ANN_REGRESSOR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d8afd9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "df= pd.read_excel('output_params_density.xlsx')\n",
    "\n",
    "\n",
    "\n",
    "data= df.drop('density',axis='columns')\n",
    "target= df['density']\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=[0, 1])\n",
    "data_scaled = scaler.fit_transform(data)\n",
    "data = pd.DataFrame(data = data_scaled, columns =['Ni', 'Al', 'Co', 'Fe', 'Cr', 'Δsₘᵢₓ', 'VEC', 'δ', 'Δχ', 'ΔHₘᵢₓ', 'Ω','Tm'])\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "xtrain,xtest,ytrain,ytest= train_test_split(data,target,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1d39542",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "747ffb1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# xtrain= xtrain.to_numpy()\n",
    "# xtest = xtest.to_numpy()\n",
    "# ytrain= ytrain.to_numpy()\n",
    "# ytest = ytest.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3225b856",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "import tensorflow\n",
    "tensorflow.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4e7aeff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def FindLayerNodesLinear(n_layers, first_layer_nodes, last_layer_nodes):\n",
    "    layers = []\n",
    "    \n",
    "    nodes_increment = (last_layer_nodes - first_layer_nodes)/ (n_layers-1)\n",
    "    nodes = first_layer_nodes\n",
    "    for i in range(1, n_layers+1):\n",
    "        layers.append(math.ceil(nodes))\n",
    "        nodes = nodes + nodes_increment\n",
    "    \n",
    "    return layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "757c865f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def createmodel(n_layers, first_layer_nodes, last_layer_nodes, activation_func, loss_func):\n",
    "    clf = Sequential()\n",
    "    n_nodes = FindLayerNodesLinear(n_layers, first_layer_nodes, last_layer_nodes)\n",
    "    for i in range(1, n_layers):\n",
    "        if i==1:\n",
    "            clf.add(Dense(first_layer_nodes,input_dim=12, activation=activation_func))\n",
    "        else:\n",
    "            clf.add(Dense(n_nodes[i-1], activation=activation_func))\n",
    "            \n",
    "    #Final layer\n",
    "    clf.add(Dense(1))\n",
    "    clf.compile(optimizer='adam', loss=loss_func, metrics = ['mae'])\n",
    "    \n",
    "    return clf\n",
    "\n",
    "#Wrap model/classifier into scikit-learn\n",
    "clf =  KerasRegressor(build_fn=createmodel, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "37b483d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################  BEST PARAMS  ###################################################################\n",
    "# {'activation_func': ['relu', 'elu'],\n",
    "#                          'batch_size': [1000], 'epochs': [300],\n",
    "#                          'first_layer_nodes': [128, 256, 512],\n",
    "#                          'last_layer_nodes': [16, 32, 64], 'loss_func': ['mse'],\n",
    "#                          'n_layers': [7, 8, 9]}\n",
    "\n",
    "\n",
    "# {'activation_func': 'relu',\n",
    "#  'batch_size': 1000,\n",
    "#  'epochs': 300,\n",
    "#  'first_layer_nodes': 512,\n",
    "#  'last_layer_nodes': 32,\n",
    "#  'loss_func': 'mse',\n",
    "#  'n_layers': 8}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f54892d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "activation_funcs = ['relu'] \n",
    "loss_funcs = ['mse']\n",
    "param_grid = dict(n_layers=[8], first_layer_nodes = [512], last_layer_nodes = [32],  activation_func = activation_funcs, loss_func = loss_funcs, batch_size = [1000], epochs = [300])\n",
    "grid = GridSearchCV(estimator = clf, param_grid = param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb201354",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "9/9 [==============================] - 3s 9ms/step - loss: 48747244.0000 - mae: 6968.5391\n",
      "Epoch 2/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 46870676.0000 - mae: 6831.6069\n",
      "Epoch 3/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 20288168.0000 - mae: 4031.0571\n",
      "Epoch 4/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 4386702.5000 - mae: 1791.8528\n",
      "Epoch 5/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2053680.7500 - mae: 1228.3458\n",
      "Epoch 6/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 867107.3750 - mae: 784.7031\n",
      "Epoch 7/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 408621.7812 - mae: 530.2477\n",
      "Epoch 8/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 196640.0469 - mae: 356.0683\n",
      "Epoch 9/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 112165.6016 - mae: 265.2531\n",
      "Epoch 10/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 74504.5391 - mae: 200.7467\n",
      "Epoch 11/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 55607.7148 - mae: 184.9803\n",
      "Epoch 12/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 46111.5781 - mae: 156.7507\n",
      "Epoch 13/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 36762.9023 - mae: 143.9860\n",
      "Epoch 14/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 30628.1270 - mae: 132.9069\n",
      "Epoch 15/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 26203.8945 - mae: 119.0816\n",
      "Epoch 16/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 22240.4062 - mae: 111.3296\n",
      "Epoch 17/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 19271.3613 - mae: 104.9846\n",
      "Epoch 18/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 16725.6992 - mae: 97.5104\n",
      "Epoch 19/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 14673.1689 - mae: 92.1037\n",
      "Epoch 20/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 12806.9102 - mae: 87.0994\n",
      "Epoch 21/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 11232.3906 - mae: 80.2749\n",
      "Epoch 22/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 9896.1924 - mae: 75.8668\n",
      "Epoch 23/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 8685.0146 - mae: 70.3484\n",
      "Epoch 24/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7573.1045 - mae: 64.9466\n",
      "Epoch 25/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 6591.6172 - mae: 60.7118\n",
      "Epoch 26/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 5732.7124 - mae: 55.6290\n",
      "Epoch 27/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 5009.2075 - mae: 52.5515\n",
      "Epoch 28/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 4326.5918 - mae: 48.3123\n",
      "Epoch 29/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 3771.9199 - mae: 45.1647\n",
      "Epoch 30/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 3286.2773 - mae: 41.9040\n",
      "Epoch 31/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2878.8335 - mae: 38.9869\n",
      "Epoch 32/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2504.9524 - mae: 36.4019\n",
      "Epoch 33/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2157.4951 - mae: 33.9178\n",
      "Epoch 34/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1836.5321 - mae: 31.3737\n",
      "Epoch 35/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1578.1700 - mae: 29.1165\n",
      "Epoch 36/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1369.1770 - mae: 27.0435\n",
      "Epoch 37/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1211.8009 - mae: 25.4106\n",
      "Epoch 38/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1107.4471 - mae: 24.3141\n",
      "Epoch 39/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1028.0228 - mae: 23.4278\n",
      "Epoch 40/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 969.0838 - mae: 22.6986\n",
      "Epoch 41/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 925.1193 - mae: 22.1286\n",
      "Epoch 42/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 888.7328 - mae: 21.6713\n",
      "Epoch 43/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 863.4810 - mae: 21.3885\n",
      "Epoch 44/300\n",
      "9/9 [==============================] - 0s 15ms/step - loss: 832.4669 - mae: 20.9404\n",
      "Epoch 45/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 807.0915 - mae: 20.5570\n",
      "Epoch 46/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 787.3817 - mae: 20.3625\n",
      "Epoch 47/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 769.9426 - mae: 20.0596\n",
      "Epoch 48/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 755.8984 - mae: 19.8960\n",
      "Epoch 49/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 738.9687 - mae: 19.6881\n",
      "Epoch 50/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 732.7984 - mae: 19.5461\n",
      "Epoch 51/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 724.1798 - mae: 19.4332\n",
      "Epoch 52/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 713.2804 - mae: 19.2193\n",
      "Epoch 53/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 708.5072 - mae: 19.2143\n",
      "Epoch 54/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 688.0154 - mae: 18.9225\n",
      "Epoch 55/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 672.6305 - mae: 18.6120\n",
      "Epoch 56/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 669.8181 - mae: 18.6440\n",
      "Epoch 57/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 660.2632 - mae: 18.4512\n",
      "Epoch 58/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 652.6204 - mae: 18.3284\n",
      "Epoch 59/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 640.8698 - mae: 18.1364\n",
      "Epoch 60/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 635.1710 - mae: 18.0656\n",
      "Epoch 61/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 642.1600 - mae: 18.1522\n",
      "Epoch 62/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 633.9438 - mae: 18.1001\n",
      "Epoch 63/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 610.0583 - mae: 17.5328\n",
      "Epoch 64/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 603.5937 - mae: 17.4779\n",
      "Epoch 65/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 598.3844 - mae: 17.3274\n",
      "Epoch 66/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 589.5175 - mae: 17.2807\n",
      "Epoch 67/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 595.9165 - mae: 17.2861\n",
      "Epoch 68/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 581.4495 - mae: 17.1277\n",
      "Epoch 69/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 572.8201 - mae: 16.8568\n",
      "Epoch 70/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 562.0757 - mae: 16.6851\n",
      "Epoch 71/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 557.2975 - mae: 16.5768\n",
      "Epoch 72/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 553.8856 - mae: 16.5399\n",
      "Epoch 73/300\n",
      "9/9 [==============================] - 0s 18ms/step - loss: 545.3182 - mae: 16.3721\n",
      "Epoch 74/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 541.3538 - mae: 16.3143\n",
      "Epoch 75/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 539.9749 - mae: 16.2712\n",
      "Epoch 76/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 536.4355 - mae: 16.1555\n",
      "Epoch 77/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 530.1304 - mae: 16.0462\n",
      "Epoch 78/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 522.5871 - mae: 15.8261\n",
      "Epoch 79/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 514.4098 - mae: 15.7395\n",
      "Epoch 80/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 510.8654 - mae: 15.6864\n",
      "Epoch 81/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 507.6386 - mae: 15.5948\n",
      "Epoch 82/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 500.1138 - mae: 15.4071\n",
      "Epoch 83/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 497.1951 - mae: 15.4248\n",
      "Epoch 84/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 491.8374 - mae: 15.2277\n",
      "Epoch 85/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 8ms/step - loss: 488.2608 - mae: 15.1944\n",
      "Epoch 86/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 485.1750 - mae: 15.0885\n",
      "Epoch 87/300\n",
      "9/9 [==============================] - 0s 13ms/step - loss: 483.0275 - mae: 15.0200\n",
      "Epoch 88/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 477.9126 - mae: 14.9679\n",
      "Epoch 89/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 471.8483 - mae: 14.8214\n",
      "Epoch 90/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 469.1265 - mae: 14.7488\n",
      "Epoch 91/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 468.5552 - mae: 14.7718\n",
      "Epoch 92/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 460.9972 - mae: 14.5816\n",
      "Epoch 93/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 459.3602 - mae: 14.4993\n",
      "Epoch 94/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 454.8069 - mae: 14.4105\n",
      "Epoch 95/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 451.6376 - mae: 14.3617\n",
      "Epoch 96/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 451.0596 - mae: 14.2700\n",
      "Epoch 97/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 444.8269 - mae: 14.2215\n",
      "Epoch 98/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 443.2222 - mae: 14.1603\n",
      "Epoch 99/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 440.5261 - mae: 14.1310\n",
      "Epoch 100/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 437.8219 - mae: 14.0689\n",
      "Epoch 101/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 434.8010 - mae: 13.9414\n",
      "Epoch 102/300\n",
      "9/9 [==============================] - 0s 14ms/step - loss: 432.3405 - mae: 13.8632\n",
      "Epoch 103/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 435.8383 - mae: 14.0227\n",
      "Epoch 104/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 427.1241 - mae: 13.7450\n",
      "Epoch 105/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 426.1566 - mae: 13.7061\n",
      "Epoch 106/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 421.6427 - mae: 13.6422\n",
      "Epoch 107/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 418.7137 - mae: 13.5134\n",
      "Epoch 108/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 418.4932 - mae: 13.5132\n",
      "Epoch 109/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 420.3070 - mae: 13.6130\n",
      "Epoch 110/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 424.2100 - mae: 13.6794\n",
      "Epoch 111/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 418.1645 - mae: 13.5422\n",
      "Epoch 112/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 414.4976 - mae: 13.4670\n",
      "Epoch 113/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 411.7135 - mae: 13.2806\n",
      "Epoch 114/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 410.9488 - mae: 13.3543\n",
      "Epoch 115/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 400.3902 - mae: 13.0711\n",
      "Epoch 116/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 397.6975 - mae: 12.9537\n",
      "Epoch 117/300\n",
      "9/9 [==============================] - 0s 13ms/step - loss: 396.3887 - mae: 12.9528\n",
      "Epoch 118/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 393.9987 - mae: 12.9353\n",
      "Epoch 119/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 392.3782 - mae: 12.8659\n",
      "Epoch 120/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 389.6132 - mae: 12.7961\n",
      "Epoch 121/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 388.1515 - mae: 12.7790\n",
      "Epoch 122/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 386.4066 - mae: 12.7475\n",
      "Epoch 123/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 383.1378 - mae: 12.5735\n",
      "Epoch 124/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 384.0544 - mae: 12.6529\n",
      "Epoch 125/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 381.9625 - mae: 12.5344\n",
      "Epoch 126/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 387.0183 - mae: 12.7561\n",
      "Epoch 127/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 379.4852 - mae: 12.4648\n",
      "Epoch 128/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 378.2747 - mae: 12.4252\n",
      "Epoch 129/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 376.0930 - mae: 12.3255\n",
      "Epoch 130/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 371.9333 - mae: 12.2747\n",
      "Epoch 131/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 372.1685 - mae: 12.3354\n",
      "Epoch 132/300\n",
      "9/9 [==============================] - 0s 12ms/step - loss: 368.8373 - mae: 12.1457\n",
      "Epoch 133/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 368.7658 - mae: 12.2354\n",
      "Epoch 134/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 365.1922 - mae: 12.0201\n",
      "Epoch 135/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 364.5635 - mae: 12.0368\n",
      "Epoch 136/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 363.2883 - mae: 11.9755\n",
      "Epoch 137/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 361.8999 - mae: 11.9913\n",
      "Epoch 138/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 358.5864 - mae: 11.9201\n",
      "Epoch 139/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 357.9321 - mae: 11.8958\n",
      "Epoch 140/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 358.7817 - mae: 11.8309\n",
      "Epoch 141/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 356.6034 - mae: 11.8649\n",
      "Epoch 142/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 353.6759 - mae: 11.6982\n",
      "Epoch 143/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 351.2742 - mae: 11.6872\n",
      "Epoch 144/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 349.8339 - mae: 11.6164\n",
      "Epoch 145/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 349.4084 - mae: 11.5950\n",
      "Epoch 146/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 348.3629 - mae: 11.6156\n",
      "Epoch 147/300\n",
      "9/9 [==============================] - 0s 17ms/step - loss: 348.1655 - mae: 11.5060\n",
      "Epoch 148/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 345.7872 - mae: 11.5148\n",
      "Epoch 149/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 349.7193 - mae: 11.6080\n",
      "Epoch 150/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 349.1698 - mae: 11.6719\n",
      "Epoch 151/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 341.9501 - mae: 11.2998\n",
      "Epoch 152/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 339.6140 - mae: 11.3560\n",
      "Epoch 153/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 339.8776 - mae: 11.3632\n",
      "Epoch 154/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 350.3743 - mae: 11.5338\n",
      "Epoch 155/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 350.0844 - mae: 11.7560\n",
      "Epoch 156/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 369.5711 - mae: 12.1009\n",
      "Epoch 157/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 351.2454 - mae: 11.5297\n",
      "Epoch 158/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 354.2589 - mae: 11.9395\n",
      "Epoch 159/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 333.0955 - mae: 11.0675\n",
      "Epoch 160/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 328.8783 - mae: 10.9637\n",
      "Epoch 161/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 330.6568 - mae: 11.0404\n",
      "Epoch 162/300\n",
      "9/9 [==============================] - 0s 15ms/step - loss: 340.1209 - mae: 11.3592\n",
      "Epoch 163/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 337.1063 - mae: 11.1400\n",
      "Epoch 164/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 330.0972 - mae: 10.9816\n",
      "Epoch 165/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 325.6325 - mae: 10.8981\n",
      "Epoch 166/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 322.2408 - mae: 10.7338\n",
      "Epoch 167/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 322.6639 - mae: 10.7795\n",
      "Epoch 168/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 323.4041 - mae: 10.8111\n",
      "Epoch 169/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 9ms/step - loss: 325.7990 - mae: 10.9062\n",
      "Epoch 170/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 320.1225 - mae: 10.6754\n",
      "Epoch 171/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 323.1627 - mae: 10.8583\n",
      "Epoch 172/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 323.4126 - mae: 10.7635\n",
      "Epoch 173/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 316.9986 - mae: 10.5638\n",
      "Epoch 174/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 319.1145 - mae: 10.6629\n",
      "Epoch 175/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 317.2038 - mae: 10.7382\n",
      "Epoch 176/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 317.0392 - mae: 10.5331\n",
      "Epoch 177/300\n",
      "9/9 [==============================] - 0s 12ms/step - loss: 312.8108 - mae: 10.3832\n",
      "Epoch 178/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 311.6170 - mae: 10.4970\n",
      "Epoch 179/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 311.3089 - mae: 10.3792\n",
      "Epoch 180/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 315.1822 - mae: 10.4723\n",
      "Epoch 181/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 317.6591 - mae: 10.6615\n",
      "Epoch 182/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 308.6494 - mae: 10.3650\n",
      "Epoch 183/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 306.6600 - mae: 10.3154\n",
      "Epoch 184/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 308.1648 - mae: 10.3072\n",
      "Epoch 185/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 318.6852 - mae: 10.5994\n",
      "Epoch 186/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 309.5460 - mae: 10.3592\n",
      "Epoch 187/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 304.5793 - mae: 10.2624\n",
      "Epoch 188/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 302.6441 - mae: 10.0862\n",
      "Epoch 189/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 303.0233 - mae: 10.1264\n",
      "Epoch 190/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 303.1445 - mae: 10.0771\n",
      "Epoch 191/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 304.5405 - mae: 10.1050\n",
      "Epoch 192/300\n",
      "9/9 [==============================] - 0s 12ms/step - loss: 300.1291 - mae: 9.9876\n",
      "Epoch 193/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 298.4356 - mae: 9.9248\n",
      "Epoch 194/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 300.0287 - mae: 10.0745\n",
      "Epoch 195/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 298.7438 - mae: 9.9690\n",
      "Epoch 196/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 301.1243 - mae: 10.0576\n",
      "Epoch 197/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 299.6134 - mae: 10.0260\n",
      "Epoch 198/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 295.8030 - mae: 9.9356\n",
      "Epoch 199/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 296.3700 - mae: 9.9999\n",
      "Epoch 200/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 295.8123 - mae: 9.9377\n",
      "Epoch 201/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 300.4499 - mae: 10.1150\n",
      "Epoch 202/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 297.0566 - mae: 9.8820\n",
      "Epoch 203/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 293.5759 - mae: 9.8382\n",
      "Epoch 204/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 292.8037 - mae: 9.7732\n",
      "Epoch 205/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 291.9168 - mae: 9.7148\n",
      "Epoch 206/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 290.6684 - mae: 9.6878\n",
      "Epoch 207/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 290.5621 - mae: 9.6753\n",
      "Epoch 208/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 296.2515 - mae: 9.9385\n",
      "Epoch 209/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 289.4019 - mae: 9.7329\n",
      "Epoch 210/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 287.1859 - mae: 9.5823\n",
      "Epoch 211/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 289.2722 - mae: 9.7440\n",
      "Epoch 212/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 286.3744 - mae: 9.5587\n",
      "Epoch 213/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 290.9727 - mae: 9.7784\n",
      "Epoch 214/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 287.0053 - mae: 9.6422\n",
      "Epoch 215/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 287.9822 - mae: 9.7880\n",
      "Epoch 216/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 289.3974 - mae: 9.6666\n",
      "Epoch 217/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 285.5938 - mae: 9.5929\n",
      "Epoch 218/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 286.5092 - mae: 9.5595\n",
      "Epoch 219/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 282.8159 - mae: 9.4655\n",
      "Epoch 220/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 281.9390 - mae: 9.3715\n",
      "Epoch 221/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 296.4278 - mae: 9.9957\n",
      "Epoch 222/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 288.2108 - mae: 9.6323\n",
      "Epoch 223/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 283.9442 - mae: 9.4463\n",
      "Epoch 224/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 279.8776 - mae: 9.3218\n",
      "Epoch 225/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 285.2198 - mae: 9.5910\n",
      "Epoch 226/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 281.6509 - mae: 9.4862\n",
      "Epoch 227/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 279.7477 - mae: 9.3528\n",
      "Epoch 228/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 278.6574 - mae: 9.2963\n",
      "Epoch 229/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 280.7570 - mae: 9.4076\n",
      "Epoch 230/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 282.5335 - mae: 9.4701\n",
      "Epoch 231/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 276.4349 - mae: 9.2023\n",
      "Epoch 232/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 278.4778 - mae: 9.2821\n",
      "Epoch 233/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 276.2482 - mae: 9.1494\n",
      "Epoch 234/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 275.0999 - mae: 9.1662\n",
      "Epoch 235/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 277.9833 - mae: 9.2553\n",
      "Epoch 236/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 274.1707 - mae: 9.1600\n",
      "Epoch 237/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 275.3251 - mae: 9.1612\n",
      "Epoch 238/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 275.5490 - mae: 9.1590\n",
      "Epoch 239/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 280.9887 - mae: 9.4069\n",
      "Epoch 240/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 279.0949 - mae: 9.3981\n",
      "Epoch 241/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 271.9856 - mae: 9.0079\n",
      "Epoch 242/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 274.1752 - mae: 9.2304\n",
      "Epoch 243/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 274.2120 - mae: 9.1546\n",
      "Epoch 244/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 278.8296 - mae: 9.3018\n",
      "Epoch 245/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 274.3188 - mae: 9.1428\n",
      "Epoch 246/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 270.9350 - mae: 9.0253\n",
      "Epoch 247/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 271.6761 - mae: 9.0767\n",
      "Epoch 248/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 269.2217 - mae: 8.9270\n",
      "Epoch 249/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 272.9591 - mae: 9.0988\n",
      "Epoch 250/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 269.4485 - mae: 8.9895\n",
      "Epoch 251/300\n",
      "9/9 [==============================] - 0s 13ms/step - loss: 274.4550 - mae: 9.1305\n",
      "Epoch 252/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 270.4653 - mae: 9.0138\n",
      "Epoch 253/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 272.1655 - mae: 9.0573\n",
      "Epoch 254/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 8ms/step - loss: 268.2818 - mae: 8.8875\n",
      "Epoch 255/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 267.2719 - mae: 8.8930\n",
      "Epoch 256/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 268.2821 - mae: 8.9018\n",
      "Epoch 257/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 268.3708 - mae: 8.8786\n",
      "Epoch 258/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 269.7013 - mae: 8.9998\n",
      "Epoch 259/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 275.3241 - mae: 9.2851\n",
      "Epoch 260/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 269.0874 - mae: 9.0787\n",
      "Epoch 261/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 265.2550 - mae: 8.7576\n",
      "Epoch 262/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 264.1970 - mae: 8.7493\n",
      "Epoch 263/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 271.3182 - mae: 9.1055\n",
      "Epoch 264/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 269.2077 - mae: 9.0500\n",
      "Epoch 265/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 274.2293 - mae: 9.1901\n",
      "Epoch 266/300\n",
      "9/9 [==============================] - 0s 13ms/step - loss: 270.9870 - mae: 9.1597\n",
      "Epoch 267/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 283.6584 - mae: 9.6028\n",
      "Epoch 268/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 263.9593 - mae: 8.8015\n",
      "Epoch 269/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 264.0215 - mae: 8.8357\n",
      "Epoch 270/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 266.1297 - mae: 8.8153\n",
      "Epoch 271/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 275.0807 - mae: 9.3342\n",
      "Epoch 272/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 279.9667 - mae: 9.4462\n",
      "Epoch 273/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 263.9359 - mae: 8.7724\n",
      "Epoch 274/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 275.1337 - mae: 9.2917\n",
      "Epoch 275/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 273.7516 - mae: 9.3498\n",
      "Epoch 276/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.2466 - mae: 10.0151\n",
      "Epoch 277/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 294.3960 - mae: 10.2563\n",
