{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
       "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
       "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
       "\n",
       "    \n",
       "    body {\n",
       "        font-family: 'Lora', Consolas, sans-serif;\n",
       "       \n",
       "        -webkit-print-color-adjust: exact important !;\n",
       "        \n",
       "      \n",
       "       \n",
       "    }\n",
       "    \n",
       "    .alert-block {\n",
       "        width: 95%;\n",
       "        margin: auto;\n",
       "    }\n",
       "    \n",
       "    .rendered_html code\n",
       "    {\n",
       "        color: black;\n",
       "        background: #eaf0ff;\n",
       "        background: #f5f5f5; \n",
       "        padding: 1pt;\n",
       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
       "    }\n",
       "    \n",
       "    p {\n",
       "      line-height: 140%;\n",
       "    }\n",
       "    \n",
       "    strong code {\n",
       "        background: red;\n",
       "    }\n",
       "    \n",
       "    .rendered_html strong code\n",
       "    {\n",
       "        background: #f5f5f5;\n",
       "    }\n",
       "    \n",
       "    .CodeMirror pre {\n",
       "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
       "    }\n",
       "    \n",
       "    .cm-s-ipython span.cm-keyword {\n",
       "        font-weight: normal;\n",
       "     }\n",
       "     \n",
       "     strong {\n",
       "         background: #f5f5f5;\n",
       "         margin-top: 4pt;\n",
       "         margin-bottom: 4pt;\n",
       "         padding: 2pt;\n",
       "         border: 0.5px solid #a0a0a0;\n",
       "         font-weight: bold;\n",
       "         color: darkred;\n",
       "     }\n",
       "     \n",
       "    \n",
       "    div #notebook {\n",
       "        # font-size: 10pt; \n",
       "        line-height: 145%;\n",
       "        }\n",
       "        \n",
       "    li {\n",
       "        line-height: 145%;\n",
       "    }\n",
       "\n",
       "    div.output_area pre {\n",
       "        background: #fff9d8 !important;\n",
       "        padding: 5pt;\n",
       "       \n",
       "       -webkit-print-color-adjust: exact; \n",
       "        \n",
       "    }\n",
       " \n",
       "    \n",
       " \n",
       "    h1, h2, h3, h4 {\n",
       "        font-family: Kameron, arial;\n",
       "\n",
       "\n",
       "    }\n",
       "    \n",
       "    div#maintoolbar {display: none !important;}\n",
       "</style>\n",
       "    <script>\n",
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "        return false;\n",
       "}\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IGNORE THIS CELL WHICH CUSTOMIZES LAYOUT AND STYLING OF THE NOTEBOOK !\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "mpl.rcParams['lines.linewidth'] = 3\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%config IPCompleter.greedy=True\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 8: Introduction to Neural Networks\n",
    "\n",
    "\n",
    "## History of Neural networks\n",
    "\n",
    "\n",
    "1943 - Threshold Logic\n",
    "\n",
    "1940s - Hebbian Learning\n",
    "\n",
    "1958 - Perceptron\n",
    "\n",
    "1980s - Neocognitron\n",
    "\n",
    "1982 - Hopfield Network\n",
    "\n",
    "1989 - Convolutional neural network (CNN) kernels trained via backpropagation\n",
    "\n",
    "1997 - Long-short term memory (LSTM) model\n",
    "\n",
    "1998 - LeNet-5\n",
    "\n",
    "2014 - Gated Recurrent Units (GRU), Generative Adversarial Networks (GAN)\n",
    "\n",
    "2015 - ResNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why the boom now?\n",
    "* Data\n",
    "* Data\n",
    "* Data\n",
    "* Availability of GPUs\n",
    "* Algorithmic developments which allow for efficient training and making networks networks\n",
    "* Development of high-level libraries/APIs have made the field much more accessible than it was a decade ago"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feed-Forward neural network\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/neural_net_ex.svg\" width=\"700\"/>\n",
    "<figcaption>A 3 layer densely connected Neural Network (By convention the input layer is not counted).</figcaption>\n",
    "</figure>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building blocks\n",
    "### Perceptron\n",
    "\n",
    "The smallest unit of a neural network is a **perceptron** like node.\n",
    "\n",
    "**What is a Perceptron?**\n",
    "\n",
    "It is a simple function which can have multiple inputs and has a single output.\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/perceptron_ex.svg\" width=\"400\"/>\n",
    "<figcaption>A simple perceptron with 3 inputs and 1 output.</figcaption>\n",
    "</figure>\n",
    "</center>\n",
    "\n",
    "\n",
    "It works as follows: \n",
    "\n",
    "Step 1: A **weighted sum** of the inputs is calculated\n",
    "\n",
    "\\begin{equation*}\n",
    "weighted\\_sum = w_{1} x_{1} + w_{2} x_{2} + w_{3} x_{3} + ...\n",
    "\\end{equation*}\n",
    "\n",
    "Step 2: A **step** activation function is applied\n",
    "\n",
    "$$\n",
    "f = \\left\\{\n",
    "        \\begin{array}{ll}\n",
    "            0 & \\quad weighted\\_sum < threshold \\\\\n",
    "            1 & \\quad weighted\\_sum \\geq threshold\n",
    "        \\end{array}\n",
    "    \\right.\n",
    "$$\n",
    "\n",
    "You can see that this is also a linear classifier as the ones we introduced in script 02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 392
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the step function\n",
    "x = np.arange(-2,2.1,0.01)\n",
    "y = np.zeros(len(x))\n",
    "threshold = 0.\n",
    "y[x>threshold] = 1.\n",
    "step_plot = sns.lineplot(x, y).set_title('Step function') ;\n",
    "plt.xlabel('weighted_sum') ;\n",
    "plt.ylabel('f(weighted_sum)') ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perceptron(X, w, threshold=1):\n",
    "    # This function computes sum(w_i*x_i) and\n",
    "    # applies a perceptron activation\n",
    "    linear_sum = np.dot(np.asarray(X).T, w)\n",
    "    output = np.zeros(len(linear_sum), dtype=np.int8)\n",
    "    output[linear_sum >= threshold] = 1\n",
    "    return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean AND\n",
    "\n",
    "| x$_1$ | x$_2$ | output |\n",
    "| --- | --- | --- |\n",
    "| 0 | 0 | 0 |\n",
    "| 1 | 0 | 0 |\n",
    "| 0 | 1 | 0 |\n",
    "| 1 | 1 | 1 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron output for x1, x2 =  0 , 0  is  0\n",
      "Perceptron output for x1, x2 =  1 , 0  is  0\n",
      "Perceptron output for x1, x2 =  0 , 1  is  0\n",
      "Perceptron output for x1, x2 =  1 , 1  is  1\n"
     ]
    }
   ],
   "source": [
    "# Calculating Boolean AND using a perceptron\n",
    "threshold = 1.5\n",
    "# (w1, w2)\n",
    "w = [1, 1]\n",
    "# (x1, x2) pairs\n",
    "x1 = [0, 1, 0, 1]\n",
    "x2 = [0, 0, 1, 1]\n",
    "# Calling the perceptron function\n",
    "output = perceptron([x1, x2], w, threshold)\n",
    "for i in range(len(output)):\n",
    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
    "          \" is \", output[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this simple case we can rewrite our equation to $x_2 = ...... $ which describes a line in 2D:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perceptron_DB(x1, x2, w, threshold):\n",
    "    # Plotting the decision boundary of the perceptron\n",
    "    plt.scatter(x1, x2, color=\"black\")\n",
    "    plt.xlim(-1,2)\n",
    "    plt.ylim(-1,2)\n",
    "    # The decision boundary is a line given by\n",
    "    # w_1*x_1+w_2*x_2-threshold=0\n",
    "    x1 = np.arange(-3, 4)\n",
    "    x2 = (threshold - x1*w[0])/w[1]\n",
    "    sns.lineplot(x1, x2, **{\"color\": \"black\"})\n",
    "    plt.xlabel(\"x$_1$\", fontsize=16)\n",
    "    plt.ylabel(\"x$_2$\", fontsize=16)\n",
    "    # Coloring the regions\n",
    "    pts_tmp = np.arange(-2, 2.1, 0.02)\n",
    "    points = np.array(np.meshgrid(pts_tmp, pts_tmp)).T.reshape(-1, 2)\n",
    "    outputs = perceptron(points.T, w, threshold)\n",
    "    plt.plot(points[:, 0][outputs == 0], points[:, 1][outputs == 0],\n",
    "             \"o\",\n",
    "             color=\"steelblue\",\n",
    "             markersize=1,\n",
    "             alpha=0.04,\n",
    "             )\n",
    "    plt.plot(points[:, 0][outputs == 1], points[:, 1][outputs == 1],\n",
    "             \"o\",\n",
    "             color=\"chocolate\",\n",
    "             markersize=1,\n",
    "             alpha=0.04,\n",
    "             )\n",
    "    plt.title(\"Blue color = 0 and Chocolate = 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 406
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the perceptron decision boundary\n",
    "perceptron_DB(x1, x2, w, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section\n",
    "* Compute a Boolean \"OR\" using a perceptron\n",
    "\n",
    "Hint: copy the code from the \"AND\" example and edit the weights and/or threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean OR\n",
    "\n",
    "| x$_1$ | x$_2$ | output |\n",
    "| --- | --- | --- |\n",
    "| 0 | 0 | 0 |\n",
    "| 1 | 0 | 1 |\n",
    "| 0 | 1 | 1 |\n",
    "| 1 | 1 | 1 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Boolean OR using a perceptron\n",
    "# Enter code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true,
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron output for x1, x2 =  0 , 0  is  0\n",
      "Perceptron output for x1, x2 =  1 , 0  is  1\n",
      "Perceptron output for x1, x2 =  0 , 1  is  1\n",
      "Perceptron output for x1, x2 =  1 , 1  is  1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 406
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "# Calculating Boolean OR using a perceptron\n",
    "threshold=0.6\n",
    "# (w1, w2)\n",
    "w=[1,1]\n",
    "# (x1, x2) pairs\n",
    "x1 = [0, 1, 0, 1]\n",
    "x2 = [0, 0, 1, 1]\n",
    "output = perceptron([x1, x2], w, threshold)\n",
    "for i in range(len(output)):\n",
    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
    "          \" is \", output[i])\n",
    "perceptron_DB(x1, x2, w, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section\n",
    "* Create a NAND gate using a perceptron\n",
    "\n",
    "#### Boolean NAND\n",
    "\n",
    "| x$_1$ | x$_2$ | output |\n",
    "| --- | --- | --- |\n",
    "| 0 | 0 | 1 |\n",
    "| 1 | 0 | 1 |\n",
    "| 0 | 1 | 1 |\n",
    "| 1 | 1 | 0 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Boolean NAND using a perceptron\n",
    "# Enter code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron output for x1, x2 =  0 , 0  is  1\n",
      "Perceptron output for x1, x2 =  1 , 0  is  1\n",
      "Perceptron output for x1, x2 =  0 , 1  is  1\n",
      "Perceptron output for x1, x2 =  1 , 1  is  0\n"
     ]
    },
    {
     "data": {
      "image/png": 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dbb0tvqPpvZFTo3c8iPa3PnYw0sWiRR3x3kCcn0uVUnHDMB7vpJzGbkv8GWuTWxZ4Xik1TClVX7TvRZRSpxXtf5TW+oGFPBYAWutnseOzdAEeVUUDREbjsDyMHbvmI+ygmkl/7uOnO4OVUvHAoyilFlNKXQ0cUKQtznNsM1u2+JxGFq5nsO9SjW22zzj+xbCdVNzVRt0EQUgAsYQJgtDhtNATj4f9wbo29ntnHnBYdMe5Lc7F3qXfFvhcKfU+diDFJbFjThxGsxdltdb/jHzwJwN3Rz+WvsT2BBS/THyB1vrhcuLRWv8tuqN+BPadgo+xd5MV9gfTJ8zfeLo4inV/7A/Ez7BjqawVnYdZWEtTqV6b0FrPi+60TwC2Bz5WSv0Xe9d/Xex5fRMY3MqdaFc4CnsHfgfgM6XUu9iR5hfD3p0fVMb7HETvqFyAfRl7KDBAKfUR9uX7uJenJ6PjtPVydZtorR9USl2BHV/kYaXU/7A/itfDPv2bAmzRzn1OU0r1wY4Gvzm217zro30H2M9N3BC7FXvuOoIDsE9P1gPeiXIwD/gN9pr8FNir2A6V1Ocem98DsTl8Vyn1HtautSb23LyBtYX9KtLEPXC9HemWBT5QSn1e1JPdkCj+TYH3lVLvYM/3WkB9FFf/Ej3CCYKQIPKERRCEarBVs7/NsY2Mt7EjV6+rtW5trIb50FqPxg56Nx5r+VgbO/7FUK11qwM/RgPV9cW+N5DDvgviYX/876G1busub/P9/R47wNxk7A/tdbCNoMuBDbXWnxVpA+wPxMHA49gfa7/FPiEYAWygtb6bMtBavxfV/SLsk4g1sSOcvwKcBGwWDX7nLNF4MRthx+H4HnsuCtjxcDZppRes1vZ1NXYQwGeiffwW+yP2YeznZHfs2Cm/UUq1p8vh1o73J2AQ1kK4BLaTgDHYz/RnJTYttc+vsNfFftjG+A/YvK6Nvct/J7CN1nqY1rqh0hiiY34T1fk0rPVpJWB1rPXrHOxn8s0Wtuv0z31kx1sf+47PZ9gG0q+xDZVTgM2ASZF8YNF272MbV//Ddpu8WvS+WnzON43ify2Kf23suzQ3Ar+LBgQVBMFBPGPaHKdLEARBEARBEAQhEeQJiyAIgiAIgiAIziINFkEQBEEQBEEQnEUaLIIgCIIgCIIgOIv0ElZENCbAMcAh2JfxcthuHkcBl2ut55S5nzWB87GjLf8KOxDXTcCIcnrCEQRBEARBEATBIi/dR0SNldHYEZlnYEddbsD2qrJoVN5Raz2rjf38Dtt7TS/geWzvKDtE+xiptT6oWjEIgiAIgiAIQtYQS1gTR2AbK28Ca2mtd9Ja9wPWwPa1vxm268dWUUp5wO3YxspQrfXWWutB2O4q3wQOVErtXWofgiAIgiAIgiA0IQ2WJg6Npidprb+MF2qtf8DaxMAOhlWKnbF9zj+ltb6zaB/fA8dGxRM6pLaCIAiCIAiCUANIg6WJH4D3gJdbWPd+NG1r5ORdo+kCo2drrWN72NZKqZ4LW0lBEARBEARBqCXkpfsIrfXAEqs3iaZftLGbdaPp260dBlgKO1LwS+XXThAEQRAEQRBqE3nC0gbReykXRMUH2pAvG02/bmV9vHzpSuslCIIgCIIgCLWAPGFpm0uA7YBvgcvb0C4STVvrSWx2NO3RAfUq5nVgFWzvZh928L4FQRAEQRAEAWB17O/Yj4ENOuug0mApgVLqAuB0YC6wX/TyfCniMVZa6yvaazbtKFYBekd/y3fwvgVBEARBEAShmFU682DSYGkBpVQeuAE4EpgDDNJaP1PGpjOiabdW1neNpjMrq2GLx+0dhiGFQgBhiG0zeeDnrCIMipb5VdJUa79p1LS+jTEh3377LZ9+9jmhMXgeGAPGRO1dz8fzPIwxYEKWXmY5Vlzx1+TzOXzPtnVDYzAGPI9GbVzuTE2Sx3ZBk8/7GAOFQuBk/STnpTW5CvPnQgy1mvPiay/LcWY155Vee2mJM02acrbxPY/6+samQ/ybt1OQBkszlFI9gPuwPX79DOxRZmMF4CtgfWAZbI9jzWnrHZeF5UNg+cK8Bn76eRamYQ4E8yBXj1/fHYBw3qzGZV5d16poqrXfNGra2qY+14VuXXtw7tl/5LlnnqUhDJnbEGI8Dz/fBT+fJywUCIN5+Ll6llthRS6+4FL67rIzAHPmFSiEIXnfp74ux7yGoLHcNfoy6QxNksd2QdNr0e40FEJ++Wmmk/WTnJfW9OjdraL8uRBDrea8+NrLcpxZzXml115a4kyTppxtutbnWXqpXkR06isI8tJ9EUqpxYCnsI2Vz4Ft2tFYgabewdZpYd8esBYQAO9UVtMSBPPwCPHw8AjBBGCC+ZdVS5PksV3TlLHNissvx5iHH+Kv113NYr16kfPB83w83wM8PN/D9/N4vsc333zDsCOG8oc/HM3UqVMjz6GHAQpBOF85DA1haDpFk+SxndCY6K6Uq/WTnJfWVJg/J2Ko1ZwX5S7TcWY15xVee6mJM0WacrYJQ1tKAmmwRCil6oHHgI2wDYottdatdU/cGuOj6Z4trNsSWBJ4Tms9faEr2ha5egw+BoPBBy8HXm7+ZdXSJHls1zRlboOX44ADhjBhwpP03XUAxoQQhuRMAcKQMCxgrWEGYwz33z+K7bbdnHGPPUpoQjwP8jkfzwODtZf5vofve/Mtq5YmyWO7osG4XT/JeWlNJflzJYZazXmcu6zHmdWcu/Dd6XoeXDsXvu9V7edrW0iDpYkLgM2xT1a211qXHHNFKbWaUmotpVTvosVPA/8FdlZK/b5IuyQwIipe2bHVFrLAkkstzS233MEtN9/KMov3pt40UEcDnjFgAA87BX6Y+j1/+MNR/OH4o/n2m2/sQmMlFN/8aL6sWpokj520Jp51tX6S89KauCjnIn1xxrNZjzOrOY+LWY8zbZoyr70kkHdYAKXU4sAJUfF74GqlVItarfVB0exkYCXgMOC2aF2olBoWrbtJKXU49r2W7YHFgJu11o9UJwoAY9+ZCAv2z/PBBHZV8bJqaZI8tmuasrfxMIFd5hEyYLeBbLHxhlx+6XDuf+ghgpyP8T3ynkfB9zCevcfg+T5PPjGZXadM4cwzzmKvfQcThAbPM42PbANjGpcVgnC+ckdpqrXf1GhCWw6Mo/WTnJfWVJg/J2Ko1ZwX5S7TcWY15458d7qeB9fOhVjCkmdTmnr22hA4sMRfSbTWLwObYQeZXAPYBfgUOBo4pqMrXowxEHo+JggwQcFO8exf0bJqaZI8tmuacrcJgwImaJhPs2jPnlx84SXcctOtLLfM8tR7IfVegXovBBNiMHh+Ds/PMX3mDP502kkcdshQPvn0M4IgtLdCPAiCkCAMG5c1L3eUplr7TYumELhdP8l5aU2l+XMhhlrNeXHushxnVnPuynen63lw7VwkhTxhAbTW42lnGrTWK5dY9w6wT4XVajeeB74JMfkcmDrI5/CiZ3he8bJqaZI8tmuaMrfxc10wpg4vbxbQbLPDjkx+fBuuuPwiRt11J34YkPMMAT4mDDBhDhMG+J7PCy88w377DOSEE07kyMN/T87Pkcv5GCCX88GwQBkWXLYwmmrtNy2avHG7fpLz0ppK8+dCDLWa8+LcZTnOrObcle9O1/Pg2rlICnnCkik87Ivcecjl7DR62Xu+ZdXSJHls1zTlbuPn8XL5VjU9ei/GeeddzB23381KK69MEBK9mB/gB3MhDKIX80NmzpjOxRcNZ8/dd+XDDzS56IW5nO+Rz/nzlf1m8wurqdZ+06TJO14/yXlpTSX5cyWGWs15voOP5WqcWc25C9+drufBtXPh+8k9YpEGiyCkgI033phHx07mhBNOIe/71NHQ6ov5r/37VXbaaVuuv/4aGhoamu6ImOgxYvEdkubLFkZTrf2mQRPPulo/yXlpTVyUc5G+OOPZrMeZ1ZzHxazHmTZNmddeEkiDJWs4PvZIzWjasQ1l7rdblzrOPOMsJox/nPXWWQefkLxnbFeEvjWber6H5/k0FApcc80V7LfPnrz55hvSh321NAZcGEvAtb76U6OpMH9OxFCrOS/KXabjzGrOK7z2UhNnijTlbCMv3QsdRwrGHqkJTTu2oZ3H/s16G/DAQ49x8sl/IpevpxCE843dYkz8Z3jvvXcYNGg3Lr3kfObMnk0S/bZnXePCWAKdqXG9fu3VyDgs6Y1TxmFJd85d+O50PQ+unQuxhAmC0C7q8nmOOuoYHhs7ic022WRBixiAATx7R+Rvf7ue3frvxCuvvGSXxxjwYm1L5XI0C7NNVjTxrKv1q5bG9fqVq4mLci7SF2c8m/U4s5rzuJj1ONOmKfPaSwJpsGSNlNigMq+pgiWsJc1qq63Kg/c/zAXnnc8i3bqQ9ww5HzxvfpuY7+f59PNPOXjoEM4550ymTZsmj9M7QmPABVuDa7aB1GgqzJ8TMdRqzotyl+k4s5rzCq+91MSZIo1YwoTOJSU2qMxrqmgJa67x67oy9ODDGTt2IlttvW1RT2JNNjHbk5gBYxh517/YfrsteGLyRHmc3gEaF2wNrtkG0qQRS1h64xRLWLpz7sJ3p+t5cO1ciCVMEISKWW655bn1tru47rq/sVjv3iV7Evvq6y85aOhgTjzxOH6a+pNdh13nRdNGmi9rq1xLmnjW1fpVS+N6/crVxEU5F+mLM57NepxZzXlczHqcadOUee0lgTRYskZKbFCZ13SSJay5xsew37778czTLzKg324lexLzPJ9HHx3NbgN35pFHR9NQCKLvI3mcLpawGsl5hflzIoZazXlR7jIdZ1ZzXuG1l5o4U6QRS5jQuaTEBpV5TSdawlrSLLX0Mlz/1xu59roRLP6rpUr2JPbT1KmceOKx/P7wg/j2m6/p6EfIWde4YGtwzTaQJo1YwtIbp1jC0p1zF747Xc+Da+dCLGGCIFSFvrvsyoSJT7L//kNK9iSGgYmTJtB3lx24755R9n2XGGMlmDLLtaSJZ12tX7U0rtevXE1clHORvjjj2azHmdWcx8Wsx5k2TZnXXhJIgyVrpMQGlXlNQpawljSL9e7F1ZdfxR23jeTXyy9XsiexGbNmcO55ZzL0oP356KOP5HF6WxoDLtgaXLMNpEZTYf6ciKFWc16Uu0zHmdWcV3jtpSbOFGnEEiZ0LimxQWVek7AlrCXN1ttsx8OjxzP0kGGExivZk9gLU56jz45bcdNNIwjDgEoeIWdd44KtwTXbQJo0YglLb5xiCUt3zl347nQ9D66dC7GECYLQKfTo0Z1zz7mQMWPGs+bqq5fsSWz2nNkMH34W++6zOx/o9+067DovmrZYriVNPOtq/aqlcb1+5WriopyL9MUZz2Y9zqzmPC5mPc60acq89pJAGixZIyU2qMxrHLKEtaTZZOONeXzS05xw3PHU57ySPYm98eYb7L33AK699ipmzZkTfWcl//jaCY0BF2wNrtkGUqOpMH9OxFCrOS/KXabjzGrOK7z2UhNnijRiCRM6l5TYoDKvcdAS1lzTpWt3Tj7lz9x3/2jWWXe9kj2JNTQ0cO11VzJg1z68+Z9/48Lja1c0LtgaXLMNpEkjlrD0ximWsHTn3IXvTtfz4Nq5EEuYIAiJsfba6/DAg49y9pnn0aPeL9mT2Hvvv8ve++zO//3lYmbPmt243ot1Mc2XZVUTz7pav2ppXK9fuZq4KOcifXHGs1mPM6s5j4tZjzNtmjKvvSSQBkvWSIkNKvMaxy1hzTV1OZ/jjjma8Y9NYNONNizZk5gBbrvtFvr168Ozzz0XfYe5+YhbLGEdr3G9fmIJq5GcF+Uu03FmNecVXnupiTNFGrGECZ1LSmxQmdekwBLWkmblVVbn9jtGcd55l9Cte8+SPYl99vmnDN5vd8466zSmT5vm5CNusYR1vMb1+oklrHZyLpawdOfche9O1/Pg2rkQS5ggCM7g+z4HDT2EZ56Zwk479inZkxjAqLtH0n/Azjw+cVLTToyVxJoFylnRxLOu1q9aGtfrV64mLsq5SF+c8WzW48xqzuNi1uNMm6bMay8JpMGSNVJig8q8JmWWsJY0yy27LHfeMYprr7qWXy3Wq8WexHw/j+d7fP/ddxx1zGEcddQwvvvu++h7LflH3GIJ63gW9/+aAAAgAElEQVSN6/UTS1iN5Lwod5mOM6s5r/DaS02cKdKIJUzoXFJig8q8JqWWsOYaz8+z+x57M+aRSfTvP3CBnsSKLWLGGEaPfpDtt9uM0Q8/SGjCxB9xiyWs4zWu108sYbWTc7GEpTvnLnx3up4H186FWMIEQXCaJZf4Fdde93du++ftLL/k4iUtYj/+9COnnHI8xx3ze7766ku70FgJxTdnmi9LoyaedbV+1dK4Xr9yNXFRzkX64oxnsx5nVnMeF7MeZ9o0ZV57SSANlqyREhtU5jUZsIS1pOm7885MmjCZwfvuW3KwSc/3ePKpJ+i3607cdtutBMbGaXDvMbhYwtqncb1+YgmrkZwX5S7TcWY15xVee6mJM0UasYQJnUtKbFCZ12TEEtaSplfvRbnwwkv5179GsfzyK85nESsebBJjmD5jGmee+UcOGrIPn37ysZOPwcUS1j6N6/UTS1jt5FwsYenOuQvfna7nwbVzIZYwQRBSx5ZbbcPkJ57l6MMPpQuFVgebBHjp5ZfYY49+/P3GERQKhUaNR5NmgXIaNPGsq/Wrlsb1+pWriYtyLtIXZzyb9TizmvO4mPU406Yp89pLAmmwZAoDwTwIC01/ka1nvmXV0iR5bNc07djGBI7GUIZmkS51nH36Wdx3992svcZqjYNN+r5H3vOiOzR5PN9nbkMDl192KbvttgtvvfUWgTEEoSEwhkIQzleOH0U7rwkdr18VNK7Xr12aCvPnRAy1mvOwRuLMas4rvPZSE2eKNOVsI5YwoUMwBkLPxwQBJijYKZ79K1pWLU2Sx3ZNU+42YVDABA1OxtCeONdb9zc8+MCjnHTSaeR9n3ovpN4rUO+FGBPg+bnGvzfefJ1+u+7I1ddcyew5cwmCEDwIgpAgDBvLLS1zUVMI3K5fNTSu1689mkrz50IMtZrz4txlOc6s5tyV707X8+DauUiK3PDhw5M7utBRHAqsHAYF5s6aA57BM+DV5fFzdfbzZQpNyzy/Oppq7TeNmjK3WaR7PWCY3WDci6Gdcea7dmHrbXegX7/+vP2fV5j63beYsGAbPdHL+PZpUoAxhtdee5Unn5jMuuuuw/LLLd/4tDmf86nz7b2UwJjGZTnPc07TrXsXAObNKzhZv2poXK9fezRdutUDC58/F2Ko1ZwXX3tZjjOrOa/02ktLnGnSlLNNne/Ts0fXSMWnwG10EvnOOpDQGXiQq4cwhFwIXh68XLQq37SsWpokj+2aptxt/DxeLgAvdC+GhYxz7XXW4977x3D7P2/i8quuYOasufhegB/MJYxezAcwJuTDDzSD99uTww45jJNPOYP6bt3IRS/6AeR8j9B45HyPfM4nNKax7IIm53sY3936VUPjev3aq6kkf67EUIs5L85dluPMcs5d+O50PQ+unQt56V4QhEyRz+UZNuwIxo+bzNZbbkkdDfOP3QL2BT4PTAj/+MeN9O/XhynPP+/uy4itLDMu169aGtfrV64mLsq5SF+c8WzW48xqzuNi1uNMm6bMay8JpMGSNVIy9kjmNRkdh6W9ca688krcc/d9XHrRpfTq0Z28Z8hFL+YXj9/i+3m++OoLhh0+lNNPP5Wffvppgf7fnezD3oALYwm41ld/ajQV5s+JGGo150W5y3ScWc15hddeauJMkUbGYRE6l5SMPZJ5TYbHYWnvNn5dV/YfMpSxj05k+x36EIRYW1jR+C1hWGgcu+Xe++5mu203Z/y4sc70T1/OWBCu1q+jNa7XrzPHgnAlhlrNuYzDku6cu/Dd6XoeXDsXYgkTBCHzLL3MMtx087/429/+wRKLL76gTcwAHmDgu++/ZdjhB3HcH45i6vdT7TrsOi+aNtJ8WWdq4llX61ctjev1K1cTF+VcpC/OeDbrcWY153Ex63GmTVPmtZcE0mDJGg7bg2pKI5awFjU+hr323Iunn5rCngN3xyck7xl7Z6fIIuZFvYqNHzeWAQN24sEH76ehEETflw49Tjfggq3BNdtAajQV5s+JGGo150W5y3ScWc15hddeauJMkUYsYULn4rg9qGY0YgkrqVliyaW4+poRjBhxM0sutSyFYH6LmDHxn+Hnn3/m1D+dyGEHD+bLLz+n1ONqsYRVX+N6/cQSVjs5F0tYunPuwnen63lw7VyIJUwQhJpkxx37MH7CEwwdekjJnsQw8MRTT9Cvbx9G3nE7YRg27cRYCaaVcjU18WwSx05S43r9ytXERTkX6Yszns16nFnNeVzMepxp05R57SWBNFiyRorsQZnWiCWsbE3vnj247JK/MGrkvay04vIlexKbNWcWF108nCH778MHH3yQ/ON0Ay7YGlyzDaRGU2H+nIihVnNelLtMx5nVnFd47aUmzhRpxBImdC4ptAdlUiOWsHZrNtt8S0aPnsCww4+y60r0JPbKqy+xU5+tueGGaygUGogfV4slrPoa1+snlrDayblYwtKdcxe+O13Pg2vnQixhgiAIQPfuXTnjjHN57LFJrLPW2iV7Eps7by4XX3w+g/YayLv//a9dh13nRdNGmi/rKE08m8Sxk9S4Xr9yNXFRzkX64oxnsx5nVnMeF7MeZ9o0ZV57SSANlqyRcntQZjRiCatIs/7v1mfC+MmccuIpdMl7JXsSe+fdt9l33z248srLmDlrVvSdKpYwsVCUoakwf07EUKs5L8pdpuPMas4rvPZSE2eKNGIJEzqXjNiDUq8RS1jFmvou3Tj+hFN48KGx/PZ365fsSSwIAm4YcS39dtmB1159GYNYwsRCUZ5GLGHpjVMsYenOuQvfna7nwbVzIZYwQRCEVlhjjTW5974xXDD8Inp1zZfsSezDjz5g8OBBXHThcGbOmNG0E2MlmFbKC6uJZzt6v65rXK9fuZq4KOcifXHGs1mPM6s5j4tZjzNtmjKvvSSQBkvWyKA9KJUasYR1qCbve/z+8MOZMG4SW26+ecmexPBg5Mjb2XXXPjzxxGSxhHWwxvX6iSWsRnJelLtMx5nVnFd47aUmzhRpxBImdC4ZtQelTiOWsKpoVvz1ytx66x1cfPEVLNKjd8mexL786gsOOGBvTjrpWH75+ScMYgkTC4VYwrIUp1jC0p1zF747Xc+Da+dCLGGCIAhl4nke+w0ewrPPvki/vv1K9iQGcO+9d7PLLjswcfz4xmUYK6H4ZlHzZeVo4tmO3q/rGtfrV64mLsq5SF+c8WzW48xqzuNi1uNMm6bMay8JpMGSNWrAHpQKjVjCqq5ZeqmluPWftzPi+hEs+atFS/YkNvXHHzjxpGM55rjf8/XX30Tfu2IJq3kLRYX5cyKGWs15Ue4yHWdWc17htZeaOFOkEUuY0LnUkD3IaY1YwjpF4/l5+vXfnUcemcTAPQaV7EkMY5gw/jF23H5LHrjvXkITdqgtxfXH/WKhaFkjlrD0ximWsHTn3IXvTtfz4Nq5EEuYIAhCBSy++GJcdeX13HXn3ay07FIlexL7ZfrPnPbnk/n94YfwxeefNe3EWAmmlXIry0x7t8mCxvX6lauJi3Iu0hdnPJv1OLOa87iY9TjTpinz2ksCabBkjRq1BzmnEUtYIpodttueCeMmcdABB5TsSczzPZ5/4Tn69duZm2/+Ow2FQvRdLJawmrJQVJg/J2Ko1ZwX5S7TcWY15xVee6mJM0UasYQJnUsN24Oc0oglLDFNj569OPfcC7jrrgdYaeVVS/YkNnPWDM4553QG7dmfjz78AINYwmrNQiGWsPTGKZawdOfche9O1/Pg2rkQS5ggCEIHs8mmmzP5iec5/rgT6eqFJXsSe+XVl9ltt5256e8jaGhosAuNlVB8Q6mFZaa4XM42WdC4Xr9yNXFRzkX64oxnsx5nVnMeF7MeZ9o0ZV57SSANlqwh9iA3NGIJc0LTrUs9Z591Dg8/NJp11lIlexJrCApcfc0V7LnHbrz++uvRd7NYwjJtoagwf07EUKs5L8pdpuPMas4rvPZSE2eKNGIJEzoXsQe5oRFLmFOa36y3PvfdN5qTT/4zvp8v2ZPYu+/9l90H7MJll13CnDlzyraluP64XywULWvEEpbeOMUSlu6cu/Dd6XoeXDsXYgkTBEGoMnV1eY497gQmP/4Um22wXsmexAITcOPfb2CP3Xfl5ZemNO3EWAlmvkVN5RbWL7AsCxrX61euJi7KuUhfnPFs1uPMas7jYtbjTJumzGsvCfLJHl7oWAwE8yAs2D/PBxPYVcXLqqVJ8tiuacrexsME0TLXYshozldfdRXuHXU/d9/xTy678koKwUwCfHzfI+95FHwPTB7P9/nsi88ZcsB+DD3gQM4881xyXboRhAbPMxSCkCA09s9EZWMa18ePzouXZUHjev3apakwf07EUKs5L8pdpuPMas4d+e50PQ+unYskLWHSYCmBUupQ4FZgG631c2VukwdmAF1akXyptV6hY2o4P8ZA6PkQBBAUgBzGtosxxcvqqqSp1n7TqClzmzAoQNCACQL3Yshwzn1jOHDIAeyww0786ZxzeOqZZ6j3QvJeAd8LmW0CfL8Lnp/D80Nu+9ctTJo4gQsuuYottt4aD6jL+wRBaL/kg7CxHIShPWq9vS6Ll2VB43r92qupJH+uxFCrOY9zl/U4s5pzF747Xc+Da+ciXpYE0mBpBaXUFsD1C7HpOtjGyv+AF1tY/2Ml9SqF54FvQkw+B6YO8jm86BmeV7ysWpokj+2apsxt/FwXjKnDyxv3YqiBnC+/0srcffcD3HffKC654Bxmz5qBHwbkPY8wDDBhDhMGeHh89fWXHHnkwey+xz6cdeY5dF3yV+RyPnkDuZwP0dTQVIYFl2VB43r92qOpNH8uxFCrOS/OXZbjzGrOXfnudD0Prp2LpJAGSwsopQYBtwE9FmLzDaLprVrrizusUmXhQa4ewhByIXh58HLRqnzTsmppkjy2a5pyt/HzeLkAvNC9GGok556fZ7/BB7HdVltxwfCzGDt+HIXA4HsBfjCXMHoxH8CYkEfGPMhzz0zmovMv5oCDh5D3PXK+Rz7nExpDaGw5fjkx53uNy7Kgcb1+7dWYCvLnSgy1mPPi3GU5ziznvJJrL01xpkVTzjby0r0jKKVWUErdDjwA5IBvF2I3cYPltQ6rmCAIVWfJpZbm+utHMOKGm1hqiSWpo6HVF/OnTp3KUccMY8iQIXz97bdNd50M9rF58V2o5suyoHG9fuVq4qKci/TFGc9mPc6s5jwuZj3OtGnKvPaSQBos83MRMBR4FdgceG8h9hE3WP7dUZVqFzImhxsaGYcltZp+/frx1BPPsO+gvfEJyXuGnA+eN//4Lb6fZ9y4cWy95Zbcc8/dNBSC6PvczT72ZdyCFjQGZByWlMZZlLtMx5nVnFd47aUmzhRpZByWdPEecAiwmdb6rfZurJTygPWBb4DdlVIvK6WmK6W+V0rdrZRSHVzfBZExOdzQyDgsqdYstsRSXHb5tdx0020ss+wKBCHWFlY0fksYFjDG8Msvv3DGmady0JBBfPbZJxg6v/981/rqT5NGxmFJb5wyDku6cy7jsLilcX0cFs+Y5FpLrqOUegrYjjJ7CVNKrQZ8GBVD4HngZ+xTlxWA6UA/rfXzHVzVp4DtTBBgjCFsmAPBXMh1IdelOwDB3FmNy/y6rlXRVGu/adS4Xj+Js3zN9FnzOPeCixkxYgT1eY8636chDJlbMHj5Lvj5PGGhQBjMo0eP3px1zrkce+zR9OhmOwqcPa9AQ8H2hNOlLsfchqCx3K0+nzqN6/WTcyFxSpxyLiTO6pyLbvV5fK+x0fI0sD2dhDxh6VhiO9iXwEZa62211rsDqwBXAj2Be5RSXatzeANh6zajxmXV0iR5bNc0rtdP4ixb06vnIlx37dU88/STrLXGao02sXxuQYvY7LlzOOfcc9ll5114++3/zm99MFAohPOVF7BHpEDjev3kXEicEqecC4mzOuciSUuY9BLWsTwA/BoItNZfxQu11gWl1GnYluhGwJ7AqI4+eENDwE8/z8U0zIFgHuTq8evthyucN6txmVfXtSqaau03jZpyt/lV7zrA8P0PM52LQXI+v2aNNdfnodETGXHdlfztxr9TCAJ8LyTnFShEFjHf9/GM4ZVXX2HjjTbipJNP5fdHH4+Xy1OX86nP55hXCGgIQupyPl3r7FfwnIZC4zLXNa7Xrz2aHr27gTFMnTqj5s9F2uLstWj3xtxlOc6s5rzSay8tcaZJU842XevyLL1UL5JAnrB0IFpro7X+vLixUrQuBB6Liht1bs0EQegIunbpwimnnMro0Y/x29/8ZsGexAzgAQYaCg1cfvml7Ll7f9568w27DrvOi6aNNF/musb1+pWriYtyLtIXZzyb9TizmvO4mPU406Yp89pLAmmwdC7fRNPuVTuC9BjlhkZ6Ccu0Zt111+HRMeO44Lxz6Vqfa9Ei5nk+nuejP3iP/fcfxKWXXsT0GTOj73w3e4mpuV51TGR1kHORvjiLcpfpOLOa8wqvvdTEmSKN9BJWQyiljlNK3aOU2qkVySrR9IuqVUJ6jHJDI72EZV5T17U7J5/yR5566hk23HATCkEIRb2IGRP/GcIg5OZ//I1dd96Ol16cgsHNXmJqsVcdjJyLtMYZ5y7rcWY155Vce2mKMy0a13sJkwZLx7IqsB+2a+T5iF603zcqTuzMSgmCUD3WXHM17r7