{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f4eiDCT3/fPPvfvq9vL3d7H/++fm0i6Wzt7dXk9Qkt2qLn82lNh/QnVVKuZVkb29vL7du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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122a1e0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def getXORdata(sigma=0.1, number=1000):\n",
    "\n",
    "    X = np.array([[0,0],[0,1],[1,0],[1,1]])\n",
    "    t = np.array([[0],[1],[1],[0]])\n",
    "    Xtmp = []\n",
    "    ttmp = []\n",
    "    for i in range(number):\n",
    "        idx = np.random.randint(len(t))\n",
    "        ttmp.append(t[idx])\n",
    "        Xtmp.append(X[idx]+np.random.normal(scale=sigma,size=2))\n",
    "\n",
    "    return np.asarray(Xtmp), np.asarray(ttmp)\n",
    "    \n",
    "X, t = getXORdata(sigma=0.3)\n",
    "\n",
    "Xunif = []\n",
    "grid = np.linspace(-1,2,100)\n",
    "for x1 in grid:\n",
    "    for x2 in grid:\n",
    "        Xunif.append([x1,x2])\n",
    "Xunif = np.asarray(Xunif)\n",
    "    \n",
    "def display(X, T, y=None, model=None):\n",
    "    plt.figure(figsize=(6,6))\n",
    "    if model is not None:\n",
    "        ygrid = model.predict_proba(Xunif).round()\n",
    "        tcolors = [int(i % 23) for i in ygrid]\n",
    "        idxes = np.where(ygrid == 0)\n",
    "        plt.scatter(Xunif[idxes,0], Xunif[idxes,1], s=10, c=\"black\", alpha=0.6, marker='o', linewidths=0)\n",
    "    #tcolors = [int(i % 23) for i in t]\n",
    "    #markers = ['o' if i==0 else 'x' for i in ]\n",
    "    for x,t in zip(X,T):\n",
    "        plt.scatter(x[0], x[1], s=30, c='blue' if t==0 else 'red', marker='s' if t==0 else 'x', linewidths=0 if t==0 else 2)\n",
    "    if y is not None:\n",
    "        ycolors = [int(i % 23) for i in y]\n",
    "        mistakes = np.where(T!=y)\n",
    "        plt.scatter(X[mistakes,0], X[mistakes,1], s=20, c='yellow', marker='o')\n",
    "        #plt.scatter(X[:,0], X[:,1], s=40, c=ycolors, marker='2')\n",
    "    plt.xlim([-0.5,1.5])\n",
    "    plt.ylim([-0.5,1.5])\n",
    "    \n",
    "        \n",
    "        \n",
    "display(X,t)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 32)                96        \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 129\n",
      "Trainable params: 129\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.models.Sequential at 0x12064e050>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_linear_model():\n",
    "\n",
    "    linmodel = Sequential()\n",
    "    linmodel.add(Dense(32, input_dim=2, activation='linear', name='H1'))\n",
    "    linmodel.add(Dense(1, activation='linear', name='Y'))\n",
    "\n",
    "    opt = Adam()\n",
    "    linmodel.compile(loss='binary_crossentropy', \n",
    "                optimizer=opt,\n",
    "                metrics=['binary_accuracy'])\n",
    "\n",
    "    linmodel.summary()\n",
    "    return linmodel\n",
    "build_linear_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Train\n",
    "#log = model.fit(X, t, nb_epoch=500)\n",
    "\n",
    "def train(model, Xtrain, ttrain, Xvalid, tvalid, nb_epoch = 500, verbose = 0):\n",
    "    log = {'loss': [], 'acc': [], 'val_loss': [], 'val_acc': []}\n",
    "    for epoch in range(1,nb_epoch+1):\n",
    "        loss, acc = model.train_on_batch(Xtrain,ttrain)\n",
    "        log['loss'].append(loss)\n",
    "        log['acc'].append(acc)\n",
    "        val_loss, val_acc = model.test_on_batch(Xvalid,tvalid)\n",
    "        log['val_loss'].append(val_loss)\n",
    "        log['val_acc'].append(val_acc)\n",
    "        if verbose == 0:\n",
    "            print(\"Epoch %i/%i - loss: %.4f - accuracy: %.4f - val_loss: %.4f - val_accuracy: %.4f \"%(epoch,nb_epoch,loss,acc,val_loss,val_acc))\n",
    "        if epoch % 20 == 0 and verbose == 1:\n",
    "            y = model.predict(Xtrain).round()\n",
    "            display(Xtrain, ttrain, y=None, model=model)\n",
    "            plt.title(\"Epoch: %i, loss: %.3f, val_loss: %.3f\"%(epoch, loss, val_loss))\n",
    "            plt.savefig(\"fig/\"+str(epoch)+\".png\")\n",
    "            \n",
    "            #plt.show()\n",
    "            \n",
    "    return log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 32)                96        \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 129\n",
      "Trainable params: 129\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 7.8387 - accuracy: 0.4500 - val_loss: 4.8492 - val_accuracy: 0.7000 \n",
      "Epoch 2/500 - loss: 7.5357 - accuracy: 0.4500 - val_loss: 4.2647 - val_accuracy: 0.7000 \n",
      "Epoch 3/500 - loss: 7.2489 - accuracy: 0.4500 - val_loss: 3.6431 - val_accuracy: 0.7000 \n",
      "Epoch 4/500 - loss: 6.7669 - accuracy: 0.4500 - val_loss: 3.5754 - val_accuracy: 0.7000 \n",
      "Epoch 5/500 - loss: 6.0962 - accuracy: 0.4500 - val_loss: 3.5371 - val_accuracy: 0.7000 \n",
      "Epoch 6/500 - loss: 5.7180 - accuracy: 0.4500 - val_loss: 3.5109 - val_accuracy: 0.7000 \n",
      "Epoch 7/500 - loss: 5.5044 - accuracy: 0.4500 - val_loss: 3.4914 - val_accuracy: 0.7000 \n",
      "Epoch 8/500 - loss: 4.7154 - accuracy: 0.4500 - val_loss: 3.4764 - val_accuracy: 0.7000 \n",
      "Epoch 9/500 - loss: 4.4751 - accuracy: 0.4500 - val_loss: 3.4641 - val_accuracy: 0.7000 \n",
      "Epoch 10/500 - loss: 4.2300 - accuracy: 0.4500 - val_loss: 3.4543 - val_accuracy: 0.7000 \n",
      "Epoch 11/500 - loss: 4.1591 - accuracy: 0.4500 - val_loss: 2.9375 - val_accuracy: 0.7000 \n",
      "Epoch 12/500 - loss: 4.1085 - accuracy: 0.4500 - val_loss: 2.8430 - val_accuracy: 0.7000 \n",
      "Epoch 13/500 - loss: 4.0689 - accuracy: 0.4500 - val_loss: 2.8107 - val_accuracy: 0.7000 \n",
      "Epoch 14/500 - loss: 3.9006 - accuracy: 0.4500 - val_loss: 2.7902 - val_accuracy: 0.7000 \n",
      "Epoch 15/500 - loss: 3.8599 - accuracy: 0.4500 - val_loss: 2.2970 - val_accuracy: 0.7000 \n",
      "Epoch 16/500 - loss: 3.8303 - accuracy: 0.4500 - val_loss: 2.1819 - val_accuracy: 0.7000 \n",
      "Epoch 17/500 - loss: 3.8063 - accuracy: 0.4500 - val_loss: 2.1437 - val_accuracy: 0.7000 \n",
      "Epoch 18/500 - loss: 3.7863 - accuracy: 0.4500 - val_loss: 2.1197 - val_accuracy: 0.7000 \n",
      "Epoch 19/500 - loss: 3.7691 - accuracy: 0.4500 - val_loss: 2.1024 - val_accuracy: 0.7000 \n",
      "Epoch 20/500 - loss: 3.6181 - accuracy: 0.4500 - val_loss: 2.0880 - val_accuracy: 0.7000 \n",
      "Epoch 21/500 - loss: 3.5924 - accuracy: 0.4500 - val_loss: 2.0771 - val_accuracy: 0.7000 \n",
      "Epoch 22/500 - loss: 3.5750 - accuracy: 0.4500 - val_loss: 2.0693 - val_accuracy: 0.7000 \n",
      "Epoch 23/500 - loss: 3.5613 - accuracy: 0.4625 - val_loss: 2.0632 - val_accuracy: 0.7000 \n",
      "Epoch 24/500 - loss: 3.4124 - accuracy: 0.4625 - val_loss: 1.5108 - val_accuracy: 0.7000 \n",
      "Epoch 25/500 - loss: 3.2535 - accuracy: 0.4625 - val_loss: 1.4684 - val_accuracy: 0.7000 \n",
      "Epoch 26/500 - loss: 3.2244 - accuracy: 0.4625 - val_loss: 1.4433 - val_accuracy: 0.7000 \n",
      "Epoch 27/500 - loss: 2.9382 - accuracy: 0.4625 - val_loss: 1.4236 - val_accuracy: 0.7000 \n",
      "Epoch 28/500 - loss: 2.8899 - accuracy: 0.4625 - val_loss: 1.4090 - val_accuracy: 0.7000 \n",
      "Epoch 29/500 - loss: 2.5742 - accuracy: 0.4625 - val_loss: 1.3969 - val_accuracy: 0.7000 \n",
      "Epoch 30/500 - loss: 2.3854 - accuracy: 0.4625 - val_loss: 1.3871 - val_accuracy: 0.7000 \n",
      "Epoch 31/500 - loss: 2.2197 - accuracy: 0.4625 - val_loss: 0.7928 - val_accuracy: 0.7000 \n",
      "Epoch 32/500 - loss: 2.1674 - accuracy: 0.4625 - val_loss: 0.7469 - val_accuracy: 0.7000 \n",
      "Epoch 33/500 - loss: 2.1387 - accuracy: 0.5000 - val_loss: 0.7210 - val_accuracy: 0.7000 \n",
      "Epoch 34/500 - loss: 2.1173 - accuracy: 0.5000 - val_loss: 0.7036 - val_accuracy: 0.6500 \n",
      "Epoch 35/500 - loss: 2.1007 - accuracy: 0.5250 - val_loss: 0.6910 - val_accuracy: 0.6500 \n",
      "Epoch 36/500 - loss: 1.9566 - accuracy: 0.5375 - val_loss: 0.6801 - val_accuracy: 0.6500 \n",
      "Epoch 37/500 - loss: 1.7908 - accuracy: 0.5500 - val_loss: 0.6710 - val_accuracy: 0.6500 \n",
      "Epoch 38/500 - loss: 1.6106 - accuracy: 0.5500 - val_loss: 0.6638 - val_accuracy: 0.6500 \n",
      "Epoch 39/500 - loss: 1.1688 - accuracy: 0.5500 - val_loss: 0.6574 - val_accuracy: 0.6500 \n",
      "Epoch 40/500 - loss: 1.0906 - accuracy: 0.5750 - val_loss: 0.6527 - val_accuracy: 0.6500 \n",
      "Epoch 41/500 - loss: 1.0527 - accuracy: 0.5875 - val_loss: 0.6494 - val_accuracy: 0.6500 \n",
      "Epoch 42/500 - loss: 1.0265 - accuracy: 0.5750 - val_loss: 0.6474 - val_accuracy: 0.6500 \n",
      "Epoch 43/500 - loss: 1.0072 - accuracy: 0.5750 - val_loss: 0.6464 - val_accuracy: 0.6500 \n",
      "Epoch 44/500 - loss: 0.9921 - accuracy: 0.5875 - val_loss: 0.6462 - val_accuracy: 0.6500 \n",
      "Epoch 45/500 - loss: 0.9801 - accuracy: 0.5625 - val_loss: 0.6467 - val_accuracy: 0.6500 \n",
      "Epoch 46/500 - loss: 0.9705 - accuracy: 0.5250 - val_loss: 0.6477 - val_accuracy: 0.7000 \n",
      "Epoch 47/500 - loss: 0.9627 - accuracy: 0.5125 - val_loss: 0.6490 - val_accuracy: 0.6500 \n",
      "Epoch 48/500 - loss: 0.8125 - accuracy: 0.5000 - val_loss: 0.6507 - val_accuracy: 0.6000 \n",
      "Epoch 49/500 - loss: 0.7966 - accuracy: 0.4875 - val_loss: 0.6526 - val_accuracy: 0.6000 \n",
      "Epoch 50/500 - loss: 0.7865 - accuracy: 0.4875 - val_loss: 0.6547 - val_accuracy: 0.6000 \n",
      "Epoch 51/500 - loss: 0.7792 - accuracy: 0.4875 - val_loss: 0.6569 - val_accuracy: 0.6000 \n",
      "Epoch 52/500 - loss: 0.7735 - accuracy: 0.4875 - val_loss: 0.6593 - val_accuracy: 0.6000 \n",
      "Epoch 53/500 - loss: 0.7690 - accuracy: 0.5000 - val_loss: 0.6617 - val_accuracy: 0.6000 \n",
      "Epoch 54/500 - loss: 0.7655 - accuracy: 0.5000 - val_loss: 0.6642 - val_accuracy: 0.6000 \n",
      "Epoch 55/500 - loss: 0.7626 - accuracy: 0.5125 - val_loss: 0.6666 - val_accuracy: 0.6000 \n",
      "Epoch 56/500 - loss: 0.7604 - accuracy: 0.5250 - val_loss: 0.6690 - val_accuracy: 0.6000 \n",
      "Epoch 57/500 - loss: 0.7586 - accuracy: 0.5250 - val_loss: 0.6713 - val_accuracy: 0.6000 \n",
      "Epoch 58/500 - loss: 0.7571 - accuracy: 0.5250 - val_loss: 0.6736 - val_accuracy: 0.6000 \n",
      "Epoch 59/500 - loss: 0.7560 - accuracy: 0.5375 - val_loss: 0.6757 - val_accuracy: 0.6000 \n",
      "Epoch 60/500 - loss: 0.7551 - accuracy: 0.5375 - val_loss: 0.6777 - val_accuracy: 0.6000 \n",
      "Epoch 61/500 - loss: 0.7543 - accuracy: 0.5375 - val_loss: 0.6795 - val_accuracy: 0.6000 \n",
      "Epoch 62/500 - loss: 0.7538 - accuracy: 0.5500 - val_loss: 0.6813 - val_accuracy: 0.6000 \n",
      "Epoch 63/500 - loss: 0.7533 - accuracy: 0.5375 - val_loss: 0.6828 - val_accuracy: 0.6000 \n",
      "Epoch 64/500 - loss: 0.7529 - accuracy: 0.5500 - val_loss: 0.6842 - val_accuracy: 0.6000 \n",
      "Epoch 65/500 - loss: 0.7526 - accuracy: 0.5500 - val_loss: 0.6854 - val_accuracy: 0.6000 \n",
      "Epoch 66/500 - loss: 0.7523 - accuracy: 0.5500 - val_loss: 0.6865 - val_accuracy: 0.6000 \n",
      "Epoch 67/500 - loss: 0.7520 - accuracy: 0.5625 - val_loss: 0.6874 - val_accuracy: 0.6000 \n",
      "Epoch 68/500 - loss: 0.7517 - accuracy: 0.5625 - val_loss: 0.6882 - val_accuracy: 0.6000 \n",
      "Epoch 69/500 - loss: 0.7515 - accuracy: 0.5625 - val_loss: 0.6888 - val_accuracy: 0.6000 \n",
      "Epoch 70/500 - loss: 0.7512 - accuracy: 0.5625 - val_loss: 0.6892 - val_accuracy: 0.6000 \n",
      "Epoch 71/500 - loss: 0.7509 - accuracy: 0.5625 - val_loss: 0.6895 - val_accuracy: 0.6000 \n",
      "Epoch 72/500 - loss: 0.7506 - accuracy: 0.5625 - val_loss: 0.6897 - val_accuracy: 0.6000 \n",
      "Epoch 73/500 - loss: 0.7503 - accuracy: 0.5625 - val_loss: 0.6897 - val_accuracy: 0.6000 \n",
      "Epoch 74/500 - loss: 0.7499 - accuracy: 0.5625 - val_loss: 0.6897 - val_accuracy: 0.6000 \n",
      "Epoch 75/500 - loss: 0.7495 - accuracy: 0.5625 - val_loss: 0.6895 - val_accuracy: 0.6000 \n",
      "Epoch 76/500 - loss: 0.7491 - accuracy: 0.5625 - val_loss: 0.6893 - val_accuracy: 0.6000 \n",
      "Epoch 77/500 - loss: 0.7487 - accuracy: 0.5625 - val_loss: 0.6890 - val_accuracy: 0.6000 \n",
      "Epoch 78/500 - loss: 0.7483 - accuracy: 0.5625 - val_loss: 0.6886 - val_accuracy: 0.6000 \n",
      "Epoch 79/500 - loss: 0.7478 - accuracy: 0.5625 - val_loss: 0.6881 - val_accuracy: 0.6000 \n",
      "Epoch 80/500 - loss: 0.7474 - accuracy: 0.5625 - val_loss: 0.6876 - val_accuracy: 0.6000 \n",
      "Epoch 81/500 - loss: 0.7469 - accuracy: 0.5625 - val_loss: 0.6870 - val_accuracy: 0.6000 \n",
      "Epoch 82/500 - loss: 0.7464 - accuracy: 0.5625 - val_loss: 0.6864 - val_accuracy: 0.6000 \n",
      "Epoch 83/500 - loss: 0.7459 - accuracy: 0.5625 - val_loss: 0.6857 - val_accuracy: 0.6000 \n",
      "Epoch 84/500 - loss: 0.7454 - accuracy: 0.5625 - val_loss: 0.6851 - val_accuracy: 0.6000 \n",
      "Epoch 85/500 - loss: 0.7449 - accuracy: 0.5625 - val_loss: 0.6844 - val_accuracy: 0.6000 \n",
      "Epoch 86/500 - loss: 0.7444 - accuracy: 0.5625 - val_loss: 0.6837 - val_accuracy: 0.6000 \n",
      "Epoch 87/500 - loss: 0.7439 - accuracy: 0.5625 - val_loss: 0.6830 - val_accuracy: 0.6000 \n",
      "Epoch 88/500 - loss: 0.7434 - accuracy: 0.5625 - val_loss: 0.6822 - val_accuracy: 0.6000 \n",
      "Epoch 89/500 - loss: 0.7430 - accuracy: 0.5625 - val_loss: 0.6815 - val_accuracy: 0.6000 \n",
      "Epoch 90/500 - loss: 0.7425 - accuracy: 0.5625 - val_loss: 0.6808 - val_accuracy: 0.6000 \n",
      "Epoch 91/500 - loss: 0.7420 - accuracy: 0.5625 - val_loss: 0.6801 - val_accuracy: 0.6000 \n",
      "Epoch 92/500 - loss: 0.7415 - accuracy: 0.5500 - val_loss: 0.6794 - val_accuracy: 0.6000 \n",
      "Epoch 93/500 - loss: 0.7411 - accuracy: 0.5500 - val_loss: 0.6787 - val_accuracy: 0.6000 \n",
      "Epoch 94/500 - loss: 0.7406 - accuracy: 0.5500 - val_loss: 0.6781 - val_accuracy: 0.6000 \n",
      "Epoch 95/500 - loss: 0.7401 - accuracy: 0.5500 - val_loss: 0.6774 - val_accuracy: 0.6000 \n",
      "Epoch 96/500 - loss: 0.7397 - accuracy: 0.5500 - val_loss: 0.6768 - val_accuracy: 0.6000 \n",
      "Epoch 97/500 - loss: 0.7393 - accuracy: 0.5500 - val_loss: 0.6762 - val_accuracy: 0.6000 \n",
      "Epoch 98/500 - loss: 0.7388 - accuracy: 0.5500 - val_loss: 0.6756 - val_accuracy: 0.6000 \n",
      "Epoch 99/500 - loss: 0.7384 - accuracy: 0.5500 - val_loss: 0.6750 - val_accuracy: 0.6000 \n",
      "Epoch 100/500 - loss: 0.7380 - accuracy: 0.5500 - val_loss: 0.6745 - val_accuracy: 0.6000 \n",
      "Epoch 101/500 - loss: 0.7376 - accuracy: 0.5500 - val_loss: 0.6739 - val_accuracy: 0.6000 \n",
      "Epoch 102/500 - loss: 0.7372 - accuracy: 0.5500 - val_loss: 0.6734 - val_accuracy: 0.6000 \n",
      "Epoch 103/500 - loss: 0.7368 - accuracy: 0.5500 - val_loss: 0.6730 - val_accuracy: 0.6000 \n",
      "Epoch 104/500 - loss: 0.7364 - accuracy: 0.5500 - val_loss: 0.6725 - val_accuracy: 0.6000 \n",
      "Epoch 105/500 - loss: 0.7360 - accuracy: 0.5500 - val_loss: 0.6721 - val_accuracy: 0.6000 \n",
      "Epoch 106/500 - loss: 0.7356 - accuracy: 0.5500 - val_loss: 0.6717 - val_accuracy: 0.6000 \n",
      "Epoch 107/500 - loss: 0.7353 - accuracy: 0.5375 - val_loss: 0.6713 - val_accuracy: 0.6000 \n",
      "Epoch 108/500 - loss: 0.7349 - accuracy: 0.5375 - val_loss: 0.6709 - val_accuracy: 0.6000 \n",
      "Epoch 109/500 - loss: 0.7345 - accuracy: 0.5375 - val_loss: 0.6706 - val_accuracy: 0.6000 \n",
      "Epoch 110/500 - loss: 0.7342 - accuracy: 0.5375 - val_loss: 0.6703 - val_accuracy: 0.6000 \n",
      "Epoch 111/500 - loss: 0.7338 - accuracy: 0.5375 - val_loss: 0.6700 - val_accuracy: 0.6000 \n",
      "Epoch 112/500 - loss: 0.7335 - accuracy: 0.5500 - val_loss: 0.6697 - val_accuracy: 0.6000 \n",
      "Epoch 113/500 - loss: 0.7331 - accuracy: 0.5500 - val_loss: 0.6694 - val_accuracy: 0.6000 \n",
      "Epoch 114/500 - loss: 0.7328 - accuracy: 0.5500 - val_loss: 0.6692 - val_accuracy: 0.6000 \n",
      "Epoch 115/500 - loss: 0.7324 - accuracy: 0.5500 - val_loss: 0.6690 - val_accuracy: 0.6000 \n",
      "Epoch 116/500 - loss: 0.7321 - accuracy: 0.5500 - val_loss: 0.6688 - val_accuracy: 0.6000 \n",
      "Epoch 117/500 - loss: 0.7318 - accuracy: 0.5375 - val_loss: 0.6686 - val_accuracy: 0.6000 \n",
      "Epoch 118/500 - loss: 0.7314 - accuracy: 0.5375 - val_loss: 0.6684 - val_accuracy: 0.6000 \n",
      "Epoch 119/500 - loss: 0.7311 - accuracy: 0.5375 - val_loss: 0.6683 - val_accuracy: 0.6000 \n",
      "Epoch 120/500 - loss: 0.7308 - accuracy: 0.5375 - val_loss: 0.6681 - val_accuracy: 0.6000 \n",
      "Epoch 121/500 - loss: 0.7304 - accuracy: 0.5375 - val_loss: 0.6680 - val_accuracy: 0.6000 \n",
      "Epoch 122/500 - loss: 0.7301 - accuracy: 0.5375 - val_loss: 0.6679 - val_accuracy: 0.6000 \n",
      "Epoch 123/500 - loss: 0.7298 - accuracy: 0.5375 - val_loss: 0.6678 - val_accuracy: 0.6000 \n",
      "Epoch 124/500 - loss: 0.7295 - accuracy: 0.5375 - val_loss: 0.6678 - val_accuracy: 0.6000 \n",
      "Epoch 125/500 - loss: 0.7291 - accuracy: 0.5375 - val_loss: 0.6677 - val_accuracy: 0.6000 \n",
      "Epoch 126/500 - loss: 0.7288 - accuracy: 0.5375 - val_loss: 0.6677 - val_accuracy: 0.6000 \n",
      "Epoch 127/500 - loss: 0.7285 - accuracy: 0.5375 - val_loss: 0.6676 - val_accuracy: 0.6000 \n",
      "Epoch 128/500 - loss: 0.7282 - accuracy: 0.5375 - val_loss: 0.6676 - val_accuracy: 0.6000 \n",
      "Epoch 129/500 - loss: 0.7278 - accuracy: 0.5375 - val_loss: 0.6676 - val_accuracy: 0.6000 \n",
      "Epoch 130/500 - loss: 0.7275 - accuracy: 0.5375 - val_loss: 0.6676 - val_accuracy: 0.6000 \n",
      "Epoch 131/500 - loss: 0.7272 - accuracy: 0.5375 - val_loss: 0.6676 - val_accuracy: 0.6000 \n",
      "Epoch 132/500 - loss: 0.7269 - accuracy: 0.5375 - val_loss: 0.6676 - val_accuracy: 0.6000 \n",
      "Epoch 133/500 - loss: 0.7266 - accuracy: 0.5375 - val_loss: 0.6676 - val_accuracy: 0.6000 \n",
      "Epoch 134/500 - loss: 0.7263 - accuracy: 0.5375 - val_loss: 0.6677 - val_accuracy: 0.6000 \n",
      "Epoch 135/500 - loss: 0.7259 - accuracy: 0.5500 - val_loss: 0.6677 - val_accuracy: 0.6000 \n",
      "Epoch 136/500 - loss: 0.7256 - accuracy: 0.5500 - val_loss: 0.6678 - val_accuracy: 0.6000 \n",
      "Epoch 137/500 - loss: 0.7253 - accuracy: 0.5500 - val_loss: 0.6678 - val_accuracy: 0.6000 \n",
      "Epoch 138/500 - loss: 0.7250 - accuracy: 0.5500 - val_loss: 0.6679 - val_accuracy: 0.6000 \n",
      "Epoch 139/500 - loss: 0.7247 - accuracy: 0.5500 - val_loss: 0.6680 - val_accuracy: 0.6000 \n",
      "Epoch 140/500 - loss: 0.7244 - accuracy: 0.5500 - val_loss: 0.6680 - val_accuracy: 0.6000 \n",
      "Epoch 141/500 - loss: 0.7241 - accuracy: 0.5500 - val_loss: 0.6681 - val_accuracy: 0.6000 \n",
      "Epoch 142/500 - loss: 0.7238 - accuracy: 0.5500 - val_loss: 0.6682 - val_accuracy: 0.6000 \n",
      "Epoch 143/500 - loss: 0.7235 - accuracy: 0.5500 - val_loss: 0.6683 - val_accuracy: 0.6000 \n",
      "Epoch 144/500 - loss: 0.7232 - accuracy: 0.5500 - val_loss: 0.6684 - val_accuracy: 0.6000 \n",
      "Epoch 145/500 - loss: 0.7229 - accuracy: 0.5500 - val_loss: 0.6685 - val_accuracy: 0.6000 \n",
      "Epoch 146/500 - loss: 0.7226 - accuracy: 0.5500 - val_loss: 0.6686 - val_accuracy: 0.6000 \n",
      "Epoch 147/500 - loss: 0.7223 - accuracy: 0.5500 - val_loss: 0.6687 - val_accuracy: 0.6000 \n",
      "Epoch 148/500 - loss: 0.7220 - accuracy: 0.5500 - val_loss: 0.6689 - val_accuracy: 0.6000 \n",
      "Epoch 149/500 - loss: 0.7217 - accuracy: 0.5500 - val_loss: 0.6690 - val_accuracy: 0.6000 \n",
      "Epoch 150/500 - loss: 0.7214 - accuracy: 0.5500 - val_loss: 0.6691 - val_accuracy: 0.6000 \n",
      "Epoch 151/500 - loss: 0.7211 - accuracy: 0.5500 - val_loss: 0.6692 - val_accuracy: 0.6000 \n",
      "Epoch 152/500 - loss: 0.7208 - accuracy: 0.5500 - val_loss: 0.6694 - val_accuracy: 0.6000 \n",
      "Epoch 153/500 - loss: 0.7205 - accuracy: 0.5500 - val_loss: 0.6695 - val_accuracy: 0.6000 \n",
      "Epoch 154/500 - loss: 0.7202 - accuracy: 0.5500 - val_loss: 0.6696 - val_accuracy: 0.6000 \n",
      "Epoch 155/500 - loss: 0.7199 - accuracy: 0.5500 - val_loss: 0.6698 - val_accuracy: 0.6000 \n",
      "Epoch 156/500 - loss: 0.7196 - accuracy: 0.5500 - val_loss: 0.6699 - val_accuracy: 0.6000 \n",
      "Epoch 157/500 - loss: 0.7193 - accuracy: 0.5500 - val_loss: 0.6701 - val_accuracy: 0.6000 \n",
      "Epoch 158/500 - loss: 0.7190 - accuracy: 0.5500 - val_loss: 0.6702 - val_accuracy: 0.6000 \n",
      "Epoch 159/500 - loss: 0.7187 - accuracy: 0.5500 - val_loss: 0.6704 - val_accuracy: 0.6000 \n",
      "Epoch 160/500 - loss: 0.7184 - accuracy: 0.5500 - val_loss: 0.6705 - val_accuracy: 0.6000 \n",
      "Epoch 161/500 - loss: 0.7181 - accuracy: 0.5500 - val_loss: 0.6707 - val_accuracy: 0.6000 \n",
      "Epoch 162/500 - loss: 0.7179 - accuracy: 0.5500 - val_loss: 0.6708 - val_accuracy: 0.6000 \n",
      "Epoch 163/500 - loss: 0.7176 - accuracy: 0.5500 - val_loss: 0.6710 - val_accuracy: 0.6000 \n",
      "Epoch 164/500 - loss: 0.7173 - accuracy: 0.5500 - val_loss: 0.6711 - val_accuracy: 0.6000 \n",
      "Epoch 165/500 - loss: 0.7170 - accuracy: 0.5500 - val_loss: 0.6713 - val_accuracy: 0.5500 \n",
      "Epoch 166/500 - loss: 0.7167 - accuracy: 0.5500 - val_loss: 0.6715 - val_accuracy: 0.5500 \n",
      "Epoch 167/500 - loss: 0.7164 - accuracy: 0.5500 - val_loss: 0.6716 - val_accuracy: 0.5500 \n",
      "Epoch 168/500 - loss: 0.7162 - accuracy: 0.5375 - val_loss: 0.6718 - val_accuracy: 0.5500 \n",
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      "Epoch 348/500 - loss: 0.6841 - accuracy: 0.4750 - val_loss: 0.7156 - val_accuracy: 0.5000 \n",
      "Epoch 349/500 - loss: 0.6840 - accuracy: 0.4750 - val_loss: 0.7158 - val_accuracy: 0.5000 \n",
      "Epoch 350/500 - loss: 0.6839 - accuracy: 0.4750 - val_loss: 0.7161 - val_accuracy: 0.5000 \n",
      "Epoch 351/500 - loss: 0.6838 - accuracy: 0.4750 - val_loss: 0.7164 - val_accuracy: 0.5000 \n",
      "Epoch 352/500 - loss: 0.6837 - accuracy: 0.4750 - val_loss: 0.7166 - val_accuracy: 0.5000 \n",
      "Epoch 353/500 - loss: 0.6837 - accuracy: 0.4750 - val_loss: 0.7169 - val_accuracy: 0.4500 \n",
      "Epoch 354/500 - loss: 0.6836 - accuracy: 0.4750 - val_loss: 0.7172 - val_accuracy: 0.4500 \n",
      "Epoch 355/500 - loss: 0.6835 - accuracy: 0.4750 - val_loss: 0.7174 - val_accuracy: 0.4500 \n",
      "Epoch 356/500 - loss: 0.6834 - accuracy: 0.4750 - val_loss: 0.7177 - val_accuracy: 0.4000 \n",
      "Epoch 357/500 - loss: 0.6833 - accuracy: 0.4750 - val_loss: 0.7179 - val_accuracy: 0.4000 \n",
      "Epoch 358/500 - loss: 0.6833 - accuracy: 0.4750 - val_loss: 0.7182 - val_accuracy: 0.4000 \n",
      "Epoch 359/500 - loss: 0.6832 - accuracy: 0.4750 - val_loss: 0.7185 - val_accuracy: 0.4000 \n",
      "Epoch 360/500 - loss: 0.6831 - accuracy: 0.4750 - val_loss: 0.7187 - val_accuracy: 0.4000 \n",
      "Epoch 361/500 - loss: 0.6830 - accuracy: 0.4750 - val_loss: 0.7190 - val_accuracy: 0.4000 \n",
      "Epoch 362/500 - loss: 0.6830 - accuracy: 0.4750 - val_loss: 0.7192 - val_accuracy: 0.4000 \n",
      "Epoch 363/500 - loss: 0.6829 - accuracy: 0.4750 - val_loss: 0.7195 - val_accuracy: 0.4000 \n",
      "Epoch 364/500 - loss: 0.6828 - accuracy: 0.4750 - val_loss: 0.7197 - val_accuracy: 0.4500 \n",
      "Epoch 365/500 - loss: 0.6827 - accuracy: 0.4750 - val_loss: 0.7200 - val_accuracy: 0.4500 \n",
      "Epoch 366/500 - loss: 0.6827 - accuracy: 0.4750 - val_loss: 0.7203 - val_accuracy: 0.4500 \n",
      "Epoch 367/500 - loss: 0.6826 - accuracy: 0.4750 - val_loss: 0.7205 - val_accuracy: 0.4500 \n",
      "Epoch 368/500 - loss: 0.6825 - accuracy: 0.4750 - val_loss: 0.7208 - val_accuracy: 0.4500 \n",
      "Epoch 369/500 - loss: 0.6825 - accuracy: 0.4750 - val_loss: 0.7210 - val_accuracy: 0.4500 \n",
      "Epoch 370/500 - loss: 0.6824 - accuracy: 0.4750 - val_loss: 0.7213 - val_accuracy: 0.4500 \n",
      "Epoch 371/500 - loss: 0.6823 - accuracy: 0.4750 - val_loss: 0.7215 - val_accuracy: 0.4500 \n",
      "Epoch 372/500 - loss: 0.6823 - accuracy: 0.4625 - val_loss: 0.7218 - val_accuracy: 0.4500 \n",
      "Epoch 373/500 - loss: 0.6822 - accuracy: 0.4625 - val_loss: 0.7220 - val_accuracy: 0.4500 \n",
      "Epoch 374/500 - loss: 0.6821 - accuracy: 0.4625 - val_loss: 0.7223 - val_accuracy: 0.4500 \n",
      "Epoch 375/500 - loss: 0.6821 - accuracy: 0.4625 - val_loss: 0.7225 - val_accuracy: 0.4500 \n",
      "Epoch 376/500 - loss: 0.6820 - accuracy: 0.4625 - val_loss: 0.7228 - val_accuracy: 0.4500 \n",
      "Epoch 377/500 - loss: 0.6819 - accuracy: 0.4625 - val_loss: 0.7230 - val_accuracy: 0.4500 \n",
      "Epoch 378/500 - loss: 0.6819 - accuracy: 0.4750 - val_loss: 0.7233 - val_accuracy: 0.4500 \n",
      "Epoch 379/500 - loss: 0.6818 - accuracy: 0.4625 - val_loss: 0.7235 - val_accuracy: 0.4500 \n",
      "Epoch 380/500 - loss: 0.6817 - accuracy: 0.4500 - val_loss: 0.7238 - val_accuracy: 0.4500 \n",
      "Epoch 381/500 - loss: 0.6817 - accuracy: 0.4500 - val_loss: 0.7240 - val_accuracy: 0.4500 \n",
      "Epoch 382/500 - loss: 0.6816 - accuracy: 0.4500 - val_loss: 0.7242 - val_accuracy: 0.4000 \n",
      "Epoch 383/500 - loss: 0.6816 - accuracy: 0.4500 - val_loss: 0.7245 - val_accuracy: 0.4000 \n",
      "Epoch 384/500 - loss: 0.6815 - accuracy: 0.4500 - val_loss: 0.7247 - val_accuracy: 0.4000 \n",
      "Epoch 385/500 - loss: 0.6814 - accuracy: 0.4500 - val_loss: 0.7250 - val_accuracy: 0.4000 \n",
      "Epoch 386/500 - loss: 0.6814 - accuracy: 0.4500 - val_loss: 0.7252 - val_accuracy: 0.4000 \n",
      "Epoch 387/500 - loss: 0.6813 - accuracy: 0.4500 - val_loss: 0.7255 - val_accuracy: 0.4000 \n",
      "Epoch 388/500 - loss: 0.6813 - accuracy: 0.4500 - val_loss: 0.7257 - val_accuracy: 0.4000 \n",
      "Epoch 389/500 - loss: 0.6812 - accuracy: 0.4500 - val_loss: 0.7259 - val_accuracy: 0.4000 \n",
      "Epoch 390/500 - loss: 0.6812 - accuracy: 0.4500 - val_loss: 0.7262 - val_accuracy: 0.4000 \n",
      "Epoch 391/500 - loss: 0.6811 - accuracy: 0.4500 - val_loss: 0.7264 - val_accuracy: 0.3500 \n",
      "Epoch 392/500 - loss: 0.6811 - accuracy: 0.4500 - val_loss: 0.7267 - val_accuracy: 0.3500 \n",
      "Epoch 393/500 - loss: 0.6810 - accuracy: 0.4500 - val_loss: 0.7269 - val_accuracy: 0.3500 \n",
      "Epoch 394/500 - loss: 0.6810 - accuracy: 0.4500 - val_loss: 0.7271 - val_accuracy: 0.3500 \n",
      "Epoch 395/500 - loss: 0.6809 - accuracy: 0.4500 - val_loss: 0.7274 - val_accuracy: 0.3500 \n",
      "Epoch 396/500 - loss: 0.6808 - accuracy: 0.4500 - val_loss: 0.7276 - val_accuracy: 0.3500 \n",
      "Epoch 397/500 - loss: 0.6808 - accuracy: 0.4500 - val_loss: 0.7278 - val_accuracy: 0.3500 \n",
      "Epoch 398/500 - loss: 0.6807 - accuracy: 0.4500 - val_loss: 0.7281 - val_accuracy: 0.3500 \n",
      "Epoch 399/500 - loss: 0.6807 - accuracy: 0.4500 - val_loss: 0.7283 - val_accuracy: 0.3500 \n",
      "Epoch 400/500 - loss: 0.6806 - accuracy: 0.4500 - val_loss: 0.7285 - val_accuracy: 0.3500 \n",
      "Epoch 401/500 - loss: 0.6806 - accuracy: 0.4500 - val_loss: 0.7288 - val_accuracy: 0.3500 \n",
      "Epoch 402/500 - loss: 0.6806 - accuracy: 0.4500 - val_loss: 0.7290 - val_accuracy: 0.3500 \n",
      "Epoch 403/500 - loss: 0.6805 - accuracy: 0.4500 - val_loss: 0.7292 - val_accuracy: 0.3500 \n",
      "Epoch 404/500 - loss: 0.6805 - accuracy: 0.4500 - val_loss: 0.7294 - val_accuracy: 0.3500 \n",
      "Epoch 405/500 - loss: 0.6804 - accuracy: 0.4500 - val_loss: 0.7297 - val_accuracy: 0.3500 \n",
      "Epoch 406/500 - loss: 0.6804 - accuracy: 0.4375 - val_loss: 0.7299 - val_accuracy: 0.3500 \n",
      "Epoch 407/500 - loss: 0.6803 - accuracy: 0.4375 - val_loss: 0.7301 - val_accuracy: 0.3500 \n",
      "Epoch 408/500 - loss: 0.6803 - accuracy: 0.4375 - val_loss: 0.7303 - val_accuracy: 0.3500 \n",
      "Epoch 409/500 - loss: 0.6802 - accuracy: 0.4375 - val_loss: 0.7306 - val_accuracy: 0.3500 \n",
      "Epoch 410/500 - loss: 0.6802 - accuracy: 0.4375 - val_loss: 0.7308 - val_accuracy: 0.3500 \n",
      "Epoch 411/500 - loss: 0.6801 - accuracy: 0.4375 - val_loss: 0.7310 - val_accuracy: 0.3500 \n",
      "Epoch 412/500 - loss: 0.6801 - accuracy: 0.4375 - val_loss: 0.7312 - val_accuracy: 0.3500 \n",
      "Epoch 413/500 - loss: 0.6801 - accuracy: 0.4250 - val_loss: 0.7315 - val_accuracy: 0.3500 \n",
      "Epoch 414/500 - loss: 0.6800 - accuracy: 0.4250 - val_loss: 0.7317 - val_accuracy: 0.3500 \n",
      "Epoch 415/500 - loss: 0.6800 - accuracy: 0.4250 - val_loss: 0.7319 - val_accuracy: 0.3500 \n",
      "Epoch 416/500 - loss: 0.6799 - accuracy: 0.4250 - val_loss: 0.7321 - val_accuracy: 0.3500 \n",
      "Epoch 417/500 - loss: 0.6799 - accuracy: 0.4250 - val_loss: 0.7323 - val_accuracy: 0.3500 \n",
      "Epoch 418/500 - loss: 0.6799 - accuracy: 0.4250 - val_loss: 0.7325 - val_accuracy: 0.3500 \n",
      "Epoch 419/500 - loss: 0.6798 - accuracy: 0.4250 - val_loss: 0.7328 - val_accuracy: 0.3500 \n",
      "Epoch 420/500 - loss: 0.6798 - accuracy: 0.4250 - val_loss: 0.7330 - val_accuracy: 0.3500 \n",
      "Epoch 421/500 - loss: 0.6797 - accuracy: 0.4250 - val_loss: 0.7332 - val_accuracy: 0.3500 \n",
      "Epoch 422/500 - loss: 0.6797 - accuracy: 0.4250 - val_loss: 0.7334 - val_accuracy: 0.3500 \n",
      "Epoch 423/500 - loss: 0.6797 - accuracy: 0.4250 - val_loss: 0.7336 - val_accuracy: 0.3500 \n",
      "Epoch 424/500 - loss: 0.6796 - accuracy: 0.4250 - val_loss: 0.7338 - val_accuracy: 0.3500 \n",
      "Epoch 425/500 - loss: 0.6796 - accuracy: 0.4250 - val_loss: 0.7340 - val_accuracy: 0.3500 \n",
      "Epoch 426/500 - loss: 0.6796 - accuracy: 0.4250 - val_loss: 0.7342 - val_accuracy: 0.3500 \n",
      "Epoch 427/500 - loss: 0.6795 - accuracy: 0.4250 - val_loss: 0.7345 - val_accuracy: 0.3500 \n",
      "Epoch 428/500 - loss: 0.6795 - accuracy: 0.4250 - val_loss: 0.7347 - val_accuracy: 0.3500 \n",
      "Epoch 429/500 - loss: 0.6795 - accuracy: 0.4250 - val_loss: 0.7349 - val_accuracy: 0.3500 \n",
      "Epoch 430/500 - loss: 0.6794 - accuracy: 0.4125 - val_loss: 0.7351 - val_accuracy: 0.3000 \n",
      "Epoch 431/500 - loss: 0.6794 - accuracy: 0.4125 - val_loss: 0.7353 - val_accuracy: 0.3000 \n",
      "Epoch 432/500 - loss: 0.6794 - accuracy: 0.4125 - val_loss: 0.7355 - val_accuracy: 0.3000 \n",
      "Epoch 433/500 - loss: 0.6793 - accuracy: 0.4125 - val_loss: 0.7357 - val_accuracy: 0.3000 \n",
      "Epoch 434/500 - loss: 0.6793 - accuracy: 0.4125 - val_loss: 0.7359 - val_accuracy: 0.3000 \n",
      "Epoch 435/500 - loss: 0.6793 - accuracy: 0.4250 - val_loss: 0.7361 - val_accuracy: 0.3000 \n",
      "Epoch 436/500 - loss: 0.6792 - accuracy: 0.4250 - val_loss: 0.7363 - val_accuracy: 0.3000 \n",
      "Epoch 437/500 - loss: 0.6792 - accuracy: 0.4125 - val_loss: 0.7365 - val_accuracy: 0.3000 \n",
      "Epoch 438/500 - loss: 0.6792 - accuracy: 0.4125 - val_loss: 0.7367 - val_accuracy: 0.3000 \n",
      "Epoch 439/500 - loss: 0.6791 - accuracy: 0.4125 - val_loss: 0.7369 - val_accuracy: 0.3000 \n",
      "Epoch 440/500 - loss: 0.6791 - accuracy: 0.4125 - val_loss: 0.7371 - val_accuracy: 0.3000 \n",
      "Epoch 441/500 - loss: 0.6791 - accuracy: 0.4125 - val_loss: 0.7373 - val_accuracy: 0.3000 \n",
      "Epoch 442/500 - loss: 0.6790 - accuracy: 0.4125 - val_loss: 0.7375 - val_accuracy: 0.3000 \n",
      "Epoch 443/500 - loss: 0.6790 - accuracy: 0.4125 - val_loss: 0.7377 - val_accuracy: 0.3000 \n",
      "Epoch 444/500 - loss: 0.6790 - accuracy: 0.4125 - val_loss: 0.7379 - val_accuracy: 0.3000 \n",
      "Epoch 445/500 - loss: 0.6790 - accuracy: 0.4125 - val_loss: 0.7381 - val_accuracy: 0.2500 \n",
      "Epoch 446/500 - loss: 0.6789 - accuracy: 0.4125 - val_loss: 0.7382 - val_accuracy: 0.2500 \n",
      "Epoch 447/500 - loss: 0.6789 - accuracy: 0.4125 - val_loss: 0.7384 - val_accuracy: 0.2500 \n",
      "Epoch 448/500 - loss: 0.6789 - accuracy: 0.4125 - val_loss: 0.7386 - val_accuracy: 0.2500 \n",
      "Epoch 449/500 - loss: 0.6789 - accuracy: 0.4125 - val_loss: 0.7388 - val_accuracy: 0.2500 \n",
      "Epoch 450/500 - loss: 0.6788 - accuracy: 0.4125 - val_loss: 0.7390 - val_accuracy: 0.2500 \n",
      "Epoch 451/500 - loss: 0.6788 - accuracy: 0.4125 - val_loss: 0.7392 - val_accuracy: 0.2500 \n",
      "Epoch 452/500 - loss: 0.6788 - accuracy: 0.4125 - val_loss: 0.7394 - val_accuracy: 0.2500 \n",
      "Epoch 453/500 - loss: 0.6788 - accuracy: 0.4125 - val_loss: 0.7396 - val_accuracy: 0.2500 \n",
      "Epoch 454/500 - loss: 0.6787 - accuracy: 0.4125 - val_loss: 0.7397 - val_accuracy: 0.2500 \n",
      "Epoch 455/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7399 - val_accuracy: 0.2500 \n",
      "Epoch 456/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7401 - val_accuracy: 0.2500 \n",
      "Epoch 457/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7403 - val_accuracy: 0.2500 \n",
      "Epoch 458/500 - loss: 0.6786 - accuracy: 0.4375 - val_loss: 0.7405 - val_accuracy: 0.2500 \n",
      "Epoch 459/500 - loss: 0.6786 - accuracy: 0.4375 - val_loss: 0.7406 - val_accuracy: 0.2500 \n",
      "Epoch 460/500 - loss: 0.6786 - accuracy: 0.4375 - val_loss: 0.7408 - val_accuracy: 0.2500 \n",
      "Epoch 461/500 - loss: 0.6786 - accuracy: 0.4375 - val_loss: 0.7410 - val_accuracy: 0.2500 \n",
      "Epoch 462/500 - loss: 0.6785 - accuracy: 0.4375 - val_loss: 0.7412 - val_accuracy: 0.2500 \n",
      "Epoch 463/500 - loss: 0.6785 - accuracy: 0.4375 - val_loss: 0.7413 - val_accuracy: 0.2500 \n",
      "Epoch 464/500 - loss: 0.6785 - accuracy: 0.4375 - val_loss: 0.7415 - val_accuracy: 0.2500 \n",
      "Epoch 465/500 - loss: 0.6785 - accuracy: 0.4375 - val_loss: 0.7417 - val_accuracy: 0.2500 \n",
      "Epoch 466/500 - loss: 0.6785 - accuracy: 0.4375 - val_loss: 0.7419 - val_accuracy: 0.2500 \n",
      "Epoch 467/500 - loss: 0.6784 - accuracy: 0.4375 - val_loss: 0.7420 - val_accuracy: 0.2500 \n",
      "Epoch 468/500 - loss: 0.6784 - accuracy: 0.4375 - val_loss: 0.7422 - val_accuracy: 0.2500 \n",
      "Epoch 469/500 - loss: 0.6784 - accuracy: 0.4375 - val_loss: 0.7424 - val_accuracy: 0.2500 \n",
      "Epoch 470/500 - loss: 0.6784 - accuracy: 0.4375 - val_loss: 0.7425 - val_accuracy: 0.2500 \n",
      "Epoch 471/500 - loss: 0.6784 - accuracy: 0.4375 - val_loss: 0.7427 - val_accuracy: 0.2500 \n",
      "Epoch 472/500 - loss: 0.6783 - accuracy: 0.4375 - val_loss: 0.7429 - val_accuracy: 0.2500 \n",
      "Epoch 473/500 - loss: 0.6783 - accuracy: 0.4375 - val_loss: 0.7430 - val_accuracy: 0.2500 \n",
      "Epoch 474/500 - loss: 0.6783 - accuracy: 0.4375 - val_loss: 0.7432 - val_accuracy: 0.2500 \n",
      "Epoch 475/500 - loss: 0.6783 - accuracy: 0.4375 - val_loss: 0.7434 - val_accuracy: 0.2500 \n",
      "Epoch 476/500 - loss: 0.6783 - accuracy: 0.4375 - val_loss: 0.7435 - val_accuracy: 0.2500 \n",
      "Epoch 477/500 - loss: 0.6783 - accuracy: 0.4375 - val_loss: 0.7437 - val_accuracy: 0.2500 \n",
      "Epoch 478/500 - loss: 0.6782 - accuracy: 0.4375 - val_loss: 0.7439 - val_accuracy: 0.2500 \n",
      "Epoch 479/500 - loss: 0.6782 - accuracy: 0.4375 - val_loss: 0.7440 - val_accuracy: 0.2500 \n",
      "Epoch 480/500 - loss: 0.6782 - accuracy: 0.4375 - val_loss: 0.7442 - val_accuracy: 0.2500 \n",
      "Epoch 481/500 - loss: 0.6782 - accuracy: 0.4375 - val_loss: 0.7443 - val_accuracy: 0.2500 \n",
      "Epoch 482/500 - loss: 0.6782 - accuracy: 0.4375 - val_loss: 0.7445 - val_accuracy: 0.2500 \n",
      "Epoch 483/500 - loss: 0.6782 - accuracy: 0.4375 - val_loss: 0.7447 - val_accuracy: 0.2500 \n",
      "Epoch 484/500 - loss: 0.6781 - accuracy: 0.4375 - val_loss: 0.7448 - val_accuracy: 0.2500 \n",
      "Epoch 485/500 - loss: 0.6781 - accuracy: 0.4500 - val_loss: 0.7450 - val_accuracy: 0.2500 \n",
      "Epoch 486/500 - loss: 0.6781 - accuracy: 0.4500 - val_loss: 0.7451 - val_accuracy: 0.2500 \n",
      "Epoch 487/500 - loss: 0.6781 - accuracy: 0.4375 - val_loss: 0.7453 - val_accuracy: 0.2500 \n",
      "Epoch 488/500 - loss: 0.6781 - accuracy: 0.4375 - val_loss: 0.7454 - val_accuracy: 0.2000 \n",
      "Epoch 489/500 - loss: 0.6781 - accuracy: 0.4375 - val_loss: 0.7456 - val_accuracy: 0.2000 \n",
      "Epoch 490/500 - loss: 0.6780 - accuracy: 0.4375 - val_loss: 0.7457 - val_accuracy: 0.2000 \n",
      "Epoch 491/500 - loss: 0.6780 - accuracy: 0.4375 - val_loss: 0.7459 - val_accuracy: 0.2000 \n",
      "Epoch 492/500 - loss: 0.6780 - accuracy: 0.4375 - val_loss: 0.7460 - val_accuracy: 0.2000 \n",
      "Epoch 493/500 - loss: 0.6780 - accuracy: 0.4375 - val_loss: 0.7462 - val_accuracy: 0.2000 \n",
      "Epoch 494/500 - loss: 0.6780 - accuracy: 0.4375 - val_loss: 0.7463 - val_accuracy: 0.2000 \n",
      "Epoch 495/500 - loss: 0.6780 - accuracy: 0.4375 - val_loss: 0.7465 - val_accuracy: 0.2000 \n",
      "Epoch 496/500 - loss: 0.6780 - accuracy: 0.4375 - val_loss: 0.7466 - val_accuracy: 0.2000 \n",
      "Epoch 497/500 - loss: 0.6780 - accuracy: 0.4375 - val_loss: 0.7467 - val_accuracy: 0.2000 \n",
      "Epoch 498/500 - loss: 0.6779 - accuracy: 0.4375 - val_loss: 0.7469 - val_accuracy: 0.2000 \n",
      "Epoch 499/500 - loss: 0.6779 - accuracy: 0.4375 - val_loss: 0.7470 - val_accuracy: 0.2000 \n",
      "Epoch 500/500 - loss: 0.6779 - accuracy: 0.4375 - val_loss: 0.7472 - val_accuracy: 0.2000 \n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 32)                96        \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 129\n",
      "Trainable params: 129\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 2.5024 - accuracy: 0.4625 - val_loss: 3.9643 - val_accuracy: 0.3500 \n",
      "Epoch 2/500 - loss: 2.4446 - accuracy: 0.4625 - val_loss: 3.9608 - val_accuracy: 0.3500 \n",
      "Epoch 3/500 - loss: 2.4416 - accuracy: 0.4625 - val_loss: 3.9620 - val_accuracy: 0.3500 \n",
      "Epoch 4/500 - loss: 2.4436 - accuracy: 0.4625 - val_loss: 3.9642 - val_accuracy: 0.3500 \n",
      "Epoch 5/500 - loss: 2.3036 - accuracy: 0.4625 - val_loss: 3.9665 - val_accuracy: 0.3500 \n",
      "Epoch 6/500 - loss: 2.1587 - accuracy: 0.4625 - val_loss: 3.9689 - val_accuracy: 0.3500 \n",
      "Epoch 7/500 - loss: 2.1457 - accuracy: 0.4625 - val_loss: 3.9708 - val_accuracy: 0.3500 \n",
      "Epoch 8/500 - loss: 2.1387 - accuracy: 0.4625 - val_loss: 3.9715 - val_accuracy: 0.3500 \n",
      "Epoch 9/500 - loss: 1.9931 - accuracy: 0.4625 - val_loss: 3.9720 - val_accuracy: 0.3500 \n",
      "Epoch 10/500 - loss: 1.9775 - accuracy: 0.4625 - val_loss: 3.9717 - val_accuracy: 0.3500 \n",
      "Epoch 11/500 - loss: 1.9655 - accuracy: 0.4625 - val_loss: 3.9706 - val_accuracy: 0.3500 \n",
      "Epoch 12/500 - loss: 1.9545 - accuracy: 0.4625 - val_loss: 3.9688 - val_accuracy: 0.3500 \n",
      "Epoch 13/500 - loss: 1.8084 - accuracy: 0.4625 - val_loss: 3.9675 - val_accuracy: 0.3500 \n",
      "Epoch 14/500 - loss: 1.7888 - accuracy: 0.4625 - val_loss: 3.9662 - val_accuracy: 0.3500 \n",
      "Epoch 15/500 - loss: 1.6456 - accuracy: 0.4625 - val_loss: 3.9669 - val_accuracy: 0.3500 \n",
      "Epoch 16/500 - loss: 1.6169 - accuracy: 0.4625 - val_loss: 3.4039 - val_accuracy: 0.3500 \n",
      "Epoch 17/500 - loss: 1.6000 - accuracy: 0.4625 - val_loss: 3.3702 - val_accuracy: 0.3500 \n",
      "Epoch 18/500 - loss: 1.5870 - accuracy: 0.4750 - val_loss: 3.3515 - val_accuracy: 0.3500 \n",
      "Epoch 19/500 - loss: 1.5758 - accuracy: 0.4625 - val_loss: 3.3386 - val_accuracy: 0.3500 \n",
      "Epoch 20/500 - loss: 1.5658 - accuracy: 0.4625 - val_loss: 3.3287 - val_accuracy: 0.3500 \n",
      "Epoch 21/500 - loss: 1.5564 - accuracy: 0.4625 - val_loss: 3.3205 - val_accuracy: 0.3500 \n",
      "Epoch 22/500 - loss: 1.5476 - accuracy: 0.4625 - val_loss: 3.3135 - val_accuracy: 0.3500 \n",
      "Epoch 23/500 - loss: 1.5392 - accuracy: 0.4625 - val_loss: 3.3073 - val_accuracy: 0.3500 \n",
      "Epoch 24/500 - loss: 1.5312 - accuracy: 0.4625 - val_loss: 3.3015 - val_accuracy: 0.3500 \n",
      "Epoch 25/500 - loss: 1.5235 - accuracy: 0.4500 - val_loss: 3.2962 - val_accuracy: 0.3500 \n",
      "Epoch 26/500 - loss: 1.5162 - accuracy: 0.4500 - val_loss: 3.2913 - val_accuracy: 0.3500 \n",
      "Epoch 27/500 - loss: 1.5092 - accuracy: 0.4500 - val_loss: 3.2866 - val_accuracy: 0.3500 \n",
      "Epoch 28/500 - loss: 1.3790 - accuracy: 0.4500 - val_loss: 2.7926 - val_accuracy: 0.3500 \n",
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      "Epoch 211/500 - loss: 0.7098 - accuracy: 0.3750 - val_loss: 0.8458 - val_accuracy: 0.2500 \n",
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      "Epoch 394/500 - loss: 0.6852 - accuracy: 0.5000 - val_loss: 0.7521 - val_accuracy: 0.4000 \n",
      "Epoch 395/500 - loss: 0.6852 - accuracy: 0.5000 - val_loss: 0.7518 - val_accuracy: 0.4500 \n",
      "Epoch 396/500 - loss: 0.6851 - accuracy: 0.5000 - val_loss: 0.7516 - val_accuracy: 0.5000 \n",
      "Epoch 397/500 - loss: 0.6851 - accuracy: 0.5000 - val_loss: 0.7513 - val_accuracy: 0.5000 \n",
      "Epoch 398/500 - loss: 0.6851 - accuracy: 0.5000 - val_loss: 0.7511 - val_accuracy: 0.5000 \n",
      "Epoch 399/500 - loss: 0.6850 - accuracy: 0.5000 - val_loss: 0.7508 - val_accuracy: 0.5000 \n",
      "Epoch 400/500 - loss: 0.6850 - accuracy: 0.5000 - val_loss: 0.7506 - val_accuracy: 0.5000 \n",
      "Epoch 401/500 - loss: 0.6850 - accuracy: 0.5000 - val_loss: 0.7503 - val_accuracy: 0.5000 \n",
      "Epoch 402/500 - loss: 0.6849 - accuracy: 0.5000 - val_loss: 0.7501 - val_accuracy: 0.5000 \n",
      "Epoch 403/500 - loss: 0.6849 - accuracy: 0.5000 - val_loss: 0.7499 - val_accuracy: 0.5000 \n",
      "Epoch 404/500 - loss: 0.6849 - accuracy: 0.5000 - val_loss: 0.7496 - val_accuracy: 0.5000 \n",
      "Epoch 405/500 - loss: 0.6848 - accuracy: 0.5000 - val_loss: 0.7494 - val_accuracy: 0.5000 \n",
      "Epoch 406/500 - loss: 0.6848 - accuracy: 0.5000 - val_loss: 0.7492 - val_accuracy: 0.5000 \n",
      "Epoch 407/500 - loss: 0.6848 - accuracy: 0.5000 - val_loss: 0.7489 - val_accuracy: 0.5000 \n",
      "Epoch 408/500 - loss: 0.6848 - accuracy: 0.5000 - val_loss: 0.7487 - val_accuracy: 0.5000 \n",
      "Epoch 409/500 - loss: 0.6847 - accuracy: 0.5000 - val_loss: 0.7485 - val_accuracy: 0.5000 \n",
      "Epoch 410/500 - loss: 0.6847 - accuracy: 0.5000 - val_loss: 0.7483 - val_accuracy: 0.5000 \n",
      "Epoch 411/500 - loss: 0.6847 - accuracy: 0.5000 - val_loss: 0.7480 - val_accuracy: 0.5000 \n",
      "Epoch 412/500 - loss: 0.6846 - accuracy: 0.5000 - val_loss: 0.7478 - val_accuracy: 0.5000 \n",
      "Epoch 413/500 - loss: 0.6846 - accuracy: 0.5000 - val_loss: 0.7476 - val_accuracy: 0.5000 \n",
      "Epoch 414/500 - loss: 0.6846 - accuracy: 0.5000 - val_loss: 0.7474 - val_accuracy: 0.5000 \n",
      "Epoch 415/500 - loss: 0.6846 - accuracy: 0.5125 - val_loss: 0.7472 - val_accuracy: 0.5000 \n",
      "Epoch 416/500 - loss: 0.6845 - accuracy: 0.5125 - val_loss: 0.7470 - val_accuracy: 0.5000 \n",
      "Epoch 417/500 - loss: 0.6845 - accuracy: 0.5125 - val_loss: 0.7468 - val_accuracy: 0.5000 \n",
      "Epoch 418/500 - loss: 0.6845 - accuracy: 0.5125 - val_loss: 0.7465 - val_accuracy: 0.5000 \n",
      "Epoch 419/500 - loss: 0.6845 - accuracy: 0.5125 - val_loss: 0.7463 - val_accuracy: 0.5000 \n",
      "Epoch 420/500 - loss: 0.6844 - accuracy: 0.5125 - val_loss: 0.7461 - val_accuracy: 0.5000 \n",
      "Epoch 421/500 - loss: 0.6844 - accuracy: 0.5125 - val_loss: 0.7459 - val_accuracy: 0.5000 \n",
      "Epoch 422/500 - loss: 0.6844 - accuracy: 0.5125 - val_loss: 0.7457 - val_accuracy: 0.5000 \n",
      "Epoch 423/500 - loss: 0.6844 - accuracy: 0.5125 - val_loss: 0.7455 - val_accuracy: 0.5000 \n",
      "Epoch 424/500 - loss: 0.6843 - accuracy: 0.5125 - val_loss: 0.7453 - val_accuracy: 0.5000 \n",
      "Epoch 425/500 - loss: 0.6843 - accuracy: 0.5125 - val_loss: 0.7451 - val_accuracy: 0.5000 \n",
      "Epoch 426/500 - loss: 0.6843 - accuracy: 0.5125 - val_loss: 0.7449 - val_accuracy: 0.5000 \n",
      "Epoch 427/500 - loss: 0.6843 - accuracy: 0.5125 - val_loss: 0.7447 - val_accuracy: 0.5000 \n",
      "Epoch 428/500 - loss: 0.6843 - accuracy: 0.5125 - val_loss: 0.7445 - val_accuracy: 0.5000 \n",
      "Epoch 429/500 - loss: 0.6842 - accuracy: 0.5250 - val_loss: 0.7444 - val_accuracy: 0.5000 \n",
      "Epoch 430/500 - loss: 0.6842 - accuracy: 0.5250 - val_loss: 0.7442 - val_accuracy: 0.5000 \n",
      "Epoch 431/500 - loss: 0.6842 - accuracy: 0.5375 - val_loss: 0.7440 - val_accuracy: 0.5000 \n",
      "Epoch 432/500 - loss: 0.6842 - accuracy: 0.5375 - val_loss: 0.7438 - val_accuracy: 0.5000 \n",
      "Epoch 433/500 - loss: 0.6842 - accuracy: 0.5375 - val_loss: 0.7436 - val_accuracy: 0.5000 \n",
      "Epoch 434/500 - loss: 0.6841 - accuracy: 0.5375 - val_loss: 0.7434 - val_accuracy: 0.5000 \n",
      "Epoch 435/500 - loss: 0.6841 - accuracy: 0.5375 - val_loss: 0.7432 - val_accuracy: 0.5000 \n",
      "Epoch 436/500 - loss: 0.6841 - accuracy: 0.5375 - val_loss: 0.7431 - val_accuracy: 0.5000 \n",
      "Epoch 437/500 - loss: 0.6841 - accuracy: 0.5375 - val_loss: 0.7429 - val_accuracy: 0.5000 \n",
      "Epoch 438/500 - loss: 0.6841 - accuracy: 0.5375 - val_loss: 0.7427 - val_accuracy: 0.5000 \n",
      "Epoch 439/500 - loss: 0.6840 - accuracy: 0.5375 - val_loss: 0.7425 - val_accuracy: 0.5000 \n",
      "Epoch 440/500 - loss: 0.6840 - accuracy: 0.5375 - val_loss: 0.7424 - val_accuracy: 0.5000 \n",
      "Epoch 441/500 - loss: 0.6840 - accuracy: 0.5375 - val_loss: 0.7422 - val_accuracy: 0.5000 \n",
      "Epoch 442/500 - loss: 0.6840 - accuracy: 0.5375 - val_loss: 0.7420 - val_accuracy: 0.5000 \n",
      "Epoch 443/500 - loss: 0.6840 - accuracy: 0.5500 - val_loss: 0.7418 - val_accuracy: 0.5000 \n",
      "Epoch 444/500 - loss: 0.6840 - accuracy: 0.5500 - val_loss: 0.7417 - val_accuracy: 0.5000 \n",
      "Epoch 445/500 - loss: 0.6839 - accuracy: 0.5500 - val_loss: 0.7415 - val_accuracy: 0.5000 \n",
      "Epoch 446/500 - loss: 0.6839 - accuracy: 0.5625 - val_loss: 0.7414 - val_accuracy: 0.5000 \n",
      "Epoch 447/500 - loss: 0.6839 - accuracy: 0.5625 - val_loss: 0.7412 - val_accuracy: 0.5000 \n",
      "Epoch 448/500 - loss: 0.6839 - accuracy: 0.5625 - val_loss: 0.7410 - val_accuracy: 0.5000 \n",
      "Epoch 449/500 - loss: 0.6839 - accuracy: 0.5625 - val_loss: 0.7409 - val_accuracy: 0.5000 \n",
      "Epoch 450/500 - loss: 0.6839 - accuracy: 0.5625 - val_loss: 0.7407 - val_accuracy: 0.5000 \n",
      "Epoch 451/500 - loss: 0.6839 - accuracy: 0.5625 - val_loss: 0.7405 - val_accuracy: 0.5000 \n",
      "Epoch 452/500 - loss: 0.6838 - accuracy: 0.5625 - val_loss: 0.7404 - val_accuracy: 0.5000 \n",
      "Epoch 453/500 - loss: 0.6838 - accuracy: 0.5625 - val_loss: 0.7402 - val_accuracy: 0.5000 \n",
      "Epoch 454/500 - loss: 0.6838 - accuracy: 0.5625 - val_loss: 0.7401 - val_accuracy: 0.5000 \n",
      "Epoch 455/500 - loss: 0.6838 - accuracy: 0.5625 - val_loss: 0.7399 - val_accuracy: 0.5000 \n",
      "Epoch 456/500 - loss: 0.6838 - accuracy: 0.5625 - val_loss: 0.7398 - val_accuracy: 0.5000 \n",
      "Epoch 457/500 - loss: 0.6838 - accuracy: 0.5625 - val_loss: 0.7396 - val_accuracy: 0.5000 \n",
      "Epoch 458/500 - loss: 0.6838 - accuracy: 0.5625 - val_loss: 0.7395 - val_accuracy: 0.5000 \n",
      "Epoch 459/500 - loss: 0.6837 - accuracy: 0.5875 - val_loss: 0.7393 - val_accuracy: 0.5000 \n",
      "Epoch 460/500 - loss: 0.6837 - accuracy: 0.5875 - val_loss: 0.7392 - val_accuracy: 0.5000 \n",
