Part 2 - Beat IBB Index Using Deep Portfolio Theory.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2, Beat IBB Index\n",
    "\n",
    "The second part of application in the paper is to select a subset of stocks from IBB Index to beat the Index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "%matplotlib inline \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "\n",
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "from keras import regularizers\n",
    "from keras.models import load_model\n",
    "from sklearn.preprocessing import StandardScaler  \n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Load data **\n",
    "\n",
    "- `index_col = 0`: since first column is date (useless for training), let's keep it as index\n",
    "- `dropna(axis=1, how='any')`: drop any stock with any n/a (missing) data\n",
    "- `astype('float32')`: datatype\n",
    "\n",
    "Finally we only have **83** stocks to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load data\n",
    "\n",
    "# stock componenet data\n",
    "stock = defaultdict(defaultdict)\n",
    "\n",
    "stock_lp = pd.read_csv('data/last_price.csv', index_col=0).dropna(axis=1, how='any').astype('float32')\n",
    "stock['calibrate']['lp'] = stock_lp.iloc[0:104, :]\n",
    "stock['validate']['lp'] = stock_lp.iloc[104:, :]\n",
    "\n",
    "stock_net = pd.read_csv('data/net_change.csv', index_col=0).dropna(axis=1, how='any').astype('float32')\n",
    "stock['calibrate']['net'] = stock_net.iloc[0:104, :]\n",
    "stock['validate']['net'] = stock_net.iloc[104:, :]\n",
    "\n",
    "stock_percentage = pd.read_csv('data/percentage_change.csv', index_col=0).dropna(axis=1, how='any').astype('float32')\n",
    "stock['calibrate']['percentage'] = stock_percentage.iloc[0:104, :]\n",
    "stock['validate']['percentage'] = stock_percentage.iloc[104:, :]\n",
    "\n",
    "\n",
    "# ibb data\n",
    "ibb = defaultdict(defaultdict)\n",
    "ibb_full = pd.read_csv('data/ibb.csv', index_col=0).astype('float32')\n",
    "\n",
    "ibb_lp = ibb_full.iloc[:,0] # Series\n",
    "ibb['calibrate']['lp'] = ibb_lp[0:104]\n",
    "ibb['validate']['lp'] = ibb_lp[104:]\n",
    "\n",
    "ibb_net = ibb_full.iloc[:,1] # Series\n",
    "ibb['calibrate']['net'] = ibb_net[0:104]\n",
    "ibb['validate']['net'] = ibb_net[104:]\n",
    "\n",
    "ibb_percentage = ibb_full.iloc[:,2] # Series\n",
    "ibb['calibrate']['percentage'] = ibb_percentage[0:104]\n",
    "ibb['validate']['percentage'] = ibb_percentage[104:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 1. Auto-encoding\n",
    "\n",
    "1. Train an auto-encoder model compressing market information.\n",
    "2. Rank stocks by 2-norm difference then select subset of them as our portfolio.\n",
    "3. Page 7 of the paper: ReLU + linear as activation functions\n",
    "4. Weights $W = (W_1, W_2)$, L2 regularization\n",
    "\n",
    "\n",
    "Some problems I did not figure out (or the paper did not mention about the details):\n",
    "1. which weekly return/what data to be used? (what can represent best market information?)\n",
    "2. How to do the data preprocessing?\n",
    "3. How to perform 4-fold validation in Deep Learning?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "104/104 [==============================] - 1s 9ms/step - loss: 5.7999\n",
      "Epoch 2/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 5.6047\n",
      "Epoch 3/500\n",
      "104/104 [==============================] - 0s 237us/step - loss: 5.5153\n",
      "Epoch 4/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 5.4541\n",
      "Epoch 5/500\n",
      "104/104 [==============================] - 0s 268us/step - loss: 5.4033\n",
      "Epoch 6/500\n",
      "104/104 [==============================] - 0s 263us/step - loss: 5.3548\n",
      "Epoch 7/500\n",
      "104/104 [==============================] - 0s 238us/step - loss: 5.3053\n",
      "Epoch 8/500\n",
      "104/104 [==============================] - 0s 263us/step - loss: 5.2516\n",
      "Epoch 9/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 5.1890\n",
      "Epoch 10/500\n",
      "104/104 [==============================] - 0s 267us/step - loss: 5.1137\n",
      "Epoch 11/500\n",
      "104/104 [==============================] - 0s 256us/step - loss: 5.0237\n",
      "Epoch 12/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 4.9151\n",
      "Epoch 13/500\n",
      "104/104 [==============================] - 0s 272us/step - loss: 4.7915\n",
      "Epoch 14/500\n",
      "104/104 [==============================] - 0s 278us/step - loss: 4.6537\n",
      "Epoch 15/500\n",
      "104/104 [==============================] - 0s 302us/step - loss: 4.5069\n",
      "Epoch 16/500\n",
      "104/104 [==============================] - 0s 279us/step - loss: 4.3591\n",
      "Epoch 17/500\n",
      "104/104 [==============================] - 0s 291us/step - loss: 4.2174\n",
      "Epoch 18/500\n",
      "104/104 [==============================] - 0s 285us/step - loss: 4.0883\n",
      "Epoch 19/500\n",
      "104/104 [==============================] - 0s 256us/step - loss: 3.9758\n",
      "Epoch 20/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 3.8803\n",
      "Epoch 21/500\n",
      "104/104 [==============================] - 0s 266us/step - loss: 3.7998\n",
      "Epoch 22/500\n",
      "104/104 [==============================] - 0s 280us/step - loss: 3.7313\n",
      "Epoch 23/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 3.6720\n",
      "Epoch 24/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 3.6188\n",
      "Epoch 25/500\n",
      "104/104 [==============================] - 0s 282us/step - loss: 3.5682\n",
      "Epoch 26/500\n",
      "104/104 [==============================] - 0s 268us/step - loss: 3.5210\n",
      "Epoch 27/500\n",
      "104/104 [==============================] - 0s 287us/step - loss: 3.4763\n",