nQS656P/o3a2+1cEmMfDxpx9xwAH7MPzcs5k+bVrTToyVYFopu6ZxvX7lauKinIv0xRnPZj3OrOY8LmY9zrRpyrz2kkAaLAuJUurXSqm1lFJLFC2+BQiAA5VSexdp64DrgZWAcVrr16pWMbEHuaERS1jNaMAj58FhhxzMpAmPs902W5ccbNLzPe659y769uvDxInjnbIE1JyFwoBYwlIaZ1HuMh1nVnNe4bWXmjhTpBFLWHa5HXgX+EO8QGv9DnBKVLw/GjjyfuAj4AjswJSHVrVWYg9yQyOWsJrRUFRebvkVuemmW7nssmvp1XsxWhtsEmP45uuvOPjg/TnuuCP4cepUDMlbAmrRQiGWsPTGKZawdOdcLGFuacQSVmNora8DdgYmAGsAA4BZwMXAJlrr7xKsniAIVcbzPPYatA9PP/MSewzco2RPYgAPPfQAfftuz9hHx2CKbGQeTZoWlyWpcb1+5WriopyL9MUZz2Y9zqzmPC5mPc60acq89pJAxmEpgdZ6+4VcNxmYXIUqtU2RTYXYygLzL6uWJslju6Ype5schiLbkUsxSM7bl79m2yy1xK+46cZbGPfIQ1x0wVl88913BDmfsMgi5nn2ntFPP//EqaeexGOPjuaiCy9lyaWXAeZ/BG+iGhhafnTfWZokj93hGgOlbCk1dS7SFmdR7jIdZ0Ia16+91MSZIk0524glTOg4xB7khkYsYTWjoY1t+u7an0cemcigfQaX7EkMY3h88kT67LAVo+4aSWCC1NoG0qQRS1h64xRLWLpzLpYwtzRiCRMEQahxFl20N//3l6u4d9T9rLr80iV7Eps+cxpnnX0aww45iE8+/qhpJ8ZKMK2UO1OT5LE7UhMX5VykL854NutxZjXncTHrcaZNU+a1lwTSYMka0mOUGxrpJaxmNLRjm2222opxjz3OsEMPpc6jZE9iL738Irvttgs3jLieeQ0N0f8V6ehJJjUaA9JLWErjLMpdpuPMas4rvPZSE2eKNNJLmNC5iD3IDY1YwmpGQzu3WaRHD04//WzuufchVlt9zZI9ic2eM5sLLziHPQb25f333sWQDttAmjRiCUtvnGIJS3fOxRLmlkYsYYIgCMICbLDhJkx6/FlOOelUuvlhyZ7EXv/Pvxk4sC9/ve4a5s6daxcaK6H4hlfzZdXSJHnsjtTERTkX6Yszns16nFnNeVzMepxp05R57SWBNFiyhtiD3NCIJaxmNFSw3y51ef582umMGT2W3/5mXfKesXe5iixinufjeT6BCblhxHXsuUd/Xnn1lej/DjdtA6nRGBBLWErjLMpdpuPMas4rvPZSE2eKNGIJEzoXsQe5oRFLWM1o6ID9rr3Obxg16kH+9KezyefrS/Ykpt9/jz332JWLLh7O7FmznbQNpEkjlrD0ximWsHTnXCxhbmnEEiYIgiC0ST6f48ijjuGJyU+x1SYblOxJzBjDrbfczMCBuzDl+ReadmKsBNNKuaM01dpvZ2viopyL9MUZz2Y9zqzmPC5mPc60acq89pJABo7MFAaCeRAW7J/nEw94N9+yammSPLZrmrK38TBBtMy1GCTn7ctfB+13lZVW4u6R93DvXf/iL5dfztRp0wnw8X2PvOdR8D0weTzf58uvv+Kggwez/777cc4551PXbRGC0OB5hkIQEhjTWI4f5RcvWxhNtfabiCa0ywIj5yJ1cRblLtNxZjXnFV57qYkzRZpythFLmNAhGAOh52OCABMU7BTP/hUtq5YmyWO7pil3mzAoYIIGJ2OQnLcvfx25Xy8MGbzvfowfO54dd9wJTEi9F1LvFaj3QowJ8Pxc49/Iu25nu223YOKESQRhSBCE4EEQhPOVW1q2MJpq7TcJTSGQc5HWOItzl+U4s5rzSq+9tMSZJk052yRFbvjw4ckdXegoDgVWDoMCc2fNAc/gGfDq8vi5Ovv5MoWmZZ5fHU219ptGTZnbLNK9HjDMbjDuxSA5b1/+qnDsHostxqB9hrDaqqvy6ovP0TB3NiYs2EZO9DK+fcITMHPWTMZNGMunn3zCZptuTo9FFml8gp/P+dT59v5UEFnM8jmfnOe1W7Mw27iq6dKtHoB58wo1fy7SFme37l0Am7ssx5nVnFd67aUlzjRpytmmzvfp2aNrpOJT4DY6CXnCkik8yNWDl4dczk6jl3rnW1YtTZLHdk1T7jZ+Hi+XdzMGyXn78lelY3t+nkF7789j45+k3679CEL76J4wwA/mQhgUvZgfMu6xR+jXdztGP3g/vmdfksxFL08Wz+d8z/4n1Wx5W5qF2cZlTV7ORWrjzHfwsVyNM6s5r+TaS1OcadGUs43vJ/eIRRosgiAIKWDJJZbgqquu46Yb/8mySy9DHQ2tvpj/088/c/yJR/P7ww/mm6+/lpdUW9PERTkX6Yszns16nFnNeVzMepxp05R57SWBNFiyhozJ4YZGxmGpGQ2dfOydd96ZJyc/zYH7D8EnJO8Zcj543vzjt/h+nieffpKBA/py5523UwgCGbegucaAjMOS0jiLcpfpOLOa8wqvvdTEmSKNjMMidC4yJocbGhmHpWY0JHDs3osvwUUXX86tt45kueVXJAjBmBCKxm8JwwLGGGbMmM65553BPoMG8PHHH3ZKX/1p0sg4LOmNU8ZhSXfOZRwWtzTlbCOWMEEQBKHdbLHFFowb/yRHHX0cOc9b0CZmAA8w8OJLU+iz4zb8/cYRBEHBrsOu86JpI82XtVVOqyYuyrlIX5zxbNbjzGrO42LW40ybpsxrLwmkwZI1xB7khkYsYTWjIcFje3gs0q0L5593AY8+Mo6111yj0SaWz81vEfM8n7nz5nH5ZZew336DeOedd8RCYUAsYSmNsyh3mY4zqzmv8NpLTZwp0oglTOhcxB7khkYsYTWjIcFjF2s23GgzHh4zkWOPPQHPz0c9iTVZxJp6EjO88/Zb7LnHrlx15aXMmzcXQ+1aKMQSlt44xRKW7pyLJcwtjVjCBEEQhE6hvq6OE044iUfGjGfD9X9XsiexQhBwzTVXsvuAvrz+n9fs8hgDXqwtp5xWTVysZD9Jx9BZGtfqF89mPc6s5jwuZj3OtGnKvPaSQBosWUPsQW5oxBJWMxoSPHZrmrXWUox5+DHOPuNsunetK9mT2IcffciBQ/bjwovOY/r0GbVloTAglrCUxlmUu0zHmdWcV3jtpSbOFGnEEiZ0LmIPckMjlrCa0ZDgsUtpcvVdOfyIoxk9ehybbLp5yZ7ETBhy663/oM+OW/HsM0/VlIVCLGHpjVMsYenOuVjC3NKIJUwQBEFIjJVWWpmRd93PFVdcS68ePUv2JPbZ558yeP+9OO20U5j+yzS7DrvOi6YtltOqiYuV7CfpGDpL41r94tmsx5nVnMfFrMeZNk2Z114SSIMla4g9yA2NWMJqRkOCxy5X42M46MCDePbpF9ilz06U6knM83weeOBe+g/YmQkTxmfbQmFALGEpjbMod5mOM6s5r/DaS02cKdKIJUzoXMQe5IZGLGE1oyHBY7dXs8xyK/D3G2/lqquupfeii5fsSeyH777jmGMP55ijh/H9d99hSN6y4JotxZUYqq1xtX5iCUt3zsUS5pZGLGGCIAiCM3ieR//+A5kw8WkG7TWoZE9iGBg7dgy79t2e0Q89iCla78W6mObL0qCJi5XsJ+kYOkvjWv3i2azHmdWcx8Wsx5k2TZnXXhJIgyVriD3IDY1YwmpGQ4LHrkSzxOKLcsN1N3DLTf9k2WWWKtmT2C/Tp3H6Gacy7LChfPrpZ9H/W27aGsQS1vEaJ+tXlLtMx5nVnFd47aUmzhRpxBImdC5iD3JDI5awmtGQ4LE7QrNjn114ZMwEBg8ZWrInMYzh6WeepM8OW3HHv24lMIGTtgaxhHW8xtX6iSUs3TkXS5hbGrGECYIgCE7Tq1dPLrrwLzz00KOsuvLKJXsSmzVnJsPPP5uhBwzm4//9r2knxkowrZRd1MTFSvaTdAydpXGtfvFs1uPMas7jYtbjTJumzGsvCaTBkjXEHuSGRixhNaMhwWN3tGaLzbdg8uPPcvQRR5H3abEnMd/P4/ke/379NXYbsAvXXnc1c+fNi/4vS97WIJawjtc4Wb+i3GU6zqzmvMJrLzVxpkgjljChcxF7kBsasYTVjIYEj10NTfdFevDnM87hnnseYPU11AI9iRVbxObMncOll5zPgP478+5/38KQvK1BLGEdr3G1fmIJS3fOxRLmlkYsYYIgCELqWG+93zF6zHhOO/V0uudMqxYxgLfefoM99ujPNa3xSqAAACAASURBVFddwZw5c+xCYyUU35BrvixpTVysZD9Jx9BZGtfqF89mPc6s5jwuZj3OtGnKvPaSQBosWUPsQW5oxBJWMxoSPHa1NV3q8px84omMfXQ8G6z/25KDTYYYbrxpBAMH9OWFF6dE/7e5aX0QS1gG4izKXabjzGrOK7z2UhNnijRiCRM6F7EHuaERS1jNaEjw2J2lWWPNtbhr5P2ceeZ51Nd3bXWwSYzhfx99yD6DduO84Wcxa+ZMJ60PYgnLRpxiCUt3zsUS5pZGLGGCIAhC6snlfA4bdiRPPvEM222xacnBJgHuvP02dtttZ5556ummnRgriTULlDtbExcr2U/SMXSWxrX6xbNZjzOrOY+LWY8zbZoyr70kkAZL1hB7kBsasYTVjIYEj52E5tcrLM/IO+7i0osvZdGePVsdbNLzPb7+5msOO/xAjj/+GH788cfo/7vkrQ9iCctAnEW5y3ScWc15hddeauJMkUYsYULnIvYgNzRiCasZDQkeO6mc4+XYe+99mTjhSXbepX/JwSaNMdx3391su81mjB83FkPy1gexhGUjTrGEpTvnYglzSyOWMEEQBCGTLLn0Mtx660j+cdM/WWbx3iV7Evth6vccd9yRHH/C0Xz37bd2obESim/aNV9WTU1crGQ/ScfQWRrX6hfPZj3OrOY8LmY9zrRpyrz2kiCf7OGFjsVAMA/Cgv3zfDCBXVW8rFqaJI/tmqbsbTxMEC1zLQbJefvy52T9qp9zj5CBA3Zni4035LJLzuPB0aMJcj7G98h7HgXfw3j23pjn+0x+/HFefmEKZ515NnvsvS9BaPA802g1CIxpXFYIwvnKHa4J7bLALNx+ql4/RzRO1q8od5mOM6s5r/DaS02cKdKUs41YwoQOwRgIPR8TBJigYKd49q9oWbU0SR7bNU2524RBARM0OBmD5Lx9+XOxfp2Z88V69eLSi//CP268hWWXXo56L6TeK1DvhWBCDAbPz+H5OabNmM4fTz2BYYcezKeffUYQhPYWngdBEBKEYeOy5uWO1hSCyvZT7fq5onGxfsW5y3KcWc15pddeWuJMk6acbZIiN3z48OSOLnQUhwIrh0GBubPmgGfwDHh1efxcnf18mULTMs+vjqZa+02jpsxtFuleDxhmNxj3YpCcty9/DtYviZyvtPoaHHDAwcyY9iPvvPUGJiwQGvv+i+fbMVtMUMAzhs+/+JzRDz/IIt26scH66+N7PkHU61g+55PzvEYXQj7nU+fbe2wdpenSrR6AefMKC7WfatfPFY2L9evWvQtgc5flOLOa80qvvbTEmSZNOdvU+T49e3SNVHwK3EYnIU9YMoUXvRCbh1zOTqOXaOdbVi1Nksd2TVPuNn4eL5d3MwbJefvy52L9Esp5j96LMXz4Jdxx+12s+OtfE4REL+YH+MFcCIPoxfyQmTOmc+FF57LXHv3434fvk4te9Mz5nv1PtKjsN5vvCE2+gv10Rv1c0Lhav3wHH8vVOLOa80quvTTFmRZNOdv4fnKPWKTBIgiCIFSFjTfehLGPPcHxx59M3vepo6HVF/Nffe0V+vTZhhtuuI5CoSE9L/5Ws34uaVyrXzyb9TizmvO4mPU406Yp89pLAmmwZA0Zk8MNjYzDUjMaEjx2GnLevWs9Z515NuMem8hv1l4bn5C8Z2wXmr41SXu+h+f5NBQKXHXVZey795689dabzo8F4fq4Cpkeq6Iod5mOM6s5r/DaS02cKdLIOCxC5yJjcrihkXFYakZDgsdOU85/+7uNePDhcZx00qn4uToKQUjx2C3GxH+G9957h0F77cZfLr2AuXPmYDBVG5NAxmFJb5wyDku6cy7jsLilKWcbsYQJgiAImacun+foo4/lsbGT2HTjjRe0iAEYwIMgDBkx4jp2678Tr776kl0eY8CLtS2Vy9XExUr2s7DHTpvGtfrFs1mPM6s5j4tZjzNtmjKvvSSQBkvWSJFVJNMasYTVjIYEj53WnK+++mo89MBozj93OIt060LeM+R88Lz5bWK+n+eTzz5h6EFDOPfcM5k2bZpTthTXLR6Zts0U5S7TcWY15xVee6mJM0UasYQJnUsKrSKZ1IglrGY0JHjsNOfcr+vKwYccwaOPTmCLLbcu6kmsySZmexIzYAx3jvwXO2y/JU8+MckZW4rrFo+s22bEEpbunIslzC2NWMIEQRAEoRWWX34F/nX7KK69dgSL9updsiexL7/6ggMP2o+TTz6en3/82a7DrvOiaSPNl7WkiYuV7Gdhj502jWv1i2ezHmdWcx4Xsx5n2jRlXntJIA2WrJFyq0hmNGIJqxkNCR47Kzn3MQzebzDPPD2F/n37UaonMc/zGTPmIXYbuDNjH3uEhkIQ/T8qlrCass0U5S7TcWY15xVee6mJM0UasYQJnUsGrCKZ0IglrGY0JHjsrOV86WWW5YYRN3PttSNYbPElS/Yk9uMPP3D88Ufz+yMO5rtvv8EglrBas82IJSzdORdLmFsasYQJgiAIQjvo23dXJkx8ksGD9y/ZkxgGJk4cR99dduD+e++x77vEGCvBtFJuWlxa09Z+FmabNGpcq188m/U4s5rzuJj1ONOmKfPaSwJpsGSNjFlFUqsRS1jNaEjw2FnO+eKL9uaaK67m9lvvZMXllivZk9j0mdM559wzOHjoED7++GOxhHWwxsn6FeUu03FmNecVXnupiTNFGrGECZ1LRq0iqdOIJaxmNCR47FrI+Tbbbs/oMeM56ODDCI1Xsiex5194lh132JKbbx5BGAYYxBKWZduMWMLSnXOxhLmlEUuYIAiCIFRAjx7dOe/cixg9ehxrrLZayZ7EZs+ZzXnnncV+++7Bh+9/YNdh13nRtBhTvKwlTfNlbZWzqnGtfvFs1uPMas7jYtbjTJumzGsvCaTBkjVqwCqSCo1YwmpGQ4LHrrWcb7rJJjw+6Rn+cMwfqPO9kj2J/eeN/zBo0G5cf/01zJozJ/q/VixhmYmzKHeZjjOrOa/w2ktNnCnSiCUsxSilDlVKGaXU1u3cbjml1I1KqY+UUrOVUlopdY5Sqku16tpIDVlFnNaIJaxmNCR47FrMeddu3fnjqadz3/0Ps9ba65bsSayhoYGrr7mcgf124q03XscglrAsxSmWsHTnXCxhbmlct4TlEzuy4yiltgCuX4jtVgCmACsArwP/BrYCLgB2VErtorVu6Mi6CoLQ+byv3+WN155n5vRfwOvCltv1YY1VV0m6WjXDOuusy4MPjeWmf/yT6675P0zQgEdIaAzG/g8LHmDgXf0Og/YeyGHDjuaUk/9I1949G/djGv+xU6+43NKytspZ1ThUv/ffe5dX//0KP8+YRp56dthuW9Raa2cuzsQ11Tx2XMx6nGnTlLNNQkiDpQWUUoOA24AeC7H5CGxj5Ryt9UXR/hYBHgZ2Ak4AruyYmrZAkU2F2GYB8y+rlibJY7umKXubHIYiS4xLMUjOW9S8MOUFrvvrdbz48ovkfY+c5xEYQ+HCc9l808054Q8nsOXW2zodQ1ZyXp/P8Ydjj6H/Ln04+4xTefm1Vyj4ENCsJzGTxwC33vYPHp80jssvu4Itt9i6TVsKMN+yciwUbW2TRo0r9XtxyhRu+OvVvPTi83i5HJ6fx4QFzh8+j80335LjT/wTm26+ZerjdEFT9WNXeO2lJs4UacrZRixhjqCUWkEpdTvwAJADvm3n9goYAPwPuCRerrWeCRwOBMDxHVbhlqhhq4hTGrGEZVJzzz33MGzYUF56+SXmFSAIAQxBCPMK8NLLLzFs2FDuvfdeZ2PIYs5XWXV17rhzFOeeezFdu/Uo2ZPYp599wj57D+S0005m+rRfEEtYOuK8755RDBt2EC+9PGW+fBpjMCZkypTnOPjgwTxw/72pjtMVjVjCak/juiVMGizzcxEwFHgV2Bx4r53b9wU84BGtdVi8Qmv9GdYetpJSap0OqKsgCJ3Is88+xZlnnWZ/DJfAmJAzzvwTzz77VOdUTADA932GHnwoTz/9An122LFkT2IAd955G5tuuikTJk1qXIaxEopvIjZf1lY5q5oEj/3sM09z5lmnEppwPk1xPsHe/T3nnNN54bnnUxmnc5pqHjsuZj3OtGnK2SYhpMEyP+8BhwCbaa3fWojt142mb5fYP8B6C7Hv8qjR3oOc00gvYZnTXH3VZY35yvlQn4ecT4tlj5Brrr7cuRhqIecrLL88I++8h6uvuJrFF+1Zsiexr7/5hgMPOICTTjqO7777Ifr/WHoYci3Oq66+vHFZPFhoS/n0fI/QGP56wzWpjNMljfQSVnsa6SUsRWit/6K1vr3505F2sGw0/bqV9fHypRdy/20jVhE3NGIJy5RGv/8+L0x5YQELWKny8y88j37/fWdiqKWce36ePffal0cemUS//gNL9iSGMYx55GF23H5zxox+iNCEYidxKM4P3n+PKVOem88C1pIlrDifL770Ah99oFMVp2sasYTVnsZ1S5i8dN+xLBJNZ7WyfnY0XZiX+dukri7Pkkv0JGyog2Au5LqQ69IdgGBurnGZX9e1Kppq7TeNmvK3mQPAEkv0dC4GyXmT5t4HX1qoa/L1119ii01+40QMtZjzJRZbmbvuGskB48Zz0gkn8OMP3+PRck9iP/78Iyef/AfGT3qMa666khWXWwmA2fMKNBRC6vI+XepyzG0IWi13q8+3uU0aNUke++H7o2uvKFctlpste/31l9l4841SE6drms44tsH+35f1ONOiKWebeFkSyBOWjiV+MtPaMzOv2bSDMRC2bjNqXFYtTZLHdk3jev0kznZppk/7hZzfugWspXLOh+nTfnEmhlrO+e4D+vPqyy9y8EFDyHuGnA+xhQjmtxlNmjiJzTbdjBtvvIlCIWiyrhgoFMKS5QXsLhnRJHnsX6ZNazVXcbnYEhYv+3n6tFTF6ZrG9fpJnMmciyQtYfKEpWOZEU27tbK+azSdWY2DNzQE/PTzXEzDHAjmQa4ev95+uMJ5sxqXeXVdq6Kp1n7TqCl3m1/1rgMM3/8w07kYJOdFcfpdCEIIQsjlmixgXr71chCC53fh+x9mOhFDzec8qOPssy9khz678cfTT+PTzz+3NjGvQCHqScz3fTxjmDZ9Gsceewwj7xzJxX+5muV/vRJ1OZ/6fI55hYCGIGyx3LXO/pc6p6GQKU2Sx8759cR2L880WcLiXMWWMGA+Td6rZ+rU6amJ0zVNtY/do3c3MIapU2dkOs40acrZpmtdnqWX6kUSyBOWjuWraLpMK+vbesdFEAQH2Wab7Tp1O6F6bLX1tjz51BSOOvJo6r1CyZ7Enn/hOf6fvfOOk5pa//CTZHZZimIB2/UqNmL3WukC0os0EQteC3bFBoqKBcSODRVFsXdFRJr0KoK911iu9WdHBKTs7iTn90dmdmeBHWZ3ZzZnkve5n7mzOfM9ec87L4mbzTfndO/WgUceHU88HvcblS9JtSZV2A6rJqDYZcfQBrXZnCWsTZsj8ypPLTW5jJ3cDHue+abJpE9AyAVLdknODlbZtMX7JN6rMwNZBij/L4pevPyVsEdUaMuVJsjYummq0Ee5muYgNS972U2b0rJFS2ImxEyDWMIClm67VctW2E2bapOD1Lx8u37dIkaNvIH5s2ewn707BYmZxEzTIGYkHzT1bUbFpaXccftojj22D5988imuUrie8hcLdb0K20kLRdg0Qcbeq+netGjRGsM0MUwLwzQTlrDy7XJLmN/WvHlLdt/Lzqs8ddPkPLYXkTzzSJNJH5klLDzMSrz3sm27wndr2/YuwMHA947jfJaL4EqBZ5go10W5cf8dw3+ltOVKE2Rs3TSZ9vHcOMot1TIHqXlFzSWXXErMsrAMMA2DeNx/39R2zLK4+OKh2uUgNa+oOfSQQ5gzaxbnn3M+JiaFhuffdTE8UB4Klfil2OLjTz6he9cOjLn7LtatL8Z1PTDAdT1czyvb3lRbGDRBxr7k4suwYrGyWnjKK/vZMC1U4n+GaWFZBZxz7sV5madumlzHjrvRyDOfNJn0CQq5YKkmtm3vYtv23rZtN0q2OY7zLf5Fiw2MStHWBx4GLOCOXI3JMMBUHkbMwjAL/Pfkf5pT2nKlCTK2bppM+5hWDMPSMwepeUVNmzZtuW7UzXiYeEoRi4GnFHGPCtseJqOuv4U2bdpql4PUvKLGtGIU1avHRUOHMWvmbA7cdx9ML47plWIZCgMD5bkoz0N5Lh4e94+9ixOO68vHH70PCizLxDJNLMv0LRObaAuDJsjYrdscyfXX3YyBQnkupmFWqIuR/J9SjBxxPa1at8zLPHXT5Dp2zIpGnvmkyaRPUMgFS/V5EvgcGLxB+/nAr8BVtm1/bNv2ROAroBMwExiXuyEZYBWC/+Sv/55Yt6BCW640QcbWTZNpHzOGYcX0zEFqvpHm2OMG8uBDT3Lo4c0oiUPcw7+Vnnjo/tDDm/HgQ0/Sf8CJ2uYgNU/RpBx/++z/H158aSpDhwwjVlCI6+E/zO25mG4xeC7++h8eX335BQMG9Ob6UddQvG4dpmlgJdYsSP3ZMg3/l7IN2vNNo8P4jjv+eB556AmaHX5EWR2Um3hXHi2at+CJx5/lmP798zpPXTS1ETsWkTzzRZNJH9MM7hZLLLDIIcVxnP/Ztn0E/h2WbsCewP+Ae4AxjuPEgxyfIAg1o2XLlrRs046v/vctH767lDWrV4JRh5ZtO7DX7rv5z0sIeUnMinHGGWfSqUs3hlw+jNdfX0YBpRQoEwN//RYUYPiPyTz00DjmzJnLjTffTutWrcp3pNNDtNnSaDC+5i1a0LrVS3z3zZe8897b/P3PKmIU0r7tkdh778P6En/Gt3zPUxtNLmMnN8OeZ75pMukTEHLBkgbHcdpV87MfgdNyMKTN45avSUByTQKo2JYrTZCxddNk3MfCN55omIPUPK2m6Z570PKI/VFunOUr1oEZ8x/q1mR8UvNMNEb58ZeiadKkCS9NnMLTTz/BbTdfR/G6NcQMhWuZeKbh9zQNTBXjp59/5NTTBjKg/wCuvfpattyyYeK/6wYKiLtehe3kQ6v5pNFtfLvvZXNos0NxPcWKv/6hTszy14sIWZ6hrrnCX9sj7HnmkSaTPvLQvZA9rEIUJv4jiCZJ60OFtlxpgoytm6YKfdA1B6l51eqn6fik5uk1ldXPtAo4+ZQzmD17AUce2Q7X8/+DjudhqTgk1m5RSoFSPP/CM7Q9sjlzZs/EMPy9GgbELLPCdtJmkU8aXceHikaeYa15sn5hzzNfNJn0CdISJhcsgiAIglAJO+z4Lx588BHG3DWWbbfemgJKK1275bfff+XU005k8OBzWP7H8uSfJkH5ElL/OLlhm+4a3caX/DHseYa15snNsOeZb5oMj70gkAuWsJGwNfhzpiSsD8qt2JYrTZCxddNUoQ+65iA1r1r9NB2f1Dy9JpP6mYZJ7969WbTgNXr3PBoTj1hi7RYjxSKWXA9k5szp9Dy6Ey9PnkRp3E38d77cUpGJfUknjZbjU5C0FIU6z7DWPKV+oc4zjzSZ9BFLmJA9xCqih0YsYZHREGBsqXnuLGGb2k+j7XdgzN3jGDt2PI0a77CRRSw5Y5VSir9XrGDo0AsYdMrx/PzzTyj0tIHks21GLGH5XXOxhOmlEUuYIAiCIISIjh07MnvOQk466b8bW8QAFGD47/MXzqdr56N47pmn8TyvfCfKl6Aq2dZNo9v4kj+GPc+w1jy5GfY8802T4bEXBHLBEjbEKqKHRixhkdEQYGypee1Ywja1n4ZbNOC2m0fz7FPPs+vOOxEzFJYJhlHRJmaaMdauX8uo66/lxBOO5euvv9LKBpK3thkFYgnL45qn1C/UeeaRRixhQu0iVhE9NGIJi4yGAGNLzWvXErap76JFy9ZMmTqHUwed5X+mKtrEUmcSe+vtN+jYoQ33338PrhtHEbwNJJ9tM2IJy++aiyVML41YwgRBEAQhxNSrV8RVw0cwffps9rHttDOJrS9ezw03jKBf35588dnn/mf4nxmJ9zI2bAtSo9v4kj+GPc+w1jy5GfY8802T4bEXBHLBEjbEKqKHRixhkdEQYGypeXCWsE1tH3LwIcyZvZBLLryEOjEj7Uxin372Ccce24s77xzNmrVrE78L5K9VRCxh4dKIJSx6GrGECbWLWEX00IglLDIaAowtNQ/eErZhn8I6dbnwoqG8NGk6Bxx4UNqZxOLxOGPvu5vuXY7ivXffRpG/VhGxhIVLI5aw6GnEEiYIgiAIEaNpU5sXJ05j5LXXs0UdK+1MYl998yUDBvTlphuvY80/a8p3onwJqpLt2tQEGbuSNhWFPMNa8+Rm2PPMN02Gx14QyAVL2BCriB4asYRFRkOAsaXmelnCNuwTMw3OPvMMZs+cS4tmR6SdSQwDnnrqCbp378iiRQvzzioilrBwacQSFj2NWMKE2kWsInpoxBIWGQ0Bxpaa62cJ21SfXXbdjccff4brrx9Nvfpbpp1J7MeffuD44/syZMhgVq38G0V+WEXEEhYujVjCoqcRS5ggCIIgRBzDMDj+hIEsWfIGXTt3STuTGMDzzz9D587tmTt7dlkbypeQ+kfODdtypQkydiVtKgp5hrXmyc2w55lvmgyPvSCQC5awIVYRPTRiCYuMhgBjS831toRtSrPD9tvz+GNPc98999F4263SziT25/I/uPCiczlv8Fn88stvid8X9LSKiCUsXBqxhEVPI5YwoXYRq4geGrGERUZDgLGl5vlhCdtQY5gxuvfozdSpc+lxdJ+0M4mhFLNmvkKH9i2YNPFFPOVpaRURS1i4NGIJi55GLGGCIAiCIGzEtttuzZi77uPpJ59hlx0ap51J7O9Vf3PZsIs5+8zT+OmnH8t3onwJqpLtbGlytd/qapI/hj3PIDW5jJ3cDHue+abJ8NgLArlgCRtiFdFDI5awyGgIMLbUPP8sYZvSdGh/FHNmzePE409IO5OYYRosee1VunXtyCOPjCfuuonfIYK3ioglLFwasYRFTyOWMKF2EauIHhqxhEVGQ4Cxpeb5aQnblKbBFlsycuT1PPPMRHbZdbe0M4mtWfsPV101jH59uvPtN1+jCN4qIpawcGnEEhY9jVjCBEEQBEHIiCOatWD+gqWcf+4F1DHctDOJvfX2m3Tv3pHxD95PaWmp36h8Cal/CN2wrTqaXO23uprkj2HPM0hNLmMnN8OeZ75pMjz2gkAuWMKGWEX00IglLDIaAowtNQ+HJWxDTb2iOlx7zQgmT5rC3k33SjuTWKkb5667bqdf36P58MMPE79TRMA2o0AsYXlslUqpX6jzzCONWMKE2kWsInpoxBIWGQ0Bxpaah8cStinNAQcezMSJU7noosswzVjamcQ+/exjenbvyG233Uxx8fpI2GbEEpbfVimxhOmlEUuYIAiCIAjVorCwgMEXXMy8uQs54j/7p51JzFUuD4wbS+9e3Xj7rTfLd6J8CaqS7Uw01emTS03yx2zH0i3PIDW5jJ3cDHue+abJ8NgLgliw4YXsosAtAS/uvwwTlOt/lNqWK02QsXXTZNzHQLmJNt1ykJpXrX5ajk9qnl6jala/Wsxhrz1258UXXuLZJx/mtjvvJO6uxcXENA1ihkHcNEDFMEyT73/8geNP6M/JA09i+PBrMQuLcD2FYSjiroerVNl20uKR2rahpjp9cq7x/G1XZS+WlnkGpMl57JT6hTrPPNJk0kcsYUJWUAo8w0S5LsqN++8Y/iulLVeaIGPrpsm0j+fGUW6pljlIzatWPx3HJzVPr6lp/Wo7B1MpTjrxJF6ZOpNWrY4E5VFoeBQacQoND6VcDNMqez32+MO0b9eKxYsW43oeruuBAa7rVdjeVNvmtnXQxN3sx9Ixz6A0uY6dWr8w55lPmkz6BIU1cuTI4KIL2eJUoInnxileux4MhaHAKIhhWgX+vy8VL28zzNxocrXffNRk2Kd+vUJAsa5U6ZeD1Lxq9dNwfFLz9Jp6RVbN6hdQDg233ZZjjzuJf++8M2+//hpuaTHKi/sXOYb/ML5/58hl9epVTH9lKr/8/AvNjjiC+vXqlTk7YpZJgen/3dJNWMxilomVeBi6su1M+uRaU7deHQBKSuJZi6VjnkFpch27Tt1CwK9fmPPMJ00mfQpMky0aFCVUfA88Ti0hd1hChQFWIRgxsCz/PfHwZoW2XGmCjK2bJtM+ZgzDiumZg9S8avXTcXxS8/SamtYvwBwMM8bxJ5zMnNkLOKp9B1zPt3TguZhuMXhuyoP5HlMmT6Rrl/bMnjEd0/AfnrUSD9Wm/myZhv/LS5rtTPrUhiaW5Vi65hmEpjZix2oxltQ8O9+FPHQvCIIgCEKVabz9DowdO477xj5A420bUUBppQ/mL1/+J2eefSrnnHsmf/7+R34/mJz8MduxdMszSE0uYyc3w55nvmkyPPaCQC5Ywoasz6CHRtZhiYyGAGNLzWuuqVH9NMnBNEy6d+/BogWv0r9vP0w8YobCMsEwKq7fYpox5s2dRc+enZk4cQKu6+XnWhUKZB2WPF6fJKV+oc4zjzSyDotQu8j6DHpoZB2WyGgIMLbUvOaaGtVPkxySbds03p7bbr+HBx98lO13+BeuB0p5pK7f4nlxlFKsXPk3wy6/hBOO78ePP36fl2tVyDos+b0+iazDopdG1mERBEEQBKHWaNu2HbNmL+C0QWdgKLWxTUwBBqDg1SWLaN+uJY89+jCe5/qf4X9WZatIbWqSP2Y7lm55BqnJZezkZtjzzDdNhsdeEMgFS9gQq4geGrGERUZDgLGl5mIJq0yzRf163HzjrUyZPJ09d2tC0iYWsypaxAzDZN369dxw40gGnjiAr776Kj9sMwqxhOVQI5aw6GnEEibULmIV0UMjlrDIaAgwttRcLGGb0zRr3pppr8znjDPOAazETGLlFrHymcQUH7z/Hkf37MTYe+8kHi9FEbxNxA40cAAAIABJREFURSxhwWjEEhY9jVjCBEEQBEEIjKI6dbj00mFMnjydA/bbL+1MYiWlpYwefRN9enXn048/9tsTn+fEglJdTfLHbMfSLc8gNbmMndwMe575psnw2AsCuWAJG2IV0UMjlrDIaAgwttRcLGFV0ey///68Mm0Ww4YOo06BmXYmsS++/JwBx/Xh1ltv4p81a9mcVUQsYeHSiCUsehqxhAm1i1hF9NCIJSwyGgKMLTUXS1hVNQVF9Tj3vAuZMmUm/zn40LQziXmux4Pj76Nrp7a8/dbrKPSysoglLL+tUmIJ00sjljBBEARBELRi99334IUJk7nxxlupX7de2pnE/vfdNxx/fH9GjbyGf1avLt9Jdewk2dIkf8x2rNrMQXdNLmMnN8OeZ75pMjz2gkAuWMKGWEX00IglLDIaAowtNRdLWE00lgGnDzqDxYteo23rNlQ2k5hpxjBMg+eef4YuXY9i3rw5wVtZFGIJy6FGLGHR04glTKhdxCqih0YsYZHREGBsqblYwrKh+fcuu/HY489y882jabBFw41mEktaxFCKX375mZNOGsDgwWex4q+/UIglLIwasYRFTyOWMEEQBEEQtMYwDPr27c+c2Ys4ukfPtBYxgEmTXqRLl3bMnDHdv5jB/6zWrCzJH7MdqzZz0F2Ty9jJzbDnmW+aDI+9IJALlrAhVhE9NGIJi4yGAGNLzcUSlm3Ndts1YvwDD/HA2AfYrtE2lS42aRgmf634iyFDLuTsc87g559/YXN2ErGE5Y9GLGHR04glTKhdxCqih0YsYZHREGBsqblYwnKl6dKtJ9OmzaFPv2PTLjaJUsybO4uj2rVkwvPP4SlPLGEh0IglLHoasYQJgiAIgpB3bL31Vtw2egzPPzuB3XbavtLFJlGw6p+VXDn8Uk4/7b98/9235TupjuUkQ1uKysZ+cjW+MGhyGTu5GfY8802T4bEXBHLBEjbEKqKHRixhkdEQYGypuVjCakPTtk0bZs2cx6knn0yBQaWLTRqmwetvLKNHj86Me+A+SuNxampTEUtYMBqxhEVPI5YwoXYRq4geGrGERUZDgLGl5mIJq62a12/QgOHDr+W5519m9z32qnSxSZRi7bq1XDfyKnof3YWvnC9QiCUs3zRiCYueRixhgiAIgiCEgkMPO5y585ZwyUVDKDK8tDOJvff+uxx9dBfuH3sPJSUlfmN1bCqVtKls7CcbfcKqyWXs5GbY88w3TYbHXhDIBUvYEKuIHhqxhEVGQ4CxpeZiCQui5kWFBVxx+XCmTpnO/vvuk3Ymsbjncu/YMfTp3Z13332XrFlZFGIJy6FGLGHR04glTKhdxCqih0YsYZHREGBsqblYwoKs+b77HcCECZMZOnQ4llWQdiaxL5zP6d2rCzfdNIp169aJJUxzjVjCoqcRS5ggCIIgCKEkFrM459zzWTB/MS0PPSjtTGKe8njk4QfpdXQXXl/2evlOqmlLUZvTZLKfbPQJqyaXsZObYc8z3zQZHntBIBcsYUMj24B2tgZNvwt0zUFqXrX6aTo+qblYwmqj5rs32ZUXnp/IiGtGsEW9emlnEvvxpx856b/Hcumll7By5UqqZWVRiCUshxqxhEVPI5YwoXbR0DYQSY1YwiKjIcDYUnOxhOlUc8OMMXDgf5k9ewFt23VIO5OYUoqnn36Mtkc2Z8G8eSjEEqaTRixh0dNE3hJm23Y327Yn2bb9qW3b79i2Pca27d0202eJbdvxXI9NEARBEITssuNOO/PMMxMZe884Gjesn3YmsV9/+4UzzzqFS4dcyPI/l/uNGdpS1OY0mewnG33Cqsll7ORm2PPMN02Gx14Q5PSCxbbtkcB0oDewD3AIcAHwmW3bQzbTPbjLuLxFgVsCXrz8lbh1X6EtV5ogY+umqUIf5Wqag9S8avXTdHxS8/SaGtVPkxx0rLmBx7H9j2XOzHkc3a0rBYmZxEzTIGYk/3JrJmxjJjNmzaBL16N46aWJxJWH6ylcpcpsKa5SZW1xN/H55jSqaprq9AmrJuexvYjkmUeaTPqE0hJm23Y74Fr8C49ZwBDgSuADoA5wm23bT9q2beVqDFFDKfAME+W6KDfuv2P4r5S2XGmCjK2bJtM+nhtHuaVa5iA1r1r9dByf1Dy9pqb10yEH3WveaJutuX307dx37zgabdOYQsOj0IhTaHigPBQKw7QwTIu/V67kggvP4bxzzuTnX37BdT3/NwgDXNfD9byyNtf1/F+wNqNJ3c5EU50+YdXkOnZq/cKcZz5pMukTFLEc7nsw/g2kmx3HuTql/Vbbts8CxgADgYa2bQ9wHKc4h2OJBIYBpvJQMQtUAcQsjMQ9PCO1LVeaIGPrpsmwj2nVQakCjJjSLwepedXqp+H4pObpNaYVq1n9NMghX2reqWt3jmjVnltuHsnkSRMxPRfLULiYKM9FeRbKczENk/nz5/D2O+9w6ZDLOOXk/2IaJpZlogDLMkH57zFVvk3isw01qduZaKrTJ6yaXMdOrV+Y88wnTSZ9giKXlrAWwD/AyA0/cBxnPHAk8DvQE5hh23b9HI4lIhhgFYIRA8vy3xMPR1Zoy5UmyNi6aTLtY8YwrJieOUjNq1Y/HccnNU+vqWn9dMghj2q+1baNufnmO3j4ocfZaaedcT0SD+a7mG4xeG7iwXyP1av+5pprL2dA/1589903WIkHgC3TIGaZ/nti20z5bEONWQVNdfqEVVMbsWO1GEtqnp3vIsiH7mM53Hdj4CPHceKb+tBxnHds224FzAXaAfNs2+7qOM7KHI5ps9i23REYDhwIFALvArc4jjM7w/7/Bn5II1nqOE7rGg9UEARBEPKQVq1aMWPWAm676w4eGv8ABZRSoEwMPLwNHsxf9vpSjmrfmiGXX83JpwyiwCws248q+7/yBnkAO4uaXMZOboY9z3zTZNInIHJ5h2UNkPauieM4/wNaAZ8BzYBXbdvePodjSott26fiX0C1BN4CXk+Mb1bCxpYJByfePwKe2cQrowufaqP5XP2R0cg6LJHREGBsqXnNNTWqnyY55GPNG9QrYtTIG5g+bSZ777UnJh6xxIP5qWu3GIZJcUkJt95yA8cffwyff/65rMNSCxpZhyV6miivw/I5sKdt2w3TiRzH+RXfHvYWcACwBNguh+PaJLZt7wg8AKwEDnMcp7vjOF3wL1hWAXfbtv2vDHaVvGAZ7TjOSZt4XZ+bDBLkwVz9kdDIOiyR0RBgbKl5zTU1qp8mOeRzzQ89rDmTp87hnHMGYxgx4q5H6totSiVfik8++ojevbpw1523UlpSjKzDkt/rk8g6LHpporwOy7zE/o/bnNBxnBVAB2AhsGfiVdtcgD972V2O43ySMra3gdFAEZDJXZbkBcu7WR+hIAiCIISMOoWFXHzxEKZOncl/DjyQAkorrt0CoAAD4q7LXXfdRpvWbXjnnXf89iS1ZYmJiiaXsZObYc8z3zSZ9AmIXF6wTMbPcaht25uN4zjOGqAbMCXRr7bpmnifvInPXk68d8tgPwfjTzbwZTYGVWVCZhvIW41YwiKjIcDYUnOxhIWp5vvsszfTpsxg+OVXUbdOjJihsEwSa7UYfq1MA9OM8cWXDt26d+eGG0fyzz9rxB6UZY1YwqKniawlzHGc94COwHlAvQz7lADHACcCg3I1tg2xbdsA9gU8fCvbhnyZ+Gy/hLay/WwD7JLQD7Ft+0Pbttfatv2zbdvjbdveKQfDr0hIbQN5pxFLWGQ0BBhbai6WsLDVPFanLmeedS5Tp87ksMObpcwkVm4T82cSUyjP49FHH+Ko9q1Y+tqrYg/KM6uUWML00uhuCTOUCu5qSRcSFxrLgT8cx9nk8zO2bf+G/2xNQ8dxVlWi6YBvhQMoBRYDJcDh+LOm/Qq0cxzHyW4GLALaKtdFKYVXuh7cYrDqYNXxrxXd4rVlbWZBUU40udpvPmp0H5/kKd+F5Cnfhe55ekYBjz31LMOGDaNk/RoKTJNSz6M4rjBidTBjMbx4HC/uL+N2yulnct11o2i07TbUKbAoLnUpjXsUxEzqFvqToq4riZe1baipTp+wanQfn+QZzHdRtzCGaZRdtCwG2lFLZOUOi23bW9ew/3nZGEcNSM5mtjaNZl3ivUEaTfL5lU8B23GcTo7j9AB2A54DdsCfKSxHKPAqtxmVteVKE2Rs3TS6j0/ylO9C8pTvQvM8LRPOOvN0Pvn4I7p36Uy6mcQMw+Tpp56iVavWTH9lBvF4ykxiahNWJMVGmur0CatG9/FJnsF8F0FawrK1DstHtm2f5DjO4qp0SlikHsO3jt2fpbFUBy/xnq4Sxgbvm+Iu4CVgteM4fyYbHcdZY9v2GfizoR1q23Zzx3HeqMmAN0VpqcuKv4tRpevBLQGrELPQT8krWVvWZhQU5USTq/3moybTPts2LAAUf/y5RrscpOZVq5+O45Oap9dss6VVo/rpkEMUal6naGvG3vcIr0ydyIhR1/HLnyswDQ/LiBNPzCQGoJTit19/5cQTjqdHt+5cO+Imttp2Wwosk6IC/9ed9aVxSl2PAsukMGZREncr3c6kT1g1uY7doGFdUIrly/8JdZ75pMmkT1FBjO2325IgyNYzLP/CX/jxRtu2rUw62LY9EPgE6JSlMdSEfxLvddNoihLvayoTOI7jOo7zberFSspna4EFic1DqzVKQRAEQYgghmHQs2dvZs9ZTL8+fdLOJIaCadMm07VLO6ZOmUwF67tK/NVRZbgdZU0uYyc3w55nvmky6RMQ2Xzo3gSuAJbatr17ZSLbtrexbftF4EkguUbL8iyOozqswr9oaWTb9kZ3nRJtjYD1juP8XYM4vybeM5qEoFpEYCaZvNBUoc+m7BFa5CA1r1r9NB2f1Dy9pkb10ySHKNW88bZbc9+945jw3LPstMN2aWcS+3vVSi6/fAhnnH4KP/74o8wYVQWNzBIWPU1UZgk7A/8XfgP/AfMPbNs+ZUORbds98e+q9EtoDeAFYL8sjaNaOI6jgM8AC2i6CYmN/119nG4/tm2PsG17om3bB1Qi2S3x/lN1x7pZIjKTjPaaKvRB1xyk5lWrn6bjk5rLLGFhrHm37j1YuvQ1jj1uYNqZxFCKhYvm065tC554/BF/EUpkxigdZs+SWcL00ug+S1hWLlgcx3kUOAhYin8R0gB41Lbt52zbbmjbdgPbth/BX2Nl+4Tm/4BejuOc4DjOH9kYRw2ZlXjvs4nPkm0zNrOPA/GnZR6w4Qe2bW8HdMafPWxhNccoCIIgCALQsOGW3HTjaF56aSq77brrxjYxhf/bhoI1a//hyuGXcuLxx/D9t9/6n+F/po0dRzdNLmMnN8OeZ75pMukTEFmzhDmO8x3+Q+XD8afyNfB/cf8Q+Ag4NdEG8ACwr+M407MVPws8BqwHLrdtu+wZE9u2DwOG4c8Sdn9K+x62be9t23bDlH08mHgfatt2qxRtA+BRYEvgYcdxfiVXiG1AD41YwiKjIcDYUnOxhEW55snatWrZigXzX+PsM84kZpJ2JrG3332b3r27Me7B+1hfUoIudhzdNGIJi54mKpYwwLdWOY5zC3AE8AH+BcouQJPEz58DbR3HOc9xnNXZjF1TEhdcQ/EvKl63bXumbduzgGXAFsBZjuP8ntJlPn4+fVP2MQe4E//h/Vdt237Vtu1JwLdAD2AJcGlOExHbgB4asYRFRkOAsaXmYgmLcs1Tz5316jfgiitH8PzzE9lzr6bE3YoWMaWSL0VxcTG3jb6JPj278sVnn6II3o6jm0YsYdHTRMIStgn+Ar5P/Kw2eJXkKGaNcRznfuBo4A2gDf7zOK8BnRzHeTrDfQzFv7O0FH9dlq7AL/h3aTokZgsTBEEQBCHLHHjgf5gydTaXDhlGPUulnUnso08+oHfvbtwz5g7Wr19f9rnWlp3a1OQydnIz7HnmmyaTPgGRrXVYALBt2wAuAkZRvhhjKfAnsBOwD/4sYvcAVzuOs26TOwqQhE1ts1Y1x3GapPnsReDFLA4rc1JulZO8nQ4V23KlCTK2bpqM+1goUqwPOuUgNa9a/bQcn9Q8vcaoWf20yCGqNa/83FmnwGLoJZfQs2tnrrnqUt774APiJrhsMJOYiuEqj3EP3MfsWa9wy623c/DBhwEV7S+bs82EUZPz2Iq0lrDQ5JlHmkz6hMISZtv2/vh3Ju7Af+jewH925XD8C5UnE20WcDHwsW3b7bMVX0ggtgE9NGIJi4yGAGNLzcUSFuWab+7c2dTeh2effYkrrxxBYZ26aWcS+/qbrzimb3euG3UNa9es1dKyo5s9SCxh4dJEwhJm2/b1wLuA/6cJ8IBbgcMdx/nYcZzVjuOciv+8x+8Jze74i02Ot207mGUzBUEQBEEILZZlMuj0s1i4cCltWrVOO5OYUoonH3+Unj07sWTxq+U7yYbVJh81uYyd3Ax7nvmmyaRPQGTrDstV+PYyA/gGONJxnCsdxylNFTmOMwXYH5ic0nw68GmWxiHITDJ6aGSWsMhoCDC21LzmmhrVT5Mcolrzqpw7m+y6Ky9OeJlbb7qVLRvU2+RMYqYZwzANfv7lZ04ddCIXXXQ+K1asYHO2GZ1mesqb2bMUaS1hockzjzRRmiXMwJ/W9yDHcZZVJnIc50/HcfrhT3O8KtFvpyyOI9qIbUAPjVjCIqMhwNhSc7GERbnmVT13GmaMAccNZMYrc2l/VMeNZhJLtYgppXjhhWdoe2RzZs+ciSJ4y45u9iCxhIVLEwlLGPAz0NVxnHMznQXLcZwngQPwpwcWBEEQBEHIOdttvz0Pjn+cB8c9xA7bbFmpRQzg9z9+47zzz+DCC8/l998SKxvobuvJliaXsZObYc8z3zSZ9AmIbF2wHJBYg6RKOI7zk+M4nYALszQOQWwDemjEEhYZDQHGlpqLJSzKNa/JudNE0atnT+bMWkCvnj3TLjZpmAZz58ymW7ejeP755/CUf1Wjo60nb6xSCrGEaaaJhCXMcZwVNex/XzbGISC2AV00YgmLjIYAY0vNxRIW5Zpn49y5zbbbcvvtYxg//nG2227HChax1MUmUYoVf69gyJDzGXTqifz0049a2nryySolljC9NFGxhAmCIAiCIOQl7Y/qxKLFr3HqwOPTLjYJ8OqSV+l1dFeeeOxRPM8r02hj68mWJpexk5thzzPfNJn0CYisLhwpBI0CtwS8uP8yTFD+gloV2nKlCTK2bpqM+xgoN9GmWw5S86rVT8vxSc3Ta1TN6qdFDlGtefbPnVvULeKGkdfTu2tXrrz6Cpz/fY+LiWkaxAyDuGmAimGYJuuK1zPqhhG8Mv1lbr/9bv616264nsIwFHHXw1WqbDtpo0lt012T89ie3+aqkOeZR5pM+uS9JUzQA6XAM0yU66LcuP+O4b9S2nKlCTK2bppM+3huHOWWapmD1Lxq9dNxfFLz9Jqa1k+HHKJa81ycO5Pbhx1yMFOnzOKccy7ENKDQ8Cg04hQaHkq5GKZV9nrr7Tfo2KEN4x64n/UlJbiuBwa4rofreWXbm2rTXZPr2HE3GnnmkyaTPkFhjRw5MrjoQrY4FWjiuXGK164HQ2EoMApimFaB/+9LxcvbDDM3mlztNx81GfapX68QUKwrVfrlIDWvWv00HJ/UPL2mXpFVs/ppkENUa56Tc2fKdkHdItod1YmOHTrywTtv8vdff6K8uH/RY/gP4/t3eFw8z+PNN99gyZIl/OeAA9hh+x3K3DMxy6TA9P827CYsZjHLxEo8cK6rJtex69QtBKCkJB7qPPNJk0mfAtNkiwZFCRXfA49TS8gdllBhgFUIRgwsy39PPGhYoS1XmiBj66bJtI8Zw7BieuYgNa9a/XQcn9Q8vaam9dMhh6jWPBfnzk30Oeg/h/HylJlcMPgiTCuWWLvFxXSLwXNTHsz3+PzTj+jXtwejb72B0uJiTNPASjy4nPqzZRr+L4gbtOukqY3YsYjkmS+aTPrIQ/eCIAiCIAgaUlhQwHnnDeaV6XM47JBDKKC00gfzXc9j7Ngx9OzRiXfffVvfh6sz0eQydnIz7HnmmyaTPgEhFyxhQ+bq10Mj67BERkOAsaXmNdfUqH6a5BDVmmf93LmZPnvttSeTJ01j5NXXUr9uITFDYZmQXKsFDAzTwDRjfPv9t5w08DhGjLya1atXa7XehhbrkyiQdVj00kRiHRZBI2Sufj00sg5LZDQEGFtqXnNNjeqnSQ5RrXnWz50Z9LEKizjltLOYNm02zVu0xvVAKY/U9Vs8L162dstTTz1G+3YtWbxovjbrbeiyPomsw6KXRtZhEQRBEARBCBE77/xvnnzqee66aywNt9hyY5uYAgxAwU//9yMnnNifoUMvYuWKv/3P8D/Le3tQdTXJzbDnmW+aTPoEhFywhA2xDeihEUtYZDQEGFtqLpawKNc86+fOKvYxUZxw/AksWbyMrp26YOIRM5T/l+oUi5iRmFVs8uSX6N6zEzNmTqc07pLv9iCxhIVLI5YwoXYR24AeGrGERUZDgLGl5mIJi3LNs37urOb4tt/xX9w/7mHuvvs+ttq6UWImsXKLWPlMYoq//vyTwYPP5qwzT+GP339Dkb/2ILGEhUsjljBBEARBEIQQYxgGXbp0Y/achRx77IC0M4mhYPbsGXTu1I5JE1/0n3dJkm/2oOpqkpthzzPfNJn0CQi5YAkbYhvQQyOWsMhoCDC21FwsYVGuedbPnVkY37Zbb8U9d97NE48+yc477Zh2JrHVa1Zz1dWXc8rJA/nuu+/yzh4klrBwacQSJtQuYhvQQyOWsMhoCDC21FwsYVGuedbPnVkc35Ftj2Lq1FmceNIpm51J7LWlizmqfUseefgBPM9FkR/2ILGEhUsjljBBEARBEISI0aBBfa4beRNTpsxgz913TzuT2Np1a7nm2is5bkAfvvnqa/8z/M+0tgdVV5PcDHue+abJpE9AyAVL2BDbgB4asYRFRkOAsaXmYgmLcs2zfu7MUQ7NjmjG/HlLOP+c8ykwjbQzib3/wfv069eD++67h7Xr16OzPUgsYeHSiCVMqF3ENqCHRixhkdEQYGypuVjColzzrJ87c5hDUd16XHrZlUx4cRL23vumnUmspKSEO+68lV7dO/PJRx+g0NMeJJawcGnEEiYIgiAIgiCw334H8PLkGQy/4mrqFxhpZxL77ItP6NuvJ7ePvoV169aV70Qne1B1NcnNfLFBRUWTSZ+AkAuWsCG2AT00YgmLjIYAY0vNxRIW5Zpn/dxZSzkUxiwuOP98Zr4ym0MPPjjtTGIKeOTR8fTo3omly17Tyh4klrBwacQSJtQuYhvQQyOWsMhoCDC21FwsYVGuedbPnbWcw+577MUzz7zANddcT52i+mlnEvvu+285pl9PrrhiKGv+WY0ieHuQWMLCpRFLmCAIgiAIgrARpmly8imDWLx4Ge3btks7kxjAk08+Spcu7VmyeFFZG/lolUpu5osNKiqaTPoEhFywhA2xDeihEUtYZDQEGFtqLpawKNc86+fOAPP8984789yzL3LH6DvYumGDtDOJ/frbr5x19iAuueQC/vhjOXlplVKIJUwzjVjChNpFbAN6aMQSFhkNAcaWmoslLMo1z/q5M+A8DTNGv2OOY/q0uXTp2iPtTGIoxZSpkziqXXOmT5uCp7y8s0qJJUwvjVjCBEEQBEEQhIxovF1jxo4dzyMPPcaO226Vdiax5SuWc9FF5zF48Nn8+ssv5TvR3SqV3MwXG1RUNJn0CQi5YAkbYhvQQyOWsMhoCDC21FwsYVGuedbPnZrl2b1rV+bNWUD/fv3SziRmmAYL5s+ja9cOPPXUE7hZsD2JJSx6GrGECbWL2Ab00IglLDIaAowtNRdLWJRrnvVzp4Z5Ntxqa266aTSPPvoMO/1rl7Qzia1avZLLLruI4wb05vvvv0Wht1VKLGF6acQSJgiCIAiCIFSbNke2Y+GiZZx5xlkUGvG0M4m9tnQJPbp15LHHH8Z1Xb9RN6tUcjNfbFBR0WTSJyDkgiVsiG1AD41YwiKjIcDYUnOxhEW55lk/d2qaZ3K7Qb263DDqJia+MIk9d2+Sdiax9SXFjL7lJo49tjeffvoZ2lmlFGIJ00wjljChdhHbgB4asYRFRkOAsaXmYgmLcs2zfu7UNM8NNYccejiTJs3gnHMuAMy0M4l98MH7dO/annvuuZOS0mKtrFJiCdNLI5YwQRAEQRAEIWsUFRUy9NIrmDVzLgfvt3famcRK3VLuHnMH/fv14sP33yvfSZBWqeRmvtigoqLJpE9AxIINL2QXBW4JeHH/ZZj+bWao2JYrTZCxddNk3MdAuYk23XKQmletflqOT2qeXqNqVj8tcohqzXNw7tQyz/Sa/fbZm5dfmszjDz/AXfeMIe4W42JimgYxwyBuGqBiGKbJV998Tf8BfTn91EFcetkVGLFCXE9hGIq46+EqVel20gqUNY3nt7mqFmKl0QQZWzdNJn3EEiZkBaXAM0yU66LcuP+O4b9S2nKlCTK2bppM+3huHOWWapmD1Lxq9dNxfFLz9Jqa1k+HHKJa81ycO3XMMxONBZx+2mm8MnUGRxzRHJRHoeH5D+cbHkq5GKaFYVooDB54cCwd2rdm2bI3cD0P1/XAANf10m5nWxN3ay9WkHnmkyaTPkFhjRw5MrjoQrY4FWjiuXGK164HQ2EoMApimFaB/+9LxcvbDDM3mlztNx81GfapX68QUKwrVfrlIDWvWv00HJ/UPL2mXpFVs/ppkENUa56Tc6eGeVZFs1Xjxhx3wsls17gxb7/+GipegvLi/kWO4T+M79+Vclm5aiVTpr7MX8tX0OyII6hbVFTm+IlZJlbigfjkdoHp/33bTVjOaqqpU7cQgJKSeM5jpdMEGVs3TSZ9CkyTLRoUJVR8DzxOLSF3WEKFAVYhGDGwLP898ZBehbZcaYKMrZsm0z5mDMOK6ZmD1Lxq9dNxfFLb7n1EAAAgAElEQVTz9Jqa1k+HHKJa81ycO3XMs4oa0yrglFPPZPbs+bRu1RrX860+eC6mWwyem/JgvseEF56ma5f2LJw3FyvxkLVlGv4vsCnb5gY/Z0MTq8VYlWmCjK2bJpM+8tC9IAiCIAiCkBV23Glnxo9/lDvvuIdtttqKAkorfTD/t99+5ZTTTuDCC89lxfIVNXtoO1NNcrM2Ym1OE2Rs3TSZ9AkIuWAJGzJXvx4aWYclMhoCjC01r7mmRvXTJIeo1jzr505N86yuxjRM+vbty6IFr9GrR09MPGKGwjLx12sx/YcUDNPANGO88so0evTsyJSpL1MadxO/n1a+JoeswxIujazDItQuMle/HhpZhyUyGgKMLTWvuaZG9dMkh6jWPOvnTk3zrKmm8Q47cvc9D3DPvQ+wbaPtcT1QyiN1/RbPi6OUYsVff3HJJYM547QT+eWX/0OhZB2WiGhkHRZBEARBEAQhUDp36szsOQs5ceB/MZTa2CaWsIihYO78uXTtfBQvPPcsnueV70QsYeHWiCVMqDXENqCHRixhkdEQYGypuVjColzzrJ87Nc0zm5qtttyCO28fw4sTXmbXnXcmaROLWRtbxNasW8PI667mpIHH8c03X4slLOQasYQJtYvYBvTQiCUsMhoCjC01F0tYlGue9XOnpnnmQtPmyPa8MnMBp546CE8ZiZnENraIoRRvvLmMDke1Zty4e3HdOAqxhIVRI5YwQRAEQRAEQSvq16vHFVdczUsTJ7N3073SWsTWF6/n+uuvpf8xR+N8/oX/Gf5nYgkLkUYsYUKtIbYBPTRiCYuMhgBjS83FEhblmmf93KlpnrnWHHzwIcyaMY+LBl9EoWVs0iJmJBae/PiTj+nf/2juuut21q5bh1jCwqMRS5hQu4htQA+NWMIioyHA2FJzsYRFueZZP3dqmmdtaOrUa8DFl1zGxJemst/+B2xkEStfbFIRj8e5d+xd9OjagQ/efweFWMLCoBFLmCAIgiAIgqA9e++9DxNfms61V19Hg0Kz0sUmUeB89QX9+/fhlpuvZ+2ateU7EUtY/mrEEibUGmIb0EMjlrDIaAgwttRcLGFRrnnWz52a5lnbmgLL5Nyzz2L2zLk0O/ywtItNYsATTzxGt24dWbx4kVjC8lgjljChdhHbgB4asYRFRkOAsaXmYgmLcs2zfu7UNM+gar5rk9154olnGTXqVurW26LSxSZRih9/+p7jjuvD0KEXsHrVShRiCcs3jVjCBEEQBEEQhLzDNE1OOPEklix5g84dO6WdSQzgueeepnPn9syfO7esDbGE5Y9GLGFCrSG2AT00YgmLjIYAY0vNxRIW5Zpn/dypaZ461HzHHXbgySee5d4x99Jom4ZpZxL748/fGXzB2Qy+4Bx+/fV3xBKWHxqxhOUZtm13tG17gW3bf9q2vcq27YW2bXep4j6a2rb9nG3bP9q2vda27Y9s2x5s23buv2+xDeihEUtYZDQEGFtqLpawKNc86+dOTfPUpeaGGaPn0X2ZOnUuPXr2SjuTGEoxY8Y0OrRvweRJL+EpTyxhmmt0t4TFAousIbZtnwo8BhQDCwALaA/Msm37bMdxxmewj4OAV4EtgaXA24l93As0B07KyeAFQahVvnQ+58N3l7Jm9Uow6tCybQf22n23oIclCKFno2OvXSdse5+ghxUZGjXahjFjxtGz9wCuuWoYy//4DQMPTymU/9stJGxiK1auYOilFzJ9xjRGjbqBPZo0KduPWMI01GhsCZMLlgS2be8IPACsBFo7jvNJov1wYB5wt23brziO839p9mEAT+JfrPzXcZynE+2NE/sYaNv2y47jvJSzRFJulZO89QsV23KlCTK2bpqM+1io1Nv0OuUgNd+kZtnry7hn7D288dYbxEwDyzBwlSJ+/bU0P6I5Fw6+kJatj9Q6B6l5UmOUH3+R/y70z7OyY2/9ddfSrFlLhlx4Ea2aN8/7PLXQZNCnU4cONDt0HrePvpFnn3+GuAkuG8wkpmIYpsHiVxfRrWtHrhh2BSefctpmLWFAhbZcaYKMrZsmkz5iCdODC4A6wF3JixUAx3HeBkYDRcBZm9lHJ+BAYFHyYiWxjz+A8xKbF2Zz0Buh2S1krW5xa/pdoGsOUvONNC+88AKDBv2XN996k5I4uB6AwvWgJA5vvvUmgwb9lwkTJmibg9RcLGH5mGe6Y8/1YNnryzj5lBOZ+NILeZ2nNpoM+2yxZUOuu+4GnnpqAv/epUnamcT+WbOa4cMv5Zi+Pfj6qy8RS5heGt0tYXLBUk7XxPvkTXz2cuK9W3X34TjOUuB3oLVt21tUa4SCIATGkiWLGH7VMP8/yGlQyuPK4ZexZMmi2hmYIIScTI89z/O4+uorWbZ0SS2NTEjSvEUrFixcxnnnDKaO4aadSezNt96gZYuWjBkzhng87jfqboOKikZjS5hcsFBm5doX8IDPNyH5MvHZfgltZeyXeP+kks8d/O9832oOdfNoNKuIdrOeaPpdoGsOUvMKmrvuHF1WL8uEwhhYJpvcNvAYc9dt2uUgNd9YQ032o0kOYa/55o691JdSirFjx+RlnlppqtGnXlEdRlw7kpcnvszeTfdMO5NYSbyUG268kX79evHhhx+xoe1Ip5mxoqKRWcLyg63x7WDLHccp2fBDx3HiwJ9APSDd3ZEdE++/VPJ5sn37ao5z8+TBLeRIaMQSFiqN8+WXLHt92UYWsHTbS5ctxfnyS21ykJqLJSwf88zk2Et9geKNN9/gy6+/yas8tdPUYL8H/udQJk6cxgUXDMUwrAoWsQ1nEvvkkw/p2b0Dd9xxK8XF67W0QUVFo7slTB6696mfeF+bRrMu8d4AWFXN/aTuI+sUFMRo3GgLvNICcIvBqoNVpx4AbrFV1mYWFOVEk6v95qMm8z7rAWjUaAvtcpCal2smTHqzWsfk+++/SYvD99ciB6n5pjRry44/+S70zLP6x95btDjiwLzJUzdNzfdbyKjrR3DCKSdx/lln8sH772Ow6ZnEXOVy/333MH/+XO67927aHtkagHUlcUrjHgUxkzoFFsWlbtl23cJYVjS52m8+ajLpk2wLArnD4uMl3tPd6zI2eK/OfjLZRw1Q4FVuMypry5UmyNi6aXQfn+RZJc3qVSvLLCeweUtYUrt61UptcpCay3eRj3lmcuylvpJt/6z6O6/y1E6Tpf3uZ+/FvLmzufH6kdQvqoNl4tvCUmcSM/2ZxL76+ms6d+nKRRddzKpVq8tnElMQj3sVtjdagLKamlztNx81mfQJ0hImd1h8/km8102jKUq8r6nBfjLZR7UpLXVZ8XcxqnQ9uCVgFWIW+v+4vJK1ZW1GQVFONLnabz5qMu2zbcMCQPHHn2u0y0FqnpKnWafMcmJZ5bYUI1b5tuuBYdbhjz/XaJGD1HxjzTZbWmXHX9S/C13zzOTY861gFTWYRRWOPd3z1E2T7f0ee+xJNG/ZgUuvvJxXX1sCnodlxIknZhIzTRNDKTzlMXbsvUyZPIWbbhlD89atKbBMCmMWJXGXUtejwDIpKvB/fV1fGi9rq44mV/vNR00mfYoKYmy/3ZYEgdxh8VmFf7HRyLbtjS7iEm2NgPWO4/ydZj8/J953qOTzzT3jIgiChrRp07ZW+wmC4FP9Y+/ILI9EqCn/3mVXJkyYwp233802DYrSziT2408/cMqpJzD8isv4e8UKv1H5kgoelg3bqqPJ1X7zUZNJn4CQCxbAcRwFfAZYQNNNSGz87+rjzewqOTvYRrOAJWYX2xtwE7FygPL/quHFy1+JW7QV2nKlCTK2bpoq9FGupjlIzctedtOmtGzRkpgJMdMglrClpNtu1bIVdtOm2uQgNd+0puz4k+9CyzwzOfaSdrBkW4tmzWi65x55lad2mhzt18Bj4IkDeeet1+nZrRMFiZnETNMgZiQf8jYTtjGTyVNfpnPXjkybNg1XKVxP4SpVZldKbYu7XpU11ekTVk0mfWSWMD2YlXjvs4nPkm0zarCPlkBj4DXHcVZXfXibRynwDBPluig37r/jr+Sc2pYrTZCxddNk2sdz4yi3VMscpOYVNZdccikxy8IywDQM4nH/fVPbMcvi4ouHapeD1LyiJvX4i/p3oXOemzv2PA88z/85ZpoMPndwXuapkybXsXfYrjGPPfwgd995N9tstQ2FhkehEafQ8EB5KBSGaWGYFn/9tZyzzzmN8y88l19/+wPX9fw/+Rvguh6u55W1bbidiaY6fcKqyaRPUMgFSzmPAeuBy23bPjTZaNv2YcAw/Bm+7k9p38O27b1t226Yso/FwKdAJ9u2z0zRNk7pe0euEjAMMJWHEbMwzAL/PXl6SGnLlSbI2LppMu1jWjEMS88cpOYVNW3atOW6UTfjYeIpRSwGnlLEPSpse5iMuv4W2rRpq10OUvOKmtTjL+rfhc55bu7YM00wTVAYXDvyBlq0OTIv89RJUxvHnhkrpNvRfVgw/1V69+yB6cUxvVIsQ2FgoDwX5Xkoz8U0TObOfIVj+nVj6uRJKM+3kVmWiWWaWJa5ye1MNNXpE1ZNJn2CQi5YEjiO8x0wFNgSeN227Zm2bc8CluGvvXKW4zi/p3SZj7/IZN+UfXjAIPznYcbbtv2GbduT8BeMPBB4yHGcabnLwgCrMPk0ov+emBe9QluuNEHG1k2TaR8zhmHF9MxBar6R5tjjBvLgQ09y6OHNKIlD3MO/lZ54EPjQw5vx4ENP0n/AidrmIDVP0aQef1H/LjTPM92x53rQrHkrHnv8Wfr1Pz6v89RGU4vH3rbbbc+dd45l3P3j2W77HXE9UMoDz8V0i8Fz8bw4Snn8/ddfDLv8YgaeeAw//fQDVmKtEMs0iFlmhW1zg583palOn7BqMukj67BoguM499u2/QP+HZU2QDHwGnCj4zjzM9zHW7ZtNwNGAe2B/YGvgCuBh3MycEEQao2WLVvSsk07vvrft3z47lLWrF4JRh1atu3AXrvv5nu2BUHIOpUee+06Ydv7lM9OJeQl7dq1Z9bsBdw0+laeePxRCiilQJkY+Ou3kPJg/qLFC2nXtiWXXzmCE/57MqjE39/lofuaaTR+6F4uWDbAcZzpwPQMdE3SfPYZ0D+Lw8oct3xedJLzokPFtlxpgoytmybjPhb+zW8Nc5Cap9U03XMPWh6xP8qNs3zFOjBj/oOlmoxPap6Jxig//iL/XeRPnhsee0ZBXVBu6PIMd803fext2aA+t958G31792P4sAv46YfviRkK1zLxUtZuMQyTdevXMeqGa5k+czo33ziapk33SvxObaCg7AHx1La466XdzqRPWDWZ9JGH7oXsYRWiMFEoFCbJ268V2nKlCTK2bpoq9EHXHKTmVaufpuOTmqfX1Kh+muQQ1Zpn/dypaZ5hrXm6Y695i9ZMe2U+gwadhcIk7nrgeVgqDp6HUsmX4v333qVnj47cf98Y4vFSFMp/ptdMzjpGWVvMMtNuZ9InrJpM+gRpCTMDiywIgiAIgiAIm6BuURHDhl3By5Omsd8++1BAacW1W8D/878BJaWl3HLLDfTr05PPPvkk+zaoqGg0toTJBUvYSNxaNUixGSVulZe15UoTZGzdNFXog645SM2rVj9Nxyc1T6+pUf00ySGqNc/6uVPTPMNa80yPvQMPPJCZr8zhsiGXUafAJGYoLJPEWi3lNjHTjPHZF58yYEAfbrvtFtasXYvnKapqg9pcn7BqMukjljAhe4T8FnLeaMQSFhkNAcaWmufWlhK17yLf8sz6uVPTPMNa86ocewVF9Tjv/IuYPHkGB/3nkJSZxMptYv5MYgrXdRn3wL107ngkb7/1uljCxBImCIIgCIIgCLXDHnvsyQsTJnPDDbdQv27djW1iCYsYCr7539f07tOda64dzro1a/3P8D/TxoKlm0YsYUKtEfJbyHmjEUtYZDQEGFtqXnu2lCh8F/mWZ9bPnZrmGdaaV/fYi5kGZ5x+JosWvkablq0x8YgZyr9DsMFMYoZh8uwzT9KjZycWL14olrA0GrGECbVLyG8h541GLGGR0RBgbKl57dpSwv5d5FueWT93appnWGte03PnLrvuzhNPPsdNN91K/QYN084k9svPP3PaoJO45OLz+HvFXyiCt2DpphFLmCAIgiAIgiBkGcMw6NfvWObOWUiPbt3TziSGgpdemkDnzu2ZPWsGKuVzrW1atakRS5hQa4T8FnLeaMQSFhkNAcaWmgdnS9Eph6jWPOvnTk3zDGvNs3nu3G67xjw8/hEeGPsA2zXaJu1MYn+tWM7FFw/mnHPP5JdffkVnm5ZYwsqRC5awEfJbyHmjEUtYZDQEGFtqHrAtRZMcolrzrJ87Nc0zrDXPxbmzS7eeTJs2h959+6edSQylmDtnJu3btmDihBfwlKelTUssYeWYgUUWBEEQBEEQhCyy9dZbcfttd/PccxP59792TjuT2Kp/VnL5FUM4/bST+eH778p3opNNqzY1YgkTao2Q30LOG41YwiKjIcDYUvOAbSma5BDVmmf93KlpnmGtea7Pne3btWfRwtc47eRTqGwmMdOMYZgGr7+xlB49OvPgg/dTGo+ji01LLGHlyAVL2Aj5LeS80YglLDIaAowtNQ/YlqJJDlGtedbPnZrmGdaa18a5s8EWDbl2xI08/fQEdm2y20YziaVaxNasXcOIEcPp06sr33z1JYrgbVpiCSvHDCyyIAiCIAiCIOSYww47jOmvzOfCwRdTZHiVWsQA3n3vHXr27MwD94+lpKTEb9TdypUtjVjChFoj5LeQ80YjlrDIaAgwttQ8YFuKJjlEteZZP3dqmmdYa17b5866dQq48vLLmTplGvvts3faxSZL3Th333Mnffv05L333kNnK5dYwoT8JOS3kPNGI5awyGgIMLbUPGBbiiY5RLXmWT93appnWGse1Llz3/0OZMKEyQwZcgWWVVDpYpMoxedffEqvoztzyy03sG7dOi2tXGIJEwRBEARBEISQUVAQ49zzLmD+vEW0OOSgtItNesrjofHj6N2rK2++8Xr5TnSycmVLo7ElLBZseCHrpNwqJ3lLFCq25UoTZGzdNBn3sVCpt691ykFqXrX6aTk+qXl6jVGz+mmRQ1RrnoNzp5Z5hrXmNTz2sqTZY7cmTHhhIs889Ri33jaauLsGlw1mElP+TGI//PgDJw48loHHD+Tqq0dQUFQPqNxOBVRo012TSR+xhAnZI+S3kPNGI5awyGjEEqbZ+MQSFpmaiyUsv2uuxbnTKsQwY5x00inMnr2AI9selXaxSaUUTz75CO3atmDRgvkogrdyiSVMEARBEARBECLATv/6N88++xL33n0/jbasl3YmsZ9/+T9OP+NkLht6MX8t/8tv1N3ulYlGY0uYXLCEjZDPKpI3miBnutE0z7BqCDC21LzmmhrVT5McolrzrJ87Nc0zrDXX4ty5wbaBx4BjBzB39kK6d+2adiYxwzSY/spUOndtz8svT8JT/lWNjjOAySxhgn6E/BZy3mjEEhYZDQHGlpoHbEvRJIeo1jzr505N8wxrzbU4d1bSp/F22zFmzFjGjh3PNts0Jt1MYn8t/5Pzzz+Dc84ZxG+//aql3UssYYIgCIIgCIIQQrp07cHiV5dyQv++aWcSA5g3dw49e3Tm+Wef9S9mEhpt7F6ZaDS2hMksYaFCgVsCXtx/GSbJWTIqtOVKE2Rs3TQZ9zFQbqJNtxyk5lWrn5bjk5qn16ia1U+LHKJa8xycO7XMM6w1r+GxV4t5btWgPrfdPJo+3bpx9Yhr+Pr7H3AxMU2DmGEQNw1QMQzT5J+1a7jqmsuZPu0lRo++i+3/9W9cT2EYirjr4SpVtp20V6W2BanJpI9YwoSsoBR4holyXZQb99/xpw5MbcuVJsjYumky7eO5cZRbqmUOUvOq1U/H8UnN02tqWj8dcohqzXNx7tQxz7DWXJdzZ1X6tGzejKlTZjDojLMxUBQaHoVGnELDQykXw7TKXq8tfZUOR7Xm4ccepiReiut6YIDrerieV7a9qbYgNZn0CQpr5MiRwUUXssWpQBPPjVO8dj0YCkOBURDDtAr8f18qXt5mmLnR5Gq/+ajJsE/9eoWAYl2p0i8HqXnV6qfh+KTm6TX1iqya1U+DHKJa85ycOzXMM6w1r/GxF1CederVo0PHbrRr244P3nmdVSv+Qnlx/yLH8B/GV24cPJe4G2fZsqW8/sbrHHLQwTRu3BiFT8wyKTD9ewZuwj4Ws0wswwhMk0mfAtNkiwZFCRXfA49TS8gdllBhgFUIRgwsy39PPEBWoS1XmiBj66bJtI8Zw7BieuYgNa9a/XQcn9Q8vaam9dMhh6jWPBfnTh3zDGvNdTl3VnO/hx3eginT5nD2WedgGBZx1wPPxXSLwXNTHsz3+Oj99+jdqwt3j7kNN16KaRpYiQfaU3+2TMO/cNigvbY0mfSRh+4FQRAEQRAEIU+oU1jIxRcPZcqUGfznwAMpoLTSB/NL43HuuONW+vTqxkcffkDZrYyERh663zxywRI2NJrPvFZj66YJci0BTfMMq4YAY0vNA14LQpMcolrzrJ87Nc0zrDXX4tyZhf3uu+8+TJsygyuHDadunRgxQ2GZlK3VAgaGaWCaMZyvHE444RhuvGkU/6xZI+uwVAG5YAkbGs5nHklNkGsJaJpnWDUEGFtqXnNNjeqnSQ5RrXnWz52a5hnWmmtx7szSfmN16nLW2ecxZcoMDjn0cFwPlPJIXb/F8+IopfBcj0ceeZAO7Vvx+rLXZB2WDDEDiywIgiAIgiAIIaFJk9157vlJ3HLLHWxRv8HGNrGERQwF3//wHcf0P5rhw4exZvU//mf4n4klbGPkgiVs5Pmt1dBoxBIWGQ0BxpaaB2xL0SSHqNY86+dOTfMMa821OHfmYL+WAaeeciqvLl7KUW3bYeIRM5R/ByPFImYkZhV74YVn6d6jE/PnzxNLWBrkgiVshODWaig0YgmLjIYAY0vNA7alaJJDVGue9XOnpnmGteZanDtzGHunf+3Cw488xW233cmWDbdOzCRWbhErn0lM8ftvv3LmWadwweAzWf7nnyjEErYhZmCRBUEQBEEQBCGkGIbB0Uf3YdbsRfTt3TvtTGIomDLlZbp2ace0qZNRKuVuhljC5IIldITs1mreasQSFhkNAcaWmgdsS9Ekh6jWPOvnTk3zDGvNtTh31lLs7Rptw/1jH2D8uIfZYbtGaWcSW7Hyb4YNG8JZZ57GTz/9JJawBHLBEjZCems17zRiCYuMhgBjS80DtqVokkNUa571c6emeYa15lqcO2s5dqfOXZk2bQ79B5yQdiYxlGL+grm0a9uCp558zLeOIZYwQRAEQRAEQRByTMOGW3LzTbfz4ouTabLLLmlnEvtnzWouv2IIJ514LD98953/Gf5nYgkT8psI3FrNC41YwiKjIcDYUvOAbSma5BDVmmf93KlpnmGtuRbnzgBjt2ndhgXzX+Os088kZpJ2JrE3336T3r278eBD41hfUoJYwoT8J0K3VrXWiCUsMhoCjC01D9iWokkOUa151s+dmuYZ1pprce4MuA71G2zBlcNH8NxzL7L7HnumnUls/fr13HrLDfQ9uhtffPYpCrGECYIgCIIgCIJQCxx00MFMnTaXoZdcRl3TSzuT2Icfv0/v3t249+47KS4uLt+JWMKEvCKit1a104glLDIaAowtNQ/YlqJJDlGtedbPnZrmGdaaa3Hu1KgORYUxLh0yhOnTZnLQAfulnUnMVR73jxtLr6O78tbbb4olTMhDInxrVSuNWMIioyHA2FLzgG0pmuQQ1Zpn/dypaZ5hrbkW504N62DvvS/PPTeJyy+/loLCorQziX319Zf07tWVESOuZN3atSjEEiYIgiAIgiAIQo6JxSzOOPNsFi5cSuuWrdLOJKaU4qGHHqBb16N4Y9ky/zP8z8JkCYsFG17IOim3yknecoSKbbnSBBlbN03GfSxU6u1hnXKQmletflqOT2qeXmPUrH5a5BDVmufg3KllnmGteQ2PvbzJs2aa3Zo04cUJL/P8049y++gbWbl6Na5l4m0wkxjATz//xGmD/r+9O4+WrKoPPf6tqtsNMqjI0GBAokn8EUQMIqgMTxklEBAHFKMoT4OCJqJhRTQGaXmE5RBeEhAEHFAjyynIJJMxDCpOKMlTI2wzCApoQAKJMvatqvfHOdV9+/Yd6tZwa1ed72etXnXvqd85tXf9+pxbu/bvnPMaXv7Sl3PKX5zKJpttDqxfyrXUkrDZ61gSpsFxajWPGEvCKhOTRVmDOe85xpKw8e2nJWHjnfMsjp2556HWoN5YwdGvOoYrv/RlXrjfAQteSYx2m89/4TPs/8K9+Idrr6WNJWGSJEmSlsGqbbflgo98kg+fcwGrtth8wSuJ/ee9v+D4E17P2058C/fec++6jYxxSZgDlknj1TbyiBnllW4y7eekxjDC1zbn/cf0lb9M+lDVnA/82JlpPyc151kcO3PPw6yYOm2OPOJwvnzNdRxx2GELXkmsVq9x7bVXc8gh+/G5z32G5iIlYF4lTMvLqdU8YiwJq0wMI3xtcz7ispRM+lDVnA/82JlpPyc151kcO3PPwzwxW261FX915t9y/vkXss2qJy94JbH7H7ifE088gWNecxR3330nbSwJkyRJkrQM9j/gYG786jd57THHLnglMYDrrv9HDjl4fy76u0/RarWKhZaEaWScWs0jxpKwysQwwtc25yMuS8mkD1XN+cCPnZn2c1JznsWxM/c8dBHz+M0244Pv/ys+e9Hn2XGH32Cq1i5mS2ZdSaxWq/PQIw9x+l+u5lVHv5wf//hfsSRMo+PUah4xloRVJoYRvrY5H3FZSiZ9qGrOB37szLSfk5rzLI6duedhCTHPfd5eXHbZtbz+DW+iVYwu5r2S2M3f/TYvOuh/cd7557Bm+jFLwiRJkiQN3yabbMy73vUertNqADcAACAASURBVPrSNezy9N9a8Epij655lA++/wxecdRL+dEPf7D2+VxLwrxx5ERpQ/MxaE0X/2r1YjoR1l82rJhRvnZuMV2vU6PdLJfl1gdzvrT8Zdk+c75wTLu//GXRh6rmfAjHziz7Oak573PfG5t+jibmWc98Jpdf+iU+et5ZnH3uOUw3WzSpU6/XmKrVmK7XoD1FrV7ntnQrR77kcI5/0wkc/5YTaUytoFZrry3/arbbNFvt9ZaNQn1kr6yBa7ehVavTbjZpN6eLR4q7yc5cNqyYUb52bjHdrtNqTtNursmyD+Z8afnLsX3mfOGYfvOXQx+qmvNhHDtz7Oek5jyXY2fueegnZkW9xglvOp7LL/kSu+32bGi3WFlrsbI2zcpai3a7Sa3eoFZv0ALOOvv/cuSLD+W7t3yXZrMFxSkwNJstmq3WumUj0li9evXoXl2Dcizwm63mNI8+9AjU2tTaUFsxRb2xovj/1Z5et6xWH07MsLY7jjFdrrPpJiuBNg+vaefXB3O+tPxl2D5zvnDMJhs3+stfBn2oas6HcuzMsJ+TmvO+970x6WcOMVuuWsXRf3gsWzzxCXzv298oBoqt6WKQUytOxm83p6m129z/wANcfvllPPDAfTxvz+excsVKmmU52VSjzop6nc0325jSHcAnWCbOsEyUGjRWQm0KGo3isTwZa71lw4oZ5WvnFtPtOvUpao2pPPtgzpeWvxzbZ84Xjuk3fzn0oao5H8axM8d+TmrOczl25p6HAcU0plZy3Bv/mKuu+gp77vFcmq02080WtJrUm49Cq1neu6VFa3oNn/rkx9l/v7346o3X0yhPvu88jsrUyF45QxHxCuDtwM5AE/gGcFpK6TtL2Ma+wFcXCLkopfSavhoqSZIkLcEOT9mRT3zi7/jCxV/k1P9zGo88/CtWtOvUaNGade+Wn935U45+1Us56uhjeOc738OWT9pipG13wFKKiNXAqcCvgOuALYBDgRdFxBEppau73NRu5eM3gJ/M8fxNfTZ1YTOuT0/nGt6w/rJhxYzytXOL6XqdBm1mXG89pz6Y86XlL8v2mfOFY2r95S+LPlQ150M4dmbZz0nNeZ/73tj0M7+YWq3OK1/5Svbb/0BO+YuTueG6LzNVa9Ns1GnNuncLwBe/+AW++vWvsfqU03jx4YczKg5YgIjYnWKwcgewd0rprnL5YcClwIUR8bSU0kNdbK4zYHlHSmm4g5O5NFbSbrWAaaBOrdYAoLjmeblsWDGjfO3cYrpdZ8a16LPrgzlfWv5ybJ857yKmj/xl04cK5nwYx84c+znROc/g2Jl7HoYYs82Td+DD532Ma6+6lFNWn8pd99xHvdaiUZtmurx3C0C73ea+e+/lLX98HFdcdjBfvPjzbLTRRiw3z2EpnFQ+ntoZrACklK6kOKFoFfDKLre1G9AC/nmQDZQkSZIGpVarccghh3Htl2/g5S8/ihWsWfDeLVdddQU333zzSNrqDEvhEIqUXD7Hc5cAfwT8PnDhQhuJiJUU57/cllJ6cNCN7IpTq3nEWBJWkRhLwrJrnyVhFcm5JWHjnXNLwnKK2XKLJ3L2X5/Fyw4/nNXveRd33n0X03VoUqc2q0xsenqaUaj8DEtEbEdxvspdKaX75wi5rXx8Zheb2wVYAdweEadHxK0R8XBE/CQi/ioinjigZs+vnCpvl1OtnatHrLdsWDGjfO3cYpawDoN+7Uz7OakxjPC1zXn/MX3lL5M+VDXnAz92ZtrPSc15FsfO3POwzO/FC154AJdffg2vevVrabYoysJaLRrtaZhRJjYKzrDAduXjz+d5vrN8VRfb6py/cijwAuBG4E5gD4qys8MjYp+U0r09tnVBK1ZMsfVWm9NaswKaj0JjIxobbQJA89HG2mX1FRsPJWZY2x3HmO7XeQSArbbaPLs+mPOl5S/P9pnzhWMe6it/efShqjkf/LEzz35Oas772/fGp5/jE9P5fastVnLuOR/iNa87luOOO46f3v4f664kBlCejL/cJnLAEhEXAbt3EXoJcFX583wn1D9SPm7WxfY6A5YbgaM6A5OI2Ar4LHAAcB7wsi621YM2tIopvrmmytcuG1bMKF87t5jc22c/fS/sp++F/czntXOLyb199nPo78W+++zNP93yPc44bTUf/tCHaLeLK4nVaozERA5YgB2B6CJuO6BV/txeJLabFL0dOAv4eUrpV52FKaVfRsRrgR8DL4mI7VJK883o9GzNmib3P/Ao7TWPQPMxaKykvrLoVuuxh9Yuq63YeCgxw9ruOMZ0u86WT1gBtLn3lw9m1wdzvrT85dg+c75wzJMe3+grfzn0oao5H8axM8d+TmrO+933xqWf4xQz3zp//Cd/xgv/136c/Ofv5J9+eCudc/GX20QOWFJK+3QbGxHPKn983DwhG5ePi55En1JaQzEomeu5uyPiFmBf4NnAld22UZIkSRqFXXbZlUsvu5oPX/CRrr69H4aJHLAsUecyxtvO8/x25eMgZkR+UT5uMoBtza3p1TayiOl6Ha8SNt4xXiUsu/Yt55WKsuhDVXPuVcLGO+deJSy7mEXWWTnV4G0nvp1NN1/+e7CAVwkjpfRL4B5g+4jYfI6Q3y0ff7DYtiLirIi4JCK2mSfkqeXjnUtvaZcaXm0ji5glrEOufTDnS8tfpu0z50O8UlEmfahqzgd+7My0n5Oa8yyOnbnnIbf3otZg0003HdpH2IXUR/Kq+bkGaACHz/HckeXjVXM8N9veZfwG24mIXShOyr8P+F5vzZQkSZKqxZKwwoeBY4D3R8Q3U0o/AYiIw4BjKcrBPjNzhYjYqfzxpymlzhXGzi//nRERN6WUbitjt6a46WQD+EBK6bGh9STj6cRKxVgSVpEYS8Kya58lYRXJuSVh451zS8Kyi+lmnXLZKDjDAqSUvgV8ENge+GFEXB4R1wNXAC3g1SmlR2etdmv5b88Zyz4K/D2wDfD/IuIrEXEZ8O/Ac4DPA2cOtTO5TydWJcaSsMrEZFHWYM57jrEkbHz7aUnYeOc8i2Nn7nnI7b2oNYb6EXYh9ZG9cmZSSidTzKbcChwI7ExxJa/np5Su73IbLeAVwPHA94G9KO69citwHHB0Sml0w1NJkiRpzFgSNkNK6ZPAJ7uMrc2zvM260rDll/t0YlViLAmrSIwlYdm1z5KwiuTckrDxzrklYdnFWBKmZZX7dGJVYiwJq0xMFmUN5rznGEvCxrefloSNd86zOHbmnofc3ouaJWGSJEmStAFLwiZN7tOJVYmxJKwiMZaEZdc+S8IqknNLwsY755aEZRdjSZiWVe7TiVWJsSSsMjFZlDWY855jLAkb335aEjbeOc/i2Jl7HnJ7L2qWhEmSJEnSBiwJmzS5TydWJcaSsIrEWBKWXfssCatIzi0JG++cWxKWXYwlYVpWuU8nViXGkrDKxGRR1mDOe46xJGx8+2lJ2HjnPItjZ+55yO29qFkSJkmSJEkbsCRsorSh+Ri0pot/tfq66buZy4YVM8rXzi2m63VqtJvlstz6YM6Xlr8s22fOF45p95e/LPpQ1ZwP4diZZT8nNed97ntj088xiulmnRGWhDlgmSDtNrRqdWg2oTkNZY0vQHvmshVDihnWdscxpst1Ws1paK6h3Wzm1wdzvrT8Zdg+c75wTN/5y6APVc35UI6dGfZzUnOezbEz9zzk9l6Uy0bBAcsEqdWg3m7RnmpAewVMNajRLp6buWxYMaN87dxiulyn3tiIdnsFtal2fn0w50vLX4btM+cLx9QbU/3lL4M+VDXnQzl2ZtjPSc153/vemPRzrGK6WadcNgoOWCZKDRorodWCRgtqU6w9Qao2tW7ZsGJG+dq5xXS7Tn2KWqMJtVZ+fTDnS8tfju0z5wvH1Bv95S+HPlQ158M4dubYz0nNeb/73rj0c5xiulnHk+4lSZIkaUNTo26ABiz3a3hXJWaU9xLIsp+TGjMjf1m2z5wvHON9WMa3n0M4dmbZz0nNufdhyS6mm3VGeNK9MyyTJvdreFclZpT3Esi0n5Mawwhf25z3H9NX/jLpQ1VzPvBjZ6b9nNScZ3HszD0Pub0XNUvCJEmSJGkDloRNmtynE6sSY0lYRWIsCcuufZaEVSTnloSNd84tCcsuxpIwLavcpxOrEmNJWGVisihrMOc9x1gSNr79tCRsvHOexbEz9zzk9l7ULAmTJEmSpA1YEjZpcp9OrEqMJWEVibEkLLv2WRJWkZxbEjbeObckLLsYS8K0rHKfTqxKjCVhlYnJoqzBnPccY0nY+PbTkrDxznkWx87c85Dbe1GzJEySJEmSNmBJ2KTJfTqxKjGWhFUkxpKw7NpnSVhFcm5J2Hjn3JKw7GIsCdOyyn06sSoxloRVJiaLsgZz3nOMJWHj209LwsY751kcO3PPQ27vRc2SMEmSJEnagCVhkyb36cSqxFgSVpEYS8Kya58lYRXJuSVh451zS8Kyi7EkTMsq9+nEqsRYElaZmCzKGsx5zzGWhI1vPy0JG++cZ3HszD0Pub0XNUvCJEmSJGkDloRNmtynE6sSY0lYRWIsCcuufZaEVSTnloSNd84tCcsuxpIwLavcpxOrEmNJWGVisihrMOc9x1gSNr79tCRsvHOexbEz9zzk9l7ULAmTJEmSpA1YEjZpcp9OrEqMJWEVibEkLLv2WRJWkZxbEjbeObckLLsYS8K0rHKfTqxKjCVhlYnJoqzBnPccY0nY+PbTkrDxznkWx87c85Dbe1GzJEySJEmSNmBJ2KTJfTqxKjGWhFUkxpKw7NpnSVhFcm5J2Hjn3JKw7GIsCdOyyn06sSoxloRVJiaLsgZz3nOMJWHj209LwsY751kcO3PPQ27vRc2SMEmSJEnagCVhE6UNzcegNV38q9XXTd/NXDasmFG+dm4xXa9To90sl+XWB3O+tPxl2T5zvnBMu7/8ZdGHquZ8CMfOLPs5qTnvc98bm36OUUw364ywJMwBywRpt6FVq0OzCc1pKGt8Adozl60YUsywtjuOMV2u02pOQ3MN7WYzvz6Y86XlL8P2mfOFY/rOXwZ9qGrOh3LszLCfk5rzbI6duecht/eiXDYKDlgmSK0G9XaL9lQD2itgqkGNdvHczGXDihnla+cW0+U69cZGtNsrqE218+uDOV9a/jJsnzlfOKbemOovfxn0oao5H8qxM8N+TmrO+973xqSfYxXTzTrlslFwwDJRatBYCa0WNFpQm2LtCVK1qXXLhhUzytfOLabbdepT1BpNqLXy64M5X1r+cmyfOV84pt7oL3859KGqOR/GsTPHfk5qzvvd98aln+MU0806nnQvSZIkSRuaGnUDNGC5X8O7KjGjvJdAlv2c1JgZ+cuyfeZ84RjvwzK+/RzCsTPLfk5qzr0PS3Yx3awzwpPunWGZNLlfw7sqMaO8l0Cm/ZzUGEb42ua8/5i+8pdJH6qa84EfOzPt56TmPItjZ+55yO29qFkSJkmSJEkbsCRs0uQ+nViVGEvCKhJjSVh27bMkrCI5tyRsvHNuSVh2MZaEaVnlPp1YlRhLwioTk0VZgznvOcaSsPHtpyVh453zLI6duecht/eiZkmYJEmSJG3AkrBJk/t0YlViLAmrSIwlYdm1z5KwiuTckrDxzrklYdnFWBKmZZX7dGJVYiwJq0xMFmUN5rznGEvCxrefloSNd86zOHbmnofc3ouaJWGSJEmStAFLwiZN7tOJVYmxJKwiMZaEZdc+S8IqknNLwsY755aEZRdjSZiWVe7TiVWJsSSsMjFZlDWY855jLAkb335aEjbeOc/i2Jl7HnJ7L2qWhEmSJEnSBiwJmzS5TydWJcaSsIrEWBKWXfssCatIzi0JG++cWxKWXUzmJWEOWOYREauBU4EdUkp3LnHdpwPvBfYBtgT+DbgAODel1BpwU9fXWEm71QKmgTq1cvqumH4tlw0rZpSvnVtMt+vMmBbPrg/mfGn5y7F95ryLmD7yl00fKpjzYRw7c+znROc8g2Nn7nnI7b2wJCwvEXEk8O4e130WcDNwNHAHcA2wA3A28KlBtVGSJEmqAmdYZomINwN/Qw/vTUTUKAYljweOSSl9uly+NfAV4NURcUlK6eIBNnl9uU8nViXGkrCKxFgSll37LAmrSM4tCRvvnFsSll1M5iVhzrCUImKniLgSOAf4b+BXPWzmIGBX4IbOYAUgpXQv8Oby17f229YF5X6FiarEjPJKN5n2c1JjGOFrm/P+Y/rKXyZ9qGrOB37szLSfk5rzLI6duecht/eiZklYDs4DDgX+Adgd+K8etnFI+Xjp7CdSSjcB9wD7RMTmvTZSkiRJqhJLwta5GTgzpXQFQET0so1nlI8/nOf5BGwD7Ax8u5cXWFTu04lVibEkrCIxloRl1z5LwiqSc0vCxjvnloRlF5N5SZgDllJK6c8GsJntysefz/N8Z/mqAbzW3HK/wkRVYkZ5pZsc+zmpMTPzl2P7zHkXMX3kL5s+VDDnwzh25tjPic55BsfO3POQ23sxwpKwiRywRMRFFGVdi7kkpfSuAb70puXjQ/M8/3D5uNkAXxPgtwFWbLSSbVathFYLaAF1qJdVf63N1l82rJhRvnZuMV2vsxGrHpdpH8z50vKXZfvM+fwxA8jfyPtQ5ZwP4diZZT8nMecZHTtzz0Nu78U6v80ymsgBC7Aj0E1N13aLhyxJq3xsz/N8bdbjoGwGUKuVm200gFmj4NnLhhUzytfOLSb39g0qJvf2LWdM7u0bVEzu7VvOmNzbN6iY3Ns3qJjc27ecMbm3b1AxubdvOWO6WWedQX/5vqCJHLCklPYZ0Uv/unx83DzPb1w+Pjjg1/0J8NTy9f9twNuWJEmSoJhZ2Yzis+eymcgBywjdDfwesC1w2xzPL3aOS692G/D2JEmSpCzUFw/REnSuDrbz7CfKm0ruBDSBHy1noyRJkqRx5YBlsK4pH4+c47m9gK2Br6eUerkppSRJklQ5Dlh6FBG/FRE7RcQTZiy+EfgX4KCIOG5G7NbAueWvZy5jMyVJkqSx5oCld/8I3Aq8pLMgpdQCXk9x8vsFEfGtiPgixQ0jdwU+0rkxpSRJkqTFOWAZsJTSd4DnAhcDvwMcDNwBHA+cMMKmSZIkSWOn1m7Pd8sQSZIkSRotZ1gkSZIkZcsBiyRJkqRsOWCRJEmSlC0HLJIkSZKy5YBFkiRJUrYcsEiSJEnKlgMWSZIkSdlywCJJkiQpWw5YJEmSJGXLAYskSZKkbE2NugHqTUSsBk4Fdkgp3bnEdZ8OvBfYB9gS+DfgAuDclFJrwE1VKSJeAbwd2BloAt8ATkspfWcJ29gX+OoCIRellF7TV0NFRBwI/DmwK7AS+B7wvpTStUvYhvvZCPSbu4jYAfjpAiE3pZT26buhWlBEHAtcCOybUvr6EtZ7MsXfxoOA7Shy+WngAymlR4fQVM2hl/xFxBTwa2CjeULuSiltP5gWaqaIaAAnAK8DfhdoAP8BfBb4YErpkS63M7S/ew5YxlBEHAm8u8d1n0XxgffxwE3AzcB+wNnA8wA/7A7BjAHmr4DrgC2AQ4EXRcQRKaWru9zUbuXjN4CfzPH8TX02tfJm/KF9lCJXDYp95JqIeFNK6YIutuF+NgKDyB3r9rHvAz+Y4/k0gKZqARHxfIp9ZanrbQ98E9ge+CfgFmBv4DRg/4g4OKW0ZpBt1YZ6zR/Fl3kbAf8OfGuO5/+rn3ZpbuVg5TLgMIoB47eANRR/q04DDouI/VNKDy2ynaH+3XPAMmYi4s3A39BD7iKiBnyK4j/TMSmlT5fLtwa+Arw6Ii5JKV08wCZXXkTsTjFYuQPYO6V0V7n8MOBS4MKIeNpiB4NS58PUO1JKDk4GLCK2A84D/hvYJ6X0w3L5HhT7yN9GxJWdHM6zDfezERhE7kqdfewDKaWLhtZgzSkiXgp8Atish9XPpRisnJJSOr3c3qYUx9kDgbcCZw6mpZpLn/nr7HsXppT+cmCN0mL+iGKw8n3g0BmfUbYCLgeeD5wCvGu+DSzH3z3PYRkTEbFTRFwJnEPxB/lXPWzmIIoyiRs6/5kAUkr3Am8uf31rv23VBk4qH0+d+WEppXQlxYF9FfDKLre1G9AC/nmQDdRaf0LxDd9fdz7wAqSUbgY+AGwMvHGRbbifjcYgcgfrPjR9b+At1LwiYvuI+BRwMcXM2H8ucf0A/oDi2/kzOstTSg8Cb6Aow/2TgTVY6+k3fyX3vdE4tnx826zPKL+kKBMDOHqRbQz9754DlvFxHkUJ0T8Au9Pb1Ogh5eOls58ov62/B9gnIjbvtZGa0yFAm+KbitkuKR9/f7GNRMRKiinz28o/whq8efcRus+V+9loDCJ3UHxo+jXw40E0Sl07HTgG+C5F+chtS1z/RUANuGJ2rXxK6acU5WE7RsTOA2irNtRv/mDdgOWWQTVKXfklRb7mOp+2cxx88iLbGPrfPUvCxsfNwJkppSsAii+TluwZ5eMP53k+AdtQfCj+di8voPWVZSpbAHemlO6fI6RzUH9mF5vbBVgB3B4RpwMvA34T+AXFt1qnp5Qe6LvRFVVOae9MMYN16xwhPy6fe0ZE1FJK7Xk25X62zAaVu4h4EvAUig9MfxoRxwC/AzwAfAlYnVK6ewhdUHEsfB3w6ZRSq4e/cYvtd7cBe1Aca3/UUwu1kL7yV+7Dv0fx9+yIiHgjxcnfj1CUFK1OKXn+2BCklA5f4Ok9ysfFLu409L97zrCMiZTSn3UGK33Yrnz8+TzPd5av6vN1tM4g3/POt0+HAm+juILH1ykGRCcB3y7rRdWbLShKiu5LKT02+8mU0jTFN1GbAAt9S+R+tvwGlbvOPvZsirKie4DrKb7cOw74XvT4bZEWllJ6X0rpU31cScj9boQGkL+nUZz/sC1wPsVA5fry8Wjg5ojYeyCNVVfKQeRp5a+LnXsy9P3PGZYRiIiLKMq6FnNJSmnek5x6sGn5ON/J3Q+Xj72cLFcZS8kfcFX583zveedSgd28550PUzcCR5W1oZ0T4z4LHEBROviyLralDS22f8D6+8j/9Lgd97PBG1TuOvvYvwCHp5R+AmtP3P4I8CrgIuA5fbVWw+B+N946+95dwB+klP4Z1l7q+H0UX8p9LiJ+u9tL7KpvZwAvoDgf6YOLxA59/3OGZTR2BKKLf9vNt4Eedb75mK+UpTbrUXNbSv4We887unnP315u9/DOYAXWnhj3WuBB4CVlGZqWrptcdbOPuJ8tv0Hl7q8pvul9YWewAmtP3P4jig9Tu0fE8/poq4bD/W68XUxRjrlnZ7ACa2dH30FxIv5vAEeOpnnVEhGnAe+kuET8K2Z+5pjH0Pc/Z1hGYIQ3Hft1+fi4eZ7fuHz0hO4FLCV/5XXJYQDveXn/gDlPBE4p3R0RtwD7UpSzXNltG7XWYvsHdJcv97PlN5DcpZSazH1/I1JKD0XEdRQnFu/O3PeJ0Oi4342x8ryyn83zXCsirqLY73anqCjQEJQzWudQXFHxEeClKaWFblbdMfT9zxmWaumcLLrtPM8vVoOopetcInA53vNflI+bDGBbVfQ/FAfdrcqD9nrKZVsBjyxycQP3s+U3qNwtxn0sX+53k819b8giYjPgCorBygPAi5ZwU+uh738OWKqlc/WGDS7rWJ5ctRPFteq9gsqAlOVa9wDbz3M5v98tH+e6o/Z6IuKsiLgkIraZJ+Sp5eNiV/PQHMpv+H5EcQ+Bp88REhTHzMVy5X62zAaVu4g4NSL+PiLmu2qf+1i+5t3vSl0fa7X8IuItEfG5iDhwnhD3vSGKiC2AGyguT/wzYN8uZ1Y6hv53zwFLtVxTPs5VA7oXsDXw9ZRSLzel1PyuofggNdelAzu5uGqO52bbu4zfYDsRsQvFSYv34U23+rHQPtJtrtzPRmMQuduV4qIVr5j9RPlFwcHAGoqrFykvnfwfERHrfbaJiKdQHB/vSCn5RUGenkax371u9hMRsTFwVPnrl5ezUVVQ3uOtU3L3I2CvmTff7dLQ/+45YJlQEfFbEbFTRDxhxuIbKa5+c1BEHDcjdmvg3PLXM5exmVXxYYoT0d4fEZ1viYiIwyjuMPtz4DMzVyhzt1NEzJz+Pr98PCMidpoRuzVwIcWg6ANzXdZVXbuQom735IhYeyW4iHgOxYmfD7NuX3E/y8sgctfZx06aeQnVslTi4xSXXf1oSukXaGQi4ill7rbqLCsvknANxWzaaTNiNwU+SnF8dL/LwFz5Az5G8Q38qyPiZTNiVwBnU1zs5uqUkl/IDd5pFDf7/BnFBUcWnMUa1d+9Wru92MWLlKOIuJ1iB95hrv9cM57/3ymlT8xYvifwjxSXlvs2Rd3hCynuY/CRlNIbh9rwioqI91N8aHqI4v3fnOJygWuAQ1JK18+K7+yY+6WUbiiX1YHPAS8HHgO+RnEC237l9j4P/GF54rB6FBFvpjjpcA1FrmrA/hQXKXltSunTM2Jvx/0sGwPK3ZnAn1Jc9eYmivu37EtxDszXKPbXhS6frAGIiBsojpH7ppS+Ps9z700prZ6x/GkUOduWokQlUXy7ux1wNXBEedUpDVmP+Xsr8DcU++3NwE+B5wLbU9yY8gUppXuWofmVUd4s906Kk+VvYe4b7wKQUnpNuc7tjODvnjMsFZNS+g7FAeBiijs4HwzcARwPnDDCpk20lNLJFLMptwIHUtR5Xgk8f/ZgZYFttCimzI8Hvk/xh/iAcpvHAUc7WOlfSulcirK7b1F8UN2D4gadB838wLvINtzPRmBAuTuJYj+7iaKM6BCKWdB3AAc4WMlXSuk/gD2BT1CUoBwG3A+8i+JqRw5WMpZSOgs4CLiW4rj5BxRf8v0lsIeDlaHYk3VX9no28OoF/i1o2H/3nGGRJEmSlC1nWCRJkiRlywGLJEmSpGw5YJEkSZKULQcskiRJkrLlgEWSJElSthywSJIkScqWAxZJkiRJ2XLAlsLCFAAAA2ZJREFUIkmSJClbDlgkSZIkZcsBiyRJkqRsOWCRJEmSlC0HLJIkSZKy5YBFkiRJUrYcsEiSJEnKlgMWSZIkSdmaGnUDJEnqR0Q8CfgB8ORy0RkppXfPE/t64GPlr3cDu6aU7ht+KyVJvaq12+1Rt0GSpL5ExCHA1eWv08DuKaXvz4r5TeD7wOZACzgopXTdcrZTkrR0loRJksZeSuka4Pzy1yngYxHR6DwfEXXg7ygGKwAfdLAiSePBAYskaVKcBPx7+fNzgBNnPPcOYJ/y5+8CpyxjuyRJfbAkTJI0MSJib+CrFF/IPQjsDDwRuBlYWS7bLaX0ryNrpCRpSRywSJImSkS8Dzi5/PUyYEfg98rf35BS+vg86z0VOBDYs/z3DKABvDeltHqYbZYkzc+rhEmSJs17gN8HdgVePGP5F+YbrJROZP0yMklSBjyHRZI0UVJKjwHHAI/NWPwz4E2LrPpL4EusG/BcPJQGSpKWxBkWSdIkup1iANK5N0sLaC60Qkrp9Jm/R8TRQ2mZJGlJnGGRJE2is1g3WIHiPJa/HVFbJEl9cMAiSZooEfFi4HXlr7cCt5U/HxsRR4ymVZKkXjlgkSRNjIjYGrig/LUFvAF4I9C5JOYFEbHVKNomSeqNAxZJ0iQ5H9im/PnslNI3U0pfK5cDrJrxsyRpDDhgkSRNhIh4LfCS8tfbgXfPePpk4K7y55dGxDHL2DRJUh8csEiSxl5E7EBxon3HcSmlBzu/pJT+BzhhxvNnR8T2y9U+SVLvHLBIksZaRNSAjwNPKBd9PKX0ldlxKaUrgM+Vvz4B+Hi5riQpYw5YJEnj7i3AgeXPPwdOWiD2rcB95c8HletKkjLmjSMlSWMtpfQh4ENdxt4DeJUwSRojzrBIkiRJypYDFkmSJEnZqrXb7cWjJEmacBGxN3DZjEWbARsBDwMPzVi+W0rpZ8vZNkmqMs9hkSSpsALYco7ljyv/dTSWpzmSJHCGRZIkSVLGPIdFkiRJUrYcsEiSJEnKlgMWSZIkSdlywCJJkiQpWw5YJEmSJGXLAYskSZKkbDlgkSRJkpQtByySJEmSsuWARZIkSVK2HLBIkiRJypYDFkmSJEnZcsAiSZIkKVsOWCRJkiRlywGLJEmSpGw5YJEkSZKULQcskiRJkrL1/wHzVps3T/HyhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 406
