Neural_Network_Back_Propagation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54d80880",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d1624b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b47d0e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a736c8c2",
   "metadata": {},
   "source": [
    "![Filter Method](https://miro.medium.com/max/1400/1*OG1d4edy5BFYeQ0yHjBOJA.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0587a34f",
   "metadata": {},
   "source": [
    "![F](https://www.charlesbordet.com/assets/images/gradient-descent/gradientdescent-alpha0.2.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d2961f4",
   "metadata": {},
   "source": [
    "### Gradient Descent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340cc1b2",
   "metadata": {},
   "source": [
    "#### Learning Rate = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4fdc0f6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(4,6,20)\n",
    "y = (x-10)**2\n",
    "#plt.scatter(x,y,color='black')\n",
    "starting_point=100\n",
    "updated_point = starting_point\n",
    "l = []\n",
    "plt.figure(figsize=(12,8))\n",
    "for i in range(100):\n",
    "    eta=+0.1\n",
    "    updated_point=updated_point-eta*2*(updated_point-10)\n",
    "    l.append(updated_point)\n",
    "    plt.scatter(updated_point,(updated_point-10)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d0bcb9f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[64.0,\n",
       " 42.4,\n",
       " 29.439999999999998,\n",
       " 21.663999999999998,\n",
       " 16.998399999999997,\n",
       " 14.199039999999998,\n",
       " 12.519423999999999,\n",
       " 11.5116544,\n",
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       " 10.19591041024,\n",
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       " 10.000710757200748,\n",
       " 10.000426454320449,\n",
       " 10.00025587259227,\n",
       " 10.000153523555362,\n",
       " 10.000092114133217,\n",
       " 10.00005526847993,\n",
       " 10.000033161087957,\n",
       " 10.000019896652775,\n",
       " 10.000011937991665,\n",
       " 10.000007162794999,\n",
       " 10.000004297677,\n",
       " 10.0000025786062,\n",
       " 10.00000154716372,\n",
       " 10.000000928298231,\n",
       " 10.00000055697894,\n",
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       " 10.00000020051242,\n",
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       " 10.00000007218447,\n",
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       " 10.000000015591846,\n",
       " 10.000000009355107,\n",
       " 10.000000005613064,\n",
       " 10.000000003367838,\n",
       " 10.000000002020702,\n",
       " 10.00000000121242,\n",
       " 10.000000000727452,\n",
       " 10.00000000043647,\n",
       " 10.000000000261883,\n",
       " 10.00000000015713,\n",
       " 10.000000000094278,\n",
       " 10.000000000056566,\n",
       " 10.000000000033939,\n",
       " 10.000000000020364,\n",
       " 10.000000000012218,\n",
       " 10.000000000007331,\n",
       " 10.000000000004398,\n",
       " 10.00000000000264,\n",
       " 10.000000000001585,\n",
       " 10.00000000000095,\n",
       " 10.00000000000057,\n",
       " 10.000000000000343,\n",
       " 10.000000000000206,\n",
       " 10.000000000000124,\n",
       " 10.000000000000075,\n",
       " 10.000000000000044,\n",
       " 10.000000000000027,\n",
       " 10.000000000000016,\n",
       " 10.000000000000009,\n",
       " 10.000000000000005,\n",
       " 10.000000000000004,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f253dca0",
   "metadata": {},
   "source": [
    "#### Learning Rate = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "84f04842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(4,6,20)\n",
    "y = (x-10)**2\n",
    "#plt.scatter(x,y,color='black')\n",
    "starting_point=100\n",
    "updated_point = starting_point\n",
    "l = []\n",
    "plt.figure(figsize=(12,8))\n",
    "for i in range(100):\n",
    "    eta=+0.2\n",
    "    updated_point=updated_point-eta*2*(updated_point-10)\n",
    "    l.append(updated_point)\n",
    "    plt.scatter(updated_point,(updated_point-10)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff6f53d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[64.0,\n",
       " 42.4,\n",
       " 29.439999999999998,\n",
       " 21.663999999999998,\n",
       " 16.998399999999997,\n",
       " 14.199039999999998,\n",
       " 12.519423999999999,\n",
       " 11.5116544,\n",
       " 10.90699264,\n",
       " 10.544195584,\n",
       " 10.3265173504,\n",
       " 10.19591041024,\n",
       " 10.117546246144,\n",
       " 10.0705277476864,\n",
       " 10.04231664861184,\n",
       " 10.025389989167104,\n",
       " 10.015233993500262,\n",
       " 10.009140396100157,\n",
       " 10.005484237660095,\n",
       " 10.003290542596057,\n",
       " 10.001974325557635,\n",
       " 10.00118459533458,\n",
       " 10.000710757200748,\n",
       " 10.000426454320449,\n",
       " 10.00025587259227,\n",
       " 10.000153523555362,\n",
       " 10.000092114133217,\n",
       " 10.00005526847993,\n",
       " 10.000033161087957,\n",
       " 10.000019896652775,\n",
       " 10.000011937991665,\n",
       " 10.000007162794999,\n",
       " 10.000004297677,\n",
       " 10.0000025786062,\n",
       " 10.00000154716372,\n",
       " 10.000000928298231,\n",
       " 10.00000055697894,\n",
       " 10.000000334187364,\n",
       " 10.00000020051242,\n",
       " 10.000000120307451,\n",
       " 10.00000007218447,\n",
       " 10.000000043310681,\n",
       " 10.00000002598641,\n",
       " 10.000000015591846,\n",
       " 10.000000009355107,\n",
       " 10.000000005613064,\n",
       " 10.000000003367838,\n",
       " 10.000000002020702,\n",
       " 10.00000000121242,\n",
       " 10.000000000727452,\n",
       " 10.00000000043647,\n",
       " 10.000000000261883,\n",
       " 10.00000000015713,\n",
       " 10.000000000094278,\n",
       " 10.000000000056566,\n",
       " 10.000000000033939,\n",
       " 10.000000000020364,\n",
       " 10.000000000012218,\n",
       " 10.000000000007331,\n",
       " 10.000000000004398,\n",
       " 10.00000000000264,\n",
       " 10.000000000001585,\n",
       " 10.00000000000095,\n",
       " 10.00000000000057,\n",
       " 10.000000000000343,\n",
       " 10.000000000000206,\n",
       " 10.000000000000124,\n",
       " 10.000000000000075,\n",
       " 10.000000000000044,\n",
       " 10.000000000000027,\n",
       " 10.000000000000016,\n",
       " 10.000000000000009,\n",
       " 10.000000000000005,\n",
       " 10.000000000000004,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002,\n",
       " 10.000000000000002]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d89729",
   "metadata": {},
   "source": [
    "#### Learning Rate = 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "062c3f18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAHSCAYAAAD45Z1sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAadElEQVR4nO3df7DldX3f8dfbXQSUKCCLwi4txG5UtEbNHdQ6aVVSoSG49A8y2yaRaZmhzZAGM05SSCaJTcYZpzZRnFZnaEzdNFRmY2wAiRpmY5O0MZCLGpFfshEDKwu7SjViYGHh3T/uwd5d7v3g3nN379318Zi5c875fL/fcz77me/sPufs95xb3R0AAGBhz1rpCQAAwGommAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABtau9ASeyUknndSnn376Sk8DAIAj2C233PK17l630LZVH8ynn356ZmdnV3oaAAAcwarqbxbb5pIMAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAG1q70BAAA4A8+99W851N35f5vPJJTjz82P3/OS3LBq9ev9LSSCGYAAFbYH3zuq7niY7fmkcefSJJ89RuP5IqP3ZokqyKaXZIBAMCKes+n7vpOLD/lkcefyHs+ddcKzWhfghkAgBV1/zceOaDxQ00wAwCwok49/tgDGj/UBDMAACvq5895SY49as0+Y8cetSY/f85LVmhG+/KhPwAAVtRTH+zzLRkAALCIC169ftUE8v5ckgEAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYeMZgrqrfrqpdVfXFeWPvqao7q+oLVfU/q+r4eduuqKrtVXVXVZ0zb/yHqurWybb3V1Ut+58GAACW2XfzDvOHk5y739iNSV7R3a9M8qUkVyRJVZ2ZZHOSl0+O+UBVrZkc88EklyTZOPnZ/zkBAGDVecZg7u4/TfLQfmN/1N17Jw//IsmGyf1NSa7p7j3dfU+S7UnOqqpTkjyvuz/T3Z3kd5JcsEx/BgAAOGiW4xrmf53kE5P765PcN2/bjsnY+sn9/ccBAGBVmyqYq+qXkuxNcvVTQwvs1oPxxZ73kqqararZ3bt3TzNFAACYypKDuaouSvJjSX5icplFMvfO8WnzdtuQ5P7J+IYFxhfU3Vd190x3z6xbt26pUwQAgKktKZir6twk/z7JW7v77+Ztui7J5qo6uqrOyNyH+27u7p1JvlVVr5t8O8bbklw75dwBAOCgW/tMO1TVR5K8MclJVbUjya9m7lsxjk5y4+Tb4f6iu/9td99WVVuT3J65SzUu7e4nJk/105n7xo1jM3fN8ycCAACrXP3/qylWp5mZmZ6dnV3paQAAcASrqlu6e2ahbX7THwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYOAZg7mqfruqdlXVF+eNnVhVN1bV3ZPbE+Ztu6KqtlfVXVV1zrzxH6qqWyfb3l9Vtfx/HAAAWF7fzTvMH05y7n5jlyfZ1t0bk2ybPE5VnZlkc5KXT475QFWtmRzzwSSXJNk4+dn/OQEAYNV5xmDu7j9N8tB+w5uSbJnc35Lkgnnj13T3nu6+J8n2JGdV1SlJntfdn+nuTvI7844BAIBVa6nXML+wu3cmyeT25Mn4+iT3zdtvx2Rs/eT+/uMAALCqLfeH/ha6LrkH4ws/SdUlVTVbVbO7d+9etskBAMCBWmowPzi5zCKT212T8R1JTpu334Yk90/GNywwvqDuvqq7Z7p7Zt26dUucIgAATG+pwXxdkosm9y9Kcu288c1VdXRVnZG5D/fdPLls41tV9brJt2O8bd4xAACwaq19ph2q6iNJ3pjkpKrakeRXk7w7ydaqujjJvUkuTJLuvq2qtia5PcneJJd29xOTp/rpzH3jxrFJPjH5AQCAVa3mvrRi9ZqZmenZ2dmVngYAAEewqrqlu2cW2uY3/QEAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAamCuaq+rmquq2qvlhVH6mqY6rqxKq6saruntyeMG//K6pqe1XdVVXnTD99AAA4uJYczFW1PsnPJpnp7lckWZNkc5LLk2zr7o1Jtk0ep6rOnGx/eZJzk3ygqtZMN30AADi4pr0kY22SY6tqbZLnJLk/yaYkWybbtyS5YHJ/U5JruntPd9+TZHuSs6Z8fQAAOKiWHMzd/dUk/ynJvUl2Jvlmd/9Rkhd2987JPjuTnDw5ZH2S++Y9xY7JGAAArFrTXJJxQubeNT4jyalJnltVPzk6ZIGxXuS5L6mq2aqa3b1791KnCAAAU5vmkowfSXJPd+/u7seTfCzJP0ryYFWdkiST212T/XckOW3e8RsydwnH03T3Vd09090z69atm2KKAAAwnWmC+d4kr6uq51RVJTk7yR1Jrkty0WSfi5JcO7l/XZLNVXV0VZ2RZGOSm6d4fQAAOOjWLvXA7r6pqj6a5LNJ9ib5XJKrkhyXZGtVXZy5qL5wsv9tVbU1ye2T/S/t7iemnD8AABxU1b3gZcSrxszMTM/Ozq70NAAAOIJV1S3dPbPQNr/pDwAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMDBVMFfV8VX10aq6s6ruqKrXV9WJVXVjVd09uT1h3v5XVNX2qrqrqs6ZfvoAAHBwTfsO85VJPtndL03yg0nuSHJ5km3dvTHJtsnjVNWZSTYneXmSc5N8oKrWTPn6AABwUC05mKvqeUn+cZIPJUl3P9bd30iyKcmWyW5bklwwub8pyTXdvae770myPclZS319AAA4FKZ5h/n7k+xO8t+q6nNV9VtV9dwkL+zunUkyuT15sv/6JPfNO37HZAwAAFataYJ5bZLXJPlgd786ybczufxiEbXAWC+4Y9UlVTVbVbO7d++eYooAADCdaYJ5R5Id3X3T5PFHMxfQD1bVKUkyud01b//T5h2/Icn9Cz1xd1/V3TPdPbNu3boppggAANNZcjB39wNJ7quql0yGzk5ye5Lrklw0GbsoybWT+9cl2VxVR1fVGUk2Jrl5qa8PAACHwtopj/93Sa6uqmcn+XKSf5W5CN9aVRcnuTfJhUnS3bdV1dbMRfXeJJd29xNTvj4AABxUUwVzd38+ycwCm85eZP93JXnXNK8JAACHkt/0BwAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGJg6mKtqTVV9rqo+Pnl8YlXdWFV3T25PmLfvFVW1varuqqpzpn1tAAA42JbjHebLktwx7/HlSbZ198Yk2yaPU1VnJtmc5OVJzk3ygapaswyvDwAAB81UwVxVG5Kcl+S35g1vSrJlcn9LkgvmjV/T3Xu6+54k25OcNc3rAwDAwTbtO8zvS/ILSZ6cN/bC7t6ZJJPbkyfj65PcN2+/HZMxAABYtZYczFX1Y0l2dfct3+0hC4z1Is99SVXNVtXs7t27lzpFAACY2jTvML8hyVur6itJrkny5qr63SQPVtUpSTK53TXZf0eS0+YdvyHJ/Qs9cXdf1d0z3T2zbt26KaYIAADTWXIwd/cV3b2hu0/P3If5/ri7fzLJdUkumux2UZJrJ/evS7K5qo6uqjOSbExy85JnDgAAh8Dag/Cc706ytaouTnJvkguTpLtvq6qtSW5PsjfJpd39xEF4fQAAWDbVveBlxKvGzMxMz87OrvQ0AAA4glXVLd09s9A2v+kPAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMwJHrC1uT974ieefxc7df2LrSMwLgMLTkYK6q06rq01V1R1XdVlWXTcZPrKobq+ruye0J8465oqq2V9VdVXXOcvwBABb0ha3J9T+bfPO+JD13e/3PimYADtg07zDvTfKO7n5ZktclubSqzkxyeZJt3b0xybbJ40y2bU7y8iTnJvlAVa2ZZvIAi9r2a8njj+w79vgjc+MAcACWHMzdvbO7Pzu5/60kdyRZn2RTki2T3bYkuWByf1OSa7p7T3ffk2R7krOW+voAQ9/ccWDjALCIZbmGuapOT/LqJDcleWF370zmojrJyZPd1ie5b95hOyZjAMvv+RsObBwAFjF1MFfVcUl+P8nbu/tvR7suMNaLPOclVTVbVbO7d++edorA96KzfyU56th9x446dm4cAA7AVMFcVUdlLpav7u6PTYYfrKpTJttPSbJrMr4jyWnzDt+Q5P6Fnre7r+rume6eWbdu3TRTBL5XvfLHk/Pfnzz/tCQ1d3v+++fGAeAArF3qgVVVST6U5I7u/s15m65LclGSd09ur503/j+q6jeTnJpkY5Kbl/r6AM/olT8ukAGY2pKDOckbkvxUklur6vOTsV/MXChvraqLk9yb5MIk6e7bqmprktsz9w0bl3b3E1O8PgAAHHRLDubu/t9Z+LrkJDl7kWPeleRdS31NAAA41PymPwAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADAgmAEAYEAwAwDAgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDCzKt3w5Rvylo++Ja/c8sq85aNvyQ1fvmGlpwQAfI9au9ITgP3d8OUb8s4/f2cefeLRJMnOb+/MO//8nUmS877/vBWcGQDwvcg7zKw6V372yu/E8lMefeLRXPnZK1doRgDA9zLBzKrzwLcfOKBxAICDSTCz6rzouS86oHEAgINJMLPqXPaay3LMmmP2GTtmzTG57DWXrdCMAIDvZT70x6rz1Af7rvzslXng2w/kRc99US57zWU+8AcArAjBzKp03vefJ5ABgFXBJRmHkW9ef33ufvPZueNlZ+buN5+db15//UpPCQDgiHfIg7mqzq2qu6pqe1Vdfqhf/3D1zeuvz85f/pXsvf/+pDt7778/O3/5V0QzAMBBdkgvyaiqNUn+S5J/mmRHkr+squu6+/ZDOY+Rd3zoXfnk6a/N1+vEvKAfyrlfuSnnPfxw/sE3/ln+6qE/yZcevDnf94KT8sOb35aX/fCbnnb8l256IJ+59q/z8EN7ctyJR+f1m16cH3jt9N/usOu970s/uu93E/ejj2bXe9+X559//tTPDwDAwg71O8xnJdne3V/u7seSXJNk0yGew6Le8aF35ffOODtff9ZJST0rX3/WSfm9M87ODccdl+3HfyL/8Og35O8956X51td254+u+s+5488+vc/xX7rpgXz66jvz8EN7kiQPP7Qnn776znzppum/P3jvzp0HNA4AwPI41MG8Psl98x7vmIytCp88/bV5rPb9OrPH6ph88vTXZs/GP8zaZx2VV57wT5Ikex/bkz+75nf22fcz1/519j725D5jex97Mp+59q+nntvaU045oHEAAJbHoQ7mWmCsn7ZT1SVVNVtVs7t37z4E05rz9Tpx0fGjjn44SfKctc/7zvi3vv61ffZ76p3l/S02fiBO/rm3p47ZN+brmGNy8s+9fernBgBgcYc6mHckOW3e4w1J7t9/p+6+qrtnuntm3bp1h2xyL+iHFh1/fM9xSZK/2/u33xn/vhectM9+x5149ILHLzZ+IJ5//vk55dd/LWtPPTWpytpTT80pv/5rrl8GADjIDvX3MP9lko1VdUaSrybZnORfHuI5LOrcr9yU3zvj7H0uy3h2P5pzv3JTjn74R7P3ycfzhf/7J0mStc8+Oj+8+W37HP/6TS/Op6++c5/LMtY++1l5/aYXL8v8nn/++QIZAOAQO6TvMHf33iQ/k+RTSe5IsrW7bzuUcxj5jYt/KRfesy0vePJrST+ZFzz5tVx4z7bvfEvGrXv+T+79uzvzfSety1su+ZmnfUvGD7z2RXnTT7z0O+8oH3fi0XnTT7x0Wb4lAwCAlVHdT7uEeFWZmZnp2dnZlZ4GAABHsKq6pbtnFtrmN/0BAMCAYAYAgAHBDAAAA4IZAAAGBDMAAAwIZgAAGBDMAAAwIJgBAGBAMAMAwIBgBgCAAcEMAAADghkAAAYEMwAADAhmAAAYEMwAADBQ3b3Scxiqqt1J/mYFXvqkJF9bgdc9klnT5WU9l581XV7Wc/lZ0+VlPZff4bymf7+71y20YdUH80qpqtnunlnpeRxJrOnysp7Lz5ouL+u5/Kzp8rKey+9IXVOXZAAAwIBgBgCAAcG8uKtWegJHIGu6vKzn8rOmy8t6Lj9rurys5/I7ItfUNcwAADDgHWYAABgQzEmq6reraldVfXHe2IlVdWNV3T25PWEl53g4WWQ931lVX62qz09+fnQl53i4qarTqurTVXVHVd1WVZdNxp2nSzBYT+fpElXVMVV1c1X91WRN/8Nk3Dm6BIP1dI5OoarWVNXnqurjk8fOzyktsKZH5DkqmOd8OMm5+41dnmRbd29Msm3ymO/Oh/P09UyS93b3qyY/f3iI53S425vkHd39siSvS3JpVZ0Z5+lSLbaeifN0qfYkeXN3/2CSVyU5t6peF+foUi22nolzdBqXJblj3mPn5/T2X9PkCDxHBXOS7v7TJA/tN7wpyZbJ/S1JLjiUczqcLbKeTKG7d3b3Zyf3v5W5v5zWx3m6JIP1ZIl6zsOTh0dNfjrO0SUZrCdLVFUbkpyX5LfmDTs/p7DImh6RBPPiXtjdO5O5f1yTnLzC8zkS/ExVfWFyyYb/9lqiqjo9yauT3BTn6dT2W8/Eebpkk/+a/XySXUlu7G7n6BQWWc/EObpU70vyC0menDfm/JzO+/L0NU2OwHNUMHOofDDJizP3X4s7k/zGis7mMFVVxyX5/SRv7+6/Xen5HO4WWE/n6RS6+4nuflWSDUnOqqpXrPCUDmuLrKdzdAmq6seS7OruW1Z6LkeKwZoekeeoYF7cg1V1SpJMbnet8HwOa9394OQv/yeT/NckZ630nA43VXVU5uLu6u7+2GTYebpEC62n83R5dPc3kvyvzH2WwTk6pfnr6RxdsjckeWtVfSXJNUneXFW/G+fnNBZc0yP1HBXMi7suyUWT+xcluXYF53LYe+ovpIl/nuSLi+3L01VVJflQkju6+zfnbXKeLsFi6+k8XbqqWldVx0/uH5vkR5LcGefokiy2ns7RpenuK7p7Q3efnmRzkj/u7p+M83PJFlvTI/UcXbvSE1gNquojSd6Y5KSq2pHkV5O8O8nWqro4yb1JLly5GR5eFlnPN1bVqzL3oZWvJPk3KzW/w9QbkvxUklsn1zQmyS/GebpUi63nv3CeLtkpSbZU1ZrMvRmztbs/XlWfiXN0KRZbz//uHF1W/g5dfv/xSDxH/aY/AAAYcEkGAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgAHBDAAAA4IZAAAG/h9PhIh5UEFLvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(4,6,20)\n",
    "y = (x-10)**2\n",
    "#plt.scatter(x,y,color='black')\n",
    "starting_point=100\n",
    "updated_point = starting_point\n",
    "l = []\n",
    "plt.figure(figsize=(12,8))\n",
    "for i in range(100):\n",
    "    eta=+0.3\n",
    "    updated_point=updated_point-eta*2*(updated_point-10)\n",
    "    l.append(updated_point)\n",
    "    plt.scatter(updated_point,(updated_point-10)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "53269c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[46.0,\n",
       " 24.400000000000002,\n",
       " 15.760000000000002,\n",
       " 12.304,\n",
       " 10.9216,\n",
       " 10.36864,\n",
       " 10.147456,\n",
       " 10.0589824,\n",
       " 10.02359296,\n",
       " 10.009437184,\n",
       " 10.0037748736,\n",
       " 10.001509949439999,\n",
       " 10.000603979775999,\n",
       " 10.000241591910399,\n",
       " 10.000096636764159,\n",
       " 10.000038654705664,\n",
       " 10.000015461882265,\n",
       " 10.000006184752905,\n",
       " 10.000002473901162,\n",
       " 10.000000989560466,\n",
       " 10.000000395824186,\n",
       " 10.000000158329675,\n",
       " 10.00000006333187,\n",
       " 10.000000025332747,\n",
       " 10.0000000101331,\n",
       " 10.00000000405324,\n",
       " 10.000000001621295,\n",
       " 10.000000000648518,\n",
       " 10.000000000259407,\n",
       " 10.000000000103762,\n",
       " 10.000000000041505,\n",
       " 10.000000000016602,\n",
       " 10.00000000000664,\n",
       " 10.000000000002656,\n",
       " 10.000000000001062,\n",
       " 10.000000000000425,\n",
       " 10.00000000000017,\n",
       " 10.000000000000068,\n",
       " 10.000000000000027,\n",
       " 10.00000000000001,\n",
       " 10.000000000000004,\n",
       " 10.000000000000002,\n",
       " 10.0,\n",
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       " 10.0]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df427844",
   "metadata": {},
   "source": [
    "### Classification Problem uisng Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "47df3e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "np.random.seed(seed)\n",
    "# laod pima indians dataset\n",
    "dataset = np.loadtxt('C:/Users/Moin Dalvi/Documents/Data Science Material/Data Science/Python/Datasets/pima-indians-diabetes.data.csv',delimiter = \",\")\n",
    "# split into input (X) and output (Y) variables\n",
    "x = dataset[:,0:8]\n",
    "y = dataset[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "df2c05e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7455a9da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6.   , 148.   ,  72.   , ...,   0.627,  50.   ,   1.   ],\n",
       "       [  1.   ,  85.   ,  66.   , ...,   0.351,  31.   ,   0.   ],\n",
       "       [  8.   , 183.   ,  64.   , ...,   0.672,  32.   ,   1.   ],\n",
       "       ...,\n",
       "       [  5.   , 121.   ,  72.   , ...,   0.245,  30.   ,   0.   ],\n",
       "       [  1.   , 126.   ,  60.   , ...,   0.349,  47.   ,   1.   ],\n",
       "       [  1.   ,  93.   ,  70.   , ...,   0.315,  23.   ,   0.   ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a67417c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6.   , 148.   ,  72.   , ...,  33.6  ,   0.627,  50.   ],\n",
       "       [  1.   ,  85.   ,  66.   , ...,  26.6  ,   0.351,  31.   ],\n",
       "       [  8.   , 183.   ,  64.   , ...,  23.3  ,   0.672,  32.   ],\n",
       "       ...,\n",
       "       [  5.   , 121.   ,  72.   , ...,  26.2  ,   0.245,  30.   ],\n",
       "       [  1.   , 126.   ,  60.   , ...,  30.1  ,   0.349,  47.   ],\n",
       "       [  1.   ,  93.   ,  70.   , ...,  30.4  ,   0.315,  23.   ]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2779b062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       0., 0., 1., 1., 1., 0., 0., 1., 0., 1., 0., 1., 1., 0., 1., 0., 0.,\n",
       "       1., 0., 1., 1., 0., 0., 1., 0., 1., 0., 0., 1., 0., 1., 0., 1., 1.,\n",
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       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 1.,\n",
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       "       1., 0., 0., 1., 0., 0., 1., 0., 1., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 1., 0., 0., 0., 0., 1., 1., 0., 1., 0., 1., 0., 0., 0., 0., 1.,\n",
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       "       1., 0., 1., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0.,\n",
       "       1., 1., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 1.,\n",
       "       1., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 1., 0.,\n",
       "       0., 1., 0., 1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 1., 0., 1., 1.,\n",
       "       0., 0., 0., 0., 1., 1., 0., 0., 0., 1., 0., 1., 1., 0., 0., 1., 0.,\n",
       "       0., 1., 1., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "       1., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 0., 0., 1., 0.,\n",
       "       1., 1., 1., 0., 0., 1., 1., 1., 0., 1., 0., 1., 0., 1., 0., 0., 0.,\n",
       "       0., 1., 0.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf706ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create model\n",
    "model = tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Dense(12, input_dim=8,activation='sigmoid'))\n",
    "model.add(tf.keras.layers.Dense(8, activation='sigmoid'))\n",
    "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e94ae9ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_3 (Dense)             (None, 12)                108       \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 8)                 104       \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 221\n",
      "Trainable params: 221\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "05821a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7315d6f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.6187 - accuracy: 0.6710 - val_loss: 0.6743 - val_accuracy: 0.6234\n",
      "Epoch 2/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6189 - accuracy: 0.6694 - val_loss: 0.6712 - val_accuracy: 0.6299\n",
      "Epoch 3/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6185 - accuracy: 0.6694 - val_loss: 0.6731 - val_accuracy: 0.6234\n",
      "Epoch 4/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6177 - accuracy: 0.6726 - val_loss: 0.6734 - val_accuracy: 0.6234\n",
      "Epoch 5/200\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.6174 - accuracy: 0.6710 - val_loss: 0.6772 - val_accuracy: 0.6234\n",
      "Epoch 6/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6189 - accuracy: 0.6661 - val_loss: 0.6774 - val_accuracy: 0.6234\n",
      "Epoch 7/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6182 - accuracy: 0.6710 - val_loss: 0.6742 - val_accuracy: 0.6234\n",
      "Epoch 8/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6175 - accuracy: 0.6710 - val_loss: 0.6705 - val_accuracy: 0.6364\n",
      "Epoch 9/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6175 - accuracy: 0.6743 - val_loss: 0.6693 - val_accuracy: 0.6364\n",
      "Epoch 10/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6172 - accuracy: 0.6726 - val_loss: 0.6682 - val_accuracy: 0.6364\n",
      "Epoch 11/200\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.6169 - accuracy: 0.6678 - val_loss: 0.6689 - val_accuracy: 0.6364\n",
      "Epoch 12/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6167 - accuracy: 0.6710 - val_loss: 0.6704 - val_accuracy: 0.6364\n",
      "Epoch 13/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6166 - accuracy: 0.6710 - val_loss: 0.6681 - val_accuracy: 0.6364\n",
      "Epoch 14/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6175 - accuracy: 0.6661 - val_loss: 0.6685 - val_accuracy: 0.6364\n",
      "Epoch 15/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6169 - accuracy: 0.6661 - val_loss: 0.6703 - val_accuracy: 0.6364\n",
      "Epoch 16/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6165 - accuracy: 0.6710 - val_loss: 0.6712 - val_accuracy: 0.6364\n",
      "Epoch 17/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6158 - accuracy: 0.6743 - val_loss: 0.6714 - val_accuracy: 0.6364\n",
      "Epoch 18/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6153 - accuracy: 0.6678 - val_loss: 0.6708 - val_accuracy: 0.6364\n",
      "Epoch 19/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6155 - accuracy: 0.6661 - val_loss: 0.6683 - val_accuracy: 0.6364\n",
      "Epoch 20/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6159 - accuracy: 0.6743 - val_loss: 0.6667 - val_accuracy: 0.6364\n",
      "Epoch 21/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6153 - accuracy: 0.6743 - val_loss: 0.6679 - val_accuracy: 0.6364\n",
      "Epoch 22/200\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.6151 - accuracy: 0.6759 - val_loss: 0.6681 - val_accuracy: 0.6364\n",
      "Epoch 23/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6151 - accuracy: 0.6678 - val_loss: 0.6683 - val_accuracy: 0.6364\n",
      "Epoch 24/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6148 - accuracy: 0.6694 - val_loss: 0.6681 - val_accuracy: 0.6364\n",
      "Epoch 25/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6144 - accuracy: 0.6743 - val_loss: 0.6683 - val_accuracy: 0.6364\n",
      "Epoch 26/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6140 - accuracy: 0.6743 - val_loss: 0.6696 - val_accuracy: 0.6364\n",
      "Epoch 27/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6141 - accuracy: 0.6759 - val_loss: 0.6711 - val_accuracy: 0.6299\n",
      "Epoch 28/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6148 - accuracy: 0.6694 - val_loss: 0.6729 - val_accuracy: 0.6299\n",
      "Epoch 29/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6144 - accuracy: 0.6726 - val_loss: 0.6716 - val_accuracy: 0.6429\n",
      "Epoch 30/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6135 - accuracy: 0.6759 - val_loss: 0.6720 - val_accuracy: 0.6364\n",
      "Epoch 31/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6141 - accuracy: 0.6743 - val_loss: 0.6724 - val_accuracy: 0.6429\n",
      "Epoch 32/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6138 - accuracy: 0.6694 - val_loss: 0.6710 - val_accuracy: 0.6364\n",
      "Epoch 33/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6144 - accuracy: 0.6710 - val_loss: 0.6728 - val_accuracy: 0.6169\n",
      "Epoch 34/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6147 - accuracy: 0.6710 - val_loss: 0.6734 - val_accuracy: 0.6234\n",
      "Epoch 35/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6139 - accuracy: 0.6792 - val_loss: 0.6725 - val_accuracy: 0.6364\n",
      "Epoch 36/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6130 - accuracy: 0.6808 - val_loss: 0.6715 - val_accuracy: 0.6364\n",
      "Epoch 37/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6128 - accuracy: 0.6808 - val_loss: 0.6708 - val_accuracy: 0.6429\n",
      "Epoch 38/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6122 - accuracy: 0.6743 - val_loss: 0.6694 - val_accuracy: 0.6364\n",