      "Epoch 461/500 - loss: 0.6837 - accuracy: 0.5875 - val_loss: 0.7390 - val_accuracy: 0.5000 \n",
      "Epoch 462/500 - loss: 0.6837 - accuracy: 0.5875 - val_loss: 0.7389 - val_accuracy: 0.5000 \n",
      "Epoch 463/500 - loss: 0.6837 - accuracy: 0.5875 - val_loss: 0.7388 - val_accuracy: 0.5000 \n",
      "Epoch 464/500 - loss: 0.6837 - accuracy: 0.5875 - val_loss: 0.7386 - val_accuracy: 0.5000 \n",
      "Epoch 465/500 - loss: 0.6837 - accuracy: 0.5875 - val_loss: 0.7385 - val_accuracy: 0.5000 \n",
      "Epoch 466/500 - loss: 0.6837 - accuracy: 0.5875 - val_loss: 0.7383 - val_accuracy: 0.5000 \n",
      "Epoch 467/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7382 - val_accuracy: 0.5000 \n",
      "Epoch 468/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7381 - val_accuracy: 0.5000 \n",
      "Epoch 469/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7379 - val_accuracy: 0.5000 \n",
      "Epoch 470/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7378 - val_accuracy: 0.5000 \n",
      "Epoch 471/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7377 - val_accuracy: 0.5000 \n",
      "Epoch 472/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7375 - val_accuracy: 0.5000 \n",
      "Epoch 473/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7374 - val_accuracy: 0.5000 \n",
      "Epoch 474/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7373 - val_accuracy: 0.5000 \n",
      "Epoch 475/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7372 - val_accuracy: 0.5000 \n",
      "Epoch 476/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7370 - val_accuracy: 0.5000 \n",
      "Epoch 477/500 - loss: 0.6836 - accuracy: 0.5875 - val_loss: 0.7369 - val_accuracy: 0.5000 \n",
      "Epoch 478/500 - loss: 0.6835 - accuracy: 0.5875 - val_loss: 0.7368 - val_accuracy: 0.5000 \n",
      "Epoch 479/500 - loss: 0.6835 - accuracy: 0.5875 - val_loss: 0.7367 - val_accuracy: 0.5000 \n",
      "Epoch 480/500 - loss: 0.6835 - accuracy: 0.5875 - val_loss: 0.7365 - val_accuracy: 0.5000 \n",
      "Epoch 481/500 - loss: 0.6835 - accuracy: 0.5875 - val_loss: 0.7364 - val_accuracy: 0.5000 \n",
      "Epoch 482/500 - loss: 0.6835 - accuracy: 0.5875 - val_loss: 0.7363 - val_accuracy: 0.5000 \n",
      "Epoch 483/500 - loss: 0.6835 - accuracy: 0.5875 - val_loss: 0.7362 - val_accuracy: 0.5000 \n",
      "Epoch 484/500 - loss: 0.6835 - accuracy: 0.5875 - val_loss: 0.7361 - val_accuracy: 0.5000 \n",
      "Epoch 485/500 - loss: 0.6835 - accuracy: 0.6000 - val_loss: 0.7360 - val_accuracy: 0.5000 \n",
      "Epoch 486/500 - loss: 0.6835 - accuracy: 0.6000 - val_loss: 0.7358 - val_accuracy: 0.5000 \n",
      "Epoch 487/500 - loss: 0.6835 - accuracy: 0.6000 - val_loss: 0.7357 - val_accuracy: 0.5000 \n",
      "Epoch 488/500 - loss: 0.6835 - accuracy: 0.6125 - val_loss: 0.7356 - val_accuracy: 0.5000 \n",
      "Epoch 489/500 - loss: 0.6835 - accuracy: 0.6125 - val_loss: 0.7355 - val_accuracy: 0.5000 \n",
      "Epoch 490/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7354 - val_accuracy: 0.5000 \n",
      "Epoch 491/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7353 - val_accuracy: 0.5000 \n",
      "Epoch 492/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7352 - val_accuracy: 0.5000 \n",
      "Epoch 493/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7351 - val_accuracy: 0.5000 \n",
      "Epoch 494/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7350 - val_accuracy: 0.5000 \n",
      "Epoch 495/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7349 - val_accuracy: 0.5000 \n",
      "Epoch 496/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7348 - val_accuracy: 0.5000 \n",
      "Epoch 497/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7346 - val_accuracy: 0.5000 \n",
      "Epoch 498/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7345 - val_accuracy: 0.5000 \n",
      "Epoch 499/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7344 - val_accuracy: 0.5000 \n",
      "Epoch 500/500 - loss: 0.6834 - accuracy: 0.6125 - val_loss: 0.7343 - val_accuracy: 0.5000 \n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 32)                96        \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 129\n",
      "Trainable params: 129\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 1.5529 - accuracy: 0.3125 - val_loss: 0.6161 - val_accuracy: 0.5000 \n",
      "Epoch 2/500 - loss: 1.1311 - accuracy: 0.3000 - val_loss: 0.6163 - val_accuracy: 0.5000 \n",
      "Epoch 3/500 - loss: 1.0473 - accuracy: 0.3000 - val_loss: 0.6174 - val_accuracy: 0.5000 \n",
      "Epoch 4/500 - loss: 1.0159 - accuracy: 0.3000 - val_loss: 0.6191 - val_accuracy: 0.5000 \n",
      "Epoch 5/500 - loss: 0.9941 - accuracy: 0.3000 - val_loss: 0.6212 - val_accuracy: 0.4500 \n",
      "Epoch 6/500 - loss: 0.9773 - accuracy: 0.3000 - val_loss: 0.6237 - val_accuracy: 0.4500 \n",
      "Epoch 7/500 - loss: 0.9639 - accuracy: 0.3000 - val_loss: 0.6266 - val_accuracy: 0.4500 \n",
      "Epoch 8/500 - loss: 0.9523 - accuracy: 0.2875 - val_loss: 0.6297 - val_accuracy: 0.4500 \n",
      "Epoch 9/500 - loss: 0.9422 - accuracy: 0.3000 - val_loss: 0.6331 - val_accuracy: 0.4500 \n",
      "Epoch 10/500 - loss: 0.9335 - accuracy: 0.3000 - val_loss: 0.6367 - val_accuracy: 0.4500 \n",
      "Epoch 11/500 - loss: 0.9259 - accuracy: 0.3125 - val_loss: 0.6405 - val_accuracy: 0.4500 \n",
      "Epoch 12/500 - loss: 0.9192 - accuracy: 0.3125 - val_loss: 0.6446 - val_accuracy: 0.4500 \n",
      "Epoch 13/500 - loss: 0.9132 - accuracy: 0.3250 - val_loss: 0.6488 - val_accuracy: 0.4500 \n",
      "Epoch 14/500 - loss: 0.9079 - accuracy: 0.3250 - val_loss: 0.6530 - val_accuracy: 0.4500 \n",
      "Epoch 15/500 - loss: 0.9032 - accuracy: 0.3250 - val_loss: 0.6572 - val_accuracy: 0.4500 \n",
      "Epoch 16/500 - loss: 0.8990 - accuracy: 0.3000 - val_loss: 0.6615 - val_accuracy: 0.4500 \n",
      "Epoch 17/500 - loss: 0.8952 - accuracy: 0.3000 - val_loss: 0.6656 - val_accuracy: 0.4500 \n",
      "Epoch 18/500 - loss: 0.8919 - accuracy: 0.3000 - val_loss: 0.6697 - val_accuracy: 0.4500 \n",
      "Epoch 19/500 - loss: 0.8889 - accuracy: 0.3000 - val_loss: 0.6736 - val_accuracy: 0.4500 \n",
      "Epoch 20/500 - loss: 0.8861 - accuracy: 0.3125 - val_loss: 0.6773 - val_accuracy: 0.4500 \n",
      "Epoch 21/500 - loss: 0.8837 - accuracy: 0.3125 - val_loss: 0.6809 - val_accuracy: 0.4500 \n",
      "Epoch 22/500 - loss: 0.8815 - accuracy: 0.3250 - val_loss: 0.6843 - val_accuracy: 0.4500 \n",
      "Epoch 23/500 - loss: 0.8795 - accuracy: 0.3375 - val_loss: 0.6874 - val_accuracy: 0.4500 \n",
      "Epoch 24/500 - loss: 0.8776 - accuracy: 0.3375 - val_loss: 0.6902 - val_accuracy: 0.4500 \n",
      "Epoch 25/500 - loss: 0.8759 - accuracy: 0.3500 - val_loss: 0.6926 - val_accuracy: 0.4500 \n",
      "Epoch 26/500 - loss: 0.8742 - accuracy: 0.3500 - val_loss: 0.6947 - val_accuracy: 0.4500 \n",
      "Epoch 27/500 - loss: 0.8726 - accuracy: 0.3500 - val_loss: 0.6964 - val_accuracy: 0.4500 \n",
      "Epoch 28/500 - loss: 0.8710 - accuracy: 0.3500 - val_loss: 0.6977 - val_accuracy: 0.4500 \n",
      "Epoch 29/500 - loss: 0.8695 - accuracy: 0.3500 - val_loss: 0.6986 - val_accuracy: 0.4500 \n",
      "Epoch 30/500 - loss: 0.8679 - accuracy: 0.3625 - val_loss: 0.6992 - val_accuracy: 0.4500 \n",
      "Epoch 31/500 - loss: 0.8664 - accuracy: 0.3625 - val_loss: 0.6995 - val_accuracy: 0.4500 \n",
      "Epoch 32/500 - loss: 0.8648 - accuracy: 0.3625 - val_loss: 0.6994 - val_accuracy: 0.4500 \n",
      "Epoch 33/500 - loss: 0.8633 - accuracy: 0.3625 - val_loss: 0.6990 - val_accuracy: 0.4500 \n",
      "Epoch 34/500 - loss: 0.8617 - accuracy: 0.3625 - val_loss: 0.6985 - val_accuracy: 0.4500 \n",
      "Epoch 35/500 - loss: 0.8601 - accuracy: 0.3625 - val_loss: 0.6977 - val_accuracy: 0.4500 \n",
      "Epoch 36/500 - loss: 0.8586 - accuracy: 0.3625 - val_loss: 0.6967 - val_accuracy: 0.4500 \n",
      "Epoch 37/500 - loss: 0.8570 - accuracy: 0.3625 - val_loss: 0.6955 - val_accuracy: 0.4500 \n",
      "Epoch 38/500 - loss: 0.8555 - accuracy: 0.3625 - val_loss: 0.6943 - val_accuracy: 0.4500 \n",
      "Epoch 39/500 - loss: 0.8539 - accuracy: 0.3625 - val_loss: 0.6929 - val_accuracy: 0.4500 \n",
      "Epoch 40/500 - loss: 0.8524 - accuracy: 0.3625 - val_loss: 0.6914 - val_accuracy: 0.4500 \n",
      "Epoch 41/500 - loss: 0.8509 - accuracy: 0.3625 - val_loss: 0.6898 - val_accuracy: 0.4500 \n",
      "Epoch 42/500 - loss: 0.8494 - accuracy: 0.3625 - val_loss: 0.6883 - val_accuracy: 0.4500 \n",
      "Epoch 43/500 - loss: 0.8480 - accuracy: 0.3625 - val_loss: 0.6867 - val_accuracy: 0.4500 \n",
      "Epoch 44/500 - loss: 0.8465 - accuracy: 0.3625 - val_loss: 0.6851 - val_accuracy: 0.4500 \n",
      "Epoch 45/500 - loss: 0.8451 - accuracy: 0.3625 - val_loss: 0.6836 - val_accuracy: 0.4500 \n",
      "Epoch 46/500 - loss: 0.8437 - accuracy: 0.3625 - val_loss: 0.6821 - val_accuracy: 0.4500 \n",
      "Epoch 47/500 - loss: 0.8423 - accuracy: 0.3625 - val_loss: 0.6807 - val_accuracy: 0.4500 \n",
      "Epoch 48/500 - loss: 0.8409 - accuracy: 0.3500 - val_loss: 0.6793 - val_accuracy: 0.4500 \n",
      "Epoch 49/500 - loss: 0.8396 - accuracy: 0.3500 - val_loss: 0.6780 - val_accuracy: 0.4500 \n",
      "Epoch 50/500 - loss: 0.8383 - accuracy: 0.3500 - val_loss: 0.6767 - val_accuracy: 0.4500 \n",
      "Epoch 51/500 - loss: 0.8370 - accuracy: 0.3500 - val_loss: 0.6756 - val_accuracy: 0.4500 \n",
      "Epoch 52/500 - loss: 0.8357 - accuracy: 0.3500 - val_loss: 0.6745 - val_accuracy: 0.4500 \n",
      "Epoch 53/500 - loss: 0.8344 - accuracy: 0.3500 - val_loss: 0.6735 - val_accuracy: 0.4500 \n",
      "Epoch 54/500 - loss: 0.8331 - accuracy: 0.3500 - val_loss: 0.6726 - val_accuracy: 0.4500 \n",
      "Epoch 55/500 - loss: 0.8319 - accuracy: 0.3500 - val_loss: 0.6718 - val_accuracy: 0.4500 \n",
      "Epoch 56/500 - loss: 0.8307 - accuracy: 0.3500 - val_loss: 0.6711 - val_accuracy: 0.4500 \n",
      "Epoch 57/500 - loss: 0.8295 - accuracy: 0.3500 - val_loss: 0.6705 - val_accuracy: 0.4500 \n",
      "Epoch 58/500 - loss: 0.8283 - accuracy: 0.3500 - val_loss: 0.6699 - val_accuracy: 0.4500 \n",
      "Epoch 59/500 - loss: 0.8271 - accuracy: 0.3500 - val_loss: 0.6695 - val_accuracy: 0.4500 \n",
      "Epoch 60/500 - loss: 0.8259 - accuracy: 0.3500 - val_loss: 0.6691 - val_accuracy: 0.4500 \n",
      "Epoch 61/500 - loss: 0.8247 - accuracy: 0.3500 - val_loss: 0.6687 - val_accuracy: 0.4500 \n",
      "Epoch 62/500 - loss: 0.8236 - accuracy: 0.3500 - val_loss: 0.6685 - val_accuracy: 0.4500 \n",
      "Epoch 63/500 - loss: 0.8224 - accuracy: 0.3500 - val_loss: 0.6683 - val_accuracy: 0.4500 \n",
      "Epoch 64/500 - loss: 0.8212 - accuracy: 0.3500 - val_loss: 0.6682 - val_accuracy: 0.4500 \n",
      "Epoch 65/500 - loss: 0.8201 - accuracy: 0.3500 - val_loss: 0.6681 - val_accuracy: 0.4500 \n",
      "Epoch 66/500 - loss: 0.8189 - accuracy: 0.3500 - val_loss: 0.6681 - val_accuracy: 0.4500 \n",
      "Epoch 67/500 - loss: 0.8178 - accuracy: 0.3500 - val_loss: 0.6681 - val_accuracy: 0.4500 \n",
      "Epoch 68/500 - loss: 0.8166 - accuracy: 0.3500 - val_loss: 0.6681 - val_accuracy: 0.4500 \n",
      "Epoch 69/500 - loss: 0.8155 - accuracy: 0.3500 - val_loss: 0.6682 - val_accuracy: 0.4500 \n",
      "Epoch 70/500 - loss: 0.8144 - accuracy: 0.3625 - val_loss: 0.6683 - val_accuracy: 0.4500 \n",
      "Epoch 71/500 - loss: 0.8132 - accuracy: 0.3625 - val_loss: 0.6685 - val_accuracy: 0.4500 \n",
      "Epoch 72/500 - loss: 0.8121 - accuracy: 0.3875 - val_loss: 0.6686 - val_accuracy: 0.4500 \n",
      "Epoch 73/500 - loss: 0.8110 - accuracy: 0.3875 - val_loss: 0.6688 - val_accuracy: 0.4500 \n",
      "Epoch 74/500 - loss: 0.8099 - accuracy: 0.3875 - val_loss: 0.6690 - val_accuracy: 0.4500 \n",
      "Epoch 75/500 - loss: 0.8088 - accuracy: 0.3875 - val_loss: 0.6692 - val_accuracy: 0.4500 \n",
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      "Epoch 255/500 - loss: 0.6927 - accuracy: 0.5750 - val_loss: 0.7059 - val_accuracy: 0.5500 \n",
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      "Epoch 438/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7561 - val_accuracy: 0.2000 \n",
      "Epoch 439/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7562 - val_accuracy: 0.2000 \n",
      "Epoch 440/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7563 - val_accuracy: 0.2000 \n",
      "Epoch 441/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7564 - val_accuracy: 0.2000 \n",
      "Epoch 442/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7565 - val_accuracy: 0.2000 \n",
      "Epoch 443/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7566 - val_accuracy: 0.2000 \n",
      "Epoch 444/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7567 - val_accuracy: 0.2000 \n",
      "Epoch 445/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7567 - val_accuracy: 0.2000 \n",
      "Epoch 446/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7568 - val_accuracy: 0.2000 \n",
      "Epoch 447/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7569 - val_accuracy: 0.2000 \n",
      "Epoch 448/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7570 - val_accuracy: 0.2000 \n",
      "Epoch 449/500 - loss: 0.6788 - accuracy: 0.4250 - val_loss: 0.7571 - val_accuracy: 0.2000 \n",
      "Epoch 450/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7572 - val_accuracy: 0.2000 \n",
      "Epoch 451/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7572 - val_accuracy: 0.2000 \n",
      "Epoch 452/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7573 - val_accuracy: 0.2000 \n",
      "Epoch 453/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7574 - val_accuracy: 0.2000 \n",
      "Epoch 454/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7575 - val_accuracy: 0.2000 \n",
      "Epoch 455/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7576 - val_accuracy: 0.2000 \n",
      "Epoch 456/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7576 - val_accuracy: 0.2000 \n",
      "Epoch 457/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7577 - val_accuracy: 0.2000 \n",
      "Epoch 458/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7578 - val_accuracy: 0.2000 \n",
      "Epoch 459/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7578 - val_accuracy: 0.2000 \n",
      "Epoch 460/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7579 - val_accuracy: 0.2000 \n",
      "Epoch 461/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7580 - val_accuracy: 0.2000 \n",
      "Epoch 462/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7580 - val_accuracy: 0.2000 \n",
      "Epoch 463/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7581 - val_accuracy: 0.2000 \n",
      "Epoch 464/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7582 - val_accuracy: 0.2000 \n",
      "Epoch 465/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7582 - val_accuracy: 0.2000 \n",
      "Epoch 466/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7583 - val_accuracy: 0.2000 \n",
      "Epoch 467/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7584 - val_accuracy: 0.2000 \n",
      "Epoch 468/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7584 - val_accuracy: 0.2000 \n",
      "Epoch 469/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7585 - val_accuracy: 0.2000 \n",
      "Epoch 470/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7585 - val_accuracy: 0.2000 \n",
      "Epoch 471/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7586 - val_accuracy: 0.2000 \n",
      "Epoch 472/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7586 - val_accuracy: 0.2000 \n",
      "Epoch 473/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7587 - val_accuracy: 0.2000 \n",
      "Epoch 474/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7588 - val_accuracy: 0.2000 \n",
      "Epoch 475/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7588 - val_accuracy: 0.2000 \n",
      "Epoch 476/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7589 - val_accuracy: 0.2000 \n",
      "Epoch 477/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7589 - val_accuracy: 0.2000 \n",
      "Epoch 478/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7590 - val_accuracy: 0.2000 \n",
      "Epoch 479/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7590 - val_accuracy: 0.2000 \n",
      "Epoch 480/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7591 - val_accuracy: 0.2000 \n",
      "Epoch 481/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7591 - val_accuracy: 0.2000 \n",
      "Epoch 482/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7592 - val_accuracy: 0.2000 \n",
      "Epoch 483/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7592 - val_accuracy: 0.2000 \n",
      "Epoch 484/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7592 - val_accuracy: 0.2000 \n",
      "Epoch 485/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7593 - val_accuracy: 0.2000 \n",
      "Epoch 486/500 - loss: 0.6787 - accuracy: 0.4250 - val_loss: 0.7593 - val_accuracy: 0.2000 \n",
      "Epoch 487/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7594 - val_accuracy: 0.2000 \n",
      "Epoch 488/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7594 - val_accuracy: 0.2000 \n",
      "Epoch 489/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7595 - val_accuracy: 0.2000 \n",
      "Epoch 490/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7595 - val_accuracy: 0.2000 \n",
      "Epoch 491/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7595 - val_accuracy: 0.2000 \n",
      "Epoch 492/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7596 - val_accuracy: 0.2000 \n",
      "Epoch 493/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7596 - val_accuracy: 0.2000 \n",
      "Epoch 494/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7596 - val_accuracy: 0.2000 \n",
      "Epoch 495/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7597 - val_accuracy: 0.2000 \n",
      "Epoch 496/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7597 - val_accuracy: 0.2000 \n",
      "Epoch 497/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7598 - val_accuracy: 0.2000 \n",
      "Epoch 498/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7598 - val_accuracy: 0.2000 \n",
      "Epoch 499/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7598 - val_accuracy: 0.2000 \n",
      "Epoch 500/500 - loss: 0.6787 - accuracy: 0.4375 - val_loss: 0.7599 - val_accuracy: 0.2000 \n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 32)                96        \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 129\n",
      "Trainable params: 129\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 6.6138 - accuracy: 0.3375 - val_loss: 7.5012 - val_accuracy: 0.2000 \n",
      "Epoch 2/500 - loss: 6.3194 - accuracy: 0.3625 - val_loss: 6.8745 - val_accuracy: 0.2000 \n",
      "Epoch 3/500 - loss: 5.9799 - accuracy: 0.3875 - val_loss: 6.8217 - val_accuracy: 0.2000 \n",
      "Epoch 4/500 - loss: 5.6536 - accuracy: 0.4000 - val_loss: 6.7872 - val_accuracy: 0.2000 \n",
      "Epoch 5/500 - loss: 5.3082 - accuracy: 0.4000 - val_loss: 6.7611 - val_accuracy: 0.2000 \n",
      "Epoch 6/500 - loss: 5.1095 - accuracy: 0.4000 - val_loss: 6.1549 - val_accuracy: 0.2000 \n",
      "Epoch 7/500 - loss: 4.9163 - accuracy: 0.4000 - val_loss: 6.0842 - val_accuracy: 0.2500 \n",
      "Epoch 8/500 - loss: 4.8701 - accuracy: 0.4000 - val_loss: 6.0456 - val_accuracy: 0.2500 \n",
      "Epoch 9/500 - loss: 4.8390 - accuracy: 0.4000 - val_loss: 6.0181 - val_accuracy: 0.3500 \n",
      "Epoch 10/500 - loss: 4.6679 - accuracy: 0.4125 - val_loss: 5.4404 - val_accuracy: 0.3500 \n",
      "Epoch 11/500 - loss: 4.4954 - accuracy: 0.4125 - val_loss: 5.3571 - val_accuracy: 0.3500 \n",
      "Epoch 12/500 - loss: 4.0293 - accuracy: 0.4250 - val_loss: 5.3114 - val_accuracy: 0.4000 \n",
      "Epoch 13/500 - loss: 3.9638 - accuracy: 0.4250 - val_loss: 5.2783 - val_accuracy: 0.4000 \n",
      "Epoch 14/500 - loss: 3.8436 - accuracy: 0.4375 - val_loss: 5.2618 - val_accuracy: 0.4000 \n",
      "Epoch 15/500 - loss: 3.7575 - accuracy: 0.4375 - val_loss: 5.2481 - val_accuracy: 0.4000 \n",
      "Epoch 16/500 - loss: 3.7343 - accuracy: 0.4375 - val_loss: 4.6992 - val_accuracy: 0.4000 \n",
      "Epoch 17/500 - loss: 3.7171 - accuracy: 0.4375 - val_loss: 4.6351 - val_accuracy: 0.4000 \n",
      "Epoch 18/500 - loss: 3.7034 - accuracy: 0.4500 - val_loss: 4.6029 - val_accuracy: 0.4000 \n",
      "Epoch 19/500 - loss: 3.6921 - accuracy: 0.4500 - val_loss: 4.5806 - val_accuracy: 0.4000 \n",
      "Epoch 20/500 - loss: 3.6824 - accuracy: 0.4500 - val_loss: 4.5635 - val_accuracy: 0.4000 \n",
      "Epoch 21/500 - loss: 3.6741 - accuracy: 0.4625 - val_loss: 4.5496 - val_accuracy: 0.4000 \n",
      "Epoch 22/500 - loss: 3.6669 - accuracy: 0.4625 - val_loss: 4.5381 - val_accuracy: 0.4000 \n",
      "Epoch 23/500 - loss: 3.6605 - accuracy: 0.4625 - val_loss: 4.5283 - val_accuracy: 0.4000 \n",
      "Epoch 24/500 - loss: 3.6549 - accuracy: 0.4875 - val_loss: 4.5198 - val_accuracy: 0.4000 \n",
      "Epoch 25/500 - loss: 3.6500 - accuracy: 0.4875 - val_loss: 4.5123 - val_accuracy: 0.4000 \n",
      "Epoch 26/500 - loss: 3.6455 - accuracy: 0.4875 - val_loss: 4.5057 - val_accuracy: 0.4000 \n",
      "Epoch 27/500 - loss: 3.6415 - accuracy: 0.4875 - val_loss: 4.4999 - val_accuracy: 0.4000 \n",
      "Epoch 28/500 - loss: 3.6379 - accuracy: 0.5000 - val_loss: 4.4947 - val_accuracy: 0.4000 \n",
      "Epoch 29/500 - loss: 3.6346 - accuracy: 0.5000 - val_loss: 4.4901 - val_accuracy: 0.4000 \n",
      "Epoch 30/500 - loss: 3.6317 - accuracy: 0.5125 - val_loss: 4.4859 - val_accuracy: 0.4000 \n",
      "Epoch 31/500 - loss: 3.6290 - accuracy: 0.5000 - val_loss: 4.4821 - val_accuracy: 0.4000 \n",
      "Epoch 32/500 - loss: 3.6266 - accuracy: 0.5000 - val_loss: 4.4786 - val_accuracy: 0.4000 \n",
      "Epoch 33/500 - loss: 3.6243 - accuracy: 0.5000 - val_loss: 4.4755 - val_accuracy: 0.4000 \n",
      "Epoch 34/500 - loss: 3.6223 - accuracy: 0.5000 - val_loss: 4.4726 - val_accuracy: 0.4000 \n",
      "Epoch 35/500 - loss: 3.6204 - accuracy: 0.5000 - val_loss: 4.4699 - val_accuracy: 0.4000 \n",
      "Epoch 36/500 - loss: 3.6186 - accuracy: 0.5000 - val_loss: 4.4675 - val_accuracy: 0.4000 \n",
      "Epoch 37/500 - loss: 3.6171 - accuracy: 0.5000 - val_loss: 4.4652 - val_accuracy: 0.4000 \n",
      "Epoch 38/500 - loss: 3.6156 - accuracy: 0.5125 - val_loss: 4.4632 - val_accuracy: 0.4000 \n",
      "Epoch 39/500 - loss: 3.6143 - accuracy: 0.5125 - val_loss: 4.4612 - val_accuracy: 0.4000 \n",
      "Epoch 40/500 - loss: 3.6130 - accuracy: 0.5125 - val_loss: 4.4595 - val_accuracy: 0.4000 \n",
      "Epoch 41/500 - loss: 3.6119 - accuracy: 0.5125 - val_loss: 4.4578 - val_accuracy: 0.4000 \n",
      "Epoch 42/500 - loss: 3.6108 - accuracy: 0.5125 - val_loss: 4.4562 - val_accuracy: 0.4000 \n",
      "Epoch 43/500 - loss: 3.6097 - accuracy: 0.5125 - val_loss: 4.4547 - val_accuracy: 0.4000 \n",
      "Epoch 44/500 - loss: 3.6088 - accuracy: 0.5125 - val_loss: 4.4534 - val_accuracy: 0.4000 \n",
      "Epoch 45/500 - loss: 3.6079 - accuracy: 0.5125 - val_loss: 4.4521 - val_accuracy: 0.4000 \n",
      "Epoch 46/500 - loss: 3.6070 - accuracy: 0.5125 - val_loss: 4.4508 - val_accuracy: 0.4000 \n",
      "Epoch 47/500 - loss: 3.6062 - accuracy: 0.5125 - val_loss: 4.4496 - val_accuracy: 0.4000 \n",
      "Epoch 48/500 - loss: 3.6054 - accuracy: 0.5125 - val_loss: 4.4485 - val_accuracy: 0.4000 \n",
      "Epoch 49/500 - loss: 3.6047 - accuracy: 0.5125 - val_loss: 4.4475 - val_accuracy: 0.4000 \n",
      "Epoch 50/500 - loss: 3.6040 - accuracy: 0.5125 - val_loss: 4.4465 - val_accuracy: 0.4000 \n",
      "Epoch 51/500 - loss: 3.6033 - accuracy: 0.5125 - val_loss: 4.4455 - val_accuracy: 0.4000 \n",
      "Epoch 52/500 - loss: 3.6027 - accuracy: 0.5125 - val_loss: 4.4446 - val_accuracy: 0.4000 \n",
      "Epoch 53/500 - loss: 3.6021 - accuracy: 0.5125 - val_loss: 4.4437 - val_accuracy: 0.4000 \n",
      "Epoch 54/500 - loss: 3.6015 - accuracy: 0.5125 - val_loss: 4.4428 - val_accuracy: 0.4000 \n",
      "Epoch 55/500 - loss: 3.6009 - accuracy: 0.5125 - val_loss: 4.4420 - val_accuracy: 0.4000 \n",
      "Epoch 56/500 - loss: 3.6004 - accuracy: 0.5125 - val_loss: 4.4411 - val_accuracy: 0.4000 \n",
      "Epoch 57/500 - loss: 3.5998 - accuracy: 0.5125 - val_loss: 4.4403 - val_accuracy: 0.4000 \n",
      "Epoch 58/500 - loss: 3.5993 - accuracy: 0.5125 - val_loss: 4.4396 - val_accuracy: 0.4000 \n",
      "Epoch 59/500 - loss: 3.5988 - accuracy: 0.5125 - val_loss: 4.4388 - val_accuracy: 0.4000 \n",
      "Epoch 60/500 - loss: 3.5983 - accuracy: 0.5125 - val_loss: 4.4381 - val_accuracy: 0.4000 \n",
      "Epoch 61/500 - loss: 3.5978 - accuracy: 0.5125 - val_loss: 4.4374 - val_accuracy: 0.4000 \n",
      "Epoch 62/500 - loss: 3.5973 - accuracy: 0.5125 - val_loss: 4.4367 - val_accuracy: 0.4000 \n",
      "Epoch 63/500 - loss: 3.5968 - accuracy: 0.5125 - val_loss: 4.4360 - val_accuracy: 0.4000 \n",
      "Epoch 64/500 - loss: 3.5964 - accuracy: 0.5125 - val_loss: 4.4353 - val_accuracy: 0.4500 \n",
      "Epoch 65/500 - loss: 3.4913 - accuracy: 0.5125 - val_loss: 4.4331 - val_accuracy: 0.4500 \n",
      "Epoch 66/500 - loss: 3.4674 - accuracy: 0.5125 - val_loss: 4.4308 - val_accuracy: 0.4500 \n",
      "Epoch 67/500 - loss: 3.4579 - accuracy: 0.5125 - val_loss: 4.4285 - val_accuracy: 0.4500 \n",
      "Epoch 68/500 - loss: 3.4516 - accuracy: 0.5250 - val_loss: 4.4264 - val_accuracy: 0.4500 \n",
      "Epoch 69/500 - loss: 3.4469 - accuracy: 0.5250 - val_loss: 4.4244 - val_accuracy: 0.4500 \n",
      "Epoch 70/500 - loss: 3.4430 - accuracy: 0.5250 - val_loss: 4.4225 - val_accuracy: 0.4500 \n",
      "Epoch 71/500 - loss: 3.4396 - accuracy: 0.5250 - val_loss: 4.4206 - val_accuracy: 0.4500 \n",
      "Epoch 72/500 - loss: 3.4368 - accuracy: 0.5250 - val_loss: 4.4189 - val_accuracy: 0.4500 \n",
      "Epoch 73/500 - loss: 3.4342 - accuracy: 0.5250 - val_loss: 4.4173 - val_accuracy: 0.4500 \n",
      "Epoch 74/500 - loss: 3.4319 - accuracy: 0.5250 - val_loss: 4.4158 - val_accuracy: 0.5000 \n",
      "Epoch 75/500 - loss: 3.4299 - accuracy: 0.5250 - val_loss: 4.4143 - val_accuracy: 0.5000 \n",
      "Epoch 76/500 - loss: 3.4280 - accuracy: 0.5250 - val_loss: 4.4129 - val_accuracy: 0.5000 \n",
      "Epoch 77/500 - loss: 3.4262 - accuracy: 0.5250 - val_loss: 4.4116 - val_accuracy: 0.5000 \n",
      "Epoch 78/500 - loss: 3.4246 - accuracy: 0.5250 - val_loss: 4.4103 - val_accuracy: 0.5000 \n",
      "Epoch 79/500 - loss: 3.4231 - accuracy: 0.5250 - val_loss: 4.4091 - val_accuracy: 0.5000 \n",
      "Epoch 80/500 - loss: 3.4217 - accuracy: 0.5250 - val_loss: 4.4080 - val_accuracy: 0.5000 \n",
      "Epoch 81/500 - loss: 3.4204 - accuracy: 0.5375 - val_loss: 4.4069 - val_accuracy: 0.5000 \n",
      "Epoch 82/500 - loss: 3.4192 - accuracy: 0.5375 - val_loss: 4.4058 - val_accuracy: 0.5000 \n",
      "Epoch 83/500 - loss: 3.4180 - accuracy: 0.5375 - val_loss: 4.4048 - val_accuracy: 0.5000 \n",
      "Epoch 84/500 - loss: 3.4169 - accuracy: 0.5375 - val_loss: 4.4038 - val_accuracy: 0.5500 \n",
      "Epoch 85/500 - loss: 3.4159 - accuracy: 0.5375 - val_loss: 4.4029 - val_accuracy: 0.5500 \n",
      "Epoch 86/500 - loss: 3.4149 - accuracy: 0.5500 - val_loss: 4.4019 - val_accuracy: 0.5500 \n",
      "Epoch 87/500 - loss: 3.4139 - accuracy: 0.5500 - val_loss: 4.4011 - val_accuracy: 0.5500 \n",
      "Epoch 88/500 - loss: 3.4130 - accuracy: 0.5500 - val_loss: 4.4002 - val_accuracy: 0.5500 \n",
      "Epoch 89/500 - loss: 3.4121 - accuracy: 0.5500 - val_loss: 4.3994 - val_accuracy: 0.5500 \n",
      "Epoch 90/500 - loss: 3.4113 - accuracy: 0.5500 - val_loss: 4.3985 - val_accuracy: 0.5500 \n",
      "Epoch 91/500 - loss: 3.4105 - accuracy: 0.5500 - val_loss: 4.3978 - val_accuracy: 0.5500 \n",
      "Epoch 92/500 - loss: 3.4097 - accuracy: 0.5500 - val_loss: 4.3970 - val_accuracy: 0.5500 \n",
      "Epoch 93/500 - loss: 3.4089 - accuracy: 0.5500 - val_loss: 4.3962 - val_accuracy: 0.5500 \n",
      "Epoch 94/500 - loss: 3.4082 - accuracy: 0.5500 - val_loss: 4.3955 - val_accuracy: 0.5500 \n",
      "Epoch 95/500 - loss: 3.4074 - accuracy: 0.5500 - val_loss: 4.3948 - val_accuracy: 0.5500 \n",
      "Epoch 96/500 - loss: 3.4067 - accuracy: 0.5500 - val_loss: 4.3940 - val_accuracy: 0.5500 \n",
      "Epoch 97/500 - loss: 3.4060 - accuracy: 0.5500 - val_loss: 4.3933 - val_accuracy: 0.5500 \n",
      "Epoch 98/500 - loss: 3.4054 - accuracy: 0.5500 - val_loss: 4.3927 - val_accuracy: 0.5500 \n",
      "Epoch 99/500 - loss: 3.4047 - accuracy: 0.5500 - val_loss: 4.3920 - val_accuracy: 0.5500 \n",
      "Epoch 100/500 - loss: 3.4041 - accuracy: 0.5500 - val_loss: 4.3913 - val_accuracy: 0.5500 \n",
      "Epoch 101/500 - loss: 3.4034 - accuracy: 0.5500 - val_loss: 4.3906 - val_accuracy: 0.5500 \n",
      "Epoch 102/500 - loss: 3.4028 - accuracy: 0.5500 - val_loss: 4.3900 - val_accuracy: 0.5500 \n",
      "Epoch 103/500 - loss: 3.4022 - accuracy: 0.5500 - val_loss: 4.3894 - val_accuracy: 0.6000 \n",
      "Epoch 104/500 - loss: 3.4016 - accuracy: 0.5500 - val_loss: 4.3887 - val_accuracy: 0.6000 \n",
      "Epoch 105/500 - loss: 3.4010 - accuracy: 0.5500 - val_loss: 4.3881 - val_accuracy: 0.6000 \n",
      "Epoch 106/500 - loss: 3.4005 - accuracy: 0.5500 - val_loss: 4.3875 - val_accuracy: 0.6000 \n",
      "Epoch 107/500 - loss: 3.3999 - accuracy: 0.5500 - val_loss: 4.3869 - val_accuracy: 0.6000 \n",
      "Epoch 108/500 - loss: 3.3993 - accuracy: 0.5500 - val_loss: 4.3863 - val_accuracy: 0.6000 \n",
      "Epoch 109/500 - loss: 3.3988 - accuracy: 0.5500 - val_loss: 4.3856 - val_accuracy: 0.6000 \n",
      "Epoch 110/500 - loss: 3.3982 - accuracy: 0.5500 - val_loss: 4.3850 - val_accuracy: 0.6000 \n",
      "Epoch 111/500 - loss: 3.3977 - accuracy: 0.5500 - val_loss: 4.3845 - val_accuracy: 0.6000 \n",
      "Epoch 112/500 - loss: 3.3972 - accuracy: 0.5500 - val_loss: 4.3839 - val_accuracy: 0.6000 \n",
      "Epoch 113/500 - loss: 3.3966 - accuracy: 0.5500 - val_loss: 4.3833 - val_accuracy: 0.6000 \n",
      "Epoch 114/500 - loss: 3.3961 - accuracy: 0.5500 - val_loss: 4.3827 - val_accuracy: 0.6000 \n",
      "Epoch 115/500 - loss: 3.3956 - accuracy: 0.5500 - val_loss: 4.3821 - val_accuracy: 0.6000 \n",
      "Epoch 116/500 - loss: 3.3951 - accuracy: 0.5500 - val_loss: 4.3816 - val_accuracy: 0.6000 \n",
      "Epoch 117/500 - loss: 3.3946 - accuracy: 0.5500 - val_loss: 4.3810 - val_accuracy: 0.6000 \n",
      "Epoch 118/500 - loss: 3.3043 - accuracy: 0.5500 - val_loss: 4.3774 - val_accuracy: 0.6000 \n",
      "Epoch 119/500 - loss: 3.2526 - accuracy: 0.5625 - val_loss: 4.3742 - val_accuracy: 0.6000 \n",
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      "Epoch 481/500 - loss: 2.9704 - accuracy: 0.6625 - val_loss: 3.6454 - val_accuracy: 0.6000 \n",
      "Epoch 482/500 - loss: 2.9703 - accuracy: 0.6625 - val_loss: 3.6454 - val_accuracy: 0.6000 \n",
      "Epoch 483/500 - loss: 2.9703 - accuracy: 0.6625 - val_loss: 3.6454 - val_accuracy: 0.6000 \n",
      "Epoch 484/500 - loss: 2.9702 - accuracy: 0.6625 - val_loss: 3.6453 - val_accuracy: 0.6000 \n",
      "Epoch 485/500 - loss: 2.9702 - accuracy: 0.6625 - val_loss: 3.6453 - val_accuracy: 0.6000 \n",
      "Epoch 486/500 - loss: 2.9702 - accuracy: 0.6625 - val_loss: 3.6453 - val_accuracy: 0.6000 \n",
      "Epoch 487/500 - loss: 2.9701 - accuracy: 0.6625 - val_loss: 3.6452 - val_accuracy: 0.6000 \n",
      "Epoch 488/500 - loss: 2.9701 - accuracy: 0.6625 - val_loss: 3.6452 - val_accuracy: 0.6000 \n",
      "Epoch 489/500 - loss: 2.9700 - accuracy: 0.6625 - val_loss: 3.6452 - val_accuracy: 0.6000 \n",
      "Epoch 490/500 - loss: 2.9700 - accuracy: 0.6625 - val_loss: 3.6451 - val_accuracy: 0.6000 \n",
      "Epoch 491/500 - loss: 2.9700 - accuracy: 0.6625 - val_loss: 3.6451 - val_accuracy: 0.6000 \n",
      "Epoch 492/500 - loss: 2.9699 - accuracy: 0.6625 - val_loss: 3.6451 - val_accuracy: 0.6000 \n",
      "Epoch 493/500 - loss: 2.9699 - accuracy: 0.6625 - val_loss: 3.6451 - val_accuracy: 0.6000 \n",
      "Epoch 494/500 - loss: 2.9698 - accuracy: 0.6625 - val_loss: 3.6450 - val_accuracy: 0.6000 \n",
      "Epoch 495/500 - loss: 2.9698 - accuracy: 0.6625 - val_loss: 3.6450 - val_accuracy: 0.6000 \n",
      "Epoch 496/500 - loss: 2.9698 - accuracy: 0.6625 - val_loss: 3.6450 - val_accuracy: 0.6000 \n",
      "Epoch 497/500 - loss: 2.9697 - accuracy: 0.6625 - val_loss: 3.6450 - val_accuracy: 0.6000 \n",
      "Epoch 498/500 - loss: 2.9697 - accuracy: 0.6625 - val_loss: 3.6449 - val_accuracy: 0.6000 \n",
      "Epoch 499/500 - loss: 2.9696 - accuracy: 0.6625 - val_loss: 3.6449 - val_accuracy: 0.6000 \n",
      "Epoch 500/500 - loss: 2.9696 - accuracy: 0.6625 - val_loss: 3.6449 - val_accuracy: 0.6000 \n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 32)                96        \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 129\n",
      "Trainable params: 129\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 4.6580 - accuracy: 0.4125 - val_loss: 9.0300 - val_accuracy: 0.3500 \n",
      "Epoch 2/500 - loss: 4.6456 - accuracy: 0.4250 - val_loss: 9.0190 - val_accuracy: 0.3500 \n",
      "Epoch 3/500 - loss: 4.5070 - accuracy: 0.4375 - val_loss: 9.0111 - val_accuracy: 0.4000 \n",
      "Epoch 4/500 - loss: 4.4802 - accuracy: 0.4500 - val_loss: 9.0042 - val_accuracy: 0.4000 \n",
      "Epoch 5/500 - loss: 4.4665 - accuracy: 0.4625 - val_loss: 8.3875 - val_accuracy: 0.4000 \n",
      "Epoch 6/500 - loss: 4.4565 - accuracy: 0.4875 - val_loss: 8.3480 - val_accuracy: 0.4000 \n",
      "Epoch 7/500 - loss: 4.3096 - accuracy: 0.5000 - val_loss: 8.3214 - val_accuracy: 0.4000 \n",
      "Epoch 8/500 - loss: 4.2886 - accuracy: 0.4875 - val_loss: 8.3019 - val_accuracy: 0.4000 \n",
      "Epoch 9/500 - loss: 4.2758 - accuracy: 0.5000 - val_loss: 8.2880 - val_accuracy: 0.4000 \n",
      "Epoch 10/500 - loss: 4.2662 - accuracy: 0.5000 - val_loss: 8.2770 - val_accuracy: 0.4000 \n",
      "Epoch 11/500 - loss: 4.2584 - accuracy: 0.5125 - val_loss: 8.2678 - val_accuracy: 0.4000 \n",
      "Epoch 12/500 - loss: 4.2518 - accuracy: 0.5375 - val_loss: 8.2602 - val_accuracy: 0.4000 \n",
      "Epoch 13/500 - loss: 4.2461 - accuracy: 0.5375 - val_loss: 8.2536 - val_accuracy: 0.4000 \n",
      "Epoch 14/500 - loss: 4.2413 - accuracy: 0.5250 - val_loss: 8.2480 - val_accuracy: 0.4000 \n",
      "Epoch 15/500 - loss: 4.2370 - accuracy: 0.5250 - val_loss: 8.2431 - val_accuracy: 0.4000 \n",
      "Epoch 16/500 - loss: 4.2333 - accuracy: 0.5375 - val_loss: 8.2388 - val_accuracy: 0.4000 \n",
      "Epoch 17/500 - loss: 4.2303 - accuracy: 0.5500 - val_loss: 8.2351 - val_accuracy: 0.4000 \n",
      "Epoch 18/500 - loss: 4.2277 - accuracy: 0.5625 - val_loss: 8.2318 - val_accuracy: 0.4000 \n",
      "Epoch 19/500 - loss: 4.2255 - accuracy: 0.5875 - val_loss: 8.2292 - val_accuracy: 0.4000 \n",
      "Epoch 20/500 - loss: 4.2237 - accuracy: 0.5875 - val_loss: 8.2271 - val_accuracy: 0.4000 \n",
      "Epoch 21/500 - loss: 4.2222 - accuracy: 0.5875 - val_loss: 8.2253 - val_accuracy: 0.4500 \n",
      "Epoch 22/500 - loss: 4.2212 - accuracy: 0.5875 - val_loss: 8.2240 - val_accuracy: 0.4500 \n",
      "Epoch 23/500 - loss: 4.2205 - accuracy: 0.5875 - val_loss: 8.2231 - val_accuracy: 0.4500 \n",
      "Epoch 24/500 - loss: 4.2200 - accuracy: 0.5875 - val_loss: 8.2223 - val_accuracy: 0.4500 \n",
      "Epoch 25/500 - loss: 4.2198 - accuracy: 0.5875 - val_loss: 8.2217 - val_accuracy: 0.4000 \n",
      "Epoch 26/500 - loss: 4.2198 - accuracy: 0.5875 - val_loss: 8.2212 - val_accuracy: 0.4000 \n",
      "Epoch 27/500 - loss: 4.2198 - accuracy: 0.5875 - val_loss: 8.2210 - val_accuracy: 0.4000 \n",
      "Epoch 28/500 - loss: 4.2198 - accuracy: 0.5875 - val_loss: 8.2211 - val_accuracy: 0.4000 \n",
      "Epoch 29/500 - loss: 4.2199 - accuracy: 0.6000 - val_loss: 8.2212 - val_accuracy: 0.4000 \n",
      "Epoch 30/500 - loss: 4.2200 - accuracy: 0.6000 - val_loss: 8.2213 - val_accuracy: 0.4000 \n",
      "Epoch 31/500 - loss: 4.2201 - accuracy: 0.6000 - val_loss: 8.2214 - val_accuracy: 0.4000 \n",
      "Epoch 32/500 - loss: 4.2202 - accuracy: 0.6000 - val_loss: 8.2215 - val_accuracy: 0.4000 \n",
      "Epoch 33/500 - loss: 4.2202 - accuracy: 0.6000 - val_loss: 8.2216 - val_accuracy: 0.4000 \n",
      "Epoch 34/500 - loss: 4.2203 - accuracy: 0.6000 - val_loss: 8.2217 - val_accuracy: 0.4000 \n",
      "Epoch 35/500 - loss: 4.2203 - accuracy: 0.6000 - val_loss: 8.2217 - val_accuracy: 0.4000 \n",
      "Epoch 36/500 - loss: 4.2204 - accuracy: 0.6000 - val_loss: 8.2218 - val_accuracy: 0.4000 \n",
      "Epoch 37/500 - loss: 4.2204 - accuracy: 0.6000 - val_loss: 8.2218 - val_accuracy: 0.4000 \n",
      "Epoch 38/500 - loss: 4.2204 - accuracy: 0.6000 - val_loss: 8.2219 - val_accuracy: 0.4000 \n",
      "Epoch 39/500 - loss: 4.2204 - accuracy: 0.6000 - val_loss: 8.2218 - val_accuracy: 0.4000 \n",
      "Epoch 40/500 - loss: 4.2203 - accuracy: 0.6000 - val_loss: 8.2218 - val_accuracy: 0.4000 \n",
      "Epoch 41/500 - loss: 4.2203 - accuracy: 0.6000 - val_loss: 8.2218 - val_accuracy: 0.4000 \n",
      "Epoch 42/500 - loss: 4.2202 - accuracy: 0.6000 - val_loss: 8.2217 - val_accuracy: 0.4000 \n",
      "Epoch 43/500 - loss: 4.2201 - accuracy: 0.6000 - val_loss: 8.2217 - val_accuracy: 0.4000 \n",
      "Epoch 44/500 - loss: 4.2200 - accuracy: 0.6000 - val_loss: 8.2216 - val_accuracy: 0.4000 \n",
      "Epoch 45/500 - loss: 4.2199 - accuracy: 0.6000 - val_loss: 8.2215 - val_accuracy: 0.4000 \n",
      "Epoch 46/500 - loss: 4.2198 - accuracy: 0.6000 - val_loss: 8.2214 - val_accuracy: 0.4000 \n",
      "Epoch 47/500 - loss: 4.2196 - accuracy: 0.6000 - val_loss: 8.2213 - val_accuracy: 0.4000 \n",
      "Epoch 48/500 - loss: 4.2195 - accuracy: 0.6000 - val_loss: 8.2212 - val_accuracy: 0.4000 \n",
      "Epoch 49/500 - loss: 4.2193 - accuracy: 0.6000 - val_loss: 8.2211 - val_accuracy: 0.4000 \n",
      "Epoch 50/500 - loss: 4.2192 - accuracy: 0.6000 - val_loss: 8.2209 - val_accuracy: 0.4000 \n",
      "Epoch 51/500 - loss: 4.2190 - accuracy: 0.6000 - val_loss: 8.2208 - val_accuracy: 0.4000 \n",
      "Epoch 52/500 - loss: 4.2188 - accuracy: 0.6000 - val_loss: 8.2207 - val_accuracy: 0.4000 \n",
      "Epoch 53/500 - loss: 4.2187 - accuracy: 0.6000 - val_loss: 8.2205 - val_accuracy: 0.4000 \n",
      "Epoch 54/500 - loss: 4.2185 - accuracy: 0.6000 - val_loss: 8.2204 - val_accuracy: 0.4000 \n",
      "Epoch 55/500 - loss: 4.2183 - accuracy: 0.6000 - val_loss: 8.2203 - val_accuracy: 0.4000 \n",
      "Epoch 56/500 - loss: 4.2182 - accuracy: 0.6000 - val_loss: 8.2202 - val_accuracy: 0.4000 \n",
      "Epoch 57/500 - loss: 4.2180 - accuracy: 0.6000 - val_loss: 8.2200 - val_accuracy: 0.4000 \n",
      "Epoch 58/500 - loss: 4.2179 - accuracy: 0.6000 - val_loss: 8.2199 - val_accuracy: 0.4000 \n",
      "Epoch 59/500 - loss: 4.2177 - accuracy: 0.6000 - val_loss: 8.2198 - val_accuracy: 0.4000 \n",
      "Epoch 60/500 - loss: 4.2176 - accuracy: 0.6000 - val_loss: 8.2198 - val_accuracy: 0.4000 \n",
      "Epoch 61/500 - loss: 4.2174 - accuracy: 0.6000 - val_loss: 8.2199 - val_accuracy: 0.4000 \n",
      "Epoch 62/500 - loss: 4.2173 - accuracy: 0.6000 - val_loss: 8.2200 - val_accuracy: 0.4000 \n",
      "Epoch 63/500 - loss: 4.2172 - accuracy: 0.6000 - val_loss: 8.2200 - val_accuracy: 0.4000 \n",
      "Epoch 64/500 - loss: 4.2170 - accuracy: 0.6000 - val_loss: 8.2201 - val_accuracy: 0.4000 \n",
      "Epoch 65/500 - loss: 4.2169 - accuracy: 0.6000 - val_loss: 8.2201 - val_accuracy: 0.4000 \n",
      "Epoch 66/500 - loss: 4.2168 - accuracy: 0.5875 - val_loss: 8.2202 - val_accuracy: 0.4000 \n",
      "Epoch 67/500 - loss: 4.2167 - accuracy: 0.5875 - val_loss: 8.2203 - val_accuracy: 0.4000 \n",
      "Epoch 68/500 - loss: 4.2166 - accuracy: 0.5875 - val_loss: 8.2203 - val_accuracy: 0.4000 \n",
      "Epoch 69/500 - loss: 4.2165 - accuracy: 0.5875 - val_loss: 8.2204 - val_accuracy: 0.4000 \n",
      "Epoch 70/500 - loss: 4.2163 - accuracy: 0.5875 - val_loss: 8.2205 - val_accuracy: 0.4000 \n",
      "Epoch 71/500 - loss: 4.2163 - accuracy: 0.5875 - val_loss: 8.2205 - val_accuracy: 0.4000 \n",
      "Epoch 72/500 - loss: 4.2162 - accuracy: 0.5875 - val_loss: 8.2206 - val_accuracy: 0.4000 \n",
      "Epoch 73/500 - loss: 4.2161 - accuracy: 0.5750 - val_loss: 8.2206 - val_accuracy: 0.4000 \n",
      "Epoch 74/500 - loss: 4.2161 - accuracy: 0.5750 - val_loss: 8.2206 - val_accuracy: 0.4000 \n",
      "Epoch 75/500 - loss: 4.2160 - accuracy: 0.5750 - val_loss: 8.2206 - val_accuracy: 0.4000 \n",
      "Epoch 76/500 - loss: 4.2160 - accuracy: 0.5750 - val_loss: 8.2206 - val_accuracy: 0.4000 \n",
      "Epoch 77/500 - loss: 4.2159 - accuracy: 0.5750 - val_loss: 8.2205 - val_accuracy: 0.4000 \n",
      "Epoch 78/500 - loss: 4.2159 - accuracy: 0.5750 - val_loss: 8.2205 - val_accuracy: 0.4000 \n",
      "Epoch 79/500 - loss: 4.2158 - accuracy: 0.5750 - val_loss: 8.2204 - val_accuracy: 0.4000 \n",
      "Epoch 80/500 - loss: 4.2158 - accuracy: 0.5750 - val_loss: 8.2204 - val_accuracy: 0.4000 \n",
      "Epoch 81/500 - loss: 4.2157 - accuracy: 0.5750 - val_loss: 8.2203 - val_accuracy: 0.4000 \n",
      "Epoch 82/500 - loss: 4.2156 - accuracy: 0.5750 - val_loss: 8.2202 - val_accuracy: 0.4000 \n",
      "Epoch 83/500 - loss: 4.2156 - accuracy: 0.5750 - val_loss: 8.2201 - val_accuracy: 0.4000 \n",
      "Epoch 84/500 - loss: 4.2155 - accuracy: 0.5875 - val_loss: 8.2199 - val_accuracy: 0.4000 \n",
      "Epoch 85/500 - loss: 4.2154 - accuracy: 0.5875 - val_loss: 8.2198 - val_accuracy: 0.4000 \n",
      "Epoch 86/500 - loss: 4.2153 - accuracy: 0.5875 - val_loss: 8.2197 - val_accuracy: 0.4000 \n",
      "Epoch 87/500 - loss: 4.2152 - accuracy: 0.5875 - val_loss: 8.2195 - val_accuracy: 0.4000 \n",
      "Epoch 88/500 - loss: 4.2152 - accuracy: 0.5875 - val_loss: 8.2194 - val_accuracy: 0.4000 \n",
      "Epoch 89/500 - loss: 4.2151 - accuracy: 0.5875 - val_loss: 8.2193 - val_accuracy: 0.4000 \n",
      "Epoch 90/500 - loss: 4.2150 - accuracy: 0.5875 - val_loss: 8.2191 - val_accuracy: 0.4000 \n",
      "Epoch 91/500 - loss: 4.2149 - accuracy: 0.5875 - val_loss: 8.2190 - val_accuracy: 0.4000 \n",
      "Epoch 92/500 - loss: 4.2149 - accuracy: 0.5875 - val_loss: 8.2188 - val_accuracy: 0.4000 \n",
      "Epoch 93/500 - loss: 4.2148 - accuracy: 0.5875 - val_loss: 8.2187 - val_accuracy: 0.4000 \n",
      "Epoch 94/500 - loss: 4.2147 - accuracy: 0.5875 - val_loss: 8.2186 - val_accuracy: 0.4000 \n",
      "Epoch 95/500 - loss: 4.2146 - accuracy: 0.5875 - val_loss: 8.2185 - val_accuracy: 0.4000 \n",
      "Epoch 96/500 - loss: 4.2146 - accuracy: 0.5875 - val_loss: 8.2183 - val_accuracy: 0.4000 \n",
      "Epoch 97/500 - loss: 4.2145 - accuracy: 0.5875 - val_loss: 8.2182 - val_accuracy: 0.4000 \n",
      "Epoch 98/500 - loss: 4.2144 - accuracy: 0.5875 - val_loss: 8.2181 - val_accuracy: 0.4000 \n",
      "Epoch 99/500 - loss: 4.2144 - accuracy: 0.5875 - val_loss: 8.2181 - val_accuracy: 0.4000 \n",
      "Epoch 100/500 - loss: 4.2143 - accuracy: 0.5875 - val_loss: 8.2180 - val_accuracy: 0.4000 \n",
      "Epoch 101/500 - loss: 4.2142 - accuracy: 0.5875 - val_loss: 8.2179 - val_accuracy: 0.4000 \n",
      "Epoch 102/500 - loss: 4.2142 - accuracy: 0.5875 - val_loss: 8.2179 - val_accuracy: 0.4000 \n",
      "Epoch 103/500 - loss: 4.2141 - accuracy: 0.5875 - val_loss: 8.2178 - val_accuracy: 0.4000 \n",
      "Epoch 104/500 - loss: 4.2140 - accuracy: 0.5875 - val_loss: 8.2178 - val_accuracy: 0.4000 \n",
      "Epoch 105/500 - loss: 4.2139 - accuracy: 0.5875 - val_loss: 8.2177 - val_accuracy: 0.4000 \n",
      "Epoch 106/500 - loss: 4.2139 - accuracy: 0.5875 - val_loss: 8.2177 - val_accuracy: 0.4000 \n",
      "Epoch 107/500 - loss: 4.2138 - accuracy: 0.5875 - val_loss: 8.2176 - val_accuracy: 0.4000 \n",
      "Epoch 108/500 - loss: 4.2137 - accuracy: 0.5875 - val_loss: 8.2175 - val_accuracy: 0.4000 \n",
      "Epoch 109/500 - loss: 4.2137 - accuracy: 0.5875 - val_loss: 8.2174 - val_accuracy: 0.4000 \n",
      "Epoch 110/500 - loss: 4.2136 - accuracy: 0.5875 - val_loss: 8.2174 - val_accuracy: 0.4000 \n",
      "Epoch 111/500 - loss: 4.2135 - accuracy: 0.5875 - val_loss: 8.2173 - val_accuracy: 0.4000 \n",
      "Epoch 112/500 - loss: 4.2134 - accuracy: 0.5875 - val_loss: 8.2172 - val_accuracy: 0.4000 \n",
      "Epoch 113/500 - loss: 4.2134 - accuracy: 0.5875 - val_loss: 8.2171 - val_accuracy: 0.4000 \n",
      "Epoch 114/500 - loss: 4.2133 - accuracy: 0.5875 - val_loss: 8.2170 - val_accuracy: 0.4000 \n",
      "Epoch 115/500 - loss: 4.2132 - accuracy: 0.5875 - val_loss: 8.2169 - val_accuracy: 0.4000 \n",
      "Epoch 116/500 - loss: 4.2132 - accuracy: 0.5875 - val_loss: 8.2168 - val_accuracy: 0.4000 \n",
      "Epoch 117/500 - loss: 4.2131 - accuracy: 0.5875 - val_loss: 8.2167 - val_accuracy: 0.4000 \n",
      "Epoch 118/500 - loss: 4.2130 - accuracy: 0.5875 - val_loss: 8.2166 - val_accuracy: 0.4000 \n",
      "Epoch 119/500 - loss: 4.2129 - accuracy: 0.5875 - val_loss: 8.2165 - val_accuracy: 0.4000 \n",
      "Epoch 120/500 - loss: 4.2129 - accuracy: 0.5875 - val_loss: 8.2164 - val_accuracy: 0.4000 \n",
      "Epoch 121/500 - loss: 4.2128 - accuracy: 0.5875 - val_loss: 8.2163 - val_accuracy: 0.4000 \n",
      "Epoch 122/500 - loss: 4.2127 - accuracy: 0.5750 - val_loss: 8.2162 - val_accuracy: 0.4000 \n",
      "Epoch 123/500 - loss: 4.2127 - accuracy: 0.5750 - val_loss: 8.2161 - val_accuracy: 0.4000 \n",
      "Epoch 124/500 - loss: 4.2126 - accuracy: 0.5750 - val_loss: 8.2161 - val_accuracy: 0.4000 \n",
      "Epoch 125/500 - loss: 4.2125 - accuracy: 0.5750 - val_loss: 8.2160 - val_accuracy: 0.4000 \n",
      "Epoch 126/500 - loss: 4.2125 - accuracy: 0.5750 - val_loss: 8.2159 - val_accuracy: 0.4000 \n",
      "Epoch 127/500 - loss: 4.2124 - accuracy: 0.5750 - val_loss: 8.2158 - val_accuracy: 0.4000 \n",
      "Epoch 128/500 - loss: 4.2123 - accuracy: 0.5750 - val_loss: 8.2157 - val_accuracy: 0.4000 \n",
      "Epoch 129/500 - loss: 4.2122 - accuracy: 0.5750 - val_loss: 8.2156 - val_accuracy: 0.4000 \n",
      "Epoch 130/500 - loss: 4.2122 - accuracy: 0.5750 - val_loss: 8.2155 - val_accuracy: 0.4000 \n",
      "Epoch 131/500 - loss: 4.2121 - accuracy: 0.5750 - val_loss: 8.2154 - val_accuracy: 0.4000 \n",
      "Epoch 132/500 - loss: 4.2120 - accuracy: 0.5750 - val_loss: 8.2153 - val_accuracy: 0.4000 \n",
      "Epoch 133/500 - loss: 4.2120 - accuracy: 0.5750 - val_loss: 8.2153 - val_accuracy: 0.4000 \n",
      "Epoch 134/500 - loss: 4.2119 - accuracy: 0.5750 - val_loss: 8.2152 - val_accuracy: 0.4000 \n",
      "Epoch 135/500 - loss: 4.2118 - accuracy: 0.5750 - val_loss: 8.2151 - val_accuracy: 0.4000 \n",
      "Epoch 136/500 - loss: 4.2118 - accuracy: 0.5750 - val_loss: 8.2150 - val_accuracy: 0.4000 \n",
      "Epoch 137/500 - loss: 4.2117 - accuracy: 0.5750 - val_loss: 8.2150 - val_accuracy: 0.4000 \n",
      "Epoch 138/500 - loss: 4.2116 - accuracy: 0.5750 - val_loss: 8.2149 - val_accuracy: 0.4000 \n",
      "Epoch 139/500 - loss: 4.2116 - accuracy: 0.5625 - val_loss: 8.2148 - val_accuracy: 0.4000 \n",