      "Epoch 278/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 275.5475 - mae: 9.3456\n",
      "Epoch 279/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 262.9933 - mae: 8.7351\n",
      "Epoch 280/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 260.5080 - mae: 8.6418\n",
      "Epoch 281/300\n",
      "9/9 [==============================] - 0s 12ms/step - loss: 264.5688 - mae: 8.8875\n",
      "Epoch 282/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 263.6701 - mae: 8.7436\n",
      "Epoch 283/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 261.6949 - mae: 8.6570\n",
      "Epoch 284/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 261.2846 - mae: 8.6208\n",
      "Epoch 285/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 257.7714 - mae: 8.5226\n",
      "Epoch 286/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 260.5445 - mae: 8.7454\n",
      "Epoch 287/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 262.1424 - mae: 8.7323\n",
      "Epoch 288/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 263.6781 - mae: 8.8621\n",
      "Epoch 289/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 267.8535 - mae: 8.9822\n",
      "Epoch 290/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 271.3310 - mae: 9.2713\n",
      "Epoch 291/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 261.3435 - mae: 8.7097\n",
      "Epoch 292/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 264.5119 - mae: 8.7881\n",
      "Epoch 293/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 255.8685 - mae: 8.5214\n",
      "Epoch 294/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 257.8406 - mae: 8.5627\n",
      "Epoch 295/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 262.0789 - mae: 8.8357\n",
      "Epoch 296/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 260.9356 - mae: 8.7846\n",
      "Epoch 297/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 261.2447 - mae: 8.6080\n",
      "Epoch 298/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 257.1835 - mae: 8.5119\n",
      "Epoch 299/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 259.5213 - mae: 8.6185\n",
      "Epoch 300/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 255.6091 - mae: 8.4861\n",
      "3/3 [==============================] - 0s 4ms/step - loss: 244.7552 - mae: 8.0519\n",
      "Epoch 1/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 48769268.0000 - mae: 6970.0972\n",
      "Epoch 2/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 46815768.0000 - mae: 6827.4897\n",
      "Epoch 3/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 19496140.0000 - mae: 3945.5552\n",
      "Epoch 4/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 4216405.5000 - mae: 1757.0029\n",
      "Epoch 5/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1955733.6250 - mae: 1195.3119\n",
      "Epoch 6/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 848446.8750 - mae: 774.7006\n",
      "Epoch 7/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 385287.5625 - mae: 516.0582\n",
      "Epoch 8/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 179491.6406 - mae: 343.0482\n",
      "Epoch 9/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 95975.1562 - mae: 251.1954\n",
      "Epoch 10/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 56274.0117 - mae: 178.8546\n",
      "Epoch 11/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 35627.8594 - mae: 148.0627\n",
      "Epoch 12/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 24843.8418 - mae: 119.2736\n",
      "Epoch 13/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 16715.3887 - mae: 95.4109\n",
      "Epoch 14/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 12770.2998 - mae: 88.7228\n",
      "Epoch 15/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 10630.7119 - mae: 77.8914\n",
      "Epoch 16/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 8818.2734 - mae: 68.9393\n",
      "Epoch 17/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 7622.9028 - mae: 65.8333\n",
      "Epoch 18/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 6624.4736 - mae: 60.7657\n",
      "Epoch 19/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 5852.0908 - mae: 56.1409\n",
      "Epoch 20/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 5200.5112 - mae: 52.4420\n",
      "Epoch 21/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 4647.2881 - mae: 48.9777\n",
      "Epoch 22/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 4182.0840 - mae: 46.3802\n",
      "Epoch 23/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 3750.2012 - mae: 43.4187\n",
      "Epoch 24/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 3352.4229 - mae: 41.0773\n",
      "Epoch 25/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2988.2791 - mae: 38.8487\n",
      "Epoch 26/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2652.1223 - mae: 36.2118\n",
      "Epoch 27/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 2336.5725 - mae: 34.0995\n",
      "Epoch 28/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2056.9629 - mae: 32.1868\n",
      "Epoch 29/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1809.2433 - mae: 30.2674\n",
      "Epoch 30/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1594.1594 - mae: 28.4367\n",
      "Epoch 31/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1404.7888 - mae: 26.6229\n",
      "Epoch 32/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1243.5599 - mae: 25.0878\n",
      "Epoch 33/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1110.5591 - mae: 23.6759\n",
      "Epoch 34/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1000.1652 - mae: 22.2784\n",
      "Epoch 35/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 900.7999 - mae: 21.1442\n",
      "Epoch 36/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 817.7967 - mae: 19.9909\n",
      "Epoch 37/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 9ms/step - loss: 749.0965 - mae: 19.0464\n",
      "Epoch 38/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 690.3155 - mae: 18.1853\n",
      "Epoch 39/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 642.0111 - mae: 17.4841\n",
      "Epoch 40/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 600.5602 - mae: 16.8495\n",
      "Epoch 41/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 566.0992 - mae: 16.3087\n",
      "Epoch 42/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 540.6552 - mae: 15.9465\n",
      "Epoch 43/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 517.6628 - mae: 15.5352\n",
      "Epoch 44/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 495.6103 - mae: 15.1526\n",
      "Epoch 45/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 480.3388 - mae: 14.8486\n",
      "Epoch 46/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 467.2618 - mae: 14.6299\n",
      "Epoch 47/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 451.9729 - mae: 14.3595\n",
      "Epoch 48/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 441.0210 - mae: 14.1389\n",
      "Epoch 49/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 435.1914 - mae: 14.0264\n",
      "Epoch 50/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 424.6961 - mae: 13.7683\n",
      "Epoch 51/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 417.5717 - mae: 13.6156\n",
      "Epoch 52/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 410.4678 - mae: 13.4787\n",
      "Epoch 53/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 404.7960 - mae: 13.3700\n",
      "Epoch 54/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 399.9694 - mae: 13.2380\n",
      "Epoch 55/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 398.1227 - mae: 13.2288\n",
      "Epoch 56/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 392.5847 - mae: 13.0878\n",
      "Epoch 57/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 388.3692 - mae: 12.9949\n",
      "Epoch 58/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 388.6752 - mae: 12.9902\n",
      "Epoch 59/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 383.7018 - mae: 12.8860\n",
      "Epoch 60/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 381.1245 - mae: 12.8184\n",
      "Epoch 61/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 383.3621 - mae: 12.8906\n",
      "Epoch 62/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 378.3928 - mae: 12.7193\n",
      "Epoch 63/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 375.3895 - mae: 12.6070\n",
      "Epoch 64/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 376.0076 - mae: 12.6977\n",
      "Epoch 65/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 376.0594 - mae: 12.7261\n",
      "Epoch 66/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 370.1534 - mae: 12.5102\n",
      "Epoch 67/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 368.3640 - mae: 12.4644\n",
      "Epoch 68/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 365.2378 - mae: 12.4066\n",
      "Epoch 69/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 363.1974 - mae: 12.3407\n",
      "Epoch 70/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 363.1427 - mae: 12.3413\n",
      "Epoch 71/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 361.0168 - mae: 12.2631\n",
      "Epoch 72/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 362.5113 - mae: 12.2861\n",
      "Epoch 73/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 358.8975 - mae: 12.2201\n",
      "Epoch 74/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 357.2121 - mae: 12.1558\n",
      "Epoch 75/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 355.7922 - mae: 12.0981\n",
      "Epoch 76/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 355.8386 - mae: 12.1049\n",
      "Epoch 77/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 355.4250 - mae: 12.1004\n",
      "Epoch 78/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 359.5512 - mae: 12.1510\n",
      "Epoch 79/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 354.6564 - mae: 12.1133\n",
      "Epoch 80/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 351.7106 - mae: 12.0030\n",
      "Epoch 81/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 350.5793 - mae: 11.9484\n",
      "Epoch 82/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 348.4090 - mae: 11.8948\n",
      "Epoch 83/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 349.0110 - mae: 11.8956\n",
      "Epoch 84/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 346.7692 - mae: 11.8266\n",
      "Epoch 85/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 345.3656 - mae: 11.8041\n",
      "Epoch 86/300\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 344.9375 - mae: 11.8002\n",
      "Epoch 87/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 345.0816 - mae: 11.8181\n",
      "Epoch 88/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 344.4742 - mae: 11.7605\n",
      "Epoch 89/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 342.5263 - mae: 11.7336\n",
      "Epoch 90/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 342.0806 - mae: 11.6645\n",
      "Epoch 91/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 343.0034 - mae: 11.7563\n",
      "Epoch 92/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 341.4944 - mae: 11.6640\n",
      "Epoch 93/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 343.0609 - mae: 11.7729\n",
      "Epoch 94/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 343.2657 - mae: 11.7105\n",
      "Epoch 95/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 338.7553 - mae: 11.6038\n",
      "Epoch 96/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 337.0645 - mae: 11.5573\n",
      "Epoch 97/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 333.9607 - mae: 11.4507\n",
      "Epoch 98/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 334.0714 - mae: 11.4680\n",
      "Epoch 99/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 331.9129 - mae: 11.4111\n",
      "Epoch 100/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 331.9904 - mae: 11.4059\n",
      "Epoch 101/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 330.7444 - mae: 11.3886\n",
      "Epoch 102/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 331.3266 - mae: 11.3916\n",
      "Epoch 103/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 329.5232 - mae: 11.3600\n",
      "Epoch 104/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 328.3120 - mae: 11.2808\n",
      "Epoch 105/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 328.1490 - mae: 11.3085\n",
      "Epoch 106/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 330.1508 - mae: 11.2992\n",
      "Epoch 107/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 326.3250 - mae: 11.2543\n",
      "Epoch 108/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 324.0500 - mae: 11.1061\n",
      "Epoch 109/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 331.8889 - mae: 11.5039\n",
      "Epoch 110/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 327.7185 - mae: 11.2778\n",
      "Epoch 111/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 327.1517 - mae: 11.3073\n",
      "Epoch 112/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 326.2143 - mae: 11.1955\n",
      "Epoch 113/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 320.2261 - mae: 11.0326\n",
      "Epoch 114/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 323.0612 - mae: 11.1445\n",
      "Epoch 115/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 319.6139 - mae: 11.0126\n",
      "Epoch 116/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 318.0877 - mae: 10.9431\n",
      "Epoch 117/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 315.1103 - mae: 10.9072\n",
      "Epoch 118/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 314.3449 - mae: 10.8906\n",
      "Epoch 119/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 314.2466 - mae: 10.8715\n",
      "Epoch 120/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 313.6674 - mae: 10.8512\n",
      "Epoch 121/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 313.1278 - mae: 10.8538\n",
      "Epoch 122/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 10ms/step - loss: 313.0603 - mae: 10.8653\n",
      "Epoch 123/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 310.6177 - mae: 10.7298\n",
      "Epoch 124/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 310.9723 - mae: 10.8511\n",
      "Epoch 125/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 311.1283 - mae: 10.7409\n",
      "Epoch 126/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 317.2832 - mae: 11.1009\n",
      "Epoch 127/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 315.2992 - mae: 10.8439\n",
      "Epoch 128/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 310.8732 - mae: 10.8132\n",
      "Epoch 129/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 313.7719 - mae: 10.7562\n",
      "Epoch 130/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 307.8766 - mae: 10.6740\n",
      "Epoch 131/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 307.8202 - mae: 10.7459\n",
      "Epoch 132/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 306.3810 - mae: 10.6457\n",
      "Epoch 133/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 304.7093 - mae: 10.5076\n",
      "Epoch 134/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 303.1699 - mae: 10.4914\n",
      "Epoch 135/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 302.5583 - mae: 10.4932\n",
      "Epoch 136/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 301.5605 - mae: 10.4568\n",
      "Epoch 137/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 301.2560 - mae: 10.5051\n",
      "Epoch 138/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 302.1832 - mae: 10.5397\n",
      "Epoch 139/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 300.7268 - mae: 10.4047\n",
      "Epoch 140/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 298.9835 - mae: 10.3525\n",
      "Epoch 141/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 300.1296 - mae: 10.4087\n",
      "Epoch 142/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 297.4794 - mae: 10.3361\n",
      "Epoch 143/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 296.9094 - mae: 10.3091\n",
      "Epoch 144/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 300.7921 - mae: 10.5407\n",
      "Epoch 145/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 306.4400 - mae: 10.6074\n",
      "Epoch 146/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 300.7052 - mae: 10.3864\n",
      "Epoch 147/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 295.9739 - mae: 10.2909\n",
      "Epoch 148/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 294.9151 - mae: 10.2201\n",
      "Epoch 149/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 296.7029 - mae: 10.2851\n",
      "Epoch 150/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 298.1870 - mae: 10.4044\n",
      "Epoch 151/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 296.0796 - mae: 10.2168\n",
      "Epoch 152/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 292.9490 - mae: 10.1797\n",
      "Epoch 153/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 292.5285 - mae: 10.1852\n",
      "Epoch 154/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 294.7930 - mae: 10.1772\n",
      "Epoch 155/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 290.1335 - mae: 10.0444\n",
      "Epoch 156/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 299.4621 - mae: 10.3609\n",
      "Epoch 157/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 294.1839 - mae: 10.1409\n",
      "Epoch 158/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 304.1584 - mae: 10.6913\n",
      "Epoch 159/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 297.1636 - mae: 10.3749\n",
      "Epoch 160/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 288.4496 - mae: 10.0536\n",
      "Epoch 161/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 289.1126 - mae: 10.1122\n",
      "Epoch 162/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 289.6377 - mae: 10.0292\n",
      "Epoch 163/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 287.1527 - mae: 9.9586\n",
      "Epoch 164/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 285.5291 - mae: 9.9271\n",
      "Epoch 165/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.4012 - mae: 10.0648\n",
      "Epoch 166/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 285.2292 - mae: 9.8875\n",
      "Epoch 167/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 282.8676 - mae: 9.9293\n",
      "Epoch 168/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 284.0174 - mae: 9.7908\n",
      "Epoch 169/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 283.2313 - mae: 9.8048\n",
      "Epoch 170/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 280.8683 - mae: 9.7638\n",
      "Epoch 171/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 284.4644 - mae: 9.9387\n",
      "Epoch 172/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 281.3105 - mae: 9.8060\n",
      "Epoch 173/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 279.0722 - mae: 9.6619\n",
      "Epoch 174/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 278.5325 - mae: 9.6788\n",
      "Epoch 175/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 279.8924 - mae: 9.7632\n",
      "Epoch 176/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 280.5865 - mae: 9.8047\n",
      "Epoch 177/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 278.0238 - mae: 9.5880\n",
      "Epoch 178/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 277.9963 - mae: 9.7167\n",
      "Epoch 179/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 279.4256 - mae: 9.7311\n",
      "Epoch 180/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 278.9119 - mae: 9.6220\n",
      "Epoch 181/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 279.2421 - mae: 9.6986\n",
      "Epoch 182/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 275.2008 - mae: 9.5454\n",
      "Epoch 183/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 275.2316 - mae: 9.5175\n",
      "Epoch 184/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 273.1147 - mae: 9.4797\n",
      "Epoch 185/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 277.0298 - mae: 9.5511\n",
      "Epoch 186/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 275.8017 - mae: 9.5765\n",
      "Epoch 187/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 271.5727 - mae: 9.4355\n",
      "Epoch 188/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 272.2015 - mae: 9.4149\n",
      "Epoch 189/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 270.9956 - mae: 9.4289\n",
      "Epoch 190/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 272.3835 - mae: 9.5248\n",
      "Epoch 191/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 280.8118 - mae: 9.7756\n",
      "Epoch 192/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 280.1279 - mae: 9.7837\n",
      "Epoch 193/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 275.8169 - mae: 9.5212\n",
      "Epoch 194/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 278.3051 - mae: 9.6170\n",
      "Epoch 195/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 274.0313 - mae: 9.4938\n",
      "Epoch 196/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 272.4888 - mae: 9.4723\n",
      "Epoch 197/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 267.8699 - mae: 9.2473\n",
      "Epoch 198/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 267.9876 - mae: 9.3300\n",
      "Epoch 199/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 266.8347 - mae: 9.1835\n",
      "Epoch 200/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 265.7155 - mae: 9.2089\n",
      "Epoch 201/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 263.8333 - mae: 9.1435\n",
      "Epoch 202/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 264.8786 - mae: 9.1664\n",
      "Epoch 203/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 263.2934 - mae: 9.1362\n",
      "Epoch 204/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 264.1640 - mae: 9.0806\n",
      "Epoch 205/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 269.1693 - mae: 9.3518\n",
      "Epoch 206/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 9ms/step - loss: 261.5263 - mae: 9.0418\n",
      "Epoch 207/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 260.5845 - mae: 9.0338\n",
      "Epoch 208/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 260.7427 - mae: 9.0498\n",
      "Epoch 209/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 259.5862 - mae: 8.9427\n",
      "Epoch 210/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 262.2050 - mae: 9.0882\n",
      "Epoch 211/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 261.6534 - mae: 9.0804\n",
      "Epoch 212/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 260.9327 - mae: 8.9604\n",
      "Epoch 213/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 258.3213 - mae: 8.8811\n",
      "Epoch 214/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 263.3112 - mae: 9.0807\n",
      "Epoch 215/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 259.9926 - mae: 8.9646\n",
      "Epoch 216/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 261.6335 - mae: 8.9263\n",
      "Epoch 217/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 266.9561 - mae: 9.2417\n",
      "Epoch 218/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 260.0210 - mae: 8.9476\n",
      "Epoch 219/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 257.5443 - mae: 8.8608\n",
      "Epoch 220/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 261.6430 - mae: 9.0331\n",
      "Epoch 221/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 264.8371 - mae: 9.1675\n",
      "Epoch 222/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 265.8953 - mae: 9.2635\n",
      "Epoch 223/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 258.4746 - mae: 8.8533\n",
      "Epoch 224/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.1808 - mae: 8.6481\n",
      "Epoch 225/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 256.1611 - mae: 8.8268\n",
      "Epoch 226/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 252.9341 - mae: 8.7236\n",
      "Epoch 227/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.9217 - mae: 8.7504\n",
      "Epoch 228/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 258.5080 - mae: 9.0081\n",
      "Epoch 229/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 262.4380 - mae: 9.1108\n",
      "Epoch 230/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.0567 - mae: 8.7651\n",
      "Epoch 231/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 251.2680 - mae: 8.6016\n",
      "Epoch 232/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 250.9889 - mae: 8.5680\n",
      "Epoch 233/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 252.3871 - mae: 8.5694\n",
      "Epoch 234/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 249.5573 - mae: 8.5650\n",
      "Epoch 235/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 249.0828 - mae: 8.4771\n",
      "Epoch 236/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.5251 - mae: 8.7293\n",
      "Epoch 237/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 254.1060 - mae: 8.6617\n",
      "Epoch 238/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.2384 - mae: 8.6625\n",
      "Epoch 239/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 256.0280 - mae: 8.7747\n",
      "Epoch 240/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 246.4970 - mae: 8.3889\n",
      "Epoch 241/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 249.2310 - mae: 8.4696\n",
      "Epoch 242/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 249.9024 - mae: 8.6705\n",
      "Epoch 243/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 248.2059 - mae: 8.4933\n",
      "Epoch 244/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 265.7874 - mae: 9.2756\n",
      "Epoch 245/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 246.8097 - mae: 8.4005\n",
      "Epoch 246/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 245.6227 - mae: 8.4451\n",
      "Epoch 247/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 250.7178 - mae: 8.4972\n",
      "Epoch 248/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 250.3979 - mae: 8.6275\n",
      "Epoch 249/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 250.5129 - mae: 8.5952\n",
      "Epoch 250/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 250.7697 - mae: 8.5986\n",
      "Epoch 251/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 244.3093 - mae: 8.3528\n",
      "Epoch 252/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 242.4522 - mae: 8.2000\n",
      "Epoch 253/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 246.8183 - mae: 8.4486\n",
      "Epoch 254/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 243.7734 - mae: 8.1842\n",
      "Epoch 255/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 242.5116 - mae: 8.2349\n",
      "Epoch 256/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 241.2937 - mae: 8.1807\n",
      "Epoch 257/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 241.1045 - mae: 8.1831\n",
      "Epoch 258/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 244.7086 - mae: 8.3422\n",
      "Epoch 259/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 246.8851 - mae: 8.4764\n",
      "Epoch 260/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 241.8166 - mae: 8.2336\n",
      "Epoch 261/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 250.6980 - mae: 8.6348\n",
      "Epoch 262/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 254.2323 - mae: 8.8324\n",
      "Epoch 263/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 258.5583 - mae: 9.0049\n",
      "Epoch 264/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 243.1221 - mae: 8.3249\n",
      "Epoch 265/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 237.6475 - mae: 8.0226\n",
      "Epoch 266/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 237.7668 - mae: 7.9824\n",
      "Epoch 267/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 242.0936 - mae: 8.2534\n",
      "Epoch 268/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 238.1782 - mae: 8.0739\n",
      "Epoch 269/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 239.9858 - mae: 8.1784\n",
      "Epoch 270/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 240.0864 - mae: 8.0878\n",
      "Epoch 271/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 236.5400 - mae: 7.9610\n",
      "Epoch 272/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 236.9260 - mae: 7.9260\n",
      "Epoch 273/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 237.6302 - mae: 8.0002\n",
      "Epoch 274/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 236.7406 - mae: 7.9430\n",
      "Epoch 275/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 236.2929 - mae: 7.8669\n",
      "Epoch 276/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 249.8779 - mae: 8.7354\n",
      "Epoch 277/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 243.7054 - mae: 8.2048\n",
      "Epoch 278/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 236.1116 - mae: 7.9781\n",
      "Epoch 279/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 240.4562 - mae: 8.0629\n",
      "Epoch 280/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 240.7939 - mae: 8.1659\n",
      "Epoch 281/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 234.6645 - mae: 7.9397\n",
      "Epoch 282/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 233.6600 - mae: 7.8503\n",
      "Epoch 283/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 235.7357 - mae: 7.9012\n",
      "Epoch 284/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 235.4703 - mae: 7.9445\n",
      "Epoch 285/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 234.6775 - mae: 7.7691\n",
      "Epoch 286/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 236.1434 - mae: 8.0706\n",
      "Epoch 287/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 231.1846 - mae: 7.6322\n",
      "Epoch 288/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 242.4444 - mae: 8.3272\n",
      "Epoch 289/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 243.0530 - mae: 8.3005\n",
      "Epoch 290/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 243.9209 - mae: 8.4349\n",
      "Epoch 291/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 9ms/step - loss: 250.0536 - mae: 8.7734\n",
      "Epoch 292/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 239.7859 - mae: 8.0500\n",
      "Epoch 293/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 232.1840 - mae: 7.7295\n",
      "Epoch 294/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 231.5946 - mae: 7.7217\n",
      "Epoch 295/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 238.0478 - mae: 8.1691\n",
      "Epoch 296/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 231.3682 - mae: 7.7836\n",