      "Epoch 39/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6115 - accuracy: 0.6775 - val_loss: 0.6646 - val_accuracy: 0.6364\n",
      "Epoch 40/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6114 - accuracy: 0.6726 - val_loss: 0.6651 - val_accuracy: 0.6364\n",
      "Epoch 41/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6107 - accuracy: 0.6824 - val_loss: 0.6662 - val_accuracy: 0.6429\n",
      "Epoch 42/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6104 - accuracy: 0.6840 - val_loss: 0.6662 - val_accuracy: 0.6494\n",
      "Epoch 43/200\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.6106 - accuracy: 0.6857 - val_loss: 0.6648 - val_accuracy: 0.6494\n",
      "Epoch 44/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6100 - accuracy: 0.6808 - val_loss: 0.6628 - val_accuracy: 0.6494\n",
      "Epoch 45/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6099 - accuracy: 0.6775 - val_loss: 0.6632 - val_accuracy: 0.6494\n",
      "Epoch 46/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6096 - accuracy: 0.6775 - val_loss: 0.6634 - val_accuracy: 0.6494\n",
      "Epoch 47/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6095 - accuracy: 0.6873 - val_loss: 0.6640 - val_accuracy: 0.6429\n",
      "Epoch 48/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6095 - accuracy: 0.6873 - val_loss: 0.6648 - val_accuracy: 0.6429\n",
      "Epoch 49/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6089 - accuracy: 0.6840 - val_loss: 0.6652 - val_accuracy: 0.6429\n",
      "Epoch 50/200\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.6086 - accuracy: 0.6824 - val_loss: 0.6650 - val_accuracy: 0.6429\n",
      "Epoch 51/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6086 - accuracy: 0.6840 - val_loss: 0.6649 - val_accuracy: 0.6429\n",
      "Epoch 52/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6084 - accuracy: 0.6857 - val_loss: 0.6665 - val_accuracy: 0.6429\n",
      "Epoch 53/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6078 - accuracy: 0.6922 - val_loss: 0.6655 - val_accuracy: 0.6429\n",
      "Epoch 54/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6078 - accuracy: 0.6889 - val_loss: 0.6648 - val_accuracy: 0.6429\n",
      "Epoch 55/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6074 - accuracy: 0.6873 - val_loss: 0.6646 - val_accuracy: 0.6364\n",
      "Epoch 56/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6073 - accuracy: 0.6873 - val_loss: 0.6641 - val_accuracy: 0.6364\n",
      "Epoch 57/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6074 - accuracy: 0.6873 - val_loss: 0.6618 - val_accuracy: 0.6364\n",
      "Epoch 58/200\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.6080 - accuracy: 0.6840 - val_loss: 0.6612 - val_accuracy: 0.6429\n",
      "Epoch 59/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6078 - accuracy: 0.6873 - val_loss: 0.6633 - val_accuracy: 0.6494\n",
      "Epoch 60/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6083 - accuracy: 0.6873 - val_loss: 0.6632 - val_accuracy: 0.6429\n",
      "Epoch 61/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6077 - accuracy: 0.6873 - val_loss: 0.6620 - val_accuracy: 0.6364\n",
      "Epoch 62/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6073 - accuracy: 0.6840 - val_loss: 0.6615 - val_accuracy: 0.6364\n",
      "Epoch 63/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6061 - accuracy: 0.6840 - val_loss: 0.6622 - val_accuracy: 0.6364\n",
      "Epoch 64/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6064 - accuracy: 0.6857 - val_loss: 0.6608 - val_accuracy: 0.6364\n",
      "Epoch 65/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6061 - accuracy: 0.6857 - val_loss: 0.6629 - val_accuracy: 0.6364\n",
      "Epoch 66/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6057 - accuracy: 0.6840 - val_loss: 0.6636 - val_accuracy: 0.6364\n",
      "Epoch 67/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6053 - accuracy: 0.6824 - val_loss: 0.6655 - val_accuracy: 0.6364\n",
      "Epoch 68/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6065 - accuracy: 0.6857 - val_loss: 0.6642 - val_accuracy: 0.6364\n",
      "Epoch 69/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6055 - accuracy: 0.6889 - val_loss: 0.6623 - val_accuracy: 0.6364\n",
      "Epoch 70/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6051 - accuracy: 0.6889 - val_loss: 0.6615 - val_accuracy: 0.6364\n",
      "Epoch 71/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6056 - accuracy: 0.6889 - val_loss: 0.6629 - val_accuracy: 0.6364\n",
      "Epoch 72/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6058 - accuracy: 0.6889 - val_loss: 0.6635 - val_accuracy: 0.6364\n",
      "Epoch 73/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6056 - accuracy: 0.6889 - val_loss: 0.6616 - val_accuracy: 0.6364\n",
      "Epoch 74/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6047 - accuracy: 0.6873 - val_loss: 0.6614 - val_accuracy: 0.6364\n",
      "Epoch 75/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6054 - accuracy: 0.6824 - val_loss: 0.6620 - val_accuracy: 0.6364\n",
      "Epoch 76/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6049 - accuracy: 0.6840 - val_loss: 0.6636 - val_accuracy: 0.6429\n",
      "Epoch 77/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6041 - accuracy: 0.6840 - val_loss: 0.6630 - val_accuracy: 0.6429\n",
      "Epoch 78/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6045 - accuracy: 0.6889 - val_loss: 0.6625 - val_accuracy: 0.6299\n",
      "Epoch 79/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6034 - accuracy: 0.6889 - val_loss: 0.6635 - val_accuracy: 0.6299\n",
      "Epoch 80/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6042 - accuracy: 0.6824 - val_loss: 0.6637 - val_accuracy: 0.6429\n",
      "Epoch 81/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6047 - accuracy: 0.6889 - val_loss: 0.6629 - val_accuracy: 0.6494\n",
      "Epoch 82/200\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.6045 - accuracy: 0.6906 - val_loss: 0.6631 - val_accuracy: 0.6429\n",
      "Epoch 83/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6039 - accuracy: 0.6857 - val_loss: 0.6631 - val_accuracy: 0.6364\n",
      "Epoch 84/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6027 - accuracy: 0.6873 - val_loss: 0.6639 - val_accuracy: 0.6299\n",
      "Epoch 85/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6020 - accuracy: 0.6889 - val_loss: 0.6637 - val_accuracy: 0.6429\n",
      "Epoch 86/200\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.6011 - accuracy: 0.6889 - val_loss: 0.6648 - val_accuracy: 0.6364\n",
      "Epoch 87/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6019 - accuracy: 0.6906 - val_loss: 0.6654 - val_accuracy: 0.6364\n",
      "Epoch 88/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6019 - accuracy: 0.6906 - val_loss: 0.6651 - val_accuracy: 0.6364\n",
      "Epoch 89/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6012 - accuracy: 0.6873 - val_loss: 0.6648 - val_accuracy: 0.6364\n",
      "Epoch 90/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6006 - accuracy: 0.6889 - val_loss: 0.6645 - val_accuracy: 0.6429\n",
      "Epoch 91/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6007 - accuracy: 0.6873 - val_loss: 0.6642 - val_accuracy: 0.6429\n",
      "Epoch 92/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6006 - accuracy: 0.6873 - val_loss: 0.6680 - val_accuracy: 0.6299\n",
      "Epoch 93/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6007 - accuracy: 0.6906 - val_loss: 0.6678 - val_accuracy: 0.6364\n",
      "Epoch 94/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6005 - accuracy: 0.6922 - val_loss: 0.6666 - val_accuracy: 0.6364\n",
      "Epoch 95/200\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.6008 - accuracy: 0.6906 - val_loss: 0.6682 - val_accuracy: 0.6429\n",
      "Epoch 96/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6005 - accuracy: 0.6889 - val_loss: 0.6651 - val_accuracy: 0.6429\n",
      "Epoch 97/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5996 - accuracy: 0.6906 - val_loss: 0.6641 - val_accuracy: 0.6364\n",
      "Epoch 98/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6001 - accuracy: 0.6906 - val_loss: 0.6654 - val_accuracy: 0.6234\n",
      "Epoch 99/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5999 - accuracy: 0.6873 - val_loss: 0.6643 - val_accuracy: 0.6234\n",
      "Epoch 100/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5991 - accuracy: 0.6873 - val_loss: 0.6634 - val_accuracy: 0.6429\n",
      "Epoch 101/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5994 - accuracy: 0.6873 - val_loss: 0.6628 - val_accuracy: 0.6429\n",
      "Epoch 102/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5997 - accuracy: 0.6873 - val_loss: 0.6618 - val_accuracy: 0.6364\n",
      "Epoch 103/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5997 - accuracy: 0.6840 - val_loss: 0.6679 - val_accuracy: 0.6429\n",
      "Epoch 104/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5997 - accuracy: 0.6873 - val_loss: 0.6658 - val_accuracy: 0.6429\n",
      "Epoch 105/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5991 - accuracy: 0.6873 - val_loss: 0.6629 - val_accuracy: 0.6364\n",
      "Epoch 106/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5986 - accuracy: 0.6873 - val_loss: 0.6626 - val_accuracy: 0.6364\n",
      "Epoch 107/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5980 - accuracy: 0.6906 - val_loss: 0.6635 - val_accuracy: 0.6364\n",
      "Epoch 108/200\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.5980 - accuracy: 0.6873 - val_loss: 0.6639 - val_accuracy: 0.6364\n",
      "Epoch 109/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5978 - accuracy: 0.6873 - val_loss: 0.6650 - val_accuracy: 0.6234\n",
      "Epoch 110/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5981 - accuracy: 0.6857 - val_loss: 0.6635 - val_accuracy: 0.6299\n",
      "Epoch 111/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5987 - accuracy: 0.6808 - val_loss: 0.6704 - val_accuracy: 0.6104\n",
      "Epoch 112/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5988 - accuracy: 0.6840 - val_loss: 0.6698 - val_accuracy: 0.6234\n",
      "Epoch 113/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5975 - accuracy: 0.6873 - val_loss: 0.6649 - val_accuracy: 0.6299\n",
      "Epoch 114/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5980 - accuracy: 0.6857 - val_loss: 0.6616 - val_accuracy: 0.6429\n",
      "Epoch 115/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5982 - accuracy: 0.6873 - val_loss: 0.6603 - val_accuracy: 0.6364\n",
      "Epoch 116/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5982 - accuracy: 0.6857 - val_loss: 0.6611 - val_accuracy: 0.6364\n",
      "Epoch 117/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5972 - accuracy: 0.6889 - val_loss: 0.6634 - val_accuracy: 0.6429\n",
      "Epoch 118/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5975 - accuracy: 0.6873 - val_loss: 0.6615 - val_accuracy: 0.6364\n",
      "Epoch 119/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5977 - accuracy: 0.6873 - val_loss: 0.6611 - val_accuracy: 0.6364\n",
      "Epoch 120/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5969 - accuracy: 0.6857 - val_loss: 0.6607 - val_accuracy: 0.6364\n",
      "Epoch 121/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5973 - accuracy: 0.6857 - val_loss: 0.6605 - val_accuracy: 0.6429\n",
      "Epoch 122/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5964 - accuracy: 0.6889 - val_loss: 0.6618 - val_accuracy: 0.6299\n",
      "Epoch 123/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5962 - accuracy: 0.6889 - val_loss: 0.6636 - val_accuracy: 0.6299\n",
      "Epoch 124/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5968 - accuracy: 0.6922 - val_loss: 0.6612 - val_accuracy: 0.6299\n",
      "Epoch 125/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5967 - accuracy: 0.6873 - val_loss: 0.6616 - val_accuracy: 0.6299\n",
      "Epoch 126/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5959 - accuracy: 0.6889 - val_loss: 0.6637 - val_accuracy: 0.6364\n",
      "Epoch 127/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5962 - accuracy: 0.6922 - val_loss: 0.6655 - val_accuracy: 0.6364\n",
      "Epoch 128/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5959 - accuracy: 0.6889 - val_loss: 0.6658 - val_accuracy: 0.6364\n",
      "Epoch 129/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5966 - accuracy: 0.6889 - val_loss: 0.6639 - val_accuracy: 0.6364\n",
      "Epoch 130/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5960 - accuracy: 0.6922 - val_loss: 0.6642 - val_accuracy: 0.6429\n",
      "Epoch 131/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5957 - accuracy: 0.6906 - val_loss: 0.6717 - val_accuracy: 0.6429\n",
      "Epoch 132/200\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.5969 - accuracy: 0.6873 - val_loss: 0.6717 - val_accuracy: 0.6429\n",
      "Epoch 133/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5964 - accuracy: 0.6873 - val_loss: 0.6694 - val_accuracy: 0.6429\n",
      "Epoch 134/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5948 - accuracy: 0.6906 - val_loss: 0.6637 - val_accuracy: 0.6364\n",
      "Epoch 135/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5951 - accuracy: 0.6840 - val_loss: 0.6582 - val_accuracy: 0.6429\n",
      "Epoch 136/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5967 - accuracy: 0.6792 - val_loss: 0.6615 - val_accuracy: 0.6364\n",
      "Epoch 137/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5947 - accuracy: 0.6873 - val_loss: 0.6651 - val_accuracy: 0.6364\n",
      "Epoch 138/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5946 - accuracy: 0.6889 - val_loss: 0.6650 - val_accuracy: 0.6429\n",
      "Epoch 139/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5946 - accuracy: 0.6906 - val_loss: 0.6636 - val_accuracy: 0.6299\n",
      "Epoch 140/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5946 - accuracy: 0.6889 - val_loss: 0.6642 - val_accuracy: 0.6234\n",
      "Epoch 141/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5950 - accuracy: 0.6873 - val_loss: 0.6672 - val_accuracy: 0.6169\n",
      "Epoch 142/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5949 - accuracy: 0.6889 - val_loss: 0.6655 - val_accuracy: 0.6234\n",
      "Epoch 143/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5940 - accuracy: 0.6906 - val_loss: 0.6651 - val_accuracy: 0.6429\n",
      "Epoch 144/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5947 - accuracy: 0.6906 - val_loss: 0.6642 - val_accuracy: 0.6429\n",
      "Epoch 145/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5944 - accuracy: 0.6889 - val_loss: 0.6617 - val_accuracy: 0.6364\n",
      "Epoch 146/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5941 - accuracy: 0.6889 - val_loss: 0.6609 - val_accuracy: 0.6364\n",
      "Epoch 147/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5939 - accuracy: 0.6857 - val_loss: 0.6609 - val_accuracy: 0.6364\n",
      "Epoch 148/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5943 - accuracy: 0.6857 - val_loss: 0.6607 - val_accuracy: 0.6364\n",
      "Epoch 149/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5939 - accuracy: 0.6873 - val_loss: 0.6644 - val_accuracy: 0.6429\n",
      "Epoch 150/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5939 - accuracy: 0.6889 - val_loss: 0.6653 - val_accuracy: 0.6234\n",
      "Epoch 151/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5932 - accuracy: 0.6873 - val_loss: 0.6655 - val_accuracy: 0.6234\n",
      "Epoch 152/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5943 - accuracy: 0.6873 - val_loss: 0.6619 - val_accuracy: 0.6234\n",
      "Epoch 153/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5957 - accuracy: 0.6857 - val_loss: 0.6623 - val_accuracy: 0.6169\n",
      "Epoch 154/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5954 - accuracy: 0.6873 - val_loss: 0.6629 - val_accuracy: 0.6234\n",
      "Epoch 155/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5948 - accuracy: 0.6873 - val_loss: 0.6618 - val_accuracy: 0.6234\n",
      "Epoch 156/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5938 - accuracy: 0.6906 - val_loss: 0.6618 - val_accuracy: 0.6299\n",
      "Epoch 157/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5927 - accuracy: 0.6906 - val_loss: 0.6627 - val_accuracy: 0.6429\n",
      "Epoch 158/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5926 - accuracy: 0.6889 - val_loss: 0.6642 - val_accuracy: 0.6364\n",
      "Epoch 159/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5926 - accuracy: 0.6857 - val_loss: 0.6647 - val_accuracy: 0.6299\n",
      "Epoch 160/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5927 - accuracy: 0.6857 - val_loss: 0.6669 - val_accuracy: 0.6364\n",
      "Epoch 161/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5926 - accuracy: 0.6873 - val_loss: 0.6663 - val_accuracy: 0.6364\n",
      "Epoch 162/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5923 - accuracy: 0.6889 - val_loss: 0.6650 - val_accuracy: 0.6299\n",
      "Epoch 163/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5921 - accuracy: 0.6889 - val_loss: 0.6644 - val_accuracy: 0.6299\n",
      "Epoch 164/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5921 - accuracy: 0.6906 - val_loss: 0.6649 - val_accuracy: 0.6299\n",
      "Epoch 165/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5919 - accuracy: 0.6906 - val_loss: 0.6631 - val_accuracy: 0.6429\n",
      "Epoch 166/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5922 - accuracy: 0.6889 - val_loss: 0.6631 - val_accuracy: 0.6429\n",
      "Epoch 167/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5923 - accuracy: 0.6889 - val_loss: 0.6644 - val_accuracy: 0.6429\n",
      "Epoch 168/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5920 - accuracy: 0.6873 - val_loss: 0.6664 - val_accuracy: 0.6429\n",
      "Epoch 169/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5918 - accuracy: 0.6873 - val_loss: 0.6633 - val_accuracy: 0.6429\n",
      "Epoch 170/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5918 - accuracy: 0.6889 - val_loss: 0.6612 - val_accuracy: 0.6364\n",
      "Epoch 171/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5917 - accuracy: 0.6873 - val_loss: 0.6613 - val_accuracy: 0.6364\n",
      "Epoch 172/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5918 - accuracy: 0.6873 - val_loss: 0.6643 - val_accuracy: 0.6364\n",
      "Epoch 173/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5916 - accuracy: 0.6889 - val_loss: 0.6618 - val_accuracy: 0.6364\n",
      "Epoch 174/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5918 - accuracy: 0.6873 - val_loss: 0.6628 - val_accuracy: 0.6299\n",
      "Epoch 175/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5918 - accuracy: 0.6873 - val_loss: 0.6631 - val_accuracy: 0.6364\n",
      "Epoch 176/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5918 - accuracy: 0.6873 - val_loss: 0.6644 - val_accuracy: 0.6299\n",
      "Epoch 177/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5915 - accuracy: 0.6873 - val_loss: 0.6667 - val_accuracy: 0.6299\n",
      "Epoch 178/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5910 - accuracy: 0.6889 - val_loss: 0.6661 - val_accuracy: 0.6364\n",
      "Epoch 179/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5911 - accuracy: 0.6906 - val_loss: 0.6637 - val_accuracy: 0.6299\n",
      "Epoch 180/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5910 - accuracy: 0.6873 - val_loss: 0.6625 - val_accuracy: 0.6429\n",
      "Epoch 181/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5908 - accuracy: 0.6857 - val_loss: 0.6652 - val_accuracy: 0.6299\n",
      "Epoch 182/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5906 - accuracy: 0.6873 - val_loss: 0.6659 - val_accuracy: 0.6364\n",
      "Epoch 183/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5907 - accuracy: 0.6873 - val_loss: 0.6643 - val_accuracy: 0.6364\n",
      "Epoch 184/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5917 - accuracy: 0.6840 - val_loss: 0.6647 - val_accuracy: 0.6364\n",
      "Epoch 185/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5905 - accuracy: 0.6873 - val_loss: 0.6650 - val_accuracy: 0.6429\n",
      "Epoch 186/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5904 - accuracy: 0.6873 - val_loss: 0.6644 - val_accuracy: 0.6299\n",
      "Epoch 187/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5902 - accuracy: 0.6889 - val_loss: 0.6631 - val_accuracy: 0.6234\n",
      "Epoch 188/200\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5901 - accuracy: 0.6906 - val_loss: 0.6644 - val_accuracy: 0.6299\n",
      "Epoch 189/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5904 - accuracy: 0.6906 - val_loss: 0.6656 - val_accuracy: 0.6299\n",
      "Epoch 190/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5918 - accuracy: 0.6873 - val_loss: 0.6662 - val_accuracy: 0.6299\n",
      "Epoch 191/200\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5910 - accuracy: 0.6873 - val_loss: 0.6664 - val_accuracy: 0.6234\n",
      "Epoch 192/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5900 - accuracy: 0.6889 - val_loss: 0.6629 - val_accuracy: 0.6299\n",
      "Epoch 193/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5902 - accuracy: 0.6906 - val_loss: 0.6648 - val_accuracy: 0.6299\n",
      "Epoch 194/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5906 - accuracy: 0.6906 - val_loss: 0.6670 - val_accuracy: 0.6364\n",
      "Epoch 195/200\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5897 - accuracy: 0.6889 - val_loss: 0.6624 - val_accuracy: 0.6364\n",
      "Epoch 196/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5898 - accuracy: 0.6889 - val_loss: 0.6618 - val_accuracy: 0.6364\n",
      "Epoch 197/200\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5893 - accuracy: 0.6889 - val_loss: 0.6632 - val_accuracy: 0.6364\n",
      "Epoch 198/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5891 - accuracy: 0.6889 - val_loss: 0.6660 - val_accuracy: 0.6299\n",
      "Epoch 199/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5896 - accuracy: 0.6873 - val_loss: 0.6659 - val_accuracy: 0.6234\n",
      "Epoch 200/200\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5897 - accuracy: 0.6873 - val_loss: 0.6644 - val_accuracy: 0.6234\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "history = model.fit(x, y, validation_split = 0.20, epochs=200, batch_size = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72c4f084",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save_weight(\"mywt.kmw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "23e5ae62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 0s 2ms/step - loss: 0.6040 - accuracy: 0.6745\n",
      "accuracy: 67.45%\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "scores = model.evaluate(x, y)\n",
    "print('%s: %.2f%%' % (model.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5da8245c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize training history\n",
    "\n",
    "# list all data in history\n",