      "Epoch 28/500\n",
      "104/104 [==============================] - 0s 246us/step - loss: 3.4336\n",
      "Epoch 29/500\n",
      "104/104 [==============================] - 0s 270us/step - loss: 3.3930\n",
      "Epoch 30/500\n",
      "104/104 [==============================] - 0s 344us/step - loss: 3.3544\n",
      "Epoch 31/500\n",
      "104/104 [==============================] - 0s 434us/step - loss: 3.3181\n",
      "Epoch 32/500\n",
      "104/104 [==============================] - 0s 325us/step - loss: 3.2843\n",
      "Epoch 33/500\n",
      "104/104 [==============================] - 0s 328us/step - loss: 3.2530\n",
      "Epoch 34/500\n",
      "104/104 [==============================] - 0s 347us/step - loss: 3.2242\n",
      "Epoch 35/500\n",
      "104/104 [==============================] - 0s 300us/step - loss: 3.1980\n",
      "Epoch 36/500\n",
      "104/104 [==============================] - 0s 239us/step - loss: 3.1739\n",
      "Epoch 37/500\n",
      "104/104 [==============================] - 0s 266us/step - loss: 3.1519\n",
      "Epoch 38/500\n",
      "104/104 [==============================] - 0s 259us/step - loss: 3.1320\n",
      "Epoch 39/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 3.1138\n",
      "Epoch 40/500\n",
      "104/104 [==============================] - 0s 285us/step - loss: 3.0970\n",
      "Epoch 41/500\n",
      "104/104 [==============================] - 0s 252us/step - loss: 3.0816\n",
      "Epoch 42/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 3.0673\n",
      "Epoch 43/500\n",
      "104/104 [==============================] - 0s 262us/step - loss: 3.0539\n",
      "Epoch 44/500\n",
      "104/104 [==============================] - 0s 252us/step - loss: 3.0413\n",
      "Epoch 45/500\n",
      "104/104 [==============================] - 0s 243us/step - loss: 3.0294\n",
      "Epoch 46/500\n",
      "104/104 [==============================] - 0s 235us/step - loss: 3.0179\n",
      "Epoch 47/500\n",
      "104/104 [==============================] - 0s 278us/step - loss: 3.0069\n",
      "Epoch 48/500\n",
      "104/104 [==============================] - 0s 268us/step - loss: 2.9961\n",
      "Epoch 49/500\n",
      "104/104 [==============================] - 0s 246us/step - loss: 2.9856\n",
      "Epoch 50/500\n",
      "104/104 [==============================] - 0s 224us/step - loss: 2.9754\n",
      "Epoch 51/500\n",
      "104/104 [==============================] - 0s 248us/step - loss: 2.9653\n",
      "Epoch 52/500\n",
      "104/104 [==============================] - 0s 253us/step - loss: 2.9553\n",
      "Epoch 53/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 2.9454\n",
      "Epoch 54/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 2.9356\n",
      "Epoch 55/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 1.393 - 0s 237us/step - loss: 2.9258\n",
      "Epoch 56/500\n",
      "104/104 [==============================] - 0s 262us/step - loss: 2.9161\n",
      "Epoch 57/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 2.9064\n",
      "Epoch 58/500\n",
      "104/104 [==============================] - 0s 314us/step - loss: 2.8968\n",
      "Epoch 59/500\n",
      "104/104 [==============================] - 0s 426us/step - loss: 2.8872\n",
      "Epoch 60/500\n",
      "104/104 [==============================] - 0s 502us/step - loss: 2.8776\n",
      "Epoch 61/500\n",
      "104/104 [==============================] - 0s 253us/step - loss: 2.8680\n",
      "Epoch 62/500\n",
      "104/104 [==============================] - 0s 319us/step - loss: 2.8586\n",
      "Epoch 63/500\n",
      "104/104 [==============================] - 0s 299us/step - loss: 2.8491\n",
      "Epoch 64/500\n",
      "104/104 [==============================] - 0s 283us/step - loss: 2.8399\n",
      "Epoch 65/500\n",
      "104/104 [==============================] - 0s 262us/step - loss: 2.8306\n",
      "Epoch 66/500\n",
      "104/104 [==============================] - 0s 215us/step - loss: 2.8216\n",
      "Epoch 67/500\n",
      "104/104 [==============================] - 0s 246us/step - loss: 2.8126\n",
      "Epoch 68/500\n",
      "104/104 [==============================] - 0s 257us/step - loss: 2.8038\n",
      "Epoch 69/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 2.7952\n",
      "Epoch 70/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 2.7866\n",
      "Epoch 71/500\n",
      "104/104 [==============================] - 0s 237us/step - loss: 2.7783\n",
      "Epoch 72/500\n",
      "104/104 [==============================] - 0s 261us/step - loss: 2.7701\n",
      "Epoch 73/500\n",
      "104/104 [==============================] - 0s 241us/step - loss: 2.7620\n",
      "Epoch 74/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 2.7541\n",
      "Epoch 75/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 2.7463\n",
      "Epoch 76/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 2.7386\n",
      "Epoch 77/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 2.7311\n",
      "Epoch 78/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 2.7237\n",
      "Epoch 79/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 2.7165\n",
      "Epoch 80/500\n",
      "104/104 [==============================] - 0s 260us/step - loss: 2.7094\n",
      "Epoch 81/500\n",
      "104/104 [==============================] - 0s 256us/step - loss: 2.7024\n",
      "Epoch 82/500\n",
      "104/104 [==============================] - 0s 269us/step - loss: 2.6956\n",
      "Epoch 83/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 2.6889\n",
      "Epoch 84/500\n",
      "104/104 [==============================] - 0s 224us/step - loss: 2.6824\n",
      "Epoch 85/500\n",
      "104/104 [==============================] - 0s 294us/step - loss: 2.6759\n",
      "Epoch 86/500\n",