      "Epoch 297/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 232.5105 - mae: 7.6874\n",
      "Epoch 298/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 236.2313 - mae: 8.1310\n",
      "Epoch 299/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 237.0268 - mae: 7.9182\n",
      "Epoch 300/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 235.1258 - mae: 7.9998\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 282.1138 - mae: 8.7294\n",
      "Epoch 1/300\n",
      "9/9 [==============================] - 1s 9ms/step - loss: 48788184.0000 - mae: 6971.5718\n",
      "Epoch 2/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 47285632.0000 - mae: 6862.3228\n",
      "Epoch 3/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 23358748.0000 - mae: 4481.5742\n",
      "Epoch 4/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 4556829.0000 - mae: 1824.2252\n",
      "Epoch 5/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2271142.2500 - mae: 1302.5396\n",
      "Epoch 6/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 906134.1875 - mae: 804.7008\n",
      "Epoch 7/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 418806.4688 - mae: 538.1779\n",
      "Epoch 8/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 203477.9219 - mae: 364.8747\n",
      "Epoch 9/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 108434.1172 - mae: 260.8732\n",
      "Epoch 10/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 67026.5000 - mae: 190.8655\n",
      "Epoch 11/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 46342.6680 - mae: 167.7259\n",
      "Epoch 12/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 35276.1953 - mae: 134.7108\n",
      "Epoch 13/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 26711.2109 - mae: 121.9715\n",
      "Epoch 14/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 20655.4375 - mae: 108.3740\n",
      "Epoch 15/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 16884.5508 - mae: 94.8647\n",
      "Epoch 16/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 14250.6191 - mae: 90.4958\n",
      "Epoch 17/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 12335.3906 - mae: 84.6075\n",
      "Epoch 18/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 10832.2031 - mae: 79.0438\n",
      "Epoch 19/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 9671.2031 - mae: 74.8503\n",
      "Epoch 20/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 8698.5469 - mae: 71.6079\n",
      "Epoch 21/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7837.0679 - mae: 67.8865\n",
      "Epoch 22/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7120.2061 - mae: 64.8462\n",
      "Epoch 23/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 6514.4805 - mae: 60.4877\n",
      "Epoch 24/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 5961.9292 - mae: 58.2824\n",
      "Epoch 25/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 5472.3540 - mae: 56.4636\n",
      "Epoch 26/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 5023.1968 - mae: 53.0463\n",
      "Epoch 27/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 4588.3906 - mae: 50.4231\n",
      "Epoch 28/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 4241.3066 - mae: 48.1245\n",
      "Epoch 29/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 3921.3909 - mae: 46.2134\n",
      "Epoch 30/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 3631.0476 - mae: 43.8911\n",
      "Epoch 31/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 3371.1389 - mae: 42.1231\n",
      "Epoch 32/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 3131.3860 - mae: 40.3016\n",
      "Epoch 33/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2917.7441 - mae: 38.5585\n",
      "Epoch 34/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2732.9822 - mae: 37.1799\n",
      "Epoch 35/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2555.2764 - mae: 35.8255\n",
      "Epoch 36/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2397.9233 - mae: 34.5466\n",
      "Epoch 37/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 2259.5645 - mae: 33.5290\n",
      "Epoch 38/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2139.6777 - mae: 32.5863\n",
      "Epoch 39/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2020.3704 - mae: 31.4760\n",
      "Epoch 40/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1915.3356 - mae: 30.6027\n",
      "Epoch 41/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1818.8795 - mae: 29.8416\n",
      "Epoch 42/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1745.7345 - mae: 29.3754\n",
      "Epoch 43/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1660.4862 - mae: 28.7521\n",
      "Epoch 44/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1583.5558 - mae: 28.0452\n",
      "Epoch 45/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1514.5210 - mae: 27.4798\n",
      "Epoch 46/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1456.2192 - mae: 26.9927\n",
      "Epoch 47/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1399.6309 - mae: 26.4134\n",
      "Epoch 48/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1347.1470 - mae: 25.8479\n",
      "Epoch 49/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1307.3038 - mae: 25.5968\n",
      "Epoch 50/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 1252.9529 - mae: 25.0414\n",
      "Epoch 51/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1208.4083 - mae: 24.5667\n",
      "Epoch 52/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1169.8843 - mae: 24.2264\n",
      "Epoch 53/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1130.5657 - mae: 23.8226\n",
      "Epoch 54/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1095.9587 - mae: 23.4180\n",
      "Epoch 55/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1064.0511 - mae: 23.0117\n",
      "Epoch 56/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1032.8456 - mae: 22.7375\n",
      "Epoch 57/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 1002.2591 - mae: 22.4433\n",
      "Epoch 58/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 975.8195 - mae: 22.1803\n",
      "Epoch 59/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 950.9862 - mae: 21.8631\n",
      "Epoch 60/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 925.6729 - mae: 21.5709\n",
      "Epoch 61/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 909.0872 - mae: 21.4933\n",
      "Epoch 62/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 882.6843 - mae: 21.1192\n",
      "Epoch 63/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 861.9631 - mae: 20.9260\n",
      "Epoch 64/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 839.5062 - mae: 20.6528\n",
      "Epoch 65/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 823.4310 - mae: 20.4887\n",
      "Epoch 66/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 805.7993 - mae: 20.2326\n",
      "Epoch 67/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 784.8856 - mae: 19.9675\n",
      "Epoch 68/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 766.8669 - mae: 19.7478\n",
      "Epoch 69/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 757.7335 - mae: 19.6694\n",
      "Epoch 70/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 737.2239 - mae: 19.3261\n",
      "Epoch 71/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 726.3471 - mae: 19.1755\n",
      "Epoch 72/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 720.4536 - mae: 19.1783\n",
      "Epoch 73/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 708.1506 - mae: 19.0520\n",
      "Epoch 74/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 9ms/step - loss: 687.1406 - mae: 18.7149\n",
      "Epoch 75/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 675.1620 - mae: 18.4970\n",
      "Epoch 76/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 668.5825 - mae: 18.4670\n",
      "Epoch 77/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 655.5697 - mae: 18.3227\n",
      "Epoch 78/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 646.4151 - mae: 18.1665\n",
      "Epoch 79/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 629.9492 - mae: 17.8613\n",
      "Epoch 80/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 618.9017 - mae: 17.7075\n",
      "Epoch 81/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 616.3724 - mae: 17.7502\n",
      "Epoch 82/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 602.9853 - mae: 17.4865\n",
      "Epoch 83/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 592.0552 - mae: 17.3204\n",
      "Epoch 84/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 584.3071 - mae: 17.2309\n",
      "Epoch 85/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 575.4187 - mae: 17.0542\n",
      "Epoch 86/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 567.7733 - mae: 16.9808\n",
      "Epoch 87/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 561.4625 - mae: 16.9056\n",
      "Epoch 88/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 552.7139 - mae: 16.7688\n",
      "Epoch 89/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 545.7191 - mae: 16.5736\n",
      "Epoch 90/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 538.7852 - mae: 16.4172\n",
      "Epoch 91/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 534.3342 - mae: 16.4099\n",
      "Epoch 92/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 531.0115 - mae: 16.2798\n",
      "Epoch 93/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 520.4084 - mae: 16.1035\n",
      "Epoch 94/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 513.1519 - mae: 16.0098\n",
      "Epoch 95/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 507.3724 - mae: 15.8721\n",
      "Epoch 96/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 501.9643 - mae: 15.7535\n",
      "Epoch 97/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 496.2034 - mae: 15.6761\n",
      "Epoch 98/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 491.1119 - mae: 15.6054\n",
      "Epoch 99/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 485.7511 - mae: 15.5037\n",
      "Epoch 100/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 480.6522 - mae: 15.3470\n",
      "Epoch 101/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 476.6183 - mae: 15.2963\n",
      "Epoch 102/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 470.9283 - mae: 15.1971\n",
      "Epoch 103/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 467.3641 - mae: 15.0994\n",
      "Epoch 104/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 462.1291 - mae: 14.9591\n",
      "Epoch 105/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 465.1005 - mae: 15.1271\n",
      "Epoch 106/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 461.8287 - mae: 14.9287\n",
      "Epoch 107/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 453.9690 - mae: 14.8073\n",
      "Epoch 108/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 450.2312 - mae: 14.6840\n",
      "Epoch 109/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 449.3138 - mae: 14.8045\n",
      "Epoch 110/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 449.1832 - mae: 14.7268\n",
      "Epoch 111/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 442.4401 - mae: 14.6533\n",
      "Epoch 112/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 430.2242 - mae: 14.2848\n",
      "Epoch 113/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 428.9361 - mae: 14.2052\n",
      "Epoch 114/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 423.6462 - mae: 14.1613\n",
      "Epoch 115/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 428.6929 - mae: 14.1738\n",
      "Epoch 116/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 416.5021 - mae: 13.9495\n",
      "Epoch 117/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 419.5074 - mae: 14.0359\n",
      "Epoch 118/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 415.0655 - mae: 13.9831\n",
      "Epoch 119/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 408.3265 - mae: 13.7999\n",
      "Epoch 120/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 407.1569 - mae: 13.7837\n",
      "Epoch 121/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 402.8219 - mae: 13.6294\n",
      "Epoch 122/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 398.7778 - mae: 13.5687\n",
      "Epoch 123/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 395.5161 - mae: 13.5015\n",
      "Epoch 124/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 393.9464 - mae: 13.4257\n",
      "Epoch 125/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 390.9115 - mae: 13.3914\n",
      "Epoch 126/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 387.3217 - mae: 13.2587\n",
      "Epoch 127/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 388.6974 - mae: 13.3386\n",
      "Epoch 128/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 389.7890 - mae: 13.2486\n",
      "Epoch 129/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 381.9080 - mae: 13.1521\n",
      "Epoch 130/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 377.7891 - mae: 13.0474\n",
      "Epoch 131/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 377.2723 - mae: 13.0723\n",
      "Epoch 132/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 372.8468 - mae: 12.8718\n",
      "Epoch 133/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 371.3513 - mae: 12.7949\n",
      "Epoch 134/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 368.7762 - mae: 12.7956\n",
      "Epoch 135/300\n",
      "9/9 [==============================] - 0s 18ms/step - loss: 366.5192 - mae: 12.7159\n",
      "Epoch 136/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 367.3261 - mae: 12.7534\n",
      "Epoch 137/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 364.5082 - mae: 12.6670\n",
      "Epoch 138/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 360.3313 - mae: 12.5851\n",
      "Epoch 139/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 359.3910 - mae: 12.4652\n",
      "Epoch 140/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 359.0455 - mae: 12.5669\n",
      "Epoch 141/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 355.2331 - mae: 12.3840\n",
      "Epoch 142/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 351.9889 - mae: 12.3241\n",
      "Epoch 143/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 349.3116 - mae: 12.2259\n",
      "Epoch 144/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 352.0240 - mae: 12.3914\n",
      "Epoch 145/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 353.1533 - mae: 12.2838\n",
      "Epoch 146/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 356.0970 - mae: 12.3740\n",
      "Epoch 147/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 349.6550 - mae: 12.3619\n",
      "Epoch 148/300\n",
      "9/9 [==============================] - 0s 14ms/step - loss: 356.1254 - mae: 12.2961\n",
      "Epoch 149/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 355.9743 - mae: 12.5566\n",
      "Epoch 150/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 346.7866 - mae: 12.1381\n",
      "Epoch 151/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 339.4707 - mae: 11.8687\n",
      "Epoch 152/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 335.7514 - mae: 11.8214\n",
      "Epoch 153/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 336.0558 - mae: 11.8028\n",
      "Epoch 154/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 332.6756 - mae: 11.7333\n",
      "Epoch 155/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 330.8894 - mae: 11.7463\n",
      "Epoch 156/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 337.5544 - mae: 11.8231\n",
      "Epoch 157/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 333.7244 - mae: 11.7023\n",
      "Epoch 158/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 10ms/step - loss: 348.3037 - mae: 12.4015\n",
      "Epoch 159/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 334.6851 - mae: 11.7843\n",
      "Epoch 160/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 331.1614 - mae: 11.7260\n",
      "Epoch 161/300\n",
      "9/9 [==============================] - 0s 18ms/step - loss: 327.9420 - mae: 11.7053\n",
      "Epoch 162/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 322.5912 - mae: 11.4805\n",
      "Epoch 163/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 318.6004 - mae: 11.2872\n",
      "Epoch 164/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 316.1303 - mae: 11.2544\n",
      "Epoch 165/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 321.0700 - mae: 11.3602\n",
      "Epoch 166/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 317.8846 - mae: 11.2989\n",
      "Epoch 167/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 313.0539 - mae: 11.2192\n",
      "Epoch 168/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 312.7304 - mae: 11.1285\n",
      "Epoch 169/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 314.0971 - mae: 11.1872\n",
      "Epoch 170/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 308.8129 - mae: 11.0635\n",
      "Epoch 171/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 310.3209 - mae: 11.0701\n",
      "Epoch 172/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 307.7819 - mae: 10.9964\n",
      "Epoch 173/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 308.6862 - mae: 11.0322\n",
      "Epoch 174/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 308.7078 - mae: 11.1056\n",
      "Epoch 175/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 305.8769 - mae: 10.9647\n",
      "Epoch 176/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 304.9458 - mae: 10.9727\n",
      "Epoch 177/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 300.7297 - mae: 10.8205\n",
      "Epoch 178/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 299.8206 - mae: 10.7827\n",
      "Epoch 179/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 305.6013 - mae: 10.9269\n",
      "Epoch 180/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 307.7831 - mae: 10.8992\n",
      "Epoch 181/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 306.5033 - mae: 10.9780\n",
      "Epoch 182/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 297.8674 - mae: 10.7615\n",
      "Epoch 183/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 294.9308 - mae: 10.6252\n",
      "Epoch 184/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 294.9941 - mae: 10.5835\n",
      "Epoch 185/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 294.5765 - mae: 10.5313\n",
      "Epoch 186/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 293.3636 - mae: 10.4848\n",
      "Epoch 187/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 291.6687 - mae: 10.4236\n",
      "Epoch 188/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 292.3607 - mae: 10.4512\n",
      "Epoch 189/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 291.5616 - mae: 10.3646\n",
      "Epoch 190/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 288.4653 - mae: 10.3430\n",
      "Epoch 191/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 292.8556 - mae: 10.4522\n",
      "Epoch 192/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 291.5326 - mae: 10.3922\n",
      "Epoch 193/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 289.4254 - mae: 10.3079\n",
      "Epoch 194/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 285.2736 - mae: 10.1904\n",
      "Epoch 195/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 285.2216 - mae: 10.2029\n",
      "Epoch 196/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 289.0871 - mae: 10.4241\n",
      "Epoch 197/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 281.8558 - mae: 10.1419\n",
      "Epoch 198/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 283.2661 - mae: 10.1457\n",
      "Epoch 199/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 282.8584 - mae: 10.0725\n",
      "Epoch 200/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 287.5052 - mae: 10.3128\n",
      "Epoch 201/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 280.8457 - mae: 10.1309\n",
      "Epoch 202/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 285.9679 - mae: 10.2229\n",
      "Epoch 203/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 279.6095 - mae: 10.0805\n",
      "Epoch 204/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 278.0273 - mae: 9.8872\n",
      "Epoch 205/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 290.8210 - mae: 10.4913\n",
      "Epoch 206/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 282.1875 - mae: 10.2406\n",
      "Epoch 207/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 277.3836 - mae: 10.0631\n",
      "Epoch 208/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 273.9332 - mae: 9.8467\n",
      "Epoch 209/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 275.3572 - mae: 9.8775\n",
      "Epoch 210/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 271.8789 - mae: 9.7209\n",
      "Epoch 211/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 271.4410 - mae: 9.7382\n",
      "Epoch 212/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 274.1265 - mae: 9.7623\n",
      "Epoch 213/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 271.5886 - mae: 9.6740\n",
      "Epoch 214/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.1138 - mae: 9.5723\n",
      "Epoch 215/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 283.1653 - mae: 10.2532\n",
      "Epoch 216/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 287.5603 - mae: 10.3562\n",
      "Epoch 217/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 277.2873 - mae: 9.9488\n",
      "Epoch 218/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 280.3037 - mae: 10.0280\n",
      "Epoch 219/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 271.1039 - mae: 9.6640\n",
      "Epoch 220/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.4890 - mae: 9.5712\n",
      "Epoch 221/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 277.6816 - mae: 10.0236\n",
      "Epoch 222/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 271.0381 - mae: 9.7079\n",
      "Epoch 223/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 272.5441 - mae: 9.7232\n",
      "Epoch 224/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.6305 - mae: 9.5874\n",
      "Epoch 225/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.3069 - mae: 9.6488\n",
      "Epoch 226/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 263.0891 - mae: 9.3824\n",
      "Epoch 227/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 261.4077 - mae: 9.3568\n",
      "Epoch 228/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 267.4933 - mae: 9.5672\n",
      "Epoch 229/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.1123 - mae: 9.6080\n",
      "Epoch 230/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 263.2601 - mae: 9.4184\n",
      "Epoch 231/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 260.1896 - mae: 9.2326\n",
      "Epoch 232/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 258.6253 - mae: 9.2198\n",
      "Epoch 233/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 259.3623 - mae: 9.2074\n",
      "Epoch 234/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 259.1074 - mae: 9.3041\n",
      "Epoch 235/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 260.4909 - mae: 9.2477\n",
      "Epoch 236/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 259.7778 - mae: 9.2811\n",
      "Epoch 237/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 258.3033 - mae: 9.1613\n",
      "Epoch 238/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 257.9337 - mae: 9.1660\n",
      "Epoch 239/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 264.4679 - mae: 9.4449\n",
      "Epoch 240/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 256.4800 - mae: 9.0700\n",
      "Epoch 241/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 255.3783 - mae: 9.0707\n",
      "Epoch 242/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 9ms/step - loss: 257.7692 - mae: 9.2461\n",
      "Epoch 243/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 257.6037 - mae: 9.2788\n",
      "Epoch 244/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 266.4624 - mae: 9.5865\n",
      "Epoch 245/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 262.4848 - mae: 9.3701\n",
      "Epoch 246/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.7628 - mae: 9.0200\n",
      "Epoch 247/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 252.3474 - mae: 8.9498\n",
      "Epoch 248/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 251.8157 - mae: 8.9094\n",
      "Epoch 249/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 262.2890 - mae: 9.4135\n",
      "Epoch 250/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 257.2699 - mae: 9.1422\n",
      "Epoch 251/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 260.2699 - mae: 9.3587\n",
      "Epoch 252/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 254.6847 - mae: 9.0665\n",
      "Epoch 253/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 249.6732 - mae: 8.8858\n",
      "Epoch 254/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 260.9729 - mae: 9.1926\n",
      "Epoch 255/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 250.5150 - mae: 8.9931\n",
      "Epoch 256/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 248.0060 - mae: 8.8010\n",
      "Epoch 257/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 251.2580 - mae: 8.9614\n",
      "Epoch 258/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 257.3246 - mae: 9.1911\n",
      "Epoch 259/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 257.1394 - mae: 9.1841\n",
      "Epoch 260/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 248.8352 - mae: 8.8379\n",
      "Epoch 261/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 252.7747 - mae: 8.9173\n",
      "Epoch 262/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.3615 - mae: 9.1030\n",
      "Epoch 263/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 252.4332 - mae: 9.0255\n",
      "Epoch 264/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 246.3741 - mae: 8.7566\n",
      "Epoch 265/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 247.4659 - mae: 8.8097\n",
      "Epoch 266/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 246.8459 - mae: 8.8031\n",
      "Epoch 267/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 250.3733 - mae: 8.9112\n",
      "Epoch 268/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 252.9161 - mae: 9.0503\n",
      "Epoch 269/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.3192 - mae: 9.0830\n",
      "Epoch 270/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.8610 - mae: 9.0128\n",
      "Epoch 271/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 250.0932 - mae: 8.9562\n",
      "Epoch 272/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 263.4385 - mae: 9.4093\n",
      "Epoch 273/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 242.8782 - mae: 8.6089\n",
      "Epoch 274/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 241.9283 - mae: 8.4626\n",
      "Epoch 275/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 247.0359 - mae: 8.8322\n",
      "Epoch 276/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 242.8415 - mae: 8.5808\n",
      "Epoch 277/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 246.9472 - mae: 8.8171\n",
      "Epoch 278/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 243.5201 - mae: 8.6371\n",
      "Epoch 279/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 256.6408 - mae: 9.1386\n",
      "Epoch 280/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 252.4038 - mae: 9.1109\n",
      "Epoch 281/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 248.4001 - mae: 8.9444\n",
      "Epoch 282/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 240.6511 - mae: 8.4561\n",
      "Epoch 283/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 242.8100 - mae: 8.6274\n",
      "Epoch 284/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 257.8171 - mae: 9.2808\n",
      "Epoch 285/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 258.1985 - mae: 9.3009\n",
      "Epoch 286/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 252.1317 - mae: 8.9488\n",
      "Epoch 287/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 245.0707 - mae: 8.7495\n",
      "Epoch 288/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 238.6934 - mae: 8.4580\n",
      "Epoch 289/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 240.3652 - mae: 8.5374\n",
      "Epoch 290/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 236.8175 - mae: 8.2468\n",
      "Epoch 291/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 266.4371 - mae: 9.8669\n",
      "Epoch 292/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 249.5901 - mae: 8.8017\n",
      "Epoch 293/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 237.0295 - mae: 8.3144\n",
      "Epoch 294/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 248.8141 - mae: 8.8512\n",
      "Epoch 295/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 240.8835 - mae: 8.6397\n",
      "Epoch 296/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 238.4847 - mae: 8.3805\n",
      "Epoch 297/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 240.7849 - mae: 8.5132\n",
      "Epoch 298/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 253.1866 - mae: 9.0865\n",
      "Epoch 299/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 266.7381 - mae: 9.5259\n",
      "Epoch 300/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 256.5932 - mae: 9.3263\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 317.7899 - mae: 8.9169\n",
      "Epoch 1/300\n",
      "9/9 [==============================] - 1s 10ms/step - loss: 48698616.0000 - mae: 6965.0166\n",
      "Epoch 2/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 46518748.0000 - mae: 6805.3579\n",
      "Epoch 3/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 17961228.0000 - mae: 3769.7380\n",
      "Epoch 4/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 3954839.7500 - mae: 1722.3971\n",
      "Epoch 5/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1843084.2500 - mae: 1166.0537\n",
      "Epoch 6/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 753171.0625 - mae: 733.5691\n",
      "Epoch 7/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 340935.8125 - mae: 491.0597\n",
      "Epoch 8/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 167536.2188 - mae: 329.9235\n",
      "Epoch 9/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 89057.1562 - mae: 243.0955\n",
      "Epoch 10/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 55696.0039 - mae: 172.0071\n",
      "Epoch 11/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 38039.1016 - mae: 152.6320\n",
      "Epoch 12/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 28100.4297 - mae: 125.3624\n",
      "Epoch 13/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 20871.0762 - mae: 104.8198\n",