    "model.history.history.keys()\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a8757b5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "648891c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fikpnYUKYQ2N+XP6tZ6GcACzWbyYfnj88iQ/8+GXc/5fnYUsR9tXHf9GL5w9PyY83rarGI5+8wNB7f/rCMfzzQwfkxzYG/P5TFy7b65ZlthDGmB3A9wBcCWATgPczxjaplqkF8H0A13HOuwDclHi+G1JgfRaAzQCuYYxtsGqsxeKxxx7DY489hjPPPBNbt27FG2+8gUOHDuH000/HE088gc9+9rP44x//iJqamsUeKlHiTPhDcDps2LqmDi6HjXzXFvDw/mHMBiM4MGI+YXTEO48nD47hxq3t+OEHt+H9Z63GsakgJgOh7G9WsX/QC7uNYfvaupTnL9jQhLaa8rTExt/uGUQ0zvGtW7bghx/chvbaCuw5rl+DuZC8/PY0fAvRJe8FPzjqQ5wDXW3V6KyvAAAMqqwhgVAURyfn0N0una+726rRN+RNS2rsG/Khuy15Tj+52Y1GtxP7MzQCGvct4O3JORweD+guo4c3GMHuvcMY9i7gD2+MG3rPr14+gZOb3fjhB7fhztu3odJpx29eOWH6s61iz4kZnNFRgx9+cBu+dM0mqSzl66MFW/9dPQOw2xi+d9tW/PCD23B6ew1+/cqJku+q+cuXpBuO3mPTln/W0YkAnj88hdvOXo0ffnAbbtzajoOjPswZKAnKOcevXj6O09ulffi927bCbmO4axnb2qxUrs8CcJhzfhQAGGN3AXgPgAOKZW4DcC/n/AQAcM7FmeA0AC9xzoOJ9z4D4AYA/57XiDIozMWAc47Pf/7z+PjHP5722p49e/DII4/g85//PN797nfjS1/60iKMkFgqjPtDaHK7UGa34dRV1RnLgBG5IU78epUiMnF37yDiHPjkOzdgTUMVPOVl+M0rJ9A/7MNFG5tMratv2IsNze401dRuY3jf9k585w+HMDgTREddJWJxjt/2DuCCDU2yP3v3vmHsH5w1vQ250C/XfPbhjI7aonymFYjt6G6vwahXCqoHZoI4udktL3MwcdPV3V4NQArEf7tnEGO+EFprJMuCNxjBiekgbj0rOevAGMOmthq59rUW4vc84ddvXqPH7n1DCEXjqHLasbNnAJd3tWZc/sCwD/sHvfjytZvw7sSyf3hjFR7cN4wvXrMJVa7FdY+Go3G8OerHn56/Hu/uagXnHL986Th29pzA+7Z15L3+UDSGe18dxLs3tcozQN75CD5z9370Hp/BjrX1eX+GFUwGQvLMWDHElV29g7DbGD71zg1ori6H3cZw76tDODjiw/Ys31Hv8RkcnZjDN953hnyMPfz6sK6tbTlgZUJjOwDlbclg4jklGwHUMcaeZoztYYx9KPF8H4ALGWMNjLFKAFcB6IQGjLGPMcZ6GWO9ExOll4Ht8Xjg90sqzuWXX46f/OQnCAQkNWJoaAjj4+MYHh5GZWUlbr/9dnz605/Gq6++mvZeglAy4Q+huVry2XW3VWcsA0aY560xP147MYtKp123UoQe8TjHrj0DOHd9A9Y0SL7VTW1SAGb2Isg5R9+QF11t2rNZNyWCi7v3DAIA/nhoAiPeBTnBDpCCvoHpeXgTHmyrmAqEMOyVgsGlfrPXN+RDXWUZ2mrK0Vkn+ULVN1liXwpVWijYyn3cP5K6jKC7rRqHxvxYSJTT1Pp8IJlbYYadPQPoaqvGh89bi6ffHMeoN3OAvqt3AE67DddvSV6eb9nRiblwDA/vHzH9+YXmrTE/IjGOrsRviDGGm3d0oufYDI5MmFf21TxxYBwzwQhu3pH8zVx9xiq4XY6SThi+79UhROMcJzVVZbxRKwSRWBz3vDqIS05pRnO1dOMozklGzmk7ewbgdjlS7Gs3b+/ETDCCJw4Ym11ZalgZXGtlAqiv/g4A2wBcDeByAF9kjG3knB8E8P8BeBzAowD2AdCce+Cc/5Bzvp1zvr2pyZwiVAwaGhpw3nnnobu7G48//jhuu+02nHvuuTj99NPxvve9D36/H6+//jrOOussbNmyBV/72tfwhS98AQDwsY99DFdeeSUuucS8T5NY3oz7F9CUSIbqbq/RLQNG5MbOngGU2RluP2cNZoIRU90QXzw6hYHp+RS1sqaiDKvrK003/Bn3hzAZCMvqqJrO+kqcf3Ijftsr1U7e2TOA+ion3rWpWV5GBHZWNxsSfuNKp33J117vH/Giu70GjDE0eVxwOWxpN1l9Qz40eVxysHHaqmowllp3XtTKFoGhoLu9BtG4fndVEbBM+EOIm6gE1DfkRf+wD7fs6MTN2zsR58Dde/QDRJEse3l3a0oS6tbVdTipqQp39Sy+NURZbUVw49Z2OGwMuwoQ/N7VcwJtNeU4/+RkblOl04FrN6/Cw/tH4Fuw9qY0FzjnuKvnBLaursW1m9twZCKAYNi6jq1PvTGOCX8ItypuQFqqXWh0O7MG9v6FCB7eP4JrN7eleOSFra0UjjErsDK4HkSq2twBQN1SbBDAo5zzOc75JIBnIXmswTn/X875Vs75hQCmARyycKyW8utf/xp9fX34xje+gU9+8pN4/fXX8frrr+PFF1/ESSedhMsvvxz79+/H3r170dPTg+3btwMA/uqv/gpvvPEGVQoh0hhPUa4TCsISVwtLhVBUCjgu29SCMzqk79ZMy+qdPQOoLnekTcd3t1ebDjq1Ags1t+zoxNDsPB7YN4QnDo7hhjPbU6ZZRWBndcAr1n/tGW04OOKTK08sNYQNYZNCKe2oq0i7ee0f9qYEzVUuB9Y3VqX8DvuGvFhVU44GVVWQ5A2P9j4Rz0fjHDPBsOGx7+wZgMthw3s2t2NNQxXOXd+AXb2DugH67/tH4Z2PpMx0ANI237pjNV49MYtDi+yf7x/2we1yYI2iskSzpxyXntqMe14dzOs4G5wJ4rnDk7hpe2daZZBbdqzGfCSGB0uwE+qrJ2ZwZGIOt+zoRFdbDTgHDo5Yt5929Q6g2ePCxackBUzGGLraarKeVx7cN4L5SAy37Eg9xoSt7bnDkxjM0AF1qWJlcN0DYANjbB1jzAngVgAPqJbZDeACxpgjYf84G8BBAGCMNSf+Xw3gRgC/sXCsBLFkCEVjmA1G5FJUG1vdchkwIn+eODCO6bkwbtmxWrYEGA2uZ4NhPNo/ihvObE/zSHe11eD4VLLclxH6hnxgTFJF9bhsUwvqKsvwj/f1IRLjaRexBrcLbTXllt989Q170VlfgXNPakA4Gi/IlP1iIGwISitHZ31lSgv0hUgMh8YDaXaPrrYa2a8NAH3DPk1LT2d9BTzlDs3frOj6uDlxYzdhMAl2IRLD/XuHcGV3q1yC75YdnTgxHcRLR6c037OzZwAddRV4x0kNaa/dkFCHF9sa0Tfkxaa2atjSgt9OTAbCePJg7rYCYae6aXu6d3tzRw1OafEURB0vNDt7BlDltOOaM9rkWS2rZqbGfAt46s0JvHdbBxyqyjPd7ZntTdJYT+DUVo98PCtR29qWE5ZlKnDOo4yxTwD4PaRSfD/hnPczxu5IvH4n5/wgY+xRAPsBxCGV6+tLrOIexlgDgAiAv+ScFyfdnSBKHNGdsTlR21SUAbPad2cFw7PzppPt2morckqW45zjhSNT8CemeZ0OG84/uQlOR+oFY2fvgDxNLAJhvSYiAHB8ak5Obnvp6DTC0Thu2bE6bTmhch4Y9uHcRDATDEdxYjqIU1u1g+e+IS/WNVTBnSGpzOWw44YzO/CT59/G1tW12KhR2mpTW43lN1/9ibrQ8sV+yKe7XVbw9uQc6iudhms7K5meC+OVt6UA9OW3pcoLytmCzrpKvKqouPLmqB+xOE+z63S3V+OBfcO4/7UhlNltODIRwDVnpNa9BoTqV635mxVK4CWnNmPfoBfjvhBOzZyTCAD4Xd8I/AvRlGPviu5WVO92YGfvAN5xcmpJ1xNTQbxwZAp/e9nGtMAVABpFLfXXhrBtTR0Yk461CzY0pgVZehwa86O1phyecvP7BJBqIx8Y8eH9Z6X/ni7a2ISWahd29Q7gim4DXxCkc8Azb03IweCungGcf3IjOurS6y2LTqj/9NAB/Orl42iocgJgOPekBtRU6G/P0Ow8HDaGlmr9OtyzwTCm58JY3+TWXUaPQCiKh/aP4Noz2lDlcqDSaUdDldOy3/fdeyTLmboUKCDdTAp7k9Y5+eCID/sGvfjSNZvAWPoxJmxtu3oGcGpr5pJ8DpsNF25MP1+XKpamAXPOHwHwiOq5O1WPvwHgGxrvNVY8kSBWGCLJSdhCAElleXj/iGYt3lLmb3ftxUtHzZWRctgYnv/cpRkvXlr0DfnwgR+/nPLct99/Jq7b3CY/XojE8MdDE7jjopNgtzHUVZbB7XLoKtfxOMcH//eVFD/uls5a2VKgpEvhfRbB9b8/+iZ++dJxPPfZS+UKE0r6h33YuqYu7Xk17z+rEz9/8RhuP2eN5uvd7dV48o0xzIWillR/8C1EcGwqiJu2d2JdoxsVZXb0DXvx3gJUczAC5xw33fkCdqytxw9u32b6/f/6yMEU9azR7UyxIXTWV8C3EIV3PiLVWE4owaerAopta6SqCZ/auVfxnPb+626rwS9eOo5oLJ4SrIoZhktOaca3njhkOKnxrlcGsKahEuesT1ZuKC+z4/oz23FXzwD+KRhJufH47Z4B2BgyVtz4wNlr8Lu+Ufz5r16Vn7vz9q24ojv9hkHNzFwY13znOVy3uQ3fuGmzoW1Qc3QigIVIPG2GAAAcdhtuOLMDP3z2CAKhaMYbUMHv+kbxF4ptAYAvXbtJZ2mpE+o3H3sT/3hfn/zc+7Z14Js628M5x+0/fhmecgce+MT5uuv97D378eKRKbz8D+9ChdPc+fqhfcMIhmO4JZHTIVWfqZaTYAsJ5xy7egdw9rp6rNNoKiRbEnWqA+3skZJlbzhTXcsiyQfOXoM7frkHd/zyVd1lBP941Wn46IWlXXtcsCI6NHLONe+alhNUKWLlMO4TynUyGLtuSxvu6hnAo32jcgm2Uice59g/6MUNZ7bjYwZPmJOBED74v6/g7j2Dphu7HJ2UbAo/+tB2NHlcuP57z2NIpUiP+0LgHFifuJAk/bbawfWLR6dwYjqIL1x9Gs5LKIMddRWayzZ5XGitLpeVyYWIVAIsGue459X07RH2gA+dqx0wK9nQ4sFL//DOhLqWTnfCl/nGqE8OAAvJQUXint0mXez7LbjY6zE4M4/JQBiPHxjLqWPdvoFZnLu+QQ60mj2uFDV3dX3SHlTdVo1dvQPYvqYO7bWp+3rbmjo885mLEQxLymh5mR1rG7S70HW31yAUjePIxBxOUah2ouujmIEYN1CO7+3JObz89jQ+c/kpade6W3Z04ucvHsf9e4fw/96xFgASJRsHceHGJrTVah+vAHD+hkY8/emLMR+JIRbnuP57z2PfoNdQcH3/Xqkk4EP7R/ClazflpF6L34pezsGOtXW48xlJITVSMu83r5xAe20FfvSh7WBMmr1an6ETZV2VE09/+mJMzUm+9/955ggeTmxPtcb2vHR0Gm8nOhj2DXk1xz3uX8ATB8cRi3M88vqI6RvQu3oGsKHZjTMVTWO622vwo2ePIhSNFbSs3UtHp3F8KohPvUu7zYiwN2lZUoRN6d1dLRk7tl7e1YIn/+4ihKOZvfOfv/d13NVzAn92wbolEc8tDX09D8rLyzE1NbWsg0/OOaamplBeXth2sERpIjyYzYoA4px1DVhdX7no/kgzvD01h2A4hnNPasBpq6oN/btgQxPOXlePXb0DpqooAEnf9PknN2JLZy2qnPa0wEU8blao4mq/rZKdPQOoqSjD7eeskceYKYjobq+Wp28f7RuFbyGKRrdLc3uyBRZqGt0u3YtOskycNQFvnxxc1yT+r0b/sNf0Psr58xPfaTTOcd9r5vybohvjjnX18j5UJyAK28DgTFCu2XvzDs3qsFjTUCWvZ11jVYZ9ol2esX/Ii+72alQ47fC4HPLNdCZ29eqr0F0Jq85dPQPydfDZtyYw6ltIqf6gx9pGaXu622uwscVjyH7AuVS5ptHtSiQF5lbSr2/IC5fDhpOatANgrfKHeojkxfdt68CmNmn/nNTkzhqoNVeXy/vzw+ety5jkuLPnBDwuB5wOG3b1ap+L79kzhFico9HtTGsAlY03R/3YOzCLW3Z0poy7O2HPULaILwS7egfgKXfgSp2bKcYYunVqtj92YAyzwQhu1bDIqddxUpM767n/trNW48jEHF49sTQcwsteue7o6MDg4CBKsQZ2ISkvL0dHR3GmYInFZcK3ABtDSgBgszHcvL0D33zsLRyfmpPrK5cycvCoU8NZj1vP6sTf7NyHl9+elu0VRhiYnkej2yVPwzZXl6dNuY/7029cOusq8dyhybQZMJG8+P4dnYatOJvaavCHN8YxH45hZ88AVtdX4lPv2oC/3bUPL709hXeclPTFCnuAuoxbLrRUuyz1ZfYPedFS7ZIV4+62Gvz8xeM4Ph3UnE4u+OcP+2C3ST7mu3oG8NEL1htWt0Q3xu4M33NnQrk+MR3EEwfHUeW04+rTs6u3mdCyz/gXIjg6OSfPPjVVu+QcCz2isTju2TOIS09t1rVK3bJjNb54fx/6hnw4vaMGd/WcQEOVE5ee2mJqzN3t1Xjy4HjW2eD9g168MerHv1zfjV+8KDV8ue3szEGWFn3DXpy6qlrX493scaHR7TJUCee3vfrJi0bZ3FGDU1ulJMcPnJ06o+QNRvC7vlHcvL0TvoUI7n9tCP9w1Wkp5wZhs9ixtg6XnNqMf3/0TRydCBj2XosSoTduTd0G5Y2a0ZvxbEhdMEdw8/bM57eutmr8/KXjiMTiKFPsp10ZkmVz4eozVuGrD/bjrlcGLJl9KzTLPrguKyvDunXrFnsYBFEwxv0hNLhdaaWj3retE//5+FvY1TuAz1x+6iKNzjj9Q1447TZsaDGX1HNl9yp8aXc/dvUOmAuuZ4JyK2tAsmlMqFTBcV9CuVYG1/UVmI/EMDUXRqPihub+14Z0kxf16G6rRpxLJdBePDqFT797I646fRW+/EA/dvUMpAbXQ16011agtlJ/StUojDF0tWfuCpgPfcPelJukLsXFvhjBtehiefs5a/D3d+/HnuMzWbvGCUR1j64MQUlNRRmqyx04OOJPWK/a8vau220Mp63ypNhnRDk1ESw1e1xZbSFPvzmBcX9IM+FMcN3mNvzLQwews/cEWms24smD4/jI+etMJ4d1tdVgV+8gRn0LWFWjbye5q2cA5WU2XLelDeFoHP/00AEcHPFlrHqjhnOO/mFfSk6EGjkxNMtNo+hcqpe8aBTGGG7e3qm5PaIz5i07pOB6997hNJveK29LtpG/vORkXLihEf/x2FvY1TuIz12Z/XwtlQiVOknWq2wWnXWV8LgcBa0I9MDe5PZkoru9Rq4OJBKYB6alWQK9ZNlcqHI5cO3mNuzeO5yzzaiYLHtbCEEsN0TrczWtNeW4+JRm3L1nENElUGO4b9iLU1o9KWqHEcrL7Lh+SzseeX3EVFm7gZmgXFoP0A5cxv0hOGwMdYqAdrVCtRRITRwGcHp7jWbyoh5CVfr6795ITON3JrenbzSli+KBYZ9u85hcEF0BQ1H9slm5MB+O4fB4ICU43dDsgdNuK1rt9b4hqeTd1adLnfXuMmGPUnZjzERnfSUe3DeM+UgsYyBrhu72GhwY8cn2GbmueeJGpcmTPrui5q6eATR5XLjk1GbdZWoqynDV6auw+7Vh/PrlE4jqVH/IPl5x06R/kxYMR/HgvmFcdfoqVJeX4YYz2+G020xb1gam5+FfiGZVYrvbq3FoPJCxHNxzhycx7F3IalEwgt72iM6Y3e01uja9nb1Sp8KrTm9Fc3U5Lkmcr43U6tbqJCmw2Qqf1LizN7k9mVBWBxL8tncALEuybC7csqMT85EYHiqBzqHZoOCaIJYY4/6FlEohSm7Z0YkxXwjPvFXaNiihSuUaPN6yoxOhaBwP7B0ytHw0Fsfw7EKacq1lC2lSJbN11qfXun59SJr21vPd6rGqphz1VU6M+hZwySnNcoWQW3Z0IhyNY/c+aXuEPcCsZSYTclfA0cL6Mt/QsFU4HTac0uqROxRaybhvAZOBELrbqxPqltRZz2+ws17fcLIbYyY66yoRjXOc0uLBFkUyWT50t9UgEIrieOLYUnd9bPZktoWM+xbw1JvjeO/Wjqw3qbfs6IQ/FMV3/nAI29fU4eRm82XgRBfKTErxw/tHEAhF5UC2rsqJd3e1JBIcjd/Y9aluNPTobqtBLM7x5qh+E5WdPSdQV1mW0rk0V8T23PfakBzQi86YwsNus0ll/F48OoXjU1KCo29BsllctyXZqfDWHZ2YDITw1BvZa3Xf1SMlY56vKqko6G6vwRujvoIIK31DXvQN+bKq1kCqvQlIzBLsGcRFWZJlc2FLZy02triXRG7RsreFEESp8WjfqNyRijGGq09fpVmGTY9xXwibdKZXLz21GY1uF7771GE5a11Ja005rjkjdZp1PhzD3XsGENLI1naV2XHTto6Cl/cbmp3HbDCi2WDDCN3tNbK/9oPnrs26/Ih3AbE4l1VoQKq2EgzHUsp4jWtUmhDVPwYVlUXkae8MU9ZaiGnsPx6aTAnMxfb89PljCEfjGPFKinpXAZVr4d3+3+eOZlWjzju5MW0K//C4H0+/mX7Ttn9Q21bR1VaNR14fwY//eBQAUFfpxI1b2w15oTnneLRvFBef0pxWquwPb4xhc0etnHOQ9KZLn3/z9k785pUB/NODB+QqHOee1KB5rIWjcbw15sefnp+9Wo24MbtZlUyWD2LW4wdPH8bGFg9eOjqV4rFv9rjSjlEld78qahBnVwjPXlePtQ2VODYVNH1TKKh0OnBSkzujx3lX7wDWN1Zhx9pkCcJbdnTiof0j+KcHD8g2oYs2NmGDqiZ735BXbnjz3OFJOGwMG1sz3wSIY7l/2IfNGjc9U4EQHj8whg+du7ZglTTE9vzzQ9L2PPPWBFwOG67bkrSAvHdrB/7jsTfxLw8fxNnr6nFwxI+FSDylG+bFpzSh2ePC954+kjIzpiYci+O5w5P460s3pNkBBV1t1ViIxHF0ci6t1v3uvUPyTZqNMVy7uS3tPPf84Um5Xv+zhybhTHT6zIawNz3z1gR+/MejGJ5dwIh3AV+6Rr/EYa4IW86/PHwQ//3EIVS5pP25dU0dtq7OXrK0mFBwTRBFJBiO4s9/tQfK4jXHp+bwT+/pNvT+WJxjMhBKKcOnpMxuwwfPWYP/euItvHZiVnOZbWvqUvySD78+gi/u7tf9zOpyB96zpbDl/cT0ZT7JetdvacfXHjkoKfk634dAqM5qWwggNeURgcuEP4T22tR1VTodaHQ75XXMh2N4cO8wrupelbGZhB4XbWzC0Ow8LlVN43/4HWvxmbv3418ePggAcLsc2NJZuAvG6vpKrK6vxP17h3H/3swtnXesrcNv73hHynP/9NBBPKszI7K+sSrNVnHeyY24q2dA3h5AUu7VzUy0ODDiw5//6lV86l0b8Kl3bZSfPzwewEd+2oubt3fg398n1RoWx5IIVLd01mJLZy1+q6hbvb6pCk/+7UVpQbHoxmjkONy2pg737x3OWLPXLBtbPGj2uLCrNznWPzlvrfy3mKEa9y3ArZH09tyhSXS3VxtKiGOM4SPnr8P3njqcVzJmV1s1Xnlbuzb94fEAeo7N4HNXnpryXZ93UiM2trjxq5dPyM89sG84pRY05xyf+PWrODaVDDLfcVJD1oC4o64C1eX6fuP7XhvS7FyaD1rb8/6zVqecD1prynHV6avw0P4RPH5gDACwdXUtzlB0KnTYbfjQuWvwzcfewr6B2YyfWem0Z7wpOjMRXD771kRKcL1/cBafvGtvyrJj/gV8/srT5MfxOMcdv9gDfyiauj0GGzKdv6EJ337ykPxb76irwDtPM5csa5Qbt3bgO384jP964i35ub+9bCMF1wSxkvHOR8A58OVrN+F92zrwkZ/2mKrgMD0XRpxD1xYCAJ981wb86QXr0spP7h/04gM/fhl9Q76U4LpvyItKpx0vfv6dUIoisTjHWf/6JPqHfQUPrvuHvQnFI/fg+qRmSQEbmA5mD64TMwWdSuVaEbgINW3Cv6A55d9RlyzH98jrI/CHojlfrP/sgvX40/PTa7XetL0TV5+xCrGE/9bpsBW0Zi1jDE/+3UUZvamA5Ae/77UhxONctsdwztE35MWNW9vx1eu60t5TUWZP255rN7fhnac1IxbnCEfjuOSbT2t2CtRC/CZ+2zuIv750gzwOUd5Mqp3cBbdLaiG+vjHZxZIxhnv+/B0IhqVA4YF9w/jH+/rQe3wmrRay7HE2UGHhiu5VuLyrtaA1dp0OG57/3KXyPmGMpSjU4rge94c0A+jpuXDKbEw2PnTuWnzIwExPJrrbarB77zAmA6GUBF9A8trabQw3bk09X9hsDI/89QWYT2znb145gX995I2UpMCX357Gsakg/u3G0+WulsI+kQlpNii17bxAlASU7ASZOwCaQb09ADRnFr7z/jPxbzeeLj+udDrSjp9PXLoBHz4v/XytJtv5YF1jFTZ31mJX70DK+UXMsj3zmUtQ6bTj1h++lFaD/vh0EP5QFP/8ni45AdNIUx7B3162ER+7cL28DeVldtO5NEapr3Ki5x/flWIxKsWujaU3IoJYxogEvGaP1BL49PZaHBjxyQFVNuQ6zFmaZLhdDnjKy1L+nbm6VtMv2TfkxaZV1aipSF2+ttKJU1uN1bU1S/+wDyc3ufOymyQbe+i3JhcMTM/DbmNYpVBXlYELIPmyp+bCmt/t6vpK+XN29gxgXWMVzlqXezkovQCt0pncb4UMrAVldlvacaH+t7mzFsFwDG9PJW1FI94FTM+FsaWzVvM9eqXSxPY0uF24/sx2/E6VtKmHUKOHZufx/JFJAJKF495XB7G+sQrBcAwP75fU9/5hX5olxW5j8tiu39IOt8uh6dPsG/LB7XKkdGPMhBXNK5T7RB3QiGNRL6lxei6cVjnCaoRVSW0NicTiuOfVQbzz1GbNm12HYjtv2taZlhS4q2cAHpcD129pl5fTs0Co6W6vxsFRf1pi4KsnZnFoPGConrdZHKrfktaxwRhLWUZve7TO1+p/Rs4Ht+7oxFtjAbyWUMGD4Sge2Csll7ZUi2tODfqGvSnBvDjHn7m6LuP2ZEK5DVYF1gKnw2b6uyk2FFwTRBERgYWYPuxuT/jkJowlmYmLbFMWpVaLpF8yGSzH4hwHRny6yl1XWw36hrwFb8LUN+TN208sSmrpdU9UMjATxKqa8pQgUB24TAbC4DqzAp31FRiancehMT9eOTaNm7cXzndbaiRbGiePE/F3rh55QPJCh6Nx3G8gCbVv2IstnbWoqSiTK3/84Y0xTAbC+MI1p+HkZjfu6hmQu1hmsnUokxx9qiTH/mEvNrVVF6xcWKGRbwB96eX4OOeYCYYzdr+zAnEMqLvyPXlwHJOBsKEZHXVSoHc+gocTyX5m24EDqeXglOzqGUCl045rTOZGLFWuOWMVKsrs2JX4zTzy+mhKcikg5UbMBiMYmk2KEn3DXpTZWUHV/ZUOBdcEUUR8C9JUdXWFpFDJHcYMliyb8KU3OTFDt6pc07FEl0S94KS7vRq+hWhKMl++jPsWMO4P5RWoAdLUY5PHpds9UcnAdGoZPgCorSyD026TZwOSswLpNy6ddZWIxTn++8lDsNsY3rttabSYz4UNLW447bYUZbJ/2AcbA05blfvFt7s9vVOgFrE4x8ERH7aursMNZ7bj8f4xTM+FsbNnAK3V5bhwQxNu3dGJ107MyoF6tooSt+xYndZZT9xYFqJJj1VUV0jd/rQqhgRCUURiHPUFqINuhpqKMnTWV6RZC3b2nECzx4WLNjYZWs+tO1bDOx/BYwfG8MC+YYSi8ZxL5Yl9qDy3BUJRPLh/GNecscqUxWEp4ykvwzVnrMKD+4YxF4piZ8+JtOTSbo3v6sCwD6e0ekrSXrFUoW+SIIqIsIUI5Xp9YxXKy2xpFyo9RACozvQ2Snd7DUYTpcuApCKpp1x366hU+ZDszJh/UNNZV2HMFjIzn1KGD5CmbJWNZMYz3LgIr/ZD+0dwqc6093KhzG7Dqas8Kfu8f9iLk5rchjywmbhleycOjvgy1uM9OhHAQiSO7vZqqURhLI4fPH0Yz7w1gfdt64DDbsMNZ7ajzM7wX49LSU3ZAuTNHTU4pcUjK3opn1PAcoeFhjGWqMeeHlzPzEnnklqDSWeFRGp5nTw+RrzzeOatCdy0vUPXIqTmHSc1oKOuAjt7TmBnz4lEi/XczglyOTjFbMvD+4cRDMcKmsi4FLhlRyfmwjF8+w+H0HNsJq26zWmrqmG3MRxI7D+RT1HKv4OlCAXXBFFEfKrg2mG34dTWasPK9bg/hOpyR85eZVFRQQS4/cM+OB023Zq3p7R6YLexgjYnEEGbmeYrenTWV2ZVrufDMUz4Q2nKNZBa6zppudEIrhXvvaVADURKma7EDIdQmKUmLfnvr+u2tMPlsGFn7wndZfoUSYanrarG5o4a/OiPbyPOITc+aXC78K7TWuBbiKK9tiKrNYIxhpt3dGLfoFcuN9ZnIplxMWnSqXU9HQwDQNE914D0nR2fCso2m7t7B1P2jxFsNoabtnXi+cNTUk3l7R05W63siSYqyhvCu3oGcFJTVclVkbCabWvqsL6pCv/zzFHN5NLyMjtOaqqSu7UOexcwE4yU9AzOUoSCa4LIg7lQFD94+ojh5ghCuVa2bu1ur0b/ULJLWyYm/CG5wUQudKn8tP3DXpyaoUtieZkdG5rduso15xw/fPYIvry7D1/e3YevPNCPN0YzB+J9Qz6sa6wqSPvazrpKDM/OZ+xwJmqKr25ID66VTTrE/+oKCACwqrYcNiYtf/Epxqa9lzJdbTXwzkcwODOPyUAIo76FggShyk6B82Ht30zfkA8uhw3rExVcRHv5d5zUkLIPhSJpVO0UnfW+cL90rP70heNwOWw4qcn69uz5oNcCfWZOCq6L7bkGkjMF/3Dv6/jy7j784qXjOHd9A9Y0mPsub9reAcakBLXr8yxxKCxvX97dh8/f+zpeOzGLW3esXra5EXowxuQETr3k0u5ELg0AucqKOimYyA8KrgkiD37zygn8f4++gReOTBla3rcQgcflSMka726rgT8UzarARmNx9B6fwckGatrqUVNRhtX1lehPZIuLttGZ6GqrkVUONfsGvfjXR97APa8OYfe+YfzypeP4+u/eyLi+vkQSWSFYXV+JOAdGZtODD4H4Xjt0leuk57q+yqnpOyyz23Dl6avwiUtPNjztvZRJNubwyrMc+XrkBVefvgr+UFR3tqZ/2IvTVlXL3/O1m1fhjI4afPyik1KWu2BDEy7Y0IirDNZsrq9y4oPnrsGRiQB27xvG8ak5XH3GqpLfn806LdCnE8F1sT3XgFRVYn1jFZ47PInd+4YR5xwfvXCd6fW01VbgtrNW40/OW4vaPLfjnae1oMplx+59w/hd3wjWNValqbYrhfdu7cCmVdX46IXazZG62msw7g9h3L+APpFP0UrKdSFZGS5/grAAzrlcyWDQQMUKQFKuq1WNR5LZ976Mys9Tb05gwh/Ce7dl78aWia62avQP+zA4Mw/vfPbpwK62atzz6iDGfQtpqvnOnhMoL7Phhc9fiuryMvzHY2/iu08dxvDsvGbrW29QUkM/cPaavLZB0JHwUQ/MBDWVaSBZqk/tuQakwGUmGEE4Gse4P5QxUfR7t20twIiXBqcm7ED9wz7ZglSoG6J1Tcn65Oq60/E4R/+QD+85M1ndwVNeltJsRGC3MfziT8829dlfvGYTvmhB5zgrafa4MBuMIBSNpZQcmwkunnJdU1GGP3z64oKs62s3nJ59IQNcuLEJvV+4rCDrWuo0uF145JMX6L7epbAH9g9J+RS5VGkh9CntW3aCKGFePTGLw+NS6acBg9U0fBrB9cZWNxw2lrWe9M6eE2jyuHBJnrYE4Zd8MdFmONt0v7K9sJJgOIoH943g6tPbUJ2weNy8vROcA3cruuMpSTbtKEyg1mmgHN/AdBDlZTY0adg9RNm9iUBIs/X5SqW8zI6Tm9zoG/LiwLAPq+src+pGqUV74qZLKxF1YEZqZkHJVUnkY1SlXs8Ew7DbGKrLSSMjzCHn3gxJM1OlnnewFKHgmiByZGfPCVQ67WirKTdUaxkAfPNR1FSkXgxdDjs2tnh0rRcAMOZbwFNvJqsl5INQLUQ3tVNbM5dX2ySXbkoN/h/eP4KAqlNhZ30lzju5Abt6BzQ95MIKUCiLwaqacthtLKOlZmAmiI66Sk3vpVzr2reACV/2Nuoria72avQN+9A37C3YzRAgBe4t1dolFEXibKGOj+WAutmRYHougrpK54rzFBP5U11ehrUNlXj6zQmM+hYomdECKLgmiBwIhKJ4aP8Irj2jDRtbPYZqLQMJW4hGIp+U1KjfrOXuPYOIxbmpbHw9RODSc2wGG5qzd0l0uxxY31iV5pHd2TOQVkMVkBLQBmfmNX3ofUM+tNWUF6zCgcNuQ1ttecZyfAPT8+isS7eEAMnAZcwXwkSAlGsl3W01mPCHcHwqWPBgt7OuUvOGtH/YC4eNYWNr7nkFy40m+QZQpVzPhVFfVfwyfMTyoKu9Br3HZwCUfsWcpQgF1wSRAw/tk2qo3ryjE511lTgxZVC5XohoTq93tdVgai6MMV964hLnHLt6B3D2unqsa8y/skGTx4WWxFSzUR/tpoRPW3B4PIDe4zO4ZUd6p8J3b2pJdNZLL7fWP+wteFZ6Z51+OT7OudRARqe9tZhyPzTmRyTGc27OsxxRqlmFVrY667WD675hHza2eEqynfFikbSFpCbtTgfDqFuEZEZieaD8TRcqn4JIQsE1QeTAzt4BbGh2Y+vqWnTWV8C3EJXL7GVCK6ERSHqQtXzXLx2dxvGpYEGbIQhPq1Fva3d7DQZn5jGbSKLa1TsAh43hxq3pyZXlZXbccGY7Husfk8uFAVLZwqOTcwX3067WCdQA6fv2h6JYrRNcN1Q5wVjSrqLV+nylsikluC7wDVF9JUZ8CwhHkyUUOefoH/LSFLWKhioXbEzDcz0XXpQa18TyQJyH1zRUas6mEvlBwTVBmOStMT9eOzErq7ZGkuoAIBKLIxiOaSrXp62qBmPA6xrB9a7eAXjKHbiy21jJMSMI9djodKA4EX/05734yE978JuXT+CdpzXr2ihEZ737XhuSnzs44gPnhUtmFHTWV2IyEEYwLLWWH5qdxx2/2IOP/LQHH//FHgDaZfgAyVbSUOWSVXnyXCfxlJdhXWMVWqvLC26X6ayrAOfA8GzSzjPqW8DUXJimqFXYbQwN7vQujTPBcN7l64iVi7iJpeRha6DgmiBMsrNnAGV2hhsSTQ+E5WAwi+9adGfUyu6vdDpwRkctft8/muK79s5H8MjrI3jPlraClkq69oxVuPoMqX6wEbauqcUFGxqxEIljwh/CSc3utLrDSkRnvZ09A/L2FLpesqAj4aceTFRs+clzb+Pxg2OY8IcQDMdw9rp6bF+r36Wt2eOS30u2kFQ+ct5a/NkF5usXZ0P8ZpR2nv5EMmOhb76WA6tqyjGkuBGJxzlmghHyXBM50+B24f+duwY3bc+vtCuhDdXwIQgThKIx3PvqIC7b1IKGRGm3pHKduRyfsI3UVGpfEG/e3oF/vK8P+we92NxZCwB4YO8QQtE4bk10qSsUG1o8puo2VzodpmsK37yjE/94Xx/2DXqxpbMWfUNeNLqdst+7UMiB2nQQaxoqcd9rQ3j3phb84PZtht7f5HEBI9LfZAtJ5YPnrrVkvcl9lvzN9A17wZh0Y0ak0llXiQMjyZwH/0IUsTgnzzWRF199T/diD2HZQso1QZjgiQPjmAlG5JbMgBQsV5c7slYM8S1ItgW9esHXbm5DeZlNbkwDAHf1DGDTquolOVV+3eY2VJTZsTOxPX3DUjfIQpcOU9pynjgwjum5sCl/ulCr3S4HKp2kNxSD1upylNlTSyj2DfmwvrGK9oEGnfWVGJqZl8tbigYy5LkmiNKEgmuCMMFdPSfQVlOO809uTHler/qBEq9sC9EOrqvLy3D16W14cN8wguEo+hIF/m89q3CJjMXEU16Gq05fhQf3DWM2GMahMb8lyWqNbicqyuwYmJnHzt4BtNWU44INxhvtCLWayvAVD7uNoa22IuU30z/sXZI3kcWgs74C4VgcY4mKIdOL2J2RIIjsUHBNEAYZnAniucOTuGl7J+y2VPW1s64SJ7IE18JznanT3S07OhEIRfHw/hHs7BmA02HDeza35z/4ReLWs6Tt+a/H30I0zi0Jnhhj6KyvwCtvT+OPhybwPo39kwmRxEjBdXFZXV8pdzadCoQw4l2g5Cod1NYzUYWnnmwhBFGSUHBNEAb5ba/U0lsrAaSzvgKDM/O6TWAAhXKdIbjesbYO6xur8MuXjuP+vUO4qrtV16O9FNi+pg7rm6rwi5eOA7AuM72zrlKutHLTNnMJOsIWQsmMxaVD0UhGTnalZEZNhEdd3MBPz5EthCBKGQquCcIAsTjH3XsGcf7JjZpl3TrrKxGKxtNq0SrxLWRXrhljuHlHJ/YNeuFfiOLmAta2XgwYY7hleyfiHPCUO9BZr90pMV9E8HH+yY26DWP0ELYQKsNXXDrrKzA9F8ZcKCrXGe9aRcq1Fm215WAsWe5zhmwhBFHSUHBNEAZ48cgUhmbndRPl5GnbDEmN3vkInA5b1nbjN25th93GsLq+Euesa8h90CXCjVs74LAxdLVVFzyZUSDK8eXSHl4E1VQppLgofzP9Qz501lcs6VkaK3E57GitLpfPL9NzETjtNlQVsDwnQRCFg9KyCcIARyYCAIBz1msHu0KRHZiex7Y12uvwzUcMdcJq9pTjK9duQkddJWwmvMOlSpPHha9c12VaUTbDVaevwoQ/hMu7Wk2/t722An916cm4+vTCNekhsqMsx9c/7CW/dRY66yoxqPBc11WVWXazShBEflBwTRAG8GZJRuww0KXRNx9FTYWxn5xV9YUXi9vP0bnjKBBttRX4/FWn5fRem43h7959SoFHRGSjMzHbcHDEh2NTQbzPpFd+pdFRX4EXj0wBkKqFUI1rgihdyBZCEAbwzUdQ6bSjzK79kykvs6PZ48pYMcQ7H8mYzEgQK4n6KicqnXY82jcKAOiiMnwZWV1fiVHfAkLRmKRcU3BNECULBdcEYQDvfCRjIiKQqHWdwXPtW8i+DoJYKTAm5RWIzoNkC8lMZ10lOAeGZuYxEwxTpRCCKGEouCYIA3gN+KU76yoytkA3EqATxEpC2Klaql1UZzwLskd9Zh4zwQjqquhcQhClCgXXBGEAI6pzZ30lRrzziMTi2uswmNBIECsFkQjcRap1VsR3dWJqDrPBMDWQIYgShoJrgjCAdz6a1S/dWVeJOAdGZhfSXuOcw7cQJeWaIBSIcnzdbdQ8JhstnnI47Tb0DfkQ51TjmiBKGaoWQqwY/u13B/HzF47Lj887uRE//n/bDb3XNx/Baas8GZdRdlFb3ZBadi4QiiIW56g2WC2EIFYCqxO/mU2kXGfFZmNor6vAvsFZANSdkSBKGUuVa8bYFYyxNxljhxljn9NZ5mLG2F7GWD9j7BnF83+TeK6PMfYbxhi1TyNyxr8Qwc9fOI7TVnnwwXPXYGOrBy8cmTT8fp8Bv/SGFjcA4I1RX/r7F6IAMndnJIiVxoUbm/CVazfh0lObF3soS4KOugq8NeYHAKoWQhAljGXBNWPMDuB7AK4EsAnA+xljm1TL1AL4PoDrOOddAG5KPN8O4K8BbOecdwOwA7jVqrESy58H941gPhLDF6/ZhH+46jRc1d2KYDiGQCia9b2xOIc/FM3ql250u9BaXY6+IW/aa95g9tbnBLHScDps+PB56+B0kEPRCKvrJesZQMo1QZQyVp7RzgJwmHN+lHMeBnAXgPeolrkNwL2c8xMAwDkfV7zmAFDBGHMAqAQwbOFYiWXOzt4BnNLiwZbOWgDJVtfjvnR/tBr/gvHAuLu9Gv3DWsq1tA5KaCQIIleUXU7Jc00QpYuVwXU7gAHF48HEc0o2AqhjjD3NGNvDGPsQAHDOhwB8E8AJACMAvJzzxywcK7GMeWPUh30Ds7h5R6fcLrjZI7mMxv2hrO8X3RmNNIDpaqvBkYkAguFURdzMOgiCILQQCaAAUFdJ5xKCKFWsDK6ZxnNc9dgBYBuAqwFcDuCLjLGNjLE6SCr3OgBtAKoYY7drfghjH2OM9TLGeicmJgo3emLZsLNnAE67DTecmby3EzV1jQTXvnnjfunu9hrEOXBwxK9aB9lCCILID1GOz+WwoaLMvsijIQhCDyuD60EAnYrHHUi3dgwCeJRzPsc5nwTwLIDNAN4F4G3O+QTnPALgXgDv0PoQzvkPOefbOefbm5qaCr4RxNImFI3hvteGcFlXS4pHsdlj3BbiNREYdyVKivUPp/quSbkmCCJfhHJdX+WUZ+EIgig9rAyuewBsYIytY4w5ISUkPqBaZjeACxhjDsZYJYCzARyEZAc5hzFWyaQzyDsTzxOEKR7rH8NsMIJbd3SmPF9TUQanw4YJI8q18EsbKKO3qqYc9VVO9A+l+q598xEwBnhcVIqPIIjcqK0sg9vloEohBFHiWHal55xHGWOfAPB7SNU+fsI572eM3ZF4/U7O+UHG2KMA9gOIA/gx57wPABhjdwN4FUAUwGsAfmjVWInlywP7htFeW4HzTmpMeZ4xhia3y5Tn2ohyzRhDV1s1+lTKtW8hCo/LAZuN1CaCIHKDMYZ1jVVocFNwTRCljKUyGuf8EQCPqJ67U/X4GwC+ofHeLwP4spXjI5Y/w7PzOLXVoxnUNle7MO7PbgsRfmmjlT662mrwv88dRSgag8sh+SK98xHUUAISQRB58p83b0aZnUoXEkQpQ79QYlkzMxfWLVnV7HEZsoV45yNw2BgqncYSiLrbqxGJcRwaC8jP+eYjVIaPIIi82dDiwdrGqsUeBkEQGaDgmljWTAfDus0Wmj3lhm0h1RVlhhOIuhOtnJVJjV4DHR4JgiAIglj6UHBNLFvmwzEsROKo1bFjNHlcmA1GEIrGMq7HtxA1FRivrq+Ex+VAnyKp0bdAyjVBEARBrAQouCaWLdPBMACgXiezXpTjy2YNEcq1UWw2htNUSY2kXBMEQRDEyoCCa2LZMjMnBde6nutqY41kJL+0udzf7rYaHBzxIRbniXVEKaGRIAiCIFYAVHSXWLZMJ4LrTJ5rABj3JYPrSCwO73wEjW6X/JxvPoKOugpTn93dXo2FSByP9o2ipdqF+UjMdIBOEARBEMTSg672xLJlJmEL0Wu4kLSFJMvx/e9zb+P7Tx3Gni9eJpe78i2Ys4UAwObOWgDAX/76VcXnlZtaB0EQBEEQSw8Krolly0wW5brB7YKNpdpCXnl7Gr6FKEZmF7C6oRKc85z80ic1uXHfX7wDs4ka2WU2G85aV5/jlhAEQRAEsVSg4JpYtkwHpZbjeoGx3cbQ4Hal2EJE+byBmSBWN1RiPhJDJMZzqvRx5uq63AZOEARBEMSShRIaiWXLzFwYtRVlsGdoOd7scWEiIAXX4/4FjCUC7YHpIAApEREw1vqcIAiCIAiCgmti2TId1O/OKGjyJFug9w8n61KfSATX3oStg4JrgiAIgiCMQME1sWyZmQvr1rgWNHuStpADieC6ocqJgZl5AFIyIwBUV5CDiiAIgiCI7FDEQCxbpufC6KirzLhMs6cck4EQYnGOviEv1jZUoq22QraFeIOkXBMEQRAEYRxSrom8mAtF5SRAs3DOsef4NOKJRit6vD7oxXw4c4tyLWaCYdRXZQ6Km6tdiHNgai6EvmEvutpr0FlXicGZhOdaKNfUupwgCIIgCANQcE3kxb/97iBu/P4LiMbipt/71JvjeO8PXsQfD0/qLuMNRnDD95/HNx9709S6OeeYmYtk9VyLWteHxwMYmJ5Hd1sNVjdUYjIQRjAcJc81QRAEQRCmoOCayJlgOIrdrw0jFI3LQagZfvPKAADIKrEW/cNeROMc9746iFDUuHo9F44hHItn9Vw3JRq7PP3mBACgq61a7sY4ODMvb5eHuisSBEEQBGEACq6JnHnk9VH4Q1KpOtEN0SjjvgX84Y3xxN8h3eX6EpaTmWAEjx8YM7x+0UDGqHItxtLVVo3OesmnfWIqCN98FG6XAw47/VQIgiAIgsgORQxEzuzqGYAoIT09Z065vufVIcTiHE6HLaVDopr+YR9aq8vRXluBnT0Dhtcvgv3synXSFtJWU44GtwudiSTIgZlgTt0ZCYIgCIJYuVBwTeTEkYkAXjk2jWvOaAMgVeYwCuccu3oHcNbaeqxvrMJEos60Fn1DXpzeUYP3bevAc4cnM1pIlEwbVK7Ly+yoTlg+NrXVAAAa3U5UlNkxMD0P30KELCEEQRAEQRiGgmsiJ3b1DMBuY/izC9YBMGcLeeXtabw9OYdbdnSiubocEzrK9VwoiqOTc+huq8FN2zsAAL/tHTT0GbJynSW4BoDmasl33d1eDQBgjKGjroKUa4IgCIIgTEPBNWGaSCyOe14dxKWnNmNjiweAOeV6Z+8APC4Hrjp9ldTERSe4PjjiA+ciybAS55/ciN/2DiCWpXSfNB7JppLNFgIkfdfdCeUaAFbXV2JgOgjffATVFFwTBEEQBGEQmu8ucSKxOB7cN4zrNrdZllS3b2AWFU67HChrcXQigEf7RwEAQzPzmAyEceuOTpSX2VHptMsJhFoEw1H85pUBhKIxcA488voI3ru1AxVOO5o9Lkz4Q4jHOWzCwJ1AtCPvbpeC3lt3rMZf/vpVPHd4EhdtbMq4TTNzYdiYsSofIrjuSijXANBZX4mX355GdbmDlGuCIAiCIAxDwXWJ8+TBMfztrn2IxTlu2t5pyWf8zc696KyvxM8+cpbuMt/9w2Hc+9qQ/Hh9U5Uc4NZVOjGdwRbyWP8Y/vmhA/Jjp8OGD5y9BoCUUBiNc8wEw2hwu1Le1zfkRaPbiZZq6fl3bWpGRZkdT785njW4ng6GUVfpTAvYtdi6pg5HJ+fQmrCHAEBHXQUCoSiC4SgF1wRBEARBGIaC6xLn9SGpFN2u3gFLgmv/QgRHJ+dQlkUVn5wL44yOGuz6+LkAAKfdJgeu9VXOjMr18SkpCXHfl98Nl8MGu43Jn9ecqDM97g+lB9fDPnS11YAx6XNcDrts18jGzFw4azKj4EPnrsWHzl2b8pwoxxfn1J2RIAiCIAjjkOe6xOkbkqwRPcdmcHg8UPD1H0hYL8YzVOwApGC1ocqJ8jI7ysvsKYpwXZUT00H9UnwDM0G0VLtQU1GG8jJ7SiDfnFCl1b7rUDSGQ2N+dLVVpzzfWV+Bgen5rNs1PRc25LfWQ5TjA4CaCroHJQiCIAjCGBRclzCcc/QNeXHxKU2w2xh+22u8zrNR+hLB9UwwgnBUv4X5dAYluL6yDLMZbCED08GUYFWJ8DuP+1KD+7dGA4jGuey3FnTWV2JgJgjOMyc1zgYjqKvKXXHurK+Q/6aERoIgCIIgjELBdQkz5gthai6Mizc24dJTm3HPq4OIxPQD4FzoT9hOAGAioN/MZSbhYdaittKZsVrI4My8bLNQo7SFKBGdGZUVPABJUQ6GY1mrk0wHw4bK8OnhKS9DXaUUVJPnmiAIgiAIo1BwXcL0iwCzvQa37ujEZCCMJw+OF/gzfHA5pMNArR4LFiIxBMMx3WC1vsoJ/0JUM/APR+MY8c6js65C451AhdMOj8uRVuu6b8gLT7kjRUEGkl7ogRl9awjnXPJc52ELUX4WKdcEQRAEQRiFgusSpm/IB8aA01ZV46KNTWj2uLCrgNaQ+XAMh8b9eMdJDQDS1WPBbMJPrResCruIViOZ4dl5xDl0lWsAaKp2pQXX/cM+dLVVy8mMAhFsZ0pq9IeiiMZ5Xso1kPRdk3JNEARBEIRRKLguAgeGfbjrlROm39c37MW6xipUuRxw2G24aXsHnn5zHCPe7Al9Rnhj1Ic4By49tRkAdDslCgtGvY6HWSQOzsylJzUOJNqVZwqupUYySdU8Govj4IgvzRICJAPegQxt0EXlknyV645EIE/BNUEQBEEQRqHgugj8z7NH8Ll7X8eJqewl5JT0D3lTAsyrTl+FOJcqhxQC0aTlwo1NYExfuRaKtL5yLQWfWj5oUdkjo3LtKU/57CMTcwhF42nJjABQ5XKgvsqZUblO3gzkF1xfckozzj+5Me/1EARBEASxcqDgugj0KWpVG2V6Loxh7wK6FV0D1zZUAchsiTBD/7AXtZVlWF1fiYYqFyZ0yvFlC1brM9hCBmaCKLOzlAYtapo9Loz7QnIFEPF9qcvwCTrrKzOW4xPjqK3MT3E+Z30DfvlnZ2etAU4QBEEQBCGgqMFiguEojk7OAQDu3jOIqMFqH/0a1TKqXA40VDkxmMESYYa+Icl6wRiTA1wtZOVatxSf9Ly2ch1EW20F7Bk6JTZ7XJiPxBAIRQFIinp5mQ3rm9yay3fWVWS0hUwn7CmkOBMEQRAEUWwouLaYgyM+cA7ctK0Do74FPHtowtD7RPOYTSr1tiOLamuUSCyON0eTTVqaPC5dW4gImmt1vMe1sudaO7hencESAqQ3kukb9mLTqmrdgLyzvhLDs/OIxbVrXcueawquCYIgCIIoMhRcW4wIkv/6nRvQ6HZiZ48xa0jfsBcddRVy4CrIptoa5dBYAOFYHF0JX7M6qVDJzFwYNRVlcOjYI5wOG9wuB6Y1bSHz6NBpICOQa137QojHOQ4M+zT91oLOukpEYhyjOqUDZ4JhOGwMHhd1ViQIgiAIorhQcG0xfUNeNFQ50VFXgfdu7cCTB8d1q3IoOTCsUy2jvhJDM/qqreFxybYTSblurnZhMhDWXO90MCI3VNGjrqpMLtknmAtFMT0XTqtVrUbu0uhfwInpIAKhqK7fGshejm8mKHWTVJfxIwiCIAiCsBoKri2mf9iHrnbJ13zT9k5E4xz3vjqY8T3+hQjenpzTDDA76yoRjfO8y/H1D3lR5bTLSZLNnnLE4lzTNz2TofW5oF6jS6Nchs+gcj3hD8lBf5fGjYVALsenE1xPz4VlHzhBEARBEEQxoXnzPPm3Rw4iFuf4wjWb0l4LRWN4a8yPi09pAgCc3OzGjrV12Nk7gI9fdFLKskcmAvjawwcRicUxl0js07JGJFXbpN0iEovjs/fslxVxG2P4u3dvxBkdtbrj7h/2YVNbNWwJX7NQjyf8ITQl/hZMz4Wxqka/2gcg+ZvTgmsDZfgAoLrCAafDhnF/CJOBMMrsDBtbPLrLt9VWgLHU4Po7Tx7CK8emAQCvD3lxaqv++wmCIAiCIKyClOs8mQmG8etXTsiVLpS8NRpANM5TguTLNrXg6MQcvCoLxRMHxvCHN8bl9Vx8ShO2r61LW+fq+vQmKm+M+HHvq0MY9S5gLhTFc4cn8fDrIxnHfXgigA2KADaZVJjuY54N5qZcn5gWynVmWwhjDE1uqUtj/7AXG1s8cDr0D02nw4a2mgq5BfqIdx7/9cRbODEdxFwoivWNVbh+S3vGzyQIgiAIgrACS5VrxtgVAP4bgB3AjznnX9dY5mIA3wJQBmCSc34RY+wUADsVi60H8CXO+besHG8u3LJjNXb1DuLh/cO4ZcfqlNeSFoekvUMZHNdUJoPugZkgairKcN9fnJfx89pqK2BjwKBCtRWf87//bwdWN1Tikm8+jcEMFUX8CxHMBiMpVTzkpEINP/h0MJy1rF1dlTOtWsjAdBBVTruhknjN1VJC5cERPy47rSXr8h11FbJyfXfvIOIc+MVHzsbqhswqOUEQBEEQhJVYplwzxuwAvgfgSgCbALyfMbZJtUwtgO8DuI5z3gXgJgDgnL/JOd/COd8CYBuAIID7rBprPmxdXYuTm92aVUD6h73wlDtSgtgOHb/wwPR81sQ/ACiz27BKodoCUtKkp9whv78jS0UR2a6h8EI3KWwhSubDMSxE4llbiddXOTEXjmEhEpOfG5wJorO+0lBiYbPHhdcHvZieC6c0ztGjs74SAzNBxOMcu/YM4B0nNVBgTRAEQRDEomOlLeQsAIc550c552EAdwF4j2qZ2wDcyzk/AQCc83GN9bwTwBHO+XELx5ozjDHcuqMTr56YxaExf8prfUM+dLVVpwSXnRq2DvE4W+KfoKOuQrZcAImkScXnrK6vzNjFUU40VATz5WV2eModGFeVtxPl9eqrMlcLEd0QlRVDlL7wbDR7yuFbkCwxXRnK8Ak66yox5gvh6bfGMTA9j1t2dBr6HIIgCIIgCCuxMrhuB6CUcwcTzynZCKCOMfY0Y2wPY+xDGuu5FcBvLBpjQbjhzHaU2VmKeh2NxXFwxJdW9aKmogzV5Y6URjDxOMfg9HzWZiuCTkXwLD5HWbavs74SM8EI/AsRzfcPTGtX8WjWaCQjN2TJplyrujRyzqUbBgNqvPhsALAx4LRWI8q1tN7/fPwt1FSU4fKuVkOfQxAEQRAEYSVWBtdaXgB1EWUHJNvH1QAuB/BFxthGeQWMOQFcB+C3uh/C2McYY72Msd6JCWPdDwtNg9uFyza14N7XhhCOSu3Nj0zMIRSNa1ochKVBMO4PIRyLo8NocF1XiXF/CAuRmPw5XYrPSZaq0/ZdD0wH4XE5ZLVZ0OwpTwuuRbBsxHMNJFulT82FEQzHDKvxIqHypCY3Kpz2rMuLGYC+IR+u39KG8rLs7yEIgiAIgrAaK4PrQQDKufoOAMMayzzKOZ/jnE8CeBbAZsXrVwJ4lXM+pvchnPMfcs63c863NzU1FWjo5rl5eyem58J44qA01H65SUu6xWF1fWWKrSNZD9qYyru6QVpucGZe83Pkcn06vuuBmXl0aHihRVKhEhEsqztFqhHBtwjGhTpuVI0XCZWZOjMqUa73ZrKEEARBEARRIlhZLaQHwAbG2DoAQ5DsHbepltkN4LuMMQcAJ4CzAfyX4vX3o8QtIYILNjShraYcn71nP77x+zcxEwyjvMyG9U3utGU76yvx5BvjiMc5bDaWtGmYUK4BKXjuG/KlfU62JisD00Gsa6xKe77Z48K4LwTOuRx4G1auE8H3bCIYPz5lbptEQmWmzowpy7tdcDpsOKXFk7HhDEEQBEEQRDGxLLjmnEcZY58A8HtIpfh+wjnvZ4zdkXj9Ts75QcbYowD2A4hDKtfXBwCMsUoAlwH4uFVjLCR2G8NX39ONB/clxflta+pgt6W7YzrrKhCOxjERCKGluly2b7TXGlOuRcA6OB1E37AXm1ZVp3xObWUZ3C4HBmfSbSGccwzOzOPCjekqf7OnHKFoHP5QFNXlkmVkZi4MxiSveCaExWR6TvJ5P9o3ivoqp2YQr8WprR584pKT8R6D9altNoYvXrOJmsUQBEEQBFFSWFrnmnP+CIBHVM/dqXr8DQDf0HhvEECDleMrNJdtasFlmwzUaK5PKsst1eUYmAmipdpl2DcsVNvjU0EcHPbh+jNTA1LGGDpV1hPBZCCM+UhM04Ii1ONxX0gOrqeDYdRWlGneJCgps9tQXe7ATDCMCX8ITxwcw5+ctzZjMxglDrsNn778FEPLCj54zhpTyxMEQRAEQVgNdWhcBJS2DkDqZGg08Q+QVNuOugo8d3gS/lBUO2lS0WRFyYkMFhRRsUPpu56Zi2TtziioT7RAv++1QUTjnMrjEQRBEASx4qDgehHoSKjGwg4yOB00nPgn6KyrxBujUl1tLc9xZ30lBmfmwXlqgZbBmQzBdXV6I5mZYFgus5eN2kQL9J09A9i2pg4nN5NlgyAIgiCIlQUF14tAeZkdzR4XBqaDCEfjGPEtGC7DJxAVQcrsDBtb0oPYzroKzEdimAyktySXXk//vCbRAt2XDK6n58KmlOve49M4MjGHW7aTak0QBEEQxMqDgutFYnWi1vXw7Dw4N16GT/l+ANjY4tH0Net2gpyeR6PbpVlLurrcAZfDlmoLMaFc11U6sRCJo8ppx9VnrDK8LQRBEARBEMsFCq4XCanL4ryiFbl5WwigXUcbSAbfat91pq6JjLFErWtJueacY2Yugtosrc8FokX6tZvbUOWyNFeWIAiCIAiiJKHgepHorKvAiHceb0/OSY9N20ISwbVGMiMAdOjUus6WPNnsKcdQooTfXDiGcCxuWLlucEuebWrqQhAEQRDESoXkxUWio74ScQ68fHQaZXaG1upyU+/vaqvG1288HddubtN8vcJpR6PbldICPRqLY8S7oKtcA8A7TmrAd586jOHZecTiUjKkUc/1jVvb0exx4czOWuMbQhAEQRAEsYwg5XqREOrxi0en0FZbkbWOtBrGGG49a3VG+0VnfUWK53rEu4BYnGdUrm/a1gnOgbv3DCa7MxpUrps95bhxa0daW3WCIAiCIIiVAgXXi4RQj6fnwqZqXJv6jLrKlODaSJv11Q2VOO/kBuzqHcDUnOS9NqpcEwRBEARBrHQouF4kVtVUwJFQq836rY3SWV+B4dkFRGNxAMnKIdlqat+8vRODM/N4aP8IAKnEHkEQBEEQBJEdCq4XCbuNoT1Rfi+TBzofVtdXIhbnGPFKpfVOTAdhtzGsqsns7768qxU1FWV4YO8wAOO2EIIgCIIgiJUOBdeLiLCDWGkLAZJ2kIHpeayqKYfDnnm3l5fZccOZ7YjGOew2Bk855b0SBEEQBEEYgYLrRUQo1tbZQqT19g/75LJ/RgP5WxLl9Ooqy2AzmWxJEARBEASxUiFJchFZ11gFGwPWWBRcr6oph9Nhw9ceOYivPXIQAPD+s4zVoD5tVTU2d9QgHOOWjI0gCIIgCGI5QsH1IvKBs9fgzNV1llXjcNht+PlHzsKxRKMaxoCLT2k2/P7v3rYV85GYJWMjCIIgCIJYjlBwvYhUuRzYsbbe0s84Z30DzlnfkNN7rbKrEARBEARBLFfIc00QBEEQBEEQBYKCa4IgCIIgCIIoEBRcEwRBEARBEESBoOCaIAiCIAiCIAoEBdcEQRAEQRAEUSAouCYIgiAIgiCIAkHBNUEQBEEQBEEUCAquCYIgCIIgCKJAUHBNEARBEARBEAWCgmuCIAiCIAiCKBAUXBMEQRAEQRBEgaDgmiAIgiAIgiAKBAXXBEEQBEEQBFEgKLgmCIIgCIIgiAJBwTVBEARBEARBFAgKrgmCIAiCIAiiQFBwTRAEQRAEQRAFgoJrgiAIgiAIgigQFFwTBEEQBEEQRIGg4JogCIIgCIIgCgQF1wRBEARBEARRICi4JgiCIAiCIIgCQcE1QRAEQRAEQRQICq4JgiAIgiAIokBYGlwzxq5gjL3JGDvMGPuczjIXM8b2Msb6GWPPKJ6vZYzdzRh7gzF2kDF2rpVjJQiCIAiCIIh8cVi1YsaYHcD3AFwGYBBAD2PsAc75AcUytQC+D+AKzvkJxlizYhX/DeBRzvn7GGNOAJVWjZUgCIIgCIIgCoGVyvVZAA5zzo9yzsMA7gLwHtUytwG4l3N+AgA45+MAwBirBnAhgP9NPB/mnM9aOFaCIAiCIAiCyBsrg+t2AAOKx4OJ55RsBFDHGHuaMbaHMfahxPPrAUwA+D/G2GuMsR8zxqq0PoQx9jHGWC9jrHdiYqLQ20AQBEEQBEEQhrEyuGYaz3HVYweAbQCuBnA5gC8yxjYmnt8K4Aec8zMBzAHQ9Gxzzn/IOd/OOd/e1NRUsMETBEEQBEEQhFmsDK4HAXQqHncAGNZY5lHO+RznfBLAswA2J54f5Jy/nFjubkjBNkEQBEEQBEGULFYG1z0ANjDG1iUSEm8F8IBqmd0ALmCMORhjlQDOBnCQcz4KYIAxdkpiuXcCOACCIAiCIAiCKGEsqxbCOY8yxj4B4PcA7AB+wjnvZ4zdkXj9Ts75QcbYowD2A4gD+DHnvC+xir8C8KtEYH4UwJ9YNVaCIAiCIAiCKASMc7UNeumyfft23tvbu9jDIAiCIAiCIJYxjLE9nPPtWq9Rh0aCIAiCIAiCKBAUXBMEQRAEQRBEgaDgmiAIgiAIgiAKBAXXBEEQBEEQBFEgKLgmCIIgCIIgiAJBwTVBEARBEARBFAgKrgmCIAiCIAiiQFBwTRAEQRAEQRAFgoJrgiAIgiAIgigQFFwTBEEQBEEQRIGg4JogCIIgCIIgCgQF1wRBEARBEARRICi4JgiCIAiCIIgCQcE1QRAEQRAEQRQICq4JgiAIgiAIokBQcE0QBEEQBEEQBcJQcM0Yu4cxdjVjjIJxgiAIgiAIgtDBaLD8AwC3ATjEGPs6Y+xUC8dEEARBEARBEEsSQ8E15/wJzvkHAGwFcAzA44yxFxhjf8IYK7NygARBEARBEASxVDBs82CMNQD4MIA/A/AagP+GFGw/bsnICIIgCIIgCGKJ4TCyEGPsXgCnAvgFgGs55yOJl3YyxnqtGhxBEARBEARBLCUMBdcAvss5/4PWC5zz7QUcD0EQBEEQBEEsWYzaQk5jjNWKB4yxOsbYX1gzJIIgCIIgCIJYmhgNrj/KOZ8VDzjnMwA+asmICIIgCIIgCGKJYjS4tjHGmHjAGLMDcFozJIIgCIIgCIJYmhj1XP8ewC7G2J0AOIA7ADxq2agIgiAIgiAIYgliNLj+LICPA/hzAAzAYwB+bNWgCIIgCIIgCGIpYii45pzHIXVp/IG1wyEIgiAIgiCIpYvROtcbAPwbgE0AysXznPP1Fo2LIAiCIAiCIJYcRhMa/w+Sah0FcAmAn0NqKEMQBEEQBEEQRAKjwXUF5/xJAIxzfpxz/hUAl1o3LIIgCIIgCIJYehhNaFxgjNkAHGKMfQLAEIBm64ZFEARBEARBEEsPo8r1pwBUAvhrANsA3A7g/1k0JoIgCIIgCIJYkmRVrhMNY27mnH8GQADAn1g+KoIgCIIgCIJYgmRVrjnnMQDblB0aCYIgCIIgCIJIx6jn+jUAuxljvwUwJ57knN9ryagIgiAIgiAIYgliNLiuBzCF1AohHAAF1wRBEARBEASRwGiHRvJZEwRBEARBEEQWjHZo/D9ISnUKnPOPFHxEBEEQBEEQBLFEMWoLeUjxdzmAGwAMZ3sTY+wKAP8NwA7gx5zzr2ssczGAbwEoAzDJOb8o8fwxAH4AMQBRzvl2g2MlCIIgCIIgiEXBqC3kHuVjxthvADyR6T2JEn7fA3AZgEEAPYyxBzjnBxTL1AL4PoArOOcnGGPqxjSXcM4njYyRIAiCIAiCIBYbo01k1GwAsDrLMmcBOMw5P8o5DwO4C8B7VMvcBuBezvkJAOCcj+c4HoIgCIIgCIJYdAwF14wxP2PMJ/4BeBDAZ7O8rR3AgOLxYOI5JRsB1DHGnmaM7WGMfUjxGgfwWOL5jxkZJ0EQBEEQBEEsJkZtIZ4c1q3VdEadFOmA1E79nQAqALzIGHuJc/4WgPM458MJq8jjjLE3OOfPpn2IFHh/DABWr84mphMEQRAEQRCEdRhVrm9gjNUoHtcyxq7P8rZBAJ2Kxx1IT4IcBPAo53wu4a1+FsBmAOCcDyf+HwdwHySbSRqc8x9yzrdzzrc3NTUZ2RyCyJ9oGIjMG/8XDS32iAmCIAiCKAJGq4V8mXN+n3jAOZ9ljH0ZwP0Z3tMDYANjbB2AIQC3QvJYK9kN4LuMMQcAJ4CzAfwXY6wKgI1z7k/8/W4A/2RwrARhLQM9wP9dAcSjJt7EgPf/BjjlSsuGlTPDrwG/uhm44znA07LYo1lcYlHg++cAl3we6H7vYo+GIAiCWIIYDa61FO6M7+WcRxljnwDwe0il+H7COe9njN2ReP1OzvlBxtijAPYDiEMq19fHGFsP4D7GmPicX3POHzU4VoKwltH9UmB94WcAZ1X25TkHnvwqMLK/dIPruXFg4g0KrufGgalDwOAeCq4JgiCInDAaXPcyxv4TUmk9DuCvAOzJ9ibO+SMAHlE9d6fq8TcAfEP13FEk7CEEUXIExgAw4KLPAXaDP6EXvwsERi0dVs74x6T/A2OLO45SwJ/YR6W6rwiCIIiSx2gpvr8CEAawE8AuAPMA/tKqQRFESeMfBaoajQfWAOBuTQaxpYYIJP0UUMo3GKW6rwiCIIiSx2i1kDkAn7N4LASxNAiMScGyGTwtpauGknKdhJRrgiAIIk+MVgt5PNFNUTyuY4z93rJREUQp4x81700m5XppQMo1QRAEkSdGbSGNnPNZ8YBzPgNA3aqcIFYGOSvXY1JyY6lBynUScYMR9gPhucUdC0EQBLEkMRpcxxljcocWxthapDeEIYjlTzwGBMZzU67jESA4bc24ciUelypkAKRcA6k3GPR9EARBEDlgNCPrHwE8xxh7JvH4QiS6IhLEiiI4BfBYbso1IFkwqhoKP65cCU5JZQXtTlKuASmgtjuBWFj6PhpOWuwREQRBEEsMQ8p1osb0dgBvQqoY8neQKoYQxMpCqJmmleuW1PeXCsJv3bwJCPmAcHBxx7PYBMak7wIovX1FEARBLAmMJjT+GYAnIQXVfwfgFwC+Yt2wCKJEEequWeVaBNelpg4Lv/WqRFn5lVwlIx6X9o/8XZTYviIIgiCWBEY9158EsAPAcc75JQDOBDBh2agIolTJVbn2tKa+v1QQwbQIKFdylYz5acki03waYCsrvX1FEARBLAmMBtcLnPMFAGCMuTjnbwA4xbphEUSJIoJRt8ng2lkFOD2lp4b6VcH1Slau5RunVmn/ltq+IgiCIJYERhMaBxN1ru8H8DhjbAbAsFWDIoiSxT8GuGqAsgrz7/W0lJ4aGkhsT91a6fFKVq7lG6fW0txXBEEQxJLAaIfGGxJ/foUx9hSAGgCPWjYqgihVAjk0kBG4W0tPDRUNcSrqAZtjhSvXiX3jaZH21czbizsegiAIYkli1BYiwzl/hnP+AOc8bMWACKKk8Y+Zt4QISlENDSS2x2aT/iflmpRrgiAIIi9MB9cEsaIJjCaTE80ilOtS6tLoV2yPu4WUa1c14KyU9tX8NBAlDYEgCIIwBwXXBGEUzvNXriNBIBwo7LhyhfOkcg1IQfZKV67l76JESycSBEEQJQ8F1wRhlAUvEAvlp1wDpRPAhnxAdEGlXJfI2BYD/5jiu0j8HxhfvPEQBEEQSxIKrgnCKLk2kBEoW6CXAn7V9nhageAkEIss3pgWE03lukT2FUEQBLFkoOCaIIySawMZgaxcl0jAFlBtj9xFcgWqtcLyo1auS2VfEQRBEEsGCq4JwigFU65LxHqhpVwDK1OtDfmA6HzyBqOqCQArnX1FEARBLBkouCYIo+SrXJfXAnZX6aihesp1qXjCi4lc4zpxg2F3SAF2qewrgiAIYslAwTVBGCUwBjgqpHJtucCYFMiWihrqH03dnpWsXGu1tS+lfUUQBEEsGSi4JgijiG6GjOW+Dndr6aihgbHU7alqBsBIuRaU0r4iCIIglgwUXBOEUQJjufutBaWkhvpHU7fH7gCqGkm5FpTSviIIgiCWDBRcE4RRhHKdD6WkhgbGAHdz6nMrtQW6fxRwlAPlNcnn3C1S5ZR4bPHGRRAEQSw5KLgmCKMUQrl2twALs0BkoSBDygtl6TnBSm2BHhiXtl1p+XG3AjwGBKcWb1wEQRDEkoOCa4IwQjgolWvLV7kulXJ8kXkg5E1v5b5SW6AHRtNvNMS+KpWZBoIgCGJJQME1QRhB9uTmq1yLihyLHMDKZQU1lOu5cSAeL/6YFhP/WPqNRqnsK4IgCGJJQcE1QRhBriZRIOV6sdVQvYY4nlYgHl15VghSrgmCIIgC4VjsARBEGpwDz34D2HIbUNOR+tpLdwJrzwdau/P7jBMvAVOHgTNvN7Z8oZXr578FHNid37rUrD4HOOujxpbVa4gjt0AfBdxNhRtbMQmMA09+Nelrd7mBd/8L4PJoLx+ZBxY0LDKycp0luD6wGyirAja8y/xYR/uAF76dTJqs6QDe9RVj5R4j88Dv/wFY8EmPHS7g0i8A1W3GPvulO4HBnuTjLbcBJ78zdZmDDwL99ycfr78Y2PpBY+svFJwDz34T2HwrUNupv9yenwHNm4DOHcbWe/QZYG4COP19hRlnJmJR4LF/BOYmMy/HbMA5fw60b019fv8u6eZv3YX6781ne7xDwFNfA6Ih6XF5DXD5vwJl5ebX9cJ3gI1XAI0bzL/XCCP7gYGXjZ/rzMI58MSXpe/EDFveD5ycwzmgUMQiwFP/Cpz3SaCi1vz7X/050HQq0HlWwYe2EqHgmig9fMPSid5RDpz318nno2Hg0c8CZ30MuOob+X3GSz8A3n7GeHCtVQc5F6qapBPw9NvA/Gv5rUtJcBI4/ITxC04m5RqQtrf19MKNr5gcfhJ47ZdA7WopaPUNAV03Ausv0l5+fkb6v7Ih9fmyRPWQbB70Z74BlFfnFlzvv0sKnOrXSwF+cBJ4x19JJRGzMbQH6P0JUN0O2MuAmWNAx3Zg+0eMffaz/y4FfVWNgHcQCAfSg+vnvw2M9QGeVVLgNvBK8YNr/yjw1L9IpSLP/xv95X7/D8ApVxkPrl/4tvQ7LEZwPXEQePlO6Xssq9RfbuaYdCypg+vHvyz9HjMF1y98B5g+ktv2HHkS2PsroHaNNHPlGwLOuFm6YTfD/Czw2BeA8Bxw8efMj8MIe38FvPJD6Ti32Qu//sAY8Px/S+dqow3DvINSTs5iBtcj+4Hn/hNYtRnout78+3//j8Cp11BwXSAouCZKj3BA+l/tdRWPCzFNHxiTgqpoSFL8si4/CtgcQEV9fp9rswG335PfOrR45t+lG5LIgjG1yZ/YHnVAqVSulypi7H/+AjB1BPjhRcljSotQ4jUtZdvdmv27CPuBSDC3sfrHgJpO4K9fBfb+Grj/z4GQ31hwLX4Ht98DNJwM/HOTuWTUaAjY9mHg8q8Bv7hR+3cVGJUuuO/9EfDYF6UAkfP8GimZRey7TNsWCkjLmTlu/WOZj4tCIsZ+008zB6zfOzt9P8TjUh5Etm0LjCaPZbMIxfqjfwD8I8Cd5+d2nhXn6KiF1ZBCAYDHpVmAfG16eusHgHd/Ddh8i7H3/PJ9i28fC/sT/+dwDIiEfbEOIm/Ic02UHvLFVHWyEifuQiSYiXUbXZdIeLOV6E/GbbIKSWBM6sio3h5ZuV7CwbV/TLJpuDzJgDk8p7+8ON6c7vTXPAbqfofncj8mA4ra6eLzM4015b1i9qFFUq4rG8wFl5F5aXYIkPa7ehs4T5RrbEkuEwsnlf5iId9sZ9g2+cbbxH4IjBr/rvNFq0mRFm6NxkXBKUlNzrZt/rHctycyL/3vcOWXyCvOG1aWGjVyPBRi/S6N84EepdBwSuz7XI4B8V0W6/ewAijRSIFY0YR0lGtx4s438OPc/MU4MJr9wriYeExeEPUa4pRVAK6axb9Q5ENKwFol/R/KoMjIwXVV+mtGlGuhmuaiGiqrlIjPN6o8+UcBuwuoqJMemymjGItKNbxFcK3VMGdhFoiFksGW2Ru4QhEyoFzLN8sGzw2xqKR8hgPFqYyjV51HjdY+FNs0l6GhUSwq2XYic7ltj1CuHeXSTZrNUbrKtZGZjEKsX+t8oIe7Vfr+F7PhlPidZDrX6SG+y1xnPog0KLgmSg9x95ymXIsL6LgUIOdKSDGNb/RiHBjP329tJSLwMXpBDIzrJ2eWggqTD35Fsx8jarB4TetiKpRrveMtHgOiCdUvl+9MWaVEVtkNXuACY6mNb8w0ABLBj7AQeTQa5qjzDBZrVkPsn4zKdeK1BW9Shc3E3DiAxD7N1dJjhsCYdNNaVpF5ObEPlceb2A/CCqHF3ASS25OD+hhdAJhdmgGx2aRZrXyUaxGsW4GR46EQ63fqJEBr4WlN7J8Ja8ZkBHHeIOW6JKDgmig99DzX4iITnZf8YbkSGFf8bULpXU7KtVLdVbPUW6AHFFYGkTyWj+c6FpKCNi2U6zUbjEQWElVKxI2AUNlNBNfKfWhGuRbBtVK5BlIDZ7WVQbYLKH4/xUA+H2T43JTftIHxKfdVMXzX6n2lh5b1RhlE6h1jymVyCZCiC8ljAUjcVOajXBu4wckVIzMZea0/ofyaUq5LoDmYHFzncDyL30yxchBWABRcE6WH+IGHfFKihUB54srnxKq8EBlZTywiVXEo5eC6qkkq42Xk5C6mxHWVawNWiFJG2abeZpPUa0Oeay3lOstNi3K9ZoMRsc5cPdfqxjdmGgDJwXUimdejETiLv2XlepGSXZVBg96Nh99AAJqyvDK4LoJap9WkSAs5SNMRAPS2Tbk9uUztRxdSE7vdGh58IxRFubbac504Hkx5rhVVlhaLfDzXYr9RcF0wKLgmSo+QjhqY8nceJ1YtdS4TcpBRwsG1zS4F2EYCPDElnk25zsd6s1gI/7Ny25xVmS8amRIas9ltlBcys8GIuhyiHFwbVa5HU61KZhoAyR7bhE1Bq0qM2GbxmssjJYoWO4DQOx8oSbnxNvKbViq9xVCuR43ZyuSbHB0BQG/b8t2e6EKqZSVv5boYnmurbSG5KNeLKEqE8lGuE/uNbCEFg4JrovTQC1j8o1IdViBP5Trx3to1xtZTqAYyVqNVaUALf5bt8bTmb71ZLLTqdzvdmdW8jJ7rLMq1MnnI7MVe3cjHZSK4joYk64ByO81MTSurQwDafurAmGSrUdplPCZ83YXCyOyA8txgVrm2OolLVF0xpFxrKKCBUalmu/hbixQlPoftiWgo18FJadbODEWpFiI811YnNJpQruWb8BKwheSUWC1mHBakmU0ibyi4JkoP5cVBfbFftTnxd57Ktd0FNJ1ibD2Fan1uNZ5Wg6pdlu3RusAvFbQ6TzqrMisyIb8URGo1pCimcm13SlUajFwctfahluqph7I6BKBdJUbkGShrWrtN+LoLRYqvXU+5HQNauqSkPNPKtcVqXcgn3awaUq61ZhDGpBuHTA2NrPBcA+b99VYr1/GYIhndwuDa7pKSO41SVg6U1y6ucp1XQmORcxBWAJYG14yxKxhjbzLGDjPGPqezzMWMsb2MsX7G2DOq1+yMsdcYYw9ZOU6ixAgHpEADSP7o4zHpRN+4QboI5DMlKJKLjCburTjlugSmOHNFa1+5PFlsIXP6U8AujxR463quE+u1O3NTrpkt2TCGsez+cPm9Ggq9GfVMXS0ESLcCBMbSA8JFUa4V5wO9bfOPSt0P3c3Gb5jFOq1unKG1r/TQst4IS0mmspDK7cmlFFs0lBpcy8mrJva1aEQi1mcF4rdhd1pnXQsFzPmtBUbFDauQPdc5Ktfy74GC60JgWXDNGLMD+B6AKwFsAvB+xtgm1TK1AL4P4DrOeReAm1Sr+SSAg1aNkShRQgGppbOy1mpwSioV5m41HkTq4R+V1uNpzVw7Vl5+DACTLtyljKfVWK3VQJbtWdLKtap8HGDMc603BcxY4iYsi3Jdty4H5Xo00chHoZg73cYubiLoyVm5FrYQZUDVoq1cK1kU5XpOCpztLu1ti4aB+elEAGrihrluXXL9VqK1rzKhvIFRWkoyNTTKd3v0lGuzTXnk9VlULUT5e4uFpFrsVnyGGb+1IN/rUr7k6rkWCfvF+j2sEKxUrs8CcJhzfpRzHgZwF4D3qJa5DcC9nPMTAMA5l+egGGMdAK4G8GMLx0iUIuE5ScFRnqyU0/35KgSBMSmwdLdkrh0rLz8qNVYwM024GMjbk6XWqj/L9ix15druTDZWAYx5rjP5K7W6FwqESthwUg7KtUZ5NpfB4Fpr9kFYOwwp18IWovDZqn9Xesp12F/cC3AokDwfaG2bbK9pNl7r2z8m7TOxfisxo1yL5eSmHglLibsl87YFxpPbk2tCo9pzDZgLFuXE7zYLlevEtoltteJGLxwwV+NaYKYUphXk6rkW+61Yv4cVgpXBdTuAAcXjwcRzSjYCqGOMPc0Y28MY+5DitW8B+HsARWifRZQUQklUKoZKf2ohlGuhcgHZL8Z+jSCjFDHa5EM0H9HDVZ2/9WaxECqf0ieczXMdzjIN7G7Orlw3nCSpp9Gw8bEGRtMDLmeVcc+10lIiMGrbkEvxKSpEiN8V58kpfi3lGijusREOSN+L3rYpzw1G2tWLDq316xPrL2HlWjkTo1fFpxDbo64WIma1zJxnxTFRt8Y6z7UIIMW2WiEAiOPNLFoNgIpJrp5r8R3Kxw8F14XAyuCaaTynPuocALZBUqgvB/BFxthGxtg1AMY553uyfghjH2OM9TLGeicmFrE7ElE4RLCjVAzTlOscg+vIgjSVKGwhQPZ1lXrrc4HRahF6rc8FwgqxFLs0au2rbFaLUJaLaaaav2kXezPByFi6NSfbjYD83lGp9KI6CdOoLUJUc1Ar19EFqbGNuoGMvP4cgq58SbnZ1vhc5blBtKHOVPEgOA3EI0BNp2Q1sdxzPSrdrLqqjS2v3E7lfvC0alsh5mekxjM1HdL25OK5VlcLsZdJs1tmbqLkKkyrrasWEiqCcp3tfKCHuyW9AVAxEeeN6Ly5ih/iO8xn5oNIw8rgehBAp+JxB4BhjWUe5ZzPcc4nATwLYDOA8wBcxxg7BslOcilj7JdaH8I5/yHnfDvnfHtTU1Oht4FYDMTJTakYKhPV3C1AyGCbYzXKKgvLTbk22gJd2WRFj8VOzskVrX0lrBZ6ilJWW0hLekMj+b0BSf31tEmPjQadsagUBKrH6sySfCnQm30w2gBI3aERSLUC6FXIWYwW6MIDq2eLUJ4bPC0AeGZrlLx8s/GbmXxQt6nPhlthvUlRrnXEAL9ie1wGE2LVqBMaAfONZPyjUp5MdZuFynVi2+oTgaAlyvVc7gmNwOKJEqGANJsFABETx4CsXIvgmjzXhcDK4LoHwAbG2DrGmBPArQAeUC2zG8AFjDEHY6wSwNkADnLOP8857+Ccr0287w+c89stHCtRSsgX09ZkrVX/mFSKqqw8vwu82l4CZFY/4nEp6XG5KNfxeLJaSrZ1LRvlukryouvdjGVKaAQyV04IKSwLgPFjcm4CANceq1HPtdYNn9EGQJqea8U26FXIycWLmy/Cc+1plVRBtZ9XJBxXNRmrciEr3a1SEGW551pnX+mhPL+lKNc6YoByXxk9ftSoPdeA+UYygTEpQbesUko+t6Jestg2zyrpcyzzXOcQXBsVN6yAc2ncVQmB0cwxLX4/YvYtl5kPIg3LgmvOeRTAJwD8HlLFj12c837G2B2MsTsSyxwE8CiA/QBeAfBjznmfVWMilgjhOUnBU9ZaVfpTzTTLUKOcQjZSmzQ4JXW9WwrKtdieTCf3+Wlpewwp10ssuBaNVdLU4CxtxbN5rjNVThAql9nSZQFFgKfEaLCXSbk20gBIVHNI8dlqKdeq8VXWA7ayxfFc6/3uAwmLjN1hzOol32C3GK/Okg/ZchzUKLdTWErKazIo14p95fTkUS2kIvW5XJRrT0tSAbeiYojYVy638eTVXD4jl+B6MZXrSBApN+tmjgGRsF9Ra/69hC4OK1fOOX8EwCOq5+5UPf4GgG9kWMfTAJ62YHhEKcK5NCUqlGtA+vErKyvkE1yrG3dksz/oeU9LlUyVLQDtJitaKK03ZRWZly0VlEGTEjm49gNQWcc4N+a5BrQv5OJCXNUEgBm/IdGrIGHEphCPaVtKgNTZmPIa/XUI9VfUtgVSlev5aWmKv6I+9X3F9uMLRc7pTg2cRcdC8Tjt3GBQuS5GcO0fA9ZfbHz5FOVaYSnJqly3FF65DoxJs102AzpcYEzyscvBdSi1u2chEDeezirrBIB8PNfA4ijX4pzhaQVG95vLIxBWurKq1HUReUEdGonSIrogTeG73KmKoVK5NpqIqIW6cUe2QEFPwStVMtVkBvSn+9Ushrc2X/T2lSuDch0NSVPY2UrxKdevRAR+docUYJtWrtU3Ap7sCUlzE9JvROuGz2gegahrrPQBu6ol9VIo1+4W7aDKrF0gH2JhaaYlRbnWCC7Vs1rZlGunR1qn1Z7ryLx0k2pKuVbOICgsJWL/aCnXTrd0nOdic4nHpe9Zy3Mdj0o3WkaQletEkG6F71rsK6dFynU0LCW75uK5ztZwykqElUMkHJtVrsVvvSzHmzMiDQquidJCVibc+sp1ZaPU5jiXE6u6cUc29WPZKdc6iWpqFsNbmy96+0qoUFpBR1hxvOlRUS+puJk814CxMnACWbnWGWumhCSl8qrG6I2nVgKbUEeF11fvmDdrF8gH5flA74ZPeW5wOKX9lU25Fstb7bnOtK/0UFpvlJYSsX80by4Sy+Rys6DVrRMwl0cgGpG4W5MzXVbUug77pRsMm90a5drI+UCPbA2nrETsc3HeNuu5FsdnrjMfRBoUXBOlhfLk5m4GwICJt6QSVOICYrMlKonkolyrkvmy1SbN5eK4mChrFWthWLlexCnOXNHbV6IhhFbQofRw6mGz6ZeBU1YWyNSeWk1gVGp0o56KF+vKdHFUW5uUGFWuI/PpwbVYp1Cu9Y75YirXyv1T1STNOikD+3gskXCsGGvWG2ZFtRyj7eZzJdO+0kNpvVHvB60OmSnBkcFqM0q0Kscox2zkmJYbyCiU61yqOWUj5femqKpSsPXnEVwD2cUNq5ATPRP7zOh3ok7YL0aC7wqBgmuitAgrPHWi1urIPuk55QUq1ylBdRk6T6s0JarXRjcwLnW9Wyq+Y7E9erVWA+PS9LKzMvN65AvreOblSonAeMLyo/JVCzVYy4eo9HBmQu94UyY/eVqMf1+Bce2AK1vyJZBaTlJNeY2xBkDRUHpgL9ZpRLk22zAnV5TnA5s9Yb1RBC/BKcki41GfG7IE1+K7c7qtrXOdaV9lwtMCzBxLt5R4NLYtTbk2G1xrVI4RnwUYEzGUN+1Kz3WhSZkpssC6ZvR8oMdiVVmSlWuR0GjwmBYJ7inKNXmuCwEF10RpIX7YQp0QCRpA6gUq1ylBdRm6bB7NwKj5C+Niki3Z058haFJS2aBvhShVRNUIdWOVTJ5rpYczE55W7cBZGVy7E8vEY9nHqtfIRw6uMwRIepYSwHjCobojn8DdCviGpaA1k3INSIqX1cj7JzH7oJ5BkGs8q84N2axR7iIFE2ZbnwvcrcDo69LfauVavW1K5ToX5VGrW6f4LMDYOUBpN5ODa4s818pjAShsMCtff3JMxFysKku5Ktfq308uMx+EJhRcE6VFSDUt525J/tjzVa5FlQW1cg3or0skdi0Vsqk5gQzT/UpsNsmbvpTK8entK2cGq4VQeLIF11peSnWlEU+rlBwZnMo+Vr1GPrLKnskWomMpERhpAKRVHQKQgqOISgVTo1cSzgpEopbyO1b+VmVlWEO51rJGhfzS9imV61jYOhU+MCrdpFY2mHufR+e8p25oJLZHqVzHI+a2Rw6uVceDs1Ka5cpZubbCFuK3VrkOq443s1hhVTGCOLeJhEajN1jqkqDkuS4YFFwTpYXa8+ZRXVjkv1uBuUlzjQpElYUU5TpLoBAYXTp+ayB7IqJR5RrQTp4qZfT2VSY1WFZGs1xMPa3JhkYCUWlE6QEFsl/sOU+fQRFkUtkF/tHMSqhR5VrPcy3IplwX49hQz2QZVa71rFFqJdllYKYgH/xjiQRqk5dadUCtfl589+oKOXJ+gYnt0fNcA8ZFDNGIxN2sqBZiRUKjKscBsEa5zsdzDRQ/V0X+nVRLFUuM7n/1LBh5rgsGBddEaRFWed7Ej95RIZ04BO4WSG2OTUxNyxdijQuX1gWE8yWoXGcI8OSgzuDNglbyVCmjt68cTqn6glbAKi4k2UpvyVPQiuNNfSE22kRifkYK/jJ5rrMlNGayKhmZmtaqFgJoW6bUuIsYQKjPB55W6TcvrDdaFWIy3eSoSyDKMwUWKY252sq0Amrl82L/qrffyMyHGhEEq6uFAMZtDqIRib1MUS3EAluIcqbIioZGRm+29bDCqmIE5QycGauTpnJNnutCQME1UVqoPW/iR+9pSa3Jm4tCoDWF7PJItT21LiAhnzS1uZSUa7E9Wif3kF/q5LUclWtRNUJvX7l0moWY8VwDqd+H2lJiVLnO1MjHqOc6m3ItGgDpkalaiEDvuxQNc4oRQGh5rnlcmrUCEs1yalMDw0xWL/UNtpHvOx+y7Ss9xHvUlpI05VoVHBmpNqNGHCf5KtdiDHK1EIs911Y0NBI2pHw818DiKNd2pyQkmGmM5B9LTdgnz3XBoOCaKC3UHksRsKgvULlMCWpNIQP6QWSuyUiLjV6pNK2bi0y4c7DeLBZzk/qNVQD9kmtGS29pJb6qVS6jZfAylUPMpjxynl0NNXKB160WIsbEJDuDFqJhTjECCC3PNZD8DrWsQJmSlNXVO4xUZ8mHfJVrtaVEXcdc3ZU0l+3RqxYiPs+fobSnQFmxxNKERn+qqlzospB5K9cWWFWMoFT0zZSXVB+fwnOdbX8TWaHgmigtwnPSVJ840SuVayW51GHWa4+tZ3/Q66JX6ug1+dC7udDDk4P1ZrFQT2+qcbqTgZqScEBqSKSXHCjQUkPVlpKycklFzTaNnqnrZzabQiZLicDIBV6vWohomFPVKAXRemiVhLOC8JxUXlGMVZ0joWUFyqZc213SfgIUSq8F5fhiUemmLx/lWn3uUTc0EttTUSc9loNrE9ujVy0EkL7b6Lw0i5eJFOVaBNcF9lxznuq5BvTPdbkSDgBgkm85F6ywqhhBqei7dM51Wqh/Py63JFJYUaN8hZHh7EksGvE4MNhjTbZ1KeD0AO1bU20egnAgVTXQU66FqpbtxDo3CYz1SX+P7JMuTg5n6jKeFmCwFzj6dOrzx57T/uxSx9MCDPRobM/ziddNKNcA8NajQP36gg3PEoZfk/7X21d6XkJxsdY6FpVUJRoapSjXGqq3pxUYP5D+3SsZeDkxVo2bHJtdurArL47K84F3KPE5mZRrAzeeetVCRMOcinr99wLS9zx1JPN2AtK6mk/LvEwmwnPS9yv2j9i2Y3+UfsfeAWDdhanvcVZJ55jh19LHN9aXajHL1XMdi0r7JJYhgJyfBcBzuzkX1hv18Sz2z+jr0raN9UmP89kevWohQPJc8cYjQPUq7fdzntqIxKpqIbGwVJNZrVyfeFFjWdX+cVVL15xsqI83swiritg/mahbB9StSX0uGpKuabWr9d/HOTB9FGg4STFuhaLvrAKCqpb14we1r5WzJ4C15yUfK2c+svVCyId4DBh4JfPvB5Bu+Dp2mE8ILgEouC5FDj8B/PqmxR6FtXz0Ke2TXXgu1e/mWSX94Bs3pC7ncEpqzdxE5s/Z/ZdScCho0/jMunVA/33Az9+T/prdqX9RKVUybY+tDKhuM7ae+nXS/w/9TeHGZilM/6Kk57kOBbJbQoCEFaJR5bnWCK7r1gFv/U77u1dS2aifRKme1tU6H9St1V+3CHIy/Tb0qoUAQMPJkgKXifp1wOHHs2+nzQF89ljuHtawP/X7dbdIF9wXvi39A7S/i/q1wIHd0j81ymA8V8/1gfuBe/7U2LKZ9pUedod0Q6sMoOT1rZOOicNPSI/XXpB8La/gWuN4qEucA+6/I/t6xHZaVS1EXaYVSG1opBRNDu4G7v5I6vv/4mWg+dQsn+HPntycjfp1wJEnpX+ZqF0DfGp/6nM9Pwae+lfg799OF4EER58GfnE98Ine5HVRqeg73VLQLJifAX5wnlTZSAvl8ZliS2vSWrowHLg/ff/o8YF7gA3vsm4sFkHBdSkipuFv+lmybuVyYXYAuO9jUvcxreA6pPLUlZVLJ5GqxvRly6qyT18FxoH27cC7/1l63HBy+jIX/T2w4d0ANHxmVc25BwaLRaG2p/k04I7ns08JlwqVDfoqodOdTIJTEjYYXAPpZeC0/JnXfx+YeCP7umo69F9T15qdOSb9//6dQHm19HrrGfrvF/s309SwXrUQALj5Z5JVJhPv+grQdUPmZd76PfD8tyQFN+fgei71+3W4gL94IanKMxuwakv6+z5wDzB9RHudjack/841uBb75EMPSBUy9Cir0B6fET7ye23v780/AybfSj5Wbo+Rfa9G9lxrHA8d24GPP5s9WLeVAW1nSn8zJq2r0J5rzZkiRXWO2s7k88r9M/M28OAngdnj2YNr9fGWCzf9NHX/aPHar4B9v5YUXGXTq6nD0naqt0fJ1GHp/+mjyeA6k+faOyQF1pd8IVWlBtJ/P1Yn+Ark/bNbErC0WPACv7lV2n9LEAquSxHxw1h3YXYFaanRlJiu0rNzaJ3c9JTjMgMn8PCcdEJd8w79ZcoqgDXnZl7PUqKQ29PaXZj1LDbOKn3PtdGLqbpygpaSVlmf+VgzgvriGBiVgt0N7zY2Peool5bXC4g4168WAiT9uxnHWJV9O/0j0v/5JAuGAulKYv367DYlT4sxO0Yu1TUA6fxVXgOsv8jc+8zg1lEOqxq1xQYgN+VaCBRapfgYA1ZtNr4ugcNV+Goh6rKMQGp+gTIYFVUw1l8EzCaOFSM+aDPnAz0y7R/B+AFg7y+l2SWlTU+ZqKoXXItrp3J7wnNJIU5dq1qcs9aen/26YHVpSoF/TLLqrL9Yf5l4TAr+F6OdfAFYekaWlYA6Q345UVGXOeHDjJLoKM8+9WhmfcTyRbdayJzxaWB1zV9xsc93GlmNOiHJPyZdOI36DhnLXI4rFgHAsydx5kshVDDhgbWKskoAzHwwYaYZUzGRtyeHOtf2Ah4PlijXqoZCQDKgVF9PAqPJ19wG83PEZziLMFOpV7ZTXWJRC/GacnuU9il1xQ+56pWBWXB55sNi5Vq5f/Sw2YtXlcgCKLguRdQVM5YT2WqTmrmYOlwGlGsKrgnot/U16rkGpON2blxKMAQSlUZs+gpwrqiTLwM5BHJ6HnMgs8e2kBQkuPZb+/vNdiOiR6BEm0vJ22PSc21zZK4OYxaHywLPtaquPKBfGUZdd7uizliQprYlWoVeRR91cyAt9JRrpS2Ex5K/82yVlJTk0oQoF4zWfy90HfMiQsF1KVKIqalSJlNtUjMJJY6KzFOPnKd60YiVi8sjXWzUNbvN3Hx5WqVqBcGpxHsTKleulQX0UAd7ykDB8Dqq9NUncdHVsgEUEvG7y0cFK4QHNht6N16Z8GvU1y4VzJRiAxLJrRpl+PLBUVH4aiFaDZ+0qvgA6TekRkv2mZnJygetij6igy6QuZynlnKttE+pa537x6TzlJHfUbE810brv3taSbkmCoi6YsZyI9OJzszFNJtyHQ1Jd/DFOFkSpY2eImPWcw0klSCrbty0PNdmVdJM6mWxlGtxDiu057rQqD2q2RBBUCkq14D5FtZ6ZRnzwQrlWstzrVXFh/P0G1KjzWaKJWxptUkPTgPxSOJ5E8p1LCKVtBOBsbp2u5lGRlY3VQKS+4eUa6LoFGtqarHIdKIrpOfaaPc9Yvmjd9Ew67kGkqpS2KLATxnsiUYkuSjXuraQDNUhCol8Q5NHgxarPdeA+WB0wSsFpKWqXJu1uWSqHJMrlnquVcKTuglYyCep5rkq18XwXGtZVZQBtZ5yHY8lS2wGFOchINVzDaQq10Z7NVjZVEkg9o9R5XpuQtruJQYF16VIMS4oi4myNqmSWFQ6IRvd9rLyzFOPFFwTAi3lOh4DIkFznmsgeRG0SuVSJiTNjQPgOXiuPSXkuc5RBYvHgUgxgusM35UWcqfXUg6uTVYLKbRFqMxAsrlZ9BL91d1CtTqgimUytfWOx4tryVQH/CLQdlXrK9dzE1IHRVd1cnvUJUHV1g4zyrXdKfnvrVSu/SZ+P+4WaXuz9bMoQSi4LkVWgucaSFcSzFZfyKZchzSmEYmViWxRUARRWh7OTMjKtQiuLQr8lAlJ4rMK6bmOFCm4Fi2kc/VcRzTqiFuBWc+1vE9K1BZi2nNtkXJd6Bba4TmpxKR6rOogVQSmauU6FpYaqugRCUr/F8tGqJ7BFdvQeoa+ci2Wbz0juT0h1XVTGVybsWAAuSf4miFg4vejPucuISi4LkVWguca0AiuTV5Ms3mutUo3ESsTreQ6LQ9nJsoqpNq54rg1U2nEDErFN1eVNJPVIVO760Jis5lXUZWogwarMOu5Lnnlehl7rrVak3tapGZhwjqgp1wDmYM0s+eDfNFTrledkahKpGGFEMuL2uP+0XSRQFm73YwFQ5DPb9YIppRrnVhhCUDBdSmyEjzXgL5ybdhznaVaSFijdBOxMtGyKOh5ODOhnIK20nMNSOcB8VlmVdJM6pMIesoKXCFCcxxVuXuuzc4s5IrZYLTUlWvTnmurqoUU2nOt83tzt0ozPaKKj55yDWQO0uTjrUjCltqqEkhU9ahfL1khxPYoUQfXgbH065zScx1IdHs2cyNodubDLGbOaUZuikoUCq5LkZXguQbSfzCmg2uDyvVy/i4JY2gF17k0a1K2QLeqTJzy4ig+q8pAA4iUdbilaW4t9UvkKRSjjn4+Klixbo6dHnNjDIxJwaOr2rox5UMuda4tUa4LHFzrVedRB2D+Uck6Ul6jWMZIcL0IyrXSquJPeKP1GswAyfPBqjOk/wNjGp5rRZWeXG4Ezd5smiUwKjUsKq/Nvqyc5zJu3XgsgoLrUqQYtV0Xk6omACz9RGd2GthRLpUu0sskJs81IRDHlFJFzeXmy9OqSmi00hYSkD6rsgFwOM2tw6VxMyEoVrUQIL8LtVmbWK6oO9plQwRBha5vXihcieBaNDvKxlKqFqL1e1Or0oFxKShT7p9MAaugWDYkgXoGNzAubUumG4HAqFRlpCbRGt0/qhh3IqhWVunJxcJUDFuI0d+PqKqSqTRhiULBdakRi0rK0nL2XNsd2m1Nc/FcA/on8Vym/Ynlibo8lfLvXJRrs5VGzKAMrs0kI6WsQ2N7BcWqFgJIv71cExqL6bkGTya0ZSOQ4z4pFs4qmNqe6MLSqBail+ivVq4Do+kJwC639LsyZAspUnCtnsEVVT0yKtej0vuU26NW3B2uZMWPnJTrIiQ0mvn9uFvJFkIUgGJlyC826vJJgOIkYTAYFp5RvZN4OIdpf2J5UpYhodGU57pVuvmVy2ZZ6bkOmCujpcSpUR1FIKo4FE25zvFCXaxSmmY7Sfpz3CfFItONlRaRBeuqhRidDTCC3kyRrFwLW4hOgx93lkYyxc7RUSvU4kZaXfJTSWAseeyJ7VH/ThhLVgsyY8EQ5PObNYJ/zNzvRytWWAJQcF1qhIp0QVlstO5GzXrejCjXWqWbiJWHzSYF2GGN4NqUcp24IE4dNv9eo6g913kp1xoXSdkWUizPdakH1xluRLQoeeXa5PZY5bkGl7oHFgq9bp1lCX+18CNrKddAwtJlRLkuludaoVCH/JKw5mlJbE+tdjk+5flAbI8Ytyh9CSTzCMxYMARmq+eYJSflmoJrIl+K/QNfLLTuRnPxXAP69VSFR69UvZFEcXGpAr1cPPlCcZk+knivBZYjsU5RLSQXlVSpfqsRN6NFqxayBDzXgLFgNByUypstCeXaaHBthedazCoW0HedKRfJnciFiMxLHTRzUa6L7blWWjvU5emUuR0CzlNnssT2iJKgNkU4J6r0mA1k5fda5LkW+8e0cj1a2FmQIkDBdakhpqaWu0/Y3ZpamxTQvgPPhLgg6NlC9JQOYmWivmjk4rGUlWsRXFuoXHsHpIRdKzzXzCb5Mq2mEJ5rq5XrTMmfauQybyWsXGe6sdIiOm9BcJ1lVjEXMiUQexK5EHKZtzyU67IiClsiQFY3VlFWJRLMz0jVRdKUaw0vukhqNWvBAKSb++i8lP9VaHJJsDTSAKgEoeC61FgxyrWqNikgnSTKKgGb3dg65OBazxayzDtdEuZQdy0M+yWFzejxBiQvVFYG1w6XZGcSn1Foz3U04bEtxoyO2UocSsIBaZx2i28CnCaCUblBSSkr1yZuFmJRIB61xnMNFC64zpZALJTrTA1K3C3SMaW3n8N+qf232co8+SACZL/qpk1LuVbXhxbbExhL/16UnutclGsgmf9VSLQa/GRDry9GiUPBdamxUsrHaWVEmy1tltVzbVGpNGJp4vSoPNdz5mc2XNVS4CA811bMjDAmrVd8hhWe62LlIZitXKGkWPX+ldVZsrEUlGt5eww0AolZ5L/PlmxulmytyWXlOkN1jGy1rhejv4SsXIubgubk//6x1JtSOQBvSf1/6kh6vOD0AMFJ8xYMwPzMhxm0GvxkQ68vRolDwXWpUewuUYuFW+NuVK9JgB5lWXx9y71eOGEOdRa82eMNkAJfdwswcyyxTosuxk538jPcJhvIAJmtDhELbAB6mFFR1RRr5smMR1lWRktZuTZRLUR0uC20/14E63r5MGbJJjq5W6QbhYm3ko/Tlkn8jvSCtNAiiDHulqRybU/UdAYSVogQsDCbXFZtqRBB88wxbeV65njyM8yQz282G7n8frRihSUABdelxkopH6fV1tSskigr15k818v8JoUwjvAhCsJzud3EelolLzRgbXAtPsPMFKpAq/SgIBoqTqUQQGG5yKGdcniuOL9fs55rm0Nq7FOqmFEe5Zrnha4WkiUfxizZRCcRcI7slSxVlY36y+g1JNFrr24lnoS1Y/poauMb8ZtX+q7VNavF9sQj6eN2Kc4fpm0hJmY+zBIYlfI9qjT2jx5LtAU6Bdelhtz4ZJnbGbROdKZtIdmqhZDnmlCg5bnO5fhQqi5WHV/KVsa5fIbdIfnJM3mui4GZwFVNKMf9Yxaznuuq5tTKDKWGGeXRqoZChU5ozCY6iQBsZL+kUGvtH62ANeUzFuF6Id8U7E+1b2jVug6MSTfN4oZTedOdZgtRPDad0GiyTroZ5N+PiTwXl0fablKuibwQJ/hiZiwvBurapEDuwbVuExnyXBMKRO1XQS6ea0B1UbPo+BLjyidxTq8ZhBUd+TKNAcit1nWxbF32MmlK3qjnupSTGQHF9hhQHi0Lrgtcii+b6CSCVO8JfctBRZ2UsKirXC+C51ocS+px6ynXymNPbA+gYQtRKPxmlWurPde5/H48WcooliAUXJca4YAUWJeyMlIoRIa3wKwHNmu1EPJcEwpE7VeRJJSL5xpIXgStrGQhLpb5JM6pbTCCYirXctWSXD3XRQp2jHaly7WpT7ExWqt4qSjX2TzXHo3AVI3Il9BTrnM9H+SD8lhSjltPuVYuL7YH0PZcA+YtGMp1WaVc5/L7cWcpo1iCrIAIbomxkqwMHtWJLmfPtcYJXJRuIs81IXC5AR5PHi/5eK4BawM/ZyGUa51Oa0X1XAvvd46e62IF13o3ImpybepTbIxuj1XdOgvuuRbBtc7v1VWdVMszJcu5WzIr18W+XqQE1Iq/XR6pLG0m5RpIbquW5xowb8EAzFXPMQsp18SiketU9VJErVybvZhmqhayUuqFE8ZRe2tz9lyL4NrCY0usOx+VVK/1eHQhGYhYTb6e62KdC53u7DcAsYhU3mxJKNcGtgdI5qsUulqIsB0VzBaSRblmLBm0ZUoA9rRm8FwXyeOvRGntUAadQpXOpFwDiht9tec6j5vzfKxcmYhFgbkcfz+kXKfCGLuCMfYmY+wwY+xzOstczBjbyxjrZ4w9k3iunDH2CmNsX+L5r1o5zpJiMaamFgtPi9SlkXPpX9hvLri2Z6gWIp+MV8iNCpEdpSLDeR6ea6EWWahyWem5jiwUv1pIKXuugcSNSJYbgMC49P9SUK6NbA9gvXJdqFJ8RhL9RdCWVbkuoTrXSmuHVuAsjrlQQPoN6SnXabaQPGxlViU0zk0A4Lkr15kaAJUglgXXjDE7gO8BuBLAJgDvZ4xtUi1TC+D7AK7jnHcBuCnxUgjApZzzzQC2ALiCMXaOVWMtKcKB5V/jWuBulZSNBa/0P4+bu5jabNJdv9YJPJfW1sTyRnnRiIWlznQlq1wvF891jsF1NCSVEiuq5zpbcL0EGsgIjHrILfdcF8gWYiTR36hyPT8NRMOpz0fD0jlhMa4XIkDWCpyFFUKvbbieRU2cm3IJZG12yZKSi5UrE/n8fuTqYktHvbZSuT4LwGHO+VHOeRjAXQDeo1rmNgD3cs5PAADnfDzxP+ecizNDWeJfDv1zlyArynOt+MGIk6dZNdBRoX0CFyeGlWKxIbLjUgR6oSwezkxUNki1jpey57pY1UIcTsBWZl5xKvbNsUvHQqNkKbQ+Fyx6QmOhq4UEsicQy8p1huBNryGJ2PeLcb3w6IxbtEYH0lufC7J5rnO9ETR6/Jghl9bngiXYAt2iVHcAQDuAAcXjQQBnq5bZCKCMMfY0AA+A/+ac/xyQle89AE4G8D3O+ctaH8IY+xiAjwHA6tWrCzl+87z8Q8A3BFyWh4slPAfUrS3YkEoacWL42bVS4X/A/I2Fw0Wea8IYIlDbebsU8AG5HR82m5QoZOWxle/FEcjguS5ih0YgXUGfPgrs/ivg/b+WynFqESpyMy2nG5h4E/iv0/WXEd/lUlCuXR6TTWQKfDzYywCwwgbX2Y4Fj44CnLJMYt/9+F1JrzMgzWIBi3O9cLdoV/VwtwAhH/Bf3cnZWcOea1ELO8cbQb1zx7PfBPb8TPs9a94B3Pg/qc8dehx45NNAPK74/eQwpiXYAt3K4JppPKdWnx0AtgF4J4AKAC8yxl7inL/FOY8B2JKwjtzHGOvmnPelrZDzHwL4IQBs3759cdXtgw8A02/nH1yvlICwYwdw1seTF1KHCzjpnebW4SjXCa7Jc02oaD0DOPsOYMEnPXa4gJNNHm+Cy78GVLcVbmxqTrlaSv5p3Jj7OoQ1gPNk5zeguNVCgHT/77HngePPAVNHgPat2u8JTkr/F6sT4rY/kQIcnuUSUtNu7X4vFBX10neo3vdqrPJcM6Z/bs6Fucnsx8IZt0gzStXt+suseQew46PaqqzDCZx8WX7jzIUdfwqsOiO9qkfXDcDM21IiIABUNQBNp6Qus+5C4KLPAqvfkfp8/Xrg0i8Am67PbUyV9dJ3rubNRwAeA9ZdlPr8yD7gwG7ghjtTj7ejTwPeIeD0hOO3ug2o6TA/HuUs9xLByuB6EECn4nEHgGGNZSY553MA5hhjzwLYDOAtsQDnfDahbF8BIC24Lin8o9LOz3ZCy0RoBXmuy8qBq/49/3VkVK4puCYSlJUDV/5/hVlX942FWY8e7ibgwk/ntw5nlaTIxcLJ4Inz4lYLARLBtcK/KbyXmfy4xbZgrD5b+rdc8LRIpUhDfqC8Wn85q6qFAIlzc4E814Gx7IpnTQdw3iczL+PyAFd/szBjKhQtXdI/NfXrgOu+k/m9ZRXAJf+Q/rzNBlz4mdzH5G6VAns1/jEpsL7hB6nPv/Ad4LEvSEq7cjbKPyrdkKqXN4uoqrKElGsrPdc9ADYwxtYxxpwAbgXwgGqZ3QAuYIw5GGOVkGwjBxljTQnFGoyxCgDvAvCGhWMtDIExKQknOJ3b+zlfWZ7rQuBwkeeaILRwaTRwsUqpzITavykC52iGShJLKXmwFDGaACaOB7sFx4OjvHDVQvyjuXl1idzQqivNuX6dd9m2oTretMoH5oJcmnDpKNeWBdec8yiATwD4PYCDAHZxzvsZY3cwxu5ILHMQwKMA9gN4BcCPE9aPVQCeYozthxSkP845f8iqsRaEcFC6awP0i9RnI7ogTblQQGgcvRM4ea6JlY5WAxerPLaZcKkSKw0r1wxwN1s6tGWLCICyKX3RBUkRtKIjsJ7wYRYR1OXi1SVyw61RVSU4LYmHWsGynHCoOt60Gt/kPKal1UjGSlsIOOePAHhE9dydqsffAPAN1XP7AZxp5dgKjvKg8o9qT/Nkg6wM5nHoTD0Kz3Wm0k0EsZzRamMsfivFqhYixhGYSD6WlesMftzAqOSxtZdZO7blihnl2qobLUdFYTzXolQrKdfFQ1mdozbh7hUxjlnl+qRLCzSmVikZeolAHRoLhfKgynXqQs6Qp+DaMJkSGh0VmUs3EcRyRqvGtLBiFFO51vNcRzIEXv4xCqbywbBybWHlGL1KTmbRq/FMWIfWzZk4lowq12I2f4Uq1xRcFwq1cp0LZGUwT6ZSfPQ9EisZl1Zwvciea86NK9dkA8id8lrJR53Nomipcl2gaiH+DIopYQ1aN2d6tbYBwFUtiVkpyxc4b0KvAVCJQsF1oRAXDGbLXbmWy8dRUGgYvRN4KEDedWJlI3uulcG18FwXs1pIVXIMC7NALBHgZ/Nck3KdO4wlktKy2UIWrLvRKlS1EFKui4+sXGuIhlo3veJ4S1G6C1zxR68BUIlCwXWhCIxKNTbr1uWhXOfYpXAlo3cCD8+RvYZY2Wh5roUVo5jKtcsjBdSxSGqwp1ctJB4H5sZJuc4Xd2t25TqyYJ3/vlDVQki5Lj5VTQBYut3V6dEX/9S2jUIr1xRcr1D8iWxmz6o8PNekXJtGt1qIn4JrYmWj6blehGoh4nwWDqQGe3qq5vy0VJ+blOv8MKxcW+m5LpBy7aiQrAdEcbA7pABbrVxnusFRl8rLp925FkbzCEoECq4LhfAIatWHNApVCzGPbrUQ8lwTK5yMnusiJzQC0m8yRbnW8eNmmn4mjGNEuV4qnmtPS+6N2YjcUN+cZatZ7WlVLZ+Yza+oL8x4tKwqJQwF14VCeATdrckujWahlt3mIc81QWjjKJdyQEIa1UKKWopP4f0WF0Zm168WIpf8IuU6LzwtUhm7TFVZLK0WUqDgulCNSAhzqG/OjCjXIW9yJlnM5heqhrqWVaWEoeC6UCiVa9F21iyy55qCQsM4yqXGO7FI6vPkuSZWOoxJHknNDo3FbCKj6BTpHwPKKqV2xrrKtUhgI+U6L4zUuo6GrPPfFzK4Jr918VEq13IjnyzKNZCceSp0xR8tq0oJQ8F1IYiGgeBUUrkGcvNdhwLSNIrdWdjxLWfEhUF9EifPNUEkyuBpdWgscik+QBqHuOCWVej7cUm5LgweI8H1EvBc+0m5XhTcrVJicTwmiYWRYBblWnW8WVHxx0geQYlAwXUhmBuX/hfKNZCb71qoreQtM05ZoqSY8iTOOXmuCQKQZsE0q4UUsxSfynPtaU2omjqVJPxjgKsm+dsmcsNt4FpkZbWQskSHxlwskoLIvGQ1IOW6+HhaAR4H5iaNlUNUxz5W1Ko3kkdQIlBwXQiUWbH5KNfhAKmtZvn/27v3IEvK8o7jv99c9joTdpFlB4HlYtAETRTdYAzBkBgNUinQRBPQKDFWkCqpSEyl1JiUllWpMhr9J+U1SqklghihRMvgLYpaJQJLFmRZkZVwWdkL951ZdmdvT/7o7jM9Z8+ZPWeme7pP7/dTtXXO6e2Zebe3T59nnn7e5+2UuT4wnXQboLwGR7t8j2mposx1+j7Maq7H1nafiCyl+xy/eONrqjpkrqWFZa/nWhUQ5Wq1vtveWzvEfOyTv5tfJDLXR5lWP8dc5nq+wTUBYX+yD4b8pB26rgCJJWM1qLnOdS1pZa7nWBqbBWSKseK4ZOLoXJnrsruFSAuru55rVUCUq1VDvaO3zPWKZyVlrZPbZ9/NL9LYhLT70aRUpeYIrovQ+q1uYmbZ2XmXhVDK0JdOF/CsxpTgGke7JWOH11wPjSSTgxZtDOk1bfejyViymuu5uoUwmXHhhoaSOwDdbqNHlN8tRCJzPajyi7b0krkeGpJWHp/uX3CP68z4RNLA4JnHi/2+JSC4LsLUDklOTqxOy4D2apqykL51uoC3Mtf8ooKjXHvNdZllAN2Mpu/DJ+5PHufKXEeQuS7S2PHdb6MfOpDU1JYeXC9glcapkoI0HFm+LGRqe5I0XLZq7q/J1vlo3c0vuLyrl3kENUFwXYTJ7dLK42ayQWMTC5vQiN616vpyF/BpWhoCktJuIe3B9SLWW0tJRmt0pfT4luT1XDXX07uS9zKZ62LMNQGs7Pr7omqui1yIBL0bXZYE05M7pKmdvS3kMzaR7NtLGcl89DKPoCYIrovQ3v9xvpnrfZMEhP1qdQvJl4WwGA8gKXkPtE9oXMxOIZmlYzPB9VzdQsq6nXy0mmsCWKtzTIndQqSF11wXuRAJ+jM+MTOhsZdAeXxtun96N5/MNRakfeWiBWWuKWXoS6fsCME1kFgyJu3fLR06lLzeX0HmWkqua3ufTp6PTXTPXOcnh2PhsglgBw8c/ndZ0FtWK77sPJtrhcgjmaT+vlJja2cmNPYyqXRsImndt2trMsFxeLT48UgD0Y6P4LoInTLXe5/q/6JCzXX/Wt1Cclkwaq6BRHYnbH/6nqii5lqaua4NjUorju1ec03muljjayVFEmC3K7tzTFHdQjgXqjOfzLVC2n53Of9v+VKVmiO4XqhDh2bqkTLz6XV96GBym5Tguj+dJjS2aq7HF388QJ20VkfMguvp8jKVc44jva6NpXWb3bqFkLkuVuuzqEOmLyvLKa3musMCX/2apOd5pcaOl3ZtS5KFvWauJWnnPeW9h7OAv+YIrhfqmceT1jCzMtfzCK5bpQxkW/vSsRUfxxKQNHsBF6nCzHX6Xsw+oLtmrrcn41t2zOKNrcnyvYrbtTLXJdXgd5ps3o+D+6VnHqMNX5XGJqRD+2eeH0n2/j64r7w7DmNzzCOoEYLrhcp+g5qVuZ5H0X2WWWJCY386rdC4bypZPKGKIAKokyW5BVykarqFSDPXtewDemRZkpRorwXOJrAdqSsBejNXjWrp3UIW2Od6Kl2IhAVkqjPeIWk4l3wATuYaC5L9BtXppOoncz3NJLx56Zi5Tlsa8gGNo12rLCQfXFfQLeSwzHWXetzJ7dTYFqmV6OmUuS67W8gCa65bJUKcD5XJB8i9lOesXDPzvLTMddq7PaKc718QguuF6pS5Xnmc5KE+M9cE1/PSrVsIdwCA3NLj6Z2xyrqFpPMfxo4QXE/tpN66SCNLkh7RnTJ9Wc17ad1Cssnm8wyuW5NbOR8qkw+Qe/klZ2RJ0iVEKu99PDYhHZxO6sBrjOB6oTotzzo0nC4DOp/gmjrhvtjJRTzfLWR6iuMISLma63QJ9APT1dZcj+VqrqUOwTWZ68KNT1STue72f