      "Epoch 140/500 - loss: 4.2115 - accuracy: 0.5625 - val_loss: 8.2147 - val_accuracy: 0.4000 \n",
      "Epoch 141/500 - loss: 4.2114 - accuracy: 0.5625 - val_loss: 8.2147 - val_accuracy: 0.4000 \n",
      "Epoch 142/500 - loss: 4.2114 - accuracy: 0.5625 - val_loss: 8.2146 - val_accuracy: 0.4000 \n",
      "Epoch 143/500 - loss: 4.2113 - accuracy: 0.5625 - val_loss: 8.2145 - val_accuracy: 0.4000 \n",
      "Epoch 144/500 - loss: 4.2112 - accuracy: 0.5625 - val_loss: 8.2144 - val_accuracy: 0.4000 \n",
      "Epoch 145/500 - loss: 4.2112 - accuracy: 0.5625 - val_loss: 8.2143 - val_accuracy: 0.4000 \n",
      "Epoch 146/500 - loss: 4.2111 - accuracy: 0.5625 - val_loss: 8.2142 - val_accuracy: 0.4000 \n",
      "Epoch 147/500 - loss: 4.2110 - accuracy: 0.5625 - val_loss: 8.2142 - val_accuracy: 0.4000 \n",
      "Epoch 148/500 - loss: 4.2110 - accuracy: 0.5625 - val_loss: 8.2141 - val_accuracy: 0.4000 \n",
      "Epoch 149/500 - loss: 4.2109 - accuracy: 0.5625 - val_loss: 8.2140 - val_accuracy: 0.4000 \n",
      "Epoch 150/500 - loss: 4.2108 - accuracy: 0.5625 - val_loss: 8.2139 - val_accuracy: 0.4000 \n",
      "Epoch 151/500 - loss: 4.2108 - accuracy: 0.5625 - val_loss: 8.2138 - val_accuracy: 0.4000 \n",
      "Epoch 152/500 - loss: 4.2107 - accuracy: 0.5625 - val_loss: 8.2138 - val_accuracy: 0.4000 \n",
      "Epoch 153/500 - loss: 4.2106 - accuracy: 0.5625 - val_loss: 8.2137 - val_accuracy: 0.4000 \n",
      "Epoch 154/500 - loss: 4.2106 - accuracy: 0.5625 - val_loss: 8.2136 - val_accuracy: 0.4000 \n",
      "Epoch 155/500 - loss: 4.2105 - accuracy: 0.5625 - val_loss: 8.2135 - val_accuracy: 0.4000 \n",
      "Epoch 156/500 - loss: 4.2105 - accuracy: 0.5625 - val_loss: 8.2135 - val_accuracy: 0.4000 \n",
      "Epoch 157/500 - loss: 4.2104 - accuracy: 0.5625 - val_loss: 8.2134 - val_accuracy: 0.4000 \n",
      "Epoch 158/500 - loss: 4.2103 - accuracy: 0.5625 - val_loss: 8.2133 - val_accuracy: 0.4000 \n",
      "Epoch 159/500 - loss: 4.2103 - accuracy: 0.5625 - val_loss: 8.2132 - val_accuracy: 0.4000 \n",
      "Epoch 160/500 - loss: 4.2102 - accuracy: 0.5625 - val_loss: 8.2131 - val_accuracy: 0.4000 \n",
      "Epoch 161/500 - loss: 4.2101 - accuracy: 0.5625 - val_loss: 8.2130 - val_accuracy: 0.4000 \n",
      "Epoch 162/500 - loss: 4.2101 - accuracy: 0.5625 - val_loss: 8.2130 - val_accuracy: 0.4000 \n",
      "Epoch 163/500 - loss: 4.2100 - accuracy: 0.5750 - val_loss: 8.2129 - val_accuracy: 0.4000 \n",
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      "Epoch 347/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2047 - val_accuracy: 0.4000 \n",
      "Epoch 348/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2046 - val_accuracy: 0.4000 \n",
      "Epoch 349/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2046 - val_accuracy: 0.4000 \n",
      "Epoch 350/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2046 - val_accuracy: 0.4000 \n",
      "Epoch 351/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2046 - val_accuracy: 0.4000 \n",
      "Epoch 352/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2046 - val_accuracy: 0.4000 \n",
      "Epoch 353/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2046 - val_accuracy: 0.4000 \n",
      "Epoch 354/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2045 - val_accuracy: 0.4000 \n",
      "Epoch 355/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2045 - val_accuracy: 0.4000 \n",
      "Epoch 356/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2045 - val_accuracy: 0.4000 \n",
      "Epoch 357/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2045 - val_accuracy: 0.4000 \n",
      "Epoch 358/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2045 - val_accuracy: 0.4000 \n",
      "Epoch 359/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2045 - val_accuracy: 0.4000 \n",
      "Epoch 360/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2045 - val_accuracy: 0.4000 \n",
      "Epoch 361/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2044 - val_accuracy: 0.4000 \n",
      "Epoch 362/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2044 - val_accuracy: 0.4000 \n",
      "Epoch 363/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2044 - val_accuracy: 0.4000 \n",
      "Epoch 364/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2044 - val_accuracy: 0.4000 \n",
      "Epoch 365/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2044 - val_accuracy: 0.4000 \n",
      "Epoch 366/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2044 - val_accuracy: 0.4000 \n",
      "Epoch 367/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2044 - val_accuracy: 0.4000 \n",
      "Epoch 368/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2043 - val_accuracy: 0.4000 \n",
      "Epoch 369/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2043 - val_accuracy: 0.4000 \n",
      "Epoch 370/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2043 - val_accuracy: 0.4000 \n",
      "Epoch 371/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2043 - val_accuracy: 0.4000 \n",
      "Epoch 372/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2043 - val_accuracy: 0.4000 \n",
      "Epoch 373/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2043 - val_accuracy: 0.4000 \n",
      "Epoch 374/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2043 - val_accuracy: 0.4000 \n",
      "Epoch 375/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 376/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 377/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 378/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 379/500 - loss: 4.2050 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 380/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 381/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 382/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 383/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 384/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2042 - val_accuracy: 0.4000 \n",
      "Epoch 385/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2041 - val_accuracy: 0.4000 \n",
      "Epoch 386/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2041 - val_accuracy: 0.4000 \n",
      "Epoch 387/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2041 - val_accuracy: 0.4000 \n",
      "Epoch 388/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2041 - val_accuracy: 0.4000 \n",
      "Epoch 389/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2041 - val_accuracy: 0.4000 \n",
      "Epoch 390/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2041 - val_accuracy: 0.4000 \n",
      "Epoch 391/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2041 - val_accuracy: 0.4000 \n",
      "Epoch 392/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2041 - val_accuracy: 0.4000 \n",
      "Epoch 393/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2040 - val_accuracy: 0.4000 \n",
      "Epoch 394/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2040 - val_accuracy: 0.4000 \n",
      "Epoch 395/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2040 - val_accuracy: 0.4000 \n",
      "Epoch 396/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2040 - val_accuracy: 0.4000 \n",
      "Epoch 397/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2040 - val_accuracy: 0.4000 \n",
      "Epoch 398/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2040 - val_accuracy: 0.4000 \n",
      "Epoch 399/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2040 - val_accuracy: 0.4000 \n",
      "Epoch 400/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 401/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 402/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 403/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 404/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 405/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 406/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 407/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 408/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 409/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 410/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2039 - val_accuracy: 0.4000 \n",
      "Epoch 411/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 412/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 413/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 414/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 415/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 416/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 417/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 418/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 419/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 420/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 421/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 422/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2038 - val_accuracy: 0.4000 \n",
      "Epoch 423/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 424/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 425/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 426/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 427/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 428/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 429/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 430/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 431/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 432/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 433/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 434/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 435/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 436/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 437/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 438/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 439/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 440/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 441/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 442/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 443/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 444/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 445/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 446/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 447/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 448/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 449/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 450/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 451/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 452/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 453/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 454/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 455/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 456/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 457/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 458/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 459/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 460/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 461/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 462/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 463/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 464/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 465/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 466/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 467/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 468/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 469/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 470/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2036 - val_accuracy: 0.4000 \n",
      "Epoch 471/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2036 - val_accuracy: 0.4000 \n",
      "Epoch 472/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2036 - val_accuracy: 0.4000 \n",
      "Epoch 473/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 474/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 475/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 476/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 477/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 478/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 479/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 480/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 481/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 482/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 483/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 484/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 485/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 486/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 487/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 488/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 489/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 490/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 491/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 492/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 493/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 494/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 495/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 496/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 497/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 498/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 499/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n",
      "Epoch 500/500 - loss: 4.2049 - accuracy: 0.4625 - val_loss: 8.2037 - val_accuracy: 0.4000 \n"
     ]
    }
   ],
   "source": [
    "def cross_val_perf(cv=5):\n",
    "    logs = []\n",
    "    X, t = getXORdata(sigma=0.3)\n",
    "    best_acc = 0.\n",
    "    P = int(len(t)/cv)\n",
    "    for r in range(cv):\n",
    "        model = build_linear_model()\n",
    "        idxes = range(r*P,(r+1)*P)\n",
    "        mask = np.ones(t.shape, dtype=bool)\n",
    "        mask[idxes] = False\n",
    "        Xvalid = X[idxes]\n",
    "        tvalid = t[idxes]\n",
    "        Xtrain = np.asarray([x for i,x in enumerate(X) if i not in idxes])\n",
    "        ttrain = np.asarray([x for i,x in enumerate(t) if i not in idxes])\n",
    "        if r == 0:\n",
    "            logs = train(model, Xtrain, ttrain, Xvalid, tvalid, verbose = 0)\n",
    "        else:\n",
    "            log = train(model, Xtrain, ttrain, Xvalid, tvalid)\n",
    "            if log['val_acc'][-1] > best_acc:\n",
    "                best_acc = log['val_acc'][-1]\n",
    "                bestmodel = model\n",
    "            for key, val in logs.items():\n",
    "                logs[key] = np.vstack([val, log[key]])         \n",
    "    return logs, bestmodel\n",
    "\n",
    "linlogs, linmodel = cross_val_perf()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k+/WSJEmS0ig1Qb7kFvkddohTt772Wt5D9t0X5s6FP/2pPHWTJEmS0iY1QX75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\nMXHiRICJSZJM63b9O6jaFvk994zfOD/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IfV3qZ9fYG1fcvCflceNSn6g7O/Uc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\nA7eAPnUbtAwNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125d4ead0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2xS4wKSsD3n1XHqH/xS8CKULcAw4G9qK2VoL7WB0/IWB3n4nzvkUUtKxig9at\ngXHj5MG9iGHvr05ccQXw8cdBl4JMpO3AKcrKyqQtxsrKwNK/4tyxU12d5Ng7Cezjui2M9YrV8RMC\ndveZOO9bREHLKjb42c+AmTMjWZHLwJ5iI20HTlH2858D06fL3wHm2Me1JtEIpJzk2Md1WxjrFavj\nJwTs7jNx3reIgqR1/j0/xMCeYiPwVJzKSuC776Qa2A2dO0tAf+CBySaS/GfUJGodyOI9ZQRSTMVx\nvi3IHqbiEAUrH+9GMrCnWDCuygM9eN96SxqktmrD3qmddwbeece1Hu+cMrbntm2BLN5TTk/4cb7I\nyccfPz8wFYcoWPl4bmNgT7FgBJ6B3m4rLwc6dAA6dQqwEO4ytmccaxOdpp8UFwMNDS07uYoDpuJ4\ng6k4RMHKx3NbVq3ixMr69cDFF1uP83//Bwwd6s7ytm4FbrkF+MMfgt/TPvgAOOkkYP/9gcLClsNL\nSoD70jZ+lL26uszdfP7mN4BVj8OzZgEPPdTko+ptbQDcj8QjfwFmzAUeecR6GX/+M/Df/5oPHzsW\nOPts63k8+mjTfqpnz45HHwcpjFqO6urMHS1HwhtvAM8/DwCoruwD4CYk/nQj8MR8Gd6li2k3oanb\nolUrH8qa6o9/bOzFerfdgAsvtB7/9NOtU8LOOgvYe+/t/zIVxxtMxSFy0csvA6++aj68f3/ghhua\nfNTi3Pb73wMrVpjP45hjgKOOyq2cAWNgX1cHzJ9vPc7Wre4t79lngeuvByZOBPbdVz5btUr2ug45\n9jDrVM+ecsGyaFH64V60da915u29ebP18I0bW8yjZpvUkidWVQBYkLkcy5dbl2PQoMzzmDpVuiI1\n2pjv1EkeeI0R42QYmzSBysrt33vNJmntILFsIbAhuS/06GE6aeq2aN/e01I2tXkzcOWV0kVu165A\nrzQ9STe3YIH1rYVm6WL5eLvaD05ScaqqvC8PUWitWQPce69USvTvn36ctWutf7fT5Em2OLeVlwOL\nF5vPY/16e+UNMQb23bsD//mPf8traJDmk/baq/GzIUOAa66R3mD9NHy4tJPup1atct/ehxwirxTV\nFQDKgOK7bgUOtjGPW27Jbtlay8mla1fgq6/k+yyIb0Zb7FJxJk+WF4DqjwDsDRQ//SiwY+ZJA9sW\nFRXy/uCDwPjx9qaZMcPRIvLxdrUfioslTTDTaYKpOJT3KiqA664DjjzSPLA/6yx5OdDi3Pbss9mX\nMSLiG5EhEqQgAAAgAElEQVSEVXk50KdP03v5HTuyuiZHvqUS/O1vcjFoXNXHOKgHmqafxE02reKk\nTucbI7D3sMnT7dti/XLPlpGP7N7xYioO5T0jBnI55zMf0wzjHZWEUXl5yx9oBvY58y2VwKhJMLqb\njrnYpeKkyKZVnNTpfFNeLheQffp4tojt2+KAsZ4tIx85CezjeIwR2VZZKe8uNz6Rj2mGDOz9lm+B\n/f/+B2zY4PlifEslML67PAnsY5eKk8LWPvPll0CbNsCcOcFti/JyoG9fT5/Y3b4tNqzy5XjNF3b3\nGabiUN7zuMY+n9IMGdj7LV1g36lT49Vq3Bx4IHDbbZ4vxrfbbT16yBnCSI+Iucin4nz5ZdOWi1LY\n2mfatpURq6qC2xaTJgG33+7pIrb/+KEmby5a/WB3n2EqDuW9qiqpRHG5AoOpOPli1izg8MP9D6br\n64ElS/Knxr62Fli2zNPcYINvB69S0qRlngQ/kQ/sH3gAuOCCtINs7TNG7VGQgf0eewAnnODpIqQJ\nT41CNOTNRasfGNgT2VRZ6UkfMAzs88WiRdKedevW/i530ya5oBg5svGz66+X8oQ1sK+tBe6+W1qA\ncWrJEmkOwofA3sij8+V2W1lZ3gT2xvaMbP5vujtkSTU1krpeZNU2mBHYV1ZGf1tYqKlJftdFRXmz\nb/vB7j5TXBzP/YrItqoqTzpL8TU2CIn8DOyrquQHrE0bf5fbsSMwfTowbpy0gbZunTRzuWxZeAP7\noiLg8suBd95xPq0RIPjQaZOvV+VlZXlTqxn5GvuKCtP9r7raxv7SqpV01BZkjb0PZFsoeTicgb1r\nWGNPZFPnzsBOO7k+W+bY5wvjlo9SwSx/9Wo5kz/wgPx/003So1oY5ZJ6EtfAvn9/4PPPrXvAi4lI\nPzyrtWWNva3AHpBzRV4E9siru1F+cBLY19VZdxZMFGtXXQW88ILrs62uluSMmLdM3UQerWoKj275\n2Na1q+xpxkN948cDu+4aXHkyyfbHvqJC2nwvKXG/TM0Yt9t8ya466ihJUaqt9WFhwVJKtmkk0wQq\nK6WHVYtUHFu1OB075k8qTmlp3tyN8oOTVBw74xGRM7bP8zHCwD4IBQXAwIHAm29K1ORh+9SuGDQI\n+PvfJcozXq+/bj3NY48BV18t6+kDX6/KjWo1s2cHjj226bZq/jr+eB8KmaPaWgnylEJiWxWqf30p\ncMQRQZfKGSNAzSUVB9j+cHthoWSmmda+zp4tvyBKyT6QidU+ohTw/PM2CueO7dvimmuAp56yHrlt\nW+tyP/aY9fSvv5553Tdvdm3dguSkxt7OeETkjO3zfIxYPTYWX0EH9gDw5JOSzjFoEFBYGGxZMrny\nSmD33Zt+likXbp99gIcfBsaM8a5cKXw9eI27F2aB/TnnAIceaj691cVOVZUENUFf7C1ZAixeDPz2\nt0g82BrVEyYD56wNtkxOZfiebO8z11wDdOgAIEMu9Jw5ctH30EP2Lmgffth6+B572CicO7Zvi0GD\nMo/8l79I3oiZffaxnn6nnTKvu98NG3iEgT1RsBjY5wuPmlVyZPfdWwbLYdWvH3DGGc6mGTZMXj7x\n9XZbRYWkF3Xtmn74xInZz/tXv5La8kx3RLxmBMVnnIHi59ugZqfdgUOCLZJjq1fLA/I9e6YdbHuf\nSblIs2y9xOhI6swz7ZXP6THlIUfHzymn5Law/v1Dte5eYioOUbCYipMvDjrIukaVIsf3Gvtkmorr\nwvLwYkoaS2Rb7Dj9dLkDYpKflc0+Y7ktLB7UDbt8rNXyg3HjgTX2RMHIx3NbftbYX3JJMMutq8vQ\naDZly/fA3qsAzgjstQ6u1SYAWLsW6NULaNMmuoE9YNmLoeuBfUUFMGCAsxmGRD7++PlBKXtNWTKw\nJ/JGPp7b8rPGPij9+wN//GPQpYilmpoYBfabNwPr13szf7suvljy7CHbNY4pAtnsM5bbwriTE0G+\nHj95xs7xY2z7OB5nREHKx3MbA3s/VVUB7dunH3bXXcBnn/lbnhiprvYxj+7UU4FJk7yZtxEYhiEd\nJ/lQd3FxPGsSs9lnTLeF1vKg+OjRrpTNb74eP05s3Rr8RW6O7Bw/ke4vgihX8+cDgwcD//2v67MO\n7bnNQwzs/bJtm/xImbXGc/31wLvv+lumGPH1dtsFFwCHHebNvI07AWEI7JMinYpjwdVUHKWAl14C\njjnGlbL5LbS3q/ffH/jtb4MuRU6YikOUwdq1wMKFnrQQGNpzm4cY2PulqkrezQL7Tp2ktR7KSmxu\nt/XoIdULIeokqEkqwcKFwKJFgZbHLa6n4kRYaI+fGHSYxVQcogwyxUc5CO25zUMM7P1i7LhmzWwm\nO8Gh7MTmdptSEsyEqMa+SSrBiSfG5jkR2/tMZaXUxq9fz7Qkv4XsWMgGU3GIMjAqNT1ohjy05zYP\nsYkWv9ipsWdgn7XqaqBdu6BL4ZJXXwW6dw+6FNslEsCaNcl/YhBoGWzfoq2oAI47Dvj4YyQSe8Yy\n+Art7eqyMtn+QbcSlQOm4hBlUFUlx7fZM4g5CO25zUP5F9hXV0urI507m7Zv7YlMV6QdOwaXijNz\nJrBggflwpYApU6zn8e671rfMBw4E9tvPfHhDA/DRR5l7rTRRUwN065bVpJlt3Ah8+61vvehi+HDz\nYe+/b50KU1oKHHig9fwff1wCJTPjxzfpgbRJKkFZGfDcc8C0aU2n+cUv0vcWOmcO8NVXwP/+J8fb\n4MFNh3fpAhx1lHV5X35ZvgNAcq7NmpRcswb44gtgwgTgvvtkf3rySdPZ2r5FaxyzL72ExLLuqNqQ\nAKa9JZ8dcEBk265PFdrb1WVlUrj77gPatgXGjrU+Pt56C1i2DNhzT2CHHdKPY+yTZuzskw7YScUp\nKpLDg6k4FDn19XKO3rKl5bC+faXfICtPPSXxQ/v2nsRkoT23eSj/AvtXXwWOP14e1ujSxb/lZqqx\n79AhuNzlH34ALroI2LQp/fCiosyB/d13A//4h/nwyZOtA/t58+QEsHlzVjVznt5ue/xxCRA/+cSj\nBTjwwAMSWJuZNClzYH/66XIyNvPMM00C+yapBGPGAFOnSstAqY45Jn1g/9JLwC23mC9rt90yB1GX\nX9544Tl5spQvnbffluFbtgCffiotLViwvc/06CGvP/0JxfgJqjGscf3/9rdYBPahvV29887Se/Cv\nfy3/P/igeWC/bh1wSLJ75PHj5SI4nZdfBm6+2XyZu+xivU/OnClByK67Zi4/7KXiKBXf1qco5t5/\nHzjhhPTDDj00c2B/zjnyu7/33u6XDSE+t3ko/wL7igqp+enc2d/l7rkn8OKL5oF927bpr3j9cNpp\nmQP3TF5+2boWOFOw/v330mrQunVA166OF+/p7baFC8PT5N5TT1nWQtu6KMoUPTSrNWmSSnDCCcDP\nf95yGrPWDG68EbjhBuCJJ+SCorJS9nUnvvtO3s84o/HvdHr2lPeKCnllCLht7zOJBLB8OdDQgMT5\nBaj+TAGf1sowP+/6eSi0t6sHDpS7Nca5xWp7GyliBx8sd9jM3HCDtEKWrYsukmZNH3rI1uh2W5WK\na+tTFHOLFsnvzqZNLSt37PweGZkKHrSIA4T43Oah/Avsjc6F/M7X7NsXOPZY6+FGYOI3pXLvETfX\ng9IIwioqsgrsPb3dFqYeRd04+Tn8rlukEjiZ3ijvkCHyvmyZeYqEGWN5AwZIqoUZ4zuqqJDjfPfd\nLWfraJ8pKAAKCpAoAWq2IXY9SIf6drXdfd4I7MePl7s3tbXpex5241zl4DmTRMLe41NxbXGJYm7c\nOOCRR4CSkuym9/hcGupzm0fiUd3khI2avEBceSXw+utBlyI4Obbf7unttgj3KOoGV1IE3Gifv6xM\nas4//hi4556Wd4j69pWL1EWLgMWLbdXYu9ZBVYTV1ckr8rery8vlF/zEE4Gnn7a+g5gLh01w2t1n\n4rhvUR4YPrxlamaI5GMqTv4F9nkepIVWju23e3q7zbjLk6dcSRHo21dqvXMJ7I3j9qWXgGuvbXnX\nrXVroE8feRaittbyO2tokD7jXOugKsKMWuJI1mq98UbjMydTpshF3+DBEtyne+bDDUaNvc0LB6bi\nEAUnH1Nx8jOwz+MgLbRybL/ds9ttW7YAq1fn9T7jSopAUZEE97l0NjRoEDBypPXFeVmZPNwIWF7A\nb9sm7+ygKuKB/UsvycPcgLRetMsuTYevWSMtNlnl3DtVVibPA21vA9aa3X0mjvsWUZC0ZipO/G3c\nKA9B5nGQFmoOc1dTeXa7zQhE83ifMVIEcs5syOH7BSAPUn71lTykZfZ9lJVJ05rG3yaMmlGm4mS/\nLUIhU2XAggXAs8+6GzGnPg9kA1NxiIJRVyd3ZyN5bstBfgX2xomYqTjhZHRG45DWHt5u4z6DREJO\njnV1Nie45pr0zQXutZc7nQ1YPSdjfE+XXWbZPbkRQDEVJ/ttEQplZcDKleZfihH0u3lhbuxjNi9S\nmYpDFIxIn9tyEK+mHTIZPBj47DPnrXLE2csvS8DVt2/QJQHOOiurTrqMq3JPDt5Ro6R9/n79PJh5\nNKT2ipmukZEWvvkmfdOtt9+ee2G0tk7Fufxy4IorLIN6ILfAvq5OugHwqHU230X6xy+19nzYsJbD\nKyqkzXk3u6rv1k26uV692tboDOyJghHpc1sO8qvGPpGQJvCctqPthhdesO7tMAgbNkgTnB98EHRJ\nxJgx0ga1Q8Zddk9ut3XrJrXPtiLaeDK2q+1sBi+fY1m3TjozMZt/584Zg3og+33G8baIAE+PH69l\nSosxLgLdbN5YKUnpPPtsW6MXF9vbX+yOR0T2RPrcloP8CuyDdN55wGuvBV2Kpry4TR2AfL0q90tq\njb0tXgb2Lj3zkEuNfer0cRDp46dfPwm0zdJivNoXHbS9zRp7iq3Zs0PdTHekz205yK9UHL/deqs0\nxwZITaNVTeIPPwDHHSftL48c6V2Ztm6VViLWr29Me/Ewf3zNGuBXv0pmZmzcIL24pnkIcwPaY5Ea\nnP4BzeqtQF090FCfdtoGVQCgPa46dw3uussih7uuFpj3tXWBhwyR2+xBmfeVrKuZfv2s89Q3bQTm\nL7BexsiR1oHJksXAmrXb/62sawdgCH4yYDMKoIHCAqBNs85ItmwGGpJfTv33wC0lwJ3WxQAg+2N9\ns/VtWwIokzqHup2AgipgYjugtgbYVms+74IC005TjEWee+hCtKvfYD6PXr3klbQ2uVkGDkxWAjfU\nA1u2mk8PSBmsekzdlv16bJe6/dNp3dq0+Ufj2YnTxn6DNgXJ5oIKlKS2JNpYLzcL550HHH+8SzNr\n3Vo69rv5ZunZ2LB6tbSW8+abTT8PgBGw77+/9XjffSfnSzezhnLRp4+0QkzUxMaNwMIF8lu8tRZo\n1QOw7gswMJs3y3u+1dgzsPfSAw9IfufOO0t0e9hh1uN/+aVcAHjp66+BV14BjjhCgsSDD/Y0v37e\nPLm2OeoooP3aRcCm74B+/VuM99XGbtiw3uQaY8M2uae2rVaS6ZsrLECP1psxot9WFPezCHprNbB0\ns3WBe9cDdWuBVauCeRZj6Rbrp1R71QK9zAejqh5Ym2Ed+2rAKrOoZhtQ3ziPng3V2KzaoU4XyEVH\n2xKgQ7NpGqolyAUk4u1cAGTMQddA1Qo566ZeaHRqY3EvsQjbF765Nn0u//ZRi4DO5gFx797A0IIq\nFNZYzKPnNiDl8Yru3Ztdi9RroMFiegDolJCLITM5rgcAQNcA9Rb7TVsFlJi3696vZy0G169HgZGx\n8uMiQLUF+g2xXq5Db78tj6y4FtgD0qdB86Yni4okuD/+eOCUU1xcmHMHHSRFqLW4dgOkyAsyXJP7\nZcUKCYry+NEiMvPlImDjd0D//kBnAH27NjlHhs3o0RKC5RMG9l6prweWLAHuvhs499zM4xs1clY/\n8G4wblk/9pg7LZRkYNwKu/deoP+s74CvvwGuP7HFeFdeCVQ+KZ2GtpQ5ZzqtjRuB//4X2HPP5CV7\nawD7ZJ7u/vuB3/wG+LQ6gCck98xx+k6wtY6WBidfTnR1vpglS4H+g4CXXwN++lPn06Nd8pWL3XKc\nvghArtWabqxHlxynb4Um+02f44BDzpKg2UWHHeZBukm6XPfOnYGHH3Z5QdkZMEBOt1FiNGr11FPB\nloNC6LS7gJJvpDM4CiXm2Htl+XKpebWb5uJXYD9xIjBrFtA1i0AsC006vznhBOD6603Hcz0P7vPP\ngf32M68Gu/nmlh3aAJKTW1cHLFvmcoGoiZg84xFLOXQWZ4WdMEUDvycyZdUqGYUCA3uv9OoFfP89\nMH68vfGNwH5zhjSKbFRUSDOfWkue/9ix7rYSYcFu5zeedDCVqcWMhQvTt3ZjnLRy6SWVzBkPUlRW\nyv7IH4nwybUzMROx64Rp/Xo5j8RM7L4nco+XjSOQK/InsP/2W+APfwCqqvxZXlGRPHzWvr298YuL\nJdj2osb+qaekKckpU3w/W9t9Kt2TDqb69JFUGqctZhifeRDYNPH55+FJqvXLddc13iX56U8luO/Q\nPGGfAjdlCnDGGa7PNnYtv1x2mcsPDIRD7L4ncs877wAXXxx0KchC/gT2n30mqRdhbY9cKWlf34vA\n3qh5fvJJyfv3UU2NrFqmze5JYF9UJA8GmwXoZj2Ytm8vObpeB/YXXCC9tOaTDh2A+fORvvkjCo3D\nD5fWs1wWu4DRozsbQYvd90TuKSuTSjMKrfwJ7MvLpUmLTM3GBamkxJvAvry8sQlNn3+EjBSbTJk/\nNTUeNUlVVpY+pUZr+dwsDcRsOjeZXVhEwX33SUqXU2Vl0qzM2rWZx6XYiV0nTGVl0iKP189G+Sx2\n3xNRHsmvwD7sQdSllwJ77+3+fMvLgXHjGv/2kd2aeE9q7AHzhwBXr5YA02yf8Ojhwe3q6oClS6Ob\nX/7dd9JzsVPG+sawlpMyi11NcEyfx4nd90SURxjYh8lllwEHHODuPI2a6aFDpeFun3+A7LZ241lg\nb3arPFMPprvtBnTJtQlBC0uXSpOoYd8n01m5ErjnHmnH2Cm/nl+gUIpdwBjT/Tl23xNRHsmfduwr\nKqRTpnyzbp20tFNa6n0tdBrbW7tZskSeIejcOe14nqbiLFsmNeSpnSAZ28Gsxtzl9rtbyLT8MPvk\nE3nP5kqsWzegTZvYBUJkT+xSPPr2lZ6BY7Y/x+57Isoj8Qvsa2ulJZTULtwz5VPHmdEUW1mZ9P70\nySfe9paitQTxya45q1d2RqKwRHre7dBBer1Nw7Ma+5NPlldRs119992BRx/Nrj3/8nLrhz+7drVu\nDWnrVuk4C4hWjf2WLdIjb/Ntmc7atdJBWDq9ewMffJC5ZYXFi1O6eE2jSxe2qOOHpUutu03t2NH0\ngh2ATLt0KQAgsaUDqrd2An5sduewTx+gtXnPuKislFfnzrK8sGjVSsr+5Zfph1dXSzeu6fTq5dFJ\nL3essc9DmzdLiqqVsjLfmsqm7MUvsD/3XOnGdMCAxs9WrZKzlF9B1IIFwNSpUo5evfxZphnjAmfg\nQOAnP5Ft4aWnnpIgPqkG9yCBccDC9+QZAhOeBfZmD0uXlQGnnprdPHfe2Tq//KGHgDPPNB8+cyZw\n0UWyb7Rtm10ZgjBhQtPeBvfay3zc666TdB0zyUDP0h57SNqPmT//WXoIJm8ddhjw1Vfmw6+8Erjh\nBvPhCxcCI0YAABI4F9W4S85Hqb74wrrf93vuAa6+Wu74rFoVruBi2DC56EjnZz8D3nor/bBZs7J7\nAN0HRmCvdbg2NXnoX/8Cjj3Wepz6eu4QERC/wL68XH4Annii8bMtW6QH0qFD/SnDvHnAX/4CXHWV\nP8uzMnKk1A536wa89pr5D5BbunUDzjoLOO44AED1HTug+Md2wD3/Bvbc03Qyz1JxvPDKK9Y1yTvs\nYD396NHAv/8drdp6QI6tKVPkDkifPtuDtbTOP7+xX/rm1q+XuxaZvPACsG2b+fBhwzLPg3L30EPA\npk3mw5sH6c316yf7O4DiN/qg9s7WaHjz301uqmacx0knSUBx1VUS2Pfsaa/sfnjySfNWcebNk4qO\nX/6y5bBM54kAGefi2lrrGykUI+PGbT9OTTGoj4T4BfZVVU1r6wH50Zgxw78ylJfLmbF7d/+Waaa4\nGNh1V/m7bVvva4gPO0xeSdXTgMQWABMnWk7mWY29F3J9wLlr14zbI5SqquShYjtlHz5cXrmw22sz\necvqzowdbdtu32cSyRswNftORJs2DuYxaJB0aHbVVXJ+DVNgb9amt9bACSdInwARO96Nc3F1NQP7\nWJk/Xy6M07W+17NnuI4rylr8WsWpqgo+B7O8XPL5C+K3eZ0KvFUcckdtrdRKBn1sUaSlBoyORa0F\nGqWAO++MXFAP5Pg9UXg9+igweXLQpSCPxS/yrKwEOnUKtgzZdjy0Zg3www/ulydA21vFySBSqTj5\nqKpK3oM+tijSjGM8qxZXOncG2rWLTmAfYTl9TxReUWj2m3IWv8A+TDX2Tt19dyRrd6wE3kEVucN4\nNiPoY4siLaeaYKXM+6UIm6OOAp55xv74n3wCnHYa0NDgXZkcYI19TEW5t3OyLV6BfXW1VDEEHXxk\ne1VcUuJO1+T33de09ZIA2UnF0ZqBfei1bg2ceGJ2nVIRJeUcMJaWRqOX12++aWzS1o6VK6UZYqtW\noHzEwD6msq10pEiJV2Cfmi4wd6705GrV3rgXtm6Vh1OCCuy1Bn7/e2lSMQTsBOxGE9kM7EOstBR4\n9llgyJCgS0IRZhzjWad4TJsm+2HYOb0ACdnzAzl/TxQ+Rn8SrLGPvXgF9jU10npCt27Slvztt0vT\nen7atAk48khpM96pkhK5MMjldmxVlXQMFJKrcjs59katEHPsybHrrzdvVpNCxzjGs64J7tHDvG+K\nMHGaMmQEWyG5G5Hz90Ths3SpxBYM7GMvXs1dlpZKQA8An34q7wsXSg2+Xy3UdO8OTJ+e3bTGD9bW\nrdk3S2n8mARx8K5cKT0xdumy/SM7qTjGjwdr7Mmx+npg9uygS0E25U2KR1mZdPhjV8eO0lt1yGrs\nY/895ZMgYwPyVbxq7FMZNdYPPwy0aQMsXx5seewwgvlc0nGCPHj/8Afg0EObfGQnFce43cvAnhwr\nK5NjmzkDkZA3KR79+wMrVjSmh2YSsgeD8+Z7yifLlsl7SO7mk3fiVWOfqkcPuZ/4z3/K7acePYIu\nUWZGjX02gf2sWdJ1+Zw58qCj044mvv8eeO45ydHfe2/g4IPNx62sBO66q+XnM2cCu+zS5COm4pCn\njB+pK64ATjkF2HnnQItD1lxP8Vi/Hvjzn63HOess806kvGL0vvXII8All9ibpqyMqTjkncmTJW0x\nCqlslJP4BvYFBcCkScB//iM7c2Fh0CXKzGgjfO1a5zXuX34J/PWv8vfPfuY89eiuu+RHqEcPoKjI\nOrDfuLFxWc0dckiTf5mKQ57aZRdgxAhpCWr58mg8WJnHXE/x2LDB/FxkOOYY/wP7CRPkHP7zn9uf\nprQU+PBD78rkAFNxYsrrnucpFOIb2ANSAx0lO+0EzJuXXcsj55wjr2yVl0vX56+8knnc/v2BJUts\nzZapOOSp7t2Bb78FpkyR7tIp1FxP8Sgrs30uamHLFrn76EXQ37Mn8OOPzqYZN06eGQkBdlBFFF3x\nzbGPokQC2HHHYHJSPOiRTmt7PcoyFScCtmwJTec5aZWWAosXB10KyqBVK3kPRU3wBRfIXd2w+MUv\ngAcfDLoUAOSGb6tWIfmeiMgRBvYkEbgHHVds2ybvTMWJgSOOAE46KehSmPv97xtbxKLQUkqO81AE\njCHKaQ+j0HxPROQIA3u31dUFXQLn1q0DNm92vcbebsDOVJwIqKoKvkdnK23bNlYHU6glEiFJ8Sgt\nlSZ6Gb2mFZrviYgciVdgf/75wMUXB1uGfv2Am24KtgxOGbVWHgX2TMWJgbAH9hQZxcUhiaVD1ilU\n2ITme6LcrFwpFXeUN+IV2H/9NbBqVbBlqKqSjkaiZJddpEWRZk1V5spuTTxTcSKAgT25JDQpHkZg\nH5K248MmNN8T5WbKFODkk4MuBfkoXoF90MFHTY2cCaMWABUUAL16Sfv3LrIbsLPGPuS0ltZDjOZY\niXIQmoCxXz9J+meNfVqh+Z4oNxUV7G02z8QrsK+sDDaoNnoZ9LMMH34IHHectC0fMnYD9poaaTo/\nCl0N5KWtW+XZkahdsFIoFReHJHe7dWugd2/W2JsIzfdE2fOoYQwKt3gF9lVVwdYqVlbKey5l2LgR\nuOgi4PPP7Y3/xRfAP/4Ryo4nnKTiMA0nxIK4YKXYClVNcFlZuAJ7raUxgxAI1fdE2Vm7VpoqZo19\nXolPYK918Kk4bgRAxcXA3XcDs2fbG7+8XG4pO+1p1gdOUnEY2IcYA3tyUagCxkcfBW69NehSNJo2\nDejWrbGt4ACF6nui7BgXrQzs80p8ep7dtEk60PEj+HjwQeCDD4Cnn276uREApdbYz5wJnHpq02Yw\nBwwAZsxIP+/WraUnxFtuAW680bwM550HXHaZecdSixYBBxxgvR7//Kd0iOURJ6k4zK8Psb59ZV8Z\nOTLoklh77z3ghBOAkhLzcd5/3/pH7tZbgfvvNx8+ciTw2mvW5Rg/XvJax40DnnrKetw8FKoUjxEj\nzIctWwbsvbf58AkTgEcecbc8paVSSTV4cMvcxI4d5Q6tT0L1PeWb6dOB3/zGfPjRRwNTp5oPr6kB\nhg+XNEqAgX2eiU9gv3SpvPfu7f2yzjlH3psH9kYqTurFxYwZ0lJP6kHatav1/MvK5ELgoIPMx9l1\nV3mvqACGDWs5vH37zE/Cd+5sPTxHTMWJifbtgcMPD7oUme24o1zwWvUl0a6d9Tx23dX6uOnTJ3M5\nJk0C3n4bePll4Mkn5QFN2i4yNcFt25rvC198AXz1lfvLHDdOmktO1zxhmzbuL89CIiFZHBSA9evl\nrodsHyUAACAASURBVM1pp6UfnqkFu4KCxn23Xz+ge3d3y0ehFp/Avn174OqrgR128G+ZW7Y0rR0c\nOxZ48UWgQ4fGz8rL5crZqva9udJSaX7SzjTl5cDEiS0/79bN2TI9wFQc8lWPHsC11+Y2j0MOkVcu\nLrkEGDgQeOMNYPVqKRdtl0g01oGEWseO/p9Di4uBK67wd5kmEonQpPvnnylT5JWtVq0C//2n4IQv\nMTtbffsC110H9Ozp3zLXr29ZhmOPbXoL1SxVxordB7q2bZMLgJDeZjNq7JmKQ3mHbaSbYopHNPB7\nIoqm+NTY+6W2Vm6tP/SQBPKZ/PSnzm+DlZUBS5YA9fXWbUAuXiz5mCEN7J30PMsae4qV1MB+jz2C\nLUvIRCYVJ8/xeyKKJgb2Ti1ZIsG03XZhL7rI+TKMHPvlyyU/zkxREXD66ZLqE0LV1fIscKYGexjY\nh9A330hO78CBQZckmrp0kRxtdn7UAgNGl8yfL5VMgwd7Mnt+T0TRxMDeqbIyeVDXywdPhw4FDjss\nc5NnZWXAX//qXTlyVFNjL2C3Ox756NJL5VbLK68EXZJoUkou/pmK00IiwRQPV5x8sjxT9thjnsye\n3xNRNGWVY6+UOl8ptUgptVUp9bFSyvRes1JqP6VUQ7NXvVIqmk+UFRRIyxhetlAwdCjw+uvAoEHe\nLcMH1dX2cuftjkc+yubZEGpq5kzgzjuDLkXoFBezJtgVZWWe3hHi90QUTY4De6XUCQDuAHANgN0A\nfAHgTaVUN4vJNIChAHolX7211qucF5eixG6KDVNxQkZrCRgY2Oema1frZ2TyFFM8XOLxHSF+T0TR\nlE2N/cUAHtRaP6G1/g7AOQC2ADBpcHW71VrrVcYri+VSxDAVJ6LWrZN2tO0+R0LkAFM8XFJWJg0o\nNDR4Mnt+TwGprpbKFaIsOQrslVKtAIwG8I7xmdZaA3gbwFirSQF8rpRappR6Syll0Z1fFlavBmbN\nkhZrgvTCC8C8ecGWIUSYihNR7IacPBSbFI/XXpNexIOKfsvK5DmslSs9mX1svqeoOfXU3PvSoLzm\n9OHZbgAKATQ/k6wEYNY0y3IAZwOYDaAYwJkAZiilxmitP7da2OzZwIYN8vfuu5t0GllRId16X3+9\njNyqle2VydmqVdJ6SH29vF9xBfCLX8gLkBNvjFoVmTMH2Lix5edaAz/80Nh7tWH2bLnWmjHDer7r\n1rHGPjRWrgTefFP+ZmDvrkWLrFMn2rbN3DTmp59adwc6YIC8zGzaJAemlTFjmna811yO62GkeLz3\nXstOeUeNatq/X6glErId/v53CcQ87sm7BeP4nD5d+k/J1KO5Q4mE/LS98076jLJevYARI1xdpH+W\nLwe+/958eGEhsO++LT7+4Qdg2bLkPwsWpP9BNHTpYn3Xs74+fe/Fn7QBhh4AzDCflKItXcfSrtJa\n234B6A2gAcCezT6/FcBHDuYzA8DjFsNHAdDAeA0cqYEj9ZAhR+ojjzxSP/PMM7qJESO0BrTu3197\nautWrevqGv9fvlzrLl1k2Wavq6/2piyvvab13Xd7M28T339vvaq5vm6+2dfVITNPPilfSNeuWjc0\nBF2aeLnySuuDYKedMs9j+HDreVx/vfX0X3yR+WD89ltP12P6dPNJf/3rzJsgNCoqtC4okIL/6lf+\nL3/DBq0TCVn+66+7Pvs33rD+mouLtd62zfXF+uORR6xXrl27FpM0NGhdUuLt7yBfcXw9o4041ngN\nGDBeS4yLUVrbj8HtvpTW2vZFQDIVZwuAn2utp6d8Pg1AR6310TbncxuAfbTW+5gMHwVgziuvzMGO\nO47CpEly8Xz//ckRGhok/aZbN6lWuPpq4IILgE6dbK+LYzfeKAVYurTxsxUrpAbsySeBm24C/v1v\n6bTKqIbq3Nn1WhQAwBlnAF98AXz2mfvzNvHJJ8BeewGvvtqyluazz+QmxaOPtqyg6Nkzc228UnJj\nI1N79+QSq47PNm6UWvuuXf2vgYy7tWtb9ladqnXrzM81lJdbpxx26SIvMzU1kpdtpbRUymImx/XQ\nWm60Nl+Nk04Chg2T02lkrFwJnHee1ADPmuX/8tesASorgd695U6Jyyoq0re6/PrrwIUXyk3y9u1d\nX6z3NmyQO+5m0vQPsG2bpCfddhtw9NGQ794qV6ltW4lRzDQ0mB+L/fvzBzHGFi+eiwMPHA0Ao7XW\nc92ev6NUHK11rVJqDoAJAKYDgFJKJf+/28GsdoWk6Fjq3x8YMgTo2LHZ8fPww8D558utsLo6uX/r\nZVAPyA9q795NP+vVS97btpU8of33d3eZxhm1+Y9sRYXvDzYa23/YMPlOUi1aJO8HHsjsjdDbvFmO\nleeeA37+85bD27eP6C91BHTtmvuFfq4HWHFxywPYqRzXQ6n0q9GpUwRzunv2BH7yE6n5CEK3btbB\nY47MfmaM76+6OqKniw4dHOd8GftmWVnyEBrSM8dCFADD+IOZj4wUc69kc0l4J4AzlVK/UkqNAPAA\ngBIA0wBAKXWzUupxY2Sl1IVKqaOUUoOVUjsqpe4CcACAe+0usLi42fNJpaVS6/jxx43/e82q+b8t\nW9yvLVm9Wqq633ij5bAA2hg3tn+6h1ythlHIlJfLxXCPaHYjQfHV4jwfFWVlknidqUPBGDHO9ZH8\nvrLE3zmKCsc9z2qtX0i2WX89gJ4APgdwiNZ6dXKUXgD6p0zSGtLufR9IGs+XACZorT+wu8wW7eka\ngfzMmfLuR5BbXi69waazZYv1w2bZ6NpVauqbP6Rm3Mf2ObA3tn+6tBqrYRQybPGGQiqRkKySyCkr\nk/Py0qWxaizBinGuj9wdlhzwd46iwnFgDwBa6/sA3Gcy7NRm/98O4PZslmNoEdgbQcnmzcAJJ3jf\njEKmYHrsWPdTgQoK0ndAsmqVbAwG9pSNigrJr+/TJ+iSEDUR2Q6R9ttPnrXyIMc9rBjYE4VXJJ7O\naNFRRrt28oDY0KGSK+y11aulLUezYProo4E//MH95abrMjygGldj+6c7qfEWZYSUl8sD3kVZXdMT\neSayHSK1bh2eoH7ZMmD0aOCjjzxdjPE7EJnv609/AkaOzGkW/J2jqIhEYN+ko4x775Va+nRBr1eM\n5fjdE2e6GnujLAHV2KdrLKO6Wj5v3iY1hVAAz2dQnnriCWDcONujs0MkF3TqBMydC/zvf54uxghu\nI/N9LVxo3hKYTayxp6iIRGDf5Bbt3LkSnKQLer0SVF5yWVnLdWzVCthnH9+bIjR6h00XvFdX82QX\nGQzsyS/19cCHH9qu1o1sKk6YlJRIKzkeV3pFLhXHiBlywMCeoiIygf323wbjAE0X9Hpl/Hjgn//0\npk16K2VlklOf2qXrz34G/Oc/vleP19SYn9CshlHIMLAnvxj7WaZ285Mim4oTNj78NkYuFceFBies\n0lGJwiQSgX2TW7RGYHLRRcBLL/lTgO7dgcMP9z/XxKhhsPnD6CWjxt7pMAqZRx6RnoCIvGacv2wG\nmUzFcYkPgX2kUnG0dqVCw1hX/tZR2EXiCbrtt2iNntrKyvKjWbFRo4C335aHHQNmlW7DVJwIOfjg\noEtA+aJ/stXj669v7E72hhsaP28mkQCqN9YCp5xpPs/evYGbb3a5oC567z3g8cflLu9pp/m33Koq\n4K67gCuvlN/HadOAU04B9t4bOOss1xeXVSrOY48B77/f+P9ll0nnXmaMbWmmbVvgL3+xXuattwLz\n5kmP2kzFoTwRmcC+pgZy8tq2rbHH17jr2BGYMCHoUgBgKg4ROVRcDJxzDvDVV8D8+fKZRSdOiQRQ\nU6sax02nttblQrqsqkoC0nff9Tew/9OfgBtvBCZOlHTN2bNlOw4e7Mnisuqg6rrrpPbcuLDbssV6\n/Koq633BTpe3y5ZJ1+gTJzp6kDsdpuJQVEQisN9+i9bovcTtNuNT1dYCZ54JnHceMGaMvWmWLpUy\nhaXJMw8wFYeIHLv/ftujFhcD1bVF0DP/E90WtiZNkueizj1Xenj2q1nZPfeU9/XrgSOOAD6w3f9j\nVgoLZdVs19jX1wNLlkirduecY2+aSZPklYs//zm36VMwFYeiIhI59ttTcaqq5AMvA/vKSrn9t2yZ\n/Wl23NHRD1gUMRWHiLxknEMsKvWjobRU0kaXLvVvmYcdJpG23dZwrrpK7irkwFErRsuWSXDvd5PR\nLqqulkbpCiIRNVE+i8QumkhI5Ud9p67AFVeY5mi6wrh46NjR/jSbN8e6th5gKg4ReStyLa2YMR7S\n9KvVNkCq0Pv3t7/Mhx+Wpkhz4KgVo6CajHYRf+coKiKTigMANT36o+Smm7xdmNPAvrZWrjpKSrwr\nUwgwFYeIvJTa0kqHDsGWJSdGrbRfHSganLSG07Fj429dlhy1YrRypbQqF/Eae/7OURREpsYe8Klp\nLad5/MYDQHkQ2DMVh4i8ErlOj8y0bSt9nvhZYw84643dhcDeUSrOz38uI9t54DWk+DtHUcHAvjmn\nNfabN8s7A3sKM62BqVM972qeKFuxCewB6R3c7+rdIUPs97XSqVNjJVaWHPcU3Lp1TssLGlNxKCoi\nEdhn1bRWtpwG9nlSY19TY/47ZTWMQmLNGuCSS6RNZ6IQsn2e37wZ+OMfgW++8bxMWfvHP4Df/tbf\nZV55pf28eZdScSL/PIQDTMWhqIhEYO97Kk7btvabKTMC+5g/PMsa+4iLwcNrFG+2z/OtWkmrLh99\n5HmZYiuIGvuI4+8cRQUD++b69gWOPNL++HlSY8/APuKMwD7CD69RvNk+z7duLT3Q+p3DHlbl5c7v\nxPmdYx8D/J2jqIhEYO9rKs7xxwPPPmt//B13BGbNAgYN8q5MIcBUnIirqJCLz65dgy4JUVqOzvNO\nWoCJuwcekE6pnBgxQn67cpBvqTj8naOoiERgv70mZ8FSaTYrFzNmyANG69fnXC4A8pT/2LGssWdN\nRvisXy/7ulKSXz9woP2H64h85ujO7MCBwBNPNO7fSgFz51pPc/PNMt655+ZcVsc2bGha1kyvnj1b\nzuORR9KPe8stcpHT/PMLLgCOOSb9NGeeCfz975nL8d57psMSb/wN1X9/PfN6PPus9Th+9c6bI/7O\nUVRE4ojafsK//lbg/WrgoYeyn9maNfI+fz6wxx65Fy5PMLCPoJIS6YjGsNtuwZWFKANHgf2NNwIH\nHND0s0xpZocdBrzzjtxh9Vsi0fRYXLoUWLiw6TgjRwKdOzeO39y4cU3n8cUXcsEAAIMHA336NB3/\nJz+RVNHDD09fpm3bgAULgB12MC/3sGFNl5m6Sg/vjMpVhcDZNwPduqWfPpEAxowxnQeAzJUNZ50l\nFWh33GE9nseqq2Nff0cxEYnAfvst2k11znqETWfffeV96VIG9g4wFSeCiouBM84IuhREtjhKxRk4\n0Pm+veuuwEEHSc2931q3zv1YHD5cXn4zKXfxh0BNEYDf/z7zPAYPzn75GzaEopnemprG6y6iMItW\nKs7m+twD+x49ZIbMz3TErFZea9bYE1HuUnue9UxZmTw0muODo+Tjw7MheZ6Cv3MUFfkX2BvdWvvd\n3XeEWQXvdXVAQwNPeESUm4ICqdj2NFg00nX8PP+/8w6wZIl/y/OJr4H9kiVAfb31eJdeKulWHmFg\nT1ERicC+qAgoKNCoqSuQ9ndzVVoaihqAqKitlfd0JzXjtjlPeAFbtEhSDb74IuiSEGUtkfC4pRWj\nHwe/zv9aS/PJL73kz/J85Pl3ZSgtlR+hFSvMx3nuOWkY47vvPCsGU04pKiIR2CslB1Q1ErnX2APm\nt/a0lhc1YdTKpDupWQ0jHy1aJEE9n+6iCCsu9rgWuFcvqSnyq8Z+9Wpg69ZYdgzn+XdlsHMxdttt\n0iqSnZr9LLHGnqIiEoE9ACRaN7gb2Kc7sW/dKr0avvCC/Xm9+Sbw9NO5lynEjJN3upOa1TDykfGj\n179/sOUgyoHn6R2FhdKizqhRHi4khfE7E8OO4XxNxQGsL8bKy6XVoLo6YNkyT4rBwJ6iIjqBfat6\n1KDYncB+8mS5dddcVZVc7Tup9XzuOeC++3IvU4hZpdswFSckysulNpJfBEWYL+kdl18O7LWXxwtJ\nMi64w1hjP3w4cO+9WU/uWypOhw7yu29WY79pE7BuXWOLdx7djWEqDkVFJJq7BIDitkWoPuZCYES7\n7GfyzTeSg3f00cCQIS2HV1bKu5M8/i1bYp/+wFScCCgvD2fwQOSAb+kdfikvD2+Pz1u2SKpQlnz9\nrm66ybwfDiOQHzdO3svLgX32cb0IrLGnqIhOjX2bAlS365bbkfW3vwFnn23eIYbRBJqTuwJ5FNgz\nFSfEKioY2FPk+Zbe4RfjuAxjj8+dOjVWZmUhkZDMF49S2ps67zzp4T0dI7A3Ovjy6MFoBvYUFeGu\nsZ8+HfjySwBAYsuRqPl8FTDtExn2q19J+2hm3n9fHihM9d576YOfV18F1q4Fnn9e/n/rLaBfP/Pe\nKGbOlB77AOk9cOedHaxU9DAVJ+S+/VZeJ50UdEmIcuJbekeqTZsyt1rz058C3bubD583D5g9u+Xn\nTz8tKXIvvggcd5z1Ml57rbFn9HR23NG6U0Wn69GxozxwOm1a4/CddgK++sp8+rZtt6+Hcc6vqWlW\nt+Xnevz4o2zbwkLpeddoGMPs+0izHqZS1qOuXqG+fgoSs/8DTJvv/nqYcXk90uJ6NPJjPT75RJ7l\n9JLWOnQvAKMA6DmN7dToPfGRPh0Pb/9f19ZqSyee2Dhu6uu885qOt2mT1lOmtBzvppvM5/3LXzYd\n99prrcsScTNnymp+803LYe+8I8MWLPC/XJR0zz1aK6X1888HXRKinEyYoPUJJ/i80PLy9L8Vqa+P\nP7aex+23W0/fv3/mcowZYz2PSy91dz3OOafl8Guvtb0eL78sH61dG+B6vPii/L/nnvL/9Olaf/ih\n69/HJpRoQOtncKI362EmivsV18PaBRfoOXPmaAAawCit3Y+hldba2yuHLCilRgGYM+eTTzAq2XrB\n/hMK0a+vxlNPNMhIhYXWtzfr62VTN5duuoYGed14IzB1KjB0qOTzPfywvXkXhfvGR67eflt6Yl+4\nUHpyT/X663KRu3SpVJhQAIz9N+b7IcXfEUfIbvzKKz4uVOvM+SSZfm+MY7C5+nqZFsh8fNbVWQ9X\nqnFe6Thdj3TjK5X+dzNVcj1Mz/1+roex3Zt/P2bfR5r1MJWyHmvXAt16FeFvL9bj6Ena/fUw4/J6\npMX1aOTHetTXY+4XX2D06NEAMFprPdd6AufCHQkUFW3fSIk2QE2tAopsPhZg9QU3V1AgrxUrgMGD\nM3dh7WTeMcBUnJAz9l+iiEskgM2bfV6oUtlfFNfUACefDPzud8CYMf/f3p3HyVHX+R9/fTMzmck5\nhBASQkg4Ei6XQ4IKcoOAKIcXKOpDDuUQvJB11dUfl7squ3IogogoyLKyIigKyyKnoGA4Em4JdzpA\nbsgBSWYyx/f3x3cq0z3Td1d11bfq/Xw8+jHT3dXV3+6uqu+nvt9Pfb/Dn69lvY2emNf6ORr53BSm\n4hRo5ucodewL45iYV4bugXiwY0xL9VFTg98vEPrnqIs+x6AwPkcT4kdvooGmXIGfy7nxhkuNc59R\nGhVHRJrBu1FxXn/d5e2+/XbcJWm64Jjv1e9VJ9Vz4hNvAvumjJYQDBk4fboL7BOYphSHakbF0QGv\nCdauhd12cxeBi6SQd6PiBD27KZyAqpKgPvDq96qTRn8Tn/gR2K9ZQ8dTD9O9al1072HtYGC/zTaw\n6aaDw19mXNDVWix47+4uyJiSKC1c6EaJylgqmGRHLKPiNCLDMz6XTMVJIaWcik/8COxffZX2+U/S\ntbbCRQuNeOstNyb99OlwzDGui7WWiapSrKvLBe7F4smuLrXWN02QHqbx6iWlvEvFWbgwszM+KxVH\nJJn8COxzOTrooosI96ply9zU1Qqahik3MYcm7WiiXM5duKPhhySlvEzFSVsazt//7o4xFSZ6UiqO\nSDL5EdgvXEjHiB66ekPO91i3zqU2dHfDTju51Js99wz3PVJAgX1C5HKw5ZbRT24hEhMvA/u0NQZt\nsgksXuxvYP/ww3DHHaGuUqk44hM/AvtcjvbOdrq7Q56W+8kn3cWIzz8/+FgSp/6OWXd36S7Ics9J\nyILp6UVSqr3ds5ztNAb2QQ9EhZHhguN+4n6vX/wCzjkn1FUqFUd84k1g3zFhVPgtA0GLRNoOzCFT\ni31CpLHbXySPdy32hx8O++4bdynCNWYMTJzob4v99OkVy14rpeKIT5I9lskpp8C4cTBvHh3szJtv\n93HgJk+753bbrXzrem4BrFw1/PH+fli/bmCGsW3oH/E3npvZSU/P4CLGuIFxxo8feOD112DFm6Xf\na8upMGnzWj9dLPr74bnnKPi8laxf7y6ePfDA4c+9+CJMnhxa8bLjr3+Fc88dnMVu0iT43e/Kv2bO\nnPQFESJ5OjpchmSxY01Ry5bCosUA7DLmFS6b9ZPC57fYAm64ofw6TjutsNd2qOOPd8sUc/nlVRbU\nMzNmwM9+Bnfd5e5/5SvwsY8VLNLa6i75+f734ZprYOxY+K//ggkTqnuLG2+EK64IudwAS06FZfsz\nYsI8vrf1r9in8xn3+Nlnw1FHlX7dY4/BP/9z0ae6lnwQ+BYdPW8D40qv40c/gttuK3xs550j+qDS\nFMuXw3nnebWvJzuwnzzZDTv58MMcOX0eT232EH12oJNhmi0f2K/rBopMYfjO2/D2Chi3KXR28vbY\nKax4pnBgg4ULXbw1bdrAa7q7oa/EdIhvvA5tLTDNj8B+zRpYsaK2gRwmTHDLb/w+8kybBocdFm4Z\nM+GWW+Dxx92c7FBdbfj//h985CPRlkskRocdBieeWEvDQw+sX8vza7bg6qVHctlBvy98etKkyqvY\nfPPy0912dlZbmPT4l3+BW28dvD927LBFjIHzz4f5893labfd5s6P9tqrure45Rb32g98IKQyByZ2\nQm87v1/wLu7pP5B9pg008I0ZU/517e3FKzmge/3mjDD9tI6skOQwYULhOnI5d4J08cVq7vfVnXe6\nE7OLL/YmF8vYBE7CZIzZA5g7d+5c9th9dzf85CmnwNFHN77y8893O9qSJQD84x/wrnfBgw/C+9/v\nFtlqKzjpJLjggsbfLmmeesp1djz8cPEZ0KVJPvEJd5Z1551xl0TEe7/4BZx6qmuQ0WVSzffSSzBr\nlps7r9relo99zKW43H57NGXaZhvX2fL97ze+rh//GP71X8uf/xV1331w8MHwwgvuCxL/3HOPO/t8\n8UWYOTOUVc6bN4/Zs2cDzLbWzgtlpXmS3WIPrq8vv+WgUcceC+9+98a7xa52926SlBro6v6EyOXc\nGZaINCw4nvX0wMiR8ZYli+qZrKq7O9p6KMx6vO5ryYJronI5Bfa+Cq7BzOVCC+yj5sfFs2HaeeeC\nlv9iV7t7N0lKDXR1f0KkcTQNkZhkabKkJKrn+496csMw6/G6R3/baiuXxrH99uEURJovmFU65Auy\no5T8FvuIFbva3buRGWqgq/sTwFr43Odgv/3iLolIKuSP0LJx0ANpmnpGyIl6RLUw6/G6yzpyJHzx\ni+EUQuLR3u4uMqww/GuSKLBXYC/NZowbPUFEQpHYoRczIqmBfeypOJIOM2Z41WKfvVScIYIdf2gq\nTtpz7JWKIyJpkdjJktLmT3+CqVOHfdGtra69otYc+9Sn4kg6KLD3i1rsRUT8phb7JhkzBhYvhtde\nK3jYmNrrzUyk4oi/PvtZ+N733P877wyjR8dbnhoosA8rsO/tDa1MUdLFsyLivfvvdxckLloEKLBv\nmvwRQoZIYmCvVByp24MPDo5veu65wyceS7BkB/bWRn6k7u52I2q25l1tUHMqzhln1DBVYry6u931\nPBrrWUS89fLLblzpiRMBpeI0TZkRQmqtN5WKI4nV1wevvz44XKlnkn3x7PLlMGoU3HEHHH547a9/\n5x03zWrg8cfdafcRR2x8qNiZeM0t9hMnwiuvwIIF7v7kya7cCaSWh4gtWgQbNpR+fvx4N5uyiFTv\njTcKp6N9+mnYYouN0ZZa7Jukvd19708/7eq74D7JbLFPTCpOV9fGSTFLytueM8daN+pMqQlTK9Wb\nPT3uGFFOpe939WpYudL9v3Spy8LIH5J65UoXUwYnt+DS0oaezS5bNhgDjBkzfFb5cePKlzMEyQ7s\nFy92f6dOre/1e+0Fzz5b+Ngxx4Qf2G+/vSvrNtu4+/vv77qKE0iBfcSOOQYee6z089/4BvzHfzSv\nPCJp8KEPuWmz8+UNF6vAvolmzYJLL3W3970P5swBkhnYh5mKs9lmDaxgzhw46KDSz8+aBeec4/K6\ns+iSS+Dss0s/X6nefP112Hbb8u/x6KOw556ln//5z+Gb3yx8LJiQat06F9CPH78x/Q+AT30KHnig\n/PsO9ZWvwAkn1PaaGiU7sA/OcOvpDunthfnz4ayzXKUQ2HXXgsWKdbHVnIrzqU+5Mvb0wG9/C3/4\nQ+3lbRJ1KUbs8sthzZrSz2tSKpHa/fznrrUs3047bfxXqThN9JvfwHPPuf/zJg2opd7s63NVtE+p\nOA2dhOy2G9x1V+nnOzpgn30aeAPPzZoF3/42HHxw8ecr1ZtTppT/fqHyJGHHHQd77DF4f/x42GEH\n9/+CBS7f/qKLCl9z0UWwalXhY88/P5ibv9lmw+PXrbYafD4iyQ7sFy+Gzk53q9Ubb7ijx6GHwgc+\nUHKxUFrs29rggAPc/0uWwNVXux9uzJjayx0xtdhH7L3vjbsEIv7p7oZrroEjj4Rp04Y/v9deZV+u\nFvsm2nJLdxuilnozOAHwKRWnoZOQCRPKxiGZd9RR7lavUaMa/3633trdigmuKcnL9gCK9wBUU455\n82opWc2SffHs4sX1t3Bu2ACHHOLOBMsIJbDPN3s2fOtbhfmgCaLAXkQS57XX3AydL7xQ18uDi+Bk\nVAAAIABJREFUoEuBfXxqqTebMeyyRsWR0CxcCC0t9aeFN1myA/slS+q/KnnWLLj77sEcqRJCScXJ\nt9NO8IMfwCab1LmCaCkVR0QSJ2gRq/N4P2KE6zhVKk582tuhe22Pq3fXrSu7bDMmSkxUKo74LZdz\nvVStyU5yCSQ7sG+kxb5KobfYJ5xaHkQkcYLAPn/EiRql+bjtg44O6Fqx1qW/zp9fdtlmtdgnJhVH\n/JbLeXV9nAJ7BfYiIvHK5Roe7i/Nx20fdHRA14iB2TmLjHOfr1mB/YYNpUdQrIXqzYxTYB+iSy6B\nY4+N9C2KdbF1dEB/vzeTydZEXYoR+Mxn4Kab4i6FiL8WLmy44gwzp1pq19EB3bbNXchYIbBv1sWz\n+e/VCNWbGXfYYfDBD8ZdiqolO2Fozz1LX6UckmJdbPkXYo0dG+nbN10aP1Os+vrgxhth333jLomI\nv3K5hmd5DDOnWmrnvn/jfseFC8suG/xOUefYB+/VaFDelFScdevc0ImeXKCZKeecE3cJapLsFvsm\nKJWKEzyXNupSDNmiRcNnqBOR2oTQ1a1UnHht/P6nT09MKg403mLf3+9SeiKvN7/6VTj66IjfJIFe\nfrnyrLFSk8wH9qVScYLn0kZdiiELKjAF9iL1sdYNabPddg2tRqk48dr4/c+YUbHFvpmpOI2e7G3Y\nULi+yFTxvaXS174Gp50WdylSJdmpOPV49FG49VY480yYPLni4pVScerS1QV33AEPPlh6kqrDD4e9\n967zDeqXyqv7rYXLLoO33oKJE+HLXy6//JVXDs5qXMx++7k5EEp58033fgD/+If7q8Besu7yy2H5\n8tLPH3iguw1lTMVRVKqhVJx4bfz+p093kzSuXOkmZipiYyrOj/8DxuQNjbnjjm4m95DKk/9e9eq6\n/2HgfbT/8UZ48R/DF+jsdDPcl/OLX5RvlX7/+933tny5S/sYkdfm+sEPuu/l0kuLv3b1anj8cTdr\nat5MwAW+/nV45hm4887izy9b5lrODz3U9Ry0tdX/OQ47rPTzq1a5z/H66/DSS+6xuXPd5zvvPHf/\nC18oPkld4KGHSn8OCO/3KPc5Ei59gf2558K998Ltt8MDD8Do0WUXjyQVZ/VqN0nVmjWFO2i+qVNj\nC+xT12I/f747GE2a5KaArhTY//73gwF5MaNGlQ/sV692FVfg0EN14YLITTfBiy+Wfn78+OKBfUiU\nihOvjd//AQe4YUvXrKkY2Hf89y9hxFp3Z+1adzv2WDcZUAjlgQq9OLkcXH+9mxxt002Ll/WJ+cD7\n6Ljnf+Ghe4YvsOWWlQPJP/4Rnnii9PMtLfDJT7peq1/9qvC5adPcLb/OydfTAytWuDpt5Mjiy5x2\nGjz1VOl1rFzpcvzvvNPNnr7//vV/jnIB8TvvuDKsWOF+mBEj3In9K68Mlu2YY8oH9uU+B4T3eyiw\nT5AFC9xG/OMfV7V4JKk4kyeH0gIVhVSm4ixY4P4+9lh1F+CVO9uvxrbbuhYHERl0332xvr1SceK1\n8fvff//qU3Fyz0PQOHzbbXDUUa43dcstQykPVDjZe/xx+O534ZRTSpf1UyfAt6Djt7+GD9RZmNtu\nq265oBW7mEbrnNNPd7diDjoIxo1z2Q7lfrtqP0cp06a5z7HDDnDkkXDRRbWvo9znqFajnyPh0pVj\nb23Nw6ZFkoqTYKlMxfFsumcRCZ9SceJVy/ff1eUaagsm8gzq7QoX3tZSnuC9Ssrl3BnApEklF2nG\nCD6xy+Vgp53gggvgXe+K9r2COK3BUbCktGS32L/2mssbq9Zbb7muvBo2GI2KkwKeTfcsIuHr6HBV\ngMQjaLG31gXt5QT1UMFyM2bAwQeHkoYTlAeqSMWZPr1sgZsxgk+s+vpcrDVjBpxxRvTv198Pv/kN\n7LJL9O+VUcmOhBYvrm35OkYo0ag4KeDZrHAiEj6l4sQrqFc2bKjcul20Hho/Hu4pksPeYHnKNtBV\n0cOf+sB+zRrXgLrDDs15v5YW+OhHm/NeGZXsVJwttqht+ToC+0Sk4tx3n7uoLOKpbq1NaSqOAnuR\nzFMqTrxqqTebUQ9VnYpToYc/OFlMXb0ZmDABHn64/IAR4pVkt9hXMVxlgXXrXJ51mXy5fKUmnmh6\nKk53N9x/vxt+KcIAtbfXfebUtTz86leV+35FJNU0Kk688uvNzs7yyzYjJbTqVJwKk0KlvsVeUifZ\nLfalhm4q5TOfccFxlUFeqUkymh7Yh3zRUCmpPUBtvz3MmhV3KUSyZ999EzPdugL7eNVSbzYjsK/Y\nYr9unRs3PgupONa6oWiXLYu7JNIEyQ7sI1aqi6211Q2v2rR8zaArMOJZ51LfpSgizTV/fmIinvZ2\n5djHKahXqvkNurujr4eMcW2DJQP7Vatg9myYObPselJTb+62m7toVVIv2ak4ESt3Jt7U1p8xY9yM\nqWqxFxFfrF3rZmFOyLB1arGPV9Ja7KHCBdVTp7q5TypIRb1pjNtPI248lGTIdIt9YgJ7cN2BCuxF\nxBd1DFYQJQX28UpqYN/oNhG8vtbM4MSZPr26GGPu3PIzSEviZbrFvlwXW9O7dbfe2k1tvWhR5VnR\n9tvPjTtbyje/6abIHsLrLsV169xU5cuXu/tf+5q7iUj1DjigfOX+jW/AmWeWfv7ZZ+HDH3b/BxFP\nQgJ7peLEK7RUnOeegyOOKL+Ce+6B7bYbvL9unZvxdsWKwjK9+SBd/3kz3HmzG6CiDkFZvR+fYeut\n4brr3N/At78Np51WuNxnP+vikAkTiq/nrrvKX9N26aXuVkxfn5td+DvfCWV2YSku04F9olrs//Vf\n3cxv1cye+pGPwMqVpZ8vMXOc1y32L77ouk0//3mYMgV23TXuEon455hjys/i9E//VP71Eya4ij8w\neTJstVU4ZWtQR4eLG3p7NVddHEJrsR+6jRUzfnzh/TfecBeGHnFEwah4HT9rp3v7d8Mx9Q8lnZpJ\nHb/2Ndh888LHdt55+HKXXAJ/+1vp9VQa8mjXXcv/fu3tMHZs+XVIQ9Jx+HviCdhkk8Iz0SokKrCf\nPdvdqnH22XW9hdeBfdDK+L3v1T6/gUhW9PbCK6+4Y2Gx3IGvf72x9U+dCv/2b42tIyL5gaXihuar\nNbDfdNMST06ZUvs2NmtW0fzxjj9B13v2g6/vV9v68qQmsN955+q+1w9+0N3qdfDB7iaxSUeO/amn\nuoCvRolKxWkCr1NxcjkXqNQ6t4FIlrzyiptB8sEH4y5J09WSCiLhS9qoOBBOPd6ssoqEJR2BfRXT\nQheTqBb7JvC+xX76dDcOqYgUl7ALWpup6fOPSIE0XzzrZZ0pmeV/lNTVBUuX1jXkmgJ7j9R58iaS\nKQsXuqv8pk2LuyRNp8A+XhUnhMqjwF4kOv7n2Ad5dXUEfaVmng0e875Ld8ECuPtuOOEEaGsr+3kT\n78IL3bjZIlJaLueuQfF+bL7aBcc174/bnjKm+tSX7u4EjGNfpWaVVSQsdbXYG2PONMa8aoxZb4yZ\nY4x5T5Wv28cY02OMmVfP+xbVQNdzcCZfKsfe+5afJ56AU05xM+xR/vMm3jbbVB6xQyTrcrnM9mzV\n0mIs0ai23uzqCqEeWrsWnn46lPKUE0pZRZqo5sDeGPNJ4CLgXODdwJPAn40xm1V4XSfwa+DuOspZ\nWi5Xd9dzuYknUpGKEwxLtXo14D5Payu0tMRYJhGJToYDe6XixK/aejOU9JabbnJDK65f33B5ylEq\njvimnhb7s4CfW2uvs9bOB04H1gEnV3jdlcB/A3PqeM/Scjk3BFsdXc9BF1uxiSdSkYqzySbu70CL\nvboURVJOgb3/x22PVVtvhlIXBdt5kWEuay1POao3xTc1BfbGmDZgNnBP8Ji11uJa4fcu87qTgG2A\n8+srZhkLF9Z14SyU72JLRSpOkRZ7dSmKpFRfH7z+emYDe6XixK+pqThBvX/ddW6I1yVL6i5POao3\nxTe1Xjy7GdACLB3y+FJgh2IvMMbMAr4P7Gut7Tc1zMtsLTz6KKx97a3Ss1l87lduOum/uLtr1rih\nnKtx331u9MS//GX4cytWuMnsSs2M7IW1k4GvwvWbwdPucxoz8Hl7e+GZZ8q/frvtYNy40s8vXFh+\nFsspU9ytlPXr4fnny5dhxx3VXBK3J590O2Mp06eXmW0Gd2L56qvl32OXXcrniJXa1rbcsmCmyUyz\nI+C6hTBmzMbjYZYMtF9w002VDyu1GDnSHYY00m5lvb0wZ075etNa1wqeyxWvewHefhtefrnSm00H\nvgrXbgKLjnD13JCI5tln3RgSX/pSdeUfO3Z4lfXyy+4Sr1JlFalV1OOAGFuuwh66sDFbAG8Ae1tr\nH857/EJgf2vt3kOWH4FLvbnaWnvVwGPnAUdba/co8z57AHP32GN/5s0bOn3x8QM3EREREZGkumHg\nNmjrrVezYMEDALOtteENJjOg1hb7FUAfMHT6z8nA8H4wGAfsCexujLl84LERgDHGbAAOs9b+pdSb\nnXLKJXzxi3twb/sRbHX2cXDSSRULeMopsGEDXHBB5Q8DMHFi8enH+/vhtdeqW0eiffjDcOKJcOyx\nQN7nDbrty9l8cxg1qvTz69e7bo22tuLPd3YO5vkXs6EbFhfbbPJkdOi+RAlGnipl003L9+ysWwfL\nl5dfx1ZblW8SXbFieDPH734H//VfbvSnGnoCJb1WrnS9tmGu79hj4cor4ZBDwltvWq1fD8uWVV6u\ntdV1tpVy+umu1f7736+wonfecV01nZ1FK/L+fli8uHJ5AB55BC6/HG65ZfiqttzSlVmkdsMbo197\nbR4HHzw7snesaVO11vYYY+YChwB/AhehD9z/SZGXrAGGjlF4JnAQ8HFgQbn327DB/d2h+0mm7nkq\nzKxcxpYWFyOEcRDefvvG1xG7rV6AbV91v1CBFtih0VzcUbBLFT9KSe2wczbzgb0ys9HfaDTQ4Dpm\nbobLBMzz1gz4xTMwaXX5E0iROgXpPZtuCjMbOdRJTVpbq63Hxw7cGjdhggvs3/e+8hmkIo0Ks/Gh\nmHrOQS8Grh0I8B/BjZIzGrgWwBjzA2CqtfaEgQtr/5H/YmPMMqDLWvtcpTcKAvt2uqu+QLa7Wxe6\nFHjxxbhLIBKNY45xO3xWm9I+/3nYfXf48pfjLklqBXWJRtppru5u17vcTPqtJS1qvhzIWnsj8M/A\nBcDjwK7A4dbaoK99CrBVGIULAvsOuqoe6UFjzhbx6KNw/fWNr+eWW1yftEgSjByZ3aAe4I47Kqc4\nSUM00k484qjHNQ+CpEVd1/lba6+w1m5trR1lrd3bWvtY3nMnWWsPLvPa88tdOJtvY4v9qJaqT98V\n2Bdx001wzjmNr+fuu+GKKxpfj4g0ZsMGlzxc51C/Uh1jUjL0sWcU2IvUL9EDeG3YAK0j+mjdelrV\nF8dpMokiwpq0Zvr0yhdSikj0Xn/djRuY0THrmykVkxV6Jo56XBOcSVokOrDv7oZ2s6GmVilNJlHE\nwoXhBAAzZrirPgZmspUU+8IX4Lbb4i6FlBKcYCuwj5xa7JsvjnpcaVeSFokO7DdsgI6R/XDooVW/\nRqk4ReRy4XTZB0GEWu3TrbcXrr3WDWUqyRTsg0rFiVxHh4K9ZlMqjkj9kh/YbzoGzj676tcoFWeI\nIBc3rBZ7cDPWHnggzJ3b+DoleRYtcvMcqDU4uXI5mDxZB7smUCpO8ykVR6R+iQ/sa+2OUyrOEGHm\n4k6e7EYieeIJuP9+TQqUVmoNTr6w0uukIqXiNJ9ScUTql+ix4jZsqO2svb+/9tekXjDM5aRJ9b3+\nlVcKp/EdORKuvtr9XyqwWLcOzjhj+OOnnw577VVfOaQ5rrkGrrvO/e9D4PjCC/Cxj7kZZaZNK77M\nFVfA6NGl13HNNe5EdShj3Hb8nvdULsett8LNNw/e//Sn4bDDSi//4ovw7/9efp2XXOJmzSnmkEOq\nK5c0TKk4zadUHJH6pSqw3zjuvQL7QSeeCC+/DDvvXN/ru7vhpZcG7wfDjh51lJuOsZj+/sLXADz9\ntJsWWIF9sp1/vuvhOflkGDMm7tJUNmWKm6Ly7beHb3OBvr7y61i2rPhrn3rKHUyqCaB/9COYPx9m\nzXL3K11gvn596fIGypX705+uXCYJhVJxmqu311Uhza7HNUGVpEWiA/taZ5ENzrSVipNn+nT49a/r\nf/1OO8Hf/lbba8aOHf6aY4916QOSXL29LnXrpz91vSs+GD8e/u//GlvHN7/pbkN97GPuhKEauZw7\nGfrBD6pbftdda9+vJBZKxWmuuOpxY1yHtH5r8V2iA/taW+yDHVIt9gk0fTo8+WTcpZByjHEpKVtv\nHXdJkuHmm6u7jqSvz50Q+ZC6JDVTKk5zxVmP67eWNEhVYB90oSmwT6CPfARmzoy7FFJOSwvss0/c\npUiOai8OD0YR0sXGqdTRUX3HjTQuznpcaVeSBskO7Lv66BxvgeoqWKXiJNh++7mbSNosXw7jxqnF\nPqXa291PLM0RZz2utCtJg2QH9q+8Qcczy4APV7W8UnFEpOn22MPNyGxt3CWRCCg9o7mUiiPSmESP\nY9+9ATraq68sFdiLSGw0r0MqKdhrLgX2Io1JdGDf02No76i+sgxy45SKIyIiYWhvV951M8VZj+u3\nljRIdGDf3TuCjlHVB/ZqsQ/Z//wPnHpq3KUQEYmNWnGbSy32Io1JdGC/oa+FjtEK7GMzbx7cd1/c\npRARiY2CveZSYC/SmGQH9v0ttI+u/vpepeKEbPVq2GSTuEshzbBmDZx3HixYEHdJkuWSS+CTn4y7\nFBIjpWc0l1JxRBqT7MC+r5WOsdUH9mqxD9nq1dDZGe46jzwS2trggQfCXa9PDjzQXWg59HbjjeVf\n97vfFX9d/q3SyCzHHVf8dZ2dcP758OaboX3MVFi3zv0upb5vjYOYemrFbS612Is0JtHDXXYzko6x\n1S8f7JAjR0ZTnsxZtSr8wP7LX4Z774XHHoP99w933T7o74e//x0++1k44IDC5/bcs/xrZ8+GX/yi\nsfc/7TQ47LDiz40b54ZulEGnnw5bbOF+t2LG1nCAEi8FwZ61GvioGeIcx76jQ+fq4r9EB/Y9LaNo\nf/c2VS/f3e0OBjr4hmT1athyy3DXefjhsPXWkMuFu15fLF3qplQ+9lg4+ujaXrvttu7WiEMOaez1\nWTNxIpx8ctylkBi1t7ugvrfXdTZKtLq73STYrTFEJ0rFkTRIdCpOb98IOracWPXyXV1KwwlVFC32\n4GbozGpg39bmUl522y3ukohIFYI6RSkazRFnPa5UHEmDRAf2UNsOrsA+ZFHk2IML7BcuDH+9Pths\nMzjnHPcdiEjiKbBvLgX2Io1JVWDf3a3APlRHH+3yusM2fXp2W+xFxCtBnaIUjeaIsx7v6NDvLP5L\ndI491HYBTVeXhroM1ZVXRrPeGTPgrbfgnXd08aGIJFpQp6gltznirMfb2/U7i/9S1WKvVBxP7Lor\nnHiijqAiknhKxWkupeKINCbxLfZKxUmhXXaBa66JuxQiIhUpFae5lIoj0pjEt9grFUdEROKiVJzm\nSkIqTqV5/kSSLPGBvVJxREQkLkrFaa64U3GshZ6eeN5fJAxKxWnEvfeWH7Zxm22Gzy6ar78frrtu\n+OPGuImcpkxpvIy+eOUVeOCB0s8bAyecUH4dUf0e+Q46qPxQleU+x1tvuTIccYTOQEU8oVSc5oo7\nFScog2awF18lPrCvNRVn/PjoyjLMZZfBLbeUfv744ysHkiedVPy5