      "Epoch 14/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 16896.0254 - mae: 100.0203\n",
      "Epoch 15/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 14438.4316 - mae: 91.0950\n",
      "Epoch 16/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 12574.8584 - mae: 84.8011\n",
      "Epoch 17/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 11065.0693 - mae: 80.3810\n",
      "Epoch 18/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 9749.0801 - mae: 75.5036\n",
      "Epoch 19/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 8678.3896 - mae: 70.1453\n",
      "Epoch 20/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7718.7788 - mae: 66.2699\n",
      "Epoch 21/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 6915.8511 - mae: 61.8723\n",
      "Epoch 22/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 6197.2241 - mae: 58.7418\n",
      "Epoch 23/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 5545.4097 - mae: 55.0547\n",
      "Epoch 24/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 5005.9282 - mae: 51.2465\n",
      "Epoch 25/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 10ms/step - loss: 4484.6030 - mae: 47.7578\n",
      "Epoch 26/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 4027.5559 - mae: 45.5128\n",
      "Epoch 27/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 3617.9243 - mae: 42.7395\n",
      "Epoch 28/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 3265.3398 - mae: 40.2262\n",
      "Epoch 29/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2950.4663 - mae: 37.8682\n",
      "Epoch 30/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2682.6113 - mae: 36.0426\n",
      "Epoch 31/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2463.4827 - mae: 34.2930\n",
      "Epoch 32/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2282.5359 - mae: 32.8243\n",
      "Epoch 33/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2105.8618 - mae: 31.5354\n",
      "Epoch 34/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1936.7557 - mae: 30.4328\n",
      "Epoch 35/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1812.6532 - mae: 29.4109\n",
      "Epoch 36/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1701.0315 - mae: 28.5028\n",
      "Epoch 37/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1593.1068 - mae: 27.7620\n",
      "Epoch 38/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1520.1289 - mae: 27.2417\n",
      "Epoch 39/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1424.7808 - mae: 26.3392\n",
      "Epoch 40/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1355.9012 - mae: 25.8479\n",
      "Epoch 41/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1295.4523 - mae: 25.2776\n",
      "Epoch 42/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 1239.7360 - mae: 24.7500\n",
      "Epoch 43/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1199.9358 - mae: 24.5606\n",
      "Epoch 44/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1148.4103 - mae: 23.9950\n",
      "Epoch 45/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1096.0950 - mae: 23.3168\n",
      "Epoch 46/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1050.5396 - mae: 22.8360\n",
      "Epoch 47/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1016.7540 - mae: 22.5567\n",
      "Epoch 48/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 979.7338 - mae: 22.1175\n",
      "Epoch 49/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 949.7252 - mae: 21.8221\n",
      "Epoch 50/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 915.6001 - mae: 21.3974\n",
      "Epoch 51/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 885.1278 - mae: 20.9913\n",
      "Epoch 52/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 860.8561 - mae: 20.7563\n",
      "Epoch 53/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 834.6324 - mae: 20.4480\n",
      "Epoch 54/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 812.2834 - mae: 20.1613\n",
      "Epoch 55/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 802.9584 - mae: 20.1766\n",
      "Epoch 56/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 770.4182 - mae: 19.6394\n",
      "Epoch 57/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 750.0526 - mae: 19.2923\n",
      "Epoch 58/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 727.3862 - mae: 18.9528\n",
      "Epoch 59/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 711.2762 - mae: 18.7888\n",
      "Epoch 60/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 692.8753 - mae: 18.5046\n",
      "Epoch 61/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 678.7134 - mae: 18.2455\n",
      "Epoch 62/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 661.8543 - mae: 17.9842\n",
      "Epoch 63/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 646.9808 - mae: 17.7469\n",
      "Epoch 64/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 634.3494 - mae: 17.5429\n",
      "Epoch 65/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 628.7309 - mae: 17.5416\n",
      "Epoch 66/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 609.8812 - mae: 17.2236\n",
      "Epoch 67/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 596.7889 - mae: 16.9770\n",
      "Epoch 68/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 585.9634 - mae: 16.7576\n",
      "Epoch 69/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 574.7283 - mae: 16.6229\n",
      "Epoch 70/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 566.6035 - mae: 16.4615\n",
      "Epoch 71/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 556.9191 - mae: 16.2505\n",
      "Epoch 72/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 548.9737 - mae: 16.0705\n",
      "Epoch 73/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 541.9410 - mae: 16.0185\n",
      "Epoch 74/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 533.7643 - mae: 15.8640\n",
      "Epoch 75/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 523.9496 - mae: 15.7138\n",
      "Epoch 76/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 518.0329 - mae: 15.5764\n",
      "Epoch 77/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 508.3583 - mae: 15.4042\n",
      "Epoch 78/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 511.0046 - mae: 15.4698\n",
      "Epoch 79/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 508.0238 - mae: 15.2679\n",
      "Epoch 80/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 489.5371 - mae: 14.9987\n",
      "Epoch 81/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 482.9815 - mae: 14.7992\n",
      "Epoch 82/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 476.6881 - mae: 14.7158\n",
      "Epoch 83/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 474.9299 - mae: 14.5894\n",
      "Epoch 84/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 467.2922 - mae: 14.5396\n",
      "Epoch 85/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 461.7653 - mae: 14.3861\n",
      "Epoch 86/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 455.5491 - mae: 14.2383\n",
      "Epoch 87/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 452.9621 - mae: 14.2670\n",
      "Epoch 88/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 446.9216 - mae: 14.0846\n",
      "Epoch 89/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 458.5418 - mae: 14.4889\n",
      "Epoch 90/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 438.2548 - mae: 13.8863\n",
      "Epoch 91/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 433.9076 - mae: 13.7515\n",
      "Epoch 92/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 430.9154 - mae: 13.7186\n",
      "Epoch 93/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 428.1727 - mae: 13.5720\n",
      "Epoch 94/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 423.8893 - mae: 13.5275\n",
      "Epoch 95/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 419.9071 - mae: 13.4516\n",
      "Epoch 96/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 417.7879 - mae: 13.3603\n",
      "Epoch 97/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 414.0112 - mae: 13.3286\n",
      "Epoch 98/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 414.3629 - mae: 13.2221\n",
      "Epoch 99/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 408.6571 - mae: 13.1408\n",
      "Epoch 100/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 408.2971 - mae: 13.0992\n",
      "Epoch 101/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 401.6290 - mae: 12.9441\n",
      "Epoch 102/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 405.5595 - mae: 13.1276\n",
      "Epoch 103/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 398.6152 - mae: 12.8647\n",
      "Epoch 104/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 395.5446 - mae: 12.8417\n",
      "Epoch 105/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 396.4230 - mae: 12.8538\n",
      "Epoch 106/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 390.9843 - mae: 12.6020\n",
      "Epoch 107/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 389.0692 - mae: 12.6513\n",
      "Epoch 108/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 385.2719 - mae: 12.4569\n",
      "Epoch 109/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 385.6507 - mae: 12.4454\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 110/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 385.9956 - mae: 12.5465\n",
      "Epoch 111/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 383.6347 - mae: 12.5386\n",
      "Epoch 112/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 379.9434 - mae: 12.2394\n",
      "Epoch 113/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 377.8536 - mae: 12.2045\n",
      "Epoch 114/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 383.2336 - mae: 12.5568\n",
      "Epoch 115/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 377.3274 - mae: 12.3214\n",
      "Epoch 116/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 376.4459 - mae: 12.1158\n",
      "Epoch 117/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 375.9142 - mae: 12.3101\n",
      "Epoch 118/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 374.7542 - mae: 12.2920\n",
      "Epoch 119/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 379.7365 - mae: 12.2838\n",
      "Epoch 120/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 374.0966 - mae: 12.1711\n",
      "Epoch 121/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 362.5318 - mae: 11.9367\n",
      "Epoch 122/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 360.0916 - mae: 11.8008\n",
      "Epoch 123/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 358.1765 - mae: 11.7591\n",
      "Epoch 124/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 357.6566 - mae: 11.6646\n",
      "Epoch 125/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 356.4449 - mae: 11.7112\n",
      "Epoch 126/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 354.6297 - mae: 11.6163\n",
      "Epoch 127/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 353.8463 - mae: 11.5935\n",
      "Epoch 128/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 357.8339 - mae: 11.7597\n",
      "Epoch 129/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 354.8866 - mae: 11.5701\n",
      "Epoch 130/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 355.8742 - mae: 11.7138\n",
      "Epoch 131/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 348.5626 - mae: 11.4708\n",
      "Epoch 132/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 345.4733 - mae: 11.3464\n",
      "Epoch 133/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 347.4856 - mae: 11.3448\n",
      "Epoch 134/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 344.5594 - mae: 11.3032\n",
      "Epoch 135/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 341.8620 - mae: 11.2729\n",
      "Epoch 136/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 344.5039 - mae: 11.4491\n",
      "Epoch 137/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 340.1516 - mae: 11.1860\n",
      "Epoch 138/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 341.2396 - mae: 11.1672\n",
      "Epoch 139/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 338.0795 - mae: 11.0484\n",
      "Epoch 140/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 341.9173 - mae: 11.4672\n",
      "Epoch 141/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 336.5663 - mae: 11.0276\n",
      "Epoch 142/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 334.8398 - mae: 11.0301\n",
      "Epoch 143/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 345.6381 - mae: 11.4195\n",
      "Epoch 144/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 334.1532 - mae: 10.9708\n",
      "Epoch 145/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 339.4331 - mae: 11.1877\n",
      "Epoch 146/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 332.2403 - mae: 10.9622\n",
      "Epoch 147/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 329.1300 - mae: 10.8904\n",
      "Epoch 148/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 330.4630 - mae: 10.9955\n",
      "Epoch 149/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 328.5521 - mae: 10.7822\n",
      "Epoch 150/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 327.4145 - mae: 10.7856\n",
      "Epoch 151/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 327.8552 - mae: 10.8332\n",
      "Epoch 152/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 328.6392 - mae: 10.7956\n",
      "Epoch 153/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 324.1604 - mae: 10.6970\n",
      "Epoch 154/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 322.4276 - mae: 10.6537\n",
      "Epoch 155/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 331.2115 - mae: 10.9441\n",
      "Epoch 156/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 325.1960 - mae: 10.6709\n",
      "Epoch 157/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 330.7336 - mae: 10.9022\n",
      "Epoch 158/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 325.5186 - mae: 10.9223\n",
      "Epoch 159/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 330.1836 - mae: 10.9897\n",
      "Epoch 160/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 323.2672 - mae: 10.7426\n",
      "Epoch 161/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 321.3729 - mae: 10.6533\n",
      "Epoch 162/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 317.6417 - mae: 10.5789\n",
      "Epoch 163/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 319.6655 - mae: 10.4328\n",
      "Epoch 164/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 316.1349 - mae: 10.3719\n",
      "Epoch 165/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 332.1707 - mae: 11.0972\n",
      "Epoch 166/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 319.0499 - mae: 10.6180\n",
      "Epoch 167/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 323.9562 - mae: 10.8902\n",
      "Epoch 168/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 321.9962 - mae: 10.8200\n",
      "Epoch 169/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 318.8665 - mae: 10.6749\n",
      "Epoch 170/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 330.1329 - mae: 11.0825\n",
      "Epoch 171/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 323.0135 - mae: 10.6502\n",
      "Epoch 172/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 324.7856 - mae: 10.7888\n",
      "Epoch 173/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 313.5189 - mae: 10.3394\n",
      "Epoch 174/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 311.0250 - mae: 10.2234\n",
      "Epoch 175/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 308.3779 - mae: 10.1891\n",
      "Epoch 176/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 313.5934 - mae: 10.3397\n",
      "Epoch 177/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 306.9349 - mae: 10.1903\n",
      "Epoch 178/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 316.9836 - mae: 10.5922\n",
      "Epoch 179/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 307.2610 - mae: 10.1038\n",
      "Epoch 180/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 308.5953 - mae: 10.2088\n",
      "Epoch 181/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 304.3422 - mae: 9.9945\n",
      "Epoch 182/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 303.9774 - mae: 10.0481\n",
      "Epoch 183/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 305.3452 - mae: 10.0628\n",
      "Epoch 184/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 303.4333 - mae: 10.0638\n",
      "Epoch 185/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 306.7284 - mae: 10.1133\n",
      "Epoch 186/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 309.2023 - mae: 10.3055\n",
      "Epoch 187/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 315.8844 - mae: 10.5524\n",
      "Epoch 188/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 300.3000 - mae: 9.9271\n",
      "Epoch 189/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 299.4061 - mae: 9.8726\n",
      "Epoch 190/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 299.1388 - mae: 9.8544\n",
      "Epoch 191/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 297.5447 - mae: 9.8234\n",
      "Epoch 192/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 298.1444 - mae: 9.7961\n",
      "Epoch 193/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 296.6025 - mae: 9.7771\n",
      "Epoch 194/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 9ms/step - loss: 299.7401 - mae: 9.9693\n",
      "Epoch 195/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 306.9626 - mae: 10.3194\n",
      "Epoch 196/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 307.6751 - mae: 10.2393\n",
      "Epoch 197/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 299.2657 - mae: 9.8739\n",
      "Epoch 198/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 294.0067 - mae: 9.6706\n",
      "Epoch 199/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 294.5968 - mae: 9.7008\n",
      "Epoch 200/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 293.0698 - mae: 9.6513\n",
      "Epoch 201/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 299.1458 - mae: 9.9855\n",
      "Epoch 202/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 297.4004 - mae: 9.7851\n",
      "Epoch 203/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 294.7309 - mae: 9.7682\n",
      "Epoch 204/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 298.3236 - mae: 9.9029\n",
      "Epoch 205/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 298.3624 - mae: 10.0442\n",
      "Epoch 206/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 299.6800 - mae: 9.9980\n",
      "Epoch 207/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.8251 - mae: 9.5933\n",
      "Epoch 208/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 289.2707 - mae: 9.5705\n",
      "Epoch 209/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 288.9877 - mae: 9.5127\n",
      "Epoch 210/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 290.9011 - mae: 9.6054\n",
      "Epoch 211/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.0057 - mae: 9.5770\n",
      "Epoch 212/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 301.3158 - mae: 10.0537\n",
      "Epoch 213/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 292.0909 - mae: 9.8497\n",
      "Epoch 214/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 291.8189 - mae: 9.6371\n",
      "Epoch 215/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.8281 - mae: 9.6440\n",
      "Epoch 216/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 287.1006 - mae: 9.4735\n",
      "Epoch 217/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 300.2197 - mae: 10.1219\n",
      "Epoch 218/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 297.6428 - mae: 9.9191\n",
      "Epoch 219/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 292.9244 - mae: 9.6994\n",
      "Epoch 220/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 285.7377 - mae: 9.4206\n",
      "Epoch 221/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 284.1115 - mae: 9.3930\n",
      "Epoch 222/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 283.4547 - mae: 9.3168\n",
      "Epoch 223/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 292.7885 - mae: 9.7781\n",
      "Epoch 224/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 303.8122 - mae: 10.1358\n",
      "Epoch 225/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 281.9469 - mae: 9.2959\n",
      "Epoch 226/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 284.4422 - mae: 9.4425\n",
      "Epoch 227/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 289.3751 - mae: 9.6775\n",
      "Epoch 228/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 308.3938 - mae: 10.3971\n",
      "Epoch 229/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 318.2039 - mae: 10.7191\n",
      "Epoch 230/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 280.2972 - mae: 9.3164\n",
      "Epoch 231/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 278.1984 - mae: 9.1679\n",
      "Epoch 232/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.0964 - mae: 9.6415\n",
      "Epoch 233/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 287.2268 - mae: 9.5229\n",
      "Epoch 234/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 282.8083 - mae: 9.4248\n",
      "Epoch 235/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 280.3764 - mae: 9.1850\n",
      "Epoch 236/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 278.5357 - mae: 9.2605\n",
      "Epoch 237/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 277.2733 - mae: 9.0447\n",
      "Epoch 238/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 276.3073 - mae: 9.0477\n",
      "Epoch 239/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 276.5732 - mae: 9.1179\n",
      "Epoch 240/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 277.1130 - mae: 9.1874\n",
      "Epoch 241/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 280.4096 - mae: 9.2582\n",
      "Epoch 242/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 276.3931 - mae: 9.0264\n",
      "Epoch 243/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 286.4761 - mae: 9.6032\n",
      "Epoch 244/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 274.7204 - mae: 9.0219\n",
      "Epoch 245/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 274.8178 - mae: 8.9899\n",
      "Epoch 246/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 277.0598 - mae: 9.1615\n",
      "Epoch 247/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 277.9551 - mae: 9.1417\n",
      "Epoch 248/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 283.6775 - mae: 9.4663\n",
      "Epoch 249/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 280.3831 - mae: 9.2541\n",
      "Epoch 250/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 272.1365 - mae: 8.9674\n",
      "Epoch 251/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 272.4736 - mae: 8.9436\n",
      "Epoch 252/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 283.4160 - mae: 9.4312\n",
      "Epoch 253/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 282.2238 - mae: 9.4507\n",
      "Epoch 254/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 287.1140 - mae: 9.4696\n",
      "Epoch 255/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 300.8236 - mae: 10.2826\n",
      "Epoch 256/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 287.3231 - mae: 9.5035\n",
      "Epoch 257/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 279.6953 - mae: 9.2270\n",
      "Epoch 258/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 281.5354 - mae: 9.4777\n",
      "Epoch 259/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 286.8487 - mae: 9.5624\n",
      "Epoch 260/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 276.7567 - mae: 9.1277\n",
      "Epoch 261/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 268.9583 - mae: 8.7374\n",
      "Epoch 262/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 270.1421 - mae: 8.8461\n",
      "Epoch 263/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 266.1631 - mae: 8.6791\n",
      "Epoch 264/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 269.0555 - mae: 8.7838\n",
      "Epoch 265/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 294.8290 - mae: 9.7713\n",
      "Epoch 266/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 303.0392 - mae: 10.3851\n",
      "Epoch 267/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 307.6804 - mae: 10.3214\n",
      "Epoch 268/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 266.6376 - mae: 8.7245\n",
      "Epoch 269/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.0674 - mae: 8.8455\n",
      "Epoch 270/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 272.1268 - mae: 8.9593\n",
      "Epoch 271/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 267.7410 - mae: 8.8352\n",
      "Epoch 272/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 266.4261 - mae: 8.6761\n",
      "Epoch 273/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 266.1355 - mae: 8.7025\n",
      "Epoch 274/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 274.9749 - mae: 9.0883\n",
      "Epoch 275/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 308.1326 - mae: 10.5992\n",
      "Epoch 276/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 304.2079 - mae: 10.1625\n",
      "Epoch 277/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 276.2650 - mae: 9.2189\n",
      "Epoch 278/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 291.2483 - mae: 9.7231\n",
      "Epoch 279/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 10ms/step - loss: 272.8348 - mae: 8.9717\n",
      "Epoch 280/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 272.2936 - mae: 9.0253\n",
      "Epoch 281/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 261.6246 - mae: 8.4067\n",
      "Epoch 282/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 262.9584 - mae: 8.4684\n",
      "Epoch 283/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 263.4591 - mae: 8.5569\n",
      "Epoch 284/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 263.4021 - mae: 8.5707\n",
      "Epoch 285/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 266.9229 - mae: 8.6920\n",
      "Epoch 286/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 273.3282 - mae: 9.1596\n",
      "Epoch 287/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 284.8301 - mae: 9.3326\n",
      "Epoch 288/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 291.4130 - mae: 9.7169\n",
      "Epoch 289/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 270.3235 - mae: 8.9509\n",
      "Epoch 290/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 272.0596 - mae: 8.8453\n",
      "Epoch 291/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 270.7531 - mae: 8.8137\n",
      "Epoch 292/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 277.4861 - mae: 9.4029\n",
      "Epoch 293/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.7901 - mae: 8.8892\n",
      "Epoch 294/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 265.5858 - mae: 8.6636\n",
      "Epoch 295/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 279.4568 - mae: 9.3811\n",
      "Epoch 296/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 267.6824 - mae: 8.8435\n",
      "Epoch 297/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 277.6456 - mae: 9.0728\n",
      "Epoch 298/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 283.1763 - mae: 9.5149\n",
      "Epoch 299/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 282.1882 - mae: 9.6537\n",
      "Epoch 300/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 276.5973 - mae: 9.1585\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 220.5255 - mae: 8.1921\n",
      "Epoch 1/300\n",
      "9/9 [==============================] - 1s 10ms/step - loss: 48768620.0000 - mae: 6970.0010\n",
      "Epoch 2/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 48057448.0000 - mae: 6918.7109\n",
      "Epoch 3/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 33361736.0000 - mae: 5681.3193\n",
      "Epoch 4/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 4459260.5000 - mae: 1761.0112\n",
      "Epoch 5/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 3123184.7500 - mae: 1679.8539\n",
      "Epoch 6/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 967168.0000 - mae: 836.8033\n",
      "Epoch 7/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 555360.8750 - mae: 636.0978\n",
      "Epoch 8/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 259113.3906 - mae: 423.0790\n",
      "Epoch 9/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 153560.0781 - mae: 301.5102\n",
      "Epoch 10/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 96574.9609 - mae: 242.0392\n",
      "Epoch 11/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 68356.3984 - mae: 198.9330\n",
      "Epoch 12/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 47828.2305 - mae: 161.8218\n",
      "Epoch 13/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 36694.9219 - mae: 146.2218\n",
      "Epoch 14/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 29212.7324 - mae: 125.6663\n",
      "Epoch 15/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 23287.2910 - mae: 113.3955\n",
      "Epoch 16/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 18720.1816 - mae: 104.3315\n",
      "Epoch 17/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 15327.0293 - mae: 91.8384\n",
      "Epoch 18/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 12800.0342 - mae: 84.5261\n",
      "Epoch 19/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 10876.5518 - mae: 79.2227\n",
      "Epoch 20/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 9173.0811 - mae: 72.3344\n",
      "Epoch 21/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7690.5142 - mae: 65.6772\n",
      "Epoch 22/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 6355.9692 - mae: 59.4185\n",
      "Epoch 23/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 5181.4028 - mae: 53.2168\n",
      "Epoch 24/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 4209.3867 - mae: 47.5080\n",
      "Epoch 25/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 3395.5322 - mae: 42.6115\n",
      "Epoch 26/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 2771.4084 - mae: 38.4850\n",
      "Epoch 27/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 2280.8276 - mae: 34.8614\n",
      "Epoch 28/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 1911.5033 - mae: 31.9085\n",
      "Epoch 29/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 1612.9292 - mae: 29.1217\n",
      "Epoch 30/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 1382.5706 - mae: 26.9560\n",
      "Epoch 31/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 1209.3849 - mae: 25.2219\n",