      "104/104 [==============================] - 0s 229us/step - loss: 2.6695\n",
      "Epoch 87/500\n",
      "104/104 [==============================] - 0s 261us/step - loss: 2.6633\n",
      "Epoch 88/500\n",
      "104/104 [==============================] - 0s 247us/step - loss: 2.6572\n",
      "Epoch 89/500\n",
      "104/104 [==============================] - 0s 225us/step - loss: 2.6511\n",
      "Epoch 90/500\n",
      "104/104 [==============================] - 0s 243us/step - loss: 2.6451\n",
      "Epoch 91/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 2.6392\n",
      "Epoch 92/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 2.6334\n",
      "Epoch 93/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 2.6277\n",
      "Epoch 94/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 2.6221\n",
      "Epoch 95/500\n",
      "104/104 [==============================] - 0s 223us/step - loss: 2.6166\n",
      "Epoch 96/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 2.6112\n",
      "Epoch 97/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 221us/step - loss: 2.6058\n",
      "Epoch 98/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 2.6005\n",
      "Epoch 99/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 2.5953\n",
      "Epoch 100/500\n",
      "104/104 [==============================] - 0s 277us/step - loss: 2.5901\n",
      "Epoch 101/500\n",
      "104/104 [==============================] - 0s 262us/step - loss: 2.5850\n",
      "Epoch 102/500\n",
      "104/104 [==============================] - 0s 283us/step - loss: 2.5799\n",
      "Epoch 103/500\n",
      "104/104 [==============================] - 0s 277us/step - loss: 2.5748\n",
      "Epoch 104/500\n",
      "104/104 [==============================] - 0s 270us/step - loss: 2.5698\n",
      "Epoch 105/500\n",
      "104/104 [==============================] - 0s 263us/step - loss: 2.5647\n",
      "Epoch 106/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 2.5598\n",
      "Epoch 107/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 2.5548\n",
      "Epoch 108/500\n",
      "104/104 [==============================] - 0s 268us/step - loss: 2.5499\n",
      "Epoch 109/500\n",
      "104/104 [==============================] - 0s 252us/step - loss: 2.5450\n",
      "Epoch 110/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 2.5402\n",
      "Epoch 111/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 2.5353\n",
      "Epoch 112/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 2.5305\n",
      "Epoch 113/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 2.5257\n",
      "Epoch 114/500\n",
      "104/104 [==============================] - 0s 247us/step - loss: 2.5210\n",
      "Epoch 115/500\n",
      "104/104 [==============================] - 0s 253us/step - loss: 2.5162\n",
      "Epoch 116/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 2.5115\n",
      "Epoch 117/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 2.5068\n",
      "Epoch 118/500\n",
      "104/104 [==============================] - 0s 295us/step - loss: 2.5022\n",
      "Epoch 119/500\n",
      "104/104 [==============================] - 0s 287us/step - loss: 2.4975\n",
      "Epoch 120/500\n",
      "104/104 [==============================] - 0s 231us/step - loss: 2.4930\n",
      "Epoch 121/500\n",
      "104/104 [==============================] - 0s 289us/step - loss: 2.4884\n",
      "Epoch 122/500\n",
      "104/104 [==============================] - 0s 288us/step - loss: 2.4840\n",
      "Epoch 123/500\n",
      "104/104 [==============================] - 0s 300us/step - loss: 2.4796\n",
      "Epoch 124/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 2.4752\n",
      "Epoch 125/500\n",
      "104/104 [==============================] - 0s 260us/step - loss: 2.4709\n",
      "Epoch 126/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 2.4666\n",
      "Epoch 127/500\n",
      "104/104 [==============================] - 0s 289us/step - loss: 2.4624\n",
      "Epoch 128/500\n",
      "104/104 [==============================] - 0s 261us/step - loss: 2.4583\n",
      "Epoch 129/500\n",
      "104/104 [==============================] - 0s 240us/step - loss: 2.4542\n",
      "Epoch 130/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 1.339 - 0s 252us/step - loss: 2.4501\n",
      "Epoch 131/500\n",
      "104/104 [==============================] - 0s 258us/step - loss: 2.4461\n",
      "Epoch 132/500\n",
      "104/104 [==============================] - 0s 266us/step - loss: 2.4421\n",
      "Epoch 133/500\n",
      "104/104 [==============================] - 0s 239us/step - loss: 2.4382\n",
      "Epoch 134/500\n",
      "104/104 [==============================] - 0s 253us/step - loss: 2.4344\n",
      "Epoch 135/500\n",
      "104/104 [==============================] - 0s 262us/step - loss: 2.4307\n",
      "Epoch 136/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 2.4271\n",
      "Epoch 137/500\n",
      "104/104 [==============================] - 0s 321us/step - loss: 2.4235\n",
      "Epoch 138/500\n",
      "104/104 [==============================] - 0s 262us/step - loss: 2.4200\n",
      "Epoch 139/500\n",
      "104/104 [==============================] - 0s 258us/step - loss: 2.4166\n",
      "Epoch 140/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 2.4132\n",
      "Epoch 141/500\n",
      "104/104 [==============================] - 0s 254us/step - loss: 2.4099\n",
      "Epoch 142/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 2.4067\n",
      "Epoch 143/500\n",
      "104/104 [==============================] - 0s 270us/step - loss: 2.4036\n",
      "Epoch 144/500\n",
      "104/104 [==============================] - 0s 255us/step - loss: 2.4006\n",
      "Epoch 145/500\n",
      "104/104 [==============================] - 0s 292us/step - loss: 2.3976\n",