r30NLrmksfLV6+qrYaedYJ99\nmveec+aU/i7ATUVYKbCP8vcI/P735QP7Sp+js9MF/wrsRbygVJzmijsVJyjDuHHxlEGkUYkP7GtN\nxdl88+jKMsxNN5VPxjOm/OtbWor3+R11lAv+4vKd78AZZzQ3sP/Up+C44xpbR1S/R74RFbLXKn2O\nESMqr0NEEkOpOM3V9Aa6PPqtJQ1SFdg3vQuvpaWx1xsDrUV+gr33hqVLG1t3vdavh2XLmj8zahgB\nb1S/Ry0UuIukStCKq1Sc5khKKo6IrxIf2GdyVJxzzonvvYMc9WYH9iIiCdTS4s731YrbHElJxRHx\nVcKbFvtpa6t+aY2KEwIF9iIiBTRxUfPEPSpOUAYRXyU6sG9v7a+YFp1PE1SFIJdzKSnTpsVdEhGR\nRNDERc2jVByRxiQ6sG9r7a9p+dSk4sQpl4MtttBYXyIiA4KJiyR6SsURaUyiA/v2MbUFl03pwrv9\ndvj2tyN+kxgtXKg0HBGRPErFaR6l4og0JtGBfa2Nxk05INx7L9x8c8RvEqPRo2H33eMuhYhIYiiw\nbx4F9iKNSfSoOLUE9ta6vLjIu/ByOZg+PeI3idHPfhZ3CUREEqW9XXnXzdKUerwEDW0qaZCaFvue\nHhfcR36mn8s1L1Wlv7ZrDEREJHxqsW+Ovj5Xl8fVYq8ce0mDRLfYv/EGHHhg+WUWLIBVqwYnHP3h\nD+FXvwqxEP398NRTg/fX/AiWT4cK5WqEMfCt7X7H4b88Dg44oPhCBxwA559ffkWVvrxzz4WDDqqr\njCIiWdHR4S6vqnRILae1FS66CHbbrfQyb74Jn/scrF1b//v4LGjLiqvFvrXV3X74Q7juunjKEIWW\nFrjwQthzz7hLIs2Q6MB+5szKoy7Ones22k03hUmTYKedYNSoEAvRb+G1vKPspq3wrgnQGeJ7DHHb\nbXDHVgdz+Oc+55owipk4sfKKKn15oX5RIiLpdMYZ8Ic/NLaOG26Av/61fGD/j3+4E4ijjoLx4xt7\nP1+ddFJjJ1CNOu88eO65+N4/CjfeCPffr8A+KxId2F9zDeyxR/llpk+HE0+ECy6IqhQtwD5Rrbyo\n2bOha8xE+NmvG1vR9deHUyARkQz7+MfdrRG33lo5xSN4/rLLNDhZXL7znbhLEL677lJ6UZYkOse+\nGn19russTTQZiohIulRzXA+e10SLEibFFNnifWDf2+tScdJEk6GIiKRLNcf14HlNtChhUkyRLakI\n7NPYYq+dUEQkPao5rgfPq8VewqSYIlsU2CeQdkIRkXSpJbBXi72ESTFFtiiwL+bvf4ezznID6sZA\nk6GIiKRLNcf17m43f4sxzSmTZINiimxRYF/MQw/B1VfH1hWgs2sRkXSptsVeaTgSNsUU2eJ9YB/J\nqDi5nBtHM6ZmE+2EIiLposBe4qKYIlu8DuytdYF96KPi5HKxDiKsbjMRkXSpNhVH+fUSNsUU2eJ1\nYB9Myhpqi/2iRfDyy7EG9jq7FhFJF7XYS1wUU2SL14F9b6/7G1pgf+edsOWW8OyzMHNmSCutnXZC\nEZF0UWAvcVFMkS1eDxQZemD/7LMwahTcdhvsvXdIK62dus1ERNJFqTgSF8UU2aLAPt9RR8G228LB\nB4e0wvro7FpEJF3UYi9xUUyRLV4H9qHn2M+cGWsKTkA7oYhIuiiwl7gopsiWVOTYhz4qTsza291n\nC05cRETEb0rFkbgoFSdbUhHYxzSPVGSCFhvtiCIi6aAWe4mLWuyzRYF9AgUHdu2IIiLpoMBe4qLA\nPlsU2CeQWuxFRNKlo6O6VBwF9hK2YNuzNu6SSDMosE+gIMdSZ9giIunQ3u6O6eWCq64u5dhL+Nrb\nob9/MGaSdPM6sI9k5tkEUCqOiEi6dHS4oL6np/QySsWRKCimyBavA/tQR8W59174wx9CWFHjlIoj\nIpIu1RzXlYojUVBMkS1et3WXTMVZuxYuu2zw9HSXXeDjHy+/sl/+El57DT760dDLWSul4oiIpEv+\ncX3cuOLLKBVHoqCYIlvSGdjfeSd8+9swdSq88w60tVUO7HM52GabSMpZK3WbiYikSzXHdaXiSBQU\nU2RLKlJxhgX2fX3wrnfB66/DD38Iq1ZVvhw8l4MZMyIpZ63UbSYiki5KxZG4KKbIlnQG9p/4BDzz\nDBgDnZ0u0F+3rvSKenpg0aLEBPbqNhMRSZdKx3VrlYoj0VBMkS1eB/ZVjYrT2en+rl5depk33nBj\nQU2fHlrZGqFuMxGRdKl0XO/tddWQWuwlbIopsiUVOfZlR8U5+GBYsgQmTSq9TC7n/iakxV7dZiIi\n6VLpuB48rsBewqaYIltSEdiXbbEfNcrdygkC+4S02Le2uiwinV2LiKRDpXSI4HGl4kjYlIqTLV6n\n4oQ282xPj7vYdvTohssUBmPcGbZ2QhGRdKiUDhE8rhZ7CZtScbJFgT3A5z/vLrZNEAX2IiLpocBe\n4qLAPlsU2CdUe7vy4URE0iJIh6iUY69UHAlbpW1P0sXrwL6qUXE8pRZ7EZH0qDbHXi32EraWFhcn\nKabIBq8D+6Kj4px3HuyzTxzFCZUCexGR9GhtLR9cKbCXKCmmyA6v27oLUnE2bID994dnn4Xdd4+1\nXGFQKo6ISLqUO64rFUeipJgiO1IR2Le0AC+/Cg8/DCeeCCedVLjgZZdBWxucfnqzi1g3nV2LiKRL\nueO6WuwlSoopssP7VJyWFjc85Max6M85x7Xc57v7brjttuErmDMHXnkl8nLWQzuhiEi6KLCXuCim\nyA7vA/uNF87mcjBiBEybNnzBTTaBVauGP37yyfCTn0Raxnqp20xEJF2UiiNxUUyRHV4H9n19QwL7\nqVNdys1QnZ2wenXhY9a61yRkttmhdHYtIpIu1bTYK7CXKCimyA6vA/sgFQeAFStgxoziC26yyfDA\n/s03Yd260q+JmXZCEZF0qRTYByPniIRNMUV2JPsQ8vbbZZ8uSMW58ko3Mk4xnZ3DU3GCnPwEB/bq\nNhMRSY9yx/XubuXXS3QUU2RHXS32xpgzjTGvGmPWG2PmGGPeU2bZfYwxfzPGrDDGrDPGPGeM+VpV\nb7RkSdmnCwJ7gJEjiy/Y2elOEoIZrSDxgX17u86uRUTSpNxxvatLaTgSHcUU2VFzYG+M+SRwEXAu\n8G7gSeDPxpjNSrxkLXAZsB+wI/A94N+MMV+o+GaLF5d9elhgX8omm7i/a9YMPpbLwajhQNnpAAAV\njUlEQVRRsFmpYsdL3WYiIulSKRVHLfYSFcUU2VFPi/1ZwM+ttddZa+cDpwPrgJOLLWytfcJa+1tr\n7XPW2oXW2t8Af8YF+uW9//1ln646sJ8+HQ49dHDge4CFC93jxlSxguZTt5mISLooFUfiopgiO2oK\n7I0xbcBs4J7gMWutBe4G9q5yHe8eWPYvFReuELUXjIpTznvfC3feCZMmDT72+uuJTcMBdZuJiKSN\nUnEkLoopsqPWi2c3A1qApUMeXwrsUO6FxpjXgEkDrz/PWntNje/tPPAA9PfDTjvR2zt5cFScWv32\nt25UnITq6HCXBTz0ELzvfdT/OUUk86yFRx9N9CEvE1atgldfhUsvHf7cI4/oOC/R6ehwgwf+5S/h\nrK+tzcUmGsUpeZr5k+wLjAX2Ai40xrxkrf1tuRecddZZdHZ2Fjx2/B13cHxPD3zgA/Tuchet/d2w\nvt/ly9dixAgYO7a21zTRlCmwciXssw/88Y9w9NFxl0hEfPXYY64SlmQ466zij2+6aXPLIdkxZQq8\n8AIcdFB46/zd7+ATnwhvfWl0ww03cMMNNxQ8tnro8OshqzWwXwH0AZOHPD4ZKDuEjbV2YBganjXG\nTAHOA8oG9pdccgl77LFH4YMvvwwXXQS3307vjv20vvwCXPs3+OIXq/8UHjj5ZNhvP9hhh+KT5oqI\nVGvlSvf3/vvdPH4Sj74+lwVazI9/7C79EonCd78Ln/2s671rlLWw/faKTapx/PHHc/zxxxc8Nm/e\nPGbPnh3Ze9YU2Ftre4wxc4FDgD8BGGPMwP2f1LCqFqC+bMLttoNdd4WrrqJ31Tu00pPoXPl6GQOz\nZrn/lRcnIo0IjiE77gibbx5vWbJuhxJJq//7v/DSS80ti2RHS4sLn8KinP3kqicV52Lg2oEA/xHc\nKDmjgWsBjDE/AKZaa08YuH8GsBCYP/D6A4CzgSJZhlWaMQP6+uhdtJxWet3oNilkjNt5dCW7iDQi\nOIbo4szk0rFefKLtNblqDuyttTcOjFl/AS4F5wngcGvt8oFFpgBb5b1kBPADYGugF3gZ+Ia19qq6\nSz0QyPctWuo+QApb7AMae1ZEGhUcQzScYnLpWC8+0faaXHVdPGutvQK4osRzJw25/1Pgp/W8T0kD\ngXzvkuW0tG4O48aFuvok0c4jIo0KjiGlJueW+OlYLz7R9ppc9UxQFb+xY+HII+nt7qe1Pd1jLWlS\nCRFpVDD5UULn4xN0rBe/aHtNLj8De4Bbb6W3cyKto9riLkmkdIGKiDRKkx8ln4714hNtr8nlb2AP\n9K7tpnV0uvuW1d0lIo3q6lJ+fdJ1dLjhMHt74y6JSGWKTZLL6zyW3r32pdWmu29Z3V0i0qggFUeS\nK/h9urs1m6ckn2KT5PK6xb5v5Chax6a7tlJ3l4g0Sqk4yRf8Pjreiw8UmySX14F9b6+bdCHN1N0l\nIo1SKk7yBb+PjvfiA8UmyeV9YJ/2Lkt1d4lIo5SKk3z5qTgiSafYJLkU2CecurtEpFFKxUk+peKI\nTxSbJJcC+4RTd5eINEqpOMmnVBzxiWKT5FJgn3Dq7hKRRikVJ/mUiiM+UWySXArsE07dXSLSKKXi\nJJ9SccQnik2Sy+vAvq9Po+KIiFSiVJzkUyqO+ESxSXJ5HdhnocVeO4+INEqBffIpsBefKDZJLgX2\nCdferjw2EWlMd7dScZIu+H10vBcfKDZJLgX2CaezYhFplFrsk08t9uITxSbJpcA+4bTziEijFNgn\nX2uru2ZMx3vxQRCbWBt3SWQoBfYJF3R3aecRkXopFccPSm8QX7S3u7iktzfukshQXgf2WRkVB2DD\nhnjLISL+Uou9H9RDK75Q6lhyeR3YZ6HFXjuPiDRKgb0fFNiLLxSbJJcC+4TTSAki0ghrlYrjC6Xi\niC8UmySXAvuE01mxiDSip8cF92qxTz612IsvFJsklwL7hNPOIyKNCI4dCuyTT4G9+EKxSXIpsE+4\nYOdRd5eI1CM4diiwT76ODh3rxQ+KTZLL68A+C6PiBHlsOisWkXoExw7l2Cdfe7uO9eIHxSbJ5W1g\n39/vbllpsdfOIyL1UCqOP5SKI75QbJJc3gb2fX3ub1YCe3V3iUg9lIrjD6XiiC8UmySXt4F9MNtZ\n2gN7dXeJSCOUiuMPpeKILxSbJJcC+4RTd5eINEKpOP5QKo74QrFJcimwTzh1d4lII5SK4w+l4ogv\nNEFVcnkb2Ac59hoVR0SkNKXi+EOpOOKLlhbXsKrtNXm8Deyz0mI/YgS0tWnnEZH6KBXHH0rFEZ9o\ne00mBfYeUPesiNRLqTj+0LFefKLtNZkU2HtA3bMiUi+l4vhDx3rxibbXZFJg7wF1d4lIvbq6XDrf\nCG+P9tmhY734RNtrMnl7qM9aYK/uLhGpR3e30nB8oWO9+ETbazJ5G9hnZeZZUHeXiNSvq0tpOL5o\nb4cNG6C/P+6SiFSm2CSZvA3sgxb7tA93CeruEpH6dXWpxd4XmrdEfKLYJJm8D+yz0GKvnUdE6qXA\n3h+azVN8otgkmRTYe6C9XS04IlKf7m6l4vhCs3mKTxSbJJMCew/orFhE6qUWe3+oxV58otgkmbwM\ni2+4AS680P2flRz7++6DAw+MuyTpM2YMXHcdTJwYd0mic/rpMH9+3KWQuMyfD9ttF3cppBpBYH/c\ncTB6dLxlqWTrreGaa8AYWLMGPvMZePvtuEvVPOPGwfXXQ2dn3CWJT5Zik5Ej4fLLYdasuEtSmZeB\n/R/+AMuWwZe+BNtsE3dpoveFL8CoUXGXIn3WrIFbb4XnnoN99427NNGwFq66Ct77Xpg5M+7SSBym\nTYMjj4y7FFKN3XaD006Dd96JuyTlLVgAv/41/Oxnrm564QW47Tb40IdgwoS4Sxe9lSvd533hBXjP\ne+IuTXxOOikb82P097sG5YcfVmAfma4u2HNPuOyyuEvSHEcc4W4SrgULXGCf5q7Enh4X3J9xBnzu\nc3GXRkTKGTcOrrwy7lJUdvPN8OCD7tg5atTgMfSii2DHHeMtWzPMnw+3357uuqMahx7qbmlnrQvs\nffm9vTzX0oQrEoYsDC0XfDbtLyISlqHHzqwdZ7JQd8ggY/y6UNjLwF4TrkgYgm3Il7PwegSfTfuL\niIRl6LEza8eZLNQdUsinybi8Deyz0jIg0cnCCBTBZ9P+IiJhGXrszNpxJgt1hxTyaQQgBfaSWVlo\ndclahSsi0VNg7/6mue6QQgrsI6YJVyQMI0ZAW5s/eXP1CD6b9hcRCcvQibSydpzRRGLZoxz7iKnF\nXsLi01l4PbLWkiYi0SvWYt/Wlo2hD2GwUSjNdYcU8ilW8HI3VGAvYfFpZ62HAnsRCVuxwD5rx5i0\n1x1SyKff28vAXqk4EhafutfqkbUuchGJXrFUnKwdYzo60l13SCGfYgUvA/sstg5INHw6C6+HWuxF\nJGxqsfdr+ENpnE+xgneBvbXZPIhINHzaWeuhwF5EwqbAPv11hxTy6ff2LrDv6XF/s9btJ9HwqXut\nHkrFEZGwtbW5v0rFibsU0iw+xQreBfZqgZQw+XQWXo+szQgpItEzpvDYmcUWe6XiZItPsYICe8k0\nn3bWegTD0LW0xF0SEUmTrAf2aa87pJBPv7d3gb1SCyRMPnWv1SOLXeQiEr38Y2cWjzNKxckWn2IF\n7wJ7tdhLmHw6C69HFlvSRCR6WW+xVypOtvgUKyiwl0zzaWetRxYrXBGJXtYD+7TXHVLIp9/bu8Be\nqTgSJp+61+qRxS5yEYmeUnHSXXdIIZ9iBe8Ce7XYS5h8OguvRxZb0kQkellvsVcqTrb4FCsosJdM\n82lnrUcWK1wRiV7WA/u01x1SyKff27vAXqk4EiafutfqkcUuchGJnlJx0l13SKFge7c27pJU5l1g\nrxZ7CZNPZ+H1yGJLmohETy326a47pFBHB/T3Q29v3CWpTIG9ZFraD85ZrHBFJHpZD+yVY58twfbt\nw2/uXWCvVBwJk1JxRERqp1ScdNcdUijYvn34zb0L7Lu6oK0NRnhXckkitdiLiNQu6y32aa87pJBa\n7COUxQOIRKejA/r6/Mibq4f2FxGJQtYDe6XiZIsC+whlsctPouNT91o9tL+ISBSCVJzeXtc4krXj\njE8XU0rjfIoVvAvss9gyINHx6Sy8HtpfRCQKQYt9EOhk7TiT9rpDCvn0eyuwl0zzaWeth/YXEYlC\nENhndaS6oAU3rXWHFPIpVvAysM9al59EJ+0HZ+0vIhKFIMc8OHZm7TjjU6AnjfMpVvAusO/uzl7L\ngEQn2JZ8yJurh/YXEYlCMNxj1lNx0lp3SCGffm/vAnulFkiY0t7qov1FRKIQpOKsXz94P0t8asGV\nxvkUK3gZ2Gety0+ik/aDs/YXEYlCcFx5++3C+1nhU6AnjfMpVvAusFdqgYTJp+61WgXD0Gl/EZGw\nBceV1asL72dFmusOGU7DXUZIqQUSpjS3umQ191VEohccV1atKryfFWmuO2S41lZ38+H39jKwz1qX\nn0THp+61WmV1tAoRiV5wXAla7LN2nElz3SHF+TLbsHeBvVJxJExp7k5Vi72IREWpOO5vGusOKS4Y\nCSrpvAvslYojYUpzd2pWJ44RkegpFcf9TWPdIcUFI0ElnZeBfda6/CQ6ra0wYoQfO2utlIojIlFR\nKo77m8a6Q4pTKk5ElIojQ91www0Nvd6X7rVaKRWnuEa3F8kObSul5afijBjhGkmypLUVWloK6w5t\nL+nmS6xQV2BvjDnTGPOqMWa9MWaOMeY9ZZb9qDHmTmPMMmPMamPMQ8aYw+otsFJxZKgwAnsfzsJr\npVSc4lT5SrW0rZSWn4rT0QHGxFueOAytO7S9pJsvsULNgb0x5pPARcC5wLuBJ4E/G2M2K/GS/YE7\ngSOAPYD7gFuNMbvVU2Cl4kjYfOleq5VScUQkKvmpOFk9xqS17pDifPm962mxPwv4ubX2OmvtfOB0\nYB1wcrGFrbVnWWt/ZK2da6192Vr7HeBF4Kh6CqxUHAmbL91rtVIqjohEJT8VJ6vHmLTWHVKcL793\nTYG9MaYNmA3cEzxmrbXA3cDeVa7DAOOAt2p574BScSRsvnSv1UqpOCISlaGpOFmU1rpDivPl9671\ncpfNgBZg6ZDHlwI7VLmObwBjgBvLLNMB8PvfP8djjw0+2N8PfX2wZAnMm1dtkSXtVq9ezbwGNoj+\nfrc9XXVViIVKgGDfee45GDMm3rIkSaPbi2SHtpXS+vvd3+XLYfLkbNbJ1sKjjw7WHbncaq66KoNf\nREYsXuxOZM8+u7H1jB37XPBvJKfExjW4V7mwMVsAbwB7W2sfznv8QmB/a23ZVntjzKeBnwNHW2vv\nq7Dcf1ddMBERERERf3zGWvubsFdaa4v9CqAPmDzk8cnAknIvNMZ8CrgK+ES5oH7An4HPAAsADzo+\nREREREQq6gC2xsW6oaupxR7AGDMHeNha+9WB+wZYCPzEWvufJV5zPHA18Elr7W2NFVlERERERIaq\nZ0qJi4FrjTFzgUdwo+SMBq4FMMb8AJhqrT1h4P6nB577CvCoMSZo7V9vrV3TUOlFRERERASoI7C3\n1t44MGb9BbgUnCeAw621ywcWmQJslfeSU3AX3F4+cAv8mhJDZIqIiIiISG1qTsUREREREZHkqWeC\nKhERERERSRgF9iIiIiIiKZC4wN4Yc6Yx5lVjzHpjzBxjzHviLpM0lzFmP2PMn4wxbxhj+o0xRxdZ\n5gJjzCJjzDpjzF3GmJlDnm83xlxujFlhjHnbGHOTMWbz5n0KaQZjzLeNMY8YY9YYY5YaY/5gjNm+\nyHLaXgRjzOnGmCeNMasHbg8ZYz44ZBltKzKMMeZbA/XRxUMe1/YiGGPOHdg+8m//GLJMU7aVRAX2\nxphPAhcB5wLvBp4E/jxwsa5kxxjcRdlnAMMuAjHGfBP4EnAq8F5gLW47GZm32KXAh4GPA/sDU4Gb\noy22xGA/4DLgfcAHgDbgTmPMqGABbS+S5zXgm8AewGzgXuCPxpidQNuKFDfQwHgqLibJf1zbi+R7\nBjeozJSB277BE03dVqy1ibkBc4Af5903wOvAv8RdNt1i2yb6cTMV5z+2CDgr7/54YD1wXN79buCj\necvsMLCu98b9mXSLdHvZbOB33lfbi25VbjNvAidpW9GtxPYxFngeOBi4D7g47zltL7oFv+u5wLwy\nzzdtW0lMi70xpg3XgnJP8Jh1n+xuYO+4yiXJYozZBncmnL+drAEeZnA72RM3lGv+Ms/jJlLTtpRu\nm+B6ed4CbS9SmjFmxMCM6KOBh7StSAmXA7daa+/Nf1DbixQxayCF+GVjzPXGmK2g+dtKPRNURWUz\n3Hj3S4c8vhR31iICbuewFN9Opgz8PxnYYIdPgJa/jKTMwCzYlwJ/s9YGuY3aXqSAMeafgL/jpnV/\nG9dC9rwxZm+0rUiegRO/3XFB11A6tki+OcCJuN6dLYDzgAcGjjdN3VaSFNiLiDTiCmBnYJ+4CyKJ\nNh/YDegEPgFcZ4zZP94iSdIYY6bhGgo+YK3tibs8kmzW2j/n3X3GGPMIkAOOwx1zmiYxqTjACqAP\nd9aSbzKwpPnFkYRagrv2otx2sgQYaYwZX2YZSRFjzE+BDwEHWmsX5z2l7UUKWGt7rbWvWGsft9Z+\nB3dB5FfRtiKFZgOTgHnGmB5jTA9wAPBVY8wGXEuqthcpylq7GngBmEmTjy2JCewHzojnAocEjw10\nrR8CPBRXuSRZrLWv4jby/O1kPG5UlGA7mQv0DllmB2A6rgteUmQgqD8GOMhauzD/OW0vUoURQLu2\nFRnibmAXXCrObgO3x4Drgd2sta+g7UVKMMaMxQX1i5p9bElaKs7FwLXGmLnAI8BZuAubro2zUNJc\nxpgxuB3CDDy0rTFmN+Ata+1ruO7R7xpjXgIWAN/DjZ70R3AXpRhjfglcbIxZicuj/QnwoLX2kaZ+\nGImUMeYK4HjgaGCtMSZoEVltre0a+F/biwBgjPk+8H+4C9LGAZ/BtcIeNrCIthUBwFq7Fhg6Dvla\n4E1r7XMDD2l7EQCMMf8J3IpLv9kSOB/oAf5nYJGmbSuJCuyttTcOjFl/Aa774QngcGvt8nhLJk22\nJ25YMTtwu2jg8V8DJ1tr/8MYMxr4OW4UlL8CR1hrN+St4yxcatdNQDtwB3Bmc4ovTXQ6bhv5y5DH\nTwKuA9D2Ink2xx1HtgBWA08BhwUjnmhbkQoK5lXR9iJ5pgG/ASYCy4G/AXtZa9+E5m4rZmCsTBER\nERER8VhicuxFRERERKR+CuxFRERERFJAgb2IiIiISAoosBcRERERSQEF9iIiIiIiKaDAXkREREQk\nBRTYi4iIiIikgAJ7EREREZEUUGAvIiIiIpICCuxFRERERFJAgb2IiIiISAr8f17MGHmUDrXOAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122114e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotPerf(logs):\n",
    "    plt.figure(figsize=(9,6))\n",
    "    plt.plot(logs['loss'].T, '--', c='red')\n",
    "    plt.plot(logs['val_loss'].T, c='blue')\n",
    "    plt.title('%.4f ($\\pm$ %.4f)' % (np.mean(logs['val_loss'][-1]), 2*np.std(logs['val_loss'][-1])))\n",
    "    \n",
    "    plt.figure(figsize=(9,6))\n",
    "    plt.plot(logs['acc'].T, '--', c='red')\n",
    "    plt.plot(logs['val_acc'].T, c='blue')\n",
    "    plt.title('%.4f ($\\pm$ %.4f)' % (np.mean(logs['val_acc'][-1]), 2*np.std(logs['val_acc'][-1])))\n",
    "    \n",
    "\n",
    "plotPerf(linlogs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YzmtGY9WqVVxyfTgkVEUNN1kU19EgxBpqa3HLA5U4PA54/Elg/nzg0CHgvrXA3DPAKw/z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1224ca390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = linmodel.predict_proba(X).round()\n",
    "\n",
    "display(X,t,y,linmodel)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Non linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 64)                192       \n",
      "_________________________________________________________________\n",
      "H2 (Dense)                   (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,417\n",
      "Trainable params: 4,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.models.Sequential at 0x1224d3350>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_nonlinear_model():\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Dense(64, input_dim=2, activation='relu', name='H1'))\n",
    "    model.add(Dense(64, activation='relu', name='H2'))\n",
    "    model.add(Dense(1, activation='sigmoid', name='Y'))\n",
    "\n",
    "    opt = Adam()\n",
    "    model.compile(loss='binary_crossentropy', \n",
    "                optimizer=opt,\n",
    "                metrics=['binary_accuracy'])\n",
    "\n",
    "    model.summary()\n",
    "    return model\n",
    "build_nonlinear_model()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 64)                192       \n",
      "_________________________________________________________________\n",
      "H2 (Dense)                   (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,417\n",
      "Trainable params: 4,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 0.7231 - accuracy: 0.4500 - val_loss: 0.7233 - val_accuracy: 0.3000 \n",
      "Epoch 2/500 - loss: 0.7168 - accuracy: 0.4375 - val_loss: 0.7168 - val_accuracy: 0.3000 \n",
      "Epoch 3/500 - loss: 0.7106 - accuracy: 0.4500 - val_loss: 0.7111 - val_accuracy: 0.3000 \n",
      "Epoch 4/500 - loss: 0.7052 - accuracy: 0.4500 - val_loss: 0.7049 - val_accuracy: 0.5500 \n",
      "Epoch 5/500 - loss: 0.6994 - accuracy: 0.6125 - val_loss: 0.6988 - val_accuracy: 0.5500 \n",
      "Epoch 6/500 - loss: 0.6938 - accuracy: 0.6625 - val_loss: 0.6929 - val_accuracy: 0.5500 \n",
      "Epoch 7/500 - loss: 0.6883 - accuracy: 0.6625 - val_loss: 0.6872 - val_accuracy: 0.5500 \n",
      "Epoch 8/500 - loss: 0.6831 - accuracy: 0.6625 - val_loss: 0.6818 - val_accuracy: 0.5500 \n",
      "Epoch 9/500 - loss: 0.6781 - accuracy: 0.6625 - val_loss: 0.6765 - val_accuracy: 0.6500 \n",
      "Epoch 10/500 - loss: 0.6731 - accuracy: 0.8375 - val_loss: 0.6714 - val_accuracy: 0.8500 \n",
      "Epoch 11/500 - loss: 0.6682 - accuracy: 0.9125 - val_loss: 0.6664 - val_accuracy: 0.8500 \n",
      "Epoch 12/500 - loss: 0.6634 - accuracy: 0.9250 - val_loss: 0.6615 - val_accuracy: 0.8500 \n",
      "Epoch 13/500 - loss: 0.6587 - accuracy: 0.9250 - val_loss: 0.6567 - val_accuracy: 0.8500 \n",
      "Epoch 14/500 - loss: 0.6541 - accuracy: 0.9375 - val_loss: 0.6519 - val_accuracy: 0.8500 \n",
      "Epoch 15/500 - loss: 0.6495 - accuracy: 0.9375 - val_loss: 0.6472 - val_accuracy: 0.8500 \n",
      "Epoch 16/500 - loss: 0.6449 - accuracy: 0.9375 - val_loss: 0.6426 - val_accuracy: 0.8500 \n",
      "Epoch 17/500 - loss: 0.6404 - accuracy: 0.9375 - val_loss: 0.6381 - val_accuracy: 0.8500 \n",
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      "Epoch 395/500 - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0030 - val_accuracy: 1.0000 \n",
      "Epoch 396/500 - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0030 - val_accuracy: 1.0000 \n",
      "Epoch 397/500 - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000 \n",
      "Epoch 398/500 - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000 \n",
      "Epoch 399/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000 \n",
      "Epoch 400/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000 \n",
      "Epoch 401/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000 \n",
      "Epoch 402/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000 \n",
      "Epoch 403/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000 \n",
      "Epoch 404/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000 \n",
      "Epoch 405/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000 \n",
      "Epoch 406/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000 \n",
      "Epoch 407/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000 \n",
      "Epoch 408/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000 \n",
      "Epoch 409/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000 \n",
      "Epoch 410/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000 \n",
      "Epoch 411/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000 \n",
      "Epoch 412/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000 \n",
      "Epoch 413/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000 \n",
      "Epoch 414/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000 \n",
      "Epoch 415/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000 \n",
      "Epoch 416/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000 \n",
      "Epoch 417/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000 \n",
      "Epoch 418/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000 \n",
      "Epoch 419/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000 \n",
      "Epoch 420/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000 \n",
      "Epoch 421/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000 \n",
      "Epoch 422/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000 \n",
      "Epoch 423/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000 \n",
      "Epoch 424/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000 \n",
      "Epoch 425/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000 \n",
      "Epoch 426/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 427/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 428/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 429/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 430/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 431/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 432/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 433/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 434/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 435/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 436/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 437/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 438/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 439/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 440/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 441/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 442/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 443/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 444/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 445/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 446/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 447/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 448/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 449/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 450/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 451/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 452/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 453/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 454/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 455/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 456/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 457/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 458/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 459/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 460/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 461/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 462/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 463/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 464/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 465/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 466/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 467/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 468/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 469/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 470/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 471/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 472/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 473/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 474/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 475/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 476/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 477/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 478/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 479/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 480/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 481/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 482/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 483/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 484/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 485/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 486/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 487/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 488/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 489/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 490/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 491/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 492/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 493/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 494/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 495/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 496/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 497/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 498/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 499/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "Epoch 500/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 64)                192       \n",
      "_________________________________________________________________\n",
      "H2 (Dense)                   (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,417\n",
      "Trainable params: 4,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 0.7031 - accuracy: 0.5375 - val_loss: 0.6754 - val_accuracy: 0.7000 \n",
      "Epoch 2/500 - loss: 0.6977 - accuracy: 0.5375 - val_loss: 0.6670 - val_accuracy: 0.7000 \n",
      "Epoch 3/500 - loss: 0.6907 - accuracy: 0.5375 - val_loss: 0.6589 - val_accuracy: 0.7000 \n",
      "Epoch 4/500 - loss: 0.6838 - accuracy: 0.5375 - val_loss: 0.6512 - val_accuracy: 0.7000 \n",
      "Epoch 5/500 - loss: 0.6771 - accuracy: 0.5625 - val_loss: 0.6439 - val_accuracy: 0.7000 \n",
      "Epoch 6/500 - loss: 0.6706 - accuracy: 0.5625 - val_loss: 0.6367 - val_accuracy: 0.7000 \n",
      "Epoch 7/500 - loss: 0.6643 - accuracy: 0.5500 - val_loss: 0.6300 - val_accuracy: 0.6500 \n",
      "Epoch 8/500 - loss: 0.6583 - accuracy: 0.5500 - val_loss: 0.6236 - val_accuracy: 0.6500 \n",
      "Epoch 9/500 - loss: 0.6526 - accuracy: 0.5625 - val_loss: 0.6181 - val_accuracy: 0.6500 \n",
      "Epoch 10/500 - loss: 0.6475 - accuracy: 0.5625 - val_loss: 0.6130 - val_accuracy: 0.7000 \n",
      "Epoch 11/500 - loss: 0.6427 - accuracy: 0.5750 - val_loss: 0.6080 - val_accuracy: 0.7500 \n",
      "Epoch 12/500 - loss: 0.6378 - accuracy: 0.7125 - val_loss: 0.6032 - val_accuracy: 0.8500 \n",
      "Epoch 13/500 - loss: 0.6331 - accuracy: 0.8625 - val_loss: 0.5984 - val_accuracy: 0.9000 \n",
      "Epoch 14/500 - loss: 0.6283 - accuracy: 0.8625 - val_loss: 0.5937 - val_accuracy: 0.9000 \n",
      "Epoch 15/500 - loss: 0.6236 - accuracy: 0.8875 - val_loss: 0.5891 - val_accuracy: 0.9500 \n",
      "Epoch 16/500 - loss: 0.6188 - accuracy: 0.8875 - val_loss: 0.5845 - val_accuracy: 0.9500 \n",
      "Epoch 17/500 - loss: 0.6140 - accuracy: 0.9000 - val_loss: 0.5798 - val_accuracy: 0.9500 \n",
      "Epoch 18/500 - loss: 0.6092 - accuracy: 0.9250 - val_loss: 0.5751 - val_accuracy: 0.9500 \n",
      "Epoch 19/500 - loss: 0.6042 - accuracy: 0.9250 - val_loss: 0.5705 - val_accuracy: 0.9500 \n",
      "Epoch 20/500 - loss: 0.5992 - accuracy: 0.9250 - val_loss: 0.5658 - val_accuracy: 0.9500 \n",
      "Epoch 21/500 - loss: 0.5942 - accuracy: 0.9375 - val_loss: 0.5612 - val_accuracy: 0.9500 \n",
      "Epoch 22/500 - loss: 0.5891 - accuracy: 0.9750 - val_loss: 0.5569 - val_accuracy: 0.9500 \n",
      "Epoch 23/500 - loss: 0.5842 - accuracy: 0.9750 - val_loss: 0.5525 - val_accuracy: 0.9500 \n",
      "Epoch 24/500 - loss: 0.5793 - accuracy: 0.9875 - val_loss: 0.5480 - val_accuracy: 0.9500 \n",
      "Epoch 25/500 - loss: 0.5744 - accuracy: 1.0000 - val_loss: 0.5434 - val_accuracy: 0.9500 \n",
      "Epoch 26/500 - loss: 0.5694 - accuracy: 1.0000 - val_loss: 0.5387 - val_accuracy: 0.9500 \n",
      "Epoch 27/500 - loss: 0.5644 - accuracy: 1.0000 - val_loss: 0.5340 - val_accuracy: 0.9500 \n",
      "Epoch 28/500 - loss: 0.5592 - accuracy: 1.0000 - val_loss: 0.5291 - val_accuracy: 0.9500 \n",
      "Epoch 29/500 - loss: 0.5540 - accuracy: 1.0000 - val_loss: 0.5241 - val_accuracy: 0.9500 \n",
      "Epoch 30/500 - loss: 0.5485 - accuracy: 1.0000 - val_loss: 0.5191 - val_accuracy: 0.9500 \n",
      "Epoch 31/500 - loss: 0.5431 - accuracy: 1.0000 - val_loss: 0.5140 - val_accuracy: 0.9500 \n",
      "Epoch 32/500 - loss: 0.5375 - accuracy: 1.0000 - val_loss: 0.5088 - val_accuracy: 0.9500 \n",
      "Epoch 33/500 - loss: 0.5318 - accuracy: 1.0000 - val_loss: 0.5035 - val_accuracy: 0.9500 \n",
      "Epoch 34/500 - loss: 0.5261 - accuracy: 1.0000 - val_loss: 0.4981 - val_accuracy: 1.0000 \n",
      "Epoch 35/500 - loss: 0.5204 - accuracy: 1.0000 - val_loss: 0.4925 - val_accuracy: 1.0000 \n",
      "Epoch 36/500 - loss: 0.5145 - accuracy: 1.0000 - val_loss: 0.4869 - val_accuracy: 1.0000 \n",
      "Epoch 37/500 - loss: 0.5086 - accuracy: 1.0000 - val_loss: 0.4810 - val_accuracy: 1.0000 \n",
      "Epoch 38/500 - loss: 0.5025 - accuracy: 1.0000 - val_loss: 0.4749 - val_accuracy: 1.0000 \n",
      "Epoch 39/500 - loss: 0.4964 - accuracy: 1.0000 - val_loss: 0.4688 - val_accuracy: 1.0000 \n",
      "Epoch 40/500 - loss: 0.4901 - accuracy: 1.0000 - val_loss: 0.4625 - val_accuracy: 1.0000 \n",
      "Epoch 41/500 - loss: 0.4838 - accuracy: 1.0000 - val_loss: 0.4561 - val_accuracy: 1.0000 \n",
      "Epoch 42/500 - loss: 0.4773 - accuracy: 1.0000 - val_loss: 0.4497 - val_accuracy: 1.0000 \n",
      "Epoch 43/500 - loss: 0.4708 - accuracy: 1.0000 - val_loss: 0.4433 - val_accuracy: 1.0000 \n",
      "Epoch 44/500 - loss: 0.4642 - accuracy: 1.0000 - val_loss: 0.4368 - val_accuracy: 1.0000 \n",
      "Epoch 45/500 - loss: 0.4575 - accuracy: 1.0000 - val_loss: 0.4303 - val_accuracy: 1.0000 \n",
      "Epoch 46/500 - loss: 0.4508 - accuracy: 1.0000 - val_loss: 0.4237 - val_accuracy: 1.0000 \n",
      "Epoch 47/500 - loss: 0.4439 - accuracy: 1.0000 - val_loss: 0.4170 - val_accuracy: 1.0000 \n",
      "Epoch 48/500 - loss: 0.4370 - accuracy: 1.0000 - val_loss: 0.4103 - val_accuracy: 1.0000 \n",
      "Epoch 49/500 - loss: 0.4299 - accuracy: 1.0000 - val_loss: 0.4035 - val_accuracy: 1.0000 \n",
      "Epoch 50/500 - loss: 0.4228 - accuracy: 1.0000 - val_loss: 0.3968 - val_accuracy: 1.0000 \n",
      "Epoch 51/500 - loss: 0.4157 - accuracy: 1.0000 - val_loss: 0.3900 - val_accuracy: 1.0000 \n",
      "Epoch 52/500 - loss: 0.4085 - accuracy: 1.0000 - val_loss: 0.3833 - val_accuracy: 1.0000 \n",
      "Epoch 53/500 - loss: 0.4013 - accuracy: 1.0000 - val_loss: 0.3765 - val_accuracy: 1.0000 \n",
      "Epoch 54/500 - loss: 0.3940 - accuracy: 1.0000 - val_loss: 0.3696 - val_accuracy: 1.0000 \n",
      "Epoch 55/500 - loss: 0.3866 - accuracy: 1.0000 - val_loss: 0.3627 - val_accuracy: 1.0000 \n",
      "Epoch 56/500 - loss: 0.3792 - accuracy: 1.0000 - val_loss: 0.3558 - val_accuracy: 1.0000 \n",
      "Epoch 57/500 - loss: 0.3718 - accuracy: 1.0000 - val_loss: 0.3488 - val_accuracy: 1.0000 \n",
      "Epoch 58/500 - loss: 0.3643 - accuracy: 1.0000 - val_loss: 0.3417 - val_accuracy: 1.0000 \n",
      "Epoch 59/500 - loss: 0.3568 - accuracy: 1.0000 - val_loss: 0.3347 - val_accuracy: 1.0000 \n",
      "Epoch 60/500 - loss: 0.3493 - accuracy: 1.0000 - val_loss: 0.3276 - val_accuracy: 1.0000 \n",
      "Epoch 61/500 - loss: 0.3418 - accuracy: 1.0000 - val_loss: 0.3206 - val_accuracy: 1.0000 \n",
      "Epoch 62/500 - loss: 0.3343 - accuracy: 1.0000 - val_loss: 0.3136 - val_accuracy: 1.0000 \n",
      "Epoch 63/500 - loss: 0.3268 - accuracy: 1.0000 - val_loss: 0.3066 - val_accuracy: 1.0000 \n",
      "Epoch 64/500 - loss: 0.3193 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 1.0000 \n",
      "Epoch 65/500 - loss: 0.3118 - accuracy: 1.0000 - val_loss: 0.2927 - val_accuracy: 1.0000 \n",
      "Epoch 66/500 - loss: 0.3043 - accuracy: 1.0000 - val_loss: 0.2859 - val_accuracy: 1.0000 \n",
      "Epoch 67/500 - loss: 0.2969 - accuracy: 1.0000 - val_loss: 0.2791 - val_accuracy: 1.0000 \n",
      "Epoch 68/500 - loss: 0.2895 - accuracy: 1.0000 - val_loss: 0.2723 - val_accuracy: 1.0000 \n",
      "Epoch 69/500 - loss: 0.2822 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 1.0000 \n",
      "Epoch 70/500 - loss: 0.2750 - accuracy: 1.0000 - val_loss: 0.2589 - val_accuracy: 1.0000 \n",
      "Epoch 71/500 - loss: 0.2678 - accuracy: 1.0000 - val_loss: 0.2524 - val_accuracy: 1.0000 \n",
      "Epoch 72/500 - loss: 0.2607 - accuracy: 1.0000 - val_loss: 0.2458 - val_accuracy: 1.0000 \n",
      "Epoch 73/500 - loss: 0.2537 - accuracy: 1.0000 - val_loss: 0.2394 - val_accuracy: 1.0000 \n",
      "Epoch 74/500 - loss: 0.2468 - accuracy: 1.0000 - val_loss: 0.2330 - val_accuracy: 1.0000 \n",
      "Epoch 75/500 - loss: 0.2399 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 1.0000 \n",
      "Epoch 76/500 - loss: 0.2331 - accuracy: 1.0000 - val_loss: 0.2206 - val_accuracy: 1.0000 \n",
      "Epoch 77/500 - loss: 0.2264 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 1.0000 \n",
      "Epoch 78/500 - loss: 0.2198 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 1.0000 \n",
      "Epoch 79/500 - loss: 0.2133 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 1.0000 \n",
      "Epoch 80/500 - loss: 0.2069 - accuracy: 1.0000 - val_loss: 0.1967 - val_accuracy: 1.0000 \n",
      "Epoch 81/500 - loss: 0.2007 - accuracy: 1.0000 - val_loss: 0.1911 - val_accuracy: 1.0000 \n",
      "Epoch 82/500 - loss: 0.1945 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 1.0000 \n",
      "Epoch 83/500 - loss: 0.1885 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 1.0000 \n",
      "Epoch 84/500 - loss: 0.1827 - accuracy: 1.0000 - val_loss: 0.1747 - val_accuracy: 1.0000 \n",
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      "Epoch 467/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 468/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 469/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 470/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000 \n",
      "Epoch 471/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 472/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 473/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 474/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 475/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 476/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 477/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 478/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 479/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 480/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 481/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000 \n",
      "Epoch 482/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 483/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 484/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 485/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 486/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 487/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 488/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 489/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 490/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 491/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 492/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 493/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 494/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 495/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 496/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 497/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 498/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 499/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 500/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 64)                192       \n",
      "_________________________________________________________________\n",
      "H2 (Dense)                   (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,417\n",
      "Trainable params: 4,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 0.6982 - accuracy: 0.3000 - val_loss: 0.7084 - val_accuracy: 0.5000 \n",
      "Epoch 2/500 - loss: 0.6930 - accuracy: 0.4500 - val_loss: 0.7003 - val_accuracy: 0.5000 \n",
      "Epoch 3/500 - loss: 0.6878 - accuracy: 0.5250 - val_loss: 0.6925 - val_accuracy: 0.5000 \n",
      "Epoch 4/500 - loss: 0.6826 - accuracy: 0.5250 - val_loss: 0.6851 - val_accuracy: 0.5000 \n",
      "Epoch 5/500 - loss: 0.6776 - accuracy: 0.5375 - val_loss: 0.6783 - val_accuracy: 0.7500 \n",
      "Epoch 6/500 - loss: 0.6728 - accuracy: 0.6500 - val_loss: 0.6721 - val_accuracy: 0.8000 \n",
      "Epoch 7/500 - loss: 0.6682 - accuracy: 0.7000 - val_loss: 0.6664 - val_accuracy: 0.8000 \n",
      "Epoch 8/500 - loss: 0.6638 - accuracy: 0.7625 - val_loss: 0.6609 - val_accuracy: 0.8000 \n",
      "Epoch 9/500 - loss: 0.6595 - accuracy: 0.7625 - val_loss: 0.6557 - val_accuracy: 0.8500 \n",
      "Epoch 10/500 - loss: 0.6553 - accuracy: 0.7750 - val_loss: 0.6508 - val_accuracy: 0.8500 \n",
      "Epoch 11/500 - loss: 0.6512 - accuracy: 0.7750 - val_loss: 0.6460 - val_accuracy: 0.8500 \n",
      "Epoch 12/500 - loss: 0.6472 - accuracy: 0.7875 - val_loss: 0.6413 - val_accuracy: 0.8500 \n",
      "Epoch 13/500 - loss: 0.6432 - accuracy: 0.7875 - val_loss: 0.6367 - val_accuracy: 0.8500 \n",
      "Epoch 14/500 - loss: 0.6393 - accuracy: 0.7875 - val_loss: 0.6322 - val_accuracy: 0.8500 \n",
      "Epoch 15/500 - loss: 0.6354 - accuracy: 0.7875 - val_loss: 0.6278 - val_accuracy: 0.8500 \n",
      "Epoch 16/500 - loss: 0.6316 - accuracy: 0.8000 - val_loss: 0.6233 - val_accuracy: 0.8500 \n",
      "Epoch 17/500 - loss: 0.6277 - accuracy: 0.8000 - val_loss: 0.6190 - val_accuracy: 0.8500 \n",
      "Epoch 18/500 - loss: 0.6238 - accuracy: 0.8125 - val_loss: 0.6147 - val_accuracy: 0.8500 \n",
      "Epoch 19/500 - loss: 0.6200 - accuracy: 0.8125 - val_loss: 0.6104 - val_accuracy: 0.8500 \n",
      "Epoch 20/500 - loss: 0.6160 - accuracy: 0.8125 - val_loss: 0.6060 - val_accuracy: 0.8500 \n",
      "Epoch 21/500 - loss: 0.6119 - accuracy: 0.8250 - val_loss: 0.6014 - val_accuracy: 0.8500 \n",
      "Epoch 22/500 - loss: 0.6077 - accuracy: 0.8250 - val_loss: 0.5968 - val_accuracy: 0.8500 \n",
      "Epoch 23/500 - loss: 0.6034 - accuracy: 0.8375 - val_loss: 0.5921 - val_accuracy: 0.8500 \n",
      "Epoch 24/500 - loss: 0.5991 - accuracy: 0.8500 - val_loss: 0.5875 - val_accuracy: 0.9000 \n",
      "Epoch 25/500 - loss: 0.5948 - accuracy: 0.8625 - val_loss: 0.5829 - val_accuracy: 0.9000 \n",
      "Epoch 26/500 - loss: 0.5905 - accuracy: 0.8750 - val_loss: 0.5782 - val_accuracy: 0.9000 \n",
      "Epoch 27/500 - loss: 0.5861 - accuracy: 0.8750 - val_loss: 0.5734 - val_accuracy: 0.9000 \n",
      "Epoch 28/500 - loss: 0.5815 - accuracy: 0.8750 - val_loss: 0.5684 - val_accuracy: 0.9000 \n",
      "Epoch 29/500 - loss: 0.5768 - accuracy: 0.8750 - val_loss: 0.5632 - val_accuracy: 0.9000 \n",
      "Epoch 30/500 - loss: 0.5720 - accuracy: 0.8875 - val_loss: 0.5579 - val_accuracy: 0.9000 \n",
      "Epoch 31/500 - loss: 0.5671 - accuracy: 0.9000 - val_loss: 0.5524 - val_accuracy: 0.9000 \n",
      "Epoch 32/500 - loss: 0.5621 - accuracy: 0.9000 - val_loss: 0.5468 - val_accuracy: 0.9000 \n",
      "Epoch 33/500 - loss: 0.5569 - accuracy: 0.9125 - val_loss: 0.5411 - val_accuracy: 0.9000 \n",
      "Epoch 34/500 - loss: 0.5516 - accuracy: 0.9250 - val_loss: 0.5352 - val_accuracy: 0.9000 \n",
      "Epoch 35/500 - loss: 0.5462 - accuracy: 0.9250 - val_loss: 0.5291 - val_accuracy: 0.9000 \n",
      "Epoch 36/500 - loss: 0.5407 - accuracy: 0.9250 - val_loss: 0.5229 - val_accuracy: 0.9000 \n",
      "Epoch 37/500 - loss: 0.5351 - accuracy: 0.9250 - val_loss: 0.5167 - val_accuracy: 0.9000 \n",
      "Epoch 38/500 - loss: 0.5294 - accuracy: 0.9500 - val_loss: 0.5103 - val_accuracy: 0.9500 \n",
      "Epoch 39/500 - loss: 0.5235 - accuracy: 0.9500 - val_loss: 0.5038 - val_accuracy: 0.9500 \n",
      "Epoch 40/500 - loss: 0.5175 - accuracy: 0.9625 - val_loss: 0.4971 - val_accuracy: 0.9500 \n",
      "Epoch 41/500 - loss: 0.5115 - accuracy: 0.9625 - val_loss: 0.4904 - val_accuracy: 0.9500 \n",
      "Epoch 42/500 - loss: 0.5053 - accuracy: 0.9625 - val_loss: 0.4835 - val_accuracy: 0.9500 \n",
      "Epoch 43/500 - loss: 0.4990 - accuracy: 0.9625 - val_loss: 0.4766 - val_accuracy: 0.9500 \n",
      "Epoch 44/500 - loss: 0.4926 - accuracy: 0.9625 - val_loss: 0.4696 - val_accuracy: 1.0000 \n",
      "Epoch 45/500 - loss: 0.4861 - accuracy: 0.9750 - val_loss: 0.4625 - val_accuracy: 1.0000 \n",
      "Epoch 46/500 - loss: 0.4795 - accuracy: 0.9750 - val_loss: 0.4554 - val_accuracy: 1.0000 \n",
      "Epoch 47/500 - loss: 0.4728 - accuracy: 0.9750 - val_loss: 0.4482 - val_accuracy: 1.0000 \n",
      "Epoch 48/500 - loss: 0.4660 - accuracy: 0.9750 - val_loss: 0.4409 - val_accuracy: 1.0000 \n",
      "Epoch 49/500 - loss: 0.4591 - accuracy: 0.9750 - val_loss: 0.4336 - val_accuracy: 1.0000 \n",
      "Epoch 50/500 - loss: 0.4521 - accuracy: 0.9750 - val_loss: 0.4262 - val_accuracy: 1.0000 \n",
      "Epoch 51/500 - loss: 0.4450 - accuracy: 0.9750 - val_loss: 0.4187 - val_accuracy: 1.0000 \n",
      "Epoch 52/500 - loss: 0.4379 - accuracy: 0.9750 - val_loss: 0.4112 - val_accuracy: 1.0000 \n",
      "Epoch 53/500 - loss: 0.4306 - accuracy: 0.9750 - val_loss: 0.4036 - val_accuracy: 1.0000 \n",
      "Epoch 54/500 - loss: 0.4233 - accuracy: 0.9750 - val_loss: 0.3960 - val_accuracy: 1.0000 \n",
      "Epoch 55/500 - loss: 0.4159 - accuracy: 0.9750 - val_loss: 0.3883 - val_accuracy: 1.0000 \n",
      "Epoch 56/500 - loss: 0.4084 - accuracy: 0.9750 - val_loss: 0.3806 - val_accuracy: 1.0000 \n",
      "Epoch 57/500 - loss: 0.4009 - accuracy: 0.9750 - val_loss: 0.3728 - val_accuracy: 1.0000 \n",
      "Epoch 58/500 - loss: 0.3933 - accuracy: 0.9750 - val_loss: 0.3650 - val_accuracy: 1.0000 \n",
      "Epoch 59/500 - loss: 0.3857 - accuracy: 0.9750 - val_loss: 0.3571 - val_accuracy: 1.0000 \n",
      "Epoch 60/500 - loss: 0.3781 - accuracy: 0.9750 - val_loss: 0.3492 - val_accuracy: 1.0000 \n",
      "Epoch 61/500 - loss: 0.3704 - accuracy: 0.9750 - val_loss: 0.3413 - val_accuracy: 1.0000 \n",
      "Epoch 62/500 - loss: 0.3627 - accuracy: 0.9875 - val_loss: 0.3333 - val_accuracy: 1.0000 \n",
      "Epoch 63/500 - loss: 0.3549 - accuracy: 0.9875 - val_loss: 0.3254 - val_accuracy: 1.0000 \n",
      "Epoch 64/500 - loss: 0.3472 - accuracy: 0.9875 - val_loss: 0.3175 - val_accuracy: 1.0000 \n",
      "Epoch 65/500 - loss: 0.3394 - accuracy: 0.9875 - val_loss: 0.3097 - val_accuracy: 1.0000 \n",
      "Epoch 66/500 - loss: 0.3317 - accuracy: 0.9875 - val_loss: 0.3019 - val_accuracy: 1.0000 \n",
      "Epoch 67/500 - loss: 0.3239 - accuracy: 0.9875 - val_loss: 0.2942 - val_accuracy: 1.0000 \n",
      "Epoch 68/500 - loss: 0.3162 - accuracy: 0.9875 - val_loss: 0.2865 - val_accuracy: 1.0000 \n",
      "Epoch 69/500 - loss: 0.3085 - accuracy: 0.9875 - val_loss: 0.2789 - val_accuracy: 1.0000 \n",
      "Epoch 70/500 - loss: 0.3008 - accuracy: 0.9875 - val_loss: 0.2713 - val_accuracy: 1.0000 \n",
      "Epoch 71/500 - loss: 0.2932 - accuracy: 0.9875 - val_loss: 0.2639 - val_accuracy: 1.0000 \n",
      "Epoch 72/500 - loss: 0.2856 - accuracy: 0.9875 - val_loss: 0.2565 - val_accuracy: 1.0000 \n",
      "Epoch 73/500 - loss: 0.2781 - accuracy: 0.9875 - val_loss: 0.2492 - val_accuracy: 1.0000 \n",
      "Epoch 74/500 - loss: 0.2707 - accuracy: 1.0000 - val_loss: 0.2420 - val_accuracy: 1.0000 \n",
      "Epoch 75/500 - loss: 0.2633 - accuracy: 1.0000 - val_loss: 0.2349 - val_accuracy: 1.0000 \n",
      "Epoch 76/500 - loss: 0.2560 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 1.0000 \n",
      "Epoch 77/500 - loss: 0.2488 - accuracy: 1.0000 - val_loss: 0.2210 - val_accuracy: 1.0000 \n",
      "Epoch 78/500 - loss: 0.2418 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 1.0000 \n",
      "Epoch 79/500 - loss: 0.2348 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 1.0000 \n",
      "Epoch 80/500 - loss: 0.2279 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 1.0000 \n",
      "Epoch 81/500 - loss: 0.2211 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 1.0000 \n",
      "Epoch 82/500 - loss: 0.2145 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 1.0000 \n",
      "Epoch 83/500 - loss: 0.2080 - accuracy: 1.0000 - val_loss: 0.1823 - val_accuracy: 1.0000 \n",
      "Epoch 84/500 - loss: 0.2016 - accuracy: 1.0000 - val_loss: 0.1763 - val_accuracy: 1.0000 \n",
      "Epoch 85/500 - loss: 0.1954 - accuracy: 1.0000 - val_loss: 0.1705 - val_accuracy: 1.0000 \n",
      "Epoch 86/500 - loss: 0.1893 - accuracy: 1.0000 - val_loss: 0.1649 - val_accuracy: 1.0000 \n",
      "Epoch 87/500 - loss: 0.1834 - accuracy: 1.0000 - val_loss: 0.1594 - val_accuracy: 1.0000 \n",
      "Epoch 88/500 - loss: 0.1776 - accuracy: 1.0000 - val_loss: 0.1540 - val_accuracy: 1.0000 \n",
      "Epoch 89/500 - loss: 0.1719 - accuracy: 1.0000 - val_loss: 0.1488 - val_accuracy: 1.0000 \n",
      "Epoch 90/500 - loss: 0.1664 - accuracy: 1.0000 - val_loss: 0.1437 - val_accuracy: 1.0000 \n",
      "Epoch 91/500 - loss: 0.1610 - accuracy: 1.0000 - val_loss: 0.1387 - val_accuracy: 1.0000 \n",
      "Epoch 92/500 - loss: 0.1557 - accuracy: 1.0000 - val_loss: 0.1339 - val_accuracy: 1.0000 \n",
      "Epoch 93/500 - loss: 0.1506 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 1.0000 \n",
      "Epoch 94/500 - loss: 0.1456 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 1.0000 \n",
      "Epoch 95/500 - loss: 0.1408 - accuracy: 1.0000 - val_loss: 0.1204 - val_accuracy: 1.0000 \n",
      "Epoch 96/500 - loss: 0.1361 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 1.0000 \n",
      "Epoch 97/500 - loss: 0.1316 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 1.0000 \n",
      "Epoch 98/500 - loss: 0.1272 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 1.0000 \n",
      "Epoch 99/500 - loss: 0.1230 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 1.0000 \n",
      "Epoch 100/500 - loss: 0.1189 - accuracy: 1.0000 - val_loss: 0.1009 - val_accuracy: 1.0000 \n",
      "Epoch 101/500 - loss: 0.1149 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 1.0000 \n",
      "Epoch 102/500 - loss: 0.1111 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 1.0000 \n",
      "Epoch 103/500 - loss: 0.1074 - accuracy: 1.0000 - val_loss: 0.0907 - val_accuracy: 1.0000 \n",
      "Epoch 104/500 - loss: 0.1038 - accuracy: 1.0000 - val_loss: 0.0875 - val_accuracy: 1.0000 \n",
      "Epoch 105/500 - loss: 0.1003 - accuracy: 1.0000 - val_loss: 0.0845 - val_accuracy: 1.0000 \n",
      "Epoch 106/500 - loss: 0.0970 - accuracy: 1.0000 - val_loss: 0.0815 - val_accuracy: 1.0000 \n",
      "Epoch 107/500 - loss: 0.0938 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 1.0000 \n",
      "Epoch 108/500 - loss: 0.0907 - accuracy: 1.0000 - val_loss: 0.0760 - val_accuracy: 1.0000 \n",
      "Epoch 109/500 - loss: 0.0877 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 1.0000 \n",
      "Epoch 110/500 - loss: 0.0848 - accuracy: 1.0000 - val_loss: 0.0710 - val_accuracy: 1.0000 \n",
      "Epoch 111/500 - loss: 0.0821 - accuracy: 1.0000 - val_loss: 0.0686 - val_accuracy: 1.0000 \n",
      "Epoch 112/500 - loss: 0.0794 - accuracy: 1.0000 - val_loss: 0.0664 - val_accuracy: 1.0000 \n",
      "Epoch 113/500 - loss: 0.0769 - accuracy: 1.0000 - val_loss: 0.0642 - val_accuracy: 1.0000 \n",
      "Epoch 114/500 - loss: 0.0744 - accuracy: 1.0000 - val_loss: 0.0621 - val_accuracy: 1.0000 \n",
      "Epoch 115/500 - loss: 0.0721 - accuracy: 1.0000 - val_loss: 0.0601 - val_accuracy: 1.0000 \n",
      "Epoch 116/500 - loss: 0.0698 - accuracy: 1.0000 - val_loss: 0.0581 - val_accuracy: 1.0000 \n",
      "Epoch 117/500 - loss: 0.0676 - accuracy: 1.0000 - val_loss: 0.0563 - val_accuracy: 1.0000 \n",
      "Epoch 118/500 - loss: 0.0655 - accuracy: 1.0000 - val_loss: 0.0545 - val_accuracy: 1.0000 \n",
      "Epoch 119/500 - loss: 0.0635 - accuracy: 1.0000 - val_loss: 0.0528 - val_accuracy: 1.0000 \n",
      "Epoch 120/500 - loss: 0.0615 - accuracy: 1.0000 - val_loss: 0.0511 - val_accuracy: 1.0000 \n",
      "Epoch 121/500 - loss: 0.0597 - accuracy: 1.0000 - val_loss: 0.0495 - val_accuracy: 1.0000 \n",
      "Epoch 122/500 - loss: 0.0579 - accuracy: 1.0000 - val_loss: 0.0480 - val_accuracy: 1.0000 \n",
      "Epoch 123/500 - loss: 0.0562 - accuracy: 1.0000 - val_loss: 0.0465 - val_accuracy: 1.0000 \n",
      "Epoch 124/500 - loss: 0.0545 - accuracy: 1.0000 - val_loss: 0.0451 - val_accuracy: 1.0000 \n",
      "Epoch 125/500 - loss: 0.0529 - accuracy: 1.0000 - val_loss: 0.0438 - val_accuracy: 1.0000 \n",
      "Epoch 126/500 - loss: 0.0514 - accuracy: 1.0000 - val_loss: 0.0425 - val_accuracy: 1.0000 \n",
      "Epoch 127/500 - loss: 0.0499 - accuracy: 1.0000 - val_loss: 0.0413 - val_accuracy: 1.0000 \n",
      "Epoch 128/500 - loss: 0.0485 - accuracy: 1.0000 - val_loss: 0.0401 - val_accuracy: 1.0000 \n",
      "Epoch 129/500 - loss: 0.0472 - accuracy: 1.0000 - val_loss: 0.0389 - val_accuracy: 1.0000 \n",
      "Epoch 130/500 - loss: 0.0459 - accuracy: 1.0000 - val_loss: 0.0378 - val_accuracy: 1.0000 \n",
      "Epoch 131/500 - loss: 0.0446 - accuracy: 1.0000 - val_loss: 0.0368 - val_accuracy: 1.0000 \n",
      "Epoch 132/500 - loss: 0.0434 - accuracy: 1.0000 - val_loss: 0.0358 - val_accuracy: 1.0000 \n",
      "Epoch 133/500 - loss: 0.0423 - accuracy: 1.0000 - val_loss: 0.0348 - val_accuracy: 1.0000 \n",
      "Epoch 134/500 - loss: 0.0411 - accuracy: 1.0000 - val_loss: 0.0338 - val_accuracy: 1.0000 \n",
      "Epoch 135/500 - loss: 0.0401 - accuracy: 1.0000 - val_loss: 0.0329 - val_accuracy: 1.0000 \n",
      "Epoch 136/500 - loss: 0.0390 - accuracy: 1.0000 - val_loss: 0.0321 - val_accuracy: 1.0000 \n",
      "Epoch 137/500 - loss: 0.0380 - accuracy: 1.0000 - val_loss: 0.0312 - val_accuracy: 1.0000 \n",
      "Epoch 138/500 - loss: 0.0371 - accuracy: 1.0000 - val_loss: 0.0304 - val_accuracy: 1.0000 \n",
      "Epoch 139/500 - loss: 0.0362 - accuracy: 1.0000 - val_loss: 0.0296 - val_accuracy: 1.0000 \n",
      "Epoch 140/500 - loss: 0.0353 - accuracy: 1.0000 - val_loss: 0.0289 - val_accuracy: 1.0000 \n",