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "# Calculating Boolean NAND using a perceptron\n",
    "import matplotlib.pyplot as plt\n",
    "threshold=-1.5\n",
    "# (w1, w2)\n",
    "w=[-1,-1]\n",
    "# (x1, x2) pairs\n",
    "x1 = [0, 1, 0, 1]\n",
    "x2 = [0, 0, 1, 1]\n",
    "output = perceptron([x1, x2], w, threshold)\n",
    "for i in range(len(output)):\n",
    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
    "          \" is \", output[i])\n",
    "perceptron_DB(x1, x2, w, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, a single perceptron can compute \"AND\", \"OR\" and \"NOT\" boolean functions.\n",
    "\n",
    "However, it cannot compute some other boolean functions such as \"XOR\".\n",
    "\n",
    "**WHAT CAN WE DO?**\n",
    "\n",
    "\n",
    "Hint: Think about what is the significance of the NAND gate we have created above?\n",
    "\n",
    "Answer: We said a single perceptron can't compute a \"XOR\" function. We didn't say that about **multiple Perceptrons** put together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**XOR function using multiple perceptrons**\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/perceptron_XOR.svg\" width=\"400\"/>\n",
    "<figcaption>Multiple perceptrons connected together to output a XOR function.</figcaption>\n",
    "</figure>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-layer perceptrons\n",
    "\n",
    "The normal densely connected neural network is sometimes also called \"Multi-layer\" perceptron."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning\n",
    "\n",
    "We know that we can compute complicated functions by combining a number of perceptrons.\n",
    "\n",
    "In the perceptron examples we had set the model parameters (weights and threshold) by hand.\n",
    "\n",
    "This is something we definitely **DO NOT** want to do or even can do for big networks.\n",
    "\n",
    "We want some algorithm to set/learn the model parameters for us!\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>Threshold -> bias</strong>  \n",
    "    \n",
    "Before we go further we need to introduce one change. The threshold which we saw in the step activation function above is moved to the left side of the equation and is called **bias**.\n",
    "\n",
    "$$\n",
    "f = \\left\\{\n",
    "        \\begin{array}{ll}\n",
    "            0 & \\quad weighted\\_sum + bias < 0 \\\\\n",
    "            1 & \\quad weighted\\_sum + bias \\geq 0\n",
    "        \\end{array}\n",
    "       \\quad \\quad  \\mathrm{where}, bias = -threshold\n",
    "    \\right.\n",
    "$$\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to algorithmically set/learn the weights and bias we need to choose an appropriate loss function for the problem at hand and solve an optimization problem.\n",
    "We will explain below what this means.\n",
    "\n",
    "\n",
    "### Loss function\n",
    "\n",
    "To learn using an algorithm we need to define a quantity/function which allows us to measure how close or far are the predictions of our network/setup from reality or the supplied labels. This is done by choosing a so-called \"Loss function\" (as in the case for other machine learning algorithms).\n",
    "\n",
    "Once we have this function, we need an algorithm to update the weights of the network such that this loss function decreases. \n",
    "As one can already imagine the choice of an appropriate loss function is critical to the success of the model. \n",
    "\n",
    "Fortunately, for classification and regression (which cover a large variety of problems) these loss functions are well known. \n",
    "\n",
    "Generally **crossentropy** and **mean squared error** loss functions are used for classification and regression problems, respectively.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; As we have seen before, <strong>mean squared error</strong> is defined as \n",
    "\n",
    "\n",
    "$$\n",
    "\\frac{1}{n} \\left((y_1 - \\hat{y}_1)^2 + (y_2 - \\hat{y}_2)^2 + ... + (y_n - \\hat{y}_n)^2 \\right)\n",
    "$$\n",
    "\n",
    "\n",
    "</div>\n",
    "\n",
    "### Gradient based learning\n",
    "\n",
    "As mentioned above, once we have chosen a loss function, we want to solve an **optimization problem** which minimizes this loss by updating the parameters (weights and biases) of the network. This is how the learning takes in a NN, and the \"knowledge\" is stored as the weights and biases.\n",
    "\n",
    "The most popular optimization methods used in Neural Network training are **Gradient-descent (GD)** type methods, such as gradient-descent itself, RMSprop and Adam. \n",
    "\n",
    "**Gradient-descent** uses partial derivatives of the loss function with respect to the network weights and a learning rate to updates the weights such that the loss function decreases and after some iterations reaches its (Global) minimum value.\n",
    "\n",
    "First, the loss function and its derivative are computed at the output node, and this signal is propagated backwards, using the chain rule, in the network to compute the partial derivatives. Hence, this method is called **Backpropagation**.\n",
    "\n",
    "One way to perform a single GD pass is to compute the partial derivatives using **all the samples** in our data, computing average derivatives and using them to update the weights. This is called **Batch gradient descent**. However, in deep learning we mostly work with massive datasets and using batch gradient descent can make the training very slow!\n",
    "\n",
    "The other extreme is to randomly shuffle the dataset and advance a pass of GD with the gradients computed using only **one sample** at a time. This is called **Stochastic gradient descent**.\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"stochastic-vs-batch-gradient-descent.png\" width=\"600\"/>\n",
    "<figcaption>Source: <a href=\"https://wikidocs.net/3413\">https://wikidocs.net/3413</a></figcaption>\n",
    "</figure>\n",
    "</center>\n",
    "\n",
    "\n",
    "In practice, an approach in-between these two is used. The entire dataset is divided into **m batches** and these are used one by one to compute the derivatives and apply GD. This technique is called **Mini-batch gradient descent**. \n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "One pass through the entire training dataset is called 1 epoch of training.\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(10, 4)) ;\n",
    "\n",
    "pts=np.arange(-20,20, 0.1) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activation Functions\n",
    "\n",
    "In order to train the network we need to move away from Perceptron's **step** activation function because it can not be used for training using the gradient-descent and back-propagation algorithms among other drawbacks.\n",
    "\n",
    "Non-Linear functions such as:\n",
    "\n",
    "* Sigmoid\n",
    "\n",
    "\\begin{equation*}\n",
    "f(z) = \\frac{1}{1+e^{-z}} \\quad \\quad \\mathrm{where}, z = weighted\\_sum + bias\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 376
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(pts, 1/(1+np.exp(-pts))) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* tanh\n",
    "\n",
    "\\begin{equation*}\n",
    "f(z) = \\frac{e^{z} - e^{-z}}{e^{z} + e^{-z}}\\quad \\quad \\mathrm{where}, z = weighted\\_sum + bias\n",
    "\\end{equation*}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 389
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(pts, np.tanh(pts*np.pi)) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **ReLU (Rectified linear unit)**\n",
    "\n",
    "\\begin{equation*}\n",
    "f(z) = \\mathrm{max}(0,z)   \\quad \\quad \\mathrm{where}, z = weighted\\_sum + bias\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 383
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pts_relu=[max(0,i) for i in pts];\n",
    "plt.plot(pts, pts_relu) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "are some of the commonly used as activation functions. Such non-linear activation functions allow the network to learn complex representations of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "ReLU is very popular and is widely used nowadays. There also exist other variations of ReLU, e.g. \"leaky ReLU\".\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "Why don't we just use a simple linear activation function?\n",
    "    \n",
    "Linear activations are **NOT** used because it can be mathematically shown that if they are used then the output is just a linear function of the input. So we cannot learn interesting and complex functions by adding any number of hidden layers.\n",
    "\n",
    "The only exception when we do want to use a linear activation is for the output layer of a network when solving a regression problem.\n",
    "\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section\n",
    "\n",
    "### Google Playground\n",
    "\n",
    "A great tool from Google to develop a feeling for the workings of neural networks.\n",
    "\n",
    "https://playground.tensorflow.org/\n",
    "\n",
    "<img src=\"./images/neuralnets/google_playground.png\"/>\n",
    "\n",
    "**Walkthrough by instructor**\n",
    "\n",
    "Some concepts to look at:\n",
    "\n",
    "* Simple vs Complex models (Effect of network size)\n",
    "* Optimization results\n",
    "* Effect of activation functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is Keras?\n",
    "\n",
    "* It is a high level API to create and work with neural networks\n",
    "* Supports multiple backends such as **TensorFlow** from Google, **Theano** (Although Theano is dead now) and **CNTK** (Microsoft Cognitive Toolkit)\n",
    "* Very good for creating neural nets quickly and hides away a lot of tedious work\n",
    "* Has been incorporated into official TensorFlow (which obviously only works with tensforflow) and as of TensorFlow 2.0 this will the main api to use it\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/neural_net_keras_1.svg\" width=\"700\"/>\n",
    "<figcaption>Building this model in Keras</figcaption>\n",
    "</figure>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_154 (Dense)            (None, 4)                 12        \n",
      "_________________________________________________________________\n",
      "dense_155 (Dense)            (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_156 (Dense)            (None, 1)                 5         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 37\n",
      "Trainable params: 37\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Say hello to keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "# Creating a model\n",
    "model = Sequential()\n",
    "\n",
    "# Adding layers to this model\n",
    "# 1st Hidden layer\n",
    "# A Dense/fully-connected layer which takes as input a \n",
    "# feature array of shape (samples, num_features)\n",
    "# Here input_shape = (2,) means that the layer expects an input with num_features = 2\n",
    "# and the sample size could be anything\n",
    "# The activation function for this layer is set to \"relu\"\n",
    "model.add(Dense(units=4, input_shape=(2,), activation=\"relu\"))\n",
    "\n",
    "# 2nd Hidden layer\n",
    "# This is also a fully-connected layer and we do not need to specify the\n",
    "# shape of the input anymore (We need to do that only for the first layer)\n",
    "# NOTE: Now we didn't add the activation seperately. Instead we just added it\n",
    "# while calling Dense(). This and the way used for the first layer are Equivalent!\n",
    "model.add(Dense(units=4, activation=\"relu\"))\n",
    "\n",
    "          \n",
    "# The output layer\n",
    "model.add(Dense(units=1))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XOR using neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 332
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a network to solve the XOR problem\n",
    "\n",
    "# Loading and plotting the data\n",
    "xor = pd.read_csv(\"xor.csv\")\n",
    "\n",
    "# Using x and y coordinates as featues\n",
    "features = xor.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = xor.iloc[:, -1].astype(int)\n",
    "\n",
    "colors = [[\"steelblue\", \"chocolate\"][i] for i in xor[\"label\"]]\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.xlim([-2, 2])\n",
    "plt.ylim([-2, 2])\n",
    "plt.title(\"Blue points are False\")\n",
    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\") ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a simple Keras model\n",
    "\n",
    "def a_simple_NN():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(4, input_shape = (2,), activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(4, activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 350 samples, validate on 150 samples\n",
      "Epoch 1/300\n",
      "350/350 [==============================] - 4s 10ms/step - loss: 0.7117 - acc: 0.5343 - val_loss: 0.7110 - val_acc: 0.5667\n",
      "Epoch 2/300\n",
      "350/350 [==============================] - 0s 313us/step - loss: 0.7049 - acc: 0.5400 - val_loss: 0.7072 - val_acc: 0.5600\n",
      "Epoch 3/300\n",
      "350/350 [==============================] - 0s 188us/step - loss: 0.6996 - acc: 0.5400 - val_loss: 0.7035 - val_acc: 0.5600\n",
      "Epoch 4/300\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.6946 - acc: 0.5400 - val_loss: 0.6996 - val_acc: 0.5600\n",
      "Epoch 5/300\n",
      "350/350 [==============================] - 0s 139us/step - loss: 0.6895 - acc: 0.5486 - val_loss: 0.6955 - val_acc: 0.5600\n",
      "Epoch 6/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.6845 - acc: 0.5486 - val_loss: 0.6909 - val_acc: 0.5600\n",
      "Epoch 7/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.6790 - acc: 0.5543 - val_loss: 0.6858 - val_acc: 0.5667\n",
      "Epoch 8/300\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.6736 - acc: 0.5600 - val_loss: 0.6814 - val_acc: 0.5733\n",
      "Epoch 9/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.6691 - acc: 0.5629 - val_loss: 0.6775 - val_acc: 0.5733\n",
      "Epoch 10/300\n",
      "350/350 [==============================] - 0s 115us/step - loss: 0.6654 - acc: 0.5657 - val_loss: 0.6742 - val_acc: 0.5733\n",
      "Epoch 11/300\n",
      "350/350 [==============================] - 0s 108us/step - loss: 0.6625 - acc: 0.5686 - val_loss: 0.6717 - val_acc: 0.5667\n",
      "Epoch 12/300\n",
      "350/350 [==============================] - 0s 143us/step - loss: 0.6602 - acc: 0.5771 - val_loss: 0.6698 - val_acc: 0.5667\n",
      "Epoch 13/300\n",
      "350/350 [==============================] - 0s 155us/step - loss: 0.6586 - acc: 0.5857 - val_loss: 0.6682 - val_acc: 0.5667\n",
      "Epoch 14/300\n",
      "350/350 [==============================] - 0s 156us/step - loss: 0.6570 - acc: 0.5829 - val_loss: 0.6668 - val_acc: 0.5800\n",
      "Epoch 15/300\n",
      "350/350 [==============================] - 0s 149us/step - loss: 0.6554 - acc: 0.5914 - val_loss: 0.6654 - val_acc: 0.5800\n",
      "Epoch 16/300\n",
      "350/350 [==============================] - 0s 152us/step - loss: 0.6539 - acc: 0.5943 - val_loss: 0.6639 - val_acc: 0.5800\n",
      "Epoch 17/300\n",
      "350/350 [==============================] - 0s 141us/step - loss: 0.6524 - acc: 0.6000 - val_loss: 0.6627 - val_acc: 0.5800\n",
      "Epoch 18/300\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.6509 - acc: 0.6000 - val_loss: 0.6614 - val_acc: 0.5800\n",
      "Epoch 19/300\n",
      "350/350 [==============================] - 0s 144us/step - loss: 0.6494 - acc: 0.6029 - val_loss: 0.6599 - val_acc: 0.5867\n",
      "Epoch 20/300\n",
      "350/350 [==============================] - 0s 170us/step - loss: 0.6477 - acc: 0.6114 - val_loss: 0.6583 - val_acc: 0.5867\n",
      "Epoch 21/300\n",
      "350/350 [==============================] - 0s 137us/step - loss: 0.6461 - acc: 0.6114 - val_loss: 0.6567 - val_acc: 0.5867\n",
      "Epoch 22/300\n",
      "350/350 [==============================] - 0s 156us/step - loss: 0.6444 - acc: 0.6143 - val_loss: 0.6550 - val_acc: 0.5933\n",
      "Epoch 23/300\n",
      "350/350 [==============================] - 0s 151us/step - loss: 0.6427 - acc: 0.6171 - val_loss: 0.6533 - val_acc: 0.5933\n",
      "Epoch 24/300\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.6410 - acc: 0.6171 - val_loss: 0.6516 - val_acc: 0.5933\n",
      "Epoch 25/300\n",
      "350/350 [==============================] - 0s 143us/step - loss: 0.6391 - acc: 0.6200 - val_loss: 0.6496 - val_acc: 0.6000\n",
      "Epoch 26/300\n",
      "350/350 [==============================] - 0s 162us/step - loss: 0.6372 - acc: 0.6200 - val_loss: 0.6475 - val_acc: 0.6133\n",
      "Epoch 27/300\n",
      "350/350 [==============================] - 0s 150us/step - loss: 0.6352 - acc: 0.6257 - val_loss: 0.6452 - val_acc: 0.6200\n",
      "Epoch 28/300\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.6331 - acc: 0.6257 - val_loss: 0.6429 - val_acc: 0.6200\n",
      "Epoch 29/300\n",
      "350/350 [==============================] - 0s 105us/step - loss: 0.6311 - acc: 0.6257 - val_loss: 0.6409 - val_acc: 0.6267\n",
      "Epoch 30/300\n",
      "350/350 [==============================] - 0s 191us/step - loss: 0.6292 - acc: 0.6286 - val_loss: 0.6388 - val_acc: 0.6400\n",
      "Epoch 31/300\n",
      "350/350 [==============================] - 0s 156us/step - loss: 0.6273 - acc: 0.6343 - val_loss: 0.6367 - val_acc: 0.6467\n",
      "Epoch 32/300\n",
      "350/350 [==============================] - 0s 138us/step - loss: 0.6254 - acc: 0.6429 - val_loss: 0.6348 - val_acc: 0.6533\n",
      "Epoch 33/300\n",
      "350/350 [==============================] - 0s 137us/step - loss: 0.6235 - acc: 0.6429 - val_loss: 0.6327 - val_acc: 0.6600\n",
      "Epoch 34/300\n",
      "350/350 [==============================] - 0s 137us/step - loss: 0.6215 - acc: 0.6429 - val_loss: 0.6307 - val_acc: 0.6600\n",
      "Epoch 35/300\n",
      "350/350 [==============================] - 0s 129us/step - loss: 0.6194 - acc: 0.6429 - val_loss: 0.6286 - val_acc: 0.6600\n",
      "Epoch 36/300\n",
      "350/350 [==============================] - 0s 132us/step - loss: 0.6175 - acc: 0.6429 - val_loss: 0.6266 - val_acc: 0.6600\n",
      "Epoch 37/300\n",
      "350/350 [==============================] - 0s 157us/step - loss: 0.6155 - acc: 0.6429 - val_loss: 0.6248 - val_acc: 0.6600\n",
      "Epoch 38/300\n",
      "350/350 [==============================] - 0s 145us/step - loss: 0.6135 - acc: 0.6457 - val_loss: 0.6230 - val_acc: 0.6600\n",
      "Epoch 39/300\n",
      "350/350 [==============================] - 0s 139us/step - loss: 0.6116 - acc: 0.6457 - val_loss: 0.6213 - val_acc: 0.6733\n",
      "Epoch 40/300\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.6098 - acc: 0.6457 - val_loss: 0.6195 - val_acc: 0.6733\n",
      "Epoch 41/300\n",
      "350/350 [==============================] - 0s 141us/step - loss: 0.6080 - acc: 0.6514 - val_loss: 0.6177 - val_acc: 0.6733\n",
      "Epoch 42/300\n",
      "350/350 [==============================] - 0s 158us/step - loss: 0.6062 - acc: 0.6514 - val_loss: 0.6159 - val_acc: 0.6867\n",
      "Epoch 43/300\n",
      "350/350 [==============================] - 0s 145us/step - loss: 0.6044 - acc: 0.6543 - val_loss: 0.6141 - val_acc: 0.6933\n",
      "Epoch 44/300\n",
      "350/350 [==============================] - 0s 156us/step - loss: 0.6026 - acc: 0.6514 - val_loss: 0.6123 - val_acc: 0.6933\n",
      "Epoch 45/300\n",
      "350/350 [==============================] - 0s 158us/step - loss: 0.6008 - acc: 0.6600 - val_loss: 0.6106 - val_acc: 0.6933\n",
      "Epoch 46/300\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.5990 - acc: 0.6600 - val_loss: 0.6087 - val_acc: 0.6933\n",
      "Epoch 47/300\n",
      "350/350 [==============================] - 0s 148us/step - loss: 0.5972 - acc: 0.6571 - val_loss: 0.6070 - val_acc: 0.7000\n",
      "Epoch 48/300\n",
      "350/350 [==============================] - 0s 120us/step - loss: 0.5956 - acc: 0.6657 - val_loss: 0.6053 - val_acc: 0.7067\n",
      "Epoch 49/300\n",
      "350/350 [==============================] - 0s 143us/step - loss: 0.5940 - acc: 0.6686 - val_loss: 0.6039 - val_acc: 0.7133\n",
      "Epoch 50/300\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.5924 - acc: 0.6686 - val_loss: 0.6024 - val_acc: 0.7133\n",
      "Epoch 51/300\n",
      "350/350 [==============================] - 0s 151us/step - loss: 0.5906 - acc: 0.6743 - val_loss: 0.6008 - val_acc: 0.7133\n",
      "Epoch 52/300\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.5889 - acc: 0.6743 - val_loss: 0.5992 - val_acc: 0.7133\n",
      "Epoch 53/300\n",
      "350/350 [==============================] - 0s 205us/step - loss: 0.5872 - acc: 0.6743 - val_loss: 0.5976 - val_acc: 0.7133\n",
      "Epoch 54/300\n",
      "350/350 [==============================] - 0s 146us/step - loss: 0.5856 - acc: 0.6743 - val_loss: 0.5961 - val_acc: 0.7133\n",
      "Epoch 55/300\n",
      "350/350 [==============================] - 0s 145us/step - loss: 0.5839 - acc: 0.6800 - val_loss: 0.5945 - val_acc: 0.7133\n",
      "Epoch 56/300\n",
      "350/350 [==============================] - 0s 152us/step - loss: 0.5823 - acc: 0.6771 - val_loss: 0.5930 - val_acc: 0.7133\n",
      "Epoch 57/300\n",
      "350/350 [==============================] - 0s 143us/step - loss: 0.5807 - acc: 0.6800 - val_loss: 0.5914 - val_acc: 0.7133\n",
      "Epoch 58/300\n",
      "350/350 [==============================] - 0s 112us/step - loss: 0.5792 - acc: 0.6829 - val_loss: 0.5899 - val_acc: 0.7133\n",
      "Epoch 59/300\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.5776 - acc: 0.6829 - val_loss: 0.5884 - val_acc: 0.7133\n",
      "Epoch 60/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.5763 - acc: 0.6886 - val_loss: 0.5869 - val_acc: 0.7133\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/300\n",
      "350/350 [==============================] - 0s 115us/step - loss: 0.5748 - acc: 0.6886 - val_loss: 0.5855 - val_acc: 0.7133\n",
      "Epoch 62/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.5734 - acc: 0.6914 - val_loss: 0.5841 - val_acc: 0.7133\n",
      "Epoch 63/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.5720 - acc: 0.6914 - val_loss: 0.5827 - val_acc: 0.7133\n",
      "Epoch 64/300\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.5707 - acc: 0.6943 - val_loss: 0.5813 - val_acc: 0.7133\n",