9wrMtfVy96vHkqShj19Tfr/Vdb7eK55BDVCcL1QUzuSyTftv/3P1by/E2qu529k6eHLn3MHAMjVXOdb8VVYc519MI526CSxf0/SC5vMdbHG1napuc4mNNa05rqshUjQu6Xj0uiKJFk4NNzb12R3GkrLXA9Gr+uRqgcw8Lr1fxybkLb/TLr5Q719nx2bkkeCwv6NLD98QiOZa2DmfbD560kiYHrXTGC7qOPIteKTcguM5O44ZXNUyFwXa3xCeuy+w7e3WvGVdD4MjyYTy+//gaQO9bHPu0CaeMHsbVs3SL/8XvJ8y3fLWYgEvbOT9+yyX+v9axYrc73hc9LDtybPTzlHOvWccn7ePBFcL9SeJzvXhJ38O9J935K+/6+9f68Vx/HBMh/tbb32Pi0de3p14wHqYum4tPpUact3kj+StOY3Fn8cE7+VjONZz0led8pq7n48eVzR4+1n9GZsbfKLS8TsSd6tSfQryvvZa8+UHvxx8qfdtjuli6+eve2775Me+NHM6+ddUN7Y0JvTzp2ZM9GLU14mPfHL8n6JP+YkafwEadMNyR9J+sP3Elw3zqVf73zb6+X/KP3+O/v8ZpaGqNTp28iy2cH15HZp3cuqGw9QF0PD0t9tlOLQ7G2Lbd3vSu+4c+Z1xy4/aV04iz8VazztVfzME9LKZ81sn9ohja4s93i/7Uezz73MF/9cmtx2+PZdj0hnvkZ63VXJa/N5WLkL/6O//V/85uRPWUaXS39/j2bfDalfZzCC64Wyu8+2ruJD7Gg0umzmF5wD+6Q9T1C3CWTs5PZ8nbSC61xZCPNOypGvUc0H15Pby+/E0e3c61aqMrVDGn8Vn52Y2wAkIes/QuBI8t1CpmjfBNRepxaa9PovR7cVg6d2VNeJI1+qkpmeSubLcO1GAxBcY/Dlu4VkHyC0bwLqq9UtpG0issRk5KJ1W0hmMTLX3eRLVTJcu9EgBNcYfPluIVnfcbIfQH21uoV0Cq7JXBeqlblua11WdeZamj0mrt1oEIJrDL58txAWHgDqr+OExrTmerTE7hVHoyUrk24P+cx11SUYnUpVyFyjQQiuMfjy3UKyhQdWrql0SADm0KkV3/RUkrUegMlKA2e8bSGZqgPZTqUq9DlHg3AVw+DLdwuZ2p4E1sM0wgFqq2O3EBZ/Ks1Y2xLoWQlGVasfdipVmdwuDS+Rlq+uZkxAgQiuMfhmdQvZSc0eUHeduoXsm6LeuiyHZa6z+uaKssSdSlWmdiQZbdevZzHQL4JrDL58t5Buy9EDqA/78MWf9u0mc12WsbVtmesa1De3B/yT21mfAI1BcI3Bl3ULiUgXIeACDdTeyNLZ3UKmp1idsSxja6X9u6XpdBXMqR3S0Ki04tgKxzRxeOaaems0BME1Bt/IUkmRBNhTO8lcA4PgsMw1NdelyYLWLJitQwkGmWs0WKnBte3zbd9re4vtd3fZ5zzbG21vsn1zuu1k29+3vTnd/o4yx4kBl02OevpXUhwk+wEMgpFl1Fwvlva+0lUuIJPJZ64P7JP2PMG1G41RWksF28OSPibplZK2SrrN9o0RcU9un1WSPi7p/Ih4yHY2dfmApH+IiDtsj0vaYPs7+a8FWkbT4PqpB5JHsh9A/Y0sa+sWQs11aVqZ6zS4ntohrT6tuvFISaeSrFRlz1Mz24AGKDNzfbakLRFxf0Tsk3StpIva9nmDpOsj4iFJioid6eO2iLgjfT4pabOkE0scKwZZlrl+8sHkkewHUH/5ichSElxTc12OVuY6zRTXIXOdL1Wpuu82ULAyg+sTJT2ce71VhwfIz5W02vYPbG+w/eb2b2L7VElnSfppWQPFgGsF1w8kj2SugfobXT5Tcx1BzXWZlq+WhpcmQXVWglF1IJsvVWHpczRMmSttdJopER1+/kskvULSckk/sX1LRPxCkmyPSfqqpCsjYlfHH2JfJukySVq3bl1BQ8dAyXrmPpVmrgmugfrLdwvZv0eKQ9Rcl8VOrotTuSxx1YFsvlRl71PJ86oDfqAgZWaut0o6Off6JEmPdNjnpojYHRGPSfqhpBdKku1RJYH11RFxfbcfEhGfjoj1EbF+zRqWvD4qjSxPHp98UFp2zEwNNoD6yncL2TeVPJK5Ls/42iSQrUsJRr5UZXKHJCer6wINUGZwfZukM2yfZnuJpIsl3di2z9cknWt7xPYKSS+VtNm2JX1W0uaI+GiJY0QTZJnrJx+o/gMDQG/y3UKy4Jqa6/Jkmeu6lGDkS1WmtieB9XCZN9OBxVNacB0RByRdIelbSiYkXhcRm2xfbvvydJ/Nkm6SdJekWyV9JiLulnSOpDdJ+qO0Td9G2xeUNVYMuKzmeu9T1X9gAOhNvlvINJnr0o1PzASyUvWJiHypyiSLf6FZSv01MSK+Kembbds+2fb6w5I+3Lbtx+pcsw0cLl8GUvUHBoDe5LuF7NudPFJzXZ6xiSQB8dRDqk0JRlaqsvcprt1oFFZoxOAbyQXXZD+AwZDvFtKquSa4Lk12bdx2V31KMMhco6EIrjH4sppriewHMCjy3UJaNdcE16XJro3b7qxPIDs+Ie3aJu1+lGs3GoXgGoMv6xYisYAMMCiybiER1FwvhiygrkOP68zYhDT9tBQHuXajUQiuMfhmZa5rkpEBMLeRZZJCOrifmuvFkA+oa5O5zo2DazcahOAag29WzTXZD2AgZO/bA3ulfZPJc4Lr8qw8TnL6kV+nzHWGazcahOAag294NPehQfYDGAjZHacDe5PM9dCoNLKk2jE12dCwtPL45HldAlky12gogmsMPjvJgo2uYBEKYFCMpnMlDuxNaq6ZzFi+LJitSyCbz1zXZUxAAQiu0QwjS5OLs2mPDgyEVlnIdJK5piSkfFkwW5fMdVaqsmzV7PUKgAFXg0aXQAFGltfnAwPAkWVlIfv3JDXXBNflq1vmOitVWb6q6pEAhSK4RjMsO0Y65qSqRwGgV1kLzVbmmjZ8pTtmXVLbXpfgWpJWnZxcv4EGIbhGM7zuKi7QwCBpn9BIzXX5zv5b6fQ/qFcJxms+IQ0RiqBZOKPRDGvPrHoEAPqRb8U3PZUsyY1yLV8lnXx21aOY7bgzqh4BUDgmNAIAFt9ovs/1FDXXABqD4BoAsPhmdQuZouYaQGMQXAMAFt+sbiHUXANoDoJrAMDiyzLX+3YnpSGUhQBoCIJrAMDiy4LrZx5PHgmuATQEwTUAYPG1guvHkkdqrgE0BME1AGDxDY9KsrQ7Da6puQbQEATXAIDFZ0ujyykLAdA4BNcAgGqMLJ3JXBNcA2gIgmsAQDVGllFzDaBxCK4BANUYWSbteTJ5vnS82rEAQEEIrgEA1RhZJsWh5DmZawANQXANAKhGtkqjRM01gMYguAYAVGN0ee75iurGAQAFIrgGAFQjy1yPrpSG+DgC0AxczQAA1chWaWQBGQANQnANAKhGFlwzmRFAgxBcAwCq0QquyVwDaA6CawBANbKaa4JrAA1CcA0AqEbWLYSaawANQnANAKhGK3NNzTWA5iC4BgBUg5prAA1EcA0AqAbBNYAGIrgGAFSDPtcAGojgGgBQDWquATQQwTUAoBpZtxDKQgA0CME1AKAa9LkG0EClBte2z7d9r+0ttt/dZZ/zbG+0vcn2zbntV9neafvuMscIAKgINdcAGqi04Nr2sKSPSXq1pDMlXWL7zLZ9Vkn6uKQLI+L5kl6f++vPSTq/rPEBACrW6hZCzTWA5igzc322pC0RcX9E7JN0raSL2vZ5g6TrI+IhSYqIndlfRMQPJT1R4vgAAFV69lnS81+bPAJAQ5QZXJ8o6eHc663ptrznSlpt+we2N9h+c4njAQDUyYpjpdd/Tlq+uuqRAEBhRkr83u6wLTr8/JdIeoWk5ZJ+YvuWiPhFzz/EvkzSZZK0bt26eQ4VAAAAWLgyM9dbJZ2ce32SpEc67HNTROyOiMck/VDSC/v5IRHx6YhYHxHr16xZs6ABAwAAAAtRZnB9m6QzbJ9me4mkiyXd2LbP1ySda3vE9gpJL5W0ucQxAQAAAKUpLbiOiAOSrpD0LSUB83URscn25bYvT/fZLOkmSXdJulXSZyLibkmyfY2kn0h6nu2ttt9a1lgBAACAIjiivQx6cK1fvz5uv/32qocBAACABrO9ISLWd/o7VmgEAAAACkJwDQAAABSE4BoAAAAoCME1AAAAUBCCawAAAKAgBNcAAABAQQiuAQAAgIIQXAMAAAAFIbgGAAAACkJwDQAAABSE4BoAAAAoCME1AAAAUBCCawAAAKAgjoiqx1AY249KerCCH32cpMcq+LmDiuPVP45Zfzhe/eOY9Yfj1T+OWX84Xv1bzGN2SkSs6fQXjQquq2L79ohYX/U4BgXHq38cs/5wvPrHMesPx6t/HLP+cLz6V5djRlkIAAAAUBCCawAAAKAgBNfF+HTVAxgwHK/+ccz6w/HqH8esPxyv/nHM+sPx6l8tjhk11wAAAEBByFwDAAAABSG4XgDb59u+1/YW2++uejx1ZPtk29+3vdn2JtvvSLe/3/avbG9M/1xQ9VjrwvYDtn+WHpfb023H2v6O7fvSx9VVj7MubD8vdx5ttL3L9pWcYzNsX2V7p+27c9u6nlO235Ne1+61/SfVjLpaXY7Zh23/3PZdtm+wvSrdfqrtPblz7ZOVDbwiXY5X1/cg51jXY/bl3PF6wPbGdDvnWPd4onbXMspC5sn2sKRfSHqlpK2SbpN0SUTcU+nAasb2CZJOiIg7bI9L2iDpNZL+QtJURPx7leOrI9sPSFofEY/ltn1I0hMR8cH0F7nVEfGuqsZYV+n78leSXirpLeIckyTZfrmkKUlfiIgXpNs6nlO2z5R0jaSzJT1b0nclPTciDlY0/Ep0OWavkvQ/EXHA9r9JUnrMTpX0jWy/o1GX4/V+dXgPco4lOh2ztr//iKSnI+IDnGNzxhN/rZpdy8hcz9/ZkrZExP0RsU/StZIuqnhMtRMR2yLijvT5pKTNkk6sdlQD6SJJn0+ff17JBQWHe4WkX0ZEFYtJ1VZE/FDSE22bu51TF0m6NiKmI+L/JG1Rcr07qnQ6ZhHx7Yg4kL68RdJJiz6wmupyjnXDOaa5j5ltK0lCXbOog6qxOeKJ2l3LCK7n70RJD+debxVB45zS37zPkvTTdNMV6e3VqyhzmCUkfdv2BtuXpdvWRsQ2KbnASDq+stHV28Wa/WHEOdZdt3OKa1tv/kbSf+den2b7f23fbPvcqgZVQ53eg5xjR3aupB0RcV9uG+dYqi2eqN21jOB6/txhGzU2Xdgek/RVSVdGxC5Jn5D0HEkvkrRN0keqG13tnBMRL5b0aklvT28d4ghsL5F0oaSvpJs4x+aHa9sR2H6vpAOSrk43bZO0LiLOkvROSV+y/WtVja9Gur0HOceO7BLNThRwjqU6xBNdd+2wbVHOM4Lr+dsq6eTc65MkPVLRWGrN9qiSN8LVEXG9JEXEjog4GBGHJP2njsJbgt1ExCPp405JNyg5NjvSerOs7mxndSOsrVdLuiMidkicYz3odk5xbZuD7Usl/amkN0Y6aSm97fx4+nyDpF9Kem51o6yHOd6DnGNzsD0i6c8kfTnbxjmW6BRPqIbXMoLr+btN0hm2T0szZhdLurHiMdVOWjf2WUmbI+Kjue0n5HZ7raS727/2aGR7ZTpRQ7ZXSnqVkmNzo6RL090ulfS1akZYa7MyPZxjR9TtnLpR0sW2l9o+TdIZkm6tYHy1Y/t8Se+SdGFEPJPbviadTCvbpys5ZvdXM8r6mOM9yDk2tz+W9POI2Jpt4BzrHk+ohteykcX4IU2Uzha/QtK3JA1LuioiNlU8rDo6R9KbJP0saykk6Z8kXWL7RUpu0Twg6W1VDK6G1kq6IbmGaETSlyLiJtu3SbrO9lslPSTp9RWOsXZsr1DSuSd/Hn2Icyxh+xpJ50k6zvZWSe+T9EF1OKciYpPt6yTdo6T04e1HWxcHqesxe4+kpZK+k75Hb4mIyyW9XNIHbB+QdFDS5RHR6+S+RuhyvM7r9B7kHEt0OmYR8VkdPndE4hyTuscTtbuW0YoPAAAAKAhlIQAAAEBBCK4BAACAghBcAwAAAAUhuAYAAAAKQnANAAAAFITgGgAwJ9vn2f5G1eMAgEFAcA0AAAAUhOAaABrC9l/ZvtX2Rtufsj1se8r2R2zfYft7ttek+77I9i2277J9g+3V6fZft/1d23emX/Oc9NuP2f4v2z+3fXW6WhoAoA3BNQA0gO3flPSXks6JiBcpWcXtjZJWSrojIl4s6WYlK+dJ0hckvSsiflvSz3Lbr5b0sYh4oaTfk7Qt3X6WpCslnSnpdCWrpQEA2rD8OQA0wyskvUTSbWlSebmknZIOSfpyus8XJV1v+xhJqyLi5nT75yV9xfa4pBMj4gZJioi9kpR+v1sjYmv6eqOkUyX9uPR/FQAMGIJrAGgGS/p8RLxn1kb7X9r2iyN8j26mc88Pis8PAOiIshAAaIbvSXqd7eMlyfaxtk9Rcp1/XbrPGyT9OCKelvSk7XPT7W+SdHNE7JK01fZr0u+x1PaKxfxHAMCgI/MAAA0QEffY/mdJ37Y9JGm/pLdL2i3p+bY3SHpaSV22JF0q6ZNp8Hy/pLek298k6VO2P5B+j9cv4j8DAAaeI+a6QwgAGGS2pyJirOpxAMDRgrIQAAAAoCBkrgEAAICCkLkGAAAACkJwDQAAABSE4BoAAAAoCME1AAAAUBCCawAAAKAgBNcAAABAQf4fcPtH+mb2Qv0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d8afcc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trying different approach and activation function\n",
    "# Create your first MLP in Keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8998ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "numpy.random.seed(seed)\n",
    "# load pima indians dataset\n",
    "dataset = numpy.loadtxt(\"C:/Users/Moin Dalvi/Documents/Data Science Material/Data Science/Python/Datasets/pima-indians-diabetes.data.csv\", delimiter=\",\")\n",
    "# split into input (X) and output (Y) variables\n",
    "X = dataset[:,0:8]\n",
    "Y = dataset[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "745b0a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create model\n",
    "keras_model = Sequential()\n",
    "keras_model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))\n",
    "keras_model.add(Dense(8, kernel_initializer='uniform', activation='relu'))\n",
    "keras_model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "638ca72e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile model\n",
    "keras_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a34e339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5069 - accuracy: 0.7510 - val_loss: 0.5111 - val_accuracy: 0.7520\n",
      "Epoch 2/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4932 - accuracy: 0.7704 - val_loss: 0.5410 - val_accuracy: 0.7047\n",
      "Epoch 3/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4951 - accuracy: 0.7685 - val_loss: 0.5380 - val_accuracy: 0.7402\n",
      "Epoch 4/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5078 - accuracy: 0.7529 - val_loss: 0.5240 - val_accuracy: 0.7677\n",
      "Epoch 5/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5011 - accuracy: 0.7588 - val_loss: 0.5155 - val_accuracy: 0.7480\n",
      "Epoch 6/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7626 - val_loss: 0.5088 - val_accuracy: 0.7677\n",
      "Epoch 7/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7685 - val_loss: 0.5093 - val_accuracy: 0.7717\n",
      "Epoch 8/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5000 - accuracy: 0.7549 - val_loss: 0.5181 - val_accuracy: 0.7638\n",
      "Epoch 9/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4997 - accuracy: 0.7471 - val_loss: 0.5184 - val_accuracy: 0.7402\n",
      "Epoch 10/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4971 - accuracy: 0.7665 - val_loss: 0.5057 - val_accuracy: 0.7677\n",
      "Epoch 11/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4913 - accuracy: 0.7685 - val_loss: 0.5241 - val_accuracy: 0.7480\n",
      "Epoch 12/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4983 - accuracy: 0.7704 - val_loss: 0.5067 - val_accuracy: 0.7756\n",
      "Epoch 13/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4972 - accuracy: 0.7588 - val_loss: 0.5081 - val_accuracy: 0.7598\n",
      "Epoch 14/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5029 - accuracy: 0.7451 - val_loss: 0.5679 - val_accuracy: 0.6969\n",
      "Epoch 15/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4931 - accuracy: 0.7646 - val_loss: 0.5056 - val_accuracy: 0.7677\n",
      "Epoch 16/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4948 - accuracy: 0.7743 - val_loss: 0.5134 - val_accuracy: 0.7598\n",
      "Epoch 17/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5062 - accuracy: 0.7568 - val_loss: 0.5070 - val_accuracy: 0.7638\n",
      "Epoch 18/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4978 - accuracy: 0.7646 - val_loss: 0.5135 - val_accuracy: 0.7638\n",
      "Epoch 19/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4983 - accuracy: 0.7665 - val_loss: 0.5081 - val_accuracy: 0.7638\n",
      "Epoch 20/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5006 - accuracy: 0.7626 - val_loss: 0.5083 - val_accuracy: 0.7638\n",
      "Epoch 21/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4903 - accuracy: 0.7626 - val_loss: 0.5068 - val_accuracy: 0.7638\n",
      "Epoch 22/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4952 - accuracy: 0.7704 - val_loss: 0.5212 - val_accuracy: 0.7283\n",
      "Epoch 23/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4985 - accuracy: 0.7665 - val_loss: 0.5025 - val_accuracy: 0.7795\n",
      "Epoch 24/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4924 - accuracy: 0.7588 - val_loss: 0.5102 - val_accuracy: 0.7598\n",
      "Epoch 25/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.5060 - accuracy: 0.7607 - val_loss: 0.5226 - val_accuracy: 0.7362\n",
      "Epoch 26/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4876 - accuracy: 0.7588 - val_loss: 0.5019 - val_accuracy: 0.7756\n",
      "Epoch 27/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4904 - accuracy: 0.7665 - val_loss: 0.5168 - val_accuracy: 0.7638\n",
      "Epoch 28/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4918 - accuracy: 0.7549 - val_loss: 0.5073 - val_accuracy: 0.7717\n",
      "Epoch 29/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4906 - accuracy: 0.7743 - val_loss: 0.5373 - val_accuracy: 0.7283\n",
      "Epoch 30/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4972 - accuracy: 0.7665 - val_loss: 0.5011 - val_accuracy: 0.7756\n",
      "Epoch 31/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4947 - accuracy: 0.7568 - val_loss: 0.5482 - val_accuracy: 0.7520\n",
      "Epoch 32/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4947 - accuracy: 0.7529 - val_loss: 0.5217 - val_accuracy: 0.7520\n",
      "Epoch 33/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4818 - accuracy: 0.7840 - val_loss: 0.5056 - val_accuracy: 0.7559\n",
      "Epoch 34/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5025 - accuracy: 0.7451 - val_loss: 0.5025 - val_accuracy: 0.7717\n",
      "Epoch 35/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4912 - accuracy: 0.7607 - val_loss: 0.5187 - val_accuracy: 0.7520\n",
      "Epoch 36/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4961 - accuracy: 0.7568 - val_loss: 0.5029 - val_accuracy: 0.7795\n",
      "Epoch 37/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4914 - accuracy: 0.7626 - val_loss: 0.5140 - val_accuracy: 0.7638\n",
      "Epoch 38/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4863 - accuracy: 0.7510 - val_loss: 0.5002 - val_accuracy: 0.7795\n",
      "Epoch 39/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4884 - accuracy: 0.7490 - val_loss: 0.5104 - val_accuracy: 0.7756\n",
      "Epoch 40/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4965 - accuracy: 0.7549 - val_loss: 0.5002 - val_accuracy: 0.7756\n",
      "Epoch 41/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4879 - accuracy: 0.7490 - val_loss: 0.5147 - val_accuracy: 0.7559\n",
      "Epoch 42/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4830 - accuracy: 0.7549 - val_loss: 0.5060 - val_accuracy: 0.7756\n",
      "Epoch 43/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4868 - accuracy: 0.7646 - val_loss: 0.5141 - val_accuracy: 0.7520\n",
      "Epoch 44/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4810 - accuracy: 0.7724 - val_loss: 0.5088 - val_accuracy: 0.7638\n",
      "Epoch 45/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4830 - accuracy: 0.7704 - val_loss: 0.5070 - val_accuracy: 0.7756\n",
      "Epoch 46/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4794 - accuracy: 0.7588 - val_loss: 0.5064 - val_accuracy: 0.7717\n",
      "Epoch 47/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4843 - accuracy: 0.7860 - val_loss: 0.4989 - val_accuracy: 0.7795\n",
      "Epoch 48/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4807 - accuracy: 0.7665 - val_loss: 0.5038 - val_accuracy: 0.7756\n",
      "Epoch 49/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4912 - accuracy: 0.7549 - val_loss: 0.5081 - val_accuracy: 0.7756\n",
      "Epoch 50/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4840 - accuracy: 0.7646 - val_loss: 0.5046 - val_accuracy: 0.7717\n",
      "Epoch 51/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4859 - accuracy: 0.7588 - val_loss: 0.5000 - val_accuracy: 0.7795\n",
      "Epoch 52/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4912 - accuracy: 0.7471 - val_loss: 0.4997 - val_accuracy: 0.7874\n",
      "Epoch 53/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4816 - accuracy: 0.7626 - val_loss: 0.5110 - val_accuracy: 0.7598\n",
      "Epoch 54/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4909 - accuracy: 0.7529 - val_loss: 0.5047 - val_accuracy: 0.7677\n",
      "Epoch 55/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4849 - accuracy: 0.7568 - val_loss: 0.5155 - val_accuracy: 0.7441\n",
      "Epoch 56/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4792 - accuracy: 0.7665 - val_loss: 0.5060 - val_accuracy: 0.7638\n",
      "Epoch 57/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4760 - accuracy: 0.7646 - val_loss: 0.5101 - val_accuracy: 0.7677\n",
      "Epoch 58/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4723 - accuracy: 0.7685 - val_loss: 0.4974 - val_accuracy: 0.7677\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4813 - accuracy: 0.7510 - val_loss: 0.4911 - val_accuracy: 0.7717\n",
      "Epoch 60/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4792 - accuracy: 0.7743 - val_loss: 0.5186 - val_accuracy: 0.7480\n",
      "Epoch 61/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4871 - accuracy: 0.7665 - val_loss: 0.5063 - val_accuracy: 0.7480\n",
      "Epoch 62/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4784 - accuracy: 0.7490 - val_loss: 0.4955 - val_accuracy: 0.7638\n",
      "Epoch 63/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4745 - accuracy: 0.7588 - val_loss: 0.5027 - val_accuracy: 0.7717\n",
      "Epoch 64/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4766 - accuracy: 0.7607 - val_loss: 0.5006 - val_accuracy: 0.7795\n",
      "Epoch 65/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4761 - accuracy: 0.7685 - val_loss: 0.5160 - val_accuracy: 0.7441\n",
      "Epoch 66/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4677 - accuracy: 0.7665 - val_loss: 0.4984 - val_accuracy: 0.7756\n",
      "Epoch 67/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4943 - accuracy: 0.7665 - val_loss: 0.5024 - val_accuracy: 0.7795\n",
      "Epoch 68/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4887 - accuracy: 0.7626 - val_loss: 0.5084 - val_accuracy: 0.7480\n",
      "Epoch 69/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4700 - accuracy: 0.7724 - val_loss: 0.5079 - val_accuracy: 0.7638\n",
      "Epoch 70/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4711 - accuracy: 0.7529 - val_loss: 0.4993 - val_accuracy: 0.7598\n",
      "Epoch 71/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4695 - accuracy: 0.7607 - val_loss: 0.5220 - val_accuracy: 0.7520\n",
      "Epoch 72/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4768 - accuracy: 0.7743 - val_loss: 0.5102 - val_accuracy: 0.7559\n",
      "Epoch 73/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4772 - accuracy: 0.7471 - val_loss: 0.5164 - val_accuracy: 0.7598\n",
      "Epoch 74/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4856 - accuracy: 0.7685 - val_loss: 0.4989 - val_accuracy: 0.7677\n",
      "Epoch 75/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4694 - accuracy: 0.7685 - val_loss: 0.5049 - val_accuracy: 0.7559\n",
      "Epoch 76/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4731 - accuracy: 0.7607 - val_loss: 0.5167 - val_accuracy: 0.7480\n",
      "Epoch 77/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4703 - accuracy: 0.7802 - val_loss: 0.5030 - val_accuracy: 0.7677\n",
      "Epoch 78/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4739 - accuracy: 0.7704 - val_loss: 0.4878 - val_accuracy: 0.7677\n",
      "Epoch 79/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4695 - accuracy: 0.7665 - val_loss: 0.5110 - val_accuracy: 0.7638\n",
      "Epoch 80/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4775 - accuracy: 0.7529 - val_loss: 0.5032 - val_accuracy: 0.7638\n",
      "Epoch 81/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4683 - accuracy: 0.7724 - val_loss: 0.5170 - val_accuracy: 0.7402\n",
      "Epoch 82/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4708 - accuracy: 0.7743 - val_loss: 0.4985 - val_accuracy: 0.7677\n",
      "Epoch 83/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4688 - accuracy: 0.7665 - val_loss: 0.5252 - val_accuracy: 0.7559\n",
      "Epoch 84/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4679 - accuracy: 0.7626 - val_loss: 0.5188 - val_accuracy: 0.7520\n",
      "Epoch 85/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4763 - accuracy: 0.7665 - val_loss: 0.5047 - val_accuracy: 0.7638\n",
      "Epoch 86/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4646 - accuracy: 0.7646 - val_loss: 0.5024 - val_accuracy: 0.7559\n",
      "Epoch 87/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4675 - accuracy: 0.7840 - val_loss: 0.5109 - val_accuracy: 0.7598\n",
      "Epoch 88/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4657 - accuracy: 0.7724 - val_loss: 0.5013 - val_accuracy: 0.7756\n",
      "Epoch 89/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4745 - accuracy: 0.7607 - val_loss: 0.5300 - val_accuracy: 0.7480\n",
      "Epoch 90/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4616 - accuracy: 0.7763 - val_loss: 0.5116 - val_accuracy: 0.7520\n",
      "Epoch 91/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4731 - accuracy: 0.7626 - val_loss: 0.5428 - val_accuracy: 0.7480\n",
      "Epoch 92/150\n",
      "52/52 [==============================] - 0s 7ms/step - loss: 0.4719 - accuracy: 0.7743 - val_loss: 0.5032 - val_accuracy: 0.7559\n",
      "Epoch 93/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4634 - accuracy: 0.7724 - val_loss: 0.5213 - val_accuracy: 0.7598\n",
      "Epoch 94/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4664 - accuracy: 0.7607 - val_loss: 0.5127 - val_accuracy: 0.7559\n",
      "Epoch 95/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4659 - accuracy: 0.7510 - val_loss: 0.5044 - val_accuracy: 0.7638\n",
      "Epoch 96/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4652 - accuracy: 0.7549 - val_loss: 0.5038 - val_accuracy: 0.7717\n",
      "Epoch 97/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4595 - accuracy: 0.7763 - val_loss: 0.5058 - val_accuracy: 0.7638\n",
      "Epoch 98/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4624 - accuracy: 0.7743 - val_loss: 0.5077 - val_accuracy: 0.7717\n",
      "Epoch 99/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4622 - accuracy: 0.7665 - val_loss: 0.5771 - val_accuracy: 0.7402\n",
      "Epoch 100/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4669 - accuracy: 0.7704 - val_loss: 0.5094 - val_accuracy: 0.7677\n",
      "Epoch 101/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4587 - accuracy: 0.7763 - val_loss: 0.5054 - val_accuracy: 0.7795\n",
      "Epoch 102/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4613 - accuracy: 0.7704 - val_loss: 0.5045 - val_accuracy: 0.7559\n",
      "Epoch 103/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4562 - accuracy: 0.7704 - val_loss: 0.5046 - val_accuracy: 0.7598\n",
      "Epoch 104/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4640 - accuracy: 0.7782 - val_loss: 0.5042 - val_accuracy: 0.7717\n",
      "Epoch 105/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4630 - accuracy: 0.7782 - val_loss: 0.5063 - val_accuracy: 0.7559\n",
      "Epoch 106/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4596 - accuracy: 0.7802 - val_loss: 0.5637 - val_accuracy: 0.7362\n",
      "Epoch 107/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4612 - accuracy: 0.7802 - val_loss: 0.5196 - val_accuracy: 0.7598\n",
      "Epoch 108/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4652 - accuracy: 0.7763 - val_loss: 0.5216 - val_accuracy: 0.7598\n",
      "Epoch 109/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4573 - accuracy: 0.7840 - val_loss: 0.5102 - val_accuracy: 0.7795\n",
      "Epoch 110/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4624 - accuracy: 0.7879 - val_loss: 0.5287 - val_accuracy: 0.7677\n",
      "Epoch 111/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4662 - accuracy: 0.7549 - val_loss: 0.5081 - val_accuracy: 0.7638\n",
      "Epoch 112/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4653 - accuracy: 0.7782 - val_loss: 0.5115 - val_accuracy: 0.7559\n",
      "Epoch 113/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4593 - accuracy: 0.7704 - val_loss: 0.5084 - val_accuracy: 0.7598\n",
      "Epoch 114/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4583 - accuracy: 0.7899 - val_loss: 0.5112 - val_accuracy: 0.7598\n",
      "Epoch 115/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4623 - accuracy: 0.7646 - val_loss: 0.5336 - val_accuracy: 0.7756\n",
      "Epoch 116/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4603 - accuracy: 0.7821 - val_loss: 0.5133 - val_accuracy: 0.7717\n",
      "Epoch 117/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4550 - accuracy: 0.7704 - val_loss: 0.5193 - val_accuracy: 0.7520\n",
      "Epoch 118/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4585 - accuracy: 0.7860 - val_loss: 0.5204 - val_accuracy: 0.7480\n",
      "Epoch 119/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4838 - accuracy: 0.7607 - val_loss: 0.5230 - val_accuracy: 0.7559\n",
      "Epoch 120/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4573 - accuracy: 0.7840 - val_loss: 0.5228 - val_accuracy: 0.7677\n",
      "Epoch 121/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4593 - accuracy: 0.7782 - val_loss: 0.5218 - val_accuracy: 0.7520\n",
      "Epoch 122/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4662 - accuracy: 0.7665 - val_loss: 0.5133 - val_accuracy: 0.7677\n",
      "Epoch 123/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4505 - accuracy: 0.7821 - val_loss: 0.5258 - val_accuracy: 0.7559\n",
      "Epoch 124/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4538 - accuracy: 0.7802 - val_loss: 0.5072 - val_accuracy: 0.7598\n",
      "Epoch 125/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4640 - accuracy: 0.7685 - val_loss: 0.5061 - val_accuracy: 0.7835\n",
      "Epoch 126/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4529 - accuracy: 0.7763 - val_loss: 0.5310 - val_accuracy: 0.7480\n",
      "Epoch 127/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4615 - accuracy: 0.7724 - val_loss: 0.5122 - val_accuracy: 0.7756\n",