      "Epoch 141/500 - loss: 0.0344 - accuracy: 1.0000 - val_loss: 0.0281 - val_accuracy: 1.0000 \n",
      "Epoch 142/500 - loss: 0.0336 - accuracy: 1.0000 - val_loss: 0.0274 - val_accuracy: 1.0000 \n",
      "Epoch 143/500 - loss: 0.0328 - accuracy: 1.0000 - val_loss: 0.0268 - val_accuracy: 1.0000 \n",
      "Epoch 144/500 - loss: 0.0320 - accuracy: 1.0000 - val_loss: 0.0261 - val_accuracy: 1.0000 \n",
      "Epoch 145/500 - loss: 0.0313 - accuracy: 1.0000 - val_loss: 0.0255 - val_accuracy: 1.0000 \n",
      "Epoch 146/500 - loss: 0.0305 - accuracy: 1.0000 - val_loss: 0.0249 - val_accuracy: 1.0000 \n",
      "Epoch 147/500 - loss: 0.0299 - accuracy: 1.0000 - val_loss: 0.0243 - val_accuracy: 1.0000 \n",
      "Epoch 148/500 - loss: 0.0292 - accuracy: 1.0000 - val_loss: 0.0238 - val_accuracy: 1.0000 \n",
      "Epoch 149/500 - loss: 0.0285 - accuracy: 1.0000 - val_loss: 0.0232 - val_accuracy: 1.0000 \n",
      "Epoch 150/500 - loss: 0.0279 - accuracy: 1.0000 - val_loss: 0.0227 - val_accuracy: 1.0000 \n",
      "Epoch 151/500 - loss: 0.0273 - accuracy: 1.0000 - val_loss: 0.0222 - val_accuracy: 1.0000 \n",
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      "Epoch 341/500 - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000 \n",
      "Epoch 342/500 - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 343/500 - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 344/500 - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 345/500 - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 346/500 - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 347/500 - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000 \n",
      "Epoch 348/500 - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 349/500 - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 350/500 - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 351/500 - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 352/500 - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 353/500 - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 354/500 - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000 \n",
      "Epoch 355/500 - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 356/500 - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 357/500 - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 358/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 359/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 360/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 361/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 \n",
      "Epoch 362/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 363/500 - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 364/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 365/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 366/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 367/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 368/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 369/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000 \n",
      "Epoch 370/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "Epoch 371/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "Epoch 372/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "Epoch 373/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "Epoch 374/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "Epoch 375/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "Epoch 376/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "Epoch 377/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000 \n",
      "Epoch 378/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000 \n",
      "Epoch 379/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000 \n",
      "Epoch 380/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000 \n",
      "Epoch 381/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000 \n",
      "Epoch 382/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000 \n",
      "Epoch 383/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000 \n",
      "Epoch 384/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000 \n",
      "Epoch 385/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000 \n",
      "Epoch 386/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000 \n",
      "Epoch 387/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 388/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 389/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 390/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 391/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 392/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 393/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 394/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 395/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 396/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000 \n",
      "Epoch 397/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 398/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 399/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 400/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 401/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 402/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 403/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 404/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 405/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 406/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000 \n",
      "Epoch 407/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 408/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 409/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 410/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 411/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 412/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 413/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 414/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 415/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 416/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 417/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000 \n",
      "Epoch 418/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 419/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 420/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 421/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 422/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 423/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 424/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 425/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 426/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 427/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 428/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 429/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000 \n",
      "Epoch 430/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 431/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 432/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 433/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 434/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 435/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 436/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 437/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 438/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 439/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 440/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 441/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 442/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 443/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 444/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 445/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 446/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 447/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 448/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 449/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 450/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 451/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 452/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 453/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 454/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 455/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 456/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 457/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 458/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 459/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 460/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 461/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 462/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 463/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 464/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 465/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 466/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 467/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 468/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 469/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 470/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 471/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000 \n",
      "Epoch 472/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 473/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 474/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 475/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 476/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 477/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 478/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 479/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 480/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 481/500 - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 482/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 483/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 484/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 485/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 486/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 487/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 488/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 489/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0009 - val_accuracy: 1.0000 \n",
      "Epoch 490/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 491/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 492/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 493/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 494/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 495/500 - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 496/500 - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 497/500 - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 498/500 - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 499/500 - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "Epoch 500/500 - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0008 - val_accuracy: 1.0000 \n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 64)                192       \n",
      "_________________________________________________________________\n",
      "H2 (Dense)                   (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,417\n",
      "Trainable params: 4,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 0.7093 - accuracy: 0.7250 - val_loss: 0.6905 - val_accuracy: 0.8500 \n",
      "Epoch 2/500 - loss: 0.7025 - accuracy: 0.6625 - val_loss: 0.6875 - val_accuracy: 0.7500 \n",
      "Epoch 3/500 - loss: 0.6960 - accuracy: 0.6500 - val_loss: 0.6850 - val_accuracy: 0.7000 \n",
      "Epoch 4/500 - loss: 0.6899 - accuracy: 0.6500 - val_loss: 0.6824 - val_accuracy: 0.7000 \n",
      "Epoch 5/500 - loss: 0.6840 - accuracy: 0.6375 - val_loss: 0.6798 - val_accuracy: 0.6500 \n",
      "Epoch 6/500 - loss: 0.6783 - accuracy: 0.6375 - val_loss: 0.6774 - val_accuracy: 0.7500 \n",
      "Epoch 7/500 - loss: 0.6729 - accuracy: 0.9125 - val_loss: 0.6750 - val_accuracy: 0.7500 \n",
      "Epoch 8/500 - loss: 0.6677 - accuracy: 0.9125 - val_loss: 0.6726 - val_accuracy: 0.7500 \n",
      "Epoch 9/500 - loss: 0.6625 - accuracy: 0.9125 - val_loss: 0.6702 - val_accuracy: 0.7500 \n",
      "Epoch 10/500 - loss: 0.6575 - accuracy: 0.9125 - val_loss: 0.6677 - val_accuracy: 0.7500 \n",
      "Epoch 11/500 - loss: 0.6525 - accuracy: 0.9125 - val_loss: 0.6651 - val_accuracy: 0.7500 \n",
      "Epoch 12/500 - loss: 0.6475 - accuracy: 0.9125 - val_loss: 0.6625 - val_accuracy: 0.7500 \n",
      "Epoch 13/500 - loss: 0.6425 - accuracy: 0.9125 - val_loss: 0.6597 - val_accuracy: 0.7500 \n",
      "Epoch 14/500 - loss: 0.6374 - accuracy: 0.9125 - val_loss: 0.6569 - val_accuracy: 0.7500 \n",
      "Epoch 15/500 - loss: 0.6325 - accuracy: 0.9125 - val_loss: 0.6541 - val_accuracy: 0.7500 \n",
      "Epoch 16/500 - loss: 0.6276 - accuracy: 0.9125 - val_loss: 0.6513 - val_accuracy: 0.7500 \n",
      "Epoch 17/500 - loss: 0.6229 - accuracy: 0.9125 - val_loss: 0.6486 - val_accuracy: 0.7500 \n",
      "Epoch 18/500 - loss: 0.6184 - accuracy: 0.9125 - val_loss: 0.6459 - val_accuracy: 0.7500 \n",
      "Epoch 19/500 - loss: 0.6141 - accuracy: 0.9125 - val_loss: 0.6432 - val_accuracy: 0.8000 \n",
      "Epoch 20/500 - loss: 0.6099 - accuracy: 0.9125 - val_loss: 0.6405 - val_accuracy: 0.8000 \n",
      "Epoch 21/500 - loss: 0.6056 - accuracy: 0.9125 - val_loss: 0.6375 - val_accuracy: 0.8000 \n",
      "Epoch 22/500 - loss: 0.6013 - accuracy: 0.9125 - val_loss: 0.6345 - val_accuracy: 0.8000 \n",
      "Epoch 23/500 - loss: 0.5968 - accuracy: 0.9125 - val_loss: 0.6314 - val_accuracy: 0.8000 \n",
      "Epoch 24/500 - loss: 0.5923 - accuracy: 0.9125 - val_loss: 0.6281 - val_accuracy: 0.8000 \n",
      "Epoch 25/500 - loss: 0.5877 - accuracy: 0.9125 - val_loss: 0.6247 - val_accuracy: 0.9000 \n",
      "Epoch 26/500 - loss: 0.5830 - accuracy: 0.9125 - val_loss: 0.6213 - val_accuracy: 0.9000 \n",
      "Epoch 27/500 - loss: 0.5782 - accuracy: 0.9125 - val_loss: 0.6179 - val_accuracy: 0.9000 \n",
      "Epoch 28/500 - loss: 0.5735 - accuracy: 0.9125 - val_loss: 0.6143 - val_accuracy: 0.9000 \n",
      "Epoch 29/500 - loss: 0.5687 - accuracy: 0.9125 - val_loss: 0.6106 - val_accuracy: 0.9000 \n",
      "Epoch 30/500 - loss: 0.5638 - accuracy: 0.9125 - val_loss: 0.6068 - val_accuracy: 0.9000 \n",
      "Epoch 31/500 - loss: 0.5588 - accuracy: 0.9125 - val_loss: 0.6030 - val_accuracy: 0.9000 \n",
      "Epoch 32/500 - loss: 0.5538 - accuracy: 0.9250 - val_loss: 0.5990 - val_accuracy: 0.9000 \n",
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      "Epoch 408/500 - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 1.0000 \n",
      "Epoch 409/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 1.0000 \n",
      "Epoch 410/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 1.0000 \n",
      "Epoch 411/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 1.0000 \n",
      "Epoch 412/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 1.0000 \n",
      "Epoch 413/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 1.0000 \n",
      "Epoch 414/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 1.0000 \n",
      "Epoch 415/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000 \n",
      "Epoch 416/500 - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000 \n",
      "Epoch 417/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000 \n",
      "Epoch 418/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000 \n",
      "Epoch 419/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000 \n",
      "Epoch 420/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0047 - val_accuracy: 1.0000 \n",
      "Epoch 421/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0047 - val_accuracy: 1.0000 \n",
      "Epoch 422/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0047 - val_accuracy: 1.0000 \n",
      "Epoch 423/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0047 - val_accuracy: 1.0000 \n",
      "Epoch 424/500 - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0047 - val_accuracy: 1.0000 \n",
      "Epoch 425/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0046 - val_accuracy: 1.0000 \n",
      "Epoch 426/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0046 - val_accuracy: 1.0000 \n",
      "Epoch 427/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0046 - val_accuracy: 1.0000 \n",
      "Epoch 428/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0046 - val_accuracy: 1.0000 \n",
      "Epoch 429/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0046 - val_accuracy: 1.0000 \n",
      "Epoch 430/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000 \n",
      "Epoch 431/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000 \n",
      "Epoch 432/500 - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000 \n",
      "Epoch 433/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000 \n",
      "Epoch 434/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000 \n",
      "Epoch 435/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000 \n",
      "Epoch 436/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000 \n",
      "Epoch 437/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000 \n",
      "Epoch 438/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000 \n",
      "Epoch 439/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000 \n",
      "Epoch 440/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000 \n",
      "Epoch 441/500 - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000 \n",
      "Epoch 442/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000 \n",
      "Epoch 443/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000 \n",
      "Epoch 444/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000 \n",
      "Epoch 445/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000 \n",
      "Epoch 446/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000 \n",
      "Epoch 447/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 1.0000 \n",
      "Epoch 448/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 1.0000 \n",
      "Epoch 449/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 1.0000 \n",
      "Epoch 450/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 1.0000 \n",
      "Epoch 451/500 - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 1.0000 \n",
      "Epoch 452/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 1.0000 \n",
      "Epoch 453/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000 \n",
      "Epoch 454/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000 \n",
      "Epoch 455/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000 \n",
      "Epoch 456/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000 \n",
      "Epoch 457/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000 \n",
      "Epoch 458/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000 \n",
      "Epoch 459/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000 \n",
      "Epoch 460/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0040 - val_accuracy: 1.0000 \n",
      "Epoch 461/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0040 - val_accuracy: 1.0000 \n",
      "Epoch 462/500 - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0040 - val_accuracy: 1.0000 \n",
      "Epoch 463/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0040 - val_accuracy: 1.0000 \n",
      "Epoch 464/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0040 - val_accuracy: 1.0000 \n",
      "Epoch 465/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0040 - val_accuracy: 1.0000 \n",
      "Epoch 466/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000 \n",
      "Epoch 467/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000 \n",
      "Epoch 468/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000 \n",
      "Epoch 469/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000 \n",
      "Epoch 470/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000 \n",
      "Epoch 471/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000 \n",
      "Epoch 472/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000 \n",
      "Epoch 473/500 - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 1.0000 \n",
      "Epoch 474/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 1.0000 \n",
      "Epoch 475/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 1.0000 \n",
      "Epoch 476/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 1.0000 \n",
      "Epoch 477/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 1.0000 \n",
      "Epoch 478/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 1.0000 \n",
      "Epoch 479/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 1.0000 \n",
      "Epoch 480/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000 \n",
      "Epoch 481/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000 \n",
      "Epoch 482/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000 \n",
      "Epoch 483/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000 \n",
      "Epoch 484/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000 \n",
      "Epoch 485/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000 \n",
      "Epoch 486/500 - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000 \n",
      "Epoch 487/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000 \n",
      "Epoch 488/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000 \n",
      "Epoch 489/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000 \n",
      "Epoch 490/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000 \n",
      "Epoch 491/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000 \n",
      "Epoch 492/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000 \n",
      "Epoch 493/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000 \n",
      "Epoch 494/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000 \n",
      "Epoch 495/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000 \n",
      "Epoch 496/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 1.0000 \n",
      "Epoch 497/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 1.0000 \n",
      "Epoch 498/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 1.0000 \n",
      "Epoch 499/500 - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 1.0000 \n",
      "Epoch 500/500 - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 1.0000 \n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 64)                192       \n",
      "_________________________________________________________________\n",
      "H2 (Dense)                   (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,417\n",
      "Trainable params: 4,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500 - loss: 0.6798 - accuracy: 0.6625 - val_loss: 0.6708 - val_accuracy: 0.7000 \n",
      "Epoch 2/500 - loss: 0.6744 - accuracy: 0.6375 - val_loss: 0.6649 - val_accuracy: 0.9000 \n",
      "Epoch 3/500 - loss: 0.6691 - accuracy: 0.8125 - val_loss: 0.6591 - val_accuracy: 0.9500 \n",
      "Epoch 4/500 - loss: 0.6638 - accuracy: 0.8625 - val_loss: 0.6535 - val_accuracy: 0.9500 \n",
      "Epoch 5/500 - loss: 0.6588 - accuracy: 0.8750 - val_loss: 0.6483 - val_accuracy: 0.9500 \n",
      "Epoch 6/500 - loss: 0.6540 - accuracy: 0.8750 - val_loss: 0.6428 - val_accuracy: 0.9500 \n",
      "Epoch 7/500 - loss: 0.6490 - accuracy: 0.8750 - val_loss: 0.6373 - val_accuracy: 0.9500 \n",
      "Epoch 8/500 - loss: 0.6440 - accuracy: 0.8750 - val_loss: 0.6319 - val_accuracy: 0.9500 \n",
      "Epoch 9/500 - loss: 0.6392 - accuracy: 0.8875 - val_loss: 0.6268 - val_accuracy: 0.9500 \n",
      "Epoch 10/500 - loss: 0.6346 - accuracy: 0.9000 - val_loss: 0.6218 - val_accuracy: 0.9500 \n",
      "Epoch 11/500 - loss: 0.6302 - accuracy: 0.9000 - val_loss: 0.6171 - val_accuracy: 0.9500 \n",
      "Epoch 12/500 - loss: 0.6259 - accuracy: 0.9125 - val_loss: 0.6124 - val_accuracy: 0.9500 \n",
      "Epoch 13/500 - loss: 0.6216 - accuracy: 0.9250 - val_loss: 0.6077 - val_accuracy: 1.0000 \n",
      "Epoch 14/500 - loss: 0.6173 - accuracy: 0.9250 - val_loss: 0.6030 - val_accuracy: 1.0000 \n",
      "Epoch 15/500 - loss: 0.6130 - accuracy: 0.9250 - val_loss: 0.5983 - val_accuracy: 1.0000 \n",
      "Epoch 16/500 - loss: 0.6087 - accuracy: 0.9250 - val_loss: 0.5936 - val_accuracy: 1.0000 \n",
      "Epoch 17/500 - loss: 0.6042 - accuracy: 0.9375 - val_loss: 0.5888 - val_accuracy: 1.0000 \n",
      "Epoch 18/500 - loss: 0.5998 - accuracy: 0.9500 - val_loss: 0.5840 - val_accuracy: 1.0000 \n",
      "Epoch 19/500 - loss: 0.5953 - accuracy: 0.9625 - val_loss: 0.5792 - val_accuracy: 1.0000 \n",
      "Epoch 20/500 - loss: 0.5908 - accuracy: 0.9625 - val_loss: 0.5744 - val_accuracy: 1.0000 \n",
      "Epoch 21/500 - loss: 0.5863 - accuracy: 0.9750 - val_loss: 0.5696 - val_accuracy: 1.0000 \n",
      "Epoch 22/500 - loss: 0.5818 - accuracy: 0.9750 - val_loss: 0.5647 - val_accuracy: 1.0000 \n",
      "Epoch 23/500 - loss: 0.5772 - accuracy: 0.9750 - val_loss: 0.5598 - val_accuracy: 1.0000 \n",
      "Epoch 24/500 - loss: 0.5725 - accuracy: 0.9750 - val_loss: 0.5547 - val_accuracy: 1.0000 \n",
      "Epoch 25/500 - loss: 0.5677 - accuracy: 0.9750 - val_loss: 0.5496 - val_accuracy: 1.0000 \n",
      "Epoch 26/500 - loss: 0.5629 - accuracy: 0.9875 - val_loss: 0.5443 - val_accuracy: 1.0000 \n",
      "Epoch 27/500 - loss: 0.5580 - accuracy: 0.9875 - val_loss: 0.5389 - val_accuracy: 1.0000 \n",
      "Epoch 28/500 - loss: 0.5529 - accuracy: 0.9875 - val_loss: 0.5335 - val_accuracy: 1.0000 \n",
      "Epoch 29/500 - loss: 0.5478 - accuracy: 0.9875 - val_loss: 0.5279 - val_accuracy: 1.0000 \n",
      "Epoch 30/500 - loss: 0.5426 - accuracy: 0.9875 - val_loss: 0.5222 - val_accuracy: 1.0000 \n",
      "Epoch 31/500 - loss: 0.5373 - accuracy: 0.9875 - val_loss: 0.5165 - val_accuracy: 1.0000 \n",
      "Epoch 32/500 - loss: 0.5319 - accuracy: 0.9875 - val_loss: 0.5106 - val_accuracy: 1.0000 \n",
      "Epoch 33/500 - loss: 0.5264 - accuracy: 0.9875 - val_loss: 0.5047 - val_accuracy: 1.0000 \n",
      "Epoch 34/500 - loss: 0.5208 - accuracy: 0.9875 - val_loss: 0.4986 - val_accuracy: 1.0000 \n",
      "Epoch 35/500 - loss: 0.5151 - accuracy: 0.9875 - val_loss: 0.4925 - val_accuracy: 1.0000 \n",
      "Epoch 36/500 - loss: 0.5093 - accuracy: 0.9875 - val_loss: 0.4863 - val_accuracy: 1.0000 \n",
      "Epoch 37/500 - loss: 0.5034 - accuracy: 0.9875 - val_loss: 0.4800 - val_accuracy: 1.0000 \n",
      "Epoch 38/500 - loss: 0.4975 - accuracy: 0.9875 - val_loss: 0.4737 - val_accuracy: 1.0000 \n",
      "Epoch 39/500 - loss: 0.4914 - accuracy: 0.9875 - val_loss: 0.4673 - val_accuracy: 1.0000 \n",
      "Epoch 40/500 - loss: 0.4853 - accuracy: 0.9875 - val_loss: 0.4608 - val_accuracy: 1.0000 \n",
      "Epoch 41/500 - loss: 0.4791 - accuracy: 0.9875 - val_loss: 0.4543 - val_accuracy: 1.0000 \n",
      "Epoch 42/500 - loss: 0.4728 - accuracy: 0.9875 - val_loss: 0.4476 - val_accuracy: 1.0000 \n",
      "Epoch 43/500 - loss: 0.4664 - accuracy: 0.9875 - val_loss: 0.4408 - val_accuracy: 1.0000 \n",
      "Epoch 44/500 - loss: 0.4600 - accuracy: 0.9875 - val_loss: 0.4339 - val_accuracy: 1.0000 \n",
      "Epoch 45/500 - loss: 0.4534 - accuracy: 0.9875 - val_loss: 0.4269 - val_accuracy: 1.0000 \n",
      "Epoch 46/500 - loss: 0.4467 - accuracy: 0.9875 - val_loss: 0.4199 - val_accuracy: 1.0000 \n",
      "Epoch 47/500 - loss: 0.4400 - accuracy: 0.9875 - val_loss: 0.4129 - val_accuracy: 1.0000 \n",
      "Epoch 48/500 - loss: 0.4332 - accuracy: 0.9875 - val_loss: 0.4057 - val_accuracy: 1.0000 \n",
      "Epoch 49/500 - loss: 0.4263 - accuracy: 0.9875 - val_loss: 0.3986 - val_accuracy: 1.0000 \n",
      "Epoch 50/500 - loss: 0.4194 - accuracy: 0.9875 - val_loss: 0.3914 - val_accuracy: 1.0000 \n",
      "Epoch 51/500 - loss: 0.4125 - accuracy: 0.9875 - val_loss: 0.3842 - val_accuracy: 1.0000 \n",
      "Epoch 52/500 - loss: 0.4054 - accuracy: 0.9875 - val_loss: 0.3769 - val_accuracy: 1.0000 \n",
      "Epoch 53/500 - loss: 0.3983 - accuracy: 1.0000 - val_loss: 0.3696 - val_accuracy: 1.0000 \n",
      "Epoch 54/500 - loss: 0.3912 - accuracy: 1.0000 - val_loss: 0.3623 - val_accuracy: 1.0000 \n",
      "Epoch 55/500 - loss: 0.3840 - accuracy: 1.0000 - val_loss: 0.3550 - val_accuracy: 1.0000 \n",
      "Epoch 56/500 - loss: 0.3768 - accuracy: 1.0000 - val_loss: 0.3477 - val_accuracy: 1.0000 \n",
      "Epoch 57/500 - loss: 0.3696 - accuracy: 1.0000 - val_loss: 0.3404 - val_accuracy: 1.0000 \n",
      "Epoch 58/500 - loss: 0.3624 - accuracy: 1.0000 - val_loss: 0.3331 - val_accuracy: 1.0000 \n",
      "Epoch 59/500 - loss: 0.3551 - accuracy: 1.0000 - val_loss: 0.3258 - val_accuracy: 1.0000 \n",
      "Epoch 60/500 - loss: 0.3478 - accuracy: 1.0000 - val_loss: 0.3185 - val_accuracy: 1.0000 \n",
      "Epoch 61/500 - loss: 0.3406 - accuracy: 1.0000 - val_loss: 0.3113 - val_accuracy: 1.0000 \n",
      "Epoch 62/500 - loss: 0.3332 - accuracy: 1.0000 - val_loss: 0.3040 - val_accuracy: 1.0000 \n",
      "Epoch 63/500 - loss: 0.3260 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 1.0000 \n",
      "Epoch 64/500 - loss: 0.3187 - accuracy: 1.0000 - val_loss: 0.2897 - val_accuracy: 1.0000 \n",
      "Epoch 65/500 - loss: 0.3115 - accuracy: 1.0000 - val_loss: 0.2827 - val_accuracy: 1.0000 \n",
      "Epoch 66/500 - loss: 0.3043 - accuracy: 1.0000 - val_loss: 0.2756 - val_accuracy: 1.0000 \n",
      "Epoch 67/500 - loss: 0.2971 - accuracy: 1.0000 - val_loss: 0.2687 - val_accuracy: 1.0000 \n",
      "Epoch 68/500 - loss: 0.2900 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 1.0000 \n",
      "Epoch 69/500 - loss: 0.2829 - accuracy: 1.0000 - val_loss: 0.2550 - val_accuracy: 1.0000 \n",
      "Epoch 70/500 - loss: 0.2759 - accuracy: 1.0000 - val_loss: 0.2483 - val_accuracy: 1.0000 \n",
      "Epoch 71/500 - loss: 0.2690 - accuracy: 1.0000 - val_loss: 0.2417 - val_accuracy: 1.0000 \n",
      "Epoch 72/500 - loss: 0.2621 - accuracy: 1.0000 - val_loss: 0.2352 - val_accuracy: 1.0000 \n",
      "Epoch 73/500 - loss: 0.2553 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 1.0000 \n",
      "Epoch 74/500 - loss: 0.2486 - accuracy: 1.0000 - val_loss: 0.2224 - val_accuracy: 1.0000 \n",
      "Epoch 75/500 - loss: 0.2419 - accuracy: 1.0000 - val_loss: 0.2161 - val_accuracy: 1.0000 \n",
      "Epoch 76/500 - loss: 0.2354 - accuracy: 1.0000 - val_loss: 0.2099 - val_accuracy: 1.0000 \n",
      "Epoch 77/500 - loss: 0.2289 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 1.0000 \n",
      "Epoch 78/500 - loss: 0.2225 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 1.0000 \n",
      "Epoch 79/500 - loss: 0.2162 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 1.0000 \n",
      "Epoch 80/500 - loss: 0.2101 - accuracy: 1.0000 - val_loss: 0.1864 - val_accuracy: 1.0000 \n",
      "Epoch 81/500 - loss: 0.2040 - accuracy: 1.0000 - val_loss: 0.1807 - val_accuracy: 1.0000 \n",
      "Epoch 82/500 - loss: 0.1980 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 1.0000 \n",
      "Epoch 83/500 - loss: 0.1922 - accuracy: 1.0000 - val_loss: 0.1698 - val_accuracy: 1.0000 \n",
      "Epoch 84/500 - loss: 0.1865 - accuracy: 1.0000 - val_loss: 0.1646 - val_accuracy: 1.0000 \n",
      "Epoch 85/500 - loss: 0.1809 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 1.0000 \n",
      "Epoch 86/500 - loss: 0.1755 - accuracy: 1.0000 - val_loss: 0.1546 - val_accuracy: 1.0000 \n",
      "Epoch 87/500 - loss: 0.1701 - accuracy: 1.0000 - val_loss: 0.1497 - val_accuracy: 1.0000 \n",
      "Epoch 88/500 - loss: 0.1649 - accuracy: 1.0000 - val_loss: 0.1450 - val_accuracy: 1.0000 \n",
      "Epoch 89/500 - loss: 0.1598 - accuracy: 1.0000 - val_loss: 0.1404 - val_accuracy: 1.0000 \n",
      "Epoch 90/500 - loss: 0.1549 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 1.0000 \n",
      "Epoch 91/500 - loss: 0.1500 - accuracy: 1.0000 - val_loss: 0.1317 - val_accuracy: 1.0000 \n",
      "Epoch 92/500 - loss: 0.1453 - accuracy: 1.0000 - val_loss: 0.1274 - val_accuracy: 1.0000 \n",
      "Epoch 93/500 - loss: 0.1407 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 1.0000 \n",
      "Epoch 94/500 - loss: 0.1363 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 1.0000 \n",
      "Epoch 95/500 - loss: 0.1319 - accuracy: 1.0000 - val_loss: 0.1155 - val_accuracy: 1.0000 \n",
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      "Epoch 480/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 481/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 482/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 483/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 484/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000 \n",
      "Epoch 485/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 486/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 487/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 488/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 489/500 - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 490/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 491/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 492/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 493/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 494/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 495/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 496/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 497/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 498/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 499/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n",
      "Epoch 500/500 - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 \n"
     ]
    }
   ],
   "source": [
    "def cross_val_perf(cv=5):\n",
    "    logs = []\n",
    "    X, t = getXORdata(sigma=0.1)\n",
    "    best_acc = 0.\n",
    "    P = int(len(t)/cv)\n",
    "    for r in range(cv):\n",
    "        model = build_nonlinear_model()\n",
    "        idxes = range(r*P,(r+1)*P)\n",
    "        mask = np.ones(t.shape, dtype=bool)\n",
    "        mask[idxes] = False\n",
    "        Xvalid = X[idxes]\n",
    "        tvalid = t[idxes]\n",
    "        Xtrain = np.asarray([x for i,x in enumerate(X) if i not in idxes])\n",
    "        ttrain = np.asarray([x for i,x in enumerate(t) if i not in idxes])\n",
    "        if r == 0:\n",
    "            logs = train(model, Xtrain, ttrain, Xvalid, tvalid)\n",
    "        else:\n",
    "            log = train(model, Xtrain, ttrain, Xvalid, tvalid)\n",
    "            if log['val_acc'][-1] > best_acc:\n",
    "                best_acc = log['val_acc'][-1]\n",
    "                bestmodel = model\n",
    "            for key, val in logs.items():\n",
    "                logs[key] = np.vstack([val, log[key]])         \n",
    "    return logs, bestmodel\n",
    "\n",
    "logs, model = cross_val_perf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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td17Hoqy1tRP0/89am9daO9mX+9SK/QSABdMPA3B9tZzULLWN0R9fAxmcoyQiIiIikpVc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\n6GjDq6WnwZgx8adM9e3LgEuv8/sf6ZhR8yO7K/bKFZfmf+cd2LABZs1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lIfGsskSfcBfyoifyIiS4HvYEfOfB9ARP5RRArnqP8OsEBE/klElojIp4APBsdRSimllFJKXQKj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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12482dd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gPzFt9ftCoX5f/NRWnJhK+F2Nbx+wx16SJEmLTaaD/fj41PdDQzCQ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\nJUmSpGzIdrA/4gigpmL/2U+nKv5llwG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SJDVQdmSug72r4kiSJEmd7Vawj4iLI+LJiHg9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\nYLCXJEmSFoH/BzSlto6mvJYVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12eb02710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gZwvQ9fDDNd8nq6jMosii+prVYhU1TNms2SfDMzoKnDlTeA7G2BjXuyBqQLW1ugvPncO7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12eb150d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerf(logs)\n",
    "\n",
    "y = model.predict_proba(X).round()\n",
    "\n",
    "display(X,t,y, model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "H1 (Dense)                   (None, 64)                192       \n",
      "_________________________________________________________________\n",
      "H2 (Dense)                   (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "Y (Dense)                    (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,417\n",
      "Trainable params: 4,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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So9JgCEuZZgZjjF/qDiBcLLsxLmtcgslSo2EIi2Uzxi91Byh14LG+lHIxyZ07d+7k4sWL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\nANCUcAEANCVcAABN/X+2GsUe9XKWzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12af42750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DHvPwkPMfc/cnh/2gO+64Q6urq7PWEwCAQhv0gfvg4EBra2vBf1ai3SLu/i+S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\ninABAACCIlwAAICg/j8ASMHfZvsaQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125313590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qDP5M7he/Rz0nx9L+2SIPUWhm3qnQP3y+wi+/Xyox+ZTCxeazCs3yvydpg0IT+Kei8kUK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\nAAAgUwQXAAAgU/8f8f/p4TNOZzMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125925890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pj5eS0voKbVjkdknHthw7VtLPu/VabNy4Ueecc07TZceOHYU1FBU2POx7LFKb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\nMuX7AXqDrH8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12556e790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ddjazZ0m6VtIHJb1a0gZJf2FmP3DO/WN2zQRQNXnEFUNpy/r167V//3hDzcyk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a0ded90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yf/A/b2Y75JMEm+UPi19Zte+Y/AfLMklyzu1V1dEdm14P4jvOuX+pqn1HPkL739Ft/Ln8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\nbUXnAgAAtNX/D4/jCwhhTbJ8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121c6c990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J+j/RqU+VjwjfJOlL8hP9Xi4/GTR+c32tmf2m/CXhg5J+VH4M+7uKOouRMUmvkZ9X02pS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\nhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a0df310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wGZgrQsVXtH4K85r/hoh83jt2GqJbsvu7MN/Tz4hIJ0yHYSVMbHtj3s/xed6EyBthrmYc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\niChU7FwQERFRqNi5ICIiolD9f82yl77rSd21AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1283b5ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WNxRxj6SPy5/1aMg5d7eZ3SJpdzTvJ16h8xmSLq67etL77a/lk2k3SvpJq11b51Hn3D9U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\nQVsJNapILaUoasIfdd898UT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HBoCmiKICgMSqikAOFGpYJKQYI7X8R1HzUisdVlUEgleo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\nslPozsXk5GSQ8bky10JrD7Xi1gBkpzDDIlNT0g0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e912bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J85Rz7ptmtlfS38ufEbq26ipfln/PfzR6XMl3kpJasvs/5L+nf29my+U7DGfKx7avKPs5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\nAPHD+QCkwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126bd59d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f7m0oisT+QXR1dbTtz6Pr33fOfST6/wckbZa0K/oF9S1Jp0naIB/Tm5zTQv4X6B9Ler/5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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122df28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5P2ZFqYXWnm5qWQ2L0LkAELRY4oFZ3MeF1KTW7ZqcnMz9MStKLbT2EEUFgDZi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\nACwMUVRqQcUDqcVT+8Qnzmj53v3AB1YH+1oDyoI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1216c9490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oYjywH7WxsatnPrZsuUhmkpk0PLxFY2NXB9POMtRCaw9RVABoo4rxwH7VWtmw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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125e27410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Jf89/zMzeVdfeLPyj/Nf0zyrzUCblv36/KWln1c/x5eaXzr5e/mrGSfJDgv8tP9lU8kMk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122ddad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Iz6B/o/zp3uwb+b+ebpafEPg+SS+rmde2xyXrYDjnfp6M039cPur51/Jj12+RP+V9XcXt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\nXrc3fwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12663df90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8aGYPyn9Pb3HOXVtX+4akY865lvFF59xXzeyApD+RvyJ0Td0p/yr/mv+Imb0jeYzq3eqg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\nAIBM/f/xZF2tc/5howAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122a81fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LJX1flSsWjkh6rfy8mkaTOt8kH2v8vJn9tXxn7TL5FM1/lR13nqQvyP9c3lK67jVm9tvy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\nVGpR1kJsD1AUUQ2LhBSDo5ZeC6091OKthdgeoCi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1211603d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5vNEo6iMl3WNmH5Of8/Ez+Zjd65I2/uXsNzD7Lfnx57skfcvMKodMPueSCK78L9DXS7rG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\nBmgQck3ipwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a2fc090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5eTxKp8YekfQa+fNq6p3U+Wb5WOPnzOzv5Dtrl8inaEpPmtso/4d8WH64RpJebWZvkO90\nHkjac4784fvrnHNfKLn9Z8zsO/InE98nfz7B6+QPs28u2e90SXuT39N/yx8FeZn8UNAe51zZN2Yz\n+5akY865NXUep5xzXzOz/ZL+WP6I0LUVu/yb/OvpI2b2nvQxqnNTdv+T/O/0j81stXyH4Rz52PbV\nJe/jK5IP4Bvkj2acJH9C97flTzaV/BDJPfLnZtwr6Zfln8frK87puF3+Q3xTrYY5524xs49L2pmc\n95PO0PlYzZ81M/P1ZmZph/BX5N8TrzGzZyX3/8fJPo+RHxo7Jh/F3lxxzurXnXPfSI6eZr23Xyrp\nTOfcP9V6PGijbkZTuHTvorn4ZrXLa5L90ijqZfIfoN+SP9T/eUm/mnG/z5Mfb/6x/B/YTymJe1Xs\nd4p8h+Ce5P7+Sz5f/wsV7Tu94nbzZq5Ug1FU+W9eV8kfpj8kf8b6AfnDoDXjlsntM6OoSe0E+WTL\nffJHCcaVEfWU74zVnbUy63Em218h/03+AfnO3YclrSqpnyifcvmm/PDT9+U/7F5Wss9T5ee1uCu5\nn4PyH+ynVfyshqKoJfufJz/XxQPyH17Dko6r8rj+qGTbGZL+rqQ9P5Q/ivS7mh8hfKOkL8p/8P00\nef18SiUx0GS/xyX3uV/JPBfycyv8VpW236cGZows2f/tyeO4o0r96fIf0D+WP8/jnfKdpcrX7jWS\n9jf53p13G/mO/O7kZx2VP5K0tWKf58p/8N4t/y39bvmTTNeW7PNb8u/t+zQ3pLdT0sMr7quhKGqy\n7/HyJ4p+N7nPmyWdXeVxzXu9yf/9yfob9fOM11W1yxUN/E5/0MzzwKW1iyW/eBRUciLjXZK2Oeeu\n6nV78s7MviLpLufcQs5tQAeYn1zq3yW90DGhEtAVHT3nwvwKh9eZnyL3mJmdV2f/dBnqyhU850XV\ngNCY2SPkh2Gu6HVbUOa58sOAdCyALun0ORdL5U8C/KD84bpGOPlx5R/NbnDuvvY3DWgv59yP1NlJ\ng7AAzrn3yc/w2VPJCZe1EhkPuuSEVSDvOtq5SL4p3CjNztzYqGk3d9IfOs+pcyejAfA+KX/uQDXf\nkl9aHMi9ENMiJmmf+ZUJ/13SsCuZdhft5Zz7H/nptgF01mWqPfNsR2azBHohtM7FQUmD8mfLP0w+\nPvcFM9voKqJlqSRDf458r/9o1j4AEIiaE221a24YoAmL5NNXNznnZtp1p0F1Lpxzd6p8tsObzWyt\n/GQxr61ys3Mk/U2n2wYAQMQukPS37bqzoDoXVdwi6Zk16t+SpI9+9KNaty5rrijk0datW3X11Vf3\nuhloE57PuPB8xuP222/Xq1/9amluqYe2yEPn4qmaWzgpy1FJWrdunU4/nSOKsTjhhBN4PiPSq+fT\nOaeJiQnt379fa9eu1aZNm5SeW05t4bXDhw/r0KFDQbQlhFpo7Wmmduqpp6YPoa2nFXS0c5EstPN4\nzU1zvMb8yo3fd87dbWY7JZ3snHttsv+l8hM6fVN+HOhi+Rkhn9/JdgKI08TEhK691s/ePTk5KUka\nGBig1mLt8OHDs/v0ui0h1EJrTzO1005LVz1or04vXLZBfirmSfmo45XyK06m61CslF+dMnV8ss/X\n5ee1f7KkAVe+9gAANGT//v1l1w8cOECNWttrobWnmdp3vvMddUJHOxfOuS865x7inDuu4vL6pH6h\nc25Tyf5/4px7gnNuqXNuhXNuwNVf/AkAMq1du7bs+po1a6hRa3sttPY0UzvllFPUCXk45wIFdP75\n5/e6CWijXj2fmzb57y4HDhzQmjVrZq9Ta6127Ngxbd68uW33efbZc8MLo6MqMSBpQKOjHwjmsWfV\nQmtPM7Vly5apE3K/cFmSC5+cnJzkBEAAyKF68zfn/GMqaLfddpvOOOMMSTrDOXdbu+630+dcAACA\ngmFYBEBuxRoPLFptLlCYbXR0NIh2xvha69SwCJ0LALkVazywaDV/bkV1k5OTQbQzxtdaXqOoANAx\nscYDi1yrJaR2xvJay2UUFQA6KdZ4YJFrtYTUzlhea0RRAaBCrPHAItZq2bBhQzDtjO21RhS1CqKo\nAAAsDFFUAACQCwyLAMitWOOB1PJVC609RFEBoAWxxgOp5asWWnuIogJAC2KNB1LLVy209hBFBYAW\nxBoPpJavWmjtIYoKAC2INR5Irfe1LVsuzlyhNfX+95cnLUN9HERRF4goKgCg3YqyUitRVAAAkAsM\niwDIrVjjgdR6X5O21H3txfBaI4oKABVijQdS632tnomJiShea0RRAaBCrPFAamHUaonltUYUFQAq\nxBoPpBZGrZZYXmtEUQGgAlFUap2qlcdQ54vltUYUtQqiqAAALAxRVAAAkAsMiwDILaKo1EKohdYe\noqgA0AKiqNRCqIXWHqKoANACoqjUQqiF1h6iqADQAqKo1EKohdYeoqgA0AKiqNRCqIXWHqKobUAU\nFQCAhSGKCgAAcoFhEQC5FWs8kFq+aqG1hygqALQg1nggtXzVQmsPUVQAaEGs8UBq+aqF1h6iqADQ\ngljjgdTyVQutPURRAaAFscYDqeWrFlp7iKK2AVFUAAAWhigqAADIBYZFADSlJH2XqZsHQ2ONB1LL\nVy209hBFBYAWxBoPpJavWmjtIYoak+np5rYDaFms8UBq+aqF1h6iqLGYmpLWrZNGRsq3j4z47VNT\nvWkXELlY44HU8lULrT1EUWMwPS0NDEgzM9L27X7b0JDvWKTXBwak22+XVqzoXTuBCMUaD6SWr1po\n7SGK2gZBRFFLOxKStGyZdPjw3PWdO32HA4hASCd0AmgNUdSQDQ35DkSKjgUAoMAYFmmXoSFp167y\njsWyZXQsgA6KNR5ILV+10NpDFDUmIyPlHQvJXx8ZoYOBqIQ07BFrPJBavmqhtYcoaiyyzrlIbd8+\nP0UCoC1ijQdSy1cttPYQRY3B9LS0e/fc9Z07pUOHys/B2L2b+S6ADog1HkgtX7XQ2kMUNQYrVkjj\n4z5uum3b3BBI+u/u3b5ODBVou1jjgdTyVQutPURR2yCIKKrkj0xkdSCqbQcAoMeIooauWgeCjgUA\noGAYFgGQW7HGA6nlqxZae4iiAkALYo0HUstXLbT2EEUFgBbEGg+klq9aaO0higoALYg1HkgtX7XQ\n2kMUFQBaEGs8kFq+aqG1hyhqGwQTRQUAIGeIogIAgFxgWARAbsUaD6SWr1po7SGKCgAtiDUeSC1f\ntdDaQxQVAFoQazyQWr5qobWHKCoAtCDWeCC1fNVCaw9RVABoQazxQGr5qoXWHqKobUAUFQCAhSGK\nCgAAcoFhEQC5FWs8kFq+aqG1hygq0KrpaWnFisa3IyqxxgOp5asWWnuIogKtmJqS1q2TRkbKt4+M\n+O1TU71pF9prerrq9ljjgdTyVQutPURRgYWanpYGBqSZGWn79rkOxsiIvz4z4+vVPpiQD3U6kOsr\ndo8lHkgtX7XQ2hNCFPW44eHhjtxxt+zYsWOVpMHBwUGtWrWq181BtyxdKh07Jo2P++vj49K73y3d\ncMPcPldcIZ1zTm/ah9ZNT0sbN/qO4vi4tGiR1N8/14E8ckS/+OUv64Tf+z09bPly9ff3zxsHX716\ntZYsWaLFixfPq1Oj1q5aaO1pprZ27VqNjo5K0ujw8PDBuu/LBhFFRb6lHzSVdu6Uhoa63x60V+Xz\nu2yZdPjw3HWeZ6AlRFGBLEND/gOn1LJlnf3AqXEOANpsaMh3IFJ0LIBcYFgE+TYyUj4UIklHj84d\nQm+3qSl/qP7YsfL7HxmRLrjAD8OsXNn+n1tk/f1+yOvo0blty5ZJ118/G6sbGxvT4cOHtXr16sx4\nYFadGrV21UJrTzO1RYsWdWRYhCgq8qvWIfN0ezu/2VaeRJref2k7Bgak228nBttOIyPlRywkf31k\nRBNnnhllPJBavmqhtYcoKrBQ09PS7t1z13fulA4dKj+Evnt3e4cqVqyQtm2bu759u7R8eXkHZ9s2\nOhbtlNWBTG3froe/971lu8cSD6SWr1po7SGKCizUihU+QdDXVz72no7R9/X5ers/6DkHoHsa6ECe\nNj6uhx85Mns9lnggtXzVQmtPCFFU0iLIt17N0Ll8eXnHYtky/8GH9pqa8kNN27aVd9xGRqTdu+XG\nxjQxM1O2+mPWOHhWnRq1dtVCa08ztWXLlmnDhg1Sm9MidC6AZhF/7S6meAc6higqEII65wDMm0kS\nravWgaBjAQSLzgXQqF6cRAoAOUQUFWhUehJp5TkA6b+7d3fmJFJUFesy2NTyVQutPSy5DuTN+vXZ\n81gMDUkXXUTHostinXuAWr5qobWHeS6APOIcgGDEOvcAtXzVQmsP81wAQAtinXuAWr5qobUnhHku\nGBYBkFubNm2SpLI8f6N1atTaVQutPc3UOnXOBfNcAABQUMxzgbixjDkARIMl19F7LGOOBYp1GWxq\n+aqF1h6WXAdYxhwtiDUeSC1ftdDaQxQVYBlztCDWeCC1fNVCaw9RVEBiGXMsWKzxQGr5qoXWHqKo\nQGpoSNq1a/4y5nQsUEOs8UBq+aqF1h6iqG1AFDUSLGMOAF1HFBXxYhlzAIgKUVT01vS0j5seOeKv\n79wpXX+9tGiRX2FUkvbtky68UFq6tHftRJBijQdSy1cttPYQRQVYxhwtiDUeSC1ftdDaQxQVkOaW\nMa88t2JoyG9fv7437ULwYo0HUstXLbT2EEUFUixjjgWINR7Yjdro6J7Zy5YtF8tMMpMGB7dodHRP\nMO3MQy209hBFBYAWxBoP7Fatlg0bNgTTztBrobWHKGobEEUFgOaVnIuYKecfDWhQp6KoHLkAgA7j\ngxxFQ+cCQG6lsbr9+/dr7dq12rRpU2Y8MKve7dpCH0enalLtdo2Ojvb8d5aXWmjtaabWqWEROhcA\ncitP8cCFPo5O1aTa7ZqcnOz57ywvtdDaQxQVAFqQp3hgLb1uZy2lt/vuvn2z/x8d3aOzzx6YTZmc\nffZAWQIlpLglUdTIoqhm9iwzu87Mvmtmx8zsvAZu81wzmzSzo2Z2p5m9tpNtBJBfeYoH1tLrdmY5\nJ+lIzN5uZESvetvbdMrMTN3bdqqdodZCa08RoqhLJe2T9EFJn6y3s5k9TtL1kt4n6TclnS3pL83s\ne865z3WumQDyKE/xwIU+jk7VxsbGy2pmJk1Py61bJ5uZkW6RnvLkJ2vtpk2z6/8cL+nyz31OH3vr\nWzXa4GPq1ePrZi209hQqimpmxyS9xDl3XY19dkl6gXPuKSXb9ko6wTn3wiq3IYoKIGi5SotkLSR4\n+PDc9WSl4lw9JlRVlFVRny5prGLbTZKe0YO2IHbT081tB4pgaMh3IFIZHQugntDSIisl3Vux7V5J\njzSzhznnftqDNiFGU1PzF0uT/Le2dLE01jQJQgzxQOfCaUtDtTPPVP+SJXrYAw/MPRHLlsldfrkm\nxseTkwK31H3egn18RFGJogJtNz3tOxYzM3OHf4eGyg