mlp test sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tenso_flow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn as sk\n",
    "from sklearn.inspection import DecisionBoundaryDisplay\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Common logic gate truth table\n",
    "\n",
    "- Classification\n",
    "\n",
    "- 2 features\n",
    "\n",
    "- 5 labels\n",
    "\n",
    "- 2 layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      X1    X2  AND  OR  NAND  NOR  XOR\n",
      "0  -0.93 -1.00    0   0     1    1    0\n",
      "1   0.25 -0.83    0   0     1    1    0\n",
      "2   0.08 -0.73    0   0     1    1    0\n",
      "3  -0.63 -0.99    0   0     1    1    0\n",
      "4  -0.18 -0.51    0   0     1    1    0\n",
      "5   0.63  0.47    0   1     1    0    1\n",
      "6   0.35 -0.96    0   0     1    1    0\n",
      "7   0.43  0.28    0   0     1    1    0\n",
      "8  -0.35  0.99    0   1     1    0    1\n",
      "9   0.84 -0.32    0   1     1    0    1\n",
      "10 -0.83 -0.88    0   0     1    1    0\n",
      "11  0.56  0.00    0   1     1    0    1\n",
      "12 -0.79 -0.46    0   0     1    1    0\n",
      "13  0.29  0.11    0   0     1    1    0\n",
      "14  0.02  0.93    0   1     1    0    1\n",
      "15 -0.32 -0.69    0   0     1    1    0\n",
      "16 -0.60 -0.46    0   0     1    1    0\n",
      "17  0.26  0.89    0   1     1    0    1\n",
      "18  0.99 -0.22    0   1     1    0    1\n",
      "19  0.44  0.88    0   1     1    0    1\n",
      "20  0.87  0.96    1   1     0    0    0\n",
      "21  0.20 -0.65    0   0     1    1    0\n",
      "22  0.94 -0.17    0   1     1    0    1\n",
      "23  0.21 -0.22    0   0     1    1    0\n",
      "24  0.63  0.81    1   1     0    0    0\n"
     ]
    }
   ],
   "source": [
    "filepath = \"portas-logicas - portas-logicas.csv\"\n",
    "\n",
    "dataset = pd.read_csv(filepath)\n",
    "\n",
    "dataset = dataset.replace(\"FALSO\", 0)\n",
    "dataset = dataset.replace(\"VERDADEIRO\", 1)\n",
    "dataset = dataset.replace(r\",\", \".\", regex=True)\n",
    "dataset[\"X1\"] = dataset[\"X1\"].astype(float)\n",
    "dataset[\"X2\"] = dataset[\"X2\"].astype(float)\n",
    "dataset = dataset.reset_index(drop=True)\n",
    "xor_dataset = dataset.copy()\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_columns = set(range(len(xor_dataset.columns)))\n",
    "desired_columns = {0, 1,2,3,4,5, 6}\n",
    "undesired_columns = total_columns- desired_columns\n",
    "undesired_columns = list(undesired_columns)\n",
    "\n",
    "xor_dataset = xor_dataset.drop(xor_dataset.columns[undesired_columns], axis = 1)\n",
    "\n",
    "\n",
    "\n",
    "xor_net = tf.Models.Sequential()\n",
    "xor_net.add(tf.Layers.Input(2))\n",
    "# xor_net.add(tf.Layers.Dense(units=3, activation=tf.Activation.tanh()))\n",
    "xor_net.add(tf.Layers.Dense(2, activation=tf.Activation.sigmoid()))#, aggregation = tf.Aggregation.mean()))\n",
    "xor_net.add(tf.Layers.Dense(5, activation=tf.Activation.sigmoid()))#, use_bias = False))\n",
    "\n",
    "xor_net.compile(optimizer = tf.Optimizers.StochasticGradientDescent(learning_rate = 0.8), loss = tf.Losses.MSE, seed = 65560)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/650\n",
      "25/25 [==============================] - elapsed: 126034ms - loss 0.9046\n",
      "Epoch 2/650\n",
      "25/25 [==============================] - elapsed: 132760ms - loss 0.8606\n",
      "Epoch 3/650\n",
      "25/25 [==============================] - elapsed: 121804ms - loss 0.8157\n",
      "Epoch 4/650\n",
      "25/25 [==============================] - elapsed: 121795ms - loss 0.7337\n",
      "Epoch 5/650\n",
      "25/25 [==============================] - elapsed: 123826ms - loss 0.6270\n",
      "Epoch 6/650\n",
      "25/25 [==============================] - elapsed: 123563ms - loss 0.5397\n",
      "Epoch 7/650\n",
      "25/25 [==============================] - elapsed: 122664ms - loss 0.4841\n",
      "Epoch 8/650\n",
      "25/25 [==============================] - elapsed: 124924ms - loss 0.4502\n",
      "Epoch 9/650\n",
      "25/25 [==============================] - elapsed: 131343ms - loss 0.4283\n",
      "Epoch 10/650\n",
      "25/25 [==============================] - elapsed: 122571ms - loss 0.4133\n",
      "Epoch 11/650\n",
      "25/25 [==============================] - elapsed: 126828ms - loss 0.4022\n",
      "Epoch 12/650\n",
      "25/25 [==============================] - elapsed: 123679ms - loss 0.3936\n",
      "Epoch 13/650\n",
      "25/25 [==============================] - elapsed: 122904ms - loss 0.3867\n",
      "Epoch 14/650\n",
      "25/25 [==============================] - elapsed: 124625ms - loss 0.3810\n",
      "Epoch 15/650\n",
      "25/25 [==============================] - elapsed: 120917ms - loss 0.3762\n",
      "Epoch 16/650\n",
      "25/25 [==============================] - elapsed: 127468ms - loss 0.3720\n",
      "Epoch 17/650\n",
      "25/25 [==============================] - elapsed: 122045ms - loss 0.3683\n",
      "Epoch 18/650\n",
      "25/25 [==============================] - elapsed: 129711ms - loss 0.3651\n",
      "Epoch 19/650\n",
      "25/25 [==============================] - elapsed: 124472ms - loss 0.3621\n",
      "Epoch 20/650\n",
      "25/25 [==============================] - elapsed: 126980ms - loss 0.3595\n",
      "Epoch 21/650\n",
      "25/25 [==============================] - elapsed: 124168ms - loss 0.3571\n",
      "Epoch 22/650\n",
      "25/25 [==============================] - elapsed: 124832ms - loss 0.3548\n",
      "Epoch 23/650\n",
      "25/25 [==============================] - elapsed: 122203ms - loss 0.3528\n",
      "Epoch 24/650\n",
      "25/25 [==============================] - elapsed: 124858ms - loss 0.3508\n",
      "Epoch 25/650\n",
      "25/25 [==============================] - elapsed: 123340ms - loss 0.3490\n",
      "Epoch 26/650\n",
      "25/25 [==============================] - elapsed: 129198ms - loss 0.3472\n",
      "Epoch 27/650\n",
      "25/25 [==============================] - elapsed: 124726ms - loss 0.3456\n",
      "Epoch 28/650\n",
      "25/25 [==============================] - elapsed: 123205ms - loss 0.3440\n",
      "Epoch 29/650\n",
      "25/25 [==============================] - elapsed: 126039ms - loss 0.3425\n",
      "Epoch 30/650\n",
      "25/25 [==============================] - elapsed: 124478ms - loss 0.3410\n",
      "Epoch 31/650\n",
      "25/25 [==============================] - elapsed: 154788ms - loss 0.3396\n",
      "Epoch 32/650\n",
      "25/25 [==============================] - elapsed: 127246ms - loss 0.3382\n",
      "Epoch 33/650\n",
      "25/25 [==============================] - elapsed: 122298ms - loss 0.3368\n",
      "Epoch 34/650\n",
      "25/25 [==============================] - elapsed: 128473ms - loss 0.3355\n",
      "Epoch 35/650\n",
      "25/25 [==============================] - elapsed: 134776ms - loss 0.3342\n",
      "Epoch 36/650\n",
      "25/25 [==============================] - elapsed: 126077ms - loss 0.3329\n",
      "Epoch 37/650\n",
      "25/25 [==============================] - elapsed: 126174ms - loss 0.3316\n",
      "Epoch 38/650\n",
      "25/25 [==============================] - elapsed: 129783ms - loss 0.3304\n",
      "Epoch 39/650\n",
      "25/25 [==============================] - elapsed: 126140ms - loss 0.3291\n",
      "Epoch 40/650\n",
      "25/25 [==============================] - elapsed: 128421ms - loss 0.3279\n",
      "Epoch 41/650\n",
      "25/25 [==============================] - elapsed: 128239ms - loss 0.3266\n",
      "Epoch 42/650\n",
      "25/25 [==============================] - elapsed: 128333ms - loss 0.3253\n",
      "Epoch 43/650\n",
      "25/25 [==============================] - elapsed: 129063ms - loss 0.3240\n",
      "Epoch 44/650\n",
      "25/25 [==============================] - elapsed: 127981ms - loss 0.3227\n",
      "Epoch 45/650\n",
      "25/25 [==============================] - elapsed: 126840ms - loss 0.3214\n",
      "Epoch 46/650\n",
      "25/25 [==============================] - elapsed: 126783ms - loss 0.3201\n",
      "Epoch 47/650\n",
      "25/25 [==============================] - elapsed: 128410ms - loss 0.3187\n",
      "Epoch 48/650\n",
      "25/25 [==============================] - elapsed: 125405ms - loss 0.3174\n",
      "Epoch 49/650\n",
      "25/25 [==============================] - elapsed: 124909ms - loss 0.3160\n",
      "Epoch 50/650\n",
      "25/25 [==============================] - elapsed: 126803ms - loss 0.3145\n",
      "Epoch 51/650\n",
      "25/25 [==============================] - elapsed: 125754ms - loss 0.3131\n",
      "Epoch 52/650\n",
      "25/25 [==============================] - elapsed: 125761ms - loss 0.3116\n",
      "Epoch 53/650\n",
      "25/25 [==============================] - elapsed: 124397ms - loss 0.3101\n",
      "Epoch 54/650\n",
      "25/25 [==============================] - elapsed: 124967ms - loss 0.3086\n",
      "Epoch 55/650\n",
      "25/25 [==============================] - elapsed: 124722ms - loss 0.3071\n",
      "Epoch 56/650\n",
      "25/25 [==============================] - elapsed: 124825ms - loss 0.3055\n",
      "Epoch 57/650\n",
      "25/25 [==============================] - elapsed: 124544ms - loss 0.3040\n",
      "Epoch 58/650\n",
      "25/25 [==============================] - elapsed: 126090ms - loss 0.3024\n",
      "Epoch 59/650\n",
      "25/25 [==============================] - elapsed: 126174ms - loss 0.3009\n",
      "Epoch 60/650\n",
      "25/25 [==============================] - elapsed: 128204ms - loss 0.2993\n",
      "Epoch 61/650\n",
      "25/25 [==============================] - elapsed: 128095ms - loss 0.2978\n",
      "Epoch 62/650\n",
      "25/25 [==============================] - elapsed: 124557ms - loss 0.2963\n",
      "Epoch 63/650\n",
      "25/25 [==============================] - elapsed: 128473ms - loss 0.2948\n",
      "Epoch 64/650\n",
      "25/25 [==============================] - elapsed: 134851ms - loss 0.2933\n",
      "Epoch 65/650\n",
      "25/25 [==============================] - elapsed: 126212ms - loss 0.2918\n",
      "Epoch 66/650\n",
      "25/25 [==============================] - elapsed: 125470ms - loss 0.2904\n",
      "Epoch 67/650\n",
      "25/25 [==============================] - elapsed: 129729ms - loss 0.2890\n",
      "Epoch 68/650\n",
      "25/25 [==============================] - elapsed: 125466ms - loss 0.2876\n",
      "Epoch 69/650\n",
      "25/25 [==============================] - elapsed: 124474ms - loss 0.2863\n",
      "Epoch 70/650\n",
      "25/25 [==============================] - elapsed: 127982ms - loss 0.2849\n",
      "Epoch 71/650\n",
      "25/25 [==============================] - elapsed: 127435ms - loss 0.2836\n",
      "Epoch 72/650\n",
      "25/25 [==============================] - elapsed: 126823ms - loss 0.2823\n",
      "Epoch 73/650\n",
      "25/25 [==============================] - elapsed: 125468ms - loss 0.2811\n",
      "Epoch 74/650\n",
      "25/25 [==============================] - elapsed: 131999ms - loss 0.2799\n",
      "Epoch 75/650\n",
      "25/25 [==============================] - elapsed: 127215ms - loss 0.2786\n",
      "Epoch 76/650\n",
      "25/25 [==============================] - elapsed: 125471ms - loss 0.2775\n",
      "Epoch 77/650\n",
      "25/25 [==============================] - elapsed: 125386ms - loss 0.2763\n",
      "Epoch 78/650\n",
      "25/25 [==============================] - elapsed: 130125ms - loss 0.2752\n",
      "Epoch 79/650\n",
      "25/25 [==============================] - elapsed: 124067ms - loss 0.2741\n",
      "Epoch 80/650\n",
      "25/25 [==============================] - elapsed: 124281ms - loss 0.2730\n",
      "Epoch 81/650\n",
      "25/25 [==============================] - elapsed: 126497ms - loss 0.2719\n",
      "Epoch 82/650\n",
      "25/25 [==============================] - elapsed: 124541ms - loss 0.2709\n",
      "Epoch 83/650\n",
      "25/25 [==============================] - elapsed: 122436ms - loss 0.2699\n",
      "Epoch 84/650\n",
      "25/25 [==============================] - elapsed: 121053ms - loss 0.2689\n",
      "Epoch 85/650\n",
      "25/25 [==============================] - elapsed: 122436ms - loss 0.2679\n",
      "Epoch 86/650\n",
      "25/25 [==============================] - elapsed: 123231ms - loss 0.2669\n",
      "Epoch 87/650\n",
      "25/25 [==============================] - elapsed: 121391ms - loss 0.2659\n",
      "Epoch 88/650\n",
      "25/25 [==============================] - elapsed: 124745ms - loss 0.2650\n",
      "Epoch 89/650\n",
      "25/25 [==============================] - elapsed: 124363ms - loss 0.2641\n",
      "Epoch 90/650\n",
      "25/25 [==============================] - elapsed: 128112ms - loss 0.2631\n",
      "Epoch 91/650\n",
      "25/25 [==============================] - elapsed: 127383ms - loss 0.2622\n",
      "Epoch 92/650\n",
      "25/25 [==============================] - elapsed: 127645ms - loss 0.2613\n",
      "Epoch 93/650\n",
      "25/25 [==============================] - elapsed: 121962ms - loss 0.2605\n",
      "Epoch 94/650\n",
      "25/25 [==============================] - elapsed: 122871ms - loss 0.2596\n",
      "Epoch 95/650\n",
      "25/25 [==============================] - elapsed: 132964ms - loss 0.2587\n",
      "Epoch 96/650\n",
      "25/25 [==============================] - elapsed: 127155ms - loss 0.2579\n",
      "Epoch 97/650\n",
      "25/25 [==============================] - elapsed: 121636ms - loss 0.2570\n",
      "Epoch 98/650\n",
      "25/25 [==============================] - elapsed: 122970ms - loss 0.2562\n",
      "Epoch 99/650\n",
      "25/25 [==============================] - elapsed: 122230ms - loss 0.2553\n",
      "Epoch 100/650\n",
      "25/25 [==============================] - elapsed: 123276ms - loss 0.2545\n",
      "Epoch 101/650\n",
      "25/25 [==============================] - elapsed: 122199ms - loss 0.2537\n",
      "Epoch 102/650\n",
      "25/25 [==============================] - elapsed: 123890ms - loss 0.2528\n",
      "Epoch 103/650\n",
      "25/25 [==============================] - elapsed: 123412ms - loss 0.2520\n",
      "Epoch 104/650\n",
      "25/25 [==============================] - elapsed: 121582ms - loss 0.2512\n",
      "Epoch 105/650\n",
      "25/25 [==============================] - elapsed: 120680ms - loss 0.2503\n",
      "Epoch 106/650\n",
      "25/25 [==============================] - elapsed: 121308ms - loss 0.2495\n",
      "Epoch 107/650\n",
      "25/25 [==============================] - elapsed: 121641ms - loss 0.2487\n",
      "Epoch 108/650\n",
      "25/25 [==============================] - elapsed: 120797ms - loss 0.2479\n",
      "Epoch 109/650\n",
      "25/25 [==============================] - elapsed: 122975ms - loss 0.2470\n",
      "Epoch 110/650\n",
      "25/25 [==============================] - elapsed: 120629ms - loss 0.2462\n",
      "Epoch 111/650\n",
      "25/25 [==============================] - elapsed: 122687ms - loss 0.2454\n",
      "Epoch 112/650\n",
      "25/25 [==============================] - elapsed: 120648ms - loss 0.2445\n",
      "Epoch 113/650\n",
      "25/25 [==============================] - elapsed: 125768ms - loss 0.2437\n",
      "Epoch 114/650\n",
      "25/25 [==============================] - elapsed: 121389ms - loss 0.2429\n",
      "Epoch 115/650\n",
      "25/25 [==============================] - elapsed: 125067ms - loss 0.2420\n",
      "Epoch 116/650\n",
      "25/25 [==============================] - elapsed: 124816ms - loss 0.2412\n",
      "Epoch 117/650\n",
      "25/25 [==============================] - elapsed: 122412ms - loss 0.2403\n",
      "Epoch 118/650\n",
      "25/25 [==============================] - elapsed: 123760ms - loss 0.2394\n",
      "Epoch 119/650\n",
      "25/25 [==============================] - elapsed: 121401ms - loss 0.2386\n",
      "Epoch 120/650\n",
      "25/25 [==============================] - elapsed: 124021ms - loss 0.2377\n",
      "Epoch 121/650\n",
      "25/25 [==============================] - elapsed: 123848ms - loss 0.2368\n",
      "Epoch 122/650\n",
      "25/25 [==============================] - elapsed: 120183ms - loss 0.2360\n",
      "Epoch 123/650\n",
      "25/25 [==============================] - elapsed: 123517ms - loss 0.2351\n",
      "Epoch 124/650\n",
      "25/25 [==============================] - elapsed: 124491ms - loss 0.2342\n",
      "Epoch 125/650\n",
      "25/25 [==============================] - elapsed: 121074ms - loss 0.2333\n",
      "Epoch 126/650\n",
      "25/25 [==============================] - elapsed: 124444ms - loss 0.2324\n",
      "Epoch 127/650\n",
      "25/25 [==============================] - elapsed: 122113ms - loss 0.2315\n",
      "Epoch 128/650\n",
      "25/25 [==============================] - elapsed: 124693ms - loss 0.2306\n",
      "Epoch 129/650\n",
      "25/25 [==============================] - elapsed: 123658ms - loss 0.2297\n",
      "Epoch 130/650\n",
      "25/25 [==============================] - elapsed: 123548ms - loss 0.2289\n",
      "Epoch 131/650\n",
      "25/25 [==============================] - elapsed: 124609ms - loss 0.2280\n",
      "Epoch 132/650\n",
      "25/25 [==============================] - elapsed: 123707ms - loss 0.2271\n",
      "Epoch 133/650\n",
      "25/25 [==============================] - elapsed: 128296ms - loss 0.2262\n",
      "Epoch 134/650\n",
      "25/25 [==============================] - elapsed: 122690ms - loss 0.2253\n",
      "Epoch 135/650\n",
      "25/25 [==============================] - elapsed: 122118ms - loss 0.2244\n",
      "Epoch 136/650\n",
      "25/25 [==============================] - elapsed: 124647ms - loss 0.2235\n",
      "Epoch 137/650\n",
      "25/25 [==============================] - elapsed: 129211ms - loss 0.2226\n",
      "Epoch 138/650\n",
      "25/25 [==============================] - elapsed: 127251ms - loss 0.2217\n",
      "Epoch 139/650\n",
      "25/25 [==============================] - elapsed: 123733ms - loss 0.2209\n",
      "Epoch 140/650\n",
      "25/25 [==============================] - elapsed: 126490ms - loss 0.2200\n",
      "Epoch 141/650\n",
      "25/25 [==============================] - elapsed: 126458ms - loss 0.2191\n",
      "Epoch 142/650\n",
      "25/25 [==============================] - elapsed: 129067ms - loss 0.2183\n",
      "Epoch 143/650\n",
      "25/25 [==============================] - elapsed: 129131ms - loss 0.2174\n",
      "Epoch 144/650\n",
      "25/25 [==============================] - elapsed: 126921ms - loss 0.2166\n",
      "Epoch 145/650\n",
      "25/25 [==============================] - elapsed: 123254ms - loss 0.2157\n",
      "Epoch 146/650\n",
      "25/25 [==============================] - elapsed: 129723ms - loss 0.2149\n",
      "Epoch 147/650\n",
      "25/25 [==============================] - elapsed: 124910ms - loss 0.2141\n",
      "Epoch 148/650\n",
      "25/25 [==============================] - elapsed: 133257ms - loss 0.2133\n",
      "Epoch 149/650\n",
      "25/25 [==============================] - elapsed: 122739ms - loss 0.2124\n",
      "Epoch 150/650\n",
      "25/25 [==============================] - elapsed: 133724ms - loss 0.2116\n",
      "Epoch 151/650\n",
      "25/25 [==============================] - elapsed: 129162ms - loss 0.2108\n",
      "Epoch 152/650\n",
      "25/25 [==============================] - elapsed: 125569ms - loss 0.2101\n",
      "Epoch 153/650\n",
      "25/25 [==============================] - elapsed: 129526ms - loss 0.2093\n",
      "Epoch 154/650\n",
      "25/25 [==============================] - elapsed: 128028ms - loss 0.2085\n",
      "Epoch 155/650\n",
      "25/25 [==============================] - elapsed: 125265ms - loss 0.2078\n",
      "Epoch 156/650\n",
      "25/25 [==============================] - elapsed: 124967ms - loss 0.2070\n",
      "Epoch 157/650\n",
      "25/25 [==============================] - elapsed: 124917ms - loss 0.2063\n",
      "Epoch 158/650\n",
      "25/25 [==============================] - elapsed: 127264ms - loss 0.2055\n",
      "Epoch 159/650\n",
      "25/25 [==============================] - elapsed: 129004ms - loss 0.2048\n",
      "Epoch 160/650\n",
      "25/25 [==============================] - elapsed: 127765ms - loss 0.2041\n",
      "Epoch 161/650\n",
      "25/25 [==============================] - elapsed: 124159ms - loss 0.2034\n",
      "Epoch 162/650\n",
      "25/25 [==============================] - elapsed: 122380ms - loss 0.2027\n",
      "Epoch 163/650\n",
      "25/25 [==============================] - elapsed: 124435ms - loss 0.2020\n",
      "Epoch 164/650\n",
      "25/25 [==============================] - elapsed: 128697ms - loss 0.2013\n",
      "Epoch 165/650\n",
      "25/25 [==============================] - elapsed: 127503ms - loss 0.2006\n",
      "Epoch 166/650\n",
      "25/25 [==============================] - elapsed: 126906ms - loss 0.1999\n",
      "Epoch 167/650\n",
      "25/25 [==============================] - elapsed: 123830ms - loss 0.1992\n",
      "Epoch 168/650\n",
      "25/25 [==============================] - elapsed: 125251ms - loss 0.1986\n",
      "Epoch 169/650\n",
      "25/25 [==============================] - elapsed: 121409ms - loss 0.1979\n",
      "Epoch 170/650\n",
      "25/25 [==============================] - elapsed: 122851ms - loss 0.1973\n",
      "Epoch 171/650\n",
      "25/25 [==============================] - elapsed: 128978ms - loss 0.1966\n",
      "Epoch 172/650\n",
      "25/25 [==============================] - elapsed: 126113ms - loss 0.1960\n",
      "Epoch 173/650\n",
      "25/25 [==============================] - elapsed: 134508ms - loss 0.1954\n",
      "Epoch 174/650\n",
      "25/25 [==============================] - elapsed: 137700ms - loss 0.1948\n",
      "Epoch 175/650\n",
      "25/25 [==============================] - elapsed: 137157ms - loss 0.1942\n",
      "Epoch 176/650\n",
      "25/25 [==============================] - elapsed: 120656ms - loss 0.1935\n",
      "Epoch 177/650\n",
      "25/25 [==============================] - elapsed: 122981ms - loss 0.1929\n",
      "Epoch 178/650\n",
      "25/25 [==============================] - elapsed: 123164ms - loss 0.1924\n",
      "Epoch 179/650\n",
      "25/25 [==============================] - elapsed: 135589ms - loss 0.1918\n",
      "Epoch 180/650\n",
      "25/25 [==============================] - elapsed: 126315ms - loss 0.1912\n",
      "Epoch 181/650\n",
      "25/25 [==============================] - elapsed: 126646ms - loss 0.1906\n",
      "Epoch 182/650\n",
      "25/25 [==============================] - elapsed: 125943ms - loss 0.1900\n",
      "Epoch 183/650\n",
      "25/25 [==============================] - elapsed: 126472ms - loss 0.1895\n",
      "Epoch 184/650\n",
      "25/25 [==============================] - elapsed: 126668ms - loss 0.1889\n",
      "Epoch 185/650\n",
      "25/25 [==============================] - elapsed: 121278ms - loss 0.1883\n",
      "Epoch 186/650\n",
      "25/25 [==============================] - elapsed: 119473ms - loss 0.1878\n",
      "Epoch 187/650\n",
      "25/25 [==============================] - elapsed: 121156ms - loss 0.1872\n",
