03_HandwrittenDigitsRecognition_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handwritten digits recognition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loader for the MNIST image data was taken from Nielsen's online book,\n",
    "\n",
    "http://neuralnetworksanddeeplearning.com/chap1.html\n",
    "\n",
    "See specifically the following link, for downloading the MNIST image data (we only need the mnist.pkl.gz package inside the 'data' subdirectory; store it inside the present directory of the notebook):\n",
    "https://github.com/mnielsen/neural-networks-and-deep-learning/archive/master.zip\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Import keras library. Also import some of the layers, so we do not need to\n",
    "# write things like \"layers.Dense\", but can just write \"Dense\" instead\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import GaussianDropout\n",
    "import tensorflow.keras.optimizers as optimizers\n",
    "\n",
    "# Import the numpy library for matrix manipulations etc.\n",
    "\n",
    "#from numpy import *\n",
    "from numpy import array, zeros, exp, random, dot, shape, transpose, reshape, meshgrid, linspace, sqrt\n",
    "\n",
    "# Set up the graphics by importing the matplotlib plotting library\n",
    "import matplotlib.pyplot as plt # for plotting\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.dpi']=300 # highres display\n",
    "\n",
    "# Set up a random number generator with a fixed seed, so that\n",
    "# running this whole notebook repeatedly should always give\n",
    "# the same result (useful for debugging)\n",
    "rng = random.RandomState(23455)\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "mnist_loader\n",
    "~~~~~~~~~~~~\n",
    "\n",
    "taken from Nielsen's online book:\n",
    "http://neuralnetworksanddeeplearning.com/chap1.html\n",
    "\n",
    "\n",
    "A library to load the MNIST image data.  For details of the data\n",
    "structures that are returned, see the doc strings for ``load_data``\n",
    "and ``load_data_wrapper``.  In practice, ``load_data_wrapper`` is the\n",
    "function usually called by our neural network code.\n",
    "\"\"\"\n",
    "\n",
    "#### Libraries\n",
    "# Standard library\n",
    "import pickle\n",
    "import gzip\n",
    "\n",
    "def load_data():\n",
    "    \"\"\"Return the MNIST data as a tuple containing the training data,\n",
    "    the validation data, and the test data.\n",
    "\n",
    "    The ``training_data`` is returned as a tuple with two entries.\n",
    "    The first entry contains the actual training images.  This is a\n",
    "    numpy ndarray with 50,000 entries.  Each entry is, in turn, a\n",
    "    numpy ndarray with 784 values, representing the 28 * 28 = 784\n",
    "    pixels in a single MNIST image.\n",
    "\n",
    "    The second entry in the ``training_data`` tuple is a numpy ndarray\n",
    "    containing 50,000 entries.  Those entries are just the digit\n",
    "    values (0...9) for the corresponding images contained in the first\n",
    "    entry of the tuple.\n",
    "\n",
    "    The ``validation_data`` and ``test_data`` are similar, except\n",
    "    each contains only 10,000 images.\n",
    "\n",
    "    This is a nice data format, but for use in neural networks it's\n",
    "    helpful to modify the format of the ``training_data`` a little.\n",
    "    That's done in the wrapper function ``load_data_wrapper()``, see\n",
    "    below.\n",
    "    \"\"\"\n",
    "    f = gzip.open('mnist.pkl.gz', 'rb')\n",
    "    training_data, validation_data, test_data = pickle.load(f,encoding='bytes')\n",
    "    f.close()\n",
    "    return (training_data, validation_data, test_data)\n",
    "\n",
    "def load_data_wrapper():\n",
    "    \"\"\"Return a tuple containing ``(training_data, validation_data,\n",
    "    test_data)``. Based on ``load_data``, but the format is more\n",
    "    convenient for use in our implementation of neural networks.\n",
    "\n",
    "    In particular, ``training_data`` is a list containing 50,000\n",
    "    2-tuples ``(x, y)``.  ``x`` is a 784-dimensional numpy.ndarray\n",
    "    containing the input image.  ``y`` is a 10-dimensional\n",
    "    numpy.ndarray representing the unit vector corresponding to the\n",
    "    correct digit for ``x``.\n",
    "\n",
    "    ``validation_data`` and ``test_data`` are lists containing 10,000\n",
    "    2-tuples ``(x, y)``.  In each case, ``x`` is a 784-dimensional\n",
    "    numpy.ndarry containing the input image, and ``y`` is the\n",
    "    corresponding classification, i.e., the digit values (integers)\n",
    "    corresponding to ``x``.\n",
    "\n",
    "    Obviously, this means we're using slightly different formats for\n",
    "    the training data and the validation / test data.  These formats\n",
    "    turn out to be the most convenient for use in our neural network\n",
    "    code.\"\"\"\n",
    "    \n",
    "    global training_inputs, training_results\n",
    "    global validation_inputs, validation_results\n",
    "    global test_inputs, test_results\n",
    "    global num_samples, numpixels, num_test_samples\n",
    "    \n",
    "    tr_d, va_d, te_d = load_data()\n",
    "    \n",
    "    num_samples=len(tr_d[0])\n",
    "    training_inputs=zeros([num_samples,numpixels])\n",
    "    training_results=zeros([num_samples,10])    \n",
    "    for j in range(num_samples):\n",
    "        training_inputs[j,:] = reshape(tr_d[0][j], (numpixels))\n",
    "        training_results[j,:] = vectorized_result(tr_d[1][j])\n",
    "#    validation_inputs = [reshape(x, (numpixels)) for x in va_d[0]]\n",
    "#    validation_results = [vectorized_result(y) for y in va_d[1]]\n",
    "\n",
    "    num_test_samples=len(te_d[0])\n",
    "    test_inputs=zeros([num_test_samples,numpixels])\n",
    "    test_results=zeros([num_test_samples,10])    \n",
    "    for j in range(num_test_samples):\n",
    "        test_inputs[j,:] = reshape(te_d[0][j], (numpixels))\n",
    "        test_results[j,:] = vectorized_result(te_d[1][j])\n",
    "\n",
    "def vectorized_result(j):\n",
    "    \"\"\"Return a 10-dimensional unit vector with a 1.0 in the jth\n",
    "    position and zeroes elsewhere.  This is used to convert a digit\n",
    "    (0...9) into a corresponding desired output from the neural\n",
    "    network.\"\"\"\n",
    "    e = zeros((10))\n",
    "    e[j] = 1.0\n",
    "    return e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# initialize the network\n",
    "def init_net():\n",
    "    global net, numpixels\n",
    "    net = Sequential()\n",
    "    # note: batch_input_shape is (batchsize,timesteps,data_dim)\n",
    "    net.add(Dense(30, input_shape=(numpixels,), activation='relu'))\n",
    "    net.add(Dense(10, activation='softmax'))\n",
    "    net.compile(loss='categorical_crossentropy', optimizer=optimizers.SGD(lr=1.0), metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# initialize the network\n",
    "def init_net_large():\n",
    "    global net, numpixels\n",
    "    net = Sequential()\n",
    "    # note: batch_input_shape is (batchsize,timesteps,data_dim)\n",
    "    net.add(Dense(100, input_shape=(numpixels,), activation='relu'))\n",
    "    net.add(GaussianDropout(0.1))\n",
    "    net.add(Dense(50, activation='relu'))\n",
    "    net.add(Dense(10, activation='softmax'))\n",
    "    net.compile(loss='categorical_crossentropy', optimizer=optimizers.SGD(lr=1.0), metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def test_on(start,stop,dontprint=False):\n",
    "    global test_inputs, test_results\n",
    "    global net, predictions_probs, predictions, true_labels\n",
    "    \n",
    "    predictions_probs=net.predict_on_batch(test_inputs[start:stop,:])\n",
    "    predictions=argmax(predictions_probs,axis=1)\n",
    "    if dontprint==False:\n",
    "        print(\"Predictions: \", predictions)\n",
    "    true_labels=argmax(test_results[start:stop,:], axis=1)\n",
    "    if dontprint==False:\n",
    "        print(\"True labels: \", true_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_image(which):\n",
    "    global test_inputs\n",
    "    \n",
    "    plt.imshow(reshape(test_inputs[which,:],[28,28]),interpolation='nearest', cmap='binary')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_image_array(which):\n",
    "    global test_inputs\n",
    "    \n",
    "    numcolumns=8\n",
    "    BigImage=zeros([28*numcolumns,28*numcolumns])\n",
    "    for j in range(len(which)):\n",
    "        x=(j%numcolumns)*28\n",
    "        y=int(j/numcolumns)*28\n",
    "        BigImage[x:x+28,y:y+28]=reshape(test_inputs[which[j],:],[28,28])\n",
    "    plt.imshow(BigImage,interpolation='nearest', cmap='binary')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_mistakes(maxnum):\n",
    "    global test_inputs, rest_results, num_test_samples\n",
    "    global true_labels, predictions, predictions_probs\n",
    "    \n",
    "    test_on(0,num_test_samples,dontprint=True)\n",
    "    which=where(true_labels!=predictions)[0]\n",
    "    for j in which:\n",
    "        if j<maxnum:\n",
    "            display_image(j)\n",
    "            print(\"True \", true_labels[j], \" - Predicted \", predictions[j], \" with prob. \", predictions_probs[j,predictions[j]])\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test out things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "numpixels=784\n",