      "Epoch 65/300\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.5693 - acc: 0.6914 - val_loss: 0.5800 - val_acc: 0.7133\n",
      "Epoch 66/300\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.5681 - acc: 0.6943 - val_loss: 0.5787 - val_acc: 0.7133\n",
      "Epoch 67/300\n",
      "350/350 [==============================] - 0s 109us/step - loss: 0.5668 - acc: 0.6971 - val_loss: 0.5774 - val_acc: 0.7133\n",
      "Epoch 68/300\n",
      "350/350 [==============================] - 0s 142us/step - loss: 0.5656 - acc: 0.6943 - val_loss: 0.5762 - val_acc: 0.7133\n",
      "Epoch 69/300\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.5644 - acc: 0.6971 - val_loss: 0.5751 - val_acc: 0.7133\n",
      "Epoch 70/300\n",
      "350/350 [==============================] - 0s 125us/step - loss: 0.5632 - acc: 0.6971 - val_loss: 0.5738 - val_acc: 0.7133\n",
      "Epoch 71/300\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.5619 - acc: 0.6971 - val_loss: 0.5726 - val_acc: 0.7133\n",
      "Epoch 72/300\n",
      "350/350 [==============================] - 0s 129us/step - loss: 0.5609 - acc: 0.6971 - val_loss: 0.5714 - val_acc: 0.7133\n",
      "Epoch 73/300\n",
      "350/350 [==============================] - 0s 125us/step - loss: 0.5597 - acc: 0.7000 - val_loss: 0.5703 - val_acc: 0.7133\n",
      "Epoch 74/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.5584 - acc: 0.7000 - val_loss: 0.5691 - val_acc: 0.7133\n",
      "Epoch 75/300\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.5574 - acc: 0.7000 - val_loss: 0.5681 - val_acc: 0.7133\n",
      "Epoch 76/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.5562 - acc: 0.7000 - val_loss: 0.5671 - val_acc: 0.7133\n",
      "Epoch 77/300\n",
      "350/350 [==============================] - 0s 122us/step - loss: 0.5551 - acc: 0.7029 - val_loss: 0.5661 - val_acc: 0.7133\n",
      "Epoch 78/300\n",
      "350/350 [==============================] - 0s 125us/step - loss: 0.5539 - acc: 0.7057 - val_loss: 0.5650 - val_acc: 0.7200\n",
      "Epoch 79/300\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.5529 - acc: 0.7086 - val_loss: 0.5640 - val_acc: 0.7200\n",
      "Epoch 80/300\n",
      "350/350 [==============================] - 0s 118us/step - loss: 0.5518 - acc: 0.7086 - val_loss: 0.5629 - val_acc: 0.7200\n",
      "Epoch 81/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.5506 - acc: 0.7086 - val_loss: 0.5619 - val_acc: 0.7200\n",
      "Epoch 82/300\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.5495 - acc: 0.7086 - val_loss: 0.5609 - val_acc: 0.7200\n",
      "Epoch 83/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.5485 - acc: 0.7200 - val_loss: 0.5599 - val_acc: 0.7200\n",
      "Epoch 84/300\n",
      "350/350 [==============================] - 0s 136us/step - loss: 0.5474 - acc: 0.7171 - val_loss: 0.5589 - val_acc: 0.7200\n",
      "Epoch 85/300\n",
      "350/350 [==============================] - 0s 136us/step - loss: 0.5463 - acc: 0.7229 - val_loss: 0.5579 - val_acc: 0.7200\n",
      "Epoch 86/300\n",
      "350/350 [==============================] - 0s 119us/step - loss: 0.5454 - acc: 0.7229 - val_loss: 0.5570 - val_acc: 0.7200\n",
      "Epoch 87/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.5444 - acc: 0.7229 - val_loss: 0.5561 - val_acc: 0.7200\n",
      "Epoch 88/300\n",
      "350/350 [==============================] - 0s 137us/step - loss: 0.5435 - acc: 0.7257 - val_loss: 0.5551 - val_acc: 0.7200\n",
      "Epoch 89/300\n",
      "350/350 [==============================] - 0s 120us/step - loss: 0.5426 - acc: 0.7257 - val_loss: 0.5543 - val_acc: 0.7267\n",
      "Epoch 90/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.5416 - acc: 0.7257 - val_loss: 0.5534 - val_acc: 0.7267\n",
      "Epoch 91/300\n",
      "350/350 [==============================] - 0s 129us/step - loss: 0.5406 - acc: 0.7229 - val_loss: 0.5524 - val_acc: 0.7267\n",
      "Epoch 92/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.5397 - acc: 0.7257 - val_loss: 0.5514 - val_acc: 0.7267\n",
      "Epoch 93/300\n",
      "350/350 [==============================] - 0s 132us/step - loss: 0.5388 - acc: 0.7257 - val_loss: 0.5505 - val_acc: 0.7267\n",
      "Epoch 94/300\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.5379 - acc: 0.7257 - val_loss: 0.5496 - val_acc: 0.7267\n",
      "Epoch 95/300\n",
      "350/350 [==============================] - 0s 129us/step - loss: 0.5370 - acc: 0.7257 - val_loss: 0.5487 - val_acc: 0.7267\n",
      "Epoch 96/300\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.5362 - acc: 0.7257 - val_loss: 0.5478 - val_acc: 0.7333\n",
      "Epoch 97/300\n",
      "350/350 [==============================] - 0s 122us/step - loss: 0.5353 - acc: 0.7286 - val_loss: 0.5470 - val_acc: 0.7333\n",
      "Epoch 98/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.5344 - acc: 0.7257 - val_loss: 0.5462 - val_acc: 0.7333\n",
      "Epoch 99/300\n",
      "350/350 [==============================] - 0s 125us/step - loss: 0.5335 - acc: 0.7257 - val_loss: 0.5454 - val_acc: 0.7333\n",
      "Epoch 100/300\n",
      "350/350 [==============================] - 0s 125us/step - loss: 0.5327 - acc: 0.7257 - val_loss: 0.5446 - val_acc: 0.7333\n",
      "Epoch 101/300\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.5318 - acc: 0.7286 - val_loss: 0.5438 - val_acc: 0.7400\n",
      "Epoch 102/300\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.5309 - acc: 0.7257 - val_loss: 0.5431 - val_acc: 0.7400\n",
      "Epoch 103/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.5301 - acc: 0.7257 - val_loss: 0.5423 - val_acc: 0.7400\n",
      "Epoch 104/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.5292 - acc: 0.7286 - val_loss: 0.5415 - val_acc: 0.7400\n",
      "Epoch 105/300\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.5283 - acc: 0.7257 - val_loss: 0.5406 - val_acc: 0.7400\n",
      "Epoch 106/300\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.5268 - acc: 0.7286 - val_loss: 0.5396 - val_acc: 0.7400\n",
      "Epoch 107/300\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.5251 - acc: 0.7286 - val_loss: 0.5386 - val_acc: 0.7400\n",
      "Epoch 108/300\n",
      "350/350 [==============================] - 0s 136us/step - loss: 0.5238 - acc: 0.7286 - val_loss: 0.5375 - val_acc: 0.7400\n",
      "Epoch 109/300\n",
      "350/350 [==============================] - 0s 102us/step - loss: 0.5223 - acc: 0.7286 - val_loss: 0.5361 - val_acc: 0.7400\n",
      "Epoch 110/300\n",
      "350/350 [==============================] - 0s 105us/step - loss: 0.5206 - acc: 0.7286 - val_loss: 0.5348 - val_acc: 0.7400\n",
      "Epoch 111/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.5190 - acc: 0.7286 - val_loss: 0.5335 - val_acc: 0.7400\n",
      "Epoch 112/300\n",
      "350/350 [==============================] - 0s 139us/step - loss: 0.5176 - acc: 0.7286 - val_loss: 0.5322 - val_acc: 0.7467\n",
      "Epoch 113/300\n",
      "350/350 [==============================] - 0s 149us/step - loss: 0.5160 - acc: 0.7314 - val_loss: 0.5309 - val_acc: 0.7467\n",
      "Epoch 114/300\n",
      "350/350 [==============================] - 0s 113us/step - loss: 0.5145 - acc: 0.7314 - val_loss: 0.5296 - val_acc: 0.7467\n",
      "Epoch 115/300\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.5128 - acc: 0.7314 - val_loss: 0.5283 - val_acc: 0.7467\n",
      "Epoch 116/300\n",
      "350/350 [==============================] - 0s 119us/step - loss: 0.5113 - acc: 0.7314 - val_loss: 0.5271 - val_acc: 0.7467\n",
      "Epoch 117/300\n",
      "350/350 [==============================] - 0s 115us/step - loss: 0.5099 - acc: 0.7343 - val_loss: 0.5258 - val_acc: 0.7467\n",
      "Epoch 118/300\n",
      "350/350 [==============================] - 0s 115us/step - loss: 0.5085 - acc: 0.7314 - val_loss: 0.5247 - val_acc: 0.7467\n",
      "Epoch 119/300\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.5070 - acc: 0.7314 - val_loss: 0.5233 - val_acc: 0.7467\n",
      "Epoch 120/300\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.5055 - acc: 0.7343 - val_loss: 0.5222 - val_acc: 0.7467\n",
      "Epoch 121/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 107us/step - loss: 0.5039 - acc: 0.7343 - val_loss: 0.5209 - val_acc: 0.7467\n",
      "Epoch 122/300\n",
      "350/350 [==============================] - 0s 109us/step - loss: 0.5025 - acc: 0.7371 - val_loss: 0.5197 - val_acc: 0.7467\n",
      "Epoch 123/300\n",
      "350/350 [==============================] - 0s 108us/step - loss: 0.5007 - acc: 0.7343 - val_loss: 0.5185 - val_acc: 0.7467\n",
      "Epoch 124/300\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.4990 - acc: 0.7343 - val_loss: 0.5174 - val_acc: 0.7467\n",
      "Epoch 125/300\n",
      "350/350 [==============================] - 0s 143us/step - loss: 0.4974 - acc: 0.7371 - val_loss: 0.5163 - val_acc: 0.7467\n",
      "Epoch 126/300\n",
      "350/350 [==============================] - 0s 113us/step - loss: 0.4957 - acc: 0.7400 - val_loss: 0.5153 - val_acc: 0.7467\n",
      "Epoch 127/300\n",
      "350/350 [==============================] - 0s 132us/step - loss: 0.4943 - acc: 0.7400 - val_loss: 0.5143 - val_acc: 0.7533\n",
      "Epoch 128/300\n",
      "350/350 [==============================] - 0s 145us/step - loss: 0.4928 - acc: 0.7400 - val_loss: 0.5133 - val_acc: 0.7533\n",
      "Epoch 129/300\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.4914 - acc: 0.7429 - val_loss: 0.5123 - val_acc: 0.7533\n",
      "Epoch 130/300\n",
      "350/350 [==============================] - 0s 148us/step - loss: 0.4900 - acc: 0.7429 - val_loss: 0.5113 - val_acc: 0.7533\n",
      "Epoch 131/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.4884 - acc: 0.7429 - val_loss: 0.5104 - val_acc: 0.7533\n",
      "Epoch 132/300\n",
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      "Epoch 133/300\n",
      "350/350 [==============================] - 0s 129us/step - loss: 0.4854 - acc: 0.7429 - val_loss: 0.5086 - val_acc: 0.7533\n",
      "Epoch 134/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.4840 - acc: 0.7429 - val_loss: 0.5075 - val_acc: 0.7533\n",
      "Epoch 135/300\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.4824 - acc: 0.7429 - val_loss: 0.5065 - val_acc: 0.7533\n",
      "Epoch 136/300\n",
      "350/350 [==============================] - 0s 141us/step - loss: 0.4809 - acc: 0.7429 - val_loss: 0.5054 - val_acc: 0.7533\n",
      "Epoch 137/300\n",
      "350/350 [==============================] - 0s 132us/step - loss: 0.4793 - acc: 0.7429 - val_loss: 0.5043 - val_acc: 0.7533\n",
      "Epoch 138/300\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.4777 - acc: 0.7429 - val_loss: 0.5032 - val_acc: 0.7533\n",
      "Epoch 139/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.4761 - acc: 0.7429 - val_loss: 0.5021 - val_acc: 0.7533\n",
      "Epoch 140/300\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.4745 - acc: 0.7429 - val_loss: 0.5012 - val_acc: 0.7533\n",
      "Epoch 141/300\n",
      "350/350 [==============================] - 0s 148us/step - loss: 0.4729 - acc: 0.7429 - val_loss: 0.5002 - val_acc: 0.7533\n",
      "Epoch 142/300\n",
      "350/350 [==============================] - 0s 178us/step - loss: 0.4714 - acc: 0.7429 - val_loss: 0.4993 - val_acc: 0.7533\n",
      "Epoch 143/300\n",
      "350/350 [==============================] - 0s 139us/step - loss: 0.4698 - acc: 0.7429 - val_loss: 0.4984 - val_acc: 0.7533\n",
      "Epoch 144/300\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.4683 - acc: 0.7429 - val_loss: 0.4975 - val_acc: 0.7533\n",
      "Epoch 145/300\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.4671 - acc: 0.7457 - val_loss: 0.4967 - val_acc: 0.7533\n",
      "Epoch 146/300\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.4657 - acc: 0.7457 - val_loss: 0.4959 - val_acc: 0.7533\n",
      "Epoch 147/300\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.4643 - acc: 0.7457 - val_loss: 0.4951 - val_acc: 0.7533\n",
      "Epoch 148/300\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.4630 - acc: 0.7457 - val_loss: 0.4944 - val_acc: 0.7533\n",
      "Epoch 149/300\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.4618 - acc: 0.7457 - val_loss: 0.4937 - val_acc: 0.7533\n",
      "Epoch 150/300\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.4605 - acc: 0.7457 - val_loss: 0.4930 - val_acc: 0.7533\n",
      "Epoch 151/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.4592 - acc: 0.7457 - val_loss: 0.4922 - val_acc: 0.7533\n",
      "Epoch 152/300\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.4581 - acc: 0.7457 - val_loss: 0.4914 - val_acc: 0.7533\n",
      "Epoch 153/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.4569 - acc: 0.7457 - val_loss: 0.4906 - val_acc: 0.7533\n",
      "Epoch 154/300\n",
      "350/350 [==============================] - 0s 113us/step - loss: 0.4558 - acc: 0.7457 - val_loss: 0.4900 - val_acc: 0.7533\n",
      "Epoch 155/300\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.4547 - acc: 0.7457 - val_loss: 0.4892 - val_acc: 0.7533\n",
      "Epoch 156/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.4536 - acc: 0.7457 - val_loss: 0.4885 - val_acc: 0.7533\n",
      "Epoch 157/300\n",
      "350/350 [==============================] - 0s 132us/step - loss: 0.4527 - acc: 0.7457 - val_loss: 0.4877 - val_acc: 0.7533\n",
      "Epoch 158/300\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.4515 - acc: 0.7429 - val_loss: 0.4870 - val_acc: 0.7533\n",
      "Epoch 159/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.4506 - acc: 0.7457 - val_loss: 0.4863 - val_acc: 0.7533\n",
      "Epoch 160/300\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.4496 - acc: 0.7429 - val_loss: 0.4854 - val_acc: 0.7533\n",
      "Epoch 161/300\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.4485 - acc: 0.7457 - val_loss: 0.4845 - val_acc: 0.7533\n",
      "Epoch 162/300\n",
      "350/350 [==============================] - 0s 139us/step - loss: 0.4476 - acc: 0.7457 - val_loss: 0.4837 - val_acc: 0.7533\n",
      "Epoch 163/300\n",
      "350/350 [==============================] - 0s 137us/step - loss: 0.4465 - acc: 0.7457 - val_loss: 0.4830 - val_acc: 0.7533\n",
      "Epoch 164/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.4456 - acc: 0.7457 - val_loss: 0.4823 - val_acc: 0.7533\n",
      "Epoch 165/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.4446 - acc: 0.7457 - val_loss: 0.4813 - val_acc: 0.7533\n",
      "Epoch 166/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.4435 - acc: 0.7457 - val_loss: 0.4805 - val_acc: 0.7533\n",
      "Epoch 167/300\n",
      "350/350 [==============================] - 0s 109us/step - loss: 0.4425 - acc: 0.7457 - val_loss: 0.4797 - val_acc: 0.7533\n",
      "Epoch 168/300\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.4416 - acc: 0.7457 - val_loss: 0.4790 - val_acc: 0.7533\n",
      "Epoch 169/300\n",
      "350/350 [==============================] - 0s 120us/step - loss: 0.4406 - acc: 0.7457 - val_loss: 0.4783 - val_acc: 0.7533\n",
      "Epoch 170/300\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.4398 - acc: 0.7457 - val_loss: 0.4777 - val_acc: 0.7533\n",
      "Epoch 171/300\n",
      "350/350 [==============================] - 0s 139us/step - loss: 0.4387 - acc: 0.7457 - val_loss: 0.4769 - val_acc: 0.7533\n",
      "Epoch 172/300\n",
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      "Epoch 173/300\n",
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      "Epoch 174/300\n",
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      "Epoch 175/300\n",
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      "Epoch 176/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.4339 - acc: 0.7429 - val_loss: 0.4719 - val_acc: 0.7533\n",
      "Epoch 177/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.4333 - acc: 0.7457 - val_loss: 0.4710 - val_acc: 0.7533\n",
      "Epoch 178/300\n",
      "350/350 [==============================] - 0s 118us/step - loss: 0.4321 - acc: 0.7457 - val_loss: 0.4701 - val_acc: 0.7533\n",
      "Epoch 179/300\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.4313 - acc: 0.7429 - val_loss: 0.4692 - val_acc: 0.7533\n",
      "Epoch 180/300\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.4303 - acc: 0.7429 - val_loss: 0.4682 - val_acc: 0.7533\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 181/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.4294 - acc: 0.7429 - val_loss: 0.4672 - val_acc: 0.7533\n",
      "Epoch 182/300\n",
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      "Epoch 183/300\n",
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      "Epoch 184/300\n",
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      "Epoch 185/300\n",
      "350/350 [==============================] - 0s 151us/step - loss: 0.4254 - acc: 0.7429 - val_loss: 0.4630 - val_acc: 0.7533\n",
      "Epoch 186/300\n",
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      "Epoch 187/300\n",
      "350/350 [==============================] - 0s 144us/step - loss: 0.4235 - acc: 0.7429 - val_loss: 0.4609 - val_acc: 0.7533\n",
      "Epoch 188/300\n",
      "350/350 [==============================] - 0s 157us/step - loss: 0.4226 - acc: 0.7429 - val_loss: 0.4601 - val_acc: 0.7533\n",
      "Epoch 189/300\n",
      "350/350 [==============================] - 0s 141us/step - loss: 0.4216 - acc: 0.7429 - val_loss: 0.4593 - val_acc: 0.7533\n",
      "Epoch 190/300\n",
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      "Epoch 191/300\n",
      "350/350 [==============================] - 0s 139us/step - loss: 0.4198 - acc: 0.7429 - val_loss: 0.4575 - val_acc: 0.7533\n",
      "Epoch 192/300\n",
      "350/350 [==============================] - 0s 120us/step - loss: 0.4189 - acc: 0.7400 - val_loss: 0.4568 - val_acc: 0.7533\n",
      "Epoch 193/300\n",
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      "Epoch 194/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.4173 - acc: 0.7429 - val_loss: 0.4552 - val_acc: 0.7533\n",
      "Epoch 195/300\n",
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      "Epoch 196/300\n",
      "350/350 [==============================] - 0s 125us/step - loss: 0.4158 - acc: 0.7400 - val_loss: 0.4536 - val_acc: 0.7533\n",
      "Epoch 197/300\n",
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      "Epoch 198/300\n",
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      "Epoch 199/300\n",
      "350/350 [==============================] - 0s 136us/step - loss: 0.4134 - acc: 0.7400 - val_loss: 0.4512 - val_acc: 0.7533\n",
      "Epoch 200/300\n",
      "350/350 [==============================] - 0s 140us/step - loss: 0.4127 - acc: 0.7429 - val_loss: 0.4505 - val_acc: 0.7533\n",
      "Epoch 201/300\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.4119 - acc: 0.7429 - val_loss: 0.4498 - val_acc: 0.7533\n",
      "Epoch 202/300\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.4112 - acc: 0.7429 - val_loss: 0.4489 - val_acc: 0.7533\n",
      "Epoch 203/300\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.4103 - acc: 0.7457 - val_loss: 0.4482 - val_acc: 0.7533\n",
      "Epoch 204/300\n",
      "350/350 [==============================] - 0s 138us/step - loss: 0.4097 - acc: 0.7429 - val_loss: 0.4477 - val_acc: 0.7533\n",
      "Epoch 205/300\n",
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      "Epoch 206/300\n",
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      "Epoch 207/300\n",
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      "Epoch 208/300\n",
      "350/350 [==============================] - 0s 105us/step - loss: 0.4071 - acc: 0.7457 - val_loss: 0.4455 - val_acc: 0.7533\n",
      "Epoch 209/300\n",
      "350/350 [==============================] - 0s 152us/step - loss: 0.4065 - acc: 0.7429 - val_loss: 0.4450 - val_acc: 0.7533\n",
      "Epoch 210/300\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.4059 - acc: 0.7457 - val_loss: 0.4443 - val_acc: 0.7533\n",
      "Epoch 211/300\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.4052 - acc: 0.7457 - val_loss: 0.4437 - val_acc: 0.7533\n",
      "Epoch 212/300\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.4046 - acc: 0.7457 - val_loss: 0.4431 - val_acc: 0.7533\n",
      "Epoch 213/300\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.4040 - acc: 0.7457 - val_loss: 0.4422 - val_acc: 0.7533\n",
      "Epoch 214/300\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.4034 - acc: 0.7457 - val_loss: 0.4412 - val_acc: 0.7533\n",
      "Epoch 215/300\n",
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      "Epoch 216/300\n",
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      "Epoch 217/300\n",
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      "Epoch 218/300\n",
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      "Epoch 219/300\n",
      "350/350 [==============================] - 0s 158us/step - loss: 0.4007 - acc: 0.7486 - val_loss: 0.4376 - val_acc: 0.7533\n",
      "Epoch 220/300\n",
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      "Epoch 221/300\n",
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      "Epoch 222/300\n",
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      "Epoch 223/300\n",
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      "Epoch 224/300\n",
      "350/350 [==============================] - 0s 147us/step - loss: 0.3982 - acc: 0.7486 - val_loss: 0.4351 - val_acc: 0.7533\n",
      "Epoch 225/300\n",
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      "Epoch 226/300\n",
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      "Epoch 227/300\n",
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      "Epoch 228/300\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.3966 - acc: 0.7457 - val_loss: 0.4336 - val_acc: 0.7533\n",
      "Epoch 229/300\n",
      "350/350 [==============================] - 0s 119us/step - loss: 0.3962 - acc: 0.7457 - val_loss: 0.4333 - val_acc: 0.7533\n",
      "Epoch 230/300\n",
      "350/350 [==============================] - 0s 132us/step - loss: 0.3959 - acc: 0.7457 - val_loss: 0.4330 - val_acc: 0.7533\n",
      "Epoch 231/300\n",
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      "Epoch 232/300\n",
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      "Epoch 233/300\n",
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      "Epoch 234/300\n",
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      "Epoch 235/300\n",
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      "Epoch 236/300\n",
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      "Epoch 237/300\n",
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      "Epoch 238/300\n",
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      "Epoch 239/300\n",
      "350/350 [==============================] - 0s 120us/step - loss: 0.3927 - acc: 0.7457 - val_loss: 0.4303 - val_acc: 0.7533\n",
      "Epoch 240/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.3926 - acc: 0.7457 - val_loss: 0.4300 - val_acc: 0.7533\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 241/300\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.3923 - acc: 0.7457 - val_loss: 0.4298 - val_acc: 0.7533\n",
      "Epoch 242/300\n",
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      "Epoch 243/300\n",
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      "Epoch 244/300\n",
      "350/350 [==============================] - 0s 136us/step - loss: 0.3916 - acc: 0.7457 - val_loss: 0.4288 - val_acc: 0.7533\n",
      "Epoch 245/300\n",
      "350/350 [==============================] - 0s 119us/step - loss: 0.3912 - acc: 0.7457 - val_loss: 0.4285 - val_acc: 0.7533\n",
      "Epoch 246/300\n",
      "350/350 [==============================] - 0s 115us/step - loss: 0.3910 - acc: 0.7457 - val_loss: 0.4281 - val_acc: 0.7533\n",
