diff --git a/08_neural_networks.ipynb b/08_neural_networks.ipynb
index 971e02c2fa581f0d21ac5a83edc96bbab68a8021..f80f89549a370663b6559d55beda1969471440f1 100644
--- a/08_neural_networks.ipynb
+++ b/08_neural_networks.ipynb
@@ -2,17 +2,130 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "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"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "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())"
+    "from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
    ]
   },
   {
@@ -42,7 +155,6 @@
    "source": [
     "## History of Neural networks\n",
     "\n",
-    "**TODO: Make it more complete and format properly**\n",
     "\n",
     "1943 - Threshold Logic\n",
     "\n",
@@ -50,17 +162,32 @@
     "\n",
     "1958 - Perceptron\n",
     "\n",
-    "1975 - Backpropagation\n",
-    "\n",
     "1980s - Neocognitron\n",
     "\n",
-    "1982: Hopfield Network\n",
+    "1982 - Hopfield Network\n",
+    "\n",
+    "1989 - CNN kernels trained via backpropagation\n",
     "\n",
-    "1986: Convolutional Neural Networks\n",
+    "1997 - Long-short term memory (LSTM) model\n",
     "\n",
-    "1997: Long-short term memory (LSTM) model\n",
+    "1998 - LeNet-5\n",
     "\n",
-    "2014: Gated Recurrent Units, Generative Adversarial Networks(Check)?"
+    "2014 - Gated Recurrent Units (GRU), Generative Adversarial Networks (GAN)\n",
+    "\n",
+    "2015 - ResNet"
+   ]
+  },
+  {
+   "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>"
    ]
   },
   {
@@ -72,8 +199,8 @@
     "* Data\n",
     "* Data\n",
     "* Availability of GPUs\n",
-    "* Algorithmic developments which allow for efficient training and training for deeper networks\n",
-    "* Much easier access than a decade ago"
+    "* 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"
    ]
   },
   {
@@ -102,7 +229,7 @@
     "Step 1: A **weighted sum** of the inputs is calculated\n",
     "\n",
     "\\begin{equation*}\n",
-    "weighted\\_sum = \\sum_{k=1}^{num\\_inputs} w_{i} x_{i}\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",
@@ -116,39 +243,63 @@
     "    \\right.\n",
     "$$\n",
     "\n",
-    "You can see that this is also a linear classifier as we introduced in script 02."
+    "You can see that this is also a linear classifier as the ones we introduced in script 02."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {
+    "tags": [
+     "hidecode"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb5a5c5f390>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 283,
+       "width": 395
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "%matplotlib inline\n",
-    "%config IPCompleter.greedy=True\n",
-    "import matplotlib as mpl\n",
-    "mpl.rcParams['lines.linewidth'] = 3\n",
-    "#mpl.rcParams['font.size'] = 16"
+    "# Plotting the step function\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\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": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
     "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(X, w)\n",
-    "    output = 0\n",
-    "    if linear_sum >= threshold:\n",
-    "        output = 1\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"
    ]
   },
@@ -168,28 +319,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 31,
    "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"
+      "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",
-    "X = [[0, 0], [1, 0], [0, 1], [1, 1]]\n",
-    "for i in X:\n",
-    "    print(\"Perceptron output for x1, x2 = \", i,\n",
-    "          \" is \", perceptron(i, w, threshold))"
+    "# (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])"
    ]
   },
   {
@@ -201,52 +357,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def perceptron_DB(X, w, threshold):\n",
-    "    # Plotting the decision boundary\n",
-    "    for i in X:\n",
-    "        plt.plot(i, \"o\", color=\"b\")\n",
-    "    plt.xlim(-1, 2)\n",
-    "    plt.ylim(-1, 2)\n",
+    "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",
-    "    plt.plot(x1, x2, \"--\", color=\"black\")\n",
+    "    sns.lineplot(x1, x2, **{\"color\": \"black\"})\n",
     "    plt.xlabel(\"x$_1$\", fontsize=16)\n",
-    "    plt.ylabel(\"x$_2$\", fontsize=16)"
+    "    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": 8,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe7148256d8>"
+       "<matplotlib.figure.Figure at 0x7fb5986fbb70>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 286,
+       "width": 413
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "perceptron_DB(X, w, threshold)"
+    "# Plotting the perceptron decision boundary\n",
+    "perceptron_DB(x1, x2, w, threshold)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Exercise 1 : Compute a Boolean \"OR\" using a perceptron?**\n",
+    "### 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"
    ]
@@ -267,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,27 +455,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 37,
    "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  1\n",
-      "Perceptron output for x1, x2 =  [0, 1]  is  1\n",
-      "Perceptron output for x1, x2 =  [1, 1]  is  1\n"
+      "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",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe7145e6c50>"
+       "<matplotlib.figure.Figure at 0x7fb59ae1fa20>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 286,
+       "width": 413
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
@@ -307,19 +489,24 @@
     "# Solution\n",
     "# Calculating Boolean OR using a perceptron\n",
     "threshold=0.6\n",
+    "# (w1, w2)\n",
     "w=[1,1]\n",
-    "X=[[0,0],[1,0],[0,1],[1,1]]\n",
-    "for i in X:\n",
-    "    print(\"Perceptron output for x1, x2 = \" , i , \" is \" , perceptron(i,w,threshold))\n",
-    "# Plotting the decision boundary\n",
-    "perceptron_DB(X,w,threshold)"
+    "# (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 2 : Create a NAND gate using a perceptron**\n",
+    "### Exercise section\n",
+    "* Create a NAND gate using a perceptron\n",
     "\n",
     "#### Boolean NAND\n",
     "\n",
@@ -333,29 +520,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Calculating Boolean NAND using a perceptron"
+    "# Calculating Boolean NAND using a perceptron\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "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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opQYqpZrf711wvI6o9n2RLH47qsR08rX6nOp2lFL9lVJLJ3+0vJcWK65XlS3qKgjdigx2BEEoxeZKqS+K/r7GfHkfj/GNzwZGVLBuBkEQTMT8+u0BFwIzlFLfYWZ52xXYt8S2H2GsIZ9hvqxMBGbH933Mw9zAfRHmHqKyF8aMvf77YaYE7g1cBUxXSk2P4/035gpD219zf49Zk6MHcB0wM44lALbBzO60XxAE71XQlheBIzEDnr2BT5RS32Lsf+djztsdmHPnPPG53wuT/9WAaUqpmZgrV5dg/p+6NgiCGyrcb0DLOjJHAd/FufkO8x55Eri2S4JYmOMxA7jewF8x74vvMV/c1wWOqHL/BwFjMO+vwcAUYI5S6lul1FzgU8xaPqtjFuos+/1XiniwuSfwMeY+mqnALKXUbMwg4keY+/iGx+vVFG87HXO/zH8xeZ4Qb/sNpo89g1lENF+0TaePVyKGat8XyZpax8Vt+Vgp9ZFS6pKiY3xEDT6nUmICxjqZ/CX3C13Spn5kKq0ThDKQwY4gCKVou6jo0pgvoW8ClwLrBkHwaCf2OxI4HTMoKGDu+XkA2DQIgimlNgyC4FXgJxg714txexbHfGF6DTNQGRgEQUVrmQRB8D3Gq38g5ovUt0BfzK+8z2K+3DzcZps5mC9wvwGej9vQG/Pl7EZg/SAIJlTSjni/12HuXboTY9/qg7HfTAX2DoJg/3Jsca4QBMF/gQ2ACzBXI/KYqZGfBvYJgqBTg4MgCE7AWAzfwAwMcvHz44BhtEyW0aXE04ZvA4ymZaAxD3OlYmOqXwdJB0FwNubK4dmYfvA15n3ShBmc3wBsGwTBlkEQlJoGu9Jjf4CZDe4coHihz39h1t76aUeD+yAI3sIM9s7A9NW5mOmdP8Es0jqS1tO1V3W8EjF0+n0RBMHNGKvbK7FuZcykC0u30dXkc0oQhMrxtLb9RwVBEARBEARBEITKkSs7giAIgiAIgiBkEhnsCIIgCIIgCIKQSWSwIwiCIAiCIAhCJskvWiLE02PuiLlxcSPMDYlLYG4InQb8OQiCZzq57/0wM/P8FHOT5LvAzcA1HazsLAiCIAiCIAhCGcgEBWWglNoeMwsSmFWgX8dMubsOZlpJgHODIBhd4X6vxkwxOw8z3WUTMAgzA9R4YC8Z8AiCIAiCIAhC55ArO+URYabFvTIIgueLX1BKjcCseXGmUurpIAjKWuFbKbUnZqDzBbB1EATvx/XLYaZb3QM4Griyy6IQBEEQBEEQhDpCrux0AUqpG4FDgL8EQXBImdu8hlkp/sAgCG5t89pAzAJrXwAD5OqOIAiCIAiCIFSOTFDQNbwRP65UjlgptRJmoLMAuK/t6/EiY/8Dlgc27aI2CoIgCIIgCEJdIYOdrmHN+PHzMvU/jx//HQTB3A40r7bRCoIgCIIgCIJQATLYqRKl1PLAQXHxgTI3Wy1+/LiE5pM2WkEQBEEQBEEQKkAmKKgCpVQeuB3oDzwZBMEjZW7aJ36cXUIzK37s28nmleINzCBqFvBBDfYvCIIgCIIgCGtgvvd+SEpuJRnsVMe1mKmiPwX2T7ktlbAaZoDWHxiQclsEQRAEQRCEbJOaU0kGO51EKXUlZga2L4BBQRB8UcHmyVWbxUpokqs/MzvRvHKO3z+KIgqFEKII0IAHfs4oorCozq+Rplb7dVHT8TZaR3z55Zd8/MmnRFrjeaA1aB1P0uf5eJ6H1hp0xHLLr8jKK/+IfD6H73lmN1qjNXgezdqk3J2aNI9tgyaf99EaCoXQyvZJzktrclXmz4YY6jXnxX0vy3FmNefV9j1X4nRJU842vufR2Ng81Ei++3Y7MtjpBEqpS4FjgK8xA533K9zFR/HjKiU0K7fRdiUfAAMKC5r47vs56KZ5EC6AXCN+Y28AogVzmuu8hp410dRqvy5qFrVNY64HvXr2YfQZv+dvzz1PUxQxvylCex5+vgd+Pk9UKBCFC/Bzjay40sqcf85YBu+4AwDzFhQoRBF536exIceCprC53DP+IOoOTZrHtkHTb/HeNBUifvhutpXtk5yX1vTp36uq/NkQQ73mvLjvZTnOrOa82r7nSpwuacrZpmdjnuWW7UdMardNyAQFFaKUugg4AZgObB8Ewdud2E0yVfW6SqleHWg2aqOtDeECPCI8PDwi0CHosHVdrTRpHts2TRnbrDxgRR5+aDx/vupylujXj5wPnufj+R7g4fkevp/H8z2++OILDv7NARx11OFMnz4dDYCHBgph1KocRZoo0t2iSfPYVmh0/GuYre2TnJfWVJk/K2Ko15wX5S7TcWY151X2PWfidEhTzjZRZEppI4OdClBKXQj8AfgO2CEIgjc7s58gCD4F/g40Anu3c5yBmDV7vgCmdbrB5ZBrROOj0Wh88HLg5VrX1UqT5rFt05S5DV6O/fYbyeTJTzN4p53ROoIoIqcLEEVEUQFjZ9Norbn//rsZuPWmTHz8USId4XmQz/l4HmiMJc73PXzfa1VXK02ax7ZFg7a7fZLz0ppq8mdLDPWa8yR3WY8zqzm34bPT9jzYdi5836vpV9hykcFOmSilzgNOBr7HDHQWecVFKTVWKfWuUmpsOy8ndX9USq1RtM2ywLi4eGEQBFGVTRcyyDLLLsdNN93GTTfczPJL9qdRN9FAE57WoAEP8wh8M/1rjjrqMI46+nC+/CK+tUwbCcU/urStq5UmzWOnrUme2to+yXlpTVKUc+FenMnTrMeZ1ZwnxazH6ZqmzL6XNnLPThkopXYFTo+LHwBHK6Xak74bBMGFReUVABU/tiIIgvuVUtcARwBvKaWeAJows7v1Ax4C/txlQbSLNveIRAXz5/mgQ/NScV2tNGke2zZN2dt46NDUeUTsPGwXNtvwF1w8dgz3jx9PmPPRvkfe8yj4Htozv2d4vs/TTz3JTtOmcdqpp7PH3iMII43n6ebLzKHWzXWFMGpV7ipNrfbrjCYy5VBb2j7JeWlNlfmzIoZ6zXlR7jIdZ1Zzbslnp+15sO1ciI3NLZYser4hcGAHfztVstMgCI4EfoWxtA0EBmMGU0cBewZBEFbd8hJoDZHno8MQHRbMI575K6qrlSbNY9umKXebKCygw6ZWmsX79uX8cy/gputvZsXlB9DoRTR6BRq9CHSERuP5OTw/x8zZs/jDScfx6wMP4KOPPyEMI/MTjAdhGBFGUXNd23JXaWq1X1c0hdDu9knOS2uqzZ8NMdRrzotzl+U4s5pzWz47bc+DbefCBuTKThkEQXALcEsntjsIOGgRmjuBOzvRrKrxPPB1hM7nQDdAPocXX3f0iutqpUnz2LZpytzGz/VA6wa8vF5Is9W22/HkE1txycXncfedt+NHITlPE+KjoxAd5dBRiO/5vPjic+yz1y4cc8yx/PaQQ8n5OXI5Hw3kcj5oFirDwnWd0dRqv65o8tru9knOS2uqzZ8NMdRrzotzl+U4s5pzWz47bc+DbefCBuTKTl3jYW56z0MuZx7jG+Nb1dVKk+axbdOUu42fx8vlO9T06b8EZ511PrfdeherrLoqYUQ8iUGIH86HKIwnMYiYPWsm5583ht133YkP3g/IxTcX5nyPfM5vVfbbPO+splb7dUmTt7x9kvPSmmryZ0sM9ZrzfBcfy9Y4s5pzGz47bc+DbefC9+24tCODHUHIIBtuuCGPPvYkxxxzAnnfp4GmDicxeP3vr7H99lvzpz9dQVNTU8svMTq+Al38y0zbus5oarVfFzTJU1vbJzkvrUmKci7cizN5mvU4s5rzpJj1OF3TlNn30kYGO/WO5WvL1I2mgm0oc7+9ejRw2qmnM3nSE6y/zjr4ROQ9baaL9I251vM9PM+nqVDgiisuYZ+9dufNN/8paxTUSqPBhrUibFuLwRlNlfmzIoZ6zXlR7jIdZ1ZzXmXfcyZOhzTlbCMTFAh24MDaMnWhqWAbKjz2euv/nAfGP87xx/+BXL6RQhi1WptH6+RP8+67bzN8+DDGXnA28+bOJY15+bOusWGtiO7U2N6+SjWyzo67cco6O27n3IbPTtvzYNu5EBubIAjdRkM+z2GHHcHjj01lk402WtjWBqABz/wSc801f2LY0O159dWXTX2CBi/RtlcuR9OZbbKiSZ7a2r5aaWxvX7mapCjnwr04k6dZjzOrOU+KWY/TNU2ZfS9tZLBT7zhi3cq8pgY2tvY0q6/+Yx68/yHOOetsFuvVg7ynyfngea2tbb6f5+NPP2bUASM588zTmDFjhlgAukKjwQYrhm1WB2c0VebPihjqNedFuct0nFnNeZV9z5k4HdKIjU1wB0esW5nX1NDG1lbjN/TkgFGH8NhjU9hiy62LZmxrsbaZGds0aM0dd/6VbQZuxlNPThELQBdobLBi2GZ1cEkjNjZ34xQbm9s5t+Gz0/Y82HYuxMYmCEKqrLjiAG6+5U6uuuoalujfv+SMbZ99/j/2P2AExx77O76b/p15DfOaFz8207ZuUeV60iRPbW1frTS2t69cTVKUc+FenMnTrMeZ1ZwnxazH6ZqmzL6XNjLYqXccsW5lXtNNNra2Gh/NPnvvw3PPvsTOQ4aVnLHN83wefXQCw3bZgUcenUBTIYw/y8QCIDa2Osl5lfmzIoZ6zXlR7jIdZ1ZzXmXfcyZOhzRiYxPcwRHrVuY13Whja0+z7HLL86c/X8eVV41jyaWWLTlj23fTp3PssUdy6CH78+UXn9PVl72zrrHBimGb1cEljdjY3I1TbGxu59yGz07b82DbuRAbmyAI1jF4x52YPOVp9t13ZMkZ29AwZepkBu+4Lffdc7e5vydBGwm6zHI9aZKntravVhrb21euJinKuXAvzuRp1uPMas6TYtbjdE1TZt9LGxns1DuOWLcyr0nJxtaeZon+/bj84su47ZY7+NGAFUvO2DZrzixGn3UaB+y/L//5z3/EArAojQYbrBi2WR2c0VSZPytiqNecF+Uu03FmNedV9j1n4nRIIzY2wR0csW5lXpOyja09zZZbDeShCZM44MCDibRXcsa2F6f9jUHbbcH1148jikKqueyddY0NVgzbrA4uacTG5m6cYmNzO+c2fHbangfbzoXY2ARBsJ4+fXoz+sxzefjhSay1xholZ2ybO28uY8aczt577cr7wXvmNcxrXvzYbrmeNMlTW9tXK43t7StXkxTlXLgXZ/I063FmNedJMetxuqYps++ljQx26h1HrFuZ11hkY2tPs9GGG/LE1Gc55ndH05jzSs7Y9s83/8mee+7MlVdexpx58+LPu/QvuVuh0WCDFcM2q4MzmirzZ0UM9ZrzotxlOs6s5rzKvudMnA5pxMYmuIMj1q3Mayy0sbXV9OjZm+NPOJn77p/AOuuuX3LGtqamJq686lJ23mkQb/7j79hwyd0WjQ1WDNusDi5pxMbmbpxiY3M75zZ8dtqeB9vOhdjYBEFwkrXXXocHHnyUM047iz6NfskZ29597x323GtX/njh+cydM7f5dS/RJbSty6omeWpr+2qlsb195WqSopwL9+JMnmY9zqzmPClmPU7XNGX2vbSRwU6944h1K/May21sbTUNOZ/fHXE4kx6fzMa//EXJGds0cMstNzFkyCCe/9vf4s8/Oy/Li42t6zW2t09sbHWS86LcZTrOrOa8yr7nTJwOacTGJriDI9atzGscsLG1p1l1tTW49ba7OeusC+jVu2/JGds++fRjRuyzK6effhIzZ8yw8rK82Ni6XmN7+8TGVj85Fxub2zm34bPT9jzYdi7ExiYIQibwfZ/9DziQ556bxvbbDSo5YxvA3XfdwdCdd+CJKVNbdqKNJNEsVM6KJnlqa/tqpbG9feVqkqKcC/fiTJ5mPc6s5jwpZj1O1zRl9r20kcFOveOIdSvzGsdsbO1pVlxhBW6/7W6uvOxKllqiX7sztvl+Hs/3+PqrrzjsiF9z2GEH89VXX8efielflhcbW9drbG+f2NjqJOdFuct0nFnNeZV9z5k4HdKIjU1wB0esW5nXOGpja6vx/Dy77rYnDz8ylaFDd1loxrZiW5vWmgl/TwuiAAAgAElEQVQTHmSbgZsw4aEHiXSU+mV5sbF1vcb29omNrX5yLjY2t3Nuw2en7Xmw7VyIjU0QhMyyzNJLceVV13LLX25lwDJLlrS1ffvdt5xwwtH87ohD+eyz/5lKbSQU/yjUts5FTfLU1vbVSmN7+8rVJEU5F+7FmTzNepxZzXlSzHqcrmnK7HtpI4OdescR61bmNRmwsbWnGbzDDkyd/CQj9t675EKknu/x9DNPMWSn7bnllpsJtYlTY9+le7GxVaaxvX1iY6uTnBflLtNxZjXnVfY9Z+J0SCM2NsEdHLFuZV6TERtbe5p+/Rfn3HPH8te/3s2AASu3srUVL0SK1sycNYPTTvs9+4/ci48/+tDKS/diY6tMY3v7xMZWPzkXG5vbObfhs9P2PNh2LsTGJghCXbH5Flvx5FPPc/ghB9GDQocLkQK8/MrL7LbbEK69bhyFQqFZ49GiWajsgiZ5amv7aqWxvX3lapKinAv34kyeZj3OrOY8KWY9Ttc0Zfa9tJHBTl2jIVwAUaHlL7YitaqrlSbNY9umqWAbHVoaQxmaxXo0cMYpp3PfXXex9pqrNy9E6vseec+LfxnK4/k+85uauPiisQwbtiNvvfUWodaEkSbUmkIYtSonl8+t10SWt68GGtvbV5GmyvxZEUO95jyqkzizmvMq+54zcTqkKWcbsbEJqaM1RJ6PDkN0WDCPeOavqK5WmjSPbZum3G2isIAOm6yMoZI41193PR584FGOO+4k8r5PoxfR6BVo9CK0DvH8XPPfP998gyE7bcflV1zK3HnzCcMIPAjDiDCKmsvt1dmoKYR2t68WGtvbV4mm2vzZEEO95rw4d1mOM6s5t+Wz0/Y82HYubCA3ZsyYtNsgdD8HAatGYYH5c+aBp/E0eA15/FyDeW/qQkud59dGU6v9uqgpc5vFejcCmrlN2r4YKowz37MHW269LUOGDOVf/3iV6V99iY4KZsAUT1xgrmKFaK15/fXXePqpJ1l33XUYsOKA5ivk+ZxPg29+twm1bq7LeZ51ml69ewCwYEHByvbVQmN7+yrR9OjVCHQ+fzbEUK85L+57WY4zqzmvtu+5EqdLmnK2afB9+vbpGav4GLiFFMincVDBFjzINUIUQS4CLw9eLn4p31JXK02ax7ZNU+42fh4vF4IX2RdDJ+Nce531uff+h7n1L9dz8WWXMHvOfHwvxA/nE8WTGABoHfHB+wEj9tmdXx/4a44/4VQae/UiF98UCZDzPSLtkfM98jmfSOvmsg2anO+hfXvbVwuN7e2rVFNN/myJoR5zXpy7LMeZ5Zzb8Nlpex5sOxcyQYEgCEJMPpfn4IN/w6SJT7Ll5pvTQFPrtXnA3OzogY7gxhuvY+iQQUx74QV7b9zsoE7b3L5aaWxvX7mapCjnwr04k6dZjzOrOU+KWY/TNU2ZfS9tZLBT7ziytkzmNRldZ6fSOFdddRXuues+xp43ln59epP3NLl4EoPi9Xl8P89/P/svBx9yAKecciLffffdQvP7W7lGgQYb1oqwbS0GZzRV5s+KGOo150W5y3ScWc15lX3PmTgd0sg6O4I7OLK2TOY1GV5np9Jt/Iae7DvyAB57dArbbDuIMMJY2YrW54miQvPaPPfedxcDt96USRMfs2b9gXLW+rC1fV2tsb193bnWhy0x1GvOZZ0dt3Nuw2en7Xmw7VyIjU0QBKEEyy2/PNff8FeuueZGll5yyYWtbRrwAA1fff0lBx+yP7876jCmfz3dvIZ5zYsfm2lb152a5Kmt7auVxvb2latJinIu3IszeZr1OLOa86SY9Thd05TZ99JGBjv1jsWWprrSiI2tXY2PZo/d9+DZZ6ax+y674hOR97T5RanI1ubFs7dNmvgYO++8PQ8+eD9NhTD+rLXIAqDBBiuGbVYHZzRV5s+KGOo150W5y3ScWc15lX3PmTgd0oiNTXAHyy1NdaMRG1tJzdLLLMvlV4xj3LgbWGbZFSiErW1tWid/mu+//54T/3Asvx41gv/971NKXWIXG1vtNba3T2xs9ZNzsbG5nXMbPjttz4Nt50JsbIIgCBWy3XaDmDT5KQ444MCSM7ah4alnnmLI4EHccdutRFHUshNtJOgOyrXUJE/TOHaaGtvbV64mKcq5cC/O5GnW48xqzpNi1uN0TVNm30sbGezUOw5ZmjKtERtb2Zr+fftw0QUXcvcd97LKygNKztg2Z94czjt/DCP33Yv3338/fQuABhusGLZZHZzRVJk/K2Ko15wX5S7TcWY151X2PWfidEgjNjbBHRy0NGVSIza2ijWbbLo5EyZM5uBDDjOvlZix7dXXXmb7QVty9dVXUCg0kVxiFxtb7TW2t09sbPWTc7GxuZ1zGz47bc+DbedCbGyCIAhV0rt3T049dTSPPz6VdX6ydskZ2+YvmM/555/N8D124Z1//9u8hnnNix+baVvXVZrkaRrHTlNje/vK1SRFORfuxZk8zXqcWc15Usx6nK5pyux7aSODnXrHcUtTZjRiY6tKs8HPNmDypCc54dgT6JH3Ss7Y9vY7/2LvvXfj0ksvYvacOfHnsdjYxPZRhqbK/FkRQ73mvCh3mY4zqzmvsu85E6dDGrGxCe6QEUuT8xqxsVWtaezRi6OPOYEHxz/GT3+2QckZ28Iw5OpxVzJkx215/bVX0IiNTWwf5WnExuZunGJjczvnNnx22p4H286F2NgEQRBqwJprrsW99z3MOWPOo1/PfMkZ2z74z/uMGDGc884dw+xZs1p2oo0E3UG5s5rkaVfv13aN7e0rV5MU5Vy4F2fyNOtxZjXnSTHrcbqmKbPvpY0MduqdDFqanNSIja1LNXnf49BDDmHyxKlsvummJWdsw4M77riVnXYaxFNPPSk2ti7W2N4+sbHVSc6LcpfpOLOa8yr7njNxOqQRG5vgDhm1NDmnERtbTTQr/2hVbr75Ns4//xIW69O/5Ixt//vsv+y3354cd9yR/PD9d2jExia2D7GxZSlOsbG5nXMbPjttz4Nt50JsbIIgCN2A53nsM2Ikzz//EkMGDyk5YxvAvffexY47bsuUSZOa69BGQvGPVG3rytEkT7t6v7ZrbG9fuZqkKOfCvTiTp1mPM6s5T4pZj9M1TZl9L21ksFPv1IGlyQmN2Nhqrllu2WW5+S+3Mu5P41hmqcVLztg2/dtvOPa4Iznid4fy+edfxJ/ZYmOre9tHlfmzIoZ6zXlR7jIdZ1ZzXmXfcyZOhzRiYxPcoY4sTVZrxMbWLRrPzzNk6K488shUdtlteMkZ29CayZMeZ7ttNueB++4l0lGXWmlstyiI7aN9jdjY3I1TbGxu59yGz07b82DbuRAbmyAIQkosueQSXHbpn7jz9rtYZYVlS87Y9sPM7znp5OM59JAD+e+nn7TsRBsJuoNyB3W60m2yoLG9feVqkqKcC/fiTJ5mPc6s5jwpZj1O1zRl9r20kcFOvVOnlibrNGJjS0Wz7cBtmDxxKvvvt1/JGds83+OFF//GkCE7cMMN19JUKMSf42JjqyvbR5X5syKGes15Ue4yHWdWc15l33MmToc0YmMT3KGOLU1WacTGlpqmT99+jB59Dnfe+QCrrPrjkjO2zZ4zizPPPIXhuw/lPx+8j0ZsbPVm+xAbm7txio3N7Zzb8Nlpex5sOxdiYxMEQbCIjTbelCefeoGjf3csPb2o5Ixtr772CsOG7cD1146jqanJVGojofiHrHbqdHG5nG2yoLG9feVqkqKcC/fiTJ5mPc6s5jwpZj1O1zRl9r20kcFOvSOWJjs0YmOzQtOrRyNnnH4mD42fwDo/USVnbGsKC1x+xSXsvtsw3njjjfhzXWxsmbZ9VJk/K2Ko15wX5S7TcWY151X2PWfidEgjNjbBHcTSZIdGbGxWadZbfwPuu28Cxx9/Mr6fLzlj2zvv/ptdd96Riy66gHnz5pVtpbHdoiC2j/Y1YmNzN06xsbmdcxs+O23Pg23nQmxsgiAIFtPQkOfI3x3Dk088wyY/X7/kjG2hDrnu2qvZbdedeOXlaS070UaCblXVUm7n9YXqsqCxvX3lapKinAv34kyeZj3OrOY8KWY9Ttc0Zfa9tMmn3QAhTTSECyAqmD/PBx2al4rraqVJ89i2acrexkOHcZ1tMWQ052v8eDXuvft+7rrtL1x06aUUwtmE+Pi+R97zKPge6Dye7/PJfz9l5H77cMB+v+K000aT69GLMNJ4nqYQRoSRNn86Lmvd/Hpyub+4Lgsa29tXkabK/FkRQ73mvCh3mY4zqzm35LPT9jzYdi5ssbHJYKdMlFIK2AnYCNgQWAszeN07CIL7O7G/W4ADS0iCIAh+0ommlo3WEHk+hCGEBSCHNuNxdHFdQ400tdqvi5oyt4nCAoRN6DC0L4YM59zXml+N3I9tt92eP5x5Js889xyNXkTeK+B7EXN1iO/3wPNzeH7ELX+9ialTJnPOBZex2ZZb4gENeZ8wjMx/EGHUXA6jyBy10fTL4rosaGxvX6WaavJnSwz1mvMkd1mPM6s5t+Gz0/Y82HYukrq0kcFO+RwBHFuD/b4AfNBO/ec1OFYrPA98HaHzOdANkM/hxdcdveK6WmnSPLZtmjK38XM90LoBL6/ti6EOcj5glVW5664HuO++u7ngnDOZO2cWfhSS9