      "Epoch 128/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4603 - accuracy: 0.7763 - val_loss: 0.5296 - val_accuracy: 0.7520\n",
      "Epoch 129/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4633 - accuracy: 0.7665 - val_loss: 0.5150 - val_accuracy: 0.7638\n",
      "Epoch 130/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4513 - accuracy: 0.7743 - val_loss: 0.6557 - val_accuracy: 0.6890\n",
      "Epoch 131/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4731 - accuracy: 0.7704 - val_loss: 0.5209 - val_accuracy: 0.7638\n",
      "Epoch 132/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4844 - accuracy: 0.7471 - val_loss: 0.5611 - val_accuracy: 0.7756\n",
      "Epoch 133/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4591 - accuracy: 0.7607 - val_loss: 0.5136 - val_accuracy: 0.7598\n",
      "Epoch 134/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4592 - accuracy: 0.7743 - val_loss: 0.5064 - val_accuracy: 0.7717\n",
      "Epoch 135/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4520 - accuracy: 0.7763 - val_loss: 0.5302 - val_accuracy: 0.7756\n",
      "Epoch 136/150\n",
      "52/52 [==============================] - 0s 5ms/step - loss: 0.4626 - accuracy: 0.7763 - val_loss: 0.5301 - val_accuracy: 0.7559\n",
      "Epoch 137/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4627 - accuracy: 0.7510 - val_loss: 0.5072 - val_accuracy: 0.7677\n",
      "Epoch 138/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4559 - accuracy: 0.7743 - val_loss: 0.5278 - val_accuracy: 0.7480\n",
      "Epoch 139/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4678 - accuracy: 0.7646 - val_loss: 0.5377 - val_accuracy: 0.7441\n",
      "Epoch 140/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4604 - accuracy: 0.7704 - val_loss: 0.5364 - val_accuracy: 0.7717\n",
      "Epoch 141/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4475 - accuracy: 0.7724 - val_loss: 0.5214 - val_accuracy: 0.7677\n",
      "Epoch 142/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4428 - accuracy: 0.7879 - val_loss: 0.5188 - val_accuracy: 0.7559\n",
      "Epoch 143/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4625 - accuracy: 0.7568 - val_loss: 0.5236 - val_accuracy: 0.7717\n",
      "Epoch 144/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4748 - accuracy: 0.7802 - val_loss: 0.5287 - val_accuracy: 0.7520\n",
      "Epoch 145/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4574 - accuracy: 0.7879 - val_loss: 0.5413 - val_accuracy: 0.7717\n",
      "Epoch 146/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4626 - accuracy: 0.7782 - val_loss: 0.5193 - val_accuracy: 0.7717\n",
      "Epoch 147/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4513 - accuracy: 0.7743 - val_loss: 0.5147 - val_accuracy: 0.7598\n",
      "Epoch 148/150\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.4697 - accuracy: 0.7724 - val_loss: 0.5572 - val_accuracy: 0.7717\n",
      "Epoch 149/150\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4560 - accuracy: 0.7802 - val_loss: 0.5026 - val_accuracy: 0.7756\n",
      "Epoch 150/150\n",
      "52/52 [==============================] - 0s 2ms/step - loss: 0.4499 - accuracy: 0.7665 - val_loss: 0.5178 - val_accuracy: 0.7677\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "history = keras_model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "527fed5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 0s 2ms/step - loss: 0.4663 - accuracy: 0.7760\n",
      "accuracy: 77.60%\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "scores = keras_model.evaluate(X, Y)\n",
    "print(\"%s: %.2f%%\" % (keras_model.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d232cb53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize training history\n",
    "\n",
    "# list all data in history\n",
    "keras_model.history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5ea01847",
   "metadata": {},
   "outputs": [],
   "source": [
    "history.history.keys()\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e2d41741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e02d0c",
   "metadata": {},
   "source": [
    "#### Let's Try another method or activation technique to get better results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e89700fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aef49a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "np.random.seed(seed)\n",
    "# laod pima indians dataset\n",
    "dataset = np.loadtxt('C:/Users/Moin Dalvi/Documents/Data Science Material/Data Science/Python/Datasets/pima-indians-diabetes.data.csv',delimiter = \",\")\n",
    "# split into input (X) and output (Y) variables\n",
    "x = dataset[:,0:8]\n",
    "y = dataset[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "118f8953",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create model\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=8,activation='relu')) # input layer\n",
    "model.add(Dense(8, activation='relu')) # hidden layer\n",
    "model.add(Dense(1, activation='sigmoid')) # output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "096e2cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a14a42c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e0dd1146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "52/52 [==============================] - 1s 5ms/step - loss: 10.2439 - accuracy: 0.5058 - val_loss: 3.8564 - val_accuracy: 0.6260\n",
      "Epoch 2/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 2.5378 - accuracy: 0.6109 - val_loss: 1.5517 - val_accuracy: 0.6417\n",
      "Epoch 3/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 1.5155 - accuracy: 0.5720 - val_loss: 1.2693 - val_accuracy: 0.5906\n",
      "Epoch 4/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 1.3338 - accuracy: 0.5934 - val_loss: 1.2440 - val_accuracy: 0.5197\n",
      "Epoch 5/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 1.2151 - accuracy: 0.5798 - val_loss: 1.1761 - val_accuracy: 0.5315\n",
      "Epoch 6/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 1.2463 - accuracy: 0.5739 - val_loss: 1.2929 - val_accuracy: 0.6457\n",
      "Epoch 7/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 1.1887 - accuracy: 0.5856 - val_loss: 1.1075 - val_accuracy: 0.5354\n",
      "Epoch 8/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.9690 - accuracy: 0.5973 - val_loss: 0.9186 - val_accuracy: 0.6102\n",
      "Epoch 9/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.9762 - accuracy: 0.5992 - val_loss: 1.1174 - val_accuracy: 0.6614\n",
      "Epoch 10/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.8894 - accuracy: 0.6128 - val_loss: 0.8501 - val_accuracy: 0.5866\n",
      "Epoch 11/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.8823 - accuracy: 0.6051 - val_loss: 0.8169 - val_accuracy: 0.6220\n",
      "Epoch 12/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.8726 - accuracy: 0.6128 - val_loss: 1.0920 - val_accuracy: 0.5197\n",
      "Epoch 13/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 1.0323 - accuracy: 0.5914 - val_loss: 0.7461 - val_accuracy: 0.6929\n",
      "Epoch 14/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.8002 - accuracy: 0.6284 - val_loss: 0.7714 - val_accuracy: 0.5984\n",
      "Epoch 15/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.8456 - accuracy: 0.5895 - val_loss: 1.1340 - val_accuracy: 0.5236\n",
      "Epoch 16/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.7814 - accuracy: 0.6304 - val_loss: 0.7219 - val_accuracy: 0.6811\n",
      "Epoch 17/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.7720 - accuracy: 0.6595 - val_loss: 0.7365 - val_accuracy: 0.6181\n",
      "Epoch 18/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.7414 - accuracy: 0.6381 - val_loss: 0.6652 - val_accuracy: 0.6811\n",
      "Epoch 19/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6835 - accuracy: 0.6556 - val_loss: 0.7804 - val_accuracy: 0.5866\n",
      "Epoch 20/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.7958 - accuracy: 0.6342 - val_loss: 0.7133 - val_accuracy: 0.5984\n",
      "Epoch 21/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.7404 - accuracy: 0.6537 - val_loss: 0.6855 - val_accuracy: 0.6220\n",
      "Epoch 22/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6553 - accuracy: 0.6770 - val_loss: 0.6514 - val_accuracy: 0.6732\n",
      "Epoch 23/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6656 - accuracy: 0.6926 - val_loss: 0.7077 - val_accuracy: 0.5512\n",
      "Epoch 24/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6711 - accuracy: 0.6479 - val_loss: 0.6389 - val_accuracy: 0.6732\n",
      "Epoch 25/250\n",
      "52/52 [==============================] - 0s 5ms/step - loss: 0.6446 - accuracy: 0.6848 - val_loss: 0.6354 - val_accuracy: 0.6772\n",
      "Epoch 26/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6294 - accuracy: 0.6829 - val_loss: 0.6176 - val_accuracy: 0.7008\n",
      "Epoch 27/250\n",
      "52/52 [==============================] - 0s 5ms/step - loss: 0.6950 - accuracy: 0.6712 - val_loss: 0.7253 - val_accuracy: 0.5866\n",
      "Epoch 28/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6656 - accuracy: 0.6712 - val_loss: 0.7616 - val_accuracy: 0.6969\n",
      "Epoch 29/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6485 - accuracy: 0.6615 - val_loss: 0.6163 - val_accuracy: 0.7008\n",
      "Epoch 30/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.7083 - accuracy: 0.6420 - val_loss: 0.6222 - val_accuracy: 0.6772\n",
      "Epoch 31/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6302 - accuracy: 0.6829 - val_loss: 0.6099 - val_accuracy: 0.6969\n",
      "Epoch 32/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6836 - accuracy: 0.6595 - val_loss: 0.6261 - val_accuracy: 0.6693\n",
      "Epoch 33/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6242 - accuracy: 0.6732 - val_loss: 0.6162 - val_accuracy: 0.7165\n",
      "Epoch 34/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6825 - accuracy: 0.6479 - val_loss: 0.7169 - val_accuracy: 0.6969\n",
      "Epoch 35/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6967 - accuracy: 0.6284 - val_loss: 0.7973 - val_accuracy: 0.5276\n",
      "Epoch 36/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6726 - accuracy: 0.6673 - val_loss: 0.6276 - val_accuracy: 0.6535\n",
      "Epoch 37/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6196 - accuracy: 0.6907 - val_loss: 0.6328 - val_accuracy: 0.6535\n",
      "Epoch 38/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6502 - accuracy: 0.6868 - val_loss: 0.7734 - val_accuracy: 0.5394\n",
      "Epoch 39/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.7058 - accuracy: 0.6848 - val_loss: 0.9187 - val_accuracy: 0.5079\n",
      "Epoch 40/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.8207 - accuracy: 0.6576 - val_loss: 0.6646 - val_accuracy: 0.6890\n",
      "Epoch 41/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.7011 - accuracy: 0.6654 - val_loss: 0.6462 - val_accuracy: 0.6811\n",
      "Epoch 42/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.5915 - accuracy: 0.6946 - val_loss: 0.6099 - val_accuracy: 0.6850\n",
      "Epoch 43/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6346 - accuracy: 0.7023 - val_loss: 0.5926 - val_accuracy: 0.6811\n",
      "Epoch 44/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6049 - accuracy: 0.7101 - val_loss: 0.5922 - val_accuracy: 0.7047\n",
      "Epoch 45/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6334 - accuracy: 0.6829 - val_loss: 0.6666 - val_accuracy: 0.6929\n",
      "Epoch 46/250\n",
      "52/52 [==============================] - 0s 5ms/step - loss: 0.6195 - accuracy: 0.7043 - val_loss: 0.5859 - val_accuracy: 0.6929\n",
      "Epoch 47/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6144 - accuracy: 0.6926 - val_loss: 0.5856 - val_accuracy: 0.7126\n",
      "Epoch 48/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5938 - accuracy: 0.6984 - val_loss: 0.6484 - val_accuracy: 0.5984\n",
      "Epoch 49/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6181 - accuracy: 0.6946 - val_loss: 0.7298 - val_accuracy: 0.6969\n",
      "Epoch 50/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6694 - accuracy: 0.7062 - val_loss: 0.6945 - val_accuracy: 0.6024\n",
      "Epoch 51/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.7421 - accuracy: 0.6751 - val_loss: 0.6883 - val_accuracy: 0.5787\n",
      "Epoch 52/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6887 - accuracy: 0.6848 - val_loss: 0.6035 - val_accuracy: 0.7244\n",
      "Epoch 53/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5943 - accuracy: 0.7062 - val_loss: 0.6338 - val_accuracy: 0.7087\n",
      "Epoch 54/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6572 - accuracy: 0.6829 - val_loss: 0.6315 - val_accuracy: 0.6772\n",
      "Epoch 55/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6007 - accuracy: 0.7082 - val_loss: 0.6457 - val_accuracy: 0.7047\n",
      "Epoch 56/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6477 - accuracy: 0.6809 - val_loss: 0.5838 - val_accuracy: 0.6969\n",
      "Epoch 57/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5761 - accuracy: 0.7237 - val_loss: 0.5734 - val_accuracy: 0.7165\n",
      "Epoch 58/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6104 - accuracy: 0.6848 - val_loss: 0.6203 - val_accuracy: 0.7047\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6099 - accuracy: 0.6770 - val_loss: 0.5738 - val_accuracy: 0.7244\n",
      "Epoch 60/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.5838 - accuracy: 0.7121 - val_loss: 0.5689 - val_accuracy: 0.7087\n",
      "Epoch 61/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6627 - accuracy: 0.6887 - val_loss: 0.5923 - val_accuracy: 0.6850\n",
      "Epoch 62/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.6170 - accuracy: 0.6848 - val_loss: 0.5806 - val_accuracy: 0.7205\n",
      "Epoch 63/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6522 - accuracy: 0.6946 - val_loss: 0.8015 - val_accuracy: 0.5787\n",
      "Epoch 64/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6304 - accuracy: 0.6984 - val_loss: 0.7084 - val_accuracy: 0.7047\n",
      "Epoch 65/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.7474 - accuracy: 0.6732 - val_loss: 0.6844 - val_accuracy: 0.5827\n",
      "Epoch 66/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6081 - accuracy: 0.6984 - val_loss: 0.5641 - val_accuracy: 0.7441\n",
      "Epoch 67/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5900 - accuracy: 0.7140 - val_loss: 0.6168 - val_accuracy: 0.6614\n",
      "Epoch 68/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5888 - accuracy: 0.7062 - val_loss: 0.5829 - val_accuracy: 0.6693\n",
      "Epoch 69/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.5790 - accuracy: 0.7062 - val_loss: 0.6016 - val_accuracy: 0.6811\n",
      "Epoch 70/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6454 - accuracy: 0.6809 - val_loss: 0.5614 - val_accuracy: 0.6929\n",
      "Epoch 71/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5976 - accuracy: 0.6984 - val_loss: 0.6169 - val_accuracy: 0.7126\n",
      "Epoch 72/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6016 - accuracy: 0.6926 - val_loss: 0.5744 - val_accuracy: 0.7126\n",
      "Epoch 73/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6163 - accuracy: 0.7082 - val_loss: 0.6318 - val_accuracy: 0.7008\n",
      "Epoch 74/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6476 - accuracy: 0.6946 - val_loss: 0.6042 - val_accuracy: 0.7323\n",
      "Epoch 75/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5990 - accuracy: 0.6887 - val_loss: 0.6749 - val_accuracy: 0.6929\n",
      "Epoch 76/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6038 - accuracy: 0.7101 - val_loss: 0.6392 - val_accuracy: 0.6220\n",
      "Epoch 77/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6041 - accuracy: 0.6984 - val_loss: 0.6246 - val_accuracy: 0.7087\n",
      "Epoch 78/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5716 - accuracy: 0.7140 - val_loss: 0.5789 - val_accuracy: 0.6969\n",
      "Epoch 79/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5657 - accuracy: 0.7101 - val_loss: 0.6191 - val_accuracy: 0.6535\n",
      "Epoch 80/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6317 - accuracy: 0.6946 - val_loss: 0.5554 - val_accuracy: 0.7323\n",
      "Epoch 81/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6610 - accuracy: 0.7004 - val_loss: 0.6600 - val_accuracy: 0.6969\n",
      "Epoch 82/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5975 - accuracy: 0.7082 - val_loss: 0.7485 - val_accuracy: 0.7165\n",
      "Epoch 83/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.7501 - accuracy: 0.6498 - val_loss: 0.5572 - val_accuracy: 0.7244\n",
      "Epoch 84/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6198 - accuracy: 0.6965 - val_loss: 0.6094 - val_accuracy: 0.7087\n",
      "Epoch 85/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5592 - accuracy: 0.7257 - val_loss: 0.5583 - val_accuracy: 0.7283\n",
      "Epoch 86/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5569 - accuracy: 0.7101 - val_loss: 0.5604 - val_accuracy: 0.7362\n",
      "Epoch 87/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6057 - accuracy: 0.7023 - val_loss: 0.5819 - val_accuracy: 0.6811\n",
      "Epoch 88/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5743 - accuracy: 0.6926 - val_loss: 0.5700 - val_accuracy: 0.7402\n",
      "Epoch 89/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6269 - accuracy: 0.7218 - val_loss: 0.6149 - val_accuracy: 0.6496\n",
      "Epoch 90/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6048 - accuracy: 0.6868 - val_loss: 0.6296 - val_accuracy: 0.7165\n",
      "Epoch 91/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5667 - accuracy: 0.7179 - val_loss: 0.5527 - val_accuracy: 0.7520\n",
      "Epoch 92/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5564 - accuracy: 0.7179 - val_loss: 0.5571 - val_accuracy: 0.7480\n",
      "Epoch 93/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5674 - accuracy: 0.7140 - val_loss: 0.6150 - val_accuracy: 0.7047\n",
      "Epoch 94/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5637 - accuracy: 0.7198 - val_loss: 0.5494 - val_accuracy: 0.7402\n",
      "Epoch 95/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5733 - accuracy: 0.7179 - val_loss: 0.5554 - val_accuracy: 0.7008\n",
      "Epoch 96/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5578 - accuracy: 0.7198 - val_loss: 0.5846 - val_accuracy: 0.7087\n",
      "Epoch 97/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5832 - accuracy: 0.7140 - val_loss: 0.5752 - val_accuracy: 0.6929\n",
      "Epoch 98/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5670 - accuracy: 0.7179 - val_loss: 0.6268 - val_accuracy: 0.6299\n",
      "Epoch 99/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5927 - accuracy: 0.7082 - val_loss: 0.5655 - val_accuracy: 0.7126\n",
      "Epoch 100/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5803 - accuracy: 0.7218 - val_loss: 0.5591 - val_accuracy: 0.7244\n",
      "Epoch 101/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6057 - accuracy: 0.6946 - val_loss: 0.7204 - val_accuracy: 0.5827\n",
      "Epoch 102/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6115 - accuracy: 0.6907 - val_loss: 0.6749 - val_accuracy: 0.7047\n",
      "Epoch 103/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5903 - accuracy: 0.7023 - val_loss: 0.5510 - val_accuracy: 0.7205\n",
      "Epoch 104/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5678 - accuracy: 0.7121 - val_loss: 0.5663 - val_accuracy: 0.7244\n",
      "Epoch 105/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5630 - accuracy: 0.7529 - val_loss: 0.5316 - val_accuracy: 0.7283\n",
      "Epoch 106/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5599 - accuracy: 0.7160 - val_loss: 0.5408 - val_accuracy: 0.7441\n",
      "Epoch 107/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5724 - accuracy: 0.7237 - val_loss: 0.5562 - val_accuracy: 0.7362\n",
      "Epoch 108/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5573 - accuracy: 0.7140 - val_loss: 0.7263 - val_accuracy: 0.7244\n",
      "Epoch 109/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5485 - accuracy: 0.7374 - val_loss: 0.5566 - val_accuracy: 0.7165\n",
      "Epoch 110/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5876 - accuracy: 0.7121 - val_loss: 0.6245 - val_accuracy: 0.7205\n",
      "Epoch 111/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5803 - accuracy: 0.7276 - val_loss: 1.1004 - val_accuracy: 0.6772\n",
      "Epoch 112/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.6472 - accuracy: 0.6809 - val_loss: 0.5719 - val_accuracy: 0.6929\n",
      "Epoch 113/250\n",
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      "Epoch 162/250\n",
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      "Epoch 163/250\n",
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      "Epoch 164/250\n",
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      "Epoch 165/250\n",
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      "Epoch 166/250\n",
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      "Epoch 169/250\n",
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      "Epoch 170/250\n",
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      "Epoch 171/250\n",
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      "Epoch 172/250\n",
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      "Epoch 173/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5528 - accuracy: 0.7296 - val_loss: 0.5923 - val_accuracy: 0.7480\n",
      "Epoch 174/250\n",
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      "Epoch 176/250\n",
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      "Epoch 177/250\n",
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      "Epoch 178/250\n",
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      "Epoch 179/250\n",
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      "Epoch 184/250\n",
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      "Epoch 190/250\n",
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      "Epoch 192/250\n",
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      "Epoch 193/250\n",
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      "Epoch 194/250\n",
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      "Epoch 195/250\n",
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      "Epoch 196/250\n",
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      "Epoch 197/250\n",
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      "Epoch 200/250\n",
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      "Epoch 201/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5090 - accuracy: 0.7451 - val_loss: 0.5730 - val_accuracy: 0.7165\n",
      "Epoch 202/250\n",
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      "Epoch 204/250\n",
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      "Epoch 205/250\n",
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      "Epoch 206/250\n",
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      "Epoch 208/250\n",
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      "Epoch 209/250\n",
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      "Epoch 210/250\n",
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      "Epoch 212/250\n",
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      "Epoch 213/250\n",
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      "Epoch 217/250\n",
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      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
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      "Epoch 229/250\n",
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      "Epoch 230/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5167 - accuracy: 0.7471 - val_loss: 0.5298 - val_accuracy: 0.7323\n",
      "Epoch 231/250\n",
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      "Epoch 232/250\n",
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      "Epoch 233/250\n",
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      "Epoch 234/250\n",
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      "Epoch 235/250\n",
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      "Epoch 236/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4994 - accuracy: 0.7412 - val_loss: 0.5814 - val_accuracy: 0.7756\n",
      "Epoch 237/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5242 - accuracy: 0.7529 - val_loss: 0.4918 - val_accuracy: 0.7835\n",
      "Epoch 238/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5065 - accuracy: 0.7510 - val_loss: 0.5158 - val_accuracy: 0.7598\n",
      "Epoch 239/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.5425 - accuracy: 0.7296 - val_loss: 0.7733 - val_accuracy: 0.5630\n",
      "Epoch 240/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5543 - accuracy: 0.7179 - val_loss: 0.5060 - val_accuracy: 0.7677\n",
      "Epoch 241/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5196 - accuracy: 0.7490 - val_loss: 0.5387 - val_accuracy: 0.7441\n",