      "Epoch 146/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 2.3947\n",
      "Epoch 147/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 2.3918\n",
      "Epoch 148/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 2.3889\n",
      "Epoch 149/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 2.3861\n",
      "Epoch 150/500\n",
      "104/104 [==============================] - 0s 257us/step - loss: 2.3833\n",
      "Epoch 151/500\n",
      "104/104 [==============================] - 0s 261us/step - loss: 2.3806\n",
      "Epoch 152/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 2.3780\n",
      "Epoch 153/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 2.3755\n",
      "Epoch 154/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 1.335 - 0s 223us/step - loss: 2.3730\n",
      "Epoch 155/500\n",
      "104/104 [==============================] - 0s 215us/step - loss: 2.3706\n",
      "Epoch 156/500\n",
      "104/104 [==============================] - 0s 261us/step - loss: 2.3682\n",
      "Epoch 157/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 2.3659\n",
      "Epoch 158/500\n",
      "104/104 [==============================] - 0s 240us/step - loss: 2.3637\n",
      "Epoch 159/500\n",
      "104/104 [==============================] - 0s 241us/step - loss: 2.3615\n",
      "Epoch 160/500\n",
      "104/104 [==============================] - 0s 229us/step - loss: 2.3593\n",
      "Epoch 161/500\n",
      "104/104 [==============================] - 0s 262us/step - loss: 2.3572\n",
      "Epoch 162/500\n",
      "104/104 [==============================] - 0s 215us/step - loss: 2.3552\n",
      "Epoch 163/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 2.3531\n",
      "Epoch 164/500\n",
      "104/104 [==============================] - 0s 260us/step - loss: 2.3512\n",
      "Epoch 165/500\n",
      "104/104 [==============================] - 0s 331us/step - loss: 2.3492\n",
      "Epoch 166/500\n",
      "104/104 [==============================] - 0s 294us/step - loss: 2.3473\n",
      "Epoch 167/500\n",
      "104/104 [==============================] - 0s 420us/step - loss: 2.3455\n",
      "Epoch 168/500\n",
      "104/104 [==============================] - 0s 288us/step - loss: 2.3436\n",
      "Epoch 169/500\n",
      "104/104 [==============================] - 0s 301us/step - loss: 2.3419\n",
      "Epoch 170/500\n",
      "104/104 [==============================] - 0s 274us/step - loss: 2.3401\n",
      "Epoch 171/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 2.3384\n",
      "Epoch 172/500\n",
      "104/104 [==============================] - 0s 356us/step - loss: 2.3368\n",
      "Epoch 173/500\n",
      "104/104 [==============================] - 0s 238us/step - loss: 2.3351\n",
      "Epoch 174/500\n",
      "104/104 [==============================] - 0s 443us/step - loss: 2.3335\n",
      "Epoch 175/500\n",
      "104/104 [==============================] - 0s 300us/step - loss: 2.3317\n",
      "Epoch 176/500\n",
      "104/104 [==============================] - 0s 340us/step - loss: 2.3300\n",
      "Epoch 177/500\n",
      "104/104 [==============================] - 0s 343us/step - loss: 2.3283\n",
      "Epoch 178/500\n",
      "104/104 [==============================] - 0s 259us/step - loss: 2.3266\n",
      "Epoch 179/500\n",
      "104/104 [==============================] - 0s 366us/step - loss: 2.3250\n",
      "Epoch 180/500\n",
      "104/104 [==============================] - 0s 280us/step - loss: 2.3234\n",
      "Epoch 181/500\n",
      "104/104 [==============================] - 0s 225us/step - loss: 2.3219\n",
      "Epoch 182/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 2.3203\n",
      "Epoch 183/500\n",
      "104/104 [==============================] - 0s 231us/step - loss: 2.3189\n",
      "Epoch 184/500\n",
      "104/104 [==============================] - 0s 225us/step - loss: 2.3174\n",
      "Epoch 185/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 2.3160\n",
      "Epoch 186/500\n",
      "104/104 [==============================] - 0s 244us/step - loss: 2.3147\n",
      "Epoch 187/500\n",
      "104/104 [==============================] - 0s 268us/step - loss: 2.3133\n",
      "Epoch 188/500\n",
      "104/104 [==============================] - 0s 235us/step - loss: 2.3120\n",
      "Epoch 189/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 2.3107\n",
      "Epoch 190/500\n",
      "104/104 [==============================] - 0s 233us/step - loss: 2.3094\n",
      "Epoch 191/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 262us/step - loss: 2.3081\n",
      "Epoch 192/500\n",
      "104/104 [==============================] - 0s 256us/step - loss: 2.3068\n",
      "Epoch 193/500\n",
      "104/104 [==============================] - 0s 269us/step - loss: 2.3056\n",
      "Epoch 194/500\n",
      "104/104 [==============================] - 0s 266us/step - loss: 2.3043\n",
      "Epoch 195/500\n",
      "104/104 [==============================] - 0s 235us/step - loss: 2.3030\n",
      "Epoch 196/500\n",
      "104/104 [==============================] - 0s 246us/step - loss: 2.3018\n",
      "Epoch 197/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 2.3006\n",
      "Epoch 198/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 2.2994\n",
      "Epoch 199/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 2.2983\n",
      "Epoch 200/500\n",
      "104/104 [==============================] - 0s 240us/step - loss: 2.2971\n",
      "Epoch 201/500\n",
      "104/104 [==============================] - 0s 247us/step - loss: 2.2960\n",
      "Epoch 202/500\n",
      "104/104 [==============================] - 0s 282us/step - loss: 2.2950\n",
      "Epoch 203/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 2.2939\n",
      "Epoch 204/500\n",
      "104/104 [==============================] - 0s 260us/step - loss: 2.2928\n",