zyiKERHOXQU4uHx2ef/47e/HcWuu+3F6aedSc9lliKX88lryOV8iB81LWVYuC4LGtvbV4mm2vzZEEO95rw4d1mOM6s5t+Wz0/Y82HYubEAGO+XzL+Bi4DXgdeAmYGAX7PfGIAhu6YL9dAIPco0QRZCLwMuDl4tfyrfU1UqT5rFt05S7jZ/Hy4XgRfbFUCc59/w8+4zYn4FbbME5Y07nsUkTKYQa3wvxw/lE8SQGAFpHPPLwg/ztuSc57+zz2W/USPK+R873yOd8Iq2JtCknN3LmfK+5Lgsa29tXqUZXkT9bYqjHnBfnLstxZjnn1fQ9l+J0RVPONjJBgWMEQXBjEAQnBUFwbxAE/5d2ewRBSJdlll2OP/1pHOOuvp5ll16GBpo6nMRg+vTpHHbEwYwcOZLPv/yy5dcubbywrX79aluXBY3t7StXkxTlXLgXZ/I063FmNedJMetxuqYps++ljQx26h1Zc8UOjayz46xmyJAhPPPUc+w9fE98IvKeJueD57Ven8f380ycOJEtN9+ce+65i6ZCGP9fYOcaCrIuRTsaDbLOjqNxFuUu03FmNedV9j1n4nRII+vsCJWwrVLqMqXU9Uqpc5VSg5VS3ZcXWXPFDo2ss+O0Zomll+Wii6/k+utvYfkVViKMMFa2ovV5oqiA1poffviBU087kf1HDueTTz5C0/3rI9i2FoNLGllnx904ZZ0dt3Mu6+zYpXFpnR25Zyd9RrVT97ZSat8gCN6q5YEbGvIss3RfoqYGCOdDrge5Hr0BCOfnmuv8hp410dRqvy5qyt9mHgBLL93Xuhgk5w0MHz6UHXbantHnnM+4ceNooIkG7eMREWkNGvAADc+/8DzDhmzP6WeO5sgjD6dPrx4AzF1QoKlgZhzq0ZBjflPYXO7VmHdOY3v7KtXouP/JuXAvziR3WY8zqzmvpu+5FKcrmnK2SerSRq7spMc/gGOAdYA+wIrAzsA/47onlFIDatsEDVHH1qjmulpp0jy2bRrb2ydxlq3p13cxrrrycp579ml+subqzda2fG5hW9vc+fM4c/RodtxhR/71r3+3tmtoKBSiVuWFLB0OaGxvn5wLiVPilHMhcdbmXNhiY7NjyFWHBEFwRZuq2cBjSqmpwLPApsCpwFG1akNTU8h3389HN82DcAHkGvEbzRszWjCnuc5r6FkTTa3266Km3G2W6t8AaL7+ZrZ1MUjOW2vWXGsDxk+YwrirLuWa666lEIb4XkTOK1CIbW2+7+NpzauvvcqGv/wlxx1/IocefjReLk9Dzqcxn2NBIaQpjGjI+fRsMB/Z85oKzXW2a2xvXyWaPv17gdZMnz6r7s+Fa3H2W7x3c+6yHGdWc15t33MlTpc05WzTsyHPcsv2I23kyo5lBEGwABgbF4em2RZBEKqjZ48enHDCiUyY8Dg/XW+9hWds00Bsa2sqNHHxxWPZfdehvPXmP81rmNe8+LGZtnW2a2xvX7mapCjnwr04k6dZjzOrOU+KWY/TNU2ZfS9tZLBjJ+/GjzW2sSEzc9mikdnYMq1Zd911ePThiZxz1mh6NubatbV5no/n+QTvv8u++w5n7NjzmDlrdvz/hZ2z8dTd7EU6tmfIuXAvzqLcZTrOrOa8yr7nTJwOaWQ2NqFaloofZ9X8SDIzlx0amY0t85qGnr05/oTf88wzz/GLX2xEIYygaLY2rZM/TRRG3HDjNey0w0BefmkaGjtn46nH2YvQci5cjTPJXdbjzGrOq+l7LsXpisal2dhksGMn+8SPr6baCkEQupy11lqdu+55kAvO+yP9ezV2uBApGj78+D/st99ejBl9BjNnzGjZiTYSdAdl2zS2t69cTVKUc+FenMnTrMeZ1ZwnxazH6ZqmzL6XNjLYqSFKqbFKqXeVUmPb1G+glNpZKZVrU59XSv0eM0sbwOU1b6RYmuzQiI2tbjTgkfPg1weOYurkJxi41ZYlFyL1fI977r2TwUMGMWXKJKtsDHVn+9AgNjZH4yzKXabjzGrOq+x7zsTpkMYlG5vMxlYmSqlfAOOKqtaJHy9QSp2YVAZBsGmRZgVAxY/FrAqMB75VSv0d+ApjXVsfMwV1BJwUBMHkroyhXXKN6CgCCoCP55nxl7FLxXW10qR5bNs05W5TZGOzLgbJeWX5yzWy4oCVuf76m3no4Uc44+yz+Pb77421rZ0Z2774/DNGjdqXPfbYkzNGX0C/JZbA8zzyOZ9IhxjbgNdsG2ixEqSrsb19lWqKrTT1fi5cirM4d1mOM8s5r6bvuRSnK5pythEbm3v0AzYp+usb16/Zpr4c/glcCQSYQdOewEBgDnAzsHEQBBd3WcsFQbAWz/PYY/hePPvcy+y2y24lZ2wDGD/+AQYP3obHHn0YXWR9c97q4IImKcq5cC/O5GnW48xqzpNi1uN0TVNm30sbubJTJkEQPEOcvwq2OQg4qJ36D4HjuqJdVVNkrSGx30Drulpp0jy2bZqyt8mhKbJK2RSD5Lyy/LXZZtmll+L6625i4iPjOe+c0/niq68Icz5Rka3N88zvU999/x0nnngcjz86gfPOHcsyyy0PtLYN6LgFmvbtBt2lSfPYXa7RUMpKU1fnwrU4i3KX6ThT0tje95yJ0yFNOdvYYmOTKzv1jszMZYdGZmOrGw2L2GbwTkN55JEpDN9rRMkZ29CaJ56cwqBtt+DuO+8g1GGzRce1GXtc0shsbO7GKbOxuZ1zmY3NLo3MxiYIgiB0msUX788fL7yMe+++nx8PWK7kjG0zZ8/g9DNO4uAD9+ejD//TshNtJOgOyt2pSfPYXalJinIu3IszeZr1OLOa86SY9Thd05TZ99JGBjv1jszMZYdGZmOrGw0VbLPVFlsw8fEnOPigg2jwKDlj28uvvMSwYTty9bg/saCpKf5/xo0Ze5zRaJDZ2ByNsyh3mY4zqzmvsu85E6dDGpdmY5PBTr0jliY7NGJjqxsNFW6zWJ8+nHLKGdxz73hWX2MttG5tbYuiQrOtbe68uZx7zpnststg3nv3HTRuWB1c0oiNzd04xcbmds7FxmaXRmxsgiAIQpfy819sxNQnnueE406klx+VnLHtjX/8nV12Gcyfr7qC+fPnm0ptJBT/0Na2rlaaNI/dlZqkKOfCvTiTp1mPM6s5T4pZj9M1TZl9L21ksFPviKXJDo3Y2OpGQxX77dGQ5+STTuHhCY/x0/XWJe9p8+taka3N83w8zyfUEVePu4rddxvKq6+9Gv+/Y6fVwRmNBrGxORpnUe4yHWdWc15l33MmToc0YmMT3EEsTXZoxMZWNxq6YL9rr7Med9/9IH/4wxnk840lZ2wL3nuX3XfbifPOH8PcOXOttDq4pBEbm7txio3N7ZyLjc0ujdjYBEEQhJqSz+f47WFH8NSTz7DFRj8vOWOb1pqbb7qBXXbZkWkvvNiyE20k6A7KXaWp1X67W5MU5Vy4F2fyNOtxZjXnSTHrcbqmKbPvpY0sKlrXaAgXQFQwf55Pshhiq7paadI8tm2asrfx0GFcZ1sMkvPK8tdF+11tlVW46457uPfOv3LhxRczfcZMQnx83yPveRR8D3Qez/f53+efsf+oEey79z6ceebZNPRajDDSeJ6mEEaEWjeXE/tBcV1nNLXabyqayNSFWs6Fc3EW5S7TcWY151X2PWfidEhTzjZiYxNSR2uIPB8dhuiwYB7xzF9RXa00aR7bNk2520RhAR02WRmD5Lyy/HXlfr0oYsTe+zDpsUlst932oCMavYhGr0CjF6F1iOfnmv/uuPNWBm69GVMmTyWMIsIwAg/CMGpVbq+uM5pa7TcNTSGUc+FqnMW5y3KcWc15tX3PlThd0pSzjQ3kxowZk3YbhO7nIGDVKCwwf8488DSeBq8hj59rMO9NXWip8/zaaGq1Xxc1ZW6zWO9GQDO3SdsXg+S8svzV4Nh9lliC4XuNZPUf/5jXXvobTfPnoqOCGSDFExeYK0shs+fMZuLkx/j4o4/YZONN6bPYYs2ug3zOp8E3v4WFsS0un/PJeV7Fms5sY6umR69GABYsKNT9uXAtzl69ewAmd1mOM6s5r7bvuRKnS5pytmnwffr26Rmr+Bi4hRSQKzt1jQe5RvDykMuZx/gG6FZ1tdKkeWzbNOVu4+fxcnk7Y5CcV5a/Gh3b8/MM33NfHp/0NEN2GkIYGbsBUYgfzocoLJrEIGLi448wZPBAJjx4P75nbijNxTeaFj/P+Z75D65N/aI0ndnGZk1ezoWzcea7+Fi2xpnVnFfT91yK0xVNOdv4vh2XdmSwIwiCkEGWWXppLrvsKq6/7i+ssNzyNNDU4SQG333/PUcfeziHHjKKLz7/XG7o7UiTFOVcuBdn8jTrcWY150kx63G6pimz76WNDHbqHVlzxQ6NrLNTNxq6+dg77LADTz/5LL/adyQ+EXlPk/PB81qvz+P7eZ5+9ml22Xkwt99+K4UwlHUp2mo0yDo7jsZZlLtMx5nVnFfZ95yJ0yGNrLMjuIOsuWKHRtbZqRsNKRy7/5JLc975F3PzzXew4oCVCSPQOoKi9XmiqIDWmlmzZjL6rFPZa/jOfPjhB92yFoNLGllnx904ZZ0dt3Mu6+zYpSlnG7GxCYIgCN3KZpttxsRJT3PY4b8j53kLW9s04AEaXnp5GoO224prrxtHGBbMa5jXvPixmbZ1iyq7qkmKci7cizN5mvU4s5rzpJj1OF3TlNn30kYGO/WOWJrs0IiNrW40pHhsD4/FevXg7LPO4dFHJrL2Wms2W9vyuda2Ns/zmb9gARdfdAH77DOct99+W2wfGsTG5micRbnLdJxZzXmVfc+ZOB3SiI1NcAexNNmhERtb3WhI8djFml/8chMeengKRx55DJ6fj2dsa7G1tczYpnn7X2+x+247cdmlY1mwYD6a+rV9iI3N3TjFxuZ2zsXGZpdGbGyCIAiC9TQ2NHDMMcfxyMOT+MUGPys5Y1shDLniikvZdefBvPGP1019ggYv0ZZTdlWTFKvZT9oxdJfGtvYlT7MeZ1ZznhSzHqdrmjL7XtrIYKfeEUuTHRqxsdWNhhSP3ZHmJz9RPPzQ45xx6hn07tlQcsa2D/7zAb8auQ/nnncWM2fOqi/bhwaxsTkaZ1HuMh1nVnNeZd9zJk6HNGJjE9xBLE12aMTGVjcaUjx2KU2usSeH/OZwJkyYyEYbb1pyxjYdRdx8840M2m4Lnn/umbqyfYiNzd04xcbmds7FxmaXRmxsgiAIgpOsssqq3HHn/VxyyZX069O35Ixtn3z6MSP23YOTTjqBmT/MMK9hXvPix3bLrmqSYjX7STuG7tLY1r7kadbjzGrOk2LW43RNU2bfSxsZ7NQ7YmmyQyM2trrRkOKxy9X4aPb/1f48/+yL7Dhoe0rN2OZ5Pg88cC9Dd96ByZMnZdv2oUFsbI7GWZS7TMeZ1ZxX2fecidMhjdjYBHcQS5MdGrGx1Y2GFI9dqWb5FVfi2utu5rLLrqT/4kuWnLHtm6++4ogjD+GIww/m66++QpO+zcI2K40tMdRaY2v7xMbmds7FxmaXRmxsgiAIQibwPI+hQ3dh8pRnGb7H8JIztqHhscceZqfB2zBh/IPoote9RJfQts4FTVKsZj9px9BdGtvalzzNepxZzXlSzHqcrmnK7HtpI4OdekcsTXZoxMZWNxpSPHY1mqWXXJyrr7qam67/Cyssv2zJGdt+mDmDU049kYN/fQAff/xJ/H+enVYMsbF1vcbK9hXlLtNxZjXnVfY9Z+J0SCM2NsEdxNJkh0ZsbHWjIcVjd4Vmu0E78sjDkxkx8oCSM7ahNc8+9zSDtt2C2/56M6EOrbRiiI2t6zW2tk9sbG7nXGxsdmnExiYIgiBkln79+nLeuRcyfvyj/HjVVUvO2DZn3mzGnH0GB+w3gg//7/9adqKNBN1B2UZNUqxmP2nH0F0a29qXPM16nFnNeVLMepyuacrse2kjg516RyxNdmjExlY3GlI8dldrNtt0M5584nkO/81h5H3anbHN9/N4vsff33idYTvvyJVXXc78BQvi/wfTt2KIja3rNVa2ryh3mY4zqzmvsu85E6dDGrGxCe4gliY7NGJjqxsNKR67Fprei/Xh5FPP5J57HmCNNdVCM7YV29rmzZ/H2AvOZuehO/DOv99Ck74VQ2xsXa+xtX1iY3M752Jjs0sjNjZBEAShrlh//Z8x4eFJnHTiKfTO6Q5tbQBv/euf7LbbUK647BLmzZtnKrWRUPxDYNu6tDVJsZr9pB1Dd2lsa1/yNOtxZjXnSTHrcbqmKbPvpY0MduodsTTZoREbW91oSPHYtWwaCuIAACAASURBVNb0aMhz/LHH8tijk/j5Bj8tuRBphOa668exy86DefGlafH/i3baNcTGloE4i3KX6TizmvMq+54zcTqkERub4A5iabJDIza2utGQ4rG7S7PmWj/hzjvu57TTzqKxsWeHC5GiNf/3nw/Ya/gwzhpzOnNmz7bSriE2tmzEKTY2t3MuNja7NGJjEwRBEOqaXM7n1wf/lqefeo6Bm21cciFSgNtvvYVhw3bguWeebdmJNpJEs1C5uzVJsZr9pB1Dd2lsa1/yNOtxZjXnSTHrcbqmKbPvpY0MduodsTTZoREbW91oSPHYaWh+tNIA7rjtTsaeP5bF+/btcCFSz/f4/IvP+fUhv+Loo4/g22+/jf+vTN+uITa2DMRZlLtMx5nVnFfZ95yJ0yGN2NgEdxBLkx0asbHVjYYUj51WzvFy7Lnn3kyZ/DQ77Di05EKkWmvuu+8utt5qEyZNfAxN+nYNsbFlI06xsbmdc7Gx2aURG5sgCIIgtGGZ5Zbn5pvv4Mbr/8LyS/YvOWPbN9O/5ne/+y1HH3M4X335panURkLxj4Vt62qpSYrV7CftGLpLY1v7kqdZjzOrOU+KWY/TNU2ZfS9t8mk3QEgTDeECiArmz/NBh+al4rpaadI8tm2asrfx0GFcZ1sMkvPK8mdl+2qfc4+IXXbelc02/AUXXXAWD06YQJjz0b5H3vMo+B7aM7/Deb7Pk088wSsvTuP0085gtz33Jow0nqeb7RGh1s11hTBqVe5yTWTqQt25/dS8fZZorGxfUe4yHWdWc15l33MmToc05WwjNjYhdbSGyPPRYYgOC+YRz/wV1dVKk+axbdOUu00UFtBhk5UxSM4ry5+N7evOnC/Rrx9jz7+QG6+7iRWWW5FGL6LRK9DoRaAjNBrPz+H5OWbMmsnvTzyGgw8axceffEIYRuanQw/CMCKMoua6tuWu1hTC6vZT6/bZorGxfcW5y3KcWc15tX3PlThd0pSzjQ3kxowZk3YbhO7nIGDVKCwwf8488DSeBq8hj59rMO9NXWip8/zaaGq1Xxc1ZW6zWO9GQDO3SdsXg+S8svxZ2L40cr7KGmuy336jmDXjW95+65/oqECkzf0+nm/W5NFhAU9rPv3vp0x46EEW69WLn2+wAb7nE8azu+VzPjnPa3ZO5HM+Db75Pa+rND16NQKwYEGhU/updfts0djYvl69ewAmd1mOM6s5r7bvuRKnS5pytmnwffr26Rmr+Bi4hRSQKzt1jRffPJyHXM48xjcct6qrlSbNY9umKXcbP4+Xy9sZg+S8svzZ2L6Uct6n/xKMGXMBt916Jyv/6EeEEfEkBiF+OB+iMJ7EIGL2rJmce95o9thtCP/3wXvk4ptic75n/gMuKvttnneFJl/FfrqjfTZobG1fvouPZWucWc15NX3PpThd0ZSzje/bcWlHBjuCIAiCFWy44UY89vhTHH308eR9nwaaOpzE4LXXX2XQoK24+uqrKBSa3LlJupbts0ljW/uSp1mPM6s5T4pZj9M1TZl9L21ksFPvyJordmhknZ260ZDisV3Iee+ejZx+2hlMfHwK6629Nj4ReU+baU59Ywr3fA/P82kqFLjssovYe8/deeutN61f68P2dTMyvRZJUe4yHWdWc15l33MmToc0ss6O4A6y5oodGllnp240pHhsl3L+05/9kgcfmshxx52In2ugEEYUr82jdfKneffdtxm+xzAuHHsO8+fNQ6NrtuaErLPjbpyyzo7bOZd1duzSlLON2NgEQRAEoQQN+TyHH34kjz82lY033HBhWxuABjwIo4hx465i2NDtee21l019ggYv0bZXLleTFKvZT2eP7ZrGtvYlT7MeZ1ZznhSzHqdrmjL7XtrIYKfeccjekmmN2NjqRkOKx3Y152ussTrjH5jA2aPHsFivHuQ9Tc4Hz2ttbfP9PB998hEH7D+S0aNPY8aMGVZZaWy3pWTa6lOUu0zHmdWcV9n3nInTIY3Y2AR3cNDekkmN2NjqRkOKx3Y5535DT0Yd+BsefXQym22+ZdGMbS3WNjNjmwatuf2Ov7LtNpvz9FNTrbHS2G5LybrVR2xsbudcbGx2acTGJgiCIAg1YMCAlfjrrXdz5ZXjWLxf/5Iztv3vs//yq/334fjjj+b7b783r2Fe8+LHZtrWtadJitXsp7PHdk1jW/uSp1mPM6s5T4pZj9M1TZl9L21ksFPvOG5vyYxGbGx1oyHFY2cl5z6aEfuM4LlnpzF08BBKzdjmeT4PPzyeYbvswGOPP0JTIYz/DxYbW11ZfYpyl+k4s5rzKvueM3E6pBEbm+AOGbC3ZEIjNra60ZDisbOW8+WWX4Grx93AlVeOY4kllyk5Y9u333zD0UcfzqG/GcVXX36BRmxs9Wb1ERub2zkXG5tdGrGxCYIgCEI3MXjwTkye8jQjRuxbcsY2NEyZMpHBO27L/ffeY+7vSdBGgu6g3FJdWrOo/XRmGxc1trUveZr1OLOa86SY9Thd05TZ99JGBjv1TsbsLc5qxMZWNxpSPHaWc77k4v254pLLufXm21l5xRVLztg2c/ZMzhx9KqMOGMmHH34oNrYu1ljZvqLcZTrOrOa8yr7nTJwOacTGJrhDRu0tzmnExlY3GlI8dj3kfKutt2HCw5PYf9SvibRXcsa2F158nu223ZwbbhhHFIVoxMaWZauP2NjczrnY2OzSiI1NEARBEFKiT5/enDX6PCZMmMiaq69ecsa2ufPmctZZp7PP3rvxwXvvm9cwr3nxYzG6uK49Tdu6RZWzqrGtfcnTrMeZ1ZwnxazH6ZqmzL6XNjLYqXfqwN7ihEZsbHWjIcVj11vON95oI56Y+hxHHXEUDb5Xcsa2f/zzHwwfPow//ekK5sybF/8/LTa2zMRZlLtMx5nVnFfZ95yJ0yGNSza2fNoNcAWllAJ2AjYCNgTWwgxe9w6C4P4q9rsfcATwUyAHvAvcDFwTBEFUbbsXSa4RHUVAAfDxvBwAxm4T19VKk+axbdOUu02RDcq6GCTnleXPxvZlMOc9e/Xm9yeewpDBgzn5tJN5/c1/43sROa9AIZ6xDUBrTVNTE5dfcTGPPzKeP158BWuv/zM8z2u2YrS10kQ6sb611iR1bTWd2cZFjY3tK85dluPMcs6r6XsuxemKppxtbLGxyWCnfI4Aju3KHSqlrgaOBOYBTwJNwCDgz8AgpdRe3TLgEQSh5rwXvMM/X3+B2TN/AK8Hmw8cxJo/Xi3tZtUN66yzLg+Of4zrb/wLV13xR3TYhEdEpDXa/O9sfr7S8E7wNsP33IVfH3w4Jxz/e3r279u8H938j3kUC4yFx25T99677/Da31/l+1kzyNPItgO3Rv1k7czFmbqmlsdOilmP0zVNOdtYgAx2yudfwMXAa8DrwE3AwM7uTCm1J2ag8wWwdRAE78f1ywFPA3sARwNXVtfsRVBkrSGxhkDrulpp0jy2bZqyt8mhKbLx2BSD5LxdzYvTXuSqP1/FS6+8RN73yHkeodYUzh3NphtvyjFHHcPmW25tdQxZyXljPsdRRx7B0B0HccapJ/LK669S8CGkzYxtOo8Gbr7lRp6YOpGLL7qEzTfbcpFWGqBVXTm2j0Vt46LGlva9NG0aV//5cl5+6QW8XA7Pz6OjAmePWcCmm27O0cf+gY033dz5OG3Q1PzYVfY9Z+J0SFPONrbY2OSenTIJguDGIAhOCoLg3iAI/q8Ldnlq/HhyMtCJj/Ml5ioSwClKqdrmqI5mabJaI7OxZVJzzz33cPDBB/DyKy+zoABhBKAJI1hQgJdfeZmDDz6Ae++919oYspjz1X68BrfdfjejR59Pz159Ss7Y9vEnH7HXnrtw0knHM3PGD8hsbG7Eed89d3Pwwfvz8ivTWuVTa43WEdOm/Y1Ro0bwwP33Oh2nLRqZja3+NDIbm1ASpdRKwC+BBcB9bV8PguBZ4H/A8sCm3ds6QRC6gueff4bTTj+p+Z6QjtA64tTT/sDzzz/TPQ0TAPB9nwNGHcSzz77IoG23KzljG8Dtt9/CxhtvzOSpU5vr0EZC8Y+XbesWVc6qJsVjP//cs5x2+olEOmqlKc4nmF+dzzzzFF782wtOxmmdppbHTopZj9M1TTnbWIAMdtLh5/Hjv4MgmNuB5tU22tpQp7M0WaeR2dgyp7n8soua85XzoTEPOZ92yx4RV1x+sXUx1EPOVxowgDtuv4fLL7mcJRfvW3LGts+/+IJf7bcfxx33O7766pv4/3KZycm2OC+7/OLmumQh2fby6fkekdb8+eornIzTJo3MxlZ/GpdmY5PBTjqsFj9+XELzSRttbRB7ix0asbFlShO89x4vTntxIdtaqfILL75A8N571sRQTzn3/Dy777E3jzwylSFDd6EQtra1aZ38adCahx95iO222ZSHJ4wn0pFYYCyK8/333mXatL+1sq21Z2MrzudLL7/If94PnIrTNo3Y2OpP45KNTSYoSIc+8ePsEppZ8WPfEpqqaGjIs8zSfYmaGiCcD7ke5Hr0BiCcn2uu8xt61kRTq/26qCl/m3kALL10X+tikJy3aO598OVO9ck33niZzTZaz4oY6jHnSy+xKnfeeQf7TZzEccccw7fffI1H+zO2ffv9txx//FFMmvo4V1x2KSuvuAoAcxcUaCpENOR9ejTkmN8Udlju1Zhf5DYuatI89kP3x32vKFftltvUvfHGK2y46S+didM2TXccW2P+78t6nK5oytkmqUsbubJT12iIOrZGNdfVSpPmsW3T2N4+ibMizcwZP5DzO7attVfO+TBzxg/WxFDPOd9156G89spLjNp/JHlPk/MhsT1Ba2vU1ClT2WTjTbjuuuspFMIWu42GQiEqWV7IopMRTZrH/mHGjA5zlZSLbWxJ3fczZzgVp20a29sncaZzLmyxsdkx5Ko/kqs2i5XQJFd/ZtaqEU1NId99Px/dNA/CBZBrxG80b8xowZzmOq+hZ000tdqvi5pyt1mqfwOg+fqb2dbFIDkvitPvQRhBGEEu12Jb8/Idl8MIPL8HX38z24oY6j7nYQNnnHEu2w4axu9POYmPP/3UWNvixUijqIDv+3haM2PmDI488gjuuP0Ozr/wcgb8aBUacj6N+RwLCiFNYdRuuWeD+S94XlMhU5o0j53zG0ksap5usbEluUpsbEArTd5rZPr0mc7EaZum1sfu078XaM306bMyHadLmnK26dmQZ7ll+5E2cmUnHT6KH1cpoVm5jVYQBEfYaquB3bqdUDu22HJrnn5mGof99nAavULJGdteePFvDB0yiJv+cj2FQsFUaiMptlO1KmdVk9Kxm/tQm9wsysa21VZbOxWnlZpaHjspZj1O1zTlbGMBMthJhzfix3WVUr060Gz0/+ydd5wURf7+39U9uyzBDGbPbOt555klCoiSFUHBeII5YVYMZ8CIYkJFMWdREZUkCAiCCKYznXpnG35n+hrOgIqk3emu3x89szu7LMPs7sx2dffn7Wtvtmue6urPPnTf9vYzVXW0JUAHf8n00zVfmUhHrbZSacIc2zRNA/poz9AaxPPqL2eHHejYoSMpC1KWIpWJreXb7tSxE84OOxhTg3hes926ZQVXjbyGOTOns7OzDWWZGdssS5FS2Q/lBtGolVVV3HzTaAYPPpgPP/wIT2s8XwcLyXp+re1s7CNumjDH3n6HHenQoTPKslCWjbKsTIytZrsmxha0tW/fkW22dyJVp2mako/tJ6TOCGkK6WNKjE1udkLAdd2vgXeAcmBw3fcdx+kKbA58D7xWquPQGnxloT0P7aWDV1TwldNWKk2YY5umKbSP76XRXpWRNYjntTXnnHM+KdvGVmApRTodvNa3nbJtzj77PONqEM9ra/bYfXdmvfgip59yOhYW5coPnvYoH7SPRmd+obb54MMP6du7B2Nuu5XlK1bieT4o8Dwfz/ert+tri4MmzLHPOfsC7FSq2gtf+9XfK8tGZ/5Tlo1tl3HKqWdHsk7TNKUeO+0lo84oaQrpYwJys1NCHMcZ5TjOx47jjKrn7WzbDY7jbJfTZ0Pgrszm9a7r+qU6PqXA0j4qZaOssuA1+3/rOW2l0oQ5tmmaQvtYdgplm1mDeF5b06VLV668ahQ+Fr7WpFLga03ap9a2j8VVV19Ply5djatBPK+tsewUFa1acdZ5I3hxxkx2+fNOWH4ay6/CVhqFQvse2vfRvoePz11jb+WIwwbywb/eBQ22bWFbFrZtBTGPetrioAlz7M5d9uXqK0eh0Gjfw1JWLV9U9j+tGXnF1XTq3DGSdZqmKfXYKTsZdUZJU0gfE5AJCgrEcZzdqbkJAfhz5vU6x3HOzza6rts+R7MJ4GRea+G67kTHccYBpwIfOI7zElAF9ADWBiYBY4taxCoosMvB98H2QaVA2Zm3UjVtpdKEObZpmkL7WCmU7YHyzatBPF9FM/iwo9hs0824467beWXh66gUWCq44alMQ/tO+3DGaWfSsUs3Y2sQz3M0ll19/u30l1155tkpPHz/Pdw85ma8yhVo5YPvYXkr8XMmMfj0k48ZMmQAxw07nnPOvYiyigrszJoUALal8LXCthQpO7g5zm5HUWPC8R12+OFstvkW3DX2FhYtegXLsoIndZk1djp26MTwM0ewV4cOka7TFE1zjK2tZNQZFU0hfWSdneixNrBPPe3bN3aHruue5jjOq8DpQFfABj4GHgTGlfKpjiAIzUPHjh3p2KUbn/6///L+2wtZuuQ3UC3o2LUH22+zdfD5ECGSpOwUJ5xwIgf06sO5F47gtdcWUUYVZdpCEazPgwZU8LGg++4bx6xZs7l21E107tSpZkfN9WHi5tQYcHztO3Sgc6dn+eLzT/jnO2/x6x+/k6Kc7l33xdlxJ1ZUBjPrRb1OYzSlHDu7Gfc6o6YppI8ByM1OgbiuO4+Mfw3oMwwYtgbNeGB8Y4+ryXg1a06QXXMCareVShPm2KZpCu5jE4RlDKxBPM+r2WG7bem491/QXpqfFy8HKxV8AN6Q4xPPC9GomvMvR7PVVlvx7MTJPP74I9w46kpWLl9KSmk828K3VNDTUlg6xTfffs2wY49iyKFDuPzSy1l77XUyvxMoNJD2/Frb2Q/4Rklj2vFts73DHvvsgedrFv/yBy1SdrAeSMzqjLXnmmDtlrjXGSFNIX1kggLBDOxyNBbBxzUtUDYou3ZbqTRhjm2apgF9MLUG8bxh/hl6fOJ5fs3q/LPsMo4ZegIzZ85l33274fnBLwP4PrZOQybWprUGrXnq6Sfoum97Zs2cgVLBXpWClG3V2ras7Gxv0dGYenzoZNQZV8+z/sW9zqhoCuljSoxNbnYEQRAEoUhsvMlm3HPPA4y5dSwbrLceZVStdm2eH/73PcOOPZLhw0/h5x9/zv5JFHQgIfePonXbTNeYdnzZb+NeZ1w9z27Gvc6oaQo898JGbnaSTiaKEcxNk4lraK92W6k0YY5tmqYBfTC1BvG8Yf4ZenzieX5NIf5ZymLAgAHMm/sqA/ofiIVPKrM2j8qJtWXXe5kxYxr9DzyA5yc9R1Xay/yOUBMDKSRyZZLGyOPTkI1BxbrOuHqe41+s64yQppA+EmMTzEDiLWZoJMaWGA0hji2ely7GVt9+2m60MWNuG8fYsffStt3Gq8TasjODaa35dfFizjvvDI4bejjffvsNGjOjK1GO+kiMLdqeS4zNLI3E2ARBEARBAGD//fdn5qyXOfrov68aawPQgApe57w8h9499+PJJx7H9/2anehAgl7Ntmka044v+23c64yr59nNuNcZNU2B517YyM1O0pF4ixkaibElRkOIY4vnzRNjq28/66zVhhtHjWb8Y0+x5eabklIa2wKlakfbLCvFshXLuOrqyznyiMF89tmnRkVXIhv10SAxtgh7nuNfrOuMkEZibEJ0kHiLGRqJsSVGQ4hji+fNG2Or72fRoWNnJk+ZxbDjTgre07Wjbbkztr351uvs36MLd911O56XRhN+dCXKUR+JsUXbc4mxmaWRGJsgCIIgCPXSqlUF/7jkCqZNm8lOjpN3xrYVK1dwzTVXMGhgfz7+93+C9wjeU5nXauq2hakx7fiy38a9zrh6nt2Me51R0xR47oWN3OwkHYm3mKGRGFtiNIQ4tngeXoytvu3dd9udWTNf5pwzz6FFSuWdse2jf3/I4MEHccsto1m6bFnm94joxlskxhYvjcTYkqeRGJsQHSTeYoZGYmyJ0RDi2OJ5+DG2un3KW7TkzLPO49nnpvHXXf6Wd8a2dDrN2Dtvo2+v/Xjn7bfQRDfeIjG2eGkkxpY8jcTYBEEQBEEomB12cHhm4lRGXn41a7Ww887Y9unnnzBkyECuu/ZKlv6xtGYnOpCgV7PdnJowx15Nm05CnXH1PLsZ9zqjpinw3AsbudlJOhJvMUMjMbbEaAhxbPHcrBhb3T4pS3HyiScwc8ZsOuyzd94Z21Dw2GOP0Lfv/syb93Lk4i0SY4uXRmJsydNIjE2IDhJvMUMjMbbEaAhxbPHcvBhbfX3+tOXWPPzwE1x99WhatV4774xtX3/zFYcfPpBzzx3O77/9iiYa8RaJscVLIzG25GkkxiYIgiAIQqNRSnH4EUexYMHr9O7ZK++MbQBPPfUEPXt2Z/bMmdVt6EBC7h9X67aVShPm2Ktp00moM66eZzfjXmfUNAWee2EjNztJR+ItZmgkxpYYDSGOLZ6bHWOrT7PxRhvx8EOPc+ftd9Jug3Xzztj2088/cuZZp3La8JP47rsfMr9rmBlvkRhbvDQSY0ueRmJsQnSQeIsZGomxJUZDiGOL59GIsdXVKCtF334DmDJlNv0OPDjvjG1ozYszXqBH9w48N/EZfO0bGW+RGFu8NBJjS55GYmyCIAiCIBSVDTZYjzG33snjjz7BnzZul3fGtl9//5ULRpzNySceyzfffF2zEx1I0KvZLpamVPttrCb7bdzrDFNTyrGzm3GvM2qaAs+9sJGbnaQj8RYzNBJjS4yGEMcWz6MXY6tP06P7fsx68SWOPPyIvDO2KUux4NVX6NN7fx544F7Snpf5/SP8eIvE2OKlkRhb8jQSYxOig8RbzNBIjC0xGkIcWzyPZoytPk2btdZm5MireeKJifxpy63zzti2dNkf/OMfIxh0cF/++/lnaMKPt0iMLV4aibElTyMxNkEQBEEQSs7e+3RgztyFnH7qGbRQXt4Z29586w369t2fe++5i6qqqqBRBxJy/wBbt60xmlLtt7Ga7LdxrzNMTSnHzm7Gvc6oaQo898JGbnaSjsRbzNBIjC0xGkIcWzyPR4ytrqZVRQsuv+wKJj03mR132D7vjG1VXppbb72JQQMP5P3338/8PpKAqI8GibFFON6V41+s64yQRmJsQnSQeIsZGomxJUZDiGOL5/GJsdWn+esuuzFx4hTOOusCLCuVd8a2j/79Af377s+NN45i5coViYj6SIwt2vEuibGZpZEYmyAIgiAIzU55eRnDzzibl2a/zN67/iXvjG2e9rh73FgGHNSHt958o2YnOpCgV7NdiKYxfUqpyX5b7LFMqzNMTSnHzm7Gvc6oaQo898ImFfYBCGGiwasEPx18KQu0F7yV21YqTZhjm6YpuI9Ce5k202oQzxvmn5HHJ57n1+im+deMNWy/7TY88/SzjH/0fm685RbS3jI8LCxLkVKKtKVAp1CWxZdff8XhRxzKMUcdzSWXXI5VXoHna5TSpD0fT+vq7WwsJbetrqYxfUqu8YNtTxdvLCPrDElT8rFz/It1nRHSFNJHYmxC6GgNvrLQnof20sErKvjKaSuVJsyxTdMU2sf30mivysgaxPOG+Wfi8Ynn+TVN9a+5a7C05ugjj+aFKTPo1Glf0D7lyqdcpSlXPlp7KMuu/nro4fvp3q0T8+fNx/N9PM8HBZ7n19qur21N2yZo0l7xxzKxzrA0pR4717841xklTSF9TMAeOXJk2McgND/DgK18L83KZStAaZQGVZbCssuCf5s6XdOmrNJoSrXfKGoK7NO6VTmgWV6lzatBPG+YfwYen3ieX9Oqwm6afyHVsM4GGzD4sKPZYvPNeeu1V/GqVqL9dHCDpIKJC4InVh5LlvzOtBem8N2337HP3nvTulWr6jRKyrYos4K/kXqZWFzKtrAzHxxf3XYhfUqtadmqBQCVlemijWVinWFpSj12i5blQOBfnOuMkqaQPmWWxVptKjIqvgQeJgTkyU6iUWCXg0qBbQevmQ+61morlSbMsU3TFNrHSqHslJk1iOcN88/E4xPP82ua6l+INSgrxeFHHMOsmXPZr3sPPD+IoeB7WN5K8L2cSQx8Jk+aSO9e3Zk5fRqWCj5obGc+gJz7vW2p4BefPNuF9GkOTarIY5laZxia5hg71YxjiefF+VnIBAWCIAiCIDQr7TbamLFjx3Hn2Ltpt0Fbyqha7SQGP//8EyeePIxTTj2Rn/73Y7Q/xJ39tthjmVZnmJpSjp3djHudUdMUeO6FjdzsJB1Zf8MMjayzkxgNIY4tnjdd0yT/DKnBUhZ9+/Zj3txXOHTgICx8UkpjW6BU7fV5LCvFS7NfpH//nkycOAHP86O5FokGWWcnwuvP5PgX6zojpJF1doToIOtvmKGRdXYSoyHEscXzpmua5J8hNWTb1m+3ETfedDv33PMgG228GZ4PWvvkrs/j+2m01vz226+MuPAcjjh8EF9//WUk1yKRdXaivf6MrLNjlkbW2REEQRAEIRJ07dqNF2fO5djjTkBpvWq0TQMK0PDKgnl079aRhx68H9/3gvcI3mtwvKU5Ndlviz2WaXWGqSnl2NnNuNcZNU2B517YyM1O0pF4ixkaibElRkOIY4vnEmNbnWat1q0Yde0NTJ40je223opstC1l1461KWWxfMUKrrl2JEcdOYRPP/00