      "Epoch 247/300\n",
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      "Epoch 248/300\n",
      "350/350 [==============================] - 0s 161us/step - loss: 0.3905 - acc: 0.7486 - val_loss: 0.4274 - val_acc: 0.7533\n",
      "Epoch 249/300\n",
      "350/350 [==============================] - 0s 164us/step - loss: 0.3904 - acc: 0.7457 - val_loss: 0.4276 - val_acc: 0.7533\n",
      "Epoch 250/300\n",
      "350/350 [==============================] - 0s 139us/step - loss: 0.3900 - acc: 0.7457 - val_loss: 0.4273 - val_acc: 0.7533\n",
      "Epoch 251/300\n",
      "350/350 [==============================] - 0s 185us/step - loss: 0.3896 - acc: 0.7486 - val_loss: 0.4269 - val_acc: 0.7533\n",
      "Epoch 252/300\n",
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      "Epoch 253/300\n",
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      "Epoch 254/300\n",
      "350/350 [==============================] - 0s 146us/step - loss: 0.3891 - acc: 0.7457 - val_loss: 0.4257 - val_acc: 0.7533\n",
      "Epoch 255/300\n",
      "350/350 [==============================] - 0s 162us/step - loss: 0.3887 - acc: 0.7457 - val_loss: 0.4252 - val_acc: 0.7533\n",
      "Epoch 256/300\n",
      "350/350 [==============================] - 0s 156us/step - loss: 0.3885 - acc: 0.7486 - val_loss: 0.4248 - val_acc: 0.7533\n",
      "Epoch 257/300\n",
      "350/350 [==============================] - 0s 138us/step - loss: 0.3882 - acc: 0.7457 - val_loss: 0.4243 - val_acc: 0.7533\n",
      "Epoch 258/300\n",
      "350/350 [==============================] - 0s 165us/step - loss: 0.3878 - acc: 0.7457 - val_loss: 0.4239 - val_acc: 0.7533\n",
      "Epoch 259/300\n",
      "350/350 [==============================] - 0s 162us/step - loss: 0.3876 - acc: 0.7457 - val_loss: 0.4236 - val_acc: 0.7533\n",
      "Epoch 260/300\n",
      "350/350 [==============================] - 0s 173us/step - loss: 0.3875 - acc: 0.7457 - val_loss: 0.4231 - val_acc: 0.7533\n",
      "Epoch 261/300\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.3870 - acc: 0.7457 - val_loss: 0.4225 - val_acc: 0.7533\n",
      "Epoch 262/300\n",
      "350/350 [==============================] - 0s 164us/step - loss: 0.3867 - acc: 0.7457 - val_loss: 0.4218 - val_acc: 0.7533\n",
      "Epoch 263/300\n",
      "350/350 [==============================] - 0s 156us/step - loss: 0.3865 - acc: 0.7457 - val_loss: 0.4215 - val_acc: 0.7533\n",
      "Epoch 264/300\n",
      "350/350 [==============================] - 0s 164us/step - loss: 0.3862 - acc: 0.7486 - val_loss: 0.4209 - val_acc: 0.7533\n",
      "Epoch 265/300\n",
      "350/350 [==============================] - 0s 140us/step - loss: 0.3860 - acc: 0.7457 - val_loss: 0.4204 - val_acc: 0.7533\n",
      "Epoch 266/300\n",
      "350/350 [==============================] - 0s 159us/step - loss: 0.3856 - acc: 0.7457 - val_loss: 0.4198 - val_acc: 0.7533\n",
      "Epoch 267/300\n",
      "350/350 [==============================] - 0s 160us/step - loss: 0.3853 - acc: 0.7457 - val_loss: 0.4191 - val_acc: 0.7533\n",
      "Epoch 268/300\n",
      "350/350 [==============================] - 0s 150us/step - loss: 0.3851 - acc: 0.7486 - val_loss: 0.4186 - val_acc: 0.7533\n",
      "Epoch 269/300\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.3849 - acc: 0.7457 - val_loss: 0.4182 - val_acc: 0.7533\n",
      "Epoch 270/300\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.3845 - acc: 0.7457 - val_loss: 0.4177 - val_acc: 0.7533\n",
      "Epoch 271/300\n",
      "350/350 [==============================] - 0s 129us/step - loss: 0.3844 - acc: 0.7457 - val_loss: 0.4173 - val_acc: 0.7533\n",
      "Epoch 272/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.3840 - acc: 0.7457 - val_loss: 0.4169 - val_acc: 0.7533\n",
      "Epoch 273/300\n",
      "350/350 [==============================] - 0s 115us/step - loss: 0.3837 - acc: 0.7486 - val_loss: 0.4165 - val_acc: 0.7533\n",
      "Epoch 274/300\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.3837 - acc: 0.7457 - val_loss: 0.4162 - val_acc: 0.7533\n",
      "Epoch 275/300\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.3832 - acc: 0.7486 - val_loss: 0.4159 - val_acc: 0.7533\n",
      "Epoch 276/300\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.3829 - acc: 0.7457 - val_loss: 0.4153 - val_acc: 0.7533\n",
      "Epoch 277/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.3827 - acc: 0.7486 - val_loss: 0.4149 - val_acc: 0.7533\n",
      "Epoch 278/300\n",
      "350/350 [==============================] - 0s 118us/step - loss: 0.3824 - acc: 0.7457 - val_loss: 0.4146 - val_acc: 0.7533\n",
      "Epoch 279/300\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.3822 - acc: 0.7457 - val_loss: 0.4142 - val_acc: 0.7533\n",
      "Epoch 280/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.3820 - acc: 0.7486 - val_loss: 0.4138 - val_acc: 0.7533\n",
      "Epoch 281/300\n",
      "350/350 [==============================] - 0s 118us/step - loss: 0.3817 - acc: 0.7486 - val_loss: 0.4134 - val_acc: 0.7533\n",
      "Epoch 282/300\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.3815 - acc: 0.7457 - val_loss: 0.4130 - val_acc: 0.7533\n",
      "Epoch 283/300\n",
      "350/350 [==============================] - 0s 115us/step - loss: 0.3811 - acc: 0.7457 - val_loss: 0.4126 - val_acc: 0.7533\n",
      "Epoch 284/300\n",
      "350/350 [==============================] - 0s 125us/step - loss: 0.3808 - acc: 0.7486 - val_loss: 0.4122 - val_acc: 0.7533\n",
      "Epoch 285/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.3807 - acc: 0.7457 - val_loss: 0.4118 - val_acc: 0.7533\n",
      "Epoch 286/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.3805 - acc: 0.7457 - val_loss: 0.4115 - val_acc: 0.7533\n",
      "Epoch 287/300\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.3800 - acc: 0.7486 - val_loss: 0.4112 - val_acc: 0.7533\n",
      "Epoch 288/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.3799 - acc: 0.7457 - val_loss: 0.4108 - val_acc: 0.7533\n",
      "Epoch 289/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.3799 - acc: 0.7457 - val_loss: 0.4106 - val_acc: 0.7533\n",
      "Epoch 290/300\n",
      "350/350 [==============================] - 0s 141us/step - loss: 0.3794 - acc: 0.7486 - val_loss: 0.4104 - val_acc: 0.7533\n",
      "Epoch 291/300\n",
      "350/350 [==============================] - 0s 165us/step - loss: 0.3792 - acc: 0.7486 - val_loss: 0.4101 - val_acc: 0.7533\n",
      "Epoch 292/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.3789 - acc: 0.7486 - val_loss: 0.4097 - val_acc: 0.7533\n",
      "Epoch 293/300\n",
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      "Epoch 294/300\n",
      "350/350 [==============================] - 0s 127us/step - loss: 0.3784 - acc: 0.7457 - val_loss: 0.4092 - val_acc: 0.7533\n",
      "Epoch 295/300\n",
      "350/350 [==============================] - 0s 132us/step - loss: 0.3781 - acc: 0.7457 - val_loss: 0.4089 - val_acc: 0.7533\n",
      "Epoch 296/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.3780 - acc: 0.7457 - val_loss: 0.4085 - val_acc: 0.7533\n",
      "Epoch 297/300\n",
      "350/350 [==============================] - 0s 138us/step - loss: 0.3777 - acc: 0.7457 - val_loss: 0.4083 - val_acc: 0.7533\n",
      "Epoch 298/300\n",
      "350/350 [==============================] - 0s 140us/step - loss: 0.3774 - acc: 0.7486 - val_loss: 0.4081 - val_acc: 0.7533\n",
      "Epoch 299/300\n",
      "350/350 [==============================] - 0s 139us/step - loss: 0.3773 - acc: 0.7486 - val_loss: 0.4079 - val_acc: 0.7533\n",
      "Epoch 300/300\n",
      "350/350 [==============================] - 0s 122us/step - loss: 0.3770 - acc: 0.7486 - val_loss: 0.4077 - val_acc: 0.7533\n"
     ]
    }
   ],
   "source": [
    "# Instantiating the model\n",
    "model = a_simple_NN()\n",
    "\n",
    "# Splitting the dataset into training (70%) and validation sets (30%)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    features, labels, test_size=0.3)\n",
    "\n",
    "# Setting the number of passes through the entire training set\n",
    "num_epochs = 300\n",
    "\n",
    "# model.fit() is used to train the model\n",
    "# We can pass validation data while training\n",
    "model_run = model.fit(X_train, y_train, epochs=num_epochs,\n",
    "                      validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><p><i class=\"fa fa-info-circle\"></i>&nbsp;\n",
    "    NOTE: We can pass \"verbose=0\" to model.fit() to suppress the printing of model output on the terminal/notebook.\n",
    "</p></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 398
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the loss and accuracy on the training and validation sets during the training\n",
    "# This can be done by using Keras callback \"history\" which is applied by default\n",
    "history_model = model_run.history\n",
    "\n",
    "print(\"The history has the following data: \", history_model.keys())\n",
    "\n",
    "# Plotting the training and validation accuracy during the training\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
    "plt.xlabel(\"epochs\") ;\n",
    "plt.ylabel(\"accuracy\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "The plots such as above are essential for analyzing the behaviour and performance of the network and to tune it in the right direction. However, for the example above we don't expect to derive a lot of insight from this plot as the function we are trying to fit is quite simple and there is not too much noise. We will see the significance of these curves in a later example.\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before we move on forward we see how to save and load a keras model\n",
    "model.save(\"./my_first_NN.h5\")\n",
    "\n",
    "# Optional: See what is in the hdf5 file we just created above\n",
    "\n",
    "from keras.models import load_model\n",
    "model = load_model(\"./my_first_NN.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the training and validation in the example above we split our dataset into a 70-30 train-validation set. We know from previous chapters that to more robustly estimate the accuracy of our model we can use **K-fold cross-validation**.\n",
    "This is even more important when we have small datasets and cannot afford to reserve a validation set!\n",
    "\n",
    "One way to do the cross-validation here would be to write our own function to do this. However, we also know that **scikit-learn** provides several handy functions to evaluate and tune the models. So the question is:\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "    Can we somehow use the scikit-learn functions or the ones we wrote ourselves for scikit-learn models to evaluate and tune our Keras models?\n",
    "\n",
    "\n",
    "The Answer is **YES !**\n",
    "</p>\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "We show how to do this in the following section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using scikit-learn functions on keras models\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "Keras offers 2 wrappers which allow its Sequential models to be used with scikit-learn. \n",
    "\n",
    "There are: **KerasClassifier** and **KerasRegressor**.\n",
    "\n",
    "For more information:\n",
    "https://keras.io/scikit-learn-api/\n",
    "</p>\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "**Now lets see how this works!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We wrap the Keras model we created above with KerasClassifier\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# Wrapping Keras model\n",
    "# NOTE: We pass verbose=0 to suppress the model output\n",
    "num_epochs = 400\n",
    "model_scikit = KerasClassifier(\n",
    "    build_fn=a_simple_NN, epochs=num_epochs, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's reuse the function to visualize the decision boundary which we saw in chapter 2 with minimal change\n",
    "\n",
    "def list_flatten(list_of_list):\n",
    "    flattened_list = [i for j in list_of_list for i in j]\n",
    "    return flattened_list\n",
    "\n",
    "def plot_points(plt=plt, marker='o'):\n",
    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
    "\n",
    "def train_and_plot_decision_surface(\n",
    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
    "):\n",
    "\n",
    "    features_2d = np.array(features_2d)\n",
    "    xmin, ymin = features_2d.min(axis=0)\n",
    "    xmax, ymax = features_2d.max(axis=0)\n",
    "\n",
    "    x = np.linspace(xmin, xmax, N)\n",
    "    y = np.linspace(ymin, ymax, N)\n",
    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
    "\n",
    "    if preproc is not None:\n",
    "        points_for_classifier = preproc.fit_transform(points)\n",
    "        features_2d = preproc.fit_transform(features_2d)\n",
    "    else:\n",
    "        points_for_classifier = points\n",
    "\n",
    "    classifier.fit(features_2d, labels, verbose=0)\n",
    "    predicted = classifier.predict(features_2d)\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        predicted = list_flatten(predicted)\n",
    "    \n",
    "    \n",
    "    if preproc is not None:\n",
    "        name += \" (w/ preprocessing)\"\n",
    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
    "    else:\n",
    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
    "    plt.plot(\n",
    "        points[~classes][:, 0],\n",
    "        points[~classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"steelblue\",\n",
    "        markersize=1,\n",
    "        alpha=0.01,\n",
    "    )\n",
    "    plt.plot(\n",
    "        points[classes][:, 0],\n",
    "        points[classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"chocolate\",\n",
    "        markersize=1,\n",
    "        alpha=0.04,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net:\t 482 / 500 correct\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 363,
       "width": 383
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
    "plot_points(plt=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The acuracy on the  5  validation folds: [0.97 0.96 0.95 0.99 0.96]\n",
      "The Average acuracy on the  5  validation folds: 0.966\n"
     ]
    }
   ],
   "source": [
    "# Applying K-fold cross-validation\n",
    "# Here we pass the whole dataset, i.e. features and labels, instead of splitting it.\n",
    "num_folds = 5\n",
    "cross_validation = cross_val_score(\n",
    "    model_scikit, features, labels, cv=num_folds, verbose=0)\n",
    "\n",
    "print(\"The acuracy on the \", num_folds, \" validation folds:\", cross_validation)\n",
    "print(\"The Average acuracy on the \", num_folds, \" validation folds:\", np.mean(cross_validation))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "The code above took quiet long to finish even though we used only 5  CV folds and the neural network and data size are very small! This gives an indication of the enormous compute requirements of training production-grade deep neural networks.\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know from chapter 6 that there are 2 types of parameters which need to be tuned for a machine learning model.\n",
    "* Internal model parameters (weights) which can be learned for e.g. by gradient-descent\n",
    "* Hyperparameters\n",
    "\n",
    "In the model created above we made some arbitrary choices such as the choice of the optimizer we used, optimizer's learning rate, number of hidden units and so on ...\n",
    "\n",
    "Now that we have the keras model wrapped as a scikit-learn model we can use the grid search functions we have seen in chapter 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# Just to remember\n",
    "model_scikit = KerasClassifier(\n",
    "    build_fn=a_simple_NN, **{\"epochs\": num_epochs, \"verbose\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8660000003576279 {'epochs': 50}\n"
     ]
    }
   ],
   "source": [
    "HP_grid = {'epochs' : [30, 50, 100]}\n",
    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
    "search.fit(features, labels)\n",
    "print(search.best_score_, search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7820000060796738 {'batch_size': 20, 'epochs': 30}\n"
     ]
    }
   ],
   "source": [
    "HP_grid = {'epochs' : [10, 15, 30], \n",
    "           'batch_size' : [10, 20, 30] }\n",
    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
    "search.fit(features, labels)\n",
    "print(search.best_score_, search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A more general model for further Hyperparameter optimization\n",
    "from keras import optimizers\n",
    "\n",
    "def a_simple_NN(activation='relu', num_hidden_neurons=[4, 4], learning_rate=0.01):\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(num_hidden_neurons[0],\n",
    "                    input_shape=(2,), activation=activation))\n",
    "\n",
    "    model.add(Dense(num_hidden_neurons[1], activation=activation))\n",
    "\n",
    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
    "\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=optimizers.rmsprop(\n",
    "        lr=learning_rate), metrics=[\"accuracy\"])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section: \n",
    "* Look at the model above and choose a couple of hyperparameters to optimize. \n",
    "* **OPTIONAL:** What function from scikit-learn other than GridSearchCV can we use for hyperparameter optimization? Use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "Another library which you should definitely look at for doing hyperparameter optimization with keras models is the <a href=\"https://github.com/maxpumperla/hyperas\">Hyperas library</a> which is a wrapper around the <a href=\"https://github.com/hyperopt/hyperopt\">Hyperopt library</a>. \n",
    "\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section:  \n",
    "* Create a neural network to classify the 2d points example from chapter 2 learned (Optional: As you create the model read a bit on the different keras commands we have used)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras import optimizers\n",
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 309,
       "width": 332
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "circle = pd.read_csv(\"2d_points.csv\")\n",
    "# Using x and y coordinates as featues\n",
    "features = circle.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = circle.iloc[:, -1].astype(int)\n",
    "\n",
    "colors = [[\"steelblue\", \"chocolate\"][i] for i in circle[\"label\"]]\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.xlim([-2, 2])\n",
    "plt.ylim([-2, 2])\n",
    "\n",
    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The examples we saw above are really nice to show various features of the Keras library and to understand how we build and train a model. However, they are not the ideal problems one should solve using neural networks. They are too simple and can be solved easily by classical machine learning algorithms. \n",
    "\n",
    "Now we show examples where Neural Networks really shine over classical machine learning algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handwritten Digits Classification (multi-class classification)\n",
    "### MNIST Dataset\n",
    "\n",
    "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
    "\n",
    "\n",
    ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a >test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size->normalized and centered in a fixed-size image.\n",
    ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
    ">source: http://yann.lecun.com/exdb/mnist/\n",
    "\n",
    "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. \n",
    "This is a multi-class classification problem. Unlike binary classification which we have seen till now we will classify data into 10 different classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset in keras\n",
    "# Later you can explore and play with other datasets with come with Keras\n",
    "from keras.datasets import mnist\n",
    "\n",
    "# Loading the train and test data\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Looking at the dataset\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This digit is:  1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 256
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
    "i=np.random.randint(0,X_train.shape[0])\n",
    "sns.set_style(\"white\")\n",
    "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
    "sns.set(style=\"darkgrid\")\n",
    "print(\"This digit is: \" , y_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 255\n"
     ]
    }
   ],
   "source": [
    "# Look at the data values for a couple of images\n",
    "print(X_train[0].min(), X_train[1].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data consists of values between 0-255 representing the **grayscale level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n"
     ]
    }
   ],
   "source": [
    "# The labels are the digit on the image\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the data\n",
    "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
    "# We could use the previously introduced function from scikit-learn. However, here it is sufficient to\n",
    "# just divide the input data by 255\n",
    "X_train_norm = X_train/255.\n",
    "X_test_norm = X_test/255.\n",
    "\n",
    "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
    "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
    "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "One-Hot encoding\n",
    "\n",
    "In multi-class classification problems the labels are provided to the neural network as something called **One-hot encodings**. The categorical labels (0-9 here) are converted to vectors.\n",
    "\n",
    "For the MNIST problem where the data has **10 categories** we will convert every label to a vector of length 10. \n",
    "All the entries of this vector will be zero **except** for the index which is equal to the (integer) value of the label.\n",
    "\n",
    "For example:\n",
    "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
    "\n",
    "Fortunately, Keras has a built-in function to achieve this and we do not have to write a code for this ourselves.\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
    "\n",
    "print(y_train_onehot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 6s 101us/step - loss: 0.5699 - acc: 0.8498\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.2558 - acc: 0.9263\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.1982 - acc: 0.9428\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.1633 - acc: 0.9528\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.1375 - acc: 0.9599\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.1181 - acc: 0.9657\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.1037 - acc: 0.9692\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0927 - acc: 0.9724\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0823 - acc: 0.9757\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0758 - acc: 0.9771\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0680 - acc: 0.9794\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0632 - acc: 0.9808\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0575 - acc: 0.9824\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0530 - acc: 0.9844\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0479 - acc: 0.9858\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.0450 - acc: 0.9863\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0410 - acc: 0.9879\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0379 - acc: 0.9893\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.0344 - acc: 0.9899\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0323 - acc: 0.9905\n"