      "Epoch 188/650\n",
      "25/25 [==============================] - elapsed: 125001ms - loss 0.1867\n",
      "Epoch 189/650\n",
      "25/25 [==============================] - elapsed: 126294ms - loss 0.1862\n",
      "Epoch 190/650\n",
      "25/25 [==============================] - elapsed: 126334ms - loss 0.1856\n",
      "Epoch 191/650\n",
      "25/25 [==============================] - elapsed: 126599ms - loss 0.1851\n",
      "Epoch 192/650\n",
      "25/25 [==============================] - elapsed: 127514ms - loss 0.1846\n",
      "Epoch 193/650\n",
      "25/25 [==============================] - elapsed: 126984ms - loss 0.1840\n",
      "Epoch 194/650\n",
      "25/25 [==============================] - elapsed: 125942ms - loss 0.1835\n",
      "Epoch 195/650\n",
      "25/25 [==============================] - elapsed: 124931ms - loss 0.1830\n",
      "Epoch 196/650\n",
      "25/25 [==============================] - elapsed: 125593ms - loss 0.1825\n",
      "Epoch 197/650\n",
      "25/25 [==============================] - elapsed: 121444ms - loss 0.1820\n",
      "Epoch 198/650\n",
      "25/25 [==============================] - elapsed: 121325ms - loss 0.1815\n",
      "Epoch 199/650\n",
      "25/25 [==============================] - elapsed: 124765ms - loss 0.1810\n",
      "Epoch 200/650\n",
      "25/25 [==============================] - elapsed: 132340ms - loss 0.1805\n",
      "Epoch 201/650\n",
      "25/25 [==============================] - elapsed: 126573ms - loss 0.1800\n",
      "Epoch 202/650\n",
      "25/25 [==============================] - elapsed: 126081ms - loss 0.1795\n",
      "Epoch 203/650\n",
      "25/25 [==============================] - elapsed: 126234ms - loss 0.1790\n",
      "Epoch 204/650\n",
      "25/25 [==============================] - elapsed: 124094ms - loss 0.1786\n",
      "Epoch 205/650\n",
      "25/25 [==============================] - elapsed: 120112ms - loss 0.1781\n",
      "Epoch 206/650\n",
      "25/25 [==============================] - elapsed: 122422ms - loss 0.1776\n",
      "Epoch 207/650\n",
      "25/25 [==============================] - elapsed: 123762ms - loss 0.1771\n",
      "Epoch 208/650\n",
      "25/25 [==============================] - elapsed: 123843ms - loss 0.1767\n",
      "Epoch 209/650\n",
      "25/25 [==============================] - elapsed: 129854ms - loss 0.1762\n",
      "Epoch 210/650\n",
      "25/25 [==============================] - elapsed: 124924ms - loss 0.1757\n",
      "Epoch 211/650\n",
      "25/25 [==============================] - elapsed: 125581ms - loss 0.1753\n",
      "Epoch 212/650\n",
      "25/25 [==============================] - elapsed: 126873ms - loss 0.1748\n",
      "Epoch 213/650\n",
      "25/25 [==============================] - elapsed: 126026ms - loss 0.1744\n",
      "Epoch 214/650\n",
      "25/25 [==============================] - elapsed: 124208ms - loss 0.1739\n",
      "Epoch 215/650\n",
      "25/25 [==============================] - elapsed: 124940ms - loss 0.1735\n",
      "Epoch 216/650\n",
      "25/25 [==============================] - elapsed: 126265ms - loss 0.1730\n",
      "Epoch 217/650\n",
      "25/25 [==============================] - elapsed: 124467ms - loss 0.1726\n",
      "Epoch 218/650\n",
      "25/25 [==============================] - elapsed: 120611ms - loss 0.1721\n",
      "Epoch 219/650\n",
      "25/25 [==============================] - elapsed: 119582ms - loss 0.1717\n",
      "Epoch 220/650\n",
      "25/25 [==============================] - elapsed: 121661ms - loss 0.1713\n",
      "Epoch 221/650\n",
      "25/25 [==============================] - elapsed: 120049ms - loss 0.1708\n",
      "Epoch 222/650\n",
      "25/25 [==============================] - elapsed: 120810ms - loss 0.1704\n",
      "Epoch 223/650\n",
      "25/25 [==============================] - elapsed: 120688ms - loss 0.1700\n",
      "Epoch 224/650\n",
      "25/25 [==============================] - elapsed: 121216ms - loss 0.1696\n",
      "Epoch 225/650\n",
      "25/25 [==============================] - elapsed: 121321ms - loss 0.1691\n",
      "Epoch 226/650\n",
      "25/25 [==============================] - elapsed: 120708ms - loss 0.1687\n",
      "Epoch 227/650\n",
      "25/25 [==============================] - elapsed: 121840ms - loss 0.1683\n",
      "Epoch 228/650\n",
      "25/25 [==============================] - elapsed: 121150ms - loss 0.1679\n",
      "Epoch 229/650\n",
      "25/25 [==============================] - elapsed: 121344ms - loss 0.1675\n",
      "Epoch 230/650\n",
      "25/25 [==============================] - elapsed: 123043ms - loss 0.1671\n",
      "Epoch 231/650\n",
      "25/25 [==============================] - elapsed: 123694ms - loss 0.1667\n",
      "Epoch 232/650\n",
      "25/25 [==============================] - elapsed: 124032ms - loss 0.1662\n",
      "Epoch 233/650\n",
      "25/25 [==============================] - elapsed: 122451ms - loss 0.1658\n",
      "Epoch 234/650\n",
      "25/25 [==============================] - elapsed: 121310ms - loss 0.1654\n",
      "Epoch 235/650\n",
      "25/25 [==============================] - elapsed: 121183ms - loss 0.1650\n",
      "Epoch 236/650\n",
      "25/25 [==============================] - elapsed: 125023ms - loss 0.1646\n",
      "Epoch 237/650\n",
      "25/25 [==============================] - elapsed: 124061ms - loss 0.1643\n",
      "Epoch 238/650\n",
      "25/25 [==============================] - elapsed: 123002ms - loss 0.1639\n",
      "Epoch 239/650\n",
      "25/25 [==============================] - elapsed: 121386ms - loss 0.1635\n",
      "Epoch 240/650\n",
      "25/25 [==============================] - elapsed: 121746ms - loss 0.1631\n",
      "Epoch 241/650\n",
      "25/25 [==============================] - elapsed: 121098ms - loss 0.1627\n",
      "Epoch 242/650\n",
      "25/25 [==============================] - elapsed: 122169ms - loss 0.1623\n",
      "Epoch 243/650\n",
      "25/25 [==============================] - elapsed: 124614ms - loss 0.1619\n",
      "Epoch 244/650\n",
      "25/25 [==============================] - elapsed: 125378ms - loss 0.1616\n",
      "Epoch 245/650\n",
      "25/25 [==============================] - elapsed: 125072ms - loss 0.1612\n",
      "Epoch 246/650\n",
      "25/25 [==============================] - elapsed: 125016ms - loss 0.1608\n",
      "Epoch 247/650\n",
      "25/25 [==============================] - elapsed: 124668ms - loss 0.1604\n",
      "Epoch 248/650\n",
      "25/25 [==============================] - elapsed: 125898ms - loss 0.1601\n",
      "Epoch 249/650\n",
      "25/25 [==============================] - elapsed: 125913ms - loss 0.1597\n",
      "Epoch 250/650\n",
      "25/25 [==============================] - elapsed: 120367ms - loss 0.1594\n",
      "Epoch 251/650\n",
      "25/25 [==============================] - elapsed: 124688ms - loss 0.1590\n",
      "Epoch 252/650\n",
      "25/25 [==============================] - elapsed: 124025ms - loss 0.1586\n",
      "Epoch 253/650\n",
      "25/25 [==============================] - elapsed: 127902ms - loss 0.1583\n",
      "Epoch 254/650\n",
      "25/25 [==============================] - elapsed: 124162ms - loss 0.1579\n",
      "Epoch 255/650\n",
      "25/25 [==============================] - elapsed: 125198ms - loss 0.1576\n",
      "Epoch 256/650\n",
      "25/25 [==============================] - elapsed: 123443ms - loss 0.1572\n",
      "Epoch 257/650\n",
      "25/25 [==============================] - elapsed: 124247ms - loss 0.1569\n",
      "Epoch 258/650\n",
      "25/25 [==============================] - elapsed: 123143ms - loss 0.1565\n",
      "Epoch 259/650\n",
      "25/25 [==============================] - elapsed: 124327ms - loss 0.1562\n",
      "Epoch 260/650\n",
      "25/25 [==============================] - elapsed: 123466ms - loss 0.1559\n",
      "Epoch 261/650\n",
      "25/25 [==============================] - elapsed: 124826ms - loss 0.1555\n",
      "Epoch 262/650\n",
      "25/25 [==============================] - elapsed: 124303ms - loss 0.1552\n",
      "Epoch 263/650\n",
      "25/25 [==============================] - elapsed: 123841ms - loss 0.1549\n",
      "Epoch 264/650\n",
      "25/25 [==============================] - elapsed: 125204ms - loss 0.1546\n",
      "Epoch 265/650\n",
      "25/25 [==============================] - elapsed: 124457ms - loss 0.1542\n",
      "Epoch 266/650\n",
      "25/25 [==============================] - elapsed: 126431ms - loss 0.1539\n",
      "Epoch 267/650\n",
      "25/25 [==============================] - elapsed: 123760ms - loss 0.1536\n",
      "Epoch 268/650\n",
      "25/25 [==============================] - elapsed: 125549ms - loss 0.1533\n",
      "Epoch 269/650\n",
      "25/25 [==============================] - elapsed: 122981ms - loss 0.1530\n",
      "Epoch 270/650\n",
      "25/25 [==============================] - elapsed: 123947ms - loss 0.1527\n",
      "Epoch 271/650\n",
      "25/25 [==============================] - elapsed: 123448ms - loss 0.1523\n",
      "Epoch 272/650\n",
      "25/25 [==============================] - elapsed: 123012ms - loss 0.1520\n",
      "Epoch 273/650\n",
      "25/25 [==============================] - elapsed: 123140ms - loss 0.1517\n",
      "Epoch 274/650\n",
      "25/25 [==============================] - elapsed: 123905ms - loss 0.1514\n",
      "Epoch 275/650\n",
      "25/25 [==============================] - elapsed: 126085ms - loss 0.1511\n",
      "Epoch 276/650\n",
      "25/25 [==============================] - elapsed: 126132ms - loss 0.1509\n",
      "Epoch 277/650\n",
      "25/25 [==============================] - elapsed: 125746ms - loss 0.1506\n",
      "Epoch 278/650\n",
      "25/25 [==============================] - elapsed: 124696ms - loss 0.1503\n",
      "Epoch 279/650\n",
      "25/25 [==============================] - elapsed: 126896ms - loss 0.1500\n",
      "Epoch 280/650\n",
      "25/25 [==============================] - elapsed: 123893ms - loss 0.1497\n",
      "Epoch 281/650\n",
      "25/25 [==============================] - elapsed: 124898ms - loss 0.1494\n",
      "Epoch 282/650\n",
      "25/25 [==============================] - elapsed: 122808ms - loss 0.1492\n",
      "Epoch 283/650\n",
      "25/25 [==============================] - elapsed: 124538ms - loss 0.1489\n",
      "Epoch 284/650\n",
      "25/25 [==============================] - elapsed: 123923ms - loss 0.1486\n",
      "Epoch 285/650\n",
      "25/25 [==============================] - elapsed: 128087ms - loss 0.1483\n",
      "Epoch 286/650\n",
      "25/25 [==============================] - elapsed: 123984ms - loss 0.1481\n",
      "Epoch 287/650\n",
      "25/25 [==============================] - elapsed: 124535ms - loss 0.1478\n",
      "Epoch 288/650\n",
      "25/25 [==============================] - elapsed: 123181ms - loss 0.1476\n",
      "Epoch 289/650\n",
      "25/25 [==============================] - elapsed: 124138ms - loss 0.1473\n",
      "Epoch 290/650\n",
      "25/25 [==============================] - elapsed: 122427ms - loss 0.1470\n",
      "Epoch 291/650\n",
      "25/25 [==============================] - elapsed: 123994ms - loss 0.1468\n",
      "Epoch 292/650\n",
      "25/25 [==============================] - elapsed: 124479ms - loss 0.1465\n",
      "Epoch 293/650\n",
      "25/25 [==============================] - elapsed: 123493ms - loss 0.1463\n",
      "Epoch 294/650\n",
      "25/25 [==============================] - elapsed: 124228ms - loss 0.1460\n",
      "Epoch 295/650\n",
      "25/25 [==============================] - elapsed: 122619ms - loss 0.1458\n",
      "Epoch 296/650\n",
      "25/25 [==============================] - elapsed: 124178ms - loss 0.1456\n",
      "Epoch 297/650\n",
      "25/25 [==============================] - elapsed: 122524ms - loss 0.1453\n",
      "Epoch 298/650\n",
      "25/25 [==============================] - elapsed: 124294ms - loss 0.1451\n",
      "Epoch 299/650\n",
      "25/25 [==============================] - elapsed: 124064ms - loss 0.1449\n",
      "Epoch 300/650\n",
      "25/25 [==============================] - elapsed: 123487ms - loss 0.1446\n",
      "Epoch 301/650\n",
      "25/25 [==============================] - elapsed: 123101ms - loss 0.1444\n",
      "Epoch 302/650\n",
      "25/25 [==============================] - elapsed: 124647ms - loss 0.1442\n",
      "Epoch 303/650\n",
      "25/25 [==============================] - elapsed: 124559ms - loss 0.1440\n",
      "Epoch 304/650\n",
      "25/25 [==============================] - elapsed: 123112ms - loss 0.1438\n",
      "Epoch 305/650\n",
      "25/25 [==============================] - elapsed: 124257ms - loss 0.1435\n",
      "Epoch 306/650\n",
      "25/25 [==============================] - elapsed: 124380ms - loss 0.1433\n",
      "Epoch 307/650\n",
      "25/25 [==============================] - elapsed: 125278ms - loss 0.1431\n",
      "Epoch 308/650\n",
      "25/25 [==============================] - elapsed: 124195ms - loss 0.1429\n",
      "Epoch 309/650\n",
      "25/25 [==============================] - elapsed: 124281ms - loss 0.1427\n",
      "Epoch 310/650\n",
      "25/25 [==============================] - elapsed: 122639ms - loss 0.1425\n",
      "Epoch 311/650\n",
      "25/25 [==============================] - elapsed: 125308ms - loss 0.1423\n",
      "Epoch 312/650\n",
      "25/25 [==============================] - elapsed: 122986ms - loss 0.1421\n",
      "Epoch 313/650\n",
      "25/25 [==============================] - elapsed: 124888ms - loss 0.1419\n",
      "Epoch 314/650\n",
      "25/25 [==============================] - elapsed: 128432ms - loss 0.1417\n",
      "Epoch 315/650\n",
      "25/25 [==============================] - elapsed: 124199ms - loss 0.1415\n",
      "Epoch 316/650\n",
      "25/25 [==============================] - elapsed: 133585ms - loss 0.1413\n",
      "Epoch 317/650\n",
      "25/25 [==============================] - elapsed: 123726ms - loss 0.1411\n",
      "Epoch 318/650\n",
      "25/25 [==============================] - elapsed: 123290ms - loss 0.1409\n",
      "Epoch 319/650\n",
      "25/25 [==============================] - elapsed: 125949ms - loss 0.1408\n",
      "Epoch 320/650\n",
      "25/25 [==============================] - elapsed: 125000ms - loss 0.1406\n",
      "Epoch 321/650\n",
      "25/25 [==============================] - elapsed: 122893ms - loss 0.1404\n",
      "Epoch 322/650\n",
      "25/25 [==============================] - elapsed: 125791ms - loss 0.1402\n",
      "Epoch 323/650\n",
      "25/25 [==============================] - elapsed: 123299ms - loss 0.1400\n",
      "Epoch 324/650\n",
      "25/25 [==============================] - elapsed: 125699ms - loss 0.1399\n",
      "Epoch 325/650\n",
      "25/25 [==============================] - elapsed: 123172ms - loss 0.1397\n",
      "Epoch 326/650\n",
      "25/25 [==============================] - elapsed: 124768ms - loss 0.1395\n",
      "Epoch 327/650\n",
      "25/25 [==============================] - elapsed: 123920ms - loss 0.1393\n",
      "Epoch 328/650\n",
      "25/25 [==============================] - elapsed: 124456ms - loss 0.1392\n",
      "Epoch 329/650\n",
      "25/25 [==============================] - elapsed: 124224ms - loss 0.1390\n",
      "Epoch 330/650\n",
      "25/25 [==============================] - elapsed: 124901ms - loss 0.1388\n",
      "Epoch 331/650\n",
      "25/25 [==============================] - elapsed: 123367ms - loss 0.1387\n",
      "Epoch 332/650\n",
      "25/25 [==============================] - elapsed: 124192ms - loss 0.1385\n",
      "Epoch 333/650\n",
      "25/25 [==============================] - elapsed: 124853ms - loss 0.1384\n",
      "Epoch 334/650\n",
      "25/25 [==============================] - elapsed: 121102ms - loss 0.1382\n",
      "Epoch 335/650\n",
      "25/25 [==============================] - elapsed: 121804ms - loss 0.1381\n",
      "Epoch 336/650\n",
      "25/25 [==============================] - elapsed: 121561ms - loss 0.1379\n",
      "Epoch 337/650\n",
      "25/25 [==============================] - elapsed: 125400ms - loss 0.1378\n",
      "Epoch 338/650\n",
      "25/25 [==============================] - elapsed: 124217ms - loss 0.1376\n",
      "Epoch 339/650\n",
      "25/25 [==============================] - elapsed: 126265ms - loss 0.1375\n",
      "Epoch 340/650\n",
      "25/25 [==============================] - elapsed: 124564ms - loss 0.1373\n",
      "Epoch 341/650\n",
      "25/25 [==============================] - elapsed: 136794ms - loss 0.1372\n",
      "Epoch 342/650\n",
      "25/25 [==============================] - elapsed: 122404ms - loss 0.1370\n",
      "Epoch 343/650\n",
      "25/25 [==============================] - elapsed: 128997ms - loss 0.1369\n",
      "Epoch 344/650\n",
      "25/25 [==============================] - elapsed: 122805ms - loss 0.1367\n",
      "Epoch 345/650\n",
      "25/25 [==============================] - elapsed: 129852ms - loss 0.1366\n",
      "Epoch 346/650\n",
      "25/25 [==============================] - elapsed: 126542ms - loss 0.1365\n",
      "Epoch 347/650\n",
      "25/25 [==============================] - elapsed: 130588ms - loss 0.1363\n",
      "Epoch 348/650\n",
      "25/25 [==============================] - elapsed: 124910ms - loss 0.1362\n",
      "Epoch 349/650\n",
      "25/25 [==============================] - elapsed: 123939ms - loss 0.1361\n",
      "Epoch 350/650\n",
      "25/25 [==============================] - elapsed: 125558ms - loss 0.1359\n",
      "Epoch 351/650\n",
      "25/25 [==============================] - elapsed: 123266ms - loss 0.1358\n",
      "Epoch 352/650\n",
      "25/25 [==============================] - elapsed: 125224ms - loss 0.1357\n",
      "Epoch 353/650\n",
      "25/25 [==============================] - elapsed: 124572ms - loss 0.1355\n",
      "Epoch 354/650\n",
      "25/25 [==============================] - elapsed: 128838ms - loss 0.1354\n",
      "Epoch 355/650\n",
      "25/25 [==============================] - elapsed: 121609ms - loss 0.1353\n",
      "Epoch 356/650\n",
      "25/25 [==============================] - elapsed: 128147ms - loss 0.1352\n",
      "Epoch 357/650\n",
      "25/25 [==============================] - elapsed: 122075ms - loss 0.1350\n",
      "Epoch 358/650\n",
      "25/25 [==============================] - elapsed: 139632ms - loss 0.1349\n",
      "Epoch 359/650\n",
      "25/25 [==============================] - elapsed: 124859ms - loss 0.1348\n",
      "Epoch 360/650\n",
      "25/25 [==============================] - elapsed: 128791ms - loss 0.1347\n",
      "Epoch 361/650\n",
      "25/25 [==============================] - elapsed: 133851ms - loss 0.1346\n",
      "Epoch 362/650\n",
      "25/25 [==============================] - elapsed: 124542ms - loss 0.1344\n",
      "Epoch 363/650\n",
      "25/25 [==============================] - elapsed: 126895ms - loss 0.1343\n",
      "Epoch 364/650\n",
      "25/25 [==============================] - elapsed: 125375ms - loss 0.1342\n",
      "Epoch 365/650\n",
      "25/25 [==============================] - elapsed: 126645ms - loss 0.1341\n",
      "Epoch 366/650\n",
      "25/25 [==============================] - elapsed: 126039ms - loss 0.1340\n",
      "Epoch 367/650\n",
      "25/25 [==============================] - elapsed: 126259ms - loss 0.1339\n",
      "Epoch 368/650\n",
      "25/25 [==============================] - elapsed: 126271ms - loss 0.1338\n",
      "Epoch 369/650\n",
      "25/25 [==============================] - elapsed: 126025ms - loss 0.1337\n",
      "Epoch 370/650\n",
      "25/25 [==============================] - elapsed: 127010ms - loss 0.1335\n",
      "Epoch 371/650\n",
      "25/25 [==============================] - elapsed: 124604ms - loss 0.1334\n",
      "Epoch 372/650\n",
      "25/25 [==============================] - elapsed: 134067ms - loss 0.1333\n",
      "Epoch 373/650\n",
      "25/25 [==============================] - elapsed: 123956ms - loss 0.1332\n",
      "Epoch 374/650\n",
      "25/25 [==============================] - elapsed: 125588ms - loss 0.1331\n",
      "Epoch 375/650\n",
      "25/25 [==============================] - elapsed: 122951ms - loss 0.1330\n",
      "Epoch 376/650\n",
      "25/25 [==============================] - elapsed: 125432ms - loss 0.1329\n",
      "Epoch 377/650\n",
      "25/25 [==============================] - elapsed: 123470ms - loss 0.1328\n",
      "Epoch 378/650\n",
      "25/25 [==============================] - elapsed: 125551ms - loss 0.1327\n",
      "Epoch 379/650\n",
      "25/25 [==============================] - elapsed: 123619ms - loss 0.1326\n",
      "Epoch 380/650\n",
      "25/25 [==============================] - elapsed: 125089ms - loss 0.1325\n",
      "Epoch 381/650\n",
      "25/25 [==============================] - elapsed: 123345ms - loss 0.1324\n",
      "Epoch 382/650\n",
      "25/25 [==============================] - elapsed: 124510ms - loss 0.1323\n",
      "Epoch 383/650\n",
      "25/25 [==============================] - elapsed: 124736ms - loss 0.1322\n",
      "Epoch 384/650\n",
      "25/25 [==============================] - elapsed: 124567ms - loss 0.1321\n",
      "Epoch 385/650\n",
      "25/25 [==============================] - elapsed: 124326ms - loss 0.1320\n",
      "Epoch 386/650\n",
      "25/25 [==============================] - elapsed: 124786ms - loss 0.1319\n",
      "Epoch 387/650\n",
      "25/25 [==============================] - elapsed: 125795ms - loss 0.1319\n",
      "Epoch 388/650\n",
      "25/25 [==============================] - elapsed: 123093ms - loss 0.1318\n",
      "Epoch 389/650\n",
      "25/25 [==============================] - elapsed: 126196ms - loss 0.1317\n",
      "Epoch 390/650\n",
      "25/25 [==============================] - elapsed: 121555ms - loss 0.1316\n",
      "Epoch 391/650\n",
      "25/25 [==============================] - elapsed: 124206ms - loss 0.1315\n",
      "Epoch 392/650\n",
      "25/25 [==============================] - elapsed: 121877ms - loss 0.1314\n",
      "Epoch 393/650\n",
      "25/25 [==============================] - elapsed: 122615ms - loss 0.1313\n",
      "Epoch 394/650\n",
      "25/25 [==============================] - elapsed: 123063ms - loss 0.1312\n",
      "Epoch 395/650\n",
      "25/25 [==============================] - elapsed: 125279ms - loss 0.1311\n",
      "Epoch 396/650\n",
      "25/25 [==============================] - elapsed: 124929ms - loss 0.1311\n",
      "Epoch 397/650\n",
      "25/25 [==============================] - elapsed: 126332ms - loss 0.1310\n",
      "Epoch 398/650\n",
      "25/25 [==============================] - elapsed: 126436ms - loss 0.1309\n",
      "Epoch 399/650\n",
      "25/25 [==============================] - elapsed: 125547ms - loss 0.1308\n",
      "Epoch 400/650\n",
      "25/25 [==============================] - elapsed: 127695ms - loss 0.1307\n",
      "Epoch 401/650\n",
      "25/25 [==============================] - elapsed: 125178ms - loss 0.1306\n",
      "Epoch 402/650\n",
      "25/25 [==============================] - elapsed: 126992ms - loss 0.1306\n",
      "Epoch 403/650\n",