GlEfjcTYSqiRGFvyNBJjE6KDxFvM0EiMLTEaQhxbPJcY25o0+7TvzNQXiiL4UAAAIABJREFU5nDCCacAdmbGtppYW82MbZr33n2HA/sfwNg7biGdrkITfrRGYmzhaCTGljyNxNgEQRAEQYgkFS1acP75I5g0aRp/3XnnvDO2VVZVMXr0dRx8UF8++uCDoD3zfkliM43VZL8t9lim1RmmppRjZzfjXmfUNAWee2EjNztJR+ItZmgkxpYYDSGOLZ5LjK0hmr/85S+8MPVFRpw3ghZlVt4Z2z7+5D8MOexgbrjhOv5Yuow1xVskxhYvjcTYkqeRGJsQHSTeYoZGYmyJ0RDi2OK5xNgaqimraMWpp53J5Mkz2HW3PfLO2OZ7Pvfceye9D+jKW2++hsas+I3E2KId75IYm1kaibEJgiAIghAbttlmW56eMIlrr72B1i1b5Z2x7f998TmHH34oV428jD+WLKnZSWMiMMXSZL8t9ljNWYPpmlKOnd2Me51R0xR47oWN3OwkHYm3mKGRGFtiNIQ4tnguMbamaGwFxx93AvPnvUrXzl1Y3YxtlpVCWYonn3qCXr3346WXZoUfv9FIjK2EGomxJU8jMTYhOki8xQyNxNgSoyHEscVzibEVQ7PFn7bmoYfHM2rUaNqstc4qM7ZlY21ozXfffcvRRw9h+PCTWPzLL2gkxhZHjcTYkqeRGJsgCIIgCLFFKcXAgYcya+Y8DuzXP2+sDeC5556hV69uzJg+LbgRIniv2eI32W+LPVZz1mC6ppRjZzfjXmfUNAWee2EjNztJR+ItZmgkxpYYDSGOLZ5LjK3Ymg03bMu9d9/H3WPvZsO26692IVKlLH5Z/AvnnnsmJ59yAt9++x1risBIjC06GomxJU8jMTYhOki8xQyNxNgSoyHEscVzibGVStOrT3+mTp3FwYMG512IFK15afaL7NetIxOeehJf+xJji4FGYmzJ00iMTRAEQRCERLHeeuty4+gxPDV+AltvutFqFyJFw+9//MbFl5zP8cf+nS+/+G/NThoTkykwSqOLsZ9SHV8cNKUcO7sZ9zqjpinw3AsbudlJOhJvMUMjMbbEaAhxbPFcYmzNoenapQsvzniJYcccQ5litQuRKkvx2uuL6NevJ+PuvpOqdJqmRmskxhaORmJsydNIjE2IDhJvMUMjMbbEaAhxbPFcYmzN5XnrNm245JLLefKp59lm2+1XuxApWrNs+TKuHPkPBhzYi0/dj9FIjC1qGomxJU8jMTZBEARBEBLPHnvuxeyXFnDOWedSofy8M7a98+7bHHhgL+4aezuVlZVBY2OiNatp08XYTzH6xFVTyrGzm3GvM2qaAs+9sJGbnaQj8RYzNBJjS4yGEMcWzyXGFobnFeVlXHThJUyZPI2//HmnvDO2pX2PO8aO4eABfXn77bcpWvxGIzG2EmokxpY8jcTYhOgg8RYzNBJjS4yGEMcWzyXGFqbnf975r0yYMInzzrsE2y7LO2Pbx+5/GHBQL6677iqWL18uMTbDNRJjS55GYmyCIAiCIAh1SKVsTjn1dObOmU/HPf6Wd8Y2X/s8cP89HHRgL15b9FrNThoZpdFr0hSyn2L0iaumlGNnN+NeZ9Q0BZ57YZNqzsEcxzkaOAbYAvgOmAjc67puejX6h4C/u67brMeZKHKiNWQjClC7rVSaMMc2TVNwHxtNTpzEpBrE84b5Z+Txief5Napp/hlRgxmeb7PVljz91ETGP/4IN9x4A+klf+BRZ8Y2HczY9vU3X3P03wdzxJAjuOyykZS3bA2sPkoD1GqrL8ZWr6aQ/TSxT1w1JR87x79Y1xkhTSF9EhdjcxxnHPAIsD/gAN2AO4C3HMfZNk9XVfqjSzAGRh0SqZEYW2I0hDi2eC4xNpM8V1aKo476OzNnzqVrtx55Z2zTWvP44w/Rdd/2zH3pJTQSYzNJIzG25GkkxlYHx3H6AicDPvAAMBy4Dfgd+BuwyHGcXZrjWARBEARBMIdNNt2cJ56YyNjbx9FundZ5Z2z7/ofvOPGkoZx/7pn8/NPPQaPO/FU094/I9bTpNWkK2U8x+sRVU8qxs5txrzNqmgLPvbBprnjYyQRlH+m67jPZRsdxrgeeAPYD5jiO09N13Xeb6ZgENHiV4KeDL2WRjRvUaiuVJsyxTdMU3EehvUybaTWI5w3zz8jjE8/za3TT/DOiBjM9V/gMPnQw+7bfm+uvvZyp01/Asy20pUgpRdpSaBX8fVZZFtNfnM7ChQu44rKR9DlwAJ6vUUpXx2Y8ravb0p6P5wfbns6jydkuRNOYPnHVlHzsHP9iXWeENIX0SVqMbW/gw9wbHQDXdX8AegEPAhsAsx3H2b2ZjinxaA2+stCeh/bSwStBJj23rVSaMMc2TVNoH99Lo70qI2sQzxvmn4nHJ57n1zTVPxNqMN3ztuuvx02jb+LOO8bRdv12lCufcpWmXPmgfTQaZdkoy+bX337jjDNP4bRTTuTb777D8/zgT8oKPM/H8/3qNs/zg1/O1qDJ3S5E05g+cdWUeuxc/+JcZ5Q0hfQxgea62Vkf+Hd9b7iu67muewLB53fWR254mg2lwNI+KmWjrLLgNft/OTltpdKEObZpmkL7WHYKZZtZg3jeMP9MPD7xPL+mqf6ZUENUPD+gd19enjufIYcMwvLTWH4VttIoFNr30L6P9j0sZTFnziwGHdyPZ55+Et/3QYNtW9iWhW1b1dspu2Z7dZrc7UI0jekTV02px871L851RklTSB8TaK6bnV+A1vkEruueBdwKrEdww7NHcxxYslFgl4NKgW0Hr5kPktZqK5UmzLFN0xTax0qh7JSZNYjnDfPPxOMTz/NrmuqfCTVEyPN1N2jHqFE3c/99D7Ppppvj+WQmMfCwvJXge5lJDHyW/P4rl11+IUMOPYgvvvgcO/NhadtSwS/JliKV2bZy3qursRqgaUyfuGqaY+xUM44lnhfnZ2HKBAWpZhrna2CNNy+u657nOI4HnA/MAj4r9YE1FMdxjgROBXYBbOBj4CFgnOu6fgP28zAwNI/EdV13xyYcqiAIgiBEnk6dOjH9xbnceOvN3Hfv3ZRRRZkOPufj15nEYNFrC9mve2fOvfBSjhl6HGVWefV+dPX/1DTIh9WLqCnl2NnNuNcZNU0hfQyguZ7svAVs7DhOxzUJXdcdAYwmeMKzZ6kPrCE4jnMnwYQKewILgNnADsBYYKLjOI35eS4kmJK77tfzxTjmNZJZD0GRs3aL9mq3lUoT5timaRrQB1NrEM8b5p+hxyee59c0yT9Daoii521aVXDVyGuYNnUGO26/HRY+KaWD6W+t4MMCylIoZbGyspIbrr+Gww8/hP/85z/1rrPj+zrzu1jQVt+aIWvSNKZPXDUlHzvHv1jXGSFNIX2SNkHBdIIbvbMLEbuuexFwXaaPETiOcwhwGvA9sIvruv1d1x0IbA/8BxgInNGIXd/vuu6wer4uLt7R5yECazEkQiPr7CRGQ4hji+dN1zTJP0NqiLLne+zZnklTZnHKKcNRKkXa88ldm0fr7Jfmw3/9iwEH9eLWW26gqnIlss5OtNefkXV2zNLIOjurMgs4ABhXaAfXdS8FBgDHleqgGkj25uNC13U/zTZmZpQ7NbN5USOf7giCIAiCUAAtyss5++xzmTJlBrvusgtlVNVemwdAAwrSnsett95Il85d+Oc//xm0Z2muGE9SNKUcO7sZ9zqjpimkjwE0y2d2XNetAuY0ot/UEhxOg3EcZ3OCzxxVAs/Ufd913fmO4/wfsBnQHljUvEfYBHKiNWSjBFC7rVSaMMc2TVNwH5tgPiIDaxDPG+afkccnnufXqKb5Z0QN8fF8p512ZOrk6Tx4/92Mvf1G9IqVpC3wqB1ts3SKjz9x6dO3L0OPGcpFF1xMq1atWFMkB8iraUyfuGpKPrYmb4wtNnVGSFNIn6TF2KLObpnXj1zXXb4azVt1tIXS3XGcWxzHuddxnKsdx+nVrE+HYhp1iJxGYmyJ0RDi2OK5xNji5nmqRUtOPOlUpkyZwZ577ZMzY1tNtC2YsU2jfZ8HH7yP/bp3YuGrr0ikqYgaibElTxOlGFvRnuw4jrO367pvNqG/BVztuu4/inVMRWTrzOuXeTRf1dEWyjH1tP3bcZzDXdf9oIH7ahBlZSnatV0Lv6oMvJVgt8Bu0QoAb6Vd3WaVVZREU6r9RlFTeJ8VALRtu5ZxNYjnDfPPzOMTz/NrljXJPzNqiKfnbdf7KzNnzuShx8YzYsQIKlcsXe2MbV99/QWDhwxg6PEncuWVV9F2g3VoUWazssqjKu1TlrJoWR78erS8Ml3dVlfTmD5x1TTH2Dpz7sW9zqhoCumTbQubYj5BWOA4zoWN6eg4ztYE0a+Ling8xaRN5nVpHs0fmde1Ctzne8CZwJ8z+98U6A+8n2l7yXGczRp+qA1Bg7/6Gb6q20qlCXNs0zSmH5/UKT8LqVN+FobXaVtw0onH8+EH/6Jvr57km7FNKYvHH3uMTp06M+2F6aTTOTO2aVadBUyziqYxfeKqMf34pM5wfhamxNiKectVBlznOM4BwN9d1/2ukE6O4xwF3AmsTfC3l0Tguu6YOk1LgRccx5kNzCf47M/FwPBSHUNVlcfiX1eiq1aAVwl2OVZ5YIFfuay6TZVVlERTqv1GUVNonw3WKQM0P/601LgaxPOG+Wfi8Ynn+TXrr203yT8TakiC5y0q1mPsnQ/wwpSJXHHVlXz302Is5WOrNOnMjG0AWmt++P57jjzicPr16cvlV1zHuhtsQJltUVEW/Hq0oipNledTZluUp2wq095qtwvpE1dNqcdus05L0Jqff/4j1nVGSVNIn4qyFBttuDZhU8wnO+nMa3fgfcdxDswndhynjeM4jwKPUvM05MMiHk8xyT61aZ1Hk336s6QpA7muWwmMymz2bcq+BEEQBCGJKKXo338AM2fNZ9DBB+edsQ0NU6dOonevbkyZPAmdfT+jUVltIdtJ1pRy7Oxm3OuMmqaQPgZQzJudzsB/CWpsC0xyHGes4zgt6godx9mbIMZ1VEavgLuAvYt4PMXki8zrlnk0W9TRNoWPM68ljrFBdrYbVU9MoLqtVJowxzZN04A+9UU6jKhBPG+Yf4Yen3ieX9Mk/wypIUmet9tgPe68YxwTnhzPphtvSEppbIsgypY7Y5uV4tfff+PCC8/lhOOH8vXXX8sCkw3QyKKiydMkclHRzOQEuwJPZJoUwfozbzmO8+esznGci4EFBB/kV8BPwEGu6w53XXdlsY6nyLybed3ZcZyWq9HsVUfbFDbIvP6RV1UMEjJjj/GaBvTB1BrE84b5Z+jxiecyG1scPe/Ttx8LF77K4MOOyjtjG1rz8rw5dOvagUcefiBYoBSZmcuEWcpkNjazNFGaja2YT3ZwXfcP13X/DgwliHMp4C8ENzwXOI4zF7iG4PM9CngJ2MV13WnFPI5i47ru18A7QDkwuO77juN0BTYHvgdeK8KQQzKvb+VVCYIgCIJQEOusszbXXTuaZ5+dwtZbbrlqtE0T/GaiYemyP7j4kvM58vBD+PK//w3eI3jPmAiRaZpSjp3djHudUdMU0scAinqzk8V13ccIFuH8Z6apJXA90JXgZ1AFXOi6bk/Xdb8vxTGUgOznaG5wHGe7bKPjOBsSRPAArndd1895b5TjOB87jjMqZz84jrOr4zj9Hcex67SnHMc5j2CWNoBbi15FXSTqYIZGYmyJ0RDi2OK5xNiS7HnWu04dOzF3zqucfMKJpCzyztj21ttvMWBAH8bdcycrKisxJUJkmkZibMnTJDLGVhfXdT8D9idYfyZbrQJ8gtjajaUauxS4rjsRGAdsDHzgOM5Ux3GeAz4lmCp6EjC2TrdNACfzmstWwFTgf47jzHYc5wnHcV4kWMfnpoxmhOu6M0tSTC4SdTBDIzG2xGgIcWzxXGJsSfY899rZqnUbLrr4Cp56aiLbbb8Daa92rE3r7Jdm5cqV3Dj6Og7u35uP//0RmvAjRKZpJMaWPE1iY2y5OI7zF2AhNR/cVwQ3PQp40HGcHqUau1S4rnsawaQK7xA8peoFfEYwPfQhrut6Be7qfeA2wCW4UToks79lwEPA3lG7GRQEQRCEqLHLLrsyecpMzj93BK1snXfGtn99+B4DBvTh9jE3s2LFiur3jY4ZNaemlGNnN+NeZ9Q0hfQxgJIsbeo4zmnAjUAFQc2/EcTATgP+RPCkY6bjODcD/3BdN726fZmG67rjgfEFaocBw+pp/y9wdlEPrLHkPN4nGwGA2m2l0oQ5tmmagvvYaHLiGibVIJ43zD8jj088z69RTfPPiBqS6vnqr50tymzOO+cc+vfuyWX/OJ933nuPtAUedWZs0yk87TPu7juZ+eILXH/DTey2255A7cjOmqI+cdSUfGxN3hhbbOqMkKaQPrGMsTmOs57jOJOAOwg+p6MIPrC/q+u6o4G/Ac/mjH0+sCj3MzBCMyNRBzM0EmNLjIYQxxbPJcaWZM/XdO3cwdmJ8eOf5eKLr6C8Rcu8M7Z99vmnHDKwL1dedRnLli4zMmZkWqRJYmzx0iQyxuY4TjfgX8CBkP1zCtcC+7qu+yWA67q/ua47GDgJWJrR7Qm84zjOscU6FkEQBEEQhIZi2xbHHX8SL7+8kC6dOuedsU1rzaMPP0j//gewYP4rNTspRjwoippSjp3djHudUdMU0scAivlk5yVgU4Ia/w/o4bruZfV9jsV13fsJbnLeyzS1Ae53HOfpIh6PUAgyY48ZGpmNLTEaQhxbPG+6pkn+GVJDUj1vyLVzqy235JkJz3PDdTewdptW9c7YZlkplKX49rtvGXbckZx11uksXryYNUV9TJpRKzKzlGnyxthiU2eENEmdjc0iuNGZDPzNdd35+cSu67rAPsAYgvs/BRxaxOMRCkGiDmZoJMaWGA0hji2eS4wtyZ439NqprBRDDjuK6S/Mpvt++68yY1turE1rzdNPP0HXfdszc8YMNOHHjEyLNEmMLV6aRMbYgBXAaa7rDnRd95dCOriuW+W67rlAX+B/RTwWQRAEQRCEJrPhRhtxz70Pc8+4+9h4/bVXG2sD+N+PP3Da6Sdw5pmn8r8fMr/WmB5FKpamlGNnN+NeZ9Q0hfQxgGLe7Ozluu7djemYWU9mF+DFIh6PUAgSdTBDIzG2xGgIcWzxXGJsSfa8KddOC81B/fsz68W5HNS/f96FSJWlmD1rJn367MdTTz2Jr4M7IhOjSJGJd2kkxmaYJpExNtd1P2pi//+5rtuvWMcjFIhEHczQSIwtMRpCHFs8lxhbkj0vxrVz/Q024KabxnDvvQ+z4Yab1Iq15S5EitYs/nUx5557OscNO5JvvvnayChSlOJdEmMzS5PUGJsgCIIgCELs6b7fAcyb/yrDjjo870KkAK8seIWDDuzNIw89iO/71RpjokjF0pRy7Oxm3OuMmqaQPgZQkkVFhaigwasEPx18KQt0ZvK83LZSacIc2zRNwX0U2su0mVaDeN4w/4w8PvE8v0Y3zT8jakiq58W/dq7VsoJrRl7NgN69ufjSi3D/35d4WFiWIqUUaUuBTqEsi+UrV3DVNVfwwrTnuemm29hsy63xfI1SmrTn42ldvZ2N/uS2ma4p+dh+0ObpmNcZIU0hfWIXYxOih9bgKwvteWgvHbyigq+ctlJpwhzbNE2hfXwvjfaqjKxBPG+YfyYen3ieX9NU/0yoIamel+Lamd3ec/fdmDL5RU455UwsBeXKp1ylKVc+Wnsoy67+evOt19m/RxfG3X0XKyor8TwfFHiej+f71dv1tZmuKfXYaS8ZdUZJU0gfE7BHjhwZ9jEIzc8wYCvfS7Ny2QpQGqVBlaWw7LLg36ZO17QpqzSaUu03ipoC+7RuVQ5olldp82oQzxvmn4HHJ57n17SqsJvmnwE1JNXzklw7c7bLWlbQbb8D2L/H/rz3zzf49Zef0H46uGFSwcQFwZMlD9/3eeON11mwYAG7/vWvbLzRxtWJn5RtUWYFf4f2MrG4lG1hZz6cb6qm1GO3aFkOQGVlOtZ1RklTSJ8yy2KtNhUZFV8CDxMC8mQn0Siwy0GlwLaD18yHMmu1lUoT5timaQrtY6VQdsrMGsTzhvln4vGJ5/k1TfXPhBqS6nkprp319Pnbrnvy/OQZnDH8LCw7lVmbx8PyVoLv5Uxi4POfj/7FoIH9GH3DNVStXIllKezMh7xzv7ctFfxyWafdJE1zjJ1KSJ1R0RTSRyYoEARBEARBiBnlZWWcdtpwXpg2iz13350yqlY7iYHn+4wdO4b+/Q7g7bffMveD6IVoSjl2djPudUZNU0gfA5CbnaQjazGYoZF1dhKjIcSxxfOma5rknyE1JNXzol8719Bn++23Y9JzUxl56eW0bllOSmlsC7Jr8YBCWQrLSvHfL//L0UcdxhUjL2XJkiVGradixPozGmSdHbM0iVxnR4goshaDGRpZZycxGkIcWzxvuqZJ/hlSQ1I9L/q1s4A+dnkFQ489ialTZ9K+Q2c8H7T2yV2fx/fT1WvzPPbYQ3Tv1pH58+YYs56KKevPyDo7ZmlknR1BEARBEAQBgM0334JHH3uKW28dyzprrb1qtE0DCtDwzf99zRFHHsp5553Fb4t/Dd4jeC/ykabGarKbca8zappC+hiA3OwkHYk6mKGRGFtiNIQ4tnguMbYke170a2cD+1hojjj8CBbMX0TvA3ph4ZNSOvgLeU6sTWVmb5s06Vn69j+A6TOmUZX2iHqkSWJs8dJIjE2IDhJ1MEMjMbbEaAhxbPFcYmxJ9rzo185GHt9Gm2zGXePu57bb7mTd9dpmZmyribXVzNim+eWnnxg+/GROOnEoP/7vBzTRjTRJjC1eGomxCYIgCIIgCPWilKJXrz7MnPUygwcPyTtjGxpmzpxOzwO68dzEZ4LP92SJWqSpsZrsZtzrjJqmkD4GIDc7SUeiDmZoJMaWGA0hji2eS4wtyZ4X/dpZhOPbYL11uf2W23jkwUfZfNNN8s7YtmTpEv5x6YUMPeYovvjii8hFmiTGFi+NxNiE6CBRBzM0EmNLjIYQxxbPJcaWZM+Lfu0s4vHt23U/pkx5kSOPHrrGGdteXTif/bp35IH778b3PTTRiDRJjC1eGomxCYIgCIIgCAXTpk1rrhx5HZMnT2e7bbbJO2PbsuXLuOzyizlsyMF8/ulnwXsE7xkdaWqsJrsZ9zqjpimkjwHIzU7SkaiDGRqJsSVGQ4hji+cSY0uy50W/dpaohn323oc5Ly3g9FNOp8xSeWdse/e9dxk0qB933nk7y1aswORIk8TY4qWRGJsQHSTqYIZGYmyJ0RDi2OK5xNiS7HnRr50lrKGiZSvOv+BiJjzzHM6Of847Y1tlZSU333IDB/XtyYf/eg+NmZEmibHFSyMxNkEQBEEQBKFJ7LzzX3l+0nQuuehSWpepvDO2/fvjDxk4qD83jb6e5cuX1+zEpEhTYzXZzahEt5KiKaSPAcjNTtKRqIMZGomxJUZDiGOL5xJjS7LnRb92NlMN5SmbM04/nRkvzGSP3XbLO2ObBh548F769T2AhYteNSrSJDG2eGkkxiZEB4k6mKGRGFtiNIQ4tnguMbYke170a2cz17DNttvzxBNPc9llV9OionXeGdu++PK/HDKoPxdddB5L/1iCJvxIk8TY4qWRGJsgCIIgCIJQVCzL4pihxzF//iK6d+2Wd8Y2gEcffZBevbqzYP686jaiGO/KbkYlupUUTSF9DEBudpKORB3M0EiMLTEaQhxbPJcYW5I9L/q1M8Q6t9h8c54c/ww3j76Z9dZpk3fGtu9/+J6TTj6Oc845gx9//JlIxrs0EmMzTCMxNiE6SNTBDI3E2BKjIcSxxXOJsSXZ86JfO0OuU1kpBh1yGNOmzqZX7355Z2xDayZPeY79urVn2tTJ+NqPXLxLYmxmaSTGJgiCIAiCIJScdhu2Y+zYe3ngvofYZIN1887Y9vPinznrrNMYPvxkvv/uu5qdmB7vym5GJbqVFE0hfQxAbnaSjkQdzNBIjC0xGkIcWzyXGFuSPS/6tdOwOvv27s1Ls+Zy6KBBeWdsU5Zi7pyX6N27B4899gheEaJaEmNLnkZibEJ0kKiDGRqJsSVGQ4hji+cSY0uy50W/dhpY5zrrrsd1143mwQefYNPN/pR3xrbfl/zGBRecxWFDBvDll/9FY3a8S2JsZmkkxiYIgiAIgiCEQpd9u/HyvEWceMJJlKt03hnbXl24gH599uehh+/H87yg0bR4V3YzKtGtpGgK6WMAcrOTdCTqYIZGYmyJ0RDi2OK5xNiS7HnRr52G1pndbtOqJddcdR0Tn36O7bbZKu+MbSsqVzL6+usYPHgAH330b4yLd2kkxmaYRmJsQnSQqIMZGomxJUZDiGOL5xJjS7LnRb92GlpnXc3ue+zFc89N55RTzgCsvDO2vffeu/Tt3Z3bb7+FyqqVRsW7JMZmlkZibIIgCIIgCIIRVFSUc975F/HijNnstvOOeWdsq/KquG3MzRw66CDef/edmp2EGe/KbkYlupUUTSF9DCAV9gEIYaLBqwQ/HXwpK3g0DrXbSqUJc2zTNAX3UWgv02ZaDeJ5w/wz8vjE8/wa3TT/jKghqZ6X4NppZJ35NTvvtCPPPzuJh++/m1tvH0PaW4mHhWUpUkqRthToFMqy+PTzzzh0yECOH3Yc519wESpVjudrlNKkPR9P69VuZ+NLRdP4QZunm2GsPJowxzZNU0gfibEJoaM1+MpCex7aSwevqOArp61UmjDHNk1TaB/fS6O9KiNrEM8b5p+Jxyee59c01T8Takiq56W4dppYZyEaGzj+2GN5Ycp09t67PWifcuUHExkoH609lGWjLBuN4u57xtKje2cWLXodz/fxPB8UeJ6fd7vYmrTXfGOFWWeUNIX0MQF75MiRYR+D0PwMA7byvTQrl60ApVEaVFkKyy4L/m3qdE2bskqjKdV+o6gpsE/rVuWAZnmVNq8G8bxh/hnTULLMAAAgAElEQVR4fOJ5fk2rCrtp/hlQQ1I9L8m108A6G6JZt107DjviGDZs1463XnsVna5E++ngBkkFExcET8M8fvv9NyZPeZ5ffl7MPnvvTcuKiuqUUsq2sDOTB2S3y6zgb+leJibXVE2LluUAVFamSz5WPk2YY5umKaRPmWWxVpuKjIovgYcJAXmyk2gU2OWgUmDbwWvmA4212kqlCXNs0zSF9rFSKDtlZg3iecP8M/H4xPP8mqb6Z0INSfW8FNdOE+tsoMayyxg67ERmzpxD506d8fwgnoTvYXkrwfdyJjHwmfD04/Tu1Z2XX5qNnflAum2p4JffnG2rzvfF0KSacazVacIc2zRNIX1kggJBEARBEAQhdDbZdHPuvfdBbrn5dtZfd13KqFrtJAY//PA9Q489gjPPPJXFPy9u2gfcC9VkN5tjrDVpwhzbNE0hfQxAbnaSjqzFYIZG1tlJjIYQxxbPm65pkn+G1JBUz4t+7TS0zsZqLGUxcOBA5s19lYP69cfCJ6U0tkWwHo8VfChDWQrLSvHCC1Pp139/Jk95nqq0l/nddvVrrsg6O/HSyDo7QnSQtRjM0Mg6O4nREOLY4nnTNU3yz5Aakup50a+dhtbZVE27jTfhttvv5vY77maDthvh+aC1T+76PL6fRmvN4l9+4ZxzhnPCsUfy3Xf/h0bLOjsJ0cg6O4IgCIIgCEJk6XlAT2bOepkjj/o7SutVo22ZWBsaZs+ZTe+e+/H0k+Pxfb9mJxJji7dGYmxCJJCogxkaibElRkOIY4vnEmNLsudFv3YaWmcxNeuuvRa33DSGZyY8z5abb0422payV421LV2+lJFXXsrRRx3G559/JjG2mGskxiZEB4k6mKGRGFtiNIQ4tnguMbYke170a6ehdZZC02Xf7rwwYy7Dhh2Hr1VmxrZVY21ozetvLKLHfp0ZN+4OPC+NRmJscdRIjE0QBEEQBEGIDa1bteKiiy7l2YmT2HGH7fPG2lasXMHVV1/OoYcciPufj4P3CN6TGFuMNBJjEyKBRB3M0EiMLTEaQhxbPJcYW5I9L/q109A6S63ZbbfdeXH6S5w1/CzKbVVvrE1lFiX94MMPOPTQA7n11ptYtnw5EmOLj0ZibEJ0kKiDGRqJsSVGQ4hji+cSY0uy50W/dhpaZ3NoWrRqw9nnXMDEZ6ew81/+ukqsrWYhUk06neaOsbfSr3cP3nv3n2gkxhYHjcTYBEEQBEEQhFiz4447MfHZaVx+6ZW0KbdWuxApGtxPP+bQQw/m+lFXs2zpspqdSIwtuhqJsQmRQKIOZmgkxpYYDSGOLZ5LjC3Jnhf92mlonc2tKbMtTj35JGbOmM0+e+2ZdyFSFDzyyEP06bM/8+fPkxhbhDUSYxOig0QdzNBIjC0xGkIcWzyXGFuSPS/6tdPQOsPyfMuttuGRR8Zz1VU30LLVWqtdiBSt+fqbLznssIM577wzWPL7b2gkxhY1jcTYBEEQBEEQhERhWRZHHHk0Cxa8Ts/9D8g7YxvAk08+Ts+e3Zkze3Z1GxJji45GYmxCJJCogxkaibElRkOIY4vnEmNLsudFv3YaWqcJnm+y8cY8+sh47hhzB23XXyfvjG0//vQ/hp9xMsPPOIXvv/8fEmOLhiZKMbZU2AcQNRzHORI4FdgFsIGPgYeAca7r+mHvr8HY5WjfB9KAhVI2AMEj/0xbqTRhjm2aptA+OVEM42oQzxvmn4nHJ54XoGmCf8bUkEDPS3HtNLFOgzxXCvofOJD2e3fgmlFXMuH5KVjKx1Zp0pkZ2wC01iitmT59Kgvnz+XykaPoN+BgUsqujkHVjbH52iOISqlammxbqTRhjm2appA+psTY5GanATiOcydwGrACmANUAT2AsUAPx3EObcgNSrH3JwiCuXzi/of3317I0iW/gWpBx6492H6brcM+LEGIPauce90OwHF2CvuwEkPbtuszZsw4+g8YwmX/GMHPP/6AwsfXGh38ZlwdbVv822LOO/9Mpk2fylVXXcO2W21VvR+JsRmoiUiMTW52CsRxnEMIbky+B/Z1XffTTPtGwMvAQOAM4LYw9tdoch7vk31cDbXbSqUJc2zTNAX3sdG50QKTahDP69Usem0Rt4+9ndfffJ2UpbCVwtOa9NWX037v9pw5/Ew6dt7X6BrE86xG1Zx/if9ZmF/n6s69FVdezj77dOTcM8+iU/v2ka/TCE0BfQ7o0YN99niJm0Zfy/inniBtgUftaJulUyhLMf+VefTpvT8XjbiIY4Yeu8YYG1CrrVSaMMc2TVNIH1NibPKZncK5OPN6YfbGBMB13R8IYmgAFzmOU+jPtNj7axwyY48ZmjBnFDK0zjhonn76aY477u+88eYbVKbB8wE0ng+VaXjjzTc47ri/M2HCBGNrEM9ra2jKfgypIQme5zv3PB8WvbaIY4YeycRnn450ncZoCuyz1trrcOWV1/DYYxPY4k9b5Z2x7Y+lS7jkkvM5ZGA/Pvv0E2Q2NrM0MhtbzHAcZ3NgD6ASeKbu+67rzgf+D9gYaN/c+xMEwTwWLJjHJf8YUZ1LXx1a+1x8yQUsWDCveQ5MEGJOoeee7/tceunFLFq4oJmOTMjSvkMn5r68iNNOGU4L5eWdse2NN1+nY4eOjBkzhnQ6HTSaHt1KiiYiMTa52SmM3TKvH7muu3w1mrfqaJtzf43HoNlbjJtdxtCfBabWIJ7X0tx6y+hqv2wLylNgW9S7rfAZc+uNxtUgnq+qoSn7MaSGuHu+pnMv90trzdixYyJZp1GaRvRpVdGCKy4fyfMTn2fHHbbLO2NbZbqKa669lkGDDuL99/9F3aiUSTOQJUUTpdnY5GanMLbOvH6ZR/NVHW1z7q/xROCxdyI0EmOLlcb95BMWvbZoldhavu2FixbifvKJMTWI5xJji2KdhZx7uV+gef2N1/nks88jVadxmibsd5dd92DixKmcccZ5wcxvObE2rbNfQbTtww/fp3/fHtx88w2sXLnCyOhWUjRRirHJBAWF0SbzujSP5o/M61oh7K9RlJWlaNd2LfyqMvBWgt0Cu0UrALyVdnWbVVZREk2p9htFTeF9VgDQtu1axtUgntdoJjz3RqPOyXfffYMOe/3FiBrE8/o0y6rPP/lZmFln48+9N+mw9y6RqdM0TdP3W85VV1/BEUOP5vSTTuS9d99FUf+MbZ72uOvO25kzZzZ33nEbXfftDMDyyjRVaZ+ylEWLMpuVVV71dsvyVFE0pdpvFDWF9Mm2hY082Uk0GvzVR6Oq20qlCXNs0zSmH5/U2SDNkt9/q47JwJpjbFntkt9/M6YG8Vx+FlGss5BzL/cr2/bH779Gqk7jNEXa787O9rw0eybXXj2S1hUtsC2CKFvujG1WMGPbp599Rs9evTnrrLP5/fclNTO2aUin/VrbqyxO2khNqfYbRU0hfUyJsZlxy2U+2acsrfNosk9rloSwv0ZRVeWx+NeV6KoV4FWCXY5VHvzD9CuXVbepsoqSaEq13yhqCu2zwTplgObHn5YaV4N4nlOn1aI6JmPbNVEalVr9tueDslrw409LjahBPF9Vs/7advX5l/Sfhal1FnLuBfG12hqsilrnnul1mqYp9n4HDz6a9h17cP7FF/LKqwuCaFtmMVLfT2NZFkprfO0zduwdTJ40meuuH0P7zp0psy3KUzaVaY8qz6fMtqgoC37dXVGVrm5rjKZU+42ippA+FWUpNtpwbcJGnuwUxheZ1y3zaLaoo23O/QmCYBBdunRt1n6CIAQ0/tzbt8hHIjSVLf60JRMmTOaWm25j/TYVeWds+/qbrxg67AguuegCfl28OGjUgSSrqbetMZpS7TeKmkL6GIDc7BTGu5nXnR3HabkazV51tM25v0aig7+m+Omar8xj5VptpdKEObZpmgb00Z6hNYjn1V/ODjvQsUNHUhakLEUqE6XJt92pYyecHXYwpgbxvH5N9fknPwsj6yzk3MtG2LJtHfbZhx222zZSdRqnKdF+FT5HHXkU/3zzNfr3OYCyzIxtlqVIqewH4q1M1M1i0pTn6dl7f6ZOnYqnNZ6v8bSujljltqU9v8GaxvSJq6aQPqbE2ORmpwBc1/0aeAcoBwbXfd9xnK7A5sD3wGvNvb/GojX4ykJ7HtpLB68EK4TntpVKE+bYpmkK7eN7abRXZWQN4nltzTnnnE/KtrEVWEqRTgev9W2nbJuzzz7PuBrE89qa3PMv6T8Lk+tc07nn++D7wfcpy2L4qcMjWadJmlKPvfGG7Xjo/nu47ZbbWH/d9SlXPuUqTbnyQftoNMqyUZbNL7/8zMmnHMvpZ57K9z/8iOf5waMGBZ7n4/l+dVvd7UI0jekTV00hfUxAbnYKZ1Tm9QbHcbbLNjqOsyFwV2bzetd1/Zz3RjmO87HjOKNYlQbvr9goBZb2USkbZZUFr9lLS05bqTRhjm2aptA+lp1C2WbWIJ7X1nTp0pUrrxqFj4WvNakU+FqT9qm17WNx1dXX06VLV+NqEM9ra3LPv6T/LEyuc03nnmWBZYFGcfnIa+jQZd9I1mmSpjnOPStVTp8DD2bunFcY0L8flp/G8quwlUah0L6H9n2072Epi9kzXuCQQX2YMuk5tB9E32zbwrYsbNuqd7sQTWP6xFVTSB8TkJudAnFddyIwDtgY+MBxnKmO4zwHfAr8GZgEjK3TbRPAybwWY39FRoFdnv3kZvCamfe+VlupNGGObZqm0D5WCmWnzKxBPF9FM/iwo7jnvkfZY699qExD2id4/J/50PQee+3DPfc9yqFDjjS2BvE8R5N7/iX9Z2F4nfnOPc+Hfdp34qGHxzPo0MMjXacxmmY89zbYcCNuuWUs4+66lw032gTPB6198D0sbyX4Hr6fRmufX3/5hREXns1RRx7CN998hZ1ZC8a2FCnbqrVt1fm+Pk1j+sRVU0gfWWcngriue5rjOK8CpwNdARv4GHgQGNfQpzDF3p8gCObRsWNHOnbpxqf/77+8//ZCli75DVQLOnbtwfbbbB1k1AVBKDqrPfe6HYDj7FQzC5gQSbp1686LM+dy3egbeOThBymjijJtoQjW5yFnEoN581+mW9eOXHjxFRzx92NAZ/7WLxMUNE0TkQkK5GangbiuOx4YX6B2GDCsWPsrCV7NvPdk572H2m2l0oQ5tmmagvvYBA/sDaxBPM+r2WG7bem491/QXpqfFy8HKxV8CNeQ4xPPC9GomvMv8T+L6NRZ99xTZS1Be7GrM96e13/urd2mNTeMupGBAwZxyYgz+OarL0kpjWdb+Dlr8yhlsXzFcq665nKmzZjGqGtHs8MO22d+H1doqP4wfW5b2vPzbhfSJ66aQvrIBAWCGdjlaCw0Go1F9pFxrbZSacIc2zRNA/pgag3iecP8M/T4xPP8mib5Z0gNSfW86NdOQ+uMq+f5zr32HToz9YU5HHfcSWgs0p4Pvo+t0+D7aJ390rz7ztv077c/d905hnS6Co0OPsNsZWd3o7otZVt5twvpE1dNIX1MibFZYR+AIAiCIAiCIDSFlhUVjBhxEc8/N5Wdd9qJMqpqr80DwWMHBZVVVVx//TUMOrg///7ww+JHt5KiiUiMTW52kk7mcbAiJxqVebxf3VYqTZhjm6ZpQB9MrUE8b5h/hh6feJ5f0yT/DKkhqZ4X/dppaJ1x9bzQc2+XXXZhxguzuODcC2hRZpFSGtsisxZPTbTNslL8++OPGDLkYG688XqWLluG72saGt1aU5+4agrpIzE2wQxi/tg7MhqJsSVGQ4hji+eljdIk7WcRtTqLfu00tM64et6Qc6+sohWnnX4WkyZN52+77p4zY1tNtC2YsU3jeR7j7r6Dnvvvy1tvviYxNomxCYIgCIIgCIL5bLvtdjw9YRLXXHM9rVu2XDXalom1oeHz//cZAw7uy2WXX8LypcuC9wjeMyY2ZppGYmxCJIj5Y+/IaCTGlhgNIY4tnjdflCYJP4uo1Vn0a6ehdcbV88aeeylLccLxJzLv5Vfp0rEzFj4ppYMnE3VmbFPKYvwTj9Kv/wHMn/+yxNjyaCTGJkSHmD/2joxGYmyJ0RDi2OJ580Zp4v6ziFqdRb92GlpnXD1v6rXzT1tuwyOPPsl1191A6zbr5J2x7btvv+XY447mnLNP49fFv6AJPzZmmkZibIIgCIIgCIJgEEopBg0azOxZL9OvT9+8M7