      "Epoch 242/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.5128 - accuracy: 0.7471 - val_loss: 0.5987 - val_accuracy: 0.7087\n",
      "Epoch 243/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5107 - accuracy: 0.7412 - val_loss: 0.6149 - val_accuracy: 0.7047\n",
      "Epoch 244/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.5416 - accuracy: 0.7412 - val_loss: 0.5282 - val_accuracy: 0.7795\n",
      "Epoch 245/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4894 - accuracy: 0.7646 - val_loss: 0.5267 - val_accuracy: 0.7677\n",
      "Epoch 246/250\n",
      "52/52 [==============================] - 0s 4ms/step - loss: 0.5021 - accuracy: 0.7432 - val_loss: 0.5550 - val_accuracy: 0.7441\n",
      "Epoch 247/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5016 - accuracy: 0.7665 - val_loss: 0.5029 - val_accuracy: 0.7756\n",
      "Epoch 248/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5267 - accuracy: 0.7646 - val_loss: 0.5964 - val_accuracy: 0.7283\n",
      "Epoch 249/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.4919 - accuracy: 0.7685 - val_loss: 0.7298 - val_accuracy: 0.6969\n",
      "Epoch 250/250\n",
      "52/52 [==============================] - 0s 3ms/step - loss: 0.5320 - accuracy: 0.7140 - val_loss: 0.5465 - val_accuracy: 0.7559\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "history = model.fit(x, y, validation_split=0.33, epochs=250, batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ddd9f05d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 0s 2ms/step - loss: 0.4483 - accuracy: 0.8047\n",
      "accuracy: 80.47%\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "scores = model.evaluate(x, y)\n",
    "print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d73782a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'accuracy'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d140de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8046875"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6854269f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize training history\n",
    "\n",
    "# list all data in history\n",
    "model.history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5c519a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bd696048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cJM6ALc/rHs+bn4MZP4TJd/R+3QlTYfLt3Z8TPQau/K99zQPI4MtEd5MxPh0Mw+DBBx/knnvu6XDftm3b+Pzzz3nwwQe54IILePjhh/thhUIIIU4rSyuUHobI9P5eSf/I36a7LFTnw9YXYf4jrn2+5jpdlhAYYz9WngVpF+s2arafk889tefJ/FoH5995AWLGwgsLdI3vHcvAO7B316g+ruuP/cKcj9eX67ri3gaOlcd067nCPTrrP2Rmx3Maq/V9M35gPxZnzZznb4OaQv165vyqd88JOmP93u06C512CSz4fe8fOwhIJroPBAQEUFNTA8CFF17ISy+9RG1tLQD5+fkUFxdTUFCAr68vN910Ez//+c/Zvn17h8cKIYQYhNb/C56ZCdVn4aQ8iwUKdsCwC3SQte1/PdftnoqGSnhuLrww31733FgNdSU6gA6K10GrrWf0qdj8LPhHwcjLISINrv0fFB+Ajc/0/hpvXqsz2O29fRO8cU3vr1OZq9fhHaRbwnXm0BdgMevfhU1oCngH682Fm58Fv0gYeUXvnzd9kd6sGT0WvvO83nx4Fhl8meh+EBYWxqxZsxg9ejQXX3wxixcvZsaMGQD4+/vz+uuvk5GRwQMPPIDJZMLDw4NnntH/kd19991cfPHFxMTEsGrVqv58GUIIIfpaq1lPfjMsULh3QP5J2qXKjkBTtc54BsXDwU9h7/sw4aa+f67WFnj3Nig9pH8u3AWxE+ylG6FDdZAXknTq5RxlmXBkOcz+Jbh76mND5+ryiCPLYM4ve75GY7V1D5ehM8G2rPDxXbrGGvSHAp/g7q9jbtLT/iLS9L+vjc9AVX7Hmu/Nz0FYKiQ6ZKmV0s97eBnUFsPsX9hfT2+4ecA93+gWdSfyuEFCgug+8uabbzr9/OMf/9jp56FDh3LhhRd2eNyPfvQjfvSjH7l0bUIIIfrJwU91GQNA8X77SOOzha0eOn4yhA+HiHSdKR1/Y8cpeafCMODzB3R/4/mPwIpH9GY7pyA6xX5bnt3VlXpnyws6IG9fz5u6AFb/GerKOpZotHd8J2CtX978PFxpDaI3ObRzK9jR86jtqjx9G5Sgyzg2PA1bX4J5v7Wfk79NZ5sv/qvzQBPQv5vMlWBy163rTtQA2+x3Okk5hxBCCOEqm5/Tgyr8o/Xmrd4wN8FH98KSG/TXO7fA8d29f84NT8Phr05uvaeitgSW3u/ciix/G3gFQtgwHTRPvQsKd+sBGn1pywuw7WU456dwzk8gZjwcWaHvawuik623KfpYdxvwutNcpztSjLxCbyh0lDofMHS9dE9sQ05GX62z87UlOvje865uMQcdezgfXQ/f/sP5mG1TYXCCfo3DL4Jtr+hMt83m58HTH8bd0HEdtgz4yMud68hFjySIFkIIIVyhcK/+s/yUu3Q7r+JeTl07vht2vqHra6tyIesbeONqe8axO+XZsOwhWP2nU1v7ydjwbx3IbnnBfix/q84G27Kf467XHyre/27vXk9vbXoWEqbrSXqgg9m8zTqgL8+CgBjw9NP3habojhQ1hSf3XAU7oakKxl7b8b7YCXqIiK2VXHfytuq1zP4ltDbD9ldg+/903+TzHtAfPBx7OAOseRxW/t65rtzW8STIOrr73J/q1/3eHbqcqLZEB+njF3e+4XHITF2vft4DvXn1woEE0UIIIYQrbH4W3H10/W9kOpQc0p06elJ5VN9e/yZ871u440sdNL15HTT1sBF9ywuAocsAaktO+SX0WkuDDgABtryk65NbGnQPYFumE3Qgu/id3r+e3qjI0bXXo66wB+vDFug69KzVOoi2lXKAc4eOk2H7MBQ1uuN9JhMMnadbyfU00CV/O8RNhojhkDJXv29bXoTk8/S/l/jJOtC2Zcyb6yFnnX5dpYft16nKBRQEWmugE6bCpX/TgfyyB3Vw3trcde9nrwC4YcnZ2z3mFAyaINo42T/LnGHOltcphBBntPpy2P0ujL1GT12LTNctyypyen6s45/nQT/2mpd1ZtqWXexMc50enBFhHVzRXUmBxdK7gN5RXanO3tYUQlOt83173tPZz+k/0BPxDn6qN81ZzDoYdBSZrifa9fR6eivDWraROt9+LG6y7lRxZIXeBGgr5QCHIPokO3SUHNQlKl1tEk2dD/Wl1ppnq1azc/lIdYF+n2wfMKbdo3+uzoOp1ha5cZOgrtiesc/5VmepQb93NpW5ei3WjX3rM0u5eksazVPv1eVEa56AoedD+LCTe72iS4MiiPb29qasrGzQB5iGYVBWVoa3t3d/L0UIIUR3drymJ+PZAiJbls8x+OlKZa5uO+YVYD+WOh8ueRyOfGXP+La3+x1orNJZSL+I7ksKPvsJvHp5r14KoEtEHh8Kf0vTX08M01lR0MHh5mchciRc8AddrrHpOfumQsdMdNvrmWd/PTte6/06OnNkhW6zFpZqP+bmrrO7hz7XgahtyArosgeTxylkog/o32dXGyNT5wHKHtxXHoMnx+nNjja2emjbezPsAt01JChR97MG+8ATW110xnIMD18Mk0e7IPpYWylHs9nCQx/uZevRCj6J/B6MWKg/vNn+HbpYq8Wg1TK4YzFHg6I7R3x8PHl5eZSUnMY/XfUTb29v4uPj+3sZQgghumJp1WUVQ2ZBtPVP/uFp+rbkAKQv7P7xVbn2LLSjKXfqTWU53+rvHRmGzjpGjdHPO3SeDlAtrZ337s1Zp0sgaovBP7L79Wx+Xtc7j70OEqfrYxv+A2/fCN9dqXswF+6Bhf/UzzXlLlj+W93aLjCu4+Y7x9ez8vfWNm8nydwE2Wtg/A0dg9phC2D/R/p7x3ION3cIGXJyQbRh6HKOkd18APEL17XRGSt0hvmNa3WGefPzul7ZO0h/wDB56Gl8oN+3xe/qUg3b7ytqjO5pnb8NRl2JcWQ5ez3H4lNXQGz+Xnxtz1d1DBKmAfDqhhyyS+vw9XRj6e4irr75BcjbAkmnOFimFyrqmrnuuQ0khPjywq2TUX3ZfWWAGhRBtIeHB8nJyT2fKIQQQrja4S91dnDBH+zHvPx1trS3mWjHoM9R3MSOHRtAB9bF++Gyp3QwmTofdr+lN8HFt8sEtzTaSxkyVuoAtCtHVsAXv9AdH654xh7gpcyB5+fpYSGhKTowtG20m3ATrPoTFO2F9Mu6f61hQ0+orCKntA4vDxMxQT76wLEN0FLnXMph43is/fsZmgJlJxFE1xZDQwXH3IcQ22rB3a2LP+inzoe1T8BbN+oPKwt+D8sf1l09ZtyrA+Po0eDh8JfliOHO13D31ENM8rZBWSaqIpt3WmYzzc2E39HdNNc3E+ztpktDghIoq23iyZVHmD08gpGxgTy3JovyZjdCk887oZe4N7+K4VEBeLr3vlihydzKPa9t43BRLYeLalm+v4gLRnXx4WkQGRTlHEIIIcRpU7RfZz+7sulZnYEd0S7jHJnecxBtGF1nokHX+lYe67hpcPOzul/vGOuUu6Hn41RS4KjsiM54QvclHyWH9PCSyFFw1YvOGe3QFL3xsfKYznhPuNne/cI31B5Qd1bK4cjWbq69/Z/A13+0f1lb/H331a1c/u91FFRau1NkrNDZ2s4CxYBonc21PY/T8w7tuc1dUw3sesu5dty6qfCXa828sj6n68faNjbmrNXlNbN+DPFTYcvzuj66YIf+XfYkfjIc38mW5UsACBt3CeMnTifGKOa+/31Lc0W+rjsPTuBvyw9T39zKbxemc9m4WFotBp/vOd7zczjIq6hn0b+/5d1tub1+jGEY/PK93WzOKefv144jNdKfP35+gCbzCdbcn4EkiBZCCCF6q/QIvHwRvHk9mJs73l98ELK/gcl36LIBR5Hp+vGtLV1fv6ECmmt1XXFnbEGpraYWdLB36EsYtxg8rBlavzCdte4sSLYF8rET9ObDrjYYbn1JB2iL39KZ9PaGzNDZ6bDUjp0fZvxAf5AYtqDr1wo6uK3K02UZNpZW+OAuWPNX3dJtzV/hlYWUZe8mo7iW4pom7nhlCzWNLTpTPmSmPYBvb8JNupSh/fqjR+sMdu5mp8PVjS28si6bxqYm/QHiw3vgwFL7CdZe30cs8byyPqfr+t+4STponv0rmHQbn+0+Tn7azTpw3/gf/Tvu6QMGYMROhJZ6og+8QqF7HD+8agHxafpxVcf28Nhb+vf7+gGDtzYf4+bpQ0iNDGBEdACpkf4s3XVio+a3Ha3AMODg8d53TfnXygw+2lnAzy8YzncmxvPbhSM5WlbPK+tyTui5z0QSRAshhBC9UVcGb1yju2C01OlSgvY2PwduXjDpto73RaSDpUV3i+hK5TF9G9RFJjp2PCiTcxCd9Y2+rm1Dmk3qAl17W1/ufLx4v67HnfY9HbS370XseF7USD2uuytjroYfbdM1xo4i0uCn+3V/7O6EDtUZ24qj9mPl2Xoz3OX/gUcq4f494O6J93uLCaWaBy5M40hxLQ+/ukzXmHdWymEz/Xtw26cdj4+8AryCdAbfwX9WZfLI0n1s+s9dOsttcnf6IGIp2k8FgSj/CPIqGlh5oKjz5zW5wXeXw9wHsVgMfvrOTh46lAL+UbrUBTp2LWnncFENP9ugP4glqBLCxl+qy0esm1R/Oq6V+hI9efHtDEV6TCD3z9cdOJRSLBoby+accgqrGrt9Hkc7cysByCiu7f5EhzX+6+sjXDkhjh/M1Rs7Zw+P4PwRkTz1dQYlNU09XOHMJkG0EEII0RNzk95IV3McbnxPB6HtSyUaq/Sf/0dfpTeXtWfr0FHSTUlH+/Z27Xn66S4YjkF0xnLwDGjbXNZm2AI6nZ5XfFBnj4ddoAPyrko6ig/qwN+VrGUWlrJMHvxgD+sySu3vj+39Ck6EG97Cs76IF7z+wT0zY/njFaPxOrpK35/aQ7a7E9sKm1nrfwHG/o+hWpc8NDS38taWY/zIbyWzqz5mY8yNuiQnY2Vb2Udt7h4OWeJ4eNEoYoK8uy/psDpe3UiT2cKazCrqxtyiu7Z4BTl3DGnnpW+zufjJtawo9KPJXQ9I8UizjowPSQJ3b2YHl/LY+cEALH3oej6771yCfT3brrFwXAyGAZ/u7n022hZEZ5b0HEQbhsEfPt2Pn6cbv1040mkj4UOXptPY0srfvjrU6+c+E0kQLYQQ4uz23p3w16H2r9V/6XjOyt/rzPMVz8DQubqUoX0QveMNnaGe1sVQi/DhOmjtri66bfpcF+UcoMsA8rfpwM4wdJCXMrutT3Cb2AngE9pxncX7dYDqG6qv1VnddF2pbg3nggEcTu1orUF01uHdLNl8jLe25Nrfn4g0+3nxk3nM+34mqkO4/30E16+eyx88XyXfCKfUJ+mEnr/ZbOHn7+7mNwUzdOnItpcB+HhnPhMbN/FTyyvsDzyXG7IvZqPbRP3BqWgvGAae5YfJUoksGBnFzTOGsD6zjEOF3Zc+5JTWAWAx4HOvC3V2O85himM7R8vqeOyLg5w3LJzVD5yP15Ap4O4NSefoE0xu+r0psU609A0HT98O1xka4c+o2ECW7irAMIy2r640mVvZV1CNt4eJ4pomqhu7KTsCVh4oZu2RUu6fP5xQP+d/e0Mj/Ll1ZhJvb81lb35Vt9dxXFtX6yuu6X02/XSSIFoIIcTZq7kO9n2og7mRl+vNebvf7nje4S91h4rR39E/py7QwahtEIbFojeNxU/VwWtnPLz183Q3/rsqFzx8dYDblbhJ0Fip62tLDunHdFbSYHLTGwwzVtin5zXV6omIkSPtryN/uw6aHdkC2cgRXa/jJBRUNjDvb9/w2W7rhjffUPAO4uhh3eZuZ26Ffn9CkpzqnEtrm3ixYjxfjfyzLiEZeTn1I6/n4ZZbdeB9Amxt4NzCh7LGGE/rlpcwzE18s3YVT3v+G6LHMvz7SzhveBT3b7P+RSFjBc3luXhb6vCIGYW3hxs3TEnEy93EK+uzu32+nDIdRIf7e/HOwRa48lmY+5suz//T5wdwd1M8dtVYHZzOfQiu+I+93h30XwiKD+jyn67+agEsGhfLrrwqkh/8nOQHP2fkw8s4WFjd6bkHjtfQbLZw8egYALJK6trus1gMFj31LXe+soWsklqazRb++PkBhkb4cfOMIZ1e7755wwjx9eT3n+7vMjh+elUGKb/+vG19U/64kiWbj7XVmhdXN/Kzd3Zx3l9XkVte3+Xr7C8SRAshhDh7Hd8FRiuc+zNY+Hfd7q0807mOuKECyjL0OGUbW9CasdJ6u0IHtdN6GGoRma7LJLpiG5zRXY9dWy1t3lZ7KUZXdcGp8619nHV3C0qtf163BcfD5tNpyYd1A11bsN1HHvviIFmldTyydB+1TWZQiqbAJDyqcogK9CK3vAFz4YEOz7s5W/8+wqbdoH9PC/9O0DVP0Tz0Ql7beJSW1s5HbDe2tFJUbc9i2trAzUmL4KVbp/C/1gtwqy/h6Od/57fVj2B4B6FueAt3nwD+vXgCwZEJHDSGUL/vS/bv2ghAyki9sS/Ez5MrJ8TxwfZ8Kuo62WRqlVNah5e7iVtmDGFLTgUFCZdCwpROz12fWcqyfUXcO2coUYHW9nfxk3SJkKPIdGuGfF/X9fPAjdMS+cVFadw/fxg/nqfrpV9c23nQv/NYBQBXTdQ18JkOddFZpXXsya9i5cFiLvznGm5+cRPZpXX8ZuFIPLpo8xfk48FPFwxnc3Y5X+wt7HB/dmkd/1xxmBkpYdw/fxj3zx9GcrgvD36wh0VPfcvjyw4y94nVLN1VwG0zkwn29ejydfYXCaKFEEKcHUoO6x7JjtpPjrO1HStw2Gxn23jn2JIsMl13n7AFsZuf1ZvGeuqLHJGug/R9H+k2bg71toC1vV03pRygx3p7+Om1Z6zQP3eVjUydp29t62zLMFuD1JgJ4BvWecmHdxAExHS/lhOw7Wg5n+wq4IKRUZTUNPH0qgwADrdEkGwq4uGFo/DAjKk8wz663GpTVhk+Hm6MjQ9yOn77rCSKqps6DdIA/vjZAWb8eSW//WgvFXXN/G35YRqaW/nNpSNJCvdj2IzLybJEk7T9MYJUPW43vQuB+jUHeHvw8u1T2OQ2Ec/jWzi6czUAYybMaLv+bbOSaG61cPV/1/PN4c4HvmWX1jMkzJfLxukx4V3VKLdaDH6/dD9xwT5899wu+oTb2H5/tUXd/nsJ8Pbg3jmp3D9/OD9ZMJyrJsXx8a4Cymo7bvjbmVtJZIAX01JCcTcpp7roHdYA+827pnHlhDg255QzNy2CuWndD+q5fkoCI6ID+NPnB2hsce4C88fPDuDpZuKf14/n/vnDuX/+cN65ZwZP3TCByvpmnl6VyYyh4Xz1k/P41cUjCPCWIFoIIYQ4/Q59Cf+ZBl//wfl43lYdhPhH6J9jJwDKuWNF/jZ9zLFMQykdoGZ9ozPLGSt0W7v2dcntxU3S3SjevRXeuRle/w6tmavt91d20yPaxuSm15KzFo6u7747hX8kxIy3Z8yLD4C7N0eaw/QmMpNJTzfMWGkv+QD7psIeps7VN5v5aEc+5i4ywTYWi8GjS/cTHejNP68fz3cmxPHi2mz2FVTxbXkQcaqU84cFM9StEJNh7pCJ3pRdzqQhIR2ynnOGR5IU5ssr6zpmV5vNFj7ZVUBMkA9vbj7G7MdX6TZwM4aQGqlb3v1w3nDedL8cs2Fi2Yg/4RU/zukaMUE+nHvJDbjTyqyqpVS7h+EZENZ2/4joQF66dQqtFoNbX9rMd/+3pUOAmlNWR1KYH0nhfoyND2Lprs57N7+yPoeDhTX8+pJ0vD06mTLpyLHMpqcPXQ5um5lEs9nCks3HOty3M7eS8QnBeLiZGBLm6xRE78ytJMDbnenJYfz16nGs/vkc/r14Yo/P5+5m4uGFI8mraODfX2e0lXV8e6SUFQeK+MH5qUQG2AfOKKVYNC6WlT+bw+qfz+GFWyeTFN5F+8IBQIJoIYQQg1vhHnjvDh28Hv7S+b787c4ZZu9AvWkrz2EqYP42fcw70PmxqQv0aOuPvq+7dUy6vee1DL8QfriN1nu+5Q9xz9BkeHB8y8f6vqZaaCjv9s/zbeIm6mxxa3P3QTTo+3M3Q0OlDqLDh/OT9/Zw7+vbdFAzbAHUl8LxHfp822jrXmwq/GB7Pve/vZNHl3Zd9wrwwY58dudV8cuL0/D1dOcXF43AzaS46YVNHGmJwIQFn7p85gRba7MdgsSKumYOFtYwLbljnbjJpLh1ZhLbj1Wyy9pZwmbtkRKqGlr4wxWj+Py+cxkbH0xMkA/3z7NPBgzw9mDUovuYrV5i5sU3drr2lAnnY/bwJ1xVd1reMndEJMt+ch4PXjyCVYdKeMkhoG+1GBwrqyfZGgguGhvLnvwqskvt9cbZpXXc+coW/vDpfs5JDeeSMb2Y9BeUAJ7+9u97KTUygHOHhXcogamoayanrJ7xicGA3hiY6VATvTO3knHxwZhM+kPVkDA//Lx6N/R6Zmo4C8fG8O9VGSx+fhN786v4/af7SAj14Y5ZnU+b9vF0G9DBs40E0UIIIQav6uPw5nW6NOGcn+ja5vJs/v31EbbtOwhVxzoOvWjf/SJva+eDMVJm604LBdth1BUQENXzepSC8FT+tM2NFzOD2GQZgV+utVVbW3u7XmQWbXXRHr562Eh3hi3Qdd9Zq6D4ADVBw9ibX01BVSO55Q0O0w1XsiWnnNdXbNYbF3sRRG/MKgPgtY1HefHbzmttG5pb+euXBxmfEMzl4+IAiA7y5t45Q6mob8ErSvcXpjyTqf7FmA0TltDUtsdvztH10NNSwjpcG+DqSfH4ebo5Ba8AS3cVEOTjwTmpEaRFB/D6d6fx7S/nEtSutvbKCfF8+7sriA7yplNuHrinzgUgMHFMp6d4ubtxz+yhjIwJZMexyrbjx6saaG61tAWEl47VpSI/WrKde17byp2vbOGCf3zDpuxyfn3JCF66bYpTq7guKWUveenpLxft2EpgvnQogdmZp9c8PiEYgKGR/hwtq6Ol1UJDcysHC2va7jsZ/7xuPH+4fBQHCqtZ+NS3HC6q5aHeZNwHOAmihRBCDFxF++HjH3Ycc90bllZ46wadgV38th5NDRxe9yFPfHWY1V9bs9Lth17ETdKZ2cqjeqNffWnnQbR3kL0389QeNhQ6eHVDDi9+m81tM5PY4T2FkPocPWykrb1dbzLR1vUknwfuXj2cO1mvde/7UFPAriZ7nfPG7DLd0zp2AhxZzr9WHuGrVdagvocg2jAMNmWXs2hcLBePjuaPnx9g2b6OtcnfHC6huKaJn10wvC2TCXDXeSlcMDKKK+ZZR3aXZzFc5XLUiCKr0tx23qascrzcTYxLCGp/aUBnkxdPS+STXQXsK9Dt1BqaW1m+v4hLxkTj6W4PdboKUHsMXG3Z/h7ek/EJwezOq2rrLpFTqjtKDAnTLehig324dcYQzK0GR8vqya9s4OpJ8Xz989ncfd5Qp7X2yLaWE8hEg70E5mWHDx07j1WiFIyNDwZ0Jrql1SC3vJ69Bfr1nEoQ7e5m4uYZSaz++RzumJXMjdMSuXBULzLuA1zvcvFCCCHE6VZTCG9cDdX5ulvErUud23z15NAXULADrnwOYsYCYIQkU7bzMyANn+KdGO5uqOixzo+zBqhLPvwIH083rnA41sHM+yBmXIdA/P1teSzZfIx/L57olOH8+mARj3yyj/npkfx24UgeLTkPcl+ybuyzlkN0klk0DINl+wp5fm02Y+ODuP/8YQRNvRvSF7E+s5S/fnmI/MqGTpe4eGoiP0mZC/t12cjnRSFMTQolo6SWTVnlXDs5AYYtwFjzOAebj7FIWdv29dCZI7u0jpKaJmakhPGdiXEcf24jP35rB1/dP5vEMHvf4m8OF+Pv5c70dplkbw83nrtlss72Lw2EskwiG7JZYSRQd6yS1MgADMNg7ZESJiQG4+Xeddbyh3OH8d62PH6/dD9v3T2dVYeKqWtuZdHY2G5fQ6+lXwZZq/WAmm6MTwjmtY1HySypZXhUANnW9nbJDqUJj14+um/WNP5G3QbQJ/iEHmYrgXl06X6eXpXBd89NZmduJcMjA/C3lmgMjdDrzSypa+tzbSv1OBXBvp48vKhvO770J8lECyGEGHia63QZRkOl7qmbtwU+utd581tPNj8LgfFO7cH2+01jnHkPd8+MZTQZ1ASldRxUETWKFuVJXdZGSg+tx3Dz7np8ddpFcNGfnTbgfXO4hF+8v5utRyu4839bqGvSWdW9+VX88M0djIwN5MnrJ+BmUoQPGUWuJQLz4a90JtrkAf7OGbqDhdUsfn4T33t9O4VVjbyyPoe5f/+GlwLv5fvr/Fj8/CZKapqYnx7J/PQop6+UcD/+9fUR8sLPwRakf1MRzmXjY5maFMqmbF2OQep8lGFhurGT4SqXRs8Qp6mLhmHQ0OzcXWFTtq3MIhRvDzeevnEiTWYLH+7Id3rc6kMlnJMa3mUrNJSC0GQo3o9HdQ5HTYltk/M+3lnAkeLatrZrXQny9eCnF6SxKbucL/cWsnRXAeH+Xl2WgJww31C45hUI6D57ags0bd0sjpbW4e1hIiqgi1KRUzFkBlzcyWCgXrh+SiIXjIzi8WWHuOAfa9h2tMIp05wSoeutM4pr2ZFbQXyID+H+PfzF4ywkmWghxMDW0qi7EbgNvPZGwkUsFvjgbt3D+YYlkHYxuLnDikf0uOrzH+r5GsUHIHsNzPudfixQXtfMf/KSeFo18fO0Uhq3ZXLQbQFT2z30tc35pLcmMTcgl4q6ZvJ9hhPfy39/Bwur+cEb2xkeFcAP5g7lviU7uG/JDh69fBR3/m8LwT4evHjrlLZNWaPiglhtGcfi7DXg7gFB8U6T7HLL67ns3+vw8XDj95ePYvHURA4V1fDo0v38/tP9+Hi48bMFw7nrvJRO60urG1uY+/hqfn8gmueAJpMvhaYILh4dTUurhS/3FZJf2UBc3CTq3QI532MPKaqAfI9kHIdSv7Ullz99doCVP5/d1k1hU1YZ4f5epFizrHHBPkxNCuWTXfncNy8VpRSHimo4XtXI/fMjun/jQofC/o9RhoWWsBHszK2kvtnMY18cZExcUI9BNMANUxJ4fcNR/u+zA5TWNnHD1ETcTL2oL+5DyWF+BHq7szO3kuumJLZ15jCd5nX0xMdT/xVgzeESfv/pfmqbzExKCmm7P8jHg4gALzJLatl5rJKJQ0K6udrZSzLRQoiByzDgpQt1BlKcPVY+Agc/hQv/pANogFn3w4SbYM1fYeeSnq+x+Tlw84KJt7Yd+vvyQ3zTnIbFzQvPrc8RqOpZXpXg1FVi1cFifvfJPsqDx5DSksFYt6Osqk3okIW1qahr5uOd+Xy8M58Pd+Rxx8tb8PNy46XbJrNwbCyPXj5aD6j4xxrqmlp56fYp9iEawKhYHUS7mevhyPIOpRyvbsih1WLw6Y/O4ZYZSbi7mRgVG8Tbd0/n/e/PYNXP5/CjecO63KAV6O3Bzy5I46tcE1VB6RwikVmpEYT5ezEtWWdpN2WVYSgT6xnL+e67GabyOdDqXAax8kARNU1mlmzSddu2euhpKaFO9cSLxsWSWVLHQeso7NWHdC377OHd9xMmNEVvfgQCEkZzsLCGf644QmF1Iw8vGtmrINTdzcTDi0aSX9lAk9nConF9VMpxAkwmxbiE4LbNhdmldW310APRecMj+OLH5/LanVP5zoQ4p/uGRvixMauMgqrGU6qHHswkiBZCDFzZa+D4Tjj0OZi7nggmBpFt/4N1T8LkO2H69+3HlYJL/wFJ58InP4KcdV1fo6ECdr0FY64BPx0oHiys5s1Nx7hq2nBMQ2bCka8AWF2XSIZ1MltVfQs/fWcn6TGBnDf3IpS5EU+jic3NyXy8M7/D01TVt3D1f9fz47d28uO3dvKTt3dR3WjmxVunEBOka7dvnj6Ee85LobnVwtM3TmREtHObvKhALw55j8esPMDcCEH2zhx1TWbe2pLLRaOjSQh1DsSUUkwaEtp1RwkH101JID0mkOsqf8APGu5hkbVDxIjoAIJ8PNiUVU5mSS1fNIwmqLUCX6OeTXXRbS3QWi1G28TA1zcdpdlsIbe8geNVjUxv13bu4tHRuJkUS3fpgSKrDhaTHhPY8zpDrcNFTB4kDhtDq8XguTVZLBwbw5SkbkagtzPL2iIuJcKPiX1Qw3syJiSGcLiohprGFnLLGwZ8qzYPNxPnDovAvV25TWqkP3kVus5+Qj+9lwOdBNFCiIFr83P6trkWcjf271qE62Wths9+StOQOfzH925Katt9cHL3hOteg5AkePtGKMvs/Do73oCWeph2N6Czpn/4dD8B3h7cP3+4bvkGWDz9yTRiWXWoGIAnVx6hsqGFv149Fu8ke5FHTfh4Xlmf45SxbjZb+N7r2zhWXs9zN0/i65/N5uufzWbdr85ndJxzF4kHL0lnx8MXMHt4x5IGpRTJcVHscbNutnLIRH+wI5+aRjN3zErqxZvXNTeT4uGFIznYFEqRKYYLrF0RTCbFFGtd9OpDJXxjsQ8aOWCObRu2cbCwmupGM5ePj6WkponP9xzXXT3o2HYuzN+LWanhLN1dQFVDC9uOVjAnrYdSDoAwa/FI+HDGJenzvdxNPHhJz2322nvy+gl8+qNzetcqzgUmJARjMeDLvYU0t1pIDhvYQXRXhlrrot1NilGxnXdGOdtJEC2EGJgqj+kM9JS79Gar9mOJXaGpFjY+A60trn8u4awsE96+BcKG8WTIQ/z1q0zOf2I1z6/JotnssJnQJ0S3q0Ppzh1f/abj18b/QMJ03TUDWL6/iHUZZfxk/jBC/Dz1kBTAFDuBYVFBrD5UQkZxLa9uyOH6KQk6YAgeosdh+4ZxyTnTOFhYwwZrT2TDMHjwgz1syCrjr1eP5YJR0aRE+JMS4U+QT+e10/7dDKYYFRvEl03Wjg3WdmWGYfDKumzGxAUxMfHU61FnDA3jpumJ3DR9iNMap6eEklNWzztbcwmNimt7zw4b8ezLrwZ0izmABy5MIyXcj5fX57Apq5xQP0+GWSf/OVo0Nobc8gb+syoDs8XocTQ0YM9ER44g3N+LBSOj+MVFI4gLPoFuLFYebiZ8Pftvy9c4a+nDR9a/Xgz0THRXbEH0yNjAM76fs6vIxkIhxMC05QV9O+vHur3ZkRWw4Peufc5N/9VjoSNGwNC5rn0u4WzTs9DajOWGt3j/vxlMTQrFz8uNP35+gHe25vL+vTMJ9LYGf2FD4fo34b3bYcuLHa9lcodzfwpAk7mVP35+gNRIf26cPkTfHz5MDxgZsZA5pRG89G02v/1or96kd0GaPkcpGHs9YHDZhDj+/OVB7luyk+ggL5paLBwpruX++cO4ckLPG956Mio2kL+Yp/LT0I14WftOf5tRSmZJHX+7ZlyfZVT/74qOg0JsddGHi2q557wUiLgV4+DnNB8OZF9BNVdNgk3ZZcSH+BAf4stts5J4+ON9HCmq4bxhEZ2u7cLR0Tz04V5e+DabAG/33pVV+EXoUp20SwB4/pbJPTxg4Ar182RImC/rM/WHrqQzNRNt/YAk9dBdkyBaCDHwtDTA9ldhxKX6z9vDFsDyh6EqH4Lien78yWg1w9aX9PflWRJEn24ZyyH5XLZUBlBU3cRDl47ksnGxfLwznx+/tZNVB4u5fLzD737IDPjZwR4v+8q6HI6W1fO/O6baW6wpBTd/CMCczDKe/SaLDVll/PqSEc5tvC76EwDewCOXjeKTnQVtdy0aF8uPzrdP1TsVo2IDyTMi+Picj7k2PKFt3eH+niwcF9PDo0/NyNhAArzcqWkyMzstAobeiZpyJyOeXse+gios1nro80foaYzfmRjP418eoqbJzLSUzmuVA709mJMWwVf7izivk1rbTikFt33aly+tX41PCOZoWT0+Hm5EBZ6ZreFirVMlLxt/+jdonimknEMIMfDseVdvDrNNgbNNC8tc6brnPPipHuoBOojuhSZzK3MeX8WXe4+7bl1ng7JM/Z6nzueTXQX4eLgxP12XACwaG0uYn2dbl4fOHCurZ8Lvv2L5/iKn4yU1TTz1dQbnj4jstB4ZYHJSCAFe7iSF+XLbzOQun+Py8XG8eNuUtq/75g3rswxxUpgffp5ubdP2NmeX8/WhYhZPG9LtgJG+4GZSTEkOxd/LnclD7EHxqNhA9h+v5nBxDRX1LW0Bs7+XO9dM1oG+LYvdGVtnjNm9qYcehGzZ2yFhvv1Wm32qlFL84qIRHTbDCjvJRAshBp6tL0HkKEg6R/8cORICYnULsIm39P46u96GjU/Dje+Bfw91mZufg+BEcPfpdRCdW1LN32oeYP/O78PoO7s+8d3bIXa8Lk1x9MWvQJnaMp6nTcFO+PgHsPCfkDDFfrypFp4/3/5hwomCuQ/CjB84HS2rbcLNpAj29YRPf6IPLvzHia0nQ384akmZxxdfZTMvPbKtptVkUpw3PIJvDpdgsRidtjp7eX02FfUtPLp0H+cOC2+r3/zbV4dobGnloUu73pzm4Wbi2VsmEeHvdWIjl/uQyaRIj9HlE1kltdz92laSw/24c1bXQX1f+s2l6RTXNDm9/lGxQbyx6Rjvb9PTC6c7BMz3LxjGuIQg0mMCurzmJWNiaO6nNnMDgS2ITj5D66FF70gmWggxsDTV6CAvfZF9CpxSMGy+7t5wIpv+DnyiB3YsuUGXiHSlcC8cXQdTvqvrZXsZRJfmZTDJdISg4i1dn9RUA/s+hK0vOx83N8H2/8Hut3U/7NOlugCWXA9Fe2HtE8737X4LSg/pCX+TbnP+CkmCb/+h1+3g1pc3M+uxr3n9i7UY216Bba9AxdETW1PGCghNYX1FEOV1zVzWLvCakxZBeV0zu/OrOjy0tsnMu1vzGBEdQF5FAy9+mw3o6YBvb83l1plJbRukujJzaDjDoroOCE8HW+b3jle2YFKKl2+bQpDv6RkwlBLh32Ek96hYnX18a0suMUHeJITaN/gFentw+fi4bjOsbibFVZPi++2DSX8bGRtIgLc76TGSxR3Mzs5/3UKIgatgB2BAfLuNRakLoKlaj3/uDcOAvK16wl3+Nvjwe12PjN78rM5AT7hZdwkoz+7VeOmqQh1su9cXd/N6durXU5Ht3JLt6Hrdhq2+FCpPMOg8WU21epR2Uw2M+g4cXqZfK+j3a/PzEDMeFj0JF/7R6Wv3yJ9CXYn+QGBVVtvE3vxqgnw8qF33X1oNsBiK7e8/wdOrMli2r9CpLVynWhp1P/DU+SzdVUCAt3uHEoDzhkVgUrrncHvvbc2ltsnMY1eN5YKRUTy9KoOi6kZ+v3Q/Ib6e3Ddv2Km+a6fFqNgg6ptbKahq5PlbJjGknzejpUUH4GZS1DSamZYcesaWJPQXL3c3lv9kNnefl9LfSxEuJEG0EKL/FOyExmrnY/nb9G3cJOfjKbN114Ujy3t37eoCqC3UddULHoX9H8GqP3Y8r74cdr8LY68B31AdRLc2dVHS4KypLAcA3+aytsEUHdheDzi36XP83vEcgJoiKDnU8VoNldYPGe2Ym+BYD320La3wwV06A331S3oaoMnN3gUl+xvdBWXaPfa/AFhV1DVz1TJPCj0SdBcNK9sAjn9fm86dvmtZ5zGDL1onkZL7Pk8t2809r23jumc3cvDIYdj9jv2rYKf94kfXgbmB5uR5LNtbyIWjojvUAYf4eTIuIZjVh5yDaIvF4H8bjjI+IZjxCcE8dGk65laDm17YxOaccn66YHiXLecGmmkpoQT7evCPa8czaUjnG/ZOJ28PN1KtGfz2vaBF70QHeUtruEFOgmghRP9oaYAXL4BV7eqB87ZCSLIOaB15B0HcZMj5tnfXz9+qb+Mmwcz79MjotU9AyWGyS+t4dOk+HfjueB3MDfZNjLahD70o6VCVegRyOJVtk706XUdIMoQOpeXQVzy+7CCf7i7QQXTSueDuDfnbnR/z6U/glUt14Oto1Z/g+XlQldd2yDAMMt57BF66kNqSY10vdvnDuu/2RY/B8AshMAbSL4Ptr+kM9abndF/kUd/p8NAv9xXSYlG81LwACrbr3xGwKbscHw83xpYvw6O5ivNu/DUX3PYwwaqOPd+p4s/fGUN5cR7+r1+sA3jb1/Pnt9VBk7ESw82L1wrjqWkyd1lDOzctkt35VZTW2stJvjlSQnZpHbdbh5EMCfPj9nOSOFJcy4joAK6fktDptQaiIWF+7PjtAi4d69puHCfCVtIxLbn/g3ohBiIJooUQ/aP0sM74HlnmfDx/e8dSDpvoMVB8oHc1xPnbwM0TokfrzOq8R/TPm5/j1Q05vLwuh51Hy2DL8zBklj4P7EMfyruYhufAu05nqyNUJTmldV2sYzuWuMkc9J+GOWsNL6w6wNMfrtJZ3+EX6eEW1qAU0FnlrFW6dMIxY2sY+r0yWtt6Ix84Xs0tz60l6MDrAPzr3a8wd5YR3/IibPg3TL1bZ5ptpt0DTVWw5nE4/IWuffboOJ556a4C3EyKN5pmYfbwb8tGb8wqY/KQYNy3PA9Ro1FDZuGRcg5EjcZj2wvcMCGCL6OeIdJUw60tD1J+50a4dxNEpsO7t0HxARoPfMlOt9H8YdlRpiaFMmto51nPOWkRGAasOWzv0vHKuhwiA7y4eLQ98Pzh3FQuHx/LX64a27vWagPIQCuZuHpSPNdNTpDNcUJ04cz6XxghxOBRbO3xW55lrxWuLoCaAj4sjubeN7ZxtKxdYBqZDs01TpnYLuVt00G3u7VHq3+E3jC3awlbDuoa5KJtH+vJiFPvtj8uIBbcvHrMRBuGQWBzIQBhVJNTUt3xpOrjUJ3PO8cjeCwjHh+a+ffMesY3W8s3hi3QmfLju+wbJm210qB7J9uUZUJFDnj4wvb/sWL3US7911oSC5cRofRzl+Rl8LtP9jnXIWeshM8f0DXlF/7ZeX0J0yB6LKz7J6BgcscOI8XVjWzIKuPWGUk0mXzZGXoJ7PuQquJcDhXVcGVoDhTv0++hUvpr6t26bOTlS3A/vp3iBU/xTesYPsv3hcgRcMNb4OFDw4uX4V2Vydfmsfz16rG8dff0LgPf0bFBhPvrVnfNZgvPr8nim8Ml3Dx9iNPmtQBvD568fkLb1Dhx8mamhvOXq8cOuOBeiIFCgmghBprWFvjoXig90vG+Lx+EoxtO/TkOLIW1f+v+nOO74LOf9WqDXQdNtfDm9fDihfrrlYW6A4aj4v2A9f+cbX/at9YGv5obzud7Clnw9zX89cuD1DWZ9f2R1lZlxQe6f35Lq64djmuX0Z56NzTXMrnyCwCSMl6HwDgYsdB+jskEocn2DXddKKtrJsbQWVF3ZaG4qKDjSdbX815hNImTLsRw92a+x24W+uyjyBSJETZMB9HmBuv7gS7zcPPULf46q6G+6DGoLyNj1avEh/jyaNS6tuz5lSkW3th0jBfWWtdelaczvhEj4OqXKK43k1VSS1ZJLQWVDTrgtWWm0xdCUBy1TWanIPyzPccxDFg8LYHJSSE8U38+WFpwf20R73g8wsKMR8A7GMZcY1/rmGv0sYLtsOBR4mZcy7BIf5busr5HwQm0XrcE1VQJwD133s21kxM6bV9n/7XoVnerDhVz0ZNr+OPnB5ibFsGt1lIOIYQ43SSIFmKgKTkEO9+Ag585H2+ohI3/0fedqp1vwponug+QD3yqN53Vl5349fM26/KA1madCT66HvZ94HxOyUEdFIem2DOueVuxmDzYbxnCX64aw8KxMfxndSYPvLdL3x8xwvrYHoLokoPQUtdxc2LcRIqDxnKL21fckFzP6KYdtE66HdzatcwPHercSaMTuaU1xKhy6gJ0L9/q0k42IuZvxTB5sKc1kUlDY1BDZqEOL2OqsZsVzWPYnFNhX6Ntc2HGChgyU7f4y9uqNz6Cfo/CUmHiLZjD0phV9j7fG1qOe+EOmH4v+EVybkQ9l46N4U9fHOCLPcd1/XhTNVz5DO/urWLan1Zy/t++4fy/fcPMx77mqZVHYPTVul58zoNszSln8v8t59cf7m0LpJfuKiA9JpDUyADmpkWysiSQmmk/oYgwWvDEPXK47uDh6Wt/3Z6+cOnfYN7vYOZ9KKVYNC6WLTnlHK/SteNf18RzV/NPyUi7B//Yrvs4O5o3IoqaRjMY8PJtU3j59qn2UeBCCHGaSRAtxEBTlet82/54Sc+jjntUW6RLBqq62YhWV+J8eyJspRqL34FbP4GoUW11v1tyynVmuXi/DqJTF0D2Wt3qLH8bpX7DaMKTC0ZG8/frxnPnOcks319EZX2z3mwYENNzJtoWkHZSW/2Bx6WkmAp5sP4JmgwP9kVf2fHxockYFdms3H+cd7fm8u7WXD7emU+T2b7Rr7ggBw/VSmuMDoIbKzqZWpi/jcrANJrwZHhUgC7fqMjGo7WeLR6TeGV9ju6/7Bum11yZq3+/qQv0uRjUH1jOwdwiHRCnLgCl2Bp5NWNM2VxV8AR4BsC46yE4AVWVy9+uGceEhGDuf3snx7P3gTKxvjqcBz/Yw4yUMJ68fjxPXj+eS8fE8Lflh/lobxlc/jQ5pkTuenUrbkqxZPMxnvkmk9zyerYfq2SRdfT0nDQ9sOazsDu4z/MRnor/G6bbPtFBeHtjroZzf9rW6WPh2BgMAz7brd+nV9ZnkxEwlSHXPtahG0hXLhkTzZK7pvPl/ecxd0QPw3OEEMLFJIgWYqCxdnxou21/vPhgx411TbVgbu54rZaGzoeM1FpbhXUXjJ5SEL1fB4b+1n6/cZOgYAfF1fVc++wGnvx8h65FjkzXI73NDTpILNjBPpVKUpgvIX6eAFw+PpaWVoNl+3T9MREjeg6i87bqcgLbJkGrxpZW/l04ihr3MAKrDvJJ6wzWHe8kgAtNQZkbeejV5Tzw3m4eeG83P35rJx/vsJds1Bbpkgnv5OkAqNoims0OmX1LK+TvIMdrBG4mRUqEnw6CAUweJE66iGX7CsmvatTvT942e0Y+dT7ETsDwCWXL8nf467Mvgrmxbfz5U6WTqMUPr7L9MOFG8AqAoASozMXbw43nb5lMVKA3u3Ztp8E3lnuW7CUlwo//3jyJy8fHcfn4OP5x3Ximp4Tyi/d28+XeQm5/Rfff/vS+c7l8fCx//fIQv/pgN6BHbwMMj/InNsibT3YVsL+gum0UdG+kRPgzOi6QpbuPc6iwhnUZZdw8YwgeJ7D5TynFjKFhZ+0ADyHEwCL/SyTEQGPLDneVie5sY92LF8CKRzpe64O74IO7nY8Zhs5EQw9BdKn19iSC6JKDelS3TfxkaKrm8L4dGAbs3rFJH49I16O93bz0eO7mWr6pG9I2MhdgTFwQQ8J8WbrLmumNHKlLXrooRdmbX0VFxkYdmLbLcG7IKqPWbKIs/UYAVgZdyabsjuUqtf5JAFwSV8/aX8xl7S/mEu7vxYYs+7nN1h7RnkP02OwIKsmtqLdfpPQINNewwzKU5HA/3fs4bKguFUmaxXXn6Pfn/rd2sLw6AUvJQUo2va2D4Yg0MLlREDaDkQ2bOY+dNCtPSJpFXkU963IbyYi/AlAw5S79fMEJ+t+FxUKYvxcv3z6FeOM4W6uD8fZw46XbpjiVPni6m3j2psnEh/rwvde3kV/ZwPO3TCY53I+/XDWWKUkhrMsoY3xCMAmhulRDKcXstEjWZ5ZhMWBacuedNLpy2bhYduVW8n+f7cfL3cT1UxJP6PFCCDGQSBAtxEDjmIl2zDhXOpReOAa/tcW6O0JZJxsRSw7bN6zZNFToWuX212mv3hpEn2hNtGHobLmtfhnaNvhVZWxEKUhotU7oi0zX9bNJsyDzawDW1ic6BdFKKS4bF8v6zFJKapp0dwdzA1TmdPr0//pyJ4FVh2mMHN/hvm8OleDtYSL60l/Dd78mLHUKW3MqOrSFe3Gfvv3uSEgI9SUh1JdpyaFsyiprqxVWtg8y4cNp9fAnQlU6dxOx9qleVZtAmm2ktFJw8wdw5bPEBftw68wkcsrqWVoagwmDiJKN5IRMB6VobGnlpaKhRKhqFnuuYZ05nXVH69rKIUIXPgp3r4LwVH3toETdMtD6oWdohD8jPEup8UvkxVsnEx/iULNsFeTrwSu3TWVCYjD/vG48k5N0Ztnbw41nb57MOanhfH/OUKfHzLVOE/R0MzEhMbj9Jbt1qTWjvfZIKVeMjyPU+tcGIYQ4E0kQLcRA45hxbqy0H688Bn7WOlDHwNhW/9tZxriuRLdZcwzGbaUcKNeUc1Tl6bVHOmwWCx8GngF4Ht/O6Nggzg0soRFPLEFD9P3WMocWjwCyjBgmJIY4XXLRuFgsBnyx97g9w93J2htbWqnL3oabMthFaof7Vx0qZubQcLy9fSB+EtNSwqhtMrP/uL09XUZxLU9vb8CsPIi12Ms3pqWEUlDV2DZUxbu+gBq3YP0hwD+SCFVJdqlDJjp/G4ZXIOsqg3U9tE1IEgREA/C7RaPY8tB8/vUze2u5v2Yksjm7nOfWZPFxjX4PPS0N7PKezO+X7uejnQWMSwgmMToCYifYrxtsHSxi+/dTX457cxWXnDeLsfHBHd4Lm8QwXz68dxaXjHEe8hHq58nr353GhaOinY7PSg3Hw00xPiH4hKexxQX7MHmI/t3eJl01hBBnOAmihRhoKo+BjzWIdKyLrsrVA0ECYpw3F7YF0aXO12k1Q0O57lLRWGU/bivliJ1gHXhi7rgGc7P9Md0F0U21sPbvekCIjS24dSznMLlhxE4gpm4/4xOCmR5QzBFLLKuOWLPcw3QQneczAg93d9JjAp2eZnhUAGlRAXyys0CXOgAU77e3Yju6AT77OaXv3McP1bsAfFrmPPkuu7SOo2X1bZlUsE9i25RV3nbsj5/tx8vDQ08ZtPWKbq7nouoPCKCejVllNJsthLQUUuejA09TQBTRbtXOA1fytlIbNhaLYSIt2r/r9xDaxo0bJneOBk/m7te28szqTKaMToOY8QBMmHsNh4pqOHC8mkWdTbULtpZG2P5iYVt7u7rwU+Xn5c5vF47kh+d3/JDSGz+/MI1fXJTW4XcshBBnGgmihRhIWhp00Dpklv7ZsYSjMtdaLzvCORNtm3ZXV+qccW6wB4bUOHSOsGWiU+boP/9XdNIPud4hIG8fnDva/TasfBQOfmo/Zms/FznC6dTykDGkcZSJsT5ENGRxzD1Jd6cA3bot7RK+4FxGxQZ2unFs0bgYth6tIL/BnUa/OFatXcPNL27Wr3npfbD9VUKzljLClMse36l8kdWCxWJ/P5bv1xsTbR0mAKICvUkK82VTdhmltU388r3drDpUwn3zhuEePhTKsnTt9Yd3E7nhUe7wWc2m7HLyKxuIoxRzQDwAyj+KGLcqcmzlHOZmKD5Ano8O+J0y0V0Zvxg18Rb+c/tsTErRahj8+pJ0mHwHpF3KedOnMS05FKXofDR0ULtMtC2IDhva8dxTdMuMJM4bHtHziZ2YnhLGvXNOLgAXQoiBRIJoIQYSW52tLYhuK+2o14FtcIJ1Y91h3f3BYtEDLZRJ1wk3O2RCHTPI1Q6DQGyZ6JTZ+rZ9zTQ4B87dBdG2ASC2YSmgM9EBMWTUuDsN7ThgGoaHamW6aR+q5jghQ8ay9kgpR4pqQCnM177BUxXTnOqhHS0apzPLNzy3kXXVkcQ25/BtRikZG5fqjPpl/+Ji7//x06QPyFjwMqW1zewt0Nn0VovB6xuPMXlISNsmOZtpyWGsyyhj7uOreX97Hnedm6xLDUJTdCC68hE9nMbdh4u99rIpu4xjZXXEqVLcQqzZ34BowoxKexBdngmWFg5ZEvByNzEkrBdjk897ABb+gyFhfrz//Zm88d1peq2TboUb3kSZTPzrhgm8dOsUYoJ8Oj7eOxC8g+wfvMoyAQXBQ3p+biGEECdMgmghBhJbABQ7Htx97OUctuA6eIiuNTY36BHQ5Zm67CJBt1lzCpwdv3fKRBeCuzfET0HXRXfSd9r22MC4rss5zE2Q9Y3+PmOFvVtG8QHqg4cx/+9rWLLZXo6ypl4Hc9FZ7wEwesJ0vNxN/OajvTSbLRwqqqGhpbXLIHpImB+Th4RQWttESNJYhrsXEuIFDeueAb8IsqMv0OUaIyI5b1gESsHqQ3rtqw4Wc6y8nttnJXe47py0CBpaWpmUFMKyn5zHQ5eO1G3XwlL0+7zuSZ0NnnY3w5r2Ul5ezp7DGfioZvwirdfzj8THUkdZRaVuc2ctadlSH8WwKH/cupnE15nkcD+mJHVsHxcV6N19f+SgRPu/mfIsnZ328D6h5xZCCNE7EkQLMZDYMs9BCdaWZbZ2d8fsxx1HX9vqoYdfqG+7yiA7ZaKLwT8KPP30JrfuMtGR6V1noo9t1PXW6Zfp7HbRHp0dLzlEsbcOLl/8NqstG73muDtlbhGog58DEJg4lseuGsOm7HJ+9f5udhyrBGBCQkinTwfw4m1TWP+r85k4eSaqtZkHhh1nVM0GakffxKojOus8Z3gkYf5ejI0PZtUhXbryyvocYoK8uWBUVIdrXjQ6mrW/mMsrt09laIRD7XKotQxi6Plw8eOQugA3w8ws01627dL9k/2jrPXG/vq6oVRxrLxe/26UiTXlwb0r5egrwQnO5RyhHT80CCGE6BsSRAsxkFTmgnLTmwetwzP0cWsQHZxg31hXckDXQ3v6617L0Hkm2s2zYzmHNegjMr3zCYi2x0amQ1OV88ZBm4zl+toLHrX+vEJnx80NZJt01jmzpI5vM0qpazJzuKiG8uDRYGnRU/aC4rlyQjw/mT+cD3bk8/flhwn18yQhtJNSBasgHw+CfT3b2uddU/o0rZh4s3U+qw+XkBLhR2KYLteYMzyCnbmVbMoq49uMUm6a3vlgD6VUhxIPQL+nlzwB1/xPjwVPmIbhGcB8zz141+v302Qr57C+n5FU6s2FxftpDUkht8awt7c7HWz/ZgxD/5WijzcVCiGEsJMgWoiBpCpXl1C4uTtnFStzweSug2uvAP1ne1smOnaCPShuH0QrNwgf3nFjob+1JCAyHcoyOgbJdSVg8rAHYZ1lo4+sgMQZ+pyYcfpna0C+ryWWEF8Pwv29eHldDnvyq7AYYLKN4Y4c0TYI5b55qVw1MZ7yumbGJwSjejMCOiINUHhUZrHT/xye3dHAxqwy5gy3lzrMHRGJYcBP39mFl7uJG6ae4GAPNw+YepeuNQZw90SlzGae227ilPX9sG3ms77/EaqSQ0U1UHKQKn+9eW549OnMRCfq9oIVObofuAs2FQohhNAkiBbiVNSXQ+7m3p9vboJDX3Yc221Tecze7zc4UQ86aa6zBtexYLL25Y1Mh+O7oHCPnsznF25dT7tyDr9w/TjHTHRNoT3ojkgHi1kH0k6vqxT8IvRX++uCrtEuOdDWmo7U+ZC7SZd4AJvrIhka4c+N0xL5+mAxH+/M1083wrph0mEQi1KKP39nDDdMTWBxbwNdD5+2UgWvmfdSVtdMs9nC3BH2jhFj44II9fMkv7Kh7wZ7pM4nrLWY2aZdNJr8wCdYH7e+n5PDWnju6/0Y5Vnkeuhs/GnNRNv+7WRba9UlEy2EEC4jQbQQp+Lr/4OXL3HuitEVw4CPfwBLroOctZ2fY2tjBzrbDDpgrcy1/wz2DLKlRY/U9vDRZR3ta6L9InT22hZEm5t16zvHcg7oOLjEFoDbguj2mwttXTlS51tvF4DRCttfhaBEDlUYJIX7ceO0RDzcFEs255IU5ktgyhTwCbV3H7HydDfx5++MZf7IjjXLXUqcCQnTGDPjAlIj/fHxcGNqsn0znsmkmG1tw3brzKTeX7c71td7rtte6n0d+lD7hYMysXiUFxN8SlCGhQ3VEQR4uRMTdBo39tn+7WRJEC2EEK7m3t8LEOKMZRhw5CsdyBbs1KOru/PNX2CPHgRC7mZIPs/5/tYWqClwyERbbytzdSba8XzHaYBxk/StX3jHcg6/cF0eUl+qs+C2IDvAGqyGD9MlHx2C6BLnTHT7co4jyyEw3p5Rjp8CXkHQWElr/FSK9jaRHO5HZKA3l46J4aOdBbrrhpc//PywLk05VZc9BUYrymTib9eM43hVI17uzhP07ps3jBlDwxgZ20eDPYITMCJGoEoO4h/lsGnP5Aa+4fg1l/LYuUNgJbyXF8jwxIDelaf0FdvAlexvAKUHxgghhHAJyUQLcbJKD9trlm1dMrqy+x1Y/WcYt1h3fcjf3vGc6gIwLPZAyJZVLM/SNc22n8EeRAfE6nIN0AFvhyA6AgKtgzlqCu09om2ZaHcvXTfbfnNhWxAdbv/ZprVFZzqHzW+ra8bNHYbOAaDCT9cCD7Fu8LvN2lZukq1lm5uH/XGnwmTS1wLGJQRz0ejoDqckh/tx7eSEDsdPhbJmoz1D2/Vf9o+C2mKim3KwmDzIUzGMPN1T+XzDdGvE+jL94Una2wkhhMtIEC3EyTqyXN96B3UfRB/bqMs4hpwDi57U5Rf5WzvWRTu2twMIiNYZ22MbnINr0JsFlQniJtqP+UV0LOfwDdeBNuhA3DqtsJQgDhyv1scj0zu2ubOVc3gF6g4cjkF07ma9ec1WymGTah3dba0FTrIOGBmfEMwnP5zFdX0czPYbWx14cLvXExClP6QUH8AUPoyP7pvDzy4YfnrXppR9XdLeTgghXEqCaCFOVsYKXc4w9Pyug+jyLHhrsQ6Mr3sN3D11+UVtEVTnO59ra2dnC5ZNbjqbeHS99bhD0ObhA/Mehun32o/5htmD6JZGHej6hdsz0dX5etAK8Nd1ldz1qnVceEQ6lGfrqYig67tb6q11vkoH4nVl9ucpsGbRE2c6r3/kZTD5TrZ6TgEgKdw+pW9sfHCno7zPSENm6fc9/TLn49ZMNMX7ITKdEdGBuh3f6Wb7ECadOYQQwqVc+v9qSqmLlFKHlFIZSqlfdXL/A0qpndavvUqpVqVUxzFdQgw0zXVwdJ3OxsZN1lnkmiLncxoq4M3rdBb5xnfB1/pPO87a5i1vq/P5tkx0YJz9WHBiW+DrVM4BcM5PnOuw/SJ07bPFYu+m4RdhL/eotmeiVxwzyK9s0NP1ItMBQ5engD0Qt9VD+4XTWFXI06sysFgMXT/tHwV+YVQ3tvD3rw7R2NKqM/IL/87hanciArzw9xqkWy7cPOCiP3fM9PpH6pKZymP6g0l/sX0Ik02FQgjhUi4LopVSbsDTwMXASOAGpdRIx3MMw3jcMIzxhmGMBx4EvjEMo9xVaxKiz2SvhdZmaxBt3djnmI1ubYF3btEZ3uted84KRo/WJRLts9eVx3Rw6ljH6ljCERTf/Zr8InS7usZKe/mFXwR4B+s62ZrjUFuE2SuE8kZdTVJY1QiR1v8sbZsLOwTREVSXHufxZYfYnV+lz7NuKPx013H+9XUGy/YVti0jp7SepLBOhpcMdv5RukMJOG/8PN3ayjkkEy2EEK7kykz0VCDDMIwswzCagbeAy7s5/wZgiQvXI0TfyVgBHn4wZKYeNKLcnILi/GX/hOw1VC34m32aoI27F0SP6TyIbp9tbhvmEa0f1x3HThqOgbBSuqSjugBqi6hxt/+xJ6+iXmcs3Tx132dwCMDD267h0ajLOTZlluhNiNbAe2OWPr7qYHHbNbPL6trqoc8q/g7t+foziA5P6/81CCHEWcCVQXQckOvwc571WAdKKV/gIuD9Lu6/Wym1VSm1taSkpLNThDh9DEOPvE4+Twe2nr4QNVJvFgSwtOK/60U2WUaweEsKdU3mjteImwQFO6DV4b6q3I6b1dq3u+uOX5heXl2xQyCsjxFgHbhSW0yhJQgfD90KLq+yQXfWCB9uz0TXdyzn8DVXAJB55ICul44cgWEYbMrWQfSaI6W0Wgxqm8yU1DQ51UOfNdo6nnhDSFL/rSPtEvj+BqmJFkIIF3NlEN1ZD6suxrSxCFjXVSmHYRjPGYYx2TCMyREREZ2dIsTpU56lxyqnzrMfi5sM+Tt0PfLhZQQ1HectdQkHjldz35IdtFra/dOPm6yDUVtrOYtFD1XpKhPd/nhnrEHvPz/ZwJeb9zodIzAWagowagrJavDjglFRKAV5FQ36/ogRUHyA1zYe5b+fb9LHfG2Z6HC8jCZ8aKQ+b48+FjmSo2X1FFU3MTUplPK6ZnbnVXK0TA+dST6bg+iINPtkyf5gMukPdUIIIVzKlTt/8gDH/+ePBwq6OPd6pJRDDDSHl8GBTzoerziqb22tzkBnlre9DOWZsPlZSlQ4dUkX8mhaNL/9eB+//mAPF47WQVagtweT4ibqT5n523SNdM1xXWPtWAMNJ5iJ1gFzWVE+Fb4VOiPq6a/vC4yBmkIMZSLfPJpzUsPZlFVOvi2IjkyHve+x88gxRjSUYvHxxeSp65otvuGYgNFBzSTWHgUPICKNTXt0FvqBi9K47tkNrD5UwnDriOshZ2VNdKS+7c9NhUIIIU4bVwbRW4BhSqlkIB8dKC9uf5JSKgiYDdzkwrUIcWLMzfDJj3QXDu+gjvePWOj8J/t4a8eNnW9A1mpea72O5Mggbp6RRG5FA8+tyeLtrfbqpp/MG8aPvYN0Ccj4G+HT+3VddcI05+cJStT9l1MX0CNfXboRRjUejeVYQsIx2YaaBMRCazMmoNgI5uKUMOJDfHRNNLTVOLcWHSBMVdPkGYaP9bLlBBEOXDXCC6/tedR6RePvHcSmrGzC/DyZPCSE8QnBrD5U3NbG7qysifYKgPRFMOqK/l6JEEKI08BlQbRhGGal1A+BZYAb8JJhGPuUUt+z3v9f66lXAl8ZhlHnqrUIccIOfKJ7Od/4nnPGuSvhw3XWd/1TGG5evN44l19F6CzwgxeP4KqJ8boNHPDK+hz+sfII1yeMIip/O3z1kB4fvvAfEDMWwzB4c/MxFqRHERnoDTe916slGyZ3alQAUW41hBlV1HuE4m+709YrGmj1jSAh1Je4EB+2HdW1zkTqbht+VUcIp4oa9+C2ILqgxZ9wYFxIC+4e+WSbEhkDbMouZ2pyKEop5qRF8o8Vhwnz9yIywAu/wdrerjtK6U4sQgghzgou7RNtGMbnhmEMNwxjqGEYf7Qe+69DAI1hGK8YhnG9K9chxAnb9KxuETZ0Xs/ngq6BjZ0AFjOFCZdQTiBDI3U2VilFWnQA4xKCGZcQzF+uGsv0lFDeLYyGor2w6b8w44cw+Q4A9uZX89CHe7n15S3UdrYpsQuHimooafVnUoSZMFVNJQH2Ox16T0fG6JKR+BAfjlc1Ym61QHASFndvUoxcQlUNZYZ9XPXRRl2aEWWqJMkoYHtDFMfK6smvbGBasu70MTctEsOAVYeKz85NhUIIIc46g2SEmBB9qGAH5G2GqXfpTVrAl3sL+eeKw90/zlrSsTH8agBSwv07Pc3T3cSzN02mwH8UAI0pF8KC37fdb+t4cbiohh++uV0Hue20tFp4dOk+vtx7vO3Y0l0FlBFEik8jEaYajrc6BNEB9kx0UpIeEhIf4kurxaCopglMJmoDhzFc5RKuqik029eeUaf7VgeX7cTDaGZ3cxyvbcwBYFqKLiEZFRtIuL8nhsHZ2SNaCCHEWUeCaCHa2/Sc7gE93l7Cv2TzMf654gjrM0q7ftz0H8C1r7KpKZEwP09C/Loe+Rzk68Edt97JAy1381bi75y6OWzMKicpzJc/XD6a1YdKeGTpPgzD3t3DMAwe/ngfL6/L4UdLdrA+sxTDMFi66zj4hePRWEqYqianwSGY9Y/CYv3PfdSwYQDEBeuCjbxyXRdd6JVEmimPcFXdln0GyKoyqMcbU863ABw24nl94zGCfT1Is24kNJkUs4frjXWSiRZCCHE2kCBaCEd1pbD3fRh/g9OGwlzrBrzff7q/08wwAP4RMPJyMktqGRrReRbaUWp0CDvDFrI8o6btmMVisCWnnGnJYSyelsg9s1N4feMxfvbOLoqrGwF4dk0WSzYf4/ZZSSSH+/G917bxwfZ8jpXXExoZB5XH8DSayajzoclsnaDn5k61WwgtuJMQp0s74kN0EJ1fqTt0ZJBApKrEHTO5TX5UN7YAcKy8nlq3YKg6BijqA4fS0NLKlKRQTCZ7J8s5abo7SPLZuKlQCCHEWUeCaHHGKqlpcsrQnrS8bbDxv/rri19CaxNMvbvtbovFIK+8geFR/hwsrOGtLbndXAwyS+ra6qF7MndEJJuzy9sGshwsrKGqoYVpKbrW+JcXjuAHc4eydHcBc59YzYMf7OaxLw6ycGwMv710JC/dNgVPdzd+9u4uPNwU8fGJYNbBdoklgCNFtQA0NLdyzBxMnUcoylqiEmvNRNva3O1stJd8lBqBZJXovb655fU0elmnHIYMYdzQWIC2emibC0dF8+hlozg/PbJXr10IIYQ4k0kQLc5IB45XM/3PK1lzpJvyit44ugFevgi+/KX+2vseDL9ID8ywKqpppLnVwi0zkpiaHMrflx+mqqGl08uV1zVTXtfcq0w0wJzhEbS0GqyzlonY6qFttcYmk+KBC0fw1U9mM2NoGEs25zJpSAhPXDMOk0kRH+LLi7dOxtvDxLwRUXgH2UdPlxPIvoIqAL4+WMz21qG0Ro9vu9/bw42IAK+2gSvrqu2DjMoIIrO4lprGFsrrmrH4WAevRI7knFT9/SzrrY2nu4lbZybh5d6Pg0aEEEKI0+Qs7EMlBoOPduTTajE4cLya2cNPcopleRa8tVgPOLn5I/C0Zo+9g51OO1amSzmGhPnyu0UjWfjUt/zq/d3MHKoD3cQwv7Y1ZJXozO/QyN4F0ZOTQvHzdGP14RIuGBXNpqxy4kN82uqVbZLD/Xjh1insza8iOdwPbw97oDouIZiVP5tDgLc7ZB5rO17vEcq+gmpAbzrc7nMPN9/u3G0kLtiH/MoGKuub2VcXQLOfP56ttVSrQDJKakkv11063PwjoAyITOeK8XEMjwogPSYQIYQQ4mwlQbQ441gsBp/u1l0pjlk3xZ2whgp441rAgMXvdDsR0PYciaG+DAnz485ZybzwbTZf7C0EwKTgmwfmkhDqS6Y1iE7tZSba093EOcPC+eZQCRaLweaccuamdV0OMTquk8Ev2DcJto35BkIjY9lXUE1NYwtfHyrmxmmJuDnUMIOui96bX0VmSR2gaAgehmfZDnxCosksrm177V7BUXAUiEjHZFJdrkMIIYQ4W0g5hxjQKuub+fPnB9o2uQHsyK0gv7IBpXS97kn54G6oyIHr34Swod2emltej0nZa4h/s3Ak23+7gK2/mc+y+89DKcWrG3IAXQ/t5W5qO7c35qRFkl/ZwBd7Cymva26rhz4pDkF0fFwCB45X8+XeQprNFhaNi+1welyIDwWVjRwp0psbTdF6cmFYZAyZJbVt729AqLVeOlJGWgshhBAgQbQY4JbuPs6za7J4csUR+7Fdx/FyNzFneMTJZaKP79ITAs//DQyZ2ePpx8rriQ32wcPN/p9LqJ8n4f5epEUHcNHoaN7akktdk5nM4lqSw/06ZHy7Y+tq8cRXhwCYnhx2gi/IgS2I9gxgeHwE9c2tPLM6k7hgHyYkBHc4PT7El+ZWCxuzyvB0M+E76x644P9IigzhaFk9WaW1BHq74zPuSjjvAQmihRBCCCsJosWAtilLb7T73/ocMktqMbda+HT3cealR5IeE0h+RUOXLecMw+DuV7eybF9hu4s+Bx6+MOk2DhfVcPerW3lhbVaXazhWXk9iaNcDRO6YlURNo5kPd+Tr9na9rIe2iQnyYUR0ANmldcQEeZMQ2vssdgfewaDcwC+cUbG6ZjmrtI5F42JRqmNgH2/NmK89UqqD/9hxMPNHpEb6Y7YYfJtRSmKYry53Of83Tv2shRBCiLOZBNFiwDIMg03Z5ZyTGo63hxt/+uwAm7LLKa1tYtHYWBJDfTFbDI5XNXb6+CPFtXy1v4jHlx2yt8KrK4M979I06lp+91UeFz+5lq/2F/H0qgxaugjGj5U3kBDSdRA9MTGEMXFBvPRtNsfK63vdmcPRHGsd9LTk0E6D3V4zmcA3DPwiGBYZgIebvtaicTGdnm7rFV1W1+zUls/2GnJ7eO1CCCHE2UqCaDFgZZfWUVLTxMVjovnR+amsPFjMHz7dj5+nG3NHRLZlh7uqi7ZlsTOKa/nWNmlw+/+gtYnvHZrEaxuPsnhqIn+9aiwV9S32cxzUN5sprW3S2dguKKW4bWYSWaV1WAwYGnHiw0bOH6GD6Okpp1DKYROaDKHJeLqbSI8JZGiEHyO76KQRF2LPejsG/ykOr6G7LLwQQghxtpIgWgxYm7LLAZiWHMZts5IYEubLwcIaLhgVjbeHGwm2ILqi8yB6Y3Y5UYFehPt78sq6HGg1w5YXyQuewqqKMF68bQp/uGI0l0+IJdDbnaW7Cjpcw9ZDOaGHQHLhuBjC/fWY75PJRE9JCuGl2ybznYnxJ/zYDq5/Ey55HIC/XzuOZ2+e3GV229fTnVC/jusO8PYgKtAL6Pm1CyGEEGcjCaLFgLUpq4xwfy+GRvjh5e7Gby/VnSOusgaaMUHeuJtUp5sLDcNgU1Y5M1LCWDxtCF8fKqZ464dQncdfymdzwciotlZyXu5uXDQ6mq/2FdHY0up0HVuP6J6ysV7ubtw2Mwl/L3enLG5vKaU4f0QUnu598J+kX3jbyPLUyABSe6jRtrXHax/8236WTLQQQgjRkfSJFi53pKiGzJJaLhrdeV1uZ2z10I41wvNHRrH5oXlEBngD4O5mIi7Eh2PlDfYHWiyw4mFqS47xcFMx46qCiTK8GOZehMfKo5R7RLO8fiLLLnXuMrFoXCzvbM1j9aESLhod3XbcsUd0T+6dk8oNUxPx9Tyz/rOKD/FhT35Vh+B/aIQ/6zPLJIgWQgghOnFm/b+9OCP95cuDfHO4hN2/i8THs3fdHXLLGzhe1dihZ7ItgLZJDPV1zkRnLIf1T6F8YhmlDGIafPBsNjHVp4mKJniq5UpuPTeFIWHOAeOMlDDC/DxZurugQxDt7+VOiK9Hj2s2mRRh/l69en0DyXnDI2g2W/Dzcv6fgzlpEezOr3KqmxZCCCGEJkG0cKnGllbWZZTR0mqw41gFM1PDOz1vf0E1z3yTye8vG0WInycbs/WmwGk99ExOCPXly70OLew2Pwf+0fw27lW+zapi84/ngVIcz63kiqfXEe7vxaq5qR2u4+5m4pIxMby7Tfd7tgWUueX1JIT6nlrHjAHuhqmJ3DA1scPxeelRzEuP6ocVCSGEEAOf1EQLl9qSU06Dtc54o3WjYGfe25bH0l0F3PP6NprMrWzKKifE14NhPdTzJob6Ul7XTE1jC5RmQMYKjMm3syG72qkUZHxCMPfMTuGvV48hwLvzrPKicbE0tlhYcaCo7ZjuES2ZWCGEEEI4kyBauNSqgyV4upsYFunf1nKuM5uy9SbCzdnl/Or9PWzKLmNqciimHib/2dvcNcCW58HkQV7KdRRWNzKtXbu4By9O5/wRXWdWJw8JISbIm3e25gK6LrunQStCCCGEODtJEC1cavWhYmakhDF7eAQ7cis7dL8AqGpoYf/xam6clsjPLxjOhzvyyato6LGUA+xBdH5RMex4A0ZdyYYiXYoxPTm0u4d2YDIp7piVzLqMMlYdKqakpokms0WCaCGEEEJ0IEG0cJmjZXVkldYxJy2CaSlhNJst7Mqt7HDe1pxyDAOmpYTyg7mpXD1Jt7Cb1UX9tCNbD2PfA+9Ccw1Mu4eN2WWE+Xn22NqtM7fOTCI53I//+3Q/mSV1AMRLEC2EEEKIdiSIFqdkd14lW3I6r3VefagE0COtpyaFopR9gIqjTdnlRLjVM+X4m6h1/+QvUStZv+AYaZE991sO8vEg2NuN4UeXQOxEiJ/Mpqxypp7k+GxPdxMPXZJOZkkdf/nyICB9koUQQgjRkXTnECdtV24l1z23gcYWCxeOiuKhS0Y6jcdedaiYpDBfksN1MDwiOpBN2WXAMKfrbMoq48ch6/BY8TIAbkAsQFwijLi0x3U85P0eEY1HYebv+OZwCfmVDfygkw4cvTUvPZJzh4Wz9kgpStmHkQghhBBC2EgmWpyU3PJ67vzfVsL9vbh//jDWHill/j++4fFlB6lrMtPY0sqGzDLmWKcCAkxLDmXb0QqazZa2Y7VNZvYWVDPVMxtCU+ChQngwHwLjYdOzPS9k+2tc0/guH7tfTMuIK/jDp/tJCvPlqklxJ/3alFL8duFI3EyK6EBvvD1619taCCGEEGcPyUSLE1bV0MIdr2yhydzKW3dPIzUygOunJPKXLw/y9KpM3tuWx0WjomkyW5iTFtH2uOkpobyyPoc9+ZVMGqI3/W3NKafVYjCk4QCkzgYPa9Z3yp2w8lEoPgCR6Z0tA7LXwKf3kxU0jV+V3kTZxmNkFNfy/C2T8XI/tcB3eFQAP78gra09nxBCCCGEI8lEixOyr6CKW17cRHZpHc/eNInUyAAAooO8+cd143n/+zOJDvTmfxuO4uVuYrpDm7mpyWHMN20jeOmdYBiAroeOM1Xg1VAE8ZPtTzTxVnDz0sNTOlNyGN6+CcJS2Trl7zS0Kv667CDnpIYzPz2y88ecoO/PGcpPFwzvk2sJIYQQYnCRTLTolbLaJp746jBvbTlGiK8nT90wodPpg5OGhPDhvbP4eFc+CuVUChHq58k1fjsZWvo1FO2F6DFsyirjsvACqAbiJtkv5BcGY66GXW/BvN+BT7D9vroyePMacPOExe8QU+oLHKXZbOG3C0cO6umCQgghhBgYJBMtutXSauHFb7OZ88Rq3t2ay+0zk1n1szlcPCamy8eYTIorJ8RzxYSOdckjPHXHjnVfvkVFXTO786o4zy8XTB4QPcb55Kl3Q0s97HzDfszcBG8thurjcP0SCBnStnHxxmlDSIsOOPUXLYQQQgjRA8lEn6V25lYyIjqg201z6zNLefjjfWQU13LusHB+t2hkW/nGyUowjgPglrWS2Y+fh9liMKL1kA6g3b2cT44dDwnTdElHeJp14W9A7ka4+iVImAJAfIgvr985jclJIae0NiGEEEKI3pIg+iz08rpsHl26n/npkTx782TcOhmtfaysntte2kJMsDcv3DKZeemRp14m0VSDqb4EPAOY2nKE1ECDTEwEV+6F8Td2/pjp34d3b4M3rrIfm/sbGH2V02nnDOt5MIsQQgghRF+RIPoss2J/EX/4dD+pkf6sOFDM/322n98tGtXhvD99fgA3k+Ltu2cQHeTdN09enq1vxy/GtPlZ3r2gicbAZNSLdc710I5GXgH3rNVlHABe/l136xBCCCGEOE2kJvossievih8t2cGYuCCW/vAc7piVzMvrcnhlXbbTeRsyy/hyXyH3zhnadwE0QHmmvh13HXgF4pa5Er+SXfpY3OTOH6MUxIzVpRsJUySAFkIIIcSAIJnos0RVQwt3/m8LoX6ePH/rZHw83Xjo0nRyK+r5/af7aWk1uG1WEial+P2n+4kL9uGu81L6dhHlWfo2PA1SZkPGCh0kewfpQStCCCGEEGcIyUSfJVYdLKa4pol/XDeeyACdXXYzKZ68fjxz0iL54+cHuOifa3h06T4OHK/m15ek9/2kvvIs8I/SJRmpC6A6H/Z9pEs5TPJPUQghhBBnDolczhKrDhUT7u/J5CHOHSx8Pd158dbJvHDLZMwWg1c3HGVqUiiXjInu+0WUZdkzzqnz9W1jZdf10EIIIYQQA5SUc5wFWi0Gaw6XMHdEJKZOOnEopZg/Mopzh4fzyc4CZqWGu2ZgSXmWPXgOioPIkVC8v+t6aCGEEEKIAUoy0WeBXXmVVNS3MCet+3HYXu5uXDM5gdhgn75fRHMd1BZCaLL92LALACWZaCGEEEKccSQTfRZYfagEk4Lz+rOXsm1ToeMGwnN/qjPT/hH9syYhhBBCiJMkmeizwOpDxUxIDCHY17P/FmELosOG2o95B0Hyuf2zHiGEEEKIUyBB9CBXUtPE7rwq5gx3UbY3dwvkb+v5PFsQHZLc/XlCCCGEEGcACaIHuTWHSwCYO6L7euiTYhjwwXfhywd7PrcsE/wiwDuw79chhBBCCHGaSU30ILf6cAnh/l6MjHFB8FqWCRU50NLY87nl2RA6tOfzhBBCCCHOAJKJHsTMrRbWHC5hTlpEp63tTlnGCn1bWwjmpu7PLc+UqYRCCCGEGDQkiB7EduVVUdXQwpw0F9VDZyy3f1+V1/V5zXVQc1yCaCGEEEIMGhJED2Ibs8oAmJES1vcXb2mAnG8haoz+uSq363MrcvRtmATRQgghhBgcJIgexDZllzMs0p8wf6++v3jOOjA3wuTb9c+V3QTRZZn6VjLRQgghhBgkJIgepMytFrbllDMtJdQ1T5CxHNy9Yey1oExQeazrc6W9nRBCCCEGGQmiB6m9BdXUNbcyLdkFpRwAR5ZD0rngFQABMT2Xc/iEgE+wa9YihBBCCHGaSRA9SG2y1kO7JBNdnqW7baTO1z8HJXRfzlFbDAGxfb8OIYQQQoh+IkH0ILUpu5yUcD8iA7z7/uIZK/XtsAX6NjgBqrop56gtBH8XDHsRQgghhOgnEkQPQq0Wgy3ZLqyHPvIVhCTZNwoGJUB1AVhaOz+/thgCol2zFiGEEEKIfiBB9CB04Hg1NU1m19RDV+XrTHT6ZaCsA1yCE8Fi1r2g2zMMqC2STLQQQgghBhUJogehja6sh976EhgWmHKn/Vhwgr7trENHYyW0NoN/VN+vRQghhBCin0gQPQhtyi4nMdSXmCCfE39wc73OHnempRG2vQLDL9LlHDZBifq2s82FNUX6VoJoIYQQQgwiEkQPMhaLwZaccqYln0QWunAP/G0EfHRv54H0/o+gvhSm3e18PChe33a2ubDWFkRLOYcQQgghBg8JogeZZfsKqaxvYdqJjvquKYQ3r9OlF7vehDWPdzxn07MQPhxS5jof9/QF3/DOM9G1xfrWXzYWCiGEEGLwcO/vBYi+cbSsjv/77ADL9xeRFObLvBEnkPltrtMBdEMl3LkMNj4Dq/6ou2+MuVqfk7cVCrbDJU/YNxQ6Ck7ofOCKZKKFEEIIMQhJED0I7DhWwXXPbsTdTfGLi9K485xkvNzdevdgw4APvwfHd8ENSyBmHCx6EiqO6rKO/O1gcoOj68EzAMZd3/l1ghKgeH/H47VF4OYF3kEn/wKFEEIIIQYYCaIHgQ935GMywdc/m0N00AkOVyk5CAc+gTm/hrSL9TF3L7j+DXj9Kt2Nw+ac+/WY784EJ+r+0YbhnKmuLdKbCjvLXgshhBBCnKEkiD7DGYbB6kMlzBoafuIBNEDGCn074Ubn476hcPeq3l8nOBHMjVBX4ly6UVsEAdKZQwghhBCDi2wsPMNlldZxrLyeOSdSA+3oyHKISLd32DhZQbZe0e3qomuLpb2dEEIIIQYdCaLPcKsPlQAwZ3jEiT+4qRaObYBh8099IbaBK+3b3Mm0QiGEEEIMQhJEn+FWHyomNdKfhFDfE39wzlrd0i61D4LozjLRrS1QXyaZaCGEEEIMOhJEn8Hqm81syio/uSw06FIODz9InHHqi/EJBq9A59HfbT2iJRMthBBCiMFFgugz2PqMMppbLcw9mXpow4CM5ZAyW3fj6AtB7XpFt/WIlkErQgghhBhcJIg+g60+XIyvpxuTk0JO/MFlGTprnDqv7xYUkgRlmfaf2zLRUs4hhBBCiMFFgugzlGEYrDpYwqzU8N4PVnF0ZLm+TV3Qd4uKnQBlR/TkQ5BphUIIIYQYtCSIPkNlltSSX9nA3LSTDFAzlkP4cAgZ0neLipuobwt26FupiRZCCCHEICVB9BnIYjH421eHUQrmpJ3EpsLmeshZ1zddORzZguj8bfq2thB8Qvqu5loIIYQQYoCQIHqAy6uo592tuTSZW9uO/WXZQb7YW8ivL04nNtjnxC96fCe0NkHy7L5bKOiAOSzVIYguknpoIYQQQgxKMvZ7gHv44318fbCYp1dl8JtLR1Jc08Sz32Rx0/REvntu8sldNG+rvo2b1HcLtYmbDJlf6+4ftcVSyiGEEEKIQUmC6AEsu7SOrw8Wc/HoaA4X1fDdV3XwOyctgkcWjUIpdXIXzt8GwYngf5L9pbsTNwl2vwVVeToTHT+l759DCCGEEKKfSRA9gL26IQd3k+LRy0YR4ufJqxuOsjuvkj9eOQZ3t1OoxMnf5rrgNt6a3c7fas1ESzmHEEIIIQYfCaIHqNomM+9uzePSsTFEBnoDcOc5J1m+4aimSA9Emfa9U79WZ6JGg5snZH0DLfUSRAshhBBiUJKNhQPU+9vyqG0yc/usPgicHdk2/cVP7tvr2rh7QfRYOPSF/lmCaCGEEEIMQhJED0AWi8Er63MYnxDM+ITgvr14/jZQbjrQdZW4Sbq9HcjGQiGEEEIMShJED0DfHCkhu7SO22cl9f3F87dC1Cjw9O37a9s4ZrklEy2EEEKIQUiC6AHGYjF4csURogO9uXh0TF9fHPJ3uKa1nSPH6wdEu/a5hBBCCCH6gQTRA8zHu/LZmVvJzy9Mw9O9j389ZRnQVOW6emib0BTwDgaTh74VQgghhBhkJIgeQOqbzfzli0OMiw/iOxPi+v4JbJsKXZ2JVko/h38UmOSfmBBCCCEGH2lxN4D8d3UmhdWNPH3jBEymkxyk0p38reAZAOHD+/7a7V3wB90nWgghhBBiEJIgeoDIq6jn2TVZXDYulklDQl3zJPnbIHY8mNxcc31HUaP0lxBCCCHEIOTSv7UrpS5SSh1SSmUopX7VxTlzlFI7lVL7lFLfuHI9A9kzqzNRCn518QjXPEFLIxTudX09tBBCCCHEWcBlmWillBvwNLAAyAO2KKU+MQxjv8M5wcB/gIsMwzimlDprmwpvO1rBtOQwYoN9XPMEhXvA0uL6emghhBBCiLOAKzPRU4EMwzCyDMNoBt4CLm93zmLgA8MwjgEYhnFWFtE2mVvJKK5lVGyg654kf6u+jZNMtBBCCCHEqXJlEB0H5Dr8nGc95mg4EKKUWq2U2qaUusWF6xmwDhfWYrYYjIoNct2T5G+DgFgI7OPe00IIIYQQZyFXbizsrL2E0cnzTwLmAT7ABqXURsMwDjtdSKm7gbsBEhMTXbDU/rWvoArAtZnovK0QL6UcQgghhBB9wZWZ6DwgweHneKCgk3O+NAyjzjCMUmANMK79hQzDeM4wjMmGYUyOiIhw2YL7y76Cavy93EkMddEo7vpyqMiWemghhBBCiD7iyiB6CzBMKZWslPIErgc+aXfOx8C5Sil3pZQvMA044MI1DUj7CqoYGRPomt7Q4DBkReqhhRBCCCH6gsuCaMMwzMAPgWXowPgdwzD2KaW+p5T6nvWcA8CXwG5gM/CCYRh7XbWmgajVYnDgeA0jXbqpcBugdI9oIYQQQghxylw6bMUwjM+Bz9sd+2+7nx8HHnflOgay7NI6GlpaXdyZYxtEpoNXgOueQwghhBDiLOLSYSuiZ/ZNhS7qzGEYelNh3ETXXF8IIYQQ4iwkQXQ/21dQjaebiWFR/q55goocaCiXTYVCCCGEEH1Iguh+tq+giuHR/ni4uehXIZsKhRBCCCH6nATR/cgwDPYVVDMqxsVDVtx9IHKk655DCCGEEOIsI0F0PyqoaqSyvoVRcS4eshI7HtxcuodUCCGEEOKsIkF0P9qX34eTCi2t0Gp2/mppgOO7pB5aCCGEEKKPSXqyH+0rqEYpGBF9ikH0jtfh059Aa3Pn90sQLYQQQgjRpySI7kcHC6tJDvPDz+sUfg1Zq2HpjyF+Kgw9v+P9Hj6QdsnJX18IIYQQQnTQq+hNKfU+8BLwhWEYFtcu6eyRX9lAYpjvyV+g5BC8fQuEDYPFb4G3CzcoCiGEEEKINr2tiX4GWAwcUUo9ppQa4cI1nTUKqxqJDvQ+uQfXlcGb14K7Jyx+WwJoIYQQQojTqFdBtGEYKwzDuBGYCOQAy5VS65VStyulPFy5wMGq2WyhtLaZ6KCTCKLNTfDWYqgphOuXQMiQvl+gEEIIIYToUq+7cyilwoDbgO8CO4An0UH1cpesbJArqm4EOPFMtGHAxz+E3I1wxTOQMMUFqxNCCCGEEN3pbU30B8AI4DVgkWEYx613va2U2uqqxQ1mbUH0iWaiv/kr7HkHzv8tjP6OC1YmhBBCCCF60tu2EP82DOPrzu4wDEPmSfdk5R/A3Qtm/6Lt0PGqkwiiD38Fq/8E4xbDuT/r61UKIYQQQohe6m05R7pSKtj2g1IqRCl1r2uWNAgd+ARW/RE2P992yJaJjgn06f11dr4B/tGw6ElQqq9XKYQQQggheqm3QfRdhmFU2n4wDKMCuMslKxqMzDpg5otfwpEVgM5E+3i4EejTyz8GtJohaxUMm687cgghhBBCiH7T2yDapJQ99amUcgMkkuutlkYYfTVEjYR3b4Oi/RRWNxId5I3qLKPcXK87bzjK3wqNVZA6/7QsWQghhBBCdK23QfQy4B2l1Dyl1PnAEuBL1y1rkDE3gV843PA2ePrCl7/svkf0V7+B/8yA5jr7sSPLQblBytzTs2YhhBBCCNGl3gbRvwS+Br4P/ABYCfyi20cIO3Oj3lgYFAejroS8rZRU1nW+qdAw4NDn0FAOu9+2H89YDglTwSf4tC1bCCGEEEJ0rrfDViyGYTxjGMbVhmFcZRjGs4ZhtLp6cYOCYUBrE7hbA+a4SdBST0BtRudBdNE+qDkOJnfY9Jx+fG0xHN8FqfNO79qFEEIIIUSnehVEK6WGKaXeU0rtV0pl2b5cvbhBwdykb9299G3cJABGk9l5OUeGdXbNnAeh5ADkrIWMlfpY6gIXL1YIIYQQQvRGb8s5XgaeAczAXOBV9OAV0RNbZw5bJjo0BbNXMONVF5nojJUQNQZm/AB8QmHTszqw9ouE6LGnb91CCCGEEKJLvQ2ifQzDWAkowzCOGobxCHC+65Y1iLTPRCtFZcgYxpsyiWkfRDdWw7ENumzDwwcm3arrow9/pbtymHo9pV0IIYQQQrhQb6OyRqWUCTiilPqhUupKINKF6xo82meigXy/kQxXucR4tysrz/4GLGYYZi3bmHynvm2ukXpoIYQQQogBpLdB9P2AL3AfMAm4CbjVRWsaXNoy0fYg+oj7cNyUQWjNAedzjywHzwBImKZ/Dk6AtEtAmWCoJP6FEEIIIQaKHoNo62CVaw3DqDUMI88wjNutHTo2nob1nfnaMtFebYd2GakAuBVss59nGLoeOmU2uHnYj1/yBNz4HviGno7VCiGEEEKIXugxiLa2spukOh2tJ3rUSSY6s86bQlM05DsE0SUHoTrPXsphExgjpRxCCCGEEAOMey/P2wF8rJR6F2gbo2cYxgcuWdVg0kkmurCqkVzfdKLzHILoI9bWdjLWWwghhBBiwOttEB0KlOHckcMAJIjuSbtMtGEYFFY3Up44BvJWQU2hHu/97T8gdiIExffjYoUQQgghRG/0Kog2DON2Vy9k0GqXia5uNFPf3Epj1ATIQ9dBf/t3wICrXui3ZQohhBBCiN7rVRCtlHoZnXl2YhjGHX2+osGmLYj2AaCoWv/sFjcedrjDp/frTYW3fgJhQ/tnjUIIIYQQ4oT0tpzjU4fvvYErgYK+X84g1C4TfbxK/xwVGgxRo+D4LrjyWRgys58WKIQQQgghTlRvyzned/xZKbUEWOGSFQ027YatFFmD6OhAb5j7G6gtgnHX99fqhBBCCCHESehtJrq9YUBiXy5k0Go39tuWiY4M9ILQC/prVUIIIYQQ4hT0tia6Buea6ELgly5Z0WDTLhNdWN1ImJ8nXu5u/bgoIYQQQghxKnpbzhHg6oUMWu0y0YVVDUQHeXfzACGEEEIIMdD1OLEQQCl1pVIqyOHnYKXUFS5b1WBibgQ3L7AOfCyvaybM36uHBwkhhBBCiIGsV0E08DvDMKpsPxiGUQn8ziUrGmzMTU4jv2sazQR4n2wpuhBCCCGEGAh6G0R3dp5Egr1hbnQa+V3daCZQgmghhBBCiDNab4PorUqpvyulhiqlUpRS/wC2uXJhg0a7THRtUwsB3h79uCAhhBBCCHGqehtE/whoBt4G3gEagB+4alGDikMmuqXVQmOLBX8vyUQLIYQQQpzJetudow74lYvXMjg5ZKJrGs0AUhMthBBCCHGG6213juVKqWCHn0OUUstctqrBxCETXdsWREs5hxBCCCHEmay35Rzh1o4cABiGUQFEumRFg425CTx8AKhubAEkEy2EEEIIcabrbRBtUUq1jflWSiXhPMFQdMUhE91WziE10UIIIYQQZ7TeRnMPAd8qpb6x/nwecLdrljTIONRE1zZJOYcQQgghxGDQ242FXyqlJqMD553Ax+gOHaInLQ0OmWgp5xBCCCGEGAx6Fc0ppb4L/BiIRwfR04ENwPkuW9lg0Ul3Dn8JooUQQgghzmi9rYn+MTAFOGoYxlxgAlDislUNJk410ZKJFkIIIYQYDHobRDcahtEIoJTyMgzjIJDmumUNIo6Z6CYznu4mvNzd+nlRQgghhBDiVPQ2JZpn7RP9EbBcKVUBFLhqUYNKu+4cgZKFFkIIIYQ44/V2Y+GV1m8fUUqtAoKAL122qsHC0gqWFqeaaBn5LYQQQghx5jvhiM4wjG96PksAupQDHCYWtkh7OyGEEEKIQaC3NdHiZJgb9a1DJlo2FQohhBBCnPkkiHaldploKecQQgghhBgcJIh2pbZMtA+gJxZKOYcQQgghxJlPgmhXapeJrm5skXIOIYQQQohBQIJoV3KoibZYDGsmWoJoIYQQQogznQTRruSQia5vacUwZFqhEEIIIcRgIEG0Kzlkou0jv6UmWgghhBDiTCdBtCs5BdFmQDLRQgghhBCDgQTRrtQWRHu1ZaKlxZ0QQgghxJlPgmhXaquJdsxESzmHEEIIIcSZToJoV3LKROsgOlDKOYQQQgghzngSRLtSJ5lofwmihRBCCCHOeBJEu5JDJrq2SbpzCCGEEEIMFhJEu1K77hwmBX6ebv27JiGEEEIIccokiHYlcxOgwM2DmkYz/l7uKKX6e1VCCCGEEOIUSRDtSuZG8PABpahpNEsphxBCCCHEICFBtCuZm8DdC4CaxhYZtCKEEEIIMUhIEO1K5kZw9wawZqIliBZCCCGEGAwkiHYlx0x0U4tMKxRCCCGE+P/27jVGrvu87/j34S65JJcULxYpU6QutCs7VS6WLFZBYzgwkCaVnSBKCieV4hh2WsB1YBc1+sZ2r4FfJWld9EWMKi4ixEEcS0kdJYLr1HbSwmleuNbF9EW2KMsSLZK7ongRuTt7I2f36Ys5u1ouZ5Z7ljt7Zs98PwAxM+ccDh/+cWb443+f8z81YYjupkUz0Q17oiVJkmrDEN1NV/RE284hSZJUF4bobrqqJ9qZaEmSpDowRHfT5WkYHGKmOcul2TlnoiVJkmrCEN1NxUz0+HQTwBAtSZJUE4bobip6og3RkiRJ9WKI7qZiJrpRhOgdQ/ZES5Ik1YEhupsWZqIvA85ES5Ik1YUhupuKmegx2zkkSZJqxRDdTc2ZVjvHTBGibeeQJEmqha6G6Ii4LyKORcTzEfGxNvvfEREXI+Jo8es/dLOedbewOoftHJIkSXXStVQXEQPAp4CfBU4CT0TE45n53SWH/t/M/IVu1VGZ2Sbk7BVL3O0wREuSJNVCN2ei7wWez8wXMvMS8Ahwfxf/vN7SnG49FhcWbts8wOYBu2ckSZLqoJup7iBwYtHrk8W2pf5hRHwzIv4qIn60i/Wsr+ZM67HoiXYWWpIkqT66meyizbZc8vpp4LbMbETEu4C/AO646o0iPgB8AODWW29d4zK7ZNFM9Nh0035oSZKkGunmTPRJ4JZFrw8BI4sPyMyxzGwUz78IbI6IG5e+UWZ+OjOPZOaRffv2dbHkNbQQols90Tu3ujKHJElSXXQzRD8B3BERhyNiC/AA8PjiAyLi9RERxfN7i3rOdbGm9bPQzjFEY/oyO4eciZYkSaqLriW7zGxGxIeBLwEDwMOZ+UxEfLDY/xDwbuA3I6IJTAEPZObSlo+NaclM9P6dW6utR5IkSWumq9OjRYvGF5dse2jR898Dfq+bNVRmUU/0xIw90ZIkSXXimmvdsngm2tU5JEmSasUQ3S1FT3QODtGYadoTLUmSVCOG6G4pZqKncjOZMGyIliRJqg1DdLcUM9GTc62l7WznkCRJqg9DdLcUM9GN2QEAdjgTLUmSVBuG6G4pZqIbzVZ4dnUOSZKk+jBEd0sxEz1ezEQPbzFES5Ik1YUhuluKmeixS60htidakiSpPgzR3dKchk2DNC63Xu4c2lxtPZIkSVozhuhuac7A4FYaM03AmWhJkqQ6MUR3S3MaihutAAwPDVRckCRJktaKIbpbmtOtW35PN9kysImhQUO0JElSXRiiu6U5U8xEX7aVQ5IkqWYM0d1SzERPzMx6oxVJkqSaMUR3y+VWT/T4dNMQLUmSVDOG6G5pTsPgNts5JEmSasgQ3S0LPdHOREuSJNWNIbpbip7ohu0ckiRJtWOI7paFmehZ2zkkSZJqxhDdLc2phSXudjoTLUmSVCuG6G64NAkXTzF7wyGmL88xbIiWJEmqFUN0N4x+E3KWqf13A9gTLUmSVDOG6G449RQAF/f8OIA90ZIkSTVjiO6GU0/CrlsY37wXwJ5oSZKkmjFEd8Opp+DgPTSmm4Az0ZIkSXVjiF5rjTNw4SU4eA/jM60Q7YWFkiRJ9WKIXmtFPzSHjizMRNvOIUmSVC+G6LV26imIATjwFiZmbOeQJEmqI0P0Wjv1JOy/E7YM05gP0c5ES5Ik1Yohei1lFhcVvhWA8aKdY3iLIVqSJKlODNFr6dwPYPoiHDoCQGOmyfCWATZtiooLkyRJ0loyRK+l+YsKD94DQGO6aT+0JElSDRmi19KpJ2HzMOz7EQAal5r2Q0uSJNWQCe96Pfs/W20cAN//Ctx8N2waAOZnojdXWJwkSZK6wRB9PS5Pw6O/Djn32rZ73r/wtDHTZMfQwPrXJUmSpK4yRF+P8dFWgP75T8JPPAARsGV4YXdjusmNO7ZXWKAkSZK6wRB9PcZHW497DsPQjqt2t2aibeeQJEmqGy8svB5jI63HG25uu7sx02Snq3NIkiTVjiH6esyH6J0HrtqVmcVMtCFakiSpbgzR12N8tLWk3dZdV+2avjzH7FwybIiWJEmqHUP09RgbgRsOtC4oXGJ85jKAN1uRJEmqIUP09RgfbdvKAa2VOQB2OhMtSZJUO4bo6zE22vGiwomZWQB7oiVJkmrIEL1ac3MwPtIxRM+3c9gTLUmSVD+G6NWaPAtzTdjZYXm7+XYOe6IlSZJqxxC9WgtrRHfoiZ5phWjbOSRJkurHEL1a83cr7DATPTEfop2JliRJqh1D9GqNnWo9duyJdiZakiSprgzRqzU2CjEAO/a33d2YbjK4KRgadIglSZLqxoS3WuOjsOMm2DTQdndjpsmOrYNEmxuxSJIkaWMzRK/W/N0KO2hMN23lkCRJqilD9GqNdV4jGoqZaEO0JElSLRmiV2t8tOPKHGCIliRJqjND9GrMNGBmbPl2jqInWpIkSfVjiF6N+TWibzjY8RB7oiVJkurLEL0a82tE72w/E52ZnBmfYe/wlnUsSpIkSevFEL0aY/Mz0e17os9NXGJ8psntrxtex6IkSZK0XgzRqzE+0nrsMBP9w3MTABy+0RAtSZJUR4bo1Rgbha27Ycv2trtfPDsJwO2GaEmSpFoyRK/G+Oiya0QfPzvBwKbg0J5t61iUJEmS1oshejXGTnVs5QB48dwEh/ZsY/OAwytJklRHprzVGBtddo3oH56b8KJCSZKkGjNElzXbhIlXOq4RnZkcPzvpRYWSJEk1Zogua/Ic5BwM72u7+2zjEo2ZJre9rv1Fh5IkSdr4DNFlTZ5rPQ7f2Hb38WJ5O1fmkCRJqi9DdFnzIXr769ruPn62WCPanmhJkqTaMkSXda0QfW6CQZe3kyRJqjVDdFlT51uP2/a23X387CSH9mxj0OXtJEmSasukV9bCTHT7EP3i2Qn7oSVJkmrOEF3W5HnYshMGh67alZmuES1JktQHDNFlTZ7rOAt9pjHDxKVZ14iWJEmqOUN0WZPnllmZYxLANaIlSZJqzhBd1uT5jjPRC8vbORMtSZJUa4bospabiS6Wtzu42+XtJEmS6swQXdbk+WVD9K17t7u8nSRJUs2Z9spozsCl8WWWt5t0eTtJkqQ+YIguY7K40Uqbmejm7Bwvnm24vJ0kSVIfMESXsczdCp873WD68hxvuWXXOhclSZKk9WaILmPhboVXz0R/48SrANx1y+51LEiSJElVMESXsUyIPvrSBfYOb+HWva4RLUmSVHeG6DKWC9EnLvCWQ7uIiHUuSpIkSevNEF3GwoWFV/ZEj09f5vkzDe66ZU8FRUmSJGm9dTVER8R9EXEsIp6PiI8tc9w/iIjZiHh3N+u5bpPnYOgGGNh8xeZvnbxIJtx16+5q6pIkSdK66lqIjogB4FPAO4E7gQcj4s4Ox/0O8KVu1bJmOtzy++iJCwDcdWj3+tYjSZKkSnRzJvpe4PnMfCEzLwGPAPe3Oe5fAp8HXuliLWujwy2/v/HSq7xh3zC7tm9u85skSZJUN90M0QeBE4tenyy2LYiIg8AvAw91sY610yZEZyZHT1xwaTtJkqQ+0s0Q3W6Zilzy+r8CH83M2WXfKOIDEfFkRDx55syZtaqvvMnzV4Xok69OcbZxibsN0ZIkSX1jsIvvfRK4ZdHrQ8DIkmOOAI8Uy8LdCLwrIpqZ+ReLD8rMTwOfBjhy5MjSIL5+Js9ddbfChX5oV+aQJEnqG90M0U8Ad0TEYeAU8ADwa4sPyMzD888j4g+BLywN0D3j8jRcnrjqwsKjJy4wNLiJHzmws6LCJEmStN66FqIzsxkRH6a16sYA8HBmPhMRHyz2b4w+6HlT82tEX9nOcfTEBX7s4C42D7jktiRJUr/o5kw0mflF4ItLtrUNz5n5/m7Wct3a3K1wdi75zqmLvOcnb6uoKEmSJFXB6dOVahOizzZmmGnOcXjfcEVFSZIkqQqG6JVaCNGv9USfujAFwM27tlZRkSRJkipiiF6pyat7okcvTANw8+5tVVQkSZKkihiiV2o+RG97bSm7kYWZaEO0JElSPzFEr9TkOdi6CwZeu7X3yMUptm8Z4IZtXb0+U5IkST3GEL1SbW75PXphmpt3b6O4WYwkSZL6hCF6pdrcrXDk4hQHvKhQkiSp7xiiV6rNTPTIhWkOelGhJElS3zFEr9TUq1eE6JnmLGcbMxzwokJJkqS+Y4heqclzV6wR/fLF1vJ2B3bbziFJktRvDNErcWkSLk9eMRM9UqwRbTuHJElS/zFEr8TU/I1WXpuJnl8j2gsLJUmS+o8LHK/Etj3w4KNw050Lm0YvFjdacSZakiSp7xiiV2LLMLz5vis2jVycZu/wFrZuHqioKEmSJFXFdo5VGrkwxc1eVChJktSXDNGrNHph2uXtJEmS+pQhepVGLkxxsxcVSpIk9SVD9CqMT19mfKbpRYWSJEl9yhC9CqMLN1oxREuSJPUjQ/QqnCrWiD7ohYWSJEl9yRC9CqPF3Qq9sFCSJKk/GaJXYeTCFJsC9u8cqroUSZIkVcAQvQojF6d4/Q1bGRxw+CRJkvqRKXAVRi9Me1GhJElSHzNEr8LIxSmXt5MkSepjhuiSMpPRi9PeaEWSJKmPGaJLOj02w6XmHIf2OBMtSZLUrwzRJR07PQ7Am27aWXElkiRJqoohuqTnXjZES5Ik9TtDdEnHTo+zf+cQe4a3VF2KJEmSKmKILunYy+O8+fXOQkuSJPUzQ3QJs3PJ918Zt5VDkiSpzxmiSzhxfpLpy3O82RAtSZLU1wzRJSyszGE7hyRJUl8zRJcwvzLHHft3VFyJJEmSqmSILuHY6XFu2buN4aHBqkuRJElShQzRJTx3etx+aEmSJBmiV+pSc44Xzky4MockSZIM0Sv14tkJmnPpGtGSJEkyRK/Usy+PAd7uW5IkSYboFXvu9DiDm4I37nNlDkmSpH5niF6hYy83OHzjMFsGHTJJkqR+ZyJcoedOj3uTFUmSJAGG6BWZvNTkpfOTLm8nSZIkwBC9IucnLnHPbXv48YO7qi5FkiRJPcBb763AoT3b+fxv/lTVZUiSJKlHOBMtSZIklWSIliRJkkoyREuSJEklGaIlSZKkkgzRkiRJUkmGaEmSJKkkQ7QkSZJUkiFakiRJKskQLUmSJJVkiJYkSZJKMkRLkiRJJRmiJUmSpJIM0ZIkSVJJhmhJkiSpJEO0JEmSVJIhWpIkSSrJEC1JkiSVZIiWJEmSSorMrLqGUiLiDPDDiv74G4GzFf3ZG5HjVZ5jVo7jVZ5jVo7jVY7jVZ5jVs56j9dtmbmv3Y4NF6KrFBFPZuaRquvYKByv8hyzchyv8hyzchyvchyv8hyzcnppvGznkCRJkkoyREuSJEklGaLL+XTVBWwwjld5jlk5jld5jlk5jlc5jld5jlk5PTNe9kRLkiRJJTkTLUmSJJVkiF6BiLgvIo5FxPMR8bGq6+k1EXFLRPyfiPheRDwTEf+q2P5bEXEqIo4Wv95Vda29JCKOR8S3i7F5sti2NyK+EhHfLx73VF1nL4iINy86j45GxFhEfMRz7EoR8XBEvBIR31m0reM5FREfL77XjkXEP66m6mp1GLP/FBHPRsS3IuKxiNhdbL89IqYWnW8PVVZ4RTqMV8fPoedYxzF7dNF4HY+Io8V2z7HOmaLnvsts57iGiBgAngN+FjgJPAE8mJnfrbSwHhIRB4ADmfl0ROwEngJ+CfhVoJGZ/7nK+npVRBwHjmTm2UXbfhc4n5m/XfyHbU9mfrSqGntR8Zk8Bfwk8Bt4ji2IiJ8GGsAfZeaPFdvanlMRcSfwOeBe4Gbgr4E3ZeZsReVXosOY/RzwvzOzGRG/A1CM2e3AF+aP60cdxuu3aPM59BxraTdmS/Z/EriYmZ/wHFs2U7yfHvsucyb62u4Fns/MFzLzEvAIcH/FNfWUzBzNzKeL5+PA94CD1Va1Yd0PfKZ4/hlaXxy60s8AP8jMqm661LMy82+B80s2dzqn7gceycyZzHwReJ7W911faTdmmfnlzGwWL78GHFr3wnpUh3OsE88xlh+ziAhaE06fW9eietgymaLnvssM0dd2EDix6PVJDIgdFf+Lvhv4f8WmDxc/En3Y1oSrJPDliHgqIj5QbLspM0eh9UUC7K+sut71AFf+g+M5trxO55TfbSvzz4C/WvT6cER8IyK+GhFvr6qoHtTuc+g5dm1vB05n5vcXbfMcKyzJFD33XWaIvrZos80emDYiYgfweeAjmTkG/DfgjcBdwCjwyeqq60lvy8y3Au8EPlT8yE/LiIgtwC8Cf1Zs8hxbPb/briEi/i3QBD5bbBoFbs3Mu4F/DfxJRNxQVX09pNPn0HPs2h7kykkBz7FCm0zR8dA229blPDNEX9tJ4JZFrw8BIxXV0rMiYjOtk/2zmfnnAJl5OjNnM3MO+O/04Y/xlpOZI8XjK8BjtMbndNEPNt8X9kp1FfakdwJPZ+Zp8BxboU7nlN9ty4iI9wG/ALwni4uHih8XnyuePwX8AHhTdVX2hmU+h55jy4iIQeCfAI/Ob/Mca2mXKejB7zJD9LU9AdwREYeLWbAHgMcrrqmnFD1dfwB8LzP/y6LtBxYd9svAd5b+3n4VEcPFBRNExDDwc7TG53HgfcVh7wP+spoKe9YVszaeYyvS6Zx6HHggIoYi4jBwB/D1CurrORFxH/BR4Bczc3LR9n3Fha1ExBtojdkL1VTZO5b5HHqOLe8fAc9m5sn5DZ5jnTMFPfhdNrgef8hGVlyd/WHgS8AA8HBmPlNxWb3mbcB7gW/PL9MD/BvgwYi4i9aPVY4D/6KK4nrUTcBjre8KBoE/ycz/FRFPAH8aEf8ceAn4lQpr7CkRsZ3WKjmLz6Pf9Rx7TUR8DngHcGNEnAT+I/DbtDmnMvOZiPhT4Lu0WhY+1G+rJkDHMfs4MAR8pfiMfi0zPwj8NPCJiGgCs8AHM3OlF9nVQofxeke7z6HnWEu7McvMP+Dq6zvAcww6Z4qe+y5ziTtJkiSpJNs5JEmSpJIM0ZIkSVJJhmhJkiSpJEO0JEmSVJIhWpIkSSrJEC1JIiLeERFfqLoOSdooDNGSJElSSYZoSdpAIuLXI+LrEXE0In4/IgYiohERn4yIpyPibyJiX3HsXRHxtYj4VkQ8FhF7iu1/LyL+OiK+WfyeNxZvvyMi/kdEPBsRny3uHCZJasMQLUkbRET8feCfAm/LzLto3dHsPcAw8HRmvhX4Kq27yAH8EfDRzPwJ4NuLtn8W+FRmvgX4KWC02H438BHgTuANtO4cJklqw9t+S9LG8TPAPcATxSTxNuAVYA54tDjmj4E/j4hdwO7M/Gqx/TPAn0XETuBgZj4GkJnTAMX7fT0zTxavjwK3A3/X9b+VJG1AhmhJ2jgC+ExmfvyKjRH/fslxeY336GRm0fNZ/DdCkjqynUOSNo6/Ad4dEfsBImJvRNxG67v83cUxvwb8XWZeBF6NiLcX298LfDUzx4CTEfFLxXsMRcT29fxLSFIdOMsgSRtEZn43Iv4d8OWI2ARcBj4ETAA/GhFPARdp9U0DvA94qAjJLwC/UWx/L/D7EfGJ4j1+ZR3/GpJUC5G53E/9JEm9LiIambmj6jokqZ/YziFJkiSV5Ey0JEmSVJIz0ZIkSVJJhmhJkiSpJEO0JEmSVJIhWpIkSSrJEC1JkiSVZIiWJEmSSvr/nbnscxUQRWEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}