8HDwz4RdNY26TnihgP7HXtB//v/5V3LCTp\n8GHtv/hiXXvccWrExMREsI+PKGrxoqj3SDqpYttJkn5Y76jF1q1bdd5555Vd9u7d27GGIsdWrPBH\nLFLbt0vLl5ePM2/bRsciEEWMB/ay9vD3vlcvu+WW2es/XbJk9v+P/+AHZ1Mk9YT6+IocRd27d68u\nu+wy3XjjjbOXq666Sp0QWufiy5o/s8uvJdtruvrqq3XdddeVXc4///yONBIRYFw5N4oYD+xZbXpa\np42Pz27/5MaN+pfrrit7r/za1JQefuSItmwZ1NjYeDLkI42NjWvLlsHZS5CPr0O10NpTrXb++efr\nqquu0rnnnjt7ueyyy9QJHR0WMbOlkh6vuXlm15jZeknfd87dbWY7JZ3snEvnsni/pEuS1MhfyXc0\nXiEpMykCtGRoSNq1q7xjsWwZHYvAFDEe2LPaihV66Be/qP99znO0b2BAJ1xyia8lh9Td7t365jvf\nqVPNwn0MPaiF1p7oo6hm9hxJn5dU+UM+7Jx7vZldI+mxzrlNJbd5tqSrJf2ypO9Ieptz7q9r/Ayi\nqFiYyshdiiMXKLrp6exhwWrbkVu5XBXVOfdF1Rh6cc5dmLHtS5LO6GS7gJpZ/tKTPIEiqtaBoGOB\nBh03PDwqyJZtAAAgAElEQVTc6za0ZMeOHaskDQ5u3qxVDU61i4KbnpYuuEA6csRf37lTuv56adEi\nH0GVpH37pAsvlJYu7V07IWkuOjc2NqbDhw9r9erV82J1WbVWbkuNWlFea4sWLdLo6KgkjQ4PDx+s\n/W5sXDxR1OXLe90C5MWKFb4TUTnPRfpvOs8F39KCUMR4ILV81UJrD1FUoFfWr/fzWFQOfQwN+e1M\noBWM2OOB1PJfC6090a+KCgSNceVciD0eSC3/tdDaU4RVUQGgJUWMB1LLVy209kQfRe0GoqgAACxM\nUVZFXbhDh3rdAjSDFUkBIFrxRFEvvVSrVq3qdXPQiKkpaeNG6dgxqb9/bvvIiI+InnOOtHJl79qH\noBQxHkgtX7XQ2kMUFcXDiqRoUhHjgdTyVQutPURRUTysSIomFTEeSC1ftdDaQxQV4enGuRCsSIom\nFDEeSC1ftdDaE0IUNZ5zLgYHOeeiVd08F6K/X3r3u6WjR+e2LVvmp+EGSqxevVpLlizR4sWL1d/f\nr02bNs2OH9eqtXJbatSK8lpbu3ZtR865IIoKb3paWrfOnwshzR1BKD0Xoq+vfedCsCIpAPQcUVR0\nVjfPhchakbT0546MtP4zAAA9w7AI5vT3l68MWjpk0a4jCqxIiiYVMR5ILV+10NpDFBXhGRqSdu0q\nP8ly2bLmOhbT09lHONLtrEiKJhQxHkgtX7XQ2kMUFeEZGSnvWEj+eqNDFVNT/tyNyv1HRvz2qSlW\nJEVTihgPpJavWmjtIYqKsLR6LkTlBFnp/un9zsz4erUjGxJHLDBPEeOB1PJVC609RFHbgHMu2qQd\n50IsXepjrOn+4+M+bnrDDXP7XHGFj7QCDSpiPJBavmqhtYcoahsQRW2jqan550JI/shDei5EI0MW\nxEzzrd45MwCiQRQVndeucyGGhsqHVKTmTwpFbzRyzgwA1EFaBOXacS5ErZNC6WCEK9BF5dLo3P79\n+7V27dqyQ7y1aq3clhq1orzWllV+EWwTOhdor6yTQtOORukHFsKTTqSWPk/bt8+PJfdgUbkixgOp\n5asWWnuIoiIu09P+3IzUzp3SoUPli5S9613tXQQN7RXgonJFjAdSy1cttPYQRUVc0gmyTjhBWrJk\nbnv6gbVkieSc9L3v9a6NqC+wc2aKGA+klq9aaO0JIYrKsAja6+STpeOOk37wg/nDIA884C89GLdH\nEwI7Z2bTpk2S/LevNWvWzF6vV2vlttSoFeW11qlzLoiiov1qnXchEUkNGc8dUChEUZEfAY7bowGN\nnDOzezfnzACoiyMX6Jzly+cvgHboUO/a0wElSbRMuXt7tWsitTYqYjyQWr5qobWn2Sjqhg0bpDYf\nueCcC3RGYOP2aFA6kVrl+TBDQ9JFF/XkPJkixgOp5asWWnuIoiJOrS6Aht4KbFG5IsYDqeWrFlp7\niKIiPozbo82KGA+klq9aaO0hior4pHNdVI7bp/+m4/bEUNGgIsYDqeWrFlp7iKK2ASd0BioPK2u2\noY3RndAJoFCIoiJfAhu3n4fVPwGgYxgWQfEEuvpnYTR5xKiI8UBq+aqF1h5WRQV6oY2rfzLs0aQF\nzKNRxHggtXzVQmsPUVSg3aqlUCq3M4to91UeMUqHpNIjRjMzvl7xXBUxHkgtX7XQ2kMUFWinZs+j\nCGz1z+ilR4xS27f7WVxL50TJOGJUxHggtXzVQmtPCFHU44aHhztyx92yY8eOVZIGBwcHtWrVql43\nB70yPS1t3Oi//Y6PS4sWSf39c9+KjxyRPv5x6cILpaVL/W1GRqQbbii/n6NH526L9uvv97/f8XF/\n/ejRuVqVI0arV6/WkiVLtHjxYvX395eNH9eqtXJbatSK8lpbu3atRkdHJWl0eHj44Lw34AIRRUU8\nmlnRk9U/e6sA684AeUAUFT1nVvvSc42eR8Esor1Va90ZAFHgyAUalpsJoxr5Vhzg6p+FsIAjRkWM\nB1LLVy209rAqKtBuja7GGuDqn9HLOmJUOb/I7t3zfv9FjAdSy1cttPYQRQXaqdnVWEOfRTQ26boz\nfX3lRyjS4ay+vsx1Z4oYD6SWr1po7SGKCrQL51HkQ3rEqPJk2aEhvz1jKKqI8UBq+aqF1p4QoqgM\niyAOrMaaH00eMSriSpXU8lULrT3N1FgVtQpO6OyeXJzQmYfVWIuI56WqXLyvEC2iqEAjOI8iPKxA\nCxQOwyJoGN+g0LQ2rEBbhHhgLSG1k1r+X2usigog/9qwAm0R4oG1hNROavl/rRFFBRCHFlegLUI8\nsJZ69zk6umf2cvbZA7Mz5p599oBGR/d07TEUuRZae4iiAiiGFlagLUI8sJZm7rOWUB97DLXQ2kMU\nFUAxNDpzaoYixANbffy1bNiwoeePL/ZaaO0hitoGRFGBwLECbU2tRlGJsqIVRFEB5A8zp9blXO0L\neif4laADxrAIgM5pw8ypRYwHNlOTan/KjY6OBtHOPNak+mmevL/WiKICyKcWV6AtYjywmVq9D8DJ\nyckg2pnPWuOdi963lSgqgFZVG0YIdXihhZlTixgPXGitlpDamZdaM3rdVqKoAFpTsOm0ixgPbKa2\nZcvg7GVsbHz2XI2xsXFt2TIYTDvzWGtGr9tKFBXAwrVhOu28KWI8kFoYtdFRNazXbSWK2mZEUVE4\nRDszEclEuxXhNUUUFYDX4nTaANBpDIsAeTQ0NH8BsAan086bRmN10paa9zM+Ph5UBJBa+LVjx2rf\nrjQG3Ou2EkUF0LoWptPOm0ZjdfWk+4USAaQWTy209hBFRRjyFmssuqxzLlLbt89PkeRcO6ODIUUA\nqcVTC609RFHRewWLNeZeAafTbiRWV7q0eC0hRQCpxVNb0G2T92hl7UknntjVx9GpKOpxw8PDHbnj\nbtmxY8cqSYODg4NatWpVr5uTL9PT0saNPtY4Pi4tWiT19899Mz5yRPr4x6ULL5SWLu11ayH55+Gc\nc/zzcsUVc0Mg/f3++du3zz+XFX/48mz16tVasmSJFi9erP7+/rLx47T2kY/Uf7y7di3JvG2t+6VG\nrZFa07ft65M97WnSsWNa/X//72ztgrvv1lN375adc460cmVXHsfatWs16jO3o8PDwwfrvpEaRBS1\n6Ig15tP0dPY8FtW2R66RRaRy/qcOsZie9keFZ2b89fRvbOnf4r6+rs1VQxQVnUGsMZ9amE67iOhY\nIBgrVvhF/FLbt0vLl5d/ydu2LffvZdIiKFSsEfnTSKyu3gJTIawMWu/oytjYeJBRRWodWIH38st9\niDXtUJT87XXvfKcs+dtLFBX5FmOskWGDaDQWqwt/ZdB6Qo0qUutQFDXjS91Pjj9eN2/cOPtqJoqK\n/Iox1kgCJiqxRFHz0k5qzdcWdNuML3VL//d/9Yj3vrerj4MoKtovxlhj5cJeaQcj7UTNzPh6nh5T\nwbVzFctexxXz0E5qzdeave3zvvKVsi91Pzn++Nn/b/zUp2b/buU5isqwSJGtWOFjiwMD/gSidAgk\n/Xf3bl/P0zBCerJU+sbdvn3++SQRnCwVlTpDWI2s8Lhhwwdma1nj4AcObOj5apT1bN68OchVM6nV\nrzVz2yedeKLWDg7O1tw736mbN27UI977Xt+xkPzf3osu6srj6NQ5F3LO5foi6XRJbnJy0mGB7ruv\nue15sHOncz4kUH7ZubPXLUOpffuc6+ub/7zs3Om379vXm3Z1QNbLsfSCAgnodT85OekkOUmnuzZ+\nNjPPBeK1fPn8BMyhQ71rD8oFlvfvtCIs340mBHLSeafmuWBYBHGKMQETmwaHsNyjHqWJ8eZjmvXq\n3a7VM76Ax0gtjNqCbpt0IDJrXXz9EkXFwgXSQ+6aWrOOptvpYIQhfR4y8v7pkYyJ8fEoVqocG5t7\nHJI/xyKtjS/wMVILoxZae4iiovOKFsuMMQETu6Gh8gi0VDaJWxFXqqSWr1po7SGKis4qYiwzTcD0\n9ZVPX55Oc97Xl78ETOxqDWGpyytVUqO2gFpo7QkhisqqqDFbulQ6dsx/mEr+33e/W7rhhrl9rrjC\nr7IZk5Ur/UqulY+rv99vj2jF0NzLGsI6etT/P1mpt3TVyI6uVEmNWrdWRQ2oxqqoVZAWaUDlH/AU\nC5OhlwqWFgFCxKqoWLg6Y9pATzCEBUSLtEgREMusqmtzDxQtsdOo9euzj0wMDUkXXVT3d5OnKGoM\nNaBRdC5iRyyz96am5k+xLvnnJp1iff363rWv16p1IBrodMUaDwy1BjSKYZGYEcvsvSImdroo1nhg\nqDUEqNrfjh7/TaFzETPGtHsvnYUytX27n5a89GgSC6ktWKzxwFBrCEzA8xgxLBK7Fse00QYNzEKJ\nhWnXSpXU2rOyK7qo8qioND9tNTDQs7QVUVQUWlcXk2IhNQDtVOucOqmhLy9EUYE8qzMLJQA0LR3i\nTgV0VJTOBQrDbP6lK7K+XaRKT/Jsk6zH2fXHDKA7Ap3HiM4FkHBu/qVlJHYAdFKgR0XpXACdRGIn\ndzjyg9zo8lHRZtC5ADotTexUHqYcGvLbizyBVqwCnXsAEQn8qCidC6AbWpiFEjkT8NwDiEjgR0WZ\n5wIA2iXwuQcQmYDnMeLIBQC0CzOyotsCPSpK5wIA2inguQeAbqFzgcLIipq2NXYaiKI8zqAFOvcA\n0C10LgCg3QKdewDoFjoXAFCi5SM/Ac89AHQLnQsAaJfA5x7IGyY0yy86FwDQLoHPPQB0C/NcAEA7\nBTz3ANAtXTlyYWaXmNldZnbEzG42szNr7PscMztWcXnQzB7djbYCQMsCnXsA6JaOdy7M7JWSrpT0\nVkmnSZqSdJOZParGzZykJ0hamVxWOefu63RbAQBA67px5GKrpD3OuY845+6Q9AZJD0h6fZ3bTTvn\n7ksvHW8lAABoi452LszsoZLOkDSebnPOOUljkp5R66aS9pnZ98zss2Z2VifbCQBAFAJZkbfTRy4e\nJek4SfdWbL9Xfrgjy0FJg5JeLullku6W9AUze2qnGgkAQO4FtCJvcGkR59ydku4s2XSzma2VH155\nbW9aBQDoNqarb0JgK/J2unNxv6QHJZ1Usf0kSfc0cT+3SHpmrR22bt2qE044oWzb+eefr/PPP7+J\nHwMAQA6lK/KmHYnt26Vdu8qmod979tnae9FFZTf7wQ9+0JHmdLRz4Zz7mZlNShqQdJ0kmZkl19/T\nxF09VX64pKqrr75ap59++kKbipxzzmliYkL79+/X2rVrtWnTJlkyhd9CawCQK+mkbWkHo2JF3vOH\nhlT5dfu2227TGWec0famdGNY5CpJH0o6GbfID28skfQhSTKznZJOds69Nrl+qaS7JH1T0iJJF0t6\nnqTnd6GtyKmJiQlde+21kqTJyUlJ0sDAQEs1AMidoaF5Ryxqrsh76FBHmtHxKKpz7lpJ2yS9TdLX\nJD1F0jnOufTU1ZWSfqnkJsfLz4vxdUlfkPRkSQPOuS90uq3Ir/3795ddP3DgQMs1AMidZlfkXb68\nI83oygydzrn3Oece55xb7Jx7hnPuqyW1C51zm0qu/4lz7gnOuaXOuRXOuQHn3Je60U7k19q1a8uu\nr1mzpuUaAORKQCvyBpcWARZi0ybfPz1w4IDWrFkze72VGgDkRtaKvJVpkd27u7a+jbmcZ33M7HRJ\nk5OTk5zQ2Yrp6ewXXLXtAICwTE35uOm2beXnWIyM+I7F+LhfWK9EyQmdZzjnbmtXU1hyHUFNvAIA\nWKB0Rd7KkzeHhvz2io5FJ9G5KLrKiVfSDkZ6KG1mxte7PHVsoQQyXS+ACDS7Im9e0yIIXDrxSmr7\ndn/2cOlJQdu2dW1oxDmn8fFxjY6Oanx8XKXDdiHV2oajRgB6qUNpEU7oRN2JV6rmozugE/NVBDsH\nRmDT9QJAu3DkAt7QUHlsSao98UqHdGK+imDnwAjsqBEAtAudC3jNTrzSIZ2YryLoOTCGhvzRoVQP\njxoBQLswLILsiVfSD7nSw/Vd0In5KoKfA6PZ6XoBIHDMc1F009P+xMGZGX89a+KVvj7G/TupsnOX\n4sgFgA5jngt0xooVfmKVvr7yD7P0cH1fn6/TseiMgKbrBYB2YVgkRzqxrLhzThP336/vbt+uX3zq\nU7XJubna5Zfrn5/wBN3xla9o7f33t20Z8449jkBqDQtsul4AaBc6FznS6bil7rxzfu2zn23rz+vG\n4+h1rWHpUaPK6XrTf9PpeulYhI2p84F5GBbJkZCimK1EOENqT89jqgFN14sFYBI0IBOdixwJKYrZ\nSoQzpPYEEVNtdrpehIGp84GqGBbJkZCimK1EOENqT/AxVYQrnQQtPT9m+/b5kWImQUNBEUUFUBvn\nFNRGlBg5RhQVQPdxTkF9gUydD4SEYZEWhBR/zEsttPYEG1MNAQurNabW1Pl0MFBQdC5aEFL8MS+1\n0NoTbEw1BJxTUF9AU+cDIWFYpAUhxR/zUgutPUHHVEPAwmrVZU2CduhQ+e9r927SIigkOhctCCn+\nmJdaaO0JPqYaAs4pyMbU+UBVDIu0IKT4Y15qobWHmGoDOKegunQStMoOxNAQ07aj0IiiAqiu1jkF\nEkMjQCqnkW2iqAC6i3MKgMYQ2Z4nqmGRkCKH1Iii5j7CysJqQH1EtjNF1bkIKXJIjShqu39vPcE5\nBUBtRLYzRTUsElLkkFp2LbT25KXWUyysBtRGZHueqDoXIUUOqWXXQmtPXmoAAkdku0xUwyIhRQ6p\nEUUtfIQVKBIi22WIogIA0IocR7aJogIAEBoi25miGhYJKTpIjShq7qOoAOojsp0pqs5FSNFBakRR\n2/17AxAoItvzRDUsElJ0MOaamXT22QMaHd0zezn77AGZ+RpR1MiiqADqI7JdJqrORUjRwdhrtRBF\nJYoKoNiiGhYJKToYe60WoqhEUdFmOV0UC8VFFBVNq3eOYc5fUkBYpqbmnywo+fhjerLg+vW9ax9y\njSgqcis9F6PaBUAVlYtipatupvMqzMz4esFijghfVMMiIUUHY6818zxItRMPzrkgH2NINRQUi2Ih\np6LqXIQUHYy9Vsv829W+zcTERJCPMaQaCiwdCkk7GDmZ+RHFFtWwSEjRwdhrtVTerp5QH2NINRQc\ni2IhZ6LqXIQUHYy55pw0NjauLVsGZy9jY+Nyztcqb1dPiI8xtBoKrtaiWECAohoWCSk6SG2uNjqq\nmkJqa6g1RK5W1PSDH6y+KFa6nSMYCAxRVHQc0VWghlpR03e9y79B0s5Eeo5F6SqcfX3ZU08DDehU\nFDWqIxcAkCuVUVNpfufhhBOkE0+U3vxmFsVCbkTVuQgpOkhtrnbsWO1VUZ0Lp615rCHHGomaVlv8\nqsCLYiF8UXUuQooOUmNV1G7+TpFjrURN6VggUFGlRUKKDlLLroXWnhhqiABRU0Qmqs5FSNFBatm1\n0NoTQw0RIGqKyEQ1LBJSdJAaq6ISU0VDSk/elIiaIgpEUQGgV6anpXXrfFpEImqKrmNVVACIzYoV\nPkra11d+8ubQkL/e10fUFLkU1bBISPFAatVjkyG1J4Yacm79+uwjE0RNkWNRdS5CigdSI4razd8p\ncq5aB4KORXfUmn6d52BBohoWCSkeSC27Flp7YqgBaMHUlD/vpTKZMzLit09N9aZdORdV5yKkeCC1\n7Fpo7YmhBmCBKqdfTzsY6Qm1MzO+Pj3d23bmUFTDIiHFA6nlO4rqT2cYSC5e6equx46F0U4ALWhk\n+vVt2xgaWQCiqEAGVnIFCqRyrpFUvenXI0AUFQCATmD69baLalgkpHggtfxHUfPyWgPQolrTr9PB\nWJCoOhchxQOp5T+KWkte2gmgDqZf74iohkVCigdSy66F1p6FRjzz0k4ANUxPS7t3z13fuVM6dMj/\nm9q9m7TIAkTVuQgpHkgtuxZaexYa8cxLOwHUwPTrHRPVsEgoMUZq+Y+i1pOXdkaNWRXRDky/3hFE\nUQHkz9SUn9xo27by8fCREX8Ye3zcf2gAqIkoKgBIzKoI5EBUwyIhxQOp5T+KGkMtSsyqCAQvqs5F\nSPFAavmPosZQi1Y6FJJ2MEo7FgWYVREIXVTDIiHFA6ll10JrT+y1qDGrIhCsqDoXIcUDqWXXQmtP\n7LWo1ZpVEUBPRTUsElI8kFr+o6gx1KLFrIpA0IiiAsiX6Wlp3TqfCpHmzrEo7XD09WXPXZBHzOeB\nDiKKCgBSsWZVnJryHanKoZ6REb99aqo37QLqiGpYJKQIIDWiqCHUolWEWRUr5/OQ5h+hGRiI5wgN\nohJV5yKkCCA1oqgh1KJW7QM1lg9a5vNAjkU1LBJSBJBadi209sReQ86lQz0p5vNATkTVuQgpAkgt\nuxZae2KvIQLM54EcimpYJKQIIDWiqCHUEIFa83nQwUCgiKICQKhqzechMTSClhFFBYAimZ72y8en\ndu6UDh0qPwdj925Wf0WQohoWCSkCSI0oagg15Fg6n8fAgE+FlM7nIfmORSzzeSA6UXUuQooAUiOK\nGkINOVeE+TwQpaiGRUKKAFLLroXWnthriEDs83kgSlF1LkKKAFLLroXWnthrANALUQ2LhBQBpEYU\nNYQaAPQCUVQAAAqKKCoAAMiFqIZFQooAUiOKGnoNADolqs5FSBFAakRRQ68BQKdENSwSUgSQWnYt\ntPYUuQYAnRJV5yKkCCC17Fpo7SlyrXCqTZPN9Nlh4XmKwnHDw8O9bkNLduzYsUrS4ODgoM466ywt\nWbJEixcvVn9/f9n48urVq6kFUAutPUWuFcrUlLRxo3TsmNTfP7d9ZES64ALpnHOklSt71z54PE9d\nd/DgQY2OjkrS6PDw8MF23S9RVABxm56W1q2TZmb89XQl0dIVR/v6sqfZRvfwPPUEUVQAWIgVK/zC\nX6nt26Xly8uXMt+2jQ+sXuN5ikpUaZGQYn7UiKKGXiuUdCXR9IPq8OG5WvoNGb3H8+SP4GR1oKpt\nD1RUnYuQYn7UiKKGXiucoSFp167yD6xly4rxgZUnRX6epqakgQF/hKb08Y6MSLt3S+PjfqXcHIhq\nWCSkmB+17Fpo7SlyrXBGRso/sCR/fWSkN+1BtqI+T9PTvmMxM+OP3KSPNz3nZGbG13OSmomqcxFS\nzI9adi209hS5ViilJwVK/ptwqvQPeTsQpVy4bj5PoYnsnBOiqNSIoha01rDpaWnp0sa3h2Z62scY\njxzx13fulK6/Xlq0yB9mlqR9+6QLL2z98RClXLhuPk+h6u8vf7xHj87VOnTOSaeiqHLO5foi6XRJ\nbnJy0gFos337nOvrc27nzvLtO3f67fv29aZdzerG47jvPn9fkr+kP2vnzrltfX1+P2SL5fXWqmXL\n5l4zkr/eIZOTk06Sk3S6a+dnczvvrBcXOhdAh8T2YVmtne1sf+nvJv1QKL1e+aGJ+brxPIWs8jXU\n4ddOpzoXUQ2LrFy5UhMTExobG9Phw4e1evXqeZE8ar2thdYeatWfJy1d6g/vp4dox8eld79buuGG\nuX2uuMIf6s+DaofS23mIvQeHtaPTjecpVFnnnKSvofFx/9oqHW5rg04NixBFpUYUlVr1mCrzDjSv\nyFFKLNz0tI+bprJmKN29W7roolyc1BlVWiSkmB+17Fpo7aGWXSszNFR+1r7Eh2UtRY1SojUrVvij\nE3195R33oSF/va/P13PQsZAi61yEFPOjll0LrT3Usmtl+LBsXJGjlGjd+vV+7ZTKjvvQkN+ekwm0\npC4Ni5jZJZK2SVopaUrS7zjnbq2x/3MlXSnpVyR9W9IfO+c+XO/nbNq0SZL/BrZmzZrZ69TCqYXW\nHmrVnydJ2R+WaUcj3c4RDC+yw9rokWqvjby9Ztp5dmjWRdIrJR2V9BpJp0raI+n7kh5VZf/HSfqx\npHdJepKkSyT9TNLzq+xPWgTohNjSIt0QepSy6EkMzNOptEg3hkW2StrjnPuIc+4OSW+Q9ICk11fZ\n/42SDjjn/sA595/OufdK+kRyPwC6JbIx4K4I+bD21JRf0rxyaGZkxG+fmupNuxCljg6LmNlDJZ0h\n6Z3pNuecM7MxSc+ocrOnSxqr2HaTpKvr/TznwllxMq+1s8+uvaiVP1jEqqix12alH5aVHYihIemi\ni2SPrt2xSF8vhRLiYe3KdSuk+UM2AwPZzzWwAJ0+5+JRko6TdG/F9nvlhzyyrKyy/yPN7GHOuZ9W\n+2EhRfnyW2tsxUyiqHHXyoT4YYnmpOtWpB2J7dvnx2VztG4FwhdNWmTr1q267LLLdOONN85e/u7v\n/m62HlLML+Rao4iixl1DhNLhrBRzlhTO3r17dd5555Vdtm7tzBkHne5c3C/pQUknVWw/SdI9VW5z\nT5X9f1jrqMXVV1+tq666Sueee+7s5VWvetVsPaSYX8i1RhFFjbuGSDFnSaGdf/75uu6668ouV19d\n94yDBenosIhz7mdmlh5rv06SzA/qDkh6T5WbfVnSCyq2/VqyvaaQonx5rflZYOsjihp3DZGqNWcJ\nHQy0kbkOn3FlZpslfUg+JXKLfOrjFZJOdc5Nm9lOSSc7516b7P84Sd+Q9D5JfyXfEflTSS90zlWe\n6CkzO13S5OTkpE4//fSOPpYiqLcadyFP0ENVvF5ypNacJRJDIwV122236YwzzpCkM5xzt7Xrfjt+\nzoVz7lr5CbTeJulrkp4i6Rzn3HSyy0pJv1Sy/7ckvUjS2ZL2yXdGLsrqWAAAGpA1wdehQ+XnYOze\n7fcD2qArM3Q6594nfyQiq3ZhxrYvyUdYm/05wUT58lprNC1CFLW4NeRQOmfJwIBPhZTOWSL5jgVz\nlqCNWBWVWlltbGyuNj4+PluTpM2bNyvtfBBFLW6tFMMeOVJnzhI6FminaKKoUlhRPmrZtdDaQ635\nGnKMOUvQJVF1LkKK8lHLroXWHmrN1wCgnqiGRUKK8lEjihprDQDq6XgUtdOIogIAsDC5jaICAIBi\niWpYJKS4HjWiqEWsAYAUWecipLgeNaKokjQ4uEXl0tnv/b5jY+NBtLMjq6kCKKyohkVCiutRy66F\n1qgFWSoAABBgSURBVJ5u1GoJqZ3EVAG0S1Sdi5DietSya6G1pxu1WkJqJzFVAO0S1bBISHE9akRR\nDxw4UHeV2VDaSUwVQDsRRQU6iFVDAYSMKCoAAMiFqIZFQorkUSOK6k98rEyLzH/NhtBOYqoA2imq\nzkVIkTxqRFEbMTExEUQ7iakCaKeohkVCiuRRy66F1p5O17ZsGdTo6AfknD+/Ys+eUW3ZMjh7CaWd\n3agBKI6oOhchRfKoZddCaw+17tUAFEdUwyIhRfKoEUWlFlFMdXpaWrGi8e1AwRFFBYBapqakgQFp\n2zZpaGhu+8iItHu3ND4urV/fu/YBLSCKCgDdNj3tOxYzM9L27b5DIfl/t2/32wcG/H4AZkU1LBJS\n7I4aUVRqEURRV6zwRyy2b/fXt2+Xdu2SDh+e22fbNoZGgApRdS5Cit1RI4pKLZIoajoUknYwSjsW\nO3eWD5UAkBTZsEhIsTtq2bXQ2kOte7VcGxqSli0r37ZsGR0LoIqoOhchxe6oZddCaw+17tVybWSk\n/IiF5K+n52AAKHPc8PBwr9vQkh07dqySNDg4OKizzjpLS5Ys0eLFi9Xf31823rt69WpqAdRCaw+1\n7j73uZSevJlatkw6etT/f3xcWrRI6u/vTduAFh08eFCjfvnm0eHh4YPtul+iqABQzfS0tG6dT4VI\nc+dYlHY4+vqk22/npE7kElFUAOi2FSv80Ym+vvKTN4eG/PW+Pl+nYwGUiSotElLsjhpRVGqRxFTX\nr88+MjE0JF10ER0LIENUnYuQYnfUiKJSiyimWq0DQccCyBTVsEhIsTtq2bXQ2kMtjBqAuETVuQgp\ndkctuxZae6iFUQMQl6iGRUJaAZIaq6JSK8iKqQDmIYoKAEAP1TuvuZMf00RRAQBALkQ1LBJStI4a\nUVRqBYiptsv0dHbypNp2IHBRdS5CitZRI4pKrSAx1VZNTUkDA9Ib3yi9/e1z20dGpN27pY9/XHre\n83rXPmABohoWCSlaRy27Flp7qIVfi9r0tO9YzMxI73iHdO65fns6vfjMjK9//vO9bSfQpKg6FyFF\n66hl10JrD7Xwa1FbscIfsUjddJO0eHH5QmnOSb/xG74jAuREVMMiIUXrqBFFpUZMtSFvf7t0662+\nYyHNrbhaats2zr1ArhBFBYAQLF6c3bEoXTANUYoxihrVkQsAyKWRkeyOxaJFdCwKIOff8TNFdc4F\nAOROevJmlqNH507yBHIkqiMXIWXz69Ue8pDax8H27BkNop3Mc0Etr7VcmJ72cdNKixbNHcm46Sbp\nLW/xaRIgJ6LqXISUza9Xk2pn+CcnJ4NoJ/NcUMtrLRdWrPDzWAwMzB0bT8+xOPfcuZM83/9+6dJL\nOakTuRHVsEhI2fxmarWE1E7muaCWp1puPO950vi41NdXfvLmjTdKf/iHfvv4OB0L5EpUnYuQsvnN\n1GoJqZ3Mc0EtT7Vced7zpNtvn3/y5jve4bevX9+bdgELFNWwSEjZ/EZqtWzYsKHlnzc39Dyg0mGY\n0VH/r3PMc0Et3lruVDsywREL5BDzXPRIN3LNvcxOAwDCx5LrAAAgF6IaFgkpBlevJtU+rDA62noU\ntd7P6MVjD/G5oBZnrZE6gM6IpnPhj+qYSs8vGBsbDzIiNzExoS1brp1t++bNm2dr4+PjuvbaazU5\n2frPqxd37cVj78XPpFbMWiN1AJ0R9bBIqBG5kCJ5RFGpxVprpA6gM6LuXIQakQspkkcUlVqstUbq\nADojmmGRLKFG5EKK5BFFpRZrrZE6gM6IJooqTUoqj6Lm/KEBANBRRFEBAEAuHDc8PNzrNrRkx44d\nqyQNSoOSVpXV3vpWNy+yNjY2psOHD2v16tXUelALrT3U4q21elugCA4ePKhRP23z6PDw8MF23W/U\n51xMTEwEGZErci209lCLt9bqbQEsXDTDIpOT0p49o9qyZXD2EmpErsi10NpDLd5aq7cFsHDRdC6k\nsGJw1LJrobWHWry1Vm8LYOGiOedicHBQZ511lpYsWaLFixerv7+/bKrf1atXUwugFlp7qMVba/W2\nQBF06pyLaKKoeVsVFQCAXiOKCgAAciGqtEhIKzJSY1XUotcGB7fUfL8eOzY/Kp6H1xqA+qLqXIQU\ng6NGFLXotXo6HRXv5P0CqC2qYZGQYnDUsmuhtYdaZ2u15PW1BqC+qDoXIcXgqGXXQmsPtc7Wasnr\naw1AfURRqeUmHkgtX7V/+qczar53P/Sh1R1tSyfvF4gFUdQqiKICYar3WZzzPz1AFIiiAgCAXIgq\nLRJqJI8aUdQi1qTaUVTn8hlFJcIK1BdV5yLUSB41oqhFrG3ZMqjNmzfP1sbHx8tiqhMTmwv1WgOK\nJKphkV7H7qjVr4XWHmrx1kJsD1AUUXUueh27o1a/Flp7qMVbC7E9QFEQRaVGFJValLUQ2wOEhihq\nFURRAQBYGKKoAAAgF6IaFlm5cqUmJiY0Njamw4cPa/XquRkA04gYtd7WQmsPtXhrobWnXluBXujU\nsAhRVGpEUalFWQutPcRUUSRRDYuEFDujll0LrT3U4q2F1h5iqiiSqDoXIcXOqGXXQmsPtXhrobWH\nmCqKJKpzLoiihl8LrT3U4q2F1h5iqggRUdQqiKICALAwRFEBAEAuRDUsQhQ1/Fpo7aEWby209hBT\nRYiIojYgpGgZtfzHA6nluxZae4ipokiiGhYJKVpGLbsWWnuoxVsLrT3EVFEkUXUuQoqWdaI2OLhF\no6N7Zi9btlwsM8nM10JpZyzxQGr5roXWHmKqKJKozrmIPYq6Y0ft38WuXWG0M5Z4ILV810JrDzFV\nhIgoahVFiqLW+1uT86cSANBlRFEBAEAuRDUsEnsUtd6wyLOeNR5EO4sQD6QWfi209hBhRYiIojYg\npPhYLyJpobSzCPFAauHXQmtPaH8vgE6KalgkpPhYryNpIT+GkNpDLd5aaO0J+e8F0G5RdS5Cio/1\nOpIW8mMIqT3U4q2F1p6Q/14A7RbVsMimTZsk+d77mjVrZq/HUjt2zI+vltbKx143B9HOWrXQ2kMt\n3lpo7enF4wd6hSgqAAAFRRQVAADkAlFUasQDqUVZC609IdWAFFHUBoQUA6O2sHjgQx5ikgaSSyXT\nli1hPA5q4ddCa09INaDTohoWCSkGRi271ki9UaE+Rmph1EJrT0g1oNOi6lyEFAOjll1rpN6oUB8j\ntTBqobUnpBrQaVGdcxH7qqgx1OrVY1j5lVoYtdDaE1INSLEqahVEUeNS729fzl+uABAUoqgomL29\nbgDaaO9ens+Y8Hyino6lRcxsuaQ/l/Trko5J+ntJlzrnflLjNtdIem3F5hudcy9s5Gem0av9+/dr\n7dq1GTNYUut1rZG6t1fS+fOe4/Hx8SAeB7Xmanv37tWrXvWqoF5r1Oq9B6vbu3evzj9//vsTSHUy\nivq3kk6SzxQeL+lDkvZIenWd231G0uskpa/0nzb6A0OKelFbWDywnlAeB7Xwa6G1Jy81oB06Mixi\nZqdKOkfSRc65rzrn/k3S70h6lZmtrHPznzrnpp1z9yWXHzT6c0OKelHLrtWr79kzqi1bBvWYx0xp\ny5ZBjY5+QM75cy327BkN5nFQC78WWnvyUgPaoVPnXDxD0iHn3NdKto1JcpKeVue2zzWze83sDjN7\nn5md2OgPDSnqRS27Flp7qMVbC609eakB7dCRtIiZbZf0Gufcuort90q6wjm3p8rtNkt6QNJdktZK\n2inpR5Ke4ao01MzOkvSvH/3oR3Xqqafq1ltv1Xe+8x2dcsopOvPMM8vGGKn1vtboba+66ipddtll\nwT4Oas3Vtm7dqquuuirI1xq1+b+3erZu3aqrr7664f0Rrttvv12vfvWrJemZyShDWzTVuTCznZIu\nr7GLk7RO0su1gM5Fxs9bLWm/pAHn3Oer7PObkv6mkfsDAACZLnDO/W277qzZEzp3S7qmzj4HJN0j\n6dGlG83sOEknJrWGOOfuMrP7JT1eUmbnQtJNki6Q9C1JRxu9bwAAoEWSHif/Wdo2TXUunHMzkmbq\n7WdmX5a0zMxOKznvYkA+AfKVRn+emZ0iqU9S1VnDkja1rbcFAEDBtG04JNWREzqdc3fI94I+YGZn\nmtkzJf2ZpL3OudkjF8lJmy9O/r/UzN5lZk8zs8ea2YCkf5B0p9rcowIAAJ3TyRk6f1PSHfIpkesl\nfUnSYMU+T5B0QvL/ByU9RdI/SvpPSR+QdKukZzvnftbBdgIAgDbK/doiAAAgLKwtAgAA2orOBQAA\naKtcdi7MbLmZ/Y2Z/cDMDpnZX5rZ0jq3ucbMjlVcPt2tNmOOmV1iZneZ2REzu9nMzqyz/3PNbNLM\njprZnWZWubgdeqyZ59TMnpPxXnzQzB5d7TboHjN7lpldZ2bfTZ6b8xq4De/RQDX7fLbr/ZnLzoV8\n9HSdfLz1RZKeLb8oWj2fkV9MbWVyYVm/LjOzV0q6UtJbJZ0maUrSTWb2qCr7P07+hOBxSeslvVvS\nX5rZ87vRXtTX7HOacPIndKfvxVXOufs63VY0ZKmkfZLeJP881cR7NHhNPZ+Jlt+fuTuh0/yiaP8h\n6Yx0Dg0zO0fSDZJOKY26VtzuGkknOOde1rXGYh4zu1nSV5xzlybXTdLdkt7jnHtXxv67JL3AOfeU\nkm175Z/LF3ap2ahhAc/pcyRNSFrunPthVxuLppjZMUkvcc5dV2Mf3qM50eDz2Zb3Zx6PXPRkUTS0\nzsweKukM+W84kqRkzZgx+ec1y9OTeqmbauyPLlrgcyr5CfX2mdn3zOyz5tcIQj7xHo1Py+/PPHYu\nVkoqOzzjnHtQ0veTWjWfkfQaSZsk/YGk50j6tDWzWg9a9ShJx0m6t2L7var+3K2ssv8jzexh7W0e\nFmAhz+lB+TlvXi7pZfJHOb5gZk/tVCPRUbxH49KW92eza4t0TBOLoi2Ic+7akqvfNLNvyC+K9lxV\nX7cEQJs55+6Un3k3dbOZrZW0VRInAgI91K73ZzCdC4W5KBra6375mVhPqth+kqo/d/dU2f+Hzrmf\ntrd5WICFPKdZbpH0zHY1Cl3FezR+Tb8/gxkWcc7NOOfurHP5uaTZRdFKbt6RRdHQXsk07pPyz5ek\n2ZP/BlR94Zwvl+6f+LVkO3psgc9plqeK92Je8R6NX9Pvz5COXDTEOXeHmaWLor1R0vGqsiiapMud\nc/+YzIHxVkl/L9/LfrykXWJRtF64StKHzGxSvje8VdISSR+SZofHTnbOpYff3i/pkuSM9L+S/yP2\nCkmchR6Opp5TM7tU0l2Svim/3PPFkp4niehiAJK/l4+X/8ImSWvMbL2k7zvn7uY9mi/NPp/ten/m\nrnOR+E1Jfy5/hvIxSZ+QdGnFPlmLor1G0jJJ35PvVFzBomjd5Zy7Npn/4G3yh073STrHOTed7LJS\n0i+V7P8tM3uRpKsl/a6k70i6yDlXeXY6eqTZ51T+C8GVkk6W9ICkr0sacM59qXutRg0b5IeKXXK5\nMtn+YUmvF+/RvGnq+VSb3p+5m+cCAACELZhzLgAAQBzoXAAAgLaicwEAANqKzgUAAGgrOhcAAKCt\n6FwAAIC2onMBAADais4FAABoKzoXAACgrehcAACAtqJzAQAA2ur/A/fa5dtrffYrAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122fa8d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lrq4u9PT0RMYDo+qtrjX6OJKqAZXbNTY2lvr3zJWabe2pp5bUsAg7F0TkLJfi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126fb71d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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unTXttL1mW3sYRY0Bo6hERPUrmIsYyfG3BqpRUlFUXrkgIkoY38gpa9i5ICJn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\nIiKiWLFzQURERLH6//ICXaWmJ2MQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12930a650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dbmc9pbe75+DBuf+Pje3Xli0DcymTLVsGyhIoIcUtiaJGFkU1s2eb2fVmdo+Z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\nBQAAyBSdCwAAkCk6FwAAIFP/P7CZ2yrlQbT7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fa045d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OR01RVGPM7SJyLYChYN5PuELncwFsLrl65Putltc7uN6LEcyFgT1rtVxE3h98PWmMub7k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\n3j709PTM1nK5XFFMdXy8J1PvNaIs8WpYJO3YHWv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1254c9a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qWISdCyJylkvxwEbvR1I1oHK7pqamUn/MXKlpaw+jqERETXApHlhJ2u2spPB6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f4d82d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bWU/59R49cGDm/6Oje7FxY99MymTjxr6KBIqluCWjqIFFUUXkNSJyi4g8KiLH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\nD+ZPP3DFBFufAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e47dd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lb5a7vCPYbzaqCftL/UHYw8+3A/jDiPs9E3ao4Zewv2C/giDuVbLfMbAdgkeD+/t/sEcW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\nco2IKCleDYtoivmxxiiq9hoRUVIYRSU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U7fvW8V89RF5gFJWIiIic4FVaRGskjzVGUbNY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1233d8210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8qllrD6OoRDFuY05tCDUe2I3a2Nje2Y/BwUsgAogAQ0ODGBvba6adPtSstcdC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12be312d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YCBfb3lX2c3x51OG6Be5sxvFwE7r/B26yKeCGSB6Gm5vxCIBfhnsdb66a03EPXKdtfb2G\nqepdInIjgJ3RvJ94hc7nYO4qqYnvNxF5N9xz9ytwPxNvFZGXR/f/vrLrvRDRXBi4tNFSEXlX9PWU\nqt4cnT2d+Tkvu+3rAZyhqp+r93goRWnHT3ixccFsfLPW5a3R9eIo6qVwH6DfhjvVfzuAX02437Mx\n+4v/INwH2EkJ1zsRrkPwcHR//w8uX/8LVe07rep2c1auRJNRVLiJdB+HW+Hxh9H3/SbcKdIFVde9\nosHz84qE+x6D+8vohwAmkBCLhOuMNbNqZeIKnXAfpF+L2l6KHs+KsvpxcMtefwtu+On7cB92byi7\nzilw61o8EN3PAbizSadWfa+moqhl1z8Xbq2LJ+A+vIYTntf4cf1xO881XAT6drgO3I/h1hf57TrP\nX63L5VXXfxRNrBhZdv33Rvdzb436S+A+oH8ENyn5/XBzHaofz/UA9rf4szvnNnAd+dHoex2B6zxv\nrbrOWXAdrQfh/kp/EG6S6aqy6/x29Pw+itkhvZ0AnlZ1X01FUaPrHgPXeXwous87AGyo8bjmvN/g\nfv8kvYY/r7pevd9pH23iOX28ldeBl/ldJHriqaDEbQj1AIBtqnp13u3xnYjcCeABVW1nbgNlQNzi\nUv8B4DXKBZWIOiLTORci8nIRuUncErlHReTcBtePt6Gu3sGz0ap8RLkTt9z4izA7x4NsOAtuGJAd\nC6IOyXrOxRK4SYDXwZ2ua4bCjSv/cOaA6qPpN40oXar6Q+SwaBDVp6ofgltaPlfRhMt6iYwn1e1Z\nQ+S9TDsX0V8KtwIzKzc2q6Szk/4oe4rsJqMRkfMZuLkitXwbbit5Iu9ZTIsIgH3idib8DwDDWrbs\nLqVLVb8Dt9w2EWXrUtRfeTaP1SyJMmGtc3EAwCDcbPlj4eJzXxKRdaqauBhLlKffCNfrP5J0HSIi\nI+outJXW2jBELVgIFw++TVWn07pTU50LVb0Plasd3iEiq+AWi7mgxs02AvibrNtGREQUsPMB/G1a\nd2aqc1HDXXA7WtbybQD45Cc/idWrk9aKIh9t3boVu3btyrsZlBK+nmHh6xmOe+65B295y1uA2a0e\nUuFD5+IUzG6clOQIAKxevRqnncYziqFYunQpX8+AzOf1VFXs3bsX+/fvx6pVq7B+/XrE88Pr1eZz\nW9bq1w4dOoSDBw+aaIuFmrX2tFI7+eST44eQ6rSCTDsX0UY7z8PsMscrxe3c+H1VfVBEdgI4QVUv\niK5/CdyCTt+CGwe6CG5FyFdl2U4ismvv3r244Qa3Avfk5CQAoL+/v2FtPrdlrX7t0KFDM9fJuy0W\natba00rt1FNPRRay3rhsLdyOiZNwUcer4JbzjfehWA6gfHOcY6LrfBNuXfsXAuhX1S9l3E4iMmr/\n/v0VX99///1N1eZzW9ZYa6VmrT2t1L773e8iC5l2LlT1y6r6FFVdUHV5e1S/UFXXl13/g6r6fFVd\noqo9qtqvjTd/IqKArVq1quLrlStXNlWbz21ZY62VmrX2tFI78cQTkQUf5lxQAW3evDnvJlCK5vN6\nrl/v/v64//77sXLlypmvG9Xmc1vW6teOHj2KTZs2pXafGzbMDi+MjaFMP4B+jI19xMxjD+291tXV\nhSx4v3FZlAufnJyc5ARAIiIPNVq/2fOPKdO+/vWv4/TTTweA01X162ndb9ZzLoiIiKhgOCxCRKYV\nMR5YtNpsoDDZ2NiYiXaG+F7LaliEnQsiMq2I8cCi1dzcitomJydNtDPE95qvUVQionkpYjywyLV6\nLLUzlPeal1FUIqL5KmI8sMi1eiy1M5T3GqOoRFRIRYwHFrFWz9q1a820M7T3GqOoNTCKSkRE1B5G\nUYmIiMgLHBYhItOKGA9kza+atfYwikpE1EAR44Gs+VWz1h5GUYmIGihiPJA1v2rW2sMoKhFRA0WM\nB7LmV81aexhFJSJqoIjxQNbyrw0MXJS4Q2vswx+uTFpafRyMoraJUVQiIkpbUXZqZRSViIiIvMBh\nESLKHeOBrFmrAQMN37MhvNcYRSWiYDEeyJq1WiN79+4N4r3GKCoRBYvxQNYs1uoJ5b3GKCoRBYvx\nQNYs1uoJ5b3GKCoRBSuLyF1W98taMWqVMdS5QnmvMYpaA6OoRERE7WEUlYiIiLzAYREiyh2jqKz5\nXLPWHkZRiYjAKCprftestYdRVCIiMIrKmt81a+1hFJWICIyisuZ3zVp7GEUlIgKjqKz5XbPWHkZR\nU8AoKhERUXsYRSUiIiIvcFiEiHLHeCBrPtestYdRVCIiMB7Imt81a+1hFJWICIwHsuZ3zVp7GEUl\nIgLjgaz5XbPWHkZRiYjAeCBrftestYdR1BQwikpERNQeRlGJiIjICxwWIaKWlKXvEqV9MrSI8UDW\n/KpZaw+jqEREDRQxHsiaXzVr7WEUNSSlUmvHiagpRYwHsuZXzVp7GEUNxdQUsHo1MDJSeXxkxB2f\nmsqnXUQBKGI8kDW/atbawyhqCEoloL8fmJ4Gtm93x4aGXMci/rq/H7jnHqCnJ792EnmqiPFA1vyq\nWWsPo6gpMBFFLe9IAEBXF3Do0OzXO3e6DgdRADo9oZOIssMoqmVDQ64DEWPHgoiICozDImkZGgKu\nvLKyY9HVxY4F0TwVMR7Iml81a+1hFDUkIyOVHQvAfT0ywg4GBaXTwx5FjAey5lfNWnsYRQ1F0pyL\n2Pbtc1MkRNS0IsYDWfOrZq09jKKGoFQCRkdnv965Ezh4sHIOxugo17sgalMR44Gs+VWz1h5GUUPQ\n0wNMTLi46bZts0Mg8b+jo67OGCpRW4oYD2TNr5q19jCKmgITUVTAnZlI6kDUOk5ERJQzRlGtq9WB\nYMeCiIgKhsMiRGRaEeOBrPlVs9YeRlGJiBooYjyQNb9q1trDKCoRUQNFjAey5lfNWnsYRSUiaqCI\n8UDW/KpZaw+jqEREDRQxHsiaXzVr7WEUNQVmoqhERESeYRSViIiIvMBhESIyrYjxQNb8qllrD6Oo\nREQNFDEeyJpfNWvtYRSViKiBIsYDWfOrZq09jKISETVQxHgga37VrLWHUVQiogaKGA9kza+atfYw\nipoCRlGJiIjawygqEREReYHDIkRkWhHjgaz5VbPWHkZRiearVAJ6epo/Tt4pYjyQNb9q1trDKCrR\nfExNAatXAyMjlcdHRtzxqal82kWpemjfvoqvZ2J1pVKw8UDW/KpZaw+jqETtKpWA/n5gehrYvn22\ngzEy4r6ennb1UinfdtL8TE3hvPe8BxvLOhgrV66c6UCuqbp6KPFA1vyqWWuPhSjqguHh4UzuuFN2\n7NixAsDg4OAgVqxYkXdzqFOWLAGOHgUmJtzXExPANdcAt9wye53LLwc2bsynfTR/pRKwbh0WHDqE\n1Q89hOOf/Ww8/8ILsf6uuyDvfCdw+DB+8atfxdI//EMcu2wZ+vr65oyD9/b2YvHixVi0aNGcOmus\npVWz1p5WaqtWrcLY2BgAjA0PDx9o+HPZJEZRyW/xmYpqO3cCQ0Odbw+lq/r17eoCDh2a/ZqvM9G8\nMIpKlGRoyH3glOvqyvYDp9ZQC4dg0jc05DoQMXYsiLzAYRHy28hI5VAIABw5AixcCPT1pf/9pqaA\ndevckEz5/Y+MAOef74Zhli9P//sWmL7sZfj5VVdhwU9/Onuwqwu4+eaZWN34+DgOHTqE3t7exHhg\nUp011tKqWWtPK7WFCxdmMizCKCr5q94p8/h4mn/ZVk8ije+/vB39/cA99zAGm6L9F12E5/3oR5UH\nDx0CRkaw94wzgowHsuZXzVp7GEUlalepBIyOzn69cydw8GDlKfTR0XSHKnp6gG3bZr/evh1Ytqyy\ng7NtGzsWaRoZwfOuu27myx8fc8xsbft2PO3aayuuHko8kDW/atbawygqUbt6elxCpLu7cuw9HqPv\n7nb1tD/oOQegc6o6kJ9Ztw6Xvu1t+O8tW2aOnToxgacdPjzzdSjxQNb8qllrj4UoKtMi5Le8Vuhc\ntqyyY9HV5c6cULqmpqD9/dj/utfh9he/eGaHR7nySmB0FDo+jr3T0xW7PyaNgyfVWWMtrZq19rRS\n6+rqwtq1a4GU0yLsXBC1ivHXzuIS70SZYRSVyIKkSaSx8pVCKT21OhDsWBCZxc4FUbPymERKROQh\nRlGJmhVPIu3vd6mQ8kmkgOtYZDGJtOCKuA02a37VrLWHW64T+WbNmuR1LIaGgC1b2LHIQBHXHmDN\nr5q19nCdCyIfcQ5ARxVx7QHW/KpZaw/XuSAiaqCIaw+w5lfNWnssrHPBYREiMm39+vUAUJHZb6Y2\nn9uyxlpR3mtZzbngOhdEREQFxXUuKGzcxpyIKBjccp3yx23MqY4iboPNml81a+3hlutE3MacGihi\nPJA1v2rW2sMoKhG3MacGihgPZM2vmrX2MIpKBHAbc6qriPFA1vyqWWuPhSgq51yQDX19wDXXAEeO\nzB7r6gJuvjm/NpEJvb29WLx4MRYtWoS+vr6KpYzr1eZzW9ZYK8p7bdWqVZnMuWAUlWzgNuZERB3H\nKCqFi9uYExEFhcMilK9SycVNDx92X+/c6YZCFi50O4wCwL59wIUXAkuW5NdOMinUeCBrftWstYdR\nVCJuY07zEGo8kDW/atbawygqETC7jXn13IqhIXd8zZp82kXmhRoPZM2vmrX2MIpKFOM25tSGUOOB\nnaiNje2euQwMXAQRQAQYHBzA2NhuM+30oWatPRaiqBwWISJvhbpTZadq9axdu9ZMO63XrLWHu6Km\ngFFUIqLWlc1FTOT5RwM1KasoKs9cEBFljB/kVDTsXBCRt+JY3f79+7Fq1ao5qybWq3e61u7jyKoG\n1G/X2NhY7s+ZLzVr7WmlltWwCDsXROQtn+KB7T6OrGpA/XZNTk7m/pz5UrPWHkZRiYjmwad4YD15\nt7Oe8ts9tG/fzP/HxnZjw4b+mZTJhg39FQkUS3FLRlEDi6KKyMtF5CYReUhEjorIuU3c5iwRmRSR\nIyJyn4hckGUbichfPsUD68m7nUk2Rh2JmduNjOC897wHJ05PN7xtVu20WrPWniJEUZcA2AfgOgCf\naXRlEXkugJsBfAjAbwHYAOCvROR7qvrF7JpJRD7yKR7Y7uPIqjY+PlFRExGgVIKuXg2ZngbuAl70\nwhdi1fr1M/v/HAPgsi9+EZ+64gqMNfmY8np8naxZa0+hoqgichTA61T1pjrXuRLAq1X1RWXH9gBY\nqqqvqXEbRlGJyDSv0iJJGwkeOjT7dbRTsVePiWoqyq6oLwEwXnXsNgAvzaEtFLpSqbXjREUwNOQ6\nELGEjgVRI9bSIssBPFJ17BEAzxCRY1X1Jzm0iUI0NTV3szTA/dUWb5bGPU3M8yUeqGqnLU3VzjgD\nfYsX49gnnph9sru6oJddhr0TE9GkwIGGr43Zx8coKqOoRKkrlVzHYnp69vTv0FDl6eD+frdpGvc2\nMS3UeGDetcff+c7KjgUAHDqE/RddhBsWLEAz9u7da/bxMYpavCjqwwCOrzp2PIAfNDprsXXrVpx7\n7rkVlz179mTWUPJYT487YxHbvh1YtqxynHnbNnYsPBBqPDDP2tOuvRZvuOuuma9/snjxzP+fd911\nMymSRqw+viJHUffs2YNLL70Ut95668zl6quvRhasdS6+irkru/xadLyuXbt24aabbqq4bN68OZNG\nUgA4rhyEUOOBudVKJZw6MTFz/DPr1uFfbrqp4mfl16am8LTDhzEwMIjx8YloyAcYH5/AwMDgzMXk\n48uoZq09tWqbN2/G1VdfjXPOOWfmcumllyILmQ6LiMgSAM/D7DqzK0VkDYDvq+qDIrITwAmqGq9l\n8WEAF0epkY/CdTTeBCAxKUI0L0NDwJVXVnYsurrYsfBIqPHA3Go9PXjql7+Mn77yldjX34+lF1/s\natEpdR0dxbfe/36cLGL3MeRQs9ae4KOoIvJKALcDqP4mH1fVt4vI9QCeo6rry27zCgC7APwygO8C\neI+q/nWd78EoKrWnOnIX45kLKrpSKXlYsNZx8paXu6Kq6pdRZ+hFVS9MOPYVAKdn2S6iuln+8kme\nREVUqwPBjgU1acHw8HDebZiXHTt2rAAwOLhpE1Y0udQuFVypBJx/PnD4sPt6507g5puBhQtdBBUA\n9u0DLrzIIFkJAAAgAElEQVQQWLIkv3ZSQ3Gsbnx8HIcOHUJvb29iPDCpzhpradWstaeV2sKFCzE2\nNgYAY8PDwwca/tA1KZwo6rJlebeAfNHT4zoR1etcxP/G61zwrzTzQo0HsuZXzVp7GEUlysuaNW4d\ni+qhj6Ehd5wLaHkhhHgga/7XrLUn+F1RiUzjuLL3QogHsuZ/zVp7irArKhFRZkKNB7LmV81ae4KP\nonYCo6hERETtKcquqO07eDDvFlAruCMpEVGwwomiXnIJVqxYkXdzqBlTU8C6dcDRo0Bf3+zxkREX\nEd24EVi+PL/2kTdCjQey5lfNWnsYRaXi4Y6klKJQ44Gs+VWz1h5GUal4uCMppSjUeCBrftWstYdR\nVLKnE3MhuCMppSTUeCBrftWstcdCFDWcOReDg5xzMV+dnAvR1wdccw1w5Mjssa4utww3UZN6e3ux\nePFiLFq0CH19fVi/fn3FOHi9OmuspVWz1p5WaqtWrcpkzgWjqOSUSsDq1W4uBDB7BqF8LkR3d3pz\nIbgjKRFR7hhFpWx1ci5E0o6k5d93ZGT+34OIiHLDYRGa1ddXuTNo+ZBFWmcUuCMppSjUeCBrftWs\ntYdRVLJnaAi48srKSZZdXa11LEql5DMc8XHuSEopCTUeyJpfNWvtYRSV7BkZqexYAO7rZocqpqbc\n3I3q64+MuONTU9yRlFITajyQNb9q1trDKCrZMt+5ENULZMXXj+93etrVa53ZAHjGgloSajyQNb9q\n1trDKGoKOOciJWnMhViyxMVY4+tPTLi46S23zF7n8stdpJUoBaHGA1nzq2atPYyipoBR1BRNTc2d\nCwG4Mw/xXIhmhiwYM/VbozkzRBQMRlEpe2nNhRgaqhxSAVqfFEr5aGbODBFRA0yLUKU05kLUmxTK\nDoZdHm4qF8fq9u/fj1WrVs05VV2vzhpradWstaeVWlf1H4IpYeeC0pU0KTTuaJR/YJE98UJq8eu0\nffvcWLKxTeVCjQey5lfNWnsYRaWwlEpubkZs507g4MHKTco+8IF0N0GjdHm2qVyo8UDW/KpZaw+j\nqBSWeIGspUuBxYtnj8cfWIsXA6rA976XXxupMY/mzIQaD2TNr5q19liIonJYhNJ1wgnAggXA44/P\nHQZ54gl3MTZuT1U8mjOzfv16AO4vs5UrV8583UydNdbSqllrTyu1rOZcMIpK6as37wIweXqdInzt\niAqFUVTyh2fj9hRpZs7M6CjnzBBRQzxzQdlZtmzuBmgHD+bXngyUJdESeffjldZCah0SajyQNb9q\n1trTahR17dq1QMpnLjjngrLh0bg9lYkXUqueDzM0BGzZYm6eTKjxQNb8qllrD6OoFKb5boBG+fJo\nU7lQ44Gs+VWz1h5GUSk8HLenDgo1HsiaXzVr7WEUlcITr3VRPW4f/xuP2xv8K5j8E2o8kDW/atba\nwyhqCjih0ygfdtZMoY3BTegkokJhFJX8Yn3cnrt/EhFlhsMiVDwe7v4ZlBTPaoUaD2TNr5q19nBX\nVKI8pLj7J4c9WpTyOhqhxgNZ86tmrT2MohKlrVYKpfo4VxHtvOozRvGQVHzGaHra1VtIEoUaD2TN\nr5q19jCKSpSmVudReLT7ZxDiM0ax7dvdKq7la6I0ecYoFmo8kDW/atbaYyGKumB4eDiTO+6UHTt2\nrAAwODg4iBUrVuTdHMpLqQSsW+f++p2YABYuBPr6Zv8qPnwYuPFG4MILgSVL3G1GRoBbbqm8nyNH\nZm9L6evrc8/vxIT7+siR2VobZ4x6e3uxePFiLFq0CH19fXPGwevVWWMtrZq19rRSW7VqFcbGxgBg\nbHh4+EDDH7omMYpK4WhlR0/u/pmvAuw7Q+QDRlEpdyL1L7lrdh4FVxHNV719Z4goCDxzQU3zZsGo\nZv4q9mz3z2CkfMYo1Hgga37VrLWHu6ISpa3Z3Vg92/0zCElnjKrXFxkdben5DzUeyJpfNWvtYRSV\nKE2t7sZqfRXR0MT7znR3V56hiIezurtb3ncm1Hgga37VrLWHUVSitHAehR/iM0bVQx9DQ+54i0NR\nocYDWfOrZq09FqKoHBahMHA3Vn+keMYo1J0qWfOrZq09rdS4K2oNnNDZOV5M6PRhN9Yi4utSkxc/\nVxQsRlGJmsF5FPZwB1qiwuGwCDWNf0FRyzLegTaUeGC7j5E1GzVr7eGuqEQUthR3oE0SSjyw3cfI\nmo2atfYwikpE4ctwB9pQ4oH1NLrPsbHdM5cNG/pnVszdsKEfY2O7O/YYilyz1h5GUYmoGDLagTaU\neGA9rdxnPVYfewg1a+1hFJWIiqHZlVNbFEo8sN3H2Mx9rF27NvfHF3rNWnsYRU0Bo6hExnEH2rrm\nG0VllJXmg1FUIvIPV05tSLX+hfJjfidowzgsQkTZyXjl1FDjga3UgPqfcmNjYyba6WMNaJzm8f29\nxigqEfkpwx1oQ40HtlJr9AE4OTlpop1+1prvXOTfVkZRiWi+ag0jWB1eyGjl1FDjge3W6rHUTl9q\nrci7rYyiEtH8cDntGaHGA1upDQwMzlzGxydm5mqMj09gYGDQTDt9rLUi77YyikpE7ct4OW3fhBoP\nZM1GbWwMTcu7rYyipoxRVCocRjsTMZJJaSvCe4pRVCJyMlxOm4goDRwWIfLR0NDcDcBSWE7bN+Wx\nOmCg7nUnJiZMRQBZs187erT+7cpjwHm3lVFUIpq/jJbT9k15rK6R+HpWIoCshVOz1h5GUckG32KN\nRZc05yK2ffvcFEnAWo0OWooAshZOzVp7GEWl/DHW6Bcup10hjtWVby1ej6UIIGvh1Nq6bfQzWl07\n6bjjOvo4soqiLhgeHs7kjjtlx44dKwAMDg4OYsWKFXk3xy+lErBunYs1TkwACxcCfX2zfxkfPgzc\neCNw4YXAkiV5t5YA9zps3Ohel8svnx0C6etzr9++fe61rPrFF6re3l4sXrwYn/hE48d75ZWLK8ae\n49suWrQIfX19rLHWdq3l23Z3Q178YuDoUfT+7/89Uzv/wQdxyugoZONGYPnyjjyOVatWYcxlbseG\nh4cPNPxBahKjqEXHWKOfSqXkdSxqHQ9cM5tIef6rjkJRKrmzwtPT7uv4d2z57+Lu7o6tVcMoKmWD\nsUY/ZbScdqjYsSAzenrcJn6x7duBZcsq/8jbts37n2WmRYixRvJWHKtrtMGUhZ1BG51dGR+fMBlV\nZK1xreXbXnaZC7HGHYqy3736/vdDot+9jKKS30KMNXLYoBBmY3X2dwZtxGpUkbWMoqgJf9T9+Jhj\ncMe6dTPvZkZRyV8hxhqZgCkMn6KovrSTtdZrbd024Y+6JT/9KZ5+7bUdfRyMolL6Qow1Vm/sFXcw\n4k7U9LSr+/SYqKZWd7HMO67oQztZa73W6m3PvvPOij/qfnzMMTP/X/fZz8783vI5isphkSLr6XGx\nxf5+N4EoHgKJ/x0ddXWfhhHiyVLxD+727XPnkwQwWSoo8xjCind4XLv2IzO7PyaNg99//9rcd6Ns\nZNOmTSZ3zWStca2V25503HFYNTg4U9P3vx93rFuHp197retYAO5375YtHXkcWc25gKp6fQFwGgCd\nnJxUatOjj7Z23Ac7d6q6kEDlZefOvFtG5fbtU+3unvu67Nzpju/bl0+7MpD0diy/UIEYet9PTk4q\nAAVwmqb42cx1Lihcy5bNTcAcPJhfe6iSsbx/1oqwfTe1wMik86zWueCwCIUphQSMNoie0TylMITV\n6DWqV+90rZGJCUZRfa21ddvofZ1Y6+D7l1FUap+RHnLH1Ft1ND7eRAdjPrFCalL8OiTk/ZtZxM2n\nnSrHxycqdnDdtGnTTG1iYsJMO1njrqhpYFokdEWLZaaYgEkrVkgNDA1VRqCBphdxC3WnStb8qllr\nD6OolK0ixjLjBEx3d+VfvvEy593dTSdg0ooVUgP1hrAaSH2nStZYa6NmrT0WoqjcFTVkS5YAR4+6\nD1PA/XvNNcAtt8xe5/LL3S6bIVm+3O3kWv24+vrc8SY7CY12QaQUJA1hHTni/l++U28Nqe5UyRpr\nndoV1VCNu6LWwLRIE6p/gce4MRnlqWBpESKLuCsqtW8eY9pEmUlxCIuIbGFapAhC3JisBfViWe3u\nVNmyoiV2mrVmTfKZiaEhYMuWeT83luKKrHU22kv5YucidCnFMn2WxU6VLZmamrvEOuBem3iJ9TVr\nWr/fUNTqQKTQ6co75sdatvFPsovDIiELcWOyNmSxU2XTipjYMSTvmB9r2dUoUut3R86/U9i5CBnH\ntAFks1Nl0+JVKGPbt7tlycvPJnEjtczkHfNjLbsawfQ6RhwWCV3GY9o+yGKnypbMcxVKap+lnTNZ\n6+wus8GrPisKzE1b9ffnlrZiFJUKraObSXEjNSJKU705dUBTf7wwikrks3msQklElCge4o4ZOivK\nYREKQjNxtg0bWp9l3s5OlXN0OLHDrb2bw+gkBWFoaO5uwgbWMWLngoLQXJytfudiYGAwlZ0qKyQl\ndqrHRUdHCzP/xRJGJykIRtcx4rAIBaGVOFs9qUfkmNgxq9ZrKAJs2NCPsbHdM5cNG/oh4mqMTpIZ\nSWdFY+XR9xywc0FBaCXOVk8mEbk4sVP9V8TQkDte5AW0ctRuzLGZ98XTDh9Ovk+uZ0JpMb6OEYdF\nKAjNxNncxn+1bdq0KbuIXIarUFJ72o05NnpfPG3/fqy59FJ897zzsKr8PrkiK6UpPitavfpv/G/8\nXsvpdwyjqFQYRZnoWJTHmZV5PX/c6ZU6bZ77FjGKSkRkHVdkpU4zelaUwyLkjfnGAxulRSYmJhg5\npPnjiqxE7FyQP+YbDxwYcPVacdPoH+8jhxz2MMDo2gNEncJhEfKGpR0ZGTmkurgiKxUcOxfkDUs7\nMnK3xnCp1r80ZHjtAaJO4bAIecPSjozcrZEScUXWVDH55C9GUYmI0jQ1NXftAYDrXLSBnYvsZRVF\n5ZkLIqI0xSuyVp+ZGBriGQsqjI50LkTkYgDbACwHMAXgD1T17hrXfSWA26sOK4AVqvpopg2l3HV6\np0rGTSkTRtceIOqUzDsXIvJmAFcBGABwF4CtAG4TkReo6mM1bqYAXgDghzMH2LEohE7vVOlr3JSI\nyLJOpEW2Atitqp9Q1XsB/A6AJwC8vcHtSqr6aHzJvJVkgqVIKeOmRETtybRzISJPBXA6gIn4mLoZ\npOMAXlrvpgD2icj3ROQLInJmlu0kOyxFShk3JSLv1NoFtcO7o2Y9LPJMAAsAPFJ1/BEAJ9W4zQEA\ngwC+BuBYABcB+JKIrFPVfVk1lGywFCll3JSIvGIoqZRpFFVEVgB4CMBLVfXOsuNXAniFqtY7e1F+\nP18C8B1VvSChxigqEREVW5s78voaRX0MwJMAjq86fjyAh1u4n7sAvKzeFbZu3YqlS5dWHNu8eTM2\nb97cwrchIiLyULwjb9yR2L59zv42ezZswJ4tWypu9vjjj2fSnEw7F6r6MxGZhNuO8iYAEJfl6wfw\nZy3c1SlwwyU17dq1i2cuAmApbsooKhF5pcGOvJuHhlD953bZmYtUdWKdi6sBfCzqZMRR1MUAPgYA\nIrITwAnxkIeIXALgAQDfArAQbs7F2QBe1YG2Us4sxU0ZRSUi77S6I+/Bg5k0I/MoqqreALeA1nsA\nfAPAiwBsVNV46upyAL9UdpNj4NbF+CaALwF4IYB+Vf1S1m2l/FmKmzKKSkTeaXVH3mXLMmlGR3ZF\nVdUPqepzVXWRqr5UVb9WVrtQVdeXff1BVX2+qi5R1R5V7VfVr3SinZQ/S3FTRlGJyCuGduTl3iJk\niqW4KaOoROQNYzvycldUckql5DdcreNERGRLG+tcZBVF7ciwCBk3NeXy0dWnzEZG3PGpqXzaRURE\nzYt35K2evDk05I53aAEtgJ0LKpVcT3d6unJMLj6VNj3t6h1eOrZQjCzXS0QBaHVHXl/TImRcvPBK\nbPt2N3u4fFLQtm2pDo2oKiYmJjA2NoaJiQmUD835UksNzxoRUZ4ySotwQic1XHilZj66TZbWq8h1\nnYvqs0bA3AlY/f1zluslIrKOZy7IGRqqjC0B9RdemQdL61Xkus5FDmeNiIg6gZ0LclpdeGUeLK1X\nkfs6F0ND7uxQLOOzRkREncBhEUpeeCX+kCs/XZ8SS+tVmFjnotXleomIjOM6F0XX5ja9lKLqzl2M\nZy6IKGNc54Ky0dPjFlbp7q78MItP13d3uzo7FtkwtFwvEVFaOCxSAA23FX/sMTy0fTt+8ZRTsF51\ntnbZZfjn5z8f9955J1Y99lhqW5Vb2jo91+3YjS3XS0SUFnYuCqDZuCXuu29u7QtfaHi7ViOcliKl\nucZU47NG1cv1xv/Gy/WyY2Ebl84nmoPDIgVgLcJpqT25x1QNLddLbeAiaESJ2LkoAGsRTkvtMRFT\nbXW5XrKBS+cT1cRhkQKwFuG01B7zMVWyK14ELZ4fs3373EgxF0GjgmIUlYjq45yC+hglJo8xikpE\nncc5BY11cOl8Il9wWMQYS5HKrGKaltpjNqZqATdWa069pfPZwaCCYufCGEuRyqximpbaYzamagHn\nFDTW4aXziXzBYRFjLEUqGUU1uptqJ3FjtdqSFkE7eLDy+RodZVqEComdC2MsRSoZRTUQU7WAcwqS\ncel8opo4LGKMpUglo6iMqQLgnIJ64kXQqjsQQ0Nctp0KjVFUIqqt3pwCgEMjRDFPI9uMohJRZ3FO\nAVFzGNmeg8MiGbEUf7RUs9YeSzVzuLEaUWOMbCdi5yIjluKPlmrW2mOpZhLnFBDVx8h2Ig6LZMRS\n/NFSzVp7LNXM4sZqRPUxsj0HOxcZsRR/tFSz1h5LNSLyGCPbFTgskhFL8UdLNWvtsVQjIo8xsl2B\nUVQiIqL58DiyzSgqERGRNYxsJ+KwCGzFEUOvWWuPLzUiMoqR7UTsXMBWHDH0mrX2+FIjIsMY2Z6D\nwyKwFUf0oSYCbNjQj7Gx3TOXDRv6IeJqjKIWKKZKRA4j2xXYuYCtOKIvtXoYRWVMlYiKjcMisBVH\n9KVWD6OojKlSyjzdFIuKi1FUalmjOYaev6WIbJmamjtZEHDxx3iy4Jo1+bWPvMYoKnkrnotR60JE\nNVRvihXvuhmvqzA97eoFizmSfUENi1iKDoZea+V1AOonHlTV5GO0VKOC4qZY5KmgOheWooOh1+qZ\ne7v6t9m7d6/Jx2ipRgUWD4XEHQxPVn6kYgtqWMRSdDD0Wj3Vt2vE6mO0VKOC46ZY5JmgOheWooMh\n11SB8fEJDAwMzlzGxyeg6mrVt2vE4mO0VqOCq7cpFpFBQQ2LWIoOsjZbGxtDXZbaarVGgasXNb3u\nutqbYsXHeQaDjGEUlTLH6CpRHfWiph/4gPsBiTsT8RyL8l04u7uTl54makJWUdSgzlwQEXmlOmoK\nzO08LF0KHHcc8I53cFMs8kZQnQtL0UHWZmtHj9bfFVXVTlt9rJHHmoma1tr8qsCbYpF9QXUuLEUH\nWeOuqJ18Tslj84masmNBRgWVFrEUHWQtuWatPSHUKACMmlJggupcWIoOspZcs9aeEGoUAEZNKTBB\nDYtYig6yxl1RGVOlppRP3gQYNaUgMIpKRJSXUglYvdqlRQBGTanjuCsqEVFoenpclLS7u3Ly5tCQ\n+7q7m1FT8lJQwyKW4oGs1Y5NWmpPCDXy3Jo1yWcmGDUljwXVubAUD2SNUdROPqfkuVodCHYsOqPe\n8ut8DdoS1LCIpXgga8k1a+0JoUZE8zA15ea9VCdzRkbc8ampfNrluaA6F5bigawl16y1J4QaEbWp\nevn1uIMRT6idnnb1UinfdnooqGERS/FA1vyOorrpDP3RxSnf3fXoURvtJKJ5aGb59W3bODTSBkZR\niRJwJ1eiAqleayTWaPn1ADCKSkRElAUuv566oIZFLMUDWfM/ihrCe42ImlBv+XV2MNoSVOfCUjyQ\nNf+jqPVYaidjqkTzwOXXMxHUsIileCBryTVr7Wk34mmpnYypErWpVAJGR2e/3rkTOHjQ/RsbHWVa\npA1BdS4sxQNZS65Za0+7EU9L7WRMlahNXH49M0ENi1iJMbLmfxS1EUvtLGxMlasqUhq4/HomGEUl\nIv9MTbnFjbZtqxwPHxlxp7EnJtyHBhHVxSgqERHAVRWJPBDUsIilCCBr/kdRQ6gFiasqEpkXVOfC\nUgSQNf+jqCHUghUPhcQdjPKORQFWVSSyLqhhEUsRQNaSa9baE3otaFxVkcisoDoXliKArCXXrLUn\n9FrQ6q2qSES5CmpYxFIEkDX/o6gh1ILFVRWJTGMUlYj8UioBq1e7VAgwO8eivMPR3Z28doGPuJ4H\nZYhRVCIioFirKk5NuY5U9VDPyIg7PjWVT7uIGghqWMRSBJA1RlGt17xWhFUVq9fzAOaeoenvD+cM\nDQUlqM6FpQgga4yiWq95r9YHaigftFzPgzwW1LCIpQgga8k1a+0pco08EA/1xLieB3kiqM6FpQgg\na8k1a+0pco08wfU8yENBDYtYigCyxiiq9Rp5ot56HuxgkFGMohIRWVVvPQ+AQyM0b4yiEhEVSank\nto+P7dwJHDxYOQdjdJS7v5JJQQ2LWIr5scYoqvUaGRev59Hf71Ih5et5AK5jEcp6HhScoDoXlmJ+\nrDGKar1GHijCeh4UpKCGRSzF/FhLrllrT5Fr5InQ1/OgIAXVubAU82MtuWatPUWuERFlJahhEUsx\nP9YYRbVeIyLKCqOoREREBcUoKhEREXkhqGERSzE/1hhF9blGRDQfQXUuLMX8WGMU1ecaEdF8BDUs\nYinmx1pyzVp7WEuuERHNR1CdC0sxP9aSa9baw1pyLUi1lsnm8tm28HUKwoLh4eG82zAvO3bsWAFg\ncHBwEGeeeSYWL16MRYsWoa+vr2IMube3lzUDNWvtYa326xSUqSlg3Trg6FGgr2/2+MgIcP75wMaN\nwPLl+bWPHL5OHXfgwAGMjY0BwNjw8PCBtO6XUVQiClupBKxeDUxPu6/jnUTLdxzt7k5eZps6h69T\nLhhFJSJqR0+P2/grtn07sGxZ5Vbm27bxAytvfJ2CEtSwyPLly7F3716Mj4/j0KFD6O3tnRO7Yy3f\nmrX2sFb7dQpKXx+wcKHbRRQAjhyZrcV/IVP++Dq5MzhLljR/fJ6yGhZhFJU1RlFZK0YUdWgIuPJK\n4NCh2WNdXcX4wPJJkV+nqSmgv9+doSl/vCMjwOio63StWZNf+1oQ1LCIpSgfa8k1a+1hLbkWpJGR\nyg8swH09MpJPeyhZUV+nUsl1LKan3VBQ/HjjOSfT067uSWomqM6FpSgfa8k1a+1hLbkWnPJJgYD7\nSzhW/os8DYxStq+Tr5M1gc05CWrOBaOo9mvW2sNaE1HUDo8Bp65UcjHGw4fd1zt3AjffXDm2v28f\ncOGF8388jFK2r5Ovk1U5zDnJas4FVNXrC4DTAOjk5KQSUcr27VPt7lbdubPy+M6d7vi+ffm0q1Wd\neByPPuruC3CX+Hvt3Dl7rLvbXY+ShfJ+m6+urtn3DOC+zsjk5KQCUACnaZqfzWneWR4Xdi6IMhLa\nh2WtdqbZ/vLnJv5QKP+6+kOT5urE62RZ9Xso4/dOVp2LoIZFGEW1X7PWHtbq1O64A6VHH8WJ997r\nXriJCeCaa4Bbbpn9Abz8cneq3we1TqWneYqdUcr568TrZFXSnJP4PTQx4d5b5cNtKWAUtQmWonys\nMYoaRK2nBw+tW4c33HUXAFTO4ueHZbIiRympfaWSi5vGklYoHR0FtmzxYlJnUGkRS1E+1pJr1trD\nWuPabaecgp8sXlxxXX5Y1lHUKCXNT0+POzvR3V3ZcR8acl93d7u6Bx0LILDOhaUoH2vJNWvtYa1x\nbeO+fTj2iScqrssPyxqKHKWk+Vuzxu2dUt1xHxpyxz1ZQAvo0LCIiFwMYBuA5QCmAPyBqt5d5/pn\nAbgKwK8A+B8A71PVjzf6PuvXrwfg/gJbuXLlzNes2alZaw9r9WtPv/ZarIuHRAD3YRn/VR5/iPIM\nhhPYaW3KSa33hm/vmTRnhyZdALwZwBEAbwVwMoDdAL4P4Jk1rv9cAD8C8AEAJwG4GMDPALyqxvWZ\nFiHKQmhpkU6wHqUsehKD5sgqLdKJYZGtAHar6idU9V4AvwPgCQBvr3H93wVwv6r+kar+l6peC+DT\n0f0QUacENgbcEZZPa09NuS3Nq4dmRkbc8ampfNpFQcp0WEREngrgdADvj4+pqorIOICX1rjZSwCM\nVx27DcCuRt9Po2jd/v37sWrVqooVB1lrrrZhQ/2Nq9zJova/n4XHyFprtZN378bL3/AGVKzdOTQE\nbNkCeVb9jkX8fikUi6e1q/etAOYO2fT3uw4QO4uUgqznXDwTwAIAj1QdfwRuyCPJ8hrXf4aIHKuq\nP6n1zUxG+byrNbcrJqOoBaoB+FlX19wdU/kh5I9434q4I7F9+9y4rEf7VpB9waRFtm7diksvvRS3\n3nrrzOXv/u7vZupWY37Was1iFLW4NfJUPJwV45olhbNnzx6ce+65FZetW7OZcZB15+IxAE8COL7q\n+PEAHq5xm4drXP8H9c5a7Nq1C1dffTXOOeecmct55503U7ca87NWaxajqMWtkceGhirjsQDXLCmQ\nzZs346abbqq47NrVcMZBWzIdFlHVn4lIfK79JgAQN6jbD+DPatzsqwBeXXXs16LjdVmM8vlWc6vA\nNsYoanFr5LF6C3yxg0EpEs14xpWIbALwMbiUyF1wqY83AThZVUsishPACap6QXT95wL4dwAfAvBR\nuI7InwJ4japWT/SEiJwGYHJychKnnXZapo+lCJJ23C5XyAl6VBPfLx5JWuCLQyOF9/Wvfx2nn346\nAJyuql9P634zn3OhqjfALaD1HgDfAPAiABtVtRRdZTmAXyq7/rcB/DqADQD2wXVGtiR1LIiIqAlJ\nC3wdPFg5B2N01F2PKAUdWaFTVT8EdyYiqXZhwrGvwEVYW/0+JqN8PtWaTYswisqam/Q50NT7hXIW\nryLles0AABF0SURBVFnS3+9SIeVrlgCuY8E1SyhF3BWVtYra+PhsbWJiYqYGAJs2bULc+WAUlTUA\nGBgYxKZNm+bGVMmeeIGv6g5EtGYJOxaUpmCiqICtuB5ryTVr7WEt3RoZZ3GBLwpSUJ0LS3E91pJr\n1trDWro1IiIgsGERS3E91hhFLWKNiAjoQBQ1a4yiEhERtcfbKCoREREVS1DDIlbjeqwxisoaERVJ\nUJ0Lq3E91oobRR0crF4HIl793l13fHzCRDs79doTUTEENSxiKZLHWnLNWns6UavHUjsZUyWitATV\nubAUyWMtuWatPZ2o1WOpnYypElFaghoWsRTJY41R1Pvvv7/hLrNW2smYKhGliVFUogxx11AisoxR\nVCIiIvJCUMMilmJ3rDGK2syuoapqop1514goLEF1LizF7lhjFLUZe/fuNdHOvGtEFJaghkUsxe5Y\nS65Za0/WtYGBQYyNfQSqbn7F7t1jGBgYnLlYaWfeNSIKS1CdC0uxO9aSa9baw5qNGhGFZcHw8HDe\nbZiXHTt2rAAwODg4iDPPPBOLFy/GokWL0NfXVzGm29vby5qBmrX2sGajZl6pBCxZ0vxxIk8cOHAA\nYy4zPzY8PHwgrftlFJWIqJ6pKaC/H9i2DRgamj0+MgKMjgITE8CaNfm1j2geGEUlIuq0Usl1LKan\nge3bXYcCcP9u3+6O9/e76xHRjKCGRZYvX469e/difHwchw4dQm9v75wYHGv51qy1hzX7tVwtWQIc\nPerOTgDu32uuAW65ZfY6l18ObNyYT/uI5imrYRFGUVljFJU107XcxUMh27e7fw8dmq3t3Fk5VEJE\nAAIbFrEUrWMtuWatPazZr5kwNAR0dVUe6+pix4KohqA6F5aidawl16y1hzX7NRNGRirPWADu63gO\nBhFVCGrOBaOo9mvW2sOa/Vru4smbsa4u4MgR9/+JCWDhQqCvL5+2Ec0To6g1MIpKRJkplYDVq10q\nBJidY1He4ejuBu65B+jpya+dRG1iFJWIqNN6etzZie7uysmbQ0Pu6+5uV2fHgqhCUGkRS7s8ssZd\nUVkLZDfVNWuSz0wMDQFbtrBjQZQgqM6Fpfgca4yishZQTLVWB4IdC6JEQQ2LWIrPsZZcs9Ye1vyu\nEZFNQXUuLMXnWEuuWWsPa37XiMgmRlFZYxSVNW9rRDQ/jKLWwCgqERH5rFFfOcuPaUZRiYiIyAtB\npUUsReRYYxSVtfzfa94olZKTJ7WOExkXVOfCUkSONUZRWcv/veaFqSmgvx/43d8F3vve2eMjI8Do\nKHDjjcDZZ+fXPqI2BDUsYikix1pyzVp7WAu35oVSyXUspqeBP/kT4Jxz3PF4efHpaVe//fZ820nU\noqA6F5Yicqwl16y1h7Vwa17o6XFnLGK33QYsWlS5UZoq8Ju/6ToiRJ4Ialhk/fr1ANxfLytXrpz5\nmjU7NWvtYS3cmjfe+17g7rtdxwKY3XG13LZtnHtBXmEUlYjIgkWLkjsW5RumUZBCjKIGdeaCiMhL\nIyPJHYuFC9mxKADP/8ZPFNScCyIi78STN5McOTI7yZPII0GdubCUsW9Ue8pT6p8H2717zEQ7uc4F\na77WmqnnrlRycdNqCxfOnsm47Tbg3e92aRIiTwTVubCUsW9UA+pn8ScnJ020k+tcsOZrrZl67np6\n3DoW/f2z58bjORbnnDM7yfPDHwYuuYSTOskbQQ2LWMrYt1Krx1I7uc4Faz7VmqmbcPbZwMQE0N1d\nOXnz1luBd73LHZ+YYMeCvBJU58JSxr6VWj2W2sl1LljzqdZM3YyzzwbuuWfu5M0/+RN3fM2afNpF\n1KaghkUsZeybqdWzdu3aeX+/2aHlfpQPw7jddd1ZWK5zwVqotWbqptQ6M8EzFuQhrnORk07kmvPM\nThMRkX3ccp2IiIi8ENSwiKUYXKMaUP+0wtjY/KOojb5HHo/d4mvBWpi1+d6WiNoXTOfCndURlM8v\nGB+fMBmR27t3LwYGbphp+6ZNm2ZqExMTuOGGGzA5Of/v1yjumsdjz+N7slbM2nxvS0TtC3pYxGpE\nztLW09bigayxllZtvrclovYF3bmwGpGztPW0tXgga6ylVZvvbYmofcEMiySxGpHLI5LX6DmyEg9k\njTUL7zUimp9goqjAJIDKKKrnD42IiChTjKISERGRFxYMDw/n3YZ52bFjxwoAg8AggBUVtSuu0Dmx\ns/HxcRw6dAi9vb2s5VCz1h7Wwq1leb9EoThw4ADG3LLNY8PDwwfSut+g51zs3bvXZESuyDVr7WEt\n3FqW90tE9QUzLDI5CezePYaBgcGZi9WIXJFr1trDWri1LO+XiOoLpnMB2IrBsZZcs9Ye1sKtZXm/\nRFRfMHMuBgcHceaZZ2Lx4sVYtGgR+vr6Kpbz7e3tZc1AzVp7WAu3luX9EoUiqzkXwURRfdsVlYiI\nKG+MohIREZEXgkqLWNqRkTXuilr02uDgQN2f16NH50bFfX+vEZETVOfCUgyONUZRi15rJOuoeB6P\nn4icoIZFLMXgWEuuWWsPa9nW6gnxvUZETlCdC0sxONaSa9baw1q2tXpCfK8RkcMoKmtBxANZs1f7\n3OdOr/uz+7GP9Wbalrze30Q+YRS1BkZRiWxq9Hnr+a8eoiAwikpEREReCCotYjWSxxqjqEWsAfWj\nqKrhRVEZUyVygupcWI3kscYoahFrAwOD2LRp00xtYmKiIqa6d++mQr3XiIokqGGRvGN3rDWuWWsP\na+HWLLaHqCiC6lzkHbtjrXHNWntYC7dmsT1ERcEoKmuMorIWZM1ie4isYRS1BkZRiYiI2sMoKhER\nEXkhqGGR5cuXY+/evRgfH8ehQ4fQ2zu7AmAcEWMt35q19rAWbs1aexq1lSgPWQ2LMIrKGqOorAVZ\ns9YexlSpSIIaFrEUO2MtuWatPayFW7PWHsZUqUiC6lxYip2xllyz1h7Wwq1Zaw9jqlQkQc25YBTV\nfs1ae1gLt2atPYypkkWMotbAKCoREVF7GEUlIiIiLwQ1LMIoqv2atfawFm7NWnsYYSWLGEVtgqVo\nGWv+xwNZ87tmrT2MsFKRBDUsYilaxlpyzVp7WAu3Zq09jLBSkQTVubAULcuiNjg4gLGx3TOXgYGL\nIAKIuJqVdoYSD2TN75q19jDCSkUS1JyL0KOoO3bUfy6uvNJGO0OJB7Lmd81aexhhJYsYRa2hSFHU\nRr9PPH8piYiowxhFJSIiIi8ENSwSehS10bDIy18+YaKdRY8HsmajZq09jKmSRYyiNsFSRCyP2JmV\ndhY9HsiajZq19lj7fUGUpaCGRSxFxPKOnVl+DJbaw1q4NWvtsfz7gihtQXUuLEXE8o6dWX4MltrD\nWrg1a+2x/PuCKG1BDYusX78egOu9r1y5cubrUGpHj7rx1fJa5djrJhPtrFez1h7Wwq1Za08ej58o\nL4yiEhERFRSjqEREROQFRlFZYzyQtSBr1tpjqUYUYxS1CZZiYKy1Fw98ylMEQH90qSYYGLDxOFiz\nX7PWHks1oqwFNSxiKQbGWnKtmXqzrD5G1mzUrLXHUo0oa0F1LizFwFhLrjVTb5bVx8iajZq19liq\nEWUtqDkXoe+KGkKtUT2EnV9Zs1Gz1h5LNaIYd0WtgVHUsDT63ef525WIyBRGUalg9uTdAErRnj18\nPUPC15MaySwtIiLLAPwFgN8AcBTA3wO4RFV/XOc21wO4oOrwrar6mma+Zxy92r9/P1atWpWwgiVr\nedeaqTt7AGye8xpPTEyYeBystVbbs2cPzjvvPFPvNdYa/QzWtmfPHmzePPfnkyiWZRT1bwEcD5cp\nPAbAxwDsBvCWBrf7JwBvAxC/03/S7De0FPVirb14YCNWHgdr9mvW2uNLjSgNmQyLiMjJADYC2KKq\nX1PVfwPwBwDOE5HlDW7+E1Utqeqj0eXxZr+vpagXa8m1RvXdu8cwMDCIZz97CgMDgxgb+whU3VyL\n3bvHzDwO1uzXrLXHlxpRGrKac/FSAAdV9Rtlx8YBKIAXN7jtWSLyiIjcKyIfEpHjmv2mlqJerCXX\nrLWHtXBr1trjS40oDZmkRURkO4C3qurqquOPALhcVXfXuN0mAE8AeADAKgA7AfwQwEu1RkNF5EwA\n//rJT34SJ598Mu6++25897vfxYknnogzzjijYoyRtfxrzd726quvxqWXXmr2cbDWWm3r1q24+uqr\nTb7XWJv7vDWydetW7Nq1q+nrk1333HMP3vKWtwDAy6JRhlS01LkQkZ0ALqtzFQWwGsAb0UbnIuH7\n9QLYD6BfVW+vcZ3fAvA3zdwfERERJTpfVf82rTtrdULnKIDrG1znfgAPA3hW+UERWQDguKjWFFV9\nQEQeA/A8AImdCwC3ATgfwLcBHGn2vomIiAgLATwX7rM0NS11LlR1GsB0o+uJyFcBdInIqWXzLvrh\nEiB3Nvv9ROREAN0Aaq4aFrUptd4WERFRwaQ2HBLLZEKnqt4L1wv6iIicISIvA/DnAPao6syZi2jS\n5muj/y8RkQ+IyItF5Dki0g/gHwDch5R7VERERJSdLFfo/C0A98KlRG4G8BUAg1XXeT6ApdH/nwTw\nIgD/COC/AHwEwN0AXqGqP8uwnURERJQi7/cWISIiIlu4twgRERGlip0LIiIiSpWXnQsRWSYifyMi\nj4vIQRH5KxFZ0uA214vI0arL5zvVZpolIheLyAMiclhE7hCRMxpc/ywRmRSRIyJyn4hUb25HOWvl\nNRWRVyb8LD4pIs+qdRvqHBF5uYjcJCIPRa/NuU3chj+jRrX6eqb18+ll5wIueroaLt766wBeAbcp\nWiP/BLeZ2vLowm39OkxE3gzgKgBXADgVwBSA20TkmTWu/1y4CcETANYAuAbAX4nIqzrRXmqs1dc0\nonATuuOfxRWq+mjWbaWmLAGwD8Dvwb1OdfFn1LyWXs/IvH8+vZvQKW5TtP8EcHq8hoaIbARwC4AT\ny6OuVbe7HsBSVX1DxxpLc4jIHQDuVNVLoq8FwIMA/kxVP5Bw/SsBvFpVX1R2bA/ca/maDjWb6mjj\nNX0lgL0AlqnqDzraWGqJiBwF8DpVvanOdfgz6okmX89Ufj59PHORy6ZoNH8i8lQAp8P9hQMAiPaM\nGYd7XZO8JKqXu63O9amD2nxNAbeg3j4R+Z6IfEHcHkHkJ/6MhmfeP58+di6WA6g4PaOqTwL4flSr\n5Z8AvBXAegB/BOCVAD4vrezWQ/P1TAALADxSdfwR1H7tlte4/jNE5Nh0m0dtaOc1PQC35s0bAbwB\n7izHl0TklKwaSZniz2hYUvn5bHVvkcy0sClaW1T1hrIvvyUi/w63KdpZqL1vCRGlTFXvg1t5N3aH\niKwCsBUAJwIS5Sitn08znQvY3BSN0vUY3Eqsx1cdPx61X7uHa1z/B6r6k3SbR21o5zVNcheAl6XV\nKOoo/oyGr+WfTzPDIqo6rar3Nbj8HMDMpmhlN89kUzRKV7SM+yTc6wVgZvJfP2pvnPPV8utHfi06\nTjlr8zVNcgr4s+gr/oyGr+WfT0tnLpqiqveKSLwp2u8COAY1NkUDcJmq/mO0BsYVAP4erpf9PABX\ngpui5eFqAB8TkUm43vBWAIsBfAyYGR47QVXj028fBnBxNCP9o3C/xN4EgLPQ7WjpNRWRSwA8AOBb\ncNs9XwTgbACMLhoQ/b58HtwfbACwUkTWAPi+qj7In1G/tPp6pvXz6V3nIvJbAP4CbobyUQCfBnBJ\n1XWSNkV7K4AuAN+D61Rczk3ROktVb4jWP3gP3KnTfQA2qmopuspyAL9Udv1vi8ivA9gF4P8A+C6A\nLapaPTudctLqawr3B8FVAE4A8ASAbwLoV9WvdK7VVMdauKFijS5XRcc/DuDt4M+ob1p6PZHSz6d3\n61wQERGRbWbmXBAREVEY2LkgIiKiVLFzQURERKli54KIiIhSxc4FERERpYqdCyIiIkoVOxdERESU\nKnYuiIiIKFXsXBAREVGq2LkgIiKiVLFzQURERKn6/7K0MFoEWPoYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124e393d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cdBGwbFl67SSTfI0HspatmrX2MIpKxG3MqQW+xgNZy1bNWnsYRSUC5rcxL59b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1295c8f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VtLWHUVQiYH4b89K5FQMD9viaNem0i1TIYjyQNbdq2trDKCpRiNuYUxlZjAe2\nojY2tnvuo6/vAogAIkB/fx/GxnaraacLNW3t0RBF5bAIEamWxZ0qW1WrZO3atWraqb2mrT3cFTUG\njKISEdWvYC5iJMffGqhGSUVReeWCiChhfCOnrGHngoicFcbq9u/fj+7u7gWrJlaqt7rW6ONIqgZU\nbtfY2Fjq3zNXatraU08tqWERdi6IyFkuxQMbfRxJ1YDK7ZqcnEz9e+ZKTVt7GEUlImqCS/HAStJu\nZyWFt3t47965/4+N7caGDb1zKZMNG3qLEiia4paMonoWRRWRV4nIjSLysIjMishZNdzmdBGZFJHD\nInKfiJyXZBuJyF0uxQMrSbudUTYGHYm5242M4JzLLsNx09NVb5tUO7XWtLUnC1HUpQD2ArgGwJer\nnSwiLwBwE4CPA3g7gA0APi0ijxhjvpFcM4nIRS7FAxt9HEnVxsdzRTURAfJ5mNWrIdPTwN3AS1/y\nEnSvXz+3/88RAC7+xjfwxUsvxViNjymtx9fKmrb2ZCqKKiKzAN5ojLmxwjk7AbzOGPPSgmN7ACwz\nxry+zG0YRSUi1ZxKi0RtJDgzM/95sFOxU4+JysrKrqgvBzBecuw2AK9IoS3ku3y+vuNEWTAwYDsQ\noYiOBVE12tIiKwA8VnLsMQDPFpEjjTG/TKFN5KOpqYWbpQH2r7ZwszTuaaKeK/FAY/S0pabaqaei\np60NRz711Pw3u70d5uKLMZHLBZMC+6o+N2ofH6OojKISxS6ftx2L6en5y78DA8WXg3t77aZp3NtE\nNV/jgWnXnnz/+4s7FgAwM4P9F1yA6xctQi0mJibUPj5GUbMXRX0UwDElx44B8NNqVy22bt2Ks846\nq+hjz549iTWUHNbZaa9YhAYHgeXLi8eZt29nx8IBvsYD06wddfXVOPvuu+c+/2Vb29z/X3jNNXMp\nkmq0Pr4sR1H37NmDbdu24dZbb537uPLKK5EEbZ2L72Dhyi5/EByvaNeuXbjxxhuLPjZv3pxII8kD\nHFf2gq/xwNRq+TxOyuXmjn953Tp8+8Ybi35W/mBqCkcdOoS+vn6Mj+eCIR9gfDyHvr7+uQ+Vjy+h\nmrb2lKtt3rwZV155Jc4888y5j23btiEJiQ6LiMhSAC/E/Dqzq0RkDYCfGGMeFJFhAMcaY8K1LD4J\n4MIgNfIZ2I7GWwBEJkWImjIwAOzcWdyxaG9nx8IhvsYDU6t1duKZ3/wmfvWa12Bvby+WXXihrQWX\n1M3oKH4vlTQMAAAgAElEQVR4+eU4QUTvY0ihpq093kdRReQ1AG4HUPpFPmuMebeIXAvg+caY9QW3\neTWAXQB+F8BDAC4zxvxdha/BKCo1pjRyF+KVC8q6fD56WLDccXKWk7uiGmO+iQpDL8aY8yOOfQvA\nKUm2i6hilr9wkidRFpXrQLBjQTVaNDQ0lHYbmrJjx46VAPr7N23CyhqX2qWMy+eBc88FDh2ynw8P\nAzfdBCxebCOoALB3L3D++cDSpem1k6oKY3Xj4+OYmZlBV1dXZDwwqs4aa3HVtLWnntrixYsxNjYG\nAGNDQ0MHqv7Q1cifKOry5Wm3gFzR2Wk7EaXrXIT/hutc8K809XyNB7LmVk1bexhFJUrLmjV2HYvS\noY+BAXucC2g5wYd4IGvu17S1x/tdUYlU47iy83yIB7Lmfk1be7KwKyoRUWJ8jQey5lZNW3u8j6K2\nAqOoREREjcnKrqiNO3gw7RZQPbgjKRGRt/yJol50EVauXJl2c6gWU1PAunXA7CzQ0zN/fGTERkQ3\nbgRWrEivfeQMX+OBrLlV09YeRlEpe7gjKcXI13gga27VtLWHUVTKHu5ISjHyNR7Imls1be1hFJX0\nacVcCO5ISjHxNR7Imls1be3REEX1Z85Ffz/nXDSrlXMhenqAq64CDh+eP9bebpfhJqpRV1cX2tra\nsGTJEvT09GD9+vVF4+CV6qyxFldNW3vqqXV3dycy54JRVLLyeWD1ajsXApi/glA4F6KjI765ENyR\nlIgodYyiUrJaORciakfSwq87MtL81yAiotRwWITm9fQU7wxaOGQR1xUF7khKMfI1HsiaWzVt7WEU\nlfQZGAB27iyeZNneXl/HIp+PvsIRHueOpBQTX+OBrLlV09YeRlFJn5GR4o4FYD+vdahiasrO3Sg9\nf2TEHp+a4o6kFBtf44GsuVXT1h5GUUmXZudClC6QFZ4f3u/0tK2Xu7IB8IoF1cXXeCBrbtW0tYdR\n1BhwzkVM4pgLsXSpjbGG5+dyNm56883z51xyiY20EsXA13gga27VtLWHUdQYMIoao6mphXMhAHvl\nIZwLUcuQBWOmbqs2Z4aIvMEoKiUvrrkQAwPFQypA/ZNCKR21zJkhIqqCaREqFsdciEqTQtnB0MvB\nTeXCWN3+/fvR3d294FJ1pTprrMVV09aeemrtpX8IxoSdC4pX1KTQsKNR+IZF+oQLqYXP0+Dgwliy\nsk3lfI0HsuZWTVt7GEUlv+Tzdm5GaHgYOHiweJOyj3wk3k3QKF6ObSrnazyQNbdq2trDKCr5JVwg\na9kyoK1t/nj4htXWBhgDPPJIem2k6hyaM+NrPJA1t2ra2qMhisphEYrXsccCixYBTz65cBjkqafs\nh7Jxeyrh0JyZ9evXA7B/ma1atWru81rqrLEWV01be+qpJTXnglFUil+leReAysvrFOBzR5QpjKKS\nOxwbt6dALXNmRkc5Z4aIquKVC0rO8uULN0A7eDC99iSgIIkWybkfr7gWUmsRX+OBrLlV09aeeqOo\na9euBWK+csE5F5QMh8btqUC4kFrpfJiBAWDLFnXzZHyNB7LmVk1bexhFJT81uwEapcuhTeV8jQey\n5lZNW3sYRSX/cNyeWsjXeCBrbtW0tYdRVPJPuNZF6bh9+G84bq/wr2Byj6/xQNbcqmlrD6OoMeCE\nTqVc2FkzhjZ6N6GTiDKFUVRyi/Zxe+7+SUSUGA6LUPY4uPunV2K8quVrPJA1t2ra2sNdUYnSEOPu\nnxz2qFPM62j4Gg9kza2atvYwikoUt3IplNLjXEW09UqvGIVDUuEVo+lpW68jSeRrPJA1t2ra2sMo\nKlGc6p1H4dDun14IrxiFBgftKq6Fa6LUeMUo5Gs8kDW3atraoyGKumhoaCiRO26VHTt2rATQ39/f\nj5UrV6bdHEpLPg+sW2f/+s3lgMWLgZ6e+b+KDx0CbrgBOP98YOlSe5uREeDmm4vv5/Dh+dtS/Hp6\n7Pc3l7OfHz48X2vgilFXVxfa2tqwZMkS9PT0LBgHr1RnjbW4atraU0+tu7sbY2NjADA2NDR0oOoP\nXY0YRSV/