ah4dlnJ9CzZ3dmvjgdnfO+0dGy5tRIjE2IBDF/7B0ZjcTYEqMhxLHF8/CiNCbVkFTPi37tNLTOuHpezGvnhhu24/57H+DusXezYdv1887Y9svinzn77OGccuqJfPfd95gcLZMYW/3IzU7Siflj78hoJMaWGA0hji2ehxylMaSGpHpe9GunoXXG1fNSXDt79enP1KmzGDDw0LwztqE1s2fNoHvXDkyc8DS+9o2MlkmMrX6ssA9AEARBEARBEMJgvfXW5aYbb+PJJyeyxWab552x7fc/fuPCi87l+GOP4asvv6jZiUnRsubUSIxNiAQxf+wdGY3E2BKjIcSxxfOQozSG1JBUz4t+7TS0zrh6XuprZ/du3Zn38qsce8xQVjdjm2WlUJbitdcX0q9fT+655y6q0mlMiZZJjK1+5GYn6cT8sXdkNBJjS4yGEMcWz0OO0hhSQ1I9L/q109A64+p5c1w726y1DpdfcS2PPz6BLbfaepUZ23JjbUuXLeWKKy7h4IN68/mnn6AJP1omMbb6scI+AEEQBEEQBEEwhT333JNpL8zhzOFnU6H81cbaAN5+55/079+Tu+8aS2VlZdBoevysWBqJsQmRIOaPvSOjkRhbYjSEOLZ4HnKUxpAakup50a+dhtYZV8+b+9rZskUZF194IVMmT2XnnXbMuxBplZfmtttvYeDB/XnnnXcwOX4mMTYhecT8sXdkNBJjS4yGEMcWz0OO0hhSQ1I9L/q109A64+p5WNfOP++8CxMmTOLccy/CtstWuxApWvOfjz/ioAN7cv3117B8+XIj42cSYxMEQRAEQRAEoZqyshSnnnYGc16aR4fd/5Z3IVJf+9x37zgGHNSbN15/rWYnJsXPiqWJSIwtFfYBCCGT83if7GNcqN1WKk2YY5umKbiPjc595G5SDeJ5w/wz8vjE8/wa1TT/jKghqZ6X4NppZJ1x9byJ516RNNtuvRUTnp7IE489xA03jibtLcWjzoxtOpix7auvv+LIowZz1OFHcemlV1BW0QpYfQQMqNVmuqaQPhJjE8wg5o+9I6ORGFtiNBJjM+z4JMaWGM8lxhZtz424dtrlKCvF0UcPZebMuezbdb+8C5FqrXn00Qfo1rUD8+bOQRN+/ExibIIgCIIgCIIg5GXTzbZg/PhnueO2u2i7dqu8M7Z9+93/cfwJx3DBeWfzy8+/BI2mR9QK0UQkxiY3O0kn5rO3REYT5oxChtYZVw0hji2eN13TJP8MqSGpnhf92mlonXH13IhrZ51thc+QwUOYPfNl+vbunXfGNmUppr0whZ69u/P888/h6+COyMSZ1mQ2NiFexPyxd2Q0EmNLjIYQxxbPQ47SGFJDUj0v+rXT0Drj6rkR187V9Gm34YaMGTOWsWPvZf3125FvxrZffv6J008/gVNOOY4ffvjeyIiaxNgEQRAEQRAEQahFr979mP/KQo44dGDeGdsAXpo9i/79evLU+PHBjVBGY0xErRBNRGJsMhtbotHgVYKfDr6URXY2klptpdKEObZpmoL7KLSXaTOtBvG8Yf4ZeXzieX6Nbpp/RtSQVM9LcO00ss64et7Ec68Z61y3TWtuHDWag/v04dIrLuOzL7/Cw8KyFCmlSFsKdAplWfyxbCn/uOxCpk19ltGjb2WjzbbA8zVKadKej6d19XY2EpbbFqamkD4SYxNCR2vwlYX2PLSXDl4JpnfMbSuVJsyxTdMU2sf30mivysgaxPOG+Wfi8Ynn+TVN9c+EGpLqeSmunSbWGVfPTbl2NqRPx/b7MGXydI474WQUmnLlU67SlCsfrT2UZVd/vbrwFXrs15n7H7qfynQVnueDAs/z8Xy/eru+tjA1hfQxAXvkyJFhH4PQ/AwDtvK9NCuXrQClURpUWQrLLgv+bep0TZuySqMp1X6jqCmwT+tW5YBmeZU2rwbxvGH+GXh84nl+TasKu2n+GVBDUj0vybXTwDrj6nmTz72Q6mzRqhU99u9Dt67deO+fr/H74l/Qfjq4QVLBxAXaS4PvkfbSLFq0kNdef43d/7Yb7dq1QxOQsi3KrOD5hJeJvKVsC1up0DSF9CmzLNZqU5FR8SXwMCEgT3YSjQK7HFQKbDt4zXzYrlZbqTRhjm2aptA+Vgplp8ysQTxvmH8mHp94nl/TVP9MqCGpnpfi2mlinXH13JRrZyP3u+deHZg8dRYnn3QKStmkPR98D8tbCb6XM4mBz7/efYcBB/XitjE34qWrsCyFnfnwf+73tqWCm4467c2lKaSPTFAgCIIgCIIgCAmgRXk5Z599HpMnT2fXXXahjKrVTmJQlU5z8803cPBBffjX++9R/Qglo5EJChqG3OwkHYPmq2/WsU3ThLlWhKF1xlVDiGOL5yGv9WFIDUn1vOjXTkPrjKvnRlw7i7DfP/95J6ZOns7FIy6hZYsUKaWxLarX4gGFshSWlcL91OWIIw7h2uuu4o+lS2WdnUYiNztJx8D56hOpCXOtCEPrjKuGEMcWz5uuaZJ/htSQVM+Lfu00tM64em7EtbNI+021aMlJJ5/G5MnT2X2PvfB80Nond30e30+jtcb3fB544B56dO/Ea4telXV2GoEV9gEIgiAIgiAIQtLYaqttePKp57j++ptZq3WbVaNtmVgbGr786gsOOfRALrlkBEuX/BG8R/CexNjyIzc7SSfij4Njo5EYW2I0hDi2eB5ylMaQGpLqedGvnYbWGVfPjbh2lmC/toJhQ4fxyvyF7Ne1GxY+KaWDJyc5sTaVmb3t6afH07ffAcyZ85LE2ApEbnaSTgweB8dCIzG2xGgIcWzxPOQojSE1JNXzol87Da0zrp4bce0s4dibbvYn7n/gMW688RbWXme9zIxtNbG2mhnbNP/74XtOPGkoZww/kZ9/+gmNxNjyYYV9AIIgCIIgCIKQdJRSHHjgwbw4cx4DBwzIO2MbGiZPfp7evboxdcoktM55iiIxtlrIzU7Sidnj4MhqJMaWGA0hji2ehxylMaSGpHpe9GunoXXG1XMjrp3NNPaGbdfnrrF3c++4+9l4w7Z5Z2xb/NuvjBhxLiedeCzffPONxNjqQW52kk5MHwdHTiMxtsRoCHFs8TzkKI0hNSTV86JfOw2tM66eG3HtbOaxD+jZm6lTZ3HokCPyztiG1syZO5tuXTvw2KMPBXE3JMaWxQr7AARBEARBEARBWJV11lmbUdfdxDPPTGKrP/0p74xtfyxdwoUXncvRRw7mqy++CN4jeE9ibEJyScDj4EhoJMaWGA0hji2ehxylMaSGpHpe9GunoXXG1XMjrp0hjt2lcxfmznmVk44/kZRF3hnb3njrDQYM6MM9941jRWUlEmMTkk2CHgcbrZEYW2I0hDi2eB5ylMaQGpLqedGvnYbWGVfPjbh2huxD6zZrcfElV/Dkk8+wzbbb5Z2xbcWKFdxw/TUMPLAPH//7IzQSYxMEQRAEQRAEwXD+9rfdmDJ1NuedcwEtLT/vjG3vf/AuAwb04Y7bbmHlypU1O5EYm5AYEvo42DiNxNgSoyHEscXzkKM0htSQVM+Lfu00tM64em7EtdMgHyrKU5x/7rlMmzqDv/1157wztnna565xYznowN68+dYbEmMTEkaCHwcbpZEYW2I0hDi2eB5ylMaQGpLqedGvnYbWGVfPjbh2GuiDs+OfefLJ57jwwsspK6/IO2Pbp599woCDenPFFRezfNkyNBJjEwRBEARBEATBYFIpmxNOPJmXX15I546d8s7YprXmvvvupk/v/Xh90aLgPYL34hpjS4V9AELI5DzeJ/uYFGq3lUoT5timaQruY6NzH2mbVIN43jD/jDw+8Ty/RjXNPyNqSKrnJbh2GllnXD1v4rkXmTqbptl6q614ZsLzPPX4g9w0+lp+W7IE7/+3d+/hklTlocbf6t57uAyD3JSLEjWoS42CQwTUqNyi4jWoGFFAQeUIouIJJwGNCEGNeIuRi+AVlCDeEVQQURGj0RwUFD2GJXrEC4oiAQUUmN3d+aNqMz3b2T3du7t2r6p6f88zT+2u/qpqrfqmavaa9XV1u0V3wRPbAH7xy19w2IsO5oBnHcDxrz2BTTdbBaxbfjZqGdvCbSxjUxqcDk4jxjK2xsQkUYphzpccYxlbdftpGVu1c57EvTP1PGRtWu1ZDnzeIXzus19gr733HfjENno9Pvbx89hnr8dw6SWX0MMyNkmSJEmJ23a77XjPez/IGae/h223XDXwiW2/vvEGjjjyRbzq6KO48Tc3rt1JTcrYHOw0nU81SSNmmk8USrSfdY1hisc25+PHjJW/RPrQ1JxP/N6ZaD/rmvMk7p2p52FBTIse+z/j6Xzh81/mGU996sAntmWtjEsuuZj99tubj370PDobKFvzaWyqDqeD04ixjK0xMUzx2OZ8yqU0ifShqTmf+L0z0X7WNedJ3DtTz8MiMVtvsw1ve/s7efe7z+Je2+4w8IltN99yM0cffSSHHPwcfvnLX9DDMjZJkiRJidtn3ydy+Ve/wQsOOXTgE9sAvnzZl9jviftw7jkfotvt5istY1MlOR2cRoxlbI2JYYrHNudTLqVJpA9NzfnE752J9rOuOU/i3pl6HoaI2XyzzXjrm9/GR879GPfd8d7MZL18lmbBE9uyrMUf7vgDb3jjiTzvwAP44Q+vxTI2VZPTwWnEWMbWmBimeGxzPuVSmkT60NScT/zemWg/65rzJO6dqedhhJg9HvUYLrjgEl704pfSzUcmiz6x7Ypv/SdPesLjOfPdp7Nm7i7L2CRJklSyQGYAACAASURBVCSlbdNNN+bVr34dF3328zzsQTsNfGLbnWvu5K1v/mf+9jnP4gff/97d71ehjM0vFW20HnTugu5c/idr5VOgsO66smKmeezUYobeJqPXKdal1gdzPlr+kmyfOR8c0xsvf0n0oak5L+HemWQ/65rzMa+9yvRzOjG7PPzhXPjpz/K+M0/h1HedzlynS4cWrVbGTJYx18qgN0PWanFN/C/2f+bTOeKlR3LEUUfTnpkly3p3l6x1ej063d4666atNe0GaHp6PehmLXqdDr3OXL4k/5bi/nVlxUzz2KnFDLtNtzNHr7MmyT6Y89Hyl2L7zPngmHHzl0IfmprzMu6dKfazrjlP5d6Zeh7GiZltZRz50iO48PzPsnr1rtDrsiLrsiKbY0XWpdfrkLXaZK02XeCUU/+F/f/mKXzrym/R6XQh/8gPnU6XTre7dl0C2ieeeOK026Dldyhwv25njjv/cAdkPbIeZLMztNqz+d/N3tzadVmrnJiy9lvFmCG3WbnpCqDHH9f00uuDOR8tfwm2z5wPjtl04/Z4+UugD03NeSn3zgT7Wdecj33tVaSfKcRsve22HPj8Q9lyi3vw7f/8j3yQ2Z3LB0hZ/uCCXmeOrNfj5ltu4cILL+CWW27iUbs/ihWzK+gUJXAz7RazrRarNtuYwk+Bs5kCZ3YaLYP2CshmoN3Ol8UH19ZZV1bMNI+dWsyw27RmyNozafbBnI+WvxTbZ84Hx4ybvxT60NScl3HvTLGfdc15KvfO1PMwoZj2zAoO/18v56KLvsjuu+1Bp9tjrtOFbodW507odorv5unSnVvDhz74AfbZ+zF89fLLaBcPKphfpmBm2g2okhBCAI4H9gG2Bm4ALgJOijH+asR97QVctoGwR8cYv7mEpkqSJElLtuOf3Zezzz6Hj3/yU5zw+pO444+3MttrkdGlu+C7eX7+i59x4POexXMOPITjjnsdW2+15bSbfzcHO0MKIewJXAxsAlwJfBXYBTgCeHYI4bExxh8uYde/Bj6/yHs3LqWtI+n7/gHmn9EO664rK2aax04tZuht2vToe55+Sn0w56PlL8n2mfPBMdl4+UuiD03NeQn3ziT7Wdecj3ntVaaf6cVkWYvnPve57L3PX3P8a4/lK1/+AjNZj067RXfBd/MAfOpTH+erX/t3Tjz+JP7m6U8nBQ52hhBCWAl8hHyg84oY42l9770NOAY4L4TwyBhjb8TdXxNjPHRijR1VewW9bheYA1pkWRuA/Jn2xbqyYqZ57NRiht2m77sGkuuDOR8tfym2z5wPETNG/pLpQwNzXsa9M8V+1jrnCdw7U89DiTH32mFHzjjz/Vxy0ac5/sQTuP43N9HKurSzOeaK7+YB6PV63HTjjRz18sP5zAVP5FOf/BgbbbQR0+RndoZzGLAdcFn/QKdwLPBjYFfgycvdMEmSJKlsWZax335P5ZIvfIUDDngOs6wZ+N08F130Ga644oppNhlwsDOs/YvluQvfiDF2yGd9+uOqo5i6zPqn93udddeVFTPNY6cWM8I2pNoHcz5a/hJtnzkfHDNW/hLpQ1NzPvF7Z6L9rGvOk7h3pp6HZYrZesstOPUdp3D2+z/IvbffjpmsR7sFWdYiW1DaNjc3N83fcgEHO8NaXSwXG55esSBuFNuGEE4IIbwnhPCOEMKLQghbL2E/S1NM7/eK6eH5p3Sss66smGkeO7WYEbYh1T6Y89Hyl2j7zPngmLHyl0gfmprzid87E+1nXXOexL0z9Tws87nYc699ufDCz/O8g15Ap0teytbt0u7NQV9p27T5mZ0NCCFsDmxVvPzpImE/K5b3X8IhHgycuGDdqSGE42KMpy5hf0ObnZ3hntusortmFjp3Qnsj2httCkDnzvbd61qzG5cSU9Z+qxgz/DZ3ALDNNquS64M5Hy1/abbPnA+O+cNY+UujD03N+eTvnWn2s645H+/aq04/qxMz/3qbLVfwrtNP4+AXHsrhhx/Oz677/2uf2AZQPLhgmqbfgvRt1vfz7YvE3FYsV42w398B7wAeR/55oFXkn/t5H7AxcEoI4SWjNXVUPeguPr1/97qyYqZ57NRiUm+f/fRc2E/Phf1M59ipxaTePvtZ+rl43GP/iquu/DbHvOpoZlsZM1mPmXaLLGPqaj+zE0J4C/CMJWy6b4zx+km3Z16M8SrgqgWrrwIODyFcDZwCvDmEcE6M8c4y2rBmTYebb7mT3po7oHMXtFfQWpF/wKx71x/uXpfNblxKTFn7rWLMsNtsfY9ZoMeNv709uT6Y89Hyl2L7zPngmK02b4+VvxT60NScl3HvTLGfdc35uNdeVfpZpZjFtnn5K/6evR6/N8e+5jiu+v5/Mf/cgmmq/WAH2AEIS9hutlje1rduJfmMzELzsz+3LuE463M68DpgG2AP8u/0kSRJkpL2sIftzKcvuJgz3vNeEpjYqf9gJ8Z4MHDwGNv/PoRwM7AlcF/g6vWE7Vgsr1vqcRYcsxtCuJZ8sHPvSexzUZ2105DMT13CuuvKipnmsVOLGXobv1S02jF+qWhy7Rspxi8VrW4//VLRaufcLxVNLmYD26yYafOqo/83K1dN9zt2wM/sDOvKYrnbIu/vXiwXlqWNY/6JbLcNjBpX26eaJBEzwjak2gdzPlr+Em2fOS/xiVCJ9KGpOZ/4vTPRftY150ncO1PPQ2rnImuzcuXKUn+NHUZr2g2oiAuK5UEL3wghtIEDi5fnT+JgIYRdgAcBPeBbk9inJEmS1DS1L2ObkLOA1wB7hxCOijGe3vfeycBO5LM6F/dvFELYHfgQQIzxwQveeyVwbozxpgXrHz2/DfDRGOOvJtmRP5HwFGijYixja0iMZWzJtc8ytobk3DK2aufcMrbkYobZplg3bQ52hhBjvC2EcCD5YOa0EMJhwLXALsBDgN8Cz4sxLnzmxKYs/nCEk4C3hxC+A/yE/G/GA4Gdi5+/Drx00n35E+0V9LpdYA5okWVtgGLKuFhXVsw0j51azLDb9E3lJ9cHcz5a/lJsnzkfImaM/CXThwbmvIx7Z4r9rHXOE7h3pp6H1M5FsW7aLGMbUozxcmA18GHgPsCzyJ/C9m5g5xhjHHGXbyQfPG0FPAnYH9geuBQ4DNgzxvj7ybRekiRJah5ndkZQDGj+5HM7A+K/Qj5Ls7733gq8dTItG0PqU6BNibGMrSExlrEl1z7L2BqSc8vYqp1zy9iSi6lQGZszO02X+pM8mhIzzScKJdrPusYwxWOb8/FjxspfIn1oas4nfu9MtJ91zXkS987U85DaucgsY5MkSZKk0ljG1nSpT4E2JcYytobEWMaWXPssY2tIzi1jq3bOLWNLLsYyNlVG6lOgTYmxjK0xMUmUYpjzJcdYxlbdflrGVu2cJ3HvTD0PqZ2LzDI2SZIkSSqNZWxNl/oUaFNiLGNrSIxlbMm1zzK2huTcMrZq59wytuRiLGNTZaQ+BdqUGMvYGhOTRCmGOV9yjGVs1e2nZWzVznkS987U85DaucgsY5MkSZKk0ljG1mg96NwF3bn8T9ZaO+XYv66smGkeO7WYobfJ6HWKdan1wZyPlr8k22fOB8f0xstfEn1oas5LuHcm2c+65nzMa68y/axQzDDbJFLG5mCnwXo96GYt6HSgMwdFTTNAr3/dbEkxZe23ijFDbtPtzEFnDb1OJ70+mPPR8pdg+8z54Jix85dAH5qa81LunQn2s645T+bemXoeUjsXxbppc7DTYFkGrV6X3kwberMw0yajl7/Xv66smGkeO7WYIbdptTei15slm+ml1wdzPlr+EmyfOR8c02rPjJe/BPrQ1JyXcu9MsJ91zfnY115F+lmpmGG2KdZNm4OdRsugvQK6XWh3IZvh7g+TZTNr15UVM81jpxYz7DatGbJ2B7Juen0w56PlL8X2mfPBMa32ePlLoQ9NzXkZ984U+1nXnI977VWln1WKGWYbH1AgSZIkSeWZmXYDNGWpP6O9KTHT/K6IJPtZ15i+/CXZPnM+OMbv2aluP0u4dybZz7rm3O/ZSS5mmG0SeUCBMztNl/oz2psSM83viki0n3WNYYrHNufjx4yVv0T60NScT/zemWg/65rzJO6dqechtXORWcYmSZIkSaWxjK3pUp8CbUqMZWwNibGMLbn2WcbWkJxbxlbtnFvGllyMZWyqjNSnQJsSYxlbY2KSKMUw50uOsYytuv20jK3aOU/i3pl6HlI7F5llbJIkSZJUGsvYmi71KdCmxFjG1pAYy9iSa59lbA3JuWVs1c65ZWzJxVjGpspIfQq0KTGWsTUmJolSDHO+5BjL2KrbT8vYqp3zJO6dqechtXORWcYmSZIkSaWxjK3pUp8CbUqMZWwNibGMLbn2WcbWkJxbxlbtnFvGllyMZWyqjNSnQJsSYxlbY2KSKMUw50uOsYytuv20jK3aOU/i3pl6HlI7F5llbJIkSZJUGsvYmi71KdCmxFjG1pAYy9iSa59lbA3JuWVs1c65ZWzJxVjGpspIfQq0KTGWsTUmJolSDHO+5BjL2KrbT8vYqp3zJO6dqechtXORWcYmSZIkSaWxjK3pUp8CbUqMZWwNibGMLbn2WcbWkJxbxlbtnFvGllyMZWyqjNSnQJsSYxlbY2KSKMUw50uOsYytuv20jK3aOU/i3pl6HlI7F5llbJIkSZJUGsvYmi71KdCmxFjG1pAYy9iSa59lbA3JuWVs1c65ZWzJxVjGpspIfQq0KTGWsTUmJolSDHO+5BjL2KrbT8vYqp3zJO6dqechtXORWcYmSZIkSaWxjK3pUp8CbUqMZWwNibGMLbn2WcbWkJxbxlbtnFvGllyMZWyqjNSnQJsSYxlbY2KSKMUw50uOsYytuv20jK3aOU/i3pl6HlI7F5llbJIkSZJUGsvYGq0HnbugO5f/yVprpxz715UVM81jpxYz9DYZvU6xLrU+mPPR8pdk+8z54JjeePlLog9NzXkJ984k+1nXnI957VWmnxWKGWabRMrYHOw0WK8H3awFnQ505qCoaQbo9a+bLSmmrP1WMWbIbbqdOeisodfppNcHcz5a/hJsnzkfHDN2/hLoQ1NzXsq9M8F+1jXnydw7U89DaueiWDdtDnYaLMug1evSm2lDbxZm2mT08vf615UVM81jpxYz5Dat9kb0erNkM730+mDOR8tfgu0z54NjWu2Z8fKXQB+amvNS7p0J9rOuOR/72qtIPysVM8w2xbppc7DTaBm0V0C3C+0uZDPc/WGybGbturJipnns1GKG3aY1Q9buQNZNrw/mfLT8pdg+cz44ptUeL38p9KGpOS/j3pliP+ua83Gvvar0s0oxw2zjAwokSZIkqTwz026Apiz1Z7Q3JWaa3xWRZD/rGtOXvyTbZ84Hx/g9O9XtZwn3ziT7Wdec+z07ycUMs00iDyhwZqfpUn9Ge1NipvldEYn2s64xTPHY5nz8mLHyl0gfmprzid87E+1nXXOexL0z9Tykdi4yy9gkSZIkqTSWsTVd6lOgTYmxjK0hMZaxJdc+y9gaknPL2Kqdc8vYkouxjE2VkfoUaFNiLGNrTEwSpRjmfMkxlrFVt5+WsVU750ncO1PPQ2rnIrOMTZIkSZJKYxlb06U+BdqUGMvYGhJjGVty7bOMrSE5t4yt2jm3jC25GMvYVBmpT4E2JcYytsbEJFGKYc6XHGMZW3X7aRlbtXOexL0z9Tykdi4yy9gkSZIkqTSWsTVd6lOgTYmxjK0hMZaxJdc+y9gaknPL2Kqdc8vYkouxjE2VkfoUaFNiLGNrTEwSpRjmfMkxlrFVt5+WsVU750ncO1PPQ2rnIrOMTZIkSZJKYxlb06U+BdqUGMvYGhJjGVty7bOMrSE5t4yt2jm3jC25mAqVsTnYGUIIYSWwP7Bb8ecRwKbA52KMTxtz3wE4HtgH2Bq4AbgIOCnG+Ktx9j2U9gp63S4wB7TIiinHfMq4WFdWzDSPnVrMsNv0TeUn1wdzPlr+UmyfOR8iZoz8JdOHBua8jHtniv2sdc4TuHemnofUzoVlbJXyQODfgKOBx5APdMYWQtgTuAo4CPgVcD7wB+AI4LshhAdN4jiSJElSEzmzM5xbgQ8A3wK+DawGzhxnh8Vs0UeATYBXxBhP63vvbcAxwHkhhEfGGHvjHGug1KdAmxJjGVtDYixjS659lrE1JOeWsVU755axJRdjGVu9xBh/DLx4/nUI4aET2O1hwHbAZf0DncKx5GVzuwJPJi9rK0fqU6BNibGMrRkxqZRimPMxYixjq2Q/y7h3ptjPWuc8gXtn6nlI7VxYxtZ4+xfLcxe+EWPskM/69MdJkiRJGoEzO9Ozulhescj7VyyIK0fqU6BNibGMrSExlrEl1z7L2BqSc8vYqp1zy9iSi6lQGZszO1MQQtgc2Kp4+dNFwn5WLO9famOK6f1kv5CqKTEjbMOkj51oP+sawxSPbc7Hjxkrf4n0oak5n/i9M9F+1jXnSdw7U89DauciS6OMzZmd6dis7+fbF4m5rViuKuH4DwCY3WgF99p2BXS7QBdoQauVR3Q3W3ddWTHTPHZqMUNvsxHbbpJoH8z5aPlLsn3mfPGYCeRv6n1ocs5LuHcm2c865jyhe2fqeUjtXKz1AKYk6/XKe9BXCkIIbwGesYRN940xXr/IPg8FzmKJ37MTQtgBmN/3bIxxbj0xDwR+CNwVY9xo1GNswC3APSa8T0mSJGl9fgdsMY0DN2FmZwcgLGG72Uk3pM9tfT+vJP8LsND87M+tJRz/J+TlcbcBPyph/5IkSdIDyH+n/cm0GlD7wU6M8WDg4Gm3o1+M8fchhJuBLYH7AlevJ2zHYnldCU0o96EHkiRJUgJaGw5RSa4slrst8v7uxfKqZWiLJEmSVDsOdqbngmJ50MI3Qght4MDi5fnL1iJJkiSpRhzslCiEsHsI4ZoQwjXrefss4AZg7xDCUQveOxnYiXxW5+KSmylJkiTVUu2fxjYpIYTzge2Ll/cE/pz8qWaxL+z1McbP9W2zF3AZQIwxW88+9yQfzGwCfBu4FtgFeAjwW+CxMca4cDtJkiRJG+bMzvBWA3sUf/68WLdF37o9yAdBQ4sxXl7s98PAfYBnkT+x4t3Azg50JEmSpKVzZkeSJElSLTmzI0mSJKmWHOxIkiRJqiUHO5IkSZJqycGOJEmSpFpysCNJkiSplhzsSJIkSaolBzuSJEmSasnBjiRJkqRacrAjSZIkqZYc7EiSJEmqpZlpN0DlCCGsBPYHdiv+PALYFPhcjPFpY+47AMcD+wBbAzcAFwEnxRh/Nc6+ta5JnusQwl7AZRsIe3SM8ZtLaGojhRCeDxwJ7Ay0gWuAs4AzYozdae9Pg03qfIcQzgZeOCAkxhgfPEZTVSjuifuR/7v2SOBBQAY8J8b4iTH267W3DCadP6+95RNCWAE8EXgqef52BLYEbgS+AZwWY/zKEvdd6vXnYKe+Hgj826R3GkLYE7gY2AS4EvgqsAtwBPDsEMJjY4w/nPRxm6jEc/1r4POLvHfjUtraRCGE04GXAXcAXwLWAPsCpwH7hhAOGPEX5onuT4OVdL6/DvxoPev9T6DJORI4epI79NpbVhPPX8Frr3yPBz5T/HwDcAVwO/BQ4Nnkv5e8Psb4ulF2uhzXn4Od+roV+ADwLeDbwGrgzHF2WMwWfYT8l+9XxBhP63vvbcAxwHkhhEfGGHvjHKvpSj7X18QYD51YYxsohPBs8pvzDcDjY4zXFuu3JZ89eybwCuCd09ifBivxfL8vxnj2BJuqP/V94K2s/bft/cCeS92Z196ym2j++njtla8LfBJ4Z4zx3/vfCCE8FzgXOD6EcFmMcUNVJPPbLcv152d2airG+OMY44tjjGfEGP8vcOcEdnsYsB1wWf8v34VjgR8DuwJPnsCxms5znbZXF8tj52/OADHGX5P/zyXAcSGEYe+xk96fBvN8V1SM8X0xxn+IMX4sxvjjCezSvwvLqIT8aZnEGL8cYzxg4UCneO+jwNnFy4NH2O2yXH9evBrF/sXy3IVvxBg75DMR/XFaOs91okII9wH+ErgL+PjC92OMlwPXkw9WH7Xc+9Ngnm/N8++CNFFXFcv7DBO8nNefZWwaxepiecUi71+xIE5LV+a53jaEcAJwb/J62+8BF8QYb1rCvppo/pz/vxjjHxeJuYL8/K4G/mOZ96fByjzfe4cQdgY2I/9s3NeAS/28R7K89urDa2/6Hlgsh/2c1LJdfw52NJQQwubAVsXLny4S9rNief/yW1Rfy3CuHwycuGDdqSGE42KMpy5hf00zf84Xyw2Mlp9J70+DlXm+X7CedT8IIRwYY/zeiPtS+bz26sNrb4pCCNsBhxYvPznkZst2/VnGpmFt1vfz7YvE3FYsV5Xclror61z/DngH8DjyaeFV5J/7eR+wMXBKCOElozW1kebzs1huYLT8THp/GqyM8/0d4JXkTyXaDNgBeBrw3WLdF0MI9x69qSqZ1171ee1NWQhhhvzpv/cAvhRj/MwGNpm3bNefMzsJCiG8BXjGEjbdN8Z4/aTbo9Gkmr8Y41WsramddxVweAjhauAU4M0hhHNijJN4oIXUCDHGf12w6nbgcyGES4HLyevNXw28fLnbJtWZ114SziR/VPTPGe3hBMvGwU6adgDCErabnXRD+tzW9/NK8lmCheZH6beW2I4qGDd/0zjXpwOvA7YB9iD/Th+t33x+Vg6IGSU/k96fBlu28x1jvCuE8CbgAuAp4+xLpfDaqymvveURQngn8GLyR0fvG2O8YYTNl+36c7CToBjjwSQ2Oo4x/j6EcDP5t+XeF7h6PWE7FsvrlqtdKRo3f9M41zHGbgjhWvLBjlP+g11XLO87IGaU/Ex6fxrsumK5XOf7mmLpdZWe64ql1149ee2VKITwdvISwhvJBzrXbmCTha4rlqVff35mR6O4sljutsj7uxfLhaVSGt00zvXWxfK2gVGaP+d/EULYZJGY3RbELuf+NNhyn2+vq3R57dWb115JinL9vwNuAv46xviDJexm2a4/BzsaxQXF8qCFb4QQ2sCBxcvzl61F9bWs5zqEsAvwIKBH/s3WWkSM8efkg9EVwHMWvh9C2JP8ewZuAL6x3PvTYFM4339bLBd7jLymxGuv9rz2ShBCOBn4e+Bm4AkxxvVVn2zQcl5/Dna0jhDC7iGEa0II16zn7bPI/9LtHUI4asF7JwM7kY++Ly65mU2wpHM9KH8hhFeGELZez/pHA58oXn40xjjsM/Kb7E3F8s0hhAfMrwwh3At4V/Hy5P7veAghvKnIzZv4UyPvT2OZWP5CCI8IITyt+E+I/vUzIYRjyMs8IH8SoqbAa6/avPbSEUJ4A3AscAv5QGeDMy4pXH9+ZqfGQgjnA9sXL+9ZLP8qhPDNvrDXxxg/1/d6Uxb5cH2M8bYQwoHkv2CfFkI4DLgW2AV4CPBb4Hkxxt4Eu9FIY5zrRfMHnAS8PYTwHeAnQEb+JWA7Fz9/HXjppPtSRzHGT4QQzgCOBL4XQvgisIb8iTSbA58GTluw2fbkudl+wfql7k9LNOH83Y98hvW/QwhXAr8hL595OPnDSrrAP8QYLymnN80SQtiVtb8EQf54YYB/DiH8n/mVMcb+b1z32kvEhPN3P7z2lk0I4RnAPxYvfwS8IoT1/rpxTYzx5L7XU7/+HOzU22r+9INfW5A/bWvePRlBjPHyEMJq8id37Ut+U/k18G7gn5wVmJwSzvUbyb9j5y/IS9Y2Bf4buBQ4DzgnxtiZUPNrL8b4shDC14CjgD2BNvkHYj8AnDHq/0RNen8abILn+7vAO8k/R/dQ8musB/yCfIb29Bjjtyfc/CbbnHX/DZv3wPWsG4rX3rKaZP689pbXVn0/P7L4sz6Xk1egDGU5rr+s1/M/4SVJkiTVj5/ZkSRJklRLDnYkSZIk1ZKDHUmSJEm15GBHkiRJUi052JEkSZJUSw52JEmSJNWSgx1JkiRJteRgR5IkSVItOdiRJEmSVEsOdiRJkiTVkoMdSZIkSbXkYEeSJElSLTnYkSRJklRLDnYkSZIk1ZKDHUmSJEm15GBHkiRJUi3NTLsBkiQtpxDCw4ArgI2BLvCEGOOXB8TvDnwNmAXmgMfFGL+5HG2VJI3HmR1JUqPEGL8PHFO8bAHnhBC2Xl9sCGEV8GHygQ7ACQ50JKk6HOxIkhonxvgu4ILi5Q7AWYuEvgvYqfj5K8DJ5bZMkjRJDnYkSU31YuD64uenhxBe3v9mCOEQ4ODi5U3AwTHG7jK2T5I0pqzX6027DZIkTUUIYS/gS+T/+XcHsEeM8eoQwgOAK4FVRegzY4yfnk4rJUlL5WBHktRoIYQ3AP9YvPwB8BjgUmC3Yt2ZMcYjp9E2SdJ4LGOTJDXdicA3ip8fClzN2oHOD4C/m0KbJEkT4MyOJKnxQgj3A74D3KNv9R3A7jHG721g2wA8EfjL4s9DgDbwTzHGE8toryRpOM7sSJIaL8Z4HfCmBatP3NBAp3AkcArwQuBh5AMdSVICHOxIkhovhLAF8LIFq58cQhjm38nvA28DDiKf1Tlnws2TJC3RzLQbIElSAt4L/Fnxcw/IgD2BVwNvHLRhjPF9/a9DCD6eWpIS4cyOJKnRQggvAQ4oXt4APBVYU7w+MYTwqKk0TJI0Ngc7kqTGKh4u8K/Fyx7wwhjjxcBri3UzwIdDCJtPo32SpPE42JEkNVIIYQVwHrCyWPUvMcYvFD+/Ffhi8fP9gXctc/MkSRPgYEeS1FQnA6uLn68EXjP/RoyxB7wA+G2x6qAQwiHL2zxJ0rgc7EiSGieE8CTgVcXL24Hnxxjv6o+JMf4KeFHfqtNDCDstUxMlSRPgYEeS1CghhHsBHyR/4hrAq2KMcX2xMcbPsLaEbRX553d8kqkkVYSDHUlSY4QQMuBsYNti1ScXPjp6PY4h/y4dgN2B15fTOknSpPm/U5Kkxig+i/OUXLlN7AAAAOBJREFUEbe5A3h4OS2SJJXJmR1JkiRJteRgR5IkSVItZb1eb9ptkCSpskIIu7Lu9/DsBGwDXA/8om/9M4snvEmSlomf2ZEkaTybA3usZ/29iz/zNlqe5kiS5jmzI0mSJKmW/MyOJEmSpFpysCNJkiSplhzsSJIkSaolBzuSJEmSasnBjiRJkqRacrAjSZIkqZYc7EiSJEmqJQc7kiRJkmrJwY4kSZKkWnKwI0mSJKmWHOxIkiRJqiUHO5IkSZJqycGOJEmSpFpysCNJkiSplhzsSJIkSaolBzuSJEmSaul/AJuGy2+sUM7QAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb598807278>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 286,
+       "width": 413
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Solution\n",
-    "# Calculating Boolean OR using a perceptron\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",
-    "X=[[0,0],[1,0],[0,1],[1,1]]\n",
-    "for i in X:\n",
-    "    print(\"Perceptron output for x1, x2 = \" , i , \" is \" , perceptron(i,w,threshold))\n",
-    "# Plotting the decision boundary\n",
-    "perceptron_DB(X,w)"
+    "# (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)"
    ]
   },
   {
@@ -363,14 +581,15 @@
    "metadata": {},
    "source": [
     "In fact, a single perceptron can compute \"AND\", \"OR\" and \"NOT\" boolean functions.\n",
-    "However, it cannot compute some other boolean functions such as \"XOR\"\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 created above?\n",
+    "Hint: Think about what is the significance of the NAND gate we have created above?\n",
     "\n",
-    "We said a single perceptron can't compute these functions. We didn't say that about **multiple Perceptrons**."