      "Epoch 205/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 2.2918\n",
      "Epoch 206/500\n",
      "104/104 [==============================] - 0s 261us/step - loss: 2.2908\n",
      "Epoch 207/500\n",
      "104/104 [==============================] - 0s 264us/step - loss: 2.2898\n",
      "Epoch 208/500\n",
      "104/104 [==============================] - 0s 264us/step - loss: 2.2888\n",
      "Epoch 209/500\n",
      "104/104 [==============================] - 0s 269us/step - loss: 2.2879\n",
      "Epoch 210/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 2.2870\n",
      "Epoch 211/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 2.2861\n",
      "Epoch 212/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 2.2852\n",
      "Epoch 213/500\n",
      "104/104 [==============================] - 0s 231us/step - loss: 2.2843\n",
      "Epoch 214/500\n",
      "104/104 [==============================] - 0s 412us/step - loss: 2.2834\n",
      "Epoch 215/500\n",
      "104/104 [==============================] - 0s 513us/step - loss: 2.2826\n",
      "Epoch 216/500\n",
      "104/104 [==============================] - 0s 401us/step - loss: 2.2818\n",
      "Epoch 217/500\n",
      "104/104 [==============================] - 0s 302us/step - loss: 2.2810\n",
      "Epoch 218/500\n",
      "104/104 [==============================] - 0s 336us/step - loss: 2.2802\n",
      "Epoch 219/500\n",
      "104/104 [==============================] - 0s 295us/step - loss: 2.2794\n",
      "Epoch 220/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 2.2786\n",
      "Epoch 221/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 2.2779\n",
      "Epoch 222/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 2.2771\n",
      "Epoch 223/500\n",
      "104/104 [==============================] - 0s 354us/step - loss: 2.2764\n",
      "Epoch 224/500\n",
      "104/104 [==============================] - 0s 440us/step - loss: 2.2757\n",
      "Epoch 225/500\n",
      "104/104 [==============================] - 0s 314us/step - loss: 2.2750\n",
      "Epoch 226/500\n",
      "104/104 [==============================] - 0s 265us/step - loss: 2.2743\n",
      "Epoch 227/500\n",
      "104/104 [==============================] - 0s 255us/step - loss: 2.2736\n",
      "Epoch 228/500\n",
      "104/104 [==============================] - 0s 272us/step - loss: 2.2729\n",
      "Epoch 229/500\n",
      "104/104 [==============================] - 0s 448us/step - loss: 2.2722\n",
      "Epoch 230/500\n",
      "104/104 [==============================] - 0s 399us/step - loss: 2.2716\n",
      "Epoch 231/500\n",
      "104/104 [==============================] - 0s 415us/step - loss: 2.2709\n",
      "Epoch 232/500\n",
      "104/104 [==============================] - 0s 726us/step - loss: 2.2703\n",
      "Epoch 233/500\n",
      "104/104 [==============================] - 0s 574us/step - loss: 2.2697\n",
      "Epoch 234/500\n",
      "104/104 [==============================] - 0s 677us/step - loss: 2.2691\n",
      "Epoch 235/500\n",
      "104/104 [==============================] - 0s 340us/step - loss: 2.2685\n",
      "Epoch 236/500\n",
      "104/104 [==============================] - 0s 290us/step - loss: 2.2679\n",
      "Epoch 237/500\n",
      "104/104 [==============================] - 0s 353us/step - loss: 2.2673\n",
      "Epoch 238/500\n",
      "104/104 [==============================] - 0s 407us/step - loss: 2.2667\n",
      "Epoch 239/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 2.2662\n",
      "Epoch 240/500\n",
      "104/104 [==============================] - 0s 459us/step - loss: 2.2656\n",
      "Epoch 241/500\n",
      "104/104 [==============================] - 0s 399us/step - loss: 2.2651\n",
      "Epoch 242/500\n",
      "104/104 [==============================] - 0s 240us/step - loss: 2.2645\n",
      "Epoch 243/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 2.2640\n",
      "Epoch 244/500\n",
      "104/104 [==============================] - 0s 229us/step - loss: 2.2634\n",
      "Epoch 245/500\n",
      "104/104 [==============================] - 0s 424us/step - loss: 2.2629\n",
      "Epoch 246/500\n",
      "104/104 [==============================] - 0s 678us/step - loss: 2.2624\n",
      "Epoch 247/500\n",
      "104/104 [==============================] - 0s 300us/step - loss: 2.2619\n",
      "Epoch 248/500\n",
      "104/104 [==============================] - 0s 309us/step - loss: 2.2614\n",
      "Epoch 249/500\n",
      "104/104 [==============================] - 0s 296us/step - loss: 2.2609\n",
      "Epoch 250/500\n",
      "104/104 [==============================] - 0s 402us/step - loss: 2.2605\n",
      "Epoch 251/500\n",
      "104/104 [==============================] - 0s 283us/step - loss: 2.2600\n",
      "Epoch 252/500\n",
      "104/104 [==============================] - 0s 438us/step - loss: 2.2595\n",
      "Epoch 253/500\n",
      "104/104 [==============================] - 0s 358us/step - loss: 2.2591\n",
      "Epoch 254/500\n",
      "104/104 [==============================] - 0s 359us/step - loss: 2.2586\n",
      "Epoch 255/500\n",
      "104/104 [==============================] - 0s 298us/step - loss: 2.2582\n",
      "Epoch 256/500\n",
      "104/104 [==============================] - 0s 325us/step - loss: 2.2577\n",
      "Epoch 257/500\n",
      "104/104 [==============================] - 0s 485us/step - loss: 2.2573\n",
      "Epoch 258/500\n",
      "104/104 [==============================] - 0s 337us/step - loss: 2.2569\n",
      "Epoch 259/500\n",
      "104/104 [==============================] - 0s 375us/step - loss: 2.2565\n",
      "Epoch 260/500\n",
      "104/104 [==============================] - 0s 327us/step - loss: 2.2561\n",
      "Epoch 261/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 2.2557\n",
