MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The MNIST Dataset\n",
    "\n",
    "In this notebook, we will create a neural network to recognize handwritten digits from the famous MNIST dataset.\n",
    "\n",
    "We will experiment with two different networks for this task. The first one will be a *multi-layer perceptron* (MLP), which is a standard type of feedforward neural network with fully-connected layers of weights, and the second will be a *convolutional neural network* (CNN), which takes advantage of the inherently two-dimensional spatial geometry of the input images.\n",
    "\n",
    "Let's begin by reading in the MNIST dataset and printing a short description of its contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "ConX, version 3.7.4\n"
     ]
    }
   ],
   "source": [
    "import conx as cx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Dataset**: MNIST\n",
       "\n",
       "\n",
       "Original source: http://yann.lecun.com/exdb/mnist/\n",
       "\n",
       "The MNIST dataset contains 70,000 images of handwritten digits (zero\n",
       "to nine) that have been size-normalized and centered in a square grid\n",
       "of pixels.  Each image is a 28 × 28 × 1 array of floating-point numbers\n",
       "representing grayscale intensities ranging from 0 (black) to 1\n",
       "(white).  The target data consists of one-hot binary vectors of size\n",
       "10, corresponding to the digit classification categories zero through\n",
       "nine.  Some example MNIST images are shown below:\n",
       "\n",
       "![MNIST Images](https://github.com/Calysto/conx/raw/master/data/mnist_images.png)\n",
       "\n",
       "**Information**:\n",
       "   * name    : MNIST\n",
       "   * length  : 70000\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : (28, 28, 1)\n",
       "   * range  : (0.0, 1.0)\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : (10,)\n",
       "   * range  : (0.0, 1.0)\n",
       "\n"
      ],
      "text/plain": [
       "**Dataset**: MNIST\n",
       "\n",
       "\n",
       "Original source: http://yann.lecun.com/exdb/mnist/\n",
       "\n",
       "The MNIST dataset contains 70,000 images of handwritten digits (zero\n",
       "to nine) that have been size-normalized and centered in a square grid\n",
       "of pixels.  Each image is a 28 × 28 × 1 array of floating-point numbers\n",
       "representing grayscale intensities ranging from 0 (black) to 1\n",
       "(white).  The target data consists of one-hot binary vectors of size\n",
       "10, corresponding to the digit classification categories zero through\n",
       "nine.  Some example MNIST images are shown below:\n",
       "\n",
       "![MNIST Images](https://github.com/Calysto/conx/raw/master/data/mnist_images.png)\n",
       "\n",
       "**Information**:\n",
       "   * name    : MNIST\n",
       "   * length  : 70000\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : (28, 28, 1)\n",
       "   * range  : (0.0, 1.0)\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : (10,)\n",
       "   * range  : (0.0, 1.0)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist = cx.Dataset.get('mnist')\n",
    "mnist.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also print a shorter, less detailed summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "MNIST:\n",
      "Patterns    Shape                 Range                         \n",
      "=================================================================\n",
      "inputs      (28, 28, 1)           (0.0, 1.0)                    \n",
      "targets     (10,)                 (0.0, 1.0)                    \n",
      "=================================================================\n",
      "Total patterns: 70000\n",
      "   Training patterns: 70000\n",
      "   Testing patterns: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "mnist.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at some input patterns. Typing ``mnist.inputs[0]`` returns a large nested list of numbers, which isn't a particularly useful way to view a pattern.  A better way is to use ConX's ``view`` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAO4AAADuCAYAAAA+7jsiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAABqNJREFUeJzt3U+Izf8ex/FzLlmQP82GUq5YUCIWKClJkxSLiQ1lgx2ymo2dxUgZC2QxK2UhW2ZF+btQSv5syPIWZnNjkPzJzLnLuzrvmTlmfuZ1PB7b1/frfBeefdXHmWm2Wq0GkOVff/oBgKkTLgQSLgQSLgQSLgQSLgQSLgQSLgQSLgSaO5WLm82m/2YFM6zVajUnusYbFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwIJFwLN/dMPQG3OnDnlvnjx4hn9/BMnTrTd5s+fX967Zs2acj9+/Hi5Dw4Ott0OHjxY3vv9+/dyP3fuXLmfOXOm3P80b1wIJFwIJFwIJFwIJFwIJFwIJFwI5Bx3ElasWFHu8+bNK/dt27aV+/bt29tuS5YsKe/dv39/uf9Jb9++LfdLly6Ve19fX9vty5cv5b0vX74s94cPH5b7bOeNC4GEC4GEC4GEC4GEC4GEC4GarVZr8hc3m5O/OMimTZvK/e7du+U+01+tm63Gx8fL/ciRI+X+9evXjj/7/fv35f7x48dyf/PmTcefPdNarVZzomu8cSGQcCGQcCGQcCGQcCGQcCGQcCGQc9xGo9HT01PuT548KfdVq1ZN5+NMq4mefXR0tNx37tzZdvv582d57996vv27nONClxIuBBIuBBIuBBIuBBIuBBIuBPLjWRuNxocPH8q9v7+/3Pfu3Vvuz58/L/eJfkxp5cWLF+Xe29tb7hN9J3bdunVtt1OnTpX3MnO8cSGQcCGQcCGQcCGQcCGQcCGQcCGQ7+NOg0WLFpX7RL8ScmhoqO129OjR8t7Dhw+X+/Xr18ud2cf3caFLCRcCCRcCCRcCCRcCCRcCCRcC+T7uNPj8+fNv3f/p06eO7z127Fi537hxo9wn+h23zE7euBBIuBBIuBBIuBBIuBBIuBDI1/pmgQULFrTdhoeHy3t37NhR7nv27Cn3O3fulDv/PF/rgy4lXAgkXAgkXAgkXAgkXAgkXAjkHHeWW716dbk/e/as3EdHR8v9/v375f706dO225UrV8p7p/J3i/9zjgtdSrgQSLgQSLgQSLgQSLgQSLgQyDluuL6+vnK/evVquS9cuLDjzz59+nS5X7t2rdxHRkY6/uxu5hwXupRwIZBwIZBwIZBwIZBwIZBwIZBz3C63fv36cr9w4UK579q1q+PPHhoaKveBgYFyf/fuXcefncw5LnQp4UIg4UIg4UIg4UIg4UIg4UIg57h/uSVLlpT7vn372m4Tfde32ayPI+/du1fuvb295d6tnONClxIuBBIuBBIuBBIuBBIuBHIcRMd+/PhR7nPnzi33X79+lfvu3bvbbg8ePCjvTeY4CLqUcCGQcCGQcCGQcCGQcCGQcCFQfdBGvA0bNpT7gQMHyn3z5s1tt4nOaSfy6tWrcn/06NFv/fndzBsXAgkXAgkXAgkXAgkXAgkXAgkXAjnHneXWrFlT7idPniz3vr6+cl+2bNmUn2myxsbGyn1kZKTcx8fHp/Nxuoo3LgQSLgQSLgQSLgQSLgQSLgQSLgRyjvsPmOis9NChQ22348ePl/euXLmyk0eaFk+fPi33gYGBcr9169Z0Ps5fxRsXAgkXAgkXAgkXAgkXAgkXAjkOmoSlS5eW+7p168r98uXL5b527dopP9N0efLkSbmfP3++7Xbz5s3yXl/LmzneuBBIuBBIuBBIuBBIuBBIuBBIuBDorznH7enpabsNDQ2V927cuLHcV61a1dEzTYfHjx+X+4ULF8r99u3b5f7t27cpPxMzzxsXAgkXAgkXAgkXAgkXAgkXAgkXAsWc427durXc+/v7y33Lli1tt+XLl3f0TNOlOiu9ePFiee/Zs2fL/evXrx09E7ObNy4EEi4EEi4EEi4EEi4EEi4EEi4EijnH7evr+639d7x+/brch4eHy31sbKzcBwcH226jo6PlvfydvHEhkHAhkHAhkHAhkHAhkHAhkHAhULPVak3+4mZz8hcDHWm1Ws2JrvHGhUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUDChUBT/TWb/200Gv+ZiQcBGo1Go/HvyVw0pZ+rDMwO/qkMgYQLgYQLgYQLgYQLgYQLgYQLgYQLgYQLgf4HRxY3BdR2KnEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cx.view(mnist.inputs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's easy to view multiple patterns at once using Python's slice operator in conjunction with the ``layout`` keyword:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cx.view(mnist.inputs[0:20], layout=(2,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ConX allows us to refer to multiple input patterns using arbitrary indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cx.view(mnist.inputs[0, 2, 4, 77, 150, 88, 9000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing the target patterns works in a similar way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.targets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cx.view(mnist.targets[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],\n",
       " [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n",
       " [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n",
       " [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n",
       " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.targets[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to ``inputs`` and ``targets``, each dataset has a ``labels`` property that stores a collection of strings describing the classification category of each input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x144 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['5', '0', '4', '1', '9']\n"
     ]
    }
   ],
   "source": [
    "cx.view(mnist.inputs[0:5])\n",
    "print(mnist.labels[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``labels`` property is useful for selecting particular input patterns. For example, to view just the \"two\"'s in the dataset, we can first create a list containing the index numbers of all of the \"two\"'s, and then view the first twenty, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 6990 twos\n",
      "[5, 16, 25, 28, 76, 82, 109, 117, 120, 122, 143, 159, 161, 171, 178, 180, 187, 189, 190, 199]\n"
     ]
    }
   ],
   "source": [
    "indices = [i for i in range(len(mnist.inputs)) if mnist.labels[i] == '2']\n",
    "print('Found', len(indices), 'twos')\n",
    "print(indices[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cx.view(mnist.inputs[indices[:20]], layout=(2,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can accomplish the same thing more directly (and efficiently) using the `select` method, together with the `slice` keyword:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "digits = mnist.inputs.select(lambda i,ds: ds.labels[i] == '2', slice=20)\n",
    "cx.view(digits, layout=(2,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's randomly shuffle the dataset before we train the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist.shuffle()\n",
    "cx.view(mnist.inputs[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When training a neural network, it is usually best to divide the dataset into two subsets: the *training* set and the *testing* (or *validation*) set.  We train the network on the training patterns only, and test its performance on the validation set to see how well it handles novel patterns.  Calling ``split()`` returns a tuple containing the current sizes of the training and testing sets, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now split the data into 60,000 training patterns and 10,000 testing patterns, and then verify the split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.split(10000)\n",
    "mnist.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last 10,000 patterns in the dataset are now reserved for testing.  As a convenience, we can refer to the training and testing sets independently, counting from 0, via the properties ``train_inputs``, ``train_targets``, ``train_labels``, and ``test_inputs``, ``test_targets``, and ``test_labels``.  The properties `inputs`, `targets`, and `labels` refer to all 70,000 input images, regardless of the current split.  For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70000 total images\n",
      "60000 images for training\n",
      "10000 images for testing\n"
     ]
    }
   ],
   "source": [
    "print(len(mnist.inputs), 'total images')\n",
    "print(len(mnist.train_inputs), 'images for training')\n",
    "print(len(mnist.test_inputs), 'images for testing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '7', '9', '3', '5', '0', '7', '4', '8', '3']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '7', '9', '3', '5', '0', '7', '4', '8', '3']\n"
     ]
    }
   ],
   "source": [
    "cx.view(mnist.inputs[0:10])\n",
    "print(mnist.labels[0:10])\n",
    "cx.view(mnist.train_inputs[0:10])  # same as inputs[0:10]\n",
    "print(mnist.train_labels[0:10])    # same as labels[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '6', '2', '1', '3', '4', '2', '4', '4', '7']\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '6', '2', '1', '3', '4', '2', '4', '4', '7']\n"
     ]
    }
   ],
   "source": [
    "cx.view(mnist.inputs[60000:60010])\n",
    "print(mnist.labels[60000:60010])\n",
    "cx.view(mnist.test_inputs[0:10])   # same as inputs[60000:60010]\n",
    "print(mnist.test_labels[0:10])     # same as labels[60000:60010]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST digits are grayscale images, with each pixel represented as a single intensity value in the range 0 (black) to 1 (white). You can think of the whole image as consisting of 784 numbers arranged in a plane of 28 rows and 28 columns. For color (RGB) images, however, each pixel consists of three numbers (one for Red intensity, one for Green, and one for Blue). Therefore color images are represented as arrays of shape *rows* &times; *columns* &times; 3, where the 3 indicates the *depth* of the image. For consistency, the grayscale MNIST images are treated as images of depth 1, with shape *rows* &times; *columns* &times; 1. We can verify this by calling `cx.shape` on input image #0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cx.shape(mnist.inputs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A multi-layer perceptron network for MNIST classification\n",
    "\n",
    "Now we are ready to build a basic feedforward neural network to learn the MNIST data.  We will create a network with an input layer of shape 28 &times; 28 &times; 1, to match the shape of the input patterns, followed by two hidden layers of 30 units each, and an output classification layer.  However, in order to feed a 2-dimensional input image into the hidden layers, we must first \"flatten\" it into a linear vector of size 784 using a special ``FlattenLayer``.  Our output layer will have 10 units, one for each digit classification (\"zero\" to \"nine\"), and will use the *softmax* activation function.  A softmax layer outputs a *probability distribution*, which means that each of the numbers can be interpreted as a probability (in the range 0-1) representing the likelihood that the input pattern is an example of the corresponding classification category.  Here is a schematic picture of our network:\n",
    "\n",
    "<p><img src=\"mnist_mlp_picture.png\" width=\"50%\"></p>\n",
    "\n",
    "To build this network in ConX, we first create a ``Network`` object, giving it the arbitrary name \"MNIST_MLP\", then add each ``Layer`` in order.  Finally, we create the connections between layers using the single command ``connect()``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = cx.Network('MNIST_MLP')\n",
    "\n",
    "net.add(cx.Layer('input', (28,28,1)))\n",
    "net.add(cx.FlattenLayer('flat_input'))\n",
    "net.add(cx.Layer('hidden1', 30, activation='relu'))\n",
    "net.add(cx.Layer('hidden2', 30, activation='relu'))\n",
    "net.add(cx.Layer('output', 10, activation='softmax'))\n",
    "\n",
    "# creates connections between layers in the order they were added\n",
    "net.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: network is uncompiled; activations cannot be visualized\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<svg id='MNIST_MLP' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' image-rendering=\"pixelated\" width=\"245.6140350877193px\" height=\"400.0px\">\n",
       " <g >\n",
       "  <svg viewBox=\"0 0 350 570\" width=\"245.6140350877193px\" height=\"400.0px\">\n",
       "    <defs>\n",
       "        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n",
       "          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n",
       "        </marker>\n",
       "    </defs><rect x=\"74.0\" y=\"24\" width=\"202\" height=\"52\" style=\"fill:none;stroke:red;stroke-width:4\"/><image id=\"picture-static-MNIST_MLP-994319_output_1\" class=\"picture-static-MNIST_MLP-994319_output\" x=\"75.0\" y=\"25\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" image-rendering=\"optimizeSpeed\" xlink:href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n",
       " output range: (-1.0, 1.0)\n",
       " shape = (10,)\n",
       " Keras class = Dense\n",
       " activation = softmax</title></image><text x=\"280.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >output</text><path d=\"M 175.0 104 L 175.0 77 \" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\" fill=\"none\" /><rect x=\"74.0\" y=\"104\" width=\"202\" height=\"52\" style=\"fill:none;stroke:red;stroke-width:4\"/><image id=\"picture-static-MNIST_MLP-994319_hidden2_2\" class=\"picture-static-MNIST_MLP-994319_hidden2\" x=\"75.0\" y=\"105\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" image-rendering=\"optimizeSpeed\" xlink:href=\"data:image/gif;base64,R0lGODdhHgABAIcAAP/9/QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHgABAAAIDAABCBxIsKDBgwIDAgA7\"><title>Layer: hidden2 (hidden)\n",
       " output range: (-1.0, 1.0)\n",
       " shape = (30,)\n",
       " Keras class = Dense\n",
       " activation = relu</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >hidden2</text><path d=\"M 175.0 184 L 175.0 157 \" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\" fill=\"none\" /><rect x=\"74.0\" y=\"184\" width=\"202\" height=\"52\" style=\"fill:none;stroke:red;stroke-width:4\"/><image id=\"picture-static-MNIST_MLP-994319_hidden1_3\" class=\"picture-static-MNIST_MLP-994319_hidden1\" x=\"75.0\" y=\"185\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" image-rendering=\"optimizeSpeed\" xlink:href=\"data:image/gif;base64,R0lGODdhHgABAIcAAP/9/QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHgABAAAIDAABCBxIsKDBgwIDAgA7\"><title>Layer: hidden1 (hidden)\n",
       " output range: (-1.0, 1.0)\n",
       " shape = (30,)\n",
       " Keras class = Dense\n",
       " activation = relu</title></image><text x=\"280.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >hidden1</text><path d=\"M 175.0 264 L 175.0 237 \" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\" fill=\"none\" /><rect x=\"124.0\" y=\"264\" width=\"102\" height=\"52\" style=\"fill:none;stroke:red;stroke-width:4\"/><image id=\"picture-static-MNIST_MLP-994319_flat_input_4\" class=\"picture-static-MNIST_MLP-994319_flat_input\" x=\"125.0\" y=\"265\" height=\"50\" width=\"100\" preserveAspectRatio=\"none\" image-rendering=\"optimizeSpeed\" xlink:href=\"data:image/gif;base64,R0lGODdhZAABAIcAAH9/fwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAZAABAAAIEgABCBxIsKDBgwgTKlzIsGHCgAA7\"><title>Layer: flat_input (hidden)\n",
       " output range: (-Infinity, +Infinity)\n",
       " Keras class = Flatten</title></image><text x=\"230.0\" y=\"292.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >flat_input</text><path d=\"M 175.0 344 L 175.0 317 \" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\" fill=\"none\" /><rect x=\"74.0\" y=\"344\" width=\"202\" height=\"202\" style=\"fill:none;stroke:red;stroke-width:4\"/><image id=\"picture-static-MNIST_MLP-994319_input_5\" class=\"picture-static-MNIST_MLP-994319_input\" x=\"75.0\" y=\"345\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" image-rendering=\"optimizeSpeed\" xlink:href=\"data:image/gif;base64,R0lGODdhHAAcAIcAAH9/fwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHAAcAEAIMAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkwQDAgA7\"><title>Layer: input (input)\n",
       " output range: (-Infinity, +Infinity)\n",
       " shape = (28, 28, 1)\n",
       " Keras class = Input</title></image><text x=\"280.0\" y=\"447.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >input</text><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\" >MNIST_MLP</text></svg></g></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.picture()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to tell the network which dataset to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.set_dataset(mnist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step after constructing the network is to compile it, specifying an *error* function (also called a \"loss\" function), and the learning algorithm to use (the *optimizer*).  For a classification task with the *softmax* activation function, it is usually best to choose the ``categorical_crossentropy`` error function.  For our learning algorithm, we will use standard Stochastic Gradient Descent.  We can also (optionally) specify the learning rate and momentum values to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "flat_input (Flatten)         (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "hidden1 (Dense)              (None, 30)                23550     \n",
      "_________________________________________________________________\n",
      "hidden2 (Dense)              (None, 30)                930       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                310       \n",
      "=================================================================\n",
      "Total params: 24,790\n",
      "Trainable params: 24,790\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "net.compile(error='categorical_crossentropy', optimizer='SGD', lr=0.3, momentum=0.1)\n",
    "net.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network summary shows the details for each layer, along with the total number of network parameters.  The underlying Keras model on which the network is based is directly available via the ``model`` property, although normally the ConX user does not need to worry about the lower Keras level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.training.Model at 0x7f1b066515f8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can manually send an input pattern through the network using the ``propagate`` method.  For example, let's ask the network to classify input pattern #0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[0.0977730080485344,\n",
       " 0.0672101154923439,\n",
       " 0.08400549739599228,\n",
       " 0.11776035279035568,\n",
       " 0.10889045894145966,\n",
       " 0.10750816762447357,\n",
       " 0.0949573963880539,\n",
       " 0.142753466963768,\n",
       " 0.09871889650821686,\n",
       " 0.08042261749505997]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cx.view(mnist.inputs[0])\n",
    "net.propagate(mnist.inputs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not surprisingly, the output values for each classification category are all around 0.10 (representing 10% probability), because the network has not yet been trained.  After training, one of the output values will typically be much larger than the others, corresponding to the output classification category.\n",
    "\n",
    "A more user-friendly way to view the behavior of the network is to open a ``dashboard`` so that we can see the activation of each layer more clearly.  The dashboard allows us to easily step through the input patterns (for either the training or testing set) and see the output of the network for each pattern, along with the correct target classification.  Clicking on **MNIST_MLP** at the top of the dashboard will open up a panel of settings for controlling the appearance and functionality of the network display. For example, to choose between the training set and testing set images, you can select \"Train\" or \"Test\" from the *Dataset* pulldown menu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4ab98c6954e403dbadfd27ed7c539a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.dashboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the weights on connections into specific units by calling `plot_layer_weights`. For example, the command below shows the weights from the input layer into units 0, 1, and 2 of the first hidden layer, displayed as 28 &times; 28 pixel array (where each pixel represents a weight from the input layer into a hidden unit). The `wrange` keyword specifies the minimum and maximum weight values for the color coding. Since the network has not yet been trained, the weights are all small random values close to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZbdMl8lRlcv3fBcJXx+76tVvXtUfVT6tA/FVzdevO9yopqJsA4dq6ui64OnPXT5dgUwXEu+WuveZMl9RNaHHlrBJ0uv3XlbM6dnftceu6fVfJNHOuZ6vCnTQAAIDB8LgTAABgUDzuBAAAGBB30gAAAAbD484BVIGKLqjTBVW6BqyCXrtvt3bbrN767IJkXfCwK1O1Tbd/F+Tsgqyr+nT7d/Xh1qveIO9uQ3eDzqs3cI/yxnY3w0M38aRa1y3rJhNUXB9y+6lmrHDtsGj/dedud5tVfbq3ux86dKi1zSpIuhtk7c6TKmGoO9vCsrl+5eqr+gXo+ov7vOs/VfC8a1t3/XTtVZ0nrm1cv3TtVW3Tlal7Ps9JWnPX7zlJKu6YuskEhw8fttvszmyzSjzuBAAAGBB30gAAAAbEnTQAAIDBEJMGAAAwKAZpS+IC3RcNIHdBonNmB+i+TXlO8KZb1wWPus9XMyi4wNduIHsVDOuCVN3nXTmrY+8mCbhA8irwunoD+qq5+nJtUPUpVw9uWTd5Q/L9xe3fzTpRBbS75W7/rk9Xfa2bzOAC0augc9cerg+5vloFc3dnW5iTMDJK/3Vc/636Wnc2B1e3VR10Z5FxdVvVt/t8NyFlzmM016fnJFm5dRedncL97nT9352nLhFHqmf82E887gQAABgMjzsBAAAGxSANAABgMJnJ404AAIARcScNAABgQAzSlsRl9LmMT7dM8hkpLkPGZaNUmSvd6ZacKhOzmzXkstaqLKhudp3LbquyZV3WkZt+xWUxVVl8broRt6w7zYpU1/Oqdfuay6SUfDu4vtad/qzapitn1Qccd064DLE5WajdLFZXH9VUU65O3HG69qjK6abGcRmQLguvKuei03ztlW59V1mT7jjcue2uIXMyibvX76pPu2Ny67o+WWWYdzOZ59RnN8N9TnZld6q1bnmk/hsQVoXHnQAAAIPiThoAAMCAGKQBAAAMhsedAAAAg+JO2pK4QMs5gdeOC9Z1AfkuIFjyQZUu+LM7JZDkg1RdQGh3ChrJB+i6srv1qqDz7pRa7tir6UtckKk7ThdcXCVNVMG8q9at2ypI2tVtd7qbKii9WzeuT1b9wpXfBRW786y6eLr9dxOB5iQOuDZyqmmhusHx3YSNOWVaNnfMc+rQ9cvu56s2dOueOHGitV6V4OX25X6nuDZ005JJvl9X1/+dqnK6a6ir4znTr7l6ctt0n6/aqJqWbT9xJw0AAGAwTAsFAAAwKAZpAAAAA+JxJwAAwGB43LlELgDSBdtWwZvdtylXAehO9w3P7o3rVTldoGj3je1VkGn1NujOelXgp1u+sbGxa5kLMq3e9j3nzeBdi35+r7i+4tq1Crx2fdV93tVhFejuuDZ0507116hLHHCfd/23SihxXKCySyipEolcQLTr024/1fnk9u/24/pCFXjt2ng/LHpudgPdXT+vfqm6bbr+67ZZJWq4a2iVzLNTVU7X3u76P2cWA1d33USKqk91f0+460k1K88o19/tGKQBAAAM6EJ63Nn78wIAAGCfbT3urP51RMStEXFPRLxvycVdGIM0AACwNhYZpEXESyQdyszrJF0aES9deoEXwCANAACsja2pody/hpdJumv6+mOSrl1aQffASmPSXFCuC75060k+KNKNnF3gaDXC7r6N2TV+Fcztyu8CPV2gsSuP5INPuwHNcxIxXNndetWxuzZyZeq+gb4q037ovrW/Ct51x+feWu7adU4yjOP6b3VOdN9G3k26kXzgtjtP57wJvZs01E04kvozZrhlcwLZR9G91lXrurbpzuIi+euIOyfcvqvzzLVNdV3tcnXSrbsqaWHOzAzd9dz+u0kXVYLAKNffLY3szqsj4tPbvj+amUe3fX+VpC9OXx+X9MI9LuKeInEAAACsjXMM0o5l5pGz/Py4pK0U4g1Jj+xVuZaBx50AAGBtLPi4848k3TB9faOkTyytoHuAQRoAAFgLi2Z3ZuZnJJ2OiHskPZmZn1p6oRfA404AALA2Fn1PWmbevEdFWToGaQAAYG0w48CSuAxHN+VFlY3osmS6mTxV5mC1fKc50y11M8Rc1kyVCdTN0OlOVSL5v0ZcG7mMvaqc3Uwgt+8qA2vOlEjL5DI5XZndelI/E87Vd9VP3b66f2VW/cJlnbllLpOymq7GTe3k+pDLMJtznrn+N2daG1fOw4cP71rmMkarbMNqmrBVc9cGl0lZHUc3Q9L1yaoNXNan6/+uDquMUdcvXV+f04Zum66vzblWuf7fvSZX5mTi71RlJ482LRRzdwIAAAzqQpoWikEaAABYG9xJAwAAGAyPOwEAAAbF484lcRXrAjXnBPl3p9Gogh+7gZZzgvxdoKdb5v4aqIJEXfCy0516SPLBsC541LVRtU1Xzy7A1+3HtYU0zq3tbkJIFSh+8uTJXctcu7rg9aqfd4Osnar/uuXdqb1c2SuuPl0fOHjwoP28O1dcX5sT4O3q0/V19/kq8HqUXyjd5KPqGuT6gOuX7rpSXdO767o2qK6Jrg+5croEh6oN3Xnu+oqru+pa6c4z93nXf+ZMs+Xqw+2nuv6Okviy3Si/E1aBO2kAAGAt8LgTAABgUKPcnV4FBmkAAGBtcCcNAABgQNxJWxI3+p3z5nkXaOmCgt1+qpG3C6DsBm5XgdfumNznXfBn1flcQKsL5HXBtFXQuSu/W9Z9W7zk67n7Vv3qbd+jcAG0LlC36mtuXfc2fdf/58xE0Q0+XrRfzOGOqfsm9TnnmasPV+9V4HX3PHd9oQq8XrTu9kr37kN1HK5uXbu6WWRcgoDkk2nctcHtp5qJoko02WlO0kR3toU5iTPueufKNGdWEdd/Xf+fMyvEKP13CzFpAAAAg2KQBgAAMCAedwIAAAyGx50AAACDYpC2JN23S1fBm92gYLdeFSjcfWv0nDdJuwBMF+TfnYFB8oGrhw4d2rXMHeecGRzccXYDXCvdt2BX2xwlcNWVz7XhnLeru8BpdwFy/UeSNjY2di1zQcGur1bB3K4PdZMZqrZydeKC713Zq0B2VyZ37rnkoioQ3JXJ9X/XbtWxu2vMflj0PHLH4erBBe5X119X39X5s1MV6O76+iIzBki+/K5fuG1Ws1u4vu7qozsLTLVNt8yVs7r+zrnWrwqPOwEAAAbD404AAIBBMUgDAAAYEI87AQAABsPjTgAAgEExSFsSl2XlMnTmZJO4TCD3+WoKHJe1083iq7jj7E7NUXU+t3+3rstOchlDks/2cp+vsuu6XN2743GZYlJ/SpZlc33NtWuVzdXt1939VOu6fuEygauMUdcH3HniskPnZE26cnazM6ttur7q9jNnmqJutnfVviM/mnFZgtX1wrWtyxJ0faW6rrn27k7/V03B5Lbp+oU7zmqbrp6601+59SSfme3Oye5UT1L/d9ecNyBU58p+Gvmc2mtj/PYDAAA4Bx53AgAADIo7aQAAAIPhThoAAMCgGKQtiZsupBsoKflAexfQ2Z0WRPLBo27/LqjSBWNL/SljXPBmNS1JNxmhux/J3zJ27eGCg6spZlyAbrfdqnYfJXDV9RXXLi4gWPL17YKxXT1Ubej279rGtUv1yMD1q27yR1VO14dc0oPrf9UUOK6c7tx351SVpOLK2Z26qOq/VSD+qrm6mZNQ0n3E1L1+VvvqJmVUSSrueuOSCdx61bXG9TVXzjmDh+611q03J2li0WSwqu32E487AQAABsPjTgAAgEExSAMAABgQjzsBAAAGs1ePOyPiYknvl/QCSXdk5i/s+Pnd05dXSnooM38oIm6T9J2SHpN0NDN/Y+GCnMNKB2kuWPfw4cO7llWBii4osttY1ci7CkreyQVjV/t2QaYu0NglUlRvV3cBrS7wtRs0K/UDQl3AcVVvLiDVBcO6wNcqacLV3X5wx+H69JxA225AfhV83p1d4NSpU619S74NXdu4wO3qPHPldOeJq7vqnHDb7AaYu+uO1E+GcNt0595IXHu787C6XjjdRKHqDfmuX3dnMagSB9w52Z3to1rP7av7O6E6z9w23XF2Z1CQ/LnSnZnG1XtVpv22R487Xyfpf2fmP4+IOyLiOZn55a0fZub1khQRPyVpe6d6Y2b+2V4UoMOn6AEAAAwoM8t/M7xM0l3T178v6fuK9V4n6UNbu5Z0e0R8OCKed16Fn4nHnQAAYC00HndeHRGf3vb90cw8ata7StLWxKrHp++/SUR86+Yu86vTon+VmQ9HxCsk/aKkN8w+gJkYpAEAgLVxjkHascw8Uv0wIn5a0mskvVDSr02LNyS5R5g/qKfvoikzH57+/8OI+AWz/p7jcScAAFgbizzuzMz3TvFmN0m6YVr8/ZLuM6v/kKQPbn0TERvT/39P0iMLHkbLSu+kuWDzbqBjxQWezgm07L51vztbQqVbpurYXfCmC0h19VEFNM95Y/xO1bG7YFq3fxfcXh37nHpeJheQ7BI6qtkYum8Yd/VQbdMFALtzyrV1lZDh+pXb5pw3trt1u316zuwW3X7lEimk/pvpXX1W5RwlocCVzx3vnPOtOxtIlfzhEgq6SS4uoalat5uMU12DukkqTnWeubpzx+4+XyVuVX1wJ9ceVXJSd5ursocvs/2wpNdHxB9K+p3M/FJEvFjSNZn569OA7KrM/Mttn/lPEfFsbcam/cReFOJceNwJAADWxl68Jy0zH5f0xh3LHpD0wPT1CUkv3/HzH1h4xzMxSAMAAGuDGQcAAAAGw9ydAAAAg2JaKAAAgAFxJ21JXMW6zKc5mWzdKUiqKS+6mVfdDC9pseyiqpxuXZcJ5Ka7mZOx5KYqcfXpppORfBtV0z3tVLVFd+quZXP90mWYXXnllfbzrh67Uw5VmWTdKcjcelW7uH5RTaO0k8uAlXy/dueEy/isphRy23Tt0e3Tkj/P3Oe75440Tnayq1t3blXTQnUzt11frbJAXXu5THrXp+dM9efay22zmsKpmwnv6shlHEv+GuqO3dVxlTHq+q/Lgp4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      "text/plain": [
       "<Figure size 720x180 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot_layer_weights('hidden1', units=[0,1,2], vshape=(28,28), wrange=(-1.5, 1.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train the network for 30 epochs, using a default batch size of 32. This means that images from the training set will be presented to the network in batches of 32 at a time, and for each batch, the SGD algorithm will update the network's weights by an appropriate amount. Then another batch of 32 images will be presented, and so on, until all 60,000 training images in the dataset have been processed, which constitutes one epoch of training. This entire cycle will be repeated for 30 epochs. As training proceeds, the network's error (loss) on both the training and testing/validation sets will be shown on the left graph, and the accuracy on each of these sets will be shown on the right graph. The accuracy is simply the fraction of input images that the network classifies correctly.  A classification is considered correct if the largest output value on the output layer corresponds to the target classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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      "Interrupted! Cleaning up...\n",
      "========================================================\n",
      "       |  Training |  Training \n",
      "Epochs |     Error |  Accuracy \n",
      "------ | --------- | --------- \n",
      "#    2 |   0.19710 |   0.94321 \n"
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-97c60fae436b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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    "The detailed epoch-by-epoch training history of the network is available by calling `show_results()`. The optional `report_rate` keyword specifies which epochs to show."