+    "Answer: We said a single perceptron can't compute a \"XOR\" function. We didn't say that about **multiple Perceptrons** put together."
    ]
   },
   {
@@ -382,29 +601,18 @@
     "<center>\n",
     "<figure>\n",
     "<img src=\"./images/neuralnets/perceptron_XOR.svg\" width=\"400\"/>\n",
-    "<figcaption>Multiple perceptrons put together to output a XOR function.</figcaption>\n",
+    "<figcaption>Multiple perceptrons connected together to output a XOR function.</figcaption>\n",
     "</figure>\n",
     "</center>"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### Multi-layer perceptrons\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Google Playground\n",
-    "\n",
-    "https://playground.tensorflow.org/\n",
+    "### Multi-layer perceptrons\n",
     "\n",
-    "<img src=\"./images/neuralnets/google_playground.png\"/>"
+    "The normal densely connected neural network is sometimes also called \"Multi-layer\" perceptron."
    ]
   },
   {
@@ -413,265 +621,431 @@
    "source": [
     "## Learning\n",
     "\n",
-    "Now we know that we can compute complex functions if we stack together a number of perceptrons.\n",
-    "\n",
-    "However, we definitely **DO NOT** want to set the weights and thresholds by hand as we did in the examples above.\n",
+    "Now we know that we can compute complex functions by combining a number of perceptrons.\n",
     "\n",
-    "We want some algorithm to do this for us!\n",
+    "In the perceptron examples we had set the model parameters (weights and thresholds) by hand.\n",
     "\n",
-    "In order to achieve this we first need to choose a loss function for the problem at hand\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 the weights for us!\n",
     "\n",
-    "### Loss function\n",
-    "In order to learn using an algorithm for learning we need to define a quantity which allows us to measure how far are the predictions of our network/setup are from the reality. This is done by choosing a so-called \"Loss function\" (as in the case for other machine learning algorithms). In other words this function measures how close are the predictions of our network to the supplied labels. Once we have this function we need an algorithm to update the weights of the network such that this loss decreases. As one can already imagine the choice of an appropriate loss function is very important to the success of the model. Fortunately, for classification and regression (which cover a large variety of probelms) these loss functions are well known. \n",
+    "This is achieved by choosing an appropriate loss function for the problem at hand and solving an optimization problem.\n",
+    "We will explain below what this means.\n",
     "\n",
-    "Generally **crossentropy** and **mean squared error** loss functions are used for classification and regression problems, respectively.\n",
     "\n",
-    "### Gradient based learning\n",
-    "As mentioned above, once we have decided upon a loss function, we want to solve an **optimization problem** which minimizes this loss by updating the weights of the network. This is how learning happens in a NN.\n",
+    "### Loss function\n",
     "\n",
-    "The most popular optimization methods used in Neural Network training are some **Gradient-descent (GD)** type methods, such as gradient-descent, RMSprop and Adam. \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 hopefully after some iterations reaches its (Global) minimum.\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",
-    "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",
+    "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",
-    "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 very big datasets and using batch gradient descent can make the training very slow!\n",
+    "Fortunately, for classification and regression (which cover a large variety of problems) these loss functions are well known. \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",
+    "Generally **crossentropy** and **mean squared error** loss functions are used for classification and regression problems, respectively.\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",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>mean squared error</strong> is defined as \n",
     "\n",
-    "One pass through the entire training dataset is called **1 epoch** of training.\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",
-    "### Activation Functions\n",
     "\n",
-    "In order to train the network we need to change Perceptron's **step** activation function as it does not allow training using the back-propagation algorithm among other drawbacks.\n",
+    "</div>\n",
     "\n",
-    "Non-Linear functions such as:\n",
+    "### Gradient based learning\n",
     "\n",
-    "* ReLU (Rectified linear unit)\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 weights of the network. This is how the learning takes in a NN, and the \"knowledge\" is stored in the weights.\n",
     "\n",
-    "\\begin{equation*}\n",
-    "f(z) = \\mathrm{max}(0,z)\n",
-    "\\end{equation*}\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",
-    "* Sigmoid\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",
-    "\\begin{equation*}\n",
-    "f(z) = \\frac{1}{1+e^{-z}}\n",
-    "\\end{equation*}\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",
-    "* tanh\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",
-    "\\begin{equation*}\n",
-    "f(z) = \\frac{e^{z} - e^{-z}}{e^{z} + e^{-z}}\n",
-    "\\end{equation*}\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",
-    "are some of the most popular choices used as activation functions.\n",
     "\n",
-    "Linear activations are **NOT** used because it can be mathematically shown that if linear activations are used then output is just a linear function of the input. So adding any number of hidden layers does not help to learn interesting functions.\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",
-    "Non-linear activation functions allow the network to learn more complex representations."
+    "<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": 12,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe71452e0b8>"
+       "<Figure size 720x288 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "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",
+    "plt.figure(figsize=(10, 4)) ;\n",
     "\n",
-    "pts=np.arange(-20,20, 0.1)\n",
+    "pts=np.arange(-20,20, 0.1) ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Activation Functions\n",
     "\n",
-    "plt.subplot(1, 3, 1)\n",
-    "# Sigmoid\n",
-    "plt.plot(pts, 1/(1+np.exp(-pts))) ;\n",
+    "In order to train the network we need to move away from Perceptron's **step** activation function because it does not allow training using the gradient-descent and back-propagation algorithms among other drawbacks.\n",
     "\n",
-    "plt.subplot(1, 3, 2)\n",
-    "# tanh\n",
-    "plt.plot(pts, np.tanh(pts*np.pi)) ;\n",
+    "Non-Linear functions such as:\n",
     "\n",
-    "# Rectified linear unit (ReLu)\n",
-    "plt.subplot(1, 3, 3)\n",
-    "pts_relu=[max(0,i) for i in pts];\n",
-    "plt.plot(pts, pts_relu) ;"
+    "* Sigmoid\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "f(z) = \\frac{1}{1+e^{-z}}\n",
+    "\\end{equation*}"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 39,
    "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": [
-    "# Introduction to Keras"
+    "sns.lineplot(pts, 1/(1+np.exp(-pts))) ;"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### What is Keras?\n",
+    "* tanh\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 very 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 TensorFlow (check reference)\n"
+    "\\begin{equation*}\n",
+    "f(z) = \\frac{e^{z} - e^{-z}}{e^{z} + e^{-z}}\n",
+    "\\end{equation*}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "dense_1 (Dense)              (None, 4)                 36        \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 4)                 0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 4)                 20        \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 1)                 5         \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 1)                 0         \n",
-      "=================================================================\n",
-      "Total params: 61\n",
-      "Trainable params: 61\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
+     "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": [
-    "# Say hello to keras\n",
-    "\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 = (8,) means that the layer expects an input with num_features = 8 \n",
-    "# and the sample size could be anything\n",
-    "# Then we specify an activation function\n",
-    "model.add(Dense(units=4, input_shape=(8,)))\n",
-    "model.add(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()"
+    "sns.lineplot(pts, np.tanh(pts*np.pi)) ;"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### XOR using neural networks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\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"
+    "* **ReLU (Rectified linear unit)**\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "f(z) = \\mathrm{max}(0,z)\n",
+    "\\end{equation*}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe736cb4160>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 254,
+       "width": 383
+      },
       "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",
+    "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": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_13 (Dense)             (None, 4)                 12        \n",
+      "_________________________________________________________________\n",
+      "dense_14 (Dense)             (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_15 (Dense)             (None, 1)                 5         \n",
+      "_________________________________________________________________\n",
+      "activation_1 (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": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb59af71898>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 321,
+       "width": 338
+      },
+      "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": 16,
+   "execution_count": 58,
+   "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": 59,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Building a Keras model\n",
+    "# Building a simple Keras model\n",
     "\n",
     "def a_simple_NN():\n",
     "    \n",
@@ -690,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -699,617 +1073,617 @@
      "text": [
       "Train on 350 samples, validate on 150 samples\n",
       "Epoch 1/300\n",
-      "350/350 [==============================] - 0s 1ms/step - loss: 0.8080 - acc: 0.4886 - val_loss: 0.8082 - val_acc: 0.4600\n",
+      "350/350 [==============================] - 1s 2ms/step - loss: 0.7572 - acc: 0.4257 - val_loss: 0.7601 - val_acc: 0.4667\n",
       "Epoch 2/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.7893 - acc: 0.4800 - val_loss: 0.7937 - val_acc: 0.4533\n",
+      "350/350 [==============================] - 0s 110us/step - loss: 0.7437 - acc: 0.4400 - val_loss: 0.7488 - val_acc: 0.4867\n",
       "Epoch 3/300\n",
-      "350/350 [==============================] - 0s 104us/step - loss: 0.7744 - acc: 0.4771 - val_loss: 0.7807 - val_acc: 0.4400\n",
+      "350/350 [==============================] - 0s 69us/step - loss: 0.7343 - acc: 0.4400 - val_loss: 0.7398 - val_acc: 0.4867\n",
       "Epoch 4/300\n",
-      "350/350 [==============================] - 0s 71us/step - loss: 0.7610 - acc: 0.4686 - val_loss: 0.7687 - val_acc: 0.4133\n",
+      "350/350 [==============================] - 0s 70us/step - loss: 0.7265 - acc: 0.4514 - val_loss: 0.7316 - val_acc: 0.4800\n",
       "Epoch 5/300\n",
-      "350/350 [==============================] - 0s 60us/step - loss: 0.7487 - acc: 0.4457 - val_loss: 0.7580 - val_acc: 0.4067\n",
+      "350/350 [==============================] - 0s 104us/step - loss: 0.7196 - acc: 0.4686 - val_loss: 0.7242 - val_acc: 0.4733\n",
       "Epoch 6/300\n",
-      "350/350 [==============================] - 0s 61us/step - loss: 0.7369 - acc: 0.4343 - val_loss: 0.7469 - val_acc: 0.4133\n",
+      "350/350 [==============================] - 0s 332us/step - loss: 0.7131 - acc: 0.4829 - val_loss: 0.7169 - val_acc: 0.4800\n",
       "Epoch 7/300\n",
-      "350/350 [==============================] - 0s 62us/step - loss: 0.7254 - acc: 0.4314 - val_loss: 0.7366 - val_acc: 0.4067\n",
+      "350/350 [==============================] - 0s 95us/step - loss: 0.7076 - acc: 0.4857 - val_loss: 0.7111 - val_acc: 0.4667\n",
       "Epoch 8/300\n",
-      "350/350 [==============================] - 0s 58us/step - loss: 0.7143 - acc: 0.4286 - val_loss: 0.7269 - val_acc: 0.4000\n",
+      "350/350 [==============================] - 0s 102us/step - loss: 0.7025 - acc: 0.5143 - val_loss: 0.7056 - val_acc: 0.4800\n",
       "Epoch 9/300\n",
-      "350/350 [==============================] - 0s 61us/step - loss: 0.7035 - acc: 0.4314 - val_loss: 0.7171 - val_acc: 0.4067\n",
+      "350/350 [==============================] - 0s 184us/step - loss: 0.6974 - acc: 0.5143 - val_loss: 0.7003 - val_acc: 0.4867\n",
       "Epoch 10/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.6932 - acc: 0.4743 - val_loss: 0.7080 - val_acc: 0.4400\n",
+      "350/350 [==============================] - 0s 125us/step - loss: 0.6933 - acc: 0.5257 - val_loss: 0.6954 - val_acc: 0.5133\n",
       "Epoch 11/300\n",
-      "350/350 [==============================] - 0s 64us/step - loss: 0.6828 - acc: 0.5086 - val_loss: 0.6983 - val_acc: 0.4933\n",
+      "350/350 [==============================] - 0s 286us/step - loss: 0.6897 - acc: 0.5400 - val_loss: 0.6913 - val_acc: 0.5133\n",
       "Epoch 12/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.6725 - acc: 0.5600 - val_loss: 0.6885 - val_acc: 0.5333\n",
+      "350/350 [==============================] - 0s 267us/step - loss: 0.6861 - acc: 0.5286 - val_loss: 0.6865 - val_acc: 0.5000\n",
       "Epoch 13/300\n",
-      "350/350 [==============================] - 0s 223us/step - loss: 0.6623 - acc: 0.6200 - val_loss: 0.6793 - val_acc: 0.5533\n",
+      "350/350 [==============================] - 0s 240us/step - loss: 0.6828 - acc: 0.5257 - val_loss: 0.6829 - val_acc: 0.5067\n",
       "Epoch 14/300\n",
-      "350/350 [==============================] - 0s 257us/step - loss: 0.6528 - acc: 0.6543 - val_loss: 0.6709 - val_acc: 0.5667\n",
+      "350/350 [==============================] - 0s 173us/step - loss: 0.6799 - acc: 0.5229 - val_loss: 0.6796 - val_acc: 0.5000\n",
       "Epoch 15/300\n",
-      "350/350 [==============================] - 0s 125us/step - loss: 0.6435 - acc: 0.6714 - val_loss: 0.6628 - val_acc: 0.5800\n",
+      "350/350 [==============================] - 0s 132us/step - loss: 0.6769 - acc: 0.5229 - val_loss: 0.6760 - val_acc: 0.5000\n",
       "Epoch 16/300\n",
-      "350/350 [==============================] - 0s 158us/step - loss: 0.6344 - acc: 0.6886 - val_loss: 0.6545 - val_acc: 0.6067\n",
+      "350/350 [==============================] - 0s 166us/step - loss: 0.6738 - acc: 0.5286 - val_loss: 0.6724 - val_acc: 0.5200\n",
       "Epoch 17/300\n",
-      "350/350 [==============================] - 0s 161us/step - loss: 0.6252 - acc: 0.6971 - val_loss: 0.6463 - val_acc: 0.6400\n",
+      "350/350 [==============================] - 0s 159us/step - loss: 0.6706 - acc: 0.5371 - val_loss: 0.6683 - val_acc: 0.5267\n",
       "Epoch 18/300\n",
-      "350/350 [==============================] - 0s 215us/step - loss: 0.6167 - acc: 0.7314 - val_loss: 0.6391 - val_acc: 0.6667\n",
+      "350/350 [==============================] - 0s 136us/step - loss: 0.6675 - acc: 0.5400 - val_loss: 0.6645 - val_acc: 0.5333\n",
       "Epoch 19/300\n",
-      "350/350 [==============================] - 0s 105us/step - loss: 0.6083 - acc: 0.7400 - val_loss: 0.6317 - val_acc: 0.6867\n",
+      "350/350 [==============================] - 0s 141us/step - loss: 0.6642 - acc: 0.5400 - val_loss: 0.6608 - val_acc: 0.5200\n",
       "Epoch 20/300\n",
-      "350/350 [==============================] - 0s 183us/step - loss: 0.5999 - acc: 0.7657 - val_loss: 0.6240 - val_acc: 0.7267\n",
+      "350/350 [==============================] - 0s 184us/step - loss: 0.6611 - acc: 0.5400 - val_loss: 0.6573 - val_acc: 0.5067\n",
       "Epoch 21/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.5920 - acc: 0.7886 - val_loss: 0.6170 - val_acc: 0.7267\n",
+      "350/350 [==============================] - 0s 147us/step - loss: 0.6581 - acc: 0.5114 - val_loss: 0.6539 - val_acc: 0.5000\n",
       "Epoch 22/300\n",
-      "350/350 [==============================] - 0s 164us/step - loss: 0.5842 - acc: 0.8114 - val_loss: 0.6103 - val_acc: 0.7667\n",
+      "350/350 [==============================] - 0s 161us/step - loss: 0.6550 - acc: 0.5286 - val_loss: 0.6507 - val_acc: 0.5133\n",
       "Epoch 23/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.5768 - acc: 0.8229 - val_loss: 0.6039 - val_acc: 0.8067\n",
+      "350/350 [==============================] - 0s 161us/step - loss: 0.6520 - acc: 0.5686 - val_loss: 0.6473 - val_acc: 0.5467\n",
       "Epoch 24/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.5691 - acc: 0.8314 - val_loss: 0.5972 - val_acc: 0.8200\n",
+      "350/350 [==============================] - 0s 208us/step - loss: 0.6490 - acc: 0.6029 - val_loss: 0.6441 - val_acc: 0.5667\n",
       "Epoch 25/300\n",
-      "350/350 [==============================] - 0s 208us/step - loss: 0.5614 - acc: 0.8343 - val_loss: 0.5904 - val_acc: 0.8133\n",
+      "350/350 [==============================] - 0s 132us/step - loss: 0.6458 - acc: 0.6343 - val_loss: 0.6408 - val_acc: 0.5867\n",
       "Epoch 26/300\n",
-      "350/350 [==============================] - 0s 191us/step - loss: 0.5538 - acc: 0.8343 - val_loss: 0.5837 - val_acc: 0.8133\n",
+      "350/350 [==============================] - 0s 139us/step - loss: 0.6423 - acc: 0.6629 - val_loss: 0.6371 - val_acc: 0.6133\n",
       "Epoch 27/300\n",
-      "350/350 [==============================] - 0s 330us/step - loss: 0.5460 - acc: 0.8400 - val_loss: 0.5770 - val_acc: 0.8133\n",
+      "350/350 [==============================] - 0s 161us/step - loss: 0.6388 - acc: 0.6771 - val_loss: 0.6336 - val_acc: 0.6133\n",
       "Epoch 28/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.5384 - acc: 0.8457 - val_loss: 0.5706 - val_acc: 0.8200\n",
+      "350/350 [==============================] - 0s 148us/step - loss: 0.6357 - acc: 0.6829 - val_loss: 0.6303 - val_acc: 0.6267\n",
       "Epoch 29/300\n",
-      "350/350 [==============================] - 0s 140us/step - loss: 0.5309 - acc: 0.8514 - val_loss: 0.5641 - val_acc: 0.8267\n",
+      "350/350 [==============================] - 0s 176us/step - loss: 0.6328 - acc: 0.6800 - val_loss: 0.6270 - val_acc: 0.6267\n",
       "Epoch 30/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.5234 - acc: 0.8600 - val_loss: 0.5575 - val_acc: 0.8267\n",
+      "350/350 [==============================] - 0s 135us/step - loss: 0.6298 - acc: 0.6857 - val_loss: 0.6239 - val_acc: 0.6267\n",
       "Epoch 31/300\n",
-      "350/350 [==============================] - 0s 72us/step - loss: 0.5160 - acc: 0.8571 - val_loss: 0.5509 - val_acc: 0.8333\n",
+      "350/350 [==============================] - 0s 159us/step - loss: 0.6272 - acc: 0.6829 - val_loss: 0.6209 - val_acc: 0.6267\n",
       "Epoch 32/300\n",
-      "350/350 [==============================] - 0s 67us/step - loss: 0.5084 - acc: 0.8600 - val_loss: 0.5439 - val_acc: 0.8333\n",
+      "350/350 [==============================] - 0s 104us/step - loss: 0.6244 - acc: 0.6857 - val_loss: 0.6181 - val_acc: 0.6267\n",
       "Epoch 33/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.5009 - acc: 0.8657 - val_loss: 0.5373 - val_acc: 0.8333\n",
+      "350/350 [==============================] - 0s 170us/step - loss: 0.6218 - acc: 0.6857 - val_loss: 0.6153 - val_acc: 0.6267\n",
       "Epoch 34/300\n",
-      "350/350 [==============================] - 0s 68us/step - loss: 0.4933 - acc: 0.8629 - val_loss: 0.5306 - val_acc: 0.8333\n",
+      "350/350 [==============================] - 0s 181us/step - loss: 0.6190 - acc: 0.6829 - val_loss: 0.6124 - val_acc: 0.6267\n",
       "Epoch 35/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.4858 - acc: 0.8657 - val_loss: 0.5238 - val_acc: 0.8400\n",
+      "350/350 [==============================] - 0s 147us/step - loss: 0.6162 - acc: 0.6829 - val_loss: 0.6096 - val_acc: 0.6267\n",
       "Epoch 36/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.4780 - acc: 0.8657 - val_loss: 0.5167 - val_acc: 0.8400\n",
+      "350/350 [==============================] - 0s 144us/step - loss: 0.6135 - acc: 0.6857 - val_loss: 0.6069 - val_acc: 0.6267\n",
       "Epoch 37/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.4702 - acc: 0.8714 - val_loss: 0.5096 - val_acc: 0.8400\n",
+      "350/350 [==============================] - 0s 182us/step - loss: 0.6105 - acc: 0.6914 - val_loss: 0.6039 - val_acc: 0.6267\n",
       "Epoch 38/300\n",
-      "350/350 [==============================] - 0s 72us/step - loss: 0.4620 - acc: 0.8714 - val_loss: 0.5022 - val_acc: 0.8400\n",
+      "350/350 [==============================] - 0s 177us/step - loss: 0.6076 - acc: 0.6914 - val_loss: 0.6010 - val_acc: 0.6267\n",
       "Epoch 39/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.4538 - acc: 0.8743 - val_loss: 0.4949 - val_acc: 0.8400\n",
+      "350/350 [==============================] - 0s 131us/step - loss: 0.6048 - acc: 0.6943 - val_loss: 0.5981 - val_acc: 0.6333\n",
       "Epoch 40/300\n",
-      "350/350 [==============================] - 0s 270us/step - loss: 0.4455 - acc: 0.8800 - val_loss: 0.4874 - val_acc: 0.8400\n",
+      "350/350 [==============================] - 0s 163us/step - loss: 0.6020 - acc: 0.6943 - val_loss: 0.5954 - val_acc: 0.6333\n",
       "Epoch 41/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.4378 - acc: 0.8829 - val_loss: 0.4800 - val_acc: 0.8467\n",
+      "350/350 [==============================] - 0s 201us/step - loss: 0.5994 - acc: 0.6914 - val_loss: 0.5928 - val_acc: 0.6400\n",
       "Epoch 42/300\n",
-      "350/350 [==============================] - 0s 195us/step - loss: 0.4297 - acc: 0.8914 - val_loss: 0.4723 - val_acc: 0.8467\n",
+      "350/350 [==============================] - 0s 168us/step - loss: 0.5966 - acc: 0.6914 - val_loss: 0.5902 - val_acc: 0.6467\n",
       "Epoch 43/300\n",
-      "350/350 [==============================] - 0s 255us/step - loss: 0.4219 - acc: 0.8914 - val_loss: 0.4651 - val_acc: 0.8467\n",
+      "350/350 [==============================] - 0s 157us/step - loss: 0.5939 - acc: 0.6886 - val_loss: 0.5876 - val_acc: 0.6467\n",
       "Epoch 44/300\n",
-      "350/350 [==============================] - 0s 115us/step - loss: 0.4142 - acc: 0.8914 - val_loss: 0.4577 - val_acc: 0.8533\n",
+      "350/350 [==============================] - 0s 121us/step - loss: 0.5913 - acc: 0.6886 - val_loss: 0.5852 - val_acc: 0.6467\n",
       "Epoch 45/300\n",
-      "350/350 [==============================] - 0s 138us/step - loss: 0.4067 - acc: 0.9000 - val_loss: 0.4506 - val_acc: 0.8600\n",
+      "350/350 [==============================] - 0s 165us/step - loss: 0.5887 - acc: 0.6914 - val_loss: 0.5828 - val_acc: 0.6467\n",
       "Epoch 46/300\n",
-      "350/350 [==============================] - 0s 232us/step - loss: 0.3998 - acc: 0.8971 - val_loss: 0.4441 - val_acc: 0.8600\n",
+      "350/350 [==============================] - 0s 169us/step - loss: 0.5861 - acc: 0.6914 - val_loss: 0.5803 - val_acc: 0.6467\n",
       "Epoch 47/300\n",
-      "350/350 [==============================] - 0s 211us/step - loss: 0.3922 - acc: 0.9000 - val_loss: 0.4371 - val_acc: 0.8667\n",
+      "350/350 [==============================] - 0s 314us/step - loss: 0.5834 - acc: 0.6914 - val_loss: 0.5777 - val_acc: 0.6467\n",
       "Epoch 48/300\n",
-      "350/350 [==============================] - 0s 201us/step - loss: 0.3847 - acc: 0.9114 - val_loss: 0.4300 - val_acc: 0.8667\n",
+      "350/350 [==============================] - 0s 350us/step - loss: 0.5806 - acc: 0.6914 - val_loss: 0.5751 - val_acc: 0.6467\n",
       "Epoch 49/300\n",
-      "350/350 [==============================] - 0s 236us/step - loss: 0.3774 - acc: 0.9057 - val_loss: 0.4231 - val_acc: 0.8667\n",
+      "350/350 [==============================] - 0s 156us/step - loss: 0.5779 - acc: 0.6914 - val_loss: 0.5725 - val_acc: 0.6467\n",
       "Epoch 50/300\n",
-      "350/350 [==============================] - 0s 155us/step - loss: 0.3700 - acc: 0.9114 - val_loss: 0.4160 - val_acc: 0.8667\n",
+      "350/350 [==============================] - 0s 159us/step - loss: 0.5752 - acc: 0.6914 - val_loss: 0.5699 - val_acc: 0.6467\n",
       "Epoch 51/300\n",
-      "350/350 [==============================] - 0s 325us/step - loss: 0.3626 - acc: 0.9171 - val_loss: 0.4100 - val_acc: 0.8667\n",
+      "350/350 [==============================] - 0s 113us/step - loss: 0.5722 - acc: 0.6943 - val_loss: 0.5673 - val_acc: 0.6467\n",
       "Epoch 52/300\n",
-      "350/350 [==============================] - 0s 130us/step - loss: 0.3555 - acc: 0.9114 - val_loss: 0.4031 - val_acc: 0.8667\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.5694 - acc: 0.6943 - val_loss: 0.5647 - val_acc: 0.6467\n",
       "Epoch 53/300\n",
-      "350/350 [==============================] - 0s 124us/step - loss: 0.3489 - acc: 0.9229 - val_loss: 0.3968 - val_acc: 0.8667\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.5665 - acc: 0.6943 - val_loss: 0.5622 - val_acc: 0.6467\n",
       "Epoch 54/300\n",
-      "350/350 [==============================] - 0s 109us/step - loss: 0.3422 - acc: 0.9257 - val_loss: 0.3905 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.5636 - acc: 0.6943 - val_loss: 0.5597 - val_acc: 0.6467\n",
       "Epoch 55/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.3356 - acc: 0.9257 - val_loss: 0.3842 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 106us/step - loss: 0.5607 - acc: 0.6971 - val_loss: 0.5573 - val_acc: 0.6467\n",
       "Epoch 56/300\n",
-      "350/350 [==============================] - 0s 61us/step - loss: 0.3291 - acc: 0.9257 - val_loss: 0.3781 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 114us/step - loss: 0.5579 - acc: 0.6971 - val_loss: 0.5548 - val_acc: 0.6533\n",
       "Epoch 57/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.3225 - acc: 0.9257 - val_loss: 0.3719 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 104us/step - loss: 0.5549 - acc: 0.6971 - val_loss: 0.5523 - val_acc: 0.6533\n",
       "Epoch 58/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.3160 - acc: 0.9257 - val_loss: 0.3655 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 117us/step - loss: 0.5519 - acc: 0.7000 - val_loss: 0.5497 - val_acc: 0.6533\n",
       "Epoch 59/300\n",
-      "350/350 [==============================] - 0s 73us/step - loss: 0.3096 - acc: 0.9314 - val_loss: 0.3596 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 117us/step - loss: 0.5490 - acc: 0.7000 - val_loss: 0.5471 - val_acc: 0.6533\n",
       "Epoch 60/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.3036 - acc: 0.9314 - val_loss: 0.3541 - val_acc: 0.8800\n",
-      "Epoch 61/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.2978 - acc: 0.9371 - val_loss: 0.3487 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 197us/step - loss: 0.5460 - acc: 0.7000 - val_loss: 0.5447 - val_acc: 0.6533\n",
+      "Epoch 61/300\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "350/350 [==============================] - 0s 254us/step - loss: 0.5431 - acc: 0.7000 - val_loss: 0.5422 - val_acc: 0.6533\n",
       "Epoch 62/300\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.2921 - acc: 0.9343 - val_loss: 0.3434 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 144us/step - loss: 0.5402 - acc: 0.7029 - val_loss: 0.5397 - val_acc: 0.6533\n",
       "Epoch 63/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.2866 - acc: 0.9343 - val_loss: 0.3380 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 150us/step - loss: 0.5371 - acc: 0.7000 - val_loss: 0.5370 - val_acc: 0.6533\n",
       "Epoch 64/300\n",
-      "350/350 [==============================] - 0s 113us/step - loss: 0.2808 - acc: 0.9343 - val_loss: 0.3329 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 135us/step - loss: 0.5339 - acc: 0.7000 - val_loss: 0.5343 - val_acc: 0.6533\n",
       "Epoch 65/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.2757 - acc: 0.9371 - val_loss: 0.3276 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 121us/step - loss: 0.5307 - acc: 0.7057 - val_loss: 0.5315 - val_acc: 0.6600\n",
       "Epoch 66/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.2704 - acc: 0.9371 - val_loss: 0.3226 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 151us/step - loss: 0.5274 - acc: 0.7057 - val_loss: 0.5288 - val_acc: 0.6600\n",
       "Epoch 67/300\n",
-      "350/350 [==============================] - 0s 77us/step - loss: 0.2657 - acc: 0.9371 - val_loss: 0.3179 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 181us/step - loss: 0.5244 - acc: 0.7086 - val_loss: 0.5261 - val_acc: 0.6600\n",
       "Epoch 68/300\n",
-      "350/350 [==============================] - 0s 73us/step - loss: 0.2604 - acc: 0.9429 - val_loss: 0.3135 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 142us/step - loss: 0.5214 - acc: 0.7057 - val_loss: 0.5234 - val_acc: 0.6667\n",
       "Epoch 69/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.2563 - acc: 0.9457 - val_loss: 0.3096 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 141us/step - loss: 0.5183 - acc: 0.7086 - val_loss: 0.5208 - val_acc: 0.6667\n",
       "Epoch 70/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.2515 - acc: 0.9514 - val_loss: 0.3050 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 156us/step - loss: 0.5153 - acc: 0.7143 - val_loss: 0.5182 - val_acc: 0.6667\n",
       "Epoch 71/300\n",
-      "350/350 [==============================] - 0s 65us/step - loss: 0.2469 - acc: 0.9457 - val_loss: 0.3003 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 168us/step - loss: 0.5124 - acc: 0.7143 - val_loss: 0.5156 - val_acc: 0.6667\n",
       "Epoch 72/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.2424 - acc: 0.9514 - val_loss: 0.2961 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 170us/step - loss: 0.5095 - acc: 0.7143 - val_loss: 0.5130 - val_acc: 0.6667\n",
       "Epoch 73/300\n",
-      "350/350 [==============================] - 0s 63us/step - loss: 0.2384 - acc: 0.9514 - val_loss: 0.2919 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 255us/step - loss: 0.5065 - acc: 0.7143 - val_loss: 0.5103 - val_acc: 0.6667\n",
       "Epoch 74/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.2337 - acc: 0.9514 - val_loss: 0.2877 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 176us/step - loss: 0.5035 - acc: 0.7143 - val_loss: 0.5075 - val_acc: 0.6667\n",
       "Epoch 75/300\n",
-      "350/350 [==============================] - 0s 132us/step - loss: 0.2297 - acc: 0.9514 - val_loss: 0.2835 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 177us/step - loss: 0.5005 - acc: 0.7171 - val_loss: 0.5047 - val_acc: 0.6667\n",
       "Epoch 76/300\n",
-      "350/350 [==============================] - 0s 130us/step - loss: 0.2256 - acc: 0.9514 - val_loss: 0.2798 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 187us/step - loss: 0.4976 - acc: 0.7171 - val_loss: 0.5020 - val_acc: 0.6667\n",