    "load_data_wrapper() # load all the MNIST images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 784)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(training_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "psi=training_inputs-sum(training_inputs,axis=0)/num_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x13d5fe940>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AoJTYAAAAAJQSGwAAAIBSYgMAAABQSmwAAAAASokNAAAAQCmxAQAAACglNgAA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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d54c940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(reshape(psi[4,:],[28,28]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 784)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(psi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e751550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_image_array(range(8*8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 784)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(training_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "init_net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pVyW5J8n+JGckeUSSr0zygHWufVeSHx7Bmmig7YqEQW2Ilofoob//SO/E6RqM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\n0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3MjqqkiAQDevWYRfrn+EPb3JCfdbsHURtz00sWn/a4wqaDTc/v7Tv23qcBDxoeARr+W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\nou5q4O8anCO+Su1hlxb6O5ji4r2+/fnArqV6C4E3Nej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GxK71lSJiYUT8P+CvSu1fn5n3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\nJEmSJEmq9P8BXoab4RM6o24AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x140074f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batchsize=100\n",
    "batches=int(num_samples/batchsize)-1\n",
    "costs=zeros(batches)\n",
    "for j in range(batches):\n",
    "    costs[j]=net.train_on_batch(training_inputs[j*batchsize:(j+1)*batchsize,:], training_results[j*batchsize:(j+1)*batchsize,:])[0]\n",
    "plt.plot(costs,linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:  [7 3 1 0 4 1 4 4 6 9 0 8 9 0 1 5 9 7 3 4]\n",
      "True labels:  [7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\n"
     ]
    }
   ],
   "source": [
    "test_on(0,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.3496 - categorical_accuracy: 0.8998 - val_loss: 0.3399 - val_categorical_accuracy: 0.9046\n",
      "Epoch 2/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2975 - categorical_accuracy: 0.9142 - val_loss: 0.3400 - val_categorical_accuracy: 0.9046\n",
      "Epoch 3/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2821 - categorical_accuracy: 0.9188 - val_loss: 0.3549 - val_categorical_accuracy: 0.8960\n",
      "Epoch 4/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2676 - categorical_accuracy: 0.9228 - val_loss: 0.3080 - val_categorical_accuracy: 0.9166\n",
      "Epoch 5/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2619 - categorical_accuracy: 0.9240 - val_loss: 0.3057 - val_categorical_accuracy: 0.9166\n",
      "Epoch 6/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2485 - categorical_accuracy: 0.9276 - val_loss: 0.3407 - val_categorical_accuracy: 0.9024\n",
      "Epoch 7/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2432 - categorical_accuracy: 0.9298 - val_loss: 0.3227 - val_categorical_accuracy: 0.9174\n",
      "Epoch 8/30\n",
      "450/450 [==============================] - 1s 3ms/step - loss: 0.2404 - categorical_accuracy: 0.9301 - val_loss: 0.3061 - val_categorical_accuracy: 0.9158\n",
      "Epoch 9/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2354 - categorical_accuracy: 0.9311 - val_loss: 0.3024 - val_categorical_accuracy: 0.9152\n",
      "Epoch 10/30\n",
      "450/450 [==============================] - 1s 3ms/step - loss: 0.2281 - categorical_accuracy: 0.9329 - val_loss: 0.3412 - val_categorical_accuracy: 0.9114\n",
      "Epoch 11/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2267 - categorical_accuracy: 0.9338 - val_loss: 0.3202 - val_categorical_accuracy: 0.9154\n",
      "Epoch 12/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2264 - categorical_accuracy: 0.9352 - val_loss: 0.3805 - val_categorical_accuracy: 0.8980\n",
      "Epoch 13/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2246 - categorical_accuracy: 0.9344 - val_loss: 0.3346 - val_categorical_accuracy: 0.9120\n",
      "Epoch 14/30\n",
      "450/450 [==============================] - 1s 3ms/step - loss: 0.2240 - categorical_accuracy: 0.9347 - val_loss: 0.4262 - val_categorical_accuracy: 0.8892\n",
      "Epoch 15/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2169 - categorical_accuracy: 0.9354 - val_loss: 0.3222 - val_categorical_accuracy: 0.9154\n",
      "Epoch 16/30\n",
      "450/450 [==============================] - 1s 3ms/step - loss: 0.2137 - categorical_accuracy: 0.9377 - val_loss: 0.3300 - val_categorical_accuracy: 0.9104\n",
      "Epoch 17/30\n",
      "450/450 [==============================] - 1s 3ms/step - loss: 0.2153 - categorical_accuracy: 0.9349 - val_loss: 0.3203 - val_categorical_accuracy: 0.9136\n",
      "Epoch 18/30\n",
      "450/450 [==============================] - 2s 5ms/step - loss: 0.2111 - categorical_accuracy: 0.9380 - val_loss: 0.3328 - val_categorical_accuracy: 0.9066\n",
      "Epoch 19/30\n",
      "450/450 [==============================] - 1s 3ms/step - loss: 0.2101 - categorical_accuracy: 0.9374 - val_loss: 0.3084 - val_categorical_accuracy: 0.9148\n",
      "Epoch 20/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2070 - categorical_accuracy: 0.9396 - val_loss: 0.4017 - val_categorical_accuracy: 0.8970\n",
      "Epoch 21/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2098 - categorical_accuracy: 0.9392 - val_loss: 0.3243 - val_categorical_accuracy: 0.9174\n",
      "Epoch 22/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2047 - categorical_accuracy: 0.9412 - val_loss: 0.3036 - val_categorical_accuracy: 0.9190\n",
      "Epoch 23/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2019 - categorical_accuracy: 0.9399 - val_loss: 0.3206 - val_categorical_accuracy: 0.9192\n",
      "Epoch 24/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2030 - categorical_accuracy: 0.9400 - val_loss: 0.3397 - val_categorical_accuracy: 0.9128\n",
      "Epoch 25/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.2059 - categorical_accuracy: 0.9396 - val_loss: 0.3245 - val_categorical_accuracy: 0.9170\n",
      "Epoch 26/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.1985 - categorical_accuracy: 0.9415 - val_loss: 0.3446 - val_categorical_accuracy: 0.9132\n",
      "Epoch 27/30\n",
      "450/450 [==============================] - 1s 3ms/step - loss: 0.1981 - categorical_accuracy: 0.9420 - val_loss: 0.3486 - val_categorical_accuracy: 0.9116\n",
      "Epoch 28/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.1986 - categorical_accuracy: 0.9413 - val_loss: 0.3496 - val_categorical_accuracy: 0.9162\n",
      "Epoch 29/30\n",
      "450/450 [==============================] - 1s 3ms/step - loss: 0.1973 - categorical_accuracy: 0.9412 - val_loss: 0.3348 - val_categorical_accuracy: 0.9164\n",
      "Epoch 30/30\n",
      "450/450 [==============================] - 1s 2ms/step - loss: 0.1983 - categorical_accuracy: 0.9424 - val_loss: 0.3255 - val_categorical_accuracy: 0.9218\n"
     ]
    }
   ],
   "source": [
    "# use the keras \"fit\" function to go through the whole data set many times ('epochs'), \n",
    "# and even set aside some validation samples\n",
    "history=net.fit(training_inputs,training_results,batch_size=100,\n",
    "                epochs=30,validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0676\n"
     ]
    }
   ],
   "source": [
    "# see which fraction of the test samples is classified incorrectly\n",
    "test_on(0,num_test_samples,dontprint=True)\n",
    "which=where(true_labels!=predictions)[0]\n",
    "print(len(which)/num_test_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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Tfh7vhNravcova1B+2fH/qx4SZCntaLARpQlJkcpIjFJmYpRS48IVHOSyo/3j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\nNC0vU6cO7Kb4mKigrgcAAAAAQCQhvAAAAGiB6ro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wpJQH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3T3Z9zdq+Avxezak5wG83MOWPAbXv0r8D/HqdMW8Zu27cJuCHG5hLJdr6YHp40ZbNumtteBoM\nLq1/Xa2Dj8D2W8pdR+rOi7kjMG9RuWuQJLVGkaeBxz9QS915sWAlLGyzckVtEV4U2XlheCHNqBDS\ny/SNB8XjiuyGtmyUJEmaRmXCixDCPOAVudO/X29cjPEu4IaaU33A60pc2mag9nGQo8CfNzj2z5jY\nl+KVIYQFdcb8WO74j2KMx6YbMPb1P6lzH7VY6s6Ls5fOZ2huf/0Lq6yntxqlo1Kf8nLXhSS1j9Sy\nUQC7vwNH9qU3jm23klEAw+vqX5M3OsNlo9x5IbWH1LB49H44XlMtOfXPXPDnckmSNK3KhBfA84HB\nmuObY4x3TnVxzvtzxy8rZ0kAPDN3fEuMsaGOgzHGPcDXa07NBV401fUhhCXAM2pOHQc+3OA6P0TW\nH2Tcs0IIixscqybtPHCUR/ZPmzGd4eJ2Lxk1brZLR50+nf6Ul9vTJal9LDoLehLD/l13dkezboA5\nC2Be4o98+7a1Zi1TSe15EXpg/vLWrEXS1FJ3XsDE3RfJZaMCjGxIn1OSJHWNKoUX+SbTX0oY+2Xg\nZM3xE0MIK5peUWZt7nhL4vjbcsdThhfADwK1vSq+GWM80MgkMcb9wLdqTvWN3U8zYOuO/cljLlnT\n5iWjxm28Mv1Dk/07YMc3y5n/4MNw8mj962r5hJcktY/ePlhybtqYXXd1R7Pucamlo2a8bNSDadcv\nWJn9d5c0s5YWadpdG14k7rxYuAb656bPKUmSukaVwovNueNpe0rUijEe4syQ4OKmV5TJfyrb0K6L\naa6/ZJprC38PxtyUOy7re6A6thRo1r15dYfsvOjthwuny+SmcPsN9a9phI0BJanzLTs/7fo998AD\nX69/XV67NeseV/nwIrFslCWjpNlRpMfQ7prWi3vvSxvrA0WSJKmOKoUXF+WO704cf0/ueFMTa6l1\nPHc8J3F8/voLQwhTfd/za67K90B1pPa7gA4qGwWw6dr0Mbd/EmJsfm4bA0pS50vte3H6JHz382lj\nhlbDUFkbd2dYanhxZC8ca2hzbzlSG3a3W9N0qVOMbIDegbQxu+/K/nnyGIw+kDbW8EKSJNVRifBi\nrDdDfodD6iNh+esLFOyc1KO549R3U/nr5wJTvcPM10SoyvdAdWx9MK1scklzWgAAIABJREFU1PrF\ngwzPa/Nm3bXOeibMTQxjRu8vVtIjr8jOC98oSVJ7WZq48wLgVFovqrYtGQXp4QXAvhlq2n3qBBx8\nJG3MwjWtWYuk6fX2weJz0sbsGgsv9m4DEh9M8oEiSZJUR1WKyY7kjg+PlYJKsTN3XNZj7Xfkjp+a\nOH6y66daW/77kP891dOS70EIYTmwLHFY4k+97WvPoePs2HckacwlnbTrAqBvAC54EfxXo/3lx9x+\nI6y5tLm5U3de9M3NamlLktpH6s6LItqxWfe4QuHF/bBiBjbpHnyE5A80h9x5Ic2apefBrvxb4Gk8\nejecOpne7wIs5SpJkuqqSnixIHec9knw5GOGCq4l799zx+eEEK6IMdbtRxFCuBKY7HGSqdbW7Peh\nVd+DNwJvL+leHadIv4uLO6VZd61N1xQIL26A574DQig+b+rOi0UboacSm84kSY1aci6EHoinWzdH\nO++8GF6XPmZ0hnZepDbrBndeSLNp2QVnPr43ndMnYN+2gqVcDS8kSdL0qvIJXv5D+6MF7pH/4D5/\nz0JijA8DX8yd/sMQQu9040IIfcAfTvHlRsOL1O9DS74Hml6Rfhcdt/MC4Jxnw0BiXrb3Pnj4283N\nm/pGySe8JKn99M/NwudWatdm3QAjBcKLfdvKX8dkCoUXNuyWZs3SAk27d32nWClXfy6XJEl1VCW8\nyCvSxbeEzr9T+t3c8RXA/xdCmLR5dwhhLvAh4ClT3K/RxwZTf0+t/B5oClsfTA8vNq/uwPCibw5c\n8ML0cbffWHzOI/uypqMpfMJLktpTkQ/UGrVwLSxIrZBZIXOHYW6++mgd+1JbqxVUKLywbJQ0a5YW\naJu4+670slGDS2FuB+5GlyRJpapKeHEwdzyvwD3yY/L3LCzG+G/An+ZOvxa4I4TwSyGEy0MI5439\n85fJNtq+auy6o5zZ9HvfFFM1+31o1ffgL4HNib+uKWnuykvdebFmZB6L5g+0aDWzbFOB/+xbb4BY\nMHcrsj3dJ7wkqT0ta2F40c79Lsal7r6YqYbd+3ekjxly54U0a4qGF6k/l9usW5IkNaAqPS8qHV6M\n+WWyptWvrTl3FvAH04w5Dfzo2DVLas5PF14sqjmuRHgRY9xJYvPw0EwPgzYyevgE2/ektSbZ3In9\nLsad+xzonw8nDjU+Zs89sPN2WHFx+nxFtqe780KS2lMrm3a3c7+LcSMb4OHbGr9+pnZeHHgo7fp5\ni7MyYZJmx8B8GF4Powl/Ruy8HfYmlqLzZ3JJktSAquy8yD+6PhhCmJ94j+W546kCgkJijCdijK8D\nfhHY08CQ7wHPAf4ByO99f2SKMfnvQ2r9gpZ+D3SmLQVKRnVkv4tx/fPg/OenjytaOip1ezq480KS\n2tWy81t3747YebE+7frDu+F4wsMGRaWWjbJZtzT7UndfPPRfWePuFO68kCRJDahEeBFjfBTIF65P\nfAfGhtzxd4uvaGoxxj8m23HxM8A/AvcCB8jKQ90LfJpst8XFMcYvAecAtTWC7o4xThUq5Nec/z3V\nMyPfAz1mS4Fm3Rd3cngBxUpHFQ0vUrenh570D3ckSdWwtIXhxapO2HlR4O+3mSgdlRxe2O9CmnWp\nZfpioy0da/hAkSRJakAlwosxd+SOz00cn390I3+/0sQY98cY3xtjfHmM8ZwY48IY47yxf39xjPHv\nYozjtYSuyA2/ZZpbt833QJnUfhfQoc26a533POgfTBuz607YeWf6XHvuS7t+4Vro69B+I5LU6eYM\nZX+Ol214PcxfUv+6qhtO7HkBMNri8OL06fSyUQvtdyHNuiJ9L1JZNkqSJDWgSuHFltxx/kP/KY2V\nmHpcnfvNlufkjr80zbWFvwdjnl7nfipZBAb6Gn8ZrVw4l2VDc1q3oCoYGITzfjB93B2fTB+T3Bhw\nY/ockqTqaEXT7k4oGQUFd14k1qhPdfhROHU8bYxlo6TZt7QFf9bmWTZKkiQ1oCoNuwE+C7yh5viq\nhLHPYOLv5dYY41R9JWZMCGEIeEXNqYPAR6cZ8nngFNA7dnxZCGEoxnigwbkurTl1cux+aqG/eN2l\nnDh1mrt3HmTLjlG27Bjlth2j3P7Qfo6eOHP79OZOLxk1btM16aWgbr8RnvWrjV9/4mh6KQrfJElS\ne1t2AdzzxXLv2QnNuqFgeNHipt0HEv+eBhiybJQ061oRFNcaGILBDtjxJkmSWq5K4cXngCPAvLHj\nK0IIF8YYG6klc13u+J/KXFgT/gdQ23j8w9MFETHG3SGErwDPGjs1ALwOeG8Dc/0w0F9z/B8xxkYa\ni6tJ/b09XLRqIRetWsgrn5SVbDh1OnLProPfDzO27tjP1gdH2bxm4Syvdoac9zzomwsnjzY+5pEt\nsPtuWNpgtbR928j2viSwtq4ktTd3Xkxt3gjMWQjH9jc+ptU9L1IfMgDLRklVMLgE5i2CI/m2lCVZ\nfBaE0Jp7S5KkjlKZ8CLGeDiE8AmyZtfjfg348enGhRDOB15ac+ok8OHyV5gmhHAh8Bs1pw4Cv9fA\n0P/NY+EFwC+GED4QYzw2zVxzgDflTn+w0bWqfL09gfNXDHH+iiFedmlWn/v06cixkwWa2bWjOUNw\n7nPhzk+njbvjRnjGLzV27Z7EklFgbV1JanetKGWyqkPCC8h2XzySUDW01TsvDC+k9hRC9uft9q+1\n5v7+TC5JkhpUpZ4XAO8ATtQcXxdCeMlUF4cQ5gLvJ9uhMO59McZ7ppskhBBzv66qt7AQQsNBTwjh\nAuALPLaLBODXY4yNFBb+IPCdmuMLqB96vHPsunG3Ax9qYC7NoJ6ewLyB3voXdopN16SPSSk1ldrv\nAtx5IUntruydF4s2wuDicu85m1JLRxleSJpKK5t2W8pVkiQ1qFLhRYzxXuBPcqc/EUL47yGE2oCC\nEMJFwBeBp9WcfhS4vkXL+0AI4UMhhKtDCPMmuyCEsDqE8A7gVqC22+BngL9oZJIY4yngl5lYD+fN\nIYT3hhAmFAYNISwNIfw18Iu1twB+aew+0uw5//nQO1D/uloP/VfjOyrceSFJ3WdwMcxfVt79OmnX\nBcDwurTrD+2EE0dasxZIDy8GFmSlryTNvlb2vfCBIkmS1KDKlI2q8RbgYuCFY8f9wJ8BvxVC+BZw\nADibrDl1baHM48BLY4wPtWhdc4GXk/WgOBFCuAO4b2w9I8B6YHNuTQD/Arwqxthwcf4Y46dDCL8J\n/G7N6TcAPxpCuAV4GFgFPIWJuzsA3hJj/Gyjc0ktM3cYzvkBuCvxf8c7PglP/4X616XuvBhcmpWz\nkiS1t2UXwqFd5dyrU5p1jyvStHv0gdY9YZ3asHtolXXwpapoRZm+cT5QJEmSGlSpnRfw/Z0HrwI+\nmvvScuAFwCuBy5gYEuwErokxfnlGFpkFKo8DXkLWKPtFwCW5NZ0kK+f0ohjj4dQJYoy/B7yViWW0\n5gFXAa8h64tRG1ycIAsu3p06l9QyrSwdlbrzwjdJktQZynwauFOadY8rEl7sa6SqaUGpOy8sGSVV\nh2WjJElSBVQuvACIMR6MMb6GLKiYrkvYHuCvgM0zsNvg74F/BY7Wue4A8L6xNb01xniy6IQxxncC\nlwM3ku0smczxsa8/Jcb4+0XnklrighdCT+IGrx3frF+D+/Qp2Htf2n3dni5JnaHMp4FXPb68e1VB\nofCihX0v9iduiDa8kKpjZD30zS3/vr1zYMjXuiRJakwVy0Z9X4zxE2Q9L84iKxO1GphPVjZpG/DV\nGONUH+pPd9/k/eg1axkg23VxHlnppkGyXRY7yRplfzPGeGLKG6XPeytwbQhhEVl/jzXAErL+HjuA\nm2KMe8uaTyrVvEVw9lVw9xfSxt3xKbji56b++v4dcDrxZebOC0nqDGXtvFh8dvb3VCcpFF5sL38d\nAEf3w/EDaWMML6Tq6OmFJefCI1vKve+ijdBTyWcoJUlSBVU6vBgXY/weUKA7b/nGwpJvjP2ayXn3\nkjX+ltrLpmvSw4vbb5w+vCjSrNudF5LUGcoKLzqtWTdkYczAAjh+sPExrdp5caBAGzrDC6lalp5f\nfnjhA0WSJCmBjzxIaq0LXgShN23M9lumr5Od2qwbfKMkSZ1iwQqYO9z8fTqtWTdkza5Td1+0KrzY\nvyN9jKVkpGops8fQOPtdSJKkBIYXklpr/hI46xnp4+741NRfK7LzwjdKktQZQiin70UnhhcAw+vS\nrh9tUdmo1Gbd4M4LqWpa0bTb3dCSJCmB4YWk1tt0TfqY22+c+mupOy/658P8ZelrkCRVUxlPA696\nXPP3qKLUnRcHHoKTx8pfR2qzbjC8kKqmjKA4z93QkiQpgeGFpNa78GoIiX/cbLsJDjwy+ddSd14s\nPit7UleS1BmaDS+WnFtO6akqKtK0e/SB8teRWjaqpx8Gl5a/DknFLTkHKPlnaHdDS5KkBIYXklpv\nwXLY8PTEQRHu+OQkpyPsvS/tVos2Js4tSaq0ZRc2N74Tm3WPG0ksGwWwb1v560ht2D20Cnp8ayJV\nSv88WLShvPuFnvTSdpIkqav5DkHSzCirdNThPXBsf9p93J4uSZ1l6fnNje/UfhdQbOfFvhb0vUjd\neWHJKKmayiwdNbwO+gbKu58kSep4hheSZsaFV5O87XzbV+HgronnUvtdgI0BJanTDK+D/sHi4zs6\nvCjwlPS++8tfR2rDbsMLqZrKbNrtA0WSJCmR4YWkmbFwFax/atqYeBru/PTEc6n9LsA3SpLUaXp6\nmvhALXRus26AwSXpwU7Z4cWJo3D40bQxhhdSNTXbY6iW/S4kSVIiwwtJM6eM0lHuvJAkQfG+F0vP\ngzlD5a6lSkJIryk/WnLZqNR+F2B4IVVVs2X6avkzuSRJSmR4IWnmXPTi9DHf+4+sz8W4Pfemje/p\nszGgJHWiok8Dd3LJqHGpfS/K3nlRJLwYWlXuGiSVo8zwwp0XkiQpkeGFpJkzvBbWPjltTDwFd37m\nsePUslHD66C3L22MJKn6ijaRXfWEctdRRanhxf4H4eTx8uZP7XcBsHBNefNLKs/gYpi/rJx7WcpV\nkiQlMryQNLOaLR2VWjbKN0mS1JmKlo3qip0XqTsOI+zfUd78Re5l2SipusrafbFoYzn3kSRJXcPw\nQtLMuugl6WPu/RIc2QvHD8HBR9LGWltXkjrToo3QO5A4KMDKS1qxmmpJ3XkB5ZaO2p9aNirA0Mry\n5pdUrjLCiwUrYWB+8/eRJEldxfBC0sxatAFWX5o25vQJ+M5nYe996fNZW1eSOlNvHyw5N23Msgtg\nzoLWrKdKRjakjyk1vEjcebFgOfT2lze/pHKVEV64G1qSJBVgeCFp5hUtHZXa7wJ8oyRJnSy1f8Wa\nJ7VmHVUznFo2inLDi9SG3TbrlqptWRnhhQ8USZKkdIYXkmbepgKlo+75Ijz87fRxlo2SpM518UvT\nrn/8q1uzjqpZsBz65qaNGd1e3vypDbtt1i1V29ILmr+HP5NLkqQCDC8kzbzFZ8PKx6WNOXUcvvH+\n9LlsDChJneucZzf+odryi2HDla1dT1WEkL77oqydF6dPwYGH08bYrFuqtoVroH+wuXu4G1qSJBVg\neCFpdhQpHXVoZ9r1C1bCQJNvtCRJ1dXbD6/9CMwdnv66+cvhtR+Gni760Te1aXdZ4cXBnRBPpY1Z\naNkoqdJ6emDpec3dw/BCkiQV0EXv4CRVyqZrWz+Hb5IkqfMtOQd+8vOw8RmTf/2c58B1n+m+nXgj\niTsv9j8Ip042P29qySiwbJTUDpotHWXZKEmSVEDfbC9AUpdaem5WwmPn1tbN4ZskSeoOyy6A138K\ntv8nPHgr7NuWhRWrnwjrnjLbq5sdqTsv4inYvwMWbWhu3gMFwgsbdkvVt7SJpt1zR2BwcXlrkSRJ\nXcPwQtLs2XRNa8MLd15IUvcIAdZfnv0SjBQIIfbd33x44c4LqTMtayK88GdySZJUkGWjJM2eIn0v\nUrjzQpLUrVIbdgOMbm9+3kLhhTsvpMprZufF4rPLW4ckSeoqhheSZs/yC5uvnzsdn/KSJHWr1LJR\nUE7T7tTwYu4wDMxvfl5JrbX4HAi9xcb6QJEkSSrI8ELS7Grl7gvfKEmSutWCFdA7kDZmNsILS0ZJ\n7aFvoPiDQe68kCRJBRleSJpdrQov5iy0MaAkqXv19KSXjiojvEht2G2zbql9FC0d5W5oSZJUkOGF\npNm14uJsG3rZFp+VNW+VJKlbjcxweBFjgZ0Xq5ubU9LMKRpeuBtakiQVZHghaXaF0JrdF75JkiR1\nu9S+F/t3wKmTxec7shdOHk0bY3ghtY8i4UXfPBhaWf5aJElSVzC8kDT7WhFeuD1dktTtUsOL0yfh\nwEPF50vddQGGF1I7WXZB+hh3Q0uSpCYYXkiafaseDyMbyr2nOy8kSd1uODG8ABjdXny+IsGHDbul\n9rH0vPQxNuuWJElNMLyQNPtaUTrKnReSpG6XuvMCmut7sX9H+hgbdkvtY+5w+mvW8EKSJDXB8EJS\nNWy6ttz7ufNCktTtZjy8sGyU1PHOfU7i9c9tzTokSVJXMLyQVA1rLoWFa8u5V++AH4ZIkjS0Enr6\n0sbs21Z8vtTwom8uzFtUfD5JM++SVzZ+7fxlsOHprVuLJEnqeIYXkqqhzNJRIxugp7ece0mS1K56\nemE48cGAfU30vEgNLxautpGv1G7OehY8/rUNXBjg2r+C3sQAVZIkqYbhhaTqKCu8sN+FJEmZ1NJR\nzZSNSm3YPeQuSanthABX/zGc97ypr+kdgBe+G877wZlblyRJ6kiGF5KqY+2Ty2ncab8LSZIyqeHF\n6ANw+nSxuVIbdlviUWpP/XPhdR+D13wY1j0V5izMzg+vg80vhzd+DS5/w+yuUZIkdQT3cEqqjp4e\nuOgl8J/vbe4+7ryQJCkznBhenD4BBx9ODxaOH4Kjo2ljDC+k9hUCXPii7FeM2Z8BcxbM9qokSVKH\nceeFpGopo3TU4rObv4ckSZ0gdecFFCsdtT+xZBQYXkidIgSDC0mS1BKGF5KqZf1TYcGK5u5h2ShJ\nkjIzFl4klowCwwtJkiRJ0zK8kFQtPb1w0YubuEGARRtKW44kSW1tZF36mCLhRWqzbrBhtyRJkqRp\nGV5Iqp5mSkctXAN9c8pbiyRJ7WxoNYTetDHuvJAkSZJUAYYXkqpn/dNgcGmxsTbrliTpMb19MLwm\nbUyh8OLBtOtDLyxYnj6PJEmSpK5heCGpenr74KKri41dtLHUpUiS1PaGE/tezETD7qGVWalISZIk\nSZqC4YWkaipaOsqdF5IkTZTatHv0ATh9Om1MatkoS0ZJkiRJqsPwQlI1bXwGzFuUPm6R4YUkSROk\nhhenjsGhnWljUht2D61Ku16SJElS1zG8kFRNvf1w4YvSx7nzQpKkiVLDC0grHXXqBBxMDDsWJvbh\nkCRJktR1DC8kVdema9Ou7x+EpRe0Zi2SJLWrkXXpY1LCiwMPAzHt/paNkiRJklSH4YWk6jrrWTCc\n8IHL+S+AgcHWrUeSpHbU6p0X+x9Mv7/hhSRJkqQ6DC8kVVffADz3HY1dG3rgyT/ZytVIktSeFq7J\n/p5MkRReJDbrBsMLSZIkSXUZXkiqts0vh82vqH/dC98NG69s/XokSWo3vf0wlBgWjG5v/NrUZt1g\nw25JkiRJdRleSKq2EODlfwPPvR5655z59eH1cO174Ck/NfNrkySpXaSWjmp12SjDC0mSJEl19M32\nAiSprhDgyjfBk/8b3PsleOj/wrzFMLw263PRNzDbK5QkqdpG1sP9NzV+/b77Icbs7+B6UsOLwSXQ\nPzdtjCRJkqSuY3ghqX3MWQAXXZ39kiRJjRtZl3b9yaNwaBcsWF7/2tTwwn4XkiRJkhpg2ShJkiSp\n06WWjQLY12Dfi+TwYk36WiRJkiR1HcMLSZIkqdMVCi+21b/m9On0ht32u5AkSZLUAMMLSZIkqdMV\nCi8aaNp9eDecPpF2X3deSJIkSWqA4YUkSZLU6RauBRpovl1rtIGyUaklowAWuvNCkiRJUn2GF5Ik\nSVKn6xtIL9fUyM6LQuGFDbslSZIk1Wd4IUmSJHWD1NJRjYQXB4qEF5aNkiRJklSf4YUkSZLUDUbW\npV2/bzvEOP01RXZe2LBbkiRJUgMMLyRJkqRukLrz4sQhOLxn+mtSw4uBIZi7MG2MJEmSpK5keCFJ\nkiR1g9TwAmDftum/nhpe2KxbkiRJUoMMLyRJkqRuMJxYNgrq971IDi9s1i1JkiSpMYYXkiRJUjcY\n2ZA+ZnT71F+LsUB4YbNuSZIkSY0xvJAkSZK6wfDa9DHT7bw4tj/ri5HCZt2SJEmSGmR4IUmSJHWD\n/rmwYGXamOnCi9RdF2DZKEmSJEkNM7yQJEmSusVIYt+LfdOUjTK8kCRJktRChheSJElStxhZn3b9\nvvuz3haTMbyQJEmS1EKGF5IkSVK3SA0vjh+AI3sn/9qBh9LnHzK8kCRJktQYwwtJkiSpWwwnlo2C\nqfte7N+Rdp/eARhckj6/JEmSpK5keCFJkiR1i5EN6WNGp+h7kVo2amgV9Pj2Q5IkSVJjfPcgSZIk\ndYvUslEwzc6LxLJR9ruQJEmSlMDwQpIkSeoWw2vTx5RVNsrwQpIkSVICwwtJkiSpWwwMwvxlaWP2\nTVI26sRROLIn7T5Dq9KulyRJktTVDC8kSZKkbpJaOmqynRcHEvtdACxckz5GkiRJUtcyvJAkSZK6\nSRnhRWq/C7BslCRJkqQkhheSJElSNxlel3b9sVE4sm/iuf1Fdl4YXkiSJElqnOGFJEmS1E1Sd14A\njOb6XqQ26wbDC0mSJElJDC8kSZKkbjKyIX1MvnTUgdSyUQEWrEifV5IkSVLXMryQJEmSuslIYtko\nODO8SN15sWAF9PanzytJkiSpaxleSJIkSd0ktecFwL582ajEnReWjJIkSZKUyPBCkiRJ6iZzFsDg\nkrQx+7ZNPE5t2G14IUmSJCmR4YUkSZLUbVKbdteWjTp1Eg4+nDbe8EKSJElSIsMLSZIkqduklo4a\nrSkbdWgnxNNp44dWpV0vSZIkqesZXkiSJEndJnXnxZG9cHR/9u+pJaMAFq5JHyNJkiSpqxleSJIk\nSd1mZEP6mPHdF4XCC3deSJIkSUpjeCFJkiR1m5HEslEA+5oJL9x5IUmSJCmN4YUkSZLUbVLLRsFj\nTbsPFAgv7HkhSZIkKZHhhSRJktRtUht2A+zblv0zdefF3BEYGEyfT5IkSVJXM7yQJEmSus3chVmo\nkGJ850VqeGHJKEmSJEkFGF5IkiRJ3Si1dFTRht0265YkSZJUgOGFJEmS1I1Sw4t990OMBcKL1WnX\nS5IkSRKGF5IkSVJ3GtmQdv3hR2H0ATh1LG2cZaMkSZIkFWB4IUmSJHWjkQJNu7ffkj5myLJRkiRJ\nktIZXkiSJEndKLVsFMD9N6ePceeFJEmSpAIMLyRJkqRuVCi8KLDzwobdkiRJkgowvJAkSZK60XCB\nslE7t6aPsWG3JEmSpAIMLyRJkqRuNG8E5gynjYmn067vH4S5I2ljJEmSJAnDC0mSJKl7FSkdlWJo\nFYTQ2jkkSZIkdSTDC0mSJKlbjRQoHZXCklGSJEmSCjK8kCRJkrpVq3deGF5IkiRJKsjwQpIkSepW\nhheSJEmSKsrwQpIkSepWLe95YXghSZIkqRjDC0mSJKlbDdvzQpIkSVI1GV5IkiRJ3cqyUZIkSZIq\nyvBCkiRJ6lbzFsHAUOvub3ghSZIkqSDDC0mSJKlbhQAjLSod1dMH85e15t6SJEmSOp7hhSRJktTN\nWlU6asFK6Oltzb0lSZIkdTzDC0mSJKmbtSq8sGSUJEmSpCYYXkiSJEndbLhFZaMMLyRJkiQ1wfBC\nkiRJ6mbuvJAkSZJUQYYXkiRJUjczvJAkSZJUQYYXkiRJUjcb2dCa+w6tas19JUmSJHUFwwtJkiSp\nmw0uhv7B8u+7cE3595QkSZLUNQwvJEmSpG4WQmtKR1k2SpIkSVITDC8kSZKkbteK8MKyUZIkSZKa\nYHghSZIkdbvhdeXeb/4y6Bso956SJEmSuorhhSRJktTtyt554a4LSZIkSU0yvJAkSZK6Xdnhhc26\nJUmSJDXJ8EKSJEnqdqWHF+68kCRJktQcwwtJkiSp25UeXqwu936SJEmSuo7hhSRJktTt5i+Dvrnl\n3c+yUZIkSZKaZHghSZIkdbsQyt19YcNuSZIkSU3qm+0FNCKEcBbwBGA1sAB4CNgG3BRjPDGL61oM\nPAk4CxgBAjAKPAB8Pcb4cEnz9ABPBC4GlgK9PPY9uDnGeLKMeSRJktTFhtfB7rvKuZc7LyRJkiQ1\nqdLhRQjhFcCbgSumuGRPCOGjwNtijLtnaE0BeDXwc8CVda69FXgP8LdFAoYQwjLg14DrgCVTXPZo\nCOFjwPUxxkdS55AkSZKAcnde2LBbkiRJUpMqWTYqhLAghPAR4ONMHVwALAZ+FtgSQnj+DKxrJfBF\n4CPUCS7GPBF4L/C1EMK5iXNdDdwN/BJTBxeMfe1nga0hhJelzCFJkiR9X1nhxZyFMGeonHtJkiRJ\n6lqV23kRQugFPgr8UO5Lu4BbycoynUMWDISxr60AbgwhPDfG+JUWrWsZ8G/AhbkvnRhb1zbgNLAW\nuAyo7Xh4GfBvIYQrY4zbGpjr9cDfcma4dBdwO3Ac2EhWsmr8miXAx0IIL4sxfrLx35kkSZJEeeHF\nwtXl3EeSJElSV6vizot3MTG4OAH8PLA2xvj8GOOrYoyXAZuBm2uumwPcEEJo1R71P+bM4OI9Y+u6\nfGxdr4kxXgmsGvt9nK65di3ZLoxphRCePHZd7X+bbwJPiTFeEGN8aYzx1THGy4H1wMdqrusF/j6E\nsDn1NydJkqQuV1Z4YbNuSZIkSSWoVHgRQjgb+IXc6VfGGP88xni89mSM8XbgOUwMMJYAb2/BujYC\nr8udfmeM8WdjjDvz18cY98UYf50zfy/PDyFcXme6PyELYsbdDDxGG9ArAAAgAElEQVQrxvj1SebZ\nEWN8NfCXNafnAX9QZw5JkiRpotJ2XtisW5IkSVLzKhVekAUP/TXHH4gx3jjVxTHGI2TNrGuDjZ8c\nC0HK9OLc8SPA9Q2M+wvg23Xu9X0hhKuY2OPjOPAjMcZDdeb5ReA7NcfPDyH8QAPrkyRJkjLzl0Pv\nnPrX1WOzbkmSJEklqEx4EUKYB7wid/r3642LMd4F3FBzqo8zd0k0Kx+G/EuM8Vi9QTHGCHwqd/q8\naYbkg41/jDHe28A8x4E/zZ3+8XrjJEmSpO/r6YHhtc3fx54XkiRJkkpQmfACeD4wWHN8c4zxzgbH\nvj93/LJylvR983PHDySM3Z47XjTNtc/MHX8uYZ7P5o5fHEIYSBgvSZKkbldG6aghwwtJkiRJzatS\nePGC3PGXEsZ+GThZc/zEEMKKplf0mIdzx3MTxuav3TPNtflH3bY0OsnYDo3DNaeGgSsbHS9JkiSV\nEl6480KSJElSCaoUXmzOHd886VWTGOsJcVvu9MVNr+gxX84dX5ow9rLc8RmNt2sszh3vS5hnsusv\nSRwvSZKkbjayrvl72LBbkiRJUgmqFF5clDu+O3H8PbnjTU2sJe+LTGyI/YwQwuPqDQohrAFeXnPq\nBPCRaYYczx2ndkzMX1/m90CSJEmdbmRDc+N758Bg/nkcSZIkSUpXifAihLCYM3cd3J94m/z10zXG\nThJjPA38BDDepLsH+EQIYeNUY8bKVt3AxD4evxNjfHCaqR7NHa9qdI1jDc9HcqcvaHS8JEmS1HTZ\nqIWrIIRy1iJJkiSpq/XN9gLG5D90PzxWCirFztzxcBPrOUOM8aYQwtXAh4FlZOHIt0MI7yNrlr0N\niGR9K54DvAFYUnOL9wL/T51p7gBqH3d7KvCFBpf4ZKA3d66U70EIYTnZ7znFOWXMLUmSpBk03GTZ\nKJt1S5IkSSpJVcKLBbnjIwXukR8zVHAtU4oxfiGEcBHwJuCHgbPG/v1N0wy7E3hbjPHjDUzx70xs\nXP4jIYTfjTHGBsb+2CTnyvoevBF4e0n3kiRJUlUNrYSefjh9oth4m3VLkiRJKkklykZxZnhxtMA9\n8uFF/p5lGQ98jk17VeYm4H8An2jw3n8PnKw5vgD42XqDQgiXAq+f5EulBziSJEnqYD29MLy2+HjD\nC0mSJEklqUp4kdfIToMyxiQJIfwUWWPw3wQubGDI04B/ISsv9fR6F8cY7wM+lDv9RyGEV06zps3A\np5h8F83pBtYoSZIkPaaZvheGF5IkSZJKUpWyUQdzx/MK3CM/Jn/PpoQQfgP4ndzpbwB/CXwZeJAs\nLFhJ1qviDcCzx67bDPx7COEnY4wfrDPVm4FnkpWkAhgAPhZC+DTwd8DtwAlgI3ANWSPxgbFrHyDr\nuTFuX+O/w2n9JdBI2ata5wA3ljS/JEmSZspIE30vDC8kSZIklcTwogEhhB/gzGbb7wB+e5J+FPeN\n/fr7EMIbgPcAgayZ9vtCCHfHGL861Vwxxj0hhBcD/weofezt6rFfU7kHeCvw0ZpzpYQXMcadnNkQ\nfVohhDKmliRJ0kwb2VB8rA27JUmSJJWkKmWjRnPHgyGE+Yn3WJ47LmvXAcDvkgUQ4z4YY7y+XiPt\nGONfj40d1wv8Sb3JYoxbgSeT7XZopBzWx8l2e/Tnzj/cwFhJkiTpMZaNkiRJklQBlQgvYoyPAntz\np1PfNeUfEftu8RU9JoSwhiwYqHV9wi3excRm4peFEB5Xb1CMcWeM8VXA48h2fdxEVprqGFkwcxtZ\nOacrYoyvijHuBi7K3eYbCeuUJEmSYLhg2ajQAwtWlLsWSZIkSV2rKmWjAO4ga3A97tyxc406e5L7\nleEJueN7Y4zfa3RwjPFQCOFrPNb/AuBy4NsNjt8CbAHe1sDlV+SOb2lokZIkSdK4ojsvFqyA3iq9\nvZAkSZLUziqx82LMltxx/oP4KY2VmMrvZsjfr6iR3HGRUkz5MUsLrmVKY9+Dy2tOHcHwQpIkSamG\nVkFPgRDCklGSJEmSSlSl8OKzueOrEsY+g4m7SG6NMT7S9Ioy+d4Zqb04ABbkjktrJl7jNUxc28di\njAdaMI8kSZI6WW9fsSBiaFX5a5EkSZLUtaoUXnyOib0hrgghXNjg2Otyx/9UyooyD+aOLwghDCbe\n49LccamNtEMI/cCbc6ffW+YckiRJ6iIj+XZyDVi4pvx1SJIkSepalQkvYoyHgU/kTv9avXEhhPOB\nl9acOgl8uMSlfZuJzcTnAj/a6OAQwtVA/p3cV0pYV623Aptqjj8dY7y55DkkSZLULYr0vVjozgtJ\nkiRJ5alMeDHmHcCJmuPrQggvmeriEMJc4P3AQM3p98UY75lukhBCzP26aqprY4ynODNUeVcIYfN0\nc4zNsx54T+70V2OMD00zJoQQeuvdu+b6nyf7vo3bD/xMo+MlSZKkMxQKL9x5IUmSJKk8lQovYoz3\nAn+SO/2JEMJ/DyHUBhSEEC4Cvgg8reb0o8D1LVjabzOxpNUIcNPYus4oIRVCGAghvB74Jmfuuvj1\nOnPNB+4LIVwfQnjCZBeMBRyXhxD+GfjTmi9F4A0xxh115pAkSZKmNrwufYwNuyVJkiSVqK/+JTPu\nLcDFwAvHjvuBPwN+K4TwLeAAcDZZH4lQM+448NLpdjUUFWN8IITww8DHgfFdEUNj63p3COGbZL0x\nTgMrgSdxZpNugN+IMX65gSnXAm8D3hZC2AfcBjxCVhJrOdn3Z0VuzCngjTHGj6b83iRJkqQzLD0/\nfczic8pfhyRJkqSuVbnwIsZ4KoTwKuBvgFfXfGk58IIphu0EXt9gMFB0Xf8UQrgGeB8Tg4N5wJV1\nhh8C3hJj/PMCU48Az6hzzQPAdTHGLxa4vyRJkjTRmkthcCkc3t3Y9SsvseeFJEmSpFJVqmzUuBjj\nwRjja4BXAl+b5tI9wF8Bm2OMn52BdX2GrDH2W4Fp+2qMeQT4A+DihODiKPA/gTsbuPYu4JeBCwwu\nJEmSVJqeXrjklY1ff8mrWrcWSZIkSV0pxBhnew11hRDOIisTtZqsJ8TDwDay5tfHZ3Fda4HLgFVk\nOyQCMArsAm6NMd7d5P2XAE8ENgJLyBqTHwLuB7411iOkkkIIFwNbxo+3bNnCxRdfPIsrkiRJUpLj\nh+F9z4NHbpv+urOvgh/5xyzwkCRJktR2tm7dyubNm2tPbY4xbp2t9YyrXNmoycQYvwd8b7bXkRdj\nfICsZFOr7v8o8IVW3V+SJEma0sAgvPbD8E8/A9u+Ovk1F14NL/5TgwtJkiRJpWuL8EKSJEnSLBhZ\nD9d9Brb+I9z1OXhkK4QeWPV4uOglcP7zZnuFkiRJkjqU4YUkSZKkqYUAm1+e/ZIkSZKkGVLJht2S\nJEmSJEmSJKl7GV5IkiRJkiRJkqRKMbyQJEmSJEmSJEmVYnghSZIkSZIkSZIqxfBCkiRJkiRJkiRV\niuGFJEmSJEmSJEmqFMMLSZIkSZIkSZJUKYYXkiRJkiRJkiSpUgwvJEmSJEmSJElSpRheSJIkSZIk\nSZKkSjG8kCRJkiRJkiRJlWJ4IUmSJEmSJEmSKsXwQpIkSZIkSZIkVYrhhSRJkiRJkiRJqhTDC0mS\nJEmSJEmSVCmGF5IkSZIkSZIkqVIMLyRJkiRJkiRJUqUYXkiSJEmSJEmSpEoxvJAkSZIkSZIkSZVi\neCFJkiRJkiRJkirF8EKSJEmSJEmSJFWK4YUkSZIkSZIkSaoUwwtJkiRJkiRJklQphheSJEmSJEmS\nJKlSDC8kSZIkSZIkSVKlGF5IkiRJkiRJkqRKMbyQJEmSJEmSJEmVYnghSZIkSZIkSZIqxfBCkiRJ\nkiRJkiRViuGFJEmSJEmSJEmqFMMLSZIkSZIkSZJUKYYXkiRJkiRJkiSpUvpmewHqWAO1B3ffffds\nrUOSJEmSJEmSNIVJPrsdmOy6mRZijLO9BnWgEMJLgBtnex2SJEmSJEmSpCTXxBg/OduLsGyUJEmS\nJEmSJEmqFMMLSZIkSZIkSZJUKZaNUkuEEIaBZ9Wc2g4cn6XlzLRzmFgy6xrgnllai9TJfK1JM8PX\nmtR6vs6kmeFrTZoZvtak1iv7dTYArKs5/vcY42gT9yuFDbvVEmP/c896XbTZEELIn7onxrh1NtYi\ndTJfa9LM8LUmtZ6vM2lm+FqTZoavNan1WvQ6u7XJ8aWzbJQkSZIkSZIkSaoUwwtJkiRJkiRJklQp\nhheSJEmSJEmSJKlSDC8kSZIkSZIkSVKlGF5IkiRJkiRJkqRKMbyQJEmSJEmSJEmVYnghSZIkSZIk\nSZIqxfBCkiRJkiRJkiRViuGFJEmSJEmSJEmqFMMLSZIkSZIkSZJUKYYXkiRJkiRJkiSpUvpmewFS\nB9oFXJ87llQ+X2vSzPC1JrWerzNpZvhak2aGrzWp9bridRZijLO9BkmSJEmSJEmSpO+zbJQkSZIk\nSZIkSaoUwwtJkiRJkiRJklQphheSJEmSJEmSJKlSDC8kSZIkSZIkSVKlGF5IkiRJkiRJkqRKMbyQ\nJEmSJEmSJEmVYnghSZIkSZIkSZIqxfBCkiRJkiRJkiRViuGFJEmSJEmSJEmqFMMLSZIkSZIkSZJU\nKYYXkiRJkiRJkiSpUgwvJEmSJEmSJElSpfTN9gKkThNCOAt4ArAaWAA8BGwDbooxnpjNtUmS1Eoh\nhH7g6cB6YBVwEHgQuDXGeN8sLk2S1OZCCL3AucAmsvdaw8AxYC9wD/CNGOOhkuccJPt7bS2wAtgH\n7AC+HmN8uMy5pCqYjdeZ1I1CCPOAC4ENZK+1IaAf2A88CmwBtsYYT5Y0Xw9wOXD22HzHyf4+2xpj\nvKOMOVolxBhnew1SRwghvAJ4M3DFFJfsAT4KvC3GuHvGFiZJ6lohhLOBJwNPGvvnpWQ/GI/bFmPc\nWMI8y4DrgVcDi6e47Cbgf8YY/6HZ+SRJ3SGEsB54GfBc4BnAwmkuPwV8HvjzGONnmpz3LOC3gZcC\n86eY61+Bd8YY/62ZuaTZNlOvsxDCRuB7xVaZiTGGZsZLsymE8OPAD5CFCOdQvyLSQeBjwJ/FGP9v\nwTkXAL8J/ChZaDGZbwN/AfyvWMGgwPBCatLYHwT/C3hNg0MeAV4fY/xc61YldYYQwjuAtzdxiw/G\nGK8rZzVSewghXAX8OllgMVWQMK7p8CKE8ELgA8DyBod8CPhpn9pTp2hlSBhCaPbN2lnuelK7CiF8\nGHhtweGfBv5bjPGRAvNeB/wZ2S76eiLwx8CvxBhPpc4lzbaZfJ0ZXqjbhRAeANYUGHqK7O+lX0nZ\niRFCeArwEbLdFo34PPAjMcad6UtsHctGSU0Y21L5UeCHcl/aBdwKjJKlqU8Exv+SXQHcGEJ4bozx\nKzO1VklS13gC8LyZmGgsKLkBGKg5HYFvAfcCI2R/By6t+foPAwtDCNfGGE/PxDqlsiWGhJKKOX+K\n8zuA75I9FNZH9qHM45n4BOvVwH+EEJ6VUt4phPA64G957L0bwEng68B2YBlwGY89mR6AXwTmAD/X\n6DxShcz460zS9x0mK8d2P1m5qB6ynysvAVbWXNcLvAnYGEJ4RSNheQhhE/A5svdjtbYA3wEGgccx\nMUz5QeAzY6/pw4V+Ry1geCE1511MDC5OkJWO+usY4/Hxk2N/aPwNj5WUmgPcEEK4JMb40EwtVpLU\n1Y4BD5CF6k0LIawF/pGJwcVXgZ+qrZsaQpgD/DTwB2R1XAFeDPwO8NYy1iLNghkLCSUB2YNhfwv8\nc4zxnvwXQwhrgLcBb6g5fT7w8RDCMxspgxFCuBR4PxODixuBn48xbq+5bgj4NeA3aq57Ywjhv2KM\nf53we5KqpuWvs5x/AH656GKlNnQI+CTwz2QldbdM9TBXCOGpZO+XnlNz+lqyzxz/3+kmCSHMBz7D\nxODiTrIqMP9Zc10vWdnf9/DYruEnkX1++bqGf1ctZtkoqaCxEgF38tgHMQDXxhhvnOL6ecAXmdgT\n470xxp9p3Sql9jZJ2ajXAl9LuMVBe8yo24QQ3gS8G9gKfIPsadFvALeRNR2trc3dTDmb9wE/UXPq\nJuA5McajU1x/LfBPNaeOARfEGLcVmV+aTWOvsz+a5EuThYRllY26hcbLlI57oKxGj9JMCyF8neyp\n73fEGL/R4Jg3ktXtrvXaGOPfNzD2C0z8kOgTwKun+WDpF8hKRo3bBZwTYzzQyFqlKpjJ19kkZaMs\n8auuEkLojzGeSLi+B/gg8CM1p0eBFTHGY9OM+y2yvk3j7gYujzHumeL6J5M9hFb7+ebTYow3N7rW\nVjK8kAoKIXwQ+LGaUx+IMf54nTHnk314NP6U6kmyD27ubc0qpfY2SXjx7Bjjl2ZnNVJ7CCEsAo5M\nFiKMlbppOrwIIZwH3EG2hRngOLA5xvjdOuM+ALy+5tT7Y4w/McXlUmXNYEhY+2bt32OMVxW5j9SO\nQggbi/RsCSF8Anh5zan/E2N8UZ0xzyZrwD1uN3BhjPHRacaEsTFX1Zx+e4zxtycfIVXPDL/ONvL/\nt3enUdYU5QHH/w+ryCo7CBhQUNCAEgwoEUFwIUYRjnGNilHiiWsSo0bQ4BJjNCYuR2IiKGCMJoo7\nIiLIIqIiiEEUDwqCEVCQfZXtyYfq0Z56752569w7d/6/c+ZD1XR113vOW/N099NVZfJC6ktEbABc\nBazbqj4wM0/ucvxGwBX8bnlDgH0z88xFrvMW5r97OT0znzBQp0dssV3NJXXQzKJ4ZlX9rsXaZeYl\nlLXB56zBFE3FkiQtf5l5Q7fZDyP0PH6XuAD47GKJi0YdK58VEfcbXbekJXM8sEFmPiozD8vMD2fm\n9/r5mk7SwobYbL7+Iny/Htq8sCofs1DiAqBZIufdi5xHmmpLPM4k9Skzbwbq/XIfskCTg5ifuPj2\nYomLxvuB9jPkfhGxbW+9HC+TF9JgnkzZ3GbOtzLzxz22PbYqHzKaLkmStGQOrsp1bOuo2QvjO62q\ndXHfAC1DS5QklDSYC6ryOs2XqB01a34/raruKa5RNkNt72H44IjYtce20nLW1ziTNJR6uaf1Ox5V\nDPqcdgNln6eFzjURJi+kwTylKp/RR9tvUJaLmvOoiNhi6B5JkrQEImJLYLdW1T2UNVJ7dUZVPnDY\nPkmS1NJpn5e1OtTNeTSwSat8dTNjflHNfhhnVdXGNa0E/Y4zSYN7UFW+qtNBzR4ZT6yqz+jjOvWx\nUxHPTF5Ig3lEVe55E5vMvI2yHnLbw4fukSRJS6OOgRc2sa1X51RlY6AkaZTq5TTuoexh0c3Az3YN\n45pWon7HmaQBNHvn7tmqSqDbMlA7MH+VmOt7TcY3pjKembyQBrNzVf5pn+0vrcq7DNEXSZKWUh2z\njIGSpGlS7014XjNDohvjmtS/fsdZJ7tFxCci4pKIuCkifhMRV0fE/0bEMRHxgohYd/HTSLMpIrYC\nPs38vQZPWGCvmlHHs20jYqElqpaEyQupTxGxMbBxVf3zPk9TH7/j4D2SVpSXRcSpEXFlRNwZEbdE\nxOURcWZEvCMiHjfpDkorQP2lXb8x8IqqvElEPGCI/kgrxXYRcWxE/DAiboiIuyLiV0354xHxF819\nqrRiRcR6wEuq6s8t0mzYuOaznVaUAcdZJ48EnksZMxtQlp3aEti1Of/HgJ9HxJsjYs3BeywtDxGx\nRkRsFhH7RMS7gR9TxsOcy4BXLnCKoeJZM5u+3l9joc3Bl4TJC6l/9SZUt/e5XAbANVV5wyH6I60k\nzwH2B7YG1gbWo6z/uA9wOHBWRHw3Ig6YXBelmVfHwTqmLSgzbwXqjY6Ng9LitgcOpXxVtxGwJrB5\nU34+8B+UlzzvbV4sSSvROykvP+fcCByzSJuh4lqH49dv1h2XZtUg42xQGwNvA74REduM6RrSRETE\n+yIi536Auykx5UzgdZSk3pzTgX0yc6EYNWw869Rm4s9pBlSpf/XD4B0DnKNuM/FpWNIM2QM4pZmJ\nEZPujDSDjIPS9FoX+Cvg/IiYinWKpaUSEQez6hepR2Rm/RVpbdi4Vh8flLEozZwhxlnbncBXKC9n\nD6TMwNiR8hz3p8C/ATdXbfakPOPVL2elWfdF4MmZ+YTMvHKRY2fyOW2NSXdAWobqPwb116O9qP8Y\n+HWctLArgZOAc4GLKVMZ7wM2AXYH/gR4cuv4oMzEWA1445L2VJp9o4qD7aWijINSd/cAZwOnAhcC\nvwBuoYyb7YDHAS+kzMKYsxNwakTslZn1Um3SzImI3ShLzLSdAnyoh+bDxrVOL4fWo4xTaWYMOc4A\nbgNeAxyXmXVyYs75wAkRcTjwfuBFrd/tDHwUOKTnTkvL34HA6hFxZ2aetcixM/m+0uSFNLxcojbS\nSnQuJSnxtczsNm7OAT4YEXsAn2D+OsN/FxHfzswvjLmf0kpmHJTG503A0QssEfB94IsR8WbgSOAN\nlAQ+lCU9PhsReywQQ6VlLyK2A77M/BcsVwB/NuD//X7bOL4080YxzjLzWuADPR57E3BoRFwL/G3r\nVwdHxN6Z+c3eei5NtbcB72uV16F8oPlI4GDgCZRlQp8KPDUijgJek5n39nj+mXhOc9koqX+3VuV1\nBjhH3aY+pyQgM0/KzFN6uSHOzPOAvYBLql/9U0SsPpYOSiuTcVBaIpn5jkXWNp477s7MfCPwqupX\nu1M2Q5VmUkRsDnwNeGCr+pfAE5sXpb0YNq51Ot64ppkxonE2qNdTEvVtfznma0pLIjOvz8zLWz8X\nZ+bZmfnBzNyfMru2PYP2FcCHFzjlTD6nmbyQ+jeTfwykWdCstfpc5n8t8DBgv8n0SJpJxkFpSmXm\nUZS1kdtePom+SOMWERtTllPbqVX9a+CAzPxJH6caR/Litj7PIU2lEY6zgTQfsb2rqn6SextqJcjM\nsynvMq5rVf95RBzUpclMPqeZvJD6d1NVvn9E9Lsh2+ZV+cYh+iOpJTO/R1l7te0pk+iLNKPqOLhZ\nP40jYj1WvSk2Dkqj886qvJcbnGrWRMSGlPu9329V30D5EvyHfZ5uqLjGqs92N2fmfX2eQ5o6Ix5n\nw/hqVd4M2GoJry9NTGb+jLK8VNvruxw+bDyDKXxfafJC6lNmXkcJ2G3b9XmaB1XlsX+xIK0wJ1fl\nXSfSC2k21TGrjmmLqY+/PjPruCppcOcy/151dWCXCfVFGrmIWJ9yr/cHreqbgadkZr28TC9GHdd8\nttOyN4ZxNrDmPnEUL2Wl5eq/q3K3D1OGimcRcX/KnhttP+3nHONg8kIazMVV+SF9tt9hkfNJGs7l\nVdmbW2l0Rh0DfzREXyRVmi++f15VGwc1E5oZ7ydR9jmbcytwYGaeO+BpfbaTWsY0zoZ1R1UeZDkc\naVlq9j9rf5iyGrB9h0Pr+PPgPi9VH/+LzLylz3OMnMkLaTAXVeXH9NqwuRGovwKvzydpON7cSuNT\nx6xdm690erX3IueTNDzjoGZORKwDnAj8Uav6duCpmXnOEKce+NmuYVzTzBjjOBumT8GqX4P/ehJ9\nkSbo7qq8dodjLqOM1zmbRMROHY7rZirjmckLaTD1kjT79tH2ccAarfIFmfmroXskqW3TquzNrTQi\nmXk1cGGrag3mP+AuZt+q/JVh+yRpFcZBzZSIuB9lM/p9W9V3Ak/PzLOGPP13getb5a16fdkTEatR\nnu/ajGtalsY8zobxKGDNVvk+4JcT6ou05JqxWd/brfIeMTPvBU6tqvft41L1sVMRz0xeSIP5KvO/\naHtMRDysx7aHVuXPjaRHktr2rMpXTaQX0uyqY9eLe2nUxMr2+LyNshGkpBGJiE1ZdRkb46CWrYhY\nC/gscECr+jfAMzLztGHPn5n3AF+qqnuKa8CTgK1b5Usz88JuB0vTatzjbEjPq8rnZ+atE+mJNBn7\nM/8d/u3AlV2OHfQ57QHA06vqz/fUuzEzeSENIDNvB06oqt+wWLvmC56DW1X3AJ8YYdekFa/5KuGQ\nqvqMCXRFmmX/BdzbKh8SETv20K6OlZ/KzDtH1y1JwHOY/5z3K1yDX8tURKwBfAo4sFV9N/DMzPzq\nCC/1sar80oiol6np5PWLnEeaeks4zvrWfPjy8qr6C5PoizQJzQy/N1fVJ2fmXV2afB64uVXeKyIe\n38OlXs38ZUZPz8x6D7WJMHkhDe4tzF9z7tCIqLOUv9W8UD0WWKtV/ZHMvHQ83ZNWrDcAD2yV7wW+\nPKG+SDMpM38CHN+qWgs4rol1HUXEQcyffXgX8NaxdFBaoSJiC+BNVfWXMjMn0R9pGBGxOiVZflCr\n+h7g2Zl54iivlZlfB77eqtoU+PfmpVG3/r0a2K9V9WvgvaPslzRuSzXOImLPiKiXWFuszU6UjcPb\nL1SvAz44qn5JSyUiXhURW/XZZk3gI6y6ssRR3dpk5o3Ae6rqY5qZFd2u82jg8Kr6iD66OlYmL6QB\nZeZlwPur6hMi4pXNlMvfioidgdOAx7aqr8OXNlJXEfGC5iVMP20OA46sqo/LzCtG1zNp+kXENhHx\ne/UPsGV16Bqdjmt+6nVVa0cCN7TKjwVOrZdRjIi1I+JVwKer9v/i2JQ6i4iHRsTT+myzJWWT1Xbs\nvAt45yj7Ji2hjwLPquoOBy5YIHZ1++maXG95HWXMzHkm8JmI2LZ9UESsHxFvB95XtT8iM2/p898o\nTdpSjbOdgbMi4psR8Yp6XLVFxKYRcQRwHrB99eu/ycybBvmHShP2EuDSiPh4RDwtItbvdmBErBMR\nzwUuYNWl5/+zSbgv5F+By1vlhwDnNEmK9nVWa65zGvM/tP5kZn5rkWssmfAjHGlwzVcKX2L+9EqA\na4DvAbdQ1hzeHYjW7+8CDsjMbyxFP6XlKCLOAP6Q8sLzU8AZmXlbl2P3oNxkH1z96kpgj8x0Qzet\nKBFxOfCgIU9zfGYeush19qXsA9W+2U3gfOAyYENKDNysanoiZQ3le5GWqYjYhrJhfW0v4JOt8pV0\n39T+1sxcZTPtZmydDvwA+DjwuWbGU6d+rA+8iDLjok76/7nd1PEAAATbSURBVH1mvn2Bf4Y0tSJi\nlC8r9svMM3q45vMpY67tHuBc4P8oMzIeDWxQHfOhzKyXtpGm3lKNs4g4lLISRdu1wI+A6yn7oG1A\nSVY8gvnvT+Ycnpkm5LUsRcT3gd1aVQn8lJJkuJHynnB9yjPcLszfpH7OiZTl3H7Tw/V2Ac6hPI+1\n/QC4BLhf059tqt+fBzy+WS5/Kpi8kIYUEesBxwDP7rHJNcCLMvPk8fVKWv6a5EV7bcb7gJ9QgvtN\nlOWgNqEE3E4zNK6nBN2LxtpRaQotVfKiudYfA8exaoKim08Ch3VLRkrLxTjHWSt50XYTcBFlaZpb\ngPWAbSlxsFMS5cOZ+bIh+ydNzCSSF811Xwx8gDLGFpPNsa81Ia/laMLJi15dC7w0M784YHtp4jok\nL/pxB/APwD9n5t2LHdy65p6UfXZ36LHJqcDzM/Oa/rs4Pp1uciX1ITNvBZ4TEScAr6V8bdfJ9cD/\nAEdm5rVL1T9phqwGPLT5WcxpwKGZ+YvxdklSZp4UEY+gLIX4bKDbeqrfBt6TmZ9Zss5Js2VDYO8e\njrsN+OvMPHrM/ZFmUmYeGxFnAm8DngGs2+Gw+yh7ZPxjZtaJRkmrOpuylM0+wK7Mn7XbSQIXUj4U\nPd4l2TQDDgOeDuxPmZm+dg9tfkzZk+a4Qd5tZOZ3ImI3yuzcFwLd9tz4AWUvmaOncZ80Z15IIxYR\n21P+EG1NudH9JXAF8M3MvGuhtpJ+JyIOpqw1vDe9fdl6G3AKcFRmnjbOvknqrNnzaW7MbkkZl1cC\nF2TmzybZN2nUxjzzYgvg1ZQZiLszf7PSbi6hzII6utNSVJL6FxHrUpZ92wbYnLK0x1XAuZl59ST7\nJi1XzSbED6MsEbU1ZbmotSn3jTdQlmj7rntbaFY1Y2BnyoyIB1Jm+q0J3ArcTFlt4oLMvKHbOQa4\n5mqUj613oIy7uyjx7KLM/NGorjMOJi8kSVMvIjYCHk5ZHmML4P6UmRg3Um5wLwYudLq+JGnWNA+b\nOwIPpjzgbkRZp/gOSgy8mvKSx5m9kiRJmikmLyRJkiRJkiRJ0lRZbdIdkCRJkiRJkiRJajN5IUmS\nJEmSJEmSporJC0mSJEmSJEmSNFVMXkiSJEmSJEmSpKli8kKSJEmSJEmSJE0VkxeSJEmSJEmSJGmq\nmLyQJEmSJEmSJElTxeSFJEmSJEmSJEmaKiYvJEmSJEmSJEnSVDF5IUmSJEmSJEmSporJC0mSJEmS\nJEmSNFVMXkiSJEmSJEmSpKli8kKSJEmSJEmSJE0VkxeSJEmSJEmSJGmqmLyQJEmSJEmSJElTxeSF\nJEmSJEmSJEmaKiYvJEmSJEmSJEnSVDF5IUmSJEmSJEmSporJC0mSJEmSJEmSNFVMXkiSJEmSJEmS\npKli8kKSJEmSJEmSJE0VkxeSJEmSJEmSJGmqmLyQJEmSJEmSJElTxeSFJEmSJEmSJEmaKiYvJEmS\nJEmSJEnSVDF5IUmSJEmSJEmSporJC0mSJEmSJEmSNFVMXkiSJEmSJEmSpKli8kKSJEmSJEmSJE0V\nkxeSJEmSJEmSJGmqmLyQJEmSJEmSJElTxeSFJEmSJEmSJEmaKiYvJEmSJEmSJEnSVDF5IUmSJEmS\nJEmSporJC0mSJEmSJEmSNFVMXkiSJEmSJEmSpKny/7sqQHnFQLl6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ff90940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "plt.plot(history.history['categorical_accuracy'], linewidth=3)\n",
    "plt.plot(history.history['val_categorical_accuracy'], linewidth=3)\n",
    "plt.show()\n",
    "#fig.savefig(\"Handwritten_MNIST_Fig1_Accuracy.pdf\")\n",
    "#fig.savefig(\"Handwritten_MNIST_Fig2_AccuracyAndValidation.pdf\")\n",
    "#fig.savefig(\"Handwritten_MNIST_Fig4_100_50_DropOut_AccuracyAndValidation.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAA\nAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAA\nQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJ\nGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABD7Vt2\nAQDAxp1wwgnLLiHnnHPOUq//m7/5m0u9/vve976lXj9Jzj777KVe//jjj1/q9QHYPsxsAAAAAIYS\nNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYA\nAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAA\nAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABD\nCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAy1b9kFAACM8OQnP3lPXx8A\nthMzGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABD\nCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkb\nAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAA\nAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABD7Vt2ASup\nqiOS3DPJ1yW5c5LjknwxyVVJPp7k/O6+bnkVAgAAAAezbcKGqrpbkickOTXJw5Mce4jdv1xV70jy\nou5+yxqvc2KSv1lnmUmS7q6NHA8AAAC72bYIG6rqNUmeuIZDjkjyqCSPqqpzkjytuy/blOIAAACA\nNdkWYUOSkw7SfnGSjya5LLNavzbJfXPgsyYem+S9VfWI7r50U6sEAAAADmu7hA2LLkzysiR/0N0f\nn35YVXdJ8jNJfnCh+aQkv1tV39LdvcbrvT7Jj6+3WAAAAOBA2yVs6CRvSXJ2d59/yB27L07y9Kr6\nYJIXL3z0sCRnJPnfa7z2td39yTUeAwAAABzEdnn15Xd292MPFzQs6u5fyWxWwqInjy0LAAAAWKtt\nETZsYGbBiyfbp2ywFAAAAGCDtkXYsAEXTraPqqrbLqUSAAAAIMnODxtuXKHtyC2vAgAAAPgHOz1s\nuOdk+8YkVyyjEAAAAGBmu7yNYr1On2yf3903rfEc962q1yR5YJITktwqyZVJPpvk/Un+OMkbuvu6\njRYLAAAAe8GOndlQVUcn+f5J8xvXcar7JXliknslOTazZRh3SnKf+flfleTTVfXTVXWL9VcMAAAA\ne8NOntnwvMxCgf0+n+Slm3St2yX5uSSPqarTu/szI09eVccnueMaD7vHyBoAAABglB0ZNlTVaUn+\n/aT5Od195RpOc32Sdyd5V5K/SHJJkuuSHJfk7pm9RvN7M5vtsN+Dkry9qh7S3Z9fZ/kr+eEkzx14\nPgAAAFiaHRc2VNV9M1vasOjtSX51lae4LsmPJnlFd3/hIPtckOR1VXVWkl9O8tSFz05O8rIkT1h1\n0QAAALCH7KhnNlTV3ZK8JcnRC82fSvK93d2rOUd3X97dLzxE0LC479XdfWaSn598dFpVPXSVZQMA\nAMCesmNmNsyfa/COJHdZaL40yb/o7ss3+fLPSnJqZg+T3O+HkvzZoPP/SpLfXeMx90jypkHXBwAA\ngGF2RNhQVbdL8s4kJy00X5Hk1O7+6GZfv7u7qp6f5LcXmr+tqmq1MyoOc/7PZvaqzVWrqo1eFgAA\nADbFtl9GUVXHZfZMhm9YaL4qsxkNH9nCUt422b5jkq/ZwusDAADAjrCtw4aqOibJHyb5xoXmLyR5\nVHf/362spbuvSnL1pHmtr6sEAACAXW/bhg1VdZskb03y4IXma5M8urvPW05V+fvJ9lFLqQIAAAC2\nsW0ZNlTVUUnOSfKwhea/S/KY7j53STVVkttPmq9YRi0AAACwnW27sKGqbpXkzUkeudB8fZLv6O73\nLqWomfsnucXC9k2ZvQ0DAAAAWLCtwoaqOjLJGzJ7zeR+X0zy+O7+o+VU9Q+eNNm+oLuvXUolAAAA\nsI1tm7ChqvYl+Z0kj15o/lKS07t7+iaILVVV907yw5PmNy2jFgAAANjutkXYUFVHJHl1ksctNN+Y\n5IzuPmfgdR5UVQ9f4zEnZfagysWHQX4uyYtG1QUAAAC7yb5lFzD3siTfNWk7K8mFVXXiGs91aXdf\nf5DPTk7y8qo6N8lrkry5uy9aacequkOSpyd5dpJjJh8/s7unr8EEAAAAsn3Chqes0PaC+b+1OiXJ\new6zz0Pm/15UVZcn+cskVya5LsmxSe6e5OuT1ArHntXdr1pHXQAAALAnbJewYZnumOQRq9jv8iRP\n6+43b3I9AAAAsKNti2c2bKE/TfILSc5PcsMq9u8kH0zyI0nuIWgAAACAw9sWMxu6e6XlCptxnY8l\n+bEkqapbJLl3Zksm7pzZ8olbZraU4qokFyV5v2czAAAAwNpsi7BhGbr7S0k+PP8HAAAADLLXllEA\nAAAAm0zYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAA\nABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAY\nStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErY\nAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAA\nAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAA\nDCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwl\nbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwA\nAAAAhhI2AAAAAEMJGwAAAICh9i27ANbtyMWNj33sY8uqAwAAgG1ghb8Lj1xpv61Q3b2sa7MBVfUd\nSd607DoAAADYth7X3W9exoUtowAAAACGEjYAAAAAQ1lGsUNV1XFJHrHQdFGSG9ZwinvkwGUYj0vy\n8QGlwVroh2wH+iHbgX7IsumDbAf64cYdmeQfLWz/cXdfvYxCPCByh5p3mHWvvamqadPHu/sjGyoK\n1kg/ZDvQD9kO9EOWTR9kO9APh7lw2QUkllEAAAAAgwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAA\nMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUPuWXQBLc3mSn51sw1bT\nD9kO9EO2A/2QZdMH2Q70w12kunvZNQAAAAC7iGUUAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErY\nAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhq37IL\nYDmq6u5J7pfkzkmOTnJJkk8lObe7v7TM2gC2q6q6RZKHJrlbkq9Jcm2Sv01yYXd/comlAXtYVR2R\n5J5Jvi6ze7vjknwxyVVJPp7k/O6+bvA1b53ZeHjXJCck+XySi5O8v7svHXktdoZl9EO2t+ruZdfA\nFqqq05M8M8k3H2SXK5O8NsnPdPcVW1YYwDpU1dcm+aYkD5z/7wOSHLOwy6e6+8QB17ljkp9NckaS\n2x1kt3OT/EJ3v36j1wM4nKq6W5InJDk1ycOTHHuI3b+c5B1JXtTdb9ngde+e5OeSnJbkNge51ruS\nPK+7372Ra7H9bVU/rKoTk/zN+qqc6e7ayPGsnbBhj6iqo5P8RpLvXuUhlyV5ane/bfOqYi+oqrOT\nPHcDp3hld585php2g6p6ZJKfzCxgONgf/vttOGyoqkcneUWS41d5yKuTPN2vN3vHZoZeVbXRG7W7\nm3Wz+1TVa5I8cZ2Hn5Pkad192Tque2aS/5nZrNjD6SS/lOQnuvvLa70W299W9kNhw85kGcUeMJ/S\n9Nok3z756PIkFya5Osk9ktw/yf7/CE9I8qaqOrW7/3SragVYhfsl+batuNA82Pi9JEcuNHeSDyT5\nRJLbZjZ23mHh8+9JcmxVPb67b9qKOtl6awy9YLSTDtJ+cZKPZvaj0b4kX5vkvjnwOW2PTfLeqnrE\nWpY7VNWTkrwsN98rJsmNSd6f5KIkd0zyjbn5l+1K8h+T3DLJv1vtddhRtrwfsrMIG/aG/54Dg4Yv\nZbaU4iXdfcP+xqr6uiQvzc1LLG6Z5Peq6hu6+5KtKhZgnb6Y5DOZhacbVlV3TfKGHBg0/FmSH+ju\nv1rY75ZJnp7k55PcYt78r5L8lyRnjaiFbWnLQi84jAszCwH+oLs/Pv2wqu6S5GeS/OBC80lJfreq\nvqVXMc25qh6Q5OU5MGh4U5If6e6LFvY7JsmzkzxnYb8frqoPdvdL1vD/iZ1n0/vhxOuT/Ph6i2Vr\nCBt2ufnUzh+dNH9nd79pum93/2VVfWuSP8rNgcPtM5sC/283tVD2kicm+fM17H/tZhXCjvalJB9J\ncn5mv6qdn+TDmT2sbNQa4Z9N8tUL2+cmObW7r1/cqbu/mOSFVfXpJG9c+OiZVfXr3f2pQfWwMwwN\nvSb+T1a/HHK/z2xCHSxfJ3lLkrO7+/xD7th9cZKnV9UHk7x44aOHZfYcmv+9iuu9IAcGr69LcsZ0\n9lZ3X5Pkp6rq8syWUOz3X6rqt+efs3tsdT9cdK0lYtufsGH3e25u/qUtSV6xUtCwX3f//Xw93odz\n85fK91fVC7r7E5tXJnvIpb4c2KBXJvm16R/9SVI1ZjlmVd0ryVMXmm5IcuZK19yvu3+vql65cNwt\nMxuD/82QotiOtiL0WnS98ZO571xrX+juX6mqf57kXy80PzmH+SOvqk5J8q0LTVck+beHWSb2wiSP\nT/LI+fYdM1tS8XNrqZltb8v6ITvTVx1+F3aqqjoqyemT5ucf7rju/uvM1ijvty/JkwaWBrBu3X3V\nof7oH+RJSY5Y2H5Dd390FcdNx9jvqqpbjSuLbeSVSY7t7vt39w9090u6+wNeH81W2EDo9OLJ9imr\nOOYpk+2XdvfnDnXAfEr8Cw5zHna4Le6H7EDCht3tXya59cL2+7r7/63y2JdPtp8wpiSAHeG0yfZ0\nTFzR/FkO/2eh6Taxrn9X2qLQC0a7cLJ9VFXd9mA7zx8y/q8mzasaD5O8LcniM7/uUVX3WeWx7G5r\n6ofsXMKG3e1Rk+33rOHYP8nsCcP73b+qTthwRQDbXFXdKbOnZu93Y2YPhlyt90y2H73RmgAGuXGF\ntiNXaNvvmzJ7ftd+l8xnwB7WfJnFeyfNxkOStfdDdihhw+729ZPt9632wPn74T88af6nG64IYPub\njp0fmo+Jq3XuZNvYCWwX95xs35jZMxgOZt33knPGQ1ay1n7IDiVs2N1Onmx/bI3HT19b83UbqAVg\np5iOdcZOYLeYPsvr/MM86NF4yGZYaz9cyX2r6jVV9ddVdXVVfbGqLqmqD1bVS6vqyVV1m1EFsz7C\nhl2qqm6X5HaT5k+v8TTT/e+1/orgHzy9qt5ZVRdX1fVVdU1VfbKq/riq/mtVPXzZBbLnTX9xWevY\nOX3V5e2r6qtX3BPW5m5V9fKq+khVXVVVN1TVZfPt36qqH5x//8NXqKqjk3z/pPmNK+27YKPjoXtJ\nDrDOfriS+2X2OvV7JTk2s2UYd0pyn/n5X5Xk01X101V1i4OehU0lbNi9pg9Z+bs1TgNOks9Oto/b\nQD2w33dn9gqtO2f2asCjk/zjJN+S5Kwk762q91fVqcsrkT1uOn5Ox8JD6u5rk0wfHGj8ZIS7Jzkz\ns1+Hb5vZq62Pn29/T5Jfz+zm+hfnN/Sw6HmZ/TG23+eTvPQwx2xoPFxh/2Oqyt8fe9t6+uF63S6z\n163+SVXddZOuwSH4j333mt5k/P06zjE95ph11gJr9cAkb5/PdKhlF8OeY/xkJ7tNkv+Q5IKqsj6e\nJElVnZbk30+an9PdVx7m0I2Oh9P9K7M+yh60gX646Pokf5DkJzJ74Oj9Mpvd8MAk35nkV5J8YXLM\ngzK7r/TGiy22b9kFsGmmXw7reT3X9AvCryRsxMVJ3prkvCR/leTKJDdl9pTrByR5bGava92vMpvp\n8FVJfnJLK2WvGzV+Li6dMH6yETcm+dMk70zyoSSfSXJNZv3qbkkenuQpmc1y2O+kJO+sqgd393Rp\nD3tIVd03synli96e5FdXcfhGx8OVwomjM+u/7CEb7IdJcl2SH03yiu6ehgn7XZDkdVV1VpJfTvLU\nhc9OTvKyJE9YddFsmLBh7+gtOgamzsssRHhHdx+sT52b5EVV9cAkr8mBazr/U1X9eXe/aZPrhIMx\nfrJMP5XkN7r7YNPX/2+SN1fVTyd5bpJnZxbWJrOpym+oqgceYvxlF6uquyV5Sw4MDT6V5HvX2SfW\neox+x5B+2N2XJ3nhKve9OsmZVXV5kh9f+Oi0qnpod6/lddZsgGUUu9e1k+2j1nGO6THTc8Jhdfdb\nu/vtq/ky6e7zkzw4yfQd3v+9qo7YlALhKxk/2Ta6+78eImhY3O/67v7JJD8y+egBmT1EjT2mqo5P\n8o4kd1lovjTJv5j/4bYaGx0PV9rfeLiHDOqH6/WszALZRT+0yddkgbBh93KzzI40X7f3xBz4a8i9\nk5yynIrYg4yf7Fjd/eIkb540//AyamF55m8leWdmy2n2uyLJqd390TWcajPChrU+sJwdamA/XJf5\nD13PnzR/m+eBbR1hw+519WT71ut41+zxk+3Pb6AeWLXu/kBm6/gWPWoZtbAnTcfPO67l4PlbAKY3\n2MZPttLzJtsP9mC0vaOqjsvsO/QbFpqvyuyX5I+s8XQbGg/zlfeSX+jum9Z4Dnagwf1wI9422b5j\nkq/ZwuvvacKGXaq7P5fZf9CL7rbG0/zjyfamJ5Cw4A8n2/dZShXsRdOxbjoWHs50/yu7ezoew2Y6\nLwfeAxyR2esx2eWq6pjMvj+/caH5C0ke1d3T6eSrMXo8dC+5B2xCP1y3+ffvRkMz1knYsLv91WT7\nnms8/msPcz7YTJ+cbGMIdq0AAAe+SURBVPtiYKuMHjv/cgO1wJrNfzn+9KTZGLrLzWewvjWzZx/t\nd22SR3f3ees8rXtJ1mST+uFGTd+Ksp7lkayDsGF3+4vJ9jev9sD5QDH9JXl6PthMvhhYlulYd5+q\nuvUajn/oYc4HW8EYuodU1VFJzknysIXmv0vymO4+dwOnXve95JzxcA/ZxH64kZoqs9esL7piGbXs\nRcKG3W06Df2Razj24Tnw1agXdvdlG64IVu8Ok21fDGyJ7r4kyYcWmvblwBunw3nkZPsPNloTrIMx\ndI+oqltl9lDQRy40X5/kO7r7vRs8/fuTXLmw/TVVddLBdp7U9VWZ3U8uMh7uUpvcDzfi/klusbB9\nU2Zvw2ALCBt2t7flwF82vrmq7r3KY8+cbL9xSEWweg+abP/tUqpgr5qOed+3moPmY+xi370uX/mw\nU9hUVXWHfOX0dWPoLlRVRyZ5Q5JTF5q/mOTx3f1HGz1/d9+Y5PcnzasaD5N8W5I7L2x/vLs/dLCd\n2bk2ux9u0JMm2xd0tzdEbRFhwy7W3X+X5HWT5mcf7rh5Yn3aQtONSV4zsDQ4pHk6/oRJ83uWUAp7\n16uTfHlh+wlVda9VHDcdY3+nu68fVxasynfnwHu8y2Kt/K5TVfuS/E6SRy80fynJ6d09fQL/Rrxq\nsv20qppOS1/Jsw5zHnaBLeyHazb/AWD66t83LaOWvUrYsPudndl/8PudWVXfcbCd53/kvTzJkQvN\n/6u7P7455cGKnp3kLgvbX07yliXVwh40f//3KxeajkzyivkYuaKqelwOnBV2Q5Kf3ZQC4SCq6oQk\nPzVp/v35++bZJarqiMxC0cctNN+Y5IzuPmfktbr7XUnetdB0hyS/Nl8mcbD6npHklIWmK5L84si6\nWL6t6odV9aCqmi7JOdwxJ2X2oMrF59V8LsmLRtXF4e07/C7sZN39iar65SQ/vtD8uqp6ZpKXdPcN\n+xur6uQkL03ykIV9Pxc3y6xTVT05ydvX8ryPqvqBJM+dNL+iuz81tDh2tKq6a1b+DrvTZHtfVZ14\nkNNc292HWsf+3MxmeX31fPshSd5ZVU/r7v+3UMstk/xgkv8xOf5/6LesV1X9kyQndfd0CvuhjrlT\nZlPeT1hoviHJ8waXx/K9LMl3TdrOSnLhIca8g7l0FTOwfiLJ+3Lzj1GnJ3l9VT2juy/av9P8lYfP\nSvKcyfHP6e5r1lgX299W9cOTk7y8qs7NbLb1mxf73aL5MrKnZ/bD1TGTj5/Z3dPXYLKJStC9+81T\nx9/PgdObkuSzST6Q5JrM1nY+IEktfH5DklO7+0+2ok52n6p6T5J/luR3M5ti957uvu4g+z4wsy+o\n0yYfXZzkgd3tYT78g6r6ZNb+vvepV3b3mYe5ziMze/7N4myvTnJBkk8kOS6zsXP6WsFzMlur+uWw\nax0i9Hpwkt9e2L44B3/I6Iqh17zvvTvJh5P8VpI3zmfcrFTHMUmemtmMhhMmH/9Md//nQ/zfYAeq\nqpE38Kd093tWcc3vyawvLroxyXlJLspsxsM3JTl2ss+vdvd0Kju7wFb1w6o6M7OZ14suz+zV0ldm\n9nykY5PcPcnX58C/Z/Y7q7sFr1tM2LBHVNXRmc1aOGOVh3w2yVO7e/pGC1i1edjwiIWmm5J8NMkn\nk1yd2fKI2ye5b77yBjmZfYE8oru9KosDbFXYML/Wtyd5Rb4yUDiY307yAwcL1tg9NrMfLoQNi67O\n7NWBV2T2Q8HRSf5RZmPoSqHHS7r76Rusj21oGWHD/Lrfl+SFmfW9w+n5vj8meN2dlhw2rNblSZ7W\n3W9e5/FsgGUUe8T8qavfXVWvS/Jjmf3qspIrk7w2yXO7+/Ktqo8946uS/JP5v8P5oyRndvdnNrck\nOLTufmtVfX1mS8rOyM3LKqb+PMnPd/frt6w49prjkjx0Fftdl+Q/dvdvbHI97DHd/fKq+uMkP5fk\n8Ulus8JuN2X2jIf/1t3TwAzW40+T/EKSb0lynxw423AlndkrrF+aWaBrCc+SmNmwR1XV3TOb+nvn\nzL4oLk3yqSR/tvgcB9iIqjots3WdD83qfv3b/5rAF2+DVyXBV5i/3mt/f75TZn324iQXdvffLLM2\ntt4mz2w4IckzMpsd9oAc+JCzg/nrzGbh/MZhnkcCG1ZVt8lsedBdkxyf5POZvWL1vO6+ZJm1sXtV\n1S2S3DuzJRN3zmz5xC0z+z6+KrMlPe/3bIbtQdgAbImqum2Sf5rZlN8Tktw6s5kOn8/sy+GvknzI\nVEuAA82f+n+vJPfI7E09t01yqyR/n9n4eUlmN9dmJAKwbQgbAAAAgKEO+n5cAAAAgPUQNgAAAABD\nCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkb\nAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAA\nAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACA\noYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGE\nDQAAAMBQ/x8/hNcBUZE5hQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ffcf630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True  5  - Predicted  6  with prob.  0.999982\n"
     ]
    },
    {
     "data": {
      "image/png": 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K2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAA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\nQ/3/zTH7E2ipBkUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ff759b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True  5  - Predicted  3  with prob.  0.54224753\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVs\nAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFD7ll0AADvftddeu9Trn3XWWUu9fpJcddVVyy5h6W5z\nm9ss9fpHH330Uq8PANzEzAYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJ\nGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsA\nAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAA\ngKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAICh\nhA0AAADAUPuWXQAAO9/ZZ5+91Ou//e1vX+r1t4MTTzxx2SXkbW9721Kvf8oppyz1+gDATcxsAAAA\nAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACG\nEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2\nAAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAA\nAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGGrfsgsAYOc74ogjlnr9448/fqnX\nT5LnPve5S73+s571rKVeP0nueMc7LrsEAGCb2JZhQ1UdkeQeSb42yZ2SHJfki0muTPLxJOd197XL\nqxAAAAA4lG0TNlTVSUmemOS0JI9Icuxhdv9yVb0ryUu7+y1rvM5dk/zdOstMknR3beR4AAAA2M22\nRdhQVa9J8pQ1HHJEkkcneXRVvTnJM7v7M5tSHAAAALAm2yJsSHLyIdovTvKxJJ/JrNavSXK/HPhg\ny8cleV9VPbK7L93UKgEAAIAVbZewYdH5SV6R5G3d/fHph1V15yQ/leT7F5pPTvJ7VfVN3d1rvN7r\nkvzoeosFAAAADrRdwoZO8pYkP93d5x12x+6Lkzy7qj6U5GULHz08yZOT/PYar31Nd1+4xmMAAACA\nQ7jZyrtsiW/v7setFDQs6u5fzmxWwqLvGVsWAAAAsFbbImzYwMyCl022T91gKQAAAMAGbYuwYQPO\nn2zfsqqOX0olAAAAQJKdHzbccJC2I7e8CgAAAOCf7PSw4R6T7RuSXL6MQgAAAICZ7fI2ivV60mT7\nvO6+cY3nuF9VvSbJg5KckOQWSa5I8tkk70/yx0le393XbrRYAAAA2At27MyGqjo6yfdNmt+wjlPd\nP8lTktwzybGZLcM4Mcl95+f/9SSfqqqfrKqbr79iAAAA2Bt28syGF2UWCux3VZKXb9K1bpvkZ5N8\na1U9qbs/PfLkVXWHJLdf42F3H1kDAAAAjLIjw4aqOj3JD06aX9jdV6zhNNcl+aMk70ny0SSXJLk2\nyXFJ7pbZazS/O7PZDvs9OMk7q+qh3X3VOss/mB9IctbA8wEAAMDS7Liwoarul9nShkXvTPIrqzzF\ntUl+OMmruvvzh9jnA0leW1VnJvmlJE9f+OyUJK9I8sRVFw0AAAB7yI56ZkNVnZTkLUmOXmj+ZJLv\n7u5ezTm6+7LufslhgobFfa/u7jOS/Pzko9Or6mGrLBsAAAD2lB0zs2H+XIN3JbnzQvOlSf5Vd1+2\nyZd/fpLTMnuY5H7/PsmfDTr/Lyf5vTUec/ckbxx0fQAAABhmR4QNVXXbJO9OcvJC8+VJTuvuj232\n9bu7q+rnkvzWQvO3VFWtdkbFCuf/bGav2ly1qtroZQEAAGBTbPtlFFV1XGbPZPi6heYrM5vRcMEW\nlvKOyfbtk9xxC68PAAAAO8K2Dhuq6pgkb0/y9QvNn0/y6O7+f1tZS3dfmeTqSfNaX1cJAAAAu962\nDRuq6tZJ3prkIQvN1yR5THefu5yq8o+T7VsupQoAAADYxrZl2FBVt0zy5iQPX2j+hyTf2t3nLKmm\nSvJVk+bLl1ELAAAAbGfbLmyoqlskeVOSRy00X5fk27r7fUspauYBSW6+sH1jZm/DAAAAABZsq7Ch\nqo5M8vrMXjO53xeTPKG7/3A5Vf2Tp062P9Dd1yylEgAAANjGtk3YUFX7kvxukscsNH8pyZO6e/om\niC1VVfdK8gOT5jcuoxYAAADY7rZF2FBVRyT5zSSPX2i+IcmTu/vNA6/z4Kp6xBqPOTmzB1UuPgzy\nc0leOqouAAAA2E32LbuAuVck+Y5J25lJzq+qu67xXJd293WH+OyUJK+sqnOSvCbJm7r7ooPtWFW3\nS/LsJC9Icszk4+d19/Q1mAAAAEC2T9jwtIO0vXj+b61OTfLeFfZ56PzfS6vqsiR/meSKJNcmOTbJ\n3ZLcJ0kd5Ngzu/vX11EXAAAA7AnbJWxYptsneeQq9rssyTO7+02bXA8AAADsaNvimQ1b6E+T/EKS\n85Jcv4r9O8mHkvxQkrsLGgAAAGBl1d3LrmEpqurmSe6V2ZKJO2W2fOKozJZSXJnkoiTv367PZqiq\neyf56P7tj370o7n3ve+9xIoAAABYpgsuuCD3uc99Fpvu090XLKOWPbuMoru/lOQj838AAADAIHtt\nGQUAAACwyYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkb\nAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAA\nAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACA\noYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGE\nDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0A\nAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAA\nwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQ\nwgYAAABgKGEDAAAAMJSwAQAAABhq37ILYN2OXNz427/922XVAQAAwDZwkL8LjzzYfluhuntZ12YD\nqurbkrxx2XUAAACwbT2+u9+0jAtbRgEAAAAMJWwAAAAAhrKMYoeqquOSPHKh6aIk16/hFHfPgcsw\nHp/k4wNKg7XQD9kO9EO2A/2QZdMH2Q70w407Msk/W9j+4+6+ehmFeEDkDjXvMOtee1NV06aPd/cF\nGyoK1kg/ZDvQD9kO9EOWTR9kO9APhzl/2QUkllEAAAAAgwkbAAAAgKGEDQAAAMBQwgYAAABgKGED\nAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUPuWXQBLc1mSn5ls\nw1bTD9kO9EO2A/2QZdMH2Q70w12kunvZNQAAAAC7iGUUAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAA\nGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhq\n37ILYDmq6m5J7p/kTkmOTnJJkk8mOae7v7TM2gC2q6q6eZKHJTkpyR2TXJPk75Oc390XLrE0YA+r\nqiOS3CPJ12Z2b3dcki8muTLJx5Oc193XDr7mrTIbD++S5IQkVyW5OMn7u/vSkddiZ1hGP2R7q+5e\ndg1soap6UpLnJfnGQ+xyRZLfSfJT3X35lhUGsA5V9TVJviHJg+b/+8Akxyzs8snuvuuA69w+yc8k\neXKS2x5it3OS/EJ3v26j1wNYSVWdlOSJSU5L8ogkxx5m9y8neVeSl3b3WzZ43bsl+dkkpye59SGu\n9Z4kL+ruP9rItdj+tqofVtVdk/zd+qqc6e7ayPGsnbBhj6iqo5P8WpLvXOUhn0ny9O5+x+ZVxV5Q\nVT+d5KwNnOLV3X3GmGrYDarqUUl+PLOA4VB/+O+34bChqh6T5FVJ7rDKQ34zybP9erN3bGboVVUb\nvVG7m1k3u09VvSbJU9Z5+JuTPLO7P7OO656R5H9lNit2JZ3kfyb5se7+8lqvxfa3lf1Q2LAzWUax\nB8ynNP1OksdOProsyflJrk5y9yQPSLL/P8ITkryxqk7r7j/dqloBVuH+Sb5lKy40DzZ+P8mRC82d\n5INJPpHk+MzGztstfP5dSY6tqid0941bUSdbb42hF4x28iHaL07yscx+NNqX5GuS3C8HPqftcUne\nV1WPXMtyh6p6apJX5KZ7xSS5Icn7k1yU5PZJvj43/bJdSZ6b5Kgk/2G112FH2fJ+yM4ibNgb/nsO\nDBq+lNlSirO7+/r9jVX1tUlenpuWWByV5Per6uu6+5KtKhZgnb6Y5NOZhacbVlV3SfL6HBg0/FmS\nZ3X3Xy3sd1SSZyf5+SQ3nzf/myT/JcmZI2phW9qy0AtWcH5mIcDbuvvj0w+r6s5JfirJ9y80n5zk\n96rqm3oV05yr6oFJXpkDg4Y3Jvmh7r5oYb9jkrwgyQsX9vuBqvpQd5+9hv9P7Dyb3g8nXpfkR9db\nLFtD2LDLzad2/vCk+du7+43Tfbv7L6vqm5P8YW4KHL4qsynw/25TC2UveUqSv1jD/tdsViHsaF9K\nckGS8zL7Ve28JB/J7GFlo9YI/0yS2yxsn5PktO6+bnGn7v5ikpdU1aeSvGHho+dV1f/u7k8Oqoed\nYWjoNfF/s/rlkPt9ehPqYPk6yVuS/HR3n3fYHbsvTvLsqvpQkpctfPTwzJ5D89uruN6Lc2Dw+tok\nT57O3uruLyT5iaq6LLMlFPv9l6r6rfnn7B5b3Q8XXWOJ2PYnbNj9zspNv7QlyasOFjTs193/OF+P\n95Hc9KXyfVX14u7+xOaVyR5yqS8HNujVSX51+kd/klSNWY5ZVfdM8vSFpuuTnHGwa+7X3b9fVa9e\nOO6ozMbgZwwpiu1oK0KvRdcZP5n79rX2he7+5ar6l0n+7ULz92SFP/Kq6tQk37zQdHmSf7fCMrGX\nJHlCkkfNt2+f2ZKKn11LzWx7W9YP2ZlutvIu7FRVdcskT5o0/9xKx3X332S2Rnm/fUmeOrA0gHXr\n7isP90f/IE9NcsTC9uu7+2OrOG46xn5HVd1iXFlsI69Ocmx3P6C7n9XdZ3f3B70+mq2wgdDpZZPt\nU1dxzNMm2y/v7s8d7oD5lPgXr3Aedrgt7ofsQMKG3e1fJ7nVwvafd/dfr/LYV062nzimJIAd4fTJ\n9nRMPKj5sxz+70LTrWNd/660RaEXjHb+ZPuWVXX8oXaeP2T830yaVzUeJnlHksVnft29qu67ymPZ\n3dbUD9m5hA2726Mn2+9dw7F/ktkThvd7QFWdsOGKALa5qjoxs6dm73dDZg+GXK33TrYfs9GaAAa5\n4SBtRx6kbb9vyOz5XftdMp8Bu6L5Mov3TZqNhyRr74fsUMKG3e0+k+0/X+2B8/fDf2TSfO8NVwSw\n/U3Hzg/Px8TVOmeybewEtot7TLZvyOwZDIey7nvJOeMhB7PWfsgOJWzY3U6ZbP/tGo+fvrbmazdQ\nC8BOMR3rjJ3AbjF9ltd5Kzzo0XjIZlhrPzyY+1XVa6rqb6rq6qr6YlVdUlUfqqqXV9X3VNWtRxXM\n+ggbdqmqum2S206aP7XG00z3v+f6K4J/8uyqendVXVxV11XVF6rqwqr646r6r1X1iGUXyJ43/cVl\nrWPn9FWXX1VVtznonrA2J1XVK6vqgqq6sqqur6rPzLd/o6q+f/79D1+hqo5O8n2T5jccbN8FGx0P\n3UtygHX2w4O5f2avU79nkmMzW4ZxYpL7zs//60k+VVU/WVU3P+RZ2FTCht1r+pCVf1jjNOAk+exk\n+7gN1AP7fWdmr9C6U2avBjw6yVcn+aYkZyZ5X1W9v6pOW16J7HHT8XM6Fh5Wd1+TZPrgQOMnI9wt\nyRmZ/Tp8fGavtr7DfPu7kvzvzG6uf3F+Qw+LXpTZH2P7XZXk5Sscs6Hx8CD7H1NV/v7Y29bTD9fr\ntpm9bvVPquoum3QNDsN/7LvX9CbjH9dxjukxx6yzFlirByV553ymQy27GPYc4yc72a2T/MckH6gq\n6+NJklTV6Ul+cNL8wu6+YoVDNzoeTvevzPooe9AG+uGi65K8LcmPZfbA0ftnNrvhQUm+PckvJ/n8\n5JgHZ3Zf6Y0XW2zfsgtg00y/HNbzeq7pF4RfSdiIi5O8Ncm5Sf4qyRVJbszsKdcPTPK4zF7Xul9l\nNtPhZkl+fEsrZa8bNX4uLp0wfrIRNyT50yTvTvLhJJ9O8oXM+tVJSR6R5GmZzXLY7+Qk766qh3T3\ndGkPe0hV3S+zKeWL3pnkV1Zx+EbHw4OFE0dn1n/ZQzbYD5Pk2iQ/nORV3T0NE/b7QJLXVtWZSX4p\nydMXPjslySuSPHHVRbNhwoa9o7foGJg6N7MQ4V3dfag+dU6Sl1bVg5K8Jgeu6fxPVfUX3f3GTa4T\nDsX4yTL9RJJf6+5DTV//f0neVFU/meSsJC/ILKxNZlOVX19VDzrM+MsuVlUnJXlLDgwNPpnku9fZ\nJ9Z6jH7HkH7Y3Zcleckq9706yRlVdVmSH1346PSqelh3r+V11myAZRS71zWT7Vuu4xzTY6bnhBV1\n91u7+52r+TLp7vOSPCTJ9B3e/72qjtiUAuErGT/ZNrr7vx4maFjc77ru/vEkPzT56IGZPUSNPaaq\n7pDkXUnuvNB8aZJ/Nf/DbTU2Oh4ebH/j4R4yqB+u1/MzC2QX/ftNviYLhA27l5tldqT5ur2n5MBf\nQ+6V5NTlVMQeZPxkx+rulyV506T5B5ZRC8szfyvJuzNbTrPf5UlO6+6PreFUmxE2rPWB5exQA/vh\nusx/6Pq5SfO3eB7Y1hE27F5XT7ZvtY53zd5hsn3VBuqBVevuD2a2jm/Ro5dRC3vSdPy8/VoOnr8F\nYHqDbfxkK71osv0QD0bbO6rquMy+Q79uofnKzH5JvmCNp9vQeJivvJf8fHffuMZzsAMN7ocb8Y7J\n9u2T3HELr7+nCRt2qe7+XGb/QS86aY2n+erJ9qYnkLDg7ZPt+y6lCvai6Vg3HQtXMt3/iu6ejsew\nmc7NgfcAR2T2ekx2uao6JrPvz69faP58kkd393Q6+WqMHg/dS+4Bm9AP123+/bvR0Ix1Ejbsbn81\n2b7HGo//mhXOB5vpwsm2Lwa2yuix8y83UAus2fyX409Nmo2hu9x8ButbM3v20X7XJHlMd5+7ztO6\nl2RNNqkfbtT0rSjrWQsr6r0AAAeGSURBVB7JOggbdrePTra/cbUHzgeK6S/J0/PBZvLFwLJMx7r7\nVtWt1nD8w1Y4H2wFY+geUlW3TPLmJA9faP6HJN/a3eds4NTrvpecMx7uIZvYDzdSU2X2mvVFly+j\nlr1I2LC7TaehP2oNxz4iB74a9fzu/syGK4LVu91k2xcDW6K7L0ny4YWmfTnwxmklj5psv22jNcE6\nGEP3iKq6RWYPBX3UQvN1Sb6tu9+3wdO/P8kVC9t3rKqTD7XzpK6bZXY/uch4uEttcj/ciAckufnC\n9o2ZvQ2DLSBs2N3ekQN/2fjGqrrXKo89Y7L9hiEVweo9eLL990upgr1qOuZ972oOmo+xi3332nzl\nw05hU1XV7fKV09eNobtQVR2Z5PVJTlto/mKSJ3T3H270/N19Q5I/mDSvajxM8i1J7rSw/fHu/vCh\ndmbn2ux+uEFPnWx/oLu9IWqLCBt2se7+hySvnTS/YKXj5on16QtNNyR5zcDS4LDm6fgTJ83vXUIp\n7F2/meTLC9tPrKp7ruK46Rj7u9193biyYFW+Mwfe430m1srvOlW1L8nvJnnMQvOXkjypu6dP4N+I\nX59sP7OqptPSD+b5K5yHXWAL++GazX8AmL76943LqGWvEjbsfj+d2X/w+51RVd92qJ3nf+S9MsmR\nC83/p7s/vjnlwUG9IMmdF7a/nOQtS6qFPWj+/u9XLzQdmeRV8zHyoKrq8TlwVtj1SX5mUwqEQ6iq\nE5L8xKT5D+bvm2eXqKojMgtFH7/QfEOSJ3f3m0deq7vfk+Q9C023S/Kr82USh6rvOUlOXWi6PMkv\njqyL5duqflhVD66q6ZKclY45ObMHVS4+r+ZzSV46qi5Wtm/lXdjJuvsTVfVLSX50ofm1VfW8JGd3\n9/X7G6vqlCQvT/LQhX0/FzfLrFNVfU+Sd67leR9V9awkZ02aX9XdnxxaHDtaVd0lB/8OO3Gyva+q\n7nqI01zT3Ydbx35WZrO8bjPffmiSd1fVM7v7rxdqOSrJ9yf5H5Pj/4d+y3pV1T9PcnJ3T6ewH+6Y\nEzOb8n7CQvP1SV40uDyW7xVJvmPSdmaS8w8z5h3KpauYgfVjSf48N/0Y9aQkr6uq53T3Rft3mr/y\n8PlJXjg5/oXd/YU11sX2t1X98JQkr6yqczKbbf2mxX63aL6M7NmZ/XB1zOTj53X39DWYbKISdO9+\n89TxD3Lg9KYk+WySDyb5QmZrOx+YpBY+vz7Jad39J1tRJ7tPVb03yb9I8nuZTbF7b3dfe4h9H5TZ\nF9Tpk48uTvKg7vYwH/5JVV2Ytb/vferV3X3GCtd5VGbPv1mc7dVJPpDkE0mOy2zsnL5W8M2ZrVX9\ncti1DhN6PSTJby1sX5xDP2T0oKHXvO/9UZKPJPmNJG+Yz7g5WB3HJHl6ZjMaTph8/FPd/Z8P83+D\nHaiqRt7An9rd713FNb8rs7646IYk5ya5KLMZD9+Q5NjJPr/S3dOp7OwCW9UPq+qMzGZeL7oss1dL\nX5HZ85GOTXK3JPfJgX/P7Hdmdwtet5iwYY+oqqMzm7Xw5FUe8tkkT+/u6RstYNXmYcMjF5puTPKx\nJBcmuTqz5RFfleR++cob5GT2BfLI7vaqLA6wVWHD/FqPTfKqfGWgcCi/leRZhwrW2D02sx8uhA2L\nrs7s1YGXZ/ZDwdFJ/llmY+jBQo+zu/vZG6yPbWgZYcP8ut+b5CWZ9b2V9HzfHxG87k5LDhtW67Ik\nz+zuN63zeDbAMoo9Yv7U1e+sqtcm+ZHMfnU5mCuS/E6Ss7r7sq2qjz3jZkn++fzfSv4wyRnd/enN\nLQkOr7vfWlX3yWxJ2ZNz07KKqb9I8vPd/botK4695rgkD1vFftcmeW53/9om18Me092vrKo/TvKz\nSZ6Q5NYH2e3GzJ7x8N+6exqYwXr8aZJfSPJNSe6bA2cbHkxn9grrl2cW6FrCsyRmNuxRVXW3zKb+\n3imzL4pLk3wyyZ8tPscBNqKqTs9sXefDsrpf//a/JvBl2+BVSfAV5q/32t+fT8ysz16c5Pzu/rtl\n1sbW2+SZDSckeU5ms8MemAMfcnYof5PZLJxfW+F5JLBhVXXrzJYH3SXJHZJcldkrVs/t7kuWWRu7\nV1XdPMm9MlsycafMlk8cldn38ZWZLel5v2czbA/CBmBLVNXxSe6d2ZTfE5LcKrOZDldl9uXwV0k+\nbKolwIHmT/2/Z5K7Z/amnuOT3CLJP2Y2fl6S2c21GYkAbBvCBgAAAGCoQ74fFwAAAGA9hA0AAADA\nUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDC\nBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYA\nAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAA\nYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAo\nYQMAAAAw1P8HcIbA53ksEGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ea656a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True  9  - Predicted  7  with prob.  0.7327373\n"
     ]
    },
    {
     "data": {
      "image/png": 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AQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ea894e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True  4  - Predicted  0  with prob.  0.47136846\n"
     ]
    },
    {
     "data": {
      "image/png": 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66yhTAAAAwAwTG5aotbY+yeGDw/86xiwAAAAwy8SGpXtxtv37+kqSG0eaBQAA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\nZM0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13f7921d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True  5  - Predicted  3  with prob.  0.7615138\n"
     ]
    },
    {
     "data": {
      "image/png": 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K2AAAAAAMJWwAAAAAhqruXnYNbEBV3TvJBw9sf/CDH8y9733vJVYE\nAADAMp1//vm5z33us9h0n+4+fxm1mNkAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDC\nBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYA\nAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAA\nYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAo\nYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGED\nAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAA\nADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAw\nlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSw\nAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEA\nAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAA\nGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK\n2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgA\nAAAADHXksgtYSVUdkeTuSb42ye2THJfkC0kuT/KRJOd299XLqxAAAAA4lB0TNlTVnZI8PsnJSR6e\n5NjD7P6lqnprkud39xvXeZ07J/nHDZaZJOnu2szxAAAAsJftiLChql6Z5EnrOOSIJI9K8qiqekOS\np3b3p7akOAAAAGBddkTYkOSEQ7RfmORDST6VWa13TXLfHPysicckeVdVPaK7L97SKgEAAIBV7ZSw\nYdF5SV6S5I+7+yPTD6vqDkl+JskPLDSfkOQPquqburvXeb1XJ/nxjRYLAAAAHGynhA2d5I1Jfra7\nzz3sjt0XJnlaVb0vyQsWPnpYkicm+d11Xvuq7v7YOo8BAAAADmGnvPryCd39mNWChkXd/euZzUpY\n9N1jywIAAADWa0eEDZuYWfCCyfZJmywFAAAA2KQdETZswnmT7ZtW1S2WUgkAAACQZPeHDdet0HbU\ntlcBAAAA/IvdHjbcfbJ9XZJLl1EIAAAAMLNT3kaxUadOts/t7uvXeY77VtUrkzwwyfFJbpLksiSf\nTvLuJH+W5DXdffVmiwUAAID9YNfObKiqo5N836T5tRs41f2SPCnJPZIcm9kyjNslOXF+/pcn+URV\n/XRV3WjjFQMAAMD+sJtnNjwns1DggCuSvHiLrnXLJD+f5Fur6tTu/uTIk1fVbZPcZp2H3W1kDQAA\nADDKrgwbquqUJP950nxmd1+2jtNck+QdSd6e5INJLkpydZLjktwls9doPiWz2Q4HPCjJW6rqId19\nxQbLX8kPJ3n2wPMBAADA0uy6sKGq7pvZ0oZFb0nyG2s8xdVJfjTJWd392UPs854kr6qqM5L8WpLT\nFj67V5KXJHn8mosGAACAfWRXPbOhqu6U5I1Jjl5o/niSp3R3r+Uc3X1Jdz/vMEHD4r5XdvfpSX55\n8tEpVfXQNZYNAAAA+8qumdkwf67BW5PcYaH54iT/trsv2eLLPzPJyZk9TPKAH0ryl4PO/+tJ/mCd\nx9wtyesGXR8AAACG2RVhQ1XdMsnbkpyw0HxpkpO7+0Nbff3u7qr6pSS/s9D8LVVVa51Rscr5P53Z\nqzbXrKo2e1kAAADYEjt+GUVVHZfZMxm+bqH58sxmNJy/jaW8ebJ9myRftY3XBwAAgF1hR4cNVXVM\nkj9J8vULzZ9N8qju/pvtrKW7L09y5aR5va+rBAAAgD1vx4YNVXXzJG9K8uCF5quSPLq7z1lOVfnn\nyfZNl1IFAAAA7GA7MmyoqpsmeUOShy00fz7Jt3b32UuqqZLcatJ86TJqAQAAgJ1sx4UNVXWTJK9P\n8siF5muSfHt3v2spRc3cP8mNFravz+xtGAAAAMCCHRU2VNVRSV6T2WsmD/hCksd1958up6p/8eTJ\n9nu6+6qlVAIAAAA72I4JG6rqyCS/n+TRC81fTHJqd0/fBLGtquqeSX540vy6ZdQCAAAAO92OCBuq\n6ogkr0jy2IXm65I8sbvfMPA6D6qqh6/zmBMye1Dl4sMgP5Pk+aPqAgAAgL3kyGUXMPeSJN8xaTsj\nyXlVded1nuvi7r7mEJ/dK8lLq+rsJK9M8vruvmClHavq1kmeluRZSY6ZfPyM7p6+BhMAAADIzgkb\nvmeFtufO/63XSUneuco+D5n/e35VXZLkb5NcluTqJMcmuUuS+ySpFY49o7tfvoG6AAAAYF/YKWHD\nMt0mySPWsN8lSZ7a3a/f4noAAABgV9sRz2zYRn+R5FeSnJvk2jXs30nel+RHktxN0AAAAACr2xEz\nG7p7peUKW3GdDyf5sSSpqhsluWdmSyZun9nyiRtntpTi8iQXJHm3ZzMAAADA+uyIsGEZuvuLST4w\n/wcAAAAMst+WUQAAAABbTNgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAo\nYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGED\nAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAA\nADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAw\nlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSw\nAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEA\nAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAA\nGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGOXHYBbNhRixsf/vCHl1UHAAAAO8AKfxcetdJ+\n26G6e1nXZhOq6tuTvG7ZdQAAALBjPba7X7+MC1tGAQAAAAwlbAAAAACGsoxil6qq45I8YqHpgiTX\nruMUd8vByzAem+QjA0qD9dAP2Qn0Q3YC/ZBl0wfZCfTDzTsqyb9a2P6z7r5yGYV4QOQuNe8wG157\nU1XTpo909/mbKgrWST9kJ9AP2Qn0Q5ZNH2Qn0A+HOW/ZBSSWUQAAAACDCRsAAACAoYQNAAAAwFDC\nBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQRy67\nAJbmkiQ/N9mG7aYfshPoh+wE+iHLpg+yE+iHe0h197JrAAAAAPYQyygAAACAoYQNAAAAwFDCBgAA\nAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABg\nKGEDAAAAMNSRyy6A5aiquyS5X5LbJzk6yUVJPp7k7O7+4jJrA9ipqupGSR6a5E5JvirJVUn+Kcl5\n3f2xJZYG7GNVdUSSuyf52szu7Y5L8oUklyf5SJJzu/vqwde8WWbj4R2THJ/kiiQXJnl3d1888lrs\nDsvoh+xs1d3LroFtVFWnJnlGkm88xC6XJfm9JD/T3ZduW2EAG1BVd03yDUkeOP/fByQ5ZmGXj3f3\nnQdc5zZJfi7JE5Pc8hC7nZ3kV7r71Zu9HsBqqupOSR6f5OQkD09y7GF2/1KStyZ5fne/cZPXvUuS\nn09ySpKbH+Jab0/ynO5+x2auxc63Xf2wqu6c5B83VuVMd9dmjmf9hA37RFUdneRFSb5zjYd8Kslp\n3f3mrauK/aCqfjbJszdxipd19+ljqmEvqKpHJvnJzAKGQ/3hf8Cmw4aqenSSs5Lcdo2HvCLJ0/x6\ns39sZehVVZu9UbuLWTd7T1W9MsmTNnj4G5I8tbs/tYHrnp7k/2Q2K3Y1neR/J/mJ7v7Seq/Fzred\n/VDYsDtZRrEPzKc0/V6Sfz/56JIk5yW5Msndktw/yYH/CI9P8rqqOrm7/2K7agVYg/sl+ZbtuNA8\n2PjDJEctNHeS9yb5aJJbZDZ23nrh8+9KcmxVPa67r9+OOtl+6wy9YLQTDtF+YZIPZfaj0ZFJ7prk\nvjn4OW2PSfKuqnrEepY7VNWTk7wkN9wrJsl1Sd6d5IIkt0ny9bnhl+1K8l+T3DjJf1rrddhVtr0f\nsrsIG/aH/5mDg4YvZraU4oXdfe2Bxqr62iQvzg1LLG6c5A+r6uu6+6LtKhZgg76Q5JOZhaebVlV3\nTPKaHBw0/GWS7+/uv1vY78ZJnpbkl5PcaN78bUl+IckZI2phR9q20AtWcV5mIcAfd/dHph9W1R2S\n/EySH1hoPiHJH1TVN/UapjlX1QOSvDQHBw2vS/Ij3X3Bwn7HJHlWkjMX9vvhqnpfd79wHf+f2H22\nvB9OvDrJj2+0WLaHsGGPm0/t/NFJ8xO6+3XTfbv7b6vqm5P8aW4IHG6V2RT4H9zSQtlPnpTkr9ex\n/1VbVQi72heTnJ/k3Mx+VTs3yQcye1jZqDXCP5fkKxe2z05ycndfs7hTd38hyfOq6hNJXrvw0TOq\n6re6++OD6mF3GBp6Tfy/rH055AGf3II6WL5O8sYkP9vd5x52x+4Lkzytqt6X5AULHz0ss+fQ/O4a\nrvfcHBy8virJE6ezt7r7c0l+qqouyWwJxQG/UFW/M/+cvWO7++GiqywR2/mEDXvfs3PDL21JctZK\nQcMB3f3P8/V4H8gNXyrfV1XP7e6Pbl2Z7CMX+3Jgk16W5Denf/QnSdWY5ZhVdY8kpy00XZvk9JWu\neUB3/2FVvWzhuBtnNgb/xyFFsRNtR+i16BrjJ3NPWG9f6O5fr6p/k+Q/LDR/d1b5I6+qTkryzQtN\nlyb5wVWWiT0vyeOSPHK+fZvMllT8/HpqZsfbtn7I7vQVq+/CblVVN01y6qT5l1Y7rrv/IbM1ygcc\nmeTJA0sD2LDuvvxwf/QP8uQkRyxsv6a7P7SG46Zj7HdU1U3GlcUO8rIkx3b3/bv7+7v7hd39Xq+P\nZjtsInR6wWT7pDUc8z2T7Rd392cOd8B8SvxzVzkPu9w290N2IWHD3vbvktxsYfuvuvvv13jsSyfb\njx9TEsCucMpkezomrmj+LIf/t9B081jXvydtU+gFo5032b5pVd3iUDvPHzL+bZPmNY2HSd6cZPGZ\nX3erqhPXeCx727r6IbuXsGFve9Rk+53rOPbPM3vC8AH3r6rjN10RwA5XVbfL7KnZB1yX2YMh1+qd\nk+1Hb7YmgEGuW6HtqBXaDviGzJ7fdcBF8xmwq5ovs3jXpNl4SLL+fsguJWzY2+4z2f6rtR44fz/8\nBybN9950RQA733TsfP98TFyrsyfbxk5gp7j7ZPu6zJ7BcCgbvpecMx6ykvX2Q3YpYcPedq/J9ofX\nefz0tTVfu4laAHaL6Vhn7AT2iumzvM5d5UGPxkO2wnr74UruW1WvrKp/qKorq+oLVXVRVb2vql5c\nVd9dVTcfVTAbI2zYo6rqlkluOWn+xDpPM93/HhuvCP7F06rqbVV1YVVdU1Wfq6qPVdWfVdUvVtXD\nl10g+970F5f1jp3TV13eqqq+csU9YX3uVFUvrarzq+ryqrq2qj413/7tqvqB+fc/fJmqOjrJ902a\nX7vSvgs2Ox66l+QgG+yHK7lfZq9Tv0eSYzNbhnG7JCfOz//yJJ+oqp+uqhsd8ixsKWHD3jV9yMrn\n1zkNOEk+Pdk+bhP1wAHfmdkrtG6f2asBj07y1Um+KckZSd5VVe+uqpOXVyL73HT8nI6Fh9XdVyWZ\nPjjQ+MkId0lyema/Dt8is1db33a+/V1Jfiuzm+tfnd/Qw6LnZPbH2AFXJHnxKsdsajxcYf9jqsrf\nH/vbRvrhRt0ys9et/nlV3XGLrsFh+I9975reZPzzBs4xPeaYDdYC6/XAJG+Zz3SoZRfDvmP8ZDe7\neZL/kuQ9VWV9PEmSqjolyX+eNJ/Z3Zetcuhmx8Pp/pVZH2Uf2kQ/XHRNkj9O8hOZPXD0fpnNbnhg\nkick+fUkn50c86DM7iu98WKbHbnsAtgy0y+Hjbyea/oF4VcSNuPCJG9Kck6Sv0tyWZLrM3vK9QOS\nPCaz17UeUJnNdPiKJD+5rZWy340aPxeXThg/2YzrkvxFkrcleX+STyb5XGb96k5JHp7kezKb5XDA\nCUneVlUP7u7p0h72kaq6b2ZTyhe9JclvrOHwzY6HK4UTR2fWf9lHNtkPk+TqJD+a5KzunoYJB7wn\nyauq6owkv5bktIXP7pXkJUkev+ai2TRhw/7R23QMTJ2TWYjw1u4+VJ86O8nzq+qBSV6Zg9d0/req\n+uvuft0W1wmHYvxkmX4qyYu6+1DT1/8myeur6qeTPDvJszILa5PZVOXXVNUDDzP+sodV1Z2SvDEH\nhwYfT/KUDfaJ9R6j3zGkH3b3JUmet8Z9r0xyelVdkuTHFz46paoe2t3reZ01m2AZxd511WT7phs4\nx/SY6TlhVd39pu5+y1q+TLr73CQPTjJ9h/f/rKojtqRA+HLGT3aM7v7FwwQNi/td090/meRHJh89\nILOHqLHPVNVtk7w1yR0Wmi9O8m/nf7itxWbHw5X2Nx7uI4P64UY9M7NAdtEPbfE1WSBs2LvcLLMr\nzdftPSkH/xpyzyQnLaci9iHjJ7tWd78gyesnzT+8jFpYnvlbSd6W2XKaAy5NcnJ3f2gdp9qKsGG9\nDyxnlxrYDzdk/kPXL02av8XzwLaPsGHvunKyfbMNvGv2tpPtKzZRD6xZd783s3V8ix61jFrYl6bj\n523Wc/D8LQDTG2zjJ9vpOZPtB3sw2v5RVcdl9h36dQvNl2f2S/L56zzdpsbDfPm95Ge7+/p1noNd\naHA/3Iw3T7Zvk+SrtvH6+5qwYY/q7s9k9h/0ojut8zRfPdne8gQSFvzJZPvEpVTBfjQd66Zj4Wqm\n+1/W3dPxGLbSOTn4HuCIzF6PyR5XVcdk9v359QvNn03yqO6eTidfi9HjoXvJfWAL+uGGzb9/Nxua\nsUHChr3t7ybbd1/n8byUdcEAAAfGSURBVHdd5XywlT422fbFwHYZPXb+7SZqgXWb/3L8iUmzMXSP\nm89gfVNmzz464Kokj+7uczZ4WveSrMsW9cPNmr4VZSPLI9kAYcPe9sHJ9jeu9cD5QDH9JXl6PthK\nvhhYlulYd2JV3Wwdxz90lfPBdjCG7iNVddMkb0jysIXmzyf51u4+exOn3vC95JzxcB/Zwn64mZoq\ns9esL7p0GbXsR8KGvW06Df2R6zj24Tn41ajndfenNl0RrN2tJ9u+GNgW3X1RkvcvNB2Zg2+cVvPI\nyfYfb7Ym2ABj6D5RVTfJ7KGgj1xovibJt3f3uzZ5+ncnuWxh+6uq6oRD7Typ6ysyu59cZDzco7a4\nH27G/ZPcaGH7+szehsE2EDbsbW/Owb9sfGNV3XONx54+2X7tkIpg7R402f6npVTBfjUd8753LQfN\nx9jFvnt1vvxhp7ClqurW+fLp68bQPaiqjkrymiQnLzR/IcnjuvtPN3v+7r4uyR9Nmtc0Hib5liS3\nX9j+SHe//1A7s3ttdT/cpCdPtt/T3d4QtU2EDXtYd38+yasmzc9a7bh5Yn3KQtN1SV45sDQ4rHk6\n/vhJ8zuXUAr71yuSfGlh+/FVdY81HDcdY3+/u68ZVxasyXfm4Hu8T8Va+T2nqo5M8vtJHr3Q/MUk\np3b39An8m/HyyfZTq2o6LX0lz1zlPOwB29gP123+A8D01b+vW0Yt+5WwYe/72cz+gz/g9Kr69kPt\nPP8j76VJjlpo/r/d/ZGtKQ9W9Kwkd1jY/lKSNy6pFvah+fu/X7bQdFSSs+Zj5Iqq6rE5eFbYtUl+\nbksKhEOoquOT/NSk+Y/m75tnj6iqIzILRR+70Hxdkid29xtGXqu7357k7QtNt07ym/NlEoeq7+lJ\nTlpoujTJr46si+Xbrn5YVQ+qqumSnNWOOSGzB1UuPq/mM0meP6ouVnfk6ruwm3X3R6vq15L8+ELz\nq6rqGUle2N3XHmisqnsleXGShyzs+5m4WWaDquq7k7xlPc/7qKrvT/LsSfNZ3f3xocWxq1XVHbPy\nd9jtJttHVtWdD3Gaq7r7cOvYn53ZLK+vnG8/JMnbquqp3f33C7XcOMkPJPlfk+P/l37LRlXV1yQ5\nobunU9gPd8ztMpvyfvxC87VJnjO4PJbvJUm+Y9J2RpLzDjPmHcrFa5iB9RNJ/io3/Bh1apJXV9XT\nu/uCAzvNX3n4zCRnTo4/s7s/t8662Pm2qx/eK8lLq+rszGZbv36x3y2aLyN7WmY/XB0z+fgZ3T19\nDSZbqATde988dfyjHDy9KUk+neS9ST6X2drOBySphc+vTXJyd//5dtTJ3lNV70zyr5P8QWZT7N7Z\n3VcfYt8HZvYFdcrkowuTPLC7PcyHf1FVH8v63/c+9bLuPn2V6zwys+ffLM726iTvSfLRJMdlNnZO\nXyv4hszWqn4p7FmHCb0enOR3FrYvzKEfMrpi6DXve+9I8oEkv53ktfMZNyvVcUyS0zKb0XD85OOf\n6e7/fpj/G+xCVTXyBv6k7n7nGq75XZn1xUXXJTknyQWZzXj4hiTHTvb5je6eTmVnD9iuflhVp2c2\n83rRJZm9WvqyzJ6PdGySuyS5Tw7+e+aAM7pb8LrNhA37RFUdndmshSeu8ZBPJzmtu6dvtIA1m4cN\nj1houj7Jh5J8LMmVmS2PuFWS++bLb5CT2RfII7rbq7I4yHaFDfNr/fskZ+XLA4VD+Z0k33+oYI29\nYyv74ULYsOjKzF4deGlmPxQcneRfZTaGrhR6vLC7n7bJ+tiBlhE2zK/7vUmel1nfW03P9/0xweve\ntOSwYa0uSfLU7n79Bo9nEyyj2CfmT139zqp6VZIfy+xXl5VcluT3kjy7uy/ZrvrYN74iydfM/63m\nT5Oc3t2f3NqS4PC6+01VdZ/MlpQ9MTcsq5j66yS/3N2v3rbi2G+OS/LQNex3dZL/2t0v2uJ62Ge6\n+6VV9WdJfj7J45LcfIXdrs/sGQ//o7ungRlsxF8k+ZUk35TkxBw823AlndkrrF+cWaBrCc+SmNmw\nT1XVXTKb+nv7zL4oLk7y8SR/ufgcB9iMqjols3WdD83afv078JrAF+yAVyXBl5m/3utAf75dZn32\nwiTndfc/LrM2tt8Wz2w4PsnTM5sd9oAc/JCzQ/mHzGbhvGiV55HAplXVzTNbHnTHJLdNckVmr1g9\np7svWmZt7F1VdaMk98xsycTtM1s+cePMvo8vz2xJz7s9m2FnEDYA26KqbpHk3plN+T0+yc0ym+lw\nRWZfDn+X5P2mWgIcbP7U/3skuVtmb+q5RZKbJPnnzMbPizK7uTYjEYAdQ9gAAAAADHXI9+MCAAAA\nbISwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCU\nsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLAB\nAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAA\nABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAY\nStgAAAAADCVsAAAAAIb6/30I4OpbJjLlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13fa87d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True  6  - Predicted  8  with prob.  0.8517948\n"
     ]
    },
    {
     "data": {
      "image/png": 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19tUkn8rCL0vclmS/JN+V5AnZ/r+ft/Te/+cy5gMAAIC5NIuxYXc7IMlxyzjv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13f978438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True  1  - Predicted  9  with prob.  0.89239544\n"
     ]
    },
    {
     "data": {
      "image/png": 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GAAAAoCthAwAAANCVsAEAAADoStgAAAAAdCVsAAAAALoSNgAAAABd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\ncrm3kdUUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13fbd0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True  7  - Predicted  3  with prob.  0.44986218\n"
     ]
    }
   ],
   "source": [
    "display_mistakes(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}