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     "text": [
      "       |  Training |  Training |  Validate |  Validate \n",
      "Epochs |     Error |  Accuracy |     Error |  Accuracy \n",
      "------ | --------- | --------- | --------- | --------- \n",
      "#    0 |   2.34410 |   0.06307 |   2.34118 |   0.06570 \n",
      "========================================================\n",
      "#    2 |   0.19710 |   0.94321 \n"
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    "The `plot` method shows the value of various network metrics during training. The metrics `loss` and `val_loss` represent the value of the error (loss) function on the training and testing sets, respectively.  Likewise, `acc` and `val_acc` represent the accuracy of the training and testing sets, respectively. The optional `ymin` and `ymax` keywords can be used to adjust the y-axis scale. For example:"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot(['acc', 'val_acc'], ymin=0.9, ymax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rk5PDsGHDePjhh6lXrx5Tp06lR48e9OrVi8WLF3PRRRexaNEi+vTpQ69evfjNb37DzTffXAPvUnaT1UoncmtYo4x0bj/zCB6/pC/bdpRx9n1vM/75pXy7Xb3+mlDlePpmlgYsBwYCxcBc4Dx3XxrV5njgHXffYmZXAD9092HBss3uXvlF6+VoPP19p/0VR6+Og7f+DDeXQB39YVzTNm/byR9e/IDH5qyiQ7MG/OGsI/hB52bJLislxHM8/T7ACncvcvftwGRgt+sS3X2mu28JJucAbatbsEitlJkNZTvhWx1ySITMenX57ek9eOqyH+AOwyfN4X+fW8w328L1BfU1KZbQbwN8EjVdHMyrzCXAi1HTGWZWYGZzzOz0vagxZS1atIhevXrt9ujbt2+yy5Lq0F25SXHMwc146dr+XHxcRx6bs4pBd+fz5goNhxEPcb1k08wuBHKBAVGzO7j7GjPrDLxuZovc/aNy640CRgG0b9++wm27e8p9H2dOTg6FhYUJfc3a9vWXKe+70P8ichOfJEyDA+pyy4+786OcVvx82kIu+Ns7nNenPb86tStZGenJLi9lxdLTXwO0i5puG8zbjZmdBNwEDHH3bbvmu/ua4GcR8B/gyPLruvskd89199wWLVp8r4CMjAzWr1+vQKuCu7N+/XoyMjKSXcr+47sbtHQyN1lyOzbl39f0Z1ReZ6bMXc3JE/L5z7K1yS4rZcXS058LdDGzTkTCfjhwfnQDMzsSuB8Y7O5ro+Y3Aba4+zYzaw4cB9xR3SLbtm1LcXExJSUl1V01dDIyMmjbVqdU4kaHd2qFjPQ0fnXq4ZzSI5ufT1vIyIfmcvbRbfn1j7rRuIF6/dVRZei7+04zGw3MANKAB919iZmNBwrcfTpwJ5AJ/CM4BLPa3YcAhwP3m1kZkb8qfh991U+s0tPT6dSpU3VXE9l36fUho7Fu0KoljmzfhH9d3Y97XvuQ+94oIn95CbedkcNJ3Vomu7SUUeUlm4lW0SWbIkl1bx9ocSgMezzZlUiURcUbuWHaAj74fBOn92rNLT/uTpOGByS7rKSJ5yWbIuGm78qtlXLaNmb66H5cc2IX/rXwMwZOyOelxToMVxWFvkhVslrp8E4tdUDdOlw38FCmj+5Hy0b1uPzx+Vz15HzWb95W9cohpdAXqcqu8Xdq2aFQ+a9urRvxz6uO439OPpRXlnzBwAn5PL/gU13xVwGFvkhVsrKhdDt8+2WyK5E9SE+rw+gTuvCvq/vRrkl9xjz1Hpc/Po+1m+Iz8u3+QqEvUpVMfYNWKjm0ZRZPX3EsvzylKzOXlTDwrnyemV+sXn9AoS9SlaxWkZ+6QStl1E2rw88GHMyL1/TnkIMyGTt1AZc+UsDnG9XrV+iLVCV6KAZJKQe3yGTqz47h16d1482P1jFwwhtMnftJqHv9Cn2Rqnx3eEeXA6aitDrGJf068dI1eRzeqhE/f3ohIx6ay5qvvk12aUmh0BepSr1MOCBLX5Ce4jo2b8jky37A+KHdKVi5gUET8nninVWh6/Ur9EVioRu09gt16hgXHdORGdfm0bNdY256djEX/O0dPtmwpeqV9xMKfZFYZGYr9Pcj7Zo24PFL+nLbGTksLN7IoLvzeeStlZSV7f+9foW+SCyysnX1zn7GzDi/b3tevi6P3h2bcsv0JQx/YA4r132T7NJqlEJfJBZZ2ZGrd0J2/DcMWh9Yn4cv7s2dZx/B+599zeA/5fO3WUWU7qe9foW+SCwyW8LOb2HrxmRXIjXAzDgntx2vjh1Av0Oac+sL73POfW+xYu3mZJcWdwp9kVjsulZfV/Ds11o2yuCBi3K5e1gvitZ9w6n3zOK+Nz5iZ2lZskuLG4W+SCy+u0FLx/X3d2bG6Ue24eXr8jjhsIP4/YsfcNZf32LZ55uSXVpcKPRFYpGpnn7YHJSVwV8vPIp7zz+ST778ltP+PIs/v/YhO1K816/QF4lFlu7KDSMz47QjWvPKdXkM6p7NH19ZzukT32Tpp18nu7S9ptAXiUW9RpDeQOPvhFSzzHrce/5R3Hfh0Xzx9TaG3Dubu15ZzvadqdfrV+iLxMIMDj7hv8f2JZQG98jm1bF5DOnZmnte+5Ah985mUXFqXdGlL0YXEdkLr73/Bb96dhHrNm/nZ3mdufrELmSkpyWtHn0xuohIDTrx8Ja8fN0AzjqqDX/5z0ec9ufZvLe69n+7mkJfRGQvNa6fzh1n9+SRn/Zhy7adnPXXt/jdC0vZuqM02aVVSqEvIrKPBhzaghnX5TG8T3semPUxp/xpFnNXbkh2WRVS6IuIxEFWRjq3nZHDk5f2ZUdpGefe/zbjpi9hy/adyS5tNwp9EZE4OvaQ5sy4No8Rx3Tk4bdWMvjuWbz90fpkl/Udhb6ISJw1rFeXcUO6M/Vnx1DH4LwH5nDzPxexeVvye/0KfRGRGtKnU1NevCaPS/t14ol3VjNoQj6zPixJak0xhb6ZDTazZWa2wsxurGD5WDNbamYLzew1M+sQtWyEmX0YPEbEs3gRkdqu/gFp3HxaN6ZdfiwZ6XX4yd/f5canF/L11h1JqafK0DezNGAicArQDTjPzLqVa/YekOvuRwDTgDuCdZsCtwB9gT7ALWbWJH7li4ikhqM7NOGFq/tz+YCDmVrwCSfflc/MD9YmvI5Yevp9gBXuXuTu24HJwNDoBu4+0913fbPwHKBt8HwQ8Iq7b3D3L4FXgMHxKV1EJLVkpKdx4yldefbK42hUvy4XPzyXsVML2bglcb3+WEK/DfBJ1HRxMK8ylwAvVmddMxtlZgVmVlBSktzjXSIiNa1nuwN5fkw/xpxwCM8VfspJE97g5SWJ+a6GuJ7INbMLgVzgzuqs5+6T3D3X3XNbtGgRz5JERGqlenXTuP7kw3juquNonlmPUY/N49rJ71FWw9/NG0vorwHaRU23DebtxsxOAm4Chrj7tuqsKyISVj3aNGb66OMYO/BQWjbKoE4dq9HXqxtDm7lAFzPrRCSwhwPnRzcwsyOB+4HB7h59ZmIGcFvUyduTgV/uc9UiIvuR9LQ6XH1il4S8VpWh7+47zWw0kQBPAx509yVmNh4ocPfpRA7nZAL/MDOA1e4+xN03mNlvifziABjv7rVzQAoRkRDQePoiIvsBjacvIiLfo9AXEQkRhb6ISIgo9EVEQkShLyISIgp9EZEQUeiLiISIQl9EJEQU+iIiIaLQFxEJEYW+iEiIKPRFREJEoS8iEiIKfRGREFHoi4iEiEJfRCREFPoiIiGi0BcRCRGFvohIiCj0RURCRKEvIhIiCn0RkRBR6IuIhIhCX0QkRBT6IiIhotAXEQkRhb6ISIjEFPpmNtjMlpnZCjO7sYLleWY238x2mtnZ5ZaVmllh8Jger8JFRKT66lbVwMzSgInAQKAYmGtm0919aVSz1cBI4H8q2MS37t4rDrWKiMg+qjL0gT7ACncvAjCzycBQ4LvQd/eVwbKyGqhRRETiJJbDO22AT6Kmi4N5scowswIzm2Nmp1fUwMxGBW0KSkpKqrFpERGpjkScyO3g7rnA+cDdZnZw+QbuPsndc909t0WLFgkoSUQknGIJ/TVAu6jptsG8mLj7muBnEfAf4Mhq1CciInEUS+jPBbqYWSczOwAYDsR0FY6ZNTGzesHz5sBxRJ0LEBGRxKoy9N19JzAamAG8D0x19yVmNt7MhgCYWW8zKwbOAe43syXB6ocDBWa2AJgJ/L7cVT8iIpJA5u7JrmE3ubm5XlBQkOwyRERSipnNC86f7pHuyBURCRGFvohIiCj0RURCRKEvIhIiCn0RkRBR6IuIhIhCX0QkRBT6IiIhotAXEQkRhb6ISIgo9EVEQkShLyISIgp9EZEQUeiLiISIQl9EJEQU+iIiIaLQFxEJEYW+iEiIKPRFREJEoS8iEiIKfRGREFHoi4iEiEJfRCREFPoiIiGi0BcRCRGFvohIiCj0RURCJKbQN7PBZrbMzFaY2Y0VLM8zs/lmttPMzi63bISZfRg8RsSrcBERqb4qQ9/M0oCJwClAN+A8M+tWrtlqYCTwZLl1mwK3AH2BPsAtZtZk38sWEZG9EUtPvw+wwt2L3H07MBkYGt3A3Ve6+0KgrNy6g4BX3H2Du38JvAIMjkPdIiKyF2IJ/TbAJ1HTxcG8WMS0rpmNMrMCMysoKSmJcdMiIlJdteJErrtPcvdcd89t0aJFsssREdlvxRL6a4B2UdNtg3mx2Jd1RUQkzmIJ/blAFzPrZGYHAMOB6TFufwZwspk1CU7gnhzMExGRJKgy9N19JzCaSFi/D0x19yVmNt7MhgCYWW8zKwbOAe43syXBuhuA3xL5xTEXGB/MExGRJDB3T3YNu8nNzfWCgoJklyEiklLMbJ6751bVrlacyBURkcRQ6IuIhIhCX0QkRBT6IiIhotAXEQkRhb6ISIgo9EVEQkShLyISIgp9EZEQUeiLiISIQl9EJEQU+iIiIaLQFxEJEYW+iEiIKPRFREJEoS8iEiIKfRGREFHoi4iEiEJfRCREFPoiIiGi0BcRCRGFvohIiCj0RURCRKEvIhIiCn0RkRBR6IuIhIhCX0QkRGIKfTMbbGbLzGyFmd1YwfJ6ZjYlWP6OmXUM5nc0s2/NrDB43Bff8kVEpDrqVtXAzNKAicBAoBiYa2bT3X1pVLNLgC/d/RAzGw78ARgWLPvI3XvFuW4REdkLsfT0+wAr3L3I3bcDk4Gh5doMBR4Jnk8DTjQzi1+ZIiISD7GEfhvgk6jp4mBehW3cfSewEWgWLOtkZu+Z2Rtm1n8f6xURkX1Q5eGdffQZ0N7d15vZ0cA/zay7u38d3cjMRgGjANq3b1/DJYmIhFcsPf01QLuo6bbBvArbmFldoDGw3t23uft6AHefB3wEHFr+Bdx9krvnuntuixYtqv8uREQkJrGE/lygi5l1MrMDgOHA9HJtpgMjgudnA6+7u5tZi+BEMGbWGegCFMWndBERqa4qD++4+04zGw3MANKAB919iZmNBwrcfTrwd+AxM1sBbCDyiwEgDxhvZjuAMuByd99QE29ERESqZu6e7Bp2k5ub6wUFBckuQ0QkpZjZPHfPraqd7sgVEQkRhb6ISIgo9EVEQkShLyISIgp9EZEQUeiLiISIQl9EJEQU+iIiIaLQFxEJEYW+iEiIKPRFREJEoS8iEiIKfRGREFHoi4iEiEJfRCREFPoiIiGi0BcRCRGFvohIiCj0RURCRKEvIhIiCn0RkRBR6IuIhIhCX0QkRBT6IiIhotAXEQkRhb6ISIgo9EVEQkShLyISIjGFvpkNNrNlZrbCzG6sYHk9M5sSLH/HzDpGLftlMH+ZmQ2KX+kiIlJdVYa+maUBE4FTgG7AeWbWrVyzS4Av3f0QYALwh2DdbsBwoDswGPhLsD0REUmCWHr6fYAV7l7k7tuBycDQcm2GAo8Ez6cBJ5qZBfMnu/s2d/8YWBFsT0REkqBuDG3aAJ9ETRcDfStr4+47zWwj0CyYP6fcum3Kv4CZjQJGBZObzWxZTNVXrDmwbh/Wrymqq3pUV/WorurZH+vqEEujWEK/xrn7JGBSPLZlZgXunhuPbcWT6qoe1VU9qqt6wlxXLId31gDtoqbbBvMqbGNmdYHGwPoY1xURkQSJJfTnAl3MrJOZHUDkxOz0cm2mAyOC52cDr7u7B/OHB1f3dAK6AO/Gp3QREamuKg/vBMfoRwMzgDTgQXdfYmbjgQJ3nw78HXjMzFYAG4j8YiBoNxVYCuwErnL30hp6L7vE5TBRDVBd1aO6qkd1VU9o67JIh1xERMJAd+SKiISIQl9EJERSJvRr61AQMdQ11syWmtlCM3vNzDpELSs1s8LgUf7keE3XNdLMSqJe/9KoZSPM7MPgMaL8ujVc14Sompab2VdRy2pyfz1oZmvNbHEly83M7gnqXmhmR0Utq8n9VVVdFwT1LDKzt8ysZ9SylcH8QjMrSHBdPzSzjVH/Xv8btWyPn4EaruuGqJoWB5+ppsGymtxf7cxsZpAFS8zsmgraJOYz5u61/kHkBPJHQGfgAGAB0K1cmyuB+4Lnw4EpwfNuQft6QKdgO2kJrOt4oEHw/IpddQXTm5O4v0YC91awblOgKPjZJHjeJFF1lWs/hsiFAzVUqQGIAAAFOUlEQVS6v4Jt5wFHAYsrWX4q8CJgwA+Ad2p6f8VY17G7Xo/IUCnvRC1bCTRP0v76IfCvff0MxLuucm1/TORKw0Tsr1bAUcHzLGB5Bf8nE/IZS5Wefm0dCqLKutx9prtvCSbnELlXoabFsr8qMwh4xd03uPuXwCtExk1KRl3nAU/F6bX3yN3ziVx5VpmhwKMeMQc40MxaUbP7q8q63P2t4HUhcZ+vWPZXZfblsxnvuhL5+frM3ecHzzcB7/P90QkS8hlLldCvaCiI8jtst6EggOihIKpatybrinYJkd/ku2SYWYGZzTGz0+NUU3XqOiv4M3Kame26ia5W7K/gMFgn4PWo2TW1v2JRWe01ub+qq/zny4GXzWyeRYY6SbRjzGyBmb1oZt2DebVif5lZAyLB+XTU7ITsL4scej4SeKfcooR8xmrFMAxhYGYXArnAgKjZHdx9jZl1Bl43s0Xu/lGCSnoeeMrdt5nZz4j8lXRCgl47FsOBab77fR3J3F+1mpkdTyT0+0XN7hfsr4OAV8zsg6AnnAjzifx7bTazU4F/Erk5s7b4MfCmu0f/VVDj+8vMMon8ornW3b+O57ZjlSo9/do6FERM2zazk4CbgCHuvm3XfHdfE/wsAv5D5Ld/Qupy9/VRtfwNODrWdWuyrijDKfendw3ur1hUVnvShxoxsyOI/BsOdff1u+ZH7a+1wLMkcIRbd//a3TcHz/8NpJtZc2rB/grs6fNVI/vLzNKJBP4T7v5MBU0S8xmriZMW8X4Q+YukiMif+7tO/nQv1+Yqdj+ROzV43p3dT+QWEb8TubHUdSSRE1ddys1vAtQLnjcHPiROJ7RirKtV1PMzgDn+35NGHwf1NQmeN01UXUG7rkROqlki9lfUa3Sk8hOTP2L3k2zv1vT+irGu9kTOUx1bbn5DICvq+VvA4ATWlb3r349IeK4O9l1Mn4GaqitY3pjIcf+GidpfwXt/FLh7D20S8hmL246u6QeRM9vLiQToTcG88UR6zwAZwD+C/wDvAp2j1r0pWG8ZcEqC63oV+AIoDB7Tg/nHAouCD/0i4JIE13U7sCR4/ZlA16h1fxrsxxXAxYmsK5geB/y+3Ho1vb+eAj4DdhA5ZnoJcDlwebDciHyZ0EfB6+cmaH9VVdffgC+jPl8FwfzOwb5aEPw735TgukZHfb7mEPVLqaLPQKLqCtqMJHJxR/R6Nb2/+hE5Z7Aw6t/q1GR8xjQMg4hIiKTKMX0REYkDhb6ISIgo9EVEQkShLyISIgp9EZEQUehLKJUbsbMwnqM9mlnHykZ5FEk2DcMgYfWtu/dKdhEiiaaevkiUYEz1O4Jx1d81s0OC+R3N7HX77/citA/mtzSzZ4OBxRaY2bHBptLM7IFg7PSXzax+0t6USBSFvoRV/XKHd4ZFLdvo7jnAvcDdwbw/A4+4+xHAE8A9wfx7gDfcvSeRcdyXBPO7ABPdvTvwFXBWDb8fkZjojlwJJTPb7O6ZFcxfCZzg7kXBAFmfu3szM1tHZLyiHcH8z9y9uZmVAG09aiC9YOjcV9y9SzD9CyDd3W+t+Xcmsmfq6Yt8n1fyvDq2RT0vRefPpJZQ6It837Con28Hz98iMnorwAXArOD5a0S+BhMzSzOzxokqUmRvqPchYVXfzAqjpl9y912XbTYxs4VEeuvnBfPGAA+Z2Q1ACXBxMP8aYJKZXUKkR38FkVEeRWolHdMXiRIc089193XJrkWkJujwjohIiKinLyISIurpi4iEiEJfRCREFPoiIiGi0BcRCRGFvohIiPw/Hpm2PRjZd/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot(['loss', 'val_loss'], ymin=0, ymax=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training, the index of the largest output value in response to an input image corresponds to the network's digit classification.  To see this more clearly, we will make use of ConX's ``argmax`` function, which takes a list of numbers and returns the *position* of the largest number in the list (counting from 0).  For example, test input #42 is shown below, along with the network's response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cx.view(net.dataset.test_inputs[42])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.2092713303864e-05,\n",
       " 0.9586198329925537,\n",
       " 0.0066554853692650795,\n",
       " 0.000483944546431303,\n",
       " 0.01734444499015808,\n",
       " 0.0013681561686098576,\n",
       " 0.0008948856266215444,\n",
       " 0.00332481786608696,\n",
       " 0.006120710633695126,\n",
       " 0.005135755520313978]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate(net.dataset.test_inputs[42])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cx.argmax(net.propagate(net.dataset.test_inputs[42]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the weights into the same three hidden units as before shows that these units have learned to respond in different ways to different parts of the input image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot_layer_weights('hidden1', units=[0,1,2], vshape=(28,28), wrange=(-1.5, 1.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Convolutional Network for MNIST Classification\n",
    "\n",
    "Convolutional neural networks (CNNs) are loosely inspired by the neurobiology of the visual system. The key idea is that each unit in a convolutional layer receives connections from a limited number of units in the previous layer (which can be thought of as the unit's \"visual field\"), and these connections are arranged in a two-dimensional topology to take advantage of spatial information. Each convolutional layer specifies a number of independent *features* to be learned, along with the *N*&nbsp;&times;&nbsp;*N* size of the visual field (also called the *kernel size*). The units responsible for learning a feature share connections across the entire convolutional layer, which often leads to far fewer network parameters compared to a network with fully-connected layers.\n",
    "\n",
    "Another type of layer common to CNNs is the *pooling* layer, which reduces the amount of information flowing through the network by the process of subsampling. Each pooling unit receives input from a limited number of units in the previous layer, and then applies some function (like *maximum* or *average*) to these inputs. The overall effect is to produce a coarser-grained version of the information from the previous layer, which makes the network less sensitive to small variations in position.\n",
    "\n",
    "We will define a CNN for MNIST classification using two convolutional layers with 5 &times; 5 kernels, each followed by a pooling layer with 2 &times; 2 kernels that compute the maximum of their inputs. The first convolutional layer will learn 16 relatively low-level features, whereas the second will learn 32 higher-level features. These features will then feed into a hidden layer (after being flattened), followed by an output classification layer using *softmax*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import conx as cx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = cx.Network(\"MNIST_CNN\")\n",
    "\n",
    "cnn.add(cx.Layer(\"input\", (28,28,1), colormap=\"gray\"),\n",
    "        cx.Conv2DLayer(\"conv2D_1\", 16, (5,5), activation=\"relu\", dropout=0.20),\n",
    "        cx.MaxPool2DLayer(\"maxpool1\", (2,2)),\n",
    "        cx.Conv2DLayer(\"conv2D_2\", 32, (5,5), activation=\"relu\", dropout=0.20),\n",
    "        cx.MaxPool2DLayer(\"maxpool2\", (2,2)),\n",
    "        cx.FlattenLayer(\"flat\"),\n",
    "        cx.Layer(\"hidden\", 30, activation='relu'),\n",
    "        cx.Layer(\"output\", 10, activation='softmax'))\n",
    "\n",
    "cnn.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that each convolution layer in the network includes a `dropout` setting of 20%. Dropout is a technique that helps to improve a network's ability to generalize what it has learned, by making it less sensitive to noise and to irrelevant correlations that may exist in the training data.  During training, a randomly chosen subset of units in a dropout layer (here, 20% of the units) will be turned off (set to zero activation) on each training cycle, with different random subsets being chosen on each cycle. Dropout only occurs during training; after the network has learned, all units participate in the classification of input data. \n",
    "\n",
    "Next, we need to initialize our dataset and then compile the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.get_dataset(\"MNIST\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "MNIST:\n",
      "Patterns    Shape                 Range                         \n",
      "=================================================================\n",
      "inputs      (28, 28, 1)           (0.0, 1.0)                    \n",
      "targets     (10,)                 (0.0, 1.0)                    \n",
      "=================================================================\n",
      "Total patterns: 70000\n",
      "   Training patterns: 60000\n",
      "   Testing patterns: 10000\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.dataset.split(10000)\n",
    "cnn.dataset.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we will try the RMSprop algorithm, which automatically adjusts the learning rate and momentum as training proceeds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.compile(error='categorical_crossentropy', optimizer='RMSprop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2D_1 (Conv2D)            (None, 24, 24, 16)        416       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 24, 24, 16)        0         \n",
      "_________________________________________________________________\n",
      "maxpool1 (MaxPooling2D)      (None, 12, 12, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2D_2 (Conv2D)            (None, 8, 8, 32)          12832     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "maxpool2 (MaxPooling2D)      (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "flat (Flatten)               (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "hidden (Dense)               (None, 30)                15390     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                310       \n",
      "=================================================================\n",
      "Total params: 28,948\n",
      "Trainable params: 28,948\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"mnist-cnn.png\" width=\"80%\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c48b11454e624cc09945038b3070decc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn.dashboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training our CNN on the full dataset will take a long time, so in the interest of brevity, let's reduce the size of our dataset by 80% using the ``chop`` method, which deletes the specified fraction of data from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with 70000 patterns\n",
      "14000 patterns left after chop\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: dataset split reset to 0\n"
     ]
    }
   ],
   "source": [
    "print('Starting with', len(cnn.dataset), 'patterns')\n",
    "cnn.dataset.chop(0.80)\n",
    "print(len(cnn.dataset), 'patterns left after chop')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then reserve 25% of the remaining data for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10500, 3500)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.dataset.split(0.25)\n",