      "25/25 [==============================] - elapsed: 124516ms - loss 0.1305\n",
      "Epoch 404/650\n",
      "25/25 [==============================] - elapsed: 126326ms - loss 0.1304\n",
      "Epoch 405/650\n",
      "25/25 [==============================] - elapsed: 120730ms - loss 0.1303\n",
      "Epoch 406/650\n",
      "25/25 [==============================] - elapsed: 121785ms - loss 0.1302\n",
      "Epoch 407/650\n",
      "25/25 [==============================] - elapsed: 120924ms - loss 0.1302\n",
      "Epoch 408/650\n",
      "25/25 [==============================] - elapsed: 124361ms - loss 0.1301\n",
      "Epoch 409/650\n",
      "25/25 [==============================] - elapsed: 121018ms - loss 0.1300\n",
      "Epoch 410/650\n",
      "25/25 [==============================] - elapsed: 121570ms - loss 0.1299\n",
      "Epoch 411/650\n",
      "25/25 [==============================] - elapsed: 121846ms - loss 0.1299\n",
      "Epoch 412/650\n",
      "25/25 [==============================] - elapsed: 122212ms - loss 0.1298\n",
      "Epoch 413/650\n",
      "25/25 [==============================] - elapsed: 122266ms - loss 0.1297\n",
      "Epoch 414/650\n",
      "25/25 [==============================] - elapsed: 120627ms - loss 0.1296\n",
      "Epoch 415/650\n",
      "25/25 [==============================] - elapsed: 122508ms - loss 0.1296\n",
      "Epoch 416/650\n",
      "25/25 [==============================] - elapsed: 120782ms - loss 0.1295\n",
      "Epoch 417/650\n",
      "25/25 [==============================] - elapsed: 122749ms - loss 0.1294\n",
      "Epoch 418/650\n",
      "25/25 [==============================] - elapsed: 120516ms - loss 0.1293\n",
      "Epoch 419/650\n",
      "25/25 [==============================] - elapsed: 122307ms - loss 0.1293\n",
      "Epoch 420/650\n",
      "25/25 [==============================] - elapsed: 121054ms - loss 0.1292\n",
      "Epoch 421/650\n",
      "25/25 [==============================] - elapsed: 122821ms - loss 0.1291\n",
      "Epoch 422/650\n",
      "25/25 [==============================] - elapsed: 120767ms - loss 0.1291\n",
      "Epoch 423/650\n",
      "25/25 [==============================] - elapsed: 121823ms - loss 0.1290\n",
      "Epoch 424/650\n",
      "25/25 [==============================] - elapsed: 119836ms - loss 0.1289\n",
      "Epoch 425/650\n",
      "25/25 [==============================] - elapsed: 137396ms - loss 0.1289\n",
      "Epoch 426/650\n",
      "25/25 [==============================] - elapsed: 120696ms - loss 0.1288\n",
      "Epoch 427/650\n",
      "25/25 [==============================] - elapsed: 120694ms - loss 0.1287\n",
      "Epoch 428/650\n",
      "25/25 [==============================] - elapsed: 120765ms - loss 0.1287\n",
      "Epoch 429/650\n",
      "25/25 [==============================] - elapsed: 121573ms - loss 0.1286\n",
      "Epoch 430/650\n",
      "25/25 [==============================] - elapsed: 122142ms - loss 0.1285\n",
      "Epoch 431/650\n",
      "25/25 [==============================] - elapsed: 122378ms - loss 0.1285\n",
      "Epoch 432/650\n",
      "25/25 [==============================] - elapsed: 121883ms - loss 0.1284\n",
      "Epoch 433/650\n",
      "25/25 [==============================] - elapsed: 124908ms - loss 0.1283\n",
      "Epoch 434/650\n",
      "25/25 [==============================] - elapsed: 121653ms - loss 0.1283\n",
      "Epoch 435/650\n",
      "25/25 [==============================] - elapsed: 123851ms - loss 0.1282\n",
      "Epoch 436/650\n",
      "25/25 [==============================] - elapsed: 124912ms - loss 0.1282\n",
      "Epoch 437/650\n",
      "25/25 [==============================] - elapsed: 124461ms - loss 0.1281\n",
      "Epoch 438/650\n",
      "25/25 [==============================] - elapsed: 124963ms - loss 0.1280\n",
      "Epoch 439/650\n",
      "25/25 [==============================] - elapsed: 124741ms - loss 0.1280\n",
      "Epoch 440/650\n",
      "25/25 [==============================] - elapsed: 125140ms - loss 0.1279\n",
      "Epoch 441/650\n",
      "25/25 [==============================] - elapsed: 120929ms - loss 0.1279\n",
      "Epoch 442/650\n",
      "25/25 [==============================] - elapsed: 122334ms - loss 0.1278\n",
      "Epoch 443/650\n",
      "25/25 [==============================] - elapsed: 119295ms - loss 0.1277\n",
      "Epoch 444/650\n",
      "25/25 [==============================] - elapsed: 121940ms - loss 0.1277\n",
      "Epoch 445/650\n",
      "25/25 [==============================] - elapsed: 121243ms - loss 0.1276\n",
      "Epoch 446/650\n",
      "25/25 [==============================] - elapsed: 121914ms - loss 0.1276\n",
      "Epoch 447/650\n",
      "25/25 [==============================] - elapsed: 120153ms - loss 0.1275\n",
      "Epoch 448/650\n",
      "25/25 [==============================] - elapsed: 120880ms - loss 0.1274\n",
      "Epoch 449/650\n",
      "25/25 [==============================] - elapsed: 121208ms - loss 0.1274\n",
      "Epoch 450/650\n",
      "25/25 [==============================] - elapsed: 120984ms - loss 0.1273\n",
      "Epoch 451/650\n",
      "25/25 [==============================] - elapsed: 121833ms - loss 0.1273\n",
      "Epoch 452/650\n",
      "25/25 [==============================] - elapsed: 120486ms - loss 0.1272\n",
      "Epoch 453/650\n",
      "25/25 [==============================] - elapsed: 121840ms - loss 0.1272\n",
      "Epoch 454/650\n",
      "25/25 [==============================] - elapsed: 120700ms - loss 0.1271\n",
      "Epoch 455/650\n",
      "25/25 [==============================] - elapsed: 121927ms - loss 0.1271\n",
      "Epoch 456/650\n",
      "25/25 [==============================] - elapsed: 120037ms - loss 0.1270\n",
      "Epoch 457/650\n",
      "25/25 [==============================] - elapsed: 122266ms - loss 0.1269\n",
      "Epoch 458/650\n",
      "25/25 [==============================] - elapsed: 121341ms - loss 0.1269\n",
      "Epoch 459/650\n",
      "25/25 [==============================] - elapsed: 125923ms - loss 0.1268\n",
      "Epoch 460/650\n",
      "25/25 [==============================] - elapsed: 122501ms - loss 0.1268\n",
      "Epoch 461/650\n",
      "25/25 [==============================] - elapsed: 125437ms - loss 0.1267\n",
      "Epoch 462/650\n",
      "25/25 [==============================] - elapsed: 122286ms - loss 0.1267\n",
      "Epoch 463/650\n",
      "25/25 [==============================] - elapsed: 121927ms - loss 0.1266\n",
      "Epoch 464/650\n",
      "25/25 [==============================] - elapsed: 120732ms - loss 0.1266\n",
      "Epoch 465/650\n",
      "25/25 [==============================] - elapsed: 121643ms - loss 0.1265\n",
      "Epoch 466/650\n",
      "25/25 [==============================] - elapsed: 121094ms - loss 0.1265\n",
      "Epoch 467/650\n",
      "25/25 [==============================] - elapsed: 126493ms - loss 0.1264\n",
      "Epoch 468/650\n",
      "25/25 [==============================] - elapsed: 129147ms - loss 0.1264\n",
      "Epoch 469/650\n",
      "25/25 [==============================] - elapsed: 127826ms - loss 0.1263\n",
      "Epoch 470/650\n",
      "25/25 [==============================] - elapsed: 132074ms - loss 0.1263\n",
      "Epoch 471/650\n",
      "25/25 [==============================] - elapsed: 125938ms - loss 0.1262\n",
      "Epoch 472/650\n",
      "25/25 [==============================] - elapsed: 130809ms - loss 0.1262\n",
      "Epoch 473/650\n",
      "25/25 [==============================] - elapsed: 129455ms - loss 0.1261\n",
      "Epoch 474/650\n",
      "25/25 [==============================] - elapsed: 125005ms - loss 0.1261\n",
      "Epoch 475/650\n",
      "25/25 [==============================] - elapsed: 121239ms - loss 0.1260\n",
      "Epoch 476/650\n",
      "25/25 [==============================] - elapsed: 128182ms - loss 0.1260\n",
      "Epoch 477/650\n",
      "25/25 [==============================] - elapsed: 123779ms - loss 0.1259\n",
      "Epoch 478/650\n",
      "25/25 [==============================] - elapsed: 131259ms - loss 0.1259\n",
      "Epoch 479/650\n",
      "25/25 [==============================] - elapsed: 128802ms - loss 0.1259\n",
      "Epoch 480/650\n",
      "25/25 [==============================] - elapsed: 124683ms - loss 0.1258\n",
      "Epoch 481/650\n",
      "25/25 [==============================] - elapsed: 128312ms - loss 0.1258\n",
      "Epoch 482/650\n",
      "25/25 [==============================] - elapsed: 121158ms - loss 0.1257\n",
      "Epoch 483/650\n",
      "25/25 [==============================] - elapsed: 121553ms - loss 0.1257\n",
      "Epoch 484/650\n",
      "25/25 [==============================] - elapsed: 120528ms - loss 0.1256\n",
      "Epoch 485/650\n",
      "25/25 [==============================] - elapsed: 121672ms - loss 0.1256\n",
      "Epoch 486/650\n",
      "25/25 [==============================] - elapsed: 120152ms - loss 0.1255\n",
      "Epoch 487/650\n",
      "25/25 [==============================] - elapsed: 121739ms - loss 0.1255\n",
      "Epoch 488/650\n",
      "25/25 [==============================] - elapsed: 121716ms - loss 0.1254\n",
      "Epoch 489/650\n",
      "25/25 [==============================] - elapsed: 121354ms - loss 0.1254\n",
      "Epoch 490/650\n",
      "25/25 [==============================] - elapsed: 121444ms - loss 0.1254\n",
      "Epoch 491/650\n",
      "25/25 [==============================] - elapsed: 120918ms - loss 0.1253\n",
      "Epoch 492/650\n",
      "25/25 [==============================] - elapsed: 121157ms - loss 0.1253\n",
      "Epoch 493/650\n",
      "25/25 [==============================] - elapsed: 121586ms - loss 0.1252\n",
      "Epoch 494/650\n",
      "25/25 [==============================] - elapsed: 122130ms - loss 0.1252\n",
      "Epoch 495/650\n",
      "25/25 [==============================] - elapsed: 120489ms - loss 0.1251\n",
      "Epoch 496/650\n",
      "25/25 [==============================] - elapsed: 121903ms - loss 0.1251\n",
      "Epoch 497/650\n",
      "25/25 [==============================] - elapsed: 128132ms - loss 0.1251\n",
      "Epoch 498/650\n",
      "25/25 [==============================] - elapsed: 124582ms - loss 0.1250\n",
      "Epoch 499/650\n",
      "25/25 [==============================] - elapsed: 130211ms - loss 0.1250\n",
      "Epoch 500/650\n",
      "25/25 [==============================] - elapsed: 130281ms - loss 0.1249\n",
      "Epoch 501/650\n",
      "25/25 [==============================] - elapsed: 124008ms - loss 0.1249\n",
      "Epoch 502/650\n",
      "25/25 [==============================] - elapsed: 130525ms - loss 0.1249\n",
      "Epoch 503/650\n",
      "25/25 [==============================] - elapsed: 121322ms - loss 0.1248\n",
      "Epoch 504/650\n",
      "25/25 [==============================] - elapsed: 124857ms - loss 0.1248\n",
      "Epoch 505/650\n",
      "25/25 [==============================] - elapsed: 124805ms - loss 0.1247\n",
      "Epoch 506/650\n",
      "25/25 [==============================] - elapsed: 127441ms - loss 0.1247\n",
      "Epoch 507/650\n",
      "25/25 [==============================] - elapsed: 125114ms - loss 0.1247\n",
      "Epoch 508/650\n",
      "25/25 [==============================] - elapsed: 126445ms - loss 0.1246\n",
      "Epoch 509/650\n",
      "25/25 [==============================] - elapsed: 125129ms - loss 0.1246\n",
      "Epoch 510/650\n",
      "25/25 [==============================] - elapsed: 125480ms - loss 0.1245\n",
      "Epoch 511/650\n",
      "25/25 [==============================] - elapsed: 125639ms - loss 0.1245\n",
      "Epoch 512/650\n",
      "25/25 [==============================] - elapsed: 131410ms - loss 0.1245\n",
      "Epoch 513/650\n",
      "25/25 [==============================] - elapsed: 130910ms - loss 0.1244\n",
      "Epoch 514/650\n",
      "25/25 [==============================] - elapsed: 126165ms - loss 0.1244\n",
      "Epoch 515/650\n",
      "25/25 [==============================] - elapsed: 126150ms - loss 0.1243\n",
      "Epoch 516/650\n",
      "25/25 [==============================] - elapsed: 125159ms - loss 0.1243\n",
      "Epoch 517/650\n",
      "25/25 [==============================] - elapsed: 125328ms - loss 0.1243\n",
      "Epoch 518/650\n",
      "25/25 [==============================] - elapsed: 123629ms - loss 0.1242\n",
      "Epoch 519/650\n",
      "25/25 [==============================] - elapsed: 125114ms - loss 0.1242\n",
      "Epoch 520/650\n",
      "25/25 [==============================] - elapsed: 123435ms - loss 0.1242\n",
      "Epoch 521/650\n",
      "25/25 [==============================] - elapsed: 129380ms - loss 0.1241\n",
      "Epoch 522/650\n",
      "25/25 [==============================] - elapsed: 124758ms - loss 0.1241\n",
      "Epoch 523/650\n",
      "25/25 [==============================] - elapsed: 121203ms - loss 0.1240\n",
      "Epoch 524/650\n",
      "25/25 [==============================] - elapsed: 120831ms - loss 0.1240\n",
      "Epoch 525/650\n",
      "25/25 [==============================] - elapsed: 120876ms - loss 0.1240\n",
      "Epoch 526/650\n",
      "25/25 [==============================] - elapsed: 120084ms - loss 0.1239\n",
      "Epoch 527/650\n",
      "25/25 [==============================] - elapsed: 120947ms - loss 0.1239\n",
      "Epoch 528/650\n",
      "25/25 [==============================] - elapsed: 121280ms - loss 0.1239\n",
      "Epoch 529/650\n",
      "25/25 [==============================] - elapsed: 122150ms - loss 0.1238\n",
      "Epoch 530/650\n",
      "25/25 [==============================] - elapsed: 120929ms - loss 0.1238\n",
      "Epoch 531/650\n",
      "25/25 [==============================] - elapsed: 121255ms - loss 0.1238\n",
      "Epoch 532/650\n",
      "25/25 [==============================] - elapsed: 121270ms - loss 0.1237\n",
      "Epoch 533/650\n",
      "25/25 [==============================] - elapsed: 120429ms - loss 0.1237\n",
      "Epoch 534/650\n",
      "25/25 [==============================] - elapsed: 119884ms - loss 0.1237\n",
      "Epoch 535/650\n",
      "25/25 [==============================] - elapsed: 121151ms - loss 0.1236\n",
      "Epoch 536/650\n",
      "25/25 [==============================] - elapsed: 121858ms - loss 0.1236\n",
      "Epoch 537/650\n",
      "25/25 [==============================] - elapsed: 121485ms - loss 0.1236\n",
      "Epoch 538/650\n",
      "25/25 [==============================] - elapsed: 122124ms - loss 0.1235\n",
      "Epoch 539/650\n",
      "25/25 [==============================] - elapsed: 121606ms - loss 0.1235\n",
      "Epoch 540/650\n",
      "25/25 [==============================] - elapsed: 122034ms - loss 0.1235\n",
      "Epoch 541/650\n",
      "25/25 [==============================] - elapsed: 120790ms - loss 0.1234\n",
      "Epoch 542/650\n",
      "25/25 [==============================] - elapsed: 122570ms - loss 0.1234\n",
      "Epoch 543/650\n",
      "25/25 [==============================] - elapsed: 121647ms - loss 0.1234\n",
      "Epoch 544/650\n",
      "25/25 [==============================] - elapsed: 123686ms - loss 0.1233\n",
      "Epoch 545/650\n",
      "25/25 [==============================] - elapsed: 121925ms - loss 0.1233\n",
      "Epoch 546/650\n",
      "25/25 [==============================] - elapsed: 126777ms - loss 0.1233\n",
      "Epoch 547/650\n",
      "25/25 [==============================] - elapsed: 134991ms - loss 0.1232\n",
      "Epoch 548/650\n",
      "25/25 [==============================] - elapsed: 123910ms - loss 0.1232\n",
      "Epoch 549/650\n",
      "25/25 [==============================] - elapsed: 124262ms - loss 0.1232\n",
      "Epoch 550/650\n",
      "25/25 [==============================] - elapsed: 123438ms - loss 0.1231\n",
      "Epoch 551/650\n",
      "25/25 [==============================] - elapsed: 122959ms - loss 0.1231\n",
      "Epoch 552/650\n",
      "25/25 [==============================] - elapsed: 124336ms - loss 0.1231\n",
      "Epoch 553/650\n",
      "25/25 [==============================] - elapsed: 124069ms - loss 0.1230\n",
      "Epoch 554/650\n",
      "25/25 [==============================] - elapsed: 124737ms - loss 0.1230\n",
      "Epoch 555/650\n",
      "25/25 [==============================] - elapsed: 124423ms - loss 0.1230\n",
      "Epoch 556/650\n",
      "25/25 [==============================] - elapsed: 124837ms - loss 0.1229\n",
      "Epoch 557/650\n",
      "25/25 [==============================] - elapsed: 124185ms - loss 0.1229\n",
      "Epoch 558/650\n",
      "25/25 [==============================] - elapsed: 123776ms - loss 0.1229\n",
      "Epoch 559/650\n",
      "25/25 [==============================] - elapsed: 124451ms - loss 0.1229\n",
      "Epoch 560/650\n",
      "25/25 [==============================] - elapsed: 123511ms - loss 0.1228\n",
      "Epoch 561/650\n",
      "25/25 [==============================] - elapsed: 124414ms - loss 0.1228\n",
      "Epoch 562/650\n",
      "25/25 [==============================] - elapsed: 123574ms - loss 0.1228\n",
      "Epoch 563/650\n",
      "25/25 [==============================] - elapsed: 124654ms - loss 0.1227\n",
      "Epoch 564/650\n",
      "25/25 [==============================] - elapsed: 122600ms - loss 0.1227\n",
      "Epoch 565/650\n",
      "25/25 [==============================] - elapsed: 123579ms - loss 0.1227\n",
      "Epoch 566/650\n",
      "25/25 [==============================] - elapsed: 122774ms - loss 0.1226\n",
      "Epoch 567/650\n",
      "25/25 [==============================] - elapsed: 132279ms - loss 0.1226\n",
      "Epoch 568/650\n",
      "25/25 [==============================] - elapsed: 124485ms - loss 0.1226\n",
      "Epoch 569/650\n",
      "25/25 [==============================] - elapsed: 124484ms - loss 0.1226\n",
      "Epoch 570/650\n",
      "25/25 [==============================] - elapsed: 124742ms - loss 0.1225\n",
      "Epoch 571/650\n",
      "25/25 [==============================] - elapsed: 121987ms - loss 0.1225\n",
      "Epoch 572/650\n",
      "25/25 [==============================] - elapsed: 123538ms - loss 0.1225\n",
      "Epoch 573/650\n",
      "25/25 [==============================] - elapsed: 122023ms - loss 0.1224\n",
      "Epoch 574/650\n",
      "25/25 [==============================] - elapsed: 123595ms - loss 0.1224\n",
      "Epoch 575/650\n",
      "25/25 [==============================] - elapsed: 129657ms - loss 0.1224\n",
      "Epoch 576/650\n",
      "25/25 [==============================] - elapsed: 125984ms - loss 0.1224\n",
      "Epoch 577/650\n",
      "25/25 [==============================] - elapsed: 123357ms - loss 0.1223\n",
      "Epoch 578/650\n",
      "25/25 [==============================] - elapsed: 125114ms - loss 0.1223\n",
      "Epoch 579/650\n",
      "25/25 [==============================] - elapsed: 134507ms - loss 0.1223\n",
      "Epoch 580/650\n",
      "25/25 [==============================] - elapsed: 126417ms - loss 0.1222\n",
      "Epoch 581/650\n",
      "25/25 [==============================] - elapsed: 124714ms - loss 0.1222\n",
      "Epoch 582/650\n",
      "25/25 [==============================] - elapsed: 122998ms - loss 0.1222\n",
      "Epoch 583/650\n",
      "25/25 [==============================] - elapsed: 124414ms - loss 0.1222\n",
      "Epoch 584/650\n",
      "25/25 [==============================] - elapsed: 124032ms - loss 0.1221\n",
      "Epoch 585/650\n",
      "25/25 [==============================] - elapsed: 125583ms - loss 0.1221\n",
      "Epoch 586/650\n",
      "25/25 [==============================] - elapsed: 123668ms - loss 0.1221\n",
      "Epoch 587/650\n",
      "25/25 [==============================] - elapsed: 125975ms - loss 0.1221\n",
      "Epoch 588/650\n",
      "25/25 [==============================] - elapsed: 124566ms - loss 0.1220\n",
      "Epoch 589/650\n",
      "25/25 [==============================] - elapsed: 126793ms - loss 0.1220\n",
      "Epoch 590/650\n",
      "25/25 [==============================] - elapsed: 127914ms - loss 0.1220\n",
      "Epoch 591/650\n",
      "25/25 [==============================] - elapsed: 126425ms - loss 0.1220\n",
      "Epoch 592/650\n",
      "25/25 [==============================] - elapsed: 123838ms - loss 0.1219\n",
      "Epoch 593/650\n",
      "25/25 [==============================] - elapsed: 122989ms - loss 0.1219\n",
      "Epoch 594/650\n",
      "25/25 [==============================] - elapsed: 123002ms - loss 0.1219\n",
      "Epoch 595/650\n",
      "25/25 [==============================] - elapsed: 122543ms - loss 0.1218\n",
      "Epoch 596/650\n",
      "25/25 [==============================] - elapsed: 134734ms - loss 0.1218\n",
      "Epoch 597/650\n",
      "25/25 [==============================] - elapsed: 122729ms - loss 0.1218\n",
      "Epoch 598/650\n",
      "25/25 [==============================] - elapsed: 123657ms - loss 0.1218\n",
      "Epoch 599/650\n",
      "25/25 [==============================] - elapsed: 121046ms - loss 0.1217\n",
      "Epoch 600/650\n",
      "25/25 [==============================] - elapsed: 123719ms - loss 0.1217\n",
      "Epoch 601/650\n",
      "25/25 [==============================] - elapsed: 122672ms - loss 0.1217\n",
      "Epoch 602/650\n",
      "25/25 [==============================] - elapsed: 124721ms - loss 0.1217\n",
      "Epoch 603/650\n",
      "25/25 [==============================] - elapsed: 122758ms - loss 0.1216\n",
      "Epoch 604/650\n",
      "25/25 [==============================] - elapsed: 123047ms - loss 0.1216\n",
      "Epoch 605/650\n",
      "25/25 [==============================] - elapsed: 123476ms - loss 0.1216\n",
      "Epoch 606/650\n",
      "25/25 [==============================] - elapsed: 123369ms - loss 0.1216\n",
      "Epoch 607/650\n",
      "25/25 [==============================] - elapsed: 138955ms - loss 0.1215\n",
      "Epoch 608/650\n",
      "25/25 [==============================] - elapsed: 134140ms - loss 0.1215\n",
      "Epoch 609/650\n",
      "25/25 [==============================] - elapsed: 136756ms - loss 0.1215\n",
      "Epoch 610/650\n",
      "25/25 [==============================] - elapsed: 123265ms - loss 0.1215\n",
      "Epoch 611/650\n",
      "25/25 [==============================] - elapsed: 126482ms - loss 0.1214\n",
      "Epoch 612/650\n",
      "25/25 [==============================] - elapsed: 121664ms - loss 0.1214\n",
      "Epoch 613/650\n",
      "25/25 [==============================] - elapsed: 127000ms - loss 0.1214\n",
      "Epoch 614/650\n",
      "25/25 [==============================] - elapsed: 123310ms - loss 0.1214\n",
      "Epoch 615/650\n",
      "25/25 [==============================] - elapsed: 124164ms - loss 0.1213\n",
      "Epoch 616/650\n",
      "25/25 [==============================] - elapsed: 125111ms - loss 0.1213\n",
      "Epoch 617/650\n",
      "25/25 [==============================] - elapsed: 122909ms - loss 0.1213\n",
      "Epoch 618/650\n",
      "25/25 [==============================] - elapsed: 120893ms - loss 0.1213\n",