      "Epoch 32/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 1067.5591 - mae: 23.6856\n",
      "Epoch 33/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 966.0739 - mae: 22.5208\n",
      "Epoch 34/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 868.3482 - mae: 21.2222\n",
      "Epoch 35/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 792.8958 - mae: 20.2478\n",
      "Epoch 36/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 743.5768 - mae: 19.5784\n",
      "Epoch 37/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 695.5914 - mae: 18.8133\n",
      "Epoch 38/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 660.6145 - mae: 18.3877\n",
      "Epoch 39/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 634.3284 - mae: 17.9534\n",
      "Epoch 40/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 609.7004 - mae: 17.6156\n",
      "Epoch 41/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 592.9177 - mae: 17.2679\n",
      "Epoch 42/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 576.8718 - mae: 17.0863\n",
      "Epoch 43/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 564.5669 - mae: 16.8434\n",
      "Epoch 44/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 552.8446 - mae: 16.6244\n",
      "Epoch 45/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 545.0152 - mae: 16.4953\n",
      "Epoch 46/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 538.1497 - mae: 16.3823\n",
      "Epoch 47/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 528.4152 - mae: 16.1981\n",
      "Epoch 48/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 521.1567 - mae: 16.0742\n",
      "Epoch 49/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 524.5435 - mae: 16.1035\n",
      "Epoch 50/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 514.3027 - mae: 15.8935\n",
      "Epoch 51/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 504.9308 - mae: 15.7198\n",
      "Epoch 52/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 498.9809 - mae: 15.5530\n",
      "Epoch 53/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 497.8276 - mae: 15.5423\n",
      "Epoch 54/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 500.7158 - mae: 15.4722\n",
      "Epoch 55/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 500.1471 - mae: 15.6978\n",
      "Epoch 56/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 486.6730 - mae: 15.1805\n",
      "Epoch 57/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 477.2675 - mae: 15.0754\n",
      "Epoch 58/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 474.2296 - mae: 14.8886\n",
      "Epoch 59/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 470.5661 - mae: 14.9029\n",
      "Epoch 60/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 466.7775 - mae: 14.6640\n",
      "Epoch 61/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 461.3495 - mae: 14.6150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 62/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 461.1017 - mae: 14.6322\n",
      "Epoch 63/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 457.3642 - mae: 14.5080\n",
      "Epoch 64/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 456.7416 - mae: 14.4985\n",
      "Epoch 65/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 448.4659 - mae: 14.2619\n",
      "Epoch 66/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 445.8184 - mae: 14.1509\n",
      "Epoch 67/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 440.9562 - mae: 14.0579\n",
      "Epoch 68/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 438.6171 - mae: 13.9962\n",
      "Epoch 69/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 435.5868 - mae: 13.9019\n",
      "Epoch 70/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 445.9842 - mae: 14.2722\n",
      "Epoch 71/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 441.5226 - mae: 13.9909\n",
      "Epoch 72/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 432.3555 - mae: 13.8019\n",
      "Epoch 73/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 427.0669 - mae: 13.6491\n",
      "Epoch 74/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 423.4494 - mae: 13.5568\n",
      "Epoch 75/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 421.5926 - mae: 13.5243\n",
      "Epoch 76/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 419.6142 - mae: 13.4608\n",
      "Epoch 77/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 419.8308 - mae: 13.5062\n",
      "Epoch 78/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 415.3900 - mae: 13.3049\n",
      "Epoch 79/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 415.6287 - mae: 13.2346\n",
      "Epoch 80/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 411.5602 - mae: 13.2579\n",
      "Epoch 81/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 415.5275 - mae: 13.3111\n",
      "Epoch 82/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 408.3146 - mae: 13.0892\n",
      "Epoch 83/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 408.4358 - mae: 13.1279\n",
      "Epoch 84/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 407.0469 - mae: 13.0613\n",
      "Epoch 85/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 412.6554 - mae: 13.3072\n",
      "Epoch 86/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 401.0684 - mae: 12.8960\n",
      "Epoch 87/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 403.2983 - mae: 12.9465\n",
      "Epoch 88/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 397.8818 - mae: 12.8717\n",
      "Epoch 89/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 397.5829 - mae: 12.8317\n",
      "Epoch 90/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 394.1527 - mae: 12.7372\n",
      "Epoch 91/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 392.2197 - mae: 12.6740\n",
      "Epoch 92/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 392.0370 - mae: 12.6799\n",
      "Epoch 93/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 391.1240 - mae: 12.5796\n",
      "Epoch 94/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 389.4144 - mae: 12.5717\n",
      "Epoch 95/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 391.5919 - mae: 12.6927\n",
      "Epoch 96/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 387.3403 - mae: 12.5412\n",
      "Epoch 97/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 385.7148 - mae: 12.4626\n",
      "Epoch 98/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 386.1059 - mae: 12.4287\n",
      "Epoch 99/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 384.5614 - mae: 12.5304\n",
      "Epoch 100/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 384.8924 - mae: 12.4252\n",
      "Epoch 101/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 382.1331 - mae: 12.3320\n",
      "Epoch 102/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 395.4401 - mae: 12.8652\n",
      "Epoch 103/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 381.8439 - mae: 12.4037\n",
      "Epoch 104/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 378.6176 - mae: 12.2583\n",
      "Epoch 105/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 380.8492 - mae: 12.4109\n",
      "Epoch 106/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 374.1715 - mae: 12.1695\n",
      "Epoch 107/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 375.3034 - mae: 12.1462\n",
      "Epoch 108/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 372.9641 - mae: 12.1271\n",
      "Epoch 109/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 375.0695 - mae: 12.1192\n",
      "Epoch 110/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 373.9683 - mae: 12.0958\n",
      "Epoch 111/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 370.6687 - mae: 12.0967\n",
      "Epoch 112/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 369.9611 - mae: 11.9680\n",
      "Epoch 113/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 368.5448 - mae: 11.9991\n",
      "Epoch 114/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 369.9688 - mae: 12.0649\n",
      "Epoch 115/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 370.9864 - mae: 12.1971\n",
      "Epoch 116/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 369.5319 - mae: 11.9862\n",
      "Epoch 117/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 366.1581 - mae: 11.9271\n",
      "Epoch 118/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 363.3625 - mae: 11.8183\n",
      "Epoch 119/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 366.3470 - mae: 11.9488\n",
      "Epoch 120/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 364.6736 - mae: 11.8153\n",
      "Epoch 121/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 360.2795 - mae: 11.7565\n",
      "Epoch 122/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 358.0289 - mae: 11.6924\n",
      "Epoch 123/300\n",
      "9/9 [==============================] - 0s 12ms/step - loss: 357.5775 - mae: 11.7129\n",
      "Epoch 124/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 358.2672 - mae: 11.6883\n",
      "Epoch 125/300\n",
      "9/9 [==============================] - 0s 13ms/step - loss: 356.1671 - mae: 11.5907\n",
      "Epoch 126/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 358.0522 - mae: 11.7107\n",
      "Epoch 127/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 354.7377 - mae: 11.6502\n",
      "Epoch 128/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 359.3769 - mae: 11.6884\n",
      "Epoch 129/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 357.8154 - mae: 11.6422\n",
      "Epoch 130/300\n",
      "9/9 [==============================] - 0s 12ms/step - loss: 352.2638 - mae: 11.5277\n",
      "Epoch 131/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 353.2780 - mae: 11.6045\n",
      "Epoch 132/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 354.2919 - mae: 11.5913\n",
      "Epoch 133/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 351.6208 - mae: 11.4280\n",
      "Epoch 134/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 349.6414 - mae: 11.4988\n",
      "Epoch 135/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 350.1078 - mae: 11.5325\n",
      "Epoch 136/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 346.6323 - mae: 11.3578\n",
      "Epoch 137/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 347.7384 - mae: 11.3943\n",
      "Epoch 138/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 349.7849 - mae: 11.4273\n",
      "Epoch 139/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 347.2306 - mae: 11.3469\n",
      "Epoch 140/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 351.4126 - mae: 11.5714\n",
      "Epoch 141/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 351.0493 - mae: 11.6495\n",
      "Epoch 142/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 351.0897 - mae: 11.5262\n",
      "Epoch 143/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 342.7030 - mae: 11.2254\n",
      "Epoch 144/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 346.6960 - mae: 11.3817\n",
      "Epoch 145/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 342.6631 - mae: 11.3101\n",
      "Epoch 146/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 10ms/step - loss: 340.1119 - mae: 11.2242\n",
      "Epoch 147/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 340.6056 - mae: 11.1821 0s - loss: 337.4975 - mae: 11.13\n",
      "Epoch 148/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 340.3703 - mae: 11.2233\n",
      "Epoch 149/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 338.9059 - mae: 11.0961\n",
      "Epoch 150/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 337.4337 - mae: 11.1331\n",
      "Epoch 151/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 336.7576 - mae: 11.0813\n",
      "Epoch 152/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 336.3204 - mae: 11.0324\n",
      "Epoch 153/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 334.2741 - mae: 10.9411\n",
      "Epoch 154/300\n",
      "9/9 [==============================] - ETA: 0s - loss: 346.3505 - mae: 10.95 - 0s 10ms/step - loss: 334.0605 - mae: 10.9236\n",
      "Epoch 155/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 333.8663 - mae: 10.9972\n",
      "Epoch 156/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 337.0333 - mae: 10.9623\n",
      "Epoch 157/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 339.4554 - mae: 11.1255\n",
      "Epoch 158/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 339.3214 - mae: 11.1381\n",
      "Epoch 159/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 330.0511 - mae: 10.8058\n",
      "Epoch 160/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 330.7008 - mae: 10.8397\n",
      "Epoch 161/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 332.5120 - mae: 11.0135\n",
      "Epoch 162/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 330.1630 - mae: 10.8586\n",
      "Epoch 163/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 328.7296 - mae: 10.7700\n",
      "Epoch 164/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 327.0053 - mae: 10.7264\n",
      "Epoch 165/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 331.8603 - mae: 10.9248\n",
      "Epoch 166/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 328.9939 - mae: 10.8472\n",
      "Epoch 167/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 334.9499 - mae: 11.1799\n",
      "Epoch 168/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 329.9903 - mae: 10.9406\n",
      "Epoch 169/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 328.5378 - mae: 10.8582\n",
      "Epoch 170/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 325.5517 - mae: 10.7931\n",
      "Epoch 171/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 327.8679 - mae: 10.7492\n",
      "Epoch 172/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 330.8041 - mae: 10.9243\n",
      "Epoch 173/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 330.7720 - mae: 10.8661\n",
      "Epoch 174/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 326.3934 - mae: 10.7353\n",
      "Epoch 175/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 326.0005 - mae: 10.7633\n",
      "Epoch 176/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 324.1486 - mae: 10.6608\n",
      "Epoch 177/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 319.5164 - mae: 10.5158\n",
      "Epoch 178/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 321.3184 - mae: 10.5892\n",
      "Epoch 179/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 325.0291 - mae: 10.7514\n",
      "Epoch 180/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 320.0684 - mae: 10.5467\n",
      "Epoch 181/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 319.2868 - mae: 10.4966\n",
      "Epoch 182/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 317.8434 - mae: 10.4519\n",
      "Epoch 183/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 318.5181 - mae: 10.4824\n",
      "Epoch 184/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 317.0762 - mae: 10.4638\n",
      "Epoch 185/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 316.3094 - mae: 10.3914\n",
      "Epoch 186/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 315.4893 - mae: 10.3831\n",
      "Epoch 187/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 316.5448 - mae: 10.5014\n",
      "Epoch 188/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 314.6795 - mae: 10.3416\n",
      "Epoch 189/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 313.4520 - mae: 10.3922\n",
      "Epoch 190/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 316.9215 - mae: 10.4785\n",
      "Epoch 191/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 314.1730 - mae: 10.3341\n",
      "Epoch 192/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 314.8139 - mae: 10.3706\n",
      "Epoch 193/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 313.1669 - mae: 10.3239\n",
      "Epoch 194/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 313.7580 - mae: 10.3584\n",
      "Epoch 195/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 321.9535 - mae: 10.7011\n",
      "Epoch 196/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 316.4514 - mae: 10.3852\n",
      "Epoch 197/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 318.4568 - mae: 10.5916\n",
      "Epoch 198/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 315.7806 - mae: 10.4437\n",
      "Epoch 199/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 316.2895 - mae: 10.4514\n",
      "Epoch 200/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 309.0319 - mae: 10.2275\n",
      "Epoch 201/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 314.3557 - mae: 10.3794\n",
      "Epoch 202/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 309.8493 - mae: 10.1824\n",
      "Epoch 203/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 310.3928 - mae: 10.2572\n",
      "Epoch 204/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 321.5002 - mae: 10.7216\n",
      "Epoch 205/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 311.7639 - mae: 10.3759\n",
      "Epoch 206/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 313.6668 - mae: 10.4338\n",
      "Epoch 207/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 305.1182 - mae: 10.1002\n",
      "Epoch 208/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 304.0430 - mae: 10.0110\n",
      "Epoch 209/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 304.4684 - mae: 10.0664\n",
      "Epoch 210/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 305.9489 - mae: 10.0607\n",
      "Epoch 211/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 305.9526 - mae: 10.1093\n",
      "Epoch 212/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 316.5988 - mae: 10.5489\n",
      "Epoch 213/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 319.4022 - mae: 10.6901\n",
      "Epoch 214/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 308.3644 - mae: 10.2444\n",
      "Epoch 215/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 308.6683 - mae: 10.1797\n",
      "Epoch 216/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 304.9443 - mae: 10.0586\n",
      "Epoch 217/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 313.5206 - mae: 10.4326\n",
      "Epoch 218/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 305.3860 - mae: 10.1369\n",
      "Epoch 219/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 311.8390 - mae: 10.3393\n",
      "Epoch 220/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 302.2739 - mae: 10.0163\n",
      "Epoch 221/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 298.8562 - mae: 9.8597\n",
      "Epoch 222/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 300.7718 - mae: 9.9381\n",
      "Epoch 223/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 301.7434 - mae: 9.9999\n",
      "Epoch 224/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 306.7619 - mae: 10.2005\n",
      "Epoch 225/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 296.1813 - mae: 9.8027\n",
      "Epoch 226/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 298.3954 - mae: 9.8774\n",
      "Epoch 227/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 295.7440 - mae: 9.8170\n",
      "Epoch 228/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 301.2454 - mae: 9.9404\n",
      "Epoch 229/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 10ms/step - loss: 306.2494 - mae: 10.1690\n",
      "Epoch 230/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 295.9243 - mae: 9.8363\n",
      "Epoch 231/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 295.0453 - mae: 9.8105\n",
      "Epoch 232/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 307.7810 - mae: 10.2228\n",
      "Epoch 233/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 304.7707 - mae: 10.1869\n",
      "Epoch 234/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 301.1828 - mae: 10.0051\n",
      "Epoch 235/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 306.4284 - mae: 10.1536\n",
      "Epoch 236/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 302.0966 - mae: 10.1183\n",
      "Epoch 237/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 306.2699 - mae: 10.2198\n",
      "Epoch 238/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 310.5861 - mae: 10.3836\n",
      "Epoch 239/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 294.1718 - mae: 9.7970\n",
      "Epoch 240/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 289.7585 - mae: 9.6361\n",
      "Epoch 241/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 293.6051 - mae: 9.6866\n",
      "Epoch 242/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 293.5830 - mae: 9.6171\n",
      "Epoch 243/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 296.8084 - mae: 9.9046\n",
      "Epoch 244/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 291.0569 - mae: 9.6687\n",
      "Epoch 245/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.9437 - mae: 9.5673\n",
      "Epoch 246/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 287.6693 - mae: 9.4796\n",
      "Epoch 247/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 293.4039 - mae: 9.7007\n",
      "Epoch 248/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 289.0730 - mae: 9.5265\n",
      "Epoch 249/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 289.2588 - mae: 9.5930\n",
      "Epoch 250/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.5836 - mae: 9.6977\n",
      "Epoch 251/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 288.6787 - mae: 9.5442\n",
      "Epoch 252/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 297.2578 - mae: 9.8090\n",
      "Epoch 253/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 288.7411 - mae: 9.6169\n",
      "Epoch 254/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 286.6454 - mae: 9.4147\n",
      "Epoch 255/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 288.7280 - mae: 9.5008\n",
      "Epoch 256/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 292.4493 - mae: 9.7268\n",
      "Epoch 257/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 295.7275 - mae: 9.8657\n",
      "Epoch 258/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 315.6887 - mae: 10.7881\n",
      "Epoch 259/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 308.2064 - mae: 10.3207\n",
      "Epoch 260/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 286.2402 - mae: 9.4909\n",
      "Epoch 261/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 288.5762 - mae: 9.5652\n",
      "Epoch 262/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 288.0503 - mae: 9.4780\n",
      "Epoch 263/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 284.0034 - mae: 9.3385\n",
      "Epoch 264/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 282.7591 - mae: 9.3619\n",
      "Epoch 265/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 291.0620 - mae: 9.5485\n",
      "Epoch 266/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 302.8720 - mae: 10.2420\n",
      "Epoch 267/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 308.3239 - mae: 10.2911\n",
      "Epoch 268/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 280.2834 - mae: 9.2834\n",
      "Epoch 269/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 281.4695 - mae: 9.3663\n",
      "Epoch 270/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.3588 - mae: 9.6776\n",
      "Epoch 271/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 289.7481 - mae: 9.7001\n",
      "Epoch 272/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 292.5496 - mae: 9.7007\n",
      "Epoch 273/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 284.8695 - mae: 9.4639\n",
      "Epoch 274/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 295.3585 - mae: 9.9817\n",
      "Epoch 275/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 296.1905 - mae: 9.9289\n",
      "Epoch 276/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 277.6554 - mae: 9.0544\n",
      "Epoch 277/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 275.6185 - mae: 9.0656\n",
      "Epoch 278/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 276.8624 - mae: 9.0875\n",
      "Epoch 279/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 275.0537 - mae: 8.9689\n",
      "Epoch 280/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 278.4096 - mae: 9.1432\n",
      "Epoch 281/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 278.7022 - mae: 9.1305\n",
      "Epoch 282/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 284.1021 - mae: 9.3279\n",
      "Epoch 283/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 273.7014 - mae: 8.9895\n",
      "Epoch 284/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 272.5167 - mae: 8.9273\n",
      "Epoch 285/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 285.5040 - mae: 9.4257\n",
      "Epoch 286/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 275.9433 - mae: 9.1031\n",
      "Epoch 287/300\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 302.5498 - mae: 9.9773\n",
      "Epoch 288/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 286.9064 - mae: 9.5225\n",
      "Epoch 289/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 282.2985 - mae: 9.3144\n",
      "Epoch 290/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 290.0923 - mae: 9.5842\n",
      "Epoch 291/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 274.7046 - mae: 8.9545\n",
      "Epoch 292/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 301.1897 - mae: 10.3266\n",
      "Epoch 293/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 305.8065 - mae: 10.3318\n",
      "Epoch 294/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 298.8824 - mae: 10.1998\n",
      "Epoch 295/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 281.1791 - mae: 9.3154\n",
      "Epoch 296/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 268.5934 - mae: 8.7842\n",
      "Epoch 297/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 270.0925 - mae: 8.8183\n",
      "Epoch 298/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 283.4602 - mae: 9.5034\n",
      "Epoch 299/300\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 295.0586 - mae: 9.9152\n",
      "Epoch 300/300\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 279.5705 - mae: 9.1733\n",
      "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x0000014035626E50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 243.1984 - mae: 8.6259\n",
      "Epoch 1/300\n",
      "11/11 [==============================] - 1s 10ms/step - loss: 48659028.0000 - mae: 6962.2114\n",
      "Epoch 2/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 38848020.0000 - mae: 6170.5830\n",
      "Epoch 3/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 4325925.5000 - mae: 1774.5049\n",
      "Epoch 4/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 2343869.5000 - mae: 1341.3060\n",
      "Epoch 5/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 10ms/step - loss: 741544.7500 - mae: 744.9892\n",
      "Epoch 6/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 221886.2656 - mae: 388.7982\n",
      "Epoch 7/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 134598.2656 - mae: 293.2280\n",
      "Epoch 8/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 62120.8633 - mae: 187.9371\n",
      "Epoch 9/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 37320.9180 - mae: 146.7296\n",
      "Epoch 10/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 22921.9316 - mae: 111.6861\n",
      "Epoch 11/300\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 15915.5586 - mae: 92.3543\n",
      "Epoch 12/300\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 11744.7119 - mae: 78.7328\n",
      "Epoch 13/300\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 9368.8711 - mae: 71.0365\n",
      "Epoch 14/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 7935.3809 - mae: 66.6003\n",
      "Epoch 15/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 6794.6436 - mae: 61.5184\n",
      "Epoch 16/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 5986.4902 - mae: 57.2093\n",
      "Epoch 17/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 5297.5825 - mae: 53.3958\n",
      "Epoch 18/300\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 4761.7993 - mae: 50.0301\n",
      "Epoch 19/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 4293.1040 - mae: 47.1636\n",
      "Epoch 20/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 3867.9851 - mae: 44.2704\n",
      "Epoch 21/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 3515.5308 - mae: 41.8363\n",
      "Epoch 22/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 3217.2905 - mae: 39.8617\n",
      "Epoch 23/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 2945.7024 - mae: 37.8559\n",
      "Epoch 24/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 2729.5017 - mae: 36.4689\n",
      "Epoch 25/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 2527.2805 - mae: 35.0380\n",
      "Epoch 26/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 2353.3020 - mae: 33.8244\n",
      "Epoch 27/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 2210.3962 - mae: 32.8439\n",
      "Epoch 28/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 2075.9890 - mae: 31.9399\n",
      "Epoch 29/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1955.6946 - mae: 31.1085\n",
      "Epoch 30/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1867.4802 - mae: 30.6475\n",
      "Epoch 31/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1776.1218 - mae: 29.9102\n",
      "Epoch 32/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1690.9598 - mae: 29.2770\n",
      "Epoch 33/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 1610.0178 - mae: 28.5798\n",
      "Epoch 34/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 1534.7587 - mae: 27.9438\n",
      "Epoch 35/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 1469.8011 - mae: 27.4076\n",
      "Epoch 36/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1408.6554 - mae: 26.8062\n",
      "Epoch 37/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 1350.3444 - mae: 26.2196\n",
      "Epoch 38/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1298.8959 - mae: 25.7965\n",
      "Epoch 39/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1248.1060 - mae: 25.3454\n",
      "Epoch 40/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1201.5548 - mae: 24.8437\n",
      "Epoch 41/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1161.5604 - mae: 24.4659\n",
      "Epoch 42/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1117.3596 - mae: 24.0099\n",
      "Epoch 43/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 1077.6782 - mae: 23.6196\n",
      "Epoch 44/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 1047.5903 - mae: 23.3347\n",
      "Epoch 45/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 1016.6998 - mae: 23.0666\n",
      "Epoch 46/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 982.2692 - mae: 22.6598\n",