      "Epoch 262/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 2.2553\n",
      "Epoch 263/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 2.2549\n",
      "Epoch 264/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 2.2545\n",
      "Epoch 265/500\n",
      "104/104 [==============================] - 0s 480us/step - loss: 2.2541\n",
      "Epoch 266/500\n",
      "104/104 [==============================] - 0s 431us/step - loss: 2.2537\n",
      "Epoch 267/500\n",
      "104/104 [==============================] - 0s 331us/step - loss: 2.2534\n",
      "Epoch 268/500\n",
      "104/104 [==============================] - 0s 268us/step - loss: 2.2530\n",
      "Epoch 269/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 2.2526\n",
      "Epoch 270/500\n",
      "104/104 [==============================] - 0s 316us/step - loss: 2.2523\n",
      "Epoch 271/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 2.2519\n",
      "Epoch 272/500\n",
      "104/104 [==============================] - 0s 283us/step - loss: 2.2516\n",
      "Epoch 273/500\n",
      "104/104 [==============================] - 0s 275us/step - loss: 2.2512\n",
      "Epoch 274/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 2.2509\n",
      "Epoch 275/500\n",
      "104/104 [==============================] - 0s 294us/step - loss: 2.2506\n",
      "Epoch 276/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 2.2502\n",
      "Epoch 277/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 2.2499\n",
      "Epoch 278/500\n",
      "104/104 [==============================] - 0s 315us/step - loss: 2.2496\n",
      "Epoch 279/500\n",
      "104/104 [==============================] - 0s 698us/step - loss: 2.2493\n",
      "Epoch 280/500\n",
      "104/104 [==============================] - 0s 241us/step - loss: 2.2490\n",
      "Epoch 281/500\n",
      "104/104 [==============================] - 0s 258us/step - loss: 2.2487\n",
      "Epoch 282/500\n",
      "104/104 [==============================] - 0s 229us/step - loss: 2.2483\n",
      "Epoch 283/500\n",
      "104/104 [==============================] - 0s 255us/step - loss: 2.2480\n",
      "Epoch 284/500\n",
      "104/104 [==============================] - 0s 362us/step - loss: 2.2477\n",
      "Epoch 285/500\n",
      "104/104 [==============================] - 0s 239us/step - loss: 2.2474\n",
      "Epoch 286/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 202us/step - loss: 2.2471\n",
      "Epoch 287/500\n",
      "104/104 [==============================] - 0s 305us/step - loss: 2.2468\n",
      "Epoch 288/500\n",
      "104/104 [==============================] - 0s 247us/step - loss: 2.2466\n",
      "Epoch 289/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 2.2463\n",
      "Epoch 290/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 2.2460\n",
      "Epoch 291/500\n",
      "104/104 [==============================] - 0s 213us/step - loss: 2.2457\n",
      "Epoch 292/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 2.2454\n",
      "Epoch 293/500\n",
      "104/104 [==============================] - 0s 754us/step - loss: 2.2452\n",
      "Epoch 294/500\n",
      "104/104 [==============================] - 0s 344us/step - loss: 2.2449\n",
      "Epoch 295/500\n",
      "104/104 [==============================] - 0s 325us/step - loss: 2.2446\n",
      "Epoch 296/500\n",
      "104/104 [==============================] - 0s 394us/step - loss: 2.2444\n",
      "Epoch 297/500\n",
      "104/104 [==============================] - 0s 509us/step - loss: 2.2441\n",
      "Epoch 298/500\n",
      "104/104 [==============================] - 0s 258us/step - loss: 2.2439\n",
      "Epoch 299/500\n",
      "104/104 [==============================] - 0s 285us/step - loss: 2.2436\n",
      "Epoch 300/500\n",
      "104/104 [==============================] - 0s 282us/step - loss: 2.2433\n",
      "Epoch 301/500\n",
      "104/104 [==============================] - 0s 298us/step - loss: 2.2431\n",
      "Epoch 302/500\n",
      "104/104 [==============================] - 0s 466us/step - loss: 2.2428\n",
      "Epoch 303/500\n",
      "104/104 [==============================] - 0s 788us/step - loss: 2.2426\n",
      "Epoch 304/500\n",
      "104/104 [==============================] - 0s 364us/step - loss: 2.2424\n",
      "Epoch 305/500\n",
      "104/104 [==============================] - 0s 293us/step - loss: 2.2421\n",
      "Epoch 306/500\n",
      "104/104 [==============================] - 0s 320us/step - loss: 2.2419\n",
      "Epoch 307/500\n",
      "104/104 [==============================] - 0s 233us/step - loss: 2.2416\n",
      "Epoch 308/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 2.2414\n",
      "Epoch 309/500\n",
      "104/104 [==============================] - 0s 276us/step - loss: 2.2412\n",
      "Epoch 310/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 2.2410\n",
      "Epoch 311/500\n",
      "104/104 [==============================] - 0s 225us/step - loss: 2.2407\n",
      "Epoch 312/500\n",
      "104/104 [==============================] - 0s 370us/step - loss: 2.2405\n",
      "Epoch 313/500\n",
      "104/104 [==============================] - 0s 684us/step - loss: 2.2403\n",
      "Epoch 314/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 1.315 - 0s 478us/step - loss: 2.2401\n",
      "Epoch 315/500\n",
      "104/104 [==============================] - 0s 335us/step - loss: 2.2398\n",
      "Epoch 316/500\n",
      "104/104 [==============================] - 0s 338us/step - loss: 2.2396\n",
      "Epoch 317/500\n",
      "104/104 [==============================] - 0s 427us/step - loss: 2.2394\n",
      "Epoch 318/500\n",
      "104/104 [==============================] - 0s 688us/step - loss: 2.2392\n",
      "Epoch 319/500\n",
      "104/104 [==============================] - 0s 522us/step - loss: 2.2390\n",