    "cnn.dataset.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================================\n",
      "       |  Training |  Training |  Validate |  Validate \n",
      "Epochs |     Error |  Accuracy |     Error |  Accuracy \n",
      "------ | --------- | --------- | --------- | --------- \n",
      "#   10 |   0.02725 |   0.99133 |   0.06282 |   0.97943 \n"
     ]
    }
   ],
   "source": [
    "cnn.reset()\n",
    "cnn.train(epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at which test inputs the network got wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = [cnn.propagate(img) for img in cnn.dataset.test_inputs]\n",
    "classifications = [cx.argmax(r) for r in results]\n",
    "correct_answers = [cx.argmax(t) for t in cnn.dataset.test_targets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrong = [i for i in range(len(classifications)) if classifications[i] != correct_answers[i]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network classified the following 72 test inputs incorrectly:\n",
      "[144, 216, 236, 256, 300, 324, 352, 364, 384, 444, 492, 530, 539, 604, 710, 804, 842, 938, 982, 1010, 1034, 1139, 1205, 1259, 1267, 1281, 1302, 1385, 1449, 1578, 1683, 1684, 1706, 1754, 1777, 1797, 1805, 1872, 2059, 2085, 2103, 2151, 2155, 2179, 2292, 2305, 2332, 2436, 2470, 2512, 2518, 2521, 2556, 2582, 2627, 2636, 2649, 2684, 2685, 2687, 2871, 2876, 2928, 2952, 3008, 3177, 3238, 3354, 3396, 3422, 3469, 3470]\n"
     ]
    }
   ],
   "source": [
    "print('Network classified the following', len(wrong), 'test inputs incorrectly:')\n",
    "print(wrong)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at some of the incorrect classifications.  For clarity, we will label each of the misclassified images with the network's (wrong) answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ydPyaNWvo0KEDQKYsK2Qut9vbbVE2c+ZMAHr06OH6Gq9evZrx48cDsVVtmadB8JoMe5+H5JRQTloBV5d2MAoWLOieZKWHa5cuXVwN36RJk+jWrdshx+hTfC3voXrooYeA8D9BDx8+3E2wy5Qp4/pw5lTtXZSPr5QSEBmfPUlzfelhWq9ePVdCkwpdBOQFNSdJ79tUUKdOHSDY+dykSRMgeAzI86S8Yfv3339dSZU8H7z77ruunOjjjz92k9bs7kBX0SA9vmvVquXesMTLnz8/AKeddhoQvNGREqQoT1plz4z8XnPnzuWzzz7zGZJ3UioBuKOOS5Qo4ZIDYuTIke5j6egkycFE0fIApZRSSikVeqHNtB6K+O4B//d//wcES3ySyr7ooovc5iU51lGKjaPiu+++AzLvUtzTt99+y+OPPw7E+lG++eab7vOdOnWK/ElZh0oeC/Fkx2jU+jNmVeJgjKF79+6ZbpfdrPJYmjp1qiu237Fjh1vyTTVyzKBkUyZNmsSAAQOA1DuqUvot3n777UBwVLb8H8+cOdONgWxuXbFihcs4qdQnG+zGjBnjTkWT44/3JP2+ZWXr/vvvd11Lokw2ccaXQqhAnz59XOnDdddd505Ny+r4X1mx1UyrUkoppZRKeymZac3Kpk2bXHF1r169XCsYqcm47LLLvMV2MGTDzcCBA91pN1J7Em/IkCG89NJLe/0+99xzT2ICTAIpED8QUpclG1AqVarEiBEjMt2vXLlyQJCZigJ5HE+YMGGf95P+x6VKlaJTp05A0NII4O677+buu+8GgtpGeYd9xhlnJCTmRLvjjjuAzKe3yClH0usZ4LnnngNSK9N6ww03uBPApNY5/qQfgFtvvRWIZegffvjhJEYYblmtwKQa6ckbf9qgbLADXAtFiPUXTcX+zipru3btYvLkyQBMnjzZZVqvvfZagCxrnxMtbSatECsBuO+++9zGjcsvvxyAp556io8//thXaIdEugPEbySSXZ2p1p82nvzfjRs3LsvPV6pUCYht0mnevDmnn346wH6XQOMnNFEgJTHyd926dd2L7ltvveXu99RTT7mP5QnnpJNOAoIXLimXqVGjhtuwE1WyOUQ2FqWLa665Bgie59q3bw8E3VP2dPTRR3PVVVcBsYl9/OMj3Z111ll8/vnnvsNIKNlUF7/MP2zYMHe0daKXesNE3rjFH12qMtq0aROQdTInWY8V/d9RSimllFKhl1aZVmGt5eWXXwZwmYb4PoRRI0ua8ZlW6S+XSkf3bd++3bXhqVChgssQSquNFi1auI1T1apVo0aNGkAs05pVS6uNGzfyySefAFC+fHlq164NxJbEFyxYkOWJU2E3ZMgQmjdvDsCoUaNctlU2V23cuNG1epE2WC1btnRjBjBx4sRkhpzj5FhGWd7am0qVKnHnnXdmul02nkRNv379gKBUJKsMq2wyHDFihFuNaNasGRA7nlJFb7XlYMixnHsez9moUSMf4XghR2TL68OuXbt8hhNqcrLi4MGDM31uzzKsRNFMq1JKKaWUCr3ophcPkbxjiDp5l7invdV5RtmGDRsYNWoUENRjSlH4/k6LiifZE9l48+ijj7qNORdeeKE7FUzqPUePHh3JTOuIESMoXbo0EDSK7tWrFxCrSfrnn39cixdphQbw9ddfA3Dbbbfx/vvvJzPkHCebjFq0aOE2IL3++usuW9+2bVsg2GAiWVmxZMkSd/hC1MhzmxwoEa99+/YMGzYMCFobSQ3zypUrkxegB2XLlmXr1q1A7BrYn9mzZ7tVrKw2a6Yyee5IdWXLlnU14OK1117zFE34SevEE044wd0mLfT2zNYnSlpOWkuWLMljjz0GxJbD4nu7Ron0YRQbN24EMvZiTSWyu/nwww93y9pSJrCnhQsXArG+nMOHD2fnzp0A/PTTT4kO1as5c+YwZ84cIDjhRrRu3RoITtCS0hGZnC1YsIAXXngBgPXr1ycz3EM2ZMgQINgNLZ0h8ubNCwTHOssxpRs3bnTXvGxUk/vF69ixY4Zd1FHyyiuvAEHfROnjLL9/0aJF3aabjh07umsklcjG1DvuuMN9XK9ePdauXQtkvPbj35hJz1o5zvOcc85xjxF5A/T8889HrnfzwYg/ESkVyTXfsGFDd/KdHE0q5WIqo7x587oyIrFly5akHw2u5QFKKaWUUir0TDLPWzfG5MgPy5UrF7lz53b/luXbff0uuXLlcu+aZ86cScOGDYHYme2y7Lwna23mI4ZyQE6NxaJFi9zS5q5du1w2QfqM5qQwjUXevHndsvbeTnCRTGJ2TzuLLw8QRYsWzXIzW5jGwrcwjcXVV1/tToA7GLJp6+KLLz6ozEEYxkJOOWrVqhX/+c9/gFgWae7cuS67muhNV77GYuzYsQBceumlWX5eXi9y5cp1wO2Nfvzxxwx9fqXX8f4eK2F4XById955B4j1cQXc73qofVrDMBY33HADEKy+yfO7nAgmGfdkCMNY7I9kpQcNGsQtt9yS4XPjxo1zrScPVXbHIlLlAbJE07RpU7e7e/z48a6pviz9xpPJ7a233uoOFNi1a5drqi9PcFEjdWvFixd3ux2ttfucuKeSnTt3uv/vnDqCN13GLpU9/fTT7kVISmeOPfbY/X6d9B3s27cvkLylrkT4/fffgWAp+/nnn/ccTfJJZ5jXX389y4m5TOCLFy/uajfLlSvnOq5kRbosFC5cmJtuugkIykvOPPNMILXKsfLly5epZGbVqlWu3j3q2rVr5/4PFy1axP333w8kd7IaJSVLlgTIMGFdt24dENT+J5uWByillFJKqdCLVHmAHEd6/vnnu0zIr7/+6vpJxr+rvuiii4DYzugKFSq4ZbH9HW0aL+zp+0WLFrl+m9Zat4FGMgPbt2/PiR8j3z/UY5FMOhYxYR2LI444AoAePXq4XfLVqlVj+fLlQKwP7ZIlS1xZwKF2igjrWPigYxETpbFo1qxZpp3gCxcu5OSTT86R7+97LCZPnuz6Vk+bNs2dCuaD77HIDik1lK4rALNmzQIyHv97qLI7FpppVUoppZRSoRepTKvUH11++eV06NABgPr16+/za+QdwbRp09wmmwPZgBD2d0J7ZlqlzlPaQUkPtZwQ9rFIJh2LGB2LGB2LGB2LmCiNRdOmTV2m1Zgg7K+//pp69erlyPeP0lgkWhTGolixYgDcf//97sRI6fst7fNyQnbHIlKTVh/C/qBq27Yt06ZNA2D69Onu40QcwRn2sUgmHYsYHYsYHYsYHYuYqI2FdA845phjAGjevDlLly7Nke8dtbFIpKiNxciRIwHcASU5eSiJlgcopZRSSqmUoZnW/YjaO6FE0rGI0bGI0bGI0bGI0bGI0bGI0bGI0bGI0UyrUkoppZRKGTppVUoppZRSoZfU8gCllFJKKaUOhmZalVJKKaVU6OmkVSmllFJKhZ5OWpVSSimlVOjppFUppZRSSoWeTlqVUkoppVTo6aRVKaWUUkqFnk5alVJKKaVU6OmkVSmllFJKhV7kJ63GmM7GmB+MMVuMMf8zxpzmO6ZkM8b8tceff40xj/qOyxdjTBVjzGvGmI3GmDXGmFHGmDy+40o2Y0x+Y8wzxpifjDGbjTELjDFn+47LF2PMccaYd40xfxpjlhlj2vuOyRdjzFxjzLa454wlvmPyxRgzwRjzqzFmkzFmqTHmSt8x+WKM6WWM+dIYs90Y85zveMLAGHPM7mtlgu9YfAvDWER60mqMaQHcD1wGFAFOB370GpQH1trC8gcoB2wFXvYclk+PAb8DRwC1gSZAT68R+ZEH+IXg9y8G9ANeMsZU8RiTF7vftMwAZgMlgKuBCcaY6l4D86tX3HNHDd/BeDQUqGKtLQq0AQYbY+p6jsmX1cBgYKzvQEJkNPCF7yBCwvtYRHrSCgwABlprP7XW7rLWrrLWrvIdlGcdCSZsH/gOxKOjgJestdustWuAOcAJnmNKOmvtFmttf2vtit3Xx2xgOZCOL8jHAuWBEdbaf6217wIfAd38hqV8s9Z+Z63dLv/c/aeqx5C8sdZOs9ZOB9b7jiUMjDGdgT+Ad3zH4ltYxiKyk1ZjTG6gHlB691Lfyt3LwIf5js2zS4DnrbXWdyAePQx0NsYUNMZUAM4mmLimNWNMWaA68J3vWELCACf6DsKjocaYdcaYj4wxTX0H45Mx5jFjzN/AYuBX4DXPISnPjDFFgYFAX9+x+BamsYjspBUoC+QFzgdOI1gGPplgCTQtGWMqEywFj/Mdi2fvE2RWNwErgS+B6V4j8swYkxeYCIyz1i72HY8HSwhWIG4xxuQ1xpxFcK0U9BuWN7cBRwMVgCeBWcaYtMwuAlhrexKUmJ0GTAO27/srVBoYBDxjrV3pO5AQCM1YRHnSunX3349aa3+11q4DHgLO8RiTb92AD621y30H4osxJhdBVnUaUAgoBRxOUPuclnaPyXhgB9DLczheWGt3Au2Ac4E1wE3ASwRvatKOtfYza+1ma+12a+04glKJdH7uZHfZyIfAkUAP3/Eof4wxtYHmwAjfsfgWtrGI7I5qa+1GY8xKgvojd7OveEKiO3Cf7yA8KwFUAkbtrlPbbox5lmBzwa1eI/PAGGOAZwhWJs7ZPXlLS9bahQTZVQCMMR+jqxLCEpRLqOB1MW2zzgqApkAV4OfgKZTCQG5jzPHW2joe4/KhKSEaiyhnWgGeBXobY8oYYw4HbiTYHZx2jDGnEiz1pXPXAHZn3JcDPYwxeYwxxQnqfBf6jcybMcBxQGtr7db93TmVGWNOMsYU2F3rfDNBd4nnPIeVdMaY4saYlrvHIo8xpitB55W0q/ve/drR2RhT2BiT2xjTEuhCmm682f14KADkJpiYFEjHdoEEJTNVCcoOawOPA68CLX0G5UmoxiLqk9ZBBO0XlgI/AF8D93qNyJ9LgGnW2s2+AwmBDkArYC2wDNhJ8IYmreyucb6G4IlmTVxPzq6eQ/OlG8Emm9+BM4EWcbvG00legpWHtcA6oDfQzlq71GtUfliCUoCVwEbgQeAGa+1Mr1H504+g9O524OLdH6fdPhFr7d/W2jXyB/gL2GatXes7tmQL21iY9N5krpRSSimloiDqmVallFJKKZUGdNKqlFJKKaVCTyetSimllFIq9HTSqpRSSimlQi+prSyMMZHb9WWtTUjvQh2LGB2LGB2LGB2LGB2LGB2LGB2LGB2LmFQeC820KqWUUkqp0NNJq1JKKaWUCj2dtCqllFJponfv3qxdu5a1a9dSq1YtatWq5TskpbJNJ61KKaWUUir0IjFpbdGiBS1atGD8+PGMHz8eay27du1i165d3H333b7DU0oppSLBWkvJkiUpWbIkU6ZMYcqUKYwePZoCBQpQoEAB3+EptU9J7R5wsB577DEAjj76aAB27drlPnfYYYd5iUkppcKmTp06VKpUCYBvv/2WZcuWeY5Ihc2iRYv47bffAKhatSoAPXr04McffwRg+PDh3mKLqnbt2nHhhRcC0KlTJ3f76tWrATjllFP49ddfvcSWaiKRaVVKKaWUUukt9JnWQYMGUaVKlQy3bd++nQEDBgDw0EMPeYgq8U488UQAqlWrts/7NW7cmJtuugkIln2eeOIJAN544w0Avv/+e5YuXZrASMOpSpUqNGvWDIBTTz0VgAsuuIDNmzcDMG3aNAD69+/Pxo0b/QSpVA6pU6cOAC+++KJ7zvjiiy/4/vvvAZg1axYAr7zyip8AI+Ckk04C4O233wbgiSeeSMnys3nz5nHJJZcAMGfOHHd7r169AHj11VdZvHixl9iipn///gD07duXQoUKAcHr8P/+9z8A/vrrLwA2bdrkJb5UpJlWpZRSSikVesba5B2ccDCnNDz44IPceOONGW5btWqVq9tKtGSfWFGjRg0ApkyZAsDxxx9/SD/nq6++4p133gHg9ttvP6TvFfbTOwoWLOiyzn369KFEiRL7/Zpnn32WK6644oB/VtjGomLFikCQeQdo1qwZxgQhNmrUiGOPPTbD/RctWsR9990HxLLO27dvP6iYwzYWcV8PwHHHHecyjBs3bmThwoUAlC1bFoBJkyYxadIkIMiSHApfYyErCAULFszy89u2bQOCGrv4x/vvv/8OkJB02nxhAAAgAElEQVTMWlgfF1k566yzmDBhAgAlS5YE4OOPP+a0007Lke8ftrE4/PDDgdi136RJE/e5d999l+bNm+dAdFkL21gcqAIFCjB+/HgAzj33XADy5cvnxvLee+91tawyT5k/f36W3yvqYwFQvXp1AEaPHi0/mw4dOgAHlmHO7liEvjwgK2XLluW///2v+/cHH3wA4Ca3f/75p5e4DlWNGjV47733gNgLKsDPP/8MwIoVKzJ9TZkyZTJNSOLVqVOH2rVru/tCME5RHaN4stN14MCBANxwww3kyXNgD2l58o4imZRVrVqVt956C4g9Se7cuZOdO3cCsGTJErZs2ZLha0844QT3Iv3RRx8BcPrppycl7mRp1KgRECyN33rrrQBcccUV7gk1f/78ALRu3ZpFixYBwealKIpfmsyKXCtHH320e46B4LEB8NprrwHBG9t//vknkaGGysUXXwzAiBEj3GRVxlDKBFKRlERNnDgRgIYNG7rroUGDBnTs2BGAqVOn+gkwhCQxMGnSJBo0aADAH3/8AcBTTz1Fnz59Mn3N2rVrkxegJ127dgXgzDPPdLfVrVsXIMNzTU7R8gCllFJKKRV6kcy0bt261W0uatWqlWuFJe2vunXrFslswW233ZYhwwowZMgQXn/9dSBYrtrTsccey9lnn53p9v/7v/8DoGjRouTKFbw3keJ7a+1BLYmHyYknnsjLL78MxEoq9iRF8CeffLK7beTIkQBZjlmUHH744a41TePGjfn333+BWAnIrFmz9rnk+9hjj3HNNdcAcNRRRyU42uSS7PnkyZOBoFRCskibNm1yj5e8efMCMHToUJo2bQpEN9OaldWrV7NhwwYASpcuDZDp+UXGQv6uXLky1157LQDr169PVqhetGrVimHDhgFQokQJlxWSx0I6ePrppwG46aab3GOgUKFCFC1a1GdYoSKbogcNGgTgsqwQyyROnTqV6667DoBly5a5jdCprlWrVvTt2zfDbb///rvbAJoIkZy0btq0iZdeegnI+IIkfdI2b97sSgX2XBYNs9tuu43BgwdnuO2XX35xy7xZWbx4cZaTE6nbmzhxonvBEm3atMmBaP2QJ5A333yTcuXK7fV+c+fOdY8B2clZqFAht0M46mrXru3ehHz33XduEr5q1apsfb2MCcQeK6ni0ksvBWDHjh0ALFiwwNU39+zZM9NkbPPmza5vZSqQcqKuXbu6N7oyERs/fjzly5ff69d26NDBTeZnzJgBBHXfqUSeQ5599ln3hr5+/fruuSGdJq2iZ8+ebu8DBOUSAC+88AJw8PXuUVeoUCE3WW3bti0QlB+uXLkSwNX+dujQwZVrWWtdDXmXLl2AoJtP1Mfw8MMPp0KFCgDccccdQDCXkNIkcf311yf0+VTLA5RSSimlVOhFMtMKMG7cOCB4J9OiRYsMn7viiisoXLgwAFdddRUQjYzr2rVrc6xwW941X3zxxW7DzZ4Z1yhq3bo1wF6zrB9++CEQvAOU8gAxcuRI905R7HmfqFi/fr3brXr99dcf8Gkr8buDU613p2QS5TFSqFAht0wuf8crV64c//nPfwBcyUnUxGd55LEQn0GfO3cuEByJfcEFFwDBc6N0G4jfkCjX2BlnnAEEpyX17NkTgC+//DKBv0VyyGO/VKlS9O7dGwi6rEimVcYyney5MVfKA66++moAHn300aTHFAaDBw9214OM0QUXXOA26Ul3n9KlS7uNrGeddZZ7PpHn6LvvvpuhQ4cmNfZ9ka4HV155pftd3njjDXeCnsyfmjZt6vqc9+jRg2LFiu33e7/77ruJCNnRTKtSSimllAq9yGZaxV133cVnn30GxOoscufO7c7/lUzaddddt8/a0FT19ttvu+ytZFqLFi3qTnqRep2okE1p9957r7tN6oseeeQRxo4dCwT/71KvJie9SM0zxPpWPvzww4kPOgEWLlzoMmYHokePHkDQnkTqWqOaXdwbyaZKrfvEiRNp167dXu+/cOHCSG7cjCctq1q1auU2ilx11VWuNlEsXrzYXfODBg2iXr16QGyTZqVKlTjvvPOAWButunXrMnv2bAAGDBjA9OnTASJ7lrr0wF62bJn7veIls3d52En/1nTLtMpG5muuuYatW7cCsZrW999/390vfsPRvHnzABg2bBiVK1cG4OuvvwbgzjvvDFWm9aKLLgKC30l+r7/++svNl6R1ZKlSpTJ8nTy3ymtr8eLF3efkdLWsVrNyUuQnrfPnz3eNe2WDxYMPPkju3LkB3C755cuXh+pB41OePHncRRU10jXilltucReYNHr++++/3f0KFCjgXnyzmphKb9evvvoqofGGhTwJ3XnnnQDs2rXLfZwKPXvjPfDAAwDuGN/zzjvPdVV48MEHM01Qy5Qp43o9R9VTTz0FBJPWAyHL/fJ3qVKlXLeN5557DgjKJ+QN76hRo1wPz3POOQeIbXiLCnmTK3+LdD669Mcff3RH/cpyOMQmJ7lz53YdSlLd+eef754v8uXL5xI88ZPVfdm+fbvrqS4JtbD1wH7++eeBjBupChcu7MoC4slr5OzZs3nyySeB2KRXum8A7s3srl27Ehc4Wh6glFJKKaUiIPKZ1njSg7NUqVLcddddnqNRiSDZVOlRGq9evXruHXLVqlWpVavWXr+PtHJJBwUKFHBLV7JJadCgQW6ZNNVIJlU2G8yYMYMhQ4YAULNmTbc5Ux5Lqdby61CsW7fOna4m2aGBAwe6lmrFihVzGWxZDuzWrVu2W62FWTpnWv/44w93Olbz5s1dz3Mpq3nooYeyPPEplVSrVg2AsWPHki9fPiDodbyvzYeysbdNmzYuK71r1y73HCOb+8LW/1n6yJYvX57jjjsOCMqAZGOmnPQ1ZcoUVx6xtxUVycDL6XqJFslJa/ny5V2tlvTijDdjxgy6d+8OxI5e69+/v0tp16xZM0mR+nfSSSdlahS9Y8cOvvjiC08R5Rx5YpFOCaeeemq2d/7ecsstAG73cCr74IMPqFOnDhA7ljF+WSdVSQ17u3bt3G7e8ePH89133wGxJfHKlSu7koKokut53rx5Gc6RPxRS89y1a1dXdtClSxd39Kn8nBdeeCHHfqbyR3qfP/LII27SKq666qqUmbQ2b97c1XG2bNmSqlWrAhk7cIgKFSq4N2fxk9KsZPV5KUG55557cvJXyDGbN2/m888/B3B/Z8cxxxzjPpYyPelv3LZtW/d8moi6dy0PUEoppZRSoRfJTKsxxm0sycr8+fN59dVXAdyRhHny5HGp/I4dO7qMU6q74IILOPLIIzPc9tdff/HEE094iijnHHHEEQA0atRon/eTjFvu3Lndu2E5wnTRokWuuDwVyBJXxYoV3UarOnXquF2usqkgftNaqtuxY4crjzjxxBPdKoxsxitevLhbxotqJwVZnl+xYoXLelavXt0t9x3q/7dkm95++213bLb0b2zcuPEhfe+wkJPAJOMmm2hUapD+vJMnT87Qb3TPbhHx//7kk08ylb5UqFCBzZs3A0FPVghOaNy0aZO7j5wiJ5uYcqr/uk/jx493XZni518ylvHZZNnUp5lWpZRSSimVlkKfaV25cqU7s1f6LmaHZJk6dOgABG1t5B1BgwYNUj7TKoXgt956q+dIEkceF1I0Ht8zbsuWLe4kMOlNWrt2bbfJpGTJkkDQIiiqmVbJCA0fPtxtmpFMa5EiRTLcV05ukc0W/fv3d9mzqPcoPRCbNm1i1KhRQOwUqAEDBtCvXz8A9/jIbnubMLv66qtd9ue2227Lke/5zz//8NhjjwGxTGuqkI17kmmLah/ag1W2bFkgyKLtWd+ZChubpbd3sWLFXK1p586dXe22tMS89NJL3SmBF198sXudEfnz53ebj+Q5ZOPGjSn7PCq9Wk8//fR9rnDLaVrdu3d3+wYSIfST1ocffpjrrrsOwC1LAa6BdrVq1dxgxZPek9KP7Oabb050qKHQpUsXINjtCRnT+HJc26RJk5IfWAKsWbMGwHUMuPnmm5kxYwYQHCUnBxGIBQsW8N577wFBLz4Ilozkyfq3335LStw5RY7WzGqDxN9//+2WpgoXLkzt2rUBXA/OGTNmuM07spy855NzqpMX5HHjxtG1a1fP0eSMFStWuF3wxx57rHtsyKbFcePGsWDBgkP6GbLzONXUqFEDiB1rLD2h00H58uXdkaMlS5bMtGQufUejTA7SsNZSv359IOOyfYkSJdzH8tya1XNi/G2psOy/P+vWrQNg9OjR9O3bFwiSgEL6oMvnEk3LA5RSSimlVOiFPtMKsYxafKa1bt26QLAJR4qi4zNlcoLLp59+mqwwvStWrJgrh5ANGBBb8pRjTOMLxqNMsuennHIKEJx8dKDZkcKFC3PZZZcBcN999+VsgAl2ww03uI8lM/LNN98AwZL3zJkzgaBPqyx9SnlA3rx5XbZhzJgxAFx++eXJCTwkZNlr/vz5dO7cGYATTjgBiG55wMCBA91JVhMmTHCbFK+//noALrvsMrp16wbENkscKGl5lWpk9U6OAz/rrLNcb0p5Dk21JWBpCTlt2jT3mpqVxx9/nDZt2gBBxg3YZ//SMJIVRtlMtCdpg2Wtdc8H6XZ87b4MGzbMlZnJZtaPPvoox0qPskszrUoppZRSKvQikWmVDOHkyZOBjC2Oxo4d62q04k85kpM8Um2zgGQDzjvvvEyfO/roo12mVbz11luurk3Gbc9az6iSGppy5coBQcNjGZ+sTu/IlSsXBQoUyHT7119/ncAoE+fDDz8EggL4Z555BiDLU662bdvmNh7+9NNPQHCGvGRaJfMwffp0l51NB0cddRQQ1GRJ9lBqP6Ps559/BoLrXFYhpJ1TkSJFGDt2LBCsYElGWQ5r+fvvv12LuPi2QHKNjRkzhipVqmT4eatXr07Qb5I8o0ePdg3TpWa3SJEirgZeDjBJFbLKIC3e9pVlhWDl8pJLLgFi+wFkv4hYvnw5AA8++GCOxppTNmzYALDXA2hkg2HPnj0z1LeqQK5cudyJimLLli3u+SJZIjFplV2cssmmQYMGGTYYySYT+XtvZMefHEsWNRUrVnQn00h3hP2pVauWGze5EFu1auU26USZvLjIk2mtWrVcJ4DLL78808klNWvWzHKyP3/+/ARHmhhS1nAgZEnvvPPOcxMV2YQ0ceJEKlWqBAS7YVOdbMCz1rojf1Op24bshgbcMayNGjVyzwMlSpRwy33Sz3rJkiWsX78e2P8bfjmaUk4ajCJZHq9Zs6brZy2vE3feeSf333+/t9gSSV4/4zuuZFehQoWAWFcWIeMm3VyefvrpQwkxx/34449AcL1LwkNKQQD3hr1Hjx4ZShFVIE+ePK7XrfDR71vLA5RSSimlVOhFItMqhg8fDgSbjCpXrgwEZwfvmbLOyvz5813Lp7CeA7w/U6dO3e8yzp7KlCnj2lNIlk2WSaJOCsCl7KFatWquQLx48eIu0yRZBTlbO953333Hli1bkhFuqKxbt85lG6pXrw4ELWFy587tM6ykkBIRaV0jvRZTkVwDsgHvpZdecmetFylSxJUNCGn7tC9SDtCxY0eALFsORkHlypXdas0xxxzjendK1llOVUxFsrlZlvpr1qy5z/vny5fPnfK0N5LJDOsmRrkGLr/8creJt3z58gwZMgTIuAIrqwdTpkw56A2L6eDFF19M+s80e/ZjS+gPMybHf1iTJk1499139/r5//73vwC0adPmoPruWWuzLoA5RAcyFrL8PXLkSAoXLnzAP2vJkiUAnHPOOcDB99wLw1hkpX379kBQ87yv5sdZmT59eqY64OwI61gcjP/7v/8DgjdzAwYMAGJHnGZHVMfi448/BoJdxdLrV8ojDnaXeJTG4qabbsrUUH/Hjh3uaEoxf/58Fi5c6P4tXTb2N1kN61jIkb2TJ092b9jGjRvHjTfeCMR6fOeksI6FD77H4sgjj3T7Oo477rj4rwcyHuM6ePBg+vfvn4NRZuR7LA5Evnz52LZtW4bbOnXqlGNHX2d3LLQ8QCmllFJKhV7kM62JFoZ3Qq1btwaCwnbZ9Rlv7ty5QGz3JsR2hP/www8uE7lq1aqDjhfCMRb70q5dO0aOHAngNlXsjSwL9u7d+6CWN8M+FgdCSk4+//xzt8QnO6mzI6pjIct+p5xyijvSVlY1DlbUxqJChQoZ/r1z504aNmyY4bbFixdHdpUqnpQRvfnmm0CQTZeSs8GDB2fauJmTwjYWPoVhLGRF5ZZbbnE9WWWDorXWlUKMHj36kE+Q25cwjEV2aaZVKaWUUkqpbIrURqx0JRmha6+9NlMfzg8++MBtPorPpMqmoxUrVhxyhjUqpk+fzkcffQQE/SSzqlWV3oL9+vUDcJsv0tmefTfThbR2Kl26dGg3jyRaVs8NqdqrV/YDyO/co0ePlOu/qrJHehn37t2b3r17e44muho2bJhjmdbs0klrhLzyyivZ3t0tS2DpZu3atUBsV6zaOymhGDx4sLtt2LBhvsJJOikLWbt2LfPmzfMcjUo0KQmSzVdKqez7559/XEcN2cDpo3OIlgcopZRSSqnQ041Y+xGlQulE07GI0bGI0bGI0bGI0bGI0bGI0bGI0bGI0Y1YSimllFIqZeikVSmllFJKhV5SywOUUkoppZQ6GJppVUoppZRSoaeTVqWUUkopFXo6aVVKKaWUUqGnk1allFJKKRV6OmlVSimllFKhp5NWpZRSSikVejppVUoppZRSoZcSk1ZjzDHGmG3GmAm+Y/HBGJPfGPOMMeYnY8xmY8wCY8zZvuPyQcciI2NML2PMl8aY7caY53zH45OORYwxpoox5jVjzEZjzBpjzChjTB7fcflgjJlgjPnVGLPJGLPUGHOl75h8McYcZ4x51xjzpzFmmTGmve+YfNFrJCZMY5ESk1ZgNPCF7yA8ygP8AjQBigH9gJeMMVU8xuSLjkVGq4HBwFjfgYSAjkXMY8DvwBFAbYLrpafXiPwZClSx1hYF2gCDjTF1PceUdLsnITOA2UAJ4GpggjGmutfA/NFrJCY0YxH5SasxpjPwB/CO71h8sdZusdb2t9ausNbustbOBpYDaffEq2ORkbV2mrV2OrDedyy+6VhkcBTwkrV2m7V2DTAHOMFzTF5Ya7+z1m6Xf+7+U9VjSL4cC5QHRlhr/7XWvgt8BHTzG5Y3eo3EhGYsIj1pNcYUBQYCfX3HEibGmLJAdeA737H4pmOhVJYeBjobYwoaYyoAZxO8EKUlY8xjxpi/gcXAr8BrnkMKCwOc6DsIT/QaiQnNWER60goMAp6x1q70HUhYGGPyAhOBcdbaxb7j8UnHQqm9ep8gU7IJWAl8CUz3GpFH1tqeQBHgNGAasH3fX5GSlhAsAd9ijMlrjDmLYBm4oN+wvNFrJCY0YxHZSasxpjbQHBjhO5awMMbkAsYDO4BensPxSsdCqaztvjbmEEzOCgGlgMOB+33G5dvuJfEPgSOBHr7jSTZr7U6gHXAusAa4CXiJYJKSVvQaiQnbWER20go0BaoAPxtj1gA3Ax2NMV/5DMoXY4wBngHKAh13PwGlJR0LpfapBFAJGGWt3W6tXQ88C5zjN6zQyEN61rRirV1orW1irS1prW0JHA187jsuD/QaiQnVWER50vokwRNL7d1/HgdeBVr6DMqjMcBxQGtr7VbfwXimY7GbMSaPMaYAkBvIbYwpkMZtW3QsAGvtOoLNiT12j0lx4BJgod/Iks8YU8YY09kYU9gYk9sY0xLoQppu7DXGnLT7uihojLmZYLf4c57DSjq9RmLCNhaRnbRaa/+21q6RP8BfwDZr7VrfsSWbMaYycA3B5H2NMeav3X+6eg4t6XQsMukHbAVuBy7e/XE/rxH5o2MR0wFoBawFlgE7gRu9RuSHJSgFWAlsBB4EbrDWzvQalT/dCDai/Q6cCbSI66yQbvQaiQnNWBhrrY+fq5RSSimlVLZFNtOqlFJKKaXSh05alVJKKaVU6OmkVSmllFJKhZ5OWpVSSimlVOjppFUppZRSSoVeUnsUGmMi16rAWmsS8X11LGJ0LGJ0LGJ0LGJ0LGJ0LGJ0LGJ0LGJSeSw006qUUkoppUJPJ61KKaWUUir0dNKqlFJKKaVCTyetSimllFIq9JK6EUupROvfvz8ATZo0oWnTpgAMGDAAgLlz5zJ37lw/gUVAsWLFePPNNwHYsmULZ5xxhueIlFJKqRhjbfI2maXyjrYDleixaN68OQBvvvkmxgS/wuzZswF44okn3McHIuxj0b9/f+6555593kcmsDK5PVhhH4sDUaJECQCmTp1KgwYNALjsssuYPHlytr4+lcbiUEVtLOT/fs6cOQDUrVuXV155BYA777wTgKVLlx7U947aWCSSjkVM1MaiZMmSALz77rsAFCxYkGbNmgGwcuXKQ/reURuLRNLuAUoppZRSKmVopnU/ovBOKG/evAAULlyY+vXrA/Dcc88BULZs2Uz3/+uvv7j66qsBsp1Ng/CPxXvvvedKArKrWbNmB1UyEPaxyI4iRYoAMGTIEACuu+46Bg0aBLDfjHW8KIxFvnz5ADjyyCP55ZdfANi5c2e2vrZatWrccccdAGzYsAGAwYMH8+eff2a6bxTGQgwbNoybbroJgKxeBzp16gQEGfiDEaWxSDQdi5gojcWpp57K8OHDAdwqFMDnn38OQMOGDQ/p+4d1LCS7PHToUK666iogyCpXrFjx0IPbC820KqWUUkqplKGZ1v0I6zuheFJf8/bbb2f7a6SmtVu3bmzatClbXxP2sWjatGmGTKtkUCVruLcsrIzfgWRcwz4W2TFjxgwAWrduDcCsWbPo2LEjAP/880+2v4+vsahUqRIAP//8836/l2RKBw8ezI033gjAyJEj9/k1DzzwAAC9evVy15aM1d6E/XFRtGhRBg8eDMDVV1/tVmmyeh348MMPgb1fN/sT9rE4GNWqVXN1wPG+//