      "Epoch 47/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 955.0023 - mae: 22.3814\n",
      "Epoch 48/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 928.2123 - mae: 22.0427\n",
      "Epoch 49/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 900.9952 - mae: 21.6909\n",
      "Epoch 50/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 874.8662 - mae: 21.4068\n",
      "Epoch 51/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 852.8000 - mae: 21.1423\n",
      "Epoch 52/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 828.9950 - mae: 20.8520\n",
      "Epoch 53/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 806.4716 - mae: 20.5520\n",
      "Epoch 54/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 786.2660 - mae: 20.2652\n",
      "Epoch 55/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 766.9541 - mae: 20.0200\n",
      "Epoch 56/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 749.6422 - mae: 19.7676\n",
      "Epoch 57/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 730.3358 - mae: 19.4779\n",
      "Epoch 58/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 713.7057 - mae: 19.2201\n",
      "Epoch 59/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 694.5074 - mae: 18.9251\n",
      "Epoch 60/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 682.7018 - mae: 18.7703\n",
      "Epoch 61/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 664.5714 - mae: 18.4281\n",
      "Epoch 62/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 648.0245 - mae: 18.1309\n",
      "Epoch 63/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 632.6871 - mae: 17.8827\n",
      "Epoch 64/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 619.8588 - mae: 17.6750\n",
      "Epoch 65/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 605.4004 - mae: 17.4090\n",
      "Epoch 66/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 595.6946 - mae: 17.2629\n",
      "Epoch 67/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 587.2255 - mae: 17.0695\n",
      "Epoch 68/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 569.7850 - mae: 16.8038\n",
      "Epoch 69/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 559.0208 - mae: 16.5545\n",
      "Epoch 70/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 549.1872 - mae: 16.4042\n",
      "Epoch 71/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 537.5884 - mae: 16.1284\n",
      "Epoch 72/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 529.1633 - mae: 15.9942\n",
      "Epoch 73/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 519.8720 - mae: 15.8363\n",
      "Epoch 74/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 513.0070 - mae: 15.7049\n",
      "Epoch 75/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 504.1080 - mae: 15.4696\n",
      "Epoch 76/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 501.7152 - mae: 15.4346\n",
      "Epoch 77/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 493.3827 - mae: 15.3441\n",
      "Epoch 78/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 485.5026 - mae: 15.1291\n",
      "Epoch 79/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 476.8602 - mae: 14.9504\n",
      "Epoch 80/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 473.2937 - mae: 14.8262\n",
      "Epoch 81/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 464.5878 - mae: 14.6835\n",
      "Epoch 82/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 460.5547 - mae: 14.5585\n",
      "Epoch 83/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 455.8629 - mae: 14.4755\n",
      "Epoch 84/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 450.1485 - mae: 14.3345\n",
      "Epoch 85/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 447.9433 - mae: 14.2528\n",
      "Epoch 86/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 446.3974 - mae: 14.3269\n",
      "Epoch 87/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 11ms/step - loss: 438.8917 - mae: 14.0387\n",
      "Epoch 88/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 437.1814 - mae: 14.0475\n",
      "Epoch 89/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 430.0787 - mae: 13.8993\n",
      "Epoch 90/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 426.8120 - mae: 13.8219\n",
      "Epoch 91/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 422.2479 - mae: 13.7207\n",
      "Epoch 92/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 421.2574 - mae: 13.6704\n",
      "Epoch 93/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 422.6060 - mae: 13.7427\n",
      "Epoch 94/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 413.4796 - mae: 13.5132\n",
      "Epoch 95/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 414.3490 - mae: 13.4949\n",
      "Epoch 96/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 416.4385 - mae: 13.5551\n",
      "Epoch 97/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 408.2072 - mae: 13.2779\n",
      "Epoch 98/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 401.8585 - mae: 13.1691\n",
      "Epoch 99/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 399.8747 - mae: 13.1663\n",
      "Epoch 100/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 400.1886 - mae: 13.1280\n",
      "Epoch 101/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 393.4223 - mae: 12.9762\n",
      "Epoch 102/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 390.7705 - mae: 12.9097\n",
      "Epoch 103/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 394.8780 - mae: 13.0110\n",
      "Epoch 104/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 391.1503 - mae: 12.9308\n",
      "Epoch 105/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 388.6243 - mae: 12.8954\n",
      "Epoch 106/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 383.7106 - mae: 12.7819\n",
      "Epoch 107/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 382.9479 - mae: 12.7169\n",
      "Epoch 108/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 381.9718 - mae: 12.6844\n",
      "Epoch 109/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 378.1613 - mae: 12.5998\n",
      "Epoch 110/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 375.8205 - mae: 12.5014\n",
      "Epoch 111/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 375.0208 - mae: 12.4179\n",
      "Epoch 112/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 373.5044 - mae: 12.4008\n",
      "Epoch 113/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 374.4490 - mae: 12.4594\n",
      "Epoch 114/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 369.8949 - mae: 12.3312\n",
      "Epoch 115/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 367.5473 - mae: 12.2885\n",
      "Epoch 116/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 369.3031 - mae: 12.2993\n",
      "Epoch 117/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 365.5876 - mae: 12.2154\n",
      "Epoch 118/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 362.9303 - mae: 12.1108\n",
      "Epoch 119/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 364.3219 - mae: 12.1426\n",
      "Epoch 120/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 362.7505 - mae: 12.1700\n",
      "Epoch 121/300\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 359.7507 - mae: 12.0271\n",
      "Epoch 122/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 359.0210 - mae: 12.0572\n",
      "Epoch 123/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 359.2135 - mae: 12.0571\n",
      "Epoch 124/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 359.4521 - mae: 12.0285\n",
      "Epoch 125/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 354.3199 - mae: 11.8778\n",
      "Epoch 126/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 351.7781 - mae: 11.7805\n",
      "Epoch 127/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 350.4273 - mae: 11.7727\n",
      "Epoch 128/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 353.0253 - mae: 11.8471\n",
      "Epoch 129/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 349.7652 - mae: 11.7164\n",
      "Epoch 130/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 347.6245 - mae: 11.6523\n",
      "Epoch 131/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 347.8590 - mae: 11.6586\n",
      "Epoch 132/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 356.0900 - mae: 11.9766\n",
      "Epoch 133/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 346.4018 - mae: 11.6189\n",
      "Epoch 134/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 345.0325 - mae: 11.5799\n",
      "Epoch 135/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 339.2166 - mae: 11.4231\n",
      "Epoch 136/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 339.0824 - mae: 11.4283\n",
      "Epoch 137/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 337.8273 - mae: 11.3337\n",
      "Epoch 138/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 339.1735 - mae: 11.4599\n",
      "Epoch 139/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 340.4413 - mae: 11.5029\n",
      "Epoch 140/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 334.4474 - mae: 11.2600\n",
      "Epoch 141/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 335.7241 - mae: 11.3432\n",
      "Epoch 142/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 335.4860 - mae: 11.2922\n",
      "Epoch 143/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 332.3644 - mae: 11.1772\n",
      "Epoch 144/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 332.3426 - mae: 11.1958\n",
      "Epoch 145/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 328.8235 - mae: 11.0774\n",
      "Epoch 146/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 329.1968 - mae: 11.1445\n",
      "Epoch 147/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 328.8297 - mae: 11.1138\n",
      "Epoch 148/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 327.6690 - mae: 11.0071\n",
      "Epoch 149/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 332.1254 - mae: 11.2219\n",
      "Epoch 150/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 330.7700 - mae: 11.1803\n",
      "Epoch 151/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 325.1058 - mae: 10.9277\n",
      "Epoch 152/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 322.6164 - mae: 10.8820\n",
      "Epoch 153/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 324.2232 - mae: 10.9765\n",
      "Epoch 154/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 328.7289 - mae: 11.1530\n",
      "Epoch 155/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 330.1049 - mae: 11.0400\n",
      "Epoch 156/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 321.8036 - mae: 10.9071\n",
      "Epoch 157/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 321.6085 - mae: 10.8857\n",
      "Epoch 158/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 319.8170 - mae: 10.7912\n",
      "Epoch 159/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 317.9544 - mae: 10.7324\n",
      "Epoch 160/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 313.8748 - mae: 10.6043\n",
      "Epoch 161/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 314.9404 - mae: 10.6323\n",
      "Epoch 162/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 313.7658 - mae: 10.6190\n",
      "Epoch 163/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 317.2519 - mae: 10.7349\n",
      "Epoch 164/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 318.7833 - mae: 10.8370\n",
      "Epoch 165/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 317.6639 - mae: 10.7058\n",
      "Epoch 166/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 309.5900 - mae: 10.4926\n",
      "Epoch 167/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 308.8179 - mae: 10.4629\n",
      "Epoch 168/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 310.7275 - mae: 10.5685\n",
      "Epoch 169/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 9ms/step - loss: 310.2022 - mae: 10.4911\n",
      "Epoch 170/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 315.4949 - mae: 10.7078\n",
      "Epoch 171/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 315.1797 - mae: 10.7313\n",
      "Epoch 172/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 322.6014 - mae: 11.0845\n",
      "Epoch 173/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 306.1347 - mae: 10.4195\n",
      "Epoch 174/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 304.7287 - mae: 10.2966\n",
      "Epoch 175/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 302.9000 - mae: 10.2265\n",
      "Epoch 176/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 303.5949 - mae: 10.3183\n",
      "Epoch 177/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 301.8298 - mae: 10.2705\n",
      "Epoch 178/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 303.2148 - mae: 10.3163\n",
      "Epoch 179/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 301.5558 - mae: 10.2350\n",
      "Epoch 180/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 298.9934 - mae: 10.1868\n",
      "Epoch 181/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 307.9450 - mae: 10.4072\n",
      "Epoch 182/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 299.9387 - mae: 10.1356\n",
      "Epoch 183/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 300.4774 - mae: 10.2601\n",
      "Epoch 184/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 304.5847 - mae: 10.2774\n",
      "Epoch 185/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 299.2458 - mae: 10.2252\n",
      "Epoch 186/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 304.4272 - mae: 10.3959\n",
      "Epoch 187/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 298.8994 - mae: 10.1184\n",
      "Epoch 188/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 297.4901 - mae: 10.0713\n",
      "Epoch 189/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 302.0093 - mae: 10.2951\n",
      "Epoch 190/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 306.5504 - mae: 10.5450\n",
      "Epoch 191/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 305.2367 - mae: 10.4033\n",
      "Epoch 192/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 294.3214 - mae: 9.9849\n",
      "Epoch 193/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 287.7872 - mae: 9.7236\n",
      "Epoch 194/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 289.9433 - mae: 9.8395\n",
      "Epoch 195/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 286.9131 - mae: 9.6948\n",
      "Epoch 196/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 290.1898 - mae: 9.8242\n",
      "Epoch 197/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 283.9664 - mae: 9.6152\n",
      "Epoch 198/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 285.3512 - mae: 9.6785\n",
      "Epoch 199/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 284.1101 - mae: 9.6115\n",
      "Epoch 200/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 283.3658 - mae: 9.5794\n",
      "Epoch 201/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 283.4269 - mae: 9.6064\n",
      "Epoch 202/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 281.8024 - mae: 9.5157\n",
      "Epoch 203/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 285.3724 - mae: 9.6322\n",
      "Epoch 204/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 290.2230 - mae: 9.8934\n",
      "Epoch 205/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 287.2703 - mae: 9.7129\n",
      "Epoch 206/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 287.5774 - mae: 9.6788\n",
      "Epoch 207/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 290.4500 - mae: 9.8280\n",
      "Epoch 208/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 297.5522 - mae: 10.1909\n",
      "Epoch 209/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 292.5317 - mae: 9.9641\n",
      "Epoch 210/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 292.5505 - mae: 9.9945\n",
      "Epoch 211/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 282.8355 - mae: 9.5574\n",
      "Epoch 212/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 280.6664 - mae: 9.5216\n",
      "Epoch 213/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 277.9262 - mae: 9.4349\n",
      "Epoch 214/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 302.8494 - mae: 10.4733\n",
      "Epoch 215/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 286.5835 - mae: 9.8064\n",
      "Epoch 216/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 283.6952 - mae: 9.7198\n",
      "Epoch 217/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 274.1135 - mae: 9.2783\n",
      "Epoch 218/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 273.6418 - mae: 9.2299\n",
      "Epoch 219/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 283.3688 - mae: 9.6338\n",
      "Epoch 220/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 270.9583 - mae: 9.1556\n",
      "Epoch 221/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 276.6050 - mae: 9.3526\n",
      "Epoch 222/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 275.9336 - mae: 9.3447\n",
      "Epoch 223/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 279.9432 - mae: 9.4882\n",
      "Epoch 224/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 275.8328 - mae: 9.3960\n",
      "Epoch 225/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 280.1785 - mae: 9.4545\n",
      "Epoch 226/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 270.2878 - mae: 9.0942\n",
      "Epoch 227/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 269.1756 - mae: 9.1401\n",
      "Epoch 228/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 273.8877 - mae: 9.3865\n",
      "Epoch 229/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 299.0530 - mae: 10.3854\n",
      "Epoch 230/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 281.4682 - mae: 9.6692\n",
      "Epoch 231/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 277.6075 - mae: 9.4492\n",
      "Epoch 232/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 269.7404 - mae: 9.0759\n",
      "Epoch 233/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 267.2630 - mae: 9.0570\n",
      "Epoch 234/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 268.7747 - mae: 9.1614\n",
      "Epoch 235/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 266.0448 - mae: 9.0144\n",
      "Epoch 236/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 289.2249 - mae: 10.0150\n",
      "Epoch 237/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 282.7971 - mae: 9.7756\n",
      "Epoch 238/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 268.8946 - mae: 9.1058\n",
      "Epoch 239/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 266.3165 - mae: 8.9477\n",
      "Epoch 240/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 285.8521 - mae: 9.8369\n",
      "Epoch 241/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 280.0744 - mae: 9.6223\n",
      "Epoch 242/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 293.5792 - mae: 10.1214\n",
      "Epoch 243/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 285.0977 - mae: 9.9098\n",
      "Epoch 244/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 275.0679 - mae: 9.3525\n",
      "Epoch 245/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 289.6393 - mae: 10.0587\n",
      "Epoch 246/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 266.5166 - mae: 9.0053\n",
      "Epoch 247/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 264.0327 - mae: 8.9860\n",
      "Epoch 248/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 263.6516 - mae: 8.8512\n",
      "Epoch 249/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 310.2651 - mae: 10.8790\n",
      "Epoch 250/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 327.6128 - mae: 11.4708\n",
      "Epoch 251/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 300.2703 - mae: 10.5888\n",
      "Epoch 252/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 9ms/step - loss: 269.6389 - mae: 9.0142\n",
      "Epoch 253/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 263.3466 - mae: 8.9099\n",
      "Epoch 254/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 258.1163 - mae: 8.6440\n",
      "Epoch 255/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 264.2071 - mae: 8.9047\n",
      "Epoch 256/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 256.3644 - mae: 8.5351\n",
      "Epoch 257/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 260.8635 - mae: 8.7131\n",
      "Epoch 258/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 273.5471 - mae: 9.4309\n",
      "Epoch 259/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 311.3979 - mae: 10.8932\n",
      "Epoch 260/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 335.1636 - mae: 11.8157\n",
      "Epoch 261/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 284.4970 - mae: 9.8540\n",
      "Epoch 262/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 256.6893 - mae: 8.5913\n",
      "Epoch 263/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 277.8621 - mae: 9.6808\n",
      "Epoch 264/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 255.7293 - mae: 8.5614\n",
      "Epoch 265/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 267.2718 - mae: 9.0446\n",
      "Epoch 266/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 253.6019 - mae: 8.5172\n",
      "Epoch 267/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 255.2147 - mae: 8.6289\n",
      "Epoch 268/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 253.7541 - mae: 8.4555\n",
      "Epoch 269/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 262.1628 - mae: 8.8741\n",
      "Epoch 270/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 261.8512 - mae: 8.8952\n",
      "Epoch 271/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 260.7778 - mae: 8.8381\n",
      "Epoch 272/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 255.7390 - mae: 8.5862\n",
      "Epoch 273/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 254.7844 - mae: 8.5708\n",
      "Epoch 274/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 253.4866 - mae: 8.4456\n",
      "Epoch 275/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 264.3213 - mae: 8.9223\n",
      "Epoch 276/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 251.4693 - mae: 8.3730\n",
      "Epoch 277/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 253.6302 - mae: 8.4626\n",
      "Epoch 278/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 266.0820 - mae: 9.0559\n",
      "Epoch 279/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 268.6103 - mae: 9.1857\n",
      "Epoch 280/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 252.0051 - mae: 8.3755\n",
      "Epoch 281/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 264.1017 - mae: 8.8814\n",
      "Epoch 282/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 319.4384 - mae: 11.2580\n",
      "Epoch 283/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 276.0003 - mae: 9.3600\n",
      "Epoch 284/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 355.4245 - mae: 12.5164\n",
      "Epoch 285/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 377.5329 - mae: 13.4377\n",
      "Epoch 286/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 256.7472 - mae: 8.7254\n",
      "Epoch 287/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 250.3270 - mae: 8.3612\n",
      "Epoch 288/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 262.1464 - mae: 8.9196\n",
      "Epoch 289/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 252.7967 - mae: 8.4393\n",
      "Epoch 290/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 419.0186 - mae: 14.6809\n",
      "Epoch 291/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 445.2189 - mae: 15.3330\n",
      "Epoch 292/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 315.4861 - mae: 11.3003\n",
      "Epoch 293/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 287.6502 - mae: 10.0989\n",
      "Epoch 294/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 296.7876 - mae: 10.3995\n",
      "Epoch 295/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 251.2556 - mae: 8.3822\n",
      "Epoch 296/300\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 264.4433 - mae: 8.8988\n",
      "Epoch 297/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 260.3624 - mae: 8.8355\n",
      "Epoch 298/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 248.1669 - mae: 8.2374\n",
      "Epoch 299/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 254.0782 - mae: 8.5187\n",
      "Epoch 300/300\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 306.1919 - mae: 10.7411\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=<tensorflow.python.keras.wrappers.scikit_learn.KerasRegressor object at 0x0000013F20412F70>,\n",
       "             param_grid={'activation_func': ['relu'], 'batch_size': [1000],\n",
       "                         'epochs': [300], 'first_layer_nodes': [512],\n",
       "                         'last_layer_nodes': [32], 'loss_func': ['mse'],\n",
       "                         'n_layers': [8]})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79c9111d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261.67657165527345"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d358251c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'activation_func': 'relu',\n",
       " 'batch_size': 1000,\n",
       " 'epochs': 300,\n",
       " 'first_layer_nodes': 512,\n",
       " 'last_layer_nodes': 32,\n",
       " 'loss_func': 'mse',\n",
       " 'n_layers': 8}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "35ac68b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 4ms/step - loss: 532.8514 - mae: 14.7971\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "532.8514404296875"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-grid.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc8869fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 4ms/step - loss: 447.0237 - mae: 14.7602\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "447.023681640625"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-grid.score(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1856d776",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "ypred= grid.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8dbc3ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7420.549 , 6225.9966, 7728.0586, ..., 6225.7524, 6670.134 ,\n",
       "       7230.194 ], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cd54d1c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12400    7409.543649\n",
       "2613     6218.566811\n",
       "4629     7713.533791\n",
       "5524     7089.495739\n",
       "10129    6790.770906\n",
       "            ...     \n",
       "7798     7188.929145\n",
       "4093     6325.855815\n",
       "9228     6221.160282\n",
       "3710     6661.707565\n",
       "6495     7223.403018\n",
       "Name: density, Length: 2556, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "847703ef",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "518.8814088758209\n",
      "532.8515274680501\n",
      "14.79713296897385\n",
      "0.9972493728235955\n"
     ]
    }
   ],
   "source": [
    "# TEST SET SCORE AND ERROR\n",
    "from sklearn.metrics import max_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(max_error(y_true= ytest, y_pred= ypred))\n",
    "print(mean_squared_error(ytest, ypred))\n",
    "print(mean_absolute_error(ytest, ypred))\n",
    "print(r2_score(ytest, ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6dc4e1c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "trainpred= grid.predict(xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8d6862e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "321.08129075978104\n",
      "447.0237006032235\n",
      "14.76022604570674\n",
      "0.9976070656201458\n"
     ]
    }
   ],
   "source": [
    "# TRAIN SET SCORE AND ERROR\n",
    "\n",
    "print(max_error(y_true= ytrain, y_pred= trainpred))\n",
    "print(mean_squared_error(ytrain, trainpred))\n",
    "print(mean_absolute_error(ytrain, trainpred))\n",