      "Epoch 320/500\n",
      "104/104 [==============================] - 0s 558us/step - loss: 2.2388\n",
      "Epoch 321/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 2.2386\n",
      "Epoch 322/500\n",
      "104/104 [==============================] - 0s 256us/step - loss: 2.2383\n",
      "Epoch 323/500\n",
      "104/104 [==============================] - 0s 296us/step - loss: 2.2381\n",
      "Epoch 324/500\n",
      "104/104 [==============================] - 0s 261us/step - loss: 2.2378\n",
      "Epoch 325/500\n",
      "104/104 [==============================] - 0s 264us/step - loss: 2.2376\n",
      "Epoch 326/500\n",
      "104/104 [==============================] - 0s 268us/step - loss: 2.2373\n",
      "Epoch 327/500\n",
      "104/104 [==============================] - 0s 365us/step - loss: 2.2371\n",
      "Epoch 328/500\n",
      "104/104 [==============================] - 0s 272us/step - loss: 2.2369\n",
      "Epoch 329/500\n",
      "104/104 [==============================] - 0s 252us/step - loss: 2.2366\n",
      "Epoch 330/500\n",
      "104/104 [==============================] - 0s 280us/step - loss: 2.2364\n",
      "Epoch 331/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 2.2362\n",
      "Epoch 332/500\n",
      "104/104 [==============================] - 0s 272us/step - loss: 2.2360\n",
      "Epoch 333/500\n",
      "104/104 [==============================] - 0s 274us/step - loss: 2.2358\n",
      "Epoch 334/500\n",
      "104/104 [==============================] - 0s 455us/step - loss: 2.2355\n",
      "Epoch 335/500\n",
      "104/104 [==============================] - 0s 412us/step - loss: 2.2353\n",
      "Epoch 336/500\n",
      "104/104 [==============================] - 0s 385us/step - loss: 2.2351\n",
      "Epoch 337/500\n",
      "104/104 [==============================] - 0s 302us/step - loss: 2.2348\n",
      "Epoch 338/500\n",
      "104/104 [==============================] - 0s 254us/step - loss: 2.2346\n",
      "Epoch 339/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 2.2344\n",
      "Epoch 340/500\n",
      "104/104 [==============================] - 0s 281us/step - loss: 2.2341\n",
      "Epoch 341/500\n",
      "104/104 [==============================] - 0s 311us/step - loss: 2.2339\n",
      "Epoch 342/500\n",
      "104/104 [==============================] - 0s 252us/step - loss: 2.2337\n",
      "Epoch 343/500\n",
      "104/104 [==============================] - 0s 271us/step - loss: 2.2335\n",
      "Epoch 344/500\n",
      "104/104 [==============================] - 0s 327us/step - loss: 2.2333\n",
      "Epoch 345/500\n",
      "104/104 [==============================] - 0s 341us/step - loss: 2.2330\n",
      "Epoch 346/500\n",
      "104/104 [==============================] - 0s 275us/step - loss: 2.2328\n",
      "Epoch 347/500\n",
      "104/104 [==============================] - 0s 311us/step - loss: 2.2326\n",
      "Epoch 348/500\n",
      "104/104 [==============================] - 0s 331us/step - loss: 2.2324\n",
      "Epoch 349/500\n",
      "104/104 [==============================] - 0s 313us/step - loss: 2.2322\n",
      "Epoch 350/500\n",
      "104/104 [==============================] - 0s 383us/step - loss: 2.2320\n",
      "Epoch 351/500\n",
      "104/104 [==============================] - 0s 331us/step - loss: 2.2318\n",
      "Epoch 352/500\n",
      "104/104 [==============================] - 0s 300us/step - loss: 2.2316\n",
      "Epoch 353/500\n",
      "104/104 [==============================] - 0s 306us/step - loss: 2.2314\n",
      "Epoch 354/500\n",
      "104/104 [==============================] - 0s 422us/step - loss: 2.2312\n",
      "Epoch 355/500\n",
      "104/104 [==============================] - 0s 596us/step - loss: 2.2310\n",
      "Epoch 356/500\n",
      "104/104 [==============================] - 0s 887us/step - loss: 2.2308\n",
      "Epoch 357/500\n",
      "104/104 [==============================] - 0s 298us/step - loss: 2.2307\n",
      "Epoch 358/500\n",
      "104/104 [==============================] - 0s 520us/step - loss: 2.2305\n",
      "Epoch 359/500\n",
      "104/104 [==============================] - 0s 290us/step - loss: 2.2303\n",
      "Epoch 360/500\n",
      "104/104 [==============================] - 0s 262us/step - loss: 2.2301\n",
      "Epoch 361/500\n",
      "104/104 [==============================] - 0s 255us/step - loss: 2.2299\n",
      "Epoch 362/500\n",
      "104/104 [==============================] - 0s 317us/step - loss: 2.2297\n",
      "Epoch 363/500\n",
      "104/104 [==============================] - 0s 260us/step - loss: 2.2295\n",
      "Epoch 364/500\n",
      "104/104 [==============================] - 0s 578us/step - loss: 2.2293\n",
      "Epoch 365/500\n",
      "104/104 [==============================] - 0s 393us/step - loss: 2.2291\n",
      "Epoch 366/500\n",
      "104/104 [==============================] - 0s 340us/step - loss: 2.2290\n",
      "Epoch 367/500\n",
      "104/104 [==============================] - 0s 545us/step - loss: 2.2288\n",
      "Epoch 368/500\n",
      "104/104 [==============================] - 0s 411us/step - loss: 2.2286\n",
      "Epoch 369/500\n",
      "104/104 [==============================] - 0s 359us/step - loss: 2.2284\n",
      "Epoch 370/500\n",
      "104/104 [==============================] - 0s 394us/step - loss: 2.2282\n",
      "Epoch 371/500\n",
      "104/104 [==============================] - 0s 347us/step - loss: 2.2280\n",
      "Epoch 372/500\n",
      "104/104 [==============================] - 0s 268us/step - loss: 2.2279\n",
      "Epoch 373/500\n",
      "104/104 [==============================] - 0s 265us/step - loss: 2.2277\n",
      "Epoch 374/500\n",
      "104/104 [==============================] - 0s 301us/step - loss: 2.2275\n",
      "Epoch 375/500\n",