57IFjFykoqjsXBisJYyMrMli1byJ07d6bP//TTTwAcddRRh/RzwjYWkmH9+uuvAahQoQJPPfUUAKtXr2bgwIE5FGFm2R0L7R6QAmRzUTx5EVqxYoW7rVixYkCw+eK8884D4JFHHuGyyy5LfJBJsLfuAHJb06ZNee+99zJ9Xm6TDWvp4KyzzuKkk04CYP369QD06dPngCarvlWvXh3Y/6Q1b968tGrVyt33jTfe2Ot9DzvsMAC6du1K+/btARg3bpyb9EZdz5496dmzp+8wQk0mKeeccw41atQAcGN2xBFHkCdP8LIZP9GXspE333yT7t27JzPcQ1KoUCEA2rdv7zbe1ahRwz0Xyu9ojMnwsXwu/uMXXngBCBIhUdenTx+ALCesqaxgwYJAMFmFYPPZ9ddfD8COHTu8xRVPywOUUkoppVToaaY14lq0aOEyqN988w21atUCYhusunbt6u578803A3D//fe726pVq5asUL2bO3euy0ofyEajVJI/f34AzjvvPEqVKgUEjyHImJWPguyWw9x11100btwYCEoglixZkuk+kmGV5a/rr7+eMWPGAMFjJatNV1Eiv79k07LjhBNOAKBVq1auJVaqKlq0KKNHjwagUaNGAFSpUoU1a9YA8PrrrwNByYS0BPvjjz8yfR8Zs6h4/vnnAWjbtm2m7Oq+Ps7qc+3atQNipThDhw5NTNAJkitXkMO77bbbEroMHiXr168PTYZV6KQ14r755htXZ7dz5043gV21alW2vr527dp06dIFgBdffDExQYZE06ZNadKkie8wvHr66aeB4M3Md999B8Cnn37qM6SEu/baa/d7n2HDhgGxZeAZM2a4N3dRn7BCsKwNseW/7Dj88MOBoKtEKk5ajzjiCOrWrQvAww8/zNFHH53h8++99x6XXnopgOs4sT9yTYXd+PHjAVwJjLWWv//+G4DFixfzwQcfuI/3JImQxo0bZyipKly4MBCrNY8a6ZZx7733ZjmBjydvYlKNvBFbtGgRABdeeKFLdh1qT9qcouUBSimllFIq9DTTGjGylHviiScCMGLEiAyf//XXXw/o+xUsWJDixYvnTHAht2d3AZHVRrZUU6dOHSDYgAXB7lfJsqS6pUuXUrp0aSDYYCArC7NmzQLgySef5JRTTsnwNeXKlXM9jg/0mgqjt956CwiyJUceeaTnaPyQDVRXXnklECwDV65c2X1eMuqTJk0C4KabbnLZx1TSvn17t5QvmcTvv/+eCy64AMg6uxpPejlba1m3bh0A06ZNc5+X3eZRU6VKFffx/roqZadjSRRt3rwZiG1erlmzptu0/fjjj/sKKwPNtCqllFJKqdDznmmtUqUK5cuXB+Djjz/Oke9ZrVq1TK1KJDMJGVt1xL/TDrvDDjuMzp07A7h2RbNmzXLv+sJWMB0FAwYMoH///r7DSDjZPCIZx2eeeYZly5b5DClpWrdu7dqa1alThwkTJgCwceNGIFa7CbFTsj755BMWLFiQ5EgTR2rVvvjii7TNtMqmI3kO3ZPUKX700UdAUKOZipnWli1butrmr776CoCzzz7bZU335vTTTwdizyHWWu6++24gWK2IOnkd/eabb9zr64IFCzj55JMz3ffMM88E4L777ktegEkk/69nnHGG+zgsmVbvk9ZZs2a5TQKffPLJPu8rRw5ed911rmg+V65c7Nq1K8P9qlat6iateyN99aKkbNmybmOA9Nbs3LmzO2Zub2T5//zzz09ofFF0MEe4Rk27du0oU6YMEHtBTpW+o9nx559/ul6DLVu2dNeQ9CK01jJ9+nQg1j3gm2++SX6gSdC9e3c3aW/btq3naJJLJiWyWWrDhg0ULVoUgG3btrnJrPz9559/8uyzzwJBBwqArVu3JjXmRJHlb1nK39+EtXTp0u51Rr7WWpuhLCDqJk6cCMD06dPdcaWNGzfOckKe6l13pExg3rx5LvE3ZswYevTo4TMsQMsDlFJKKY9xfBgAACAASURBVKVUBHjPtC5btozjjz8eCE4g2ZesPp9VpjU7JOUdVXJSxzfffLPPd/9VqlRh6tSpQNDeak+ffvppSiztHKxUzrTKasPzzz/v+rM+8cQTwP4zK6lGjiRdtmyZOx1LlsmnTJnC5ZdfDuz9GM5Ucf7559OmTRvfYXhx++23A7Hn/p07d7rNWbt27XJLwrIMfv7559O7d28AGjZsCECXLl3cEZ5RdqCn/9WtW9dt5pSv7d69e0o+j2zZsoUff/wRiB3Pm64effRRt2mvbdu2brXbZ9mM90lrp06dKFCgAIDb1bthw4YMT6wy2ZJlHak725s333yT//3vf0Csyb7Uzcr3GTt2bA79BoknT6x9+/Z1ty1fvhyI7YDek5yd3KpVqywnq2L79u38+++/ORWqConTTz+dBx54AAhq86RJ+ksvveQzLO9KlSrllvZkmXP8+PEpP1kVBQsWdE3UIdZQPas3/vK8cO+99yYnuCSJf/2IP7ZYapjl75EjR7ojrqWe7/bbbw/FEumhksf+VVddBQS7/7OagEqHkccff9x9jfRwlb9TUceOHX2HEApLly7loYceAmD48OGuLlyuCykjSCYtD1BKKaWUUqHnPdO6Y8cOt+tdli4BXn75Zfex9E+TExni3x3vjfSck+MZIZapHTBgQGR22ufPn9+dYnTRRRe52/fWR07GasqUKQBZ7nyMJ8uiKrV07NiR+vXru3/LJryoPO5zmqw89OjRw50a9+ijjwJ7X61IJXJk7y233JLhueONN94AoHnz5pm+Rrq5pPqJafsiG7HkVLXu3bu7k9KiduxxPFnilxPB5s2b53pYr1271nXVufjii4FgI5Y8brLqdZ1KqlWrxsiRI32HERrSt/jaa691mfeZM2cCsY4cyaSZVqWUUkopFXreM63ZcaDvaFu1auWyk3IeMgTtKwA2bdqUY7ElWsGCBTNkWEV8IX2zZs0A6Nevn+uhd8IJJ+zz+0pNr9Q6pjLpw3rPPff4DSSJOnTo4D5evny5O0s6XckGgmuvvdZljPr06eMzpKSSjGr8qT8Q9G2FrDOtKrbhVVb5qlatyi+//OIzpEP21FNPuc018npRo0YNXnvtNff5rl27AtCoUSMgyL4OGTLEQ7TJI6sx9913HyVLlnS3y6aso446CjjwTWxRJycCtm3b1q1Wjx49Gghab7766qtJjScSk9b9kT6vM2bMAKB+/fruhUkGdNSoUZGarO6P9N28+eab6devHwBFihTJ1teuX7/epfy3bduWmABDYF+T1VTtGiAN0uM3Hj755JOuL+txxx0HBH1a93dcY6qoV68eN998MxD06pQX5HQifWhr1arlbhszZky2viadSX9fWRb97LPPIr9xdf78+ZQrVw7AdZZp166dKxWoU6eOm5jJ6+hXX33FI4884iHa5Bk/fjyQ8Q3/jh073LG/chSyvJFJN6tWrXIlQ/Jmpk2bNkmftGp5gFJKKaWUCr3IZ1obN25MzZo1gVhR+c8//8zs2bOBWG8+H60ZEklO85FNAfsiG9cefvhhINhckKpZNsmuNmnSZJ8bBqSkItVIP9b4JayrrrrKnSAnt2/evJnu3bsnP8AkklZ6M2fOdKfCDRs2LMeOi44SOfno7LPPdtmzW2+9NcujGWUVQk4Iipr4LNBtt912wF9fokQJIFihkZZQcgJhKrS7itetWzcgyCTLppr4jXrysdwvlTVo0CDTbTNnznRHtsZnWKO8Ce9gbd682ZWVyMmKV155JfPnzweSd5SvZlqVUkoppVToRTbTKs1tn3nmGfduUN4Nt2nTJmXPDj9Q8+fPd2eNR62Nh2RKpSa1adOmLgs0b948mjRpkuF+2ZGqGdZ9kSwr7L1VWiqSrFjZsmXdhqPnnnvOY0TJ1b59e9fqSg5Zad++PRs3bgTgzjvvdC2N4h8Xkn1du3ZtMsPNMVKv/9hjj2X7a4oXL+5qnXv16gUEm5O++uorAFfXKAcPpAo52WjixImcdtppQLAyIysycoBAKp58taf4/R3yOnPllVcyb968TPdN13pvmWPJHonnn3/ebdCT/RSJ3qgYyUlrmTJl3LKNMcYNkpyilc4TVnlQffvttwBceOGFkX3Ckclo/KQ0q9uyq1mzZim7ASu7pE+rnHKSik488UQg6McMwclO8iT722+/eYsrWY499lgg6BAimzNl0h4/6brxxhuTH1wSyPG8v//+e5afr1ixIhCUSlSvXh0I+q/KBF9OR7v33nvdqXKptIl3b2STZvwbmB9++MFXOEnXtm1bIDilUzadDR06NMPmRQiOhD6QN0Sp6JVXXgHggQcecCV5siGrZcuWruNAImh5gFJKKaWUCr3IZlqlaNpa6zZdpWKG9e+//3bnxV944YX7vO/EiRN54YUXAJgzZ07CYwuzuXPnumUdeSeYDuR0pz0z0Vu2bAGC7BKk3jKnaNq0KUOHDgWgUKFCQHANffjhhz7DSirZgBbfAk9WoU4++WTq1KkDBD2g9/TTTz9F/rlDehKPHj2ahg0bAlCuXDnXJvD4448HYo8PCDarSu/JESNGALH+nOmgffv2rjzAWpt2vUgBlixZAgQZdnncZLXx7rXXXmPnzp1JjS2sBg4c6E5ePPfccwGoXLlyQjOtJpk1bsaYHPlhRYsWZdSoUe7f1113HZCYDgHW2oRcvQcyFrIjvFOnTu5YQakj+f77711T6K1btyb0mE5fYxFfErCvnqvz5s1zHye6DCAMj4usyGEa06ZNcw3j//vf/9KxY0cgVjaSk8I0Fp9++mmG42shqM2UFx9Z1kqUMIxF7dq1Afjyyy+z832B2Juatm3b8t577x1MiJn4Ggs5QKFXr17UqFEDCDoKbN++HYi9YVu6dKn7XefMmZPQntVheFxkRUpJvvjiC/cmJn7SKjvCc7JrQljHQnTq1IkXX3wx0+1S33zuuefmWJlR2MciO6TcRjoqDBkyhLvvvvuAv092x0LLA5RSSimlVOhFMtOaTGF6J9S1a1fXS0/e9e2ZVUqkMI2FbzoWMWEYi8qVKwPBxhFZmRD169d310uihWEs5DS0uXPnZugckZX3338fiJXQyL9zQhjGQhQvXpw//vgjEeFkS5jGIl69evWA4KSv+FOw5OMnnngCSO1Ma6VKlYBYX+ITTzyRYsWKZbqfXFc5efR52MbiYOyZad2wYYM7Rn5vmyGzoplWpZRSSimVMiK5EStdTZw4MbKn1CiVSEcccQQQnFojmRCp403FDZr7snr1agDXzknhNcsaZrLSmtUpWJD4GvAw+PnnnwHcRjR1YKSHsShRogT58uVL2M/T8oD9SIX0fU7RsYjRsYjRsYjRsYjRsYgJ61jsrTxA+pT27dv3ECPMLKxj4YOORYyWByillFJKqZSh5QFKKaVUGpKjeteuXUvp0qWBINO6ePFin2EptVdaHrAfmr6P0bGI0bGI0bGI0bGI0bGI0bGI0bGI0bGI0fIApZRSSimVMpKaaVVKKaWUUupgaKZVKaWUUkqFnk5alVJKKaVU6OmkVSmllFJKhZ5OWpVSSimlVOjppFUppZRSSoWeTlqVUkoppVTo6aRVKaWUUkqFXmQnrcaY/MaYZ4wxPxljNhtjFhhjzvYdlw86FjE6FhkZY3oZY740xmw3xjznOx6fjDFVjDGvGWM2GmPWGGNGGWPS8ihrHYsYHYsYY8xfe/z51xjzqO+4fNDHRUyYxiKyk1YgD/AL0AQoBvQDXjLGVPEYky86FjE6FhmtBgYDY30HEgKPAb8DRwC1CR4jPb1G5I+ORYyOxW7W2sLyBygHbAVe9hyWL/q4iAnNWER20mqt3WKt7W+tXWGt3WWtnQ0sB+r6ji3ZdCxidCwystZOs9ZOB9b7jiUEjgJestZus9auAeYAJ3iOyRcdixgdi6x1JJiofOA7EE/0cRETmrGI7KR1T8aYskB1/p+9O4+3qeofOP5ZhsxcKUMkURpEN6mkwTX0hAyhoiQZEpWnDIUnPa4yPEWpKA2/ECrNlEQeD0WlQRooGUIDoiKUi7J+f2xr7XPOPffe495zz977nO/79fLCmaz7tc/ea3/XWt8Fa7xui9ckFi6JhQjxMNBVKVVaKVUdaI1z8k1FEguXxCK6HsAMnbp7vctx4fJNLJKi06qUKg48BzyrtV7rdXu8JLFwSSxEhPdwsgN7gB+BT4E5nrbIOxILl8QiglLqJJwh4Ge9bouH5Lhw+SYWge+0KqWKADOBg8BtHjfHUxILl8RChDpyPCwAXgPKAMcBFYH7vWyXFyQWLolFjroDy7XWm7xuiBfkuHD5LRaB7rQqpRTwDFAF6Ky1PuRxkzwjsXBJLEQUxwI1gcla6wNa61+BaUAbb5vlCYmFS2IR3Q2kdpZVjguXr2IR6E4rMAU4A2intd7vdWM8JrFwSSyOUEoVU0qVBIoCRZVSJVOxbIvW+hecBXn9j8QkDWfO3pfetizxJBYuiUV2SqkmQHVSt2qAHBch/BaLwHZaj8y5uRmn/ML2kLpy3TxuWsJJLFwSi2xG4JStGQZcf+TPIzxtkXc6Aa2AncAG4BAw0NMWeUdi4ZJYhOsBvKa13ut1Qzwmx4XLN7FQqbswUAghhBBCBEVgM61CCCGEECJ1SKdVCCGEEEL4nnRahRBCCCGE70mnVQghhBBC+F5CS98opQK36ktrrQrjcyUWLomFS2Lhkli4JBYuiYVLYuGSWLiSORaSaRVCCCGEEL4nnVYhhBBCCOF70mkVQgghhBC+J51WIYQQQgjhe4Hfg7xEiRJUrlwZgN69ewPQtm1bzjnnnGyvHTVqFAD33ntv4hooRCFLT0+3x/51110HwOrVq7nrrrsA+OijjzxrmxBCCBEvCd3GNZ4r2kqUKAHAxIkT6du3b+S/Q7Sfa//+/QBcdtllrFixIqZ/x6+r+4YPHw7AmDFjuPXWWwHo3LkzLVq0AKBChQoA7NmzpyD/TBi/xsILfohFs2bNAHj77bcpXrx45Ofw4YcfAtC8eXMOHDgQx1aG80Ms/EJi4ZJYuCQWrqDGwvQ5mjZtypQpUwA4+eSTs73u0ksvZfny5TF9ZlBjURikeoAQQgghhEgagZ0e8M9//hMgW5Y1N6VLlwZg8ODBXH311YXSrkTp3LkzAH/88QevvPIKAGvXruXCCy8E4KmnngLg7rvvZuPGjd400gNFijj3Yeeffz5PP/00AGeddRYAu3btomfPngDMnTvXmwbG0XHHHQdAsWLF+OKLLwDYsGED4EyRady4MQDDhg2zU2OEECIjIwOAkSNHhv0dYOnSpfZ8sXTp0gS3zH9KlSoFuFOvnnrqKZRykoLRRnTffPNNLr/8cgA+/vjjBLWy8FSsWBFwMs07d+4E4O+//wagePHiVKpUCYAbbrgBcK5LZcqUAaBfv37ZPq9OnTps3rw53+2RTKsQQgghhPC9wGZa161bB8D69es59dRTAWxm7eeff+b//u//AHj99dcBwhZmVa1aNZFNjav27dsDcNpppwEwb948e/ezZMkSmjRpAsCMGTMA+N///mezrhMmTAAo1PmNXihbtizg3AkPGDAAgDPOOIPPP/8cgN27dwOQlpZG7dq1vWlkITD/72+88QadOnUKe27SpEnccsstAPTv318yrULkg8lAmt+bNm0alpWM1KxZM99mJ027lyxZkufrzGvNeSMzM7MQW+Zf6enpTJ8+HYD69evbx1etWgXA+PHjAdi8eTOTJk0C4Nxzz6VOnTpA8DOtNWvW5P333wegWrVqXHbZZQB2xDItLY02bdpke19emejQWB6twHZazfBuXsO8ZsFVw4YN7dCx+T2IzMFiFt7Mnz8/7HkzTNywYUMArr76avtlMgfXwIEDA/9lAjj77LMBmDVrFgCnnnoqb7/9NgD33HMPc+bMAWDNmjUA/PXXX/a1ycBcHP16kfSr8uXLA85J2DjzzDMBZ9FaNPPmzQPci9WOHTsKs4kJV65cOcA5r/Tp0weAY489FoBLLrnE3gxHuwjVr1/ffseSQWhH1Qyfx2rkyJG+/D5mZmbm+rOE3tSGvs78OVU7rZ999lm2Y/6pp56if//+YY8NGTLEXnMBNm3alJD2FRaz6Kx169Zh58lFixYBuXdKQ19XpkwZO2XRKOi5Iri9NyGEEEIIkTICm2nNS926dQHo0qUL4NwRHD58GMD+HjRnnXUW7dq1A+C///0vADNnzoz6WjNRevbs2fauzyzYmjdvHoMHDwac6RP79u0r1HbHk7nD69Spk737r169OgAtWrSwQxkAJ510EgAnnngiAAsWLLBD6slu9+7dNlbly5e3WYDPPvvMy2Z5xpwPbrvtNptJMwv0YmHKypmMq5mmE0TmuKhTpw5XXHEF4Iy+gPtdiZTbOfPll1/mvPPOA5yFoUGUmZlJ06ZNAXId/s9LQd5bGCIXXEWKNvyfmZmZLYO2ZMkSW2Iv2bVu3dpOLwR3ell6ejoAP/zwg33OZFwfeOAB+9jw4cNjLqnpV08++SQA119/fa6v27VrF2+99RYAn376qf3djPg+99xz2d6zYMGCArUtKTutpUuXZuzYsYA7xBVq/fr1iW5SXHTs2NFObVi5cmXM7zPF5c0QaO/evbnvvvsA5yJuLsjmoPMzM2wxbtw4OzfXdORDO6zgrno0c17N0G4qmD17Nv/6178AJ2ZmLm8qdVrNd6VLly48+OCDwNHNZ//rr78ApzqD8euvv8axhYlXpUoVuwrazHE/Ghs3brRxMfPqTzvtNI455hggWJ3WvIbMo4mcG27eb6YE+GXueKzzV3Ma/jcdVPP+0Hmufpz+EA8XX3wxAFOmTAk7T7z55psAbNu2zT527bXXAu5GRVprXn75ZcAdGg+aevXq2emWpv5s5M2LqU9rOp4ffvghv/32W9hrGjVqZKsDmMoCAF9//TXgJs/yS6YHCCGEEEII30uqTOu5554LOHeNrVu3zva8SWObzGLQXHrppfbP+dmac+/evQA8/PDDvPHGG4CTGTC7d3Ts2BHALmbyo6ysLMBZxWtqwZnapJGCPIQbb2b4tqB3uUFgMqwmezRixIiw57/77jvAzSLlNMJgMifDhg1jyJAhAGzZsiXu7U2Eiy66CHC++6ELRiJ99NFHdji0Vq1aAMyZM8dmj77++msOHjwIuAsgW7VqZTMzu3btKpT2x1No9jA3S5cu5d133wVyXohkso5+z0JG1l41P09O7Y3MHI8cOdLGzUwvSTam7nXoFJmffvrJZqPN4udbbrnFPpaWlgbAJ598wkMPPQQEbzTLXPdDrw3mHHr48GH7fb/55pttVaJQZkRz4sSJAHTv3t2+/+DBgzYe5npc0OmIkmkVQgghhBC+lxSZVrPTlckeVqtWLdtcjK1bt9q7xv379ye2gXFk5mUuXLiwQJ9jsk19+vSxpaFefPFFwLnzWrx4cYE+v7CFzi/KyZ9//hn2d7OzSSoymdZU0K1bNyA8w7p27VoAHn30UZ5//nkA9uzZk+NnHH/88XZhzjXXXGMfD9oCixYtWgDYhSU1a9YMe978PGZ+6zvvvEOVKlUAN7t4xx13MHXqVMCpgW2YOH/xxRd2UZtfs0wZGRl5zu802cW8FhyZuGRmZvq27JxpT05Z0VhLWJnPCZ37G/rzJ7vQnZvMoqQRI0bY7OJLL70EwEMPPRTYMpI333wzED5/1Sy8/P777+283dAsq8k6N23alGnTpgFuCUGttc3OjhgxwtayjZfAd1rLlStnt3Q1J1uttR0Kf+211wBnSkBQO6vHH3884NQl/fbbb4H4bRBw4MABuwGDmTR9zjnn+L7TGovIC4kp+CySm6kmEcosPDQ3toCdXhLqzjvvBJzzRegiAsPUOi7oCthESE9Pt4tIzAJGcKcWdenSxS6iMAuoatWqZacHnXLKKfY9pqh46FQcM42gcePGYZ1ZP8lrQVKsxfOjfc7SpUt911mNt9BpAke7aC1oTPKmQ4cOtlM6f/58brrpJsA9N1SsWNF24EzVALPIKIheffVVAFq2bGkfMzf806dPD0sQmSlDZuOaQYMGRf3Mf/zjHwB2ek08yfQAIYQQQgjhe4HPtNarVy9qmRGTYe3Vq1eimxR3JUuWBJyJ4jnVZS0IMxRgtns15WuCLnJ7udWrV3vUksSrWLGiHRpUStlSJakgWi3eaPUCj4aZThO5A50fmZGZadOm2Qyr+Y4/8cQTtkazGcIDt9zPO++8E5aVNXLa+QZim6rjhZymBIRmD/PKlOaWqc3IyEj6TGsqmjdvns20Dh061GZYDx06BDi1zW+88Uavmhd3H374IeAsrHziiSeA6NOghg4dym233QbACSecAISfF0wN6//85z+FOo0q8J1WUzEg1FtvvRXYCgHRdOjQwf7ZzM0rTKEXsyALHe4A+OabbzxqSeJ16dLFnlCysrLCCmInO7NXeI0aNQBnqN+sDD4aGzduBJytk83wX2RNQr8pUqSIXcXcoEED+13u168fAM8++2zY682UGROzaB3Wn3/+2V7MgiCnjmasc1ZjrXEqHdbkVLRoUZ555hkArrzySnseNeeDzp07e9a2wmCSOdE64pUrV7ZTBfr27RtWsxqcBIHpn5j57KaOc2GR6QFCCCGEEML3Ap9pbdq0abYVkkuWLAnsoqtoou3qJfJWoUIFAH7//Xf7e4MGDXJ9z5o1awB3G9ygmj59up0sf8wxx1C5cmWPW5Q45v/OTBt65JFHbJ3ScePG5bp96y+//AI4CxFeeOEFwK1vHARpaWl2xytway+GZlhPP/10wFlcZRaxmjqroUyVhXiv/i1s0eqvjho1Ks/FVub5vBYchdY7Fcmna9euYaObZmpQ48aNvWpSwnXp0gVwzoNmJ81QpqpCu3btEr4ITTKtQgghhBDC93ybaTVzVYsXL8769euB6Pt+a61zXSSQDIKwy4wfVKlSxe6Hfvvtt1O3bl3AnacXSxmv8uXLAwXftcNroeWalFJJs7guP3bv3m2/Q2XLlo36GlNv0ZTGMhn3oMlpvp2p03r88cfb3QKLFi0a9bWmnI+ZBxuv8nqJEpopza2kVehjsZZziiVjK4Lp2muvBZxazmb0dvXq1XTt2hUI1ohLflSsWJGxY8cCzvxVyN6/MiM2vXv3TnwDj/Blp7VFixZh9QXXrVsHuIsgzAkYok+qnz17dgJamTimjupDDz1Eu3btAOxEcbMqWDhbVJphjVCmBmVe1QN+/vnnpFmEZo4TcBZi5bTVbSoYMGAADz74IEDYQgLTGXvkkUdsZ9UcK0EVWYs4dKpALJ599lm7QjhondVozAYR+emghoq1nmuyMnFMRq1atQKczio4U2zMoqsbb7wx0DVYY2ESHC+++GK2qTX79u2z1QVmzJhha7p6SaYHCCGEEEII31OJHFpXSuX6j5kpAW+//bZdfKSUijr8b9L30Z6LLMtQEFrr6PvgFVBesYhm0aJFdltGs5jCZKETwU+xiKZKlSo2s3TNNdfYBTfm7nHlypXx+GcA/8fiscceo3///oBTlsTsFlcY/BYLc24wi4wmTJgQNhRuagj++9//BuC///1vgdoZyutY1KxZk3feeQeAU089NebPN1MCbr311mzbH+eXV7GItWRVLOKVYfX6uMivaIvTctoaNlZ+ikW9evV4+OGHAWjevDkAX331lS3xVtil3ryORceOHe1CS7PbFbijLNdddx1z586NfwOjiDUWkmkVQgghhBC+56s5rcWLFweyLyKJJre7vQ4dOth5GDt27IhjC701depUm0Uwc/S6du0a+Hl48RJaBL1fv362LEc8M6xBcfjwYTsKUaFCBYYPHw64BbL37NnDggULcnx/w4YN7d7z5k47KHMczZzMiRMnZntu2bJldOrUCYi+sDPovv/+e84//3wAateuTffu3QG4/vrrAbJtsmDKWgV10VU0oTtembmY0cpg5UUWXYXLa1OGILrqqqvs6KU5X3755ZdRM6ylSpWy7ymMnSkTyWTOhwwZYn+uUFdddRXgjHr7ja+mB5g6aMuXLw99z1FPD1BK8dVXXwHuyXjVqlX5OiF7nb6PZCZCd+zYEXB2/7r66qsBZ8FNYfJbLKKpXbs2ABs2bLAXLzPsE09+j8X48eMZNGhQjs8fPnw4192dypUrZ6sumCkoZ5xxRtTX+iEWZtvFGTNmcPnllwPh04SmTZsGwJ133lmou1r5IRZGjRo17BC5qcMaerP//PPP06NHD6BwFnT6KRaQ97SBwlxs5bdY5CVarAo6LcDwQyyqVq0KwNatW+3PZbZ+D63AcdZZZ9ktS812ri1atLA1sM15Jb83e17FwvSbQr/3WVlZdopAtK2wAcqUKWNfC+6W0eBeH1q3bm1jOnfu3LD+XB5tkukBQgghhBAiOfhqeoCpjfj666/bTGJe1q1bZ+txhjKLcO69917AqcGWDENfJnNsatf27t3b7is/d+5cW+7LZBlj2Qe4SBHn3sVkJIsUKWIXcwRN+/bt7Z9ffvllD1virdGjR9vh7yuuuIImTZqEPV+0aNFsQ8WhQkc4/F4uq0KFCvZ4NYs5AQ4dOgTAoEGDePzxx4HoIzPJxkyzOuecc+zIg7Fv3z7eeOMNADt1IFXktTBLpgK4ImNlstDJwmRNQ88HZuenrVu32scqVKhAyZIlw96rtebuu+8GsLvmBa1vYTKsoT//1q1bmTBhApDzSLaJ0aZNmwDsVKtI5v09e/a0Wfu8Sk7GylfTA4zSpUtz1113AXDPPfdkC9zTTz/NmDFjAKfgb7ly5QDo06cP4BQQ37ZtGwCTJk0CyHf9TT8MZeSmUaNGdn5K48aN7Xzgn376CXAOuv/9738AbNmyJdv7GzZsyNlnnw1gC9CPHj2ayZMnZ3ut32NRqlQpPv74Y8BZFTplyhTAWREdb36PRahixYrZ+WimU1+pUqWoNW2NuXPnMmLE8aFuBAAAIABJREFUCMCdHpDTDZBXsTCd7rlz53LhhRfax7///nvA3X70scceK4zmReWH48LcoCxbtsw+tnv3bsCZqxaPVfWx8EMsjCVLlkSd12pu7gt7vqafYpGXjIyMfHVaTSzz2t7WD7Ewc50ff/xxO6yd01TDaI+bc4qZghVLcigar2JhtrrOqf+X2/TLo3l+7969Nhn22Wef5dpmmR4ghBBCCCGShi8zrX7ih7vCWNWoUYObbrrJ/hmcSgqm5m00K1eutIu7pk6dCuRcccHvsahZs6atGADudJP69evH4+PD+D0WiZToWFx88cUAjBs3DoCLLrrIPrdp0yaGDBkCuDvJJZLXx8VZZ53F/PnzAahevbpddGa2qIxnTdq8eB2LI23I67MK3J4Y2+F5LPJiMtEjR47MV7WFWLPWfopF9+7d7TQZU0Vg3rx5dtvnUqVK2R04zVSb7777jkWLFsWlzV7Fwix6nzlzpl2kGfF+oOCZ1sGDB9s6uHmRTKsQQgghhEgakmnNg5/uCr3m91iUKVPG1tfr1q0b7dq1A5yyYPHm91gkUqJj8eyzzwLhC4nMorMOHTrwwQcfFEZzYuL1cbF169aw3c/MfuoDBw4sjGblyutYgLugKKfMoWRaXXnFKrd5rUeziC0IsUgUr2NRoUIFW9ow4v2AU74qtNThp59+CsB7770H5J1p/f3332MuxRlrLKTTmgevDyo/kVi4JBauRMfCLCg88cQTAWdRgdkIIdpiw0Ty+rgI3VRi5syZdivf/fv3F0azcuV1LEJF61QtXbo0z0VD8eKnWHhNYuGSWLhkeoAQQgghhEgakmnNg9wJuSQWLomFK9GxqFy5MgBpaWmAk130Sy1Zr4+Lw4cP260Xhw4dGrfaiPnhdSz8RGLhkli4JBYumR4QJ3JQuSQWLomFS2Lhkli4JBYuiYVLYuGSWLhkeoAQQgghhEgaCc20CiGEEEIIkR+SaRVCCCGEEL4nnVYhhBBCCOF70mkVQgghhBC+J51WIYQQQgjhe9JpFUIIIYQQviedViGEEEII4XvSaRVCCCGEEL4nnVYhhBBCCOF7ge60KqVqKaXmK6V2KaW2K6UmK6WKed0uLyilZimltiml9iil1iml+njdJq/IceGSWLgkFi6JhUti4VBKlVBKPaOU2qKU2quU+lwp1drrdnlFKXWbUupTpdQBpdR0r9vjJT99RwLdaQUeB3YA1YB0oClwi6ct8s44oJbWujzQHhitlDrX4zZ5RY4Ll8TCJbFwSSxcEgtHMeAHnJ+/AjACeEkpVcvDNnlpKzAamOp1Q3zAN9+RoHdaTwZe0lpnaa23AwuAeh63yRNa6zVa6wPmr0d+1fGwSV6S48IlsXBJLFwSC5fEAtBa/6G1ztRab9ZaH9ZazwM2ASmZ/NBav6a1ngP86nVbfMA335Ggd1ofBroqpUorpaoDrXGCmZKUUo8rpf4E1gLbgPkeN8krcly4JBYuiYVLYuGSWEShlKoC1AXWeN0W4TnffEeC3ml9D6e3vwf4EfgUmONpizyktb4FKAdcArwGHMj9HUlLjguXxMIlsXBJLFwSiwhKqeLAc8CzWuu1XrdHeM4335HAdlqVUkVwevqvAWWA44CKwP1etstrWuu/tdbLgRpAf6/bk2hyXLgkFi6JhUti4ZJYZHckJjOBg8BtHjdHeMxv35HAdlqBY4GawGSt9QGt9a/ANKCNt83yjWKk5pxWOS5cEguXxMIlsXBJLEIopRTwDFAF6Ky1PuRxk4T3fPUdCWynVWv9C84k8f5KqWJKqTSgB/Clty1LPKVUZaVUV6VUWaVUUaXU5cC1wGKv25Zocly4JBYuiYVLYuGSWGQzBTgDaKe13u91Y7x05HgoCRQFiiqlSqZiKTS/fUcC22k9ohPQCtgJbAAOAQM9bZE3NM5UgB+BXcAE4A6t9Ruetso7cly4JBYuiYVLYuGSWABKqZOAm3FKGm1XSu078qubx03zyghgPzAMuP7In0d42iLv+OY7orTWXvy7QgghhBBCxCzomVYhhBBCCJECpNMqhBBCCCF8TzqtQgghhBDC96TTKoQQQgghfC+h5RuUUoFb9aW1VoXxuRILl8TCJbFwSSxcEguXxMIlsXBJLFzJHAvJtAohhBBCCN+TTqsQQgghhPA96bQKIYQQQgjfk06rEEIIIYTwPem0CiGEEEII30to9YDCoJSiZcuWAFStWhWAmTNncsoppwBQtmxZAFq3bs1pp50GwHnnncehQ4cA6NevHwArVqxIaLuFEMJL3bp1Y9asWQCMHj0agHvuucfLJgkfyMzMZOTIkWGPLV26lFGjRtk/C+EVybQKIYQQQgjfC3ym9ZprruGFF14A4L333gOgUqVK9q6wXLlyub6/Q4cOgGRagywjIwOAJUuWxPweky149913yczMjH+jAqRMmTIAtG3blosvvhiAK6+8khNOOAGAyZMnA3D77bd708A4MMdIaAbJPAbQrFkzILWySIcPH+bw4cMAtGnTBkjuTKs5zh944AEAVq9ezWuvvWaf37lzJ4CNSaqKzLKC810x3xfzHTHfGZE8SpQoAUCnTp0AGD58OGeddRYA77//vv2+vPPOOwCsWbMm4W2UTKsQQgghhPA9pXXiNk6I5y4NZv7qY489RseOHXN83a+//grAqlWr+PLLLwEYNGiQff7GG28EYMaMGVHf77cdKy6//HLAzYw0a9aMTZs2AXDRRRfx8ssvA/Dcc88BsHz58gK31fBbLEyGNFpm4GjkJ3Pgt1jkpnLlymRlZQGwZ88e+3j79u0B6N69OwAdO3ZEKefHCj0vvPvuuwC0aNEi6uf7NRYmM5SRkZHnMWJ+7oLyayyiWblyJenp6QB8/vnnAMyZM4dPP/0UgLfffrtAn++nWFSsWJF//etfQM4jBpdddhkAv/zyCxDfLJKfYpGXzMxMe04035vQUQmjWbNm+RqZCFIsQpUqVQqAli1b8sorrwBQrJgzWD1s2DDGjx9/1J/pp1jUqlWLsWPHAtClSxf7+N9//w1A0aJF7WPfffcdAH379j2qEc7cxBqLQE4PaNCgAQsXLgSgSpUq2Z7/4osveOihhwBYtmwZAAcPHmTBggX2NevXrwdgy5Ythd3cuClTpoydClGhQgX7eMWKFQHYv38/ffv2BaBnz56A8/Nt3rwZgHvvvRdw0vzJoGnTptkeizbsH9p5MaINE2dmZibNVIG0tDSefvppAJo3b247oWaov3379tSvXx+IX4fNL8xJNNqFNtpFNiMjw74nlRabVK5cOdtjp59+Ov/+978B9zzRpk0b/vzzz4S2LV4qVaoEwJAhQ/Kc3rJo0SLAvbG79tpr7XPbtm0DnCkFyS70HGi+B0uWLMn2fcrIyEiJ70ndunUBd+pM6HFhzqsNGjRIfMPi7NZbbw3rrAJMmDCB119/HYBGjRpx6623Am5MXn31VY499tiEtlOmBwghhBBCCN8LxPSAatWqAdC/f3/ASUmHZgm++OILAMaMGQPAW2+9xf79+wGoXr06AAsWLKBevXqAk3U1w2Jr167N9d/2U/o+LS3NZovPO+88APbu3cupp54KwKFDh+wdkLnzu+qqq+yw1++//w7As88+a7Mpe/fujfnf91MsQrNjxtKlS2Me4s9p8VasC3L8FItQZqrMlClTOO6446J9PhA+/J/X83fffTcA999/f9T3+CkW0TJCo0aNSlgG3U+xyMsPP/xgF9uZ6QFr166la9euYa9LS0s7qvOE4YdYmHPfW2+9FfPnFyni5HJCF2SZ0mC9evWK+XNC+SEWBRV5zjia823E5wQmFmeddZa9RpgRzWjuuecexo0bd9Sf74dYmHPAt99+S8mSJQG3L3XvvfeGfQ+aNGkCuCPYBw4coGHDhkDefam8JM30gIsuuoipU6cCbko69MvzxhtvcOWVV2Z7X+PGjQHsEGm9evVsbdbevXsXOMBe2L17N0OGDAFg3rx5gFMdwfy5S5cufPzxxwD29xkzZtC8eXPAmf8LcMMNN9h5a2bua9DkNMcqVqZTOmrUqKhTBYI07FW9enXbsbz55puBnDulocxQT506dYDwud7gVuOYMmVK3Npa2KIdF8ky5SNejjnmmByfmzp1KmlpaQC0atUqUU0qFJUrV6Zz585x+awzzzwTgMGDB9s53uYcmqqifdeCwtyY3HnnnSxevBhw/z+LFSvGTTfdBDjnDjP8/dVXXwFOxaKVK1cCzo0fOImgoLrhhhsAKF26NN9//z2Q8znTVFky18eMjAxbXSBRfSqZHiCEEEIIIXzPt5lWUx1g8uTJdvg7NHs0adIkwF04Eapt27Y8+uijgLMiDuCvv/6yq2GDml0EtxqAmeqwYMECO1Vg06ZNNtP21FNPAeEL0IYNGwY4Q13/+c9/AGfYbPfu3Yn7AUTcnHTSSYCzgMRkS6Mtqvrxxx/t4hIzrDNmzBguvPBCAB5//HH7WpOBWLRokV1tHVpxQASfmRoVrYb14sWL7TkmqIuvDK113GqumiHQhg0b2hGIu+66i88++ywun+930bKq0a69QXH88ccDznlwx44dAHZofOjQobRu3dq+1tT1NYtYe/bsaSsJmMXNW7duTUzDPWa+T19//TXgHBdm8daHH34IwE8//VSobZBMqxBCCCGE8D3fLsR68MEHARg4cGC25/r378+TTz5p/24yBt26dQOcuZsm43TgwAEA+vTpk68Mqx8mSuemfPnyTJs2DXAW4Zh5uya7OnbsWL799lvA3RFm2bJldp7OzJkzbc21iRMn5vpv+S0WkcdufuoGRi7cCcpCrBNPPNH+H5922mmh7wec2Jh5VsOGDbO7/RgZGRl2jnPo+82Cm4suusjeTefF61hEtCXa5+T5PjOHy/y/53dOs59ikZPbbrsNgEceecQ+ZhZinXvuuTZeZk7rkiVLbJ3fo+GHWMRrIVY0PXr0sCUI8+KHWBREfr9XOXyW57EwpTJzygqaxVcPPPCALYXWqFEj+5z5Ppx++umAWw/+aPkhFmYEdsyYMXZO68knn5zre8zOiWZ+N7hzgi+44IKja+wRgV+IZTqlHTt2tLX27rvvPgC7MAugePHidiX84MGD7ePmS9anTx8g2FMCcrNnzx672KBr16522L9t27b2dzN0sXHjRsApkmw6+rfccovddCGvTqvfhE4GB+dkEmunM7KGq2EWZfl9IdaTTz4Z1tk0TCHoqVOn2sVZoSdUs8Dg0UcfpXjx4mHvfe+99+yxtGvXrkJpd2FbunRptv/TJUuWhJ1cIxcZZGZm2v9383uy1a4NFVokPBpz7izo5gJB0r9/f3vuzG2VeKi77rqLb775BnA7/cki2rbHRjJs32pu4nv16sWIESMAbCJnzJgx9ob+4MGD9j0tW7YEnOunmTKQ386qn3zwwQcA7Nu3zx77559/PuAu6I6FqUJQ2GR6gBBCCCGE8D3fZlrXrVsHQO3atW0KPrSkgskUvvjii9lKs2RlZdG7d2+AmIdvksHs2bNt+SuTWW7VqpW9AzI1ayOHel599dUEtjJ+TPYsNLMWubNRpLy28wzNyPmZWTRgmGy6GVl455137HNpaWk2i2QyraHHgBn+6tKlS+AXXTVr1izbjlgZGRlRd0OLzNSHPiZSy+DBg+30qVjVq1fPjgIGXeTuVtF2EUym3eLM1I8ZM2bkuIW7YRZtmUXO4E5fTAZmYeGgQYPsAm6zkP3SSy8Nyzbn5mim4BSEbzutoSLrf5UoUYLnn38eiF5L8MILL7QbDqSaffv2AdChQwfAOenMnDkTcDut4NS3BWf/8dD5wUFihnnNdq45bdMaq0QWoS+ouXPn2qoRs2fPtlsMbt++3b7GDPGMGTMm6pCeqdNqbuyC3mE1In/WzMzMsGMkWmfVCMpNS7ylyurnnJxyyilH/Z4BAwbYahxBF7pZS7QpNvlZL5AsevToATjrCJLZf//7XzZt2gS4mxeNGTPG3qzs27fPduDbtWuX7f352VwhP2R6gBBCCCGE8D+tdcJ+Abogvy6++GJ98cUX699++00fPnzY/lq1apVetWqVbtmypW7ZsqU+snIuLr/8GotYf2VkZOgffvhB//DDD9r46quvdJMmTXSTJk2SIhYZGRk6IyNDF1RGRkbgY1GyZEldsmRJfffdd+s//vhD//HHH/rvv/+2v7Zu3aq3bt2qGzVqlLLfkczMTJ2ZmRn1GEiFWKxYsUKvWLEi7Lgw5854/jt+ikX37t31X3/9FdMvI6fnBw4cqAcOHBjYWOT0nYhmyZIlesmSJUl7XMTyy8TA/P8///zzukiRIrpIkSJJF4v09HSdnp6us7KydFZWlv7777/12rVr9dq1a/UjjzyiV65cqVeuXBl27jBxOfHEE/WJJ55Y6LGQTKsQQgghhPC9QMxp7d69O+CWZDJ7Y4OziGjAgAFA+Hy+VNe/f38AHn74YYoVc/6b169fDzg7eiTTvtlmrpVSKmxOaizzWpcuXZoUJVwMU6rFzMMC0FrbcmfNmzcHCn/XkqAJ8u4+R8vsiAXughRTKi1ZaX30u2PFazetIEvVOd5G+/bt7fxeU+98wIABSXtsmNJtZqHvxIkT6dmzJ+DWd440a9YsAH744YcEtNDHnVZTQ/L+++/n9ttvB8ILp5tarSNHjpTO6hFlypSxBeVNndZixYrZgvHnnnsu4BaQT0ahndZoC7QM09FNhg7r6aefbmsYd+rUCSB0mIgvv/ySK6+8EpDOKrjHRSpq1qyZLZ4P2Fq+ZhFOstq2bZutklG3bl3A3Qb5aGzevJnNmzfHs2meyq0eK6T2dwXgoYcesh3U999/H0iO2qyxGjhwoE0W3nrrrZQuXRpw6rsbc+bMSWibZHqAEEIIIYTwPd9mWs2OEybLGmrs2LG2xE+qK1KkCOeccw7gpOlNFsFYvnw5vXr1ApI7wxopcntWw2RYk2E42NQvXrhwYVg5M3B2fDE19/JT/iuZRTsuUkWzZs3CdsRauHBhjq81IzNfffVVzLUa/Wrx4sUsXrwYcHYOBPjHP/5hn2/btm3YtLOcvPrqq7z++uuF00gPRMuwjxo1KtdRqlRgsvDlypWz101TuzTVmK1dhw4daqcdGr///rtkWoUQQgghhIjky0zrBRdcYAufg1sw39wZf/LJJ7m+v2zZspQqVQqAP/74A4A///yzMJrqmQYNGgAwfPhwrrnmGsCZ82vmMY4ePRpwJlLv3r3bm0YmWGQR+WiSYQ4rOMXQzd7woVnWXbt2Ac7mEkezb3SqC8qmEvlVtmxZgLDzak6OOeYYwM3CnnzyyYHPtIaaPXt22O8An332WUyZ1mRjRpxCR2MyMzPt9yEVM63FihWzi7srVapkNzJK1Q2LojHri6LJzMy088fNPOB48lWn1WzNOnXqVLviHeCVV14BYMWKFVHfV758ecDdAejxxx+3O5xMnjwZgH/+85+F0+hCYjpfxYsX55tvvgGgX79+dsgu2k5gX375JcOGDQNgwYIFCWqpdyIXEeR1gk2WDivA008/Tc2aNe3fTWf1uOOOi+n9tWrV4oILLgDgo48+AkiqBSY5iXaMJMNUkbyYxVd5dcwaN27MI488AkDFihULvV1+MXHiRDv8a65DqSrZb+Dycvrpp3PHHXfYvydqe9IgMcmx4sWL26RJlSpVAKefErmLaTzJ9AAhhBBCCOF7vsq01qlTB4Azzjgj7PH27dsDbp3RSCVKlACgRo0a2Z7r168f4Eyg93vNuUsvvdTu32v2/lVKsX//fsAZ4jN3OOb3v/76i1dffRWAm2++OekXW5ksQKw1WMHNpCXT3tmhJa3ALV1UoUIFIHyRSfXq1e13yAzrNGjQwL7WfC9atGhR+A33WLRM68iRI1Myu7Rnzx4OHDgQ9tiAAQNo1KiRRy3yTt26dcMWqKUKc9w3bdo0z/JXyc6M7g4fPtw+9tJLL/Hiiy961STfMteR0qVLM2nSJACaNGkCODWfc1vgWVC+6rSaVWobN260HViAY489Nuz3o2EKAm/YsCEOLSxcgwYNonHjxtkeL1OmjP3zd999B8DMmTMBmDFjRkoM64Jzgo31hJpsmwbkZcKECYBTWQPczmuk0FrHwtGsWbNsndalS5cm1U2Omfce6sUXXyzUYbwgueKKK2xB9VT07rvv5lptJRUMGjQIgC5dutjHzNoQES70+tGhQwcAsrKyALjooovsdLXCINMDhBBCCCGE7/kq0/rbb78B0Lp1a1q2bAk4w56RNSgj7dy5E8AOkwN8/fXXgDulIAg7Aa1bt87GwMjKyuLBBx8E4IMPPuCzzz4DnGkBItzSpUuTcipALEy1DLNjydFkUoPw3ShMI0eOzJZlGjlyZK4rZIPGLOAMZXZRE04m2qx0rlSpUo6v69Gjh722mNGuZJCZmWnPmUuWLEmqHQPzYhZwh9Z+N5UCErU1aVB8+OGH2R4z1Z1MRaMtW7YUahsk0yqEEEIIIXxPJXJum1IqcBPptNaFkm6RWLiOJhahx6vJBpiFRIlcTON1LGrVqmXLojVq1Mju8mPmfc+ZM4eNGzcCMH369Fw/68cffwSchTn54XUsjlZui/lMZim/mfqgxaIwBS0WJsNqSvd8/vnn2V4zZcoUu1DnaGp/By0WhclvsXjppZcA6NSpE+DsHHndddcB2FrYhcVvsciLKZ3Xtm1bAJ566il69uwJFDxWscZCOq15CNpBVZgkFi6JhUti4ZJYuCQWLomFy2+xMDVZzXSZjRs3kp6eHr+G5cJvsfBSrLGQ6QFCCCGEEML3JNOaB7kTckksXBILl8TCJbFwSSxcEguXxMIlsXBJplUIIYQQQiQN6bQKIYQQQgjfS+j0ACGEEEIIIfJDMq1CCCGEEML3pNMqhBBCCCF8TzqtQgghhBDC96TTKoQQQgghfE86rUIIIYQQwvek0yqEEEIIIXxPOq1CCCGEEML3At1pVUrNUkptU0rtUUqtU0r18bpNXlJKdVVKfaOU+kMptVEpdYnXbfKCHBcOpdS+iF9/K6Umed0uLyilSiilnlFKbVFK7VVKfa6Uau11u7yilDpDKfU/pdTvSqkNSqmOXrfJK0qpWkqp+UqpXUqp7UqpyUqpYl63yytyHXHIdcTlp+9IoDutwDiglta6PNAeGK2UOtfjNnlCKXUZcD/QEygHXAp852mjvCPHBaC1Lmt+AVWB/cDLHjfLK8WAH4CmQAVgBPCSUqqWh23yxJGLzVxgHnAs0BeYpZSq62nDvPM4sAOoBqTjHCO3eNoij8h1JIxcR1y++Y4EutOqtV6jtT5g/nrkVx0Pm+SlUcC9WusVWuvDWuuftNY/ed0oL8hxEVVnnJPOMq8b4gWt9R9a60yt9eYj3495wCYgFS9CpwMnABO11n9rrf8HvA9097ZZnjkZeElrnaW13g4sAOp53CavyHXkCLmOhPHNdyTQnVYApdTjSqk/gbXANmC+x01KOKVUUaARcPyRob4fj6TvS3ndNq/IcZFND2CGln2bAVBKVQHqAmu8botPKOAsrxvhkYeBrkqp0kqp6kBrnItySpHrSHZyHbF88x0JfKdVa30LzjDGJcBrwIHc35GUqgDFgatw4pAOnIMzBJqS5LhwKaVOwhnOedbrtviBUqo48BzwrNZ6rdft8cC3OFn3O5VSxZVS/8A5Pkp72yzPvIeTNdoD/Ah8CszxtEXekOtIBLmOWL75jgS+0wpwZIhrOVAD6O91ezyw/8jvk7TW27TWvwAPAW08bJPn5LiwugPLtdabvG6I15RSRYCZwEHgNo+b4wmt9SHgSuAKYDswGHgJ52KUUo4cDwtwOiRlgOOAijjzOlONXEeiSPXriN++I0nRaQ1RjBScc6K13oVzwQkd+pVhYFdKHhchbkCyrCilFPAMTkap85HOW0rSWn+ptW6qta6ktb4cqA187HW7PHAsUBOYrLU+oLX+FZhGCnbU5DqSp1S9jvjqOxLYTqtSqvKR0hxllVJFlVKXA9cCi71um0emAQOOxKUiMBBndXBKkeMinFKqCVCd1K0aEGoKcAbQTmu9P68XJzOlVAOlVMkjc9SG4KwKnu5xsxLuSDZxE9BfKVVMKZWGM//7S29b5hm5jiDXkVB++44EttOKcwfYH+fOcBcwAbhDa/2Gp63yzn3AJ8A64BtgFTDG0xZ5Q46LcD2A17TWe71uiJeOzOu9GWee3vaQ2rXdPG6aV7rjLCzZAbQALgtZKZ1qOgGtgJ3ABuAQTmctFcl1xCHXkXC++Y4oWUwshBBCCCH8LsiZViGEEEIIkSKk0yqEEEIIIXxPOq1CCCGEEML3pNMqhBBCCCF8TzqtQgghhBDC94ol8h9TSgWuVIHWWhXG50osXBILl8TCJbFwSSxcEguXxMIlsXAlcywk0yqEEEIIIXxPOq1CCCGEEML3pNMqhBBCCCF8TzqtQgghhLAyMjLIyMhAa43Wmvfff59y5cpRrlw5r5smUlxCt3FN5snBR0ti4ZJYuCQWLomFS2Lhkli4CiMW//jHP5gzZw4AJUqUAEBrzfLlywHo2LEjALt27crX5wcpFtFkZGSwePFiAIoUcfJ+hw8fts8vXbqUFi1axPRZQY9FPMlCLCGEEEIIkTQSWvIqv5544gkA+vbtG/X5vXv3AjBt2jT72KRJkwDYuHFjIbdOCJEsjjvuOACysrLsnwF+/PFHAP766y9P2iVEYatWrRoAL7/8Msccc0yOz5csWTKh7fJasWJON6lGjRoAVK1aNSyzCuGZ1p07dyaucSlIMq1CCCGEEML3fD+ntVq1anzwwQcA1KxZM6fPBZx5N8bBgwcBGDRoEADPP/88v//++9H+8zLnJETQYrFgwQIALrvsMvvY119/DTjztgC2bduWr8/2QywyMzPtn5s2bQo4860ijRo1Ks/3F4QfYlEQGRkZ7N69G4BnnnkGgC1bttDB+L0kAAAgAElEQVSuXTv7mkcffRSAmTNnAvD5559H/awgxKJJkyYAVK5cmcGDBwPOPDyAu+++O9v5tHfv3mGjWLHyeywGDhzImDFjAChVqpR93PzcSileeOEFAH755RcA2rRpQ+3ate1rv//+e8A9n6xbty7qv+X3WLRr144JEyYAUKdOndDPB5yYzJgxA4BevXoV6N/yeywi3XHHHQCMHz8+23Ohc1rfffddADp37hxzXyNosShMscbC951WgKeffhrI+ctiLjRm+KJNmzbZXrNgwQJGjhwJwKeffhrzvy0HlcvvsWjZsiX169cHYOTIkXala7RjvHHjxsDRHQuhEh0L08E0x3Ah/dv5ep/fj4tII0aMAKBnz54AVKhQwQ77V6pUCXAuRpFDgIC9gTY3CZH8Ggtz3Jx00kn2Ju6EE06I6b179+7l5ptvBuDFF1+M+d/0ayyMZcuW2Q58QU2ePBmA22+/Perzfo1FvXr1AHj33XdJS0uzj5tpdaeccgrgnEMLes40/BqLaEaMGMHQoUOB6NMiTKf1zz//5NZbbwWwnftY+D0W7dq1Y9iwYYBzzYy28OxoFS1aNOrjshBLCCGEEEIkjUAsxOrfvz8AL730EuAM9R977LH2eZNRu/HGGwHYv3+/Ha559dVXAWjVqhWXXnop4AyBmeG+ZDJw4ED27NkDuNnnnJg76KuuuirX1/3nP/+JT+Pi7IorruDOO+8EsMN1FStWDBvmSybRMqxmSNcMS0HOQ/7mcfOejIyMbJ+ptaZZs2Zhr0tGZrpEfrIFeX2v/MAM7zZp0oSHH34YcM+ROWU5clOuXDnOPfdc4OgyrUFjjoe///4723NmFMIsyjHMKM6qVasKuXXxVaFCBcC5FgJhWdZ169bRunVrAL777jsAfv75ZzZv3pzYRnqoT58+gHOuiOU8MXbs2KPKsAZF586dueCCCwDnWDexSOQIfaRAdFrN0N2iRYsAOPXUU20HtkWLFnTp0gVwh+xuuukmrr322rDP+PXXX+0X8IorrkiaTmupUqXshalnz542VgcOHABg1qxZtoPfvHlzwJmbYzp3oSuko/Fbp7Vly5YAzJ49O9cO6po1a7jmmmsA+OSTTwAoXbp04TewkER2IkeNGnVUHcvIzuzSpUvt+5csWWIfN3Nik7nTajpu5th/4IEH7HMXX3wx4JxjounduzdwdEOAiVSnTh1mz54NQMOGDeP2uea4qF27tj2PBt2SJUvCpgeY6jTR5u+aYf+HHnrIPnbo0CEGDhwIwPTp0wuxpfFVrlw5pkyZAmDPkQDffPMN4AwJb9myJew9ixcvtt8XU63HXGOSzYgRI+yNrRkOz4k5D4wbN67Q2yUcMj1ACCGEEEL4XiAyrZF2795ts6tjx461iwSqVKkCwBtvvJHtPY899hijR48G3MUWQdaoUSPAucMzQ7oAq1evBuC9994DnLtqU+e2U6dOMX32zz//7Nu76JUrVwJOLUEzRGGyBqF++uknWxmgIJPG/SL0/zhekjmbGguzIrxXr142g28WKR0+fDjsuPniiy+A8MyUH7355pucdtppOT7/66+/2u/Fe++9x8SJE8OeP+GEE+xuSBUrVrSPm2oJW7dujXeTPTN58mTq1q0LOCM4y5Yty/YaM0xuVtZHvj/aucfvWrZsme043rp1Kx06dACIOg3guuuu47rrrgPc6+uqVavsKIVfrxf50bNnz7Dvfm7XDy+HyQuTqZzSvn37qM/PmzcPCK9bbUa6Q88bodasWROXtkmmVQghhBBC+F4gM63gzqt57rnnbKY1lLkDeuyxxwAnI2sm2O/YsSNBrYw/M7fMLE6LzMCZOVamfuDbb78dVqc00vTp07PVFnz++eftDkB+Y/a7LmitQBG9pmsqSE9PB7CZJYABAwYA7gIVwNZunTRpks0+/fzzzwlqZf6Y0aZI5nzZq1cvmyWJZvDgwVEzJSYWWVlZcWilP+zYscNmDytVqmR3MjKLrp588kk6d+4MhM9tNIvx7r333kQ2N25KlChh/2yuidOnT495rrLJvrVv355zzjkHcObMf/nll3Fuqf+Zc8ntt9/OI4884nFrCu7EE08E3Pnd5cuXt9nUH3/80Y7g3nTTTUD4osUVK1YA7ihwpOeffz4ubQxkp/Xss8+2wxJmWC+SOQHlVDcviFq2bMnrr78OkOMipPnz5wPuwfToo4/aWOzfvx/ArroHp76cbE2ZmqJ1WpNpyoC5oKSlpdnzRaVKlexK+pyGsQxz4jbfuSAYN24c999/v/276ayamrS5dVhz8tdff9nPSTZm6Hfnzp12wWqPHj0Ad9EdOIuuABYuXGjrdppKLUFhFuaFLkI2C4hyqjryzjvvAFC2bFl77Jx55pkAdOvWzXZgt23bZuuUppKzzz4bIOw7F+TO6w8//AC4N/GdO3e2C5l/+ukn+zrTvzjuuOPs8WSm2oQ6ePAg9913H4BdIFpQMj1ACCGEEEL4XqAyrffccw/gZEBCd3Mxi4/M1mlNmjSxWRQzfB5a1idoypQpAzhZ47zqkJrXGjfddBOXXHIJQNKUqolVt27dgPDhMCN0d7SctjlNZtHqtIaWwUoGpjyROf6NWHd1+fXXXwunYYVo+PDhYX83WZCCZIu3bNlisyXJqlKlSrz22muAW/YMYNOmTQBcf/31gDsEGkQmExq6EDm0xnM0ZiFaNNu3b2fIkCEA9OvXz55nTY3ToJo6depRb3FdtGjRsKlFQWemQz344INhj5uyZ6effjrgTLGKtjjVLMy777774l420/ed1mrVqtkNAs477zwgfH7RHXfcYWulmflc77//vh3qyWmOV5CUL18eyLumajRVq1a1VRPMRbyg2/D5VZEiRShbtizg7AVujotoKzzNRhPJuhlBXqLdxBVGhQIvmaHNtLQ0O4wH7s2bucGLdo4oUqQIF110EeBW4giaw4cPxzz1x9SujSyeD8m1MjyS6WiMGTMmrLMKsGHDBjsVIMidVcOsbYjXivepU6faKj41atTIde1EkIwZM4YxY8YATs3e3OJlpt2kggoVKrBgwQLAnXoVTeiUgMKo8y7TA4QQQgghhO/5NtNqthkNXdluhv/Xr19vV3WaicOhz8+ePZvbbrsNcDNq8ZoE7AWTEVq9ejXnn38+gN2xpH379nZ6RKNGjRg8eDDg1pMsUqSIvRs2tddatmzJRx99lLgfoJCYlY79+vUDnJq0t9xyS67vMXUpTdbZ1KxMFdGGvYI6JaBq1apA9lWp5tg3d/mzZ8+mZs2a9nlTAaBkyZKAk0F44YUXAKhcubJ9nVmIE9Tdbr7++uuYV7ibBa3Rhna7du0a13b5RaVKlWxGzayGBuf6AnD55Zdn2xkqWZjzXkGqYXz77bf2O7Jw4UK746AZOl67dm0BWxkf5jxhjuP27dvbChALFy609ZqjiZZJTU9Pt9cPMwoTuq18sjGjEYsWLbLVIqJln82oTmZmZtgug/EmmVYhhBBCCOF7vsy0Vq9enblz5wJOj97cDbZp0wZwd2fJyRdffGEXWeQ29yIozJzWWbNm2d1qTHY1NBPQpEmTbHdChw8ftn82d0LRdjwJoqlTpwLQvHlzwCnvs2rVKsAp7xK54GbGjBlh5b5Skdm1BNwMa1DnsprMSeRCKzOX1ZxDIpk6nKHZgmiL9YJowoQJdg77iSeeaLPOL730Uq7vMyM0qcBkxV577bWoi65atWoFkFRZ1tq1a4fN3//666/Dfs+v0FJokYuf/ZBpTU9Pt2tiQkdbzDnjq6++sjtFxnpdTEtLs++PdVFnUF199dX2mmlKpuXk7rvvBrIv3oo3X3Zahw0bZocYwB2my6uzaqxdu9Z20MxwX7NmzQJZQeD//u//uPLKKwHny2JOMmZbyVBXXXUVxYsXz/b4t99+C8C///1vwP8F0mNlVsOmpaUBzjZx5v999OjRDBo0CHA7J8m65V4szLSA0NqsQa2aYH6G7t27R33eLLqqX79+1OdjudBs3749bMg4KN5//3375woVKtgpQQsXLgTcKVSRIhchJaNjjjkGcKeNhP7M69evt53VZLmpD1W/fn07fA9ux938/ttvv+Xrc+vUqWP/bBbshdbz9Fp6enpYZzXS+vXr2b59+1F9ZmjfxMjKykqq48b8jJHTKqOdO03dXtPPKGwyPUAIIYQQQvieLzOtoVnEXr162eG+WC1fvpyDBw8CcPLJJwPOIqUgZlpXrlzJjTfeaP9u7mrM77GYNWsWgB0mSRaR28+GMv//InpN1lGjRgV2Ada1114LQIMGDQrt37jmmmvCspZBsWHDBhYvXgxAixYt7DalZpeeZC13Fwuz3XfoTlemNuuQIUOSajpApLlz59q6wyeccIKdRta2bVsAWx7waLRv3z5sdy2zoOmNN94oaHMT5o8//shza+Jhw4YB7o5PZjpBqJ9//jlfMfQbM6prfubI0UmTYTVb9j766KN24WKiSKZVCCGEEEL4nq8yraa0QseOHe1jBw4csPvcFkRQNxmYOXOmncNnynzlxuyHvWzZMgBuu+22o56zk6y++eYbr5uQUOa4CR1hMNnVo93xxU8+/PBDIOedd0I3H4n1+VdeeQVwMi9AILOsAFu3buWqq64CnHOHOe/t27fvqD/L7Kee24hGULRo0YIJEyZke9yMQCRzltWYMmUK4Owsaeb3mkUzTZo04eGHHwacbFrk/3nFihXtsTRw4EDAuR6Z9QQHDhywn+8n06dPp0ePHoBb/jJUz549bfmvnDKGN9xwAwCnnXZatufMRiUmYx1k1apVs5sCnHHGGVFfY7LJd911FwA7d+5MTONC+KrTapg0PMC9995rJ3jHuhVhnTp17M4uZkjkiSeeiHMrE2Pfvn30798fcFY9N2rUKOz5F154wa4KXbRokV3NuXz58sQ2NACee+45r5uQUNGmw+S1bWMQmF1ZTOds/PjxdvW/qckI0Rda/fLLL/z5559A+NCXWbTnp0Uk+WVuXDt06FCgzzHnkkOHDhW4TV6oUKEC48ePB5xFe5G7fc2fPz8p/r9jFVpv2GyJblb89+nTx94EHjp0iBdffBFwK23UrVvX7kgZzfjx4wtl96N4MNU0nnzyScDZvcvsAAduByyaIkWK2PNI6PnETIUwfZJELUIqDLfffjvg7C5qap9Hs2bNGl/sACbTA4QQQgghhO/5KtNqyrFccMEFbNy4EXCypqZU06mnngrA448/zv79+wGiTh2466677PDHhg0bwn4PIlOOxNRcFDkzZV1Cd/ExJb5SaXFW5PC/KW8V5GkBhpnuYrIcr7/+OrVq1QLcobyczJ07N2q5uFRlMtMmo5YMjjvuOMA5Lpo0aZLj69q0aWN3C3zqqacS0jY/GDdunB1lGD58OODuughQvHhxrr/+eiC8prHJuJtr78aNG22t7Lfffjsxjc8HszDR7LK5cePGXMtg5WXz5s12lCfo55I333zTTiMLreNrrFmzhqeffhrwz0JuX3Vajd9++43LLrsMgKFDh9r6eWZ4Y9y4ccyfPx9whgrNgWPmroSu7jPTA0RqMMfASSedZB8zc6Rz264v2QW1UkCsTI3EWLctFQ6zfWvocGlQmRsXc6znNtQJzkYMptOVasxQvpmj2L17d8qWLQu4HVlwC8Zrre2GNm+99VYimxp3ffv2tT9jLBurmP7F7t27AfjnP/9Z4E0ZvHLCCScA7tSq5s2bR91Y5YMPPgCcRJnf1sTI9AAhhBBCCOF7KpG7BCml8vWPdevWDYB//etfQPYdKaJty2i2kDO1+fK7MElrXSjjZvmNhZf8HouKFSva/+fTTjvNZgTMXWU8F5T4PRaZmZlhtVkjMwo5ZV7NUFHo82ZKQU5TC/wei0QKaizM4rTQrMuYMWMAZ2pJfiq4JDoWZvX7gAEDor7PjLSYUbzVq1cnbPvNoB4XhcFPsejRo4ftN5iFi+3bt7fP9+nTxy78LIydJBMdC5NhNlUCIpkKCmZrdFNZIRFijYVkWoUQQgghhO8FItNqHH/88YCTcTXzVmvUqJEt0zpr1iy7C9SiRYsK8k/66q7Qa36PRfXq1cPqLZq9xVesWBGPjw/j91hkZGQUeAc4k201i7hyys76PRaJFNRYvPnmm4CzOCnSwIEDw3Y+ilWiY3HnnXcCRC299MEHH3DTTTcB7ihcIgX1uCgMEgtXomNhdsRLT0/P9tyHH37I2LFjAW8W1sUai0B1Wr0gXzCX32MR2mlduHAhV1xxRTw+Niq/xwLcof7QbVxNxzOneq3m+aNZuBWEWCRKUGNRuXJlAKZNm2YXvhobN24Mq50dq0THwvwMZrVzmzZtbMWVjz76iK1btxZGc2IS1OOiMEgsXImOham40q5dO/uYWWjWunVrduzYURjNiYlMDxBCCCGEEElDMq15kLtCl8TCJbFwSSxcEguXxMIlsXBJLFyJjkXjxo0Bp5QoOBnXW265BfC+VrFMD4gT+YK5JBYuiYVLYuGSWLgkFi6JhUti4ZJYuGR6gBBCCCGESBoJzbQKIYQQQgiRH5JpFUIIIYQQviedViGEEEII4XvSaRVCCCGEEL4nnVYhhBBCCOF70mkVQgghhBC+J51WIYQQQgjhe9JpFUIIIYQQviedViGEEEII4XuB7rQqpWoppeYrpXYppbYrpSYrpYp53S4vKKWWKqWylFL7jvz61us2eU0pdeqRmMzyui1eUUrNUkptU0rtUUqtU0r18bpNXlJKdVVKfaOU+kMptVEpdYnXbfKCHBfh5LhwSSzCSTwcfjlnBHpHLKXUfGAH0A9IAxYBT2utH/W0YR5QSi0FZmmt/8/rtviFUuodoBSwRWt9vdft8YJSqh6wQWt9QCl1OrAUuEJrvdLbliWeUuoy4P+ALsDHQDUArfVPXrbLC3JcuOS4cEkswkk8XH45ZwQ60wqcDLyktc7SWm8HFgD1PG6T8AGlVFdgN7DY67Z4SWu9Rmt9wPz1yK86HjbJS6OAe7XWK7TWh7XWP6XixQfkuIggx4VLYhFO4nGEX84ZQe+0Pgx0VUqVVkpVB1rjdFxT1Til1C9KqfeVUhleN8YrSqnywL3AIK/b4gdKqceVUn8Ca4FtwHyPm5RwSqmiQCPgeKXUBqXUj0emE5Xyum1ekeNCjotQEotwEo/s/HDOCHqn9T2czOoe4EfgU2COpy3yzlCgNlAdeAp4UymVqpmT+4BntNY/et0QP9Ba3wKUAy4BXgMO5P6OpFQFKA5chROHdOAcYISXjfKSHBeAHBehJBbhJB4R/HDOCGynVSlVBCer+hpQBjgOqAjc72W7vKK1/khrvVdrfUBr/SzwPtDG63YlmlIqHWgJTPS6LX6itf5ba70cqAH097o9Hth/5PdJWuttWutfgIdIwe9IKDku5LgIIbEIJ/GIwutzRmA7rcCxQE1g8pGO2q/ANFL8gAqhAeV1IzyQAdQCvldKbQeGAJ2VUp952SgfKUYKzl3UWu/CGY0JXXka3FWo8SfHRcjDHjXHUxKLcBKPPHlyzghsp/XIXc8moL9SqphSKg3oAXzpbcsSTymVppS6XClV8kgsugGXkprze5/C+SKlH/n1BPAWcLmXjfKCUqrykXItZZVSRZVSlwPXkrqL06YBA47EpSIwEJjncZsSTo6LbOS4cEkswkk88Nc5I+g1TTvhLMYaCvwN/A/noEo1xYHRwOk4cVgLXKm1Xudpqzygtf4T+NP8XSm1D8jSWu/0rlWe0TjDN0/g3KBuAe7QWr/haau8cx/ONKJ1QBbwEjDG0xZ5Q46LcHJcuCQW4SQeDt+cMwJdp1UIIYQQQqSGwE4PEEIIIYQQqUM6rUIIIYQQwvek0yqEEEIIIXxPOq1CCCGEEML3Elo9QCkVuFVfWutCqXUqsXBJLFwSC5fEwiWxcEksXBILl8TClcyxkEyrEEIIIYTwPem0CiGEEEII35NOqxBCCCGE8D3ptAohhBBCCN8L+jauQgiRq/Lly9O8eXP791atWgFw8803AxC6K2Dp0qUByMrKSmALhR+deeaZLFy4EIBPP/0UgL59+7JzZyruCC2EP0inNaAmTJgAwKBBg/jkk08A6NatGxs2bPCyWUL4zvjx46lSpQoAr7/+OiVLlgTg8OHDXjZL+NQjjzwCwCWXXELVqlUBaNu2LQANGza0HdlkU65cOQA2b97MscceC8DatWsBaN68Odu2bfOsbV659tprueuuuwBo0KABAEWKFLHnjkcffRSAKVOmsG7dOm8amWJkeoAQQgghhPC9lMm0tm7dmvr16wOwePFiVq5c6XGLCua9994DoFq1alxzzTUAVK5cWTKtQkS48847OXToEAD79++3UwC6d+/uZbN8q0mTJgBcfPHFAAwdOtRm3qZOncrEiRMBWL16tTcNLARp/9/evcfZXO1/HH99BznT0Kg4DKWLwolzUEgUOk0hPMpljkudk8qtovhVpKhcO9SRey7dVDSkQpSS05lcyumkkkohuQ31yChqpMz+/v7YrbX3XJgZM3vv73d7Px+PeZjZs2fPmmX2d9b+rM/nsypVYsaMGQB069YNCEbize/N1KlTAVi9enVsBhhhycnJpKenA8G52LBhAwCvv/66/fzJEmmtXbs2ixYtAuCCCy7glFNOAeCjjz4CYOvWrTbqaiLxN910Ew888EAMRnvyUaRVRERERDwvbiOtJjJw5513AjBs2DDKlg3+uL/++iv33XcfEMpf8pulS5faf+vUqQNAamoq69ati+WwYmLBggVAsKDGcYKHajz++OO8//77sRxW1Jx77rm2cGj58uUAZGdnM2HCBABee+01m5Nnio4++OADvv/+eyD+czsPHjxo309OTqZz58757vP8888DcPTo0aiNy0uqV68OwMcff0xiYiIQKkqD0O9Ir169bLQxHiKtlSpVAmD27Nl06tQp1+cyMjJ45513ABg7dmzUxxYN5no5atQorrnmGgB++ukn+vbtC+D7HcmiqF27NgA333wzEIy016xZE4BDhw4xaNAgIHgdBdi7dy/nnHMOADt27Ij2cE96TnjlbMS/WZSOFmvXrh33338/ENrqyuvQoUMAXHXVVcCxn5x+OGbt7rvvBoIXnqSkpNJ62Hy8NBdnn3223c4y/8eBQICEhAT7fpkyZUpxlLl5YS7M9u0bb7zBDz/8AECNGjUAmDdvHtu2bQPgsssus3Nhquh37dplt8B27twJBLd+zfOiOLwwF0XVrFkz1q5dC4QWYjNnzmTYsGFA8A92SfhpLiA4HxAs6ATo0qVLoV9z6623AvDss88e935+mIv58+cDkJaWZm/r2LEjAJ9//rl9bpSUV+fCvJhdsmSJva1atWoR7ZDgpbkoX748L730EgDXXnuteRwb/HnwwQftC5dI8MJcmBen5cqVY8yYMUDoxVxhxo4da9NGfvzxx+IOMxcd4yoiIiIicSMu0gNMOxvTd/G+++6jfPnyx/0a097D9Gz08zbI0KFDAUotKuAH6enpNG3aFAhFzMK3uf/1r3/FZFyR1rhxYwBatWrFDTfcAASjBSZFwli9ejXt2rUDoHXr1jaSZCKpHTp04K677gKw0emMjAw++eQTIHfv0nhgotIvvviivc2kVAwcODAmY/KCUaNGAaEdJ9d1eeSRRwDstmh4mkA8qFKlCgBz5syxUVWA9u3bA7BixQog2KfVRJVNRHL27Nm88cYb0RxuRA0fPty+v2XLFiBYrHiyePjhh22E1cjIyODRRx8FiGiU1StmzZoFBNt7FZVJK+nRo4eNsJ555pmlP7gC+H7R2qZNG/vEKygVwGz39erVy+ay9ezZ036+QYMGURhl6StXrhwQ7A9XuXJlIHgRjndmO7N58+Z2kWoWXevWreOKK66I2diioVWrVgD2ogrw3HPPcc899+S7r9ny69+/v73N5ELXrVvXLvrNNu+GDRvsiz1TNR0PLrjgArtYNbmbAAcOHIjVkDzLdV3+9Kc/Adiq6XCffPKJ7/+Qm+tk+/bt7TUkIyODzz//PNf9+vbtyx133JHrtg4dOtg6iSeeeCIKo40M84L2kksusbddeOGFAHz55Ze2F63ZLt6+fXtcvZA1eazdunWzCzBj8uTJhb4wMd0kzAudtm3b+qpPq+mcMmrUqBIvNk0AcPv27QC0aNGCzMzMkg3wOJQeICIiIiKe58tIa40aNejVqxcA999/vz3hJpyp9DMFBl9//TVXX311vvsVpfDAi1JTU4FgxaOpAg9Ppo9HzZo1s8VXgUDARklM0nze7Y3BgwfnewzTY9KvXn75ZSD3roJJiymOzZs32+dQONN/0M/pMnl17tw5V4TVVL2bfpwnq7Jly+YrVkxISLDbpdnZ2fb2adOmAcEopV/TkEyRokkJCAQCvPfeewB0797dXkeNMWPG2GLFhQsXAsHUAjM/fo60mup4s0uVnZ3Nd999BwS7kdx0000A9t877riDmTNnxmCkpa927dq2y0rNmjX5+eefgdB1tLC/o61atbKRygoVKgCwbNkym0Lih4irSXtJSkqy66c5c+bYnbiimjZtmu2kcPbZZwNEtAAaFGkVERERER/wVaTVvFLu37+/bWkVzrT96d+/P2+99RYQasOQmJjIueeem+9rTATBb8ILjUwO2v79+2M1nKioWbOmfTVnIgRArjxWczrYXXfdxWWXXQaEioocx+Gxxx4DIv9qMFK++eYbILhDYF7lHzlypNiPU65cuVz5bAAvvPCCPQknHpi8b9NzEoK/C6tWrQJC56qfrK688kpat26d73aTL/3QQw9FeUSRVVCk0PTmzBtlNbetWbMGwBanTZw40f4dadCggS1c9BtznTTGjRvHk08+CcANN9xg83ZNFG3SpEm2dmL69OmAf3PC27dvz3nnnWc/Nte88CLN48nIyLC7eBMnTgTg/PPPt3mw3377LR9//DEQKpI+kVaCkeCOMRMAABe6SURBVGSKLM8991yeeuopAG677bZiP04sfi5fLFrN1p7ZGm3SpEmuz5stHHORLeiP0bBhw2jTpo392BSa/POf/yz9AUeB+cNbp04dHnzwQSDYZ9OvF9GicF230A4B5sITCATsYjW8YCueGumXpKfos88+ayvGjW+++SYuii3++Mc/AtiLcfgfqMzMTJsydLIyW4MjRozI97mcnJy4PJTjuuuus503Suqiiy4Cgh0p4uV6u2/fPtubddKkSTz99NMAjB49GgimB4wcORIIBYemT5/uy+vFP/7xD/v+kSNHGD9+fLEf45lnngFCQYRBgwbZ1KpLL73UFrma4rYRI0Z46nllFtWbNm0qMJhXGJMaYwJmAJ999hmQO60oEpQeICIiIiLe57pu1N4At7hvd955p7t582Z38+bNbk5OTr63fv36ucnJyW5ycnKBX9+iRQu3RYsW7t69e3N93ZQpU9wpU6YU+v29NBfhb+XKlXPLlSvnzpkzx/5MmZmZbocOHdwOHTqU6LG9Oheu69qfNRAIuF27dnW7du3qpqWluWlpae7atWvdQCDgBgIBNycnx925c6e7c+dONz093U1PT7e3m68PBALuo48+6su5ONG35s2bu82bN3e3b99u52Dfvn3uvn373LPOOisu5qJly5Zuy5YtC7xe7Ny5M6Lz67W5aNq0qdu0aVO3R48ebo8ePdxvvvnG3bJli7tlyxb3+eefd/fs2ePu2bPHzk92dnZczUVSUpKblJTkzpgxw/6MxqeffuqmpKS4KSkpRX68119/3X79gAEDfDUX4W+rVq1yV61a5f7222/ub7/9VujfjMmTJ+d7Ll1//fW+/L3Iyclxjx496h49etQdMmRIqV8DXnnlFfv45m3u3LmenIvivt19993u3XffbX8Hjh496v7www/uDz/84J5zzjnuOeecc8KPXdSfzbPpAa+++ioQ7H+Wt19gVlaWzclYtmxZvuPDkpKSbKeAGTNmAMEtQ3Ou+MMPP8y8efMiOv5IM+kNffr0sdvEN998s+2a8MILLwDB3M6srKzYDLKUhXcMSEhIsM3xw49x/f0JSyAQsHlbZltm165d9vfG5MQOGjTI9pTze2eB4znrrLMA+Pe//w0Ee3CauZo9ezYAu3fvjs3gSlmjRo3y3fb1118DwW3ieJecnAwEe3FOmTIFCG3pTpw40f4ObNq0iVq1agHY+7Vp08bmtvm5Ot5ISUkBgtfJvKlBM2fOtEdQFtUTTzxh08zM88fPzAEby5YtO+79RowYYXubXnPNNUDwYILFixdHdoAREJ4m9u6775b640+ePNmmXpm6g4Jyx/2mRo0auVIrALZu3WrTK3bs2BGVcSg9QEREREQ8z5OR1gEDBtgTO0wFMIT6Kw4dOtQetRfOvBIcMmSIrQo1fvvtN1uodSKJ115mKhmXL19u+6yZU79ycnIK7MfpR+EdAxzHsRFWc6JJQkICe/bsAYK9W/Mmvt977702qmqKuBISEuz71atX5957743sDxEjplgxfNfCPA9MIV+8yPvzZGZm2iNv8556FI/++te/AjBv3jy2bt0KYKODpnDE2LZtGxCal7Zt21KtWrUojdR/zE7WyebgwYO2j6npbduwYUPbm7SwSK2XhO/IRSJanpGRYXtAz507F4A//OEPdn3ihz6u4cy187nnnrO3mb/FXbt2teuyaFGkVUREREQ8z1OR1k6dOgHBvKvwPpqmhZXJRwuPFlSoUIErr7wSCJ0pXaVKFfv5X3/9FYCRI0fGXYQ1r7ffftvOoTnBpU6dOja6ZubCr/LmtIa/bz6fN481L5O3avq1BgKBXPmt8RhpHTx4cL6erK+99lqBLY/iUVZWFv/9739jPYyouPLKK5k8eTIQ7L1pIj15I6x5ma/5v//7P3sNMe0ADx8+HKHRRp6pd3j33Xdp2bJljEfjPebaV7VqVb799tvj3techGbuV61aNds320927NhhTwSLlA8++AAIthIDqFevnj1JzW+RVvN3NjwqPWbMGAC7kxNNnli01q1bFwj1DQxfsC5cuJDevXsD2OPWKlasaA8aGDZsGDfeeGO+x9y4cSOAPYTANP6Nd2+++SYQWpQ9/PDDdruwoJQKP8mbHmA+vueee4DiFVK1aNECgAULFuQ6sKBZs2bAsRe9fmL6Uk6YMME+p8zP1aNHD3JycmI2tmiqWrWqPfb47bffLpXH7NOnj21Ib4pGvWDatGl2gXqiL0rq1asHBI959TvTe3Tx4sX5Fq39+vXjlVdeASh2QVa8MEd4Xnvttbb3aGFMb9oGDRrYnrV+MnXqVHuARu/evfn000+B0usvWrFiRbuVbp5L+/fv91UKRbjwVEvzAvZEri2mKK1MmTL5iueLQ+kBIiIiIuJ5nngpbYoAwsPPpnDklltusVGijh07AsEtrIK2esxxlo899pg9ks5saZxszKtHCB0l5/dIa1paWq42V6aA6kRaVZmI43vvvWfbQQUCARYsWABgE+n9HHE1z5syZcrYFmlmuyrSp5bEyujRo6lYsWKu26pUqWJTi9asWWPb/BgVK1akatWq+R7LnKh1xhln2NtM6yiA7t27l9q4S1NBR1yf7Hbu3GmPuTb/1/Xq1aNt27YArF+//rhFeubUoIULF9odHlMAerJp0KCBfd+0T/Orm2++2abP3XTTTaXymEuXLs11tDhAenp6TLbSS0P4SXJmV7M4TKF4pUqVgOCOZklS0xRpFRERERHP80Sk1bxiDY+0jho1CoDU1FR7VvixoqsmP+V///sfcPK2JQm3Zs0aIDi3BUWR/GjRokU2Al9aunfvbou3EhISbH5rly5dAH9HWsOZCKFpWxOvatSokSsn3rj99tuBYOs70xbNaNCggW3rUpA1a9awfv16AFavXg147xpj8vpNW52SmDBhAoA9tCQeLFmyxNZGmIKYQCBgD9bYuHGj/b8t6O+ROVe+UaNGBRam+F27du0KzWk1tScm6pyVlWWfF36yYcMGWx9ToUIF27br4osvtp8vrhtvvNEWPYYXCZtixgceeKDE4/aC+vXrA9C+fftj3mf06NGcfvrp9mPTEssUOZvC+hPliUVrQUwhVUF+/fVXDh06BAS7AkyfPj1aw/IN80c47Fg3OQaTZjBo0KBcnQQA33YTSEhIyNVFwzCFKfHq559/tr/vBW3fmvSSYzlw4AAQfN7ccsstAHz88cfs2rWrlEdaukw/64SEBJsOZF64FpfpMhJv142+ffsC8M477wBQuXJle3rYX/7yF7swDe9GUpCMjAwgdOqgH40bNw6Apk2b2n9Nf16TQhQuMTGRSZMmAaET1x599FHb99pP8vZRPfPMMwFYuXIlAK+//rrtdw6h4iNzYuCFF15oPzdw4EAg+GLGPF82b95sC7HM35Z4YU7LM0GAgq4Rhw8fzlUAOWTIEAB7ImlJKT1ARERERDzPE5HWgwcPAqGWCHmZUL5ptTF+/Hjfto+INLOFY9IrgCK3MjlZmWhqZmZmrpOyIHiylkkfMK+0/eCiiy6yW9iu6zJgwIAYjyg6Bg4caNNhTIpHUfznP/8BsIU5pnDNj8z2beXKlW1brmMx0UUTgQ4EAoX2dPUr09bKXCO7dOnCrFmzgFD0sChMsWZJ2vbE2qpVq4DQSVZ/+9vf7G1dunSxRWumv/OIESNsO0CTNuLn3xNTlHzLLbfw9NNPA9iIa8+ePenRo4e9r9l5MHNSvXr1fBHGI0eO2ALIBQsWsGPHjsj+AFG0ZMkSAP7+978X+HkTiR49ejQQPGXv5Zdfjth4nGhuATmOc9xvZqrM8vYHNHlHsbhIuK4bkRLRwubiRKSmpjJ27FggVPGXnp5uf9mOtd1VVH6aixNl8qNNekBCQgLr1q0DyFUR6vW5qFWrFlu2bLEff/TRRwB2AW6O7ywNXpsL88fH9OKtWbOmff/NN9+0i1lzGAnAhx9+CJAv37W4YjUX5kCA+fPn22roDRs2MGPGDIBcuYeNGjUCgvncZnvYVPZOmDCh1A6d8NrvRUEuv/xyAJKSkhg5ciQATZo0AYLXyz59+gC5+7iaXtjF4dW5MAv4lStXUr16dSC4jfvdd98B2NsglBdvriFmkVtcXpsLsxg3W9+tW7e2qRIF5cc7jmMDaSb/dfz48SfUC95rc1EQk3qUmJhoO6aYv4Xbtm2zgR6TsnmiijoXSg8QEREREc/zVKTVi/zwSsi8Qhw3bpzd5jJFAoMGDSIrK6tUvo8f5qK0mKh/s2bNbB/Y8E4CXp+L5ORkm86QlJRkbzfR9vnz59to67x584BgBO6ll14q9vfy+lxEU6znYtiwYbaXoomeHo/ZvTLbmSYKWxpiPRde4vW5qF+/vo0q9+7d256UZWRlZdnq+pIWJXp9LgDbaSI8ZdH0iXccx+7IlfSkTT/MRbQo0ioiIiIicUOR1kLolVCI5iLED3NhiorGjx/Pn//851yf27Nnj43EmT56S5cu5ZFHHin29/HDXESLF+bC5CH27NmTzp07A6F8xNTUVJunN2nSJKZMmQIQkcIRL8yFV2guQjQXIZqLkKLOhRathdAvVYjmIsRPc9G0aVPbfN7Izs62x5nOnz8fCC5czFHIxeGnuYg0zUWI5iJEcxGiuQjRXIQoPUBERERE4oYirYXQK6EQzUWI5iJEcxGiuQjRXIRoLkI0FyGaixBFWkVEREQkbmjRKiIiIiKeF9X0ABERERGRE6FIq4iIiIh4nhatIiIiIuJ5WrSKiIiIiOdp0SoiIiIinqdFq4iIiIh4nhatIiIiIuJ5WrSKiIiIiOdp0SoiIiIinuf7RavjON0dx/nCcZyfHcfZ5jjOFbEeUyw4jvMfx3F+cRznp9/fvoz1mERERERKS9lYD6AkHMe5GhgPdAP+C6TEdkQxN8B13SdjPQgRERGR0ubrRSswEhjluu77v3+8J5aDEREREZHI8G16gOM4ZYDGQBXHcbY6jrPbcZxpjuMkxnpsMfSI4zjfO46z1nGc1rEejIiIiEhp8e2iFagKlAO6AlcADYFGwPBYDiqGhgLnAzWA2cBrjuPUiu2QREREREqHnxeth3//d6rruntd1/0emAhcG8MxxYzruutd1z3kuu4R13XnAms5SedCRERE4o9vF62u6x4AdgNu+M0xGo4XuYAT60GIiIiIlAbfLlp/9www0HGcPzqOczowGFgW4zFFneM4lRzHaeM4zh8cxynrOM4NQEtgRazHJiIiIlIa/N49YDRQGfgK+AVYCIyN6YhioxwwBqgL5ACbgetd1/0qpqMSERERKSWO62pHXURERES8ze/pASIiIiJyEtCiVUREREQ8T4tWEREREfE8LVpFRERExPOi2j3AcRzfVX25rhuRXqeaCxEREZGiU6RVRERERDxPi1YRERER8TwtWkVERETE87RoFRERERHP8/sxrnICLr/8cjp16gTAJZdcgjkV7ZlnngHgueeei9nYRERERApyUi1aq1WrBkC9evX44osvAMjMzIzlkKKiWbNmALRv3x6Afv36ccYZZwDgOI5dtJ522mkAvPrqqxw6dCgGIxUREREpmNIDRERERMTz4j7S2qtXLyAYZWzSpAkAZ511lo2wHjhwAIB7772Xt956KyZjjISzzz4bgNtuu42hQ4cC2IhquOzsbJYvXw7A+++/D6Aoq4iIiHiOIq0iIiIi4nlxFWktU6YMAB07drTFRKeeeqr9vImqfvfdd5x++ukAVK9eHYCUlJRoDjViOnToAMC4ceMAuOiiiwq835w5cwAYNWoUe/fujc7gRERERE6QU9CWccS+WYSPLp01axYAt956q73t3XffBWD27NmsXLkSgP3799OuXTsA5s2bB8CKFSvo2bNnvsf00zGuN954I8OHDwfgwgsvtLcfPHgQgC+//BKAMWPGsGzZsmI/vo5xFRERkVhReoCIiIiIeF5cpQcYq1evZsKECQC2uConJyfXfRo2bBj1cUVKnz59AJg5c6Yttlq3bh0AkydPZuPGjQB89dVXsRmgiIiISAnFVXqAyWktW7YsR44cOeb9WrRowYoVKwBITEwE4OKLL7aLu3BeTw/o378/EydOBKB8+fJ2sfrAAw8AofSI0qD0ABEREYkVpQeIiIiIiOfFVXqASQHImwpgmOKrtLQ0EhKC6/WlS5cCubsMeFmrVq0AGDFiBADNmzfnlFNOAWD58uV069YNgMOHD8dmgCIiIiIRoEiriIiIiHheXEVaU1NTAXj77bftbXXr1gVg4MCB9OjRA4DTTjvNRlg7d+4c5VEWTdmywf+alJQUG1W99dZbbYQ4EAjk+5qPPvoIx1HaqYiIiMQfTxVima3vhg0b8tlnnwG5F6AFOf/881m0aBEA9erVA2DXrl328+YQgeTkZHtbv379WLhwIVD4kaWxKsQyx7Bu374979eZcRX0mLYQy/RrzcjIKPlgf6dCLBEREYkVpQeIiIiIiOd5KtJqtrzDx7Ro0SJbXFSQ+vXrs3r1agAqVaqU63EgVJS1fv160tLSANi3b1+RxxyrSGv58uWBYOsq83PdfvvtNtL68ssvAzBx4kQaNWoEwF133cUFF1wAQFZWFgBbtmyxba+mT58OwO7du09ozIq0ioiISKx4atH64YcfAtCgQQN72xdffMEbb7wBwJAhQ/J9TbVq1WjSpAkAd955JxBc9F566aX2fYBLL73UHmNaHF7o0/rmm28CcNVVV9kF6ODBgwH45JNP7P0qVapkF/gmPSAlJcUudDdt2gRA27Zt2bt3b7HHrEWriIiIxIrSA0RERETE8zwVaX3xxRcB7Da+YYqR+vbtC8A777xT6PeaP38+EOrN2rhxY7Zt21bMEcc+0nrfffcxduxY+7E59auo+vTpw6xZs4BQ1HnLli2MHj0agHnz5hX5sRRpFRERkVhRpFVEREREPM9TfVrT09MBuO666+wpTwDnnXceACtWrACgV69eLF68GDj2yU/mdtPvNLzllZ80atSowPZWRTVnzhwOHDgAYPNdO3XqxNy5c4Fg1DozM7PkAxURERGJIE8tWpcsWQLAW2+9RYcOHfJ93myNP//887Y465577slXYJWamsr1118PQGJiIgC1a9dmw4YNERt7NJj0ieIyfWxNQVft2rVtT9tu3brx+OOPl84ARURERCJE6QEiIiIi4nmeirQaU6dO5YorrgCOva1vCqwuu+wye7qVae2UlpZmv+69994DYOXKlREdczR07NixRF9vThyrWLFiaQxHREREJGoUaRURERERz/NUy6twAwcOBChWvqWJtP7yyy+20GjYsGEA/Pjjj0V+nHCxbnn11FNP0atXL/vxmDFjAHjyyScByM7OZv/+/fbzZ555JgCnnnqqve3VV18FsCdnAfz0009A8QrU1PJKREREYsWzi9Y6deoAcPXVV9O7d28AatWqBYSKq/L66quvgOCJTzt37izRWI1YL1rr16/PmjVrAKhQoYJdmJv/t927d7N+/Xp7/2bNmgFQo0aN8O+V62s2bdrEHXfcAcDatWuLPGYtWkVERCRWlB4gIiIiIp7n2UhrQRo3bgwcu5Bo69atAOzatask3yaXWEdaIdhXFWD48OE0bNjQjKugxyzw9o0bNwKhVIvFixdz6NChYo9ZkVYRERGJFV8tWmPBC4tWo2LFijz00EMAtG/fHgguXvft2wfAJZdcwuzZswFYsGABAPv27bP5vCeyUA2nRauIiIjEitIDRERERMTzFGkthJcirbGmSKuIiIjEiiKtIiIiIuJ5WrSKiIiIiOdp0SoiIiIinqdFq4iIiIh4nhatIiIiIuJ5WrSKiIiIiOdp0SoiIiIinhfVPq0iIiIiIidCkVYRERER8TwtWkVERETE87RoFRERERHP06JVRERERDxPi1YRERER8TwtWkVERETE87RoFRERERHP06JVRERERDxPi1YRERER8TwtWkVERETE87RoFRERERHP06JVRERERDxPi1YRERER8TwtWkVERETE87RoFRERERHP06JVRERERDxPi1YRERER8TwtWkVERETE87RoFRERERHP06JVRERERDxPi1YRERER8TwtWkVERETE87RoFRERERHP+3+XYU5Q0bF8JAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2b044b9ac8>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "misclassifications = [classifications[i] for i in wrong]\n",
    "cx.view(cnn.dataset.test_inputs[wrong], labels=misclassifications, layout=(None,10), spacing=0.5)"
   ]
  }
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonModel",
      "state": {
       "icon": "save",
       "layout": "IPY_MODEL_6bce55021eb34c5ba0c76a1c49a1705e",
       "style": "IPY_MODEL_b3a7320d36c34bbfafc0321e8e87c7bf"
      }
     },
     "54d09d31f8ea4715afadef23d7130b38": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "100%"
      }
     },
     "5555567abe17459a9e5d35c21e58ad20": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_613073d402754d2d962328c8c78cd42e",
        "IPY_MODEL_4071ae1382af4cce8f1fa59ac3e7b493",
        "IPY_MODEL_22e20825e12f4f88b9e042ecaf2c444b",
        "IPY_MODEL_850ce45c94c14cd5a39d833c71f109a2",
        "IPY_MODEL_b0c282c29508432694cfe6223f33579c",
        "IPY_MODEL_22ac5e00b8844c9eb58674f46813f260",
        "IPY_MODEL_343ebdc05f1f43bfaeaddcaf1e4255e6",
        "IPY_MODEL_be306ceca1f24298943835ce15099b68"
       ],
       "layout": "IPY_MODEL_737c4886ffcf447d913b667123da685e"
      }
     },
     "55fc77e941e94ea88db4b592defd9f5c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_dd63fd368eff43ff86e776a3080edbd9",
       "style": "IPY_MODEL_b04079a3437a4877b54f9552364409b7",
       "value": "<p style=\"text-align:center\"><svg id='MNIST_MLP' xmlns='http://www.w3.org/2000/svg' image-rendering=\"pixelated\" width=\"480.0px\" height=\"266.66666666666663px\">\n <g  transform=\"rotate(90) translate(0 -480.0)\" >\n  <svg viewBox=\"0 0 350 630\" width=\"266.66666666666663px\" height=\"480.0px\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><rect x=\"74.0\" y=\"24\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-798434_output_targets_6\" class=\"picture-dashboard-MNIST_MLP-798434_output_targets\" x=\"75.0\" y=\"25\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdh+gAZAIcAAMzM/9ra/7i4/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAA+gAZAEAI/wAHCBxIsKDBgwgTKlzIsKHDhxAjSpxIsSJFABgzatzIEeOAAQJCihxJsmTIAQMAqFzJsqVLlQMGAJhJs6bNmzMHDAjAs6fPn0B5DhgQoKjRo0iTFh0wIIDTp1CjSnU6YICAq1izat16dcAAAGDDih1LFuyAAQHSql3Ltm3aAQMAyJ1Lt65duQMGCNjLt6/fv3sHDABAuLDhw4gJDxgAoLHjx5AjNx4wIIDly5gza7Y8YECAz6BDix79ecCAAKhTq17NGvWAAQJiy55Nu3bsAQMA6N7Nu7dv3QMGBBhOvLjx48MHKF/OvLnz59CjS59Ovbr169iza9/OvXt3AODDi/8fTx78gAEC0qtfz759+gEDAMifT7++ffkDBgDYz7+/f4AABA4UOGBAAIQJFS5kiHDAgAARJU6kWDHigAEBNG7k2NGjxgEDBIwkWdLkyZEDBgBg2dLlS5gsBwwIUNPmTZw5aw4YAMDnT6BBhfocMEDAUaRJlS49OmAAAKhRpU6lCnXAAABZtW7l2jXrgAEBxI4lW9as2AEDAqxl29bt27UDBgSgW9fuXbx0BwwQ0NfvX8CB+w4YAMDwYcSJFRseMCDAY8iRJU9+PGAAAMyZNW/mjHnAAAGhRY8mXTr0gAEAVK9m3dq16gEDAMymXdv27dkDBgTg3dv3b+C8BwwIUNz/+HHkyYsPGBDA+XPo0aU7HzBAwHXs2bVvvz5gAADw4cWPJw9+wIAA6dWvZ98+/YABAOTPp1/fvvwBAwTs59/fP0ABAgcKHDAAAMKEChcyRDhgAICIEidSrBhxwIAAGjdy7OhR44ABAUaSLGny5MgBAwKwbOnyJUyWAwYIqGnzJs6cNQcMAODzJ9CgQn0OGBDgKNKkSpceHTAAANSoUqdShTpggICsWrdy7Zp1wAAAYseSLWtW7IABANaybev27doBAwLQrWv3Ll66AwYE6Ov3L+DAfQcMCGD4MOLEig0PGCDgMeTIkic/HjAAAObMmjdzxjxgQIDQokeTLh16wAAA/6pXs27tWvWAAQJm065t+/bsAQMA8O7t+zdw3gMGAChu/Djy5MUHDAjg/Dn06NKdDxgQ4Dr27Nq3Xx8wIAD48OLHkwc/YICA9OrXs2+ffsAAAPLn069vX/6AAQH28+/vH2AAgQMFDhgAAGFChQsZIhwwQEBEiRMpVow4YAAAjRs5dvSoccAAACNJljR5cuSAAQFYtnT5EibLAQMC1LR5E2fOmgMGBPD5E2hQoT4HDBBwFGlSpUuPDhgAAGpUqVOpQh0wIEBWrVu5ds06YAAAsWPJljUrdsAAAWvZtnX7du2AAQDo1rV7Fy/dAQMA9PX7F3DgvgMGBDB8GHFixYYHDP8I8BhyZMmTHw8YEABzZs2bOWMeMEBAaNGjSZcOPWAAANWrWbd2rXrAgACzade2fXv2gAEAePf2/Rs47wEDBBQ3fhx58uIDBgBw/hx6dOnOBwwAcB17du3brw8YEAB8ePHjyYMfMCBAevXr2bdPP2BAAPnz6de3L3/AAAH7+ff3D1CAwIECBwwAgDChwoUMEQ4YECCixIkUK0YcMACAxo0cO3rUOGCAgJEkS5o8OXLAAAAsW7p8CZPlgAEAatq8iTNnzQEDAvj8CTSoUJ8DBgQ4ijSp0qVHBwwIADWq1KlUoQ4YICCr1q1cu2YdMACA2LFky5oVO2BAgLVs27p9u3b/wAAAdOvavYuX7oABAvr6/Qs4cN8BAwAYPow4sWLDAwYAeAw5suTJjwcMCIA5s+bNnDEPGBAgtOjRpEuHHjAggOrVrFu7Vj1ggIDZtGvbvj17wAAAvHv7/g2c94ABAYobP448efEBAwA4fw49unTnAwYIuI49u/bt1wcMAAA+vPjx5MEPGAAgvfr17NunHzAggPz59Ovblz9gQID9/Pv7BxhA4ECBAwYEQJhQ4UKGCAcMEBBR4kSKFSMOGABA40aOHT1qHDAgwEiSJU2eHDlgAACWLV2+hMlywAABNW3exJmz5oABAHz+BBpUqM8BAwAcRZpU6dKjAwYEgBpV6lSq/1AHDAiQVetWrl2zDhgQQOxYsmXNih0wQMBatm3dvl07YAAAunXt3sVLd8CAAH39/gUcuO+AAQAMH0acWLHhAQMEPIYcWfLkxwMGAMCcWfNmzpgHDAAQWvRo0qVDDxgQQPVq1q1dqx4wIMBs2rVt3549YEAA3r19/wbOe8AAAcWNH0eevPiAAQCcP4ceXbrzAQMCXMeeXfv26wMGAAAfXvx48uAHDBCQXv169u3TDxgAQP58+vXtyx8wAMB+/v39AwQgcKDAAQMCIEyocCFDhAMGBIgocSLFihEHDAigcSPHjh41DhggYCTJkiZPjhwwAADLli5fwmQ5YECAmjZv4v/MWXPAAAA+fwINKtTngAECjiJNqnTp0QEDAECNKnUqVagDBgDIqnUr165ZBwwIIHYs2bJmxQ4YEGAt27Zu364dMCAA3bp27+KlO2CAgL5+/wIO3HfAAACGDyNOrNjwgAEBHkOOLHny4wEDAGDOrHkzZ8wDBggILXo06dKhBwwAoHo169auVQ8YAGA27dq2b88eMCAA796+fwPnPWBAgOLGjyNPXnzAgADOn0OPLt35gAECrmPPrn379QEDAIAPL348efADBgRIr349+/bpBwwAIH8+/fr25Q8YIGA///7+AQoQOFDggAEAECZUuJAhwgEDAESUOJFixYgDBgTQuJH/Y0ePGgcMCDCSZEmTJ0cOGBCAZUuXL2GyHDBAQE2bN3HmrDlgAACfP4EGFepzwIAAR5EmVbr06IABAKBGlTqVKtQBAwRk1bqVa9esAwYAEDuWbFmzYgcMALCWbVu3b9cOGBCAbl27d/HSHTAgQF+/fwEH7jtgQADDhxEnVmx4wAABjyFHljz58YABADBn1ryZM+YBAwKEFj2adOnQAwYAUL2adWvXqgcMEDCbdm3bt2cPGACAd2/fv4HzHjAAQHHjx5EnLz5gQADnz6FHl+58wIAA17Fn1779+oABAcCHFz+ePPgBAwSkV7+effv0AwYAkD+ffn378gcMCLCff3//bgADCBwocMAAAAgTKlzIEOGAAQIiSpxIsWLEAQMAaNzIsaNHjQMGABhJsqTJkyMHDAjAsqXLlzBZDhgQoKbNmzhz1hwwIIDPn0CDCvU5YICAo0iTKl16dMAAAFCjSp1KFeqAAQGyat3KtWvWAQEBADs=\"><title>Layer: output (output)\n output range: (-1.0, 1.0)\n shape = (10,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"288.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 52.0) translate(2)\" >targets</text><line x1=\"87.5\" y1=\"80\" x2=\"262.5\" y2=\"80\" stroke=\"green\" stroke-width=\"2\"><title></title></line><rect x=\"74.0\" y=\"84\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-798434_output-rotated_6\" class=\"picture-dashboard-MNIST_MLP-798434_output-rotated\" x=\"75.0\" y=\"85\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdh+gAZAIcAAMzM/9ra/7i4/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAA+gAZAEAI/wAHCBxIsKDBgwgTKlzIsKHDhxAjSpxIsSJFABgzatzIEeOAAQJCihxJsmTIAQMAqFzJsqVLlQMGAJhJs6bNmzMHDAjAs6fPn0B5DhgQoKjRo0iTFh0wIIDTp1CjSnU6YICAq1izat16dcAAAGDDih1LFuyAAQHSql3Ltm3aAQMAyJ1Lt65duQMGCNjLt6/fv3sHDABAuLDhw4gJDxgAoLHjx5AjNx4wIIDly5gza7Y8YECAz6BDix79ecCAAKhTq17NGvWAAQJiy55Nu3bsAQMA6N7Nu7dv3QMGBBhOvLjx48MHKF/OvLnz59CjS59Ovbr169iza9/OvXt3AODDi/8fTx78gAEC0qtfz759+gEDAMifT7++ffkDBgDYz7+/f4AABA4UOGBAAIQJFS5kiHDAgAARJU6kWDHigAEBNG7k2NGjxgEDBIwkWdLkyZEDBgBg2dLlS5gsBwwIUNPmTZw5aw4YAMDnT6BBhfocMEDAUaRJlS49OmAAAKhRpU6lCnXAAABZtW7l2jXrgAEBxI4lW9as2AEDAqxl29bt27UDBgSgW9fuXbx0BwwQ0NfvX8CB+w4YAMDwYcSJFRseMCDAY8iRJU9+PGAAAMyZNW/mjHnAAAGhRY8mXTr0gAEAVK9m3dq16gEDAMymXdv27dkDBgTg3dv3b+C8BwwIUNz/+HHkyYsPGBDA+XPo0aU7HzBAwHXs2bVvvz5gAADw4cWPJw9+wIAA6dWvZ98+/YABAOTPp1/fvvwBAwTs59/fP0ABAgcKHDAAAMKEChcyRDhgAICIEidSrBhxwIAAGjdy7OhR44ABAUaSLGny5MgBAwKwbOnyJUyWAwYIqGnzJs6cNQcMAODzJ9CgQn0OGBDgKNKkSpceHTAAANSoUqdShTpggICsWrdy7Zp1wAAAYseSLWtW7IABANaybev27doBAwLQrWv3Ll66AwYE6Ov3L+DAfQcMCGD4MOLEig0PGCDgMeTIkic/HjAAAObMmjdzxjxgQIDQokeTLh16wAAA/6pXs27tWvWAAQJm065t+/bsAQMA8O7t+zdw3gMGAChu/Djy5MUHDAjg/Dn06NKdDxgQ4Dr27Nq3Xx8wIAD48OLHkwc/YICA9OrXs2+ffsAAAPLn069vX/6AAQH28+/vH2AAgQMFDhgAAGFChQsZIhwwQEBEiRMpVow4YAAAjRs5dvSoccAAACNJljR5cuSAAQFYtnT5EibLAQMC1LR5E2fOmgMGBPD5E2hQoT4HDBBwFGlSpUuPDhgAAGpUqVOpQh0wIEBWrVu5ds06YAAAsWPJljUrdsAAAWvZtnX7du2AAQDo1rV7Fy/dAQMA9PX7F3DgvgMGBDB8GHFixYYHDP8I8BhyZMmTHw8YEABzZs2bOWMeMEBAaNGjSZcOPWAAANWrWbd2rXrAgACzade2fXv2gAEAePf2/Rs47wEDBBQ3fhx58uIDBgBw/hx6dOnOBwwAcB17du3brw8YEAB8ePHjyYMfMCBAevXr2bdPP2BAAPnz6de3L3/AAAH7+ff3D1CAwIECBwwAgDChwoUMEQ4YECCixIkUK0YcMACAxo0cO3rUOGCAgJEkS5o8OXLAAAAsW7p8CZPlgAEAatq8iTNnzQEDAvj8CTSoUJ8DBgQ4ijSp0qVHBwwIADWq1KlUoQ4YICCr1q1cu2YdMACA2LFky5oVO2BAgLVs27p9u3b/wAAAdOvavYuX7oABAvr6/Qs4cN8BAwAYPow4sWLDAwYAeAw5suTJjwcMCIA5s+bNnDEPGBAgtOjRpEuHHjAggOrVrFu7Vj1ggIDZtGvbvj17wAAAvHv7/g2c94ABAYobP448efEBAwA4fw49unTnAwYIuI49u/bt1wcMAAA+vPjx5MEPGAAgvfr17NunHzAggPz59Ovblz9gQID9/Pv7BxhA4ECBAwYEQJhQ4UKGCAcMEBBR4kSKFSMOGABA40aOHT1qHDAgwEiSJU2eHDlgAACWLV2+hMlywAABNW3exJmz5oABAHz+BBpUqM8BAwAcRZpU6dKjAwYEgBpV6lSq/1AHDAiQVetWrl2zDhgQQOxYsmXNih0wQMBatm3dvl07YAAAunXt3sVLd8CAAH39/gUcuO+AAQAMH0acWLHhAQMEPIYcWfLkxwMGAMCcWfNmzpgHDAAQWvRo0qVDDxgQQPVq1q1dqx4wIMBs2rVt3549YEAA3r19/wbOe8AAAcWNH0eevPiAAQCcP4ceXbrzAQMCXMeeXfv26wMGAAAfXvx48uAHDBCQXv169u3TDxgAQP58+vXtyx8wAMB+/v39AwQgcKDAAQMCIEyocCFDhAMGBIgocSLFihEHDAigcSPHjh41DhggYCTJkiZPjhwwAADLli5fwmQ5YECAmjZv4v/MWXPAAAA+fwINKtTngAECjiJNqnTp0QEDAECNKnUqVagDBgDIqnUr165ZBwwIIHYs2bJmxQ4YEGAt27Zu364dMCAA3bp27+KlO2CAgL5+/wIO3HfAAACGDyNOrNjwgAEBHkOOLHny4wEDAGDOrHkzZ8wDBggILXo06dKhBwwAoHo169auVQ8YAGA27dq2b88eMCAA796+fwPnPWBAgOLGjyNPXnzAgADOn0OPLt35gAECrmPPrn379QEDAIAPL348efADBgRIr349+/bpBwwAIH8+/fr25Q8YIGA///7+AQoQOFDggAEAECZUuJAhwgEDAESUOJFixYgDBgTQuJH/Y0ePGgcMCDCSZEmTJ0cOGBCAZUuXL2GyHDBAQE2bN3HmrDlgAACfP4EGFepzwIAAR5EmVbr06IABAKBGlTqVKtQBAwRk1bqVa9esAwYAEDuWbFmzYgcMALCWbVu3b9cOGBCAbl27d/HSHTAgQF+/fwEH7jtgQADDhxEnVmx4wAABjyFHljz58YABADBn1ryZM+YBAwKEFj2adOnQAwYAUL2adWvXqgcMEDCbdm3bt2cPGACAd2/fv4HzHjAAQHHjx5EnLz5gQADnz6FHl+58wIAA17Fn1779+oABAcCHFz+ePPgBAwSkV7+effv0AwYAkD+ffn378gcMCLCff3//bgADCBwocMAAAAgTKlzIEOGAAQIiSpxIsWLEAQMAaNzIsaNHjQMGABhJsqTJkyMHDAjAsqXLlzBZDhgQoKbNmzhz1hwwIIDPn0CDCvU5YICAo0iTKl16dMAAAFCjSp1KFeqAAQGyat3KtWvWAQEBADs=\"><title>Layer: output (output)\n output range: (-1.0, 1.0)\n shape = (10,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"288.0\" y=\"112.