       "Epoch 77/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.2217 - acc: 0.9514 - val_loss: 0.2759 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 268us/step - loss: 0.4946 - acc: 0.7143 - val_loss: 0.4993 - val_acc: 0.6667\n",
       "Epoch 78/300\n",
-      "350/350 [==============================] - 0s 156us/step - loss: 0.2178 - acc: 0.9514 - val_loss: 0.2723 - val_acc: 0.8667\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.4917 - acc: 0.7171 - val_loss: 0.4966 - val_acc: 0.6667\n",
       "Epoch 79/300\n",
-      "350/350 [==============================] - 0s 161us/step - loss: 0.2141 - acc: 0.9543 - val_loss: 0.2687 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 117us/step - loss: 0.4889 - acc: 0.7171 - val_loss: 0.4940 - val_acc: 0.6667\n",
       "Epoch 80/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.2100 - acc: 0.9543 - val_loss: 0.2647 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 112us/step - loss: 0.4860 - acc: 0.7171 - val_loss: 0.4914 - val_acc: 0.6667\n",
       "Epoch 81/300\n",
-      "350/350 [==============================] - 0s 118us/step - loss: 0.2062 - acc: 0.9543 - val_loss: 0.2604 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 207us/step - loss: 0.4835 - acc: 0.7171 - val_loss: 0.4890 - val_acc: 0.6733\n",
       "Epoch 82/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.2026 - acc: 0.9571 - val_loss: 0.2569 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 169us/step - loss: 0.4809 - acc: 0.7200 - val_loss: 0.4867 - val_acc: 0.6733\n",
       "Epoch 83/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1989 - acc: 0.9571 - val_loss: 0.2534 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 114us/step - loss: 0.4782 - acc: 0.7200 - val_loss: 0.4842 - val_acc: 0.6733\n",
       "Epoch 84/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1952 - acc: 0.9571 - val_loss: 0.2497 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 219us/step - loss: 0.4758 - acc: 0.7229 - val_loss: 0.4818 - val_acc: 0.6733\n",
       "Epoch 85/300\n",
-      "350/350 [==============================] - 0s 154us/step - loss: 0.1920 - acc: 0.9571 - val_loss: 0.2462 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 121us/step - loss: 0.4735 - acc: 0.7257 - val_loss: 0.4797 - val_acc: 0.6733\n",
       "Epoch 86/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.1889 - acc: 0.9571 - val_loss: 0.2437 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 118us/step - loss: 0.4712 - acc: 0.7229 - val_loss: 0.4775 - val_acc: 0.6733\n",
       "Epoch 87/300\n",
-      "350/350 [==============================] - 0s 231us/step - loss: 0.1861 - acc: 0.9571 - val_loss: 0.2405 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 342us/step - loss: 0.4688 - acc: 0.7200 - val_loss: 0.4753 - val_acc: 0.6733\n",
       "Epoch 88/300\n",
-      "350/350 [==============================] - 0s 188us/step - loss: 0.1829 - acc: 0.9571 - val_loss: 0.2372 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 149us/step - loss: 0.4664 - acc: 0.7229 - val_loss: 0.4730 - val_acc: 0.6733\n",
       "Epoch 89/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.1798 - acc: 0.9571 - val_loss: 0.2335 - val_acc: 0.8733\n",
+      "350/350 [==============================] - 0s 150us/step - loss: 0.4641 - acc: 0.7229 - val_loss: 0.4707 - val_acc: 0.6733\n",
       "Epoch 90/300\n",
-      "350/350 [==============================] - 0s 118us/step - loss: 0.1771 - acc: 0.9571 - val_loss: 0.2305 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 319us/step - loss: 0.4617 - acc: 0.7229 - val_loss: 0.4684 - val_acc: 0.6733\n",
       "Epoch 91/300\n",
-      "350/350 [==============================] - 0s 126us/step - loss: 0.1742 - acc: 0.9571 - val_loss: 0.2272 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 138us/step - loss: 0.4597 - acc: 0.7257 - val_loss: 0.4662 - val_acc: 0.6733\n",
       "Epoch 92/300\n",
-      "350/350 [==============================] - 0s 67us/step - loss: 0.1714 - acc: 0.9571 - val_loss: 0.2247 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 237us/step - loss: 0.4573 - acc: 0.7229 - val_loss: 0.4641 - val_acc: 0.6733\n",
       "Epoch 93/300\n",
-      "350/350 [==============================] - 0s 50us/step - loss: 0.1690 - acc: 0.9543 - val_loss: 0.2217 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 123us/step - loss: 0.4552 - acc: 0.7229 - val_loss: 0.4620 - val_acc: 0.6733\n",
       "Epoch 94/300\n",
-      "350/350 [==============================] - 0s 64us/step - loss: 0.1661 - acc: 0.9571 - val_loss: 0.2190 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 170us/step - loss: 0.4531 - acc: 0.7229 - val_loss: 0.4598 - val_acc: 0.6733\n",
       "Epoch 95/300\n",
-      "350/350 [==============================] - 0s 50us/step - loss: 0.1637 - acc: 0.9600 - val_loss: 0.2165 - val_acc: 0.8800\n",
+      "350/350 [==============================] - 0s 277us/step - loss: 0.4509 - acc: 0.7229 - val_loss: 0.4577 - val_acc: 0.6733\n",
       "Epoch 96/300\n",
-      "350/350 [==============================] - 0s 62us/step - loss: 0.1613 - acc: 0.9571 - val_loss: 0.2131 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 495us/step - loss: 0.4487 - acc: 0.7257 - val_loss: 0.4557 - val_acc: 0.6733\n",
       "Epoch 97/300\n",
-      "350/350 [==============================] - 0s 59us/step - loss: 0.1589 - acc: 0.9600 - val_loss: 0.2101 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 285us/step - loss: 0.4469 - acc: 0.7257 - val_loss: 0.4538 - val_acc: 0.6733\n",
       "Epoch 98/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1565 - acc: 0.9600 - val_loss: 0.2073 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 156us/step - loss: 0.4449 - acc: 0.7229 - val_loss: 0.4520 - val_acc: 0.6733\n",
       "Epoch 99/300\n",
-      "350/350 [==============================] - 0s 53us/step - loss: 0.1543 - acc: 0.9600 - val_loss: 0.2050 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.4430 - acc: 0.7257 - val_loss: 0.4500 - val_acc: 0.6733\n",
       "Epoch 100/300\n",
-      "350/350 [==============================] - 0s 55us/step - loss: 0.1521 - acc: 0.9600 - val_loss: 0.2026 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.4411 - acc: 0.7286 - val_loss: 0.4481 - val_acc: 0.6800\n",
       "Epoch 101/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.1501 - acc: 0.9629 - val_loss: 0.2011 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 108us/step - loss: 0.4395 - acc: 0.7286 - val_loss: 0.4464 - val_acc: 0.6800\n",
       "Epoch 102/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.1479 - acc: 0.9600 - val_loss: 0.1982 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 131us/step - loss: 0.4377 - acc: 0.7286 - val_loss: 0.4447 - val_acc: 0.6800\n",
       "Epoch 103/300\n",
-      "350/350 [==============================] - 0s 53us/step - loss: 0.1460 - acc: 0.9629 - val_loss: 0.1960 - val_acc: 0.8867\n",
+      "350/350 [==============================] - 0s 149us/step - loss: 0.4359 - acc: 0.7314 - val_loss: 0.4430 - val_acc: 0.6800\n",
       "Epoch 104/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1439 - acc: 0.9629 - val_loss: 0.1939 - val_acc: 0.8933\n",
+      "350/350 [==============================] - 0s 170us/step - loss: 0.4345 - acc: 0.7314 - val_loss: 0.4416 - val_acc: 0.6800\n",
       "Epoch 105/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1420 - acc: 0.9629 - val_loss: 0.1920 - val_acc: 0.9000\n",
+      "350/350 [==============================] - 0s 281us/step - loss: 0.4329 - acc: 0.7314 - val_loss: 0.4401 - val_acc: 0.6800\n",
       "Epoch 106/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1400 - acc: 0.9629 - val_loss: 0.1889 - val_acc: 0.9000\n",
+      "350/350 [==============================] - 0s 115us/step - loss: 0.4315 - acc: 0.7343 - val_loss: 0.4386 - val_acc: 0.6800\n",
       "Epoch 107/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1387 - acc: 0.9629 - val_loss: 0.1876 - val_acc: 0.9000\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.4300 - acc: 0.7371 - val_loss: 0.4371 - val_acc: 0.6800\n",
       "Epoch 108/300\n",
-      "350/350 [==============================] - 0s 140us/step - loss: 0.1364 - acc: 0.9629 - val_loss: 0.1857 - val_acc: 0.9067\n",
+      "350/350 [==============================] - 0s 73us/step - loss: 0.4285 - acc: 0.7343 - val_loss: 0.4356 - val_acc: 0.6800\n",
       "Epoch 109/300\n",
-      "350/350 [==============================] - 0s 121us/step - loss: 0.1347 - acc: 0.9657 - val_loss: 0.1831 - val_acc: 0.9067\n",
+      "350/350 [==============================] - 0s 142us/step - loss: 0.4273 - acc: 0.7343 - val_loss: 0.4341 - val_acc: 0.6800\n",
       "Epoch 110/300\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.1329 - acc: 0.9657 - val_loss: 0.1815 - val_acc: 0.9067\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.4260 - acc: 0.7343 - val_loss: 0.4329 - val_acc: 0.6800\n",
       "Epoch 111/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1313 - acc: 0.9657 - val_loss: 0.1798 - val_acc: 0.9133\n",
+      "350/350 [==============================] - 0s 104us/step - loss: 0.4248 - acc: 0.7371 - val_loss: 0.4317 - val_acc: 0.6800\n",
       "Epoch 112/300\n",
-      "350/350 [==============================] - 0s 132us/step - loss: 0.1295 - acc: 0.9657 - val_loss: 0.1783 - val_acc: 0.9067\n",
+      "350/350 [==============================] - 0s 111us/step - loss: 0.4236 - acc: 0.7371 - val_loss: 0.4305 - val_acc: 0.6800\n",
       "Epoch 113/300\n",
-      "350/350 [==============================] - 0s 204us/step - loss: 0.1276 - acc: 0.9714 - val_loss: 0.1762 - val_acc: 0.9067\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.4225 - acc: 0.7371 - val_loss: 0.4294 - val_acc: 0.6800\n",
       "Epoch 114/300\n",
-      "350/350 [==============================] - 0s 164us/step - loss: 0.1262 - acc: 0.9714 - val_loss: 0.1748 - val_acc: 0.9000\n",
+      "350/350 [==============================] - 0s 103us/step - loss: 0.4215 - acc: 0.7371 - val_loss: 0.4283 - val_acc: 0.6800\n",
       "Epoch 115/300\n",
-      "350/350 [==============================] - 0s 235us/step - loss: 0.1244 - acc: 0.9771 - val_loss: 0.1735 - val_acc: 0.9133\n",
+      "350/350 [==============================] - 0s 94us/step - loss: 0.4204 - acc: 0.7343 - val_loss: 0.4272 - val_acc: 0.6800\n",
       "Epoch 116/300\n",
-      "350/350 [==============================] - 0s 123us/step - loss: 0.1233 - acc: 0.9714 - val_loss: 0.1716 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 103us/step - loss: 0.4193 - acc: 0.7371 - val_loss: 0.4261 - val_acc: 0.6800\n",
       "Epoch 117/300\n",
-      "350/350 [==============================] - 0s 232us/step - loss: 0.1221 - acc: 0.9743 - val_loss: 0.1699 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 71us/step - loss: 0.4184 - acc: 0.7343 - val_loss: 0.4251 - val_acc: 0.6800\n",
       "Epoch 118/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1204 - acc: 0.9743 - val_loss: 0.1680 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 68us/step - loss: 0.4175 - acc: 0.7371 - val_loss: 0.4241 - val_acc: 0.6800\n",
       "Epoch 119/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.1192 - acc: 0.9743 - val_loss: 0.1663 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.4165 - acc: 0.7371 - val_loss: 0.4231 - val_acc: 0.6800\n",
       "Epoch 120/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1179 - acc: 0.9771 - val_loss: 0.1654 - val_acc: 0.9267\n",
-      "Epoch 121/300\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "350/350 [==============================] - 0s 125us/step - loss: 0.1169 - acc: 0.9771 - val_loss: 0.1647 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.4158 - acc: 0.7343 - val_loss: 0.4222 - val_acc: 0.6800\n",
+      "Epoch 121/300\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.4148 - acc: 0.7343 - val_loss: 0.4213 - val_acc: 0.6800\n",
       "Epoch 122/300\n",
-      "350/350 [==============================] - 0s 137us/step - loss: 0.1157 - acc: 0.9743 - val_loss: 0.1633 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 52us/step - loss: 0.4140 - acc: 0.7371 - val_loss: 0.4204 - val_acc: 0.6800\n",
       "Epoch 123/300\n",
-      "350/350 [==============================] - 0s 126us/step - loss: 0.1144 - acc: 0.9771 - val_loss: 0.1615 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 58us/step - loss: 0.4133 - acc: 0.7343 - val_loss: 0.4195 - val_acc: 0.6800\n",
       "Epoch 124/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.1131 - acc: 0.9743 - val_loss: 0.1599 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 105us/step - loss: 0.4124 - acc: 0.7371 - val_loss: 0.4187 - val_acc: 0.6800\n",
       "Epoch 125/300\n",
-      "350/350 [==============================] - 0s 66us/step - loss: 0.1125 - acc: 0.9771 - val_loss: 0.1592 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 72us/step - loss: 0.4118 - acc: 0.7371 - val_loss: 0.4179 - val_acc: 0.6800\n",
       "Epoch 126/300\n",
-      "350/350 [==============================] - 0s 73us/step - loss: 0.1110 - acc: 0.9771 - val_loss: 0.1586 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.4111 - acc: 0.7343 - val_loss: 0.4171 - val_acc: 0.6800\n",
       "Epoch 127/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.1100 - acc: 0.9771 - val_loss: 0.1564 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.4104 - acc: 0.7343 - val_loss: 0.4164 - val_acc: 0.6800\n",
       "Epoch 128/300\n",
-      "350/350 [==============================] - 0s 126us/step - loss: 0.1088 - acc: 0.9771 - val_loss: 0.1547 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 115us/step - loss: 0.4098 - acc: 0.7343 - val_loss: 0.4157 - val_acc: 0.6800\n",
       "Epoch 129/300\n",
-      "350/350 [==============================] - 0s 65us/step - loss: 0.1082 - acc: 0.9771 - val_loss: 0.1538 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.4091 - acc: 0.7371 - val_loss: 0.4150 - val_acc: 0.6800\n",
       "Epoch 130/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1068 - acc: 0.9771 - val_loss: 0.1529 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 128us/step - loss: 0.4085 - acc: 0.7371 - val_loss: 0.4142 - val_acc: 0.6800\n",
       "Epoch 131/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.1063 - acc: 0.9771 - val_loss: 0.1517 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 123us/step - loss: 0.4079 - acc: 0.7371 - val_loss: 0.4135 - val_acc: 0.6800\n",
       "Epoch 132/300\n",
-      "350/350 [==============================] - 0s 71us/step - loss: 0.1048 - acc: 0.9771 - val_loss: 0.1519 - val_acc: 0.9200\n",
+      "350/350 [==============================] - 0s 213us/step - loss: 0.4071 - acc: 0.7371 - val_loss: 0.4127 - val_acc: 0.6800\n",
       "Epoch 133/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1045 - acc: 0.9771 - val_loss: 0.1507 - val_acc: 0.9200\n",
+      "350/350 [==============================] - 0s 146us/step - loss: 0.4065 - acc: 0.7371 - val_loss: 0.4119 - val_acc: 0.6867\n",
       "Epoch 134/300\n",
-      "350/350 [==============================] - 0s 73us/step - loss: 0.1033 - acc: 0.9771 - val_loss: 0.1490 - val_acc: 0.9200\n",
+      "350/350 [==============================] - 0s 136us/step - loss: 0.4060 - acc: 0.7371 - val_loss: 0.4113 - val_acc: 0.6867\n",
       "Epoch 135/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.1028 - acc: 0.9771 - val_loss: 0.1488 - val_acc: 0.9200\n",
+      "350/350 [==============================] - 0s 138us/step - loss: 0.4055 - acc: 0.7371 - val_loss: 0.4108 - val_acc: 0.6867\n",
       "Epoch 136/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1017 - acc: 0.9771 - val_loss: 0.1476 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 110us/step - loss: 0.4048 - acc: 0.7371 - val_loss: 0.4102 - val_acc: 0.6867\n",
       "Epoch 137/300\n",
-      "350/350 [==============================] - 0s 73us/step - loss: 0.1011 - acc: 0.9771 - val_loss: 0.1463 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 150us/step - loss: 0.4042 - acc: 0.7371 - val_loss: 0.4096 - val_acc: 0.6867\n",
       "Epoch 138/300\n",
-      "350/350 [==============================] - 0s 53us/step - loss: 0.1003 - acc: 0.9771 - val_loss: 0.1450 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 132us/step - loss: 0.4037 - acc: 0.7371 - val_loss: 0.4091 - val_acc: 0.6867\n",
       "Epoch 139/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.0995 - acc: 0.9743 - val_loss: 0.1454 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 208us/step - loss: 0.4033 - acc: 0.7371 - val_loss: 0.4086 - val_acc: 0.6867\n",
       "Epoch 140/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.0987 - acc: 0.9771 - val_loss: 0.1441 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 306us/step - loss: 0.4026 - acc: 0.7371 - val_loss: 0.4080 - val_acc: 0.6867\n",
       "Epoch 141/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.0982 - acc: 0.9771 - val_loss: 0.1431 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 169us/step - loss: 0.4022 - acc: 0.7371 - val_loss: 0.4075 - val_acc: 0.6867\n",
       "Epoch 142/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.0971 - acc: 0.9771 - val_loss: 0.1433 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 228us/step - loss: 0.4017 - acc: 0.7371 - val_loss: 0.4069 - val_acc: 0.6867\n",
       "Epoch 143/300\n",
-      "350/350 [==============================] - 0s 65us/step - loss: 0.0965 - acc: 0.9743 - val_loss: 0.1435 - val_acc: 0.9267\n",
+      "350/350 [==============================] - 0s 181us/step - loss: 0.4011 - acc: 0.7343 - val_loss: 0.4063 - val_acc: 0.6867\n",
       "Epoch 144/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.0960 - acc: 0.9771 - val_loss: 0.1415 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 193us/step - loss: 0.4006 - acc: 0.7371 - val_loss: 0.4056 - val_acc: 0.6867\n",
       "Epoch 145/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.0955 - acc: 0.9771 - val_loss: 0.1407 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 301us/step - loss: 0.4002 - acc: 0.7371 - val_loss: 0.4052 - val_acc: 0.6867\n",
       "Epoch 146/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.0947 - acc: 0.9771 - val_loss: 0.1405 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 225us/step - loss: 0.3998 - acc: 0.7371 - val_loss: 0.4047 - val_acc: 0.6867\n",
       "Epoch 147/300\n",
-      "350/350 [==============================] - 0s 64us/step - loss: 0.0938 - acc: 0.9800 - val_loss: 0.1381 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 73us/step - loss: 0.3991 - acc: 0.7371 - val_loss: 0.4042 - val_acc: 0.6867\n",
       "Epoch 148/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.0935 - acc: 0.9771 - val_loss: 0.1376 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 99us/step - loss: 0.3988 - acc: 0.7400 - val_loss: 0.4036 - val_acc: 0.6867\n",
       "Epoch 149/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.0928 - acc: 0.9771 - val_loss: 0.1367 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.3984 - acc: 0.7371 - val_loss: 0.4032 - val_acc: 0.6867\n",
       "Epoch 150/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.0920 - acc: 0.9771 - val_loss: 0.1365 - val_acc: 0.9333\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.3978 - acc: 0.7371 - val_loss: 0.4028 - val_acc: 0.6867\n",
       "Epoch 151/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.0913 - acc: 0.9800 - val_loss: 0.1357 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.3975 - acc: 0.7371 - val_loss: 0.4024 - val_acc: 0.6867\n",
       "Epoch 152/300\n",
-      "350/350 [==============================] - 0s 120us/step - loss: 0.0909 - acc: 0.9800 - val_loss: 0.1348 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.3970 - acc: 0.7400 - val_loss: 0.4019 - val_acc: 0.6867\n",
       "Epoch 153/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.0901 - acc: 0.9800 - val_loss: 0.1332 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 54us/step - loss: 0.3968 - acc: 0.7400 - val_loss: 0.4015 - val_acc: 0.6867\n",
       "Epoch 154/300\n",
-      "350/350 [==============================] - 0s 113us/step - loss: 0.0901 - acc: 0.9771 - val_loss: 0.1334 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.3962 - acc: 0.7400 - val_loss: 0.4012 - val_acc: 0.6867\n",
       "Epoch 155/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.0892 - acc: 0.9771 - val_loss: 0.1346 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 54us/step - loss: 0.3959 - acc: 0.7371 - val_loss: 0.4008 - val_acc: 0.6867\n",
       "Epoch 156/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.0889 - acc: 0.9800 - val_loss: 0.1333 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 55us/step - loss: 0.3956 - acc: 0.7371 - val_loss: 0.4003 - val_acc: 0.6867\n",
       "Epoch 157/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.0883 - acc: 0.9800 - val_loss: 0.1326 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.3952 - acc: 0.7400 - val_loss: 0.3999 - val_acc: 0.6867\n",
       "Epoch 158/300\n",
-      "350/350 [==============================] - 0s 148us/step - loss: 0.0877 - acc: 0.9800 - val_loss: 0.1332 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3949 - acc: 0.7400 - val_loss: 0.3996 - val_acc: 0.6867\n",
       "Epoch 159/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.0876 - acc: 0.9800 - val_loss: 0.1323 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.3946 - acc: 0.7400 - val_loss: 0.3993 - val_acc: 0.6867\n",
       "Epoch 160/300\n",
-      "350/350 [==============================] - 0s 64us/step - loss: 0.0871 - acc: 0.9800 - val_loss: 0.1307 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.3942 - acc: 0.7400 - val_loss: 0.3989 - val_acc: 0.6867\n",
       "Epoch 161/300\n",
-      "350/350 [==============================] - 0s 73us/step - loss: 0.0868 - acc: 0.9800 - val_loss: 0.1303 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 65us/step - loss: 0.3939 - acc: 0.7400 - val_loss: 0.3987 - val_acc: 0.6867\n",
       "Epoch 162/300\n",
-      "350/350 [==============================] - 0s 122us/step - loss: 0.0858 - acc: 0.9800 - val_loss: 0.1292 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.3936 - acc: 0.7400 - val_loss: 0.3984 - val_acc: 0.6867\n",
       "Epoch 163/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.0858 - acc: 0.9743 - val_loss: 0.1301 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 73us/step - loss: 0.3933 - acc: 0.7400 - val_loss: 0.3980 - val_acc: 0.6867\n",
       "Epoch 164/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.0851 - acc: 0.9771 - val_loss: 0.1296 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.3929 - acc: 0.7371 - val_loss: 0.3976 - val_acc: 0.6867\n",
       "Epoch 165/300\n",
-      "350/350 [==============================] - 0s 105us/step - loss: 0.0851 - acc: 0.9800 - val_loss: 0.1288 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 69us/step - loss: 0.3927 - acc: 0.7371 - val_loss: 0.3972 - val_acc: 0.6867\n",
       "Epoch 166/300\n",
-      "350/350 [==============================] - 0s 109us/step - loss: 0.0844 - acc: 0.9800 - val_loss: 0.1283 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 73us/step - loss: 0.3922 - acc: 0.7400 - val_loss: 0.3969 - val_acc: 0.6867\n",
       "Epoch 167/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.0843 - acc: 0.9800 - val_loss: 0.1287 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.3920 - acc: 0.7371 - val_loss: 0.3966 - val_acc: 0.6933\n",
       "Epoch 168/300\n",
-      "350/350 [==============================] - 0s 148us/step - loss: 0.0837 - acc: 0.9800 - val_loss: 0.1272 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 64us/step - loss: 0.3919 - acc: 0.7400 - val_loss: 0.3963 - val_acc: 0.6933\n",
       "Epoch 169/300\n",
-      "350/350 [==============================] - 0s 107us/step - loss: 0.0833 - acc: 0.9771 - val_loss: 0.1268 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 100us/step - loss: 0.3914 - acc: 0.7400 - val_loss: 0.3960 - val_acc: 0.6867\n",
       "Epoch 170/300\n",
-      "350/350 [==============================] - 0s 132us/step - loss: 0.0830 - acc: 0.9771 - val_loss: 0.1269 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 69us/step - loss: 0.3911 - acc: 0.7371 - val_loss: 0.3956 - val_acc: 0.6867\n",
       "Epoch 171/300\n",
-      "350/350 [==============================] - 0s 125us/step - loss: 0.0825 - acc: 0.9771 - val_loss: 0.1269 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.3908 - acc: 0.7400 - val_loss: 0.3954 - val_acc: 0.6867\n",
       "Epoch 172/300\n",
-      "350/350 [==============================] - 0s 178us/step - loss: 0.0824 - acc: 0.9800 - val_loss: 0.1262 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 63us/step - loss: 0.3904 - acc: 0.7400 - val_loss: 0.3950 - val_acc: 0.6867\n",
       "Epoch 173/300\n",
-      "350/350 [==============================] - 0s 115us/step - loss: 0.0821 - acc: 0.9771 - val_loss: 0.1261 - val_acc: 0.9467\n",
+      "350/350 [==============================] - 0s 69us/step - loss: 0.3903 - acc: 0.7400 - val_loss: 0.3948 - val_acc: 0.6867\n",
       "Epoch 174/300\n",
-      "350/350 [==============================] - 0s 132us/step - loss: 0.0815 - acc: 0.9829 - val_loss: 0.1255 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 109us/step - loss: 0.3899 - acc: 0.7400 - val_loss: 0.3945 - val_acc: 0.6867\n",
       "Epoch 175/300\n",
-      "350/350 [==============================] - 0s 141us/step - loss: 0.0813 - acc: 0.9800 - val_loss: 0.1252 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 62us/step - loss: 0.3897 - acc: 0.7400 - val_loss: 0.3942 - val_acc: 0.6867\n",
       "Epoch 176/300\n",
-      "350/350 [==============================] - 0s 127us/step - loss: 0.0810 - acc: 0.9771 - val_loss: 0.1251 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.3895 - acc: 0.7371 - val_loss: 0.3940 - val_acc: 0.6867\n",
       "Epoch 177/300\n",
-      "350/350 [==============================] - 0s 126us/step - loss: 0.0810 - acc: 0.9771 - val_loss: 0.1251 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 70us/step - loss: 0.3891 - acc: 0.7400 - val_loss: 0.3936 - val_acc: 0.6867\n",
       "Epoch 178/300\n",
-      "350/350 [==============================] - 0s 109us/step - loss: 0.0804 - acc: 0.9800 - val_loss: 0.1239 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 110us/step - loss: 0.3890 - acc: 0.7400 - val_loss: 0.3934 - val_acc: 0.6867\n",
       "Epoch 179/300\n",
-      "350/350 [==============================] - 0s 152us/step - loss: 0.0799 - acc: 0.9800 - val_loss: 0.1238 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.3887 - acc: 0.7400 - val_loss: 0.3932 - val_acc: 0.6867\n",
       "Epoch 180/300\n",
-      "350/350 [==============================] - 0s 127us/step - loss: 0.0798 - acc: 0.9771 - val_loss: 0.1236 - val_acc: 0.9533\n",
-      "Epoch 181/300\n",
-      "350/350 [==============================] - 0s 143us/step - loss: 0.0795 - acc: 0.9771 - val_loss: 0.1236 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.3884 - acc: 0.7400 - val_loss: 0.3930 - val_acc: 0.6867\n",
+      "Epoch 181/300\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "350/350 [==============================] - 0s 111us/step - loss: 0.3880 - acc: 0.7371 - val_loss: 0.3926 - val_acc: 0.6867\n",
       "Epoch 182/300\n",
-      "350/350 [==============================] - 0s 146us/step - loss: 0.0790 - acc: 0.9800 - val_loss: 0.1233 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 58us/step - loss: 0.3879 - acc: 0.7371 - val_loss: 0.3924 - val_acc: 0.6867\n",
       "Epoch 183/300\n",
-      "350/350 [==============================] - 0s 132us/step - loss: 0.0791 - acc: 0.9829 - val_loss: 0.1217 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.3878 - acc: 0.7371 - val_loss: 0.3922 - val_acc: 0.6867\n",
       "Epoch 184/300\n",
-      "350/350 [==============================] - 0s 126us/step - loss: 0.0786 - acc: 0.9800 - val_loss: 0.1219 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 71us/step - loss: 0.3873 - acc: 0.7400 - val_loss: 0.3919 - val_acc: 0.6867\n",
       "Epoch 185/300\n",
-      "350/350 [==============================] - 0s 102us/step - loss: 0.0786 - acc: 0.9771 - val_loss: 0.1221 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 63us/step - loss: 0.3872 - acc: 0.7400 - val_loss: 0.3917 - val_acc: 0.6867\n",
       "Epoch 186/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.0779 - acc: 0.9800 - val_loss: 0.1218 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.3869 - acc: 0.7371 - val_loss: 0.3915 - val_acc: 0.6867\n",
       "Epoch 187/300\n",
-      "350/350 [==============================] - 0s 106us/step - loss: 0.0780 - acc: 0.9800 - val_loss: 0.1215 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 65us/step - loss: 0.3866 - acc: 0.7371 - val_loss: 0.3912 - val_acc: 0.6867\n",
       "Epoch 188/300\n",
-      "350/350 [==============================] - 0s 136us/step - loss: 0.0777 - acc: 0.9800 - val_loss: 0.1209 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.3864 - acc: 0.7400 - val_loss: 0.3910 - val_acc: 0.6867\n",
       "Epoch 189/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.0778 - acc: 0.9800 - val_loss: 0.1206 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 89us/step - loss: 0.3862 - acc: 0.7400 - val_loss: 0.3908 - val_acc: 0.6867\n",
       "Epoch 190/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.0769 - acc: 0.9829 - val_loss: 0.1199 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.3859 - acc: 0.7400 - val_loss: 0.3905 - val_acc: 0.6867\n",
       "Epoch 191/300\n",
-      "350/350 [==============================] - 0s 124us/step - loss: 0.0769 - acc: 0.9771 - val_loss: 0.1210 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.3857 - acc: 0.7400 - val_loss: 0.3902 - val_acc: 0.6867\n",
       "Epoch 192/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.0764 - acc: 0.9829 - val_loss: 0.1192 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.3855 - acc: 0.7400 - val_loss: 0.3900 - val_acc: 0.6867\n",
       "Epoch 193/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.0766 - acc: 0.9800 - val_loss: 0.1189 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.3853 - acc: 0.7400 - val_loss: 0.3898 - val_acc: 0.6867\n",
       "Epoch 194/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.0766 - acc: 0.9800 - val_loss: 0.1191 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.3850 - acc: 0.7371 - val_loss: 0.3895 - val_acc: 0.6867\n",
       "Epoch 195/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.0760 - acc: 0.9771 - val_loss: 0.1191 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.3848 - acc: 0.7400 - val_loss: 0.3893 - val_acc: 0.6867\n",
       "Epoch 196/300\n",
-      "350/350 [==============================] - 0s 111us/step - loss: 0.0758 - acc: 0.9800 - val_loss: 0.1190 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.3846 - acc: 0.7371 - val_loss: 0.3892 - val_acc: 0.6867\n",
       "Epoch 197/300\n",
-      "350/350 [==============================] - 0s 187us/step - loss: 0.0754 - acc: 0.9771 - val_loss: 0.1190 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.3842 - acc: 0.7429 - val_loss: 0.3890 - val_acc: 0.6867\n",
       "Epoch 198/300\n",
-      "350/350 [==============================] - 0s 114us/step - loss: 0.0753 - acc: 0.9800 - val_loss: 0.1184 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.3841 - acc: 0.7371 - val_loss: 0.3888 - val_acc: 0.6867\n",
       "Epoch 199/300\n",