      "Epoch 619/650\n",
      "25/25 [==============================] - elapsed: 126429ms - loss 0.1213\n",
      "Epoch 620/650\n",
      "25/25 [==============================] - elapsed: 125687ms - loss 0.1212\n",
      "Epoch 621/650\n",
      "25/25 [==============================] - elapsed: 123801ms - loss 0.1212\n",
      "Epoch 622/650\n",
      "25/25 [==============================] - elapsed: 125953ms - loss 0.1212\n",
      "Epoch 623/650\n",
      "25/25 [==============================] - elapsed: 123002ms - loss 0.1212\n",
      "Epoch 624/650\n",
      "25/25 [==============================] - elapsed: 128466ms - loss 0.1211\n",
      "Epoch 625/650\n",
      "25/25 [==============================] - elapsed: 121367ms - loss 0.1211\n",
      "Epoch 626/650\n",
      "25/25 [==============================] - elapsed: 128068ms - loss 0.1211\n",
      "Epoch 627/650\n",
      "25/25 [==============================] - elapsed: 120500ms - loss 0.1211\n",
      "Epoch 628/650\n",
      "25/25 [==============================] - elapsed: 127609ms - loss 0.1210\n",
      "Epoch 629/650\n",
      "25/25 [==============================] - elapsed: 124505ms - loss 0.1210\n",
      "Epoch 630/650\n",
      "25/25 [==============================] - elapsed: 129837ms - loss 0.1210\n",
      "Epoch 631/650\n",
      "25/25 [==============================] - elapsed: 124937ms - loss 0.1210\n",
      "Epoch 632/650\n",
      "25/25 [==============================] - elapsed: 128235ms - loss 0.1210\n",
      "Epoch 633/650\n",
      "25/25 [==============================] - elapsed: 127425ms - loss 0.1209\n",
      "Epoch 634/650\n",
      "25/25 [==============================] - elapsed: 127728ms - loss 0.1209\n",
      "Epoch 635/650\n",
      "25/25 [==============================] - elapsed: 127884ms - loss 0.1209\n",
      "Epoch 636/650\n",
      "25/25 [==============================] - elapsed: 123637ms - loss 0.1209\n",
      "Epoch 637/650\n",
      "25/25 [==============================] - elapsed: 130124ms - loss 0.1208\n",
      "Epoch 638/650\n",
      "25/25 [==============================] - elapsed: 124299ms - loss 0.1208\n",
      "Epoch 639/650\n",
      "25/25 [==============================] - elapsed: 136113ms - loss 0.1208\n",
      "Epoch 640/650\n",
      "25/25 [==============================] - elapsed: 120294ms - loss 0.1208\n",
      "Epoch 641/650\n",
      "25/25 [==============================] - elapsed: 127835ms - loss 0.1208\n",
      "Epoch 642/650\n",
      "25/25 [==============================] - elapsed: 119986ms - loss 0.1207\n",
      "Epoch 643/650\n",
      "25/25 [==============================] - elapsed: 123880ms - loss 0.1207\n",
      "Epoch 644/650\n",
      "25/25 [==============================] - elapsed: 120879ms - loss 0.1207\n",
      "Epoch 645/650\n",
      "25/25 [==============================] - elapsed: 120866ms - loss 0.1207\n",
      "Epoch 646/650\n",
      "25/25 [==============================] - elapsed: 120147ms - loss 0.1207\n",
      "Epoch 647/650\n",
      "25/25 [==============================] - elapsed: 135838ms - loss 0.1206\n",
      "Epoch 648/650\n",
      "25/25 [==============================] - elapsed: 122658ms - loss 0.1206\n",
      "Epoch 649/650\n",
      "25/25 [==============================] - elapsed: 120439ms - loss 0.1206\n",
      "Epoch 650/650\n",
      "25/25 [==============================] - elapsed: 123296ms - loss 0.1206\n",
      "Layer 0 weights: ['neuron 0: [13.697413178311066, 12.461977805094454, 5.61892192253577]', 'neuron 1: [-13.356350270913437, -7.455569318383052, -13.41871234039236]']\n",
      "Layer 1 weights: ['neuron 0: [4.600879483759232, -10.130323728366186, -0.42816404273355957]', 'neuron 1: [8.28582777869759, -4.5152376057503005, 1.0762231478400606]', 'neuron 2: [-3.3941343262392967, 10.291278086896614, 1.7176493956521746]', 'neuron 3: [-8.105816116042794, 5.249442663495346, -0.22217519337461303]', 'neuron 4: [8.423204753172357, 6.461840320126135, 11.756098180334654]']\n",
      "seed: 65560\n"
     ]
    }
   ],
   "source": [
    "xor_net.fit(dataset = xor_dataset, epochs = 650)#, verbose = False)\n",
    "\n",
    "xor_net.net_weights()\n",
    "print(f\"seed: {xor_net.get_seed()}\")\n",
    "predicted = xor_net.predict(xor_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      X1    X2  AND   OR  NAND  NOR  XOR  predicted AND  predicted OR  \\\n",
      "0  -0.93 -1.00  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "1   0.25 -0.83  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "2   0.08 -0.73  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "3  -0.63 -0.99  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "4  -0.18 -0.51  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "5   0.63  0.47  0.0  1.0   1.0  0.0  1.0       0.037182      0.971163   \n",
      "6   0.35 -0.96  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "7   0.43  0.28  0.0  0.0   1.0  1.0  0.0       0.005663      0.925720   \n",
      "8  -0.35  0.99  0.0  1.0   1.0  0.0  1.0       0.003378      0.837403   \n",
      "9   0.84 -0.32  0.0  1.0   1.0  0.0  1.0       0.003691      0.840378   \n",
      "10 -0.83 -0.88  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "11  0.56  0.00  0.0  1.0   1.0  0.0  1.0       0.003689      0.853532   \n",
      "12 -0.79 -0.46  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "13  0.29  0.11  0.0  0.0   1.0  1.0  0.0       0.000446      0.117571   \n",
      "14  0.02  0.93  0.0  1.0   1.0  0.0  1.0       0.006120      0.936273   \n",
      "15 -0.32 -0.69  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "16 -0.60 -0.46  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "17  0.26  0.89  0.0  1.0   1.0  0.0  1.0       0.008619      0.945440   \n",
      "18  0.99 -0.22  0.0  1.0   1.0  0.0  1.0       0.023350      0.963377   \n",
      "19  0.44  0.88  0.0  1.0   1.0  0.0  1.0       0.087953      0.980650   \n",
      "20  0.87  0.96  1.0  1.0   0.0  0.0  0.0       0.993184      0.999245   \n",
      "21  0.20 -0.65  0.0  0.0   1.0  1.0  0.0       0.000061      0.003716   \n",
      "22  0.94 -0.17  0.0  1.0   1.0  0.0  1.0       0.017069      0.957916   \n",
      "23  0.21 -0.22  0.0  0.0   1.0  1.0  0.0       0.000062      0.003845   \n",
      "24  0.63  0.81  1.0  1.0   0.0  0.0  0.0       0.914770      0.997590   \n",
      "\n",
      "    predicted NAND  predicted NOR  predicted XOR  \n",
      "0         0.999811       0.995813       0.004995  \n",
      "1         0.999811       0.995813       0.004995  \n",
      "2         0.999811       0.995813       0.004995  \n",
      "3         0.999811       0.995813       0.004995  \n",
      "4         0.999811       0.995813       0.004995  \n",
      "5         0.964522       0.026841       0.875343  \n",
      "6         0.999811       0.995813       0.004995  \n",
      "7         0.994613       0.078032       0.948498  \n",
      "8         0.996358       0.167729       0.886539  \n",
      "9         0.996000       0.163594       0.877275  \n",
      "10        0.999811       0.995813       0.004995  \n",
      "11        0.996085       0.151249       0.895918  \n",
      "12        0.999811       0.995813       0.004995  \n",
      "13        0.999182       0.877970       0.159635  \n",
      "14        0.994323       0.067299       0.956883  \n",
      "15        0.999811       0.995813       0.004995  \n",
      "16        0.999811       0.995813       0.004995  \n",
      "17        0.991978       0.056356       0.947867  \n",
      "18        0.977752       0.035177       0.899726  \n",
      "19        0.914761       0.016864       0.796858  \n",
      "20        0.006283       0.000386       0.035481  \n",
      "21        0.999811       0.995813       0.004996  \n",
      "22        0.983815       0.041333       0.916383  \n",
      "23        0.999808       0.995671       0.005171  \n",
      "24        0.082121       0.001490       0.162614  \n"
     ]
    }
   ],
   "source": [
    "print(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGzCAYAAAAi6m1wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABZtElEQVR4nO3deXhTVd4H8O9N2ibd0oXuUOgCFJClCEMFURjpQAEHcHCkyCIMghsuA4PC+ygoOCLI+DoqMwjD7oLLCCJiZVGUpSwWEMomRUoLNN2bdG+TnPcP3gZj0z1ptu/nefJA7z333nPT0H6553fPlYQQAkRERERORGbrDhARERFZGgMOEREROR0GHCIiInI6DDhERETkdBhwiIiIyOkw4BAREZHTYcAhIiIip8OAQ0RERE6HAYeIiIicDgMOEdncgQMHIEkSDhw4YFw2Y8YMREVF2axPv2Wuj0RkvxhwiMipvPbaa9ixY4etu4Fz585h6tSp6NixIxQKBSIiIjBlyhScO3euXttNmzZBkiTjy83NDR07dsSMGTNw48YNG/SeyPG52boDRETmrFu3DgaDocXbvfbaa3jwwQcxYcIEy3eqmT7//HNMnjwZgYGBmDVrFqKjo5GZmYn169fjs88+w7Zt2/DAAw/U227p0qWIjo5GVVUVjh49ik2bNuHQoUNIT0+HUqm0wZkQOS4GHCJqNYPBgJqaGqv88nV3d7f4PtvDlStXMG3aNMTExOCHH35AcHCwcd2zzz6Le+65B9OmTcOZM2cQExNjsu3o0aMxcOBAAMCjjz6KoKAgrFixAjt37sRDDz3UrudB5Og4REXk4l5++WVIkoSLFy/ioYcegkqlQocOHfDss8+iqqrKpK0kSZg7dy4++OAD3HHHHVAoFEhJSQEA3LhxA3/5y18QGhoKhUKBO+64Axs2bKh3vOvXr2PChAnw9vZGSEgI/vrXv6K6urpeO3M1OAaDAf/85z/Rp08fKJVKBAcHIykpCT/++KOxf+Xl5di8ebNxuGfGjBnG7S3dR3PeeOMNVFRUYO3atSbhBgCCgoLw3nvvoby8HCtXrmxyX/fccw+AW6GJiFqGV3CICADw0EMPISoqCsuXL8fRo0fx9ttvo7i4GFu2bDFp9+233+KTTz7B3LlzERQUhKioKOTm5uKuu+4yBqDg4GB8/fXXmDVrFrRaLZ577jkAQGVlJUaMGIGsrCw888wziIiIwNatW/Htt982q4+zZs3Cpk2bMHr0aDz66KPQ6XQ4ePAgjh49ioEDB2Lr1q149NFHMWjQIMyZMwcAEBsbCwDt1scvv/wSUVFRxnDyW/feey+ioqLw1VdfNbmvzMxMAEBAQECzjk1EvyKIyKUtWbJEABDjxo0zWf7kk08KAOKnn34yLgMgZDKZOHfunEnbWbNmifDwcFFQUGCyPDk5Wfj5+YmKigohhBBvvfWWACA++eQTY5vy8nLRtWtXAUB89913xuWPPPKI6NKli/Hrb7/9VgAQzzzzTL1zMBgMxr97e3uLRx55pF4ba/Txt0pKSgQAMX78+AbbCCHEuHHjBACh1WqFEEJs3LhRABD79u0T+fn5Ijs7W3z22WciODhYKBQKkZ2d3ej+iKg+DlEREQDgqaeeMvn66aefBgDs3r3bZPmwYcPQq1cv49dCCPz3v//FH//4RwghUFBQYHyNGjUKGo0GJ0+eNO4rPDwcDz74oHF7Ly8v49WWxvz3v/+FJElYsmRJvXWSJDW6bXv1sbS0FADg6+vbaLu69Vqt1mR5YmIigoODERkZiQcffBDe3t7YuXMnOnXq1OSxicgUh6iICADQrVs3k69jY2Mhk8mMwyR1oqOjTb7Oz89HSUkJ1q5di7Vr15rdd15eHgDg2rVr6Nq1a71AEhcX12T/rly5goiICAQGBjbZ9rfaq491waUu6DSkoSC0evVqdO/eHRqNBhs2bMAPP/wAhULR5HGJqD4GHCIyq6GrIp6eniZf193KPXXqVDzyyCNmt+nbt69lO9dC7dVHPz8/hIeH48yZM422O3PmDDp27AiVSmWyfNCgQca7qCZMmIChQ4fi4YcfxqVLl+Dj49Pm/hG5EgYcIgIAXL582eTqTEZGBgwGQ5OzCQcHB8PX1xd6vR6JiYmNtu3SpQvS09MhhDAJUJcuXWqyf7Gxsfjmm29QVFTU6FUcc8GsvfoIAPfffz/WrVuHQ4cOYejQofXWHzx4EJmZmXjsscca3Y9cLsfy5cvx+9//Hu+++y4WLlzYrOMT0S2swSEiALeGR37tnXfeAXBrbpbGyOVyTJw4Ef/973+Rnp5eb31+fr7x72PGjMHNmzfx2WefGZfV3VLdlIkTJ0IIgVdeeaXeOiGE8e/e3t4oKSmxSR8BYMGCBfD09MRjjz2GwsJCk3VFRUV4/PHH4eXlhQULFjS5r+HDh2PQoEF466236t2yT0SN4xUcIgIAXL16FePGjUNSUhJSU1Px/vvv4+GHH0a/fv2a3Pb111/Hd999h4SEBMyePRu9evVCUVERTp48iX379qGoqAgAMHv2bLz77ruYPn060tLSEB4ejq1bt8LLy6vJY/z+97/HtGnT8Pbbb+Py5ctISkqCwWDAwYMH8fvf/x5z584FAAwYMAD79u3Dm2++iYiICERHRyMhIaFd+gjcqmXavHkzpkyZgj59+tSbybigoAAfffSR8fb1pixYsAB//vOfsWnTJjz++OPN2oaIwNvEiVxd3W3i58+fFw8++KDw9fUVAQEBYu7cuaKystKkLQDx1FNPmd1Pbm6ueOqpp0RkZKRwd3cXYWFhYsSIEWLt2rUm7a5duybGjRsnvLy8RFBQkHj22WdFSkpKk7eJCyGETqcTb7zxhujRo4fw8PAQwcHBYvTo0SItLc3Y5uLFi+Lee+8Vnp6eAoDJLeOW7mNjzpw5IyZPnizCw8ONx5o8ebI4e/ZsvbZ1t4mfOHGi3jq9Xi9iY2NFbGys0Ol0zTo2EQkhCfGra7tE5HJefvllvPLKK8jPz0dQUJCtu0NEZBGswSEiIiKnw4BDRERETocBh4iIiJwOa3CIiIjI6fAKDhERETkdBhwiIiJyOi450Z/BYMDNmzfh6+vb5FOIiYiIyD4IIVBaWoqIiAjIZI1fo3HJgHPz5k1ERkbauhtERETUCtnZ2ejUqVOjbVwy4Pj6+gIAhofOhJvMw8a9odao7BXe6PqSrg1/X8tiDGaXe3fRNnnc+zpdNv49dU06Lu7IwE/7uqBDoOn/JC5crsGQsdcxYuGd6Hl/lyb3+1vJ/sdavA0RkbXcvK7DC08W4uovesREuUGpkHD+Ui0CAiQs+99AxA9UtEs/yssMGHWX2vh7vDEueReVVquFn58fEsMfY8BxYJW9Oza6vjiu4e9taaz5kOMTrWnWsUd2voSyvEq8n/wNukZI+PeKEAzqr4AQwN7vKzBnQT6qPBR4+KORcFe27v8R0wKOtGo7IiJLqqw04KFRefCQSdj6bhgS7lQCADKu1uDx5/Nw/FQVPtodis5R1r9mUlZqwNDeN6HRaKBSqRptyyJjclie6TfgmX6jwfUBl2oQcKnG7DrfK+Y/+mVX/VB21a/JY+/JioNPiCcmrhmObK0bhozNRsf+mQjrm4kxD9+EIcAHf1ozvNXhBgC2Fg9p9bZERJaS8kUlrmfp8OWWCGO4AYCu0R74YnMEvDxl+HBDqQ17aB6v4PAKjlOw1dWckZ0vwaA3IPNILm7+VACZXELnu0LRMT7IYgXsvJJDRLb09MwCuNVI2Pep+ZqXBa/kY+vnWuw9EWH1vvAKDrmcxq7kAGjwSg7Q+NWcpuzJioNMLkPMPeEYOrcPhjzRG536B1v07rytxUN4NacJQgjUVAu44P/XiKyuqkIgNFje4PrQYDdUVtrfvz0GHHIatgw5e7LimmzXVgw59eXn6vGPZSUYHp+DQd1vYFjfm1j5Sgly1Xpbd43IaUR3c8OBI5WorTUfYvb9UI7Ybu7t3KumMeCQU2lOyLFmXY61MeTcdiNbh2nj87DrswrMSlZh4z9D8dg0f6Rsr8DUP+YiK1Nn6y4SOYUHp3hDnavHineK6q37ck8Z9n5fiQenetugZ41jDQ5rcJyWLetyrI11OcDjU/OhvqbHwS8iERF2u5g7N1+Heydch3+IhP98HGLDHhI5j/f+qcW/39Ri5HAvTH3QF55KGb5IKcNH20sx/A+eWPmvQMjl1p84tyU1OAw4DDhOzZlDDuC6Qefa1VqMH56LLe+GYsrE+j/kPv2yFMlz1Phsbyi6dre/S+dEjuibLyuwZW0pzp2pBQB0ipRj0iM+mDzTB25u7fNUgJYEHJec6I9ch2f6jUZDTsClmgZDju8VmdmQUzdc1VjQqRuusnbQ2Vo8xCVDzuULt37Ajr7P/GXxuuWXztUy4BBZyKg/emHUH72gKTFArxPwD5RBJrPfxx2xBoecnjXqcoDmFyBbmyvW5Xgobv1QLdGav8pWorlVZKxon8lViVyKn78MgUFyuw43AAMOuYimJgUEGr7LyveKrM13WVmbq4WcAQkKeHtL2PCh+atoG7dpoVRKGHS30ux6InJ+DDjkUpz5VnJXmi/H20eGh6b74I1/FWPDRxrodLdKCfV6gS2faPH3t4rw4BRvqPyc90fc9Swd1rylxbJFxfjXmxreNUb0GywyZpGxS7JG8TFgPwXIrlCXo9MJLF1YjJ2fViAiXI5e3TxwMaMW12/qMHq8J5b+IxDu7vZ9Cb01DAaB//27Bu+vL4PKV4bYKHf8cq0WJRoDkh/xxoIl/u1yNwuRLfAuqiYw4BDQdMgBGg46DDn242J6Db74rAL5uXoEh8hx/0Qv3NHXef9dr3tHi3/9Q4vXXwzCkzP84OUlQ2WlAWu3avC3Vwrw6FxfPDm/6auKtlJUoMf2j8uRdqwaQgB3/k6BB5K9ERTS8Ey5RHUYcJrAgEO/5sy3krtKyHEVlZUGjBqUg5mT/PDm0uB66xf9vQCrN5Vgz7FwePvY3/Bc6sEq/O2xQuj1wB/u9YIkAXu/rwAkYOW/AnHPfZ627iLZOT6LiqgFWJdDjiLtaDW0WoHHppv/fM2Z5ofyMoFjh6vbuWdNy7mhw7zZhbgnwRPZJ6OxY3MEtm+KQPapaCTe44W/PV6E7GusIyLLYcAhAh/xQI6hsuLWBfeQIPPDOaH/v7yyvOEhVFv59P1yeLhL+HhtOAIDbvc/wF+OD/8dBm8vCZ9sKbNhD8nZMOAQ/b+mQg7AW8nJtmL+/4GG+w9VmF2/7+Ct5bF2OLlh6vdVeGCMD3y86/878fKS4cH7fXDk+yob9IycFQMO0a+0Zb4cwDGGrMhxxXZ3R//feWDJykIUFZs+Mb1Eo8filYXoHe+OHr3tr7ZQrwe8PBu+u8vLUwYdR6jIghhwiMywVsixh6s5rMtxbC8tD0BuoR79RlzD8n8WYdfeMqx4pwjxI7KQdUOHxa8H2LqLZt3Rzx1f7imHXl//vhaDQWDnnjL0jre/YEaOiwGHqAF8xAPZo5hu7tj6RQgGDlVg2f8WYfz0HLz8j0L0S/DAli+C0b2nfYaEh6b5IPuGDi++Xohf37wrhMDSfxThl0wdJk03/2wxotbgbeK8TZya0Jb5cgDeSk7WU10loNUY4Osng1Jp/5P7bV1Xin+8qkHvnh6YNM4XMhnwyc5S/HSuBs+8oMJfnmz8tl8izoPTBAYcag1bzZcDWD/oMORQezmRWoUPN5bhx9RbE/0NSFBg8kwf3DWUzw2jpjHgNIEBh1qLj3ggIrIdTvRHZCWsyyEicgwMOEQtxPlyiIjsHwMOUStwvhwi16XVGPD++lI89UgBHp+aj3ff0CDnBifxsTcMOERtwPlyiFzL2dM1GDdMjbde08BXJkO4rxs+3lSGccPU2L3D/AzTZBsMOERtxLocIteg1Rjw9IwC9IjxwNUT0fjqg474bH0Erp+OQfIEX7w0rwjnzzb8nxpqXww4RBZg67ocDlkRWd8Xn5ajvMyAz9aHIyLMzbjcx1uG/7wZis6d3PDhBj4w1F4w4BBZiDXrcuzhag5DDrm6I99X4Q/DvBAe6lZvnZubhIcfUPGBoXaEAYfIwpx5yIp1OeTK9LpbDwVtiJenBF2ty00tZ7cYcIisoK1DVg2xh5AD8GoOuaY7+nlg7/cVKK8wP2nnF9+U4Q4+MNRuMOAQWQnrcoicy4MPe6O8woDnXsyv91T01RtKcPxkNZIf8bFR7+i3+KgGPqqB2oEzP+KBj3cgV7Lzs3K8vKAYMVHumPInX3gqJez4uhxH06ow7VEfzHvRD5Jk/w8+dVR8FlUTGHDIFtryVHJ7DzkAgw65jrOna/DB+lKkfl8NnV6gT7wHkmf4YFiikuHGyhhwmsCAQ7bSlpAD8KnkROTa+LBNIjvVlrocoG2PeAB4KzkRuY52CTirV69GVFQUlEolEhIScPz48QbbDh8+HJIk1XuNHTvW2GbGjBn11iclJbXHqRC1GefLISKyPqsHnI8//hjz5s3DkiVLcPLkSfTr1w+jRo1CXl6e2faff/45cnJyjK/09HTI5XL8+c9/NmmXlJRk0u6jjz6y9qkQWRTnyyEish6rB5w333wTs2fPxsyZM9GrVy+sWbMGXl5e2LBhg9n2gYGBCAsLM7727t0LLy+vegFHoVCYtAsICLD2qRBZHG8lJyKyDqsGnJqaGqSlpSExMfH2AWUyJCYmIjU1tVn7WL9+PZKTk+Ht7W2y/MCBAwgJCUFcXByeeOIJFBYWNriP6upqaLVakxeRvbBmXY69XM0hImpvVg04BQUF0Ov1CA0NNVkeGhoKtVrd5PbHjx9Heno6Hn30UZPlSUlJ2LJlC/bv348VK1bg+++/x+jRo6HX683uZ/ny5fDz8zO+IiMjW39SRFZgrbocwH6GrIiI2pNd30W1fv169OnTB4MGDTJZnpycjHHjxqFPnz6YMGECdu3ahRMnTuDAgQNm97No0SJoNBrjKzs7ux16T9Ryzl6XQ0TUXqwacIKCgiCXy5Gbm2uyPDc3F2FhYY1uW15ejm3btmHWrFlNHicmJgZBQUHIyMgwu16hUEClUpm8iOyVM9flsPiYiNqLVQOOh4cHBgwYgP379xuXGQwG7N+/H4MHD250208//RTV1dWYOnVqk8e5fv06CgsLER4e3uY+E9kD3kpORNQ2Vh+imjdvHtatW4fNmzfjwoULeOKJJ1BeXo6ZM2cCAKZPn45FixbV2279+vWYMGECOnToYLK8rKwMCxYswNGjR5GZmYn9+/dj/Pjx6Nq1K0aNGmXt0yFqVxyyIiJqHTdrH2DSpEnIz8/H4sWLoVarER8fj5SUFGPhcVZWFmQy0x/Ely5dwqFDh7Bnz556+5PL5Thz5gw2b96MkpISREREYOTIkVi2bBkUCoW1T4eo3Xmm32jyEQ8Bl2rMPuLB94qswcc7lF31a/IRD3uy4qz6eIe6kMNHPBCRpfFZVHwWFTkIPseKiFwdn0VF5IRYl0NE1HwMOEQOhnU5RERNY8AhckBtvZW8IbyVnIicBQMOkYOy5nw5vJpDRI6OAYfIgfERD0RE5jHgEDkBZ6/LYdAhopZiwCFyEs5clwPwag4RtQwDDpETYV0OEdEtDDhETqa5dTmOPGRFRNQUBhwiJ2XrIStrYl0OETWFAYfIiVlzyKoprMshIltiwCFycnzEAxG5IgYcIhfh7HU5DDpE9GsMOEQuxJnrcgBezSGi2xhwiFyMLW8lZ10OEbUXBhwiF8RHPBCRs2PAIXJhrMshImfFgEPk4mxdl8OrOURkDQw4RMRHPBCR02HAISIArvGIBwYdItfBgENEJmw9ZGVtDDlEroEBh4jqYcghIkfHgENEZrXlVnLOl0NEtsaAQ0SNYl0OETkiBhwiahKHrIjI0TDgEFGz2DrkcMiKiFqCAYeIms2WdTkA58shouZjwCGiFmNdDhHZOwYcImoVWw9ZWRtDDpFjY8AholbjreREZK8YcIiozThkRUT2hgGHiCyCQ1ZEZE8YcIjIYhhyiMheMOAQkUWxLoeI7AEDDhFZBetyiMiWGHCIyGo4ZEVEttIuAWf16tWIioqCUqlEQkICjh8/3mDbTZs2QZIkk5dSqTRpI4TA4sWLER4eDk9PTyQmJuLy5cvWPg0iagWGHCKyBasHnI8//hjz5s3DkiVLcPLkSfTr1w+jRo1CXl5eg9uoVCrk5OQYX9euXTNZv3LlSrz99ttYs2YNjh07Bm9vb4waNQpVVVXWPh0iagVXqMth0CGyL1YPOG+++SZmz56NmTNnolevXlizZg28vLywYcOGBreRJAlhYWHGV2hoqHGdEAJvvfUWXnzxRYwfPx59+/bFli1bcPPmTezYscPap0NEbeDMdTkAr+YQ2ROrBpyamhqkpaUhMTHx9gFlMiQmJiI1NbXB7crKytClSxdERkZi/PjxOHfunHHd1atXoVarTfbp5+eHhISEBvdZXV0NrVZr8iIi2+CQFRG1B6sGnIKCAuj1epMrMAAQGhoKtVptdpu4uDhs2LABX3zxBd5//30YDAYMGTIE169fBwDjdi3Z5/Lly+Hn52d8RUZGtvXUiKgN2jpk1RCGHGoPer3AwW8rsfKVEix/qRhf/rcc1VXC1t2i37C7u6gGDx6M6dOnIz4+HsOGDcPnn3+O4OBgvPfee63e56JFi6DRaIyv7OxsC/aYiFqrtUNWrMshW7mRpcOkpFw8PbMQR7+tQvrxWrw0rxhj7s7ByePVtu4e/YpVA05QUBDkcjlyc3NNlufm5iIsLKxZ+3B3d0f//v2RkZEBAMbtWrJPhUIBlUpl8iIi+8AhK3IUlZUGPD61AIYa4NCXnfDzkSic+a4LLh7ugj5xCjw9owBZmTpbd5P+n1UDjoeHBwYMGID9+/cblxkMBuzfvx+DBw9u1j70ej3Onj2L8PBwAEB0dDTCwsJM9qnVanHs2LFm75OI7AtDDjmCb3ZW4nqWDl9u6YjBAz0hSRIAoFuMB77YHAEvTxk+3FBq415SHasPUc2bNw/r1q3D5s2bceHCBTzxxBMoLy/HzJkzAQDTp0/HokWLjO2XLl2KPXv24JdffsHJkycxdepUXLt2DY8++iiAW3dYPffcc3j11Vexc+dOnD17FtOnT0dERAQmTJhg7dMhIiux5q3kTWHIoebYn1KJ4Xd7okc3j3rrfLxlmPagL75N4XQl9sLN2geYNGkS8vPzsXjxYqjVasTHxyMlJcVYJJyVlQWZ7PYPpuLiYsyePRtqtRoBAQEYMGAAjhw5gl69ehnbPP/88ygvL8ecOXNQUlKCoUOHIiUlpd6EgETkeDzTb6Cyd8cG19eFnOK4+r9kfK/IUBprqLe8LuT4RGsa3G9dyBnZ+VKL+tsSdSFnWsARqx2DrKeqQiA2vOFfm6HBbqioqP/5I9uQhBAuV/qt1Wrh5+eHxPDH4Car/0OSiGyvsZBTx1zIAWA25NRpLOTUsWbIqcOQ43hee7EYB/dW4erxaLi7S/XWj558A2qNDlt2hNigd66hrNSAob1vQqPRNFlPa3d3URERAazLIfsz8WFv5Kj1WPlucb11u/aWYc+BCjw41dsGPSNzGHCIyG7Z+hEP1saQ41jienngsed8sXhlIcZOuYGPtpdix9dlmPmsGhP/koPfj1Ji7ANetu4m/T8OUXGIisghcMiK7MXXX1Rgy9pSXEivBQBEdJJj0nQfTJnlAze3+kNXZDktGaJiwGHAIXIYDDlkL4QQKCk2QK8DAoNkkMkYbNoDa3CIyCnxEQ9kLyRJQkCgHEEhcoYbO8WAQ0QOx9nrchh0iNqOAYeIHFJrn2MFtO1qTns8xwrg1RyitmLAISKHxVvJiaghDDhE5ND4iAciMocBh4icQmtDDtDw1RzW5RA5LgYcInIarMshojoMOETkVHgrOREBDDhE5KRYl0Pk2hhwiMhpsS6HyHUx4BCRU2NdDpFrYsAhIqfHuhwi18OAQ0Qug3U5RK6DAYeIXArrcohcAwMOEbmcpoasWJdD5PgYcIjIZbEuh8h5MeAQkUtjXQ6Rc2LAISKXx7ocIufDgENEBNblEDkbBhwiol/hkBWRc2DAISL6DWsNWTWFQ1ZElsOAQ0RkRnOGrBpi73U5AK/mkPNjwCEiagTrcogcEwMOEVETWJdD5HgYcIiImoF1OW1jMAicOVmNA3srcfFcDYQQVj0ekSRc8FOm1Wrh5+eHxPDH4CbzsHV3iMjBVPbu2OC64riGf6aUxhoaXOcTrWnyuCM7X2qyTVtNCzhi8X3u/aoCb72mxY3rOuOy7j088MJSPwxIUFj8eOS8ykoNGNr7JjQaDVQqVaNteQWHiKiFbFGXAzjmkNXuHRVY8GQRam4EYwCG4R7cj3jcjbyfffDYwwU4ebzaoscjqsOAQ0TUCqzLaVptjcAbL2sQKnVCXzEEAVIwFJISQVI47jQMh4/BH6teafrKFVFrMOAQEbUS63Iad+hAFYqL9YgWvSBJksk6mSRDZ0NPnE+vQcbPtW06DpE5DDhERG1gjUc8NHe+HHu/mpObo4dMkuAjma+VUMEfAJCXo2/1MYgawoBDRGQBHLKqLzBIBoMQqBBlZteXQ3urXQf+KiLLa5dP1erVqxEVFQWlUomEhAQcP368wbbr1q3DPffcg4CAAAQEBCAxMbFe+xkzZkCSJJNXUlKStU+DiKhRzj5k1VL33KeEj48M11D/7i8hBK5JlxAd6464O9wt0UUiE1YPOB9//DHmzZuHJUuW4OTJk+jXrx9GjRqFvLw8s+0PHDiAyZMn47vvvkNqaioiIyMxcuRI3Lhh+oMjKSkJOTk5xtdHH31k7VMhImqSLUOOtYNOS+tyPD1lmPu8CjdwFefFjygXpRBCQCuKcUY6jGLkY96Lqnr1OUSWYPV5cBISEvC73/0O7777LgDAYDAgMjISTz/9NBYuXNjk9nq9HgEBAXj33Xcxffp0ALeu4JSUlGDHjh2t6hPnwSGi9tDYfDlAw3PmONt8OZ9sLcO7K7XQam+fV3CIGxa96of7Rnlao3vkpOxmHpyamhqkpaUhMTHx9gFlMiQmJiI1NbVZ+6ioqEBtbS0CAwNNlh84cAAhISGIi4vDE088gcLCwgb3UV1dDa1Wa/IiIrI21uXc8tA0H+w9EY4313bAS6/7419bgpByNJThhqzKqgGnoKAAer0eoaGhJstDQ0OhVqubtY8XXngBERERJiEpKSkJW7Zswf79+7FixQp8//33GD16NPR685X4y5cvh5+fn/EVGRnZ+pMiImoB1uXcolBKuG+UJyZO9sGQYUrI5RyWIuuy69L1119/Hdu2bcP27duhVCqNy5OTkzFu3Dj06dMHEyZMwK5du3DixAkcOHDA7H4WLVoEjUZjfGVnZ7fTGRARsS6HyBasGnCCgoIgl8uRm5trsjw3NxdhYWGNbrtq1Sq8/vrr2LNnD/r27dto25iYGAQFBSEjI8PseoVCAZVKZfIiImpPtpovB7C/qzlE7cGqAcfDwwMDBgzA/v37jcsMBgP279+PwYMHN7jdypUrsWzZMqSkpGDgwIFNHuf69esoLCxEeHi4RfpNRGQtrMshah9WH6KaN28e1q1bh82bN+PChQt44oknUF5ejpkzZwIApk+fjkWLFhnbr1ixAi+99BI2bNiAqKgoqNVqqNVqlJXdmiiqrKwMCxYswNGjR5GZmYn9+/dj/Pjx6Nq1K0aNGmXt0yEiajNnH7IisgdWDziTJk3CqlWrsHjxYsTHx+P06dNISUkxFh5nZWUhJyfH2P7f//43ampq8OCDDyI8PNz4WrVqFQBALpfjzJkzGDduHLp3745Zs2ZhwIABOHjwIBQKhbVPh4jIIqwVcuzhag7rcsgeWH0eHHvEeXCIyF40NVcO4Drz5RA1xW7mwSEiosY1VXwMsC6HqDUYcIiI7ADrcogsiwGHiMhOOHtdDlF7YsAhIrIjzQk5jjpkxeJjak8MOEREdqYtdTmAcz3igai1GHCIiOwU63KIWo8Bh4jIjrEuh6h1GHCIiOwc63KIWo4Bh4jIAdi6LodXc8jRMOAQETkQDlkRNQ8DDhGRg2nrkFVDGHLImTDgEBE5oKZCDuC4j3hgXQ5ZAgMOEZGDYl0OUcMYcIiIHBzrcojqY8AhInICvw45BoMOuTdP4dyprThz4j/IuPAlPNKyWZdDLkUSQghbd6K9abVa+Pn5ITH8MbjJPGzdHSIiiymO9caZE/9BZWUB/BAEBRQokQpRI6oQ1fUP6BI7AsVxDf/cK401mF3uE61p8tgjO19qdb+ba1rAEasfg+xXWakBQ3vfhEajgUqlarStWzv1iYiIrEwIgfNH1kHoqnAX/gAf6dbVF4MwIBMX8UvGXnh6BSEE/RoMOb5XZGZDTtlVvyZDTt2VHGsGna3FQxhyqFk4REVE5CQKq7NRqivEHRhoDDcAIJNkiJF6oQPCcP3qDwBYl0POjwGHiMhJFFRfgxLe8EeQ2fXh6ILS0huoqSkDwPlyyLkx4BAROQmDMEAOOSRJMrteDjkAQBhMh6CsOV+ONYMOQw41hgGHiMhJ+HmEoBxaVIgys+vzcRMKhQoeCp966xx1yIqTAlJDGHCIiJxEmGc3eEhKXMRJ6IXOZF2hyEUOshDReTAkyfyPfg5ZkTNhwCEichJyyQ39OoyGBkU4gm+QIc4iS1zGaXEYp3AIHRSR6FYe2+R+HPkRD0R1GHCIiJxIB0UnDA5JRoh3LG5K15CBdNS46XCH/3Dc2WEsZJK8Tc+xAuz7EQ8MOVSHE/1xoj8icmGVvTs2ur41kwIC9jExIOfLcT4tmeiPV3CIiFxYc55jxbocckQMOERELq4tQ1asyyF7xYBDRESsyyGnw4BDREQAboWc5gxZNYTz5ZA9YcAhIiITrMshZ8CAQ0RE9di6LodDVtRWDDhERGSWsw9ZkXNjwCEiokZZI+QAth+yYl2Oc2PAISKiJrEuhxwNAw4RETWLM9TlVGlrkHlEjauHc1BZUm1czpDjfPioBj6qgYioxRztEQ+1lToc+MdpXPgyE7XVt47v5iFDz7FdMHxBPDy83I1t+YgH+2V3j2pYvXo1oqKioFQqkZCQgOPHjzfa/tNPP0WPHj2gVCrRp08f7N6922S9EAKLFy9GeHg4PD09kZiYiMuXL1vzFIiI6FccachKX2vAjmcOImN3Jpb8NQCXj0Yh41gUli0IwC97r2H7kz9AV6M3tufVHOdg9YDz8ccfY968eViyZAlOnjyJfv36YdSoUcjLyzPb/siRI5g8eTJmzZqFU6dOYcKECZgwYQLS09ONbVauXIm3334ba9aswbFjx+Dt7Y1Ro0ahqqrK2qdDRET/r61DVg2xdMi5vP86sk7kY9fWcCx6NhAxXdwR3dkdz88NxDcfReD66UJc+ibbZBuGHMdn9SGqhIQE/O53v8O7774LADAYDIiMjMTTTz+NhQsX1ms/adIklJeXY9euXcZld911F+Lj47FmzRoIIRAREYH58+fjb3/7GwBAo9EgNDQUmzZtQnJycpN94hAVEZHlNDVcBbRuyKo5w1VA00NWnz/xPTpBiwOfdzK7flTyDfxc7oU//+e+eus4XGVf7GaIqqamBmlpaUhMTLx9QJkMiYmJSE1NNbtNamqqSXsAGDVqlLH91atXoVarTdr4+fkhISGhwX1WV1dDq9WavIiIyDLsfb6c8rwK9L9D0eD6/r0VKFNXmF3HW8kdl1UDTkFBAfR6PUJDQ02Wh4aGQq1Wm91GrVY32r7uz5bsc/ny5fDz8zO+IiMjW3U+RETUMHuty1EGKHHxSsMB62JGDbw6KBvdP0OO43GJ28QXLVoEjUZjfGVnZze9ERERtZg91uX0uL8L9h6owOn06nrrzl2qxld7y9Hj/qgm98+Q41isGnCCgoIgl8uRm5trsjw3NxdhYWFmtwkLC2u0fd2fLdmnQqGASqUyeRERkXVYc76cpoKOuflyeiR1RmicH0Ym38DGbRpUVBhQWWnAlk+0SHzoJoJiVOjVjIADMOQ4EqsGHA8PDwwYMAD79+83LjMYDNi/fz8GDx5sdpvBgwebtAeAvXv3GttHR0cjLCzMpI1Wq8WxY8ca3CcREbUva9XlAC2/muOudMOf1gxHYJ9QzJ6XB9/YK/CJuYKZz+bCNy4IE9cOh7unW5P7rMO6HMfQ/O9oK82bNw+PPPIIBg4ciEGDBuGtt95CeXk5Zs6cCQCYPn06OnbsiOXLlwMAnn32WQwbNgz/+Mc/MHbsWGzbtg0//vgj1q5dCwCQJAnPPfccXn31VXTr1g3R0dF46aWXEBERgQkTJlj7dIiIqAU80280epdVXcgxd5eV7xVZg3dYlV31a/Iuqz1ZccY7rDz9FRj31lCUXC/D9R/zISDQqX8wArr4NvdU6tlaPIR3WdkxqwecSZMmIT8/H4sXL4ZarUZ8fDxSUlKMRcJZWVmQyW4n9SFDhuDDDz/Eiy++iP/5n/9Bt27dsGPHDvTu3dvY5vnnn0d5eTnmzJmDkpISDB06FCkpKVAqGy8SIyKi9tdUyAFuBR1rhRzg9q3k/p184N/JpzndbhaGHPvFRzVwHhwionZhrflyAOs94qG5GHLah93Mg0NERFSnuXU59jD7cUuxLsf+MOAQEVG7ssdbyS2FIcd+MOAQEVG7s+at5E0xdyu5JTHk2AcGHCIisgl7f8RDWzDk2B4DDhER2RTrcsgaGHCIiMjmWJdDlsaAQ0REdsHeHvFgSQw57Y8Bh4iI7IY9PeLB0hhy2hcDDhER2R3W5VBbMeAQEZFdYl0OtQUDDhER2a22DFmxLse1MeAQEZHdc+YhK7IOBhwiInIIzjpkxboc62DAISIih+GsIQfg1RxLY8AhIiKHwrocag4GHCIickisy6HGMOAQEZHDctYhK9bltB0DDhEROTRnDTkAr+a0BQMOERE5PNbl0G8x4BARkdNgXY79q6w0YPu2cjw3uwBPTs/HP1/X4HqWzuLHYcAhIiKn4qxDVs5Ql3M1oxZ/GpGLpQuLgXIJIV5u2P5hOcYNU+OTrWUWPRYDDhEROZ22hpy2DllZk6OGnJpqgbmPFCDAR46Lh7tg/2ed8N8NEcg+GY0nZvjhtRdLcOxQlcWOx4BDREROqbl1Oda4msO6nPr27q7Ejet6fPxeGLpGexiXe3nJ8M9Xg3FnXwW2rrPcVRwGHCIicmrOPGTlSI58X4WB8Qr0ilPUWydJEqb/WYUjP1TBYBAWOR4DDhEROT1nDjmOEnRqawW8vRp+L728JBgMgF5vmeMx4BARkUuw9a3k1uQIIeeOvh5I/bESBYXmE8yX35Qjrpc73N0lixyPAYeIiFwK63JsY/xDXpDLJTy5MBe1tabDUJ/sLMWuveWYNN3HYsdjwCEiIpfjzENW9so/QI6/vxWInd+Uo8fQTLyyqhBvrytGUvJ1TH5MjaRxnpgwyctix5OEEJap5nEgWq0Wfn5+SAx/DG4yj6Y3ICKyIoPQI78qE5X6UrjLlAhRRsNdVr8QkyyvsnfHJtsUx5n/PVEaa2hwG59oTZP7Hdn5UpNtWmtawBGr7butLp6rwfvry/DD3krU1ABxvdzx52neGDPBCzJZw8NT167W4od9VfjHqxpoNBqoVKpGj8OAw4BDRDakrszA+ZIDqDFUQgY5DNBDDjfEqAYixmcgJMky9QjUuKaCTkMhB2hb0LFmyAHsO+g0l1ZjwJIFRfjumyrI5beKkJsTcDhERURkI3mVV3G66Gv4GwJxF/6A+6QHMBRj0RHRuKw9il/KfrR1F10G63Lsk04n8MzMApw+VoP1b4Ui+2R0s7dlwCEisgEhBH7WHkEgQtAHd8FHuvWLUCl5orvUD53RDb9of0StodrGPXUdrMuxP9/vrcLptBps3xiBGZNU8PRs/hVNBhwiIhso0xWhTFeEzuhmdhiqC7pDDx3yqn6xQe9cV1tvJW8IQ07rfP1FBX7XX4F77vJs8bYMOERENlBjqAQAeMH8bbEKyRNyuKFGb7ln81DztXbIyp7ny3GkSQHrlBTr0TWqdbWyDDhERDbgKfcFAGhRbHZ9udBCDx083Xzbs1v0K7YasmJdzm1hHd3w409VaM39UFYNOEVFRZgyZQpUKhX8/f0xa9YslJU1/CCtoqIiPP3004iLi4Onpyc6d+6MZ555BhqNaRW6JEn1Xtu2bbPmqRARWZSXmx8CPToiE5egFzqTdUII/ILz8JA8EaJsflElWR7rcmzrgUneuPxLLT74b2mLt7VqwJkyZQrOnTuHvXv3YteuXfjhhx8wZ86cBtvfvHkTN2/exKpVq5Ceno5NmzYhJSUFs2bNqtd248aNyMnJMb4mTJhgxTMhIrK8Hn73oFKqwAl8B7XIQoUoQ6FQ4xQOIRfX0dP/Xsgkua276fKs+YiHprh6yLlzkAfGPuCFvzyXi3mL83E6vflF91abB+fChQvo1asXTpw4gYEDBwIAUlJSMGbMGFy/fh0RERHN2s+nn36KqVOnory8HG5ubrc6LUnYvn17q0MN58EhInuhrcnHBc1BFNfc/gXq4xaI7qrBCPGMsWHPyBxbTQzoqpMCArduFV/3jhYfby5HSfGt99CmE/1t2LAB8+fPR3Hx7fFlnU4HpVKJTz/9FA888ECz9vOf//wHixYtQn5+vnGZJEmIiIhAdXU1YmJi8Pjjj2PmzJkNTohVXV2N6urbqU+r1SIyMpIBh4jsRoVOg0q9Fh4yT/i4deAEf3aMsx/bRnWVQNqxKjw5vdC2E/2p1WqEhISYLHNzc0NgYCDUanWz9lFQUIBly5bVG9ZaunQpPvnkE+zduxcTJ07Ek08+iXfeeafB/Sxfvhx+fn7GV2RkZMtPiIjIirzc/NBBEQlf9yCGGzvHW8ltQ6GU0PfO5j/CpMUBZ+HChWaLfH/9unjxYkt3W49Wq8XYsWPRq1cvvPzyyybrXnrpJdx9993o378/XnjhBTz//PN44403GtzXokWLoNFojK/s7Ow294+IiFwb63Lsm1tLN5g/fz5mzJjRaJuYmBiEhYUhLy/PZLlOp0NRURHCwsIa3b60tBRJSUnw9fXF9u3b4e7u3mj7hIQELFu2DNXV1VAo6qc7hUJhdjkREVFbeKbfaHTIqi7kmBuy8r0iMztkVRdyGhuy2pMVZ7Xhqq3FQ+x+uKo5WhxwgoODERwc3GS7wYMHo6SkBGlpaRgwYAAA4Ntvv4XBYEBCQkKD22m1WowaNQoKhQI7d+6EUqls8linT59GQEAAQwwREbW7pkIOcCvotCTkALeCDkNO61mtBqdnz55ISkrC7Nmzcfz4cRw+fBhz585FcnKy8Q6qGzduoEePHjh+/DiAW+Fm5MiRKC8vx/r166HVaqFWq6FWq6HX6wEAX375Jf7zn/8gPT0dGRkZ+Pe//43XXnsNTz/9tLVOhYiIqFG2qsux5qSAjjjz8a9ZdR6cDz74AD169MCIESMwZswYDB06FGvXrjWur62txaVLl1BRUQEAOHnyJI4dO4azZ8+ia9euCA8PN77q6mbc3d2xevVqDB48GPHx8Xjvvffw5ptvYsmSJdY8FSIioiaxLsd+WO02cXvGeXCIiMiaOF+OdZSVGjC0903b3iZORETkqmz5HCtrcbQrObyCwys4RERkRU1dzbGXSQGrtDU4+/kvuPxNFqq1NVBF+qD3n2LRbURHyOS3Q5ctr+TwCg4REZGdcIS6nJLsMnww6Rsc/ddZDIvT4dEHFIgQpdj1fCq+nH8Y+trbYctRruS0+DZxIiIiahl7ni9HCIGv/nYYHTz1OH2kCyI73p577qt95fjTX3JwdN153P1kb+PyupBjD3U5DeEVHCIionZgy1vJG3M9LR/qSxr8+/Vgk3ADAGMTvfH4dBXOfpYBXY2+3rb2fDWHAYeIiKgd2WLIqrH5cm6cKoC/vxz3DfU0u/7Pf/RFeVENijNLza6315DDgENERNTOWhtygIav5rSlLkeIWy9zDHWjY408A9YeQw4DDhERkQ00J+S0dMiq7Kpfi4esIgeGQKPRY+/3FWbbf/xFKXyDFAiM8m10v/YWchhwiIiIbMQe6nIi4jsg4o4APLEwH1cybx9LCIHPdpVi3fsa9J3UDXJ3eaP7BOzr8Q6cB4fz4BARkR1o7Xw5QMNz5jR3vhztzXL897ED0Nwsx5hEb0R1csehE1U4daYKcSM7Ycxrd0Hm1rJrIta4w6ol8+Aw4DDgEBGRnbBlyKkpr8W5LzN/NdGfL3r/KQYxQ8MhyRopwGmEpUMOA04TGHCIiMie2WL2Y2s9x8qSIYczGRMRETkwW9flWJKtanIYcIiIiOyQvc2X0xa2CDkMOERERHbK3ubLaYv2vsOKAYeIiMiONXUreXvNl2Mp7RVyGHCIiIgcgK3qchx1yIoBh4iIyEHYoi4HsN6QlTUx4BARETmQ5gxZNcSVQg4DDhERkQNylroca4UcBhwiIiIH5Sy3klvjDisGHCIiIgfmbLeSWwoDDhERkYNjXU59DDhEREROgnU5tzHgEBERORFnqstpCwYcIiIiJ+MsdTltKT5mwCEiInJCrl6XIwkhhMV7Yue0Wi38/PyQGP4Y3GQeNutHtb4C2eXpUFdmQC9q4eMWiEjv3ghWRkGSJJv1qyVqDVW4Xn4eORU/o9ZQDS83P0T63IFQZSwkifmZiMgeVPbu2Oj64jjzvwtLYw0NbuMTrWl0nyM7X2q6Yy30gNshDO19ExqNBiqVqtG2/A1kI9rafBzO/QC/lP4IX50PQvRhqKrW4GTRLqQX74cj5M4KnQaHcz/Cz9pUeOqUCDGEQ19TidNFKThVtBsGobd1F4mICM5Tl7OtJKHZbd0semRqFoPQ42TBV1AIJe7CH+AhKYzrcsQ1nKs8AZVHCLr49LVhLxsnhMCpwt2QGYC7kQSl5GVcly9u4kzVUVwp/RHdVM3/MBIRkfV4pt9o9EpOwKWaBq/k+F6Rmb2aU3bVr8krOXuy4qxyNacpvIJjA3lVV1FlKEUvDDQJNwAQLnVBKCJxrey0XV/FKa65iVJdAeLQ3yTcAECwFIFOiEF22RlexSEisiPOUnzcHAw4NlBcfRNe8IWv5G92fSg6oUKvQbWhon071gLF1TfhDg8EIsTs+lB0Qo2oQrmuuJ17RkREjWlO8bG15stpz6DDgGOHBG5dubHrMmMJaOz6kmh0LRER2Zot6nKA9ruaw4BjA4GKjqhAKbTC/NWNXGTDWx4AD5mX2fX2INCjI3SoQRFyza7PRTY8ZJ7wdgto554REVFzOfOQFQOODQQro+EpV+E8fkS1qDQuF0LghvgFebiBLr7xdn2ruL9HOFTuIbiIU6gQZcblQgjkiuu4gavo4t0PMkluw14SEVFTnDXkcB4cG82DU1pbiB8LdqDWUIUgREABJYqQh3JoEenVG738h9t1wAGASp0Wxwu2o0pfiiCEQwkvlKAQpShGmLIr+gaOgoxz4RAROQxbzJcDNH/OnOqyWrw7dLvt58EpKirClClToFKp4O/vj1mzZqGsrKzRbYYPv/WL/devxx9/3KRNVlYWxo4dCy8vL4SEhGDBggXQ6XTWPBWL83XvgLtDHkY31WBUu9eiWF4IH2UIftdhgkOEGwDwdFPh7pDJ6OF3D2rdBYrlhVAq/XFn4P3oF5jEcGOn9EKH6+UXcL7ke1zUHEJh9XW7vmOPiNqPM9XlWHUenClTpiAnJwd79+5FbW0tZs6ciTlz5uDDDz9sdLvZs2dj6dKlxq+9vG7Xouj1eowdOxZhYWE4cuQIcnJyMH36dLi7u+O1116z2rlYg4fcE9G+dyLa905bd6XV3GQe6OLTD118+tm6K9QMBVVZ+KnoG9SKKnhDBR10yCw7BT/3EPTvcD+Ucm9bd5GIbMxZ5sux2hDVhQsX0KtXL5w4cQIDBw4EAKSkpGDMmDG4fv06IiIizG43fPhwxMfH46233jK7/uuvv8b999+PmzdvIjQ0FACwZs0avPDCC8jPz4eHR/03vbq6GtXV1cavtVotIiMjbf6oBqL2VFpbiNS8jxGAIMShP7wkHwghUIQ8nMeP8HDzwuCQZF55IyIATQ9XAS0fsmrrcJVdDFGlpqbC39/fGG4AIDExETKZDMeOHWt02w8++ABBQUHo3bs3Fi1ahIqK2/PBpKamok+fPsZwAwCjRo2CVqvFuXPnzO5v+fLl8PPzM74iIyPbeHZEjudq6Ul4QIG+GAIvyQcAIEkSOkih6IvBKNUVIr/qqo17SUT2oqn5coDGh6zMac/5cqwWcNRqNUJCTCeBc3NzQ2BgINRqdYPbPfzww3j//ffx3XffYdGiRdi6dSumTp1qst9fhxsAxq8b2u+iRYug0WiMr+zs7NaeFpHDyq26gghEQW7mzjY/KRC+8Ie6MsMGPSMie+aodTktrsFZuHAhVqxY0WibCxcutLpDc+bMMf69T58+CA8Px4gRI3DlyhXExsa2ap8KhQIKhaLphkROSggBvaiFB5QNtvGAEnpR2469IiJH4Yh1OS0OOPPnz8eMGTMabRMTE4OwsDDk5eWZLNfpdCgqKkJYWFizj5eQcOthjRkZGYiNjUVYWBiOHz9u0iY399Zkcy3ZL5ErkSQJ3vIAFOvz0Akx9dbrhR4aFCHSrbcNekdEjqA5IQcwX5fTWMgBGq/NaW3IaXHACQ4ORnBwcJPtBg8ejJKSEqSlpWHAgAEAgG+//RYGg8EYWprj9OnTAIDw8HDjfv/+978jLy/POAS2d+9eqFQq9OrVq4VnQ+Q6In1645LmEIpFPgKk2/+GhRC4ivPQoQaR3gw4RNSwuuGq1lzNqRuuas3VnLrhqmGB6c3uq9VqcHr27ImkpCTMnj0bx48fx+HDhzF37lwkJycb76C6ceMGevToYbwic+XKFSxbtgxpaWnIzMzEzp07MX36dNx7773o27cvAGDkyJHo1asXpk2bhp9++gnffPMNXnzxRTz11FMchiJqRGfvPgjwiMApHMQFcRIFIgdqkYVTOIRMXEJ31RB4uTU9Lk5EZKvZj7+93q3JNnWsej/oBx98gB49emDEiBEYM2YMhg4dirVr1xrX19bW4tKlS8a7pDw8PLBv3z6MHDkSPXr0wPz58zFx4kR8+eWXxm3kcjl27doFuVyOwYMHY+rUqZg+fbrJvDlEVJ9MkmNA0DjE+A5EgUyN0ziMdByHzl0gPnA0YnwH2LqLRORAbBVymouPauA8OOSCDMKAGkMFZJDDQ+5p6+4QkQOzxnw5gPm6HH1FNc4nr7T9oxqIyD7JJBmUch+GGyJqs7bOl2OtqzkMOERERNRm9jZkxYBDREREFmFPIYcBh4iIiCymOSHHGo94+C0GHCIiIrIoa9XllF9rvLD41xhwiIiIyCqsMWTVXAw4REREZDW2CjkMOERERGRV1qjLaQoDDhEREVldUyEHaF1dTkMYcIiIiKhdtKX4GAB8fml+bGHAISIionbVlpDTXAw4RERE1O7aUpfTHAw4REREZBNtHbJqDAMOERER2ZQ1Qg4DDhEREdlcc0KOf0bzg45bWztEREQtZxAG5Fdloqj61g/1QEUEgpXRkEn8fye5Ls/0G6js3dEi+2LAISJqZ2W1RThZuAsVeg084QMAuFZ+Gp5yX9zZ4X74ugfZuIdEtmOpkMOAQ0TUjmoMVThRsB1uBncMwn1QSYEAAK0oxnl9Gk7k78DdoQ9DIfeycU+JbKduuKotQYfXQomI2tGN8vOoNVThTgw1hhsAUEkBuBNDoRM1uF5xzoY9JLIfzZn9uCEMOERE7UhdmYFgREAhedZb5yEpEYKOUFdctkHPiOxTa0MOAw4RUTvSG2rgAWWD6xVQQidq27FHRPavOfPl/BYDDhFRO/J2D0Qx8iGEMLu+GPnwcQs0u47I1Xmez2l2WwYconagM9SiXFeCan2FrbtCNhbp3Rtl0OAmMuutyxFZ0KIYkT69279jRE6Gd1ERWVG1vgKXtam4WfEzDNABAAI8ItDVdxA6KCNt3DuyhQ6KSHTyugMXKtJQJHIRikgAEvKQDTWyEeHZA8GKKFt3k8jhMeAQWUm1vhxH8z+FTl+DKHSHP4JQjUpcr7mCE4VfID5wFMI8u9m6m9TOJEnCHf6/h8o9GJllp5GrTwUAeMn90NPnXnT27gtJkmzcSyLHx4BDZCU/a1Oh19dgEO6Dp+RtXB4mOiMdx5Be/C2CFFFwk7nbsJdkC5IkobNPH0R690a1oRwAoJB5M9gQWRBrcIisQGeoQU7Fz4hEV5NwA9z65RaL3tCJGuRWZtioh2QPJEmCUu4DpdyH4YbIwhhwiKygSl8GA/QIQLDZ9V6SD5TwRrmuuJ17RkTkGhhwiKxALt0adqpGpdn1eqFHLarhJvNoz24REbkMBhwiK/B084Wfeyiy8YvZ+U7UuAY9dAj17GqD3hEROT8WGRNZSVfVIKQVfonz+BGxojeUkicMQo8cZOESfkK4Z3d4u/nbupsurcZQhRvl51FUfWuG1ABFBDp59YKHvP5jFIjIsTDgEFlJsDIKfQIScb7ke6hFFjyFD2pQBR1qEebZDb0DRti6iy6tsPo6ThV+Bb3QIRDBACRkVB/FldIT6B84BkHKzrbuIhG1AQMOkRV19OqJUGUsblb+jApdCdwkD4R5doWPO6fit6UqfRlOFu6CnwhAbwyCh3Tr2VA1ohrnxAmcLPwKQ0Mfhpebn417SkStxYBDZGVuMg909ubU+/Ykq/wsIIC+GAw36fY8RB6SAn3FXTiI3cguT0ec39027CURtQWLjInI5RRUXkMIIkzCTR255IZQdERe5VUb9IyILMWqAaeoqAhTpkyBSqWCv78/Zs2ahbKysgbbZ2ZmQpIks69PP/3U2M7c+m3btlnzVIjIiRigh7yRC9hyuEHA0I49IiJLs+oQ1ZQpU5CTk4O9e/eitrYWM2fOxJw5c/Dhhx+abR8ZGYmcHNNHoa9duxZvvPEGRo8ebbJ848aNSEpKMn7t7+9v8f4TkXNSuYegUJcNIUS9GYSFEChADlQe4TbqHRFZgtUCzoULF5CSkoITJ05g4MCBAIB33nkHY8aMwapVqxAREVFvG7lcjrCwMJNl27dvx0MPPQQfHx+T5f7+/vXaEhE1RxefvrhZeRG/4BxixB3GkCOEwFVcRAXK0Me7r417SURtYbUhqtTUVPj7+xvDDQAkJiZCJpPh2LFjzdpHWloaTp8+jVmzZtVb99RTTyEoKAiDBg3Chg0bzE6mVqe6uhpardbkRUSuy88jFN1VQ3AVF3Ec3yJTXMI18TNO4Dv8gnPo6puAAEX9/4QRkeOw2hUctVqNkJAQ04O5uSEwMBBqtbpZ+1i/fj169uyJIUOGmCxfunQp7rvvPnh5eWHPnj148sknUVZWhmeeecbsfpYvX45XXnmldSdCRE4pxncAVO7ByCw7jV+qzwMAAhQdMcDnbgQro2zbOSJqsxYHnIULF2LFihWNtrlw4UKrO1SnsrISH374IV566aV66369rH///igvL8cbb7zRYMBZtGgR5s2bZ/xaq9UiMjKyzX0kIscWpOzMCf2InFSLA878+fMxY8aMRtvExMQgLCwMeXl5Jst1Oh2KioqaVTvz2WefoaKiAtOnT2+ybUJCApYtW4bq6mooFIp66xUKhdnlRERE5JxaHHCCg4MRHBzcZLvBgwejpKQEaWlpGDBgAADg22+/hcFgQEJCQpPbr1+/HuPGjWvWsU6fPo2AgACGGCIiIgJgxRqcnj17IikpCbNnz8aaNWtQW1uLuXPnIjk52XgH1Y0bNzBixAhs2bIFgwYNMm6bkZGBH374Abt376633y+//BK5ubm46667oFQqsXfvXrz22mv429/+Zq1TISIiIgdj1XlwPvjgA8ydOxcjRoyATCbDxIkT8fbbbxvX19bW4tKlS6ioqDDZbsOGDejUqRNGjhxZb5/u7u5YvXo1/vrXv0IIga5du+LNN9/E7NmzrXkqRERE5EAk0dj91U5Kq9XCz88PieGPwU3mYevuEBERUTPoDDXYl/MeNBoNVCpVo235LCoiIiJyOgw4RERE5HSsWoNDRESWZRAGVOlLIUGCUu5b71laRHQLAw4RkQMwCD2ulp1EVtlZVBvKAQBecn9E+cYj0qs3gw7RbzDgEBHZOYMw4FThbhRUZyECUQhBBAwwQK3PwvmSAyirLUIv/2G27iaRXWHAISKyczmVPyO/OhPxuBtBUrhxeTAi4C8ycKn8NMI9uyNAEd7IXohcC4uMiYjsXHZZOgIRahJu6nRCLDzhjezydBv0jMh+MeAQEdm5Cl0xAhBkdp0kSfBHECp0Je3bKSI7x4BDRGTn5DIPVKOqwfXVqIRc5t6OPSKyfww4RER2LsyzK9TIQq2oqbeuXGhRhDyEeXazQc+I7BcDDhGRnevi0w+SJMMpHIRWFAMAhBAoEnk4jcPwkvsh3LO7jXtJZF94FxURkZ1Tyn0wMGgCThXuxnHDfiiFNwQMqEYlfN2CcGeHsXDjEBWRCQYcK9DU5OFa2U/Q1KgBSAhSdkZn7z7wdg+wddeIyEH5eYTg3rDpyK+6ipK6ny2KSAQqOnGSPyIzGHAsLLPsNC5qDkIJbwQjHAIG3Cy/iKzys4gPTEKoZ6ytu0hEDkomyRDqGcufI0TNwIBjQcXVObioOYgu6I6u6GP8X1U30Q/ncAI/FX2De0KnwdPN18Y9JSIicm4sMraga2U/wQu+JuEGAOSSHL0wEBIkZFdwMi4iIiJrY8CxoKKaGwhFR7Pj4W6SG4IQjqKqGzboGRERkWthwLEgCYBotIUAawGJiIisjwHHggIUHZGH6xCifszRiVoUQI1ARScb9IyIiMi1MOBYUJRPPCpQhp/xEwzCYFyuFzqcwwkICER697ZhD4mIiFwD76KyIH+PMPTyG4bzmu+RhxsIFhEwQI883IQBesQHjoZS7mPrbhIRETk9BhwL6+zTF/6KcGSVnUVJ9U1IkgydlHegs3cfeLn52bp7RERELoEBxwpU7sHoHXCfrbtBRETksliDQ0RERE6HAYeIiIicDgMOEREROR0GHCIiMsvcnF5EjoJFxkREZGQQemSXn0N2+VmU6YoghxtCPGMQ7XMnVB7Btu4eUbMx4BAREYBb4eZk4S4UVmcjGBHohDtRg2rkVGYitTID/TuMQYgy2tbdJGoWBhwiIgIAZJadRmH1dcRjKDpIocblUaI7zuAozhTtwfCwmXCTediwl0TNwxocIiKCEAJZZWcRhs4m4QYAZJIcPdAfelGLm5WXbNRDopZhwCEiItQaqlBlKEUQwsyuV0pe8IE/tDV57dwzotbhEBU1ySD0KKi6hkp9KdxlSoQoo3mJmsjJyCQ5AEAPndn1QgjoUWtsR2TvGHCoUerKDFwo+R7VhgrIIIcBesgld8T6/g7RPndCkiRbd5GILMBN5oEAj3DcrMlEuOhS79+2BoWoQBmCWWRMDsKlh6hyKjNgEHpbd8Nu5Vdl4nTR11AZ/HEX/oD7pAcwFGPQUUThZ+0R/FKWZusuEpEFRfsMQAkKcBlnoBe3r+RoRTHScRw+bh0QpOhswx4SNZ/VAs7f//53DBkyBF5eXvD392/WNkIILF68GOHh4fD09ERiYiIuX75s0qaoqAhTpkyBSqWCv78/Zs2ahbKyslb18VzJfnyv3oTi6pxWbe/MhBC4pDmCAASjLwbDR7r1JHSl5IXuUj90Rjf8oj0BnaHGxj0lIksJ8YxGD797kYUMHMRXOCUO4bjYj+PYD7mbAgM6/JFXbclhWC3g1NTU4M9//jOeeOKJZm+zcuVKvP3221izZg2OHTsGb29vjBo1ClVVVcY2U6ZMwblz57B3717s2rULP/zwA+bMmdOqPg7EffA0eCGtcCcqdJpW7cNZlemKUKYrRBd0N/sDrTO6Qw8dcqt+sUHviMhaonz64d7Qaejs0w9ypSe8PYMRH5iEu0Mmw9PN19bdI2o2q9XgvPLKKwCATZs2Nau9EAJvvfUWXnzxRYwfPx4AsGXLFoSGhmLHjh1ITk7GhQsXkJKSghMnTmDgwIEAgHfeeQdjxozBqlWrEBER0aI++sAX8bgbh0UKrpX9hJ7+97Zoe2dWY6gEAHjBx+x6peQJuXBDjb6yPbtFRO3Ay80P3f0G27obRG1iN0XGV69ehVqtRmJionGZn58fEhISkJqaiuTkZKSmpsLf398YbgAgMTERMpkMx44dwwMPPGB239XV1aiurjZ+rdHculqjQy3c4I5QdEROxc/oprrLSmfneNwlBQCgGAXwEMp668tRCj108JArOUxFRETtou73TXOek2Y3AUetVgMAQkNNJ5gKDQ01rlOr1QgJCTFZ7+bmhsDAQGMbc5YvX268ovRrh7D79hcC2JfzXmu777QuIA0X0HAx8dnifTiLfe3YIyIicnWlpaXw8/NrtE2LAs7ChQuxYsWKRttcuHABPXr0aMlurW7RokWYN2+e8WuDwYCioiJ06NDB4QrmtFotIiMjkZ2dDZVKZevutDtXP3+A74Grnz/A9wDge+Cq5y+EQGlpabNKUloUcObPn48ZM2Y02iYmJqYluzQKC7s1e2Zubi7Cw8ONy3NzcxEfH29sk5dnOoumTqdDUVGRcXtzFAoFFAqFybLm3tllr1QqlUt9qH/L1c8f4Hvg6ucP8D0A+B644vk3deWmTosCTnBwMIKDg1vVoaZER0cjLCwM+/fvNwYarVaLY8eOGe/EGjx4MEpKSpCWloYBAwYAAL799lsYDAYkJCRYpV9ERETkeKx2m3hWVhZOnz6NrKws6PV6nD59GqdPnzaZs6ZHjx7Yvn07AECSJDz33HN49dVXsXPnTpw9exbTp09HREQEJkyYAADo2bMnkpKSMHv2bBw/fhyHDx/G3LlzkZyc3OI7qIiIiMh5Wa3IePHixdi8ebPx6/79+wMAvvvuOwwfPhwAcOnSJeMdTQDw/PPPo7y8HHPmzEFJSQmGDh2KlJQUKJW37+L54IMPMHfuXIwYMQIymQwTJ07E22+/ba3TsDsKhQJLliypN+TmKlz9/AG+B65+/gDfA4Dvgauff3NIojn3WhERERE5EJd+FhURERE5JwYcIiIicjoMOEREROR0GHCIiIjI6TDgEBERkdNhwLEzRUVFmDJlClQqFfz9/TFr1iyTuYN+KzMzE5IkmX19+umnxnbm1m/btq09TqnFWvoeAMDw4cPrnd/jjz9u0iYrKwtjx46Fl5cXQkJCsGDBAuh0OmueSqu09PyLiorw9NNPIy4uDp6enujcuTOeeeYZkykYAPv+DKxevRpRUVFQKpVISEjA8ePHG23/6aefokePHlAqlejTpw92795tsl4IgcWLFyM8PByenp5ITEzE5cuXrXkKbdKS81+3bh3uueceBAQEICAgAImJifXaz5gxo973Oikpydqn0SYteQ82bdpU7/x+PZ0I4HifAaBl74G5n3mSJGHs2LHGNo74ObAoQXYlKSlJ9OvXTxw9elQcPHhQdO3aVUyePLnB9jqdTuTk5Ji8XnnlFeHj4yNKS0uN7QCIjRs3mrSrrKxsj1NqsZa+B0IIMWzYMDF79myT89NoNMb1Op1O9O7dWyQmJopTp06J3bt3i6CgILFo0SJrn06LtfT8z549K/70pz+JnTt3ioyMDLF//37RrVs3MXHiRJN29voZ2LZtm/Dw8BAbNmwQ586dE7Nnzxb+/v4iNzfXbPvDhw8LuVwuVq5cKc6fPy9efPFF4e7uLs6ePWts8/rrrws/Pz+xY8cO8dNPP4lx48aJ6Ohouzjf32rp+T/88MNi9erV4tSpU+LChQtixowZws/PT1y/ft3Y5pFHHhFJSUkm3+uioqL2OqUWa+l7sHHjRqFSqUzOT61Wm7RxpM+AEC1/DwoLC03OPz09XcjlcrFx40ZjG0f7HFgaA44dOX/+vAAgTpw4YVz29ddfC0mSxI0bN5q9n/j4ePGXv/zFZBkAsX37dkt11Wpa+x4MGzZMPPvssw2u3717t5DJZCY/BP/9738LlUolqqurLdJ3S7DUZ+CTTz4RHh4eora21rjMXj8DgwYNEk899ZTxa71eLyIiIsTy5cvNtn/ooYfE2LFjTZYlJCSIxx57TAghhMFgEGFhYeKNN94wri8pKREKhUJ89NFHVjiDtmnp+f+WTqcTvr6+YvPmzcZljzzyiBg/frylu2o1LX0PNm7cKPz8/Brcn6N9BoRo++fgf//3f4Wvr68oKyszLnO0z4GlcYjKjqSmpsLf3x8DBw40LktMTIRMJsOxY8eatY+0tDScPn0as2bNqrfuqaeeQlBQEAYNGoQNGzZA2OEcj215Dz744AMEBQWhd+/eWLRoESoqKkz226dPH4SGhhqXjRo1ClqtFufOnbP8ibSSJT4DAKDRaKBSqeDmZjpZub19BmpqapCWlobExETjMplMhsTERKSmpprdJjU11aQ9cOt7Wdf+6tWrUKvVJm38/PyQkJDQ4D5tpTXn/1sVFRWora1FYGCgyfIDBw4gJCQEcXFxeOKJJ1BYWGjRvltKa9+DsrIydOnSBZGRkRg/frzJv2NH+gwAlvkcrF+/HsnJyfD29jZZ7iifA2uw2qMaqOXUajVCQkJMlrm5uSEwMBBqtbpZ+1i/fj169uyJIUOGmCxfunQp7rvvPnh5eWHPnj148sknUVZWhmeeecZi/beE1r4HDz/8MLp06YKIiAicOXMGL7zwAi5duoTPP//cuN9fhxsAxq+b+962B0t8BgoKCrBs2TLMmTPHZLk9fgYKCgqg1+vNfm8uXrxodpuGvpd170/dn421sRetOf/feuGFFxAREWHyyzEpKQl/+tOfEB0djStXruB//ud/MHr0aKSmpkIul1v0HNqqNe9BXFwcNmzYgL59+0Kj0WDVqlUYMmQIzp07h06dOjnUZwBo++fg+PHjSE9Px/r1602WO9LnwBoYcNrBwoULsWLFikbbXLhwoc3HqaysxIcffoiXXnqp3rpfL+vfvz/Ky8vxxhtvtNsvN2u/B7/+Zd6nTx+Eh4djxIgRuHLlCmJjY1u9X0tpr8+AVqvF2LFj0atXL7z88ssm62z9GSDLe/3117Ft2zYcOHDApMg2OTnZ+Pc+ffqgb9++iI2NxYEDBzBixAhbdNWiBg8ejMGDBxu/HjJkCHr27In33nsPy5Yts2HPbGP9+vXo06cPBg0aZLLc2T8HTWHAaQfz58/HjBkzGm0TExODsLAw5OXlmSzX6XQoKipCWFhYk8f57LPPUFFRgenTpzfZNiEhAcuWLUN1dXW7PKytvd6DOgkJCQCAjIwMxMbGIiwsrN4dCbm5uQDQov22Vnucf2lpKZKSkuDr64vt27fD3d290fbt/RkwJygoCHK53Pi9qJObm9vg+YaFhTXavu7P3NxchIeHm7SJj4+3YO/brjXnX2fVqlV4/fXXsW/fPvTt27fRtjExMQgKCkJGRobd/WJry3tQx93dHf3790dGRgYAx/oMAG17D8rLy7Ft2zYsXbq0yePY8+fAKmxdBES31RWY/vjjj8Zl33zzTbMLTIcNG1bvzpmGvPrqqyIgIKDVfbWWtr4HdQ4dOiQAiJ9++kkIcbvI+Nd3JLz33ntCpVKJqqoqy51AG7X2/DUajbjrrrvEsGHDRHl5ebOOZS+fgUGDBom5c+cav9br9aJjx46NFhnff//9JssGDx5cr8h41apVxvUajcZuC0xbev5CCLFixQqhUqlEampqs46RnZ0tJEkSX3zxRZv7aw2teQ9+TafTibi4OPHXv/5VCOF4nwEhWv8ebNy4USgUClFQUNDkMez9c2BpDDh2JikpSfTv318cO3ZMHDp0SHTr1s3kFuHr16+LuLg4cezYMZPtLl++LCRJEl9//XW9fe7cuVOsW7dOnD17Vly+fFn861//El5eXmLx4sVWP5/WaOl7kJGRIZYuXSp+/PFHcfXqVfHFF1+ImJgYce+99xq3qbtNfOTIkeL06dMiJSVFBAcH2+1t4i05f41GIxISEkSfPn1ERkaGyS2hOp1OCGHfn4Ft27YJhUIhNm3aJM6fPy/mzJkj/P39jXe8TZs2TSxcuNDY/vDhw8LNzU2sWrVKXLhwQSxZssTsbeL+/v7iiy++EGfOnBHjx4+321uEW3r+r7/+uvDw8BCfffaZyfe6blqI0tJS8be//U2kpqaKq1evin379ok777xTdOvWza7C/K+19D145ZVXxDfffCOuXLki0tLSRHJyslAqleLcuXPGNo70GRCi5e9BnaFDh4pJkybVW+6InwNLY8CxM4WFhWLy5MnCx8dHqFQqMXPmTJP5bK5evSoAiO+++85ku0WLFonIyEih1+vr7fPrr78W8fHxwsfHR3h7e4t+/fqJNWvWmG1rD1r6HmRlZYl7771XBAYGCoVCIbp27SoWLFhgMg+OEEJkZmaK0aNHC09PTxEUFCTmz59vchu1vWjp+X/33XcCgNnX1atXhRD2/xl45513ROfOnYWHh4cYNGiQOHr0qHHdsGHDxCOPPGLS/pNPPhHdu3cXHh4e4o477hBfffWVyXqDwSBeeuklERoaKhQKhRgxYoS4dOlSe5xKq7Tk/Lt06WL2e71kyRIhhBAVFRVi5MiRIjg4WLi7u4suXbqI2bNn15snxt605D147rnnjG1DQ0PFmDFjxMmTJ03252ifASFa/u/g4sWLAoDYs2dPvX056ufAkiQh7PBeYSIiIqI24Dw4RERE5HQYcIiIiMjpMOAQERGR02HAISIiIqfDgENEREROhwGHiIiInA4DDhERETkdBhwiIiJyOgw4RERE5HQYcIiIiMjpMOAQERGR0/k/5JMMlqbos8YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGzCAYAAAAi6m1wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABWDElEQVR4nO3deXxU5d3//9eZyb4nZIewI4usggTQikoqQVq11SpWRShCa6XaYlvl/iluvYsLt7dfrXddbnErllbrVqsIoliXCAii7BIMexJIQjLZl5nr94c3o2MmIQmZzGTyfj4e89Cc6zpnPmcYydtzrnNdljHGICIiIhJEbP4uQERERKSzKeCIiIhI0FHAERERkaCjgCMiIiJBRwFHREREgo4CjoiIiAQdBRwREREJOgo4IiIiEnQUcERERCToKOCISJdZt24dlmWxbt0697Y5c+bQv39/v9X0Xd5qFJHuRwFHRLqlP/7xj7z66qv+LgOA//mf/8GyLLKzs1vsY1kWlmXxX//1X83annnmGSzL4tNPP3Vvu/POO937WJZFVFQUffv25Yc//CFPP/009fX1PjkXkWChgCMifvXkk0+ye/fudu8XSAFnxYoV9O/fnw0bNpCfn99q3wceeICampo2H/vPf/4zzz//PI888gjXXXcdZWVl/OxnP2PixIkcPHjwVEsXCVoKOCJyUi6Xi7q6Op8cOzQ0lPDwcJ8cuysUFBTw8ccf8+CDD5KSksKKFSta7Dt27FiKi4t57LHH2nz8yy67jKuvvpp58+axZMkSPvroI/7yl7+wbds2fvKTn3TGKYgEJQUckR7ixC2PXbt2cfnllxMXF0evXr246aabmoUXy7JYuHAhK1as4PTTTyc8PJxVq1YBcPjwYX72s5+RlpZGeHg4p59+OsuXL2/2focOHeKSSy4hOjqa1NRUfvOb33i9reJtDI7L5eL//b//x6hRo4iIiCAlJYXc3Fz3LRzLsqiurubZZ59138KZM2eOe//OrrE1K1asIDExkZkzZ3LZZZe1GnDOOusszj//fO6//35qa2vb9T7fdtVVV3Hdddexfv161qxZ0+HjiASzEH8XICJd6/LLL6d///4sXbqUTz75hIcffpjjx4/z3HPPefR79913+fvf/87ChQtJTk6mf//+FBcXM2nSJHcASklJ4a233mLevHk4HA5+/etfA1BbW8u0adM4cOAAN954I5mZmTz//PO8++67bapx3rx5PPPMM8yYMYPrrruOpqYmPvjgAz755BMmTJjA888/z3XXXcfEiRNZsGABAIMGDQLoshpPWLFiBT/+8Y8JCwvjyiuv5M9//jMbN27kzDPP9Nr/zjvv5JxzzuHPf/4zixYtatd7fds111zDE088werVq/n+97/f4eOIBC0jIj3CHXfcYQBz0UUXeWz/5S9/aQDz+eefu7cBxmazme3bt3v0nTdvnsnIyDAlJSUe22fNmmXi4+NNTU2NMcaYhx56yADm73//u7tPdXW1GTx4sAHMe++9595+7bXXmn79+rl/fvfddw1gbrzxxmbn4HK53P8eHR1trr322mZ9fFFjSz799FMDmDVr1rjr69Onj7npppua9QXMDTfcYIwx5rzzzjPp6enuWp5++mkDmI0bN7r7n/jzOnbsmNf3Pn78uAHMj370o5PWKdIT6RaVSA9zww03ePz8q1/9CoA333zTY/vUqVMZMWKE+2djDP/4xz/44Q9/iDGGkpIS92v69OlUVFSwefNm97EyMjK47LLL3PtHRUW5r7a05h//+AeWZXHHHXc0a7Msq9V9u6rGE1asWEFaWhrnnXeeu74rrriClStX4nQ6W9zvzjvvpKioqF1jcb4rJiYGgMrKyg4fQySYKeCI9DBDhgzx+HnQoEHYbDb27dvnsX3AgAEePx87dozy8nKeeOIJUlJSPF5z584F4OjRowDs37+fwYMHNwskQ4cOPWl9e/fuJTMzk6SkpPaeWpfVCOB0Olm5ciXnnXceBQUF5Ofnk5+fT3Z2NsXFxaxdu7bFfc855xzOO++8UxqLU1VVBUBsbGyH9hcJdhqDI9LDtXRVJDIy0uNnl8sFwNVXX821117rdZ/Ro0d3bnHt1JU1vvvuuxQWFrJy5UpWrlzZrH3FihVccMEFLe5/xx13cO655/L444+TkJDQ7vfftm0bAIMHD273viI9gQKOSA+zZ88ej6sz+fn5uFyuk84mnJKSQmxsLE6nk5ycnFb79uvXj23btmGM8QhQbZnvZtCgQbz99tuUlZW1ehXHWzDrqhrh6wCTmprKo48+2qzt5Zdf5pVXXuGxxx5rFhRPmDp1Kueeey733XcfS5YsadN7ftvzzz8PwPTp09u9r0hPoFtUIj3Md38hP/LIIwDMmDGj1f3sdjuXXnop//jHP9xXD77t2LFj7n+/8MILOXLkCC+99JJ7W01NDU888cRJ67v00ksxxnDXXXc1azPGuP89Ojqa8vJyv9RYW1vLyy+/zA9+8AMuu+yyZq+FCxdSWVnJ66+/3upxTozFact7ftsLL7zA//7v/zJ58mSmTZvWrn1FegpdwRHpYQoKCrjooovIzc0lLy+Pv/zlL/z0pz9lzJgxJ9333nvv5b333iM7O5v58+czYsQIysrK2Lx5M++88w5lZWUAzJ8/nz/96U/Mnj2bTZs2kZGRwfPPP09UVNRJ3+O8887jmmuu4eGHH2bPnj3k5ubicrn44IMPOO+881i4cCEA48eP55133uHBBx8kMzOTAQMGkJ2d3SU1vv7661RWVnLRRRd5bZ80aZJ70r8rrriixeNMnTqVqVOn8v7777fY56WXXiImJoaGhgYOHz7M22+/zUcffcSYMWN48cUXT1qrSI/lvwe4RKQrnXjseMeOHeayyy4zsbGxJjEx0SxcuNDU1tZ69OVbjzR/V3FxsbnhhhtMVlaWCQ0NNenp6WbatGnmiSee8Oi3f/9+c9FFF5moqCiTnJxsbrrpJrNq1aqTPiZujDFNTU3mgQceMMOGDTNhYWEmJSXFzJgxw2zatMndZ9euXeacc84xkZGRBvB4ZLyza/yuH/7whyYiIsJUV1e32GfOnDkmNDTU/bh6S5/pe++9Z4AWHxM/8YqIiDB9+vQxP/jBD8zy5ctNXV1di+8tIsZYxnzrmq+IBK0777yTu+66i2PHjpGcnOzvckREfEpjcERERCToKOCIiIhI0FHAERERkaCjMTgiIiISdHQFR0RERIKOAo6IiIgEnR450Z/L5eLIkSPExsaedHViERERCQzGGCorK8nMzMRma/0aTY8MOEeOHCErK8vfZYiIiEgHHDx4kD59+rTap0cGnNjYWADOTZtLiC3Mz9VId5XvWM+B6m1MIocwwj3aqnDwKe8xPH4qvaOG+alCEf+oHZHRYlv54OZ/51YNdDXbFt3P4XX/8/vsaVMNsxLWt6mftM2RQ03c8stSCr5yMrB/CBHhFjt2N5KYaHHPfycxdkL4yQ/SCaqrXEyfVOT+Pd6aHhlwTtyWCrGFKeBIh/WPHceRml1sNZ8wjHHE8fXK12UUs5PNRNsT6BM9ArvVI/8zkx4sdlcptSN7e21L3gfHh3r+vRt/GCoHeYac2uIIYgZUNNv//bKRXND35Cu+x8RqiGlnqa11cfPPywiz2fj4X5lknxEBQH5BA7/4/VFuuaGMv76ZRt/+Xfd3XVuGl+gbINJBEfYYzkz5EU67YSPv8SFv8m/e4DM+JDz06zaFG5HmEnc3NNsWu7f5r6Oqgniv+68+MPSk7/H88SntL0y8WvVaLYcONPHP574JNwCDB4Tx2rOZREXaeGF5pR8r9E4BR+QUxIYm8720qxnf64f0jj2dvrFjyE6+lEkplxNhj/F3eSJ+E7ntcKvtpxpy2kIhp3O8+3Yt554VyVAvtxejo2xcc1ksa9+q9UNlrVPAETlFlmUjJaI/p8VNZkhcNonhmXo6r4sZY3AZJ5q3tOdoy1UcUMjpDHU1hrQUe4vtaSkh1NYG3n97Cjgi0m3VOavZVfEB7xY+yeoj/8PawifYWf5v6pxV/i5N8P1VHIWcrjFgSAjrPq6lsdF7iHnn39UMGhLaxVWdnAKOiHRLNU0OPjn6Nw5X7SDD9GUEZ9Lb9OdI9U7yjv6N6qZyf5coKOQEg8uuiqao2Ml9j5Q1a/vn6irWvF/LZVdH+6Gy1ingiEi3tP34u1gumMT3Oc0aQ6bVjyHWaCZxAXaXjW3H1/q7RPk/gRJypGNOGx7G9YviuOOBMmZceZgV/3Dw8r+quPZXRVz6s0Km5UZy4SVR/i6zGQUcEel2qpvKKW04yEBOJ9yK9GgLtyIYxEiONxyhsrHUTxXKd50s5HijQceB4+c3xXHfn5I4XNbI7IXF/OS6Qtatr+HXi+O579Ek7PbAG3eoZ1hFpNupbCwBIJl0r+29/m97ZWMJsaG9uqwu6bjE3Q3N5sdpj9UHhrZpfpznj0/hmsSPO/w+Pdn0H0Yx/YdRVJS7cDYZEpJs2GyBF2xO0BUcEel2bHz9REcjzW9tADT933ab1fKTH9L1dKsqOMQn2EhKtgd0uAEFHBHphpLCe2O3QjnCPq/tR9iHDTu9wrXmXKAJhJCjW1U9gwKOiHQ7IbYw+kaPYj+7OWwKcJmvp/k3xnDE7KeAXWRFjyTU1jXr4/hDTVMF+Y71bDv+Lnscn3Srp8Z8PR5HIUdAY3BEpJsaEjeZemcNO2s38RU7iDaxVFNFPTVkRJ7G0Piz/F2iTxhj2O34kH1VWwghlEhiqKWKvZUb6Rs9iuHx52BZgf//rpHbDre4XlVL43Fi99qarVl1KjQeJ7hZpgdO/elwOIiPjycn4+dabFOkm3M0HONwzU7qnNWE26PoHTWM+LA0f5flM3srN7LH8QmDGUUWg7BbITiNk8N8xZd8waDYCQyJm+TvMltU76zhUM0OyuoPA4bYrGFk9JlIWHjz1aG9hRxvAcfbopxAmwYdAwo53UhVpYuzRx6hoqKCuLi4VvsGfswXEWlFXFgKwxPOYVyvGYxImBrU4cbpaqSgcjNZDKa/NdS9mKvdstPXGkI/TmNf1RaaXN4HX/tbSd0B/l38HHsdG7DqG7HVOzmQ/y7r/30/pcd2NeuvSQDlVCjgiIh0E2UNh2kyDfRhkNf2PgzEaRoprT/YxZWdXG1TJZ+V/osE04vvMZOx1lmMsabwPWaS5Ephx2d/obam+bxFerJKOkoBR0Skm3CaJgDC8D54+sR2p2nsspra6kD1ViwsRjGJUOubW0+hVhijmIgdO0cO5LX5eN5CzqnQVZzgo4AjItJNxIQkAVDGUa/tJ7bHhATe5IYldftJoTchVvNnW+xWCKmmN2XHvI+Z8XYVxxvdqpJvU8AREekmYkKTSAjN4Ct20Gg8f+k3mgb2sp340FTiwlL8VGHLDC7stDzxoh07xuVssV3jcaS9FHBERLqRkYnn02A18AlrKDA7OWaOsM/sYj3vUGfVMTJxmr9L9Co+LJ0SCvH24K4xhmMUkmC1P5h19npVEjwUcEREupGY0CQmp15Or8h+FLCLz/mYr9hJUmQWk1MvJzY02d8letU3ehR11JDPNo+QY4zhK3ZQSxX9Yka3eoyWblW1NeToKk7PonlwNA+OiHRTTtNEo6ueUFu4+5HxQFZQ+Rm7HR8SQzxp9AEsijlEFeWcFjeZgbETAFqcAPCEts6PA97nyNH8ON2X5sEREekB7FYIEfbobhFuAAbEjuPM5B8RFdGL/dYe9ltfEhmewIReF7vDDXTeelUt0aPjPYOu4OgKjohIQOqsKzmnMtOxruIEFl3BERGRbs/XK4+3hcbjdF8KOCIiErA6a+VxbzToOLgp4IiISEBrLeT4YxJAR4WLvzxVyQ3XlvCLq4/xpwcqKDzc1KbjSNdRwBERkW6tKycB3LqlgYumFvHQHyuItdnIiA3hb89UcdHUIt58taZ9hYtPKeCIiEjA8/V4nLaEnDpHAz+/9jjDBoZRsHEA/1rRm5eeyuTQloHMuiSW2xeVsWNrYK7k3hMp4IiISLfg78fHt79WQEN1Ey89lUFm+jeP5sdE2/jfB9Po2yeEF5ZXtfn9xLcUcEREpNvoyKDj7+roU1X7Pi7igqmRZKQ1n3coJMTipz+K4+P36061POkkCjgiIhI0fHmryjhdREW2/GszKtKiqbHHTS0XsBRwRESkW/HXeJzU03vx9vs1VNd4XxbitberOH2sJo8NFAo4IiLS7XTW/DjtuV015rKB1NQYbrrtGE6n55WaR5eXs2FzPbOujWl3XeIbCjgiItItdWR+nFNZeTy+dwzfX3Imz/zNwbDvHeDu/yrlgUfLOOsHB7nx/zvGNdfFcM60iHacgfhS91ihTUREpJ0Sdzd4Xa+qrVYfGNpsvarTL+pP0oBYNq/4kgeeKMLldDF2XAgP/W8vpuZEYFnWqZYtnUQBR0REuq3IbYdbXZTTW8iJ3WtrtihnVUF8i4tyflfGqF7MvHeyx7ZztShnwNEtKhER6dZ8OR5H61V1X10ScB599FH69+9PREQE2dnZbNiwocW+5557LpZlNXvNnDnT3WfOnDnN2nNzc7viVEREJAB19XgcbxRyAovPA87f/vY3Fi1axB133MHmzZsZM2YM06dP5+jRo177v/zyyxQWFrpf27Ztw26385Of/MSjX25urke/v/71r74+FRERCWCdsShnSxRyuh+fB5wHH3yQ+fPnM3fuXEaMGMFjjz1GVFQUy5cv99o/KSmJ9PR092vNmjVERUU1Czjh4eEe/RITE319KiIi0o115aKc4n8+DTgNDQ1s2rSJnJycb97QZiMnJ4e8vLw2HeOpp55i1qxZREdHe2xft24dqampDB06lOuvv57S0tIWj1FfX4/D4fB4iYhI8AmERTlBIScQ+DTglJSU4HQ6SUtL89ielpZGUVHRSfffsGED27Zt47rrrvPYnpuby3PPPcfatWu57777eP/995kxYwZOp9PrcZYuXUp8fLz7lZWV1fGTEhGRgKaQIxDgj4k/9dRTjBo1iokTJ3psnzVrlvvfR40axejRoxk0aBDr1q1j2rRpzY6zePFiFi1a5P7Z4XAo5IiIiAQxn17BSU5Oxm63U1xc7LG9uLiY9PT0Vvetrq5m5cqVzJs376TvM3DgQJKTk8nPz/faHh4eTlxcnMdLRESCl67iiE8DTlhYGOPHj2ft2rXubS6Xi7Vr1zJ58uRW9oQXX3yR+vp6rr766pO+z6FDhygtLSUjI+OUaxYRkeCgkNOz+fwpqkWLFvHkk0/y7LPPsnPnTq6//nqqq6uZO3cuALNnz2bx4sXN9nvqqae45JJL6NWrl8f2qqoqfve73/HJJ5+wb98+1q5dy8UXX8zgwYOZPn26r09HRES6EV+HHAlcPh+Dc8UVV3Ds2DGWLFlCUVERY8eOZdWqVe6BxwcOHMBm8/wy7d69mw8//JDVq1c3O57dbueLL77g2Wefpby8nMzMTC644ALuuecewsPDfX06IiLSzZxsOQdv2rqcg7f1qrx5/vgUrtFyDl3KMsaYk3cLLg6Hg/j4eHIyfk6IreMLsYmISPfRWsjxtijndwMO0OJ6VW0JOQo4p66q0sXZI49QUVFx0vG0WotKRER6hPbOdNzZ43E0FqdrKeCIiEiP0d6FORVyui8FHBER6VFaCjmnuignKOQEEgUcERGR/9OekHMqFHJ8TwFHRER6nM5YeVzz4wQ2BRwREZHv0CSA3Z8CjoiI9EiBMtOx+IYCjoiI9FjtfaoKOn+mY13F8Q0FHBERkRa0dTxOS3Sryn8UcEREpEcLlFtVCjmdSwFHRER6vEAJOdJ5FHBERETQeJxgo4AjIiLyfzoyP05bQ45uVXUtBRwREZFv6YxJAFuikNN1FHBERETaQeNxugcFHBERke/w9XgcLcrpewo4IiIiXnTWeJyWKOT4lgKOiIhIC3y5KGdbKeR0jAKOiIhIB2kSwMClgCMiItKKQJkEUCGnfRRwRERETiIQJgGU9lHAERERaQPNj9O9KOCIiIh0At2qCiwKOCIiIm3k6/E40nkUcERERNrBl+NxdBWn8yjgiIiItJMW5Qx8CjgiIiKdrKsGHUvLFHBEREQ6IBDmx9FVnJYp4IiIiHSQQk7gUsARERE5BYHwZJVCTnMKOCIiIqeoI09WtZUGHXeMAo6IiIiPaRLArqeAIyIi0gkCYTwOKOScoIAjIiLSSRRyAocCjoiISCfqrJmO5dToExUREelCbZ0EUFdxTk2XBJxHH32U/v37ExERQXZ2Nhs2bGix7zPPPINlWR6viIgIjz7GGJYsWUJGRgaRkZHk5OSwZ88eX5+GiIhImwTKraqezOcB529/+xuLFi3ijjvuYPPmzYwZM4bp06dz9OjRFveJi4ujsLDQ/dq/f79H+/3338/DDz/MY489xvr164mOjmb69OnU1dX5+nRERETaxJeLcrZVT76K4/OA8+CDDzJ//nzmzp3LiBEjeOyxx4iKimL58uUt7mNZFunp6e5XWlqau80Yw0MPPcRtt93GxRdfzOjRo3nuuec4cuQIr776qq9PR0REpM20KKf/+DTgNDQ0sGnTJnJycr55Q5uNnJwc8vLyWtyvqqqKfv36kZWVxcUXX8z27dvdbQUFBRQVFXkcMz4+nuzs7BaPWV9fj8Ph8HiJiIh0hUAIOT2RTwNOSUkJTqfT4woMQFpaGkVFRV73GTp0KMuXL+e1117jL3/5Cy6XiylTpnDo0CEA937tOebSpUuJj493v7Kysk711EREpIcyxsWxun3sLP83O8rXcbhmJ07T1Oo+HQk5baX1qrwLuKeoJk+ezOzZsxk7dixTp07l5ZdfJiUlhccff7zDx1y8eDEVFRXu18GDBzuxYhER6Slqmir46Ohf2VT6T45W76W0+gBbj7/D+4XPUFbf+pib9oYcLcp5anwacJKTk7Hb7RQXF3tsLy4uJj09vU3HCA0NZdy4ceTn5wO492vPMcPDw4mLi/N4iYiItIfT1cjGkldxNjUwgfOYwnQmW99nCtOJNjFsKv0n1U3lnfqeGnTccT4NOGFhYYwfP561a9e6t7lcLtauXcvkyZPbdAyn08nWrVvJyMgAYMCAAaSnp3sc0+FwsH79+jYfU0REpL0Ka/dQ63QwlikkWL2wLAuAKCuWMZyF3djYX/V5q8cIhPE4PSXk+PwW1aJFi3jyySd59tln2blzJ9dffz3V1dXMnTsXgNmzZ7N48WJ3/7vvvpvVq1fz1VdfsXnzZq6++mr279/PddddB3z9hNWvf/1r/vCHP/D666+zdetWZs+eTWZmJpdccomvT0dERHqo4tp8Ekkh2mp+FyDECiGdvhTX5p/Se3TFeBzoGSEnxNdvcMUVV3Ds2DGWLFlCUVERY8eOZdWqVe5BwgcOHMBm+yZnHT9+nPnz51NUVERiYiLjx4/n448/ZsSIEe4+v//976murmbBggWUl5dz9tlns2rVqmYTAoqIiHSWJtNEGC3/ngkjAqdpPOlxIrcdpnZk7xbbE3c3cHxomMe22L02Kge5PLZVFcQTM6Ci2f6rDwzlgr67T1rH88encE3ixyft111Zxhjj7yK6msPhID4+npyMnxNiCzv5DiIi0uPtKF9HcXU+ZzEDm9X8Bshn5gOaQmFy6uVtOl5rIQdoFnKAZiEH8BpygDaFHKBbhZyqShdnjzxCRUXFScfTBtxTVCIiIoGoT/Tp1FPLPpoHh2PmCKUU0zd6VKe9n1YePzU+v0UlIiISDOJCUxgUO5G9lRuoMKVk0Bcbdo5xhCIOkBoxkMyotk+8d7JbVXJqdAVHRESkjYbEZTM6cTqNIU62sYEvyOO4rYzT4qYwNmkGlpdbV60JlEU5g/Eqjq7giIiItENm1GlkRA6h0VWHwUWYLcr9yHhHBMqg42CjKzgiIiLtZFkWYfZIwu3RpxRuTtDK451PAUdERCQAaBLAzqWAIyIiEiD8vSgnBE/IUcAREREJIP5elBOCI+Qo4IiIiASY9o7J8UXI6e4UcERERLqRUx2PA20LOd39Ko4CjoiISADy5XicturOIUcBR0REpBvSeJzWKeCIiIgEKM103HEKOCIiIgEsECYB7I4UcERERLqx9ozH6UmTACrgiIiIBLjOulUFPSfkKOCIiIh0A50ZcrwJtvlxFHBERES6CV8POm6L7nIVRwFHRESkG/HloONgulWlgCMiItLNaFHOk1PAERER6Ya0KGfrFHBERESCUE8POQo4IiIi3ZRmOm6ZAo6IiEg31lmDjlvSXR8fV8AREREJYm0ddHyqSzkE2lUcBRwREZFuTreqmlPAERERCQKBEnIChQKOiIhIkAiElccD5SqOAo6IiPicMYbyhiKO1n6Fo+EYxhh/l9Qj9aRJAEP8XYCIiAS3otp8vqz4mBpnhXtbbEgywxPOISm8tx8rC06R2w5TO7LlzzVxdwPHh4Z5bIvda6NykMtjW1VBPDEDKviu1QeGckHf3Z1TrA/pCo6IiPjMkZrdbCl7iyhnFOOZyvf4AWM5C1uTYWPJq5TVt/+WipycblUp4IiIiI+4jJNdFR+QRh/GMIVEK4VwK4JkK4PxTCWOBHZXfOjvMoNWR9arCqZFORVwRETEJ47V7afBVcsARmBZlkebzbLRj2FUNB6lsrHUTxUGv54cchRwRETEJ+qcVVhYxFhxXtvjSHD3E9/pqSuPK+CIiIhPhNsjMRhqjPcAU43j6362qK4sS74jWCcB7JKA8+ijj9K/f38iIiLIzs5mw4YNLfZ98skn+d73vkdiYiKJiYnk5OQ06z9nzhwsy/J45ebm+vo0RESkHVLC+xNihbOf5k/cGGPYz5fEhCQRG5rsh+p6lp44CaDPA87f/vY3Fi1axB133MHmzZsZM2YM06dP5+jRo177r1u3jiuvvJL33nuPvLw8srKyuOCCCzh82PMPJzc3l8LCQvfrr3/9q69PRURE2sFuC2VI3CQOU8AO8ynVphJjDA5znM/5mDKOMTT+rGbjc8Q3OivktKQtIacrr+JYxsezLWVnZ3PmmWfypz/9CQCXy0VWVha/+tWvuPXWW0+6v9PpJDExkT/96U/Mnj0b+PoKTnl5Oa+++mqHanI4HMTHx5OT8XNCbGEn30FERDrsQNVW9jg+odHUubdF2GIYnnAOaZGD/FhZz9Pa/DhAs/lxgGbz4wBe58cB2jw/zjWJH7ep33dVVbo4e+QRKioqiIvzPrbrBJ9ewWloaGDTpk3k5OR884Y2Gzk5OeTl5bXpGDU1NTQ2NpKUlOSxfd26daSmpjJ06FCuv/56SktbHoVfX1+Pw+HweImISNfoGzOKczPmMi7pQk5POI/xvS5iavq1Cjd+ECi3qrriSo5PA05JSQlOp5O0tDSP7WlpaRQVFbXpGLfccguZmZkeISk3N5fnnnuOtWvXct999/H+++8zY8YMnE6n12MsXbqU+Ph49ysrK6vjJyUiIu1mt0JIixxEVvRIUiL6YVl6xsVfekrICehv2L333svKlSt55ZVXiIiIcG+fNWsWF110EaNGjeKSSy7hjTfeYOPGjaxbt87rcRYvXkxFRYX7dfDgwS46AxERkcDj65ATCHwacJKTk7Hb7RQXF3tsLy4uJj09vdV9ly1bxr333svq1asZPXp0q30HDhxIcnIy+fn5XtvDw8OJi4vzeImIiPRknbWcgzeBcBXHpwEnLCyM8ePHs3btWvc2l8vF2rVrmTx5cov73X///dxzzz2sWrWKCRMmnPR9Dh06RGlpKRkZGZ1St4iISE/QGZMABuqj4z6/RbVo0SKefPJJnn32WXbu3Mn1119PdXU1c+fOBWD27NksXrzY3f++++7j9ttvZ/ny5fTv35+ioiKKioqoqvp6oqiqqip+97vf8cknn7Bv3z7Wrl3LxRdfzODBg5k+fbqvT0dERKTH6IrxOL66iuPzgHPFFVewbNkylixZwtixY9myZQurVq1yDzw+cOAAhYWF7v5//vOfaWho4LLLLiMjI8P9WrZsGQB2u50vvviCiy66iNNOO4158+Yxfvx4PvjgA8LDw319OiIiIkElWFce9/k8OIFI8+CIiIh4am2OHG/z40Db58jprPlxAmYeHBEREekeOmvlcW/8MehYAUdERESA4Bp0rIAjIiIibdKdBh0r4IiIiIhbIEwC2BkhRwFHREREPHTkyaq26qrxOAo4IiIi0i7dYb0qBRwRERFpprsvyqmAIyIiIl5155CjgCMiIiKdKhBWHlfAERERkRZ15CpOe/jqKo4CjoiIiLQqUG5VtYcCjoiIiJxUIIScleXZJ+1zggKOiIiItEkgrDzeVgo4IiIi0maBsChnWyjgiIgEGZdxcqRmN5tL/8XGklfZWf5vqhrL/F2WBJHusCinAo6ISBCpbarko+IX+OL4ahrqHNjrXRRW7+bDoyvId2zwd3nSQwTCoGMFHBGRIGGMYXPpP3E6G5nE95lgnctoazJncyEDGUF+5XoKa770d5kSJAJh0HFrFHBERIJEaf1BKptKOZ0JxFjf/NKwWTYGWiPoRRoFVZ/5sUIJNp016NgXFHBERIJESf1+IogmgWSv7Rn0x9F4lAZnbRdXJj2VP8fjKOCIiAQJl3Fhx45lWV7b7di/7oezK8uSIBeot6oUcEREgkR8WCrVOKgxVV7bj3GECFsM4baoLq5Mgl0ghhwFHBGRIJEeOYQwK4JdbMZpmjzaSk0xhRwgK2YUlqW/+iUw+HISQH3LRUSChN0KYUyvGVRQxse8Tb7ZygGzhy3mIz7jQ3qFZzEgZpy/y5Qg1ZmLcnoLOe29iqOAIyISRHqF92Fy6ixSowdxxNpPPttoCGni9IRzOaPXTGyW3d8lShDrrFtVLXn30JA29w1pc08REekWYkITOT3hPE5POM/fpUgPFLntMLUje7fYnri7geNDwzy2xe61UTnI5bGtqiCemAEVHa5DV3BERESkU/l60HFbKOCIiIhIp/P3yuMKOCIiIuITnbEoZ0cp4IiIiIhf+PJWlQKOiIiI+Iy/xuMo4IiIiIhP+frxcW8UcERERMTnOjLo+Luq98e1ua8CjoiIiPhdZ1/FUcARERGRLtGVt6oUcEREpMdodNVxrG4/x+r20+Cs9Xc5PVJnzY9zMlqqQUREgl6Tq5FdFR9wpGYXLpwA2LCTETWU4fHfI8QWdpIjSGdqbTkHb0s5wNchp6LlFSCa6ZIrOI8++ij9+/cnIiKC7OxsNmzY0Gr/F198kWHDhhEREcGoUaN48803PdqNMSxZsoSMjAwiIyPJyclhz549vjwFERHpplzGyebSf1JYs5sBDGMKuZzFDAYynKKaL/m05HVcxunvMuVbOmMSQJ8HnL/97W8sWrSIO+64g82bNzNmzBimT5/O0aNHvfb/+OOPufLKK5k3bx6fffYZl1xyCZdccgnbtm1z97n//vt5+OGHeeyxx1i/fj3R0dFMnz6duro6X5+OiIh0M8W1eylrOMxYzmKANZwoK4ZIK5r+1jDG8T3KGwsprP3S32X2OB0ZjxPzVdtji2WMMe2uqh2ys7M588wz+dOf/gSAy+UiKyuLX/3qV9x6663N+l9xxRVUV1fzxhtvuLdNmjSJsWPH8thjj2GMITMzk5tvvpnf/va3AFRUVJCWlsYzzzzDrFmzTlqTw+EgPj6enIyf67KkiEiQ23jsVZwNdUywpnpt32w+wITZyU65tIsrE6DVlce/e6vKWV/H9sf/g4qKCuLiWn9k3KdXcBoaGti0aRM5OTnfvKHNRk5ODnl5eV73ycvL8+gPMH36dHf/goICioqKPPrEx8eTnZ3d4jHr6+txOBweLxER6RnqnFXE0vIsuLHEU9dU1YUVybf5ar0qnwackpISnE4naWlpHtvT0tIoKiryuk9RUVGr/U/8sz3HXLp0KfHx8e5XVlZWh85HRES6nzB7FDVUttheQxXh9qgurEi+yxchp0c8Jr548WIqKircr4MHD/q7JBER6SK9o4ZRSjGVprxZW5Wp4BiFZEYP6/rCxENnhxyfBpzk5GTsdjvFxcUe24uLi0lPT/e6T3p6eqv9T/yzPccMDw8nLi7O4yUiIj1DRtRpxIYks5kPOGL24TRNOI2TI2Y/m/mAmJBEMiMVcAJde0OOTwNOWFgY48ePZ+3ate5tLpeLtWvXMnnyZK/7TJ482aM/wJo1a9z9BwwYQHp6ukcfh8PB+vXrWzymiIj0XHYrhDOTLyExIpMdfMp7vMp7vMIONhIfnsaZyT8ixBbq7zKFkz9ZlZDf9pDj84n+Fi1axLXXXsuECROYOHEiDz30ENXV1cydOxeA2bNn07t3b5YuXQrATTfdxNSpU/mv//ovZs6cycqVK/n000954oknALAsi1//+tf84Q9/YMiQIQwYMIDbb7+dzMxMLrnkEl+fjoiIdENh9kjO6PUDapoqKKv/+pdoYngm0SEJ/i1MfMbnAeeKK67g2LFjLFmyhKKiIsaOHcuqVavcg4QPHDiAzfbNhaQpU6bwwgsvcNttt/Ef//EfDBkyhFdffZWRI0e6+/z+97+nurqaBQsWUF5eztlnn82qVauIiIjw9emIiEg3FhUST1RIy09Uif+1Nstxe/h8HpxApHlwREREApu3kNPUVMdHa+/0/zw4IiIiIh3RkUU5v00BR0RERALSqYQcBRwREREJWB0NOQo4IiIiEtA6EnIUcERERCTgtTfkKOCIiIhItxC5o7DNfRVwREREJOgo4IiIiEjQUcARERGRoKOAIyIiIkFHAUdERESCjgKOiIiIBB0FHBEREQk6CjgiIiISdBRwREREJOgo4IiIiEjQUcARERGRoKOAIyIiIkFHAUdERESCToi/CxAREZGew+lqpLD2S47WFeA0TuJCk8mKHklUSHynvo8CjoiIiHSJqsYyPi15jTpXFYmkEEIoB+u3UlD1GSPip9I3ZlSnvZcCjoiIiPicyzjZVPI6dpeNKeQSZcUA4DRN7GErOyrWER2SQK+IrE55P43BEREREZ8rqs2n1lXJKCa5ww2A3QphKGOJJZGCqs867f0UcERERMTnSur2E0ciMVZcszbLssigLyX1BzDGdMr7KeCIiIiIz7lwYm9lZMzXbQaDq1PeTwFHREREfC4+NI1ySmkw9V7bj3GE2JBe2Cx7p7yfAo6IiIj4XO+o4diwsYvNuIznVZpic5ASCukbM6bT3k9PUYmIiIjPhdkjGZ10AVvKVpHH26SbvoQSRglFlFFMRuRp9Ika0Wnvp4AjIuJnLuPkWN0+ap2VhNoiSI0YQKgt3N9liXS6tMhBTE65nH1Vn3Gwbi8u4yQuNIVRMd8nM3IolmW1uG91UzlHawva/F4KOCIiflRUm8+O8nU0uGqxYXcPxBwYN4GBMRNa/QtfpDuKC0thdNIFbe7f6Kpn6/F3OFr3VbveRwFHRMRPjtYWsKXsLVLpzUBGEGPFU2dqOcCX7HF8AsCg2DP9XKWI/7iMi00lr1PVWMYIJtCLdD7gjTbtq4AjIuIHxhi+dHxMEqmMYpL7Sk2EFclpjAEDXzk+pW/0aN2ukh7raN1XlDcWMZ6pJFopNJnGNu+rp6hERPygqqmMqqYy+jLE622ofpyGk6Z2X5YXCSaFNV8SRxKJVkq791XAERHxgwZXLQBRxHhtD7cisRNCg7OuK8sSCSgNrlqiiO7Qvgo4IiJ+EGmPBcDBca/t1caBkyYiQ2K7siyRgBJpj8VBeYeWb/BpwCkrK+Oqq64iLi6OhIQE5s2bR1VVVav9f/WrXzF06FAiIyPp27cvN954IxUVFR79LMtq9lq5cqUvT0VEpFNFhcSTFNabfezGaZo82owxfMUOwqxIUiMG+KlCEf/rE306NVRSxIF27+vTQcZXXXUVhYWFrFmzhsbGRubOncuCBQt44YUXvPY/cuQIR44cYdmyZYwYMYL9+/fzi1/8giNHjvDSSy959H366afJzc11/5yQkODLUxER6XTD4r/H+pJ/sNG8R38zlDiSqKWK/eyhjGLGJEzvtGnrRbqjxLBMMiKHsqP2UxzmOKn0bvO+lumsZTu/Y+fOnYwYMYKNGzcyYcIEAFatWsWFF17IoUOHyMzMbNNxXnzxRa6++mqqq6sJCfk6j1mWxSuvvMIll1zSodocDgfx8fHkZPycEFtYh44hItIZHA3H2FnxAccbDru3xYQkcVrcZFIjB/qxMpHA4DIu9lZu5EDVFzSar8ekVVRUEBfXfFXyb/PZFZy8vDwSEhLc4QYgJycHm83G+vXr+dGPftSm45w4iRPh5oQbbriB6667joEDB/KLX/yCuXPntjghVn19PfX13yzu5XA4OnBGIiKdLy4sheyUH1PTVEGt00GYLZKYkF6a4E/k/9gsG0PishkYO56yusNsKnu9Tfv5LOAUFRWRmprq+WYhISQlJVFUVNSmY5SUlHDPPfewYMECj+133303559/PlFRUaxevZpf/vKXVFVVceONN3o9ztKlS7nrrrs6diIiIl0gKiSeqJB4f5chErDsVgiJ4Rlt7t/uQca33nqr10G+337t2rWrvYdtxuFwMHPmTEaMGMGdd97p0Xb77bdz1llnMW7cOG655RZ+//vf88ADD7R4rMWLF1NRUeF+HTx48JTrExERkcDV7is4N998M3PmzGm1z8CBA0lPT+fo0aMe25uamigrKyM9Pb3V/SsrK8nNzSU2NpZXXnmF0NDQVvtnZ2dzzz33UF9fT3h48xk/w8PDvW4XERGR4NTugJOSkkJKyslnFJw8eTLl5eVs2rSJ8ePHA/Duu+/icrnIzs5ucT+Hw8H06dMJDw/n9ddfJyIi4qTvtWXLFhITExViREREBPDhGJzhw4eTm5vL/Pnzeeyxx2hsbGThwoXMmjXL/QTV4cOHmTZtGs899xwTJ07E4XBwwQUXUFNTw1/+8hccDod7QHBKSgp2u51//vOfFBcXM2nSJCIiIlizZg1//OMf+e1vf+urUxEREZFuxqfz4KxYsYKFCxcybdo0bDYbl156KQ8//LC7vbGxkd27d1NTUwPA5s2bWb9+PQCDBw/2OFZBQQH9+/cnNDSURx99lN/85jcYYxg8eDAPPvgg8+fP9+WpiIiISDfis3lwApnmwREREel+mlwNvFP4eJvmwdFaVCIiIhJ0fHqLSkRERLqHRlcdh6p3UFjzJY2uOqJCEsiKOZ20iEFYVve7HqKAIyIi0sPVNFWw4djLNLhqSKE3iSRR0VDKlrJVpEQMYFzSjG63LpoCjoiISA9mjOGz0jexuSymkEuEFeVuKzGFfF6Xx97KjQyJm+THKtuv+11zEhERkU5zvOEIlU0lDGOsR7gBSLYy6MNADlZtxWWcfqqwYxRwREREerDj9UcIJYxEUr22p9GHBlNHddPxLq7s1CjgiIiI9GQWtDZfjGm1NXAp4IiIiPRgSWG9aaKBMoq9thdzkDBbFNEhiV1c2alRwBEREenBEsIyiAtNZRefUWOq3NuNMRSbQxymgH7Ro/UUlYiIiHQflmUxLmkGG0peIc/5NskmgwiiKKeUSo6THjGYAbHj/V1muyngiIiI9HCRIXGclXolh2t2UVjzJbWuUqJCExgSdRYpEf2xLMvfJbabAo6IiIgQYgujX8xo+sWM9ncpnUJjcERERCToKOCIiIhI0FHAERERkaCjgCMiIiJBRwFHREREgo4CjoiIiAQdBRwREREJOgo4IiIiEnQUcERERCToKOCIiIhI0FHAERERkaCjgCMiIiJBRwFHREREgo4CjoiIiAQdBRwREREJOiH+LqAnq3fWcLB6G0W1+ThNIzEhSWRFjyQloj+WZfm7vDZpdNVxqHoHhTVf0uiqJyoknqyY00mLGIRlKT+LiIh/KOD4iaPxGJ8ee5Um00gafQgjnDLnMTbXv0HvyOGMTJwW8CGnpqmCDcdept5VQyq9SSSJioZStpStIjViAGOTZmCz7P4uU0REeiAFHD9wGSebS/5FuIlgEt8nzAp3txWa/Wyv3UhcWCr9Ykb7scrWGWP4rPRNbC44i1wirCh32zFzhC/qPmFv5acMicv2Y5UiItJT6R6CHxytK6DOVckIJniEG4AMqx9pZLG/agvGGD9VeHLHG45Q2VTCUMZ5hBuAFCuTPgzkYNUXuIzTTxWKiEhPpoDjB8frjxBFLLFWgtf2NPpQ46yg3lXTtYW1w/H6I4QSRhKpXtvT6EODqaO66XgXVyYiIqKAE5AMX1+5CegROBa0dn3JtNoqIiLiWwo4fpAU3psaKnEY71c3ijlItD2RMFuU1/ZAkBTWmyYaKKPYa3sxBwmzRRIdktjFlYmIiCjg+EVKxAAi7XHs4FPqTa17uzGGw+YrjnKYfrFjA/opqoSwDOJCU9nFZ9SYKvd2YwzF5hCHKaBf9Bg9RSUiIn6hp6j8wGbZOKPXD/i05FU+cr1FsskknAjKOEo1DrKiRpIVdbq/y2yVZVmMS5rBhpJXyHO+TbLJIIIoyimlkuOkRwxmQOx4f5cpIiI9lE+v4JSVlXHVVVcRFxdHQkIC8+bNo6qqqtV9zj33XCzL8nj94he/8Ohz4MABZs6cSVRUFKmpqfzud7+jqanJl6fS6WJDe3FW6k8ZEjeZ+tBGjttLiYlI5cxelzAi4dyAvnpzQmRIHGelXsmw+O/RGGo4bi8lIiKBM5J+wJikXGya6C8gOU0Th6p3sqP8fXZVfEhp/aGAfmJPRKQjfHoF56qrrqKwsJA1a9bQ2NjI3LlzWbBgAS+88EKr+82fP5+7777b/XNU1DdjUZxOJzNnziQ9PZ2PP/6YwsJCZs+eTWhoKH/84x99di6+EGaPZEDsGQyIPcPfpXRYiC2MfjFj6Bczxt+lSBuU1B3g87K3aTR1RBNHE03sq/qM+NBUxvX6ARH2aH+XKCLSKXwWcHbu3MmqVavYuHEjEyZMAOCRRx7hwgsvZNmyZWRmZra4b1RUFOnp6V7bVq9ezY4dO3jnnXdIS0tj7Nix3HPPPdxyyy3ceeedhIWFNdunvr6e+vp6988Oh+MUz06k+6lsLGVz6RskksxQziXKisEYQxlH2dH4KZtKXmNy6ixdeRORoOCzv8ny8vJISEhwhxuAnJwcbDYb69evb3XfFStWkJyczMiRI1m8eDE1Nd/MB5OXl8eoUaNIS0tzb5s+fToOh4Pt27d7Pd7SpUuJj493v7Kysk7x7ES6n4LKzYQRzmimEGXFAF+PpeplpTGayVQ2lXKsrsDPVYqIdA6fXcEpKioiNdVzEriQkBCSkpIoKipqcb+f/vSn9OvXj8zMTL744gtuueUWdu/ezcsvv+w+7rfDDeD+uaXjLl68mEWLFrl/djgcCjnS4xTX7aUfQ7B7ebIt3koi1iRQVJtPWuQgP1QnItK52h1wbr31Vu67775W++zcubPDBS1YsMD976NGjSIjI4Np06axd+9eBg3q2F+84eHhhIeHn7yjSJAyxuA0jYQR0WKfMCJwmsYurEpExHfaHXBuvvlm5syZ02qfgQMHkp6eztGjRz22NzU1UVZW1uL4Gm+ys79erDE/P59BgwaRnp7Ohg0bPPoUF3892Vx7jivSk1iWRbQ9kePOo/RhYLN2p3FSQRlZISP9UJ2ISOdrd8BJSUkhJSXlpP0mT55MeXk5mzZtYvz4r+dDeffdd3G5XO7Q0hZbtmwBICMjw33c//zP/+To0aPuW2Br1qwhLi6OESNGtPNsRHqOrJiR7K74kOPmGInWN/8NG2MoYAdNNJAVrYAjIsHBZ4OMhw8fTm5uLvPnz2fDhg189NFHLFy4kFmzZrmfoDp8+DDDhg1zX5HZu3cv99xzD5s2bWLfvn28/vrrzJ49m3POOYfRo0cDcMEFFzBixAiuueYaPv/8c95++21uu+02brjhBt2GEmlF3+hRJIZl8hkfsNNspsQUUmQO8Bkfso/dnBY3haiQeH+XKSLSKXw6D86KFStYuHAh06ZNw2azcemll/Lwww+72xsbG9m9e7f7KamwsDDeeecdHnroIaqrq8nKyuLSSy/ltttuc+9jt9t54403uP7665k8eTLR0dFce+21HvPmiEhzNsvO+OSLKKjcxMHqbRx2fQVAXGgqY2NnkB452M8Vioh0Hsv0wClMHQ4H8fHx5GT8nBBb83lzRIKdy7hocNVgw06YPdLf5YiItEmTq4F3Ch+noqKCuLi4VvtqLSqRHshm2Yiwx/i7DBERn9GUpSIiIhJ0FHBEREQk6CjgiIiISNBRwBEREZGgo4AjIiIiQUcBR0RERIKOAo6IiIgEHQUcERERCToKOCIiIhJ0FHBEREQk6CjgiIiISNBRwBEREZGgo4AjIiIiQUcBR0RERIJOiL8LEBHpiVzGxbG6fZTVHwYgKTyTlIgB2Cz9f6dIZ1DAERHpYlWNZWwufYMaZwWRxACwv3oLkfZYzuj1A2JDk/1coUj3p4AjItKFGlx1bCx5hRBXKBM5nzgrCQCHOc4O5yY2HnuVs9J+Srg9ys+VinRvuhYqItKFDlfvoNFVxxmc7Q43AHFWImdwNk2mgUM12/1YoUhwUMAREelCRbX5pJBJuBXZrC3MiiCV3hTV7PFDZSLBRQFHRKQLOV0NhBHRYns4ETSZxi6sSCQ4KeCIiHSh6NAkjnMMY4zX9uMcIyYkyWubiLSdAo5IF2hyNVLdVE69s8bfpYifZUWPpIoKjrCvWVuhOYCD42TFjOz6wkSCjJ6iEvGhemcNexx5HKn5EhdNACSGZTI4diK9IrL8XJ34Q6/wLPpEnc7Omk2UmWLSyAIsjnKQIg6SGTmMlPD+/i5TpNtTwBHxkXpnNZ8ce5EmZwP9OY0EkqmnlkMNe9lY+hpjk6aTHjnE32VKF7Msi9MTziMuNIV9VVsoduYBEGWPZ3jMOfSNHo1lWX6uUqT7U8AR8ZEvHXk4nQ1M5HwirWj39nTTl22sZ9vxd0kO70+ILdSPVYo/WJZF35hRZEWPpN5VDUC4LVrBRqQTaQyOiA80uRoorPmSLAZ7hBv4+pfbIEbSZBoors33U4USCCzLIsIeQ4Q9RuFGpJMp4Ij4QJ2zChdOEknx2h5lxRBBNNVNx7u4MhGRnkEBR8QH7NbXt53qqfXa7jROGqknxBbWlWWJiPQYCjgiPhAZEkt8aBoH+crrfCdF7MdJE2mRg/1QnYhI8NMgYxEfGRw3kU2l/2QHnzLIjCTCisRlnBRygN18TkbkaUSHJPi7zB6twVXH4eodlNUfBiAxPJM+USMIszdfRkFEuhcFHBEfSYnoz6jEHHaUv0+ROUCkiaGBOppoJD1yCCMTp/m7xB6ttP4Qn5X+C6dpIokUwCK//hP2Vm5kXNKFJEf09XeJInIKFHBEfKh31HDSIgZxpPZLaprKCbHCSI8cTEyopuL3pzpnFZtL3yDeJDKSiYRZX68N1WDq2W42srn0X5yd9lOiQuL9XKmIdJQCjoiPhdjC6ButqfcDyYHqrWBgNJMJsb6ZhyjMCme0mcQHvMnB6m0MjT/Lj1WKyKnQIGMR6XFKaveTSqZHuDnBboWQRm+O1hb4oTIR6Sw+DThlZWVcddVVxMXFkZCQwLx586iqqmqx/759+7Asy+vrxRdfdPfz1r5y5UpfnoqIBBEXTuytXMC2E4LB1YUViUhn8+ktqquuuorCwkLWrFlDY2Mjc+fOZcGCBbzwwgte+2dlZVFYWOix7YknnuCBBx5gxowZHtuffvppcnNz3T8nJCR0ev0iEpziQlMpbTqIMabZDMLGGEooJC4sw0/ViUhn8FnA2blzJ6tWrWLjxo1MmDABgEceeYQLL7yQZcuWkZmZ2Wwfu91Oenq6x7ZXXnmFyy+/nJiYGI/tCQkJzfqKiLRFv5jRHKndxVdsZ6A53R1yjDEUsIsaqhgVPdrPVYrIqfDZLaq8vDwSEhLc4QYgJycHm83G+vXr23SMTZs2sWXLFubNm9es7YYbbiA5OZmJEyeyfPlyr5OpnVBfX4/D4fB4iUjPFR+WxmlxUyhgFxt4l31mN/vNl2zkPb5iO4Njs0kMb/4/YSLSffjsCk5RURGpqamebxYSQlJSEkVFRW06xlNPPcXw4cOZMmWKx/a7776b888/n6ioKFavXs0vf/lLqqqquPHGG70eZ+nSpdx1110dOxERCUoDY8cTF5rCvqotfFW/A4DE8N6MjzmLlIj+/i1ORE5ZuwPOrbfeyn333ddqn507d3a4oBNqa2t54YUXuP3225u1fXvbuHHjqK6u5oEHHmgx4CxevJhFixa5f3Y4HGRlZZ1yjSLSvSVH9NWEfiJBqt0B5+abb2bOnDmt9hk4cCDp6ekcPXrUY3tTUxNlZWVtGjvz0ksvUVNTw+zZs0/aNzs7m3vuuYf6+nrCw8ObtYeHh3vdLiIiIsGp3QEnJSWFlJSUk/abPHky5eXlbNq0ifHjxwPw7rvv4nK5yM7OPun+Tz31FBdddFGb3mvLli0kJiYqxIiIiAjgwzE4w4cPJzc3l/nz5/PYY4/R2NjIwoULmTVrlvsJqsOHDzNt2jSee+45Jk6c6N43Pz+ff//737z55pvNjvvPf/6T4uJiJk2aREREBGvWrOGPf/wjv/3tb311KiIiItLN+HQenBUrVrBw4UKmTZuGzWbj0ksv5eGHH3a3NzY2snv3bmpqajz2W758OX369OGCCy5odszQ0FAeffRRfvOb32CMYfDgwTz44IPMnz/fl6ciIiIi3YhlWnu+Okg5HA7i4+PJyfg5IbYwf5cjIiIibdDkauCdwsepqKggLi6u1b5ai0pERESCjgKOiIiIBB2fjsEREZHO5TIu6pyVWFhE2GObraUlIl9TwBER6QZcxklB1WYOVG2l3lUNQJQ9gf6xY8mKGqmgI/IdCjgiIgHOZVx8VvomJfUHyKQ/qWTiwkWR8wA7ytdR1VjGiISp/i5TJKAo4IiIBLjC2i85Vr+PsZxFspXh3p5CJgkmn93VW8iIPI3E8IxWjiLSs2iQsYhIgDtYtY0k0jzCzQl9GEQk0Rys3uaHykQClwKOiEiAq2k6TiLJXtssyyKBZGqayru2KJEAp4AjIhLg7LYw6qlrsb2eWuy20C6sSCTwKeCIiAS49MjBFHGARtPQrK3aOCjjKOmRQ/xQmUjgUsAREQlw/WLGYFk2PuMDHOY4AMYYysxRtvARUfZ4MiJP83OVIoFFT1GJiAS4CHsME5Iv4bPSN9ngWkuEicbgop5aYkOSOaPXTEJ0i0rEgwKOD1Q0HGV/1edUNBQBFskRfekbPYro0ER/lyYi3VR8WCrnpM/mWF0B5Sf+bgnPIim8jyb5E/FCAaeT7avawq6KD4ggmhQyMLg4Ur2LA9VbGZuUS1rkIH+XKCLdlM2ykRY5SH+PiLSBAk4nOl5fyK6KD+jHaQxmlPv/qoaYMWxnI5+Xvc330q4hMiTWz5WKiIgENw0y7kT7qz4niliPcANgt+yMYAIWFgdrNBmXiIiIryngdKKyhsOk0dvr/fAQK4RkMiirO+yHykRERHoWBZxOZAGm1R4GjQUUERHxPQWcTpQY3pujHMKY5jGnyTRSQhFJ4X38UJmIiEjPooDTifrHjKWGKr7kc1zG5d7uNE1sZyMGQ1b0SD9WKCIi0jPoKapOlBCWzoj4qeyoeJ+jHCbFZOLCyVGO4MLJ2KQZRNhj/F2miIhI0FPA6WR9Y0aTEJ7BgaqtlNcfwbJs9Ik4nb7Ro4gKifd3eSIiIj2CAo4PxIWmMDLxfH+XISIi0mNpDI6IiIgEHQUcERERCToKOCIiIhJ0FHBERMQrb3N6iXQXGmQsIiJuLuPkYPV2DlZvpaqpDDshpEYOZEDMGcSFpfi7PJE2U8ARERHg63CzufQNSusPkkImfTiDBuoprN1HXm0+43pdSGrEAH+XKdImCjgiIgLAvqotlNYfYixn08tKc2/vb07jCz7hi7LVnJs+lxBbmB+rFGkbjcERERGMMRyo2ko6fT3CDYDNsjOMcThNI0dqd/upQpH2UcAREREaXXXUuSpJJt1re4QVRQwJOBqOdnFlIh2jW1RyUi7jpKRuP7XOSkJtEaRGDNAlapEgY7PsADhp8tpujMFJo7ufSKBTwJFWFdXms7P8fepdNdiw48KJ3QplUOyZDIg5A8uy/F2iiHSCEFsYiWEZHGnYR4bp1+y/7QpKqaGKFA0ylm6iR9+iKqzNx2Wc/i4jYB2r28eWsreIcyUwie9zvvUjzuZCepv+fOn4mK+qNvm7RBHpRANixlNOCXv4Aqf55kqOwxxnGxuICelFcnhfP1Yo0nY+Czj/+Z//yZQpU4iKiiIhIaFN+xhjWLJkCRkZGURGRpKTk8OePXs8+pSVlXHVVVcRFxdHQkIC8+bNo6qqqkM1bi9fy/tFz3C8vrBD+wczYwy7Kz4mkRRGM5kY6+uV0COsKE6zxtCXIXzl2EiTq8HPlYpIZ0mNHMCw+HM4QD4f8C8+Mx+ywaxlA2uxh4QzvtcPddVWug2fBZyGhgZ+8pOfcP3117d5n/vvv5+HH36Yxx57jPXr1xMdHc306dOpq6tz97nqqqvYvn07a9as4Y033uDf//43CxYs6FCNEzifSFcUm0pfp6apokPHCFZVTWVUNZXSj9O8/oXWl9Nw0kRx3Vd+qE5EfKV/zBjOSbuGvjFjsEdEEh2ZwtikXM5KvZLIkFh/lyfSZj4bg3PXXXcB8Mwzz7SpvzGGhx56iNtuu42LL74YgOeee460tDReffVVZs2axc6dO1m1ahUbN25kwoQJADzyyCNceOGFLFu2jMzMzHbVGEMsYzmLj8wq9ld9zvCEc9q1fzBrcNUCEEWM1/YIKxK7CaHBWduVZYlIF4gKiee0+Mn+LkPklATMIOOCggKKiorIyclxb4uPjyc7O5u8vDxmzZpFXl4eCQkJ7nADkJOTg81mY/369fzoRz/yeuz6+nrq6+vdP1dUfH21polGQggljd4U1nzJkLhJPjq77ifUCgfgOCWEmYhm7dVU4qSJMHuEblOJiEiXOPH7pi3rpAVMwCkqKgIgLc1zgqm0tDR3W1FREampqR7tISEhJCUluft4s3TpUvcVpW/7kDe/+cHAO4WPd7T8oLWTTeyk5cHEW4+/w1be6cKKRESkp6usrCQ+Pr7VPu0KOLfeeiv33Xdfq3127tzJsGHD2nNYn1u8eDGLFi1y/+xyuSgrK6NXr17dbsCcw+EgKyuLgwcPEhcX5+9yulxPP3/QZ9DTzx/0GYA+g556/sYYKisr2zQkpV0B5+abb2bOnDmt9hk4cGB7DumWnv717JnFxcVkZGS4txcXFzN27Fh3n6NHPWfRbGpqoqyszL2/N+Hh4YSHh3tsa+uTXYEqLi6uR32pv6unnz/oM+jp5w/6DECfQU88/5NduTmhXQEnJSWFlJSUDhV0MgMGDCA9PZ21a9e6A43D4WD9+vXuJ7EmT55MeXk5mzZtYvz48QC8++67uFwusrOzfVKXiIiIdD8+e0z8wIEDbNmyhQMHDuB0OtmyZQtbtmzxmLNm2LBhvPLKKwBYlsWvf/1r/vCHP/D666+zdetWZs+eTWZmJpdccgkAw4cPJzc3l/nz57NhwwY++ugjFi5cyKxZs9r9BJWIiIgEL58NMl6yZAnPPvus++dx48YB8N5773HuuecCsHv3bvcTTQC///3vqa6uZsGCBZSXl3P22WezatUqIiK+eYpnxYoVLFy4kGnTpmGz2bj00kt5+OGHfXUaASc8PJw77rij2S23nqKnnz/oM+jp5w/6DECfQU8//7awTFuetRIRERHpRnr0WlQiIiISnBRwREREJOgo4IiIiEjQUcARERGRoKOAIyIiIkFHASfAlJWVcdVVVxEXF0dCQgLz5s3zmDvou/bt24dlWV5fL774oruft/aVK1d2xSm1W3s/A4Bzzz232fn94he/8Ohz4MABZs6cSVRUFKmpqfzud7+jqanJl6fSIe09/7KyMn71q18xdOhQIiMj6du3LzfeeKPHFAwQ2N+BRx99lP79+xMREUF2djYbNmxotf+LL77IsGHDiIiIYNSoUbz55pse7cYYlixZQkZGBpGRkeTk5LBnzx5fnsIpac/5P/nkk3zve98jMTGRxMREcnJymvWfM2dOsz/r3NxcX5/GKWnPZ/DMM880O79vTycC3e87AO37DLz9nWdZFjNnznT36Y7fg05lJKDk5uaaMWPGmE8++cR88MEHZvDgwebKK69ssX9TU5MpLCz0eN11110mJibGVFZWuvsB5umnn/boV1tb2xWn1G7t/QyMMWbq1Klm/vz5HudXUVHhbm9qajIjR440OTk55rPPPjNvvvmmSU5ONosXL/b16bRbe89/69at5sc//rF5/fXXTX5+vlm7dq0ZMmSIufTSSz36Bep3YOXKlSYsLMwsX77cbN++3cyfP98kJCSY4uJir/0/+ugjY7fbzf3332927NhhbrvtNhMaGmq2bt3q7nPvvfea+Ph48+qrr5rPP//cXHTRRWbAgAEBcb7f1d7z/+lPf2oeffRR89lnn5mdO3eaOXPmmPj4eHPo0CF3n2uvvdbk5uZ6/FmXlZV11Sm1W3s/g6efftrExcV5nF9RUZFHn+70HTCm/Z9BaWmpx/lv27bN2O128/TTT7v7dLfvQWdTwAkgO3bsMIDZuHGje9tbb71lLMsyhw8fbvNxxo4da372s595bAPMK6+80lml+kxHP4OpU6eam266qcX2N99809hsNo+/BP/85z+buLg4U19f3ym1d4bO+g78/e9/N2FhYaaxsdG9LVC/AxMnTjQ33HCD+2en02kyMzPN0qVLvfa//PLLzcyZMz22ZWdnm5///OfGGGNcLpdJT083DzzwgLu9vLzchIeHm7/+9a8+OINT097z/66mpiYTGxtrnn32Wfe2a6+91lx88cWdXarPtPczePrpp018fHyLx+tu3wFjTv178N///d8mNjbWVFVVubd1t+9BZ9MtqgCSl5dHQkICEyZMcG/LycnBZrOxfv36Nh1j06ZNbNmyhXnz5jVru+GGG0hOTmbixIksX74cE4BzPJ7KZ7BixQqSk5MZOXIkixcvpqamxuO4o0aNIi0tzb1t+vTpOBwOtm/f3vkn0kGd8R0AqKioIC4ujpAQz8nKA+070NDQwKZNm8jJyXFvs9ls5OTkkJeX53WfvLw8j/7w9Z/lif4FBQUUFRV59ImPjyc7O7vFY/pLR87/u2pqamhsbCQpKclj+7p160hNTWXo0KFcf/31lJaWdmrtnaWjn0FVVRX9+vUjKyuLiy++2OO/4+70HYDO+R489dRTzJo1i+joaI/t3eV74As+W6pB2q+oqIjU1FSPbSEhISQlJVFUVNSmYzz11FMMHz6cKVOmeGy/++67Of/884mKimL16tX88pe/pKqqihtvvLHT6u8MHf0MfvrTn9KvXz8yMzP54osvuOWWW9i9ezcvv/yy+7jfDjeA++e2frZdoTO+AyUlJdxzzz0sWLDAY3sgfgdKSkpwOp1e/2x27drldZ+W/ixPfD4n/tlan0DRkfP/rltuuYXMzEyPX465ubn8+Mc/ZsCAAezdu5f/+I//YMaMGeTl5WG32zv1HE5VRz6DoUOHsnz5ckaPHk1FRQXLli1jypQpbN++nT59+nSr7wCc+vdgw4YNbNu2jaeeespje3f6HviCAk4XuPXWW7nvvvta7bNz585Tfp/a2lpeeOEFbr/99mZt3942btw4qqureeCBB7rsl5uvP4Nv/zIfNWoUGRkZTJs2jb179zJo0KAOH7ezdNV3wOFwMHPmTEaMGMGdd97p0ebv74B0vnvvvZeVK1eybt06j0G2s2bNcv/7qFGjGD16NIMGDWLdunVMmzbNH6V2qsmTJzN58mT3z1OmTGH48OE8/vjj3HPPPX6szD+eeuopRo0axcSJEz22B/v34GQUcLrAzTffzJw5c1rtM3DgQNLT0zl69KjH9qamJsrKykhPTz/p+7z00kvU1NQwe/bsk/bNzs7mnnvuob6+vksWa+uqz+CE7OxsAPLz8xk0aBDp6enNnkgoLi4GaNdxO6orzr+yspLc3FxiY2N55ZVXCA0NbbV/V38HvElOTsZut7v/LE4oLi5u8XzT09Nb7X/in8XFxWRkZHj0GTt2bCdWf+o6cv4nLFu2jHvvvZd33nmH0aNHt9p34MCBJCcnk5+fH3C/2E7lMzghNDSUcePGkZ+fD3Sv7wCc2mdQXV3NypUrufvuu0/6PoH8PfAJfw8Ckm+cGGD66aefure9/fbbbR5gOnXq1GZPzrTkD3/4g0lMTOxwrb5yqp/BCR9++KEBzOeff26M+WaQ8befSHj88cdNXFycqaur67wTOEUdPf+KigozadIkM3XqVFNdXd2m9wqU78DEiRPNwoUL3T87nU7Tu3fvVgcZ/+AHP/DYNnny5GaDjJctW+Zur6ioCNgBpu09f2OMue+++0xcXJzJy8tr03scPHjQWJZlXnvttVOu1xc68hl8W1NTkxk6dKj5zW9+Y4zpft8BYzr+GTz99NMmPDzclJSUnPQ9Av170NkUcAJMbm6uGTdunFm/fr358MMPzZAhQzweET506JAZOnSoWb9+vcd+e/bsMZZlmbfeeqvZMV9//XXz5JNPmq1bt5o9e/aY//mf/zFRUVFmyZIlPj+fjmjvZ5Cfn2/uvvtu8+mnn5qCggLz2muvmYEDB5pzzjnHvc+Jx8QvuOACs2XLFrNq1SqTkpISsI+Jt+f8KyoqTHZ2thk1apTJz8/3eCS0qanJGBPY34GVK1ea8PBw88wzz5gdO3aYBQsWmISEBPcTb9dcc4259dZb3f0/+ugjExISYpYtW2Z27txp7rjjDq+PiSckJJjXXnvNfPHFF+biiy8O2EeE23v+9957rwkLCzMvvfSSx5/1iWkhKisrzW9/+1uTl5dnCgoKzDvvvGPOOOMMM2TIkIAK89/W3s/grrvuMm+//bbZu3ev2bRpk5k1a5aJiIgw27dvd/fpTt8BY9r/GZxw9tlnmyuuuKLZ9u74PehsCjgBprS01Fx55ZUmJibGxMXFmblz53rMZ1NQUGAA895773nst3jxYpOVlWWcTmezY7711ltm7NixJiYmxkRHR5sxY8aYxx57zGvfQNDez+DAgQPmnHPOMUlJSSY8PNwMHjzY/O53v/OYB8cYY/bt22dmzJhhIiMjTXJysrn55ps9HqMOFO09//fee88AXl8FBQXGmMD/DjzyyCOmb9++JiwszEycONF88skn7rapU6eaa6+91qP/3//+d3PaaaeZsLAwc/rpp5t//etfHu0ul8vcfvvtJi0tzYSHh5tp06aZ3bt3d8WpdEh7zr9fv35e/6zvuOMOY4wxNTU15oILLjApKSkmNDTU9OvXz8yfP7/ZPDGBpj2fwa9//Wt337S0NHPhhReazZs3exyvu30HjGn/fwe7du0ygFm9enWzY3XX70FnsowJwGeFRURERE6B5sERERGRoKOAIyIiIkFHAUdERESCjgKOiIiIBB0FHBEREQk6CjgiIiISdBRwREREJOgo4IiIiEjQUcARERGRoKOAIyIiIkFHAUdERESCzv8PQt0kY5Ir2bAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_1, feature_2 = np.meshgrid(\n",
    "    np.linspace(min(list(xor_dataset.iloc[:, 0])), max(list(xor_dataset.iloc[:, 0]))),\n",
    "    np.linspace(min(list(xor_dataset.iloc[:, 1])), max(list(xor_dataset.iloc[:, 1])))\n",
    ")\n",
    "\n",
    "grid = np.vstack([feature_1.ravel(), feature_2.ravel()]).T\n",
    "grid = pd.DataFrame(grid)\n",
    "\n",
    "grid_pred = xor_net.predict(grid)\n",
    "\n",
    "for i in range(5):\n",
    "    space = np.array(grid_pred.iloc[:,-(i+1)])\n",
    "\n",
    "    y_pred = np.reshape(space, feature_1.shape)\n",
    "\n",
    "    display = DecisionBoundaryDisplay(\n",
    "        xx0=feature_1, xx1=feature_2, response=y_pred\n",
    "    )\n",
    "\n",
    "    display.plot()\n",
    "\n",
    "    display.ax_.scatter(\n",
    "        dataset.iloc[:,0], dataset.iloc[:,1],c= xor_dataset.iloc[:,-(i+1)], edgecolors= \"black\"\n",
    "    )\n",
    "    plt.title(f\"{predicted.columns[-(i+1)]}\")\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XOR problem\n",
    "\n",
    "- Classification\n",
    "\n",
    "- 2 features\n",
    "\n",
    "- 1 label\n",
    "\n",
    "- 3 layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 =np.concatenate([np.random.uniform(0, 10, 10), np.random.uniform(-10, 0, 10)])\n",
    "y1 =np.concatenate([np.random.uniform(-10, 0, 10), np.random.uniform(0, 10, 10)])\n",
    "\n",
    "x2 =np.concatenate([np.random.uniform(0, 10, 10), np.random.uniform(-10, 0, 10)])\n",
    "y2 =np.concatenate([np.random.uniform(0, 10, 10), np.random.uniform(-10, 0, 10)])\n",
    "xor_dataset = pd.DataFrame()\n",
    "xor_dataset[\"X1\"] = np.concatenate([x1,x2])\n",
    "xor_dataset[\"X2\"] = np.concatenate([y1,y2])\n",
    "xor_dataset[\"XOR\"] = np.concatenate([[0 for i in range(20)], [1 for i in range(20)]])\n",
    "\n",
    "xor_net = tf.Models.Sequential()\n",
    "xor_net.add(tf.Layers.Input(2))\n",
    "xor_net.add(tf.Layers.Dense(units=3, activation=tf.Activation.tanh()))\n",