     ]
    }
   ],
   "source": [
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "def mnist_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\"))\n",
    "\n",
    "    model.add(Dense(64, activation=\"relu\"))\n",
    "\n",
    "    model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "    model.compile(loss=\"categorical_crossentropy\",\n",
    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    return model\n",
    "\n",
    "model = mnist_model()\n",
    "\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=20,\n",
    "                      batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 3s 274us/step\n",
      "The [loss, accuracy] on test dataset are:  [0.0877833616821561, 0.9727]\n"
     ]
    }
   ],
   "source": [
    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section\n",
    "* Reinitialize and run the model again with validation dataset, plot the accuracy as a function of epochs, play with number of epochs and observe what is happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 7s 116us/step - loss: 0.5683 - acc: 0.8505 - val_loss: 0.3052 - val_acc: 0.9128\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.2550 - acc: 0.9262 - val_loss: 0.2355 - val_acc: 0.9279\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.2021 - acc: 0.9407 - val_loss: 0.1751 - val_acc: 0.9448\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.1675 - acc: 0.9507 - val_loss: 0.1661 - val_acc: 0.9489\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.1435 - acc: 0.9580 - val_loss: 0.1612 - val_acc: 0.9485\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.1240 - acc: 0.9633 - val_loss: 0.1358 - val_acc: 0.9570\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1088 - acc: 0.9674 - val_loss: 0.1284 - val_acc: 0.9615\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.0959 - acc: 0.9712 - val_loss: 0.1252 - val_acc: 0.9608\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0871 - acc: 0.9736 - val_loss: 0.1122 - val_acc: 0.9660\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0785 - acc: 0.9772 - val_loss: 0.1041 - val_acc: 0.9681\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0714 - acc: 0.9790 - val_loss: 0.1041 - val_acc: 0.9677\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.0650 - acc: 0.9808 - val_loss: 0.1217 - val_acc: 0.9628\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0596 - acc: 0.9821 - val_loss: 0.1072 - val_acc: 0.9651\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0540 - acc: 0.9840 - val_loss: 0.1041 - val_acc: 0.9685\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0504 - acc: 0.9849 - val_loss: 0.1036 - val_acc: 0.9656\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0459 - acc: 0.9862 - val_loss: 0.0892 - val_acc: 0.9723\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0423 - acc: 0.9874 - val_loss: 0.0876 - val_acc: 0.9736\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0390 - acc: 0.9889 - val_loss: 0.0950 - val_acc: 0.9720\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0357 - acc: 0.9898 - val_loss: 0.0957 - val_acc: 0.9719\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0324 - acc: 0.9910 - val_loss: 0.0931 - val_acc: 0.9717\n",
      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 398
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution:\n",
    "num_epochs = 20\n",
    "model = mnist_model()\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))\n",
    "# Evaluating the model on test dataset\n",
    "#print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))\n",
    "history_model = model_run.history\n",
    "print(\"The history has the following data: \", history_model.keys())\n",
    "\n",
    "# Plotting the training and validation accuracy during the training\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
    "plt.xlabel(\"epochs\") ;\n",
    "plt.ylabel(\"accuracy\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we see here is **overfitting**. After the first few epochs the training and validation datasets show a similar accuracy but thereafter the network starts to over fit to the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "Keep in mind that neural networks are quite prone to overfitting so always check for it.\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 7s 118us/step - loss: 1.5830 - acc: 0.8313 - val_loss: 1.0330 - val_acc: 0.8796\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.8571 - acc: 0.8997 - val_loss: 0.7363 - val_acc: 0.9086\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.6914 - acc: 0.9073 - val_loss: 0.6694 - val_acc: 0.8920\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.6105 - acc: 0.9121 - val_loss: 0.5905 - val_acc: 0.9062\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.5626 - acc: 0.9163 - val_loss: 0.5815 - val_acc: 0.8957\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.5282 - acc: 0.9198 - val_loss: 0.5588 - val_acc: 0.9052\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.5033 - acc: 0.9229 - val_loss: 0.5025 - val_acc: 0.9223\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.4838 - acc: 0.9253 - val_loss: 0.4875 - val_acc: 0.9200\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.4656 - acc: 0.9284 - val_loss: 0.4792 - val_acc: 0.9161\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.4517 - acc: 0.9312 - val_loss: 0.5833 - val_acc: 0.8833\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.4387 - acc: 0.9336 - val_loss: 0.4403 - val_acc: 0.9305\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.4279 - acc: 0.9350 - val_loss: 0.4342 - val_acc: 0.9349\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.4154 - acc: 0.9383 - val_loss: 0.4060 - val_acc: 0.9381\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.4066 - acc: 0.9385 - val_loss: 0.4100 - val_acc: 0.9346\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.3962 - acc: 0.9417 - val_loss: 0.4068 - val_acc: 0.9357\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.3863 - acc: 0.9430 - val_loss: 0.4106 - val_acc: 0.9287\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.3800 - acc: 0.9453 - val_loss: 0.4057 - val_acc: 0.9388\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 2s 27us/step - loss: 0.3720 - acc: 0.9455 - val_loss: 0.3719 - val_acc: 0.9443\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 2s 31us/step - loss: 0.3656 - acc: 0.9466 - val_loss: 0.3556 - val_acc: 0.9504\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.3594 - acc: 0.9485 - val_loss: 0.3805 - val_acc: 0.9337\n"
     ]
    }
   ],
   "source": [
    "# Adding l2 regularization\n",
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.regularizers import l2\n",
    "\n",
    "def mnist_model():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\", \n",
    "                   kernel_regularizer=l2(0.01)))\n",
    "\n",
    "    model.add(Dense(64, activation=\"relu\", \n",
    "                   kernel_regularizer=l2(0.01)))\n",
    "\n",
    "    model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "    model.compile(loss=\"categorical_crossentropy\",\n",
    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    return model\n",
    "\n",
    "model = mnist_model()\n",
    "\n",
    "num_epochs = 20\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 398
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluating the model on test dataset\n",
    "history_model = model_run.history\n",
    "print(\"The history has the following data: \", history_model.keys())\n",
    "\n",
    "# Plotting the training and validation accuracy during the training\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
    "plt.xlabel(\"epochs\") ;\n",
    "plt.ylabel(\"accuracy\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "Another way to add regularization and to make the network more robust is by applying Dropout. When we add dropout to a layer a specified percentage of units in that layer are switched off. \n",
    "    \n",
    "Both L2 regularization and Dropout make the model simpler and thus reducing overfitting.\n",
    "</p>\n",
    "</div>\n",
    "\n",
    "### Exercise section\n",
    "* Add dropout instead of L2 regularization in the network above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding dropout is easy in keras\n",
    "# We import a layer called Dropout and add as follows\n",
    "# model.add(Dropout(0.2)) to randomly drop 20% of the hidden units\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 7s 124us/step - loss: 0.6387 - acc: 0.8222 - val_loss: 0.2917 - val_acc: 0.9119\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 2s 31us/step - loss: 0.2846 - acc: 0.9158 - val_loss: 0.1995 - val_acc: 0.9402\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 2s 31us/step - loss: 0.2187 - acc: 0.9348 - val_loss: 0.1717 - val_acc: 0.9463\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1823 - acc: 0.9460 - val_loss: 0.1526 - val_acc: 0.9519\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1574 - acc: 0.9530 - val_loss: 0.1383 - val_acc: 0.9569\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 2s 32us/step - loss: 0.1407 - acc: 0.9582 - val_loss: 0.1228 - val_acc: 0.9635\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 2s 32us/step - loss: 0.1279 - acc: 0.9619 - val_loss: 0.1111 - val_acc: 0.9650\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1160 - acc: 0.9645 - val_loss: 0.1107 - val_acc: 0.9653\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 2s 32us/step - loss: 0.1078 - acc: 0.9670 - val_loss: 0.1032 - val_acc: 0.9680\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 2s 32us/step - loss: 0.1010 - acc: 0.9693 - val_loss: 0.0945 - val_acc: 0.9717\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 2s 31us/step - loss: 0.0938 - acc: 0.9711 - val_loss: 0.0936 - val_acc: 0.9729\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 2s 32us/step - loss: 0.0882 - acc: 0.9734 - val_loss: 0.0861 - val_acc: 0.9735\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 2s 32us/step - loss: 0.0836 - acc: 0.9744 - val_loss: 0.0935 - val_acc: 0.9709\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0794 - acc: 0.9753 - val_loss: 0.0849 - val_acc: 0.9739\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 2s 32us/step - loss: 0.0764 - acc: 0.9762 - val_loss: 0.0844 - val_acc: 0.9742\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 2s 37us/step - loss: 0.0735 - acc: 0.9767 - val_loss: 0.0855 - val_acc: 0.9741\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 2s 33us/step - loss: 0.0689 - acc: 0.9791 - val_loss: 0.0826 - val_acc: 0.9752\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 2s 32us/step - loss: 0.0668 - acc: 0.9790 - val_loss: 0.0857 - val_acc: 0.9742\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 2s 33us/step - loss: 0.0639 - acc: 0.9805 - val_loss: 0.0841 - val_acc: 0.9753\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 2s 33us/step - loss: 0.0617 - acc: 0.9798 - val_loss: 0.0806 - val_acc: 0.9760\n",
      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 398
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "# Adding Dropout\n",
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "\n",
    "def mnist_model():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\"))\n",
    "              \n",
    "    model.add(Dropout(0.15))\n",
    "\n",
    "    model.add(Dense(64, activation=\"relu\"))\n",
    "    \n",
    "    model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "    model.compile(loss=\"categorical_crossentropy\",\n",
    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "              \n",
    "    return model\n",
    "\n",
    "model = mnist_model()\n",
    "\n",
    "num_epochs = 20\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))\n",
    "\n",
    "# Evaluating the model on test dataset\n",
    "history_model = model_run.history\n",
    "print(\"The history has the following data: \", history_model.keys())\n",
    "\n",
    "# Plotting the training and validation accuracy during the training\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
    "plt.xlabel(\"epochs\") ;\n",
    "plt.ylabel(\"accuracy\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Architecture\n",
    "\n",
    "The neural networks which we have seen till now are the simplest kind of neural networks.\n",
    "There exist more sophisticated network architectures especially designed for specific applications.\n",
    "Some of them are as follows:\n",
    "\n",
    "###  Convolution Neural Networks (CNNs)\n",
    "\n",
    "These networks are used mostly for computer vision like tasks such as image classification and object detection. \n",
    "One of the old CNN networks is shown below.\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/CNN_lecun.png\" width=\"800\"/>\n",
    "<figcaption>source: LeCun et al., Gradient-based learning applied to document recognition (1998).</figcaption>\n",
    "</figure>\n",
    "</center>\n",
    "\n",
    "CNNs consist of new type of layers such as convolution and pooling layers.\n",
    "\n",
    "###  Recurrent Neural Networks (RNNs)\n",
    "\n",
    "RNNs are used for problems such as time-series data, speech recognition and translation.\n",
    "\n",
    "### Generative adversarial networks (GANs)\n",
    "\n",
    "GANs consist of 2 parts, a generative network and a discriminative network. The generative network produces data which is then fed to the discriminative network which judges if the new data belongs to a specified dataset. Then via feedback loops the generative network becomes better and better at creating images similar to the dataset the discriminative network is judging against. At the same time the discriminative network get better and better at identifyig **fake** instances which are not from the reference dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN in a bit more detail\n",
    "\n",
    "The standard CNN srchtecture can be seen as 2 parts:\n",
    "\n",
    "* Feature extraction\n",
    "* Classification\n",
    "\n",
    "For the **classification** part we use the denly connected network as shown in the keras examples above.\n",
    "\n",
    "However, for the **feature extraction** part we use new types of layers called **convolution** layers\n",
    "\n",
    "### What is a Convolution?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a51a73dd8>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RAACAoYgAAAAMRQQAAGAoIgAAAEMRAQAAGIoIAADAUEQAAACGIgIAADAUEQAAgKGIAAAADEUEAABgKCIAAABDEQEAABiKCAAAwFBEAAAAhiICAAAwFBEAAIChiAAAAAxFBAAAYCgiAAAAQxEBAAAYiggAAMBQRAAAAIbyc3oAEwwcONCxtV0ul2NrnzhxwrG1Gzdu7NjaTzzxhGNrR0VFObZ2vXr1HFtbkjZt2uTY2vHx8Y6tXbduXcfWdpKTv9vuJrwSAACAoYgAAAAMRQQAAGAoIgAAAEMRAQAAGIoIAADAUI59RHD9+vVasWKF/va3v6mwsFARERF68skn9eyzz8rX19epsQAAMIYjEfD+++9r0aJFCggIUMeOHeXr66t9+/Zp6tSpSkxM1KxZs/gMKAAAlcz2CEhLS9Onn36qkJAQrVy5UhEREZKkjIwM/e53v9PWrVu1ZcsW9e7d2+7RAAAwiu3nBOzevVuWZemxxx4rDgBJCgsLK76y3t69e+0eCwAA49geAUUv82dkZNyy7dKlS5KkmjVr2joTAAAmsj0CunXrJpfLpU2bNmn+/PnKzMzUlStXtGbNGi1dulT33HOPnnrqKbvHAgDAOLZHQNOmTRUbG6sqVaroww8/VNeuXdWxY0dNnjxZrVq1Unx8vMLDw+0eCwAA4zhynYD27dura9euCgoKUpcuXfTP//zPqlatmg4ePKiVK1fKsiwnxgIAwCi2fzogOTlZzz//vBo0aKCEhAQ1bNhQ0k/nCLz44otaunSpgoOD9fvf/97u0QAAMIrtrwRMnTpV165d07vvvlscANJPnw746KOP5OfnpyVLlig3N9fu0QAAMIqtEXD9+nWlpKSoevXqioqKumV7o0aNFBERoZycHJ04ccLO0QAAMI6tEZCdnS3Lssq8LHDRths3btg1FgAARrI1AkJDQ1WzZk1dvnxZKSkpt2zPyMjQkSNH5O/vr8jISDtHAwDAOLZGgI+Pj/r16ydJmjx58k0XDMrMzNTYsWN148YNPfXUU6pWrZqdowEAYBzbPx0wZswYpaSkKDExUb169VLHjh3lcrl04MABXblyRffff78mTJhg91gAABjH9ggIDAzUp59+qpUrV2r9+vXav3+/CgsL1aRJE/3Hf/yHhg4dqoCAALvHAgDAOI58lbC/v7+GDBmiIUOGOLE8AACQQ1cMBAAAziMCAAAwFBEAAIChiAAAAAxFBAAAYCgiAAAAQxEBAAAYypHrBJjGz8+5p3nw4MGOrQ3Yad++fY6tff/99zu2to+Pc3/LeTwex9Yu64voUHG8EgAAgKGIAAAADEUEAABgKCIAAABDEQEAABiKCAAAwFBEAAAAhiICAAAwFBEAAIChiAAAAAxFBAAAYCgiAAAAQxEBAAAYiggAAMBQRAAAAIYiAgAAMBQRAACAoYgAAAAMRQQAAGAoIgAAAEMRAQAAGIoIAADAUEQAAACGIgIAADAUEQAAgKGIAAAADEUEAABgKCIAAABDEQEAABiKCAAAwFB+Tg+AyuXxeBxb27Isx9Z2uVxGrl1YWOjY2n5+zv46qVatmmNrZ2VlOba2k3x8+DvyTsd/QQAADEUEAABgKCIAAABDEQEAABiKCAAAwFBEAAAAhiICAAAwVKVEwNq1a9WiRQvt27evxO3Hjh3TK6+8ou7du6tt27bq27evli9f7uhnnAEAMI3XIyApKUmxsbGlbv/+++/Vr18/bdiwQfXr11e3bt107tw5xcbGavz48d4eBwAAlMKrl/jasmWLXnvtNeXk5JS43bIsjR8/XlevXtX777+vxx9/XJKUmZmpoUOHKiEhQb169VLv3r29ORYAACiBV14JOHfunMaPH6/Ro0ersLBQtWvXLvF+u3btUlpamjp16lQcAJIUEhKit956S5K0bNkyb4wEAADK4ZUImDFjhtavX6/WrVsrPj5ekZGRJd5v586dkqSePXvesq1Dhw4KDQ3V/v37dfXqVW+MBQAAyuCVCIiMjNT06dO1evVqtWjRotT7paenS5LcbneJ2yMiIlRYWKgjR454YywAAFAGr5wTMGzYsArd7/z585KkOnXqlLi96PYff/zRG2MBAIAy2HqdgNzcXElSlSpVStxedHtpJxYCAADvsTUCir57urTvWy/6/nknv4ceAABT2BoBQUFBkqTr16+XuD0vL++m+wEAgMpjawTUrVtXUunv+V+4cEFS6ecMAAAA77E1Apo3by7pfz8l8I8sy9LRo0fl6+urpk2b2jkWAABGsjUCunXrJkn605/+dMu27777TpmZmerQoYOCg4PtHAsAACPZGgGdOnVS8+bNtWvXLn3++efFt2dmZuqdd96RJEVHR9s5EgAAxvLqdweUx8fHR1OnTtWQIUP0xhtvaM2aNapbt64SExOVlZWlAQMG6JFHHrFzJAAAjGVrBEhSVFSUVq9erbi4OO3Zs0c//PCDGjdurFdeeUX9+/e3exwAAIxVKRFQ3pcANWvWTHFxcZWxNAAAqCBbzwkAAAC3DyIAAABDEQEAABiKCAAAwFBEAAAAhiICAAAwlO3XCYC9fH19nR4BNjL5a7gLCwsdWzs5OdmxtZ1U2tfC28HJY93Jx+1tvBIAAIChiAAAAAxFBAAAYCgiAAAAQxEBAAAYiggAAMBQRAAAAIYiAgAAMBQRAACAoYgAAAAMRQQAAGAoIgAAAEMRAQAAGIoIAADAUEQAAACGIgIAADAUEQAAgKGIAAAADEUEAABgKCIAAABDEQEAABiKCAAAwFBEAAAAhiICAAAwFBEAAIChiAAAAAxFBAAAYCgiAAAAQxEBAAAYiggAAMBQfk4PAMB7fHzM7fq+ffs6tnZgYKBjawO/hrm/MQAAMBwRAACAoYgAAAAMRQQAAGAoIgAAAEMRAQAAGIoIAADAUJUSAWvXrlWLFi20b9++Erdv375dL7zwgjp16qTWrVvr4Ycf1ptvvqlz585VxjgAAKAEXo+ApKQkxcbGlrp9/vz5GjZsmHbv3q2IiAg99NBDkqT4+Hj99re/1ZEjR7w9EgAAKIFXI2DLli164YUXlJOTU+L29PR0zZgxQ0FBQVq5cqXi4+M1Z84cbdmyRQMHDlRmZqYmTZrkzZEAAEApvBIB586d0/jx4zV69GgVFhaqdu3aJd5v/fr18ng8io6OVrt27Ypv9/f316RJkxQSEqLk5GSdOXPGG2MBAIAyeCUCZsyYofXr16t169aKj49XZGRkiffz9/dXixYt1LFjxxK3NWzYUJJ0/vx5b4wFAADK4JUvEIqMjNT06dP12GOPlfkFJmPGjNGYMWNK3JaTk6P09HRJUr169bwxFgAAKINXImDYsGG/eh8LFixQTk6O2rRpo/DwcC9MBQAAynJbXCdg+/btmjdvnnx8fDRu3DinxwEAwAiOR8C2bds0evRoeTwevfzyy+rcubPTIwEAYARHI2DNmjUaNWqU8vLyNGrUKK+8rQAAACrGK+cE/BIzZszQ3Llz5XK5NHHiRA0dOtSpUQAAMJLtEWBZll5//XWtWbNGAQEBmj59uvr06WP3GAAAGM/2CJg2bZrWrFmj4OBgzZ07V506dbJ7BAAAIJsjYMeOHVqyZIn8/Pw0b948PfDAA3YuDwAA/oGtETBr1ixJUmhoqFatWqVVq1aVeL8RI0aoadOmdo4GAIBxbIuA3NxcHTx4UJKUkZGhhISEUu/bv39/IgAAgEpWKRGwbNmyW26rWrWqDh8+XBnLAQCAX8DxiwUBAABnEAEAABiKCAAAwFBEAAAAhiICAAAwFBEAAIChHPsCIQDe53K5HFvbsizH1pYkt9tt5NqmcvJYv5vwSgAAAIYiAgAAMBQRAACAoYgAAAAMRQQAAGAoIgAAAEMRAQAAGIoIAADAUEQAAACGIgIAADAUEQAAgKGIAAAADEUEAABgKCIAAABDEQEAABiKCAAAwFBEAAAAhiICAAAwFBEAAIChiAAAAAxFBAAAYCgiAAAAQxEBAAAYiggAAMBQRAAAAIYiAgAAMBQRAACAoYgAAAAMRQQAAGAoP6cHsMOJEyckSYcPH9bgwYMdnga4O1mW5ej6LpfL0fWBynb48GFJ//tvmjcYEQE5OTmSpOzsbCUmJjo8DQAAv1zRv2neYEQENGzYUKdPn1ZQUJAaN27s9DgAAPxsJ06cUE5Ojho2bOi1fbosp1/DAwAAjuDEQAAADEUEAABgKCIAAABDEQEAABiKCAAAwFBEAAAAhiICAAAwFBEAAIChiAAAAAxFBAAAYCgiAAAAQxEBAAAYiggAAMBQRAAAAIYiAgAAMBQRAACAofycHsApu3fv1ieffKK0tDTduHFDrVq10rBhw9StW7cK7+PYsWOaOXOm9u/fr8uXL+vee+/V008/rYEDB8rH5+7qK4/Ho88++0zr1q3T0aNH5fF41KhRI/Xp00cxMTEKDAwsdx9nz55Vjx49St3evn17ffbZZ16c2nlffPGFJkyYUOr24cOH6+WXXy53P6Ycay1atKjQ/ZYuXarOnTuXeZ+CggK1a9dO+fn5JW4PCwvTjh07fvaMt5u1a9dq4sSJWrFihR544IFbtnvr2Lly5YrmzZunrVu36uzZs6pdu7YeffRRvfjiiwoODvbmQ7JFec/b9u3btXTpUh08eFA5OTmqU6eOunXrppEjR6pevXoVXqdnz546depUqdtTU1Pl5+fcP8VGRkDRf/yAgAB16dJFhYWF2rNnj2JiYjRlyhQ9/fTT5e7j+++/16BBg3T16lW1b99ebdq00Z49exQbG6vk5GR98MEHNjwSe3g8Ho0cOVLbtm1TUFCQ2rZtKz8/Px04cEBxcXHavn27/vjHP6pq1apl7ufQoUOSfvpF73a7b9keERFRKfM76fDhw5Kkf/mXf1FISMgt21u2bFnuPkw61vr27VvqtlOnTik5OVnBwcFq1KhRuftKT09Xfn6+7r33XrVt2/aW7TVr1vxVs94OkpKSFBsbW+p2bx07V69e1bPPPqu0tDRFRESoR48eSk1N1eLFi7Vz506tWrVK1atX99bDqnTlPW/z58/Xhx9+KB8fH0VFRSk0NFSHDx9WfHy8vvnmGy1fvlxNmzYtd53s7GydPn1atWvXVteuXUu8j+MRbxkmIyPDat26tdWhQwcrLS2t+PYDBw5Y7du3t9q0aWOdO3euzH0UFhZaffv2tdxut/XFF18U337x4sXi2zdt2lRpj8Fun332meV2u62+ffve9NxcvHjRevrppy2322198MEH5e5n5syZltvtttavX1+Z495Wnn32Wcvtdpd7TJXGtGOtNDk5OdZvfvMby+12W1u3bq3Qz6xdu9Zyu93WnDlzKnk6Z2zevNlq166d5Xa7Lbfbbe3du/em7d48dmJjYy232229/vrrlsfjsSzLsm7cuGGNGzfOcrvd1pQpU7z3wCpZec/bDz/8YLVs2dK6//77re+++6749vz8fOvtt9+23G63NWDAgAqttWfPHsvtdluTJ0/26mPwprvndcQKWr58ufLz8zV06NCb/hqNiopSTEyM8vLyFB8fX+Y+du3apbS0NHXq1EmPP/548e0hISF66623JEnLli2rnAfggHXr1kmSJk2apLCwsOLbQ0JC9Pbbb0uSNmzYUO5+il4JaNWqlfeHvE19//33ql279k3P289h2rFWmqlTp+rIkSMaMGCA/vVf/7VCP3O3Hm/nzp3T+PHjNXr0aBUWFqp27dol3s9bx86VK1e0evVqBQcHa8KECcV/ufr5+emtt97SPffcozVr1ignJ8cLj67yVPR5W79+vTwej6Kjo9WuXbvi2/39/TVp0iSFhIQoOTlZZ86cKXfNO+EYNC4Cdu7cKemn92n+r169eklSue8TlrWPDh06KDQ0VPv379fVq1d/7bi3hVq1aikyMlJRUVG3bGvSpIkk6fz58+Xu5/DhwwoKCrorX/YvyalTp3TlypVf9QvAtGOtJCkpKVq9erVCQ0M1bty4Cv9c0Vsxt/Mv4F9ixowZWr9+vVq3bq34+HhFRkaWeD9vHTt79+7V9evX1aVLl1ve+69WrZq6du2q69eva+/evb/wEdmjos+bv7+/WrRooY4dO5a4rWHDhpIq/jtPur2PQaMiwLJETZRzAAALfklEQVQspaeny8fHp8QDoEmTJvLx8VF6erosyyp1P+np6ZJU4vva0k/vbRcWFurIkSPeGdxhn3zyib7++msFBQXdsu3gwYOSVO6JMpcvX9bf//53RUREaPHixXrsscfUtm1bPfjgg3rjjTeUkZFRKbM7qegXQGhoqGJjY9WrVy+1adNGvXv31uzZs5WXl1fuPkw71koydepUWZal0aNHq0aNGhX6GcuydPjwYdWpU0f/8z//o379+qldu3bq0qWLXnnlFR09erSSp648kZGRmj59ulavXl3miZTeOnaK9tO8efNS55GktLS0cmd3UkWftzFjxujLL78s8T38nJyc4uejIicHHjp0SL6+vjp27JgGDRqkBx54QB07dtTw4cOVkpLyyx+MFxkVAVlZWcrPz1fNmjUVEBBwy3Y/Pz/VqlVLubm5unbtWqn7KSrAOnXqlLi96PYff/zRC1PfvizLUlxcnCTp0UcfLfO+Rf8gpqam6uOPP1ZoaKg6d+4sj8ejzz//XE899dQd/Yu5JEUvBa5du1YJCQlq1qyZ2rZtq4yMDMXFxWnIkCG6fv16mfsw/Vjbvn27kpKSVK9ePfXr16/CP3fq1CldvXpVFy5c0JtvvqnAwEB17txZgYGB2rBhg/r166f9+/dX4uSVZ9iwYXriiSfKPaHMW8fOhQsXKrSfixcvlrkfp1X0eSvLggULlJOTozZt2ig8PLzM++bn5xd/kmr8+PHKy8tT586dVatWLX377bcaOHCgNm3a9Itn8RajPh2Qm5srSWWexV6lShVJ0rVr10r92EvRforuW9o+bvf3yH6tjz76SImJiapdu7ZiYmLKvG/RP4jNmzfX3Llzi8/uzsnJ0RtvvKGvvvpKY8eO1dq1ayt9brsUhc9vfvMbTZ06tfiVlNOnT2vUqFFKSkrSjBkz9Nprr5W6D9OPtSVLlkiSoqOj5e/vX+GfKzrewsLCNG/evOJPYRQUFOjDDz/Up59+qpdfflnffPNNhT7eeify1rFTtL2035t3+zFYZPv27Zo3b558fHwq9LZUWlqaCgoKVK1aNc2ePfumVxaWLFmi9957TxMnTlSHDh1KDSw7GPVKQEUKsKy3Af7vflwuV5n7qMi+7lR/+MMfNH/+fAUEBGjGjBklfvztHw0dOlRbt27V0qVLb/p4V1BQkP7zP/9TYWFhSk1NVXJycmWPbpu4uDht2LBB77///k1vpTRs2FDTpk2Ty+VSfHy8bty4Ueo+TD7W0tPTtXv3blWvXl0DBgz4WT/bu3dvbdu2TatXr77pY5h+fn4aN26cWrVqpYyMDG3dutXbY982vHXsmHwMFtm2bZtGjx4tj8ejl19+udxrVEhSmzZt9Oc//1kJCQm3vLUwdOhQ9ezZUzk5OcUnXjvFqAgo+kVc1nuxRdvKerWgaD+lvZRbtI+S3kO/0xUUFOjNN9/UnDlzFBgYqFmzZpV4As3/5evrq0aNGpUYC1WrVlWXLl0k/fR2wd0iMDBQzZo1K/Gtp5YtW6pevXrKycnR8ePHS92Hycfaxo0bJf10wu7PfXwul0vh4eElfirDx8dH3bt3lyT99a9//fWD3qa8dexUdD/lXSfkTrVmzRqNGjVKeXl5GjVqlIYNG1bhn61Tp44aNGhQ4raHH35YkvPHoFEREBwcrKCgIF26dEkFBQW3bC8oKNClS5cUGBhY5glIdevWlVT6e2nlvYd2p7p27ZqGDx+u+Ph41ahRQ4sWLSr+ZfprFX1cp+glTBNU5DGbeqxJ0jfffCNJ6tOnj9f3XfTcl3dOxp3MW8eOycfgjBkzNHnyZHk8Hk2cOFFjxozx2r6Lni+nj0GjIsDlcqlZs2byeDwl/vV17NgxFRYWlno2bZGis2SLzhL9R5Zl6ejRo/L19a3QFaXuFFlZWRo8eLB27typ8PBwrVixokKvABSZNWuWxowZU+oZxKdPn5ZUsTNu7wRXr17VG2+8oTFjxpQYnNL/PuayriFg4rEm/XSJ6b/97W+qXr16qVdaK8uKFSv00ksvaffu3SVuv9uOt5J469gpaz+Sij9dUNFLPt8JLMvS5MmTNXfuXAUEBOijjz7S0KFDf9Y+Nm7cqFdffVUJCQklbr9djkGjIkBS8XcDlPReYNFt5f11W7SPP/3pT7ds++6775SZmakOHTrckdfTLkl+fr6GDRum1NRUNWvWTKtWrSo3lP6vtLQ0bd68WV9//fUt2y5evKhdu3bJ39+/Qu+13QmqVaumb775Rps3by7x89M7duzQpUuX5Ha7y4wA0461IgcOHJD000W8fsl11U+dOqWvv/66xPdb8/LytHnzZkk/Xc75buWtY6djx46qUqWK/vKXv9xy8t+1a9f0l7/8RUFBQerQoYP3hnfYtGnTtGbNGgUHB2vRokW/6NWoixcv6quvvirx+1Asy9KXX34pSXrwwQd/9by/hnER8OSTTyowMFALFiy46b2YgwcPauHChapSpYoGDhxYfPvJkyd15MgRZWdnF9/WqVMnNW/eXLt27dLnn39efHtmZqbeeecdST+dzXy3iIuLU3JyssLDw7Vs2bJyy7Wk56zo+xgWL15800ezrl27pkmTJunq1avq16/fXfOSosvlKj6ZLTY29qbrIJw8ebL4OBkxYsRNt5t+rBUp+v9mmzZtyr3v3//+dx05ckSZmZnFt/Xr10++vr5KSEgo/gdfkm7cuKHY2FidOXNGDz30kFq3bu394W8Tv+TYOX/+vI4cOXLThXCCgoL0xBNPKCsrS++8807xK1sFBQWaMmWKrly5oqeffvquCdEdO3ZoyZIl8vPz07x589SpU6dyf6ak5+3f/u3fFBwcrP379xd/ykX6KQBmz56t5ORkud1uPfLII5XxMCrMZd3Np3SWYsWKFZoyZYr8/f3VpUsXWZalPXv2qKCgQNOnT7/pEpuPPPKIzpw5o/fee09PPvlk8e0pKSkaMmSIcnJy1LZtW9WtW1eJiYnKysrSgAEDyvxyijvJ5cuX1b17d12/fl2tWrUq9Spbkoq/jKS052zatGlavHixfHx81L59e9WqVUv79u3TpUuX9MADD2jhwoV31clF169f1/PPP6/9+/ff9JfSnj17lJ+fr+jo6Js+Hmj6sfaPXnrpJX399dd69913y70+wODBg5WYmKgXX3xRo0ePLr596dKlxRcaatOmjerXr68DBw7o3LlzioyM1PLlyxUaGlrZD6XSFT3+kr4N7+ceO6+99prWrVun3/72t5o2bVrx7ZcvX9YzzzyjY8eOqVGjRrrvvvt06NAhnTp1Svfdd5+WL1+uatWq2fJ4vaW0523AgAE6cOCAwsLCygyAESNGFL+VUtrztnnzZr366qu6ceOGmjdvrsjISKWlpen48eOqU6eOli9fXnzVVacYdZ2AIoMGDVL9+vW1cOFC7d+/XwEBAWrfvr1GjBhR4fcfo6KitHr1asXFxWnPnj364Ycf1LhxY73yyivq379/JT8C+6SkpBSfuJKamlrm2fvlfSPZa6+9prZt22r58uU6dOiQCgsLde+99yomJkZDhgz5WZ8DvxNUqVJFS5Ys0ZIlS5SQkKA9e/YoICBA999/vwYPHlzuBZaKmHKs/aOiv+p/zfulzz33nJo3b66FCxcqJSVFaWlpql+/voYPH65hw4bdcf9o/RLeOnZq1qypVatWadasWdq6dau+/fZbhYeHKyYmRsOHD79rnsvc3Nziq6BmZGSU+n6+JPXv37/c8yl69+6tBg0aaN68edq7d6+OHz+uunXravDgwRo5cmS5H622g5GvBAAAAAPPCQAAAD8hAgAAMBQRAACAoYgAAAAMRQQAAGAoIgAAAEMRAQAAGIoIAADAUEQAAACGIgIAADAUEQAAgKGIAAAADEUEAABgKCIAAABDEQEAABiKCAAAwFBEAAAAhvp/uKHgdQHaYYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 256
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "# Loading the train and test data\n",
    "digit = np.genfromtxt(\"digit_4_14x14.csv\", delimiter=\",\").astype(np.int16) ;\n",
    "plt.imshow(digit, \"gray_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This image in matrix form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_astable(matrix, hw=0.15):\n",
    "    matrix = plt.table(cellText=matrix, loc=(0,0), cellLoc='center') ;\n",
    "    matrix.set_fontsize(14)\n",
    "    cells=matrix.get_celld() ;\n",
    "    for i in cells:\n",
    "        cells[i].set_height(hw) ;\n",
    "        cells[i].set_width(hw) ;\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S4sWLc7N7lsKcZR4yMVNu3oOQPxgjzlmm0Lx582bH48wOJIKCghwHCd9//32B9MuqyMM8ZGIW8jAPmZiFPMzTv39/rV+/XnPnzlWlSpWybM8NsvIXYyT/9O/fX3v37pW3t7dCQkK0YMGCLLexLynTpEkT3XbbbU7bPPbYY5KkpKQk/fjjj5l+ry1btmj+/PkKCAjQW2+9lYs9KDrOnj2rEiVKqEyZMqpdu3am7apXry5JSk5O1sWLFyVJFy9elLe3t+6+++5Mt/Px8XGsUXn27FnH81FRUY5/P/roo0639fb2doy99evX6+bNmznYM+thzjIPmZgpN+9ByB+MEecsU2iOioqSJFWsWFGBgYGZtmvQoIEk6cCBAwXSL6siD/OQiVnIwzxkYhbyMI+Xl5eqVKmiZs2aOX09KSlJ8+fPlyRVqlRJdevWLcjuWQ5jJP94eXmpRYsWWrZsmUaOHKkSJUq4bJ+cnKwjR45Iksszz+rWrStfX19JqesvO3Pp0iW9/vrrstlsGj16tMtsrSA0NFR79uxxnFGWmd9//11Sanb2Qv+6deu0f/9+vfLKK5lud/PmTZ08eVKSHGc2S3+OL8l1pvbiwtWrV3X06FHXO2NxzFnmIRMz5fQ9CPmHMeKcZU4lsd8lsWrVqi7bVa5cWZIUGxurq1evqlSpUvneNysiD/OQiVnIwzxkYhbyKBwSEhL0xx9/aNeuXQoLC1N0dLR8fHw0duxY+fn5ebp7RRpjJP8sWbLEsSRCdpw5c0Y3btyQ9Ocd3J3x8vJSUFCQjh8/7ihu3mr06NE6e/as/vGPf6ht27Y563gRduvlymnFxcVp1apVkqS77rorXVGmWLFiLv/Pr1q1SleuXJGUeja6nX182f/Alhn7+JKkkydP6o477shiT6yLOcs8ZGKmnL4HIf8wRpyzTKHZfolUZpeq2ZUpU8bx+MqVK0X+P4CnkId5yMQs5GEeMjELeRQOISEh6c7eCAoK0nvvvafGjRt7sFfWwBjJPzn9gG/PQsp+HvbiZlorV67U2rVrVbFiRY0ZMyZHfbCy8ePH69KlS5Kk7t27Z3u7s2fPatKkSZKksmXL6qmnnnK8Zs+0RIkSLv9olnZ8Xb58OUf9thrmLPOQiZkoMpuDMeKcZZbOuH79uiRleVlB2tft2yDvkYd5yMQs5GEeMjELeRQOp0+fzvDvsWPHaufOnR7qkXUwRsyRlJTkeJxVHsWLF5eUMYuYmBiNHz9ekvTWW2+pbNmyedzLomnmzJlauXKlJOmBBx5Qu3btsrVdXFyc+vfvr/Pnz0uSXnvttXSFBHs+9rwykzbvtP8PkBFzlnnIBHCNMeKcZQrNxYoVy1Y7+93Skb/IwzxkYhbyMA+ZmIU8Cod58+Zp37592rp1q958800FBAQoKipKvXv31q5duzzdvSKNMWIOb+8/P3J5eXm5bOssj5SUFA0dOlRxcXHq3LmzWrVqled9LIpmzJihDz74QFLqZctTp05Nl0VmYmNj9dxzzzmuxujcubP++c9/pmtjH1+5yRPOMWeZh0wA1xgjzlmm0FyyZElJWf/1IO3rLKqef8jDPGRiFvIwD5mYhTwKhzp16sjPz0/ly5dX586dNXfuXPn5+SkxMdFxSTryB2PEHGkvkU1MTHTZ1n7Wa9osPvvsM+3YsUNVq1bVsGHD8qeTRcjNmzc1evRoTZ8+XVLqzUfnzJmjihUrZrntiRMn1K1bN+3bt0+S9NRTTzldpiQ34yurs5+tjjnLPGQCuMYYcc4yhWb7mihxcXEu26VdD+3222/P1z5ZGXmYh0zMQh7mIROzkEfhVL9+fcel67t371ZsbKyHe1R0MUbMkXZtxuzmYc/i0KFDmjZtmry9vTVhwgT5+/vnX0eLgPj4ePXt21eLFi2SJNWsWVMLFixQjRo1stx2586d6tSpk44ePSpJ6tKliyZPnuz0jDX7MhrXrl1TcnJypt8zbd6ML9eYs8xDJoBrjBHnLFNoti+Yfutagbeyv16xYkXuhp6PyMM8ZGIW8jAPmZiFPAqvRo0aOR6fPHnSgz0p2hgj5ggKCnKcweQqD5vNpjNnzkiSqlSpIklat26dkpKSlJKSoh49eqhevXoZvuzrCH/55ZeO51asWJHPe2WemJgYde3aVVu2bJEkNW7cWAsXLnT8Ll1ZtWqVnnvuOV28eFFeXl56+eWXNXbs2EyX2qhZs6ak9Jll1ie77PTDypizzEMmgGuMEecsU2iuV6+epNSA0975+VaRkZGSpDvvvLNA+mVV5GEeMjELeZiHTMxCHuaZMmWKunTpoqFDh7psl3bpACtcPugpjBFzeHt764477pAkRUVFZdouOjracXYseeTM0aNH1blzZ0VHR0uSnnzySYWFhalcuXJZbjt37lwNGTJESUlJ8vPz05QpUzRgwACX29jHlyQdPHgw03b28VWqVKlsnVVtZcxZ5iETwDXGiHOWKTS3bNlSUupfnTdu3Oi0zenTpx0Hf9xkI3+Rh3nIxCzkYR4yMQt5mCcmJka7d+/W2rVr010ieKvNmzdLkvz9/R1nBSLvMUbMYv/97tixQ/Hx8U7brF+/XpLk6+ur5s2bS5L69eunXbt2ufwqX768JOl//ud/HM/Zl6ixgpiYGPXq1Uvnzp2TJPXp00dTp07N1prIS5cu1VtvvSWbzaayZctqzpw5atu2bZbb1a1b13GGsj23W6WkpGjDhg2SpL/+9a/ZvmmUVTFnmYdMANcYI85ZptBcrVo1NW3aVJI0c+ZMXb58OUObiRMnKiUlRWXLllXHjh0LuouWQh7mIROzkId5yMQs5GEee2ErMTFR77//vtM2X3/9tX766SdJ0tNPP22Jywc9hTFilnbt2qlYsWKKj4933KQurZiYGM2ZM0eS1LFjR8cajn5+fvL393f55eXlJUny8fFxPOfj41Ng++ZJN27c0CuvvOJYvuLVV1/VkCFDHL8TVw4cOKCxY8dKksqWLasvvvhC999/f7Z/docOHSRJ33zzjePmgWktXLhQx44dkyT16tUr29/XqpizzEMmgGuMEecsU2iWpOHDh6tYsWI6ceKEunfvrs2bNys2NlaRkZEKDQ3VmjVrJEkDBw7kRhsFgDzMQyZmIQ/zkIlZyMMsrVq1UuvWrSVJ8+fPV2hoqCIiIhQbG6vo6Oj/a+/uY72s7/uPvw4gA0FRW2Y5Bw2d6bHc1Luoiy0pxXUknc2sXfAGPNWGYU2XHpeOrK5QldWV9XZNU9M0k1m8IRHF2VbDar1r2QpdEMFSKERmGntmu9bTImIOgnx/f/A7R458PQgX55zP4ft4JCRHvtd1/MAzn8tz3l7n+mbJkiU9j9WYNGlSPvWpTw3mchuCPVKOSZMmpa2tLUnyne98J4sWLcqzzz6bzs7OPPLII7n66qvzhz/8IePGjcsnPvGJQV7t0HHvvfdmw4YNSZKLL744V199dXbt2tXnr1qtliS55ZZbeh5VcvPNN6elpaXP8w587E+SzJs3LxMmTMiePXsyb9683Hffffntb3+b559/Pl//+tfzT//0T0mSD37wgznvvPMG8G9l6HLNKo8m0Dd75GBNte7/0h6G7h8T6ujoOOoL6m8PPvhgFi5cmL1799Z9va2tLYsWLRrgVVU3VJvoUR5NyqJHeTQpix5l2bVrV9rb23vejKueqVOn5pvf/Gaam5sHcGXVDNUeiT0yULqf0zh//vwsWLCg7jGvvvpq2tvbex6n8EajR4/O0qVLD+uu2iR53/vel9/97ne57LLL8s///M+Ht/CjZLB6zJo1K7/85S8P65zHHnssv/71rzN37tzDOu/CCy/MXXfd1ev3fvGLX+TjH/94Ojs7655z7rnn5o477sjo0aMP6991NJS2R94q16zyaFK+t/LfoNIN5R72SG+N8TNVB/jIRz6SKVOmZOnSpVm7dm1efPHFjB49OtOmTctVV12VWbNmDfYSG4oe5dGkLHqUR5Oy6FGWMWPG5Pbbb8+qVavywAMPZNOmTdm5c2dOOOGETJkyJZdcckkuvfTShvmx/hLYI+UYOXJkvvWtb+W73/1uVq5cma1bt+aVV17J+PHj8773vS/XXXddTj/99MFe5pDR2dl52EPmbhs3bjwqa3j3u9+dhx9+OEuXLs3jjz+ejo6ODBs2LH/yJ3+SD3/4w7n66qs9IugwuWaVRxPomz3SW8Pd0Xys0qQsepRHk7LoUR5NyqJHWfQojyZl0aM8mpRFj/JoUhY9ynOkTRrqGc0AAAAAABx9Bs0AAAAAAFRi0AwAAAAAQCUGzQAAAAAAVGLQDAAAAABAJQbNAAAAAABUYtAMAAAAAEAlBs0AAAAAAFRi0AwAAAAAQCUGzQAAAAAAVGLQDAAAAABAJQbNAAAAAABUYtAMAAAAAEAlBs0AAAAAAFRi0AwAAAAAQCUGzQAAAAAAVGLQDAAAAABAJQbNAAAAAABUYtAMAAAAAEAlBs0AAAAAAFRi0AwAAAAAQCUGzQAAAAAAVGLQDAAAAABAJU21Wq12uCe1tLT0x1oAAAAAAChAR0fHYR3vjmYAAAAAACoZUeXkw51q03+67zLXpAx6lEeTsuhRHk3KokdZ9CiPJmXRozyalEWP8mhSFj3Kc6RPs3BHMwAAAAAAlRg0AwAAAABQiUEzAAAAAACVGDQDAAAAAFCJQTMAAAAAAJUYNAMAAAAAUIlBMwAAAAAAlRg0AwAAAABQiUEzAAAAAACVGDQDAAAAAFCJQTMAAAAAAJUYNAMAAAAAUIlBMwAAAAAAlRg0AwAAAABQiUEzAAAAAACVGDQDAAAAAFCJQTMAAAAAAJUYNAMAAAAAUMmIwV7AYNi6dWtuv/32/PSnP01nZ2fGjRuXadOmZc6cOZkxY8ZgL6/h6FEeTcqiR3k0KYse5Vu2bFm+8IUvZP78+VmwYMFgL6fh2CMDq1ar5aGHHsoDDzyQzZs3Z9euXXn729+e8847L7Nnz85FF11U97yurq6cd955ee211w7571i/fn3GjBlztJd+zPrhD3+Y++67Lz/72c+yc+fOnHLKKbngggvysY99LGeffXavY3/1q1/lz/7szw7r87e0tOTxxx8/mktuaK5Z5dGkbL7OGnz2yOuaarVa7XBPamlpSZJ0dHQc9QX1t8ceeyw33HBD9uzZU/f1OXPm5Oabbx7gVVU3VJvoUR5NyqJHeTQpix7l27BhQ6655pp0dXUN2W+AhnIPe2RgdXV1pb29PT/60Y/e9JjLL788//iP/5impqZev79hw4ZcccUVb+nfU9qgueQef//3f58f/OAHb3pMe3t7/uZv/qbnn49k0Nza2prvf//7R7zO/lBqk0NxzSqPJmU7Fr7OSoZ2D3ukt4Z6dMbmzZvz6U9/Onv27MnUqVOzbNmyrFmzJitXrsysWbOSJMuXL8+yZcsGeaWNQY/yaFIWPcqjSVn0KN+6desyb968dHV1DfZSGpI9MvAWL17cM2S+9NJLc//99+cnP/lJVqxY0fN3vmLFitx2220Hnfvzn/88STJ27Ng89dRTWb9+/Zv+KmnIXLKbbrqpZ8h89tln54477sjatWvz8MMPp62tLUnyjW98o1ePlpaWPv/uu3919xw1alSWLFky8H+4Y5BrVnk0KZuvswafPVJH7Qg0NzfXmpubj+TUQXXdddfVWltbaxdffHFt586dvV7bt29frb29vdba2lo7//zzazt27BikVR6ZodhEj/JoUhY9yqNJWfQo2x133FGbOnVqrbW1tefXl7/85cFe1hEZqj3skYH17LPP1s4888xaa2trbdGiRXWP+cQnPlFrbW2tnXvuubWXX36512s33nhjrbW1tfaxj31sIJZ7VJXYY/369T3Xnra2ttru3bsPOubb3/52rbW1tTZt2rTac88995Y/9913393zuVesWHEUV330lNjkUFyzyqNJuY6lr7NqtaHbwx45WMPc0bx9+/Y8+eSTSZL58+dn7NixvV5vamrKjTfemGHDhuWll17KqlWrBmGVjUOP8mhSFj3Ko0lZ9CjXunXrMnv27CxZsqTn7g4Gnj0y8B555JHU/v9TCQ98FMOBLr300iTJrl278uyzz/Z6rfuO5ve85z39uMrG8d3vfjdJMmzYsNx6660ZOXLkQcf89V//dSZOnJhXX3013/nOd97S533uuefypS99KUkyc+bMzJ49+6ituZG5ZpVHkzL5Oqsc9kh9DTNoXr16dc/HM2fOrHvMhAkTMnny5CT73zCC/qNHeTQpix7l0aQsepTr+uuvzzPPPJNhw4alra0ty5cvH+wlNSR7ZOBdf/31efzxx3PnnXfmHe94xyGPHzHi9fdl3717d7Zv354kOeuss/ptjY2ke3A/efLknH766XWPGTZsWN773vcmSX784x+/pc97yy23pKurK8cff3wWL158dBaLa1aBNCmTr7PKYY/U1zCD5i1btiRJxo8fn1NPPfVNj5syZUqS178woX/oUR5NyqJHeTQpix7lampqyvTp03P//fdn0aJFGTVq1GAvqSHZIwOvqakpLS0t+dM//dO6r7/66qu55557kiTveMc78q53vavnta1bt2bv3r1JknHjxuXzn/98Zs2alWnTpuXCCy/Mtddem4ceeqjnjmkObceOHUlefzOjN3PKKack2f9mRy+//HKfxz7yyCNZu3Ztkv3Dnr72FofHNas8mpTJ11nlsEfqG3HoQ44N3e+SOHHixD6Pa25uTpJ0dnbmlVdeyfHHH9/va2tEepRHk7LoUR5NyqJHuVasWJF3vvOdg72MhmePlGHXrl35zW9+k/Xr12fZsmXZtm1bRowYkcWLF/d6lMOmTZt6Pp43b16vd67fsWNH1qxZkzVr1uShhx7K1772NZ3egu43TNy1a1efx3UPpJPkN7/5zUE/+txt3759+drXvpZk/1Dh2muvPToLJYlrVok0KZOvs8phj9TXMHc0//73v0+SnHjiiX0ed8IJJ/R8/NJLL/XrmhqZHuXRpCx6lEeTsuhRLt/8lMEeKUNbW1s+9KEPZeHChdm2bVsmTJiQu+++Ox/4wAd6HXfgXU5ve9vbcuutt+axxx7LmjVrsnTp0px//vlJkieeeCKf+cxnBvKPMGS1trYmSTZs2NDnsPm///u/ez7u647mRx99NM8991yS5OMf/3j+6I/+6CitlMQ1q0SalMnXWeWwR+prmEHz7t27k+SQP1Zw4Ovd53D06VEeTcqiR3k0KYse0Dd7pAwvvPDCQf+8ePHirFu3rtfvd3V1ZcyYMZk0aVL+/d//PbNnz87EiRNzyimnZPr06bnzzjtz8cUXJ9n/+IbuNx/izX3oQx9Ksv+O5q985St1j1mxYkXPs7GT9LqT/I3+7d/+Lcn+R5tcccUVR3GlJK5ZJdIE+maP1Ncwg+bhw4e/peM892xg6FEeTcqiR3k0KYse0Dd7pAx33313fvazn+UnP/lJPv/5z2fcuHHZsmVL5s2bl/Xr1/cc99WvfjXr16/P97///Z5nBh9o+PDhueWWW3LcccclSVauXDlgf4ah6gMf+EAuuuiiJMny5cvT3t6ejRs3ZseOHdm+fXu+8pWv5Oabb+71XM3uv9832rhxY55++ukkyZw5c9708RocOdes8mgCfbNH6muYQfPo0aOTHPr/Hhz4uoeq9x89yqNJWfQojyZl0QP6Zo+U4YwzzsjIkSPztre9LZdffnnuvPPOjBw5Ml1dXfnSl7500PEHPrf5jU499dRMmzYtyf7HQXBo//Iv/5Jzzz03SfKDH/wgl19+eS688ML8xV/8Rf71X/81Z511VhYuXNhz/Js9N/N73/tez8d/9Vd/1b+LblCuWeXRBPpmj9TXMIPm7mei7Ny5s8/jDnxeysknn9yva2pkepRHk7LoUR5NyqIH9M0eKdO73/3u/OVf/mWS5Omnn05nZ+dhnd/S0pLk9edC0reTTz45d911V2666aacddZZOf744zN27Nicc845ueWWW7J8+fK89tprPcePHz/+oM9Rq9Xy6KOPJkne85735LTTThuw9TcS16zyaAJ9s0fqa5hBc/cD09/4nLQ36n59/Pjxfd5RQDV6lEeTsuhRHk3Kogf0zR4pV/ddyUnyq1/9qtdrh/rx2u5nCHffRcWhHXfccZk7d27uu+++PP3003nqqady77335qqrrsrw4cOzdevWJPu/+T/ppJMOOv+ZZ57Jr3/96yTJJZdcMqBrbySuWeXRBPpmj9TXMIPmM888M8n+wH3dAbB58+YkyeTJkwdkXY1Kj/JoUhY9yqNJWfSAvtkjA++rX/1qrrzyynzmM5/p87iurq6ej0eNGpUtW7Zk5syZOfvss3PPPff0ee6zzz6b5PVvbunbvn37Dnn393/+538mSc4555y6r69evbrn4z//8z8/eoujF9es8mgCfbNH6muYQfP73//+JPvvEnizd2l+4YUXsmXLliTJjBkzBmppDUmP8mhSFj3Ko0lZ9IC+2SMD73//93/z9NNP5z/+4z96/ZjsG3UPLseMGZNJkyalpaUl//d//5eurq786Ec/etPzNm3alO3btyd5vS9vbuXKlZk2bVqmT5+eHTt21D3mF7/4RTZt2pQk+eAHP1j3mKeeeipJ8sd//MeZOHFi/ywW8hgf2wAADPdJREFU16wCaQJ9s0fqa5hB82mnnZYLLrggSXLbbbfV/WLji1/8Yvbt25eTTjopl1122UAvsaHoUR5NyqJHeTQpix7QN3tk4HU/e7mrqytf//rX6x7zve99L//1X/+VJPnoRz+akSNH5sQTT+z55vPHP/5xnnjiiYPOe/nll/O5z30uSTJ27NhceeWV/fFHOKacc845ee2117J3797ce++9B72+e/fu3HTTTUn2D5E//OEP1/083Xeidb+pIP3DNas8mkDf7JH6GmbQnCSf/exnM3z48Dz//POZO3duVq9enc7OzmzevDnt7e1ZtWpVkuSTn/xkxowZM8irPfbpUR5NyqJHeTQpix7QN3tkYM2YMSMzZ85Mktxzzz1pb2/Phg0b0tnZmW3btmXJkiU9j9WYNGlSPvWpT/Wc+3d/93c5/vjjkyQ33HBDvvWtb2X79u3p7OzMo48+miuvvLJn4Pm5z30ub3/72wf4Tzf0nHHGGT09vvGNb+Tb3/52fvnLX+Z3v/tdHn/88VxxxRXZuHFjmpqasnjx4owaNeqgz/Hb3/42f/jDH5Ik73rXuwZ0/Y3INas8mkDf7JGDNdUO9Y4TdXS/23FHR8dRX1B/e/DBB7Nw4cLs3bu37uttbW1ZtGjRAK+quqHaRI/yaFIWPcqjSVn0GBq6n2E3f/78LFiwYJBXc/iGcg97ZGDt2rUr7e3tPc/9rWfq1Kn55je/mebm5l6/v2bNmvzt3/5tz2DzjY477rj8wz/8Q+bOnXtU13w0lNqjs7Mz11xzTbZt21b39ZEjR2bx4sX56Ec/Wvf1jRs35vLLL0+S3HrrrZk9e3a/rfVoK7XJobhmlUeT8g31r7OSod3DHultRH8spmQf+chHMmXKlCxdujRr167Niy++mNGjR2fatGm56qqrMmvWrMFeYkPRozyalEWP8mhSFj2gb/bIwBozZkxuv/32rFq1Kg888EA2bdqUnTt35oQTTsiUKVNyySWX5NJLL82IEQd/G3bRRRfl4Ycfzl133ZUnnngizz//fPbt25dTTz01733ve9PW1pYzzjhjEP5UQ9cpp5yS++67L3feeWdWrVqV//mf/8lrr72W5ubmTJ8+Pddee21OP/30Nz3/wGdtT5gwYSCW3PBcs8qjCfTNHumt4e5oPlZpUhY9yqNJWfQojyZl0aMsepRHk7LoUR5NyqJHeTQpix7lOdImDfWMZgAAAAAAjj6DZgAAAAAAKjFoBgAAAACgEoNmAAAAAAAqMWgGAAAAAKASg2YAAAAAACoxaAYAAAAAoBKDZgAAAAAAKjFoBgAAAACgEoNmAAAAAAAqMWgGAAAAAKASg2YAAAAAACoxaAYAAAAAoBKDZgAAAAAAKjFoBgAAAACgEoNmAAAAAAAqMWgGAAAAAKASg2YAAAAAACoxaAYAAAAAoBKDZgAAAAAAKjFoBgAAAACgEoNmAAAAAAAqMWgGAAAAAKCSplqtVjvck1paWvpjLQAAAAAAFKCjo+OwjndHMwAAAAAAlYyocvLhTrXpP913mWtSBj3Ko0lZ9CiPJmXRoyx6lEeTsuhRHk3Kokd5NCmLHuU50qdZuKMZAAAAAIBKDJoBAAAAAKjEoBkAAAAAgEoMmgEAAAAAqMSgGQAAAACASgyaAQAAAACoxKAZAAAAAIBKDJoBAAAAAKjEoBkAAAAAgEoMmgEAAAAAqMSgGQAAAACASgyaAQAAAACoxKAZAAAAAIBKDJoBAAAAAKjEoBkAAAAAgEoMmgEAAAAAqMSgGQAAAACASgyaAQAAAACoZMRgL2AwbN26Nbfffnt++tOfprOzM+PGjcu0adMyZ86czJgxY7CX13D0KI8mZdGjPJqURY/yaFK2ZcuW5Qtf+ELmz5+fBQsWDPZyGpI9UhY9yqJHeTQpix7l0eR1TbVarXa4J7W0tCRJOjo6jvqC+ttjjz2WG264IXv27Kn7+pw5c3LzzTcP8KqqG6pN9CiPJmXRozyalEWP8hyLTYZyjzfasGFDrrnmmnR1dQ3pQfNQbmKPlOVY7JEM3SZ6lEeTsuhRHk16a6hHZ2zevDmf/vSns2fPnkydOjXLli3LmjVrsnLlysyaNStJsnz58ixbtmyQV9oY9CiPJmXRozyalEWP8mhStnXr1mXevHnp6uoa7KU0LHukLHqURY/yaFIWPcqjSR21I9Dc3Fxrbm4+klMH1XXXXVdrbW2tXXzxxbWdO3f2em3fvn219vb2Wmtra+3888+v7dixY5BWeWSGYhM9yqNJWfQojyZl0aM8x2qTodrjQHfccUdt6tSptdbW1p5fX/7ylwd7WUdsqDaxR8pyrPao1YZmEz3Ko0lZ9CiPJgdrmDuat2/fnieffDJJMn/+/IwdO7bX601NTbnxxhszbNiwvPTSS1m1atUgrLJx6FEeTcqiR3k0KYse5dGkTOvWrcvs2bOzZMmSnrttGBz2SFn0KIse5dGkLHqUR5P6GmbQvHr16p6PZ86cWfeYCRMmZPLkyUmSH/7whwOyrkalR3k0KYse5dGkLHqUR5MyXX/99XnmmWcybNiwtLW1Zfny5YO9pIZlj5RFj7LoUR5NyqJHeTSpr2EGzVu2bEmSjB8/PqeeeuqbHjdlypQkyc9//vMBWVej0qM8mpRFj/JoUhY9yqNJmZqamjJ9+vTcf//9WbRoUUaNGjXYS2pY9khZ9CiLHuXRpCx6lEeT+hpm0Nz9LokTJ07s87jm5uYkSWdnZ1555ZV+X1ej0qM8mpRFj/JoUhY9yqNJmVasWJGlS5d6ZEYB7JGy6FEWPcqjSVn0KI8m9TXMoPn3v/99kuTEE0/s87gTTjih5+OXXnqpX9fUyPQojyZl0aM8mpRFj/JoUqZ3vvOdg70E/j97pCx6lEWP8mhSFj3Ko0l9DTNo3r17d5Ic8scFD3y9+xyOPj3Ko0lZ9CiPJmXRozyaQN/skbLoURY9yqNJWfQojyb1Ncygefjw4W/puFqt1s8rIdGjRJqURY/yaFIWPcqjCfTNHimLHmXRozyalEWP8mhSX8MMmkePHp3k0P/34MDXvVlK/9GjPJqURY/yaFIWPcqjCfTNHimLHmXRozyalEWP8mhSX8MMmrufibJz584+jzvweSknn3xyv66pkelRHk3Kokd5NCmLHuXRBPpmj5RFj7LoUR5NyqJHeTSpr2EGzd1vhPLCCy/0eVz36+PHj8/IkSP7fV2NSo/yaFIWPcqjSVn0KI8m0Dd7pCx6lEWP8mhSFj3Ko0l9DTNoPvPMM5PsD9z9zpD1bN68OUkyefLkAVlXo9KjPJqURY/yaFIWPcqjCfTNHimLHmXRozyalEWP8mhSX8MMmt///vcn2f8Q7ieffLLuMS+88EK2bNmSJJkxY8ZALa0h6VEeTcqiR3k0KYse5dEE+maPlEWPsuhRHk3Kokd5NKmvYQbNp512Wi644IIkyW233ZYdO3YcdMwXv/jF7Nu3LyeddFIuu+yygV5iQ9GjPJqURY/yaFIWPcqjCfTNHimLHmXRozyalEWP8mhSX8MMmpPks5/9bIYPH57nn38+c+fOzerVq9PZ2ZnNmzenvb09q1atSpJ88pOfzJgxYwZ5tcc+PcqjSVn0KI8mZdGjPJpA3+yRsuhRFj3Ko0lZ9CiPJgdrqtVqtcM9qaWlJUnS0dFx1BfU3x588MEsXLgwe/furft6W1tbFi1aNMCrqm6oNtGjPJqURY/yaFIWPcpzLDYZyj3q6X6m4Pz587NgwYJBXs2RGcpN7JGyHIs9kqHbRI/yaFIWPcqjSW8NN2hOkm3btmXp0qVZu3ZtXnzxxYwePTrTpk3LVVddlVmzZg328o7IUG6iR3k0KYse5dGkLHqU51hrMtR7vJFB8+CzR8pyrPVIhnYTPcqjSVn0KI8mr2vIQfOxSJOy6FEeTcqiR3k0KYseZdGjPJqURY/yaFIWPcqjSVn0KM+RNmmoZzQDAAAAAHD0GTQDAAAAAFCJQTMAAAAAAJUYNAMAAAAAUIlBMwAAAAAAlRg0AwAAAABQiUEzAAAAAACVGDQDAAAAAFCJQTMAAAAAAJUYNAMAAAAAUIlBMwAAAAAAlRg0AwAAAABQiUEzAAAAAACVGDQDAAAAAFCJQTMAAAAAAJUYNAMAAAAAUIlBMwAAAAAAlRg0AwAAAABQiUEzAAAAAACVGDQDAAAAAFCJQTMAAAAAAJUYNAMAAAAAUIlBMwAAAAAAlRg0AwAAAABQSVOtVqsd7kktLS39sRYAAAAAAArQ0dFxWMe7oxkAAAAAgEqO6I5mAAAAAADo5o5mAAAAAAAqMWgGAAAAAKASg2YAAAAAACoxaAYAAAAAoBKDZgAAAAAAKjFoBgAAAACgEoNmAAAAAAAqMWgGAAAAAKASg2YAAAAAACoxaAYAAAAAoBKDZgAAAAAAKjFoBgAAAACgkv8HsMmgLtOveP8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 471,
       "width": 717
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_astable(digit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 255,
       "width": 381
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vertical edge detection\n",
    "vertical_edge_kernel = np.array([[-1, 2, -1], [-1, 2, -1], [-1, 2, -1]])\n",
    "plot_astable(vertical_edge_kernel, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 256
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def convolution(matrix, kernel):\n",
    "    # This function computes a convolution between a matrix and a kernel/filter without any padding\n",
    "    width_kernel = kernel.shape[0]\n",
    "    height_kernel = kernel.shape[1]\n",
    "    convolution = np.zeros((matrix.shape[0] - width_kernel + 1,\n",
    "                            matrix.shape[1] - height_kernel + 1))\n",
    "    for i in range(matrix.shape[0] - width_kernel + 1):\n",
    "        for j in range(matrix.shape[1] - height_kernel + 1):\n",
    "            convolution[i, j] = np.sum(np.multiply(\n",
    "                matrix[i:i+width_kernel, j:j+height_kernel], kernel))\n",
    "    return convolution\n",
    "\n",
    "\n",
    "vertical_detect = convolution(digit, vertical_edge_kernel)\n",
    "plt.imshow(vertical_detect, cmap=\"gray_r\") ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQv0HhJtOZol3IBgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 255,
       "width": 381
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Horizontal edge detection\n",
    "horizontal_edge_kernel = np.array([[-1, -1, -1], [2, 2, 2], [-1, -1, -1]])\n",
    "plot_astable(horizontal_edge_kernel, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 256
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "horizontal_detect = convolution(digit, horizontal_edge_kernel)\n",
    "plt.imshow(horizontal_detect, cmap=\"gray_r\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maxpooling\n",
    "Taking maximum in n x n sized sliding windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def maxpool_2x2(matrix):\n",
    "    out_dim = np.array([matrix.shape[0]/2, matrix.shape[1]/2]).astype(int)\n",
    "    subsample = np.zeros((out_dim))\n",
    "    for i in range(out_dim[0]):\n",
    "        for j in range(out_dim[1]):\n",
    "            subsample[i,j] = np.max(matrix[i*2:i*2+2, j*2:j*2+2])\n",
    "    return subsample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 266,
       "width": 250
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "subsampled_image = maxpool_2x2(vertical_detect)\n",
    "plt.imshow(subsampled_image, cmap=\"gray_r\")\n",
    "plt.title(\"Max Pooled vertical edge detection filter\") ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 266,
       "width": 252
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subsampled_image = maxpool_2x2(horizontal_detect)\n",
    "plt.imshow(subsampled_image, cmap=\"gray_r\") ;\n",
    "plt.title(\"Max Pooled horizontal edge detection filter\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's explore some more of such filters/kernels!!\n",
    "\n",
    "http://setosa.io/ev/image-kernels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we will work with a dataset called fashion-MNIST which is quite similar to the MNIST data above.\n",
    "> Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.\n",
    "source: https://github.com/zalandoresearch/fashion-mnist\n",
    "\n",
    "The 10 classes of this dataset are:\n",
    "\n",
    "| Label| Item |\n",
    "| --- | --- |\n",
    "| 0 |\tT-shirt/top |\n",
    "| 1\t| Trouser |\n",
    "|2|\tPullover|\n",
    "|3|\tDress|\n",
    "|4|\tCoat|\n",
    "|5|\tSandal|\n",
    "|6|\tShirt|\n",
    "|7|\tSneaker|\n",
    "|8|\tBag|\n",
    "|9|\tAnkle boot|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset in keras\n",
    "# Later you can explore and play with other datasets with come with Keras\n",
    "from keras.datasets import fashion_mnist\n",
    "\n",
    "# Loading the train and test data\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
    "\n",
    "items =['T-shirt/top', 'Trouser', \n",
    "        'Pullover', 'Dress', \n",
    "        'Coat', 'Sandal', \n",
    "        'Shirt', 'Sneaker',\n",
    "        'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This item is a:  Ankle boot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 256
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "i=np.random.randint(0,X_train.shape[0])\n",
    "plt.imshow(X_train[i], cmap=\"gray_r\") ; \n",
    "print(\"This item is a: \" , items[y_train[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "# Also we need to reshape the input data such that each sample is a 4D matrix of dimension\n",
    "# (num_samples, width, height, channels). Even though these images are grayscale we need to add\n",
    "# channel dimension as this is expected by the Conv function\n",
    "X_train_prep = X_train.reshape(X_train.shape[0],28,28,1)/255.\n",
    "X_test_prep = X_test.reshape(X_test.shape[0],28,28,1)/255.\n",
    "\n",
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
    "\n",
    "print(y_train_onehot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 26, 26, 6)         60        \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 6)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 11, 11, 16)        880       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 5, 5, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 400)               0         \n",
      "_________________________________________________________________\n",
      "dense_304 (Dense)            (None, 120)               48120     \n",
      "_________________________________________________________________\n",
      "dense_305 (Dense)            (None, 84)                10164     \n",
      "_________________________________________________________________\n",
      "dense_306 (Dense)            (None, 10)                850       \n",
      "=================================================================\n",
      "Total params: 60,074\n",
      "Trainable params: 60,074\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Creating a CNN similar to the one shown in the figure from LeCun paper\n",
    "# In the original implementation Average pooling was used. However, we will use maxpooling as this \n",
    "# is what us used in the more recent architectures and is found to be a better choice\n",
    "# Convolution -> Pooling -> Convolution -> Pooling -> Flatten -> Dense -> Dense -> Output layer\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout, BatchNormalization\n",
    "\n",
    "def simple_CNN():\n",
    "    \n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv2D(6, (3,3), input_shape=(28,28,1), activation='relu'))\n",
    "    \n",
    "    model.add(MaxPool2D((2,2)))\n",
    "    \n",
    "    model.add(Conv2D(16, (3,3), activation='relu'))\n",
    "    \n",
    "    model.add(MaxPool2D((2,2)))\n",
    "    \n",
    "    model.add(Flatten())\n",
    "    \n",
    "    model.add(Dense(120, activation='relu'))\n",
    "    \n",
    "    model.add(Dense(84, activation='relu'))\n",
    "    \n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "    \n",
    "    model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = simple_CNN()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 45s 756us/step - loss: 0.5710 - acc: 0.7896 - val_loss: 0.4820 - val_acc: 0.8232\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 38s 631us/step - loss: 0.3682 - acc: 0.8648 - val_loss: 0.4050 - val_acc: 0.8527\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 38s 630us/step - loss: 0.3175 - acc: 0.8829 - val_loss: 0.3660 - val_acc: 0.8663\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 38s 631us/step - loss: 0.2908 - acc: 0.8921 - val_loss: 0.3247 - val_acc: 0.8808\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 39s 643us/step - loss: 0.2710 - acc: 0.9001 - val_loss: 0.3236 - val_acc: 0.8863\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 39s 650us/step - loss: 0.2541 - acc: 0.9060 - val_loss: 0.2934 - val_acc: 0.8947\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 39s 648us/step - loss: 0.2419 - acc: 0.9103 - val_loss: 0.2874 - val_acc: 0.8957\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 40s 660us/step - loss: 0.2301 - acc: 0.9157 - val_loss: 0.2959 - val_acc: 0.8972\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 40s 660us/step - loss: 0.2205 - acc: 0.9183 - val_loss: 0.3209 - val_acc: 0.8884\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 39s 650us/step - loss: 0.2121 - acc: 0.9210 - val_loss: 0.3127 - val_acc: 0.8842\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs, \n",
    "                      batch_size=64, validation_data=(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section\n",
    "* Use the above model or improve it (change number of filters, add more layers etc. on the MNIST example and see if you can get a better accuracy than what we achieved with a vanilla neural network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section\n",
    "* Explore the CIFAR10 (https://www.cs.toronto.edu/~kriz/cifar.html) dataset included with Keras and build+train a simple CNN to classify it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import cifar10\n",
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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