-      "350/350 [==============================] - 0s 147us/step - loss: 0.0755 - acc: 0.9829 - val_loss: 0.1182 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 66us/step - loss: 0.3839 - acc: 0.7400 - val_loss: 0.3885 - val_acc: 0.6867\n",
       "Epoch 200/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.0748 - acc: 0.9800 - val_loss: 0.1192 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.3835 - acc: 0.7429 - val_loss: 0.3885 - val_acc: 0.6867\n",
       "Epoch 201/300\n",
-      "350/350 [==============================] - 0s 176us/step - loss: 0.0751 - acc: 0.9800 - val_loss: 0.1181 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3833 - acc: 0.7400 - val_loss: 0.3883 - val_acc: 0.6867\n",
       "Epoch 202/300\n",
-      "350/350 [==============================] - 0s 124us/step - loss: 0.0742 - acc: 0.9800 - val_loss: 0.1173 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 102us/step - loss: 0.3832 - acc: 0.7400 - val_loss: 0.3881 - val_acc: 0.6867\n",
       "Epoch 203/300\n",
-      "350/350 [==============================] - 0s 114us/step - loss: 0.0743 - acc: 0.9771 - val_loss: 0.1169 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.3830 - acc: 0.7457 - val_loss: 0.3880 - val_acc: 0.6867\n",
       "Epoch 204/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.0742 - acc: 0.9829 - val_loss: 0.1155 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 67us/step - loss: 0.3828 - acc: 0.7429 - val_loss: 0.3880 - val_acc: 0.6867\n",
       "Epoch 205/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.0738 - acc: 0.9800 - val_loss: 0.1158 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 77us/step - loss: 0.3826 - acc: 0.7371 - val_loss: 0.3876 - val_acc: 0.6867\n",
       "Epoch 206/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.0737 - acc: 0.9800 - val_loss: 0.1163 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 66us/step - loss: 0.3824 - acc: 0.7400 - val_loss: 0.3875 - val_acc: 0.6867\n",
       "Epoch 207/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.0735 - acc: 0.9771 - val_loss: 0.1166 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 95us/step - loss: 0.3823 - acc: 0.7429 - val_loss: 0.3874 - val_acc: 0.6867\n",
       "Epoch 208/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.0734 - acc: 0.9771 - val_loss: 0.1168 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.3820 - acc: 0.7400 - val_loss: 0.3872 - val_acc: 0.6867\n",
       "Epoch 209/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.0733 - acc: 0.9800 - val_loss: 0.1157 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 107us/step - loss: 0.3819 - acc: 0.7429 - val_loss: 0.3871 - val_acc: 0.6867\n",
       "Epoch 210/300\n",
-      "350/350 [==============================] - 0s 69us/step - loss: 0.0731 - acc: 0.9800 - val_loss: 0.1152 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 102us/step - loss: 0.3816 - acc: 0.7429 - val_loss: 0.3869 - val_acc: 0.6867\n",
       "Epoch 211/300\n",
-      "350/350 [==============================] - 0s 77us/step - loss: 0.0727 - acc: 0.9829 - val_loss: 0.1147 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 66us/step - loss: 0.3815 - acc: 0.7429 - val_loss: 0.3867 - val_acc: 0.6867\n",
       "Epoch 212/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.0726 - acc: 0.9829 - val_loss: 0.1141 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.3813 - acc: 0.7457 - val_loss: 0.3867 - val_acc: 0.6867\n",
       "Epoch 213/300\n",
-      "350/350 [==============================] - 0s 64us/step - loss: 0.0724 - acc: 0.9800 - val_loss: 0.1149 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 102us/step - loss: 0.3810 - acc: 0.7457 - val_loss: 0.3865 - val_acc: 0.6867\n",
       "Epoch 214/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.0723 - acc: 0.9771 - val_loss: 0.1153 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.3809 - acc: 0.7457 - val_loss: 0.3864 - val_acc: 0.6867\n",
       "Epoch 215/300\n",
-      "350/350 [==============================] - 0s 148us/step - loss: 0.0720 - acc: 0.9800 - val_loss: 0.1159 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 77us/step - loss: 0.3808 - acc: 0.7457 - val_loss: 0.3863 - val_acc: 0.6867\n",
       "Epoch 216/300\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.0721 - acc: 0.9771 - val_loss: 0.1155 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.3806 - acc: 0.7457 - val_loss: 0.3862 - val_acc: 0.6867\n",
       "Epoch 217/300\n",
-      "350/350 [==============================] - 0s 72us/step - loss: 0.0714 - acc: 0.9800 - val_loss: 0.1157 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 106us/step - loss: 0.3806 - acc: 0.7400 - val_loss: 0.3861 - val_acc: 0.6867\n",
       "Epoch 218/300\n",
-      "350/350 [==============================] - 0s 102us/step - loss: 0.0716 - acc: 0.9771 - val_loss: 0.1151 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 72us/step - loss: 0.3803 - acc: 0.7400 - val_loss: 0.3859 - val_acc: 0.6867\n",
       "Epoch 219/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.0718 - acc: 0.9771 - val_loss: 0.1141 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 89us/step - loss: 0.3800 - acc: 0.7400 - val_loss: 0.3857 - val_acc: 0.6867\n",
       "Epoch 220/300\n",
-      "350/350 [==============================] - 0s 177us/step - loss: 0.0713 - acc: 0.9771 - val_loss: 0.1138 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 98us/step - loss: 0.3801 - acc: 0.7429 - val_loss: 0.3857 - val_acc: 0.6867\n",
       "Epoch 221/300\n",
-      "350/350 [==============================] - 0s 122us/step - loss: 0.0714 - acc: 0.9800 - val_loss: 0.1143 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3797 - acc: 0.7457 - val_loss: 0.3855 - val_acc: 0.6867\n",
       "Epoch 222/300\n",
-      "350/350 [==============================] - 0s 113us/step - loss: 0.0709 - acc: 0.9771 - val_loss: 0.1137 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 63us/step - loss: 0.3797 - acc: 0.7457 - val_loss: 0.3856 - val_acc: 0.6867\n",
       "Epoch 223/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.0710 - acc: 0.9800 - val_loss: 0.1141 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 67us/step - loss: 0.3794 - acc: 0.7457 - val_loss: 0.3855 - val_acc: 0.6867\n",
       "Epoch 224/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.0707 - acc: 0.9771 - val_loss: 0.1149 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 49us/step - loss: 0.3793 - acc: 0.7457 - val_loss: 0.3856 - val_acc: 0.6867\n",
       "Epoch 225/300\n",
-      "350/350 [==============================] - 0s 60us/step - loss: 0.0707 - acc: 0.9800 - val_loss: 0.1135 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 121us/step - loss: 0.3792 - acc: 0.7457 - val_loss: 0.3854 - val_acc: 0.6867\n",
       "Epoch 226/300\n",
-      "350/350 [==============================] - 0s 68us/step - loss: 0.0706 - acc: 0.9771 - val_loss: 0.1126 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 118us/step - loss: 0.3791 - acc: 0.7429 - val_loss: 0.3852 - val_acc: 0.6867\n",
       "Epoch 227/300\n",
-      "350/350 [==============================] - 0s 65us/step - loss: 0.0703 - acc: 0.9800 - val_loss: 0.1136 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 107us/step - loss: 0.3789 - acc: 0.7457 - val_loss: 0.3851 - val_acc: 0.6867\n",
       "Epoch 228/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.0700 - acc: 0.9800 - val_loss: 0.1128 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 161us/step - loss: 0.3789 - acc: 0.7457 - val_loss: 0.3849 - val_acc: 0.6867\n",
       "Epoch 229/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.0701 - acc: 0.9829 - val_loss: 0.1132 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.3785 - acc: 0.7429 - val_loss: 0.3849 - val_acc: 0.6867\n",
       "Epoch 230/300\n",
-      "350/350 [==============================] - 0s 106us/step - loss: 0.0698 - acc: 0.9800 - val_loss: 0.1143 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.3783 - acc: 0.7457 - val_loss: 0.3849 - val_acc: 0.6867\n",
       "Epoch 231/300\n",
-      "350/350 [==============================] - 0s 155us/step - loss: 0.0702 - acc: 0.9800 - val_loss: 0.1132 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3782 - acc: 0.7457 - val_loss: 0.3848 - val_acc: 0.6867\n",
       "Epoch 232/300\n",
-      "350/350 [==============================] - 0s 175us/step - loss: 0.0695 - acc: 0.9771 - val_loss: 0.1139 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 102us/step - loss: 0.3781 - acc: 0.7457 - val_loss: 0.3848 - val_acc: 0.6867\n",
       "Epoch 233/300\n",
-      "350/350 [==============================] - 0s 115us/step - loss: 0.0694 - acc: 0.9771 - val_loss: 0.1136 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 141us/step - loss: 0.3779 - acc: 0.7429 - val_loss: 0.3845 - val_acc: 0.6867\n",
       "Epoch 234/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.0694 - acc: 0.9800 - val_loss: 0.1130 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.3779 - acc: 0.7457 - val_loss: 0.3845 - val_acc: 0.6867\n",
       "Epoch 235/300\n",
-      "350/350 [==============================] - 0s 133us/step - loss: 0.0694 - acc: 0.9800 - val_loss: 0.1125 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 105us/step - loss: 0.3777 - acc: 0.7457 - val_loss: 0.3844 - val_acc: 0.6867\n",
       "Epoch 236/300\n",
-      "350/350 [==============================] - 0s 145us/step - loss: 0.0691 - acc: 0.9800 - val_loss: 0.1125 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 110us/step - loss: 0.3776 - acc: 0.7400 - val_loss: 0.3842 - val_acc: 0.6867\n",
       "Epoch 237/300\n",
-      "350/350 [==============================] - 0s 177us/step - loss: 0.0689 - acc: 0.9800 - val_loss: 0.1128 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3774 - acc: 0.7457 - val_loss: 0.3841 - val_acc: 0.6867\n",
       "Epoch 238/300\n",
-      "350/350 [==============================] - 0s 107us/step - loss: 0.0692 - acc: 0.9771 - val_loss: 0.1131 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.3771 - acc: 0.7457 - val_loss: 0.3839 - val_acc: 0.6867\n",
       "Epoch 239/300\n",
-      "350/350 [==============================] - 0s 123us/step - loss: 0.0689 - acc: 0.9800 - val_loss: 0.1134 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 76us/step - loss: 0.3772 - acc: 0.7457 - val_loss: 0.3838 - val_acc: 0.6867\n",
       "Epoch 240/300\n",
-      "350/350 [==============================] - 0s 108us/step - loss: 0.0688 - acc: 0.9800 - val_loss: 0.1121 - val_acc: 0.9600\n",
-      "Epoch 241/300\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "350/350 [==============================] - 0s 102us/step - loss: 0.0685 - acc: 0.9800 - val_loss: 0.1114 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.3770 - acc: 0.7457 - val_loss: 0.3837 - val_acc: 0.6867\n",
+      "Epoch 241/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.3769 - acc: 0.7457 - val_loss: 0.3837 - val_acc: 0.6867\n",
       "Epoch 242/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.0682 - acc: 0.9771 - val_loss: 0.1122 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 67us/step - loss: 0.3768 - acc: 0.7457 - val_loss: 0.3838 - val_acc: 0.6867\n",
       "Epoch 243/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.0689 - acc: 0.9800 - val_loss: 0.1117 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.3766 - acc: 0.7429 - val_loss: 0.3835 - val_acc: 0.6867\n",
       "Epoch 244/300\n",
-      "350/350 [==============================] - 0s 106us/step - loss: 0.0682 - acc: 0.9800 - val_loss: 0.1113 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 110us/step - loss: 0.3767 - acc: 0.7429 - val_loss: 0.3835 - val_acc: 0.6867\n",
       "Epoch 245/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.0681 - acc: 0.9800 - val_loss: 0.1116 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 127us/step - loss: 0.3764 - acc: 0.7457 - val_loss: 0.3835 - val_acc: 0.6867\n",
       "Epoch 246/300\n",
-      "350/350 [==============================] - 0s 152us/step - loss: 0.0682 - acc: 0.9771 - val_loss: 0.1125 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 118us/step - loss: 0.3763 - acc: 0.7457 - val_loss: 0.3835 - val_acc: 0.6867\n",
       "Epoch 247/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.0679 - acc: 0.9800 - val_loss: 0.1113 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3762 - acc: 0.7457 - val_loss: 0.3835 - val_acc: 0.6867\n",
       "Epoch 248/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.0678 - acc: 0.9800 - val_loss: 0.1111 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 123us/step - loss: 0.3762 - acc: 0.7429 - val_loss: 0.3833 - val_acc: 0.6867\n",
       "Epoch 249/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.0677 - acc: 0.9800 - val_loss: 0.1125 - val_acc: 0.9533\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.3759 - acc: 0.7457 - val_loss: 0.3831 - val_acc: 0.6867\n",
       "Epoch 250/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.0678 - acc: 0.9800 - val_loss: 0.1112 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 111us/step - loss: 0.3760 - acc: 0.7457 - val_loss: 0.3831 - val_acc: 0.6867\n",
       "Epoch 251/300\n",
-      "350/350 [==============================] - 0s 148us/step - loss: 0.0675 - acc: 0.9771 - val_loss: 0.1111 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 60us/step - loss: 0.3759 - acc: 0.7457 - val_loss: 0.3830 - val_acc: 0.6867\n",
       "Epoch 252/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.0673 - acc: 0.9771 - val_loss: 0.1116 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 103us/step - loss: 0.3756 - acc: 0.7457 - val_loss: 0.3829 - val_acc: 0.6867\n",
       "Epoch 253/300\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.0672 - acc: 0.9800 - val_loss: 0.1105 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 117us/step - loss: 0.3757 - acc: 0.7429 - val_loss: 0.3827 - val_acc: 0.6867\n",
       "Epoch 254/300\n",
-      "350/350 [==============================] - 0s 73us/step - loss: 0.0674 - acc: 0.9771 - val_loss: 0.1107 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 72us/step - loss: 0.3755 - acc: 0.7457 - val_loss: 0.3827 - val_acc: 0.6867\n",
       "Epoch 255/300\n",
-      "350/350 [==============================] - 0s 151us/step - loss: 0.0672 - acc: 0.9771 - val_loss: 0.1115 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.3754 - acc: 0.7457 - val_loss: 0.3826 - val_acc: 0.6867\n",
       "Epoch 256/300\n",
-      "350/350 [==============================] - 0s 113us/step - loss: 0.0671 - acc: 0.9800 - val_loss: 0.1106 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.3755 - acc: 0.7457 - val_loss: 0.3825 - val_acc: 0.6867\n",
       "Epoch 257/300\n",
-      "350/350 [==============================] - 0s 72us/step - loss: 0.0671 - acc: 0.9771 - val_loss: 0.1109 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.3751 - acc: 0.7457 - val_loss: 0.3825 - val_acc: 0.6867\n",
       "Epoch 258/300\n",
-      "350/350 [==============================] - 0s 111us/step - loss: 0.0667 - acc: 0.9800 - val_loss: 0.1099 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3752 - acc: 0.7457 - val_loss: 0.3824 - val_acc: 0.6867\n",
       "Epoch 259/300\n",
-      "350/350 [==============================] - 0s 145us/step - loss: 0.0669 - acc: 0.9771 - val_loss: 0.1106 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 106us/step - loss: 0.3750 - acc: 0.7457 - val_loss: 0.3823 - val_acc: 0.6867\n",
       "Epoch 260/300\n",
-      "350/350 [==============================] - 0s 127us/step - loss: 0.0664 - acc: 0.9800 - val_loss: 0.1094 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 129us/step - loss: 0.3749 - acc: 0.7457 - val_loss: 0.3823 - val_acc: 0.6867\n",
       "Epoch 261/300\n",
-      "350/350 [==============================] - 0s 69us/step - loss: 0.0668 - acc: 0.9771 - val_loss: 0.1090 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.3748 - acc: 0.7457 - val_loss: 0.3822 - val_acc: 0.6867\n",
       "Epoch 262/300\n",
-      "350/350 [==============================] - 0s 112us/step - loss: 0.0665 - acc: 0.9743 - val_loss: 0.1101 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.3746 - acc: 0.7457 - val_loss: 0.3821 - val_acc: 0.6867\n",
       "Epoch 263/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.0664 - acc: 0.9771 - val_loss: 0.1111 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 100us/step - loss: 0.3746 - acc: 0.7457 - val_loss: 0.3821 - val_acc: 0.6867\n",
       "Epoch 264/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.0662 - acc: 0.9743 - val_loss: 0.1102 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.3746 - acc: 0.7457 - val_loss: 0.3821 - val_acc: 0.6867\n",
       "Epoch 265/300\n",
-      "350/350 [==============================] - 0s 110us/step - loss: 0.0662 - acc: 0.9800 - val_loss: 0.1099 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.3747 - acc: 0.7429 - val_loss: 0.3821 - val_acc: 0.6867\n",
       "Epoch 266/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.0661 - acc: 0.9800 - val_loss: 0.1098 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 72us/step - loss: 0.3743 - acc: 0.7457 - val_loss: 0.3821 - val_acc: 0.6867\n",
       "Epoch 267/300\n",
-      "350/350 [==============================] - 0s 109us/step - loss: 0.0662 - acc: 0.9771 - val_loss: 0.1093 - val_acc: 0.9600\n",
+      "350/350 [==============================] - ETA: 0s - loss: 0.2986 - acc: 0.781 - 0s 85us/step - loss: 0.3743 - acc: 0.7457 - val_loss: 0.3819 - val_acc: 0.6867\n",
       "Epoch 268/300\n",
-      "350/350 [==============================] - 0s 122us/step - loss: 0.0658 - acc: 0.9800 - val_loss: 0.1095 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 131us/step - loss: 0.3741 - acc: 0.7457 - val_loss: 0.3819 - val_acc: 0.6867\n",
       "Epoch 269/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.0661 - acc: 0.9743 - val_loss: 0.1098 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 141us/step - loss: 0.3742 - acc: 0.7457 - val_loss: 0.3817 - val_acc: 0.6867\n",
       "Epoch 270/300\n",
-      "350/350 [==============================] - 0s 192us/step - loss: 0.0664 - acc: 0.9743 - val_loss: 0.1104 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 134us/step - loss: 0.3740 - acc: 0.7457 - val_loss: 0.3816 - val_acc: 0.6867\n",
       "Epoch 271/300\n",
-      "350/350 [==============================] - 0s 138us/step - loss: 0.0657 - acc: 0.9829 - val_loss: 0.1103 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 139us/step - loss: 0.3738 - acc: 0.7457 - val_loss: 0.3815 - val_acc: 0.6867\n",
       "Epoch 272/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.0654 - acc: 0.9743 - val_loss: 0.1100 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.3738 - acc: 0.7457 - val_loss: 0.3813 - val_acc: 0.6867\n",
       "Epoch 273/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.0657 - acc: 0.9800 - val_loss: 0.1104 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.3736 - acc: 0.7457 - val_loss: 0.3811 - val_acc: 0.6867\n",
       "Epoch 274/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.0655 - acc: 0.9771 - val_loss: 0.1097 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 144us/step - loss: 0.3738 - acc: 0.7457 - val_loss: 0.3810 - val_acc: 0.6867\n",
       "Epoch 275/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.0649 - acc: 0.9743 - val_loss: 0.1103 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.3735 - acc: 0.7457 - val_loss: 0.3811 - val_acc: 0.6867\n",
       "Epoch 276/300\n",
-      "350/350 [==============================] - 0s 171us/step - loss: 0.0656 - acc: 0.9800 - val_loss: 0.1098 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 121us/step - loss: 0.3734 - acc: 0.7457 - val_loss: 0.3811 - val_acc: 0.6867\n",
       "Epoch 277/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.0651 - acc: 0.9800 - val_loss: 0.1085 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 59us/step - loss: 0.3734 - acc: 0.7457 - val_loss: 0.3810 - val_acc: 0.6867\n",
       "Epoch 278/300\n",
-      "350/350 [==============================] - 0s 191us/step - loss: 0.0652 - acc: 0.9771 - val_loss: 0.1081 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.3732 - acc: 0.7457 - val_loss: 0.3809 - val_acc: 0.6867\n",
       "Epoch 279/300\n",
-      "350/350 [==============================] - 0s 166us/step - loss: 0.0650 - acc: 0.9771 - val_loss: 0.1082 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.3733 - acc: 0.7429 - val_loss: 0.3810 - val_acc: 0.6867\n",
       "Epoch 280/300\n",
-      "350/350 [==============================] - 0s 219us/step - loss: 0.0652 - acc: 0.9743 - val_loss: 0.1083 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 65us/step - loss: 0.3730 - acc: 0.7457 - val_loss: 0.3808 - val_acc: 0.6867\n",
       "Epoch 281/300\n",
-      "350/350 [==============================] - 0s 227us/step - loss: 0.0652 - acc: 0.9771 - val_loss: 0.1081 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 73us/step - loss: 0.3731 - acc: 0.7457 - val_loss: 0.3807 - val_acc: 0.6867\n",
       "Epoch 282/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.0651 - acc: 0.9743 - val_loss: 0.1087 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 95us/step - loss: 0.3729 - acc: 0.7457 - val_loss: 0.3807 - val_acc: 0.6867\n",
       "Epoch 283/300\n",
-      "350/350 [==============================] - 0s 129us/step - loss: 0.0648 - acc: 0.9771 - val_loss: 0.1081 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 72us/step - loss: 0.3728 - acc: 0.7457 - val_loss: 0.3807 - val_acc: 0.6867\n",
       "Epoch 284/300\n",
-      "350/350 [==============================] - 0s 170us/step - loss: 0.0649 - acc: 0.9771 - val_loss: 0.1080 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 132us/step - loss: 0.3728 - acc: 0.7457 - val_loss: 0.3807 - val_acc: 0.6867\n",
       "Epoch 285/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.0646 - acc: 0.9743 - val_loss: 0.1080 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.3727 - acc: 0.7457 - val_loss: 0.3805 - val_acc: 0.6867\n",
       "Epoch 286/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.0648 - acc: 0.9800 - val_loss: 0.1075 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 73us/step - loss: 0.3725 - acc: 0.7457 - val_loss: 0.3803 - val_acc: 0.6867\n",
       "Epoch 287/300\n",
-      "350/350 [==============================] - 0s 124us/step - loss: 0.0647 - acc: 0.9771 - val_loss: 0.1084 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.3725 - acc: 0.7457 - val_loss: 0.3803 - val_acc: 0.6867\n",
       "Epoch 288/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.0645 - acc: 0.9800 - val_loss: 0.1084 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3722 - acc: 0.7457 - val_loss: 0.3802 - val_acc: 0.6867\n",
       "Epoch 289/300\n",
-      "350/350 [==============================] - 0s 109us/step - loss: 0.0643 - acc: 0.9743 - val_loss: 0.1081 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 107us/step - loss: 0.3723 - acc: 0.7457 - val_loss: 0.3801 - val_acc: 0.6867\n",
       "Epoch 290/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.0643 - acc: 0.9771 - val_loss: 0.1074 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 69us/step - loss: 0.3723 - acc: 0.7457 - val_loss: 0.3801 - val_acc: 0.6867\n",
       "Epoch 291/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.0643 - acc: 0.9743 - val_loss: 0.1079 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.3721 - acc: 0.7457 - val_loss: 0.3800 - val_acc: 0.6867\n",
       "Epoch 292/300\n",
-      "350/350 [==============================] - 0s 104us/step - loss: 0.0643 - acc: 0.9771 - val_loss: 0.1074 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 116us/step - loss: 0.3720 - acc: 0.7457 - val_loss: 0.3800 - val_acc: 0.6867\n",
       "Epoch 293/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.0638 - acc: 0.9771 - val_loss: 0.1074 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 70us/step - loss: 0.3720 - acc: 0.7457 - val_loss: 0.3800 - val_acc: 0.6867\n",
       "Epoch 294/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.0640 - acc: 0.9800 - val_loss: 0.1080 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.3718 - acc: 0.7457 - val_loss: 0.3798 - val_acc: 0.6867\n",
       "Epoch 295/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.0641 - acc: 0.9743 - val_loss: 0.1079 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 105us/step - loss: 0.3718 - acc: 0.7457 - val_loss: 0.3798 - val_acc: 0.6867\n",
       "Epoch 296/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.0640 - acc: 0.9800 - val_loss: 0.1068 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.3718 - acc: 0.7457 - val_loss: 0.3797 - val_acc: 0.6867\n",
       "Epoch 297/300\n",
-      "350/350 [==============================] - 0s 123us/step - loss: 0.0638 - acc: 0.9743 - val_loss: 0.1076 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 107us/step - loss: 0.3716 - acc: 0.7457 - val_loss: 0.3797 - val_acc: 0.6867\n",
       "Epoch 298/300\n",
-      "350/350 [==============================] - 0s 67us/step - loss: 0.0641 - acc: 0.9771 - val_loss: 0.1079 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.3714 - acc: 0.7486 - val_loss: 0.3800 - val_acc: 0.6867\n",
       "Epoch 299/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.0637 - acc: 0.9771 - val_loss: 0.1082 - val_acc: 0.9600\n",
+      "350/350 [==============================] - 0s 115us/step - loss: 0.3714 - acc: 0.7457 - val_loss: 0.3798 - val_acc: 0.6867\n",
       "Epoch 300/300\n",
-      "350/350 [==============================] - 0s 131us/step - loss: 0.0636 - acc: 0.9800 - val_loss: 0.1076 - val_acc: 0.9600\n"
+      "350/350 [==============================] - 0s 64us/step - loss: 0.3713 - acc: 0.7457 - val_loss: 0.3797 - val_acc: 0.6867\n"
      ]
     }
    ],
@@ -1324,14 +1698,24 @@
     "# 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": 18,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1343,49 +1727,81 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe6d85f0080>"
+       "<matplotlib.figure.Figure at 0x7fb4f7e945c0>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 270,
+       "width": 402
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# Looking at the loss and accuracy on the training and validation sets during the training\n",
+    "# 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",
-    "plt.plot(np.arange(1, num_epochs+1), history_model[\"acc\"], \"blue\") ;\n",
-    "\n",
-    "plt.plot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], \"red\") ;"
+    "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": [
-    "**Here we dont't really see a big difference between the training and validation data because the function we are trying to fit is quiet simple and there is not too much noise. We will come back to these curves in a later example**"
+    "<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": 70,
+   "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": [
-    "In the example above we splitted our dataset into a 70-30 train-validation set. We know from previous chapters that to more robustly calculate accuracy we can use **K-fold crossvalidation**.\n",
+    "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",
+    "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",
-    "Can we somehow use these **Scikit learn** functions or ones we wrote ourselves for **Scikit learn** models to evaluate and tune our Keras models?\n",
     "\n",
     "The Answer is **YES !**\n",
+    "</p>\n",
+    "</div>\n",
+    "\n",
+    "\n",
     "\n",
     "We show how to do this in the following section."
    ]
@@ -1396,19 +1812,26 @@
    "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": 19,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1419,12 +1842,12 @@
     "# 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})"
+    "    build_fn=a_simple_NN, epochs=num_epochs, verbose=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1491,24 +1914,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Neural Net:\t 486 / 500 correct\n"
+      "Neural Net:\t 488 / 500 correct\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe7143e61d0>"
+       "<matplotlib.figure.Figure at 0x7fb4f46168d0>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 363,
+       "width": 388
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
@@ -1523,15 +1950,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The acuracy on the  5  validation folds: [ 0.96  0.96  0.94  0.97  0.96]\n",
-      "The Average acuracy on the  5  validation folds: 0.958\n"
+      "The acuracy on the  5  validation folds: [0.97 0.95 0.96 0.97 0.96]\n",
+      "The Average acuracy on the  5  validation folds: 0.962\n"
      ]
     }
    ],
@@ -1550,7 +1977,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### NOTE: The above code took quiet long even though we used only 5  CV folds and the neural network and data size are very small!"
+    "<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>"
    ]
   },
   {
@@ -1568,14 +1999,14 @@
     "* Internal model parameters (weights) which can be learned for e.g. by gradient-descent\n",
     "* Hyperparameters\n",
     "\n",
-    "In the model which we created above we made some arbitrary choices like which optimizer we use, what is its learning rate, number of hidden units and so on ...\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 model we can use the grid search functions we have seen in chapter 6."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1587,32 +2018,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "metadata": {},
-   "outputs": [],
+   "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": [
+      "0.894 {'epochs': 300}\n"
+     ]
+    }
+   ],
    "source": [
     "HP_grid = {'epochs' : [300, 500, 1000]}\n",
-    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
+    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid, n_jobs=3)\n",
     "search.fit(features, labels)\n",
     "print(search.best_score_, search.best_params_)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
-   "outputs": [],
+   "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": [
+      "0.8119999953508377 {'batch_size': 10, '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 = GridSearchCV(estimator=model_scikit, param_grid=HP_grid, n_jobs=4)\n",
     "search.fit(features, labels)\n",
     "print(search.best_score_, search.best_params_)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1640,9 +2103,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exercise: \n",
+    "### 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."
+    "* **OPTIONAL:** What function from SciKit learn other than GridSearchCV can we use for hyperparameter optimization? Use it."
    ]
   },
   {
@@ -1658,26 +2121,47 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exercise: Create a neural network to classify the 2d points example from chapter 2 learned \n",
-    "(Optional: As you create the model read a bit on the different keras commands we have used)"
+    "### 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": 23,
+   "execution_count": 72,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe7177f6a58>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+   "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": 73,
+   "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": [
@@ -1708,14 +2192,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The examples above are not the ideal use problems one should use neural networks for. They are too simple and can be easily solved by classical machine learning algorithms. Below we show examples which are the more common applications of Neural Networks."