1LOjJ3f/TFcG9p0hcgGjqJQ6kcofqat1HgVXEU1XpX1niMgLvHJBNXNmwaha/ip2bPdP\nb8R8xcjXeCBrbtW0tYe7ohLFrdbdWB3b/dMLUVeMStcXGR2t6/vvazyQNbdq2trDKCpRnOrdjVX7\nKqK+Cfed6egovkIRDmd1dNS974yv8UDW3Kppaw+jqERx4TwKN4RXjEqHPgYG7PE6h6J8jQey5lZN\nW3s0RFE5LEJ+4G6s7ojxipGvO1Wy5lZNW3vqqXFX1DI4obN1nJjQ6cJurFnE56UsJ36uyFuMohLV\ngvMo9OEOtESZw2ERqhn/gqK6JbwDrS/xwEYfI2s6atraw11RichvMe5AG8WXeGCjj5E1HTVt7WEU\nlYj8l+AOtL7EAyupdp9jY7vnPjZs6J1bMXfDhl6Mje1u2WPIck1bexhFJaJsSGgHWl/igZXUc5+V\naH3sPtS0tYdRVCLKhlpXTq2TL/HARh9jLfexdu3a1B+f7zVt7WEUNQaMohIpxx1oK2o2isooKzWD\nUVQicg9XTq3KmMoflB71O0ErxmERIkpOwiun+hoPrKcGVH6XGxsbU9FOF2tA9TSP6681RlGJyE0J\n7kDrazywnlq1N8DJyUkV7XSzVnvnIv22MopKRM0qN4ygdXghoZVTfY0HNlqrRFM7XanVI+22MopK\nRM3hctpzfI0H1lPr6+uf+xgfz83N1Rgfz6Gvr19NO12s1SPttjKKSkSNS3g5bdf4Gg9kTUdtbAw1\nS7utjKLGjFFUyhxGOyMxkklxy8JrilFUIrISXE6biCgOHBYhctHAwMINwGJYTts1hbE6oK/iublc\nTlUEkDX9tdnZyrcrjAGn3VZGUYmoeQktp+2awlhdNeF5WiKArPlT09YeRlFJB9dijVkXNeciNDi4\nMEXisXqjg5oigKz5U9PWHkZRKX2MNbqFy2kXCWN1hVuLV6IpAsiaP7WGbhv8jJbWjj/66JY+jqSi\nqIuGhoYSueNW2bFjx0oA/f39/Vi5cmXazXFLPg+sW2djjbkcsHgx0NMz/5fxoUPADTcA558PLF2a\ndmsJsM/Dxo32ebnkkvkhkJ4e+/zt3Wufy5JffL7q6upCW1sbPve56o935862orHn8LZLlixBT08P\na6w1XKv7th0dkJe9DJidRdcf//Fc7dwHH8SJo6OQjRuBFSta8ji6u7sxZjO3Y0NDQweq/iDViFHU\nrGOs0U35fPQ6FuWOe66WTaQc/1VHvsjn7VXh6Wn7efg7tvB3cUdHy9aqYRSVksFYo5sSWk7bV+xY\nkBqdnXYTv9DgILB8efEfedu3O/+zzLQIMdZYRrUdNyl94XNUbYMpDTuDVnvpjI/nVEYVWateq/u2\nF19sQ6xhh6Lgd6+5/HJI8LuXUVRym4+xxhiGDapFzyh988+R/p1Bq9EaVWQtoShqxB91vzjiCNy5\nbt3cq5lRVHKXj7HGmBIw1aJnlD6XoqiutJO1+msN3Tbij7qlv/oVnnX11S19HIyiUvx8jDWWbuwV\ndjDCTtT0tK3X8JiqRc8offXuYpl2XNGFdrJWf63e255x111Ff9T94ogj5v6/7itfmfu95XIUlcMi\nWdbZaWOLvb12AlE4BBL+Ozpq6y5NLAonS4U/uIODC+eT1DhZqtLOghSjJoawwudk7dpPzT1HUePg\n99+/NvXdKKvZtGmTyl0zWateq+e2xx99NLr7++dq5vLLcee6dXjW1VfbjgVgf/du2dKSx5HUnAsY\nY5z+AHAyADM5OWmoQY8/Xt9xFwwPG2NDAsUfw8Npt4wK7d1rTEfHwudleNge37s3nXYlIOrlWPhB\nGaLodT85OWkAGAAnmxjfm7nOBflr+fKFCZiDB9NrDxVTlvdPWha276Y6KFmrJql1LjgsQn6qMQFj\nmoieUZNiGMKq9hw1+vwmUasml2MU1dVaQ7cNXteRtRa+fhlFpcYp6SG3TKVVR8PjQQcjqVgh1Sjs\n6EXk/WtZxM2lnSrHx3NFO7hu2rRprpbL5dS0kzXuihoHpkV8l7WNyepMwLQiVkhVDAwUR6CBmhdx\n83WnStbcqmlrD6OolKwYY5nOCBMwHR3Ff/mGy5x3dBQlYFoRK6QqKg1hVRH7TpWssdZATVt7NERR\nuSuqz5YuBWZn7ZspYP+96irg5pvnz7nkErvLpk9WrLA7uZY+rp4ee7zgB62ZXRApBlFDWIcP2/8X\n7tRbRqw7VbLGWqt2RVVU466oZTAtUoPSX+AhbkxGacpYWoRII+6KSo1rYkybKDF1DmERkTuYFskC\nHzcmK9FoLCuJnSojZS2xU6s1a6KvTAwMAFu2NP290RRXZK210V5KFzsXvqsjlukyTTtVLjA1tXCJ\ndcA+N+ES62vW1NQeL5XrQMTQ6Uo75sdasvFP0ovDIj7zcWOyMjTtVFkki4kdRdKO+bGWXI0C5X53\npPw7hZ0Ln2VoTFvTTpVFwlUoQ4ODdlnywqtJNW6kRvVLO+bHWnI1gup1jDgs4ruEx7S1aGY3w0oa\n2alygSZXoaTGado5k7XW7jLrvdKrosDCtFVvb2ppK0ZRKdNaupkUN1IjojhVmlMH1PTHC6OoRC5r\nYhVKIqJI4RB3SNFVUQ6LkDOajbNt2FD/LPNGdqpcoMWJHW7tXRtGJ8kLAwMLdxNWsI4ROxfkjObj\nbJU7F319/bHsVFkkKrFTOi46OurV/BdXMDpJXlC6jhGHRcgZccXZKok9IpehxI5ryj2HIsCGDb0Y\nG9s997FhQy9EbI3RSVIj6qpoqDD6ngJ2LsgZccXZKkkkIhcmdkr/ihgYsMezvIBWihqNOdbyujjq\n0KHo++R6JhQX5esYcViEnNFsnM1u/Ffepk2bkovIJbgKJTWm0ZhjtdfFUfv3Y822bXjonHPQXXif\nXJGV4hReFS1d/Tf8N3ytpfQ7hlFUyoysTHTMyuNMSlPfP+70Sq3W5L5FjKISEWnHFVmp1ZReFeWw\nCKmSZDywWlokl8sxckjN44qsROxckC5JxgP7+my9XNw0+Mf5yCGHPRRQuvYAUatwWIRU0bTrIiOH\n1DCuyEoZx84FqaJp10Xu1phNxlT+qErx2gNErcJhEVJF066L3K2R6sYVWWPF5JO7GEUlIorT1NTC\ntQcArnPRAHYukpdUFJVXLoiI4hSuyFp6ZWJggFcsKDNa0rkQkQsBbAewAsAUgPcaY+4pc+5rANxe\nctgAWGmMeTzRhlLqWr1TJXe/pEQoXXuAqFUS71yIyNsAXAGgD8DdALYCuE1EXmyMeaLMzQyAFwP4\n2dwBdiwyodU7VXL3SyKi+LUiLbIVwG5jzOeMMfcC+BMATwF4d5Xb5Y0xj4cfibeSVNAUKWUUlYio\nMYl2LkTkmQBOAZALjxk7g3QcwCsq3RTAXhF5RES+LiKnJdlO0kNTpJRRVCJyTrldUFu8O2rSwyLP\nAbAIwGMlxx8DcHyZ2xwA0A/guwCOBHABgDtEZJ0xZm9SDSUdNEVKGUUlIqcoSiolGkUVkZUAHgbw\nCmPMXQXHdwJ4tTGm0tWLwvu5A8CPjTHnRdQYRSUiomxrcEdeV6OoTwB4GsAxJcePAfBoHfdzN4BX\nVjph69atWLZsWdGxzZs3Y/PmzXV8GSIiIgeFO/KGHYnBwQX72+zZsAF7tmwputmTTz6ZSHMS7VwY\nY34tIpOw21HeCABi83q9AP6mjrs6EXa4pKxdu3bxyoUHNEVKGTclIqdU2ZF388AASv/cLrhyEatW\nrHNxJYDrgk5GGEVtA3AdAIjIMIBjwyEPEbkIwAMAfghgMeycizMAvLYFbaWUaYqUMm5KRM6pd0fe\ngwcTaUbiUVRjzPWwC2hdBuD7AF4KYKMxJpy6ugLA7xTc5AjYdTH+HcAdAF4CoNcYc0fSbaX0aYqU\nMm5KRM6pd0fe5csTaUZLdkU1xnzcGPMCY8wSY8wrjDHfLaidb4xZX/D5R40xLzLGLDXGdBpjeo0x\n32pFOyl9miKljJsSkVMU7cjLvUVIFU2RUsZNicgZynbk5a6oZOXz0S+4cseJiEiXBta5SCqK2pJh\nEVJuasrmo0svmY2M2ONTU+m0i4iIahfuyFs6eXNgwB5v0QJaADsXlM/bnu70dPGYXHgpbXra1lu8\ndGymKFmul4g8UO+OvK6mRUi5cOGV0OCgnT1cOClo+/ZYh0aMMcjlchgbG0Mul0Ph0JwrtdjwqhER\npSmhtAgndFLVhVfK5qMbpGm9ilTXuSi9agQsnIDV27tguV4iIu145YKsgYHi2BJQeeGVJmharyLV\ndS5SuGpERNQK7FyQVe/CK03QtF5F6utcDAzYq0OhhK8aERG1AodFKHrhlfBNrvByfUw0rVehYp2L\nepfrJSJSjutcZF2D2/RSjEo7dyFeuSCihHGdC0pGZ6ddWKWjo/jNLLxc39Fh6+xYJEPRcr1ERHHh\nsAjNL7xS0oEwF1+Mf3nRi3DvXXeh+4knYtuqXNPW6alux65suV4ioriwc0FWxJvXxMQErv/61wHE\nG+HUFClNNaYaXjUqXa43/DdcrpcdC924dD7RAhwWobKSinBqipSmHlNVtFwvNYCLoBFFYueCykoq\nwqkpUqoiplrvcr2kA5fOJyqLwyJUVlIRTk2RUvUxVdIrXAQtnB8zOLgwUsxF0CijGEUloso4p6Ay\nRonJYYyiElHrcU5BdS1cOp/IFRwWcYimuCWjqAnHVDXgxmq1qbR0PjsYlFHsXDhEU9ySUdQUd1Nt\nFc4pqK7FS+cTuYLDIg7RFLdkFDXF3VRbiRurlRe1CNrBg8Xfr9FRpkUok9i5cIimuCWjqCnvptpK\nnFMQjUvnE5XFYRGHaIpbMoqaoZgq5xSUV2bpfAwMcNl2yjRGUYmovEpzCgAOjRCFHI1sM4pKRK3F\nOQVEtWFkewEOiyREU/xRU01bezTV1OHGakTVMbIdiZ2LhGiKP2qqaWuPpppKnFNAVBkj25E4LJIQ\nTfFHTTVt7dFUU4sbqxFVxsj2AuxcJERT/FFTTVt7NNWIyGGMbBfhsEhCNMUfNdW0tUdTjYgcxsh2\nEUZRiYiImuFwZJtRVCIiIm0Y2Y7EYZEqNEUVfahpa48rNSJSipHtSOxcVKEpquhDTVt7XKkRkWKM\nbC/AYZEqNEUVtdREgA0bejE2tnvuY8OGXojYGqOoGYqpEpHFyHYRdi6q0BRV1FSrhFFUxlSJKNs4\nLFKFpqiiplqj3zNtj8OVGmWco5tiUXYxikp1qzbH0PGXFJEuU1MLJwsCNv4YThZcsya99pHTGEUl\nZ4VzMcp9EFEZpZtihbtuhusqTE/besZijqSfV8MimqKDvtfqeR6AyokHY4zKx6ipRhnFTbHIUV51\nLjRFB32vVbLwdpVvMzExofIxaqpRhoVDIWEHw5GVHynbvBoW0RQd9L1WSentqtH6GDXVKOO4KRY5\nxqvOhabooM81Y4Dx8Rz6+vrnPsbHczDG1kpvV43Gx6itRhlXaVMsIoW8GhbRFB1kbb42NoaKNLVV\na408Vylqes015TfFCo/zCgYpwygqJY7RVaIKKkVNP/IR+wMSdibCORaFu3B2dEQvPU1Ug6SiqF5d\nuSAickpp1BRY2HlYtgw4+mjgfe/jpljkDK86F5qig6zN12ZnK++KaoyetrpYI4fVEjUtt/lVhjfF\nIv286lxoig6yxl1RW/k9JYc1EzVlx4KU8iotoik6yFp0TVt7fKiRBxg1Jc941bnQFB1kLbqmrT0+\n1MgDjJqSZ7waFtEUHWSNu6Iypko1KZy8CTBqSl5gFJWIKC35PLB6tU2LAIyaUstxV1QiIt90dtoo\naUdH8eTNgQH7eUcHo6bkJK+GRTTFA1krH5vU1B4fauS4NWuir0wwakoO86pzoSkeyBqjqK38npLj\nynUg2LFojUrLr/M5aIhXwyKa4oGsRde0tceHGhE1YWrKznspTeaMjNjjU1PptMtxXnUuNMUDWYuu\naWuPDzUialDp8uthByOcUDs9bev5fLrtdJBXwyKa4oGsuR1FtdMZeoMPq3B319lZHe0koibUsvz6\n9u0cGmkAo6hEEbiTK1GGlK41Eqq2/LoHGEUlIiJKApdfj51XwyKa4oGsuR9F9eG1RkQ1qLT8OjsY\nDfGqc6EpHsia+1HUSjS1kzFVoiZw+fVEeDUsoikeyFp0TVt7Go14amonY6pEDcrngdHR+c+Hh4GD\nB+2/odFRpkUa4FXnQlM8kLXomrb2NBrx1NROxlSJGsTl1xPj1bCIlhgja+5HUavR1M7MxlS5qiLF\ngcuvJ4JRVCJyz9SUXdxo+/bi8fCREXsZO5ezbxpEVBGjqEREAFdVJHKAV8MimiKArLkfRfWh5iWu\nqkiknledC00RQNbcj6L6UPNWOBQSdjAKOxYZWFWRSDuvhkU0RQBZi65pa4/vNa9xVUUitbzqXGiK\nALIWXdPWHt9rXqu0qiIRpcqrYRFNEUDW3I+i+lDzFldVJFKNUVQicks+D6xebVMhwPwci8IOR0dH\n9NoFLuJ6HpQgRlGJiIBsrao4NWU7UqVDPSMj9vjUVDrtIqrCq2ERTRFA1hhF1V5zWhZWVSxdzwNY\neIWmt9efKzTkFa86F5oigKwxiqq95rxyb6i+vNFyPQ9ymFfDIpoigKxF17S1J8s1ckA41BPieh7k\nCK86F5oigKxF17S1J8s1cgTX8yAHeTUsoikCyBqjqNpr5IhK63mwg0FKMYpKRKRVpfU8AA6NUNMY\nRSUiypJ83m4fHxoeBg4eLJ6DMTrK3V9JJa+GRTTF/FhjFFV7jZQL1/Po7bWpkML1PADbsfBlPQ/y\njledC00xP9YYRdVeIwdkYT0P8pJXwyKaYn6sRde0tSfLNXKE7+t5kJe86lxoivmxFl3T1p4s14iI\nkuLVsIimmB9rjKJqrxERJYVRVCIiooxiFJWIiIic4NWwiKaYH2uMorpcIyJqhledC00xP9YYRXW5\nRkTUDK+GRTTF/FiLrmlrD2vRNSKiZnjVudAU82MtuqatPaxF17xUbplsLp+tC58nLywaGhpKuw1N\n2bFjx0oA/f39/TjttNPQ1taGJUuWoKenp2gMuaurizUFNW3tYa388+SVqSlg3Tpgdhbo6Zk/PjIC\nnHsusHEjsGJFeu0ji89Tyx04cABjY2MAMDY0NHQgrvtlFJWI/JbPA6tXA9PT9vNwJ9HCHUc7OqKX\n2abW4fOUCkZRiYga0dlpN/4KDQ4Cy5cXb2W+fTvfsNLG58krXg2LrFixAhMTExgfH8fMzAy6uroW\nxO5YS7emrT2slX+evNLTAyxebHcRBYDDh+dr4V/IlD4+T/YKztKltR9vUlLDIoyissYoKmvZiKIO\nDAA7dwIzM/PH2tuz8Yblkiw/T1NTQG+vvUJT+HhHRoDRUdvpWrMmvfbVwathEU1RPtaia9raw1p0\nzUsjI8VvWID9fGQknfZQtKw+T/m87VhMT9uhoPDxhnNOpqdt3ZHUjFedC01RPtaia9raw1p0zTuF\nkwIB+5dwqPAXeRwYpWxcK58nbTybc+LVnAtGUfXXtLWHtRqiqC0eA45dPm9jjIcO2c+Hh4Gbbioe\n29+7Fzj//OYfD6OUjWvl86RVCnNOkppzAWOM0x8ATgZgJicnDRHFbO9eYzo6jBkeLj4+PGyP792b\nTrvq1YrH8fjj9r4A+xF+reHh+WMdHfY8iubL661Z7e3zrxnAfp6QyclJA8AAONnE+d4c552l8cHO\nBVFCfHuzLNfOONtf+L0J3xQKPy9906SFWvE8aVb6Gkr4tZNU58KrYRFGUfXXtLWHtQq1O+9E/vHH\ncdy999onLpcDrroKuPnm+R/ASy6xl/pdUO5SepyX2BmlbF4rnietouachK+hXM6+tgqH22LAKGoN\nNEX5WGMU1YtaZyceXrcOZ999NwAUz+Lnm2W0LEcpqXH5vI2bhqJWKB0dBbZscWJSp1dpEU1RPtai\na9raw1r12m0nnohftrUVncs3ywqyGqWk5nR22qsTHR3FHfeBAft5R4etO9CxADzrXGiK8rEWXdPW\nHtaq1zbu3Ysjn3qq6Fy+WZaR5SglNW/NGrt3SmnHfWDAHndkAS2gRcMiInIhgO0AVgCYAvBeY8w9\nFc4/HcAVAH4PwP8A+LAx5rPVvs769esB2L/AVq1aNfc5a3pq2trDWuXas66+GuvCIRHAvlmGf5WH\nb6K8gmF5dlmbUlLuteHaaybO2aFRHwDeBuAwgHcCOAHAbgA/AfCcMue/AMDPAXwEwPEALgTwawCv\nLXM+0yJESfAtLdIK2qOUWU9i0AJJpUVaMSyyFcBuY8znjDH3AvgTAE8BeHeZ898D4H5jzF8YY/7L\nGHM1gC8F90NEreLZGHBLaL6sPTVltzQvHZoZGbHHp6bSaRd5KdFhERF5JoBTAFweHjPGGBEZB/CK\nMjd7OYDxkmO3AdhV7euZIFq3f/9+dHd3F604yFpttQ0bKm9cZS8WNf71NDxG1uqrnbB7N1519tkI\nn0FjDCZOPRUPDw7ivBMrv1mGr5dM0XhZu3TfCmDhkE1vr+0AsbNIMUh6zsVzACwC8FjJ8cdghzyi\nrChz/rNF5EhjzC/LfTGVUT7narXtiskoaoZqAH7d3h5ZI0eE+1aEHYnBwYVxWYf2rSD9vEmLbN26\nFdu2bcOtt9469/EP//APc3WtMT9ttVoxisoaOSYczgpxzZLM2bNnD84666yij61bk5lxkHTn4gkA\nTwM4puT4MQAeLXObR8uc/9NKVy127dqFK6+8EmeeeebcxznnnDNX1xrz01arFaOorJGDBgaK47EA\n1yzJkM2bN+PGG28s+ti1q+qMg4YkOixijPm1iITX2m8EALGDur0A/qbMzb4D4HUlx/4gOF6Rxiif\nazW7Cmx1jKKydv/999f8eiElKi3wxQ4GxUhMwjOuRGQTgOtgUyJ3w6Y+3gLgBGNMXkSGARxrjDkv\nOMP6iLwAABIrSURBVP8FAP4DwMcBfAa2I/LXAF5vjCmd6AkRORnA5OTkJE4++eREH0sWRO24XSiT\nE/SoLL5eHBK1wBeHRjLve9/7Hk455RQAOMUY87247jfxORfGmOthF9C6DMD3AbwUwEZjTD44ZQWA\n3yk4/0cA3gBgA4C9sJ2RLVEdCyIiqkHUAl8HDxbPwRgdtecRxaAlK3QaYz4OeyUiqnZ+xLFvwUZY\n6/06KqN8LtVqTYswisqandjZV/V1ItUub1DywjVLenttKqRwzRLAdiy4ZgnFiLuislZUGx+fr+Vy\nuaLI4aZNmxB2PhhFZQ0A+vr6sWnTprKvmYmJTUXPPaUoXOCrtAMxMMAlySl23kRRgfQjeaxVr2lr\nD2utq5ECGhf4Ii951blIO5LHWvWatvaw1roaEWWHV8MiWuN6rDGKyhoRZUniUdSkMYpKRETUGGej\nqERERJQtXg2LaI3rscYoKmtElCVedS60xvVYy24Utb+/dB2IcPV7e+74eE5FO1v13BNRNng1LKIp\ndsdadE1be9LeNVRTOxlFJaK4eNW50BS7Yy26pq09ae8aqqmdjKISUVy8GhbRFLtjjVHUWnYN1dJO\nRlGJKE6MohIliLuGEpFmjKISERGRE7waFtEUu2ONUdR6dw3V+hgYUyWiennVudAUu2ONUdRaTExM\nqGhn2jUi8otXwyKaYnesRde0tSfpWl9fP8bGPgVj7PyK3bvH0NfXP/ehpZ1p14jIL151LjTF7liL\nrmlrD2s6akTkl0VDQ0Npt6EpO3bsWAmgv7+/H6eddhra2tqwZMkS9PT0FI3pdnV1saagpq09rOmo\nqZfPA0uX1n6cyBEHDhzAmM3Mjw0NDR2I634ZRSUiqmRqCujtBbZvBwYG5o+PjACjo0AuB6xZk177\niJrAKCoRUavl87ZjMT0NDA7aDgVg/x0ctMd7e+15RDTHq2GRFStWYGJiAuPj45iZmUFXV9eCGBxr\n6da0tYc1/bVULV0KzM7aqxOA/feqq4Cbb54/55JLgI0b02kfUZOSGhZhFJU1RlFZU11LXTgUMjho\n/52Zma8NDxcPlRARAM+GRTRF61iLrmlrD2v6ayoMDADt7cXH2tvZsSAqw6vOhaZoHWvRNW3tYU1/\nTYWRkeIrFoD9PJyDQURFvJpzwSiq/pq29rCmv5a6cPJmqL0dOHzY/j+XAxYvBnp60mkbUZMYRS2D\nUVQiSkw+D6xebVMhwPwci8IOR0cHsG8f0NmZXjuJGsQoKhFRq3V22qsTHR3FkzcHBuznHR22zo4F\nURGv0iKadnlkjbuisubJbqpr1kRfmRgYALZsYceCKIJXnQtN8TnWGEVlzaOYarkOBDsWRJG8GhbR\nFJ9jLbqmrT2suV0jIp286lxois+xFl3T1h7W3K4RkU6MorLGKCprztaIqDmMopbBKCoREbmsWl85\nybdpRlGJiIjICV6lRTRF5FhjFJW19F9rzsjno5Mn5Y4TKedV50JTRI41RlFZS/+15oSpKaC3F3jP\ne4APfWj++MgIMDoK3HADcMYZ6bWPqAFeDYtoisixFl3T1h7W/K05IZ+3HYvpaeCv/go480x7PFxe\nfHra1m+/Pd12EtXJq86Fpogca9E1be1hzd+aEzo77RWL0G23AUuWFG+UZgzw1rfajgiRI7waFlm/\nfj0A+9fLqlWr5j5nTU9NW3tY87fmjA99CLjnHtuxAOZ3XC20fTvnXpBTGEUlItJgyZLojkXhhmnk\nJR+jqF5duSAictLISHTHYvFidiwywPG/8SN5NeeCiMg54eTNKIcPz0/yJHKIV1cuNGXsq9We8YzK\n18F27x5T0U6uc8Gaq7Va6qnL523ctNTixfNXMm67DfjgB22ahMgRXnUuNGXsq9WAyln8yclJFe3k\nOhesuVqrpZ66zk67jkVv7/y18XCOxZlnzk/y/OQngYsu4qROcoZXwyKaMvb11CrR1E6uc8GaS7Va\n6iqccQaQywEdHcWTN2+9FfjAB+zxXI4dC3KKV50LTRn7emqVaGon17lgzaVaLXU1zjgD2Ldv4eTN\nv/ore3zNmnTaRdQgr4ZFNGXsa6lVsnbt2qa/3vzQci8Kh2Hs7rr2KizXuWDN11otdVXKXZngFQty\nENe5SEkrcs1pZqeJiEg/brlORERETvBqWERTDK5aDah8WWFsrPkoarWvkcZj1/hcsOZnrdnbElHj\nvOlc2Ks6gsL5BePjOZURuYmJCfT1XT/X9k2bNs3Vcrkcrr/+ekxONv/1qsVd03jsaXxN1rJZa/a2\nRNQ4r4dFtEbkNG09rS0eyBprcdWavS0RNc7rzoXWiJymrae1xQNZYy2uWrO3JaLGeTMsEkVrRC6N\nSF6175GWeCBrrGl4rRFRc7yJogKTAIqjqI4/NCIiokQxikpEREROWDQ0NJR2G5qyY8eOlQD6gX4A\nK4tql15qFsTOxsfHMTMzg66uLtZSqGlrD2v+1pK8XyJfHDhwAGN22eaxoaGhA3Hdr9dzLiYmJlRG\n5LJc09Ye1vytJXm/RFSZN8Mik5PA7t1j6Ovrn/vQGpHLck1be1jzt5bk/RJRZd50LgBdMTjWomva\n2sOav7Uk75eIKvNmzkV/fz9OO+00tLW1YcmSJejp6Slazrerq4s1BTVt7WHN31qS90vki6TmXHgT\nRXVtV1QiIqK0MYpKRERETvAqLaJpR0bWuCtq1mv9/X0Vf15nZxdGxV1/rRGR5VXnQlMMjjVGUbNe\nqybpqHgaj5+ILK+GRTTF4FiLrmlrD2vJ1irx8bVGRJZXnQtNMTjWomva2sNasrVKfHytEZHFKCpr\nXsQDWdNX++pXT6n4s3vddV2JtiWt1zeRSxhFLYNRVCKdqr3fOv6rh8gLjKISERGRE7xKi2iN5LHG\nKGoWa0DlKKox/kVRGVMlsrzqXGiN5LHGKGoWa319/di0adNcLZfLFcVUJyY2Zeq1RpQlXg2LpB27\nY616TVt7WPO3prE9RFnhVeci7dgda9Vr2trDmr81je0hygpGUVljFJU1L2sa20OkDaOoZTCKSkRE\n1BhGUYmIiMgJXg2LrFixAhMTExgfH8fMzAy6uuZXAAwjYqylW9PWHtb8rWlrTzOPgygpSQ2LMIrK\nGqOorHlZ09YeRlgpS7waFtEUO2MtuqatPaz5W9PWHkZYKUu86lxoip2xFl3T1h7W/K1paw8jrJQl\nXs25YBRVf01be1jzt6atPYywkkaMopbBKCoREVFjGEUlIiIiJ3g1LMIoqv6atvaw5m9NW3sYUyWN\nGEWtgab4GGt+xwNZ01/T1h7GVClLvBoW0RQfYy26pq09rPlb09YexlQpS7zqXGiKjyVR6+/vw9jY\n7rmPvr4LIAKI2JqWdmYhHsia/pq29jCmSlni1ZwL36OoO3ZU/l7s3KmjnVmIB7Kmv6atPYypkkaM\nopaRpShqtd8njj+VRETUYoyiEhERkRO8GhbxPYpabVjkVa/KqWhn1uOBrOmoaWsPY6qkEaOoNdAU\nEUsjdqalnVmPB7Kmo6atPdp+XxAlyathEU0RsbRjZ5ofg6b2sOZvTVt7NP++IIqbV50LTRGxtGNn\nmh+Dpvaw5m9NW3s0/74giptXwyLr168HYHvvq1atmvvcl9rsrB1fLawVj71uUtHOSjVt7WHN35q2\n9qTx+InSwigqERFRRjGKSkRERE5gFJU1xgNZ87KmrT2aakQhRlFroCkGxlpj8cBnPEMA9AYfpQR9\nfToeB2v6a9rao6lGlDSvhkU0xcBYi67VUq+V1sfImo6atvZoqhElzavOhaYYGGvRtVrqtdL6GFnT\nUdPWHk01oqR5NefC911RfahVq/uw8ytrOmra2qOpRhTirqhlMIrql2q/+xx/uRIRqcIoKmXMnrQb\nQDHas4fPp0/4fFI1iaVFRGQ5gI8B+EMAswD+EcBFxphfVLjNtQDOKzl8qzHm9bV8zTB6tX//fnR3\nd0esYMla2rVa6tYeAJsXPMe5XE7F42CtvtqePXtwzjnnqHqtsVbtZ7C8PXv2YPPmhT+fRKEko6hf\nAHAMbKbwCADXAdgN4B1Vbvc1AO8CEL7Sf1nrF9QU9WKtsXhgNVoeB2v6a9ra40qNKA6JDIuIyAkA\nNgLYYoz5rjHm3wC8F8A5IrKiys1/aYzJG2MeDz6erPXraop6sRZdq1bfvXsMfX39eN7zptDX14+x\nsU/BGDvXYvfuMTWPgzX9NW3tcaVGFIek5ly8AsBBY8z3C46NAzAAXlbltqeLyGMicq+IfFxEjq71\ni2qKerEWXdPWHtb8rWlrjys1ojgkkhYRkUEA7zTGrC45/hiAS4wxu8vcbhOApwA8AKAbwDCAnwF4\nhSnTUBE5DcC/fv7zn8cJJ5yAe+65Bw899BCOO+44nHrqqUVjjKylX6v1tldeeSW2bdum9nGwVl9t\n69atuPLKK1W+1lhb+H2rZuvWrdi1a1fN55Ne+/btwzve8Q4AeGUwyhCLujoXIjIM4OIKpxgAqwG8\nGQ10LiK+XheA/QB6jTG3lznn7QD+vpb7IyIiokjnGmO+ENed1TuhcxTAtVXOuR/AowCeW3hQRBYB\nODqo1cQY84CIPAHghQAiOxcAbgNwLoAfAThc630TERERFgN4Aex7aWzq6lwYY6YBTFc7T0S+A6Bd\nRE4qmHfRC5sAuavWrycixwHoAFB21bCgTbH1toiIiDImtuGQUCITOo0x98L2gj4lIqeKyCsB/C2A\nPcaYuSsXwaTNPwr+v1REPiIiLxOR54tIL4B/AnAfYu5RERERUXKSXKHz7QDuhU2J3ATgWwD6S855\nEYBlwf+fBvBSAP8M4L8AfArAPQBebYz5dYLtJCIiohg5v7cIERER6cK9RYiIiChW7FwQERFRrJzs\nXIjIchH5exF5UkQOisinRWRpldtcKyKzJR+3tKrNNE9ELhSRB0TkkIjcKSKnVjn/dBGZFJHDInKf\niJRubkcpq+c5FZHXRPwsPi0izy13G2odEXmViNwoIg8Hz81ZNdyGP6NK1ft8xvXz6WTnAjZ6uho2\n3voGAK+G3RStmq/Bbqa2Ivjgtn4tJiJvA3AFgEsBnARgCsBtIvKcMue/AHZCcA7AGgBXAfi0iLy2\nFe2l6up9TgMGdkJ3+LO40hjzeNJtpZosBbAXwJ/CPk8V8WdUvbqez0DTP5/OTegUuynafwI4JVxD\nQ0Q2ArgZwHGFUdeS210LYJkx5uyWNZYWEJE7AdxljLko+FwAPAjgb4wxH4k4fyeA1xljXlpwbA/s\nc/n6FjWbKmjgOX0NgAkAy40xP21pY6kuIjIL4I3GmBsrnMOfUUfU+HzG8vPp4pWLVDZFo+aJyDMB\nnAL7Fw4AINgzZhz2eY3y8qBe6LYK51MLNficAnZBvb0i8oiIfF3sHkHkJv6M+qfpn08XOxcrABRd\nnjHGPA3gJ0GtnK8BeCeA9QD+AsBrANwi9ezWQ816DoBFAB4rOf4Yyj93K8qc/2wROTLe5lEDGnlO\nD8CuefNmAGfDXuW4Q0ROTKqRlCj+jPollp/PevcWSUwdm6I1xBhzfcGnPxSR/4DdFO10lN+3hIhi\nZoy5D3bl3dCdItINYCsATgQkSlFcP59qOhfQuSkaxesJ2JVYjyk5fgzKP3ePljn/p8aYX8bbPGpA\nI89plLsBvDKuRlFL8WfUf3X/fKoZFjHGTBtj7qvy8RsAc5uiFdw8kU3RKF7BMu6TsM8XgLnJf70o\nv3HOdwrPD/xBcJxS1uBzGuVE8GfRVfwZ9V/dP5+arlzUxBhzr4iEm6K9B8ARKLMpGoCLjTH/HKyB\ncSmAf4TtZb8QwE5wU7Q0XAngOhGZhO0NbwXQBuA6YG547FhjTHj57ZMALgxmpH8G9pfYWwBwFroe\ndT2nInIRgAcA/BB2u+cLAJwBgNFFBYLfly+E/YMNAFaJyBoAPzHGPMifUbfU+3zG9fPpXOci8HYA\nH4OdoTwL4EsALio5J2pTtHcCaAfwCGyn4hJuitZaxpjrg/UPLoO9dLoXwEZjTD44ZQWA3yk4/0ci\n8gYAuwD8GYCHAGwxxpTOTqeU1Pucwv5BcAWAYwE8BeDfAfQaY77VulZTBWthh4pN8HFFcPyzAN4N\n/oy6pq7nEzH9fDq3zgURERHppmbOBREREfmBnQsiIiKKFTsXREREFCt2LoiIiChW7FwQERFRrNi5\nICIiolixc0FERESxYueCiIiIYsXOBREREcWKnQsiIiKKFTsXREREFKv/D7wf0wG780cvAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124e1f090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YiPDNsItQ/Qr20vZzYWO/gO0A/kFwH/bDrh1xKewqpbdHvv8I7JvxCwBUm9T5XthY41dE\n5G9hL7m/GzZF81+R89bBzmUZgh2ugYi8AXas/wEA/yUiF5V87S8bY8KrSiOwcxf2Bs+dQ7BvCqcC\neL8pXufg/bBzDMLn2W/Axi/vNMZ8JfoNROQhAEeMMauq3E8YY74tIvthk0dHY2FH5V9hn/OfEZG/\nCI69Dekt2f0l2J/ph0WkC7bDsBE2tr0z8jq+MnhDvQ32asZxsMNM/wM7ZwWwQySPwf7cHgfwm7CP\n460lczrug40Fr6/UMGPMXhG5BcBwMO8nXKHz+QAuKTk99vkmIn8C+7P7LdjXxNtF5BXB1/9w5LwX\nI5gLA5s2WiYi7w8+nzbG3Br8/2PB9/6SiHws+FmcA3vV7s5gsjqlrdXxFH605gPz8c1yH28Pzguj\nqJfDvoE+BHup/6sAfjvm654LO978U9hfsF8EcFLMeSfAdggeC77ef8Pm63+tpH2nldxuwcqVqDGK\nCjuR7tMA/gv2r9ynYJcU/mMEK/uVnL8SdvLbd2HfxB6BjRMeW+Zrj8FeXfgJgAnERD1hO2O1rFoZ\nu0InbAfsm0HbC8H9WRmpHwsb1fwO7PDTD2Hf7C6InHMK7LoW3wu+zgHYq0mnlnyvmqKokfPPh11X\n4inYX9hDpT/XyP3608ixq6o8F0t/Bq+CXWb88eBx2Qfgf5dp05mwcwd+FjzXrkVkxc7IeU+ghhUj\nI+d/MGjb/WXqL4N9g/4p7KTkj8DOdSh97t4IYH+dr90Ft4HtyI8G3+swbOd5a8k558CugfJw8HN7\nGLaj1h0553/DvrafwPyQ3jCAZ5Z8rZqiqMG5R8N2Hh8NvubdiFkhtdzzDfb3T9zz4lcl51X6nfap\nknNfCHvF6aHg5/UgbOdmcT2PBT8a/5DggaCcEpHnw74JbTPGXJN1e1wnIvcA+J4xppG5DZQCsYtL\n/QeA1xpj7si6PUR5kOqcCxF5hYjsEbtE7pGSnHrc+eE21KU7eD43zXYSJUHsktQvwfwcD9LhHNhh\nQHYsiFok7TkXS2Evad4Ae7muFgZ2XPkncweMKV0jnkgdY8xPkN2iQVSGMeZ62KXlMxVMuKyUyHja\n2D1riJyXauci+EvhDmBu5cZaFcz8pD9Kn0F6k9GIyPoC7JyUch6C3VaeyHka0yICYJ/YnQn/A8CQ\niSy7S8kyxnwf8ZlwIkrW5ai88ix36yRvaOtcHADQDztb/hjY+NxdIrLOGBO7GEuQp9+I+VnBRERa\nVVxoK6m1YYjqsBg2HnynMWYmqS+qqnNh7Fa40dUO7xaRbtjFYi4uc7ONAP467bYRERF57CIAf5PU\nF1PVuShjL4r3eSj1EAB89rOfxerVcWtFkYu2bt2KnTt3Zt0MSggfT7/w8fTHfffdh7e97W3A/FYP\niXChc3EK5jdOinMYAFavXo3TTuMVRV8sW7aMj6dHmnk8jTGYnJzE/v370d3djfXr1yOcH16p1sxt\nWatcm52dxcGDB1W0RUNNW3vqqZ188twmsYlOK0i1cxGs+X8i5pc5XiV258YfGmMeFpFhAMcbYy4O\nzr8MdkGn78COA10KuyLkq9JsJxHpNTk5iZtvtitwT01NAQB6e3ur1pq5LWuVa7Ozs3PnZN0WDTVt\n7amnduqppyINaW9cthZ2k5gp2Kjj1QC+hfl9KFbA7gcQOjo4599g17V/MYBeY8xdKbeTiJTav39/\n0ecPPvhgTbVmbssaa/XUtLWnntojjzyCNKTauTDGfM0Yc5QxZlHJxzuD+iXGmPWR8//MGPNCY8xS\nY0ynMabXVN/8iYg81t3dXfT5qlWraqo1c1vWWKunpq099dROOOEEpMGFOReUQ5s3b866CZSgZh7P\n9evt3x8PPvggVq1aNfd5tVozt2Wtcu3IkSPYtGlTYl9zw4b54YWxMUT0AujF2Ngn1Nx3355r7e3t\nSIPzG5cFufCpqakpTgAkInJQtfWbHX+bUu1b3/oWTj/9dAA43RjzraS+btpzLoiIiChnOCxCRKrl\nMR6Yt9p8oDDe2NiYinb6+FxLa1iEnQsiUi2P8cC81ezcivKmpqZUtNPH55qrUVQioqbkMR6Y51ol\nmtrpy3PNySgqEVGz8hgPzHOtEk3t9OW5xigqEeVSHuOBeaxVsnbtWjXt9O25xihqGYyiEhERNYZR\nVCIiInICh0WIKHOMB7Lmck1bexhFJSIC44GsuV3T1h5GUYmIwHgga27XtLWHUVQiIjAeyJrbNW3t\nYRSViAiMB7Kmr9bXd2nsDq2hj3+8OGmp9X4witogRlGJiChpedmplVFUIiIicgKHRYgoc4wHsqat\nBvRVfc768FxjFJWIvMV4IGvaatVMTk568VxjFJWIvMV4IGsaa5X48lxjFJWIvMV4IGsaa5X48lxj\nFJWIvMUoKmvaasUx1IV8ea4xiloGo6hERESNYRSViIiInMBhESLKHKOorLlc09YeRlGJiMAoKmtu\n17S1h1FUIiIwisqa2zVt7WEUlYgIjKKy5nZNW3sYRSUiAqOorLld09YeRlETwCgqERFRYxhFJSIi\nIidwWISIMsd4IGsu17S1h1FUIiIwHsia2zVt7WEUlYgIjAey5nZNW3sYRSUiAuOBrLld09YeRlGJ\niMB4IGtu17S1h1HUBDCKSkRE1BhGUYmIiMgJHBYhorpE0nexGrkYynggay7XtLWHUVQiIjAeyJrb\nNW3tYRTVJ4VCfceJaA7jgay5XNPWHkZRfTE9DaxeDYyMFB8fGbHHp6ezaReRIxgPZM3lmrb2MIrq\ng0IB6O0FZmaAwUF7bGDAdizCz3t7gfvuAzo7s2snkWKMB7Lmck1bexhFTYCKKGq0IwEA7e3A7Oz8\n58PDtsNB5IE0JnQSUTYYRdVsYMB2IELsWBARUY5xWCQpAwPAjh3FHYv2dnYsiGrAeCBrLte0tYdR\nVJ+MjBR3LAD7+cgIOxjklTSGPRgPZM3lmrb2MIrqi7g5F6HBwYUpEiIqwnggay7XtLWHUVQfFArA\n6Oj858PDwMGDxXMwRke53gVRBYwHsuZyTVt7NERRFw0NDaXyhVtl+/btKwH09/f3Y+XKla1vwNKl\nwMaNwC23AFdeOT8E0tMDLF4M7NsHTEwAJU9EIprX1dWFtrY2LFmyBD09PUVjxI3W0vq6rLHm03Ot\nu7sbY2NjADA2NDR0oMLLtC6MoialUIhfx6LccSIioowxiqpduQ4EOxZERJQzTIsQkWp5jAey5lZN\nW3sYRSUiqiKP8UDW3Kppaw+jqEREVeQxHsiaWzVt7WEUlYioijzGA1lzq6atPRqiqBwWISLV8rhT\nJWtu1bS1p54ad0UtQ00UlYiIyDGMohIREZETOCxCRKrlMR7Imls1be1hFJWIqIo8xgNZc6umrT2M\nohIRVZHHeCBrbtW0tYdRVCKiKvIYD2TNrZq29jCKSkRURR7jgay5VdPWHkZRE8AoKhERUWMYRSUi\nIiIncFiEiFTLYzyQNbdq2trDKCpRswoFoLOz9uPknDzGA1lzq6atPYyiEjVjehpYvRoYGSk+PjJi\nj09PZ9MuStSj+/YVfT4XqysUvI0HsuZWTVt7GEUlalShAPT2AjMzwODgfAdjZMR+PjNj64VCtu2k\n5kxP48IPfAAbIx2MVatWzXUg15Sc7ks8kDW3atraoyGKumhoaCiVL9wq27dvXwmgv7+/HytXrsy6\nOdQqS5cCR44AExP284kJ4Nprgdtumz/nyiuBjRuzaR81r1AA1q3DotlZrH70URz3vOfhhZdcgvV7\n90Le9z7g0CH8+je+gWV/9Ec4Zvly9PT0LBgH7+rqQltbG5YsWbKgzhprSdW0taeeWnd3N8bGxgBg\nbGho6EDV12WNGEUlt4VXKkoNDwMDA61vDyWr9PFtbwdmZ+c/5+NM1BRGUYniDAzYN5yo9vZ033DK\nDbVwCCZ5AwO2AxFix4LICRwWIbeNjBQPhQDA4cPA4sVAT0/y3296Gli3zg7JRL/+yAhw0UV2GGbF\niuS/b46Zs87Cr66+Got+8Yv5g+3twK23zsXqxsfHMTs7i66urth4YFydNdaSqmlrTz21xYsXpzIs\nwigquavSJfPweJJ/2ZZOIg2/frQdvb3AffcxBpug/ZdeihN/+tPig7OzwMgIJs84w8t4IGtu1bS1\nh1FUokYVCsDo6Pznw8PAwYPFl9BHR5MdqujsBLZtm/98cBBYvry4g7NtGzsWSRoZwYk33DD36c+O\nPnq+NjiIZ153XdHpvsQDWXOrpq09jKISNaqz0yZEOjqKx97DMfqODltP+o2ecwBap6QD+YV163D5\nO96B727ZMnfs1IkJPPPQobnPfYkHsuZWTVt7NERRmRYht2W1Qufy5cUdi/Z2e+WEkjU9DdPbi/2v\nfz2++tKXzu3wKDt2AKOjMOPjmJyZKdr9MW4cPK7OGmtJ1bS1p55ae3s71q5dCyScFmHngqhejL+2\nFpd4J0oNo6hEGsRNIg1FVwql5JTrQLBjQaQWOxdEtcpiEikRkYMYRSWqVTiJtLfXpkKik0gB27FI\nYxJpzuVxG2zW3Kppaw+3XCdyzZo18etYDAwAW7awY5GCPK49wJpbNW3t4ToXRC7iHICWyuPaA6y5\nVdPWHq5zQURURR7XHmDNrZq29mhY54LDIkSk2vr16wGgKLNfS62Z27LGWl6ea2nNueA6F0RERDnF\ndS7Ib9zGnIjIG9xynbLHbcypgjxug82aWzVt7eGW60TcxpyqyGM8kDW3atrawygqEbcxpyryGA9k\nza2atvYwikoEcBtzqiiP8UDW3Kppa4+GKCrnXJAOPT3AtdcChw/PH2tvB269Nbs2kQpdXV1oa2vD\nkiVL0NPTU7SUcaVaM7dljbW8PNe6u7tTmXPBKCrpwG3MiYhajlFU8he3MSci8gqHRShbhYKNmx46\nZD8fHrZDIYsX2x1GAWDfPuCSS4ClS7NrJ6nkazyQNbdq2trDKCoRtzGnJvgaD2TNrZq29jCKSgTM\nb2NeOrdiYMAeX7Mmm3aRer7GA1lzq6atPYyiEoW4jTk1wNd4YCtqY2O75j76+i6FCCAC9Pf3YWxs\nl5p2ulDT1h4NUVQOixCRs3zdqbJVtUrWrl2rpp3aa9raw11RE8AoKhFR/SJzEWM5/tZANUorisor\nF0REKeMbOeUNOxdE5KwwVrd//350d3cvWDWxUr3VtUbvR1o1oHK7xsbGMv+ZuVLT1p56amkNi7Bz\nQUTOcike2Oj9SKsGVG7X1NRU5j8zV2ra2sMoKhFRE1yKB1aSdTsrid7u0X375v4/NrYLGzb0zqVM\nNmzoLUqgaIpbMorqWRRVRF4hIntE5FEROSIi59dwm3NEZEpEDovIAyJycZptJCJ3uRQPrCTrdsbZ\nGHQk5m43MoILP/ABnDAzU/W2abVTa01be/IQRV0KYB+AGwB8odrJIvICALcCuB7AWwFsAPBJEfmB\nMeYr6TWTiFzkUjyw0fuRVm18fKKoJiJAoQCzejVkZgbYC7zkxS9G9/r1c/v/HA3giq98BZ+76iqM\n1Xifsrp/raxpa0+uoqgicgTA640xeyqcswPAa4wxL4kc2w1gmTHmtWVuwygqEanmVFokbiPB2dn5\nz4Odip26T1RWXnZFfRmA8ZJjdwJ4eQZtId8VCvUdJ8qDgQHbgQjFdCyIqtGWFlkB4PGSY48DeLaI\nHGOM+XkGbSIfTU8v3CwNsH+1hZulcU8T9VyJBxqjpy011c44Az1tbTjmqafmf9jt7TBXXIHJiYlg\nUmBf1cdG7f1jFJVRVKLEFQq2YzEzM3/5d2Cg+HJwb6/dNI17m6jmazww69qP3ve+4o4FAMzOYv+l\nl+LmRYtQi8nJSbX3j1HU/EVRHwNwXMmx4wD8uNpVi61bt+L8888v+ti9e3dqDSWHdXbaKxahwUFg\n+fLiceZt29ixcICv8cAsa8+87jpcsHfv3Oc/b2ub+/+JN9wwlyKpRuv9y3MUdffu3bj88stxxx13\nzH1cc801SIO2zsU3sHBll1cHxyvauXMn9uzZU/SxefPmVBpJHuC4shd8jQdmVisUcOrExNzxL6xb\nh3/es6fotfLq6Wk889Ah9PX1Y3x8IhjyAcbHJ9DX1z/3ofL+pVTT1p5ytc2bN+Oaa67BeeedN/dx\n+eWXIw0pwz3ZAAAgAElEQVSpDouIyFIAJ2J+ndlVIrIGwA+NMQ+LyDCA440x4VoWHwfw7iA18inY\njsabAMQmRYiaMjAA7NhR3LFob2fHwiG+xgMzq3V24hlf+xp+cfbZ2Nfbi2XvfretBZfUzegovvOR\nj+BkEb33IYOatvZ4H0UVkbMBfBVA6Tf5tDHmnSJyI4DnG2PWR27zSgA7AfwmgEcAfMAY81cVvgej\nqNSY0shdiFcuKO8KhfhhwXLHyVlO7opqjPkaKgy9GGMuiTn2dQCnp9kuoopZ/ugkT6I8KteBYMeC\narRoaGgo6zY0Zfv27SsB9Pdv2oSVNS61SzlXKAAXXQQcOmQ/Hx4Gbr0VWLzYRlABYN8+4JJLgKVL\ns2snVRXG6sbHxzE7O4uurq7YeGBcnTXWkqppa089tcWLF2NsbAwAxoaGhg5UfdHVyJ8o6vLlWbeA\nXNHZaTsRpetchP+G61zwrzT1fI0HsuZWTVt7GEUlysqaNXYdi9Khj4EBe5wLaDnBh3gga+7XtLXH\n+11RiVTjuLLzfIgHsuZ+TVt78rArKhFRanyNB7LmVk1be7yPorYCo6hERESNycuuqI07eDDrFlA9\nuCMpEZG3/ImiXnYZVq5cmXVzqBbT08C6dcCRI0BPz/zxkREbEd24EVixIrv2kTN8jQey5lZNW3sY\nRaX84Y6klCBf44GsuVXT1h5GUSl/uCMpJcjXeCBrbtW0tYdRVNKnFXMhuCMpJcTXeCBrbtW0tUdD\nFNWfORf9/Zxz0axWzoXo6QGuvRY4fHj+WHu7XYabqEZdXV1oa2vDkiVL0NPTg/Xr1xeNg1eqs8Za\nUjVt7amn1t3dncqcC0ZRySoUgNWr7VwIYP4KQnQuREdHcnMhuCMpEVHmGEWldLVyLkTcjqTR7zsy\n0vz3ICKizHBYhOb19BTvDBodskjqigJ3JKUE+RoPZM2tmrb2MIpK+gwMADt2FE+ybG+vr2NRKMRf\n4QiPc0dSSoiv8UDW3Kppaw+jqKTPyEhxxwKwn9c6VDE9bedulJ4/MmKPT09zR1JKjK/xQNbcqmlr\nD6OopEuzcyFKF8gKzw+/7syMrZe7sgHwigXVxdd4IGtu1bS1h1HUBHDORUKSmAuxdKmNsYbnT0zY\nuOltt82fc+WVNtJKlABf44GsuVXT1h5GURPAKGqCpqcXzoUA7JWHcC5ELUMWjJm6rdqcGSLyBqOo\nlL6k5kIMDBQPqQD1TwqlbNQyZ4aIqAqmRahYEnMhKk0KZQdDLwc3lQtjdfv370d3d/eCS9WV6qyx\nllRNW3vqqbWX/iGYEHYuKFlxk0LDjkb0DYv0CRdSCx+nwcGFsWRlm8r5Gg9kza2atvYwikp+KRTs\n3IzQ8DBw8GDxJmUf/Wiym6BRshzbVM7XeCBrbtW0tYdRVPJLuEDWsmVAW9v88fANq60NMAb4wQ+y\nayNV59CcGV/jgay5VdPWHg1RVA6LULKOPx5YtAj40Y8WDoM89ZT9UDZuTyUcmjOzfv16APYvs1Wr\nVs19XkudNdaSqmlrTz21tOZcMIpKyas07wJQeXmdAnzsiHKFUVRyh2Pj9hSoZc7M6CjnzBBRVbxy\nQelZvnzhBmgHD2bXnhREkmixnHt5JbWQWov4Gg9kza2atvbUG0Vdu3YtkPCVC865oHQ4NG5PEeFC\naqXzYQYGgC1b1M2T8TUeyJpbNW3tYRSV/NTsBmiULYc2lfM1HsiaWzVt7WEUlfzDcXtqIV/jgay5\nVdPWHkZRyT/hWhel4/bhv+G4vcK/gsk9vsYDWXOrpq09jKImgBM6lXJhZ80E2ujdhE4iyhVGUckt\n2sftufsnEVFqOCxC+ePg7p9eSfCqlq/xQNbcqmlrD3dFJcpCgrt/ctijTgmvo+FrPJA1t2ra2sMo\nKlHSyqVQSo9zFdHWK71iFA5JhVeMZmZsvY4kka/xQNbcqmlrD6OoREmqdx6FQ7t/eiG8YhQaHLSr\nuEbXRKnxilHI13gga27VtLVHQxR10dDQUCpfuFW2b9++EkB/f38/Vq5cmXVzKCuFArBunf3rd2IC\nWLwY6OmZ/6v40CHglluASy4Bli61txkZAW67rfjrHD48f1tKXk+P/flOTNjPDx+erzVwxairqwtt\nbW1YsmQJenp6FoyDV6qzxlpSNW3tqafW3d2NsbExABgbGho6UPVFVyNGUckf9ezoyd0/s5WDfWeI\nXMAoKmVOpPJH5mqdR8FVRLNVad8ZIvICr1xQzZxZMKqWv4od2/3TGwlfMfI1HsiaWzVt7eGuqERJ\nq3U3Vsd2//RC3BWj0vVFRkfr+vn7Gg9kza2atvYwikqUpHp3Y9W+iqhvwn1nOjqKr1CEw1kdHXXv\nO+NrPJA1t2ra2sMoKlFSOI/CDeEVo9Khj4EBe7zOoShf44GsuVXT1h4NUVQOi5AfuBurOxK8YuTr\nTpWsuVXT1p56atwVtQxO6GwdJyZ0urAbax7xcSnLidcVeYtRVKJacB6FPtyBlih3OCxCNeNfUFS3\nlHeg9SUe2Oh9ZE1HTVt7uCsqEfktwR1o4/gSD2z0PrKmo6atPYyiEpH/UtyB1pd4YCXVvubY2K65\njw0beudWzN2woRdjY7tadh/yXNPWHkZRiSgfUtqB1pd4YCX1fM1KtN53H2ra2sMoKhHlQ60rp9bJ\nl3hgo/exlq+xdu3azO+f7zVt7WEUNQGMohIpxx1oK2o2isooKzWDUVQicg9XTq3KmMoflB31O0Er\nxmERIkpPyiun+hoPrKcGVH6XGxsbU9FOF2tA9TSP6881RlGJyE0p7kDrazywnlq1N8CpqSkV7XSz\nVnvnIvu2MopKRM0qN4ygdXghpZVTfY0HNlqrRFM7XanVI+u2MopKRM3hctpzfI0H1lPr6+uf+xgf\nn5ibqzE+PoG+vn417XSxVo+s28ooKhE1LuXltF3jazyQNR21sTHULOu2MoqaMEZRKXcY7YzFSCYl\nLQ/PKUZRichKcTltIqIkcFiEyEUDAws3AEtgOW3XRGN1QF/FcycmJlRFAFnTXztypPLtojHgrNvK\nKCoRNS+l5bRdE43VVROepyUCyJo/NW3tYRSVdHAt1ph3cXMuQoODC1MkHqs3OqgpAsiaPzVt7WEU\nlbLHWKNbuJx2kTBWF91avBJNEUDW/Kk1dNvgNVpaO+nYY1t6P9KKoi4aGhpK5Qu3yvbt21cC6O/v\n78fKlSuzbo5bCgVg3Toba5yYABYvBnp65v8yPnQIuOUW4JJLgKVLs24tAfZx2LjRPi5XXjk/BNLT\nYx+/ffvsY1nyi89XXV1daGtrw2c+U/3+7tjRVjT2HN52yZIl6OnpYY21hmt137ajA/LSlwJHjqDr\n935vrnbRww/jlNFRyMaNwIoVLbkf3d3dGLOZ27GhoaEDVV9INWIUNe8Ya3RToRC/jkW5456rZRMp\nx3/VkS8KBXtVeGbGfh7+jo3+Lu7oaNlaNYyiUjoYa3RTSstp+4odC1Kjs9Nu4hcaHASWLy/+I2/b\nNudfy0yLEGONZVTbcZOyFz5G1TaY0rAzaLWnzvj4hMqoImvVa3Xf9oorbIg17FBEfveaj3wEEvzu\nZRSV3OZjrDGBYYNq0TPK3vxjpH9n0Gq0RhVZSymKGvNH3c+OPhp3r1s392xmFJXc5WOsMaEETLXo\nGWXPpSiqK+1krf5aQ7eN+aNu6S9+gWddd11L7wejqJQ8H2ONpRt7hR2MsBM1M2PrNdynatEzyl69\nu1hmHVd0oZ2s1V+r97bn3nNP0R91Pzv66Ln/r/viF+d+b7kcReWwSJ51dtrYYm+vnUAUDoGE/46O\n2rpLE4vCyVLhC3dwcOF8khonS1XaWZAS1MQQVviYrF37ibnHKG4c/MEH12a+G2U1mzZtUrlrJmvV\na/Xc9qRjj0V3f/9czXzkI7h73To867rrbMcCsL97t2xpyf1Ia84FjDFOfwA4DYCZmpoy1KAnnqjv\nuAuGh42xIYHij+HhrFtGUfv2GdPRsfBxGR62x/fty6ZdKYh7OkY/KEcUPe+npqYMAAPgNJPgezPX\nuSB/LV++MAFz8GB27aFiyvL+acvD9t1UByVr1aS1zgWHRchPNSZgTEqxNKpBAkNY1R6HNB7ftJ4X\nExOMorpaa+i2wfM6ttbC5y+jqNQ4JT3klqm06mh4POhgZBE5pIiwoxeT969lETeXdqocH58o2sF1\n06ZNc7WJiQk17WSNu6ImgWkR3+VtY7I6EzBZRw4JtgNR+tdTjYu4+bpTJWtu1bS1h1FUSleCsUxn\nhAmYjo7iv3zDZc47OooSMFlHDgmVh7CqSHynStZYa6CmrT0aoqjcFdVnS5cCR47YN1PA/nvttcBt\nt82fc+WVdpdNn6xYYXdyLb1fPT32eOSFltYOiVSjuCGsw4ft/6M79ZaR6E6VrLHWql1RFdW4K2oZ\nTIvUoPQXeIgbk1GWcpYWIdKIu6JS45oY0yZKTZ1DWETkDqZF8sDHjclKNBrLSmOnylh5S+zUas2a\n+CsTAwPAli1N/2w0xRVZ0xP7pfSxc+G7OmKZLtO0U+UC09MLl1gH7GMTLrG+Zk1N7fFSuQ5EAp2u\nrGN+rGUXDaVscVjEZz5uTFaGpp0qi+QxsaNI1jE/1rKp5Uq53x0Z/05h58JnORrT1rRTZZFwFcrQ\n4KBdljx6NanGjdSoflnH/FjLppYbitcx4rCI71Ie09aimd0MK2lkp8oFmlyFkhqnaedM1vTsQOuF\n0quiwMK0VW9vZmkrRlEp11q6mRQ3UiOiJFWaUwfU9McLo6hELmtiFUoioljhEHdI0VVRDouQM5qN\nrG3YUP9M8kZ2qlygxYkdbu1dG0YnyQsDAwt3E1awjhE7F+SM5iNrlTsXfX39iexUWSQusVM6Ljo6\n6tX8F1cwOkleULqOEYdFyBlJRdYqSTwGl6PEjmvKPYYiwIYNvRgb2zX3sWFDL0RsjdFJUiPuqmgo\nGn3PADsX5IykImuVpBKDCxM7pX9FDAzY43leQCtDjUYZa3lePPPQodhaeLye70cUS/k6RhwWIWc0\nG1mzG/+Vt2nTpvRicCmuQkmNaTTKWO158cz9+7Hm8svxyIUXojta27sXr/iHf8CXLrsM7Wefnb/o\nJCUrvCpauvpv+G+4+m9Gv2MYRaXcyMtEx7zcz7Q09fPjTq/Uak3uW8QoKhGRdlyRlVpN6VVRDouQ\nKmlGAKulRSYmJhgrpOZxRVYidi5IlzQjgH19tl4ubhr843yskMMeCihde4CoVTgsQqpo2lmRsUJq\nGFdkpZxj54JU0bSzIndkzCdjKn9UpXjtAaJW4bAIqaJpZ0XuyEh144qsiWLyyV2MohIRJWl6euHa\nA4DtYIRrD3DhtJqwc5G+tKKovHJBRJSkcEXW0isTAwO8YkG50ZLOhYi8G8A2ACsATAN4jzHm3jLn\nng3gqyWHDYCVxpgnUm0oZU7TTpWMolLDlK49QNQqqXcuROQtAK4G0AdgL4CtAO4UkRcZY54sczMD\n4EUAfjJ3gB2LXNC0U6WrUVQioqy1Ii2yFcAuY8xnjDH3A/h9AE8BeGeV2xWMMU+EH6m3klTQFCll\nFJWIqDGpdi5E5BkATgcwER4zdgbpOICXV7opgH0i8gMR+bKInJlmO0kPTZFSRlGJyDnldkFt8e6o\naQ+LPAfAIgCPlxx/HMBJZW5zAEA/gG8COAbApQDuEpF1xph9aTWUdNAUKWUUlYicoiiplGoUVURW\nAngUwMuNMfdEju8A8EpjTKWrF9GvcxeA7xtjLo6pMYpKRET51uCOvK5GUZ8E8DSA40qOHwfgsTq+\nzl4AZ1U6YevWrVi2bFnRsc2bN2Pz5s11fBsiIiIHhTvyhh2JwcEF+9vs3rABu7dsKbrZj370o1Sa\nk2rnwhjzSxGZgt2Ocg8AiM3r9QL4izq+1CmwwyVl7dy5k1cuPKApUsq4KRE5pcqOvJsHBlD653bk\nykWiWrHOxTUAbgo6GWEUtQ3ATQAgIsMAjg+HPETkMgDfA/AdAIth51ycC+BVLWgrZUxTpJRxUyJy\nTr078h48mEozUo+iGmNuhl1A6wMAvg3gJQA2GmPCqasrAPxG5CZHw66L8W8A7gLwYgC9xpi70m4r\nZU9TpJRxUyJyTr078i5fnkozWrIrqjHmemPMC4wxS4wxLzfGfDNSu8QYsz7y+Z8ZY15ojFlqjOk0\nxvQaY77einZS9jRFShk3JSKnKNqRl3uLkCqaIqWMmxKRM5TtyMtdUckqFOKfcOWOExGRLg2sc5FW\nFLUlwyKk3PS0zUeXXjIbGbHHp6ezaRcREdUu3JG3dPLmwIA93qIFtAB2LqhQsD3dmZniMbnwUtrM\njK23eOnYXFGyXC8ReaDeHXldTYuQcuHCK6HBQTt7ODopaNu2RIdGjDGYmJjA2NgYJiYmEB2ac6WW\nGF41IqIspZQW4YROqrrwStl8dIM0rVeR6ToXpVeNgIUTsHp7FyzXS0SkHa9ckDUwUBxbAiovvNIE\nTetVZLrORQZXjYiIWoGdC7LqXXilCZrWq8h8nYuBAXt1KJTyVSMiolbgsAjFL7wSvslFL9cnRNN6\nFSrWuah3uV4iIuW4zkXeNbhNLyWotHMX4pULIkoZ17mgdHR22oVVOjqK38zCy/UdHbbOjkU6FC3X\nS0SUFA6L0PzCKyUdCHPFFfinF74Q999zD7qffDKxrco1bZ2e6ZbrypbrJSJKCjsXZMW8eU1OTuLm\nL38ZQLIRTk2R0kyjqOFVo9LlesN/w+V62bHQjUvnEy3AYREqK60Ip6ZIaeZbritarpcawEXQiGKx\nc0FlpRXh1BQpzTyKCtS/XC/pwKXzicrisAiVlVaEU1OkVEUUldwULoIWzo8ZHFwYKeYiaJRTjKIS\nUWWcU1AZo8TkMEZRiaj1OKeguhYunU/kCg6LOERTFDPvUdRMI6ytwo3ValNp6Xx2MCin2LlwiKYo\nZt6jqJlGWFuFcwqqa/HS+USu4LCIQzRFMfMeRc08wtoq3FitvLhF0A4eLP55jY4yLUK5xM6FQzRF\nMfMeRVURYW0VzimIx6XzicrisIhDNEUx8x5FzVWElXMKyiuzdD4GBrhsO+Uao6hEVF6lOQUAh0aI\nQo5GthlFJaLW4pwCotowsr0Ah0VSoin+qKmmrT2aaupwYzWi6hjZjsXORUo0xR811bS1R1NNJc4p\nIKqMke1YHBZJiab4o6aatvZoqqnFjdWIKmNkewF2LlKiKf6oqaatPZpqROQwRraLcFgkJZrij5pq\n2tqjqUZEDmNkuwijqERERM1wOLLNKCoREZE2jGzH4rBIFZqiij7UtLXHlRoRKcXIdix2LqrQFFX0\noaatPa7UiEgxRrYX4LBIFZqiilpqIsCGDb0YG9s197FhQy9EbI1R1BzFVInIYmS7CDsXVWiKKmqq\nVcIoKmOqRJRvHBapQlNUUVOt0Z+ZtvvhSo1yztFNsSi/GEWlulWbY+j4U4pIl+nphZMFARt/DCcL\nrlmTXfvIaYyikrPCuRjlPoiojNJNscJdN8N1FWZmbD1nMUfSLzfDIppihT7U6vlZA5UTD8YYlfdR\nU41yiptikaNy07nQFCv0oVbJwttVvs3k5KTK+6ipRjkWDoWEHQxHVn6kfMvNsIimWKEPtUpKb1eN\n1vuoqUY5x02xyDG56VxoihW6XjMGGB+fQF9f/9zH+PgEjLG10ttVo/E+aqtRzlXaFItIodwMi2iK\nFeatNjaGijS1VWuNPFcpanrDDeU3xQqP8woGKcMoKqWO0VWiCipFTT/6UfsCCTsT4RyL6C6cHR3x\nS08T1SCtKGpurlwQEalTGjUFFnYeli0Djj0WeO97uSkWOcOrzoWm6CBr87UjRyrvimqMnra6WCOH\n1RI1Lbf5VY43xSL9vOpcaIoOssZdUVv5MyWHNRM1ZceClPIqLaIpOshafE1be3yokQcYNSXPeNW5\n0BQdZC2+pq09PtTIA4yakme8GhbRFB1kjbuiMqZKNYlO3gQYNSUvMIpKRJSVQgFYvdqmRQBGTanl\nuCsqEZFvOjttlLSjo3jy5sCA/byjg1FTcpJXwyKa4oGslY9NamqPDzVy3Jo18VcmGDUlh3nVudAU\nD2SNUdRW/kzJceU6EOxYtEal5df5GDTEq2ERTfFA1uJr2trjQ42ImjA9bee9lCZzRkbs8enpbNrl\nOK86F5rigazF17S1x4caETWodPn1sIMRTqidmbH1QiHbdjrIq2ERTfFA1tyOotrpDL3BhxXd3fXI\nER3tJKIm1LL8+rZtHBppAKOoRDG4kytRjpSuNRKqtvy6BxhFJSIiSgOXX0+cV8MimuKBrLkfRfXh\nuUZENai0/Do7GA3xqnOhKR7ImvtR1Eo0tZMxVaImcPn1VHg1LKIpHshafE1bexqNeGpqJ2OqRA0q\nFIDR0fnPh4eBgwftv6HRUaZFGuBV50JTPJC1+Jq29jQa8dTUTsZUiRrE5ddT49WwiJYYI2vuR1Gr\n0dTO3MZUuaoiJYHLr6eCUVQics/0tF3caNu24vHwkRF7GXtiwr5pEFFFjKISEQFcVZHIAV4Ni2iK\nALLmfhTVh5qXuKoikXpedS40RQBZcz+K6kPNW+FQSNjBiHYscrCqIpF2Xg2LaIoAshZf09Ye32te\n46qKRGp51bnQFAFkLb6mrT2+17xWaVVFIsqUV8MimiKArLkfRfWh5i2uqkikGqOoROSWQgFYvdqm\nQoD5ORbRDkdHR/zaBS7ieh6UIkZRiYiAfK2qOD1tO1KlQz0jI/b49HQ27SKqwqthEU0RQNYYRdVe\nc1oeVlUsXc8DWHiFprfXnys05BWvOheaIoCsMYqqvea8cm+ovrzRcj0PcphXwyKaIoCsxde0tSfP\nNXJAONQT4noe5AivOheaIoCsxde0tSfPNXIE1/MgB3k1LKIpAsgao6jaa+SISut5sINBSjGKSkSk\nVaX1PAAOjVDTGEUlIsqTQsFuHx8aHgYOHiyegzE6yt1fSSWvhkU0xfxYYxRVe42UC9fz6O21qZDo\neh6A7Vj4sp4HecerzoWmmB9rjKJqr5ED8rCeB3nJq2ERTTE/1uJr2tqT5xo5wvf1PMhLXnUuNMX8\nWIuvaWtPnmtERGnxalhEU8yPNUZRtdeIiNLCKCoREVFOMYpKRERETvBqWERTzI81RlFdrhERNcOr\nzoWmmB9rjKK6XCMiaoZXwyKaYn6sxde0tYe1+BoRUTO86lxoivmxFl/T1h7W4mteKrdMNpfP1oWP\nkxcWDQ0NZd2Gpmzfvn0lgP7+/n6ceeaZaGtrw5IlS9DT01M0htzV1cWagpq29rBW/nHyyvQ0sG4d\ncOQI0NMzf3xkBLjoImDjRmDFiuzaRxYfp5Y7cOAAxsbGAGBsaGjoQFJfl1FUIvJboQCsXg3MzNjP\nw51EozuOdnTEL7NNrcPHKROMohIRNaKz0278FRocBJYvL97KfNs2vmFljY+TV7waFlmxYgUmJycx\nPj6O2dlZdHV1LYjdsZZtTVt7WCv/OHmlpwdYvNjuIgoAhw/P18K/kCl7fJzsFZylS2s/3qS0hkUY\nRWWNUVTW8hFFHRgAduwAZmfnj7W35+MNyyV5fpymp4HeXnuFJnp/R0aA0VHb6VqzJrv21cGrYRFN\nUT7W4mva2sNafM1LIyPFb1iA/XxkJJv2ULy8Pk6Fgu1YzMzYoaDw/oZzTmZmbN2R1IxXnQtNUT7W\n4mva2sNafM070UmBgP1LOBT9RZ4ERikb18rHSRvP5px4NeeCUVT9NW3tYa2GKGqLx4ATVyjYGOOh\nQ/bz4WHg1luLx/b37QMuuaT5+8MoZeNa+ThplcGck7TmXMAY4/QHgNMAmKmpKUNECdu3z5iODmOG\nh4uPDw/b4/v2ZdOuerXifjzxhP1agP0Iv9fw8Pyxjg57HsXz5fnWrPb2+ecMYD9PydTUlAFgAJxm\nknxvTvKLZfHBzgVRSnx7syzXziTbH/3ZhG8K0c9L3zRpoVY8TpqVPodSfu6k1bnwaliEUVT9NW3t\nYa1C7e67UXjiCZxw//32gZuYAK69FrjttvkX4JVX2kv9Lih3KT3JS+yMUjavFY+TVnFzTsLn0MSE\nfW5Fh9sSwChqDTRF+VhjFNWLWmcnHl23Dhfs3QsAxbP4+WYZL89RSmpcoWDjpqG4FUpHR4EtW5yY\n1OlVWkRTlI+1+Jq29rBWvXbnKafg521tRefyzbKCvEYpqTmdnfbqREdHccd9YMB+3tFh6w50LADP\nOheaonysxde0tYe16rWN+/bhmKeeKjqXb5Zl5DlKSc1bs8bunVLacR8YsMcdWUALaNGwiIi8G8A2\nACsATAN4jzHm3grnnwPgagC/BeB/AHzYGPPpat9n/fr1AOxfYKtWrZr7nDU9NW3tYa1y7VnXXYd1\n4ZAIYN8sw7/KwzdRXsGwPLusTRkp99xw7TmT5OzQuA8AbwFwGMDbAZwMYBeAHwJ4TpnzXwDgpwA+\nCuAkAO8G8EsArypzPtMiRGnwLS3SCtqjlHlPYtACaaVFWjEsshXALmPMZ4wx9wP4fQBPAXhnmfPf\nBeBBY8wfG2P+yxhzHYDPB1+HiFrFszHgltB8WXt62m5pXjo0MzJij09PZ9Mu8lKqwyIi8gwApwP4\nSHjMGGNEZBzAy8vc7GUAxkuO3QlgZ7XvZ4Jo3f79+9Hd3V204iBrtdU2bKi8cZW9WNT499NwH1mr\nr3byrl14xQUXIHwEjTGYPOMMPDo4iItPqfxmGT5fckXjZe3SfSuAhUM2vb22A8TOIiUg7TkXzwGw\nCMDjJccfhx3yiLOizPnPFpFjjDE/L/fNVEb5nKvVtismo6g5qgH4ZXt7bI0cEe5bEXYkBgcXxmUd\n2reC9PMmLbJ161ZcfvnluOOOO+Y+/vZv/3aurjXmp61WK0ZRWSPHhMNZIa5Zkju7d+/G+eefX/Sx\ndWs6Mw7S7lw8CeBpAMeVHD8OwGNlbvNYmfN/XOmqxc6dO3HNNdfgvPPOm/u48MIL5+paY37aarVi\nFOg4uQIAABKtSURBVJU1ctDAQHE8FuCaJTmyefNm7Nmzp+hj586qMw4akuqwiDHmlyISXmvfAwBi\nB3V7AfxFmZt9A8BrSo69OjhekcYon2s1uwpsdYyisvbggw/W/HwhJSot8MUOBiVITMozrkRkE4Cb\nYFMie2FTH28CcLIxpiAiwwCON8ZcHJz/AgD/DuB6AJ+C7Yj8OYDXGmNKJ3pCRE4DMDU1NYXTTjst\n1fuSB3E7bkflcoIelcXni0PiFvji0Ejufetb38Lpp58OAKcbY76V1NdNfc6FMeZm2AW0PgDg2wBe\nAmCjMaYQnLICwG9Ezn8IwO8A2ABgH2xnZEtcx4KIiGoQt8DXwYPFczBGR+15RAloyQqdxpjrYa9E\nxNUuiTn2ddgIa73fR2WUz6VarWkRRlFZsxM7+6o+T6Ta5Q1KX7hmSW+vTYVE1ywBbMeCa5ZQgrgr\nKmtFtfHx+drExERR5HDTpk0IOx+MorIGAH19/di0aVPZ58zk5Kaix54yFC7wVdqBGBjgkuSUOG+i\nqED2kTzWqte0tYe11tVIAY0LfJGXvOpcZB3JY616TVt7WGtdjYjyw6thEa1xPdYYRWWNiPIk9Shq\n2hhFJSIiaoyzUVQiIiLKF6+GRbTG9VhjFJW16s8LIvKHV50LrXE91vIbRe3vL10HIlz93p47Pj6h\nop1Z14jIL14Ni2iK3bEWX9PWnqx3DdXUzqyfF0TkD686F5pid6zF17S1J+tdQzW1M+vnBRH5w6th\nEU2xO9YYRa1l11At7cy6RkR+YRSVKEXcNZSINGMUlYiIiJzg1bCIpmgda4yi1rtrqNb7wCgqEdXL\nq86Fpmgda4yi1mJyclJFO7OuEZFfvBoW0RStYy2+pq09adf6+voxNvYJGGPnV+zaNYa+vv65Dy3t\nzLpGRH7xqnOhKVrHWnxNW3tY01EjIr8sGhoayroNTdm+fftKAP39/f0488wz0dbWhiVLlqCnp6do\nTLerq4s1BTVt7WFNR029QgFYurT240SOOHDgAMZsZn5saGjoQFJfl1FUIqJKpqeB3l5g2zZgYGD+\n+MgIMDoKTEwAa9Zk1z6iJjCKSkTUaoWC7VjMzACDg7ZDAdh/Bwft8d5eex4RzfFqWGTFihWYnJzE\n+Pg4Zmdn0dXVtSAGx1q2NW3tYU1/LVNLlwJHjtirE4D999prgdtumz/nyiuBjRuzaR9Rk9IaFmEU\nlTVGUVlTXctcOBQyOGj/nZ2drw0PFw+VEBEAz4ZFNEXrWIuvaWsPa/prKgwMAO3txcfa29mxICrD\nq86Fpmgda/E1be1hTX9NhZGR4isWgP08nINBREW8mnPBKKr+mrb2sKa/lrlw8maovR04fNj+f2IC\nWLwY6OnJpm1ETWIUtQxGUYkoNYUCsHq1TYUA83Msoh2Ojg7gvvuAzs7s2knUIEZRiYharbPTXp3o\n6CievDkwYD/v6LB1diyIiniVFtG0yyNr3BWVNU92U12zJv7KxMAAsGULOxZEMbzqXGiKz7HGKCpr\nHsVUy3Ug2LEgiuXVsIim+Bxr8TVt7WHN7RoR6eRV50JTfI61+Jq29rDmdo2IdGIUlTVGUVlztkZE\nzWEUtQxGUYmIyGXV+sppvk0zikpERERO8CotoikixxqjqKxl/1xzRqEQnzwpd5xIOa86F5oicqwx\nispa9s81J0xPA729wLveBXzwg/PHR0aA0VHglluAc8/Nrn1EDfBqWERTRI61+Jq29rDmb80JhYLt\nWMzMAB/6EHDeefZ4uLz4zIytf/Wr2baTqE5edS40ReRYi69paw9r/tac0Nlpr1iE7rwTWLKkeKM0\nY4A3v9l2RIgc4dWwyPr16wHYv15WrVo19zlremra2sOavzVnfPCDwL332o4FML/jatS2bZx7QU5h\nFJWISIMlS+I7FtEN08hLPkZRvbpyQUTkpJGR+I7F4sXsWOSA43/jx/JqzgURkXPCyZtxDh+en+RJ\n5BCvrlxoythXqx11VOXrYLt2jaloJ9e5YM3VWi31zBUKNm5aavHi+SsZd94J/Mmf2DQJkSO86lxo\nythXqwGVs/hTU1Mq2sl1LlhztVZLPXOdnXYdi97e+Wvj4RyL886bn+T58Y8Dl13GSZ3kDK+GRTRl\n7OupVaKpnVzngjWXarXUVTj3XGBiAujoKJ68eccdwPvfb49PTLBjQU7xqnOhKWNfT60STe3kOhes\nuVSrpa7GuecC9923cPLmhz5kj69Zk027iBrk1bCIpox9LbVK1q5d2/T3mx9a7kV0GMburmuvwnKd\nC9Z8rdVSV6XclQlesSAHcZ2LjLQi15xldpqIiPTjlutERETkBK+GRTTF4KrVgMqXFcbGmo+iVvse\nWdx3jY8Fa37Wmr0tETXOm86FvaojiM4vGB+fUBmRm5ycRF/fzXNt37Rp01xtYmICN998M6ammv9+\n1eKuWdz3LL4na/msNXtbImqc18MiWiNymrae1hYPZI21pGrN3paIGud150JrRE7T1tPa4oGssZZU\nrdnbElHjvBkWiaM1IpdFJK/az0hLPJA11jQ814ioOd5EUYEpAMVRVMfvGhERUaoYRSUiIiInLBoa\nGsq6DU3Zvn37SgD9QD+AlUW1q64yC2Jn4+PjmJ2dRVdXF2sZ1LS1hzV/a2l+XSJfHDhwAGN22eax\noaGhA0l9Xa/nXExOTqqMyOW5pq09rPlbS/PrElFl3gyLTE0Bu3aNoa+vf+5Da0QuzzVt7WHN31qa\nX5eIKvOmcwHoisGxFl/T1h7W/K2l+XWJqDJv5lz09/fjzDPPRFtbG5YsWYKenp6i5Xy7urpYU1DT\n1h7W/K2l+XWJfJHWnAtvoqiu7YpKRESUNUZRiYiIyAlepUU07cjIGndFzXutv7+v4uv1yJGFUXHX\nn2tEZHnVudAUg2ONUdS816pJOyqexf0nIsurYRFNMTjW4mva2sNaurVKfHyuEZHlVedCUwyOtfia\ntvawlm6tEh+fa0RkMYrKmhfxQNb01b70pdMrvnZvuqkr1bZk9fwmcgmjqGUwikqkU7X3W8d/9RB5\ngVFUIiIicoJXaRGtkTzWGEXNYw2oHEU1xr8oKiOsRJZXnQutkTzWGEXNY62vrx+bNm2aq01MTBTF\nVCcnN/G5xggrecqrYZGsY3esVa9paw9r/ta0tYcRVsoTrzoXWcfuWKte09Ye1vytaWsPI6yUJ4yi\nssYoKmte1rS1hxFW0ohR1DIYRSUiImoMo6hERETkBK+GRVasWIHJyUmMj49jdnYWXV3zKwCGMTDW\nsq1paw9r/ta0taeZ+0GUlrSGRRhFZY1RVNa8rGlrD2OqlCdeDYtoipaxFl/T1h7W/K1paw9jqpQn\nXnUuNEXLWIuvaWsPa/7WtLWHMVXKE6/mXDCKqr+mrT2s+VvT1h7GVEkjRlHLYBSViIioMYyiEhER\nkRO8GhZhFFV/TVt7WPO3pq09jKmSRoyi1kBTfIw1v+OBrOmvaWsPY6qUJ14Ni2iKj7EWX9PWHtb8\nrWlrD2OqlCdedS40xcfSqPX392FsbNfcR1/fpRABRGxNSzvzEA9kTX9NW3sYU6U88WrOhe9R1O3b\nK/8sduzQ0c48xANZ01/T1h7GVEkjRlHLyFMUtdrvE8cfSiIiajFGUYmIiMgJXg2L+B5FrTYs8opX\nTKhoZ97jgazpqGlrD2OqpBGjqDXQFBHLInampZ15jweypqOmrT3afl8QpcmrYRFNEbGsY2ea74Om\n9rDmb01bezT/viBKmledC00RsaxjZ5rvg6b2sOZvTVt7NP++IEqaV8Mi69evB2B776tWrZr73Jfa\nkSN2fDVaKx573aSinZVq2trDmr81be3J4v4TZYVRVCIiopxiFJWIiIicwCgqa4wHsuZlTVt7NNWI\nQoyi1kBTDIy1xuKBRx0lAHqDj1KCvj4d94M1/TVt7dFUI0qbV8MimmJgrMXXaqnXSut9ZE1HTVt7\nNNWI0uZV50JTDIy1+Fot9VppvY+s6ahpa4+mGlHavJpz4fuuqD7UqtV92PmVNR01be3RVCMKcVfU\nMhhF9Uu1332OP12JiFRhFJVyZnfWDaAE7d7Nx9MnfDypmtTSIiKyHMDHAPwugCMA/g7AZcaYn1W4\nzY0ALi45fIcx5rW1fM8werV//350d3fHrGDJWta1WurWbgCbFzzGExMTKu4Ha/XVdu/ejQsvvFDV\nc421xn6mgO1cbN688PVJFEozivo3AI6DzRQeDeAmALsAvK3K7f4RwDsAhM/mn9f6DTVFvVhrLB5Y\njZb7wZr+mrb2+FAjqlUqwyIicjKAjQC2GGO+aYz5VwDvAXChiKyocvOfG2MKxpgngo8f1fp9NUW9\nWIuvVavv2jWGvr5+PO950+jr68fY2CdgjJ1rsWvXmJr7wZr+mrb2+FAjqlVacy5eDuCgMebbkWPj\nAAyAl1a57Tki8riI3C8i14vIsbV+U01RL9bia9raw5q/NW3t8aFGVKtU0iIiMgjg7caY1SXHHwdw\npTFmV5nbbQLwFIDvAegGMAzgJwBebso0VETOBPAvn/3sZ3HyySfj3nvvxSOPPIITTjgBZ5xxRtE4\nImvZ12q97TXXXIPLL79c7f1grb7a1q1bcc0116h8rrFW388UALZu3YqdO3eC3HfffffhbW97GwCc\nFYwyJKKuzoWIDAO4osIpBsBqAG9EA52LmO/XBWA/gF5jzFfLnPNWAH9dy9cjIiKiWBcZY/4mqS9W\n74TOUQA3VjnnQQCPAXhu9KCILAJwbFCriTHmeyLyJIATAcR2LgDcCeAiAA8BOFzr1yYiIiIsBvAC\n2PfSxNTVuTDGzACYqXaeiHwDQLuInBqZd9ELmwC5p9bvJyInAOgAUHbVsKBNifW2iIiIciax4ZBQ\nKhM6jTH3w/aCPiEiZ4jIWQD+EsBuY8zclYtg0ubrgv8vFZGPishLReT5ItIL4O8BPICEe1RERESU\nnjRX6HwrgPthUyK3Avg6gP6Sc14IYFnw/6cBvATAPwD4LwCfAHAvgFcaY36ZYjuJiIgoQc7vLUJE\nRES6cG8RIiIiShQ7F0RERJQoJzsXIrJcRP5aRH4kIgdF5JMisrTKbW4UkSMlH7e3qs00T0TeLSLf\nE5FDInK3iJxR5fxzRGRKRA6LyAMiUrq5HWWsnsdURM6OeS0+LSLPLXcbah0ReYWI7BGRR4PH5vwa\nbsPXqFL1Pp5JvT6d7FzARk9Xw8ZbfwfAK2E3RavmH2E3U1sRfHBbvxYTkbcAuBrAVQBOBTAN4E4R\neU6Z818AOyF4AsAaANcC+KSIvKoV7aXq6n1MAwZ2Qnf4WlxpjHki7bZSTZYC2AfgD2Afp4r4GlWv\nrscz0PTr07kJnWI3RftPAKeHa2iIyEYAtwE4IRp1LbndjQCWGWMuaFljaQERuRvAPcaYy4LPBcDD\nAP7CGPPRmPN3AHiNMeYlkWO7YR/L17ao2VRBA4/p2QAmASw3xvy4pY2luojIEQCvN8bsqXAOX6OO\nqPHxTOT16eKVi0w2RaPmicgzAJwO+xcOACDYM2Yc9nGN87KgHnVnhfOphRp8TAG7oN4+EfmBiHxZ\n7B5B5Ca+Rv3T9OvTxc7FCgBFl2eMMU8D+GFQK+cfAbwdwHoAfwzgbAC3S+mOPJSm5wBYBODxkuOP\no/xjt6LM+c8WkWOSbR41oJHH9ADsmjdvBHAB7FWOu0TklLQaSania9Qvibw+691bJDV1bIrWEGPM\nzZFPvyMi/w67Kdo5KL9vCRElzBjzAOzKu6G7RaQbwFYAnAhIlKGkXp9qOhfQuSkaJetJ2JVYjys5\nfhzKP3aPlTn/x8aYnyfbPGpAI49pnL0AzkqqUdRSfI36r+7Xp5phEWPMjDHmgSofvwIwtyla5Oap\nbIpGyQqWcZ+CfbwAzE3+60X5jXO+ET0/8OrgOGWswcc0zinga9FVfI36r+7Xp6YrFzUxxtwvIuGm\naO8CcDTKbIoG4ApjzD8Ea2BcBeDvYHvZJwLYAW6KloVrANwkIlOwveGtANoA3ATMDY8db4wJL799\nHMC7gxnpn4L9JfYmAJyFrkddj6mIXAbgewC+A7vd86UAzgXA6KICwe/LE2H/YAOAVSKyBsAPjTEP\n8zXqlnofz6Ren851LgJvBfAx2BnKRwB8HsBlJefEbYr2dgDtAH4A26m4kpuitZYx5uZg/YMPwF46\n3QdgozGmEJyyAsBvRM5/SER+B8BOAH8I4BEAW4wxpbPTKSP1PqawfxBcDeB4AE8B+DcAvcaYr7eu\n1VTBWtihYhN8XB0c/zSAd4KvUdfU9Xgiodenc+tcEBERkW5q5lwQERGRH9i5ICIiokSxc0FERESJ\nYueCiIiIEsXOBRERESWKnQsiIiJKFDsXRERElCh2LoiIiChR7FwQERFRoti5ICIiokSxc0FERESJ\n+v89ng3HaCmnfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122c30450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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884ElS9JrJ6nkazyQNbdq2trDKCoRtzGnJvgaD2TNrZq29jCKSgTMbWNeOrei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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136f6b9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vjrjfs2DHm5+A/QX7DQAvijjuONgOwcPB/f03bL7+D0rad3LJ7easXIkao6gFxz8ftkNw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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1233be190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8b9XidRm+BOB9AK4Vs/bHYzBvJM+AidAW3scQzFyHM1T1O1XaWhit/J2IfABm+OI7IrIL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\nERERxYqdCyIiIooVOxdEREQUq/8P4OPZQ4pQgMoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1268ed850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dsL+4/zR4fHcAkIL9ngM7k+eTwWN+f/BYpgG8tOQ2B0rbX6ad5R7ns7Dplt+HjQQ/DXs+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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128227510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rcLqq/rBKW6cfp6r+UUQ+AjN88UMR2QFgCUzM8T4A/zc49WUwEeEbYBah+iPMpe3nw/zi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\nJTa1k+tcsOZSba63bZozzgByOaCtrXjy5q23Ah/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ec9d810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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L4VI0/513vbVwKyP2ww1dQETeCPeBfw+A/xaRC4vu+xuqGp6y/ncR+T5cZ+txuE7FZri/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\nREREsWLngoiIiGLFzgURERHF6v8DBc2Zkw7l1JIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1234c2090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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j8iWY1/yHReRvgm2XIL0puz8N85i+U0S6YToMm2Bi27sj7+Orgw/gO2Aer5NgTuj+X5iT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\n/w980PMBXVshvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122cba290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l9/FdAHOquq7K44SqflNEDgJ4D9wZodKzLP8G95r/mIh8IDh2IZJbsvtzcD/T94hIF1yH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\nihU7F0RERBQrdi6IiIgoVv8fedl/NWVZJ8QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136e19b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mVPX2onO/C+Cwqq6u8Tihqt8WkX0A3gN3Raj8KsvX4F7zHxeRDwTHLkRyS3Z/Hu57+h4R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\nRESxYueCiIiIYvX/AeMO4eImtva5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12484dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ph4aA224DFi60O4wCwN69wKWXAkuWZNdOUsnXeCBrbtW0tYdRVCJuY07z4Gs8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12612b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MeVxEPgZgc2S8+TmwHYaFiMQEjTGHReSvAHxIRHbDRjdfA/sH6L2mNHv/Wdhx+JvEzv3x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\noti5ICIiokSxc0FERESJ+v+pMuuwt/EkOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129645590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FAB6H11n0DAN4K+x5NfVO6nw37C/tr4rIZ2APub8TNkXzg7L91gP4GuzzcqX3nNR7rr9q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1287a1050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MGEUlIqpfwVzESI5/NVCNkoqi8swFEVHC+EVOWcPOBRE5K4zV7d+/H11dXejp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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12be5a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PwLVi5/54DPaL5BmwEdrZhosMwJ7rcIYx5rul2lr8OI0xvxeRD8AOX3xXRHYCWOq15x7M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\nfaeOMdBXj3kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c6cf6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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P2wrvtbka9svmXbBfpE8DeIO3z8WwJ7UOe49rI+wXyL6i1+l6rw1l0yLevq+FPalwFMBf\nw8YZ/wjgX0s8rkvztv2lt+1uBL8X24tu40OwXyb/4V3vU97PHyzabznsUbKHYIck+r3/7wWwtGjf\n+1FDagP2C+px73k9sah2JOwsrffBfsFvgI3p3oWitAPCSYsI7Pv5j9579N2wHfinUZjm2ua143IA\nF8JGLx/wHvuzvH1+BNtRHYBNVIzAfgZuLGrDQQBjVbb3C7C/F7Z477fveT+/vGi/wPdbta93mc9Q\n8SRaL4TtbD8Oe55Vb95tf6OW14KX+i+xN4CXiF7Y2S+qUpe3e/v5nYuNAN7n/SJ6EvZkxzlxPtjD\nq9+B/dLbBxttOz5gv2O9X5IPe7f3v7BfSM8sal9Q56L4F3QtUdRnwP4FOe79cnkcNs0SFGdcCnvy\n2y+8X7APwh5iPTJg30WwHZdHvV9cWQREPQF8HNXNWjnncXrb3+R9ATwJ22n7DPK+KGG/2P4Z9oTZ\n38DG7b6f/wsWdm6Bz8F++T0J+2X7leL2osooat7+53jP65Owh+IHAcwr8bj+IW/bZRXei8XPwcES\n+/0xoE0nws7p8RvYzt6XAHQG7PcoqpgxMm//D3v3eXeJ+kthv0SfgP0C/yjsuQ5BnYs9NX5251wH\n9iTTEe++DsB21DYU7XMm7BwoD3jv5wdgTzLtzNvnr2E/249idkhvCMDhRbdVVRTV2/dQ2I7FQ95t\n3gFgdYnHNef9VsvrXeK9FhRFfQFsp+d+7/m6F7az3VLLa8FL/RfxXghKKe9EqPsAbDLGbI27PUkn\nIj8EcJ8xpp5zGygC3uRS/w3gNcaYW+NuD1EaRHrOhYicLiI3iZ0i96CInFNhf38Z6uIVPI+Ksp1E\nYRCRZwF4MWwElPQ4E8D32bEgap6oz5pdCDuWfy3s4bpqGMyOmdkNxhTPEU+kjrFrmIQ5aRCFwBhz\nNew5KrHyTrgsl8h42tg1a4gSL9LOhfeXwq3AzMyN1coZY34TTasogEHA0sVEFKovw54nUMr9AIpn\nkSVKJI15XwEwIXZlwv8GMGjypt2lcBljfongTDgRhWsjyk/uxdU6yRnaOhd7AfTBni1/GGys6XYR\nWWWMCZyMxcvTr8HsWcFERFqVnWgrrLlhiGrQArvMw23GmKmwblRV58LYpXDzZzu8Q0Q6YXPkF5S4\n2hoA/x5124iIiBx2Pux8IKFQ1bkoYRcK13kodj8AfO5zn8Py5cvL7EZJsmHDBmzbti3uZlBI+Hq6\nha+nO3bv3o23ve1twOxSD6FIQufiRMwunBTkAAAsX74cJ5/MI4quWLRoEV9Ph1R6PY0xGBsbw549\ne9DZ2Ynu7m7454DXW4vqdlkbw/T0NPbt26eiLRpq2tpTS+2EE2YWiQ31tIJIOxfeQjvHYXaFy2Xe\nyo2/NsY8ICJDAI4xxlzg7X8x7IROP4MdB7oIdkbIV0bZTiKK19jYGHbutGuZjY+PAwB6enoaqkV1\nu6ztxPT09Mw+cbdFQ01be2qpnXTSSYhC1AuXrYRdJGYcNup4Bez8+5u9+hLY1e58h3r7/ATA7bBL\nBvcYY26PuJ1EFKM9e/YU/Hzvvfc2XIvqdlljrbimrT211B588EFEIdLOhTHm28aYZxhj5hVd3unV\n1xtjuvP2/7gx5gXGmIXGmHZjTI8x5jtRtpGI4tfZ2Vnw87JlyxquRXW7rLFWXNPWnlpqxx57LKKQ\nhHMuKIXWrVsXdxMoRJVez+5u+zfGvffei2XLls383EgtqttlDTh48CDWrl0b2m2uXj07vJDJIE8P\ngB5kMteoeeyuvddaW1sRhcQvXOblwsfHx8d5AiARUQJVmr854V9Tqt1111045ZRTAOAUY8xdYd1u\n1OdcEBERUcpwWISIYsd4YLprs4HCYJlMRkU7XXyvRTUsws4FEcWO8cB01+y5FaWNj4+raKeL77Wk\nRlGJiCpiPJC1amhqpyvvtURGUYmIqsF4IGvV0NROV95rjKISkbMYD2StnJUrV6ppp2vvNUZRS2AU\nlYiIqD6MohIREVEicFiEiJqC8UDWXK1paw+jqESUGowHsuZqTVt7GEUlotRgPJA1V2va2sMoKhGl\nBuOBrLla09YeRlGJKDUYD2QtSbXe3osCV2j1ffKThUlLrY+DUdQ6MYpKRERhS8tKrYyiEhERUSJw\nWISImoLxQNaSVAN6K76fXXivMYpKRInGeCBrSapVMjY25sR7jVFUIko0xgNZS1qtHFfea4yiElGi\nMR7IWtJq5bjyXmMUlYgSjVFU1pJUK4yhzuXKe41R1BIYRSUiIqoPo6hERESUCBwWIaLQxB2rYxSV\nNb7XGEUlIsfEHavLr2lrD2vu1rS1h1FUInJK3LE6V+KBrCWrpq09jKISkVPijtW5Eg9kLVk1be1h\nFJWInBJ3rM6VeCBryappaw+jqCFgFJWIiKg+jKISERFRInBYhIhCE3eszpV4IGvJqmlrD6OoROSU\nuGN1+TVt7WHN3Zq29jCKSkROiTtW50o8kLVk1bS1h1FUInJK3LE6V+KBrCWrpq09jKISkVPijtW5\nEg9kLVk1be1hFDUEjKISERHVh1FUIiIiSgQOixBRTfLSd4FGR7MqIndx3Cdr6axpaw+jqETkHC2R\nuzjuk7V01rS1h1FUl+RytW0nSgHGA1lLQ01bexhFdcXkJLB8OTA8XLh9eNhun5yMp11EMWM8kLU0\n1LS1h1FUF+RyQE8PMDUFDAzYbf39tmPh/9zTA+zeDbS3x9dOoiZZu3atishdHPfJWjpr2trDKGoI\nVERR8zsSANDaCkxPz/48NGQ7HEQOqHRCZ8J/pRClCqOomvX32w6Ejx0LIiJKMQ6LhKW/H9iypbBj\n0drKjgWlTjbLKCpr6appaw+jqC4ZHi7sWAD25+FhdjDIKeWGPbLZrJrIXRz3yVo6a9rawyiqK4LO\nufANDMxNkRA5Ku5YnSvxQNaSVdPWHkZRXZDLASMjsz8PDQH79hWegzEywvkuKBXijtW5Eg9kLVk1\nbe3REEWdNzg4GMkNN8vmzZuXAujr6+vD0qVLm9+AhQuBNWuAG24ALr10dgikqwtoaQEmJoBsFih6\nIxK5qKOjAwsWLMD8+fPR1dVVMNbb7Jq29rDmbk1be2qpdXZ2IpPJAEBmcHBwb+AHuw6MooYllwue\nx6LUdiIiopgxiqpdqQ4EOxZERJQyTIsQUVMwHsiaqzVt7WEUlYhSg/FA1lytaWsPo6hElBqMB7Lm\nak1bexhFJaLUYDyQNVdr2tqjIYrKYREiagquVMmaqzVt7amlxlVRS1ATRSUiIkoYRlGJiIgoETgs\nQkSxYzyQtSTXtLWHUVQiIjAeyFqya9rawygqEREYD2Qt2TVt7WEUlYgIjAeyluyatvYwikpEBMYD\nWUt2TVt7GEUNAaOoRERE9WEUlYiIiBKBwyJEpFoa44GsJaumrT2MohI1KpcD2tur306Jk8Z4IGvJ\nqmlrD6OoRI2YnASWLweGhwu3Dw/b7ZOT8bSLQvXQxETBzzOxulzO2Xgga8mqaWsPo6hE9crlgJ4e\nYGoKGBiY7WAMD9ufp6ZsPZeLt53UmMlJnHf55ViT18FYtmzZTAdyRdHursQDWUtWTVt7NERR5w0O\nDkZyw82yefPmpQD6+vr6sHTp0ribQ82ycCFw8CCQzdqfs1ngyiuBW26Z3efSS4E1a+JpHzUulwNW\nrcK86Wksf+ghHP3c5+IF69eje9cuyCWXAPv3409+8AMset/7cNjixejq6pozDt7R0YEFCxZg/vz5\nc+qssRZWTVt7aql1dnYik8kAQGZwcHBvxc9llRhFpWTzj1QUGxoC+vub3x4KV/Hr29oKTE/P/szX\nmaghjKISBenvt184+Vpbo/3CKTXUwiGY8PX32w6Ejx0LokTgsAgl2/Bw4VAIABw4ALS0AF1d4d/f\n5CSwapUdksm//eFh4Pzz7TDMkiXh32+adXXZIa8DB2a3tbYCN988E6sbHR3F9PQ0Ojo6AuOBQXXW\nWAurpq09tdRaWloiGRZhFJWSq9whc397mH/ZFp9E6t9+fjt6eoDduxmDDdPwcOERC8D+PDyMsVNP\ndTIeyFqyatrawygqUb1yOWBkZPbnoSFg377CQ+gjI+EOVbS3A5s2zf48MAAsXlzYwdm0iR2LMAV1\nIH0DAzj8qqsKdnclHshasmra2sMoKlG92tttQqStrXDs3R+jb2uz9bC/6HkOQPNU0YE8KZvF4fv3\nz/zsSjyQtWTVtLVHQxSVaRFKtrhm6Fy8uLBj0dpqv/goXJOTdqhp06bCjtvwMDAyAjM6irGpqYLV\nH4PGwYPqrLEWVk1be2qptba2YuXKlUDIaRF2Lohqxfhrc3GKd6LIMIpKpEGFcwDmTEVOjSvVgWDH\ngkgtdi6IqhXHSaRERAnEKCpRtfyTSIvPAfD/HRmJ5iRSKsnVZbBZS1ZNW3u45DpR0qxYETyPRX8/\ncOGF7Fg0matzD7CWrJq29nCeC6Ik4jkAarg69wBryappaw/nuSAiaoCrcw+wlqyatvZomOeCwyJE\nlFjd3d0AUJDnr7bOGmth1bS1p5ZaVOdccJ4LIiKilOI8F+Q2LmNOROQMDotQ/CpM8Yxs1qY0iGoU\nd8yPtXTUtLWHUVQiLmNOEYo75sdaOmra2sMoKhGXMacIxR3zYy0dNW3tYRSVCOAy5hSZuGN+rKWj\npq09jKIS+fr7gS1b5i5jzo4FNSDumB9r6ahpaw+jqCFgFNURXMaciKjpGEUld3EZcyIip8wbHByM\nuw0N2bx581IAfX19fVi6dGnczaFa5XLA+ecD+/fbn4eGgJtvBlpabAQVACYmgPXrgYUL42snqeTH\n6kZHRzE9PY2Ojo7AeGBQnTXWwqppa08ttZaWFmQyGQDIDA4O7q34oasSz7mgeHEZc2qAq/FA1pJV\n09YeRlGJgNllzIvPrejvt9s5gRaV4Go8kLVk1bS1h1FUIh+XMac6uBoPbEYtk9k+c+ntvQgigAjQ\n19eLTGa7mnYmoaatPYyiEhE1wNV4YLNq5axcuVJNO7XXtLWHUdQQMIpKRFS7vHMRAyX8q4GqFFUU\nlUcuiIgixi9ySht2LogosZK0UmW9jyOqGlC+XZlMJvbnLCk1be3hqqhERA1IUjyw3scRVQ0o367x\n8fHYn7Ok1LS1h1FUIqIGJCkeWE7c7Swn/3oPTUzM/D+T2Y7Vq3tmUiarV/cUJFA0xS0ZRXUsiioi\np4vITSLykIgcFJFzqrjOmSIyLiIHROQeEbkgyjYSUXIlKR5YTtztDLLG60jMXG94GOddfjmOnZqq\neN2o2qm1pq09aYiiLgQwAeBaAF+utLOIPB/AzQCuBvBWAKsBfEpEfmWM+WZ0zSSiJEpSPLDexxFV\nbXQ0W1ATESCXg1m+HDI1BewCXvyiF6Gzu3tm/Z9DAXzgm9/EFy67DJkqH1Ncj6+ZNW3tSVUUVUQO\nAniDMeamMvtsAfBqY8yL87btALDIGPOaEtdhFJWIVEtUWiRoIcHp6dmfvZWKE/WYqKS0rIr6UgCj\nRdtuA/CyGNpCrsvlattOlAb9/bYD4QvoWBBVoi0tsgTAI0XbHgFwhIgcZox5KoY2kYsmJ+culgbY\nv9r8xdK4pol6SYkHGqOnLVXVTj0VXQsW4LAnn5x9sltbYT7wAYxls95Jgb0VXxu1j49RVEZRiUKX\ny9mOxdTU7OHf/v7Cw8E9PXbRNK5topqr8cC4a49fcklhxwIApqex56KLsHPePFRjbGxM7eNjFDV9\nUdSHARxdtO1oAL+pdNRiw4YNOOeccwouO3bsiKyhlGDt7faIhW9gAFi8uHCcedMmdiwSwNV4YJy1\nw6+6Cufu2jXz81MLFsz8/7hrr51JkVSi9fGlOYq6Y8cObNy4EbfeeuvMZevWrYiCts7FDzB3ZpdX\nedvL2rZtG2666aaCy7p16yJpJDmA48pOcDUeGFstl8NJ2ezM9i+vWoXv3nRTwWflVZOTOHz/fvT2\n9mF0NOsN+QCjo1n09vbNXFQ+vohq2tpTqrZu3Tps3boVZ5999sxl48aNiEKkwyIishDAcZidZ3aZ\niKwA8GtjzAMiMgTgGGOMP5fFJwG8x0uNfBq2o/EmAIFJEaKG9PcDW7YUdixaW9mxSBBX44Gx1drb\ncci3v40cOKFuAAAgAElEQVTfn3EGJnp6sOg977E175C6GRnBzz76UZwgovcxxFDT1h7no6gicgaA\nbwEovpPPGGPeKSLXAXieMaY77zqvALANwJ8CeBDA5caYfytzH4yiUn2KI3c+HrmgtMvlgocFS22n\nxErkqqjGmG+jzNCLMWZ9wLbvADglynYRlc3y55/kSZRGpToQ7FhQleYNDg7G3YaGbN68eSmAvr61\na7G0yql2KeVyOeD884H9++3PQ0PAzTcDLS02ggoAExPA+vXAwoXxtZMq8mN1o6OjmJ6eRkdHR2A8\nMKjOGmth1bS1p5ZaS0sLMpkMAGQGBwf3VvzQVcmdKOrixXG3gJKivd12IornufD/9ee54F9p6rka\nD2QtWTVt7WEUlSguK1bYeSyKhz76++12TqCVCC7EA1lLfk1be5xfFZVINY4rJ54L8UDWkl/T1p40\nrIpKRBQZV+OBrCWrpq09zkdRm4FRVCIiovqkZVXU+u3bF3cLqBZckZSIyFnuRFEvvhhLly6NuzlU\njclJYNUq4OBBoKtrdvvwsI2IrlkDLFkSX/soMVyNB7KWrJq29jCKSunDFUkpRK7GA1lLVk1bexhF\npfThiqQUIlfjgawlq6atPYyikj7NOBeCK5JSSFyNB7KWrJq29miIorpzzkVfH8+5aFQzz4Xo6gKu\nvBI4cGB2W2urnYabqEodHR1YsGAB5s+fj66uLnR3dxeMg5ers8ZaWDVt7aml1tnZGck5F4yikpXL\nAcuX23MhgNkjCPnnQrS1hXcuBFckJSKKHaOoFK1mngsRtCJp/v0ODzd+H0REFBsOi9Csrq7ClUHz\nhyzCOqLAFUkpRK7GA1lLVk1bexhFJX36+4EtWwpPsmxtra1jkcsFH+Hwt3NFUgqJq/FA1pJV09Ye\nRlFJn+Hhwo4FYH+udqhictKeu1G8//Cw3T45yRVJKTSuxgNZS1ZNW3sYRSVdGj0XoniCLH9//3an\npmy91JENgEcsqCauxgNZS1ZNW3sYRQ0Bz7kISRjnQixcaGOs/v7ZrI2b3nLL7D6XXmojrUQhcDUe\nyFqyatrawyhqCBhFDdHk5NxzIQB75ME/F6KaIQvGTJOt0jkzROQMRlEpemGdC9HfXzikAtR+UijF\no5pzZoiIKmBahAqFcS5EuZNC2cHQK4GLyvmxuj179qCzs3POoepyddZYC6umrT211FqL/xAMCTsX\nFK6gk0L9jkb+Fxbp40+k5r9OAwNzY8nKFpVzNR7IWrJq2trDKCq5JZez52b4hoaAffsKFyn72MfC\nXQSNwpWwReVcjQeylqyatvYwikpu8SfIWrQIWLBgdrv/hbVgAWAM8KtfxddGqixB58y4Gg9kLVk1\nbe3REEXlsAiF65hjgHnzgMcfnzsM8uST9qJs3J6KJOicme7ubgD2L7Nly5bN/FxNnTXWwqppa08t\ntajOuWAUlcJX7rwLQOXhdfLwtSNKFUZRKTkSNm5PnmrOmRkZ4TkzRFQRj1xQdBYvnrsA2r598bUn\nAnlJtEDFH69KcbbYhTWRWpO4Gg9kLVk1be2pNYq6cuVKIOQjFzzngqKRoHH7ZqoUZ4udP5Fa8fkw\n/f3AhReqO0/G1Xgga8mqaWsPo6jkpkYXQHNYpTibCglaVM7VeCBryappaw+jqOQejtuXVSnORrVx\nNR7IWrJq2trDKCq5x5/ronjc3v/XH7dX+FdwM1SKs1FtXI0Hspasmrb2MIoaAp7QqVQSVtYMoY21\nntBJRKQJo6iULNrH7bn6JxFRZDgsQukT4+qf6qOozRDiUS1X44GsJaumrT1cFZUoDiGu/lnrsIf6\nKGrUQp5Hw9V4IGvJqmlrD6OoRGErlUIp3h7TLKKJiKJGpfiIkT8k5R8xmpqy9RqSRK7GA1lLVk1b\nexhFJQpTredRxLD6Z6qjqP4RI9/AgJ3FNX9OlCqPGPlcjQeylqyatvZoiKLOGxwcjOSGm2Xz5s1L\nAfT19fVh6dKlcTeH4pLLAatW2b9+s1mgpQXo6pr9q3j/fuCGG4D164GFC+11hoeBW24pvJ0DB2av\nG4GOjg4sWLAA8+fPR1dXV/rOuejqss9vNmt/PnBgtlbHEaNKz2e5OmushVXT1p5aap2dnchkMgCQ\nGRwc3FvxQ1clRlHJHbWs6MnVP+OVgnVniJKAUVSKnUj5S+yqPY+Cs4jGq9y6M0TkBB65oKolZsKo\nav4qjnD1z0bibM4L+YiRq/FA1pJV09YeropKFLZqV2ONcPXPRuJsTgs6YlQ8v8jISE3Pv6vxQNaS\nVdPWHkZRicJU62qsEc0i2kiczWn+ujNtbYVHKPzhrLa2mtedcTUeyFqyatrawygqUVgUnUfRSJzN\nef4Ro+Khj/5+u73GoShX44GsJaumrT0aoqgcFiE3KFqNtZGVFVMhxCNGrq5UyVqyatraU0uNq6KW\nwBM6mycRJ3QmYTXWNOLrUlIiPlfkLEZRiaqhfTXWNOIKtESpw2ERqhr/gprFuGmVIl6B1pV4YL2P\nkTUdNW3t4aqoRAnFuGmVQlyBNogr8cB6HyNrOmra2sMoKlFCMW5agwhXoHUlHlhOpdvMZLbPXFav\n7pmZMXf16h5kMtub9hjSXNPWHkZRiRKKcdMaRbQCrSvxwHJquc1ytD52F2ra2sMoKlFCMW5ao2pn\nTq2RK/HAeh9jNbexcuXK2B+f6zVt7WEUNQSMohIpxxVoy2o0isooKzWCUVQiSh5FM6dqZUz5C8VH\n/UrQinFYhKgOjKJWKeKZU12NB9ZSA8q/tzKZjIp2JrEGVJ/yirutjKISOYBR1BrEtAJtpbortUpf\ngOPj4yramcxa9Z/b+NvKKCpR4sUeRS01jKB1eCGGFWgr1V2slaOpnUmp1SLutjKKSuSAWKOonE57\nhqvxwFpqvb19M5fR0ezMuRqjo1n09vapaWcSa7WIu62MohI5ILYoasTTaSeNq/FA1nTUMhlULe62\nMooaMkZRKXUY7QzESCaFLQ3vKUZRiciKcDptIqIwcFiEqIRmr35Zk/7+uQuAhTCddtLkP9dAb9l9\ns9msqggga/prBw+Wv15+DDjutjKKSpQQzV79siYRTaedNPnPdSX+floigKy5U9PWHkZRSYekxRqb\npNmrX1Yt6JwL38DA3BSJw2qNDmqKALLmTk1bexhFpfgx1lhSs1e/rAqn0y7gP9f5S4uXoykCyJo7\ntbqu631Gi2vHH3lkUx9HVFHUeYODg5HccLNs3rx5KYC+vr4+LF26NO7mJEsuB6xaZWON2SzQ0gJ0\ndc3+Zbx/P3DDDcD69cDChXG3tuk6OjqwYMECzJ8/H11dXQXjlvXWGrZwIbBmjX1dLr10dgikq8u+\nfhMT9rUMe24Npfzn+rOfrfx4t2xZEMpryBprQZ/rmq7b1gZ5yUuAgwfR8Vd/NVM7/4EHcOLICGTN\nGmDJkqY8js7OTmRs5jYzODi4t+IHqUqMoqYdY43JlMsFz2NRarvjqum7JfxXHbkil7NHhaem7M/+\n79j838VtbU2bq4ZRVIoGY43JFNF02q5ix4LUaG+3i/j5BgaAxYsL/8jbtCnxn2WmRcj5WGPcUa9I\noqgEYPa5rrTAlIaVQSu9BUZHsyreo6zV97mu6bof+IANsfodirzfveajH4V4v3sZRaVkczHWmDc8\nkB+9+vl3vwsg3sgahWf2uda/MmglWqOKrEUURQ34o+53hx6KO1atmnk3M4pKyeVirLEoAeNHr9ZM\nTGDzzp2Y/va3Z3aNI7JG4UlSFDUp7WSt9lpd1w34o27h73+PZ111VVMfB6OoFD4XY43FC3sND6Oz\nsxNrJiZw7q5dOPypp/AXV15ZMgbWjMgahafWVSzjjismoZ2s1V6r9bpn/fCHBX/U/e7QQ2f+v+rG\nG2f+MEpyFJXDImnW3m5jiz099gQifwjE/3dkxNaTdGKRf7KU/8EdGEB3aysk7y+EQ/r7Zx5Ts1ck\npAANJF/853blymtmnuugcfB7710Z+2qUlaxdu1blqpmsVa7Vct3jjzwSnX19MzXz0Y/ijlWr8Kyr\nrrIdC8D+7r3wwqY8jqjOuYAxJtEXACcDMOPj44bq9OijtW1PgqEhY2xIoPAyNBR3yyjfxIQxbW1z\nX5ehIbt9YiKedkUg6O2Yf6EUUfS+Hx8fNwAMgJNNiN/NnOeC3LV48dwEzL598bWHCinL+0ctDct3\nUw2UzFUT1TwXHBahxDC1RK927SoYCgEATE/jF3/91+i85hpGUTUIGMKaE4mukPev9Fw3+/Vt5LXP\nZhlFTWqtLo7PVcPORRoo6SE3qtp41bOvvRaya9fM9f5w+OE45IknAADHXXstfgHguE99qqbbZBQ1\nIv75PQF5/2omcUvSSpWjo9mCFVzXrl07U8tms2rayRo/12FgWsR1Di1MVk286vD9+/Gq/Mc0NITr\nrrgCX161ambTsZ///ExaRHMcMTX6+wsj0EDVk7i5ulIla8mq0VzsXLgsIJYJYHZMe2rK1hMSNa0m\nXvXE/PnY9rrX4fdHHDHzl29nZyduO/FEfHnVKjxx2GGY3Lp15oiN5jhiapSbxK2C0FeqZI21Omo0\nF4dFXBbCmLYmtcSrDrn6auCoowprK1firiOPxOnnnlvXbTKKGoFyC+f528scwQgrHsgaa43UaC6m\nRdKg+Be4jwuTUZxSlhYh0oirolL9GhjTJoqMP4lbW1thR9dfqbetLXmTuBERAA6LpEOCFiZrdoSs\naStVPvaYE4md0K1YEXxkor8fuPDCis9NkqKorDGenSbsXLiuwTHtZtMWIQvj/g7fswcvueSSwinW\nAfva+FOsr1hRVXuc1EDeP0lRVNYY40wTDou4LIELk2mOkNVzf4fv348VGzc6k9jRhlHU5NeoQaV+\nd8T8O4WdC5clcExbc4Ssnvt7Yv58PHjeebM7DgzYacnzjyYlKLGjDaOoya9RAxTPY8RhEdc1OKbd\nbNoiZGGsVNnZ3Q0cd1zds1BSaYyiJr9GdSqexwiYm7bq6YktbcUoKqVaUxeT4kJqRBSmcufUAVX9\n8cIoKlGSNTALJRFRIH+I26foqCiHRUiVKGNwq1fXfnZ6KCtV7toFueSS2RuNOLGTpqW90xBFJSqr\nv3/uzMsK5jFi54JUiTYGV75z0dvbF/pKlT//7ndx+le/ikP9OwmahXJkROX5L0mQhigqUVlK5zHi\nsAip0owYXDlh398T8+fjaxdfnKjETpJEEUUVAVav7kEms33msnp1D0RsjVFNUiPonAtffvQ9Buxc\nkCrNiMGVE8X9tZ5xhj1ju/iviP5+uz3NE2g1KKooar336dcO378/sOZvD6stlGLK5zHisAipEmUM\nLpMpf99r166NLnZXavycRywaElUUtd777O7uxuF79mDFxo148LzzbAzZr+3ahdO/+lV87eKL0XrG\nGYxqUmP8eYx6egpn//X/9Wf/jel3DKOolBppOdExLY8zKg09f1zplZqt1PpEVa5bxCgqEZF27e32\nr0gfZ2SlqDWwNk+UOCxCqkQZ86uUFslms4wVOiaK16ni9fzD0pyRlVKMnQtSJcqYX2+vrZeKm3r/\nJD5WyGGPWVG8TlVdT+ncA0TNwmERUkXTao2MFSZfFK9TVdfjjKyUcuxckCqaVmvkCpDJV8/rZAww\nOppFb2/fzGV0NAtjbK3i66t47gGiZuGwCKmiabVGrgCZfE1/fYPmHuCMrHVj8im5GEUlIgrT5OTc\nuQcA28Hw5x7gxGlVYecielFFUXnkgogoTCtWBM9j0d/PIxaUGk3pXIjIewBsArAEwCSA9xpj7iyx\n7xkAvlW02QBYaox5NNKGUuw0rUbJuCnVTencA0TNEnnnQkTeAuAKAL0AdgHYAOA2EXmhMeaxElcz\nAF4I4LczG9ixSAVNq1FqjpsSEWnWjLTIBgDbjTGfNcbcDeBdAJ4E8M4K18sZYx71L5G3klTQFBtl\n3JSIqD6Rdi5E5BAApwDI+tuMPYN0FMDLyl0VwISI/EpE/lNETouynaSHptgo46ZElDilVkFt8uqo\nUQ+LPBvAPACPFG1/BMDxJa6zF0AfgB8BOAzARQBuF5FVxpiJqBpKOmiKjTJuSkSJoiipFGkUVUSW\nAngIwMuMMT/M274FwCuMMeWOXuTfzu0AfmmMuSCgxigqERGlW50r8iY1ivoYgKcBHF20/WgAD9dw\nO7sAvLzcDhs2bMCiRYsKtq1btw7r1q2r4W6IiIgSyF+R1+9IDAzMWd9mx+rV2HHhhQVXe/zxxyNp\nTqSdC2PMH0RkHHY5ypsAQGxerwfAP9dwUyfCDpeUtG3bNh65cICmSCnjpkSUKBVW5F3X34/iP7fz\njlyEqhnzXGwFcL3XyfCjqAsAXA8AIjIE4Bh/yENELgZwH4CfAWiBPefiLACvbEJbKWaaIqWMmxJR\n4tS6Iu++fZE0I/IoqjFmJ+wEWpcD+DGAFwNYY4zxT11dAuA5eVc5FHZejJ8AuB3AiwD0GGNuj7qt\nFD9NkVLGTYkocWpdkXfx4kia0ZRVUY0xVxtjnm+MmW+MeZkx5kd5tfXGmO68nz9ujHmBMWahMabd\nGNNjjPlOM9pJ8dMUKWXclIgSRdGKvFxbhFTRFCll3JSIEkPZirxcFZWsXC74DVdqOxER6VLHPBdR\nRVGbMixCyk1O2nx08SGz4WG7fXIynnYREVH1/BV5i0/e7O+325s0gRbAzgXlcranOzVVOCbnH0qb\nmrL1Jk8dmypKpuslIgfUuiJvUtMipJw/8YpvYMCePZx/UtCmTU0bGjHGIJvNIpPJIJvNIn/YTlMt\nNDxqRERxiigtwhM6qeLEKyXz0RHQNJdF5PNcFB81AuaegNXTM2e6XiIi7Xjkgqz+/sLYElB+4pWI\naJrLIvJ5LpQdNSIiCgs7F2TVOvFKRDTNZdGUeS76++3RIV+MR42IiMLCYREKnnjF/5LLP1zfBJrm\nsmjaPBe1TtdLRKQc57lIuzqX6aUQFXfufDxyQUQR4zwXFI32djuxSltb4ZeZf7i+rc3W2bGIhqLp\neomIwsIjF2Q1cYZOTUunx7rkOo8aEVHMojpywXMuyKp14pUGaIqUxhpF9Y8aFU/X6//rT9fLjoVu\nnDqfaA4Oi1DTaYqUxr7kuqLpeqkOnASNKBA7F9R0miKlsUdRgaYeNaIQcep8opI4LEJNpylSqiKK\nSsnkT4Lmnx8zMDA3UsxJ0CileEInEZXHcwrKY5SYEoxRVCJqPp5TUJmSqfOJNOGwCJUUVYRTU6Q0\n1iiqdlxYrTrlps5nB4NSip0LKimqCKemSGmsUVTteE5BZYqmzifShMMiVFJUEU5NkdLYo6jacWG1\n0nI5OxeJb2gI2Lev8PkaGWFahFKJnQsqKaoIp6ZIqYooqnY8pyAYp84nKonDIlRSVBFOTZFSRlGr\nwHMKSvMnQSvuQPT3AxdeyI4FpRajqERUWrlzCgAOjRD5EhrZZhSViJqL5xQQVYeR7Tk4LJJycUQ4\nNUVKGVMtgwurEVXGyHYgdi5SLo4Ip6ZIKWOqFfCcAqLyGNkOxGGRlIsjwqkpUsqYahW4sBpReYxs\nz8HORcrFEeHUFCllTJWIQsHIdgEOi6RcHBFOTZFSxlSJKBSMbBdgFJWIiKgRCY5sM4pKRESkDSPb\ngTgs4ghNUcy0R1FTEVMlIouR7UDsXDhCUxQz7VHU1MRUichiZHsODos4oplxSxFg9eoeZDLbZy6r\nV/dAxNbSHkVNVUyViCxGtguwc+EITXHLtEdRGVMlorTjsIgjNMUt0x5FZUyVQpfQRbEovRhFpZpV\nOjcx4W8pIl0mJ+eeLAjY+KN/suCKFfG1jxKNUVRKLP9cjFIXIiqheFEsf9VNf16FqSlbT1nMkfTj\nsIgymmKT9UYqi68HlE9KGGNUPkZGUSl2XBSLEoqdC2U0xSbrjVTOvV7564yNjal8jIyikgr+UIjf\nwUjIzI+UbhwWUUZTbLLeSGXx9SrR+hgZRSU1uCgWJQw7F8poik2WqhkDjI5m0dvbN3MZHc3CGFsr\nvl4lGh9jVDWiupRbFItIIQ6LKKMpNhlWLZOp7jFraCujqBSLclHTa68tvSiWv51HMEgZRlEpcoyu\nEpVRLmr6sY/ZD4jfmfDPschfhbOtLXjqaaIqRBVF5ZELIqK4FEdNgbmdh0WLgCOPBN7/fi6KRYnB\nzkVENMUf464dPFh+VVRj9LSVMVVqqmqipqUWv0rxolikHzsXEdEUf9RU09YeTTVKqUaipuxYkFJM\ni0REU/xRU01bezTVKMUYNSXHsHMREU3xR001be3RVKMUY9SUHMNhkYhoij9qqmlrj6YapVT+yZsA\no6bkBEZRiYjikssBy5fbtAjAqCk1HVdFJSJyTXu7jZK2tRWevNnfb39ua2PUlBKJwyIVaIoqulDT\n1p6k1MhhK1YEH5lg1JQSjJ2LCjRFFV2oaWtPUmrkuFIdCHYsmqPc9Ot8DerCYZEKNEUVXahpa09S\nakQUkclJe95LcTJneNhun5yMp10Jx85FBZqiii7UtLUnKTUiikDx9Ot+B8M/oXZqytZzuXjbmUAc\nFqlAU1TRhZq29pSq2VMderyLlb+668GDjKkSJV41069v2sShkTowikoUgCu5EqVI8VwjvkrTrzuA\nUVQiIqIocPr10Dk1LKIpOsha8qOomt5rRBShctOvs4NRF6c6F5qig6wlP4pajqa2EFEDOP16JJwa\nFtEUHWQtuKatPfXGPzW1hYjqlMsBIyOzPw8NAfv22X99IyNMi9TBqc6Fpugga8E1be2pN/6pqS1E\nVCdOvx4Zp4ZFtMQYWUt+FLUSTW1JLc6qSGHg9OuRYBSViJJnctJObrRpU+F4+PCwPYydzdovDSIq\ni1FUIiKAsyoSJYBTwyKaYoysJT+KmpRa6nBWRSL1nOpcaIoxspb8KGpSaqnkD4X4HYz8jkUKZlUk\n0s6pYRFNMUbWgmva2uNCLbU4qyKRWk51LjTFGFkLrmlrjwu11Co3qyIRxcqpYRFNMUbWkh9FTUot\nlTirIpFqjKISUbLkcsDy5TYVAsyeY5Hf4WhrC567IIk4nwdFiFFUIiIgXbMqTk7ajlTxUM/wsN0+\nORlPu4gqcGpYRFM8kDVGURlFjVAaZlUsns8DmHuEpqfHnSM05BSnOhea4oGsMYrazOc0lUp9obry\nRcv5PCjBnBoW0RQPZC24pq09LtTIYf5Qj4/zeVBCONW50BQPZC24pq09LtTIcZzPgxLIqWERTfFA\n1hhFZRSVQlFuPg92MEgpRlGJiLQqN58HwKERahijqEREaZLL2eXjfUNDwL59hedgjIxw9VdSyalh\nEU3xQNYYRdVQowTz5/Po6bGpkPz5PADbsXBlPg9yjlOdC03xQNYYRdVQo4RLw3we5CSnhkU0xQNZ\nC65pa4/rNXKA6/N5kJOc6lxoigeyFlzT1h7Xa0REcXBqWERTPJA1RlE11IiI4sAoKhERUUoxikpE\nRESJ4NSwiKYIIGuMomqvERFFxanOhaYIIGuMomqvERFFxalhEU0RQNaCa9rak+YaEVFUnOpcaIoA\nshZc09aeNNdSp9Q02Zw+Wxe+Tk6YNzg4GHcbGrJ58+alAPr6+vpw2mmnYcGCBZg/fz66uroKxpc7\nOjpYU1DT1p4011JlchJYtQo4eBDo6prdPjwMnH8+sGYNsGRJfO0ji69T0+3duxeZTAYAMoODg3vD\nul1GUYnIbbkcsHw5MDVlf/ZXEs1fcbStLXiabWoevk6xYBSViKge7e124S/fwACweHHhUuabNvEL\nK258nZzi1LDIkiVLMDY2htHRUUxPT6Ojo2NOJI+1eGva2sNa6dfJKV1dQEuLXUUUAA4cmK35fyFT\n/Pg62SM4CxdWv71BUQ2LMIrKGqOorKUjptrfD2zZAkxPz25rbU3HF1aSpPl1mpwEenrsEZr8xzs8\nDIyM2E7XihXxta8GTg2LaIr5sRZc09Ye1oJrThoeLvzCAuzPw8PxtIeCpfV1yuVsx2Jqyg4F+Y/X\nP+dkasrWE5KacapzoSnmx1pwTVt7WAuuOSf/pEDA/iXsy/9FHgZGKevXzNdJG8fOOXHqnAtGUfXX\ntLWHtSpiqk0eAw5dLmdjjPv325+HhoCbby4c25+YANavb/zxMEpZv2a+TlrFcM5JVOdcwBiT6AuA\nkwGY8fFxQ0Qhm5gwpq3NmKGhwu1DQ3b7xEQ87apVMx7Ho4/a2wLsxb+voaHZbW1tdj8K5sr7rVGt\nrbPvGcD+HJHx8XEDwAA42YT53RzmjcVxYeeCKCKufVmWameY7c9/bvwvhfyfi780aa5mvE6aFb+H\nIn7vRNW5cGpYhFFU/TVt7WGtTO2OO5B79FEce/fd9oXLZoErrwRuuWX2A3jppfZQfxKUOpQe5iF2\nRikb14zXSaugc07891A2a99b+cNtIWAUtQqaonysMYrqRK29HQ+tWoVzd+0CgMKz+PllGSzNUUqq\nXy5n46a+oBlKR0aACy9MxEmdTqVFNEX5WAuuaWsPa5Vrt514Ip5asKBgX35ZlpHWKCU1pr3dHp1o\nayvsuPf325/b2mw9AR0LwLHOhaYoH2vBNW3tYa1ybc3EBA578smCffllWUKao5TUuBUr7NopxR33\n/n67PSETaAFNGhYRkfcA2ARgCYBJAO81xtxZZv8zAVwB4M8A/B+AfzTGfKbS/XR3dwOwf4EtW7Zs\n5mfW9NS0tYe18rVnXXUVVvlDIoD9svT/Kve/RHkEw3LssDbFpNR7I2nvmTDPDg26AHgLgAMA3g7g\nBADbAfwawLNL7P98AE8A+BiA4wG8B8AfALyyxP5MixBFwbW0SDNoj1KmPYlBc0SVFmnGsMgGANuN\nMZ81xtwN4F0AngTwzhL7vxvAvcaYvzfG/NwYcxWAL3q3Q0TN4tgYcFNoPqw9OWmXNC8emhkettsn\nJ+NpFzkp0mERETkEwCkAPupvM8YYERkF8LISV3spgNGibbcB2Fbp/owXrduzZw86OzsLZhxkrbra\n6ltRXmAAABRqSURBVNXlF66yB4vqvz8Nj5G12monbN+O0889F/4raIzB2Kmn4qGBAVxwYvkvS//9\nkioaD2sXr1sBzB2y6emxHSB2FikEUZ9z8WwA8wA8UrT9EdghjyBLSux/hIgcZox5qtSdqYzyJa5W\n3aqYjKKmqAbgD62tgTVKCH/dCr8jMTAwNy6boHUrSD9n0iIbNmzAxo0bceutt85cPv/5z8/Utcb8\ntNWqxSgqa5Qw/nCWj3OWpM6OHTtwzjnnFFw2bIjmjIOoOxePAXgawNFF248G8HCJ6zxcYv/flDtq\nsW3bNmzduhVnn332zOW8886bqWuN+WmrVYtRVNYogfr7C+OxAOcsSZF169bhpptuKrhs21bxjIO6\nRDosYoz5g4j4x9pvAgCxg7o9AP65xNV+AODVRdte5W0vS2OUL2k1OwtsZYyisnbvvfdW/X4hJcpN\n8MUOBoVITMRnXInIWgDXw6ZEdsGmPt4E4ARjTE5EhgAcY4y5wNv/+QB+CuBqAJ+G7Yj8E4DXGGOK\nT/SEiJwMYHx8fBwnn3xypI8lDYJW3M6XyhP0qCS+XxIkaIIvDo2k3l133YVTTjkFAE4xxtwV1u1G\nfs6FMWYn7ARalwP4MYAXA1hjjMl5uywB8Jy8/e8H8FoAqwFMwHZGLgzqWBARURWCJvjat6/wHIyR\nEbsfUQiaMkOnMeZq2CMRQbX1Adu+AxthrfV+VEb5klSrNi3CKCpr9sTO3orvE6l0eIOi589Z0tNj\nUyH5c5YAtmPBOUsoRFwVlbWC2ujobC2bzRZEDteuXQu/88EoKmsA0Nvbh7Vr15Z8z4yNrS147SlG\n/gRfxR2I/n5OSU6hcyaKCsQfyWOtck1be1hrXo0U0DjBFznJqc5F3JE81irXtLWHtebViCg9nBoW\n0RrXY41RVNaIKE0ij6JGjVFUIiKi+iQ2ikpERETp4tSwiNa4HmuMorLW2HuGiJLFqc6F1rgea+mN\novb1Fc8D4c9+b/cdHc2qaKfmGhElj1PDIppid6wF17S1J+5VQzW1U2uNiJLHqc6Fptgda8E1be2J\ne9VQTe3UWiOi5Jk3ODgYdxsasnnz5qUA+vr6+nDaaadhwYIFmD9/Prq6ugrGbTs6OlhTUNPWnqhr\nX/ta+Vnsr7++Q0U7NdeIKDp79+5Fxi5vnBkcHNwb1u0yikoUIa4aSkSaMYpKREREieBUWkRTfI41\nRlFrXTVU62OIu0ZEyeNU50JTfI41RlGrMTY2pqKdmmtElDxODYtois+xFlzT1p6oa729fchkroEx\n9vyK7dsz6O3tm7loaafmGhElj1OdC03xOdaCa9raw5r+GhElD6OorDGKyprqmgq5HLBwYfXbiRKC\nUdQSGEUlokhNTgI9PcCmTUB//+z24WFgZATIZoEVK+JrH1EDGEUlImq2XM52LKamgIEB26EA7L8D\nA3Z7T4/dj4hmODUssmTJEoyNjWF0dBTT09Po6Jid/dCPurEWb01be1hLdi1yCxcCBw/aoxOA/ffK\nK4Fbbpnd59JLgTVrmtMeopBFNSzCKCprjKKylthaU/hDIQMD9t/p6dna0FDhUAkRAXBsWERTfI61\n4Jq29rCW7FrT9PcDra2F21pb2bEgKsGpzoWm+BxrwTVt7WEt2bWmGR4uPGIB2J/9czCIqIBT51ww\niqq/pq09rCW71hT+yZu+1lbgwAH7/2wWaGkBurqa1x6iEDGKWgKjqEQUmVwOWL7cpkKA2XMs8jsc\nbW3A7t1Ae3t87SSqE6OoRETN1t5uj060tRWevNnfb39ua7N1diyICjg1LMIoqv6atvaw5m6tmnpV\nliwB1q+fGzft6rLbOVW5M9Lwe60Yo6hV0BSRY41RVNZ0v9dqUurIBI9YOCUNv9eaxalhEU0ROdaC\na9raw5q7tWrqRPnS8HutWZzqXGiKyLEWXNPWHtbcrVVTJ8qXht9rzeLUOReMouqvaWsPa+7WqqkT\n5UvD77VijKKWwCgqERElWaX+bpRf04yiEhERUSI4NSzCKKr+mrb2sOZurdHrNlUuZ1dgrXY7NV2U\nv9c2by7/vjvmmExkn5mWlhZGUSvRFPdhLfmRLdaSXWv0uk0zOQn09ADvfjfw4Q/Pbh8eBkZGgBtu\nAM46q/ntogJR/l4Dyr/vxsfHI/vMnHTSSbU8DVVzalhEU9yHteCatvaw5m6t0es2RS5nOxZTU8BH\nPgKcfbbd7k8vPjVl69/6VvPbRgWa9XutnCg+Mw8++GDV918LpzoXmuI+rAXXtLWHNXdrjV63Kdrb\n7REL3223AfPnFy6UZgzw5jfbjgjFplm/18qJ4jNz7LHHVn3/tXDqnAtGUfXXtLWHNXdrjV63abq7\ngTvuAPy/KP/4x7n7XHrp3OnHqami/L1W6ZyLvr6HI/vMdHZ2MooahFFUInLC/PmzS7nny18wjZzk\nYhTVqRM6iYgSaXg4uGPR0sKORQok/G/8QE6dc0FElDj+yZtBDhyYPcmTKEGcOnLh53f37NmDzs7O\ngnEmbbVnPKP8cbDt2zMq2hl2TVt7WHO3FuXthiaXs3HTYi0ts0cybrsN+NCHbJqE1NL03q+l1tra\nGsnz4VTnQlPGXnOuOc6atvaw5m4tytsNTXu7nceip2f22Lh/jsXZZ9uOBQB88pPAxRdziXfFNL33\nOc9FyDRl7DXnmpM69wBrrNVSi/J2Q3XWWUA2C7S1FZ68eeutwAc/aLdns+xYKKfpvc95LkKmKWOv\nOdec1LkHWGOtllqUtxu6s84Cdu+ee/LmRz5it69YEe39U8M0vfc1zHPh1LBId3c3ANtLW7Zs2czP\nWmvlrFy5suH7mx0i7kH+MIyNNNujsM1+7FHdLmusNeu9FplSRyZ4xCIRNL33a6lFdc4F57mISTNy\nzXFmp4mISD8uuU5ERESJ4NSwSNyRnlpqQPnDCplM41HUSvcRx2PX+Fqw5mYtrvskqkXcnxlGUSuw\nR3UE+ecXjI5m1cR9imu9vTtn2r527dqZWjabxc6dOzE+3vj9VYq7xvHY47hP1tJZi+s+iWoR92eG\nUdQ6aIr7xB3JK8WVeCBrrBXX4rpPolrE/ZlhFLUOmuI+cUfySnElHsgaa8W1uO6TqBZxf2a45HoJ\n/pLrQB+ApQW166/vULkUdLNqlZbxHRxsfju1PDesuV+L6z6JahH3Z6azk0uuB/KjqMA4gMIoasIf\nGhERUaQYRSUiIqJEcHpY5LLLzJz4zejoKKanp9HR0cFaDDVt7WHN3Zq29lRqK1FYankftrS0RDIs\n4kwUNcjY2JjKiFyaa9raw5q7NW3tYYSVmoVR1BCNjwPbt2fQ29s3c9EakUtzTVt7WHO3pq09jLBS\nszCKGrK4Iz2sVa5paw9r7ta0tYcRVmoWRlFD4J9z0dfXh9NOO01NDI41PfFA1tJZ09YeRlipWRhF\nDYEkdFVUIiKiuDGKSkRERIngVFok7tXlWKtc09Ye1qKr9fX1lv28Hjw4NyrO9xpXWiU3ONW50BQt\nYy358UDWGqtVEnVUPO7Hz5gqpZlTwyKaomWsBde0tYe1aGvl8L3GmCq5y6nOhaZoGWvBNW3tYS3a\nWjl8rzGmSu5iFJU1xgNZi6T2ta+dUvazG/WqxXE/fsZUKQn27t3LKGoQRlGJdKr03ZjwXz1ETmAU\nlYiIiBLBqbSIpvgYa27HA1mrXAPKR1GNYRSVMVVylVOdC03xMdbcjgeyVrnW29uHtWvXztSy2WxB\nTHVsbC3fa4ypkqOcGhbRFB9jLbimrT2suVvT1h7GVClNnOpcaIqPsRZc09Ye1tytaWsPY6qUJoyi\nssYoKmtO1rS1hzFV0ohR1BIYRSUiIqoPo6hERESUCE4NiyxZsgRjY2MYHR3F9PQ0OjpmZwD041ys\nxVvT1h7W3K1pa08cj5+okqiGRRhFZY1RVNacrGlrD2OqlCZODYtoioixFlzT1h7W3K1paw9jqpQm\nTnUuNEXEWAuuaWsPa+7WtLWHMVVKE6fOuWAUVX9NW3tYc7emrT2MqZJGjKKWwCgqERFRfRhFJSIi\nokRwaliEUVT9NW3tYc3dmrb2JKVG6cIoahU0xcBYYzyQNb7XklgjCoNTwyKaYmCsBde0tYc1d2va\n2pOUGlEYnOpcaIqBRVHr6+tFJrN95tLbexFEABFb09JOxgNZ01DT1p6k1IjC4NQ5F65HUTdvLv9c\nbNmio52MB7KmoaatPUmpUbowilpCmqKolT77CX8piYioyRhFJSIiokRwaljE9ShqpWGR00/Pqmgn\n44Gsaahpa48LNXIPo6hV0BTniiMipqWdjAeypqGmrT0u1Iiq5dSwiKY4V9wRMc2PQVN7WHO3pq09\nLtSIquVU50JTnCvuiJjmx6CpPay5W9PWHhdqRNVyaliku7sbgO1pL1u2bOZnV2oHD9qx0Pxa4Tjp\nWhXtLFfT1h7W3K1pa48LNaJqMYpKRESUUoyiEhERUSIwisoa44GsOVnT1p4010gvRlGroCmyxVp9\n8cBnPEMA9HiXYoLeXh2PgzX9NW3tSXON0sepYRFNkS3WgmvV1Kul9TGypqOmrT1prlH6ONW50BTZ\nYi24Vk29WlofI2s6atrak+YapY9T51y4viqqC7VKdRdWfmVNR01be9JcI724KmoJjKK6pdLvooS/\nXYmIVGEUlVJmR9wNoBDt2MHX0yV8PamSyNIiIrIYwCcAvA7AQQBfAnCxMeZ3Za5zHYALijbfaox5\nTTX36Ueh9uzZg87OzoLDcqzpqFVTt3YAWDfnNc5msyoeB2u11Xbs2IHzzjtP1XuNtfprw8PDOOqo\no0J7ncg9UUZR/wPA0bCZwkMBXA9gO4C3VbjeNwC8A4D/znuq2jvUFL1irb54YCVaHgdr+mva2uNS\nbXp6emYfxlQpSCTDIiJyAoA1AC40xvzIGPN9AO8FcJ6ILKlw9aeMMTljzKPe5fFq71dT9Iq14Fql\n+vbtGfT29uG5z51Eb28fMplrYIw912L79oyax8Ga/pq29rAWXCM3RXXOxcsA7DPG/Dhv2ygAA+Al\nFa57pog8IiJ3i8jVInJktXeqKXrFWnBNW3tYc7emrT2sBdfITZGkRURkAMDbjTHLi7Y/AuBSY8z2\nEtdbC+BJAPcB6AQwBOC3AF5mSjRURE4D8L3Pfe5zOOGEE3DnnXfiwQcfxLHHHotTTz21YMyPtfhr\n1V5369at2Lhxo9rHwVpttQ0bNmDr1q0q32us1V4L8/NJ8dq9ezfe9ra3AcDLvVGGUNTUuRCRIQAf\nKLOLAbAcwBtRR+ci4P46AOwB0GOM+VaJfd4K4N+ruT0iIiIKdL4x5j/CurFaT+gcAXBdhX3uBfAw\ngKPyN4rIPABHerWqGGPuE5HHABwHILBzAeA2AOcDuB/AgWpvm4iIiNAC4Pmw36WhqalzYYyZAjBV\naT8R+QGAVhE5Ke+8ix7YBMgPq70/ETkWQBuAkrOGeW0KrbdFRESUMqENh/giOaHTGHM3bC/oGhE5\nVUReDuBfAOwwxswcufBO2ny99/+FIvIxEXmJiDxPRHoAfAXAPQi5R0VERETRiXKGzrcCuBs2JXIz\ngO8A6Cva5wUAFnn/fxrAiwF8FcDPAVwD4E4ArzDG/CHCdhIREVGIEr+2CBEREenCtUWIiIgoVOxc\nEBERUagS2bkQkcUi8u8i8riI7BORT4nIwgrXuU5EDhZdvt6sNtMsEXmPiNwnIvtF5A4RObXC/meK\nyLiIHBCRe0SkeHE7ilktr6mInBHwWXxaRI4qdR1qHhE5XURuEpGHvNfmnCquw8+oUrW+nmF9PhPZ\nuYCNni6Hjbe+FsArYBdFq+QbsIupLfEuc5fdpEiJyFsAXAHgMgAnAZgEcJuIPLvE/s+HPSE4C2AF\ngCsBfEpEXtmM9lJltb6mHgN7Qrf/WVxqjHk06rZSVRYCmADwN7CvU1n8jKpX0+vpafjzmbgTOsUu\nivY/AE7x59AQkTUAbgFwbH7Uteh61wFYZIw5t2mNpTlE5A4APzTGXOz9LAAeAPDPxpiPBey/BcCr\njTEvztu2A/a1fE2Tmk1l1PGangFgDMBiY8xvmtpYqomIHATwBmPMTWX24Wc0Iap8PUP5fCbxyEUs\ni6JR40TkEACnwP6FAwDw1owZhX1dg7zUq+e7rcz+1ER1vqaAnVBvQkR+JSL/KXaNIEomfkbd0/Dn\nM4mdiyUACg7PGGOeBvBrr1bKNwC8HUA3gL8HcAaArwtXz2mmZwOYB+CRou2PoPRrt6TE/keIyGHh\nNo/qUM9ruhd2zps3AjgX9ijH7SJyYlSNpEjxM+qWUD6fta4tEpkaFkWrizFmZ96PPxORn8IuinYm\nSq9bQkQhM8bcAzvzru8OEekEsAEATwQkilFYn081nQvoXBSNwvUY7EysRxdtPxqlX7uHS+z/G2PM\nU+E2j+pQz2saZBeAl4fVKGoqfkbdV/PnU82wiDFmyhhzT4XLHwHMLIqWd/VIFkWjcHnTuI/Dvl4A\nZk7+60HphXN+kL+/51XedopZna9pkBPBz2JS8TPqvpo/n5qOXFTFGHO3iPiLor0bwKEosSgagA8Y\nY77qzYFxGYAvwfayjwOwBVwULQ5bAVwvIuOwveENABYAuB6YGR47xhjjH377JID3eGekfxr2l9ib\nAPAsdD1qek1F5GIA9wH4GexyzxcBOAsAo4sKeL8vj4P9gw0AlonICgC/NsY8wM9ostT6eob1+Uxc\n58LzVgCfgD1D+SCALwK4uGifoEXR3g6gFcCvYDsVl3JRtOYyxuz05j+4HPbQ6QSANcaYnLfLEgDP\nydv/fhF5LYBtAP4OwIMALjTGFJ+dTjGp9TWF/YPgCgDHAHgSwE8A9BhjvtO8VlMZK2GHio13ucLb\n/hkA7wQ/o0lT0+uJkD6fiZvngoiIiHRTc84FERERuYGdCyIiIgoVOxdEREQUKnYuiIiIKFTsXBAR\nEVGo2LkgIiKiULFzQURERKFi54KIiIhCxc4FERERhYqdCyIiIgoVOxdEREQUqv8PymP7t3b4S0AA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e0aa3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K77wKwBxpGxeRz8B0fA7DHHW6wHu8PhS8ARH5CYDDqrqqyv2Eqn5HRPYBeB/MEaHSoyz/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\n86yeglU5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12964e890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pa089taSGRdi5ICJvpR3zK6xpaydQuV2Tk5OpP2eu1LS1h1FUIqIEpR3zK40r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13086a890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DzSoWgQsuAA4edN8PDwN33AEsXOgiqACwZw9w0UXAkiXptZMyKYzdjY+PY3p6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130b469d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VKMRlzCkBacf8LNfGx3fNXfr7L4UIIAIMDPRjfHyXmXZmoWatPYyiEhElKO2Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13300a810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PtWzUtLWHUVQiLmNOMUo65sdaNmra2sMoKhHAZcwpNknH/FjLRk1bexhFJfIN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1311093d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hOxK/xEzksxPAmfltN8Y8JnZuj41eCurrsGeS3gvgi8aY8byfeSrs73bQGHNZucfq+TJs\n6mMjgJ8FXAh4C4CzReTrsMdhMYA+2GuSis/uhOEjsMMu/ykiV8N2bnphzzj8Y95+vxSRO2E/5T8J\ne8buHQA+5dVfARtFvwH29/sc7Cf7wzATswVqeG3CRtzfARuLvRJ2hs71sM/f64v2nfV8864JOcv7\n9ijYmL3/2p4wxtwC1Ha885wLe03MVys8BmqEUTCTF78a+8LMDJilvt7t7fdS2E+7F8O+gf4a9tTq\nHcib5TDv554Oezr4adhOxdcAHB2w35Gwf3Qe9X7ef8Hm5P+sqH0nFN3uVBTNypi377ureNyneo8n\n6DGPBey/FvaP517Y4Y0PlPi582GTLY/DfnrLAji+xL4nw16/8Afv8V+JvBk78/Z7HNXNWnkd7Kn1\n/G1zYac1fgj2j+J9yJsV09vnNNg/lg95j+8h2BlIu/L2ea93rB/HzBDPEICDi37W8wCyVT73DoS9\nUPS33s+8GwGzk3qP6zkALynaXur4PVe033rYN+k/wHb4vgrg2DLt+nvYN6193vN8EN6Mj3n7vMW7\nr4ozRubd5jfebfpL1C8B8ID3u/gx7DUaQcf0eQD/VOPrfNZtYFNd3wTwlPdc/Q5sLDx/nwEAP4S9\nwPRp7/d4CWZmwDwEtqPxC9hhzicB/ADA2SX+zlR8bXr7L4E9s/Z7774/B6CjxOPKlrivoK9/red4\ne/u90HsO3VDL755ftX+J9wunFBCRl8Ke7izOs1OExE529HPYT9m3xd0essTOrPlO2DlfwrrAkIgQ\n8TUXInKKiNwsdlro/SJyVoX9T5XgVT0Pi7KdRBE7DXZoih0LXU4FcBk7FkThi/qai3mwFwZei+rH\ntwzsGOD0jI3GmMdL706kmzHmasxe14RiZowpnva86bzJsDoq7Pa0mT3PCZFqkXYuvE9qtwHTM6JV\nK2dmLgSkcFU7tTMRRe/FsEOVpRjYC0irueCUSA2NaREBsENEWmDHqQeNMT+IuU1OMHaa4FCigEQU\nikdhEx/lPNCMhhCFSVvnYjdsdOvHsPnqC2GjTMuNMYETr3gT2qyCvUJ4X9A+RESKVVp2/pDiuSqI\nQtQCGxG/3RgzGdYPVdW5MMbcj8KpfO8WkS7YCYPOL3GzVeBkKERERI04FyGuEKuqc1HCdtillkv5\nNQB84QtfwJIlQfNHURKtW7cOW7ZsibsZFBIeT7fweLpj586dOO+884CZ5R9CkYTOxXGYWQgnyD4A\nWLJkCU44IdRVjSlG8+fP5/F0SCPH0xiDsbEx7Nq1C11dXVixYgX868PL1Rq5LWvla1NTU9izZ4+K\ntmioaWtPLbVjjjnGfwihXlYQaefCW3PhKMxM8bzYW2XvSWPMQyIyBOAIY8z53v4XwV45/QvYcaAL\nYWeJfGOU7SQivcbGxnDDDXY5ivFxO5tzT09PxVojt2WtfG1qamp6n7jboqGmrT211I4//nhEIeqF\ny5YB+AnsnPYGwBUA7sXM2hQLYaNYvgO9fX4KO9f8sQB6jDF3RtxOIlJq165dBd8/8MADVdUauS1r\nrNVS09aeWmoPP/wwohBp58IY811jzAuMMXOKvt7j1S8wxqzI2/+TxpiXG2PmGWM6jDE9xpi7omwj\nEenW1dVV8P3ixYurqjVyW9ZYq6WmrT211I488khEIQnXXFAKrVmzJu4mUIgaOZ4rVtjPHw888AAW\nL148/X2lWiO3Za18bf/+/Vi9enVoP3PlypnhhUwGeXoA9CCTuUbNY3ftudbW1oYoJH7hMhE5AcD4\n+Pg4LwAkIkqgSvM3J/xtSrV7770XJ554IgCcaIy5N6yfG/U1F0RERJQyHBYhotgxHpju2kygMFgm\nk1HRThefa1ENi7BzQUSxYzww3TV7bUVp4+PjKtrp4nMtqVFUIqKKGA9krRqa2unKcy2RUVQiomow\nHshaNTS105XnGqOoROQsxgNZK2fZsmVq2unac41R1BIYRSUiIqoPo6hERESUCBwWIaLYMR7IWpJr\n2trDKCoRERgPZC3ZNW3tYRSViAiMB7KW7Jq29jCKSkQExgNZS3ZNW3sYRSUiAuOBrOmr9fZeGLhC\nq+8znylMWmp9HIyi1olRVCIiCltaVmplFJWIiIgSgcMiRNQUjAeylqQa0Fvx+ezCc41RVCJKNMYD\nWUtSrZKxsTEnnmuMohJRojEeyFrSauW48lxjFJWIEo3xQNaSVivHlecao6hElGjNjtzFcZ+suVMr\njKHO5spzjVHUEhhFJSIiqg+jqERERJQIHBYhotDEHatjFJU1PtcYRSUix8Qdq8uvaWsPa+7WtLWH\nUVQickrcsTpX4oGsJaumrT2MohKRU+KO1bkSD2QtWTVt7WEUlYicEneszpV4IGvJqmlrD6OoIWAU\nlYiIqD6MohIREVEicFiEiEITd6zOlXgga8mqaWsPo6hE5JS4Y3X5NW3tYc3dmrb2MIpKRE6JO1bn\nSjyQtWTVtLWHUVQickrcsTpX4oGsJaumrT2MohKRU+KO1bkSD2QtWTVt7WEUNQSMohIREdWHUVQi\nIiJKBA6LEFFN8tJ3gUZHsyoid3HcJ2vprGlrD6OoROQcLZG7OO6TtXTWtLWHUVSX5HK1bSdKAcYD\nWUtDTVt7GEV1xcQEsGQJMDxcuH142G6fmIinXUQxYzyQtTTUtLWHUVQX5HJATw8wOQkMDNht/f22\nY+F/39MD7NwJdHTE106iJlm9erWKyF0c98laOmva2sMoaghURFHzOxIA0NYGTE3NfD80ZDscRA6o\ndEFnwv+kEKUKo6ia9ffbDoSPHQsiIkoxDouEpb8f2LSpsGPR1saOBaVONssoKmvpqmlrD6OoLhke\nLuxYAPb74WF2MMgpo6PZ6SgbYK+x8GNu2WxWTeQujvtkLZ01be1hFNUVQddc+AYGZqdIiBIs7uhc\nGuKBrCWrpq09jKK6IJcDRkZmvh8aAvbsKbwGY2SE812QM+KOzqUhHshasmra2qMhijpncHAwkh/c\nLBs3blwEoK+vrw+LFi1qfgPmzQNWrQJuvBHYsGFmCKS7G2hpAXbsALJZoOiJSJRUnZ2dmDt3Llpb\nW9Hd3V0wnquppq09rLlb09aeWmpdXV3IZDIAkBkcHNw9+xVfH0ZRw5LLBc9jUWo7ERFRzBhF1a5U\nB4IdCyIiShmmRYioKRgPZM3Vmrb2MIpKRKnBeCBrrta0tYdRVCJKDcYDWXO1pq09jKISUWowHsia\nqzVt7dEQReWwCBE1BVeqZM3Vmrb21FLjqqglqImiEhERJQyjqERERJQIHBYhItXSGA9kLVk1be1h\nFJWIqII0xgNZS1ZNW3sYRSUiqiCN8UDWklXT1h5GUYmIKkhjPJC1ZNW0tYdRVCKiCtIYD2QtWTVt\n7WEUNQSMohIREdWHUVQiIiJKBA6LEFFiuRoPZC1ZNW3tYRSVqFG5HNDRUf12coqr8UDWklXT1h5G\nUYkaMTEBLFkCDA8Xbh8ettsnJuJpF4Urlyu53dV4IGvJqmlrD6OoRPXK5YCeHmByEhgYmOlgDA/b\n7ycnbb3UGxMlQ4UO5NKi3V2JB7KWrJq29miIos4ZHByM5Ac3y8aNGxcB6Ovr68OiRYvibg41y7x5\nwP79QDZrv89mgSuvBG69dWafDRuAVaviaR81LpcDli+3HcVsFmhpAbq7ZzqQe/fiz3/4Q8z/h3/A\nQQsWoLu7e9Y4eGdnJ+bOnYvW1tZZddZYC6umrT211Lq6upDJZAAgMzg4uLvi67JKjKJSsvlvNMWG\nhoD+/ua3h8JVfHzb2oCpqZnveZyJGsIoKlGQ/n77hpOvrS3aN5wy1wBQyPr7bQfCx44FUSJwWISS\nbXi4cCgEAPbtmzmFHraJCXuqfv/+wp8/PAyce64dhlm4MPz7TbPubjvktW/fzLa2NuCWW6ZjdaOj\no5iamkJnZ2dgPDCozhprYdW0taeWWktLSyTDIoyiUnKVO2Xubw/zk23xRaT+z89vR08PsHMnY7Bh\nGh4uPGMB2O+HhzF20klOxgNZS1ZNW3sYRSWqVy4HjIzMfD80BOzZU3gKfWQk3KGKjg5g/fqZ7wcG\ngAULCjs469ezYxGmoA6kb2AAB191VcHursQDWUtWTVt7GEUlqldHh00QtLcXjr37Y/Tt7bYe9hs9\nrwFonio6kMdnszh4797p712JB7KWrJq29miIojItQskW1wydCxYUdiza2uwbH4VrYsIONa1fX9hx\nGx4GRkZgRkcxNjlZsPpj0Dh4UJ011sKqaWtPLbW2tjYsW7YMCDktws4FUa0Yf20uTvFOFBlGUYk0\nqHANwKyZJKlxpToQ7FgQqcXOBVG14riIlIgogRhFJaqWfxFp8TUA/r8jI9FcREp1i3s5a9bSUdPW\nHi65TpQ0S5cGz2PR3w+sXcuOhTJxzyHAWjpq2trDeS6IkojXACRG3HMIsJaOmrb2cJ4LIqIIxT2H\nAGvpqGlrj4Z5LjgsQkTOWrFiBQAU5P1ZYy3smrb21FKL6poLznNBRESUUpzngtzGZcyJiJzBYRGK\nX4UpnpHN2pQGUY3ijvmxlo6atvYwikrEZcwpQnHH/FhLR01bexhFJeIy5hShuGN+rKWjpq09jKIS\nAVzGnCITd8yPtXTUtLWHUVQiX38/sGnT7GXM2bGgBsQd82MtHTVt7WEUNQSMojqCy5gTETUdo6jk\nLi5jTkTklDmDg4Nxt6EhGzduXASgr6+vD4sWLYq7OVSrXA4491xg7177/dAQcMstQEuLjaACwI4d\nwAUXAPPmxddOSiQ/djc6OoqpqSl0dnbOiuSxxlqjNW3tqaXW0tKCTCYDAJnBwcHd1b62KuE1FxQv\nLmNOEYo75sdaOmra2sMoKhEws4x58bUV/f12OyfQojrFHfNjLR01be1hFJXIx2XMKQJxx/w01zKZ\nrdNfvb0XQgQQAfr6epHJbFXTziTUtLWHUVQiogjFHfPTXitn2bJlatqpvaatPYyihoBRVCKi2uVd\nixgo4W8NVKWooqg8c0FEFDG+kVPasHNBRM6Ke8XJ4pUzNbUTKN+uTCYT++8sKTVt7eGqqEREEYo7\n5pdf09ZOoHy7xsfHY/+dJaWmrT2MohIRRSjumF9xXFFrO8vJv91vd+yY/n8msxUrV/ZMp0xWruwp\nSKBoilsyiupYFFVEThGRm0XktyKyX0TOquI2p4nIuIjsE5H7ReT8KNtIRO6KO+ZXHFfU2s4gq7yO\nxPTthodxzmWX4cjJyYq3jaqdWmva2pOGKOo8ADsAXAvgq5V2FpGXAbgFwNUA3gVgJYDPisgjxpjv\nRNdMInJR3DG/aiKfcbVzdDRbUBMRIJeDWbIEMjkJbAdefeyx6FqxYnr9nwMBXPKd7+DLl16KTJWP\nKa7H18yatvakKooqIvsBvNUYc3OZfTYBeLMx5tV527YBmG+MObPEbRhFJSLVEpUWCVpIcGpq5ntv\npeJEPSYqKS2ror4WwGjRttsBvC6GtpDrcrnathOlQX+/7UD4AjoWRJVoS4ssBPBY0bbHALxIRA4y\nxvwxhjaRiyYmZi+WBthPbf5iaVzTJPHijvn5NWP0tKWq2kknoXvuXBz0zDMzv8y2NphLLsFYNutd\nFNhb8Xev9vExisooKlHocjnbsZicnDn9299feDq4p8cumsa1TRIt7phfUmtPfehDhR0LAJiawq4L\nL8QNc+YE/KZnGxsbU/v4GEVNXxT1UQCHF207HMDvKp21WLduHc4666yCr23btkXWUEqwjg57xsI3\nMAAsWFA4zrx+PTsWDog75pfE2sFXXYWzt2+f/v6Pc+dO//+oa6+dTpFUovXxpTmKum3bNlx88cW4\n7bbbpr82b96MKGjrXPwQs2d2eZO3vawtW7bg5ptvLvhas2ZNJI0kB3BcORXijvklrpbL4fhsdnr7\nV5cvx/dvvrngtfKmiQkcvHcvenv7MDqa9YZ8gNHRLHp7+6a/VD6+iGra2lOqtmbNGmzevBlnnHHG\n9NfFF1+MKEQ6LCIi8wAchZl5ZheLyFIATxpjHhKRIQBHGGP8uSw+A+D9XmrkX2E7Gu8AEJgUIWpI\nfz+waVNhx6KtjR0Lh8Qd80tcraMDB3z3u/jTqadiR08P5r///bbmnVI3IyP4xSc+gWNE9D6GGGra\n2uN8FFUy7gHzAAAgAElEQVRETgVwB4DiO/mcMeY9InIdgJcaY1bk3eYNALYA+AsADwO4zBjzb2Xu\ng1FUqk9x5M7HMxeUdrlc8LBgqe2UWIlcFdUY812UGXoxxlwQsO0uACdG2S6isln+/Is8idKoVAeC\nHQuq0pzBwcG429CQjRs3LgLQ17d6NRZVOdUupVwuB5x7LrB3r/1+aAi45RagpcVGUAFgxw7ggguA\nefPiayc1zI/djY6OYmpqCp2dnbMieayx1mhNW3tqqbW0tCCTyQBAZnBwcHe1r61K3ImiLlgQdwso\nKTo6bCeieJ4L/19/ngt+Sku8uGN+rKWjpq09jKISxWXpUjuPRfHQR3+/3c4JtJygJQLImts1be1x\nflVUItU4ruw8LRFA1tyuaWtPGlZFJSKKTdwxP9bSUdPWHuejqM3AKCoREVF90rIqav327Im7BVQL\nrkhKROQsd4ZFmBZJDq5ISk0S94qTrKWjpq09XBWV0ocrklITxR3zYy0dNW3tYRSV0ocrklITxR3z\nYy0dNW3tYRSV9GnGtRBckZSaJO6YH2vpqGlrj4YoqjvTf/f1YdGiRXE3J9kmJoDly4H9+4Hu7pnt\nw8N2uuxVq4CFC8O5r+5u4MorgX37Zra1tdlpuIlC0tnZiblz56K1tRXd3d0FY8+ssRZWTVt7aql1\ndXVFMv03o6hk5XLAkiX2Wghg5gxC/rUQ7e3hXQvBFUmJiGLHKCpFq5nXQgStSJp/v8PDjd8HERHF\nhsMiNKO7u3Bl0Pwhi7DOKHBFUgqRqytVspasmrb2cFVU0qe/H9i0qfAiy7a22joWuVzwGQ5/O1ck\npZC4Gg9kLVk1be1hFJX0GR4u7FgA9vtqhyomJuy1G8X7Dw/b7RMTXJGUQuNqPJC1ZNW0tYdRVNKl\n0WshiifI8vf3f+7kpK2XOrMB8IwF1cTVeCBryappaw+jqCHgNRchCeNaiHnzbIzV3z+btXHTW2+d\n2WfDBhtpJQqBq/FA1pJV09YeRlFDwChqiMJa84Mx02SrdM0METmDUVSKXljXQvT3Fw6pALVfFErx\nqOaaGSKiCpgWoUJhXAtR7qLQlHcwKq2sGKsELirn6kqVrCWrpq09XBWV3BN0Uajf0ch/w0qpSnG2\nWPkTqfnHaWBgdixZ2aJyrsYDWUtWTVt7GEUlt+Ry9toM39AQsGdP4SJll18e7iJoCVMpzha7hC0q\n52o8kLVk1bS1h1FUcos/Qdb8+cDcuTPb/TesuXMBY4BHHomvjTGrFGdTIUHXzLgaD2QtWTVt7dEQ\nReWwCIXriCOAOXOAp56aPQzyzDP2S9m4fTOtWLECgP0ksXjx4unvVUnQNTOVfp/l6qyxFlZNW3tq\nqUV1zQWjqBS+ctddACpPr5OHx44oVRhFpeRI2Lg9eaq5ZmZkJNXXzBBRdXjmgqKzYMHsBdD27Imv\nPRGolCItfnmpjqIC4U2k1iSuxgNZS1ZNW3tqjaIuW7YMCPnMBa+5oGgkaNy+mVRHUYGZidSKr4fp\n7wfWrlV3nYyr8UDWklXT1h5GUclNjS6A5jD1UVQgUYvKuRoPZC1ZNW3tYRSV3MNx+7ISEUVNEFfj\ngawlq6atPYyiknv8uS6Kx+39f/1xe4WfgpshEVHUBHE1Hshasmra2sMoagh4QadSSVhZM4Q21npB\nJxGRJoyiUrJoH7fn6p9ERJHhsAilTxNX/8xmszXF2VIhxLNarsYDWUtWTVt7uCoqURxCXP0zaNgj\nm81OR728f6qOszkv5Hk0XI0Hspasmrb2MIpKFLZSKZTi7RHOItpInM1pxWeM/CEp/4zR5KSt15Ak\ncjUeyFqyatrawygqUZhqvY4iotU/G4mzOc0/Y+QbGLCzuObPiVLlGSOfq/FA1pJV09YeDVHUOYOD\ng5H84GbZuHHjIgB9fX19WLRoUdzNobjkcsDy5fbTbzYLtLQA3d0zn4r37gVuvBG44AJg3jx7m+Fh\n4NZbC3/Ovn0zt61TZ2cn5s6di9bWVnR3dxeMd5arpUJ3t/39ZrP2+337Zmp1nDGq9Pus91iwxlqt\nr11N7aml1tXVhUwmAwCZwcHB3RVfdFViFJXcUcuKnlz9M14pWHeGKAkYRaXYiZT/il2111FwFtF4\nlVt3hoicwDMXVLXETBhVzafiCFf/bCTO5ryQzxi5Gg9kLVk1be3hqqhEYat2NdYIV/9sJM7mtKAz\nRsXzi4yM1PT7dzUeyFqyatrawygqUZhqXY01ollEGUUtwV93pr298AyFP5zV3l7zujOuxgNZS1ZN\nW3sYRSUKi6LrKBhFLcM/Y1Q89NHfb7fXOBTlajyQtWTVtLVHQxSVwyLkBkWrsTaysmIqhHjGyNWV\nKllLVk1be2qpcVXUEnhBZ/Mk4oLOJKzGmkY8LiUl4nVFzmIUlaga2ldjTSOuQEuUOhwWoarxE9SM\nqOJszol4BVpX4oH1PkbWdNS0tYerohIlVFRxNueEuAJtEFfigfU+RtZ01LS1h1FUooSKKs7mpJhW\noK1U11Qrp9LPzGS2Tn+tXNkzPWPuypU9yGS2Nu0xpLmmrT2MohIlVFRxNmfFsAJtpbqmWjm1/Mxy\ntD52F2ra2sMoKlFCRRVnc1a1M6fWyJV4YL2PsZqfsWzZstgfn+s1be1hFDUEjKISKccVaMtqNIrK\nKCs1glFUIkoeRTOnamVM+S+Kj/qVoBXjsAhRHRg3rVLEM6e6Gg+spQaUf25lMhkV7UxiDag+yRV3\nWxlFJXIA46Y1iGkF2kp1V2qV3gDHx8dVtDOZtepft/G3lVFUosSLPW5aahhB6/BCDCvQVqq7WCtH\nUzuTUqtF3G1lFJXIAbHGTTmd9jRX44G11Hp7+6a/Rkez09dqjI5m0dvbp6adSazVIu62MopK5IDY\n4qYRT6edNK7GA1nTUctkULW428ooasgYRaXUYbQzECOZFLY0PKcYRSUiK8LptImIwsBhEaISVK98\n2t8/ewGwEKbTTpr83zXQW3bfbDarKgLImv7a/v3lb5cfA467rYyiEiWE6pVPI5pOO2nyf9eV+Ptp\niQCy5k5NW3sYRSUdkhZrbBK1K58GXXPhGxiYnSJxWK3RQU0RQNbcqWlrD6OoFD/GGktSufIpp9Mu\n4P+u85cWL0dTBJA1d2p13dZ7jRbXjj7kkKY+jqiiqHMGBwcj+cHNsnHjxkUA+vr6+rBo0aK4m5Ms\nuRywfLmNNWazQEsL0N0988l4717gxhuBCy4A5s2Lu7VN19nZiblz56K1tRXd3d0F45b11ho2bx6w\napU9Lhs2zAyBdHfb47djhz2WYc+toZT/u/785ys/3k2b5oZyDFljLeh1XdNt29shr3kNsH8/Ov/m\nb6Zr5z70EI4bGYGsWgUsXNiUx9HV1YWMzdxmBgcHd1d8IVWJUdS0Y6wxmXK54HksSm13XDV9t4T/\nqSNX5HL2rPDkpP3e/xub/7e4vb1pc9UwikrRYKwxmSKaTttV7FiQGh0ddhE/38AAsGBB4Ye89esT\n/1pmWoRSHWtsdgyMwuX/ristMKVhZdBKT4HR0azKqCJr1b2ua7rtJZfYEKvfocj722s+8QmI97eX\nUVRKNhdjjVUOG8QRWaPwzPyu9a8MWonWqCJrEUVRAz7U/eHAA3H38uXTz2ZGUSm5XIw11pCAiSOy\nRuFJUhQ1Ke1krfZaXbcN+FA3709/wguvuqqpj4NRVAqfi7HG4oW9/A6G34manLT1EjGwZkTWKDy1\nrmIZd1wxCe1krfZarbc9/Uc/KvhQ94cDD5z+//KvfW3671aSo6gcFkmzjg4bW+zpsRcQ+UMg/r8j\nI7aepAuL/Iul/BfuwMDs60nyLpZq9oqEFKCB5Iv/u1227Jrp33XQOPgDDyyLfTXKSlavXq1y1UzW\nKtdque3RhxyCrr6+6Zr5xCdw9/LleOFVV9mOBWD/9q5d25THEdU1FzDGJPoLwAkAzPj4uKE6Pf54\nbduTYGjIGBsSKPwaGoq7ZZRvxw5j2ttnH5ehIbt9x4542hWBoKdj/heliKLn/fj4uAFgAJxgQnxv\n5jwX5K4FC2YnYPbsia89VEhZ3j9qaVi+m2qgZK6aqOa54LAIOcEUR6+2b4cEJGD++73vRdc118Qa\nWSNPjUNYQSr9rpt9fBs59tkso6hx15rK8blq2LlIAyU95Cjlx6sOvfZayPbt07VnDz4YBzz9NADg\nqGuvxX8DOOqzn511Ow1xxNTxr+8JyPtXM4lbklaqHB3NFqzgunr16ulaNptV08401yg8TIu4LiUL\nk/nxqoP37sWb8h/T0BCuu+IKfHX58ulNR37pS9NpEc1xxNTo7y+MQANVT+Lm6kqVrMVTo/Cwc+Gy\nGmOZSebHq55ubcWWv/xL/OlFL5r+5NvV1YXbjzsOX12+HE8fdBAmNm+ePmOjOY6YGuUmcasg9JUq\nWUt1jcLDYRGXhTCmnRTF8aoDrr4aOOywwtqyZbj3kENwytlnl7xd3HHE1Cm3cJ6/vcwZjLDigayx\nRuFiWiQNiv+A+7gwGcUpZWkRIo24KirVr4ExbaLI+JO4tbcXdnT9lXrb25M3iRsRAeCwSDokaGGy\nZkfPmrZS5RNPOJ/YqcvSpcFnJvr7gbVrK/5ukhRFdb1GlI+dC9c1OKbdbNqiZ2Hc38G7duE1H/pQ\n4RTrgD02/hTrS5dW1R4nNZD3T1IU1fUaUT4Oi7gsgQuTaY6e1XN/B+/di6UXX5yKxE4cGEXVU6OY\nlPrbEfPfFHYuXJbAMW3N0bN67u/p1lY8fM45MzsODNhpyfPPJjmS2IkDo6h6ahQDxfMYcVjEdQ2O\naTebtuhZGCtVdq1YARx1VN2zUFJpjKLqqVGTFc9jBMxOW/X0xJa2YhSVUq2pi0lxITUiClO5a+qA\nqj68MIpKlGQNzEJJRBTIH+L2KTorymERUiXK+NzKlbVf1R7KSpXbt0M+9KGZHxpxYidNS3u7EkUl\nqlt//+yZlxXMY8TOBakSbXyufOeit7cv9JUqf/X97+OUm27Cgf6dBM1COTKi8vqXJHAlikpUN6Xz\nGHFYhFRpRnyunLDv7+nWVnzjoosSldhJkiiiqCLAypU9yGS2Tn+tXNkDEVtjjJPUCLrmwpcffY8B\nOxekSjPic+VEcX9tp55qr9gu/hTR32+3p3kCrQZFFUWt9z792sF79wbW/O213B9RIOXzGHFYhFSJ\nMj6XyZS/79WrV0cX1ys1ts4zFg2JKopa732uWLECB+/ahaUXX4yHzznHxpD92vbtOOWmm/CNiy5C\n26mnMsZJjfHnMerpKZz91//Xn/03pr8xjKJSaqTlQse0PM6oNPT740qv1Gyl1ieqct0iRlGJiLTr\n6LCfIn2ckZWi1sDaPFHisAipEmUEsFJaJJvNMnLomCiOU8Xb+aelOSMrpRg7F6RKlBHA3l5bLxU3\n9f5JfOSQwx4zojhOVd1O6dwDRM3CYRFSRdMqj4wcJl8Ux6mq23FGVko5di5IFU2rPHLlyOSr5zgZ\nA4yOZtHb2zf9NTqahTG2VvH4Kp57gKhZOCxCqmha5ZErRyZf049v0NwDnJG1bkw+JRejqEREYZqY\nmD33AGA7GP7cA5w4rSrsXEQvqigqz1wQEYVp6dLgeSz6+3nGglKjKZ0LEXk/gPUAFgKYAPABY8w9\nJfY9FcAdRZsNgEXGmMcjbSjFTtNKlYybJl9sx0np3ANEzRJ550JE3gngCgC9ALYDWAfgdhF5hTHm\niRI3MwBeAeD30xvYsUgFTStVao6bUnV4nIji0Yy0yDoAW40xnzfG3Afg7wA8A+A9FW6XM8Y87n9F\n3kpSQVNslHHT5ONxIopHpJ0LETkAwIkAsv42Y68gHQXwunI3BbBDRB4RkW+LyMlRtpP00BQbZdw0\n+XicKHVKrYLa5NVRox4WORTAHACPFW1/DMDRJW6zG0AfgB8DOAjAhQDuFJHlxpgdUTWUdNAUG2Xc\nNPl4nChVFCWVIo2iisgiAL8F8DpjzI/ytm8C8AZjTLmzF/k/504AvzHGnB9QYxSViIjSrc4VeZMa\nRX0CwPMADi/afjiAR2v4OdsBvL7cDuvWrcP8+fMLtq1ZswZr1qyp4W6IiIgSyF+R1+9IDAzMWt9m\n28qV2LZ2bcHNnnrqqUiaE2nnwhjzrIiMwy5HeTMAiM2B9QD4VA0/6jjY4ZKStmzZwjMXDtAUKWXc\nlIgSpcKKvGv6+1H8cTvvzEWomjHPxWYA13udDD+KOhfA9QAgIkMAjvCHPETkIgAPAvgFgBbYay5O\nB/DGJrSVYqYpUsoYIxElTq0r8u7ZE0kzIo+iGmNugJ1A6zIAPwHwagCrjDH+pasLAbw47yYHws6L\n8VMAdwI4FkCPMebOqNtK8dMUKWWMkYgSp9YVeRcsiKQZTVkV1RhztTHmZcaYVmPM64wxP86rXWCM\nWZH3/SeNMS83xswzxnQYY3qMMXc1o50UP02RUsYYiShRFK3Iy7VFSBVNkVLGGIkoMZStyMtVUcnK\n5YKfcKW2ExGRLnXMcxFVFLUpwyKk3MSEzUcXnzIbHrbbJybiaRcREVXPX5G3+OLN/n67vUkTaAHs\nXFAuZ3u6k5OFY3L+qbTJSVtv8tSxqaJkul4ickCtK/ImNS1CyvkTr/gGBuzVw/kXBa1f37ShEWMM\nstksMpkMstks8oftNNVCw7NGRBSniNIivKCTKk68UjIfHQFNc1lEPs9F8VkjYPYFWD09s6brJSLS\njmcuyOrvL4wtAeUnXomIprksIp/nQtlZIyKisLBzQVatE69ERNNcFk2Z56K/354d8sV41oiIKCwc\nFqHgiVf8N7n80/VNoGkui6bNc1HrdL1ERMpxnou0q3OZXgpRcefOxzMXRBQxznNB0ejosBOrtLcX\nvpn5p+vb222dHYtoKJqul4goLDxzQVYTZ+jUtHR6rEuu86wREcUsqjMXvOaCrFonXmmApkhprFFU\n/6xR8XS9/r/+dL3sWOjGqfOJZuGwCDWdpkhp7EuuK5qul+rASdCIArFzQU2nKVIaexQVaOpZIwoR\np84nKonDItR0miKlKqKolEz+JGj+9TEDA7MjxZwEjVKKF3QSUXm8pqA8RokpwRhFJaLm4zUFlSmZ\nOp9IEw6LUOgqRTg1RUpjjaJqx4XVqlNu6nx2MCil2Lmg0FWKcGqKlMYaRdWO1xRUpmjqfCJNOCxC\noasU4dQUKY09iqodF1YrLZezc5H4hoaAPXsKf18jI0yLUCqxc0GhqxTh1BQpVRFF1Y7XFATj1PlE\nJXFYhEJXKcKpKVLKKGoVeE1Baf4kaMUdiP5+YO1adiwotRhFJaLSyl1TAHBohMiX0Mg2o6hE1Fy8\npoCoOoxsz8JhESopqginpkgpY6plcGE1osoY2Q7EzgWVFFWEU1OklDHVCnhNAVF5jGwH4rAIlRRV\nhFNTpJQx1SpwYTWi8hjZnoWdCyopqginpkgpY6pEFApGtgtwWIRKiirCqSlSypgqEYWCke0CjKIS\nERE1IsGRbUZRiYiItGFkOxCHRVJAW0xTU3sYRSWihjCyHYidixTQFtPU1B5GUYmoYYxsz8JhkRQI\nO24pAqxc2YNMZuv018qVPRCxNUZRiSh1GNkuwM5FCjQ7bskoKhFRunFYJAWaHbdkFJUoZAldFIvS\ni1FUqlml6xYT/pQi0mViYvbFgoCNP/oXCy5dGl/7KNEYRaXE8q/FKPVFRCUUL4rlr7rpz6swOWnr\nKYs5kn4cFkmQpMQti28HlE9RGGNURkoZRaXYcVEsSih2LhIkKXHL2bcrf5uxsTGVkVJGUUkFfyjE\n72AkZOZHSjcOiyRIUuKWxberRGuklFFUUoOLYlHCsHORIFrilsYAo6NZ9Pb2TX+NjmZhjK0V364S\nrZFSRlFJjXKLYhEpxGGRBNEUt6yllslU97g0tJVRVIpFuajptdeWXhTL384zGKQMo6gUOUZXicoo\nFzW9/HL7AvE7E/41FvmrcLa3B089TVSFqKKoPHNBRBSX4qgpMLvzMH8+cMghwAc/yEWxKDHYuYiB\npthkM2r795dfFdUYPW1lTJWaqpqoaanFr1K8KBbpx85FDDTFJrkqKmOqFLNGoqbsWJBSTIvEQFNs\nMo6Ypqb2MKZKKjBqSo5h5yIGmmKTccQ0NbWHMVVSgVFTcgyHRWKgKTYZR0xTU3sYU6XY5V+8CTBq\nSk5gFJWIKC65HLBkiU2LAIyaUtNxVVQiItd0dNgoaXt74cWb/f32+/Z2Rk0pkTgs0gBNEcek1LS1\nR1ONUmrp0uAzE4yaUoKxc9EATRHHpNS0tUdTjVKsVAeCHYvmKDf9Oo9BXTgs0gBNEcek1LS1R1ON\niGIwMWGveylO5gwP2+0TE/G0K+HYuWiApohjUmra2qOpRkRNVjz9ut/B8C+onZy09Vwu3nYmEIdF\nGqAp4piUmrb2lKrZyyB6vC8rf3XX/fsZUyVKvGqmX1+/nkMjdWAUlSgAV3IlSpHiuUZ8laZfdwCj\nqERERFHg9OuhS82wiKbIYZpr2tpTbzRUU1uIqEHlpl9nB6MuqelcaIocprmmrT31RkM1tYWIGsDp\n1yORmmERTZHDNNe0tafeaKimthBRnXI5YGRk5vuhIWDPHvuvb2SEaZE6pKZzoSlymOaatvbUGw3V\n1BYiqhOnX49MaoZFtEQc017T1p56o6Ga2pJanFWRwsDp1yPBKCoRJc/EhJ3caP36wvHw4WF7Gjub\ntW8aRFQWo6hERABnVSRKAKeGRTTFGFlLfhRVU43ycFZFIvWc6lxoijGylvwoqqYaFfGHQvwORn7H\nIgWzKhJp59SwiKYYI2vBNW3tSUqNAnBWRSK1nOpcaIoxshZc09aepNQoQLlZFYkoVk4Ni2iKMbKW\n/CiqphoV4ayKRKoxikpEyZLLAUuW2FQIMHONRX6Ho709eO6CJOJ8HhQhRlGJiIB0zao4MWE7UsVD\nPcPDdvvERDztIqrAqWERTdFB1hhFZUw1QmmYVbF4Pg9g9hmanh53ztCQU5zqXGiKDrLGKGozf6ep\nVOoN1ZU3Ws7nQQnm1LCIpugga8E1be1xoUYO84d6fJzPgxLCqc6Fpugga8E1be1xoUaO43welEBO\nDYtoig6yxigqY6oUinLzebCDQUoxikpEpFW5+TwADo1QwxhFJSJKk1zOLh/vGxoC9uwpvAZjZISr\nv5JKTg2LaIoHssYoqoYaJZg/n0dPj02F5M/nAdiOhSvzeZBznOpcaIoHssYoqoYaJVwa5vMgJzk1\nLKIpHshacE1be1yvkQNcn8+DnORU50JTPJC14Jq29rheIyKKg1PDIprigawxiqqhRkQUB0ZRiYiI\nUopRVCIiIkoEp4ZFNEUAWWMUVUONiCgOTnUuNEUAWWMUVUONiCgOTg2LaIoAshZc09Ye12tERHFw\nqnOhKQLIWnBNW3tcr1GeUtNkc/psXXicnDBncHAw7jY0ZOPGjYsA9PX19eHkk0/G3Llz0draiu7u\n7oKx587OTtYU1LS1x/UaeSYmgOXLgf37ge7ume3Dw8C55wKrVgELF8bXPrJ4nJpu9+7dyGQyAJAZ\nHBzcHdbPZRSViNyWywFLlgCTk/Z7fyXR/BVH29uDp9mm5uFxigWjqERE9ejosAt/+QYGgAULCpcy\nX7+eb1hx43FyilNpEU0RQNYYRdVeSxV/JVH/jWpqaqbmf0Km+PE42TM4QR2oUtuVcqpzoSkCyBqj\nqNprqdPfD2zaVPiG1daWjjesJEnzcZqYAHp67Bma/Mc7PAyMjADZrF0pNwGcGhbRFAFkLbimrT1p\nrqXO8HDhGxZgvx8ejqc9FCytxymXsx2LyUl75sZ/vP41J5OTtp6Q1IxTnQtNEUDWgmva2pPmWqrk\nXxQI2E/Cvvw/5GFglLJ+zTxO2jh2zQmjqKwxiprSWtVyOWDevOq3a5PL2Rjj3r32+6Eh4JZbgJYW\ne5oZAHbsAC64oPHHwyhl/Zp5nLTq7i58vPv2zdQiuuYkqigqjDGJ/gJwAgAzPj5uiChkO3YY095u\nzNBQ4fahIbt9x4542lWrZjyOxx+3PwuwX/59DQ3NbGtvt/tRMFeeb41qa5t5zgD2+4iMj48bAAbA\nCSbM9+Ywf1gcX+xcEEXEtTfLUu0Ms/35vxv/TSH/++I3TZqtGcdJs+LnUMTPnag6F04NiyxcuBBj\nY2MYHR3F1NQUOjs7Z0XyWIu3pq09rJU+Tpg3z57e90/RZrPAlVcCt946s8+GDfZUfxKUOpUe5in2\nGE5rO6cZx0mroGtO/OdQNmufW/nDbSGIaliEUVTWGEVlrXRMlfMO1C7NUUqqXy5n46a+oBlKR0aA\ntWsTcVGnU2kRTTE/1oJr2trDWnCtQH9/4VX7AN8sy0lrlJIa09Fhz060txd23Pv77fft7baegI4F\n4FjnQlPMj7Xgmrb2sBZcK8A3y+qlOUpJjVu61K6dUtxx7++32xMygRbQpGEREXk/gPUAFgKYAPAB\nY8w9ZfY/DcAVAF4J4H8AfNwY87lK97NixQoA9hPY4sWLp79nTU9NW3tYK32cAAS/WfodDX87z2BY\njp3WppiUem4k7TkT5tWhQV8A3glgH4B3AzgGwFYATwI4tMT+LwPwNIDLARwN4P0AngXwxhL7My1C\nFAXX0iLNoD1KmfYkBs0SVVqkGcMi6wBsNcZ83hhzH4C/A/AMgPeU2P99AB4wxvyjMeZXxpirAHzF\n+zlE1CyOjQE3hebT2hMTdknz4qGZ4WG7fWIinnaRkyIdFhGRAwCcCOAT/jZjjBGRUQCvK3Gz1wIY\nLdp2O4Atle7PGD0rTia1tnJl+UWt7MkiroqaptoxW7filLPPhn8EjTEYO+kk/HZgAOcfV/7N0n++\npIrG09rF61YAs4dsenpsB4idRQpB1NdcHApgDoDHirY/BjvkEWRhif1fJCIHGWP+WOrONEX5klur\nbsVMRlFTVAPwbFtbYI0Swl+3wu9IDAzMjssmaN0K0s+ZtMi6detw8cUX47bbbpv++tKXvjRd1xTz\n0wJMhDcAABO+SURBVFyrFqOorFHC+MNZPs5Zkjrbtm3DWWedVfC1bl00VxxE3bl4AsDzAA4v2n44\ngEdL3ObREvv/rtxZiy1btmDz5s0444wzpr/OOeec6bqmmJ/mWrUYRWWNEohzlqTamjVrcPPNNxd8\nbdlS8YqDukQ6LGKMeVZE/HPtNwOA2EHdHgCfKnGzHwJ4c9G2N3nby9IU5Utqzc4CWxmjqKw98MAD\nVT9fSIlyc5awg0EhEhPxFVcishrA9bApke2wqY93ADjGGJMTkSEARxhjzvf2fxmAnwG4GsC/wnZE\n/h+AM40xxRd6QkROADA+Pj6OE044IdLHkgaVVuNO5QV6VBKfLwlSbs4SgEMjKXXvvffixBNPBIAT\njTH3hvVzI7/mwhhzA+wEWpcB+AmAVwNYZYzJebssBPDivP1/DeAtAFYC2AHbGVkb1LEgIqIqBE3w\ntWdP4TUYIyN2P6IQNGWGTmPM1bBnIoJqFwRsuws2wlrr/aiM8iWpVm1ahFFU1uyFnb0VnyfVPi8o\nQv6cJT09NhWSP2cJYDsWnLOEQsRVUVkrqI2OztSy2WxB5HD16tXwOx+MorIGAL29fVi9enXJ58zY\n2OqqnxcUMX+Cr+IORH8/pySn0DkTRQXij+SxVrmmrT2s6ahRk2ic4Iuc5FTnIu5IHmuVa9raw5qO\nGhG5xalhEa1xPdYYRWWtylVYicgJkUdRo8YoKhERUX0SG0UlIiKidHFqWERrXI81RlFZa+w5Q0TJ\n4lTnQmtcj7X0RlH7+orngfBnv7f7jo5mVbRTc42IksepYRFN0TrWgmva2hP3qqGa2qm1RkTJ41Tn\nQlO0jrXgmrb2xL1qqKZ2aq0RUfLMGRwcjLsNDdm4ceMiAH19fX04+eSTMXfuXLS2tqK7u7tg3Laz\ns5M1BTVt7Ym69o1vlJ/F/vrrO1W0U3ONiKKze/duZOzyxpnBwcHdYf1cRlGJIsRVQ4lIM0ZRiYiI\nKBGcSotois+xxihqmKuGssaYKlGSONW50BSfY41R1GqMjY2paGdSa0Skk1PDIpric6wF17S1J+pa\nb28fMplrYIy9vmLr1gx6e/umv7S0M6k1ItLJqc6Fpvgca8E1be1hLdk1ItKJUVTWGEVlLbG1psnl\ngHnzqt9OlBCMopbAKCoRRWpiAujpAdavB/r7Z7YPDwMjI0A2CyxdGl/7iBrAKCoRUbPlcrZjMTkJ\nDAzYDgVg/x0YsNt7eux+RDTNqWGRhQsXYmxsDKOjo5iamkJn58zsh36cjbV4a9raw5q7tVDMmwfs\n32/PTgD23yuvBG69dWafDRuAVavCu0+iJopqWIRRVNYYRWXNyVpo/KGQgQH779TUTG1oqHCohIgA\nODYsoikix1pwTVt7WHO3Fqr+fqCtrXBbWxs7FkQlONW50BSRYy24pq09rLlbC9XwcOEZC8B+71+D\nQUQFnLrmglFU/TVt7WHN3Vpo/Is3fW1twL599v/ZLNDSAnR3h3ufRE3CKGoJjKISUWRyOWDJEpsK\nAWauscjvcLS3Azt3Ah0d8bWTqE6MohIRNVtHhz070d5eePFmf7/9vr3d1tmxICrg1LAIo6j6a9ra\nw5q7tWrqVVm4ELjggtlx0+5uu53Tkauj7bmmudbS0sIoaiWaYnCsMYrKmu7nWk1KnZngGQuVtD3X\nNNeOP/74ir/Pejg1LKIpBsdacE1be1hzt1ZNndyk7bmmufbwww8jCk51LjTF4FgLrmlrD2vu1qqp\nk5u0Pdc014488khEwalrLhhF1V/T1h7W3K1VUyc3aXuuaa51dXUxihqEUVQiIkqySv3dKN+mGUUl\nIiKiRHBqWIRRVP01be1hzd1ao7dtqlzOrsBa7XaKRFx/1zZuLP+8O+KIDKOocYo70sOa25Et1pJV\na/S2TTMxAfT0AO97H/DRj85sHx4GRkaAG28ETj+9+e1Kobj+rgHln3fj4+OMosYp7kgPa5Vr2trD\nmru1Rm/bFLmc7VhMTgIf+xhwxhl2uz+9+OSkrd9xR/PblkIa/q6VwyhqTOKO9LBWuaatPay5W2v0\ntk3R0WHPWPhuvx1obS1cKM0Y4K//2nZEKFIa/q6VwyhqEzGKmqyatvaw5m6t0ds2zYoVwN13A/4n\nyueem73Phg2zpx+n0MX1d63SNRd9fY8yitpsjKISkRNaW2eWcs+Xv2AaOcnFKKpTF3QSESXS8HBw\nx6KlhR2LFEj4Z/xATl1zQUSUOP7Fm0H27Zu5yJMoQZw6c+Hnd3ft2oWurq6CcSZttRe8oPx5sK1b\nMyraGXZNW3tYc7cW133WJJezcdNiLS0zZzJuvx34yEdsmoRio+25Flatra0tkt+XU50LTRl7zbnm\nOGva2sOau7W47rMmHR12Houenplz4/41FmecYTsWAPCZzwAXXcQl3mOk7bnGeS6aSFPGXnOuOalz\nD7DGWi21uO6zZqefDmSzQHt74cWbt90GfPjDdns2y45FzLQ91zjPRRNpythrzjUnde4B1lirpRbX\nfdbl9NOBnTtnX7z5sY/Z7UuXNn4f1BBtzzXt81w4NSyyYsUKALaXtnjx4unvtdbKWbZsWcP3NzMc\n2IP8YRgbabZnYZv92KP6uayxpuG51pBSZyZ4xkIFbc+1sGpRXXPBeS5i0oxcc5zZaSIi0o9LrhMR\nEVEiODUsEnekp5YaUP60QibTeBS10n3E8dg1HgvW3KxpbA+lk6bnIaOoNbJndQT51xeMjmbVxH2K\na729N0y3ffXq1dO1bDaLG264AePjjd9fpbhrHI89jvtkLZ01je2hdNL0PGQUNQSa4j5xR/JKSUM8\nkLV01jS2h9JJ0/OQUdQQaIr7xB3JKyUN8UDW0lnT2B5KJ03Pw2ZFUZ1Zch3oA7CooHb99Z0ql4Ju\nVq3SMr6Dg81vp5bfDWvu1zS2h9JJ0/OQS65XyY+iAuMACqOoCX9oREREkWIUlYiIiBLB6WGRSy81\ns+I3o6OjmJqaQmdnJ2sx1LS1hzV3a9raU6mtlE5xPw9bWloiGRZxJooaZGxsTE3chzWd8UDW3K1p\naw9jqhQk7ucho6gVjI8DW7dm0NvbN/2lKe7DGqqqs8ZaWDVt7WFMlYLE/TxkFLUKcUd6WKtc09Ye\n1tytaWsPY6oUJO7nIaOoJfjXXPT19eHkk09WG/dhTWc8kDV3a9raw5gqBYn7ecgoagmS0FVRiYiI\n4sYoKhERESWCU2mRuFeXY61yTVt7WIuu1tfXW/b1un//7Kg4n2tcaZWixVVR6xB3pIc1t+KBrDVW\nqyTqqHjcj58RVtKIUdQ6xB3pYa1yTVt7WIu2Vg6fa4ywUvMxilqHuCM9rFWuaWsPa9HWyuFzjRFW\naj5GUavEKGqyatraw1p0tW9848Syr92oVy2O+/EzwkoaMYpaJUZRiXSq9P6X8D89RE5gFJWIiIgS\nwam0iKaIGGvpjQeyNuZdNFY+imoMo6iMqZKrnOpcaIqIsZbeeCBrttbb24fVq1dP17LZbEFMdWxs\nNZ9rjKmSo5waFtEUEWMtuKatPay5W9PWHsZUKU2c6lxoioixFlzT1h7W3K1paw9jqpQmjKKyxigq\na07WtLWHMVXSaPfu3YyiBmEUlYiIqD6MohIREVEiODUssnDhQoyNjWF0dBRTU1Po7JyZAdCPbLEW\nb01be1hzt6atPZpqRL6ohkUYRWWNUVTWnKxpa4+mGlHUnBoW0RQDYy24pq09rLlb09YeTTWiqDnV\nudAUA2MtuKatPay5W9PWHk01oqg5dc0Fo6j6a9raw5q7NW3t0VQj8jGKWgKjqERERPVhFJWIiIgS\nwalhEUZR9de0tYc1d2va2uNCjdzDKGoVNEW9WGM8kDU+11yrEVXLqWERTVEv1oJr2trDmrs1be1x\noUZULac6F5qiXlHU+vp6kclsnf7q7b0QIoCIrWlpJ+OBrGmoaWuPCzWiajl1zYXrUdSNG8v/LjZt\n0tFOxgNZ01DT1h4XauQeRlFLSFMUtdLrO+GHkoiImoxRVCIiIkoEp4ZFXI+iVhoWOeWUrIp2Mh7I\nmoaatva4XqNkYhS1CpoiW3HEwLS0k/FA1jTUtLXH9RpRPqeGRTRFtuKOgWl+DJraw5q7NW3tcb1G\nlM+pzoWmyFbcMTDNj0FTe1hzt6atPa7XiPI5NSyyYsUKALY3vXjx4unvXant32/HO/NrhWOhq1W0\ns1xNW3tYc7emrT2u14jyMYpKRESUUoyiEhERUSIwisoa44GsOVnT1h7WGGHViFHUKmiKZbFWXzzw\nBS8QAD3eVzFBb6+Ox8Ga/pq29rDGCGuaODUsoimWxVpwrZp6tbQ+RtZ01LS1h7XgGrnJqc6FplgW\na8G1aurV0voYWdNR09Ye1oJr5CanrrlwfVVUF2qV6i6s/Mqajpq29rDGlVY14qqoJTCK6pZKf28S\n/nQlIlKFUVRKmW1xN4BCtG0bj6dLeDypksjSIiKyAMCnAfwlgP0A/gPARcaYP5S5zXUAzi/afJsx\n5sxq7tOPO+3atQtdXV0Fp95Y01Grpm5tA7Bm1jHOZrMqHgdrtdW2bduGc845R9VzjbX6a8PDwzjs\nsMOacgwpmaKMon4RwOGwmcIDAVwPYCuA8yrc7lsA/haA/+z6Y7V3qClexVp98cBKtDwO1vTXtLXH\npdrU1NT0PoypUpBIhkVE5BgAqwCsNcb82BjzAwAfAHCOiCyscPM/GmNyxpjHva+nqr1fTfEq1oJr\nlepbt2bQ29uHl7xkAr29fchkroEx9lqLrVszah4Ha/pr2trDWu01Sq6orrl4HYA9xpif5G0bBWAA\nvKbCbU8TkcdE5D4RuVpEDqn2TjXFq1gLrmlrD2vu1rS1h7Xaa5RckaRFRGQAwLuNMUuKtj8GYIMx\nZmuJ260G8AyABwF0ARgC8HsArzMlGioiJwP4zy984Qs45phjcM899+Dhhx/GkUceiZNOOqlgXI+1\n+GvV3nbz5s24+OKL1T4O1mqrrVu3Dps3b1b5XGOt9lqzXp8UvZ07d+K8884DgNd7owyhqKlzISJD\nAC4ps4sBsATA21FH5yLg/joB7ALQY4y5o8Q+7wLw79X8PCIiIgp0rjHmi2H9sFov6BwBcF2FfR4A\n8CiAw/I3isgcAId4taoYYx4UkScAHAUgsHMB4HYA5wL4NYB91f5sIiIiQguAl8G+l4amps6FMWYS\nwGSl/UTkhwDaROT4vOsuemATID+q9v5E5EgA7QBKzhrmtSm03hYREVHKhDYc4ovkgk5jzH2wvaBr\nROQkEXk9gH8GsM0YM33mwrto86+8/88TkctF5DUi8lIR6QHwdQD3I+QeFREREUUnyhk63wXgPtiU\nyC0A7gLQV7TPywHM9/7/PIBXA7gJwK8AXAPgHgBvMMY8G2E7iYiIKESJX1uEiIiIdOHaIkRERBQq\ndi6IiIgoVInsXIjIAhH5dxF5SkT2iMhnRWRehdtcJyL7i76+2aw20wwReb+IPCgie0XkbhE5qcL+\np4nIuIjsE5H7RaR4cTuKWS3HVERODXgtPi8ih5W6DTWPiJwiIjeLyG+9Y3NWFbfha1SpWo9nWK/P\nRHYuYKOnS2DjrW8B8AbYRdEq+RbsYmoLva/Zy25SpETknQCuAHApgOMBTAC4XUQOLbH/y2AvCM4C\nWArgSgCfFZE3NqO9VFmtx9RjYC/o9l+Li4wxj0fdVqrKPAA7APw97HEqi69R9Wo6np6GX5+Ju6BT\n7KJovwRwoj+HhoisAnArgCPzo65Ft7sOwHxjzNlNayzNIiJ3A/iRMeYi73sB8BCATxljLg/YfxOA\nNxtjXp23bRvssTyzSc2mMuo4pqcCGAOwwBjzu6Y2lmoiIvsBvNUYc3OZffgaTYgqj2cor88knrmI\nZVE0apyIHADgRNhPOAAAb82YUdjjGuS1Xj3f7WX2pyaq85gCdkK9HSLyiIh8W+waQZRMfI26p+HX\nZxI7FwsBFJyeMcY8D+BJr1bKtwC8G8AKAP8I4FQA3xSukNNMhwKYA+Cxou2PofSxW1hi/xeJyEHh\nNo/qUM8x3Q07583bAZwNe5bjThE5LqpGUqT4GnVLKK/PWtcWiUwNi6LVxRhzQ963vxCRn8EuinYa\nSq9bQkQhM8bcDzvzru9uEekCsA4ALwQkilFYr081nQvoXBSNwvUE7EyshxdtPxylj92jJfb/nTHm\nj+E2j+pQzzENsh3A68NqFDUVX6Puq/n1qWZYxBgzaYy5v8LXcwCmF0XLu3kki6JRuLxp3MdhjxeA\n6Yv/elB64Zwf5u/veZO3nWJW5zENchz4WkwqvkbdV/PrU9OZi6oYY+4TEX9RtPcBOBAlFkUDcIkx\n5iZvDoxLAfwHbC/7KACbwEXR4rAZwPUiMg7bG14HYC6A64Hp4bEjjDH+6bfPAHi/d0X6v8L+EXsH\nAF6FrkdNx1RELgLwIIBfwC73fCGA0wEwuqiA9/fyKNgPbACwWESWAnjSGPMQX6PJUuvxDOv1mbjO\nheddAD4Ne4XyfgBfAXBR0T5Bi6K9G0AbgEdgOxUbuChacxljbvDmP7gM9tTpDgCrjDE5b5eFAF6c\nt/+vReQtALYA+D8AHgaw1hhTfHU6xaTWYwr7geAKAEcAeAbATwH0GGPual6rqYxlsEPFxvu6wtv+\nOQDvAV+jSVPT8URIr8/EzXNBREREuqm55oKIiIjcwM4FERERhYqdCyIiIgoVOxdEREQUKnYuiIiI\nKFTsXBAREVGo2LkgIiKiULFzQURERKFi54KIiIhCxc4FERERhYqdCyIiIgrV/wfcbaib646V9QAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1339566d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xtrain, ttrain = getXORdata(sigma=0.4)\n",
    "Xvalid, tvalid = getXORdata(sigma=0.2, number = 100)\n",
    "display(Xtrain, ttrain)\n",
    "plt.savefig(\"fig/traindata.png\")\n",
    "display(Xvalid, tvalid)\n",
    "plt.savefig(\"fig/validdata.png\")\n",
    "log = train(build_nonlinear_model(), Xtrain, ttrain, Xvalid, tvalid, verbose=1, nb_epoch=1000)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}