    "xor_net.add(tf.Layers.Dense(2, activation=tf.Activation.sigmoid()))#, aggregation = tf.Aggregation.mean()))\n",
    "xor_net.add(tf.Layers.Dense(1, activation=tf.Activation.sigmoid()))#, use_bias = False))\n",
    "\n",
    "xor_net.compile(optimizer = tf.Optimizers.StochasticGradientDescent(learning_rate = 0.2), loss = tf.Losses.MSE, seed = 65560)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "40/40 [==============================] - elapsed: 382626ms - loss 0.2467\n",
      "Epoch 2/250\n",
      "40/40 [==============================] - elapsed: 380668ms - loss 0.2449\n",
      "Epoch 3/250\n",
      "40/40 [==============================] - elapsed: 375597ms - loss 0.2435\n",
      "Epoch 4/250\n",
      "40/40 [==============================] - elapsed: 376397ms - loss 0.2419\n",
      "Epoch 5/250\n",
      "40/40 [==============================] - elapsed: 372450ms - loss 0.2401\n",
      "Epoch 6/250\n",
      "40/40 [==============================] - elapsed: 373833ms - loss 0.2382\n",
      "Epoch 7/250\n",
      "40/40 [==============================] - elapsed: 372526ms - loss 0.2365\n",
      "Epoch 8/250\n",
      "40/40 [==============================] - elapsed: 374939ms - loss 0.2349\n",
      "Epoch 9/250\n",
      "40/40 [==============================] - elapsed: 376240ms - loss 0.2333\n",
      "Epoch 10/250\n",
      "40/40 [==============================] - elapsed: 374028ms - loss 0.2316\n",
      "Epoch 11/250\n",
      "40/40 [==============================] - elapsed: 373453ms - loss 0.2298\n",
      "Epoch 12/250\n",
      "40/40 [==============================] - elapsed: 373784ms - loss 0.2280\n",
      "Epoch 13/250\n",
      "40/40 [==============================] - elapsed: 372869ms - loss 0.2261\n",
      "Epoch 14/250\n",
      "40/40 [==============================] - elapsed: 373879ms - loss 0.2242\n",
      "Epoch 15/250\n",
      "40/40 [==============================] - elapsed: 391129ms - loss 0.2223\n",
      "Epoch 16/250\n",
      "40/40 [==============================] - elapsed: 408829ms - loss 0.2203\n",
      "Epoch 17/250\n",
      "40/40 [==============================] - elapsed: 381338ms - loss 0.2184\n",
      "Epoch 18/250\n",
      "40/40 [==============================] - elapsed: 392420ms - loss 0.2164\n",
      "Epoch 19/250\n",
      "40/40 [==============================] - elapsed: 376352ms - loss 0.2145\n",
      "Epoch 20/250\n",
      "40/40 [==============================] - elapsed: 376226ms - loss 0.2126\n",
      "Epoch 21/250\n",
      "40/40 [==============================] - elapsed: 375890ms - loss 0.2107\n",
      "Epoch 22/250\n",
      "40/40 [==============================] - elapsed: 374993ms - loss 0.2089\n",
      "Epoch 23/250\n",
      "40/40 [==============================] - elapsed: 376884ms - loss 0.2071\n",
      "Epoch 24/250\n",
      "40/40 [==============================] - elapsed: 376079ms - loss 0.2054\n",
      "Epoch 25/250\n",
      "40/40 [==============================] - elapsed: 375678ms - loss 0.2037\n",
      "Epoch 26/250\n",
      "40/40 [==============================] - elapsed: 376261ms - loss 0.2020\n",
      "Epoch 27/250\n",
      "40/40 [==============================] - elapsed: 375534ms - loss 0.2004\n",
      "Epoch 28/250\n",
      "40/40 [==============================] - elapsed: 378761ms - loss 0.1989\n",
      "Epoch 29/250\n",
      "40/40 [==============================] - elapsed: 378640ms - loss 0.1974\n",
      "Epoch 30/250\n",
      "40/40 [==============================] - elapsed: 375310ms - loss 0.1959\n",
      "Epoch 31/250\n",
      "40/40 [==============================] - elapsed: 377787ms - loss 0.1945\n",
      "Epoch 32/250\n",
      "40/40 [==============================] - elapsed: 377879ms - loss 0.1932\n",
      "Epoch 33/250\n",
      "40/40 [==============================] - elapsed: 376493ms - loss 0.1919\n",
      "Epoch 34/250\n",
      "40/40 [==============================] - elapsed: 380200ms - loss 0.1906\n",
      "Epoch 35/250\n",
      "40/40 [==============================] - elapsed: 380033ms - loss 0.1893\n",
      "Epoch 36/250\n",
      "40/40 [==============================] - elapsed: 377369ms - loss 0.1881\n",
      "Epoch 37/250\n",
      "40/40 [==============================] - elapsed: 378317ms - loss 0.1869\n",
      "Epoch 38/250\n",
      "40/40 [==============================] - elapsed: 379091ms - loss 0.1858\n",
      "Epoch 39/250\n",
      "40/40 [==============================] - elapsed: 379126ms - loss 0.1846\n",
      "Epoch 40/250\n",
      "40/40 [==============================] - elapsed: 378783ms - loss 0.1834\n",
      "Epoch 41/250\n",
      "40/40 [==============================] - elapsed: 378328ms - loss 0.1823\n",
      "Epoch 42/250\n",
      "40/40 [==============================] - elapsed: 377659ms - loss 0.1812\n",
      "Epoch 43/250\n",
      "40/40 [==============================] - elapsed: 378961ms - loss 0.1800\n",
      "Epoch 44/250\n",
      "40/40 [==============================] - elapsed: 379270ms - loss 0.1788\n",
      "Epoch 45/250\n",
      "40/40 [==============================] - elapsed: 377911ms - loss 0.1777\n",
      "Epoch 46/250\n",
      "40/40 [==============================] - elapsed: 377170ms - loss 0.1765\n",
      "Epoch 47/250\n",
      "40/40 [==============================] - elapsed: 378442ms - loss 0.1752\n",
      "Epoch 48/250\n",
      "40/40 [==============================] - elapsed: 376706ms - loss 0.1739\n",
      "Epoch 49/250\n",
      "40/40 [==============================] - elapsed: 378605ms - loss 0.1726\n",
      "Epoch 50/250\n",
      "40/40 [==============================] - elapsed: 379920ms - loss 0.1713\n",
      "Epoch 51/250\n",
      "40/40 [==============================] - elapsed: 378467ms - loss 0.1698\n",
      "Epoch 52/250\n",
      "40/40 [==============================] - elapsed: 381220ms - loss 0.1683\n",
      "Epoch 53/250\n",
      "40/40 [==============================] - elapsed: 380211ms - loss 0.1667\n",
      "Epoch 54/250\n",
      "40/40 [==============================] - elapsed: 377499ms - loss 0.1651\n",
      "Epoch 55/250\n",
      "40/40 [==============================] - elapsed: 377633ms - loss 0.1633\n",
      "Epoch 56/250\n",
      "40/40 [==============================] - elapsed: 377831ms - loss 0.1614\n",
      "Epoch 57/250\n",
      "40/40 [==============================] - elapsed: 378471ms - loss 0.1594\n",
      "Epoch 58/250\n",
      "40/40 [==============================] - elapsed: 378496ms - loss 0.1572\n",
      "Epoch 59/250\n",
      "40/40 [==============================] - elapsed: 376334ms - loss 0.1549\n",
      "Epoch 60/250\n",
      "40/40 [==============================] - elapsed: 378802ms - loss 0.1524\n",
      "Epoch 61/250\n",
      "40/40 [==============================] - elapsed: 378204ms - loss 0.1497\n",
      "Epoch 62/250\n",
      "40/40 [==============================] - elapsed: 378542ms - loss 0.1467\n",
      "Epoch 63/250\n",
      "40/40 [==============================] - elapsed: 378397ms - loss 0.1435\n",
      "Epoch 64/250\n",
      "40/40 [==============================] - elapsed: 381445ms - loss 0.1400\n",
      "Epoch 65/250\n",
      "40/40 [==============================] - elapsed: 379573ms - loss 0.1364\n",
      "Epoch 66/250\n",
      "40/40 [==============================] - elapsed: 380968ms - loss 0.1325\n",
      "Epoch 67/250\n",
      "40/40 [==============================] - elapsed: 379828ms - loss 0.1286\n",
      "Epoch 68/250\n",
      "40/40 [==============================] - elapsed: 380746ms - loss 0.1246\n",
      "Epoch 69/250\n",
      "40/40 [==============================] - elapsed: 379889ms - loss 0.1205\n",
      "Epoch 70/250\n",
      "40/40 [==============================] - elapsed: 377713ms - loss 0.1163\n",
      "Epoch 71/250\n",
      "40/40 [==============================] - elapsed: 378613ms - loss 0.1120\n",
      "Epoch 72/250\n",
      "40/40 [==============================] - elapsed: 382989ms - loss 0.1076\n",
      "Epoch 73/250\n",
      "40/40 [==============================] - elapsed: 384137ms - loss 0.1030\n",
      "Epoch 74/250\n",
      "40/40 [==============================] - elapsed: 384689ms - loss 0.0984\n",
      "Epoch 75/250\n",
      "40/40 [==============================] - elapsed: 400192ms - loss 0.0942\n",
      "Epoch 76/250\n",
      "40/40 [==============================] - elapsed: 383552ms - loss 0.0904\n",
      "Epoch 77/250\n",
      "40/40 [==============================] - elapsed: 386337ms - loss 0.0861\n",
      "Epoch 78/250\n",
      "40/40 [==============================] - elapsed: 386161ms - loss 0.0811\n",
      "Epoch 79/250\n",
      "40/40 [==============================] - elapsed: 386625ms - loss 0.0763\n",
      "Epoch 80/250\n",
      "40/40 [==============================] - elapsed: 386236ms - loss 0.0726\n",
      "Epoch 81/250\n",
      "40/40 [==============================] - elapsed: 385687ms - loss 0.0690\n",
      "Epoch 82/250\n",
      "40/40 [==============================] - elapsed: 386908ms - loss 0.0655\n",
      "Epoch 83/250\n",
      "40/40 [==============================] - elapsed: 387497ms - loss 0.0623\n",
      "Epoch 84/250\n",
      "40/40 [==============================] - elapsed: 385209ms - loss 0.0593\n",
      "Epoch 85/250\n",
      "40/40 [==============================] - elapsed: 384398ms - loss 0.0565\n",
      "Epoch 86/250\n",
      "40/40 [==============================] - elapsed: 381831ms - loss 0.0538\n",
      "Epoch 87/250\n",
      "40/40 [==============================] - elapsed: 381196ms - loss 0.0513\n",
      "Epoch 88/250\n",
      "40/40 [==============================] - elapsed: 382390ms - loss 0.0490\n",
      "Epoch 89/250\n",
      "40/40 [==============================] - elapsed: 382402ms - loss 0.0468\n",
      "Epoch 90/250\n",
      "40/40 [==============================] - elapsed: 383202ms - loss 0.0448\n",
      "Epoch 91/250\n",
      "40/40 [==============================] - elapsed: 384467ms - loss 0.0429\n",
      "Epoch 92/250\n",
      "40/40 [==============================] - elapsed: 393221ms - loss 0.0411\n",
      "Epoch 93/250\n",
      "40/40 [==============================] - elapsed: 393813ms - loss 0.0395\n",
      "Epoch 94/250\n",
      "40/40 [==============================] - elapsed: 392679ms - loss 0.0379\n",
      "Epoch 95/250\n",
      "40/40 [==============================] - elapsed: 393098ms - loss 0.0364\n",
      "Epoch 96/250\n",
      "40/40 [==============================] - elapsed: 392964ms - loss 0.0351\n",
      "Epoch 97/250\n",
      "40/40 [==============================] - elapsed: 393107ms - loss 0.0338\n",
      "Epoch 98/250\n",
      "40/40 [==============================] - elapsed: 388375ms - loss 0.0326\n",
      "Epoch 99/250\n",
      "40/40 [==============================] - elapsed: 392861ms - loss 0.0314\n",
      "Epoch 100/250\n",
      "40/40 [==============================] - elapsed: 393204ms - loss 0.0304\n",
      "Epoch 101/250\n",
      "40/40 [==============================] - elapsed: 392361ms - loss 0.0293\n",
      "Epoch 102/250\n",
      "40/40 [==============================] - elapsed: 397351ms - loss 0.0284\n",
      "Epoch 103/250\n",
      "40/40 [==============================] - elapsed: 393443ms - loss 0.0275\n",
      "Epoch 104/250\n",
      "40/40 [==============================] - elapsed: 393593ms - loss 0.0266\n",
      "Epoch 105/250\n",
      "40/40 [==============================] - elapsed: 392459ms - loss 0.0258\n",
      "Epoch 106/250\n",
      "40/40 [==============================] - elapsed: 390179ms - loss 0.0251\n",
      "Epoch 107/250\n",
      "40/40 [==============================] - elapsed: 389395ms - loss 0.0243\n",
      "Epoch 108/250\n",
      "40/40 [==============================] - elapsed: 390680ms - loss 0.0236\n",
      "Epoch 109/250\n",
      "40/40 [==============================] - elapsed: 390530ms - loss 0.0230\n",
      "Epoch 110/250\n",
      "40/40 [==============================] - elapsed: 391390ms - loss 0.0224\n",
      "Epoch 111/250\n",
      "40/40 [==============================] - elapsed: 390931ms - loss 0.0218\n",
      "Epoch 112/250\n",
      "40/40 [==============================] - elapsed: 391340ms - loss 0.0212\n",
      "Epoch 113/250\n",
      "40/40 [==============================] - elapsed: 383383ms - loss 0.0207\n",
      "Epoch 114/250\n",
      "40/40 [==============================] - elapsed: 381788ms - loss 0.0201\n",
      "Epoch 115/250\n",
      "40/40 [==============================] - elapsed: 382453ms - loss 0.0196\n",
      "Epoch 116/250\n",
      "40/40 [==============================] - elapsed: 383503ms - loss 0.0192\n",
      "Epoch 117/250\n",
      "40/40 [==============================] - elapsed: 390299ms - loss 0.0187\n",
      "Epoch 118/250\n",
      "40/40 [==============================] - elapsed: 390711ms - loss 0.0183\n",
      "Epoch 119/250\n",
      "40/40 [==============================] - elapsed: 408192ms - loss 0.0179\n",
      "Epoch 120/250\n",
      "40/40 [==============================] - elapsed: 396452ms - loss 0.0175\n",
      "Epoch 121/250\n",
      "40/40 [==============================] - elapsed: 397147ms - loss 0.0171\n",
      "Epoch 122/250\n",
      "40/40 [==============================] - elapsed: 392015ms - loss 0.0167\n",
      "Epoch 123/250\n",
      "40/40 [==============================] - elapsed: 403448ms - loss 0.0164\n",
      "Epoch 124/250\n",
      "40/40 [==============================] - elapsed: 388555ms - loss 0.0160\n",
      "Epoch 125/250\n",
      "40/40 [==============================] - elapsed: 389568ms - loss 0.0157\n",
      "Epoch 126/250\n",
      "40/40 [==============================] - elapsed: 389070ms - loss 0.0154\n",
      "Epoch 127/250\n",
      "40/40 [==============================] - elapsed: 396470ms - loss 0.0151\n",
      "Epoch 128/250\n",
      "40/40 [==============================] - elapsed: 395109ms - loss 0.0148\n",
      "Epoch 129/250\n",
      "40/40 [==============================] - elapsed: 387580ms - loss 0.0145\n",
      "Epoch 130/250\n",
      "40/40 [==============================] - elapsed: 389838ms - loss 0.0142\n",
      "Epoch 131/250\n",
      "40/40 [==============================] - elapsed: 386531ms - loss 0.0140\n",
      "Epoch 132/250\n",
      "40/40 [==============================] - elapsed: 388582ms - loss 0.0137\n",
      "Epoch 133/250\n",
      "40/40 [==============================] - elapsed: 393022ms - loss 0.0135\n",
      "Epoch 134/250\n",
      "40/40 [==============================] - elapsed: 392789ms - loss 0.0132\n",
      "Epoch 135/250\n",
      "40/40 [==============================] - elapsed: 391544ms - loss 0.0130\n",
      "Epoch 136/250\n",
      "40/40 [==============================] - elapsed: 393347ms - loss 0.0128\n",
      "Epoch 137/250\n",
      "40/40 [==============================] - elapsed: 394647ms - loss 0.0126\n",
      "Epoch 138/250\n",
      "40/40 [==============================] - elapsed: 391496ms - loss 0.0124\n",
      "Epoch 139/250\n",
      "40/40 [==============================] - elapsed: 393917ms - loss 0.0122\n",
      "Epoch 140/250\n",
      "40/40 [==============================] - elapsed: 394377ms - loss 0.0120\n",
      "Epoch 141/250\n",
      "40/40 [==============================] - elapsed: 392484ms - loss 0.0118\n",
      "Epoch 142/250\n",
      "40/40 [==============================] - elapsed: 393858ms - loss 0.0116\n",
      "Epoch 143/250\n",
      "40/40 [==============================] - elapsed: 385720ms - loss 0.0114\n",
      "Epoch 144/250\n",
      "40/40 [==============================] - elapsed: 384145ms - loss 0.0112\n",
      "Epoch 145/250\n",
      "40/40 [==============================] - elapsed: 389364ms - loss 0.0111\n",
      "Epoch 146/250\n",
      "40/40 [==============================] - elapsed: 391752ms - loss 0.0109\n",
      "Epoch 147/250\n",
      "40/40 [==============================] - elapsed: 391008ms - loss 0.0107\n",
      "Epoch 148/250\n",
      "40/40 [==============================] - elapsed: 393323ms - loss 0.0106\n",
      "Epoch 149/250\n",
      "40/40 [==============================] - elapsed: 389949ms - loss 0.0104\n",
      "Epoch 150/250\n",
      "40/40 [==============================] - elapsed: 381331ms - loss 0.0103\n",
      "Epoch 151/250\n",
      "40/40 [==============================] - elapsed: 382370ms - loss 0.0101\n",
      "Epoch 152/250\n",
      "40/40 [==============================] - elapsed: 383365ms - loss 0.0100\n",
      "Epoch 153/250\n",
      "40/40 [==============================] - elapsed: 383273ms - loss 0.0099\n",
      "Epoch 154/250\n",
      "40/40 [==============================] - elapsed: 383591ms - loss 0.0097\n",
      "Epoch 155/250\n",
      "40/40 [==============================] - elapsed: 384164ms - loss 0.0096\n",
      "Epoch 156/250\n",
      "40/40 [==============================] - elapsed: 381911ms - loss 0.0095\n",
      "Epoch 157/250\n",
      "40/40 [==============================] - elapsed: 382418ms - loss 0.0094\n",
      "Epoch 158/250\n",
      "40/40 [==============================] - elapsed: 382378ms - loss 0.0092\n",
      "Epoch 159/250\n",
      "40/40 [==============================] - elapsed: 382750ms - loss 0.0091\n",
      "Epoch 160/250\n",
      "40/40 [==============================] - elapsed: 383949ms - loss 0.0090\n",
      "Epoch 161/250\n",
      "40/40 [==============================] - elapsed: 384950ms - loss 0.0089\n",
      "Epoch 162/250\n",
      "40/40 [==============================] - elapsed: 383399ms - loss 0.0088\n",
      "Epoch 163/250\n",
      "40/40 [==============================] - elapsed: 380295ms - loss 0.0087\n",
      "Epoch 164/250\n",
      "40/40 [==============================] - elapsed: 382369ms - loss 0.0086\n",
      "Epoch 165/250\n",
      "40/40 [==============================] - elapsed: 383233ms - loss 0.0085\n",
      "Epoch 166/250\n",
      "40/40 [==============================] - elapsed: 383193ms - loss 0.0084\n",
      "Epoch 167/250\n",
      "40/40 [==============================] - elapsed: 383482ms - loss 0.0083\n",
      "Epoch 168/250\n",
      "40/40 [==============================] - elapsed: 395691ms - loss 0.0082\n",
      "Epoch 169/250\n",
      "40/40 [==============================] - elapsed: 382815ms - loss 0.0081\n",
      "Epoch 170/250\n",
      "40/40 [==============================] - elapsed: 384344ms - loss 0.0080\n",
      "Epoch 171/250\n",
      "40/40 [==============================] - elapsed: 385052ms - loss 0.0079\n",
      "Epoch 172/250\n",
      "40/40 [==============================] - elapsed: 383116ms - loss 0.0078\n",
      "Epoch 173/250\n",
      "40/40 [==============================] - elapsed: 383468ms - loss 0.0077\n",
      "Epoch 174/250\n",
      "40/40 [==============================] - elapsed: 384603ms - loss 0.0076\n",
      "Epoch 175/250\n",
      "40/40 [==============================] - elapsed: 382177ms - loss 0.0076\n",
      "Epoch 176/250\n",
      "40/40 [==============================] - elapsed: 385185ms - loss 0.0075\n",
      "Epoch 177/250\n",
      "40/40 [==============================] - elapsed: 383908ms - loss 0.0074\n",
      "Epoch 178/250\n",
      "40/40 [==============================] - elapsed: 383716ms - loss 0.0073\n",
      "Epoch 179/250\n",
      "40/40 [==============================] - elapsed: 383416ms - loss 0.0072\n",
      "Epoch 180/250\n",
      "40/40 [==============================] - elapsed: 384179ms - loss 0.0072\n",
      "Epoch 181/250\n",
      "40/40 [==============================] - elapsed: 385468ms - loss 0.0071\n",
      "Epoch 182/250\n",
      "40/40 [==============================] - elapsed: 385304ms - loss 0.0070\n",
      "Epoch 183/250\n",
      "40/40 [==============================] - elapsed: 383481ms - loss 0.0070\n",
      "Epoch 184/250\n",
      "40/40 [==============================] - elapsed: 382703ms - loss 0.0069\n",
      "Epoch 185/250\n",
      "40/40 [==============================] - elapsed: 392682ms - loss 0.0068\n",
      "Epoch 186/250\n",
      "40/40 [==============================] - elapsed: 395979ms - loss 0.0068\n",
      "Epoch 187/250\n",
      "40/40 [==============================] - elapsed: 396950ms - loss 0.0067\n",
      "Epoch 188/250\n",
      "40/40 [==============================] - elapsed: 392566ms - loss 0.0066\n",
      "Epoch 189/250\n",
      "40/40 [==============================] - elapsed: 384981ms - loss 0.0066\n",
      "Epoch 190/250\n",
      "40/40 [==============================] - elapsed: 384603ms - loss 0.0065\n",
      "Epoch 191/250\n",
      "40/40 [==============================] - elapsed: 385511ms - loss 0.0064\n",
      "Epoch 192/250\n",
      "40/40 [==============================] - elapsed: 384194ms - loss 0.0064\n",
      "Epoch 193/250\n",
      "40/40 [==============================] - elapsed: 386659ms - loss 0.0063\n",
      "Epoch 194/250\n",
      "40/40 [==============================] - elapsed: 385539ms - loss 0.0063\n",
      "Epoch 195/250\n",
      "40/40 [==============================] - elapsed: 384718ms - loss 0.0062\n",
      "Epoch 196/250\n",
      "40/40 [==============================] - elapsed: 385177ms - loss 0.0061\n",
      "Epoch 197/250\n",
      "40/40 [==============================] - elapsed: 383843ms - loss 0.0061\n",
      "Epoch 198/250\n",
      "40/40 [==============================] - elapsed: 384699ms - loss 0.0060\n",
      "Epoch 199/250\n",
      "40/40 [==============================] - elapsed: 381679ms - loss 0.0060\n",
      "Epoch 200/250\n",
      "40/40 [==============================] - elapsed: 381604ms - loss 0.0059\n",
      "Epoch 201/250\n",
      "40/40 [==============================] - elapsed: 381720ms - loss 0.0059\n",
      "Epoch 202/250\n",
      "40/40 [==============================] - elapsed: 380549ms - loss 0.0058\n",
      "Epoch 203/250\n",
      "40/40 [==============================] - elapsed: 381964ms - loss 0.0058\n",
      "Epoch 204/250\n",
      "40/40 [==============================] - elapsed: 383381ms - loss 0.0057\n",
      "Epoch 205/250\n",
      "40/40 [==============================] - elapsed: 381559ms - loss 0.0057\n",
      "Epoch 206/250\n",
      "40/40 [==============================] - elapsed: 382707ms - loss 0.0056\n",
      "Epoch 207/250\n",
      "40/40 [==============================] - elapsed: 381821ms - loss 0.0056\n",
      "Epoch 208/250\n",
      "40/40 [==============================] - elapsed: 390350ms - loss 0.0056\n",
      "Epoch 209/250\n",
      "40/40 [==============================] - elapsed: 392700ms - loss 0.0055\n",
      "Epoch 210/250\n",
      "40/40 [==============================] - elapsed: 392341ms - loss 0.0055\n",
      "Epoch 211/250\n",
      "40/40 [==============================] - elapsed: 390267ms - loss 0.0054\n",
      "Epoch 212/250\n",
      "40/40 [==============================] - elapsed: 391543ms - loss 0.0054\n",
      "Epoch 213/250\n",
      "40/40 [==============================] - elapsed: 391342ms - loss 0.0053\n",
      "Epoch 214/250\n",