    "print(r2_score(ytrain, trainpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3df2f542",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "820833d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d30be397",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db3e0df7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0ee92ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ARTIFICIAL NEURAL NETWORK')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGaCAYAAADdB0ORAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAACq1ElEQVR4nOzdd1iTV/vA8W8mIwEUpe66haoIuLUKDrQuBFScdfetXVato7Xtz9k6O6xt7a6rrbZWWVrrHmjdCC5ABTdqFQUZSgjJ74+UVARcBUS9P9fV6315nvM8OTmJyZ0z7qMwm81mhBBCCCEeEeWjroAQQgghnm4SjAghhBDikZJgRAghhBCPlAQjQgghhHikJBgRQgghxCMlwYgQQgghHikJRsRjY/fu3bi6utKsWTMMBkOe8++88w6urq55/nN3d6dt27a8++67XL16FYDz58/nWza//86fPw9Au3btaNeuXa7HvNe1r732mrVsftfniIyM5K233qJdu3bUr1+f1q1b8/bbb3P69Ok8Ze92H4AXX3wRV1dXfvjhh3zP5zz3d955p8B7FCSnjX/++ee73vvzzz/Pc839tlNO+Zx2v9OePXvu+zHye+3zs3LlSlxdXfHz8yuwzMCBA3F1dS3wfEFy6tu7d28KyqQwcODAXK9pzjX3+u/GjRv3XdbV1ZVt27bh6urKjz/+mKcOOf++3NzcSE5OznN+9OjR1KtXj/T09FzHd+3axRtvvMHzzz+Pu7s7HTp0YOLEicTFxRXYFnf+99xzz9GsWTMGDRpERERErmtWrVqFq6srq1atynM/k8nEm2++iaurK++9916B7StKPvWjroAQ9ys8PBx7e3uSk5PZvHkznTp1yrfcxIkTKV26tPXvtLQ0du3axcqVKzly5Ai///47zs7OzJkzJ9d1M2fOtF5/O2dn57vWq0aNGrzyyiv5nqtQocI9n9dnn33GV199Rc2aNenRowcuLi4kJCSwYsUKNmzYwJIlS6hfv/497wOQmJjI/v37sbe3JyQkhOHDh9/XdQ9q3rx5vPDCC5QtW/a+r7nzdbnd/bTTwzxGfq+9VqvNc13Oe+v48eMcPnwYd3f3QqnP7aKjo1m+fDn9+vW772s6dOhAhw4dCjxvZ2dHzZo187yXJ0yYkO/7slGjRqhUKg4dOpTnXrt370aj0ZCVlcWePXt44YUXcp0/ePAg7u7u6HQ6AMxmM7Nnz2bhwoXUrFmTAQMG4OLiwtmzZwkODiYsLIz3338/3+d75/PKzs4mISGBX375hREjRrB06VIaNWp0z/aZPHky69ato1u3bkyfPh2FQnHPa0TJJMGIeCwYDAbWr1+Pv78/q1evJjg4uMBgxNfXl8qVK+c6NmDAAKZMmcKyZcvYuHEjXbp0wd/fP1eZzz77DCDP8XspW7bsA1+TY+XKlSxYsIA+ffowZcoUlMp/Oyv79OlD3759eeWVV9i0aRM2Njb3vN/q1asBy/P97rvviuyL9caNG8ycOZOPP/74vq/J73UpbPf72t/u77//Zs+ePQwfPpzvv/+e4ODgImkzgE8++YSOHTtSpkyZ+yrv6up6z/dWfu+/CRMmFPi+dHNzKzAYad26Nfv372fXrl25gpGLFy9y6dKlXPdbtGgRCxcuZODAgUycOBGVSmU99+qrrzJy5EimTJlCpUqV8Pb2vq/n1aFDB/r06cPXX3/Nd999d9fn/fHHH/Pbb7/Rvn17Zs+enevfjnj8yKsnHgvbtm3jxo0bNGvWjFatWrFjxw6uXLnyQPcIDAwELL9QS4KsrCzmzJlD1apVmTx5cp4P0xo1avDSSy9x5coVNm3adF/3XL16NTVr1rQ+1+Dg4EKvN1iGilavXs2uXbuK5P6F7W6v/R9//IHJZKJjx464u7uzZs2afIcB/6t27dpx48YNZs2aVej3fhBNmzblwoULuYat0tPTOXLkCM2aNaNx48Z5XteDBw8C0KxZM8ASjM6fPx8vLy/efffdXIEIgL29PZ9++imlS5dm2rRp9z184uHhQfXq1e/5b/THH3/k22+/pVWrVsybNw+1Wn5XP+4kGBGPhfDwcBQKBU2aNKFDhw4YjUZCQ0Mf6B52dnYAJWZc+a+//iI5OZlevXrl+TDP0b9/f7Zu3Zrn13x+Tpw4QVxcHE2bNqVmzZpUr169yL5Y33//fezs7JgyZUqR3L+w3e21DwsLQ6/XU7duXTp06EBycvJ9B38PokOHDrRp04awsDB2795d6Pe/X40bNwZyB2b79+8nKyuL5s2b06xZM06fPs3ly5et5w8ePIhGo6Fhw4YArFu3joyMDPr3719gj4SjoyO9evXi3LlzREZG3nf97Ozs7vpvdNWqVcyZM4fGjRvzxRdf5DvsJh4/EoyIEi8tLY2tW7fi6elJ2bJl8fHxQavVEhIS8kD3yZkYV7du3UKtX1ZWFteuXcvzX2pq6l2vO3LkCGD5NVgQvV5/3/MpwsPDActQBVCkX6yVKlXitdde4/Tp03z77bf3dc2NGzfybadr166RnZ1d6HW8XUGv/alTpzh69Cht2rRBrVZb266oepTef/99bG1t7zuIu3nzZoFtlpGR8VB1aNy4MQqFItdQze7duyldujSurq40b94cIFfvSM58kZygLioqCgAvL6+7PlbOvQ4cOHBfdbt8+TLHjx/nueeey/f8xo0bef/996lQoQLffPONtT7i8Sd9W6LEW7duHZmZmXTs2BGwfEG3bNmSrVu3cujQIRo0aJCrfM6XXo60tDQiIiL44osvqFmzJl27di3U+h08eJAWLVrkOd60aVOWLl1a4HU53eQuLi7/uQ5ms5nVq1fj5ORk7Urv2LEj3377LcHBwXTu3Pk/P8adhg4dSlhYGN9++y1+fn5UrVr1ruVzhkryExISUuAX0IN40Nc+J4DLeW/VqFGD2rVrs2PHDv7++2+eeeaZ/1yn21WpUoXXXnuNTz75hO+//z7XKqL8/PDDDwWuiho0aBDvvffeA9ehVKlS1KlTJ1fPyJ49e2jatCkKhQJXV1dKlSrFrl27CAgI4NatW8TGxuaaDJ0zRHqv925O+/3999+5jucEWTmysrKIj4/no48+wmQy8cYbb+S51969e1mzZg0mk4mLFy9y9OhR63tdPP4kGBElXs6kzNtn33fo0IGtW7eyatWqPMFIfl96dnZ2tG/fnvfffx+NRlOo9Stomayjo+Ndr8sZmjGZTP+5DpGRkVy4cIGAgADr+Lm7uzsVK1Yssi9WjUbDlClTePHFF5k2bVqBX5o55s6dW+Dqm2efffaBHz+/lRMP+tqvXr0aW1vbXBMsO3TowIIFCwgNDeV///vfA9frXoYNG0ZoaChff/01fn5+VKlSpcCy/v7+BAQE5Hvuv6xAatKkCSEhIZhMJlJTU4mJiaFnz56ApV2bNWvG3r17ATh8+LB1CCdHzjBKQcOLOXLei3cOuxQUZNWrV48ffviBpk2b5jkXHBxMjRo1mDZtGsOGDWPixImEh4dbV/eIx5sEI6JE+/vvv9m9ezfVqlVDoVBYc0+4ubmhUCj4448/ePfdd3ONG+d86WVlZREREcHPP/9M586dmTJlyn2tSHlQTk5OtGzZ8oGvy/liTkpKolatWv+pDjm/8D08PHLl52jatCkhISFF9sXauHFjAgMDWbVqFWvWrLnrkFPDhg3vazVNzmtZ0NBNzvH85go8yGt/6NAhzpw5Q/PmzUlKSrIez1lJExISUiRtlhPEDRw4kKlTp/L9998XWLZKlSoP9d66lyZNmvDTTz+RkJDAqVOnMJlMuYKNZs2asW7dOi5dumSdL3L7kExOYJuUlET58uULfJycHpE7A+GcIMtsNluH+mxtbZkxYwZubm753qtSpUosXLiQ8uXL8+qrr/LZZ58xa9Yspk+f/tDtIEoOCUZEiZaz0uH06dO0b98+z/mUlJQ8yzVv/9Lz8fGhatWqfPDBByQnJ7NgwYISk4sg58M9Ojq6wO7mK1eu8PrrrxMUFERQUFC+ZYxGI3/++ScAU6dOzbdMUX2xAowfP57Nmzczc+bMu36x3i8nJyeAPMm1cqSkpAD59zw9yGsfFhYGWOZL5PfeOnnyZL7DgIWhadOmBAQEEBISwtq1awv9/vfSpEkTwNLrcfToUVxcXKhZs6b1fM77MTIykqioKDw8PLC1tbWeb9y4Mb///jv79u27a6K4/fv3A1gnvua4Pch6/vnn8fHxoWfPngwePJhff/2VatWq5bnXa6+9Zg18/ve///Hnn3/y22+/0bFjR1q3bv0QrSBKEpnAKkq0nFU0s2fP5ssvv8z138iRI4F7TzYcOHAg7du3Z/PmzSxevLg4qn1fGjZsSJkyZVi1alWBvQCrV68mOjq6wC9mgB07dnD9+nXatGmTp42+/PJLatSoYf1iLQrOzs6MGzeOK1euMG/evP98v5wvxRMnTuR7/vjx4wDUrl37nvcq6LXPzs5m7dq12Nvb8/nnn+dps/79+wPkm/WzsLz99ts4OTkxY8aMu76+RaFMmTLUqFGD2NhY9u/fnycYrlWrFi4uLsTFxeUbLPv6+qLT6Vi8eHGB79309HRWrFhBpUqVrCt4ClK5cmU+/PBDkpOTeeuttzAajXnK3L5qR6PR8OGHH6JSqXjvvfe4cePG/T51UUJJMCJKrFOnTnHkyBHrr0hfX99c/40YMQIXFxd27tyZaxlifqZNm4aTkxPz5s3j3LlzxfQM7k6j0TBy5EhOnTrFjBkz8oyrx8bGMn/+fFxcXOjVq1eB98kZonnppZfytJGvr6/1i3XlypVF9lx69epFw4YN2bJly3++1/PPP4+trS1Llizh1q1buc7duHGD4OBgKlSocN+JyfJ77Xft2sXVq1fp1KkTHTt2zNNmI0eORKvVFtnSaLAEcWPHjuXvv//m6NGjRfIYd9OkSRMiIyM5fvx4riGaHE2bNiUiIoKrV6/mmcPh4ODAuHHjOHz4MFOnTs0TkNy6dYtx48Zx6dIlJk+efF+9kb6+vnTr1o2jR4/mm67+Tu7u7gwePJjLly/zwQcf3LO8KNlkmEaUWDkTVwv6ItZoNPTs2ZOvv/76njlHypYty7hx4/i///s/pkyZcs/JlsWlT58+HDt2jJ9++om9e/fSrVs3HB0diYmJYdWqVWi1Wj777DP0en2+12dkZLB582aqVatm7Xq/U0BAAB9//DF//PFHrtUXBw8eZNKkSXnK29nZ5UmJfy8KhYIpU6bQo0ePfH/VgmVZZkHp4OHfzLcuLi5MmDCB6dOn06NHDwICAihTpgwXL15k1apVXLt27YHyS+T32t/rveXs7EzHjh1ZvXp1nmHA/NoMLDlhCprvUJDevXsTHBxsTSp2p7i4uLu+txs2bHjXCbB306RJE3799VeAfIORZs2asWbNGrRabb5LePv378/Vq1f58ssvOXDgAH5+fpQtW5YLFy4QGhrK5cuX+b//+z98fHzuu04TJ04kIiKCL7/8kk6dOt1zYvOoUaPYtGkToaGh1qBSPJ4kGBEl1urVq3FwcLAuu8xP7969rctX7zZ5EiAoKIiQkBB27NhBSEhIgasUipNSqWT69Ol4e3uzbNkyfvrpJ65fv46zszP+/v688sord/2y2bhxIxkZGfTo0aPAMg4ODnTt2pXff/+djRs3WudAnD59Ot+N+BwcHB44GAHLqqJBgwYV+Ks2Z++fgtyeHnzAgAFUrVqVpUuXsmTJEpKTkylbtiwNGzbkf//73wMvA77ztV+/fj3Vq1e/6/4n/fr1Y/Xq1axatSpXMJLzBX4nb2/vBw5GcoK4nJUsd9qwYQMbNmwo8PqZM2c+dDCS09tRqVKlfO+RMzTj6elZ4MTvN998k9atW7N48WJ+++03rl69iouLC82bN2fQoEEP3B5ly5Zl/PjxvP/++0yaNIlFixbdtbytrS3Tp09n8ODBTJ48mYYNG95zLylRMinMJSUdpRBCCCGeSjJnRAghhBCPlAQjQgghhHikJBgRQgghxCP1yIORZcuW0blzZzw9PfHz87MmIsqxY8cOevbsiYeHB+3atct3ctzhw4cZOHAgXl5etGrVik8++YSsrKxcZU6fPs0rr7xC48aNadasGZMnTyYtLa1In5sQQggh7u2Rrqb59ddfmTJlCsOGDaN169Zs27aN8ePHo9Fo6Ny5M5GRkbzyyit07tyZUaNGceDAAebMmYPZbLZu2nTmzBmGDBmCl5cX8+bNIz4+nk8//ZS0tDTrEryUlBQGDx6Mi4sLs2fPJikpiblz53Lp0iW++eabR9kEQgghxFPvka6m6du3L1qtliVLlliPDRgwAKVSydKlSxkyZAgZGRn89ttv1vNz587lt99+Y+fOnWi1Wt577z127tzJ+vXrrXkHfvnlFz744AO2bNlCuXLlWLBgAd999x2bN2+25jnYtm0bL7/8Mr/99ts9l4Tmx2w2I+uQCldOXiRp16Il7Vz0pI2Lh7Rz0bu9jRWK/DeoLAyPtGckMzMzTxKkUqVKcfbsWTIzM9m/fz+jR4/Odf6FF17g+++/JzIykubNm7Nz507atm2bKwFSp06dmDp1qnWIZ+fOnTRp0iTXY7Vq1QqdTse2bdseMhiBpCQZ5ilMTk52AKSk3HzENXmySTsXPWnj4iHtXPRub+MyZfQU1dZejzQYGTRoEP/3f//H2rVrad26NTt27GDr1q2MGTOGc+fOkZWVRfXq1XNdU7VqVcCSKtzDw4OLFy/mKePs7Ixer+fUqVMAJCQk0L1791xlVCoVlStXtpZ5UArFvy+SKBxqtWU7cmnXoiXtXPSkjYuHtHPRu72Ni3KP0UcajHTt2pXdu3fn6v0IDAzkpZdesqZHvjMNtk6nAyAtLY3U1NR8y+SUy5mgmpqaes8yQgghhHg0Hmkw8uqrr3Lw4EEmTpxI3bp1iY6OZsGCBej1+lzpl/OjVCrzbCyWX5l7uZ8y+TGbpWuwsEmXa/GQdi560sbFQ9q56D3xwzSRkZHs2LGDmTNnWvfVaNq0KY6OjkyaNMm6gdWdW2vn9GQ4ODhYezvy2347LS0NBwcHwNJzUlCZihUrFt6TEkIIIcQDe2R5RhITEwHLrpO3a9y4MQAxMTGoVCrOnj2b63zO39WrV0en01GuXDnOnDmTq0xSUhLp6enWuSTVq1fPUyY7O5vz58/nmW8ihBBCiOL1yIKRnCDgwIEDuY5HRUUBUKNGDRo3bsz69etzDcesW7cOBwcH6tevD8Dzzz/Pli1bMBgMucqoVCrrrpTPP/88e/bsITk52Vpmx44dZGRk0LJly6J4ekIIIYS4T49smKZevXr4+vry4YcfkpqaynPPPceRI0f48ssv8fb2xsPDg1dffZWhQ4cyZswYAgMDOXjwID/88ANjx47Fzs4yjvXSSy+xZs0aXn75ZQYPHszp06f55JNP6N27t3UIpn///vz0008MGTKE119/neTkZObOnYu3t3eenhkhhBBCFK9HmvTMYDDwxRdfEBYWRlJSEpUqVaJbt268/PLL1rwhGzZsYP78+Zw6dYpy5coxYMAAhg0blus++/fvZ86cOcTExFC6dGkCAgIYOXIkGo3GWub48ePMmDGDgwcPotPp8PX1ZcKECfmusrkfJpNZ8owUMpmMVjyknYuetHHxkHYuendOYFUqi2YG6yMNRh5nEowUPvlgKR7SzkVP2rh4SDsXTKNRoVQqMJnMZGVlP/R9iisYeaRLe4UQQghReMxmI+npKYSHhxMVdRBPTy/8/PzQ6ZxQKEruV37JrZkQQggh7pvZbCQyci/+/t1vW9SxkPHjxxEaGkbDhk1LbEDyyFbTCCGEEKLwpKen3BGIWBgMBvz9u5OenvKIanZvEowIIYQQjzmNRkV4eHieQCSHwWAgPDwcjUZVzDW7PxKMCCGEEI8ZjUaFjY0aW1sNNjZqNBoV0dFR1vPDh8OPP4KLy7/XHDoUXWQTUP+rkjl4JIQQQgirnNUxCoWZmzfTOXQoBr1ez+bNmzl27Bienl6MGTMGo/ESPXv+Qc5G9atXw6pVlv/foIEHJlPJXEArwYgQQghRQimVJlJTrxMWFkZUVBR169alU6dOKJVKWrRokWuiaocOakJCHLC3txxZuhTCwiz/X6vV4ufn95+W+RYlCUaEEEKIEsZsNnLrVhpRUQcJCgrKNRfkvffeY9GiRXTt2pXg4GBUKpg8Gd57z4hSeR2j0Y5hw26ydKmlvFarJSwsDJ3O6RE9m3uTYEQIIYQoQXKW6Lq4lM0TiIBlMuqQIUPYsGEDBw8G8/PPkLPN2v79cPToW/j7e6HVrqVBAw/JMyKEEEKIB5OdfYtKlSrm2QT2dgaDgb///pJDh1Q4OFiGXubOhffeg0GDLvHZZ93o0KHLf87AWlwkGBFCCCFKALPZSGZmGllZWRgMBusu9neyt4d586BXr98AuHwZBg2C9est5xs08CArK/uxCEJySDAihBBCPGJms5ErVxJRKBSsXLkStVpN3bp185Rr0ACWL4fnnrP8vWdPabp3v87ff1v+LukTVQsiwYgQQgjxCJlMmdy6lc7p06fp0aMHBoMBhULB1q1b0Wq11qGaN96Ajz4CGxswGODcuddo2XIBJpPlPo/DRNWCSDAihBBCPAKW3CHZ3LhxC5VKxaeffkpWVhYAZrOZefPmsWjRIsaOHczXX2dZc4ecPAmXL8+jVClf5s6tybFjx2jQoAF+ft1L/ETVgjx+NRZCCCEeY2o1ZGdncvNmFjdu3OCPP/7g8OHD+Pj4MGnSJObNm0dwcDDBwcHUr3+FU6ecsLG5CsCRI17Al1SpUgmTyczw4S+jUKgem4mqBZFgRAghhCgGZrOR9PRkzp8/T8WKFTl8+DB9+vTJtWJGq9WyaNEiVCoTHh6hvPvuDpRKyMzUcuHCu/z4Ywovv1waB4fSGI1gMJgB46N7UoVEghEhhBCiiFlyh+zhxx9/ZPDgwQB5AhGwLNmdPHkw+/bVwemfqR/79sGePa/SunUAHTokUqpUKYyPf/yRi2yUJ4QQQhSx7OxbREZGMn/+fDZv3szWrVvzzSESFAR792bh5HQUsOQOadtWg6/vK6jValxdXVEqbYq7+kVOghEhhBCiiFiGZpJYsWIFJ0+eZMmSJYwYMQJHR8dc5ezt4dtv4bffoFQpSEmxZcYMb95/X8uKFaGcP3+ec+fOodM5PpYTVO9FghEhhBCiCJjNmRw4sBs3NzdWrlzJzZs3WbNmDQ0aNKBatWoEBgYCltwh+/fD//5nue7PP2HJkrHUqTOSmJgYHBwcqFWrNg0bNnsie0VA5owIIYQQhcqyyV0qGRkZLFy4kPXr17Nt2zbi4+Px9fVl+vTpfPHFF4wZM5rKlYOZO/ff3CETJ8KXX2o4dmwYLi7PYDarcHYu/1ivlLkfEowIIYQQhUShMHLgwB4qVqzI8ePHCQwMpEOHDnlWzPzyy3xq1hzL/PmWYydOQL9+cPiwltDQUBwcSnHr1pOxUuZ+SDAihBBC/EeWuSEppKenYTAYKFOmDDVq1KBFixZ5Jqq2bGmgefNXqFjR8vehQx58800DBgzwonv37ly4cAEbGzsyM5+OQARkzogQQgjxn5hMmURG7mHHjgj0ej1ms5mQkBA2b96cKxBRq2H6dNi0CSpVAoPBhvXrX2TkSCcaNHieAQMGcP78eaKiojGZzI/wGRU/CUaEEEKIh2QyZZKefoPo6GhKly7NuXPn6N27NyaTiWPHjlnLVa0K27bB+++DUmnJHTJ5sh+xsU3Yvn076enpnD9/ni+//PKx3Ojuv5JgRAghhHgoWdjYqLlx4wY9e/Zk3rx51vwhJ06coF69eoAld0hUFLRsablqzhx4/nkoW7Y5J0+etO60O3v2bIYPH/5YbnT3X8mcESGEEOIBmM1GjMab3Lx5k+DgYCpUqMDFixdxcXEhPj4egC+//JLY2AOUKqVk+HDLtrqXLsGgQbBhg2USa7du3fD09GTlypUcPXqMWbNmP7Yb3f1XT98zFkIIIR6SWm1J2X7w4EE+/fRTXFxc6NWrF0ePHiUxMRFfX18A6tc3Ubp0R2sg8uefMHgw/P23JRBZuXIlf//9N8eOxVCqlDMKhfqpG5q5nQQjQgghxD2YzUays29x4EA0TZo0oWbNmowbN47Tp09TuXJl6tWrx+LFi5k+fRqjR6uYNSsbG5srZGeruHZtHEeOONO1ayx169YlMDCQUqWcUalsyMrK/mefmac3EAEJRoQQQoi7MpkySU5OIiwsjJiYGNLT02nQoAFHjx7l6NGjJCcnExQURExMBHZ2ffn0U0tgYckdks3Bg3MZNWoUkyZNwsnJifT0DEwmNSbT0x2A3E6CESGEEKIAZrORgwf34+/vj8FgIDAwEIPBQP369XMt292y5f8IDXXE1vYaAJcvv8CqVY1xd79A//7udO/enaysLBITE3Fxqfionk6JJcGIEEIIkY/s7Jukp6daAxGFQsHo0aNzZVRVq2HKFJg40YhSeQ2TScfatX6sWmVPhw71GTz4DQAyMzNRKlU4OZV9Kieo3ou0iBBCCHEHtTobGxtboqIiad68OREREbRu3Zpt27ZZA5Fq1eCXX6BFC8s1+/ZBZOSr9Or1DuXLn6ZGjRrcuHGD9PR0nJ2dUal0j+4JlXCSZ0QIIYT4h8mUSWZmCklJV/n+++/59ddf8fX1ZevWrfj7+1uX7vbubckdkhOI5OQO2bv3Gps3b8bJyYmNGzeSkZGB2Wx+YnfbLSzSMyKEEEJgGZa5evVvTp06Ra9evfJsbrdmzRrM5jS++w5eesly/PbcIQDu7u5UqlQJhUJBixYtMJnM2NrqZWjmHqR1hBBCPPWys2+SkZGGVqvls88+o0ePHiQmJhIREYHZbMZgMPDuu13YsaMyWq3lmttzhwDWTKo2NjY4ODhhNque6twhD0KGaYQQQjzVlEoDSiU4OztTunRphgwZgq2trXV4JjAwgJEjISIiC632FCaTirffVtGlS+5AZNWqVdy8eROtVofBgAQiD0B6RoQQQjyV1GpIS0smKyuLjIwM1qxZQ1xcHO7u7rz++uvMmDGDr76azr599alUyXLN5csOXLjwEcOHt6F06VXExcVRr149unbtip2dHXZ2MiTzMKTFhBBCPFUsk1QzSElJISUlhXPnzjFkyJA8c0Q2bJhI/fprcXY+CMDixXDt2jhOnTrCl1++SqtWrahSpQr9+/dHq9ViMqkkEHlIMkwjhBDiqWEy3SQ1NZns7GzMZjNmszlPIKJWw6RJBlq1moqz8y1SUyEkpCdLl7anYUMfvvjiC0wmE7t372bgwIGo1VrMZhsJRP4DaTkhhBBPvJyddrOysoiNjWXevHkMGTKEU6dO5QpE7swdkphYmXHjKmBj48BHH73PqVOnGDp0KPXq1aNjx45UqFAFk0l+1/9X0oJCCCGeaGazkZSUJM6dO0dWVhY9e/bExcWFtLQ0a94QyJs7ZPZsmDy5HSZTDTw9PdHpdDRs2JB69erxxx9/oNPpJRApJNIzIoQQ4olmMmWi09mjUMDq1asxGAwkJiai1+upWbMm9vbw2Wf55w6ZMcONTZs24evrS3p6Om5ubqjVasLCwtDpnB7tE3uCSDAihBDiiWQyZaJQmLh16xZqtZrU1FRu3bqFQqEgIiKC6dOnU7eugd69wdXVcs3atZbcIVeuWCaxPv/883h4eJCcnMyBAwf49NNP8fPrjk7nJHNECpG0pBBCiCeO2XyTW7dukpqaikqlIiQkhBMnTlC3bl22bt3KvHmfcvz4SIYNi0GpBIMB3nkH5s0Ds9kSiKxcuZIqVapga2uLUqmifn0PzGal5A8pAhKMCCGEeGKYTJmYzUauXLmCk5MTZ8+eJTAwMNck1YoVNezdW59KlQ4BkJn5LOfOzaFcudMMHXqcBg0a4Ofnh1KpRKlUolAoUCrtMBjMgAQiRUFm3gghhHgiKJVGNBolNjY2VKtWDYVCQY8ePXIFIm3bwr59WVSqZMkdsmgRuLic5bXXfiAoKIgpU6ZQp04dXnnlFWJiYkhNTZVN7oqB9IwIIYR4rJnNRtLTk4mOjqZu3br8+eefREVFUbduXdavX8+8efMIDw9m6lTLUIxSCampsH17Py5dasD48Vn4+Pjwzjvv8Pbbb3P8+HG++uorbGxs0WrtZW5IMZAWFkII8VhSKMBgMHDq1HG2bt2Ku7s77u7ueTKprlgxh5kzt+Lqeh2AvXuhXz/w8bHFw8OOtWvX8uGHHxISEkLVqlWpU6cO2dmyyV1xkmEaIYQQjxVLT0gSX321gJEj3yAhIQF/f/88c0MAAgIM+PiMtgYis2dDq1aQkAB169YlMzOTHj16cOTIEZycnLh8+TI3bxolEClm0jMihBDisWEyZRIZuY+AgAAMBgMKhYIJEyawYsWKXIGIvT3Mnw/Dh1v+TkvTM3lyNT755Ahg6THp0qULVatW5erVqxiNRqpUeVaGZR4RaXEhhBCPBbPZSHJyEoGBgTRv3pyKFSvyzDPPkJCQkCuTqocHLF8Obm6Wv9euhT//7Mbly2bgCFqtlkWLFqHX67l58ybPPFOBzEzpDXmUHlkwsmfPHgYNGlTg+VmzZhEYGEiHDh04e/ZsnvO7du3C2dkZgMOHDzNnzhyOHDmCTqejR48ejBw5Eo1GYy1/+vRpZs2axf79+1GpVHTq1Inx48ej1+sL/8kJIYQodNnZtzh27Bjbt29n+/btHDt2DF9fXw4dOkTNmjUBGDkS5s4FG5vcuUM++qgJFSoYqFevHm3atEGpVGJnZ4/ZbENaWuajfWLi0QUj9erV49dff811zGw2895775GRkYGPjw/p6emcO3eOsWPH0rRp01xlHR0dAThz5gxDhgzBy8uLefPmER8fz6effkpaWhqTJk0CICUlhcGDB+Pi4sLs2bNJSkpi7ty5XLp0iW+++aZ4nrAQQoiHplBYhmRsbW1p3bq1dUgmPj4eX19ffH09aNRIQZcuZgBOnIC+fSEy8t8hmS1btmBvbw9AlSpVAFmyW1I8smBEr9fj6emZ69jixYs5deoUy5cvx9nZmcjISMxmM+3bt7dGvXf69ttvcXBwYMGCBWi1Wnx8fLC1teWDDz5gxIgRlCtXjp9//pkbN24QEhJC6dKlAShXrhwvv/wy0dHReHh4FPXTFUII8RBykpidOXMGJycnPv74Y3r06EFiYiIRERFERETw+eeBVK7cB2dnSyCyaJGlhyQtzRKIhISEsH37drZt28bVq1cZN248trYOj/aJiVxKzGqaK1eu8Nlnn9GvXz9rcBATE4OtrS3VqlUr8LqdO3fStm1btFqt9VinTp3Izs5mx44d1jJNmjSxBiIArVq1QqfTsW3btqJ5QkIIIf4TszmTzMwM0tLScHFxwWAw4OPjg42NDb6+vmzbtpEVK2pTv/4YnJ1vcfOmmtjY9zl3bhq9eg1h2rRpREVF4ebmxr59+2jZsiXfffc9DRs2lUmqJUyJeTU+//xzlEolo0ePth6Li4vDycmJt956i507d5KdnU2bNm149913cXFx4ebNm1y8eJHq1avnupezszN6vZ5Tp04BkJCQQPfu3XOVUalUVK5c2VpGCCFEyWA2GzEab5KRkYG9vT0JCQmkpaXRs2dP6/BMtWrQoYOCnj1zhmVKk5m5kDVrYtFozPj5+VGrVi1SU1MpW7YsH388H5PJLJNUS6gSEYwkJSUREhLCsGHDrHNBAGJjY7l69Sq1a9dm4MCBJCQkMH/+fAYNGkRwcDCpqakA+U5C1el0pKWlAZCamnrPMg9KoQAnJ7uHulbkT61WAdKuRU3auehJGz+8jIx0VCo1CoUjWq2WxMREKlasmCuZWe/e8O234ORkCUTOnu1P/fq/MGfOaRo1akR8fDypqanY2Nhgb2+PnZ09Wq3mbg8rCnD7e1mhKMLHKbpb378VK1ZgMpnyrK55//33MZvN1mGbxo0bU7NmTfr3709YWBg+Pj53va9See9RqPspI4QQouglJ19DqVRy/XoqBw4cICkpCScnJ+rVq0fXrl1Zty44V+6QS5dg4EDw9najdev2+Pr6olAocHR0pEKFCpjNZpydy6DVykTVkq5EBCPr1q2jdevW1qW6ORo0aJCnbKNGjXBwcCA2NpauXbsCkJ6enqdcWloaDg6WCUp6vb7AMhUrVnyoOpvNkJJy86GuFfnL+RUp7Vq0pJ2LnrTxgzGZMlGrFSgUCi5evEhycjIxMTHEx8dTs2ZNKlWqxDvvdGL+/A1UrmzpzV67FgYPhitXoHLlBObOnYter8fGxoZy5cqhUKjIzlZx86aJmzfldXhYt7+Xy5TRF1nvSIHByIcffshbb72FnV3RdjNevnyZY8eOMXTo0FzHMzIyWLt2LfXq1cMtJ3MNYDKZyMrKonTp0uh0OsqVK8eZM2dyXZuUlER6erp1Lkn16tXzlMnOzub8+fO88MILRfTMhBBC3ItKlUV6ehohISEoFAqqVq1Knz59cmVTHTNGxdy5ClQqIwYDvP02fPaZ5UchgKenJ05OTtjY2KBWqzGZtAU8miipChyjWLp0KV27dmX79u1FWoHo6GjA0uNxOxsbG2bNmsUXX3yR6/jmzZu5deuWNe/I888/z5YtW3K9cdetW4dKpcpVZs+ePSQnJ1vL7Nixg4yMDFq2bFkUT0sIIcRdmEyZZGens3fvXlxdXXnrrbfw8vLKFYiULQvh4fDJJ9moVEYyM5/l9dcbMm/ev4GIVqulW7du2NracubMWUwm1aN7UuKhFRiMTJ06lfT0dEaMGMHYsWO5du1akVTg+PHj2NnZUalSpVzHVSoVr732Ghs2bOCDDz7gr7/+YtGiRbz99tu0b9+eZs2aAfDSSy9x5coVXn75ZbZs2cLChQuZOXMmvXv3tg7B9O/fH61Wy5AhQ9iwYQMrVqxg/PjxeHt707BhwyJ5XkIIIfLSaFQoFJlkZd0iOTkZf39/DAYDrVu3Ztu2bdZApG1biI6Gbt0s1y1aBF9+OZzKlf9dGanVagkNDUWhUJCZaaB69dqyZPcxVeCr1qdPHzp06MCcOXMICQlh586dvPPOOwQEBBRqBa5evZprBc3thg4dil6vZ8mSJaxYsQInJyf69u3LyJEjrWVq1qzJjz/+yJw5c3jzzTcpXbo0Q4cOzVXG2dmZJUuWMGPGDMaNG4dOp6NTp05MmDChUJ+LEEKI/JnNRm7dSiM7W4PRaCQlJYVjx47RokULKlSowHPPPUdCQgJqNUydaknjrlTCjRvw6qvwyy8wbNgZhg8fzrlz56hbty4BAQE4OjqiVtvLkt3HnMJszunsKtj+/fuZOnUqJ0+epEWLFkydOvWfVLpPL5PJTFLSwy0LFvmTSX/FQ9q56Ekb56ZQWDa40+v1pKamEhISglarpV27dmzcuJFDhw5Rp04dAgI8UKlepE6d6wDs3Qv9+kFCguU+n3zyCQMHDuTnn3+mefPm1KpVGwcHR2nnInTnBFalsmhmsN5XMAKWiaPLli1jwYIFpKen57vSRaFQsHjx4kKvZEkkwUjhkw/w4iHtXPSkjS1yekMUCsjKyuLw4cP07t2brl27EhQUxJAhQ6zDMn36wDffgJOT5dpZs2DSJMjKsvyt1Wo5fPgwf//9N5UqVUKnK0WpUpYVk097Oxel4gpG7ntwTalUYm9vj1qt5tatW+zduzdPGUVRZkQRQgjx2DCZMrly5RI3btzg+vXrODo60rt3b7Kyshg9ejQdOnTAYDBgbw+ffw7Dhlmuu3QJrlz5mHffHUfOb+WcuSGOjo5kZ2ej05WSuSFPmPt6NaOiopg2bRoxMTGUKVOGjz/+2JrjQwghhADL5FSlUkF2toHr12+gUqlQq9Vs2rSJVq1a0bx5cxQKhXWiqqcnLFsGOdkbcnKHTJyoYPny5axbtw53d3e6d++OWq3m5s1blClTXgKRJ9BdX9Hr16/z0UcfERwcDEC/fv1466238k2tLoQQ4umj0agwm42kpFwjKioKV1dXsrKyyMjIoHTp0tjZ2VGuXDnCw8Px9fWlS5cubN++jTffhDlzwMaGPLlDjh49So8ePWjVqhU2NjaYzUrUahuZpPoEKzAYWb58OZ9++ikpKSnUrVuXqVOn4u7uXpx1E0IIUUKZzUbS01OIjo7G2bk0+/fvt+6gvmbNGo4dO4a7uzuNGzdmz5491h+1X389nQMHGlC+vOU+x49D375w8OC/93Z3d8fW1hatVovRaPmakkDkyVZgMDJlyhT0ej3vvfceAwYMkD1chBBCAJZAJDJyLz/++AMffPABv/76K/379ycmJobmzZvnSkKp1WpZtGgRACkpwSxdmkX58gcAS+6QN96A23fr0Gq1+Pn5YTZjDUTEk6/AV7pz5868++67uLi4FGd9hBBClHDp6Sn8+OMP9O7dm9deew1vb28UCgU9e/bMFYgAGAwGXnppMDExvalc+d/cIRER/fnpp8ukp2+yltVqtYSFhWFvr0elkh2PnyYFBiO2trYkJiZKMCKEEMJKo1ERHh7OpEmTyMjIoFOnThiNRlatWpUnEAGoVg2WLcvi2Wd/BmDPHujfH9q0sWXhwoWsWrWKw4cPWyeqykqZp1OBr3hwcDAtW7bEw8OjOOsjhBCiBFMosgkI8Ofo0aMEBARgMBjo27cvNjY2ecremTskNNSNXr1iMRrhzTcbsHHjRpo3b86LL74IKDGZJAh5WskrL4QQ4p5yEphdvHgdOzs7Pv74Y3r06EFiYiIXL16kffv21rJ35g65eBE2bRrEd9+dxmiMRavV0rZtW2xsbHB2LiO77AoJRoQQQtxdzoTV8uXLUaFCBdLT05k0aRLXr18nOTmZ6tWrY29vzwcffEDduoZcuUP++ANeflnDzz8PIyKirTWBWdmyZVEqVUDeHhXx9LlrMLJ//36ysx9sOVVhb6QnhBDi0UpLS2bLls2MGTOG9PR00tLS2LVrF3Fxcbi7u1OvXj0UCti7dwBubgutuUMmTICvv9bw668riIqK4uOPP6ZLly7/JEOzk7khwqrAvWnc3NweKL272WxGoVAQExNTaJUryWRvmsIn+3kUD2nnovektLFGo0KjAZMpmxs3bnD+/HkSEhJy7SkDULGihv373alQIRKAa9fKMn9+C1SqJvj7+3Pz5k1KlSqFo6MjJpMZjaZwApEnpZ1LshKxN03v3r3x9PQskgcWQghRMlkSmiUTHh5OVFQUHh4e+Pn5YTAY8gQi7drB0qVZ1kBk3bqKVKiwgri4z7lyZRs+Pj7/bGynw2wGrVYyeIu87hqMNG7cGD8/v+KqixBCiEcsO/smUVGR+Pv7W4MOb29vXFxciI+Ptx5Tq2HqVHjnnX9zh2zf3p+qVd9h8uTJrFmzhkWLFmFjY4O9vT1nzpylevXaj/KpiRJM0qoKIcRTTqNRYWOjRqnMIj09NVcgAlCxYkVSUlKIj48HoHp1iIiAd9+1BCJ79oCnJwQH25KRkUHLli3Zv38/DRs2pEKFCmRlGalevbbMEREFkmBECCGeUmazkbS0JDZtWktc3BEuX77E6tWr8yQvS0xMxN7enpo1a9K3L0RFQfPmlnOzZkGrVnDqFHh4eFC5cmWqVq2KyWTi1KlT6HRO2NlJIjNxdwUGI4GBgTz77LPFWRchhBDFxGw2curUCc6fP8eJEyc4e/YslStXJjo6Ok/ZiIgIatYsx//+t5tly8DR0ZI7pEMHmDgRjEZLKvcuXbrw1ltvYWNjg5OTE15ejTGZ5DevuLcCV9OIu5PVNIVPZsYXD2nnovc4tPHNm9fZuXMnMTEx9OvXjz///JPMzEzUajXjx4/PVdbTE1av1lOpkuUz788/FQwaZObKFct5rVZLSEgImZmZ1KtXDwcHB5RKmyLvDXkc2vlx98hX04SEhDzUDSXPiBBClFwajQp7ew2pqUaee+457O3t8fT0xGAwoFAo2Lp1K1qt1jpUM2oUzJ4NNjZpGI0Kbtx4n0qVejJ27J8cP36cBg0a0L17dzQaDbdu3aJMmXIYjY/4SYrHjuQZeUjSM1L45FdO8ZB2LnolsY3NZiPZ2bfIyMjg3LlzpKSk4OzszNSpU3FxcSExMZGIiAgCAgIICgpi3LjBfPttFl27Wq4/cQLi4z+kXLnOLF68GG9vb9q1a0dcXBz29vbo9Q7odE7FOjekJLbzk+aR94zMnDmzSB5QCCFE8coJRC5evMg777zD559/zqVLlyhVqhQ+Pj7Ex8fj6+vL9OnTmTdvHrGxX5KQ4ICNzTUAoqK8SE+fiVarYfr06axZs4ZOnToRFRVFZmYmtWq5ydwQ8Z/InJGHJD0jhU9+5RQPaeeiV5La2Gw2kpp6jdWrV2MymejSpQsXLlzAzs6OBQsWcPbsWSIiIjCbzdjba9i3rzNubmEolZCZacPx42M4etQDDw8PNmzYgNFoJCAggPPnz1OlyrPF3htyu5LUzk+q4uoZkVBWCCGeQJZU7mYOHtzLiBEjMBgMNGvWjJs3b1KnTh0uXrxI3bp18fX1ZevWrbzySkc2b86ibt0wa+6QH38cSWioDk9PTxwcHABo1aoVdnZ21K/fAL2+jCzZFYXinu+iiIgIoqKiGDlypPVYQkICs2bN4sCBA9jZ2dG+fXtGjx5N6dKli7SyQggh7k6pNJGaep3Q0FA6d+5MhQoVeO2112jZsiVpaWmYTCYWLlxIfHw8NWvWxMfHh4MHJzB//qF/9qGBXbtaM22amvfe8+P69esYjUZOnz5Nly5d0Ov1KJU2ZGerHvVTFU+QAoMRk8nEmDFjWLduHQqFgldffRW1Ws2VK1fo168fN27coFatWtSoUYM//viD3bt3s3LlSvR62XdACCGKm1oN2dmZxMUd57PPPmPmzJmoVCquXr1K/fr12blzJwqFgn79+llXyuh0UKWKklGjTABkZjrTrds1KleuyYcfvo5arebZZ59Fr9dToUIFzGYlZrP0hIjCV+C7asWKFaxbt47+/fszePBg1GpL0c8//5yUlBRatWrFt99+i1Kp5Pz58/To0YPvvvuOMWPGFFvlhRDiSafRqFAqFZhMZrKysvOcz9nULiQkhCNHjtC+fXvmz59PcnIyK1asIC4ujurVq9OnTx/c3d2tgYinJyxfDq6ulkAkKak5ixb5YDDswsPDg0OHDlG6dGk8PDwwGrNRqeyL82mLp8xd84y0atWKSZMmWY+ZTKZcPSVKpWXKSeXKlQkICGDDhg0SjAghRCGwBBkp/+ycexBPTy/8/PysE0Y1GhVms5E9e/bi79+drl278u6775KRkUFkZCQBAQG5NroD8skdAgYDTJgAZcp0RqPRUqVKFdq2bYudnR3Xr19Hp3NEqbR5ZO0gng4FTmA9efIkrVq1ynXs8OHDpKSk4OTkRKNGjXKdq1OnDomJiUVTSyGEeIqYzUYiI/fi5ubKqFFvsnDhQkaNehM3N1ciI/dw82YyS5b8wIULZ62ByIABA5g7dy7lypXLFYiAZaO7+Ph4ypaF1ath3jxLIBIXZ9lj5rPPICHhFHXq1OHll18GwNbWlurV60ggIopFgT0jBoMBe/vc3XK7d+8GoHnODkm3uXXrlnUoRwghxMNLT0/B3797ng3rDAYD/v7+bNy4katXr7Bp0yaysrIYPXo0L7zwApGRkYSEhOS70d1LL1Vn5kyoUMFy7Mcf4c03IT3d8neNGjVo2rQpALa2OpkbIopVgT0jFStWJCEhIdexTZs2oVAoaNOmTZ7y+/bto2LFioVeQSGEeJpoNCrCw8PzBBQ5DAYDe/bsYdy4cdja2rJt2zaSkpKIjY3l5s2bxMbG5iqvVkPnztt58cUlVKgAN25Av34wfPi/gYhWqyUoKAgbGxtsbJwkEBHFrsB3nK+vL8uXLycgIAA3NzfWr1/PoUOH0Ov1+Pr65iq7e/duNmzYwPDhw4u8wkII8aS5fZKqUqkgKupgnjIKhYLWrVvj7+9Pt27d2LRpE97e3uj1esqXL8+KFSswGo3Uq1fPek316rBsGTRrBmDm6tWatGp1lri4LGsZrVZLaGgopUqVAmyL/LkKkZ8Cg5ERI0awefNmAgMDKVWqFMnJySgUCt59913r8t39+/ezatUqVq9ejbOzMy+99FKxVVwIIR53+U1SDQrqhaenZ65ygYGBjB49mr/++osjR46QnZ1Nr169UCgUREZG0rt37zwb3fXoYeCbb8DR0ZI7ZPZsOHTIk7CwP/j11185ffo0Hh4edOrUidKlnQGZGyIenQKDEb1ez6+//spPP/1EVFQUOp2Onj170rJlS2uZbdu2sWrVKho3bsyMGTNwcnIqlkoLIcTjLmeSau65IQuZMGE80dHR1p1zAwMDCQoKokOHDtZygYGBVK5cmcqVK1sDEcs9zXz99UfExDSnRo3tAFy8CAMHQkSElmXL+nPgwAH8/f2xs7Pj/PnzqNUaJBARj9p/2pvm77//xmQyUb58+cKs02NB9qYpfLLPRPGQdi5699PGaWlJuLm55js3JCgoiKFDhxIYGMj69etzBSI5vR/Tp0/nxRdf5M8//7TuuOvpaWb5cqhTx3KfhAQ3PvvMi+rVm/LCCy+QkJBAeno6jRo1YuXKlTRo4EHDhk0f25Tu8l4ueo/F3jTPPPPMUxmICCHEf3GvSaorVqwgM9PA+fPnOXDgQK5yrVu35sKFC8yaNYtLly5hY2ODr297Tpx4jT17FNSpY8kdMmYM/PbbIAYNGkvPnj0pX748Hh4eZGdns2bNGoKCej/WgYh4stw16dnDCAgIeMiqCCHE06GgSao5AgMDcXWtQ3x8PIcPH851rlu3btjY2NCyZUsMBgMuLrBwIdSsaTl/4YIeP780Dh6EWbOUlCtXjuzsbC5duoTZbKZLFz/MZmW+2VyFeFQKDEbeeecdFAoF9xrFUShyd9lIMCKEEHdnMpnx9PQCFuY5FxgYyLhx47h69Srnz5/H1dXVek6hUODt7Y23tzcGg4H27WHp0n9zhyxapKRWrRVERXVBq9Xg5+eHwWDAxsYGnU6Hra0DBoMZkEBElCwFBiNLliy558XZ2dksXryYrVu3AvDCCy8UWsWEEOJxV9C+MllZ2fj5+TF+/LhcQzAKhYKZM2cSGxtL7969ycrKYuvWrbRv3x4XFxdcXFxYv349JpOBmTMtadyVSkvukBEjYPlyE9Om7aNdu3a89dZbmEwmdDodGo3ssitKtgKDkZxMfAWJjo5m8uTJxMbGUqVKFSZNmkTr1q0LvYJCCPG4MRgySUq6SmhoWL77ygDodE6EhoZZV9MoFApGjx6NTqdj5cqVjBgxgipVquDk5ETnzp05fPgwbdq0Yc+eZezYkZM7BHbvhv794dQpy9+nT59myZIlGI3Gf/YPU0kgIkq8B565lJqayty5c/n9999RqVS8+uqrvPrqq2i12qKonxBCPFbMZiM7d+6le/fcS3bHjx9HaGiYddKoQqGmYcOmxMbGER0djZubK0qlkt9//x21Wk3VqlXp0qULkydPZsWKFQDUqLGHadNOYWNjyR0yaxZMngxG47+PX69ePa5du0Z6ejpVq1aXvWXEY+GBlvaGhIQwd+5ckpKSaNasGVOmTKF69epFWb8SS5b2Fj5Zplc8pJ2L1t2W7Gq1WmJj49Dry1iPmc1G/v77AmfOnKFHjx65rtNqtSxatIjw8GV06BDO0KGW44mJltwhmzfnvf/Ro0fRarXY2Ng/8YGIvJeLXnEt7b2vnpGEhASmTp3K3r17cXZ2Zs6cOXTv3r1IKiSEEI+rnCW7WVlZeHt7U7FiRRITE9mxYwetW7emXbt2/P33ZcqWLc+tW1kolSbS01NRKpV5AhGw7EMzb94gtmwpT86+pQcOlOPMmans2PEmkDtwCQsLo0yZcrl6SoR4HNw1GDEYDHz55Zf8+OOPZGdn06dPH8aOHYuDg0Nx1U8IIR4bSqUCs9nE1q1b2bZtG/Hx8XTu3JkffviB7OxsQkJC+Oabb/Dw8MDPzw8nJydsbW0JDg7Otydl9GiYPduIVnseg0HB+PFm5s+/TGDgOnbt2sWGDRuIjY21pnUvVcpZAhHxWCowGNm+fTvTp0/n/PnzPPfcc0ydOhV3d/firJsQQjxWsrON1KpVK1fGVIDJkyezaNEi9uzZQ2hoKG+++SZZWVkYDAYuXbpEdHR0rvvk5A7p2tXy96VLjsyb15z589cDEBwcjJeXF9evX2fkyJFUqlQJUD3xwzLiyVXgnBE3Nzfr/69QocI/s7LvcTOFgo0bNxZe7UowmTNS+GT8t3hIOxe+nA3v0tPTaNSoUYHzRQ4ePIi9vT1KpZKMjAxWrFiBWq1Go9Ewfvx4gDy5Q374AVJSphIauont27db77V//350Oh2lS5fBaPxPybQfW/JeLnqPfM5IkyZNiuQBhRDiSZKz4d1HH83Fx8cnTyCiUCho3bo1/v7+ZGVlkZSUxN9//01AQECunXbt7TX83/9lWXOHpKTAK6/AqlVatm9/gXHjpgCWQCQ0NBR7e3v0+tJPbSAiniwFBiNLly4tznoIIcRjKT09BX//7vTo0YP4+Phc53r06MGkSZOIj4/nxIkT2NraotVqrancwbLT7rJlH3LmzLOULWu5Pid3yIULlsAjISGBoUOH4uHhQYcOHazZVGVfGfGkkHeyEEL8o6CMqXcrn7PhXWJiIr6+vtZzM2bMwN/fn3379pGRkUHHjh3R6XT89ttvuXpP+vaF2bPX4+gIZrOCPXva8uOPzzJihBtdu3YlKysLd3d3WrZsSWpqKqmpqZQpU14CEfFEKbB/b+bMmRw7dizP8bS0NEwmU57jGzdupH379oVbOyGEKAZms5G0tCSWLPmB0aNfZ8mSH0hLS8JsvvvSlNs3vIuIiMDHxwetVkuPHj1o27YtSUlJnD17lpSUFC5evEh2djZHjhwBQKeDH3+EZcvA0dGSO6RDBzNbt3bgpZde5fLly9jZ2REREUHt2rXJyMhAp9NTrVptCUTEE6fAYGTx4sV5uhyvX79OkyZN2LNnT57yGRkZJCYmFn4NhRCiCOXM+XBzc2XUqDdZuHAho0a9iZubK5GRe+8akPy74Z1luGXevHls2LCBzz//nMuXL/P555/TsWNHFAoFv/32G8uWLWPMmDGMHduWyEisSczWrAEPD9i0CbKysrCxscHX15fx48czfvx4goODeeaZiuj1ZSQQEU+kB35XP0DCViGEKPFy5nzkl3DM3797noypOXKGdIKCejFhwniysrJ46aWXePbZZ8nIyCA2NpbBgwdbd9gFUCggKel9Zs8GlQoyMy2b3c2fb7mnVqulV69e2NjYkJWVhY+PDx9//DE6XSlMJpmoKp5cjyzE3rNnD4MGDSrw/KxZswgMDGTHjh18+umnnDx5kjJlyvDiiy8ybNiwXGUPHz7MnDlzOHLkCDqdjh49ejBy5Eg0Go21zOnTp5k1axb79+9HpVLRqVMnxo8fj16vL7LnKIQo2W6f85Efg8FAeHg4gwYNt84hUSpNpKZeJyws/J9N8Dw5fvw4JpOJy5cvc/LkSa5cuYKfnx9eXl7We7u4wKJF0KWL5T4ZGVVo1eocBy2jPGi1WkJCQnB2dub06dNUrVqVVq28MZmkJ0Q8+R7Zu7xevXr8+uuvuY6ZzWbee+89MjIy8PHxITIykldeeYXOnTszatQoDhw4wJw5czCbzQwfPhyAM2fOMGTIELy8vJg3bx7x8fF8+umnpKWlMWnSJABSUlIYPHgwLi4uzJ49m6SkJObOnculS5f45ptviv25CyFKhtvnfBTk0KHofzKrGrl1K42oqIMEBQVZg4wZM2aQmJjIM888Q9myZUlJSaFRo0b8+uuv1jK+vpbcIeXLW+75ww9w69brjBpVnm3btlG/fn3atWuHs7MzJ06coHLlytjbO0kgIp4aj+ydrtfr8fT0zHVs8eLFnDp1iuXLl+Ps7Mxbb71F3bp1mTt3LgDe3t4YjUa+/vprBg4ciFar5dtvv8XBwYEFCxag1Wrx8fHB1taWDz74gBEjRlCuXDl+/vlnbty4QUhICKVLlwagXLlyvPzyy0RHR+Ph4VHcT18IUQL8O+djYa7jOblBKlasSOfOncnONnLq1AnUalWuQESpVNK/f3+ys7Mxm80EBwdToUIFDAYD8fHxqNXwwQfw9tuW++bkDlm+HIYNO86zz97CYDDQvn17Ll26RNWq1alfv+F9reQR4klSYgYhr1y5wmeffUa/fv3w8PAgMzOT/fv307Fjx1zlXnjhBW7cuEFkZCQAO3fupG3btmi1WmuZTp06kZ2dzY4dO6xlmjRpYg1EAFq1aoVOp2Pbtm3F8OyEECVRVlY2fn5+uT4/AgMD2bVrF126dMHW1pbz589z7drfpKbeYOPGjbmGdP766y+USiXHjh2jbt26jB8/njVr1nDkyBEaNy7Njh3/BiK7doGnpyUQAahTpw4RERH07t2b0qVL4+XVFKNRKYGIeCrdtWdEocg/7WtBx/+Lzz//HKVSyejRowE4d+4cWVlZVK9ePVe5qlWrAnDq1Ck8PDy4ePFinjLOzs7o9XpOnToFWHYdvnOXYZVKReXKla1lhBBPJ53OidDQUPz9/ZkyZQoNGjTA29ubrKwsWrduTWZmJmfOnOGVV17hl19+wdvbm8qVK/PGG2+gUChQq9XMmzePrKwsAC5cuEDfvia6dAlHrQaTCWbOhClTsG5ip9Vq6d69O3369EGn0wOyp4x4ut01GMlZVnanoTnr0QpJUlISISEhDBs2DEdHRwBSU1MB8kww1el0gCXfSUFlcsqlpaVZ73WvMg9Kofg3Z78oHGq1CpB2LWrSzrkZDJk4OzuzZ88ebG1t8fDwoGvXrowePZq//vqL48ePU7FiRZRKJe+88w6rV69GrVbj4uLCqlWriI2NxcfHh0mTJvH11x/RsWM4Ob99MjJK0aNHGuvW/bs8OGeiqk6nQ693QK+XXdAflryXi97tbVwE/RD/Pk5BJ4pzb5oVK1ZgMplyra651xJipVJ5X2Xu5X7KCCGeXElJV/H29mbPnj2sWLGCrl27EhQUlGvn3cDAQCpVqsQPP/zACy+8QPXq1alXr16uIZumTdVs2FCWf35PceGCF5MmVeHdd8fy/PPbSEhIoEaNGvTu3Ru9Xo/ZbJZARIh/lIi9adatW0fr1q1xdna2HnNwsPwjTU9Pz1U2pyfDwcHB2ttxZ5mccjn30Ov1BZapWLHiQ9XZbJadIgub7MBZPKSd/6XRqAgNDaNZs2bEx8eTkJDA6NGjcwUiKpWKGTNmcOXKFXx8fGjfvj0tWrTIlTtk1CiYPduIVnsJk0nDmDFZnDtXldGjx7B3715sbGwICgqiQYMGpKSkcOHCBapVqy2vwX8k7+Wid+euvUXVO/LI141dvnyZY8eO5Rn6efbZZ1GpVJw9ezbX8Zy/q1evjk6no1y5cpw5cyZXmaSkJNLT061zSapXr56nTHZ2NufPn+eFF14o7KckhHhM5CztrVixInFxcfTp04ddu3blWrY7ePBg9u3bR+/evWnevDlAPrlDLPeLjYXt218iKuoo27eH8Mcfazl58iQqlYrs7GwyMjJwdHTCxaWiZFIV4jb39a9hy5YtREREEBsbS3JyMgqFAmdnZ1xdXa2/Eh5WdHQ0AI0aNcp13MbGhsaNG7N+/XoGDx5snTS7bt06HBwcqF+/PgDPP/88W7ZsYcKECdYZ8evWrUOlUtG0aVNrmR9//JHk5GRKlSoFwI4dO8jIyKBly5YPXXchRMl35+Z3t/9tMpmtm9E1adKEuLg4YmNjAVi+fDmOjo4kJSXRu3dvDAYDFStWtG6TkV/ukDffhN69b1KhQgW0Wi0rV65Eq9ViZ2fH/PnzGTZsGGq1JFoU4k53DUYSEhIYNWoUJ0+exGw2Y2dnh6OjI0ajkbNnz7Jv3z5+/vln6tWrx0cffUS1atUeuALHjx/Hzs6OSpUq5Tn36quvMnToUMaMGUNgYCAHDx7khx9+YOzYsdjZWbqOXnrpJdasWcPLL7/M4MGDOX36NJ988gm9e/e2DsH079+fn376iSFDhvD666+TnJzM3Llz8fb2pmHDhg9cZyFEyWc2G0lPTyE8/N9MqV27diUx8SLHjh3lueeeo1KlSly4cIGoqChOnDhB9+7dcXd3p0ePHnh5ebF//35iYmKsPSGJiYl07NiWWbNy5w4ZMQJycji6ublRtWpVPvzwQ2xtbbl69SpTp05l2LBhqFS2j6g1hCjZFOYCZoFeunQJf39/MjMzGTFiBH5+flSuXNl63mQycfLkSdauXcvixYuxt7cnODgYFxeXB6rAlClT2Lx5M9u3b8/3/IYNG5g/fz6nTp2iXLlyDBgwIE86+P379zNnzhxiYmIoXbo0AQEBedLBHz9+nBkzZnDw4EF0Oh2+vr5MmDDhodPBm0xmkpIebiWOyJ+M/xaPp6Gdcza/u33PmcDAQMaMsczhUKlUVKtWjT59+uSahNq7d28+/PBD9Hq9dRXexIkTWbx4MQA1a0Jk5HM4OsYAltwh/fvD6dOW67VaLbGxsdjb25OUlMSNGzcwGo1UqlQZnc5JhmYK2dPwXn7U7pwzolQWzaSRAoORKVOmEBISwvLly3Fzc7vrTU6ePElQUBC9e/dm4sSJRVLRkkaCkcInHyzF42lo57S0JNzcXHMFIkFBQQwZMoSsrCy2bt2aa5KqUqnk22+/pWvXriiVSvbu3UtKSgoqlYoTJ04wadIk+vWDr78GR0cwmxXMnq3g//7PlCt3SGhoKC4uLv8EIJXQ6RxRKNSSyKyIPA3v5UetuIKRAte17tixg169et0zEAGoVasWAQEBBfZuCCFEcblz8zuFQsHo0aMZMmQIBoOB1q1bs23btlyTVOPi4khNTeXdd99lyZIlVKxYkSpVqrBhwwbatm3C4sVKfvnFEogkJsKUKS1p2XILkyZNY/jw4cydO5cjR47g7u5OmTJl0Ov1lCpVGqNRIYGIEPehwGDkypUr1K5d+75v5OrqysWLFwulUkII8bDu3PzuzuDj9kmoM2bMoEGDBrz66qvcuHGDatWqsXr1alq2bMnff/9NUFBNatToxaBBJgBWrwYPD5g2bScdO3akbdu29O/fn759++Lk5ERCwinMZqhatRparWRVFeJ+FTiAmZmZib29/X3fyN7enszMzEKplBBC3K87V8vcvvmdQqGgffv2lC1bFm9vbyIiIkhMTMTX15cePXrQv39/rly5QpcuXTh8+DCurq7Mnz+f7OwsDh0aTs+ex1AossjKUnLmzOscPFiWbt1OUaNGDdq0aUOZMmXIzMzEaDRib6/H3d2yyZ0EIkI8GJlNJYR4LOVdLeOFn58fOp0Tfn5+7NgRwRtvvMHOnTvZt28fvr6+TJ8+nc8++4zevXvj7+/PoUOH6NWrV65JrF988X/s2VOXwYMtaQfS06vQps1FDhz4gtatW1OhQgW2bdtGrVq1uHr1Ks8/3wpQk51tySUihHhwD7VRnhBCPEr5rZaBhYwfP47Q0DA8Pb0YPHhwrkmqYFktM2/ePMDS+3tnIGLJHZJF+fKWQOTqVX9+/rkp33zTia1bt3LkyBGqV6+Oj48PX3zxBbNnz0ahkOW6QvxXBa6mcXNze6hgJCYm5j9X6nEgq2kKn8yMLx4lqZ3vHGK5X3eullEoFLRu3ZqKFSty9epVvv76a+rWrZvr/KJFi2jRogXBwcGYTCbUarV1I1CNBqZPz507ZNOmIAyGHvz111+4uLjQt29fJk2aRGJiInv27CEkJJSGDZvmu1y3JLXxk0zauegV12qaErFRnhDi6XK3IZacL/eCApU7V8sEBgby9ttvs3XrVmJiYmjXrh0ZGRl07dqVkJAQJk6cyPDhwzl27Bj169fHYDDQt29fbGws8zpq1IBly+CfhM3W3CFt2uho3jyF69ev89prr3Ho0CHs7e3p2bMnixYtlrwhQhSiAntGxN1Jz0jhk185xeNRt3P+Qyw5eTrC8PJqREZGWoGBio2NmtGjX2fhwoXW1TA9evTIc69ffvkFLy8vVCoVGRkZeHp6Wst4e3vj6+tLbOwkvvrKsmTXZIKZM2HKFDAaYdq0adSsWZOaNWty9uxZvvnmm38SPDpac4sU5FG38dNC2rnoPfKeESGEKArp6Sl5AhGwbD73448/MHSoiYCAgHzngjRs2BSTSYWnpxc9eqTg7++Pl5dXvveKj4+nXr167Nmzh0uXLuUqc/Dgdn77zY5y5Sx/JybCiy/Cli2Wv7VaLT4+PlSqVIn9+/czaNAgQkNDUatt7hmICCEeXIF5RoQQorDdOcRyO4VCwRtvvHFHIGJhMBjw9+9OenoKWVnZ+Pn5MXnyZFauXJnvvWbOnIm7uztvvPEGN27cwGQyWefANWwI+/dDuXLrAFizRkGDBrkDkUWLFmFra8vFixe5dOkScXFxNGzYDKVSluwKURSkZ0QIUWzuTEh2uzuTk93JYDAQHh7OoEHDcXYuS0zMMRISEvD29qZixYokJiayc+dOwsPDadWqFampqQwZMoRNmzZRr149tm7dTHz8SAYMOIJWC5mZsHRpA+rUmc877+wjJiaGOnXq0LlzZ+zs7HBwcECj0VCvnof0hghRxKRnRAhRbP5NSJbX7ZlRC3LoUDQGQzq7du0gLS2NMWPG4Ovri42NDa+//jrHjx/n+PHjjBo1iqVLl9KgQQPefPNNjh7dzM2b7Rg61BKIxMZCs2bwv/8domPHF/D19aVq1arcunWLsWPHYjQauXnzJtnZSglEhCgG0jMihCg2OUMs48ePy9MDkpMZ9W7q1atH//79aNq0KU2bNqVJkyYYDAYCAwOxsbHhueeey3XfSZMm8eefY/n6693Y2Fjm6l+82JXGjdeQnm4Zklm4cCHTpk0jODjYOkRjb2+PnZ2jrJYRopjIapqHJKtpCp/MjC8ej7qdC1pNY2NjQ3R0NA0aNMh3qEar1f6zcV1btmzZYk1oplAo8uzCC3lzhxiNOl58MZ3GjeeSnZ2NnZ0dXbt25eDBg6xdu5Zq1arRpk0bbGxsqFat9n8KRB51Gz8tpJ2LXolaTbNlyxYiIiKIjY0lOTkZhUKBs7Mzrq6u+Pr60rx58yKpnBDiyaPV2tCixfOcPn2aFSt+59ChaBo08MDPzw97ez2hoWH5LvtdsWIF8+bNo3Xr1uzZs8d6vnXr1uzevTtX+Ttzh5w/X5k9e0Zx8WI4x44d46OPPuLKlSvcuHGD8uXL06tXLzIyMqhcuYrkDxHiEbjrv7iEhARGjRrFyZMnMZvN2NnZ4ejoiNFo5OzZs+zbt4+ff/6ZevXq8dFHH1GtWrViqrYQ4nGTX6Kz7t27M3jwEMxmlTWxWcOGTYmNjSM8PJxDh6Jxd3enU6dOrF27FoVCwdKlS5k8ebL1vhUrViQ2Ntb6d//+5MkdcupUOxo10lGhQgXc3d1JS0vjypUrVKlShQoVKmBnp8dsVj5QFlghROEpMBi5dOkS/fr1IzMzkzfffBM/Pz8qV65sPW8ymTh58iRr165l8eLFvPjiiwQHB+Pi4lIsFRdCPD7utpfMihUr8PLywsZGj0KhRqFQo9eXYeDAIZw5k8C4cWMJDg7mk08+oUWLFqSnp1O3bl3rvRMTE+natSt6PXzxBQwenHP839wh06bVwt7enqtXr9K9e3eSk5MpVaoUen0pTCYlBoMZkEBEiEelwDkjU6ZMISQkhOXLl+Pm5nbXm5w8eZKgoCB69+7NxIkTi6SiJY3MGSl8Mv5bPB5FO9+5l8zttFotf/31F0ajkWrValpzeeRck5WVxeHDh3FwcODs2bN8++23vP7663h7e1vnjOzf/w0ODi9Tu7blnuHhMHQoJCX9O9fEcs80vLy8uHXrFjpdqSIbjpH3cvGQdi56xTVnpMClvTt27KBXr173DEQAatWqRUBAANu3by/UygkhSh6NRoWNjRqNRnXf5QtKdAaW/CEbN24kNjaWtLQUTKZM6zUTJkzg2rVrlCtXDoPBwJYtW1AqlSQkJLBq1SpsbDSMHm3G3X0EtWtbcoeMHAndu/8biCxatAilUkmlSpVwc3NDo9Gi15eVeSFClCAF/mu8cuUKtXN+ZtwHV1dXgoODC6VSQoiS5342t8vP3RKd5YiNjeXgwYO8/PLLhIaG0rhxM3r0CMRsNhMdHU16ejqBgYHWgGbx4sW89JIfV682Q6/fAZi5caMCKSnfUKtWAsOGHaJOnTp07doVR0dHTp48yYkTJ2jUqClms6Ywm0UIUQgK/ATJzMzE3t7+vm9kb29PZmZmoVRKCFGy3G3OR86eMQUFJP8mOltY4P1r1KjBxo0b/9mf5kdq165NWFgYhw8fxtPTk+eff56xY8cya9YszGYzHTrA9Onh6PWW6w8fboqt7Tf88Yclg+vo0aOpUqUKWVlZ7Nixg3r16mFrawdIICJESST9lEKIe7rb5nb+/t2JjY1Dry9T4PXdu+ef6AwsQyne3t5MnjyZwMBABgwYQN26dfMs7f3111+JjNzNqVMvEhh4AoCUFNi4MYj58y/j57eRSpUqUblyZRwdHcnMzMTW1pavvvqKsWPH4uXVtJBaQwhR2O6aDj5nYykhxNPrfuZ8hIeH55lDYjYbSUtLYsmSHwgNDWXFihVotdpcZXLmdHz22WcoFArmzJlD79698w163n23NzVrDrYGIrt2gacnrF6to0qVKrRu3RoPDw+mTZtGTEwMycnJLF68hG+++RYvr4J7boQQj95d/3WOHz+e8ePHF1ddhBAl0P3M+Th0KDrXLPv8hnUCAwP566+/iIiI4PDhw1SrVg0fHx/mzZsHwKlTp/jtt9/yDXr694evv87CwSEWkwnOnx+Ij89SsrIsQzwtWrQgMzOTs2fP8t133+HgUBqNRsugQcMld4gQj4ECg5EmTZoUZz2EECXU/cz5aNDAA5Pp3ywB+Q3rBAcHExISwk8//cSMGTMYMGAAU6ZM4Z133qFly5b89NNPnDx5Mtd978wdcv26PZ9/3hSFojYtWnize/du+vbti1qtJjIyEqVSSalSz5CVlU1mpuxwJ8TjosBgZOnSpcVZDyFECXW3ze3AMtTi5+dn7YG427CO2WxmwIABREREMH/+fJycnNBqtSQmJpKeno5K9e9QT8OGsHw51twhYWFw/PhINm3aRY0aCVSpUoWJEydiMpkYN24cq1evJjY2TnpChHgM3XXOiBDiyaRQ8EC5QnQ6J0JDw/Kd8xEWFoZO52Q9dq9hnVmzZlGpUiU2btzI//3f//HLL79gNpv57bffaNGiBTY2Gt56yzIn5PbcIUFBGpo06UxERAT16tVj2rRpnD17lueee47Vq1cTGhqaqx5CiMdHgT0jM2fOxN/fP1faZbBkMLS3t0epzB3HbNy4kZkzZ7Jp06aiqakQ4j8zm41cvHiBsLAwDh68/1whCoU6z54xOZvb3Xnt3YZ19u/fz99//42bm1ue1TIhISEolVdISKhLxYrRAMTEQN++EBv770RXjUZD69atycrKYu/evcycOZOAgABKlSojk1SFeEwVmA7ezc2NuXPn4ufnZz12/fp1WrZsyY8//kiLFi1ylQ8LC+Ptt98mJiamaGtcQkg6+MInqZ2LVv65QiyBwL1yhdxOo1GhVCowmcwFDonkl/792LFjqNVq6tevn+8QTpcuakJDS6FWXwXg0KFmfPllLSpXdrVOdF2zZg2LFi2iRo0apKenk5lpwNPTA5XKtkQFIvJeLh7SzkXvkaeDL0gBsYsQooS7V66Q9PSU+7pPzuTQu83NuHNY59ixYzg6OhIaGprn8TUamD0b1qwxolZfxWRypG9fBb/95ss770znueeeY8mSJTRr1owDBw7w/PPPU7NmHRo3bkGLFt6o1foSFYgIIR6c/AsW4ilwv7lC7ncp7L16R3KGdU6fPo1Go2Hv3r0sW7Ys1wRVgJo1YdkyyFm8d/KkC6dPz+CZZw7x4YcfYmNjw/PPP88nn3yCWq3GaMwmMxPMZmS1jBBPEAlGhHgKPEyukPzk7E8TGXmAq1evUrZsWRo2bGSdN5ITpGRnG7l16xYZGRkAfPLJJ3h7e1OrVi2WLFmC2Wz+J3cIODiAyQQzZoBC8Roq1VVq1qyJVqvFx8eHsmXLsmnTJpo1a45Goy+0NhFClBwSjAjxFHiYXCF3MpuNnDp1AoMhk2PHjhEfH0/NmjV55plnKF06HZ3OnvDw1URFHcTDw4MXXngBs9mM0WjEx8eH+Ph4HB0d2bFjLZmZ/6Nt23MAXLgAL74If/2lZf36tuzbt48zZ86waNEitFot4eHhBAYGolbbFnazCCFKCAlGhHgKPGiukPzcupVGQkI8Q4YMyXWPoKAgBg4cSK9evfIcHzJkSK7ddg8fXky3blCrlqVMWBgMGwapqZbVMl9++SUffvght27dQqlUMnXqVIYNG4aTk7PMCxHiCXbXf90F7U0je9YIUfzuZxXL3eRMKs1vNc2duULye+zLl1PyBCIKhYI33niDDh063PW4QgFjxsDMmaDVWnKHXL36NiEhlxk1qgY+Pj588cUXDB06FI1Gw8qVK7Gzs2POnLnY2ztKICLEE+6h9qYZOnRokVVICJFbzjyN8PBwoqLuPzfInXImlR4/fpywsDCio6Nxc3OjXbt2nD17jvT0lALvqdGoWLNmTZ5eldatW7N3716ysrLw9vamYsWKJCYmolAo2LZtGwaDgWeegUWLoHNnyzU5uUOGDi3PkCFd2L17N3/99ReTJk0iMzOTl156iY8//phnn62BUeaoCvFUkL1phCjB8s8NspDx48c9UG6QHAqFmjJlytKsWTP27NlDeHg4EyZMwGw23zPfSFxcXJ5jb731FqdPn2br1q1s27aN+Ph4fH196dWrF3PmzKFDB1iyBMqXt5T/7jsYPRoyMiA6OpqOHTuiUqlYv349HTp04I8//uDbb79Fry8tgYgQTxHZm0aIEuxeuUFiY+PQ68s80D2Tkq7SunXrfO8ZEODPqVOn0elsUSgUmM1m67CQh4cHYBmC8fHxYejQobRq1Qqz2ZxnmGbnzi1Mn262LtlNToaXX4YVK/59vAYNGvDLL78wd+5cfv75Z7Zu3coLL7yAXl9ahmWEeMoUmPTsiy++4Pjx48VZFyHEbe43N8j97i+Tc8+wsLB87xkYGMj69etZtuwXRo9+nW+++YKTJ2PYunUD16//Tffu3QkKCiIqKoqAgAC2bt3KTz/9RJkyZejatav1PjVrwvTpW2jSZCsAf/0Fnp65AxGtVou3tzfOzs7s2rULhUJBz569qFattgQiQjyFCvxX/8UXX1C1alXq1KlTnPURQvyjsHKD3HnPgwfz3jMwMJCgoKA8PRxarWWVy9tvT2DSpEkMHTqUJk2a5FsGwN4+mK+++jd3yIkTQfj6hnDzZlau8qGhoVSpUgVHR0c+/fRT5syZg1otOUSEeFrJTxAhSqjCyA2S3z29vLxYuPDfeyoUCkaPHp0nEAFL78uQIUOIiIhAo9EQEBCQb5k33hjMsWNtKVfOciwnd0hgYCt2736f0NBQTp8+Tf369enWrRvZ2dm8+uqrrF69mrCwMFQqySEixNPsgfemEUIUj5zcIDn7u9zpfnKD5HfP7t2757pn69atrStf8mMwGChdujTr1q3Lt0yjRrB7dxblyq0HLLlDPDxg61YwmUyMGjWKjRs30rRpU4YNG2adsOrj04bY2Di8vB5sEq4Q4slz10+AhIQE9u3b90A3lFU4QhSe/5IbpCBlypQlLCyM7t0t96xYsSLx8fF5yikUClq3bs2CBQtQKBRERUXdcT537pCsLCU//eTJsGGR1jq2b9+et956C7PZTI8ePVAqbXB2Ls+QIf976HwpQognz12Dka+//pqvv/76gW4YExPznyokhPhXTm6Q2Ng4wsPDOXQomgYNPB4qz0gOrdaG559vRVzccX7/fQVGoxG1Ovd9AgMDefvtt9m5cycff/wxPj4+1tU0AM88A4sXQ6dOlr9jYiAi4hV+/vnIP4+h5bfffmPZsmXWZcN+ft25dSsLIYS4010/yXx9fXF1dS2uuggh8qFQqNHryzBo0PD/lIH1dlqtDTqdMwMGDGDChAm88cYbaLVaDAYDM2bMwMPDA29vb2tvzKJFi9i2bRtarZY2bQwsWYJ1fsh338GECRq2bn2Z3bs/w9fXlzZt2qDX6wkMDLTmL9HrS/3HlhBCPKnuGox07NgRPz+/4qqLEOIuimJIQ622oX///nz88cf8+uuvZGRk0L59ewYMGEBW1r+9GGazmc8//5gjR7pQu3YI8G/ukNBQy2qanOu3bduGl5cXBw8eZN68efj5dX/oXhwhxNNBJrAK8ZSyZHfdx6FDh5g9ezYNGjTgypUrTJw4ER8fH7Zu3UpgYCBgyR0yfnyoNRBJTKzGjBlBPP/8PI4ePYpareby5cs0a9aMBQsW4ObmxqBBgxk06CX0+jISiAgh7ko+IYR4SqWnp7Bw4UImTZpEXFwc/v7++eYP8fY+y/DhB6y5Q9as8SAqyp9OnXxwd3fn/Pnz2NraMmzYMCpUqIBarcbGxp6sLCUgE1SFEPdWYM9IkyZNKFu2bHHWRQhRTBQKuHDhPLNnz0ar1fLRRx/Ro0cPvL29rbtya7UGsrNfZPRoSyBy4QK0bw9RUT3ZuHErnTt3Zv/+/Tg5OdGqVStcXV1xcSmPnV1plEqbR/wMhRCPE4XZbL7/jEl3MBgMBeZAeNKZTGaSktIedTWeKE5OdgCkpNx8xDV5sjk52XHjRjKXLl1Co9Gg0WiIiooiLi4Oo9GIj48P4eGTefnlrdSubblm505n/P2vkZqq5a+//uKPP/6gd+/e6HQ6TCYT2dkmmRdyG3kvFw9p56J3exuXKaN/oIzPD+Kec0a2bNnCiBEjyC9mmTJlCgEBAWzYsKFIKieEyEujUWFjo36gPWlud+NGMunp6Tg5OWE2m/n5558JCwv7JxBpzenTI5k1K4LateHWLfjpp2Z8/rkvqalaVq5ciVarZdiwYezfv59Dhw6h1WplXogQ4j+566fHp59+yjfffINareb06dNUr149T5mTJ0/y5ptvMnToUCZMmFBkFRXiaWc2G0lPTyE8PJyoqIN4eno9UL6R7OwMjMYszp8/T2ZmJmlpafTo0cM6T+SZZ6BZMwWDBll+eGRkPEvLlmfp1y+Qzp3L89prrxMVFUWzZs3YsWMHDRs2JDU1VYZkhBD/WYE9I3/++SfffPMNbdq0YdOmTfkGIjNmzGDr1q20aNGChQsXsmXLlgeuwL59++jXrx8eHh60atWK6dOnk56ebj3foUMHXF1d8/x37do1a5nDhw8zcOBAvLy8aNWqFZ988kmuZYkAp0+f5pVXXqFx48Y0a9aMyZMnk5Ymwyzi8WBZ+bIXNzdXRo16k4ULFzJq1Ju4ubkSGbkXtRpsbNTY2mry9JrodFrs7BScOXOaW7dukZWVhZ2dXa5ApGNHOHQIOna0BCIXL3Zl/vxBlC3bnvbt27Nw4ULatGnzT13MlC1bFltbO9llVwhRKAr8FPn5559xc3Pjq6++sk5oy0/ZsmX5+uuv6dq1K0uWLKFt27b3/eBRUVEMHTqUdu3a8dVXX3HmzBk++eQTrl27xqeffkp6ejrnzp1j7NixNG3aNNe1jo6OAJw5c4YhQ4bg5eXFvHnziI+P59NPPyUtLY1JkyYBkJKSwuDBg3FxcWH27NkkJSUxd+5cLl26xDfffHPf9RXiUUlPT8mTEh6ga9eu6HT2bN26kapVq7J582aOHTuGp6cnAQEBGI1GFi9ehdFopHPnzpw7d46zZ89y6dIlDAYDGg18+CGMH2+5X07uEHf3Zmg0GqZOncqMGTOsCc+6d++OjY097u6NJJW7EKLQFBiMHDt2jFdfffWugUgOrVaLv7+/dRvx+/XRRx/h6enJZ599hkKhoGXLlphMJhYuXMjNmzeJi4vDbDbTvn17atasme89vv32WxwcHFiwYAFarRYfHx9sbW354IMPGDFiBOXKlePnn3/mxo0bhISEULp0aQDKlSvHyy+/THR0dK4010KUNBqNivDw8DyBSGBgIEFBQXz++ecEBgbSokULa5nAwEBcXFyIjY2lf//+xMbGsnbtWurWrcutW7eIiYmhVi345RfI2U7qr7+gf384cwZ0ugR69erF2rVrCQ4ORqvVEhISgr29EwaDGVmyK4QoTAUO05hMJhwcHO77Rs888wzZ2ff/AXXt2jX2799Pv379cgU8AwYMYOPGjdjZ2RETE4OtrS3VqlUr8D47d+6kbdu2uVb1dOrUiezsbHbs2GEt06RJE2sgAtCqVSt0Oh3btm277zoL8SgolQqiog7mOqZQKBg9ejRDhw7ljTfeYMiQIRgMBhQKBT4+PnzyySc0a9aMAQMGEBISQnBwMFlZWZQrV46aNWsSEJBKZKQlEDGZYPp08Pa2BCIANWrU4LnnnuPSpUvMmjWL6OhovLwayZCMEKJIFPjJUrlyZU6cOHHfNzpx4gTly5e/7/LHjx/HbDbj5OTE6NGj2bp1KyqVim7dujFx4kRsbW2Ji4vDycmJt956i507d5KdnU2bNm149913cXFx4ebNm1y8eDHPfBZnZ2f0ej2nTp0CLLsPd+/ePVcZlUpF5cqVrWWEKKlMJjOenl7AQuux1q1bs23bNpo1a8a2bdswGAwEBgYyevRoDhw4QFRUFDY2NgQEBOTqUZk3bzrR0a2oXNkyv+vCBRgwAG6PybVaLUFBQZw6dYrAwEC8vLxQqbQyUVUIUWQKDEY6duzIwoUL+d///ke5nB2xCnD58mWCg4Pp1q3bfT9wzgTUd955hw4dOvDVV18RFxfHvHnzyMzMZNasWcTGxnL16lVq167NwIEDSUhIYP78+QwaNIjg4GBSU1MB0Ov1ee6v0+msE1RTU1PvWeZBKRT/rr8WhUOttky6lHbNy9+/O+PHj7MGFhUrViQ+Pt76v4GBgQwaNIgOHTrQrVs3OnXqhKenZ65ApFEjWL48yxqInD/fiKZND3Hx4r+TvXOGY0qVKkWZMmXQ6/WoVJrifbJPAHkvFw9p56J3exvfx6yNh3+cgk4MGDCAX3/9lYEDBzJz5kwaNWqUb7nIyEjef/99srOzGTp06H0/cM5ql4YNGzJ58mQAWrRogdlsZvbs2bz++uu8//77mM1m65yOxo0bU7NmTfr3709YWBg+Pj53fQyl8t5b79xPGSEetTJlyhIWFkb37pZJrImJifj6+rJx40brKrH69euTlZXFpEmT+P33362BiEIBb70FM2eCRmPJHbJ9e3dq1/6UdevSWbt2LXFxcXh4eODr64utrS0ODg5oNNITIoQoHgUGI87Oznz++ee89tprvPjii1SvXh0PDw/r3JCkpCSio6M5deoU9vb2fPHFF1StWvW+H1in0wHg7e2d63irVq2YNWsWcXFx+Pr65rmuUaNGODg4EBsbS9euXQFyLQXOkZaWZp3zotfrCyxTsWLF+67z7cxmyfpX2CSb4t15eTUlNjaO8PBwDh8+RFBQEEePHqVdu3YsX74cg8GAt7c38fHxxMfHA1CuHCxeDC+8YLnHsWPQty80blyGHj2OUapUKTp16kSfPn1YuHAhZ8+excurMRkZJkBeh4cl7+XiIe1c9O7MwFpUvSN3nY3m5eXFmjVrWLBgAX/++SfBwcG5zleqVInBgwfz8ssv4+zs/EAPnDMp9c4VArfnB1m5ciX16tXDzc3NesxkMpGVlUXp0qXR6XSUK1eOMzmz7v6RlJREenq6dS5J9erV85TJzs7m/PnzvJDzKS1ECadQqNHryzBo0HCMxkwMhpvMmTOHc+fOcfCgZYJrxYoViYuLo2bNmnTsCEuWWAISgG+/hTFjICMDBgxwxcvLi6ioKHbu3Im9vT1DhgzF3t5RJqkKIYrdPT91nJ2def/993nvvfe4fPkyV65cQaVSUbZsWZ555pmHfuCaNWtSqVIl/vjjD/r37289vmXLFtRqNZ6ennTu3JlmzZrxxRdfWM9v3ryZW7duWfOOPP/882zZsoUJEyZYV9SsW7cOlUqVq8yPP/5IcnIypUqVAmDHjh1kZGTQsmXLh34OQhQnjUaFUqng1q1UTCYTUVFRuLq6cu7cOfr27cvSpUtJTEykfv06DBlynP/7P8t1ycnwv//B779b/tZqtQQEBPDVV1+xZ88efvrpZ2xs9JI3RAjxyNz3TyCFQkH58uUfaMXMve43btw43nrrLcaNG0ePHj04cuQIX331FS+++CJly5bltddeY9asWXzwwQe0a9eO48eP8/nnn9O+fXuaNWsGwEsvvcSaNWt4+eWXGTx4MKdPn+aTTz6hd+/e1iGY/v3789NPPzFkyBBef/11kpOTmTt3Lt7e3jRs2LBQno8QReX2NPAvvNARGxsb1Go1tWvXJjg4mKNHj1KvXj1iYmLYvPkbBgz4FQeHOAD++ktBv35mzp613CtnguqqVauYO3cuYWFhKBQaCUSEEI9Ugbv27tu376Fu2CQng9J92rhxI19++SUnT56kTJky9OnThxEjRlgnlq5YsYIlS5Zw9uxZnJyc8PPzY+TIkdja2lrvsX//fubMmUNMTAylS5cmICCAkSNHotH8uwrg+PHjzJgxg4MHD6LT6fD19WXChAn5rrK5H7Jrb+F7UsZ/c3owTCbzf/6Sz0kD/+OPPzBlyhSMRiN6vZ5jx47Rs2fPXMOcQ4ao+O47LWr1TcxmBatWueHi8gXbtu0kISGBGjVq0Lt3b2JjYzlz5gx+ft1lp90i8qS8l0s6aeeiV1y79hYYjLi5ud1X9tU7xcTE/OdKPQ4kGCl8j/sHy3/dyC4/aWlJTJz4DtOnT8fZ2Rmj0UhWVha1atUiMzMTAL0eFiyAgQMt1xiN5QgJ6UWdOi+zefNmMjMzadCgAR4eHmRkZPDMM89gZ2dPcvLj2c6Pg8f9vfy4kHYuesUVjBT4Cfn6668/VDAixNMopwcj9/4xCxk/fhyhoWE0bNi0wICkoJ4UjUbFjRspfPbZZ5hMJjZt2sSff/5J3bp1Wb9+PfPmzePs2WCWL4datSzXhIbC+fOvotNVZezYsXz00UdkZ2dTvnx5jEYjDg6lsLW1J/+fIEII8WgU2DMi7k56Rgrf4/wrJy0tCTc31zyrw8AyTyM2Ng69vkyu4/fqScnOTic1NZXw8HAOHTpEzZo18fHxYd68efzxx2p27QrC3X0ZarWZW7dg7FhLD8nQoUNp2LAhVapUwcvLC7Vazc2bN3FwcEahUD/W7fy4kDYuHtLORe+R94wIIe5PQRvZ5TAYDISHhzNo0HBrz0dOT8pHH83FxcWFxMREFi1axNtvT+DAgUjKli3DwYMH86Rz12q1LF8+j//7v514ef0C/Js75PBhS5m6devSr18/AK5fvw4orIGIEEKURPf16bRlyxYiIiKIjY0lOTkZhUKBs7Mzrq6utG/fnhYtWhR1PYUosfLbyO5Ohw5F5/pFcetWGjqdPT4+PsTHx+Pr68vnn39OmTJlOH/+PPb2dnkCEYA2bQy0bPmaNXdIZGQjJk605fDhnYAlWAkMDCQ9PR2FQknp0i6YTJJlWAhRst01GElISGDUqFGcPHkSs9mMnZ0djo6OGI1Gzp49y759+/j555+pV68eH3300V131xXiSZXfRnZ3atDAA5PJMiKqVJqIijpIUFCQdafd1q1bc/z4ccaPH0/58uVZsWJFrkBEo4EPP4Tx4y1/37ply8yZ1Tl71p3SpS1d1FqtllWrVnHz5k0cHUsDGkymonrWQghReAqcM3Lp0iX8/f3JzMxkxIgR+Pn5UblyZet5k8nEyZMnWbt2LYsXL8be3p7g4GBcXFyKrfKPkswZKXyP8/jvg8wZSU9PwtXVNddOu9u2bePUqVPUr1+fLl26MHv2bBYtWgRYJqcuWwaNG1vut3MnBAf34vx5FY0bN8ZoNKJWqwkMDMTGxhat1v6uQzKPczs/LqSNi4e0c9F75HNGvv76azIzM1m+fHmudOw5lEolderUoU6dOnTt2pWgoCC+//57Jk6cWCQVFaIk0+mcCA0Nu2M1jSUQCQsLQ6dzAizzS8LCwq2BSFBQEB06dMh1zR9//EHnzp0BePFFy6RUBwcwmeCDD2DaNJg8uQFRUdto3769Nauwo6MTJpPssCuEePwUGIzs2LGDXr165RuI3KlWrVoEBASwfft2CUbEU0mhUNOw4b8b2R06FE2DBh558ozkzC9RKBSMHj06TyACli0Ppk0bx08/KRkwwDLOcv68JTDZts0S4Pj4+NCgQQOcnJxQqVQolSoJRIQQj60Cg5ErV65Qu3bt+76Rq6trno30hHia3L6RXUEZWI3GTDw8PGjdujXbtm3Ld1inUSMz1av3omVLSyASEgLDh8O1a5ZA5LfffqNy5cpUq1aN9evX/7OEt2lxPEUhhCgSBQYjmZmZ2Nvb3/eN7O3trRkhhXiaFZQC3mw2cuLEcXr06MGzzz6LUqnEx8eH7du3YzabUSgsuUJmzACNJh2jUU1i4lscPqyje/dT1KtXj6CgIFQqFb/88gu2traS0l0I8USQTzAhioHZbMRgSKdMmTIkJydz/Phx4uLi8Pf356OPPmLBgkn07buWjh0t5Y8dg4iIl1m2bA9ms5kqVarQo0cPypQpQ3Z2NiNGvIbZrJQN7oQQT4S7BiOSDl6I/85szsRguMWFCxdISEhgyJAhuYZnunVTs2KFjpy9H7/5Bt5+W0NoaG+2b2+LRqPhlVdeQa1Wk5mZ/U/eEDMggYgQ4skgG+U9JFnaW/ietGV6SqUJo/EWkZGRVK1alaSkJHx9fa2BiEZjGZIZN85SPitLT//+aYSFWeaFnDp1itTUVNq0aYNWq6VmzTqYzf+9M/NJa+eSSNq4eEg7F71HvrS3SZMmRfKAQjzpLEMyGaSlpaJWq9m9ezepqanExcVZA5E7c4fs2AHHjr1J796ezJzpxZkzZ7Czs6Ny5cqULVsWnU5fKIGIEEKURAV+ui1durQ46yHEE8FsNnLlSiIKhQInJyd27txJ37592bp1K/Hx8QAMHGjJHaLX584dMnjwJapWjWHBggW0b9+ePn36sH//fs6ePSurZYQQTzT5qSUElmRkBS3Hvd/rIYusLCP29vaEhIRw6NAh6tWrR6NGjXB0dOS55yqxZIklGIHcuUMA6tWrh9FoJDAwkC5dunDkyFGaN28hq2WEEE+8AueMzJw5E39/f+rWrZvreFpaGvb29iiVuTff2rhxIzNnzmTTpk1FV9sSROaMFL5HMf5rNhtJT08hPDycqKiDeHp65UlUdjdKpYnU1OtERUXRrFkzLl68yIQJE9i8eTM5/7RsbGw4dmwxFSqMxc7uApA7dwhY8occO3YMR0dH1GoNKpXNQwdG9yLj7EVP2rh4SDsXveKaM1Lgdp6LFy+2divnuH79Ok2aNGHPnj15ymdkZJCYmFj4NRSiiJjNRiIj9+Lm5sqoUW+ycOFCRo16Ezc3VyIj92I2G+967c2byezcuY19+/bRsmVLLl++zLFjx/Dx8WHr1q0EBgaiUMDIkZk8+2w/7OwukJ2tYdQoFYGBuQOR0NBQ9HrLvBCjUUVmplGW7QohnhoP3PdbQEeKEI+d9PSUPHvJABgMBvz9u+fa3O52ZrORU6dOoNPZU7lyZRwdHVmyZAlGo5H69eszZMgQfvnlFwYO7MCkSTvx9PwbMJOSUokrV+YzfHgtKlZcy/Hjx6lfvz7+/v7o9Y4oFDbF9MyFEKJkkYFo8VTSaFSEh4fnm44dLAFJeHg4gwYNt/ZQaDQqzGYj165dR6vVkJWVRWJiIrNmzeKNN95g27Zt/P777xw8eJAhQ8rj4jIeW9sUwJI7JDKyLTdurODixYu0bduW6tWr07x5c3Q6CUSEEE83CUbEUylnw7q7OXQoGqVSYZ1XEh0dhUKhIDo6mlGjRnH16lVmzpxJYGCgdcO7nNwhVapY7mEw2PPBB1WZPj2GefMaU6FCBdatW4dOp6NHjx7odI4olRKICCGebhKMiKeSyWTG09MLWFhgmQYNPMjKMhAZuZeAAH927dqFTqejbt26nD9/nrVr1/LGG29YA5E7c4fs3KngmWdCOXHiB7TaeFq0aIGdnR2dOnXCaMwmO1v++QkhBNxlAqsQT7KsrGz8/PzQarX5ntdqtfj5+XHjxnU+/vgj/vjjD8qWLcvZs2e5dOkSW7duxWg0WnfeHTgQDh60BCLZ2TB1Kvj4mFmz5ggVKlRg5cqVVK1aFbVaTUxMLMaC58YKIcRT567BSEHp4GXPGvEk0OmcCA0NyxOQaLVawsLCKFWqNAoFfPrpp1y6dImDBw+i1+t59tlnsbGxoX79+ly4EMOSJbBkiSWJ2fnz0K4dTJliCUqOHDnC22+/jV6vR6FQoNPpqV69tuQNEUKI2zzw3jSWrc4LDkZkbxrxsB51npFDh6Jp0MCDPn36YDabOHPmDOnp6Tg7O6PVavnjjz84duwY7u7udO3aldKlT6JSDcTZ2bJG987cIQDz5s2jS5cuqNVqSpcuVyKW60puhqInbVw8pJ2LnuxNI0QxUCjU6PVlGDRoOGazkeTkaxw/HkeFChWwsbFBrVZz4cIFevToYV15o1DAlSvj+eADUCqzuXUL3noLvvoq9721Wi2dOnXi2rXrVK9eu0QEIkIIURLJ3jRCAAZDJpGRe1i0aBEff/wxEREReHl5kZ2dnSsQKVfOMiTTsaMlsMjMrMW+fWP44YcxwL/LhLVaLSEhIeh0OkqXfkaGZYQQ4i4K9RMyNDQUf3//wrylEMUiPT2Z6OhoBg8ezDvvvEPPnj3RaDT89ttv1kCkUydYvBieecZyzddfw82bQ7h27SK7d+9m06ZNxMTE4O7ujr+/Pw4OpWSnXSGEuA93/aQ0Go1s3LiR6OhozGYzzz33HN26dUOlUuUqd+HCBSZNmsRff/0lwYh47NjaqoiNPY+/vz8eHh40b96c6tWrc+TIEeLj49FqLblDxo61lL9+Hf73P1i5EoYPP0WTJk1Yv349QUFBODo6YjKZAbUEIkIIcZ8K/LRMSkpi+PDhxMXFWVPAKxQKvv/+e3766SecnJwAyx428+bN4+bNmzRq1Kh4ai1EITCbjWRn3yIjQ0nVqlVZvHgxBoOBiIgISpUqxdq1a2nSxImRIyHnrb1jBwwYAGfPWv52d3enT58+AFy7dg1QyZCMEEI8oAI/NT/55BNiY2Pp168fgYGB2NnZsX37dr744gs++OADPvzwQ8aMGcPmzZtxdHTk3XffJSgoqDjrLsRDy87OICMjHQCdTsfp06dJSEgALCvGvv/+O1q1OoWvbzAqlWWZ7gcfwPTplv8Plnkh3bt3JyUlBaVShV5fWnpDhBDiIRT4yblr1y46duzI5MmTrcdq166Nra0tH330ERqNhk2bNtG+fXumTZtGmTJ5NxQToiTRaFQolQqyszO5fPkKSqWS0NBQTpw4Qd26dRkzZgwpKSls3BiMq+uHvPCC5bqMDGe6d7/Bpk3/ZirLmaCanp5OuXIVAc2jeVJCCPEEKDAYuXr1Ki1atMhz3Nvbm+nTpxMaGsr777/Piy++WKQVFOK/un1vmWeffRYnJyfOnDmTa5UMwHvvvUdY2PssWLCe8uUtvSaJiU14//0KTJo0Fh+fbSQkJFCjRg169+6NXq9HrbZBAhEhhPhvCgxGDAYDer0+z/GcY/3795dApJDk/GI3mcySi6KQmc1GIiP3smjRQsaNG8fZs2dp1qwZvXr1yhWIKBQwapSBdu0modFgzR1y6VIlxox5i/3792NjY0Pv3r2pW7cuGo0GtdpO5ocIIUQheOhPUm9v78Ksx1Pp9uyfUVEH8fT0ws/PD53O6Yn+kivO4Cs9PYVDh6L58MMPCQ4O5ujRo5w8eZLo6GhWrlzJu+++e1vuEMs1SUnl2LHjDW7ejKdJkzpUqFCBoUOHcvHiRZYvXw5Aw4ZNnujXSAghitNDf5qq1fJB/F/k/GL39+9+2y/0hYwfP47Q0DAaNmz6xH3ZFXfwpdGouHEjhbp161K3bt08QzK///47y5cPpm3bxblyh0RHv0CnTu74+dWlUaNGnDlzhpSUFCIiIhg0aBA6nQNKpU2h11cIIZ5Wd/0GSE5OJjExMdexlJQUwLKM8c5zABUrVizE6j250tNT7ghELAwGA/7+3YmNjUOvf3ImBd9P8FVYNBoVCoUJkykTvV5Pz54987QzGIiPD2D0aBOQO3fI3LnuPPPMM9jb2zNhwgRmzJiByWRi4MCBZGcrn7ggUQghHrW7fqrOmDGDGTNm5Htu3LhxeY4pFAqOHTtWODV7gmk0KsLDw/P5grQwGAyEh4czaNDwJ2YOyf0EX6VKOfynx/i35yWMjIwMhgwZQmhoaJ7HrF0bli2DRo0sgUh8fAXatbvI2bP/LtddunQpc+fOZeXKlZQu7YxSaUNWVjayYbUQQhS+AoORwMDA4qzHU0WpVBAVdfCuZQ4dii6y3RGL2/0GX6+++ir57yF9b2azkYMH9/LDDz/wxhtvcOvWLdRqNUeOHMlVbuBAWLAA9HpLvpA1azw5c2YQZ8++ZV2ue+zYMUqXLs2xY8fQ6RzJzlaTnf1kBIVCCFESFRiMzJw5szjr8VQxmcx4enoBCwss06CBxz9pxR9/9xt8KRQKa7bfB2E2G0lLu84PP/xAYGAgX375JSNHjiQhIYG6desC4OBgCUJyFoCdO2f5/35+A/D0dOfjjz+mU6dOlCpVCgcHB9q378itWxKACCFEcVDeq0BWVhYxMTEcPnyYmzdv/n97dx5WVbU3cPx7OHAAOUecyErvDSRnZUogEaVMTEhEMTUnnDK1a29a1qt2X8mpRAu9OdYtNatbmoojV9RSE7uaoOKQU6I4VgoxDwcO+/3jXE4eAUEFDsLv8zw8T2fvtTdrr9bj+bH2Wr9VHXWq9QoKDISEhKDRaEo9r9FoCAkJqTWvaP4Mvsrm5ub+AIFIGlu2bGHixIlER0czZ84c9uzZw9GjR+nevTt+ftYcPfpnIBIdDR4ecPCghmeffRYXFxd8fX1RqVTk5eWRk5MvgYgQQlSjuwYjq1evxs/Pj7CwMAYOHMjTTz9NZGQkhYWFd7tMVICDgyObN28pEZBoNBq2bNmCg4OjhWpW+SoafN3PK5rCwlx0Oi15eXlcu3aNgQMH0qFDB2bMmMHXX3+FWv0h+/cruLoac4dMmABhYZCVpWH16tVcunSJuLg4IiIiSE5OxsGhvqyUEUKIalbma5pNmzYxb948mjVrRmhoKFZWVhw6dIjVq1djMBiYPn16ddaz1lGprPHy8uHMmbNs3bqV48cTcXNzr7V5RoqDrzsnsd5v8KUohdjZqUlPzyEnJ4dWrVrh7OxM586d0ev1PPoovPPOftzdjeWzs5358ssXyMvLZtasFgQEBLBkyRLmzp1LTEwMn376KfXq1b52F0KIh4FKKWNsfODAgVhZWfH5559ja2v8S1FRFCZPnsyePXs4fPhwmX/p1gVFRQopKVmVcq+6koH19jwjpQVfjo72AKSn3/11YFFRLllZmWzbto3ExERatWrFsGHDWLt2LVOmTKFXL/j8c0y5Q86ffw4fn/24uT3NY489xo0bNzh06BAbNmzA29sbGxt76tJgX0XbWdw/aePqIe1c9W5v48aNtVW2sKLMPwMvXLjAG2+8YQpEwLh0d+TIkcTGxpKUlESbNm2qpFJ1TW0OQG6nUlmj1TYmPHzMfQVfilJIYWEuly9fZunSpVy+fJn9+/ejKAqnTp3C3t6KDz80pnEHY+6Ql18GRanP5s27+M9//sO5c+cICQlh1apVaLX1Uals61QgIoQQNVGZwUhubi46Xcm8D82bN0dRFDIyMqq0YqL2up/gS1EK+f3369jb27Fr1y7UajU9evRg9uzZLFq0iKKiM8yceY3HHjOW378fhg41rpqBaDZt2sSaNWsYPHgwWq0Wg0E2txNCiJqizGCkqKgIVSkZntRqNYDkXRDVojibak5OHsnJl0rstKvR2LBv3xjc3bdgb2/AYIDZs2HOHGMekT/vY4O3tzc3bvyKi0tLSV4mhBA1SLlLe4WwBOP8khTWrPmMN954nZs3b5YIRHQ6WLmygKefXoG9vYFbt+w5cGAW77+vMQtENBoNGzZswN6+3n8DEZmkKoQQNYnsTSNqoAISEg7Rt29f9Ho93bp1K5HW3dvbmNLd1dX4+cyZtrz1VgPCw9tw+PBhNm/ezKVLl+jYsSOhoaHodA1QFAlChBCiJipzNU2bNm1KfU0DxlU1pZ2rS3vTVOZqGmFkb29FenoamZmZTJw4EScnJ65fv06zZs3QaDR8/vnnqFQwZQrMnQs2NsbcIZMnQ17eCAoKCnn77bc5efIkTz/9NPXr18fa2paCAks/Wc0iKxCqnrRx9ZB2rnoWX00je9OI6qQohfz883kef/xx9Ho9AQEBXLhwgR49etCzZ0+SkpKIjf2cNWsgMNB4zalT8NJLcPIkzJrlSufOnbG1tcXHx5fGjR9Br1ckEBFCiIdAmSMj4u7qyshIdeVAycpKwcpKxalTp+jXr1+JxGj790+ndev3cXTMB2DFCuMS3txc4/njx49z69Yt/vKXv2Jnp5V5IXchf01WPWnj6iHtXPWqa2TE4hNYDx8+zODBg3F3d8ff35/Zs2eTnZ1tOh8XF0f//v1xd3ene/furFy5ssQ9Tpw4wfDhw/H09MTf35+oqCgK7viT+NKlS4wfP55OnTrh6+tLREQEWVm1P5i4X8Y9X4wTSCdN+htr1nxGVlYKilK5STlsbNRoNFBQoCcjI6OUQATef1+Pj8+7ODrm88cf0L+/Ma17cSCyfv16bGxsaN26Dfb2DSQQEUKIh4xF/9U+duwYo0aNonv37ixfvpzk5GSioqJITU1l4cKFHDlyhPHjxxMUFMTrr79OQkIC8+fPR1EUxowZA0BycjIjR47E09OTRYsWceHCBRYuXEhWVhYzZswAjJNuR4wYgZOTE5GRkaSkpLBgwQJ+/fVXPv74Y0s2QY2kKIUcOfLTHanbV/HWW1PYvHkLXl4+D/yFX5yNNTExEZUKFi9eTEBAgFkg0rKlcZLqU08ZP1+54kxW1gq6dj1N/frGzKs9e/Zk8+bNtG3blqIiK1myK4QQDyGLBiMffPABHh4e/OMf/0ClUuHn50dRURGrVq0iNzeXjz76iHbt2rFgwQIAunXrRmFhIStWrGD48OFoNBo++eQTdDody5YtQ6PREBAQgJ2dHXPmzGHcuHE0bdqUr776ioyMDDZt2kTDhg0BaNq0Ka+88gqJiYm4F29gIgDIzk4vsYcMgF6vJzS0D2fOnEWrbXzf9y8Odv7znx8ZPnw47du3JywsjAsXLpjKhIfD0qWg1WLKHXLlSjdee60pDg6Xyc3NZceOHcycOZNNmzah08mIiBBCPKws9pomNTWV+Ph4Bg8ebLYyZ+jQoezevRsrKyvi4+Pp2bOn2XXPP/88GRkZHDlyBIADBw7w7LPPmu2T06tXLwwGA3FxcaYy3t7epkAEwN/fHwcHB/bt21eVj/nQsbFRs3Xr1hKBSDG9Xs/WrVuxsVHf171tba1RlAJcXVswZMgQvv32W/R6PdevX8fV1RWdDr780ri3jFZrzKD67LMwcya4uXnx+OOPc+vWLRwcHOjf/0XOnDmLl5ev7LQrhBAPMYv9KXnu3DkURcHR0ZFJkyaxd+9e1Go1vXv3Ztq0aVy9epWCggJcXFzMrnviiScAuHjxIu7u7ty4caNEmUaNGqHVarl48SIASUlJ9OnTx6yMWq2mefPmpjLCyMpKxbFjR+9a5vjxxHuaxHT7BnmPP/4YPj4+JCYmcvPmTc6fPw/A/v37WbRoCEOHQosWxus2bjTuLfPHH8a5IcHBwfz444+MHDmSevV0KIpVndnXRwghajOLBSOpqakATJ06lcDAQJYvX87Zs2dZtGgR+fn5DBo0CACtVmt2nYODAwBZWVlkZmaWWqa4XPEE1czMzHLL3CuV6s9ZxrWJSgWenp6sWrWqzDLu7u7Y29tgZ1f+/i56fT4HDvzEZ599xjvvvMN3333Htm3bcHV1pWfPnjRo0IDVq1cxZYpCx44TsLY2TkydPBmKp/NoNBo2b95MkyZNeOGF3sj6rwdjbW0c1aqN/bemkDauHtLOVe/2Nq7KOXkWC0aKV7t4eXkREREBQOfOnVEUhcjISAYOHHjX662srChvVbKVVflvoSpSpi5RFOjTpw9Tpkwp9VWNRqOhT58+FQ4IUlJukZiYyMiRI/Hx8TG755w5c9i+/VOOHXsEN7ffAYXLl3X8/vtHNGt2hZEjk/Dw8KBv3340btwYjcZWAhEhhKiFLBaMFI9wdOvWzey4v78/8+bN48SJEwBmy3wB00iGTqczjXbcWaa4XPGuw1qttswy95u+XlFq79p2e/v6bN68pcQkVo1Gw5YtW7C3r1+hZ7e2No6AhYWF0b59+xLBzXPP6XFzC+eRR4yfly+HN9/MJC9vNN27dycyMhKVSkWjRo+Qm1tAbm7tbO/qJrkZqp60cfWQdq56d+YZqarREYsFI87OzgAlvqCKR0yaN2+OWq3m8uXLZueLP7u4uODg4EDTpk1JTk42K5OSkkJ2drZpLomLi0uJMgaDgatXr/L8889X2jPVFiqVNV5ePpw5c5atW7dy/Hgibm7uhISE4ODgWKFVK0VF+eTm6tHr9Xz99del5A4xJi0DyMuz49df55KRUcBLL53FxcWFQYMGkZ2dTf369cnLkzSqQghRm1nsHYWrqyvNmjUjJibG7PiePXuwtrbG09OTTp06sXPnTrPXMbGxseh0Ojp06ABAly5d2LNnj9mXXWxsLGq1Gh8fH1OZQ4cOkZaWZioTFxdHTk4Ofn5+VfiUDy+VyhqttjHh4WOIilpMePgYtNrGFQ5E8vKyKSoqYvfu3SQlJZnOtWwJ//nPn4HI/v3wzju9mTbtMI8++igtWrSgc+fOXL16lby8PLRax6p6RCGEEDWExUZGVCoVU6ZM4Y033mDKlCmEhYVx8uRJli9fzrBhw2jUqBETJkxg1KhRTJ48mX79+nH06FE+++wz3nzzTeztjUNHL7/8Mtu3b+eVV15hxIgRXLp0iaioKAYOHGh6BTNkyBC+/PJLRo4cyd/+9jfS0tJYsGAB3bp1w8vLy1JN8FC419UqRUX5/PHHLaKjozl//jwtW7Zk8uTJpKenUb/+JpYsMc8dMmcORES4kZi4D09PT7y8vLC1tcXOzg5b23qyZFcIIeoAi+9Ns3v3bpYuXcovv/xC48aNGTRoEOPGjTNNLN21axcfffQRFy9epGnTpgwdOpTRo0eb3SM+Pp758+dz+vRpGjZsSN++fXnttdewsflztce5c+d47733OHr0KA4ODvTo0YO333671FU2FVFX9qa5F4pSSELCQfr27Ws2UtW4sQ3x8d44O/8IGHOHDB1qHBXRaDTs2rWLnJwc2rVrh1arxcbGjrw8WbJbVeQ9e9WTNq4e0s5Vr7r2prF4MPKwkmCkpOzsFFq3bm0WiPj4GFO6l5U7ZMOGDbRq1YqEhAR8fX155JGm5OYWWegJ6gb5B7zqSRtXD2nnqldnNsoTtYONjZotW/7M3KpSwdtvQ1ycMRDJzYXvvnuRzMxVhIaOZNasWfz88880bNiQ2bNn06KFK4880hSNRl7LCCFEXSObeYj7Zmdng1pthcFQhKIopsytjz4Ka9ZAYKCx3MmT8NJL4O2txdk5mYKCAnr27ImNjQ0JCUeYNWs2Dg6OEogIIUQdJcGIuGdqtYGCgny2b9/Dzp07adeuHYMHD8bDw4OgIOO+Mk5OxrLG3CHGkZHhw9vw5JNP0q9fP7RaLTpdA8LDx0hKdyGEqOPkNY2oMEUpJCvrFqtXr2Tq1KkkJyczbtw4fvzxR9q2bcGwYUeIiTEGIn/8AWFh8OqrxkBEo9EQFhZGfn4+169fx8GhPoWFKglEhBBCyMiIqBhFKeTIkZ9KzcoaG7uYZcsO0ajR5wDExakYMkThypU/y2zatInffvsNP78u1KtXv0L5SoQQQtQN8o0gKiQ7O71EIAIweLCeTp3GmXKHnD07kObN32PKlBhT5tY+ffrg6NgQlcpaRkKEEEKUIMGIKJeNjZqtW7eaBSI6HaxYAUOGGD+npzty8+ZCPvwwjsWL/0J4+BisrFQUFSkUFBgoLASQQEQIIURJEoyIu7KxUaPRWJtWykDpuUO+/z6Y7t0dCQwMxGAokhEQIYQQFSYTWEWpjJNVU1iz5jM++iiKjh07lpo7ZPx46N8fmjZty7lz53j22WdlYzshhBD3REZGRAnGyaqHCA0NRa/Xo1KpiI/fyq5dKp57zpiwtzh3yKlTxgmq3bp1IyEhARsbWwwyKCKEEOIeyMiIKCE7O80UiAD06qXg6hpmCkSWLQNv7z8DkdWrV7N06VL69u2HwaC2ZNWFEEI8hGRkRJgxpnXfgl6vR6OBefNg8mQAPbm59qSkRJKSksZLLyXRokULAgICWLJkCWPGjEGrbWDh2gshhHgYSTAizNjYqElMTKRVK/jmG/D0NB7/4QcYNiwXb++9vP/++yQlJXH8+HGOHUskMnI+Dg6OkjtECCHEfZFvD3EHhRdfzGTxYnBwMOYOmTUL5s41/veVKxvx9fXltdcm4ecXYFq6K4QQQtwvCUbqMBsbtVkuEJUqHRubNwgOXg/A5cswdKhx9UwxjUZDUFAQ+fmFEoQIIYSoFBKM1EGKUkh2djpbt27l2LGjeHh48uKLzWnadDJqdTIAV6544+NzjN9++3OZbvFkVUfHBhKICCGEqDQSjNQxd+4xY8wdsgonJ1CrQVHsyMycy/XrXfj2Wz179+4lKck4WfWZZ57B1tYWOzutpR9DCCFELSLBSB1z+x4zjz4KX3wBPXoYz506pcLRcRO2tn64uBhHT9q1a8cjjzxCkyZNaN78LzJRVQghRKWTb5U65PY9ZoKC4PPPwcnJeG7ZMnjzTYXIyHOEh3eloAC02sY891yQ2bwSIYQQorJJMFKHWFmpOHkynqio4twhkJoKY8bApk3Gz8ePJ2JlpTJdIwGIEEKIqibBSJ1ylrlzvzONhhhzh8CVK3+WcHNzp6hIsUz1hBBC1EmSDr5OULC1/RKdrgtOTtcwGCAiArp3Nw9ENBoNISEhMhoihBCiWkkwUsupVOnodGOoX/9VVKocDIZmHD78AfPmacw2tNNoNGzZsgUHB0fLVVYIIUSdJK9pajFr68PUrz8GtfoSAPn5fcjM/IgWLepz5kx/tm7dyvHjibi5uRMSEiIrZYQQQliEfPPUSkXY2y/CwWEOKlUhimJHVtY88vJGASpUKuNKmfDwMbJSRgghhMVJMFLLWFn9ik73ChrNXgAKC9uRkbEKg6FtibISgAghhKgJJBipRTSaWHS68VhZpQCQm/syWVlzAXvLVkwIIYS4CwlGaoV8HBwiqFdvGQBFRQ3IzFyKXh9i4XoJIYQQ5ZNg5CGnVp9HpxuFjc1xAPR6PzIzP6WoqLmFayaEEEJUjCztfWgZc4c0bNgVG5vjKIoV2dnTSU/fLoGIEEKIh4qMjDyEVKp0tNpJ2NltAMBgaE5GxmcUFna2cM2EEEKIeyfByEPG2vqn/+YOSQb+zB2iKI0sXDMhhBDi/kgw8tAowt5+4X9zhxhK5A4RQgghHlYSjDwErKxuoNONq1DuECGEEOJhI8FIDafR7ECnmyC5Q4QQQtRaEozUWPk4OMygXr3lgOQOEUIIUXtJMFIDSe4QIYQQdYnkGalRJHeIEEKIukdGRmoIyR0ihBCirpJgpAZQq3/B0bHfbblDQv+bO6ShhWsmhBBCVD0JRmoAO7s1qNXJ/80dEkle3kgkd4gQQoi6QoKRGiA39xUUxY78/DAMhjaWro4QQghRrSQYqQGKipqTkzPd0tUQQgghLEJW0wghhBDCoiQYEUIIIYRFSTAihBBCCIuSYEQIIYQQFiXBiBBCCCEsSoIRIYQQQliUBCNCCCGEsCgJRoQQQghhURKMCCGEEMKiJBgRQgghhEVJMCKEEEIIi1IpiqJYuhIPI0VRkJarXKr/blQs7Vq1pJ2rnrRx9ZB2rnq3t7FKBSpV1ewoL8GIEEIIISxKXtMIIYQQwqIkGBFCCCGERUkwIoQQQgiLkmBECCGEEBYlwYgQQgghLEqCESGEEEJYlAQjQgghhLAoCUaEEEIIYVESjAghhBDCoiQYEUIIIYRFSTAihBBCCIuSYEQIIYQQFiXBiKh0hw8fZvDgwbi7u+Pv78/s2bPJzs42nY+Li6N///64u7vTvXt3Vq5cWeIeJ06cYPjw4Xh6euLv709UVBQFBQVmZS5dusT48ePp1KkTvr6+REREkJWVVeXPVxOU18aBgYG0bt26xE9qaqqpjLRx2Q4dOlRq+xX/REdHA9KXH0RF21j68oP7+uuvCQoKwsPDg5CQELZs2WJ2vib0Y+v7fzwhSjp27BijRo2ie/fuLF++nOTkZKKiokhNTWXhwoUcOXKE8ePHExQUxOuvv05CQgLz589HURTGjBkDQHJyMiNHjsTT05NFixZx4cIFFi5cSFZWFjNmzAAgPT2dESNG4OTkRGRkJCkpKSxYsIBff/2Vjz/+2JJNUOXKa+Ps7GyuXLnCm2++iY+Pj9m19evXB6SNy9O+fXvWrl1rdkxRFN555x1ycnIICAiQvvyAKtLG0pcf3Nq1a3n33XcZPXo0Xbt2Zd++fbz11lvY2NgQFBRUc/qxIkQlGjp0qDJ06FClqKjIdOzLL79UnnvuOSUnJ0cZMWKEMmDAALNr5s+fr3Tq1EnJz89XFEVRpk+frgQEBJg+K4qifPXVV0rbtm2VX3/9VVEURVm6dKni4eGhpKammsrs3btXadWqlXLs2LGqfESLK6+NExISlFatWim//PJLmfeQNr53q1evVtq0aWN6dunLle/ONpa+/OAGDRqkDB8+3OzYkCFDlGHDhimKUnP6sbymEZUmNTWV+Ph4Bg8ejEqlMh0fOnQou3fvxsrKivj4eHr27Gl23fPPP09GRgZHjhwB4MCBAzz77LNoNBpTmV69emEwGIiLizOV8fb2pmHDhqYy/v7+ODg4sG/fvqp8TIsqr43t7e05ffo0dnZ2ODs7l3kfaeN7c/PmTf7xj3+YXo3l5+dLX65kd7YxIH25EuTn5+Pg4GB2rEGDBqSlpdWofizBiKg0586dQ1EUHB0dmTRpEh4eHjz11FNERESQl5fHlStXKCgowMXFxey6J554AoCLFy+Sm5vLjRs3SpRp1KgRWq2WixcvApCUlFSijFqtpnnz5qYytVF5bQxw9uxZHB0deeONN+jUqROenp5MnjyZmzdvAkgb34fFixdjZWXFpEmTAKQvV4E72xikL1eG8PBw9u/fz7///W+ysrLYsWMHe/fuJTQ0tEb1Y5kzIipN8YSyqVOnEhgYyPLlyzl79iyLFi0iPz+fQYMGAaDVas2uK47as7KyyMzMLLVMcbniyVCZmZnllqmNymvjefPmcebMGW7dukXLli0ZPnw4SUlJfPTRR4SHhxMdHS1tfI9SUlLYtGkTo0ePNs1TKKsNpS/fn9LaGJC+XAleeOEFDh48aBbk9evXj5dffpmjR48CNaMfSzAiKk3xzGovLy8iIiIA6Ny5M4qiEBkZycCBA+96vZWVFYqilFumPBUp87Aqr43/9re/8fe//x1FUUxD3Z06dcLV1ZUhQ4awZcsWAgIC7vo76nob3+nbb7+lqKiI8PBw07GK9FPpyxVXWhsD0pcrwYQJEzh69CjTpk2jXbt2JCYmsmzZMrRaLcHBwXe9tjr7ce3+vyCqVXE03a1bN7Pj/v7+KIrCiRMnAMyWoAKmqFmn05ki6zvLFJfT6XSAMUovq0xp0XltUV4bnz17Fjc3N9M/3sWeeuopdDodZ86ckTa+R7GxsXTt2pVGjRqZjhW3kfTlylFaGwPSlx/QkSNHiIuL4+9//zsjR47Ex8eHsWPHMnXqVL744gvq1asH1Ix+LMGIqDTFk8z0er3Z8eK/5ps3b45areby5ctm54s/u7i44ODgQNOmTUlOTjYrk5KSQnZ2tumdpIuLS4kyBoOBq1evlnhvWZuU18YAGzZs4MyZM2bni4qKKCgooGHDhtLG9+C3337j559/JigoyOz4X//6V+nLlaSsNs7JyZG+/ICuX78OGEdSb9epUyfAOEG4pvRjCUZEpXF1daVZs2bExMSYHd+zZw/W1tZ4enrSqVMndu7caTb0Fxsbi06no0OHDgB06dKFPXv2mH3hxsbGolarTbkGunTpwqFDh0hLSzOViYuLIycnBz8/vyp8Sssqr409PDyYN28eS5YsMTv//fffk5eXZ9Z+0sblS0xMBIx/jd/O1tZW+nIluVsbS19+MMVBQEJCgtnxY8eOAdCiRYua048ruFRZiArZvn270rp1a+XNN99UDhw4oHz88cdK+/btlffee09RFEX58ccfldatWyuvv/66snfvXmXhwoVK69atlU8++cR0j19++UXp2LGjMmLECOX7779XVq5cqXTo0EGJiIgwlUlJSVF8fX2V0NBQZefOncq6desUb29v5eWXX67uR6525bXxypUrlVatWimzZ89WDhw4oKxatUrx8vJSJkyYYLqHtHHFLF68WHF3dy/1nPTlynG3Npa+/OBeffVVxdPTU1m1apVy8OBB5dNPP1U8PT1Nz15T+rEEI6LS7dq1S+nbt6/SoUMHJSAgQFm2bJliMBhM53fu3Kn07t1bad++vdK9e3fls88+K3GPw4cPKwMGDFA6dOigdO3aVfnwww8VvV5vVubs2bPKiBEjFDc3N6Vz587K//3f/ymZmZlV/nw1QXltvG7dOqV3796Km5ub0rVrV2X+/PlKbm6u2T2kjcsXERGhdO3atczz0pcfXHltLH35weTn5ysffvihEhAQoHTo0EF5/vnnlcWLF5slMKsJ/VilKOVMlRVCCCGEqEIyZ0QIIYQQFiXBiBBCCCEsSoIRIYQQQliUBCNCCCGEsCgJRoQQQghhURKMCCGEEMKiZKM8IWqggwcPMmLECBo0aMD+/fvRaDSllsvKymLdunVs27aN5ORkDAYDTz75JAMGDGDAgAGmDaqmTp1KdHR0ub+3X79+zJs3z1T+u+++o3nz5iXKHTp0iPDwcCZOnMhrr712X/Uv73eU5erVqzz33HNmx6ysrKhXrx4uLi6EhoYyZMgQ1Gr1Xa8pzaZNm2jbtq2pfLNmzdi+fTv29vZl1v/s2bNmx0+cOME///lPEhISSE9Pp0GDBnh7ezN69Gg6duxoVnb48OH89NNPpnu0bt26Qm0QFRVl2ihxx44dNGnSpEQZRVEYPHgwx48f55tvvsHNza1C9xbCEiQYEaIG2rp1K/Xq1SMtLY3vv/+eXr16lSiTlJTEhAkTuHbtGiEhIfTv3x+9Xs/u3buZMWMGhw8fZsGCBahUKgYNGkTnzp1N1yYkJLB27VoGDRpklob7r3/9a7XV/0F16tTJtBN0UVERaWlp7N+/nzlz5hAXF8eyZcvMApI7rynN448/bvb52rVrLF26lClTplSoTj/88APjx4/nySefJDw8nEaNGnHjxg02bNjAjh07WLx4MT169Cjz+vnz55t9XrFiBUlJSSWO+/r6MmnSJGbPnk1kZCQLFiwoca/169dz9OhRRo4cKYGIqPEkGBGihtHr9ezcuZPQ0FC2bdtGdHR0iS/z/Px8Xn31VdLS0li/fj1t2rQxnRs1ahQzZ87kX//6F25uboSHh+Pp6Ymnp6epjMFgYO3atXh4eBAaGlrt9a8Mf/nLX0rUfdSoUURFRfHxxx+zcuVKxo4dW+415Vm9ejWhoaG0bNmy3LJz586lTZs2rF27FhsbG9Px8PBwQkNDmTVrFs888wzW1qX/03tn3davX09SUlKpdR4yZAjR0dFs2bKFAQMGmPYIAfjjjz/44IMPaNasGa+//npFH1UIi5E5I0LUMPv27SMjIwNfX1/8/f2Ji4vj5s2bZmX+9a9/cfHiRaZNm2YWiBT73//9XxwdHfnmm2+qq9omFal/Vfqf//kfXFxcWLVqFYWFhQ90r2eeeQaDwcC7775LecmqU1NTuXTpEr6+vmaBCECDBg3o27cvt27d4urVqw9Up2JWVlbMnDkTtVrNrFmzzJ51wYIFpKWl8e6775q2iReiJpNgRIgaZuvWrahUKry9vQkMDKSwsJDNmzebldm+fTv16tXjhRdeKPUednZ2rFu3jk2bNlVDjc1VpP5VydramuDgYFJSUvj5558f6F4dOnRg8ODBxMfHs3HjxruWtbe3R61W891335UafL322mucOnUKZ2fnB6rTnfUbMmQI58+f58svvwSMO7Ju3LiR3r17061bt0r7XUJUJQlGhKhBsrKy2Lt3Lx4eHjRp0oSAgAA0Go1ZUKEoCqdPn6ZDhw4l/gK/nbOzc5kTX6tKRepfHYpfqZw5c8bsuF6vJzU1tdSfrKysUu81efJknJycWLBgAX/88UeZv9Pe3p7g4GCSk5Pp0aMHkydPZsOGDVy7dg0wBkkqlaqSnvBPkyZNwsnJiWXLlpGWlsbcuXNxdHTknXfeqfTfJURVkWBEiBokNjaW/Px8evbsCYBWq8XPz4/z589z/PhxwDgfoLCwECcnJ0tWtVQVqX91cHR0BCAtLc3s+Pbt2+ncuXOpP2+//Xap99LpdEydOtU0D+NuZs6cSXBwMHl5ecTExDB9+nS6d+9O7969+eqrrygqKqqU57udVqtl+vTppKenM2rUKI4fP860adNo1KhRpf8uIaqKTGAVogbZtm0bAIGBgaZjgYGB7N27l40bN+Lm5mZarmswGCxSx9vd+Zd+RepfHQoKCko97u/vz5gxY0o9d7cv7969e7NhwwY2bNhAWFiY2Qqk2zk4OLBw4UImTpzIv//9b+Li4jhx4gTnz59n1qxZ7N+/n6VLl5ZY5fOggoOD2bBhA3Fxcfj5+dG3b99Kvb8QVU1GRoSoIX7//XcOHjyIs7MzKpWKq1evcvXqVdq0aYNKpSImJga9Xo+joyM2NjakpqZWWV2KX++UFfAUH7/9NVBF618dikdE7gwwnJyc8PPzK/WntInAt4uIiMDGxoZ333233Imxrq6uTJw4kW+++YaDBw8yZ84cnJyc2LNnD7GxsQ/0bGUpnj8UEhJSJfcXoirJyIgQNURMTAxFRUVcunSp1ARd6enp7N69m+DgYDw9PTl58iSFhYVlLhNduHAhV65cYdq0aff8Sqf4NUd2dnap59PT0wGoX7/+fdW/qp0+fRqg3ADjXjg7OzN27FiWLl3K6tWrS5zfu3cvBw4c4K233jIL0nQ6HQMGDKBVq1YMHDiQhISEamkDIR4mEowIUUMUr0KZN28eWq3W7NyZM2dYvHgx0dHRBAcHExgYyE8//cT27dtLzUGRl5fH+vXrMRgMNGjQ4J7r4urqCsD58+dp165difPnzp0DMMu9cS/1r0pFRUXs3LmTRx99lPbt21fqvceNG8e2bdtYunRpiWyqp06dYs2aNQQGBprl/ChW3FZ2dnaVWichagN5TSNEDXDx4kVOnjyJj48Pffv2pUePHmY/48aNw8nJiQMHDvDbb78xaNAgmjVrxvz5802BQbHivBi3bt1i7Nixd11xU5YuXbpgZ2fHmjVryMvLMzuXkZFBdHQ0jz32mOkL+V7rX5WWLVvGtWvXGDNmTKWvXrG1tWXGjBnk5ORw6NAhs3MvvPACVlZWREZGkpGRUeLadevWAVQoLb0QdY2MjAhRAxRP/HzxxRdLPW9jY0P//v1ZsWIFmzdv5pVXXmHJkiWMHj2aF198kZCQEDp27EhaWho7duzg9OnT9OrVi1GjRt1XfZycnHj77beZPXs2YWFh9O3bl8aNG3Pjxg02btxIamoqS5YsMb2OuJ/6F1u4cCEODg4lrgkKCjJLYX+nK1eumPKXKIpCamoqcXFxHDhwgMDAQIYNG3bXa0rTunXrcl/t+Pv7ExwcTExMjNlxZ2dnpk2bxnvvvUdQUBB9+vShRYsW5OXlceDAAfbs2cPw4cPx8vK66/2FqIskGBGiBti2bRs6nc60JLY0AwcO5JNPPiE6OppXXnmFdu3asXnzZlavXs0PP/xATEwMiqLQunVr3nvvPcLCwh5oZGDo0KE88cQTfPHFF6xZs4a0tDSaNGmCl5cXY8eOpW3btg9U/9uvLU2LFi3uGozEx8cTHx8PGFf1aLVaWrZsycyZM802CSzrmtJMnDixQvNMpk2bxg8//FAiN0l4eDjt2rXjq6++IiYmhtTUVOzs7GjTpg1RUVFlJqkToq5TKeXlOBZCCCGEqEIyZ0QIIYQQFiXBiBBCCCEsSoIRIYQQQliUBCNCCCGEsCgJRoQQQghhURKMCCGEEMKiJBgRQgghhEVJMCKEEEIIi5JgRAghhBAWJcGIEEIIISzq/wEuKNXIPKXL+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context('talk', font_scale=1)\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.lineplot(x=ytest, y=ytest, color= 'yellow')\n",
    "sns.scatterplot(x=ytest, y=ypred, color= 'black')\n",
    "\n",
    "plt.xlabel('ACTUAL DENSITY')\n",
    "plt.ylabel('PREDICTED DENSITY')\n",
    "plt.title('ARTIFICIAL NEURAL NETWORK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f685b6ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "125cc0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "sns.set_context(font_scale=1.2)\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.scatter(range(1,2557),ytest,c='black', marker='o')\n",
    "plt.scatter(range(1,2557),ypred, c='orange', marker='*')\n",
    "plt.legend(labels=['ACTUAL DENSITY','PREDICTED DENSITY'])\n",
    "plt.xlabel('DATAPOINTS')\n",
    "plt.ylabel('DENSITY')\n",
    "plt.title('ARTIFICIAL NEURAL NETWORKS')\n",
    "plt.savefig('ACTUAL_VS_PREDICTED_ANN.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23f0cb3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd4e63b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b9309df4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 6ms/step - loss: 447.0237 - mae: 14.7602\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 165760.3438 - mae: 315.3680\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 169023.4531 - mae: 316.5846\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 167667.6719 - mae: 315.4842\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 167265.7969 - mae: 315.9953\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 168486.6719 - mae: 317.6755\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 237664.8594 - mae: 394.0522\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 242299.8750 - mae: 398.8216\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 236370.4688 - mae: 393.1936\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 235967.1406 - mae: 392.0533\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 240263.9531 - mae: 395.7377\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 100967.3281 - mae: 251.0887\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 103935.3906 - mae: 255.1718\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 102883.7422 - mae: 253.8344\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 103451.0469 - mae: 256.2792\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 102105.1484 - mae: 252.7022\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 64393.7969 - mae: 200.2070\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 64701.3281 - mae: 200.6303\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 64639.5352 - mae: 201.0531\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 62814.4375 - mae: 197.6169\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 63277.6875 - mae: 198.8266\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 24834.3125 - mae: 112.3493\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 24497.2910 - mae: 111.5338\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 24517.3105 - mae: 112.1643\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 24428.5859 - mae: 111.6408\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 24315.4414 - mae: 111.1853\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 18124.2910 - mae: 108.6654\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 17916.2734 - mae: 108.0121\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 18141.6445 - mae: 108.8388\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 17978.6699 - mae: 108.2283\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 18158.5703 - mae: 108.9884\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 213213.2969 - mae: 371.3460\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 216969.1094 - mae: 374.1836\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 212918.0000 - mae: 371.6318\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 214912.8438 - mae: 371.9336\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 216436.2969 - mae: 373.7199\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 169778.7344 - mae: 333.2784\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 173311.5938 - mae: 337.5075\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 169847.4219 - mae: 333.0531\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 167840.8906 - mae: 331.2196\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 172298.5000 - mae: 335.1340\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 77061.4141 - mae: 221.0991\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 77283.3125 - mae: 221.7882\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 76935.9531 - mae: 221.7734\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 75580.7422 - mae: 219.3782\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 77654.0078 - mae: 222.4837\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 154118.2031 - mae: 316.4353\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 155914.3594 - mae: 319.4401\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 152968.6719 - mae: 315.6846\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 151571.4844 - mae: 313.9677\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 155873.0000 - mae: 318.6283\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 61224.4492 - mae: 188.8121\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 62016.1484 - mae: 190.3479\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 61579.9375 - mae: 188.6571\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 60325.1992 - mae: 186.9348\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 61984.1523 - mae: 189.7695\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 128843.6094 - mae: 289.9644\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 130540.3906 - mae: 293.1143\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 127205.8047 - mae: 289.2043\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 126171.1875 - mae: 288.0211\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 129431.9297 - mae: 291.3653\n",
      "          importance\n",
      "Al     238066.235693\n",
      "VEC    214442.885693\n",
      "δ      170168.404443\n",
      "Ni     167193.763818\n",
      "ΔHₘᵢₓ  153642.120068\n",
      "Tm     127991.560693\n",
      "Co     102221.507568\n",
      "Δχ      76456.062256\n",
      "Fe      63518.333350\n",
      "Ω       60978.953662\n",
      "Cr      24071.564600\n",
      "Δsₘᵢₓ   17616.866162\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "\n",
    "pi= permutation_importance(grid, xtrain, ytrain,random_state=42)\n",
    "result=pi.importances_mean\n",
    "\n",
    "fi = pd.DataFrame(result, index= xtrain.columns, columns= ['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "print(fi)\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "\n",
    "sns.barplot(x=fi.index,y='importance',data=fi)\n",
    "plt.savefig('ANN.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d38ba97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0d059e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}