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 112.0) translate(2)\" >output</text><line x1=\"175.0\" y1=\"164\" x2=\"175.0\" y2=\"137\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden2 to output\n output/kernel has shape (30, 10)\n output/bias has shape (10,)</title></line><rect x=\"74.0\" y=\"164\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-798434_hidden2-rotated_7\" class=\"picture-dashboard-MNIST_MLP-798434_hidden2-rotated\" x=\"75.0\" y=\"165\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhHgABAIcAAP37/dXV/5eX/wAA97S0/4SE/0FB/6mp/xUV/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHgABAAAIIQABCDwAAAGAAAAABABgAIDDAAAEAJgIQAAAAAQGFAAQEAA7\"><title>Layer: hidden2 (hidden)\n output range: (-1.0, 1.0)\n shape = (30,)\n Keras class = Dense\n activation = relu</title></image><text x=\"288.0\" y=\"192.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 192.0) translate(2)\" >hidden2</text><line x1=\"175.0\" y1=\"244\" x2=\"175.0\" y2=\"217\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden1 to hidden2\n hidden2/kernel has shape (30, 30)\n hidden2/bias has shape (30,)</title></line><rect x=\"74.0\" y=\"244\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-798434_hidden1-rotated_8\" class=\"picture-dashboard-MNIST_MLP-798434_hidden1-rotated\" x=\"75.0\" y=\"245\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhHgABAIcAAP78/dPT/3t7/zAw/6en/wAAfgAATAAAmgAA4FVV/wAAxMHB/7Gx/2Vl/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHgABAAAIIwAHADAAIACAgwoECABAoMGCAQAYEEAQAACAAwAABEgAoEBAADs=\"><title>Layer: hidden1 (hidden)\n output range: (-1.0, 1.0)\n shape = (30,)\n Keras class = Dense\n activation = relu</title></image><text x=\"288.0\" y=\"272.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 272.0) translate(2)\" >hidden1</text><line x1=\"175.0\" y1=\"324\" x2=\"175.0\" y2=\"297\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from flat_input to hidden1\n hidden1/kernel has shape (784, 30)\n hidden1/bias has shape (30,)</title></line><rect x=\"74.0\" y=\"324\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-798434_flat_input-rotated_9\" class=\"picture-dashboard-MNIST_MLP-798434_flat_input-rotated\" x=\"75.0\" y=\"325\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: flat_input (hidden)\n output range: (0.0, 1.0)\n Keras class = Flatten</title></image><text x=\"288.0\" y=\"352.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 352.0) translate(2)\" >flat_input</text><line x1=\"175.0\" y1=\"404\" x2=\"175.0\" y2=\"377\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to flat_input</title></line><rect x=\"74.0\" y=\"404\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-798434_input-rotated_10\" class=\"picture-dashboard-MNIST_MLP-798434_input-rotated\" x=\"75.0\" y=\"405\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" 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href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n output range: (0.0, 1.0)\n shape = (10,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"288.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 52.0) translate(2)\" >targets</text><line x1=\"87.5\" y1=\"80\" x2=\"262.5\" y2=\"80\" stroke=\"green\" stroke-width=\"2\"><title></title></line><rect x=\"74.0\" y=\"84\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-397103_output-rotated_1\" class=\"picture-dashboard-MNIST_CNN-397103_output-rotated\" x=\"75.0\" y=\"85\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n output range: (0.0, 1.0)\n shape = (10,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"288.0\" y=\"112.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 112.0) translate(2)\" >output</text><line x1=\"175.0\" y1=\"164\" x2=\"175.0\" y2=\"137\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden to output\n output_4/kernel:0 has shape (30, 10)\n output_4/bias:0 has shape (10,)</title></line><rect x=\"74.0\" y=\"164\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-397103_hidden-rotated_2\" class=\"picture-dashboard-MNIST_CNN-397103_hidden-rotated\" x=\"75.0\" y=\"165\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhHgABAIcAAPT5/dzp9uTu+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHgABAAAIGwABCBwoAIBBAAIABADAkKEAABADAAAQAIDFgAA7\"><title>Layer: hidden (hidden)\n output range: (0.0, +Infinity)\n shape = (30,)\n Keras class = Dense\n activation = relu</title></image><text x=\"288.0\" y=\"192.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 192.0) translate(2)\" >hidden</text><line x1=\"175.0\" y1=\"244\" x2=\"175.0\" y2=\"217\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from flat to hidden\n hidden/kernel:0 has shape (512, 30)\n hidden/bias:0 has shape (30,)</title></line><rect x=\"74.0\" y=\"244\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-397103_flat-rotated_3\" class=\"picture-dashboard-MNIST_CNN-397103_flat-rotated\" x=\"75.0\" y=\"245\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhAAIBAIcAAPz7/dfX/8fH/+fn/7e3/6en/4aG/5eX/2tr/3V1/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAAAIBAAAI/wADCAAwAMAAAAQAKAQQAIBDhwEAABAQAMAAAQMAAAgwIECAAgEAiAQgAACAAQACAAAwAAAAAwAAEBAAIICAAAAGCAAgoIAAAQMACB06AICAAAMIAABgAACAAgEASCUAIICAAAEEBBAwAIDXrwMABBAQIAAAAAUAACgQAIDbAgAEBBgQIMCAAAEADAAQAACAAQAADABAGIAAAAAEBAAAIAABAAAECAAQIACAywMAHADAGYCAAAQAiEYAAEABAAAKECgwYEAAAgMCCCAAoDYAAwAACABAQEAAAQAAIAAAoAAAAAQKFAggIACBAAQGEABAHcAAAAACAAggIIAAAAAOAP8AECAAAAEDBAAYAEDAAAABAgAYAACAgAEA8uvXLwAAAIACBgAIICDAAAAABAwgIGBAgAEDABQAUBFAAAAABAAAQAAAAAEBBgggIAAAgAECBggQAIAAAJgCAAAIACAAgAAEAAAoAACAgAEACAggMCBAgAEBBAQgAMDpUwACAAgYEEAAAAAFAAAIIADAAAABAAwYAABAgAADBgBgKwDAW7hxBwAAMCDAAAECBgAAMABAAAECAgQAUHgAAAADACwGMAAAAAIAAAgQAECAAAADBggQMEBAAAICAIweAADAAAAEBAAoAACAAQAACAgAAGAAAQABCggYMAAAAQDBAQQAAIAYAAACAZQDAJAAAAACAKQDODCAQAAAAgICADs=\"><title>Layer: flat (hidden)\n output range: (-Infinity, +Infinity)\n Keras class = Flatten</title></image><text x=\"288.0\" y=\"272.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 272.0) translate(2)\" >flat</text><line x1=\"175.0\" y1=\"324\" x2=\"175.0\" y2=\"297\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from maxpool2 to flat</title></line><rect x=\"74.0\" y=\"324\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-397103_maxpool2-rotated_4\" class=\"picture-dashboard-MNIST_CNN-397103_maxpool2-rotated\" x=\"75.0\" y=\"325\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: maxpool2 (hidden)\n output range: (-Infinity, +Infinity)\n Keras class = MaxPooling2D</title></image><text x=\"288.0\" y=\"427.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 427.0) translate(2)\" >maxpool2</text><text x=\"88.0\" y=\"310\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 310) translate(2)\" >32</text><text x=\"273.0\" y=\"540\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 540) translate(2)\" >0</text><line x1=\"175.0\" y1=\"554\" x2=\"175.0\" y2=\"527\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from conv2D_2 to maxpool2</title></line><rect x=\"74.0\" y=\"554\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-397103_conv2D_2-rotated_5\" class=\"picture-dashboard-MNIST_CNN-397103_conv2D_2-rotated\" x=\"75.0\" y=\"555\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: conv2D_2 (hidden)\n output range: (0.0, +Infinity)\n dropout = 0.2\n Keras class = Conv2D\n activation = relu</title></image><text x=\"288.0\" y=\"657.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 657.0) translate(2)\" >conv2D_2</text><text x=\"88.0\" y=\"540\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 540) translate(2)\" >32</text><text x=\"273.0\" y=\"770\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 770) translate(2)\" >0</text><text x=\"62.0\" y=\"560\" font-family=\"monospace\" font-size=\"24.0\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 62.0 560) translate(2)\" >&#10683;</text><line x1=\"175.0\" y1=\"784\" x2=\"175.0\" y2=\"757\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from maxpool1 to conv2D_2\n conv2D_2/kernel:0 has shape (5, 5, 16, 32)\n conv2D_2/bias:0 has shape (32,)</title></line><rect x=\"74.0\" y=\"784\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-397103_maxpool1-rotated_6\" class=\"picture-dashboard-MNIST_CNN-397103_maxpool1-rotated\" x=\"75.0\" y=\"785\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: maxpool1 (hidden)\n output range: (-Infinity, +Infinity)\n Keras class = MaxPooling2D</title></image><text x=\"288.0\" y=\"887.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 887.0) translate(2)\" >maxpool1</text><text x=\"88.0\" y=\"770\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 770) translate(2)\" >16</text><text x=\"273.0\" y=\"1000\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 1000) translate(2)\" >0</text><line x1=\"175.0\" y1=\"1014\" x2=\"175.0\" y2=\"987\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from conv2D_1 to maxpool1</title></line><rect x=\"74.0\" y=\"1014\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-397103_conv2D_1-rotated_7\" class=\"picture-dashboard-MNIST_CNN-397103_conv2D_1-rotated\" x=\"75.0\" y=\"1015\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: conv2D_1 (hidden)\n output range: (0.0, +Infinity)\n dropout = 0.2\n Keras class = Conv2D\n activation = relu</title></image><text x=\"288.0\" y=\"1117.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 1117.0) translate(2)\" >conv2D_1</text><text x=\"88.0\" y=\"1000\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 1000) translate(2)\" >16</text><text x=\"273.0\" y=\"1230\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 1230) translate(2)\" >0</text><text x=\"62.0\" y=\"1020\" font-family=\"monospace\" font-size=\"24.0\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 62.0 1020) translate(2)\" >&#10683;</text><line x1=\"175.0\" y1=\"1244\" x2=\"175.0\" y2=\"1217\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to conv2D_1\n conv2D_1/kernel:0 has shape (5, 5, 1, 16)\n conv2D_1/bias:0 has shape (16,)</title></line><rect x=\"74.0\" y=\"1244\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-397103_input-rotated_8\" class=\"picture-dashboard-MNIST_CNN-397103_input-rotated\" x=\"75.0\" y=\"1245\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input (input)\n output range: (0.0, 1.0)\n shape = (28, 28, 1)\n Keras class = Input</title></image><text x=\"288.0\" y=\"1347.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 1347.0) translate(2)\" >input</text><text x=\"88.0\" y=\"1230\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 1230) translate(2)\" >1</text><text x=\"273.0\" y=\"1460\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 1460) translate(2)\" >0</text><text x=\"18\" y=\"735.0\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 18 735.0) translate(2)\" >MNIST_CNN</text></svg></g></svg></p></td>\n</tr>\n<tr>\n  <td valign=\"top\" align=\"center\"><p style=\"text-align:center\"><b>conv2D_1 features</b></p><table><tr><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature0\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 0</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature1\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 1</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature2\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 2</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature3\" src=\"data:image/gif;base64,R0lGODdhYABgAIcAAPb6/gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAYABgAEAInAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhwyQDAgA7\"/><br/><center>Feature 3</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature4\" src=\"data:image/gif;base64,R0lGODdhYABgAIcAAPb6/gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAYABgAEAInAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhwyQDAgA7\"/><br/><center>Feature 4</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature5\" src=\"data:image/gif;base64,R0lGODdhYABgAIcAAPX5/uPt+Nzp9gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAYABgAEAI/wABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgZBtgYAIDHjyBDihxJsqTJkyhTAgjAMgCAlzBjypxJs6bNmzhz6tQZoGcAAECDCh1KtKjRo0iTKl06NIDTp04BSJ1KtarVq1izat3KtWvVAGADABhLtqzZs2jTql3Ltq1btQHiBhBAVwCAu3jz6t3Lt6/fv4ADCx5MuLDhw4gTK17MuLHjx5AjS55MubLly5jvCtgsAIDnz6BDix5NurTp06hTq17NurXr16oFyBYAoLbt27hz697Nu7fv38BxBxhOfDiA48iTK1/OvLnz59CjS3ceoLr16gCya9/Ovbv37+DDi/8fT768+fPo06tfz769+/fwtQeYT38+gPv48+vfz7+/f4AABA4kWNDgQYQJFS5k2NDhQ4gRJU6kWNHiRYwZNW7k2NHjR5AhRY4sGMBkAAApVa5k2dLlS5gxZc6kCUDATQEAdO7k2dPnT6BBhQ4lWhRAAKQBACxl2tTpU6hRpU6lWtXqVaxZtW7lOjXA1wAAxI4lW9bsWbRp1a5l27asALgCAMylW9fuXbx59e7l29cvgACBBQcGUNjwYcSJFS9m3NjxY8gAAkymPBnAZcyZNW/m3NnzZ9ChRQMIUDoAANSpVa9m3dr1a9ixZc9+HcD2bdsAdO/m3dv3b+DBhQ8nLjz/wPEAApQLANDc+XPo0aVPp17d+nXs2bVv597d+3fw4cWPJ1/e/Hn06dWvZ98efAD48eEDoF/f/n38+fXv59/fP0AAAgcSLGjwIMKEChcybOjwIcSIEidSrGjxIsaMGjdy7OjxI8iQIkeSLGnyJMqUKleybOnyJUyBAWYGAGDzJs6cOnfy7OnzJ9CgAAIQDQDgKNKkSpcyber0KdSoUgEIqCoAANasWrdy7er1K9iwYscCEGBWAIC0ateybev2Ldy4cufSBRDgbgAAevfy7ev3L+DAggcTLgwgAOIAABYzbuz4MeTIkidTrmz5MubMmjdz7uz5M+jOAUYHAGD6NOrU/6pXs27t+jXs2AAC0A4A4Dbu3Lp38+7t+zfw4MJ1CyguAADy5MqXM2/u/Dn06NKnAxBgXQCA7Nq3c+/u/Tv48OLHkwcQ4Dz68wDWs2/v/j38+PLn068/PwD+/PgB8O/vHyAAgQMJFjR4EGFChQsZEgzwEOJDABMpVrR4EWNGjRs5dtwYAGRIkABIljR5EmVKlStZtnT5EkAAmQEA1LR5E2dOnTt59vT5EyiAAEMDADB6FGlSpUuZNnX6FGrUpgGoVqUKAGtWrVu5dvX6FWxYsWADlDVbFkBatWvZtnX7Fm5cuXPjBrAbQEBeAQD49vX7F3BgwYMJFzZMOEDiAAIYC18A8BhyZMmTKVe2fBlzZs2bOXf2/Bl0aNGjSZc2fRp1atWrWbd2/Rp2bNmzade2fRt3bt27effuHAB4cOAAiBc3fhx5cuXLmTd3zjxAdOnRAVS3fh17du3buXf3/r16QAA7\"/><br/><center>Feature 5</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature6\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 6</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature7\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 7</center></td></tr><tr><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature8\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 8</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature9\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 9</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature10\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 10</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature11\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 11</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature12\" src=\"data:image/gif;base64,R0lGODdhYABgAIcAAPb6/gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAYABgAEAInAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhwyQDAgA7\"/><br/><center>Feature 12</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature13\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 13</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature14\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 14</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature15\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 15</center></td></tr><tr></tr></table></td>\n</tr>\n</table>"
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       "description": "Layer:",
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       "layout": "IPY_MODEL_98edd296784c4cdd8599e87f5961cce6",
       "rows": 1,
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     "957bec2fb1be44dabc5b36736ce68cf1": {