      "40/40 [==============================] - elapsed: 391020ms - loss 0.0053\n",
      "Epoch 215/250\n",
      "40/40 [==============================] - elapsed: 391443ms - loss 0.0052\n",
      "Epoch 216/250\n",
      "40/40 [==============================] - elapsed: 390530ms - loss 0.0052\n",
      "Epoch 217/250\n",
      "40/40 [==============================] - elapsed: 401039ms - loss 0.0052\n",
      "Epoch 218/250\n",
      "40/40 [==============================] - elapsed: 383482ms - loss 0.0051\n",
      "Epoch 219/250\n",
      "40/40 [==============================] - elapsed: 383238ms - loss 0.0051\n",
      "Epoch 220/250\n",
      "40/40 [==============================] - elapsed: 383020ms - loss 0.0051\n",
      "Epoch 221/250\n",
      "40/40 [==============================] - elapsed: 384329ms - loss 0.0050\n",
      "Epoch 222/250\n",
      "40/40 [==============================] - elapsed: 383565ms - loss 0.0050\n",
      "Epoch 223/250\n",
      "40/40 [==============================] - elapsed: 400335ms - loss 0.0049\n",
      "Epoch 224/250\n",
      "40/40 [==============================] - elapsed: 389356ms - loss 0.0049\n",
      "Epoch 225/250\n",
      "40/40 [==============================] - elapsed: 386641ms - loss 0.0049\n",
      "Epoch 226/250\n",
      "40/40 [==============================] - elapsed: 390802ms - loss 0.0048\n",
      "Epoch 227/250\n",
      "40/40 [==============================] - elapsed: 391025ms - loss 0.0048\n",
      "Epoch 228/250\n",
      "40/40 [==============================] - elapsed: 386675ms - loss 0.0048\n",
      "Epoch 229/250\n",
      "40/40 [==============================] - elapsed: 390840ms - loss 0.0047\n",
      "Epoch 230/250\n",
      "40/40 [==============================] - elapsed: 391154ms - loss 0.0047\n",
      "Epoch 231/250\n",
      "40/40 [==============================] - elapsed: 387255ms - loss 0.0047\n",
      "Epoch 232/250\n",
      "40/40 [==============================] - elapsed: 390321ms - loss 0.0046\n",
      "Epoch 233/250\n",
      "40/40 [==============================] - elapsed: 389711ms - loss 0.0046\n",
      "Epoch 234/250\n",
      "40/40 [==============================] - elapsed: 384556ms - loss 0.0046\n",
      "Epoch 235/250\n",
      "40/40 [==============================] - elapsed: 383958ms - loss 0.0045\n",
      "Epoch 236/250\n",
      "40/40 [==============================] - elapsed: 384779ms - loss 0.0045\n",
      "Epoch 237/250\n",
      "40/40 [==============================] - elapsed: 383454ms - loss 0.0045\n",
      "Epoch 238/250\n",
      "40/40 [==============================] - elapsed: 382220ms - loss 0.0045\n",
      "Epoch 239/250\n",
      "40/40 [==============================] - elapsed: 383436ms - loss 0.0044\n",
      "Epoch 240/250\n",
      "40/40 [==============================] - elapsed: 384051ms - loss 0.0044\n",
      "Epoch 241/250\n",
      "40/40 [==============================] - elapsed: 386111ms - loss 0.0044\n",
      "Epoch 242/250\n",
      "40/40 [==============================] - elapsed: 383899ms - loss 0.0043\n",
      "Epoch 243/250\n",
      "40/40 [==============================] - elapsed: 383952ms - loss 0.0043\n",
      "Epoch 244/250\n",
      "40/40 [==============================] - elapsed: 385261ms - loss 0.0043\n",
      "Epoch 245/250\n",
      "40/40 [==============================] - elapsed: 382939ms - loss 0.0043\n",
      "Epoch 246/250\n",
      "40/40 [==============================] - elapsed: 383771ms - loss 0.0042\n",
      "Epoch 247/250\n",
      "40/40 [==============================] - elapsed: 388510ms - loss 0.0042\n",
      "Epoch 248/250\n",
      "40/40 [==============================] - elapsed: 382316ms - loss 0.0042\n",
      "Epoch 249/250\n",
      "40/40 [==============================] - elapsed: 384470ms - loss 0.0041\n",
      "Epoch 250/250\n",
      "40/40 [==============================] - elapsed: 382767ms - loss 0.0041\n",
      "Layer 0 weights: ['neuron 0: [1.8503698292550836, 0.16509954467833474, 0.5077468112592501]', 'neuron 1: [0.33591765256144684, 0.3643502908449471, 2.632588809353837]', 'neuron 2: [-0.15960730150939711, 1.8308985412706875, 0.4466619812012822]']\n",
      "Layer 1 weights: ['neuron 0: [3.251442500731503, -1.6265742807126895, 3.2604429362521956, -1.532944182644002]', 'neuron 1: [3.0398389249008746, 2.288876366857348, 2.970720681414515, 2.010933937653479]']\n",
      "Layer 2 weights: ['neuron 0: [-5.841548497217373, 6.071285615098867, -2.7180488581182494]']\n",
      "seed: 65560\n"
     ]
    }
   ],
   "source": [
    "xor_net.fit(dataset = xor_dataset, epochs = 250)#, verbose = False)\n",
    "\n",
    "xor_net.net_weights()\n",
    "print(f\"seed: {xor_net.get_seed()}\")\n",
    "predicted = xor_net.predict(xor_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_1, feature_2 = np.meshgrid(\n",
    "    np.linspace(min(list(xor_dataset.iloc[:, 0])), max(list(xor_dataset.iloc[:, 0]))),\n",
    "    np.linspace(min(list(xor_dataset.iloc[:, 1])), max(list(xor_dataset.iloc[:, 1])))\n",
    ")\n",
    "\n",
    "grid = np.vstack([feature_1.ravel(), feature_2.ravel()]).T\n",
    "grid = pd.DataFrame(grid)\n",
    "\n",
    "grid_pred = xor_net.predict(grid)\n",
    "\n",
    "space = np.array(grid_pred.iloc[:,-1])\n",
    "\n",
    "y_pred = np.reshape(space, feature_1.shape)\n",
    "\n",
    "display = DecisionBoundaryDisplay(\n",
    "    xx0=feature_1, xx1=feature_2, response=y_pred\n",
    ")\n",
    "\n",
    "display.plot()\n",
    "\n",
    "display.ax_.scatter(\n",
    "    predicted.iloc[:,0], predicted.iloc[:,1],c= xor_dataset.iloc[:,-1], edgecolors= \"black\"\n",
    ")\n",
    "plt.title(f\"{predicted.columns[-1]}\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iris dataset\n",
    "\n",
    "- Classification\n",
    "\n",
    "- 4 features\n",
    "\n",
    "- 3 labels\n",
    "\n",
    "- 2 layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      sl   sw   pl   pw  Iris-setosa  Iris-virginica  Iris-versicolor\n",
      "0    5.1  3.5  1.4  0.2            1               0                0\n",
      "1    4.9  3.0  1.4  0.2            1               0                0\n",
      "2    4.7  3.2  1.3  0.2            1               0                0\n",
      "3    4.6  3.1  1.5  0.2            1               0                0\n",
      "4    5.0  3.6  1.4  0.2            1               0                0\n",
      "..   ...  ...  ...  ...          ...             ...              ...\n",
      "145  6.7  3.0  5.2  2.3            0               1                0\n",
      "146  6.3  2.5  5.0  1.9            0               1                0\n",
      "147  6.5  3.0  5.2  2.0            0               1                0\n",
      "148  6.2  3.4  5.4  2.3            0               1                0\n",
      "149  5.9  3.0  5.1  1.8            0               1                0\n",
      "\n",
      "[150 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "filepath = \"iris.data\"\n",
    "\n",
    "iris_dataset = pd.read_csv(filepath, names = [\"sl\", \"sw\", \"pl\", \"pw\", \"Iris-setosa\"])\n",
    "iris_dataset[\"Iris-virginica\"] = iris_dataset[\"Iris-setosa\"]\n",
    "iris_dataset[\"Iris-versicolor\"] = iris_dataset[\"Iris-setosa\"]\n",
    "\n",
    "iris_dataset[\"Iris-setosa\"] = iris_dataset[\"Iris-setosa\"].replace(\"Iris-setosa\", 1)\n",
    "iris_dataset[\"Iris-setosa\"] = iris_dataset[\"Iris-setosa\"].replace(\"Iris-virginica\", 0)\n",
    "iris_dataset[\"Iris-setosa\"] = iris_dataset[\"Iris-setosa\"].replace(\"Iris-versicolor\", 0)\n",
    "\n",
    "iris_dataset[\"Iris-virginica\"] = iris_dataset[\"Iris-virginica\"].replace(\"Iris-setosa\", 0)\n",
    "iris_dataset[\"Iris-virginica\"] = iris_dataset[\"Iris-virginica\"].replace(\"Iris-virginica\", 1)\n",
    "iris_dataset[\"Iris-virginica\"] = iris_dataset[\"Iris-virginica\"].replace(\"Iris-versicolor\", 0)\n",
    "\n",
    "iris_dataset[\"Iris-versicolor\"] = iris_dataset[\"Iris-versicolor\"].replace(\"Iris-setosa\", 0)\n",
    "iris_dataset[\"Iris-versicolor\"] = iris_dataset[\"Iris-versicolor\"].replace(\"Iris-virginica\", 0)\n",
    "iris_dataset[\"Iris-versicolor\"] = iris_dataset[\"Iris-versicolor\"].replace(\"Iris-versicolor\", 1)\n",
    "\n",
    "iris_dataset = iris_dataset.reset_index(drop=True)\n",
    "\n",
    "# sub_iris_dataset = iris_dataset.copy()\n",
    "\n",
    "# sub_iris_dataset = sub_iris_dataset.drop(['sl','sw','label'], axis = 1)\n",
    "# sub_iris_dataset = sub_iris_dataset.drop(['pw','sl'], axis = 1)\n",
    "print(iris_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "iris_net= tf.Models.Sequential()\n",
    "iris_net.add(tf.Layers.Input(4))\n",
    "# iris_net.add(tf.Layers.Dense(4, activation=tf.Activation.tanh()))\n",
    "iris_net.add(tf.Layers.Dense(10, activation=tf.Activation.sigmoid()))\n",
    "# iris_net.add(tf.Layers.Dense(4, activation=tf.Activation.sigmoid()))\n",
    "iris_net.add(tf.Layers.Dense(3, activation=tf.Activation.sigmoid()))\n",
    "\n",
    "iris_net.compile(optimizer = tf.Optimizers.StochasticGradientDescent(learning_rate = 0.1), loss = tf.Losses.MSE, seed = 32021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "150/150 [==============================] - elapsed: 6s 198127ms - loss 0.6221\n",
      "Epoch 2/50\n",
      "150/150 [==============================] - elapsed: 6s 235240ms - loss 0.5502\n",
      "Epoch 3/50\n",
      "150/150 [==============================] - elapsed: 6s 266436ms - loss 0.4825\n",
      "Epoch 4/50\n",
      "150/150 [==============================] - elapsed: 6s 297183ms - loss 0.4338\n",
      "Epoch 5/50\n",
      "150/150 [==============================] - elapsed: 6s 290554ms - loss 0.4023\n",
      "Epoch 6/50\n",
      "150/150 [==============================] - elapsed: 6s 291528ms - loss 0.3810\n",
      "Epoch 7/50\n",
      "150/150 [==============================] - elapsed: 6s 279357ms - loss 0.3652\n",
      "Epoch 8/50\n",
      "150/150 [==============================] - elapsed: 6s 289136ms - loss 0.3519\n",
      "Epoch 9/50\n",
      "150/150 [==============================] - elapsed: 6s 372537ms - loss 0.3394\n",
      "Epoch 10/50\n",
      "150/150 [==============================] - elapsed: 6s 335853ms - loss 0.3271\n",
      "Epoch 11/50\n",
      "150/150 [==============================] - elapsed: 6s 370381ms - loss 0.3144\n",
      "Epoch 12/50\n",
      "150/150 [==============================] - elapsed: 6s 302686ms - loss 0.3012\n",
      "Epoch 13/50\n",
      "150/150 [==============================] - elapsed: 6s 192089ms - loss 0.2876\n",
      "Epoch 14/50\n",
      "150/150 [==============================] - elapsed: 6s 191451ms - loss 0.2735\n",
      "Epoch 15/50\n",
      "150/150 [==============================] - elapsed: 6s 191141ms - loss 0.2591\n",
      "Epoch 16/50\n",
      "150/150 [==============================] - elapsed: 6s 305764ms - loss 0.2447\n",
      "Epoch 17/50\n",
      "150/150 [==============================] - elapsed: 6s 319600ms - loss 0.2303\n",
      "Epoch 18/50\n",
      "150/150 [==============================] - elapsed: 6s 332796ms - loss 0.2163\n",
      "Epoch 19/50\n",
      "150/150 [==============================] - elapsed: 6s 290458ms - loss 0.2028\n",
      "Epoch 20/50\n",
      "150/150 [==============================] - elapsed: 6s 392619ms - loss 0.1900\n",
      "Epoch 21/50\n",
      "150/150 [==============================] - elapsed: 6s 409501ms - loss 0.1780\n",
      "Epoch 22/50\n",
      "150/150 [==============================] - elapsed: 6s 290833ms - loss 0.1669\n",
      "Epoch 23/50\n",
      "150/150 [==============================] - elapsed: 6s 300971ms - loss 0.1568\n",
      "Epoch 24/50\n",
      "150/150 [==============================] - elapsed: 6s 282606ms - loss 0.1477\n",
      "Epoch 25/50\n",
      "150/150 [==============================] - elapsed: 6s 315282ms - loss 0.1394\n",
      "Epoch 26/50\n",
      "150/150 [==============================] - elapsed: 6s 345071ms - loss 0.1320\n",
      "Epoch 27/50\n",
      "150/150 [==============================] - elapsed: 6s 334190ms - loss 0.1254\n",
      "Epoch 28/50\n",
      "150/150 [==============================] - elapsed: 6s 288214ms - loss 0.1196\n",
      "Epoch 29/50\n",
      "150/150 [==============================] - elapsed: 6s 360206ms - loss 0.1144\n",
      "Epoch 30/50\n",
      "150/150 [==============================] - elapsed: 6s 372227ms - loss 0.1098\n",
      "Epoch 31/50\n",
      "150/150 [==============================] - elapsed: 6s 338338ms - loss 0.1058\n",
      "Epoch 32/50\n",
      "150/150 [==============================] - elapsed: 6s 330444ms - loss 0.1023\n",
      "Epoch 33/50\n",
      "150/150 [==============================] - elapsed: 6s 270558ms - loss 0.0992\n",
      "Epoch 34/50\n",
      "150/150 [==============================] - elapsed: 6s 323848ms - loss 0.0965\n",
      "Epoch 35/50\n",
      "150/150 [==============================] - elapsed: 6s 374828ms - loss 0.0942\n",
      "Epoch 36/50\n",
      "150/150 [==============================] - elapsed: 6s 317587ms - loss 0.0921\n",
      "Epoch 37/50\n",
      "150/150 [==============================] - elapsed: 6s 474942ms - loss 0.0903\n",
      "Epoch 38/50\n",
      "150/150 [==============================] - elapsed: 6s 434183ms - loss 0.0888\n",
      "Epoch 39/50\n",
      "150/150 [==============================] - elapsed: 6s 337453ms - loss 0.0875\n",
      "Epoch 40/50\n",
      "150/150 [==============================] - elapsed: 6s 393607ms - loss 0.0863\n",
      "Epoch 41/50\n",
      "150/150 [==============================] - elapsed: 6s 313177ms - loss 0.0853\n",
      "Epoch 42/50\n",
      "150/150 [==============================] - elapsed: 6s 296335ms - loss 0.0845\n",
      "Epoch 43/50\n",
      "150/150 [==============================] - elapsed: 6s 315078ms - loss 0.0838\n",
      "Epoch 44/50\n",
      "150/150 [==============================] - elapsed: 6s 345687ms - loss 0.0832\n",
      "Epoch 45/50\n",
      "150/150 [==============================] - elapsed: 6s 376119ms - loss 0.0827\n",
      "Epoch 46/50\n",
      "150/150 [==============================] - elapsed: 6s 411895ms - loss 0.0822\n",
      "Epoch 47/50\n",
      "150/150 [==============================] - elapsed: 6s 428758ms - loss 0.0818\n",
      "Epoch 48/50\n",
      "150/150 [==============================] - elapsed: 6s 439764ms - loss 0.0815\n",
      "Epoch 49/50\n",
      "150/150 [==============================] - elapsed: 6s 501310ms - loss 0.0813\n",
      "Epoch 50/50\n",
      "150/150 [==============================] - elapsed: 6s 364259ms - loss 0.0810\n",
      "Layer 0 weights: ['neuron 0: [0.17537499991989466, 1.0062256818088187, 0.5672724877397797, 0.41250520263059026, 0.4646355735811972]', 'neuron 1: [0.5701587768285722, 0.057819707029722764, 0.9014868016636414, 0.39596093881447686, 0.1847246327884305]', 'neuron 2: [0.742546893732094, 0.5202470572015016, 0.42836145475322507, 0.4078968692795579, 0.6921186812024201]', 'neuron 3: [0.11397220062556881, 0.6987176887982735, -0.9437624711039148, -0.37956135480762726, 0.2568533196671224]', 'neuron 4: [0.5076038888318029, 0.5766139007350359, 0.5236832091299182, 0.10158583805496407, 0.07735969351165164]', 'neuron 5: [0.41917589413319567, 0.9395159569371799, 0.4284029807582646, 0.08268535992675924, 0.8286488421600867]', 'neuron 6: [-1.7602339685585522, -1.8523696762857833, 2.936608330590911, 2.823639492611839, 1.8934696332910015]', 'neuron 7: [-1.786189005997497, -1.6103224671735823, 2.670163461485946, 2.8012556675248153, 1.3308173638782905]', 'neuron 8: [0.7758902439834915, 0.31469593123670014, 0.2200593901052389, -0.01554000788480095, 0.33868339371674655]', 'neuron 9: [-0.6223737105646343, -1.3795870663219625, 2.847148648251356, 1.3305385172033724, 0.9063695892758745]']\n",
      "Layer 1 weights: ['neuron 0: [0.6513433604130844, -0.01931756012614777, 0.3250138666582039, 1.6425637985996557, 0.10538615065031122, 0.7548067622099123, -2.5229741791739553, -1.1226637777125787, 0.5573175813655592, -3.9877147570539746, 0.847829886080524]', 'neuron 1: [-0.3091252947825497, -0.028429637925911636, -0.7776860017181595, 0.08802363309224796, -0.7613017554967632, -0.5769740146161081, 3.8527645916979467, 2.608603306003898, -0.5970845376049376, 0.8883378773843182, 1.194671215348632]', 'neuron 2: [0.2308407909569158, -0.36101776335721186, 0.21028591146744013, -0.40163652338138517, -0.4825923756940842, -0.08052008855540202, -2.323565889080929, -2.677735143393715, -0.2709379815575452, 4.634632620676965, 1.4073791079009896]']\n",
      "seed: 32021\n"
     ]
    }
   ],
   "source": [
    "iris_net.fit(dataset = iris_dataset.iloc[:, :], epochs = 50)#, verbose = False)\n",
    "\n",
    "iris_net.net_weights()\n",
    "print(f\"seed: {iris_net.get_seed()}\")\n",
    "predicted = iris_net.predict(iris_dataset.iloc[:, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    plt.title(f\"{predicted.columns[-(i+1)]}\")\n",
    "    plt.scatter(iris_dataset.iloc[:, :].iloc[:,0],iris_dataset.iloc[:, :].iloc[:,2], c = predicted.iloc[:,-(i+1)])\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# f(x) = x^2 with noise\n",
    "\n",
    "- Regression\n",
    "\n",
    "- 2 features\n",
    "\n",
    "- 3 layers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(start = 0,stop = 10, num = 100)\n",
    "# y = [np.sin(np.radians(i))+ np.random.randint(-10,10)/100 for i in x]\n",
    "y = [-(i**2) + np.random.randint(-10,10) for i in x]\n",
    "scaler = sk.preprocessing.MinMaxScaler()\n",
    "scaler.fit(np.array(y).reshape(-1,1))\n",
    "y = scaler.transform(np.array(y).reshape(-1,1))\n",
    "# plt.scatter(x,y)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           x         y\n",
      "0    0.00000  0.935622\n",
      "1    0.10101  0.935534\n",
      "2    0.20202  0.866602\n",
      "3    0.30303  0.960585\n",
      "4    0.40404  0.882719\n",
      "..       ...       ...\n",
      "95   9.59596  0.136631\n",
      "96   9.69697  0.111320\n",
      "97   9.79798  0.060082\n",
      "98   9.89899  0.120257\n",
      "99  10.00000  0.000000\n",
      "\n",
      "[100 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "square_dataset = pd.DataFrame()\n",
    "square_dataset[\"x\"] = x\n",
    "square_dataset[\"y\"] = y\n",
    "\n",
    "print(square_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "square_net= tf.Models.Sequential()\n",
    "square_net.add(tf.Layers.Input(1))\n",
    "\n",
    "square_net.add(tf.Layers.Dense(2, activation=tf.Activation.linear()))\n",
    "square_net.add(tf.Layers.Dense(2, activation=tf.Activation.linear()))\n",
    "# square_net.add(tf.Layers.Dense(2, activation=tf.Activation.tanh()))\n",
    "square_net.add(tf.Layers.Dense(1, activation=tf.Activation.tanh()))\n",
    "\n",
    "square_net.compile(optimizer = tf.Optimizers.StochasticGradientDescent(learning_rate = 0.008), loss = tf.Losses.MSE,seed = 34176)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "100/100 [==============================] - elapsed: 1s 96254ms - loss 0.4571\n",
      "Epoch 2/25\n",
      "100/100 [==============================] - elapsed: 1s 93590ms - loss 0.2036\n",
      "Epoch 3/25\n",
      "100/100 [==============================] - elapsed: 1s 101566ms - loss 0.1008\n",
      "Epoch 4/25\n",
      "100/100 [==============================] - elapsed: 1s 74977ms - loss 0.0567\n",
      "Epoch 5/25\n",
      "100/100 [==============================] - elapsed: 1s 87634ms - loss 0.0353\n",
      "Epoch 6/25\n",
      "100/100 [==============================] - elapsed: 1s 128769ms - loss 0.0237\n",
      "Epoch 7/25\n",
      "100/100 [==============================] - elapsed: 1s 114288ms - loss 0.0169\n",
      "Epoch 8/25\n",
      "100/100 [==============================] - elapsed: 1s 94573ms - loss 0.0126\n",
      "Epoch 9/25\n",
      "100/100 [==============================] - elapsed: 1s 109945ms - loss 0.0098\n",
      "Epoch 10/25\n",
      "100/100 [==============================] - elapsed: 1s 108204ms - loss 0.0079\n",
      "Epoch 11/25\n",
      "100/100 [==============================] - elapsed: 1s 90070ms - loss 0.0066\n",
      "Epoch 12/25\n",
      "100/100 [==============================] - elapsed: 1s 102112ms - loss 0.0056\n",
      "Epoch 13/25\n",
      "100/100 [==============================] - elapsed: 1s 139097ms - loss 0.0049\n",
      "Epoch 14/25\n",
      "100/100 [==============================] - elapsed: 1s 101176ms - loss 0.0044\n",
      "Epoch 15/25\n",
      "100/100 [==============================] - elapsed: 1s 98079ms - loss 0.0040\n",
      "Epoch 16/25\n",
      "100/100 [==============================] - elapsed: 1s 126127ms - loss 0.0038\n",
      "Epoch 17/25\n",
      "100/100 [==============================] - elapsed: 1s 133104ms - loss 0.0035\n",
      "Epoch 18/25\n",
      "100/100 [==============================] - elapsed: 1s 131245ms - loss 0.0034\n",
      "Epoch 19/25\n",
      "100/100 [==============================] - elapsed: 1s 146518ms - loss 0.0033\n",
      "Epoch 20/25\n",
      "100/100 [==============================] - elapsed: 1s 101180ms - loss 0.0032\n",
      "Epoch 21/25\n",
      "100/100 [==============================] - elapsed: 1s 106644ms - loss 0.0031\n",
      "Epoch 22/25\n",
      "100/100 [==============================] - elapsed: 1s 95840ms - loss 0.0030\n",
      "Epoch 23/25\n",
      "100/100 [==============================] - elapsed: 1s 99988ms - loss 0.0030\n",
      "Epoch 24/25\n",
      "100/100 [==============================] - elapsed: 1s 81144ms - loss 0.0030\n",
      "Epoch 25/25\n",
      "100/100 [==============================] - elapsed: 1s 103505ms - loss 0.0029\n",
      "Layer 0 weights: ['neuron 0: [0.6472800950260899, 0.10704116642585967]', 'neuron 1: [0.19468934547331865, 1.0051155744044982]']\n",
      "Layer 1 weights: ['neuron 0: [1.0421940975641732, 0.4013338146459403, 0.5625159781579206]', 'neuron 1: [0.06621136782105308, 0.8000574239967386, 0.6109603414223052]']\n",
      "Layer 2 weights: ['neuron 0: [-0.013736736526122336, -0.721115639189966, -0.6063147687929774]']\n",
      "seed: 34176\n"
     ]
    }
   ],
   "source": [
    "# sin_net.net_weights()\n",
    "\n",
    "square_net.fit(dataset = square_dataset, epochs = 25)#, verbose = False)\n",
    "\n",
    "square_net.net_weights()\n",
    "print(f\"seed: {square_net.get_seed()}\")\n",
    "predicted = square_net.predict(square_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(predicted.iloc[:,0],predicted.iloc[:,-1])\n",
    "plt.scatter(square_dataset.iloc[:, :].iloc[:,0],square_dataset.iloc[:, :].iloc[:,-1])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Draft\n",
    "\n",
    "Studying use of Decimal module to avoid early gradient vanishing/exploding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "0.9525741268224334\n",
      "0.9525741268224332191211518479\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from decimal import *\n",
    "a = 3#-735.906310255588\n",
    "print(a)\n",
    "print(1/(1 + math.exp(-a)))\n",
    "print(Decimal(1)/Decimal(Decimal(1) + Decimal.exp(Decimal(-a))))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mastersvenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}