+    "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\n",
+    "## 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",
@@ -1725,13 +2211,13 @@
     ">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",
-    "The problem we want to solve using this dataset is: multi-class classification (FIRST TIME)\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. "
+    "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": 24,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1746,7 +2232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -1764,24 +2250,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "This digit is:  9\n"
+      "This digit is:  5\n"
      ]
     },
     {
      "data": {
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lSVJNk54T8CpgDXA3u9wyOCIOA14PfA+4ccJ1SZJUzqR/Dvhw254fESf1XoyIA4E/AQ4GPpqZD/VbWZIkdWfSEwP/HrgMOAC4KSJuiojrgHuA04AvAP9jkjVJklTVxCcGZub5wBnALTT3DTgV+CbwLuB17VUCkiRpns3LnIDMXDVH/yeBT87HviVNx5YtW8Zaf3Z2tpNlRjFu7YceemhHlUiTtdAuEZQkSRNiCJAkqShDgCRJRRkCJEkqyhAgSVJRhgBJkooyBEiSVJQhQJKkogwBkiQVZQiQJKkoQ4AkSUUZAiRJKsoQIElSUYYASZKKmpdHCUuqZ+nSpWOtPzMzM9Yyu7P+IJs3bx55XfBRwlq8HAmQJKkoQ4AkSUUZAiRJKsoQIElSUYYASZKKMgRIklSUIUCSpKIMAZIkFWUIkCSpKEOAJElFGQIkSSrKECBJUlGGAEmSijIESJJUlCFAkqSi9pt2AVrY7rvvvr6v//AP//DQfoArr7xyXmraHatXrx5r/SVLloy87tKlS8fa9zi2bNky1vqbNm0aed0zzzxzrH3Pzs52sswoVqxYMS/blRY6RwIkSSrKECBJUlGGAEmSijIESJJUlCFAkqSiDAGSJBVlCJAkqShDgCRJRRkCJEkqyhAgSVJRhgBJkooyBEiSVJQhQJKkogwBkiQVNTNfj+ZcYNYDK6ddxGK0cmX/w7Zhw4ah/QA333zzWPse59ycmZkZa9/jPEr4qKOOGti3ceNGAJYvXz7y9ofZunXrWOsPezT0XMY95sP+/96xYwcA++wz+HvL0UcfPfK+77jjjpHXlaZgA7Cqiw05EiBJUlGGAEmSijIESJJUlCFAkqSiDAGSJBVlCJAkqShDgCRJRRkCJEkqyhAgSVJRhgBJkooyBEiSVJQhQJKkogwBkiQVZQiQJKkoQ4AkSUXt18VGImJf4DzgTGApsC/wb8BfAJdm5pO7LP9TwHuA5cCBwJ3AH2Xmn3VRj7rzq7/6qyP333bbbWPte9u2bWOtP619b9q0aaxlZmdnR973zMzMyOuOu+9xLVu2bKxlPvvZz3ZZjlTC2CMBbQD4K+By4CjgVmA98BLgQmB9RCzZaflTgC8Cb6T58L8R+HHgmoj4g3HrkSRJu6eLnwPOBk4DbgeOysyfycw3Aq8A/gl4NfA7ABFxAHB1u94pmfm6zDydJgQ8AFwQEXN/HZAkSWPrIgSc1bbvzMxv9F7MzK00PxEAvLVt1wGHAddk5o07LfuvwG+2f/5KBzVJkqQ5dBECtgKbgS/36fta276kbU9t20/1WfZvgKdofiaQJEnzbOyJge1w/iDL2/aBtj2mbe/os51vR8SDwOER8cLMfHjc2iRJ0mCdXB3QT0TM0EwMBLi2bV/ctt8csNo3gcOBFwKGgAVgzZo1I/fPtW5lO3bsmHYJi9LGjRunXYK0V5nP+wRcDKyk+TC/tH3tuW37xIB1eq8fOI91SZIk5mkkICIupJno9x3gjMzc0nY9Bcxk5qCLkWd2aTVl1113Xd/Xe9/yB/UDnHnmmWPte5xr9ad5vfywffdGAPbZZ3D+Xqz3CRh338PuAdAbAVi+fPnAZca5T8Ahhxwy8rrSYtbpSEBE7BcRH6a5JPBJYHVm3rTTItuBmYjYf8Am9t9pOUmSNI86CwERcSDNDP9zgEeBN2TmrtH8wbZ90YDNzDVnQJIkdaSTEBARz6e5S+CpwP3Ail1GAHp6VwUc3WcbB9NcSrjFKwMkSZp/Xdw2+NnAZ4BlwF3AiZn5jEsAWze07Zv79J1O88yBz4xbkyRJmlsXIwEXAifQjACsyswHhix7LfAIcFZEvKn3YkT8KHAJMAtc1kFNkiRpDmNdHRARL+Dp2/xuAd4fEX2Xzcy17Q2B3k4TBv42IjYAjwGvA5YAv5WZt49TkyRJ2j0z41wSFBGnArt1XU5mfv/6oYg4EfhdmhGEGZqfES7LzE+OXMxw62nuWaAJ2rx581jr33fffSOv+6lP9bsz9fR98IMfBOC8886bY8npOOmkk0Ze9+ijnzHVZ4+87GUvG9jXu4Rv69atcy4jFbABWNXFhsYaCcjMGxjhmv7M/CJPP0dAkiRNwXzeMVCSJC1ghgBJkooyBEiSVJQhQJKkogwBkiQVZQiQJKkoQ4AkSUUZAiRJKsoQIElSUYYASZKKMgRIklSUIUCSpKIMAZIkFWUIkCSpqJnZ2dlp1zAJ64GV0y5CkqQObABWdbEhRwIkSSrKECBJUlGGAEmSijIESJJUlCFAkqSiDAGSJBVlCJAkqShDgCRJRRkCJEkqyhAgSVJRhgBJkooyBEiSVJQhQJKkogwBkiQVZQiQJKkoQ4AkSUUZAiRJKsoQIElSUYYASZKKMgRIklSUIUCSpKIMAZIkFWUIkCSpKEOAJElFGQIkSSrKECBJUlGGAEmSijIESJJUlCFAkqSiDAGSJBVlCJAkqShDgCRJRRkCJEkqyhAgSVJRhgBJkooyBEiSVJQhQJKkogwBkiQVZQiQJKkoQ4AkSUUZAiRJKsoQIElSUYYASZKKMgRIklSUIUCSpKL262IjEbEvcB5wJrAU2Bf4N+AvgEsz88mdll0B3DRkc9dk5tou6pIkSYONHQLaAPBXwGnANuBW4HvACcCFwGkR8dOZ+Xi7yrFt+0Xgnj6bvGXcmiRJ0ty6GAk4myYA3A68KTO/ARARhwB/DbwG+B3g3e3yvRDwrsz0A1+SpCnpYk7AWW37zl4AAMjMrTQ/EQC8dafljwV2AF/pYN+SJGlEXYSArcBm4Mt9+r7Wti8BiIhnA0cDmzNzewf7liRJIxr754DMPH1I9/K2faBtXwk8C7g3Ii4C3gL8CPAQcC1wUWY+Om5NkiRpbvN2iWBEzNBMDITmAx6eng/wJuCdNFcQ3Aw8Hzgf+FJEHDpfNUmSpKfN530CLgZWAg8Dl7av9ULABuCIzDwtM08BXg58HjgS+NA81iRJklrzEgIi4kLgN4HvAGdk5pa269eAAE7f6bXeJMJfALYDqyPixfNRlyRJelonNwvqiYj9gD8GzgGeBNZk5vdvDJSZ3+PpyYI/IDMfjIjbgBXAccCnu6xNkiT9oM5CQEQcCHwSOBV4FPjZnQPAbnqobZd0VZckSeqvq9sGPx/4HLAMuJ/mpkF39FnuA8DhwLmZ+UifTR3Rtg/06ZMkSR0ae05Ae+3/Z2gCwF3Aif0CQOsk4M3AMy4rjIhX0kwc/Bawady6JEnScF1MDLyQ5jkB9wOrMnPYt/gPt+3FEXFU78X2ssCraB489N7M/G4HdUmSpCFmZmdnR145Il5AM3R/AHAbcPegZTNzbUTsA/wl8HPAd4Ev0FwR8FrgIOATwM9n5lMjF9XfeprLFSVJWuw2AKu62NC4cwKOpwkA0MzoP27Ismszc0dEnEFz9cDZwIk0zxG4E7gS+Fhmjp5KJEnSbhtrJGARWY8jAZKkvUNnIwHzecdASZK0gBkCJEkqyhAgSVJRhgBJkooyBEiSVJQhQJKkogwBkiQVZQiQJKkoQ4AkSUUZAiRJKsoQIElSUYYASZKKMgRIklSUIUCSpKIMAZIkFWUIkCSpKEOAJElFGQIkSSrKECBJUlGGAEmSijIESJJUlCFAkqSiDAGSJBVlCJAkqShDgCRJRRkCJEkqyhAgSVJRhgBJkooyBEiSVFSVEPDyaRcgSVJHOvtMqxICDpx2AZIkdaSzz7T9utrQAncPcASwDfj6lGuRJGkUL6cJAPd0tcGZ2dnZrrYlSZIWkSo/B0iSpF0YAiRJKsoQIElSUYYASZKKMgRIklSUIUCSpKIMAZIkFWUIkCSpKEOAJElFGQIkSSrKECBJUlGGAEmSijIESJJUlCFAkqSiDAGSJBVlCJAkqaj9pl3ANEXEzwAXAK8Cng1sAi7JzL+bamELWESsAz4+ZJE/yMzfnlQ9C1lEnAVcBazIzJv79B8J/D5wMvBDwNeBjwBXZOaOCZa6oAw7bhFxOPDvQ1a/JTNPnsfyFoyI2Bc4DzgTWArsC/wb8BfApZn55C7L/xTwHmA5cCBwJ/BHmflnk6x7mvbkmEXECuCmIZu7JjPXzmO5E1E2BOz0RvMd4B9pTobXAjdExLmZ+ZEplreQHdu2nwMe6dP/lQnWsmBFxGuAy4f0/wTNG8zBwC3ARprz73LgBGDRv7mMYq7jxtPn3+3AV/v0Z+dFLUDth9lfAacB24Bbge/RnDsXAqdFxE9n5uPt8qcAn6YZ/d0APA68DrgmIo7JzN+a/H/FZO3pMePpc+2LwD19NnnL/FY8GSVDQES8GPgQ8B/AyZl5R/v6cuAfgD+KiE9n5jemWOZC1fuH8Ysen/4iYg3wpzTftvr1z9CMphwMrMvMq9vXD6U5/94WEddn5rWTqXhhmOu4tXrn33sz85p5L2rhOpvmw+x24E29f4sRcQjw18BrgN8B3h0RBwBXt+udkpk3tsv+GLAeuCAirsvMTZP9T5i43T5m7fK9c+1dmblXfOD3U3VOwC8DzwHe3wsAAJm5EXgvsD9wzpRqW+h+EnjYAPBMEfHSiPg4cC3NyNLDAxY9heYnqPW9AACQmVuAX2r//JX5rHUh2YPjBk+/Me/tH1hzOatt37nzv8XM3Eoz3A3w1rZdBxxGM3x9407L/ivwm+2fFc63s9p2d44ZNOfaDvby0c2qIeDUtv1Un77r2/aNE6pl0YiII4Dn4RvwIBfRvOH+M80Q4+YByw08/9pvHI8AJ0fEQfNR5AK0u8cNmjfmbcDXJlDXQraV5jh9uU9f79i8pG2Hvd/9DfAUNd7vdvuYRcSzgaOBzZm5fTLlTUe5nwPaodijaRLe3X0W+Vrbd0xEzGTm7CTrW+B638IejojLad44XgrcRzPc+IzJSMVspplwdHVm7oiIQcsd07Z3DOhPmm9uRwNf6rTChWm3jltEvAB4GXAb8OvtJNVXAI8Cfwv8XmY+OJmSpyszTx/SvbxtH2jbgedbZn47Ih4EDo+IF2bmsFGYRW0Pj9krgWcB90bERcBbgB8BHqIZsbooMx+dp1InquJIwPNpfgr4VmZ+d9fOzPxPmsS4BKjyTWx3fX8+APA2mtnFX6IJAhcCn29/fywpMy/JzI/vxsz+F7ftNwf0915/YTeVLWx7cNx6599xwMU0IyY30nyZeTuwKYYkrwraLzkXtn/25pR4vg0x4Jj1zrU3Ae+kuYLgZprPj/OBL7VzeBa9iiHguW37+JBlnmjbYROUKur9w/gEcHhm/mxmrqT5pvEvwIk0Q7sabq5z0POvv975dycQmXlKZp4GHAH8OfAioPJkQWjC0UqaeRWXtq/1zrcn+q7h+dbvmPXOtQ3AEZl5WmaeArwc+DxwJM3k8kWvYgjofdsYNsw/s0urxs/RfOCv2/l3ssy8l2bSzSxwTkQ8ayrVLR5znYOef/29H/hRYFVmfv+SrfZcPBv4BrAsIk6YUn1TFREX0kz0+w5wRjvRFJrf/GeH/LRZ9nwbcsx+DQjg9J1e600i/AVgO7C6vdJsUSs3J4BmUhHAsGHr/dt2r54Qsqfa3/vvGtD3lYh4ADicJiXfOcnaFpm5zkHPvz4y8yn6X69NZj4eEf9IM8FwGc014CVExH7AH9Nc0fQksCYzd77JzXbgeRGx/4A5O+XOt7mOWWZ+jwGTTzPzwYi4DVhB89PUp+e/4vlTcSTg2zRvwoe0J8IPaF87BHhyb5n4MUEPte2SqVax8PUmr71oQP9cv+Gqv3LnX0QcSDPD/xyaCZJvyMzP7rKY59tOdvOYzWWvOdfKhYB2SOwumuuRj+yzSNAcl353IysrIg6KiI9ExP/pF55aR7St9xAYrjdL++hdO9pJSkfRDOH2HXWpKiLe055/Pz5gkd7598CA/r1KRDyf5mY/pwL309xmud9tboedbwfTXBa3ZW++MqBnd49ZRHwgIq6PiMMGbGqvOdfKhYDWDW375j59vdc+M6FaFottwGqaS2VW7toZEafSjKB8tcplWmMYdv6dCBwK3JyZj02upEXhVTTn3xm7drRv1q+nuQ3sjbv2723a69g/Q/PTx13AiTvf+GwXw86302m+EO3173d7eMxOojlez7isMCJeSTNx8FvsBfdMqRoCrqL5Heg3ImIfV/ggAAACW0lEQVRZ78X2ARvvopkte8WUaluQ2hGUK9s/L4+I3o1Iercf7R0vrw6Y2waaOROnRMTbey+2lxz1juP7plHYAvfhtj0/Ik7qvdgO7/4JzW2YP5qZD/VbeS9zIc2Nle6nmSg57BvptTSXU54VEW/qvRgRPwpcQjNB9bJ5rHWh2JNj1jvXLo6Io3ovtv9Gr6IJTu/td5n5YjMzO1vzXjgR8Us0E0O+R3PJxwzw0zSTJX9h59u5qtHeA+DvaZ56t43mulloHnzzHOCyzDx/SuUtOBGxnmbUpN/T8I6nOe8OpLnXwoPAKprrkK/MzLK3rZ7juL0P+HWaKyxuobmnxwqaUagvAKfu9ACYvVJ706QHaCaW3kb/m54B0HvKXUT8N56+LfMG4DGaBwgtAX4rMy+e57Knak+PWUTsA/wlzRVR36U5t7bTvNcdRHOZ9M+3k1UXtYpXBwCQmVdExL/TfPNfQXOJyM00j8L9/FSLW6Ay84mIeB3N5TNraf5BfIdmJvYHMvO6ada3mGTmlyPi1TTfTl5Lc4ey/0fz8JKPTrO2hSwzz4+IW2me/3EszYfa12me+fG/21nde7vjefrKkuPa/w2yFiAz/zoiVgK/S/NteIbmQTqXZeYn57HWhWKPjll758ozaCYPnk3zM90OmhG8K4GP7S13ky07EiBJUnVV5wRIklSeIUCSpKIMAZIkFWUIkCSpKEOAJElFGQIkSSrKECBJUlGGAEmSijIESJJUlCFAkqSiDAGSJBVlCJAkqShDgCRJRRkCJEkqyhAgSVJRhgBJkooyBEiSVNT/B1uQ7sdBHovFAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe66b039c50>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 254,
+       "width": 256
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
@@ -1798,7 +2288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -1823,7 +2313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
@@ -1841,7 +2331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1861,23 +2351,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**IMPORTANT: One-Hot encoding**\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "One-Hot encoding\n",
     "\n",
-    "**TODO: Better frame the explaination**\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",
-    "In such problems the labels are provided as something called **One-hot encodings**. What this does is to convert a categorical label to a vector.\n",
-    "\n",
-    "For the MNIST problem where we have **10 categories** one-hot encoding will create a vector of length 10 for each of the labels. All the entries of this vector will be zero **except** for the index which is equal to the integer value of the label.\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, we don't have to code this ourselves because Keras has a built-in function for this."
+    "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": 30,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -1899,7 +2392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -1907,45 +2400,45 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.5722 - acc: 0.8494\n",
+      "60000/60000 [==============================] - 2s 38us/step - loss: 0.5605 - acc: 0.8502\n",
       "Epoch 2/20\n",
-      "60000/60000 [==============================] - 1s 18us/step - loss: 0.2583 - acc: 0.9256\n",
+      "60000/60000 [==============================] - 1s 19us/step - loss: 0.2453 - acc: 0.9296\n",
       "Epoch 3/20\n",
-      "60000/60000 [==============================] - 1s 17us/step - loss: 0.2006 - acc: 0.9418\n",
+      "60000/60000 [==============================] - 1s 19us/step - loss: 0.1886 - acc: 0.9447\n",
       "Epoch 4/20\n",
-      "60000/60000 [==============================] - 1s 17us/step - loss: 0.1650 - acc: 0.9516\n",
+      "60000/60000 [==============================] - 1s 19us/step - loss: 0.1546 - acc: 0.9548\n",
       "Epoch 5/20\n",
-      "60000/60000 [==============================] - 1s 16us/step - loss: 0.1422 - acc: 0.9584\n",
+      "60000/60000 [==============================] - 1s 19us/step - loss: 0.1316 - acc: 0.9619\n",
       "Epoch 6/20\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1235 - acc: 0.9638\n",
+      "60000/60000 [==============================] - 1s 19us/step - loss: 0.1145 - acc: 0.9663\n",
       "Epoch 7/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.1093 - acc: 0.9666\n",
+      "60000/60000 [==============================] - 1s 19us/step - loss: 0.0998 - acc: 0.9707\n",
       "Epoch 8/20\n",
-      "60000/60000 [==============================] - 1s 17us/step - loss: 0.0975 - acc: 0.9706\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0890 - acc: 0.9730\n",
       "Epoch 9/20\n",
-      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0891 - acc: 0.9732\n",
+      "60000/60000 [==============================] - 1s 19us/step - loss: 0.0798 - acc: 0.9765\n",
       "Epoch 10/20\n",
-      "60000/60000 [==============================] - 1s 16us/step - loss: 0.0810 - acc: 0.9757\n",
+      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0716 - acc: 0.9786\n",
       "Epoch 11/20\n",
-      "60000/60000 [==============================] - 1s 16us/step - loss: 0.0745 - acc: 0.9776\n",
+      "60000/60000 [==============================] - 1s 19us/step - loss: 0.0662 - acc: 0.9797\n",
       "Epoch 12/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0677 - acc: 0.9797\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0594 - acc: 0.9826\n",
       "Epoch 13/20\n",
-      "60000/60000 [==============================] - 1s 17us/step - loss: 0.0623 - acc: 0.9813\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0551 - acc: 0.9841\n",
       "Epoch 14/20\n",
-      "60000/60000 [==============================] - 1s 15us/step - loss: 0.0574 - acc: 0.9829\n",
+      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0509 - acc: 0.9850\n",
       "Epoch 15/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0537 - acc: 0.9841\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0468 - acc: 0.9861\n",
       "Epoch 16/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0506 - acc: 0.9845\n",
+      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0428 - acc: 0.9875\n",
       "Epoch 17/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0466 - acc: 0.9860\n",
+      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0400 - acc: 0.9887\n",
       "Epoch 18/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0439 - acc: 0.9868\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0367 - acc: 0.9897\n",
       "Epoch 19/20\n",
-      "60000/60000 [==============================] - 1s 17us/step - loss: 0.0410 - acc: 0.9877\n",
+      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0340 - acc: 0.9905\n",
       "Epoch 20/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0374 - acc: 0.9884\n"
+      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0311 - acc: 0.9914\n"
      ]
     }
    ],
@@ -1975,15 +2468,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10000/10000 [==============================] - 1s 63us/step\n",
-      "The [loss, accuracy] on test dataset are:  [0.15624154731309972, 0.95640000000000003]\n"
+      "10000/10000 [==============================] - 1s 65us/step\n",
+      "The [loss, accuracy] on test dataset are:  [0.0949482088279212, 0.971]\n"
      ]
     }
    ],
@@ -1995,12 +2488,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Optional exercise: 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."
+    "### 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": null,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2009,7 +2503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -2018,66 +2512,60 @@
      "text": [
       "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0092 - acc: 0.9976 - val_loss: 0.1240 - val_acc: 0.9700\n",
+      "60000/60000 [==============================] - 2s 41us/step - loss: 0.5688 - acc: 0.8466 - val_loss: 0.2997 - val_acc: 0.9124\n",
       "Epoch 2/20\n",
-      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0088 - acc: 0.9979 - val_loss: 0.1109 - val_acc: 0.9744\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.2601 - acc: 0.9239 - val_loss: 0.2568 - val_acc: 0.9244\n",
       "Epoch 3/20\n",
-      "60000/60000 [==============================] - 1s 19us/step - loss: 0.0079 - acc: 0.9981 - val_loss: 0.1234 - val_acc: 0.9727\n",
+      "60000/60000 [==============================] - 2s 27us/step - loss: 0.2041 - acc: 0.9398 - val_loss: 0.1820 - val_acc: 0.9427\n",
       "Epoch 4/20\n",
-      "60000/60000 [==============================] - 1s 19us/step - loss: 0.0074 - acc: 0.9983 - val_loss: 0.1047 - val_acc: 0.9764\n",
+      "60000/60000 [==============================] - 1s 22us/step - loss: 0.1691 - acc: 0.9503 - val_loss: 0.1718 - val_acc: 0.9475\n",
       "Epoch 5/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0074 - acc: 0.9981 - val_loss: 0.1147 - val_acc: 0.9748\n",
+      "60000/60000 [==============================] - 1s 22us/step - loss: 0.1450 - acc: 0.9573 - val_loss: 0.1663 - val_acc: 0.9496\n",
       "Epoch 6/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0067 - acc: 0.9983 - val_loss: 0.1150 - val_acc: 0.9765\n",
+      "60000/60000 [==============================] - 1s 22us/step - loss: 0.1272 - acc: 0.9618 - val_loss: 0.1537 - val_acc: 0.9522\n",
       "Epoch 7/20\n",
-      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0060 - acc: 0.9986 - val_loss: 0.1161 - val_acc: 0.9753\n",
+      "60000/60000 [==============================] - 1s 22us/step - loss: 0.1116 - acc: 0.9669 - val_loss: 0.1249 - val_acc: 0.9627\n",
       "Epoch 8/20\n",
-      "60000/60000 [==============================] - 1s 16us/step - loss: 0.0062 - acc: 0.9985 - val_loss: 0.1457 - val_acc: 0.9682\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.1004 - acc: 0.9701 - val_loss: 0.1349 - val_acc: 0.9578\n",
       "Epoch 9/20\n",
-      "60000/60000 [==============================] - 1s 16us/step - loss: 0.0056 - acc: 0.9986 - val_loss: 0.1162 - val_acc: 0.9758\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0905 - acc: 0.9733 - val_loss: 0.1214 - val_acc: 0.9624\n",
       "Epoch 10/20\n",
-      "60000/60000 [==============================] - 1s 25us/step - loss: 0.0050 - acc: 0.9989 - val_loss: 0.1097 - val_acc: 0.9768\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0819 - acc: 0.9757 - val_loss: 0.1303 - val_acc: 0.9607\n",
       "Epoch 11/20\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.0054 - acc: 0.9986 - val_loss: 0.1148 - val_acc: 0.9757\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0745 - acc: 0.9784 - val_loss: 0.1025 - val_acc: 0.9694\n",
       "Epoch 12/20\n",
-      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0044 - acc: 0.9990 - val_loss: 0.1148 - val_acc: 0.9772\n",
+      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0680 - acc: 0.9795 - val_loss: 0.1105 - val_acc: 0.9675\n",
       "Epoch 13/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0046 - acc: 0.9990 - val_loss: 0.1199 - val_acc: 0.9746\n",
+      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0630 - acc: 0.9811 - val_loss: 0.1209 - val_acc: 0.9632\n",
       "Epoch 14/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0042 - acc: 0.9990 - val_loss: 0.1156 - val_acc: 0.9770\n",
+      "60000/60000 [==============================] - 2s 25us/step - loss: 0.0574 - acc: 0.9831 - val_loss: 0.1206 - val_acc: 0.9653\n",
       "Epoch 15/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0035 - acc: 0.9992 - val_loss: 0.1206 - val_acc: 0.9757\n",
+      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0537 - acc: 0.9846 - val_loss: 0.1281 - val_acc: 0.9616\n",
       "Epoch 16/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0040 - acc: 0.9990 - val_loss: 0.1252 - val_acc: 0.9757\n",
+      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0488 - acc: 0.9851 - val_loss: 0.1076 - val_acc: 0.9676\n",
       "Epoch 17/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0038 - acc: 0.9993 - val_loss: 0.1305 - val_acc: 0.9741\n",
+      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0456 - acc: 0.9859 - val_loss: 0.0998 - val_acc: 0.9702\n",
       "Epoch 18/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0032 - acc: 0.9994 - val_loss: 0.1391 - val_acc: 0.9723\n",
+      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0421 - acc: 0.9877 - val_loss: 0.1129 - val_acc: 0.9674\n",
       "Epoch 19/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0033 - acc: 0.9993 - val_loss: 0.1244 - val_acc: 0.9759\n",
+      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0382 - acc: 0.9887 - val_loss: 0.0976 - val_acc: 0.9721\n",
       "Epoch 20/20\n",
-      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0031 - acc: 0.9993 - val_loss: 0.1263 - val_acc: 0.9770\n",
+      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0360 - acc: 0.9895 - val_loss: 0.1066 - val_acc: 0.9680\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": [
-       "[<matplotlib.lines.Line2D at 0x7fe6681f74e0>]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe66b03b0f0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 269,
+       "width": 398
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
@@ -2086,6 +2574,7 @@
    "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",
@@ -2094,9 +2583,28 @@
     "print(\"The history has the following data: \", history_model.keys())\n",
     "\n",
     "# Plotting the training and validation accuracy during the training\n",
-    "plt.plot(np.arange(1, num_epochs+1), history_model[\"acc\"], \"blue\")\n",
-    "\n",
-    "plt.plot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], \"red\")"
+    "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>"
    ]
   },
   {
@@ -2154,10 +2662,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Another way to add regularization and to make the network more robust we can add something called \"Dropout\". When we add dropout to a layer a specified percentage of units in that layer are switched off. \n",
-    "(MAKING MODEL SIMPLER)\n",
+    "<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: Add dropout instead of l2 regularization in the network above"
+    "### Exercise section\n",
+    "* Add dropout instead of L2 regularization in the network above"
    ]
   },
   {
@@ -2230,7 +2744,7 @@
     "\n",
     "###  Convolution Neural Networks (CNNs)\n",
     "\n",
-    "These networks are used mostly for computer vision (EXAMPLES) like tasks. \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",
@@ -2240,26 +2754,314 @@
     "</figure>\n",
     "</center>\n",
     "\n",
-    "CNNs consist of new type of layers like convolution layer and pooling layers.\n",
+    "CNNs consist of new type of layers such as convolution and pooling layers.\n",
     "\n",
     "###  Recurrent Neural Networks (RNNs)\n",
     "\n",
-    "These are used for time-series data, speech recognition, translation etc.\n",
-    "\n",
-    "IMAGE HERE\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. \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",
+    "\n",
+    "INSERT IMAGE HERE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
+      "  return f(*args, **kwds)\n",
+      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
+      "  return f(*args, **kwds)\n",
+      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216, got 192\n",
+      "  return f(*args, **kwds)\n",
+      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1a1eec9e10>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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KylD6/yc23ADG8HAOYAyhB4wh9IAxhB4whtADxhB6wBhCDxhD6AFj/gdQ2rNHDswMcgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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": 8,
+   "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": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_astable(digit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD7CAYAAACPDORaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAABxVJREFUeJzt3aFrln0bxvHzepExbstdZHEij0kZLFgMgmCxLon/gNZhEZcWxJlELMMgmDSYtK4tiQaLWJ1lQWwibIxdT1D2e8PbxutvO+7PBxa2lZMD+TouptcwjuNYAET6T+8DAPj/EXmAYCIPEEzkAYKJPEAwkQcIJvIAwUQeIJjIAwQTeYBgIg8QTOQBgok8QDCRBwgm8gDBRB4gmMgDBBN5gGAiDxBM5AGCiTxAMJEHCCbyAMFEHiCYyAMEE3mAYCIPEEzkAYKJPEAwkQcIJvIAwUQeIJjIAwQTeYBgIg8QTOQBgok8QDCRBwgm8gDBRB4gmMgDBBN5gGAiDxBM5AGCiTxAMJEHCCbyAMFEHiCYyAMEE3mAYCIPEEzkAYKJPECwM70PmGXnz5+vnZ2d3mecCJPJpH79+tX7jBPBFs3i4mJ9/fq19xmn2jCO49j7iFk1DEOZ/zdbNLZobHF8HtcABBN5gGAiDxBM5AGCiTxAMJEHCCbyAMFEHiCYyAMEE3mAYCIPEEzkAYKJPEAwkaeqqnZ2dmppaakODg56n9KdLRpbnH4iT+3u7tadO3dqb2+v9ynd2aKxRQaRn3FbW1u1srJSc3NzvU/pzhaNLXKI/Izb3t6u1dXVWltb631Kd7ZobJHD6/9m3Pr6elVVvX//vvMl/dmisUUOP8kDBBP5GbK5uVnLy8tHHx8/fux9Uje2aGyRzeOaGXLr1q26efPm0ecLCwsdr+nLFo0tson8DJlOpzWdTnufcSLYorFFNo9rAIKJPECwYRzHsfcRs2oYhjL/b7ZobNHY4vj8JA8QTOQBgok8QDCRBwgm8gDBRB4gmMgDBBN5gGAiDxBM5AGCiTxAMJEHCCbyAMFEHiCYyAMEE3mAYN7x2tFkMqlhGHqfcSLMz8/b4g9bNJPJpPcJp543Q3XkrTeNLRpbNLY4Po9rAIKJPEAwkQcIJvIAwUQeIJjIAwQTeYBgIg8QTOQBgok8QDCRBwgm8gDBRB4gmMjPqG/fvtXdu3frypUrde3atdrY2Ki9vb3eZ3Vhi8YWefx/8jNof3+/7t69W//880+9fv26fvz4UQ8ePKiqqvv373e+7u+yRWOLUCPd9Jr/w4cP46VLl8afP38efe3t27fj1atXu9wzjrb4b7ZoJOr4PK6ZQRcuXKjnz5/X2bNnj742DEPt7+93vKoPWzS2yOTNUB2dlLfeHB4e1u3bt2s6ndbm5maXG2zR2KI5KVucZp7JU48ePaovX77Umzdvep/SnS0aW2QQ+Rk2jmM9fPiwXr16VU+fPq2LFy/2PqkbWzS2yCLyM+rw8LDW1tbq3bt39eTJk7px40bvk7qxRWOLPCI/ozY2Nurdu3f17Nmzun79eu9zurJFY4s8Ij+DPn36VC9fvqx79+7V5cuX6/v370ffO3fuXMfL/j5bNLbI5LdrOur1mwOPHz+uFy9e/M/vff78uc6c+ft/99uisUXjt2uOT+Q78ge4sUVji8YWx+cfQwEEE3mAYCIPEEzkAYKJPEAwkQcIJvIAwUQeIJjIAwQTeYBgIg8QTOQBgok8QDCRBwgm8gDBRB4gmMgDBPOO144mk0kNw9D7jBNhfn7eFn/YoplMJr1POPW8/q8jrzZrbNHYorHF8XlcAxBM5AGCiTxAMJEHCCbyAMFEHiCYyAMEE3mAYCIPEEzkAYKJPEAwkQcIJvIAwUSeqqra2dmppaWlOjg46H1Kd7ZobHH6iTy1u7tbd+7cqb29vd6ndGeLxhYZRH7GbW1t1crKSs3NzfU+pTtbNLbIIfIzbnt7u1ZXV2ttba33Kd3ZorFFDq//m3Hr6+tVVfX+/fvOl/Rni8YWOfwkDxBM5GfI5uZmLS8vH318/Pix90nd2KKxRTaPa2bIrVu36ubNm0efLywsdLymL1s0tsgm8jNkOp3WdDrtfcaJYIvGFtk8rgEIJvIAwYZxHMfeR8yqYRjK/L/ZorFFY4vj85M8QDCRBwgm8gDBRB4gmMgDBBN5gGAiDxBM5AGCiTxAMJEHCCbyAMFEHiCYyAMEE3mAYCIPEEzkAYJ5x2tHi4uLNQxD7zNOhMlkYos/bNEsLi72PuHU82YogGAe1wAEE3mAYCIPEEzkAYKJPEAwkQcIJvIAwUQeIJjIAwQTeYBgIg8QTOQBgok8QDCRBwgm8gDBRB4gmMgDBBN5gGAiDxBM5AGCiTxAMJEHCCbyAMFEHiCYyAMEE3mAYCIPEEzkAYKJPEAwkQcIJvIAwUQeIJjIAwQTeYBgIg8QTOQBgok8QDCRBwgm8gDBRB4gmMgDBBN5gGAiDxBM5AGCiTxAMJEHCCbyAMFEHiCYyAMEE3mAYCIPEEzkAYKJPEAwkQcIJvIAwUQeIJjIAwQTeYBgIg8QTOQBgok8QDCRBwgm8gDBRB4gmMgDBPsXTrgLIWqnv9IAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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": 32,
+   "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": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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",
-    "IMAGE HERE"
+    "http://setosa.io/ev/image-kernels"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## CNN example"
+    "## CNN Examples"
    ]
   },
   {
@@ -2288,7 +3090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2309,29 +3111,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This item is a:  T-shirt/top\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe64931db38>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
     "import matplotlib.pyplot as plt\n",
@@ -2343,17 +3125,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000, 10)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "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",
@@ -2425,16 +3199,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exercise: 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)"
+    "### 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: Load and play with the CIFAR10 dataset also included with Keras and build+train a simple CNN using it"
+    "### 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": 70,
+   "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,