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       "value": "\n<table align=\"center\" style=\"width: 100%;\">\n <tr>\n  <td valign=\"top\"><p style=\"text-align:center\"><svg id='MNIST_CNN' xmlns='http://www.w3.org/2000/svg' image-rendering=\"pixelated\" width=\"800.0000000000001px\" height=\"190.47619047619048px\">\n <g  transform=\"rotate(90) translate(0 -800.0000000000001)\" >\n  <svg viewBox=\"0 0 350 1470\" width=\"190.47619047619048px\" height=\"800.0000000000001px\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><rect x=\"74.0\" y=\"24\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-935832_output_targets_1\" class=\"picture-dashboard-MNIST_CNN-935832_output_targets\" x=\"75.0\" y=\"25\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n output range: (0.0, 1.0)\n shape = (10,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"288.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 52.0) translate(2)\" >targets</text><line x1=\"87.5\" y1=\"80\" x2=\"262.5\" y2=\"80\" stroke=\"green\" stroke-width=\"2\"><title></title></line><rect x=\"74.0\" y=\"84\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-935832_output-rotated_1\" class=\"picture-dashboard-MNIST_CNN-935832_output-rotated\" x=\"75.0\" y=\"85\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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output (output)\n output range: (0.0, 1.0)\n shape = (10,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"288.0\" y=\"112.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 112.0) translate(2)\" >output</text><line x1=\"175.0\" y1=\"164\" x2=\"175.0\" y2=\"137\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden to output\n output/kernel has shape (30, 10)\n output/bias has shape (10,)</title></line><rect x=\"74.0\" y=\"164\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-935832_hidden-rotated_2\" class=\"picture-dashboard-MNIST_CNN-935832_hidden-rotated\" x=\"75.0\" y=\"165\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhHgABAIcAAPT5/tjn9OPt9wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHgABAAAIGgADABgYAIAAAAgRBgAgAIDDhw4FBABAkWJAADs=\"><title>Layer: hidden (hidden)\n output range: (0.0, +Infinity)\n shape = (30,)\n Keras class = Dense\n activation = relu</title></image><text x=\"288.0\" y=\"192.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 192.0) translate(2)\" >hidden</text><line x1=\"175.0\" y1=\"244\" x2=\"175.0\" y2=\"217\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from flat to hidden\n hidden/kernel has shape (512, 30)\n hidden/bias has shape (30,)</title></line><rect x=\"74.0\" y=\"244\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-935832_flat-rotated_3\" class=\"picture-dashboard-MNIST_CNN-935832_flat-rotated\" x=\"75.0\" y=\"245\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhAAIBAIcAAPv6/efn/9fX/8jI/7e3/6mp/5aW/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAAAIBAAAI/wABCAQgIIBBAQAEAFgIIACAhxAfBpgIAAABABgzFhAgAACBAAUAAAggIACAkyhPClgZAAABAQBixjQgQACAAgAKAAAQQEAAAECDAg1AIEAAAAQEAFi6dACAAAAMBBgAAEAAAQAABADAtWuAAgECABAgAEAAAGgFCAgAQACAAQDiygUQAIBduwEEBAAAQACAAAACAxgQQACAAQAIAAgAAEAAAJAjAxAQYMAAAAEIANjMeYCAAAAEACAAIACAAABSq1YdQMAAAAEEAJhNWwCA2wEACAAAIIAAAMADCAAQAACAAAICAAAgAIDz5wICDABAPQCA69cDANjOHUAAAAEACNAQAKC8eQICBgAYAEAAgPcBBACYT39+gAACAgQQAKC/f4ADAgQAQADAAAAJAQQA0NBhwwADBAQIQADARYwDAgQAQCDAAAAABAwAACAAgAAAVAIQMCCAAAACBACgCSDAAAECAggAEADAzwAAhA4dOgDA0QACBABgyrTAgAAACAAoAADAAAEAtG4NAADAALAAABQIEADAWQAFCAQAUABAAQAABggAACAAALx5BQwgEAAAgQAAAAQAEGAAAMQFAgwAECCAAACRJU8eICAAAAECAAQEADs=\"><title>Layer: flat (hidden)\n output range: (-Infinity, +Infinity)\n Keras class = Flatten</title></image><text x=\"288.0\" y=\"272.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 272.0) translate(2)\" >flat</text><line x1=\"175.0\" y1=\"324\" x2=\"175.0\" y2=\"297\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from maxpool2 to flat</title></line><rect x=\"74.0\" y=\"324\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-935832_maxpool2-rotated_4\" class=\"picture-dashboard-MNIST_CNN-935832_maxpool2-rotated\" x=\"75.0\" y=\"325\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: maxpool2 (hidden)\n output range: (-Infinity, +Infinity)\n Keras class = MaxPooling2D</title></image><text x=\"288.0\" y=\"427.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 427.0) translate(2)\" >maxpool2</text><text x=\"88.0\" y=\"310\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 310) translate(2)\" >32</text><text x=\"273.0\" y=\"540\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 540) translate(2)\" >0</text><line x1=\"175.0\" y1=\"554\" x2=\"175.0\" y2=\"527\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from conv2D_2 to maxpool2</title></line><rect x=\"74.0\" y=\"554\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-935832_conv2D_2-rotated_5\" class=\"picture-dashboard-MNIST_CNN-935832_conv2D_2-rotated\" x=\"75.0\" y=\"555\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: conv2D_2 (hidden)\n output range: (0.0, +Infinity)\n dropout = 0.2\n Keras class = Conv2D\n activation = relu</title></image><text x=\"288.0\" y=\"657.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 657.0) translate(2)\" >conv2D_2</text><text x=\"88.0\" y=\"540\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 540) translate(2)\" >32</text><text x=\"273.0\" y=\"770\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 770) translate(2)\" >0</text><text x=\"62.0\" y=\"560\" font-family=\"monospace\" font-size=\"24.0\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 62.0 560) translate(2)\" >&#10683;</text><line x1=\"175.0\" y1=\"784\" x2=\"175.0\" y2=\"757\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from maxpool1 to conv2D_2\n conv2D_2/kernel has shape (5, 5, 16, 32)\n conv2D_2/bias has shape (32,)</title></line><rect x=\"74.0\" y=\"784\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-935832_maxpool1-rotated_6\" class=\"picture-dashboard-MNIST_CNN-935832_maxpool1-rotated\" x=\"75.0\" y=\"785\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: maxpool1 (hidden)\n output range: (-Infinity, +Infinity)\n Keras class = MaxPooling2D</title></image><text x=\"288.0\" y=\"887.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 887.0) translate(2)\" >maxpool1</text><text x=\"88.0\" y=\"770\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 770) translate(2)\" >16</text><text x=\"273.0\" y=\"1000\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 1000) translate(2)\" >0</text><line x1=\"175.0\" y1=\"1014\" x2=\"175.0\" y2=\"987\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from conv2D_1 to maxpool1</title></line><rect x=\"74.0\" y=\"1014\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-935832_conv2D_1-rotated_7\" class=\"picture-dashboard-MNIST_CNN-935832_conv2D_1-rotated\" x=\"75.0\" y=\"1015\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGAAYAIcAAPT5/uHt99zp9QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGAAYAEAI/wABCBxIsKDBgwMDAFjIMAAAAAECAJhIsSIAAQAyaty4UQCAAABCAggAoKTJkyhTnhQAoCWAAABiygQQIACAmzhz6tyZMwCAn0CDCh1K9GcAAEiTJg0QAIDTp1CdCggAoGpVAQIAaAUgIACAr2DDAgggQACAs2jTBggAoK3bt3Djvg0AoK7du3jz6t3Lt69fvwEACB5MuLBhwQEAKF68WACAAAAiS54MQIBlAJgxCwDAGYAAAKBDiw4QAIBpAAEEAFjNWgCA17BjB5gNIACA27hxCwDAu3dvAQEACB9OfLiAAACSKwcQAIDz59ChCwAQAID169izawcQAID37+DDiwYfT748+IAAOw==\"><title>Layer: conv2D_1 (hidden)\n output range: (0.0, +Infinity)\n dropout = 0.2\n Keras class = Conv2D\n activation = relu</title></image><text x=\"288.0\" y=\"1117.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 1117.0) translate(2)\" >conv2D_1</text><text x=\"88.0\" y=\"1000\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 1000) translate(2)\" >16</text><text x=\"273.0\" y=\"1230\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 1230) translate(2)\" >0</text><text x=\"62.0\" y=\"1020\" font-family=\"monospace\" font-size=\"24.0\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 62.0 1020) translate(2)\" >&#10683;</text><line x1=\"175.0\" y1=\"1244\" x2=\"175.0\" y2=\"1217\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to conv2D_1\n conv2D_1/kernel has shape (5, 5, 1, 16)\n conv2D_1/bias has shape (16,)</title></line><rect x=\"74.0\" y=\"1244\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_CNN-935832_input-rotated_8\" class=\"picture-dashboard-MNIST_CNN-935832_input-rotated\" x=\"75.0\" y=\"1245\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input (input)\n output range: (0.0, 1.0)\n shape = (28, 28, 1)\n Keras class = Input</title></image><text x=\"288.0\" y=\"1347.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 1347.0) translate(2)\" >input</text><text x=\"88.0\" y=\"1230\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 1230) translate(2)\" >1</text><text x=\"273.0\" y=\"1460\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 1460) translate(2)\" >0</text><text x=\"18\" y=\"735.0\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 18 735.0) translate(2)\" >MNIST_CNN</text></svg></g></svg></p></td>\n</tr>\n<tr>\n  <td valign=\"top\" align=\"center\"><p style=\"text-align:center\"><b>conv2D_1 features</b></p><table><tr><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature0\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 0</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature1\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 1</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature2\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 2</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature3\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 3</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature4\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 4</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature5\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 5</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature6\" src=\"data:image/gif;base64,R0lGODdhYABgAIcAAPX6/gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAYABgAEAInAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhwyQDAgA7\"/><br/><center>Feature 6</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature7\" src=\"data:image/gif;base64,R0lGODdhYABgAIcAAPX5/gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAYABgAEAInAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhwyQDAgA7\"/><br/><center>Feature 7</center></td></tr><tr><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature8\" src=\"data:image/gif;base64,R0lGODdhYABgAIcAAPb6/gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAYABgAEAInAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhwyQDAgA7\"/><br/><center>Feature 8</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature9\" src=\"data:image/gif;base64,R0lGODdhYABgAIcAAPb6/gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAYABgAEAInAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhwyQDAgA7\"/><br/><center>Feature 9</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature10\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 10</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature11\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 11</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature12\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 12</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature13\" src=\"data:image/gif;base64,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\"/><br/><center>Feature 13</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature14\" src=\"data:image/gif;base64,R0lGODdhYABgAIcAAPb6/gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAYABgAEAInAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhwyQDAgA7\"/><br/><center>Feature 14</center></td><td style=\"border: 1px solid black;\"><img style=\"image-rendering: pixelated;\" class=\"MNIST_CNN_conv2D_1_feature15\" 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15</center></td></tr><tr></tr></table></td>\n</tr>\n</table>"
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output (output)\n output range: (-1.0, 1.0)\n shape = (10,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"288.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 52.0) translate(2)\" >targets</text><line x1=\"87.5\" y1=\"80\" x2=\"262.5\" y2=\"80\" stroke=\"green\" stroke-width=\"2\"><title></title></line><rect x=\"74.0\" y=\"84\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-688544_output-rotated_6\" class=\"picture-dashboard-MNIST_MLP-688544_output-rotated\" x=\"75.0\" y=\"85\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n output range: (-1.0, 1.0)\n shape = (10,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"288.0\" y=\"112.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 112.0) translate(2)\" >output</text><line x1=\"175.0\" y1=\"164\" x2=\"175.0\" y2=\"137\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden2 to output\n output/kernel:0 has shape (30, 10)\n output/bias:0 has shape (10,)</title></line><rect x=\"74.0\" y=\"164\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-688544_hidden2-rotated_7\" class=\"picture-dashboard-MNIST_MLP-688544_hidden2-rotated\" x=\"75.0\" y=\"165\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhHgABAIcAAP/9/Xt7/9XV/wAAhDAw/1lZ/2Fh/+np/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHgABAAAIIAADABgIwIAAAAAKAFh4AIBDhwMABAAQAAAAAQQAAAgIADs=\"><title>Layer: hidden2 (hidden)\n output range: (-1.0, 1.0)\n shape = (30,)\n Keras class = Dense\n activation = relu</title></image><text x=\"288.0\" y=\"192.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 192.0) translate(2)\" >hidden2</text><line x1=\"175.0\" y1=\"244\" x2=\"175.0\" y2=\"217\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden1 to hidden2\n hidden2/kernel:0 has shape (30, 30)\n hidden2/bias:0 has shape (30,)</title></line><rect x=\"74.0\" y=\"244\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-688544_hidden1-rotated_8\" class=\"picture-dashboard-MNIST_MLP-688544_hidden1-rotated\" x=\"75.0\" y=\"245\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhHgABAIcAAP/9/ZeX/wAAsQAAyouL/wAAmigo/zU1/11d/3l5/6Sk/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHgABAAAIIgALGAAAYACAgwEEAAAgAEACBAACBFAwgAAAAAcAaCQQICAAOw==\"><title>Layer: hidden1 (hidden)\n output range: (-1.0, 1.0)\n shape = (30,)\n Keras class = Dense\n activation = relu</title></image><text x=\"288.0\" y=\"272.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 272.0) translate(2)\" >hidden1</text><line x1=\"175.0\" y1=\"324\" x2=\"175.0\" y2=\"297\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from flat_input to hidden1\n hidden1/kernel:0 has shape (784, 30)\n hidden1/bias:0 has shape (30,)</title></line><rect x=\"74.0\" y=\"324\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-688544_flat_input-rotated_9\" class=\"picture-dashboard-MNIST_MLP-688544_flat_input-rotated\" x=\"75.0\" y=\"325\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: flat_input (hidden)\n output range: (0.0, 1.0)\n Keras class = Flatten</title></image><text x=\"288.0\" y=\"352.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 352.0) translate(2)\" >flat_input</text><line x1=\"175.0\" y1=\"404\" x2=\"175.0\" y2=\"377\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to flat_input</title></line><rect x=\"74.0\" y=\"404\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-MNIST_MLP-688544_input-rotated_10\" class=\"picture-dashboard-MNIST_MLP-688544_input-rotated\" x=\"75.0\" y=\"405\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhHAAcAIcAAAAAAPv7+6OjoxYWFtbW1iYmJurq6kdHR1paWomJibi4uMvLy2xsbJqamnR0dDg4OAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAHAAcAEAI/wABCBxIsKDBgwgJGgjQAIBDBAEEAJhIkaICAw8AaNzIcWOBAAESABhJsqTJkyhTqgQgIIDLAA4GAJhJs6bNmzQLEAjAkyeAn0CDAhhQgIECBQYCKF26AAGAp1CjSp1KtapVqA4CLAAAYMCCAAIAiB1LtmzZAQkCBCBQAICCAAcAyJ1Lt65dugcCBADAt29fAQECBzAgoLDhwgESCwDAuLHjx5AjS55MubLly5YLCNjMAIDnz6BDiwaAIICBAwgCBFgwAIDr17Bjw2YQIMABALgXEEAAoLfv38B/JwgQoAEAAAUMGBgAoLnz59ChJwhAvQGBAAkAaN/Ovbt37QgcEFUIEACA+fPmHxQAwL69+/cMAhAAQL8+fQUBAhBIcGAAAIAABA4EUIBAgAAHACxk2LABgQARJU6UKADARYwZNWIc0NHjAAAhRY4kWdLkSZQpVa5kOTIgADs=\"><title>Layer: input (input)\n output range: (0.0, 1.0)\n shape = (28, 28, 1)\n Keras class = Input</title></image><text x=\"288.0\" y=\"507.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 507.0) translate(2)\" >input</text><text x=\"88.0\" y=\"390\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 88.0 390) translate(2)\" >1</text><text x=\"273.0\" y=\"620\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 273.0 620) translate(2)\" >0</text><text x=\"18\" y=\"315.0\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 18 315.0) translate(2)\" >MNIST_MLP</text></svg></g></svg></p>"
      }
     },
     "af22e8fa11db4baba172d6a0e8416871": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "af647b9ef78844b68160ad1034f14d03": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "b04079a3437a4877b54f9552364409b7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "b0c282c29508432694cfe6223f33579c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HBoxModel",
      "state": {
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