      "104/104 [==============================] - 0s 307us/step - loss: 2.2274\n",
      "Epoch 376/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 1.303 - 0s 287us/step - loss: 2.2272\n",
      "Epoch 377/500\n",
      "104/104 [==============================] - 0s 292us/step - loss: 2.2270\n",
      "Epoch 378/500\n",
      "104/104 [==============================] - 0s 269us/step - loss: 2.2269\n",
      "Epoch 379/500\n",
      "104/104 [==============================] - 0s 312us/step - loss: 2.2267\n",
      "Epoch 380/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 284us/step - loss: 2.2265\n",
      "Epoch 381/500\n",
      "104/104 [==============================] - 0s 288us/step - loss: 2.2264\n",
      "Epoch 382/500\n",
      "104/104 [==============================] - 0s 288us/step - loss: 2.2262\n",
      "Epoch 383/500\n",
      "104/104 [==============================] - 0s 274us/step - loss: 2.2261\n",
      "Epoch 384/500\n",
      "104/104 [==============================] - 0s 229us/step - loss: 2.2259\n",
      "Epoch 385/500\n",
      "104/104 [==============================] - 0s 265us/step - loss: 2.2257\n",
      "Epoch 386/500\n",
      "104/104 [==============================] - 0s 275us/step - loss: 2.2256\n",
      "Epoch 387/500\n",
      "104/104 [==============================] - 0s 270us/step - loss: 2.2254\n",
      "Epoch 388/500\n",
      "104/104 [==============================] - 0s 253us/step - loss: 2.2253\n",
      "Epoch 389/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 2.2251\n",
      "Epoch 390/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 2.2250\n",
      "Epoch 391/500\n",
      "104/104 [==============================] - 0s 296us/step - loss: 2.2248\n",
      "Epoch 392/500\n",
      "104/104 [==============================] - 0s 280us/step - loss: 2.2247\n",
      "Epoch 393/500\n",
      "104/104 [==============================] - 0s 284us/step - loss: 2.2246\n",
      "Epoch 394/500\n",
      "104/104 [==============================] - 0s 256us/step - loss: 2.2244\n",
      "Epoch 395/500\n",
      "104/104 [==============================] - 0s 231us/step - loss: 2.2243\n",
      "Epoch 396/500\n",
      "104/104 [==============================] - 0s 281us/step - loss: 2.2242\n",
      "Epoch 397/500\n",
      "104/104 [==============================] - 0s 271us/step - loss: 2.2240\n",
      "Epoch 398/500\n",
      "104/104 [==============================] - 0s 271us/step - loss: 2.2239\n",
      "Epoch 399/500\n",
      "104/104 [==============================] - 0s 403us/step - loss: 2.2238\n",
      "Epoch 400/500\n",
      "104/104 [==============================] - 0s 343us/step - loss: 2.2236\n",
      "Epoch 401/500\n",
      "104/104 [==============================] - 0s 755us/step - loss: 2.2235\n",
      "Epoch 402/500\n",
      "104/104 [==============================] - 0s 334us/step - loss: 2.2234\n",
      "Epoch 403/500\n",
      "104/104 [==============================] - 0s 358us/step - loss: 2.2233\n",
      "Epoch 404/500\n",
      "104/104 [==============================] - 0s 265us/step - loss: 2.2231\n",
      "Epoch 405/500\n",
      "104/104 [==============================] - 0s 302us/step - loss: 2.2230\n",
      "Epoch 406/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 2.2229\n",
      "Epoch 407/500\n",
      "104/104 [==============================] - 0s 243us/step - loss: 2.2228\n",
      "Epoch 408/500\n",
      "104/104 [==============================] - 0s 256us/step - loss: 2.2226\n",
      "Epoch 409/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 2.2225\n",
      "Epoch 410/500\n",
      "104/104 [==============================] - 0s 320us/step - loss: 2.2224\n",
      "Epoch 411/500\n",
      "104/104 [==============================] - 0s 341us/step - loss: 2.2223\n",
      "Epoch 412/500\n",
      "104/104 [==============================] - 0s 314us/step - loss: 2.2222\n",
      "Epoch 413/500\n",
      "104/104 [==============================] - 0s 296us/step - loss: 2.2221\n",
      "Epoch 414/500\n",
      "104/104 [==============================] - 0s 306us/step - loss: 2.2219\n",
      "Epoch 415/500\n",
      "104/104 [==============================] - 0s 257us/step - loss: 2.2218\n",
      "Epoch 416/500\n",
      "104/104 [==============================] - 0s 314us/step - loss: 2.2217\n",
      "Epoch 417/500\n",
      "104/104 [==============================] - 0s 296us/step - loss: 2.2216\n",
      "Epoch 418/500\n",
      "104/104 [==============================] - 0s 232us/step - loss: 2.2215\n",
      "Epoch 419/500\n",
      "104/104 [==============================] - 0s 264us/step - loss: 2.2214\n",
      "Epoch 420/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 2.2213\n",
      "Epoch 421/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 2.2212\n",
      "Epoch 422/500\n",
      "104/104 [==============================] - 0s 283us/step - loss: 2.2210\n",
      "Epoch 423/500\n",
      "104/104 [==============================] - 0s 306us/step - loss: 2.2209\n",
      "Epoch 424/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 2.2208\n",
      "Epoch 425/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 1.295 - 0s 244us/step - loss: 2.2207\n",
      "Epoch 426/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 2.2206\n",
      "Epoch 427/500\n",
      "104/104 [==============================] - 0s 264us/step - loss: 2.2205\n",
      "Epoch 428/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 2.2204\n",
      "Epoch 429/500\n",
      "104/104 [==============================] - 0s 357us/step - loss: 2.2203\n",
      "Epoch 430/500\n",
      "104/104 [==============================] - 0s 345us/step - loss: 2.2202\n",
      "Epoch 431/500\n",
      "104/104 [==============================] - 0s 490us/step - loss: 2.2201\n",
      "Epoch 432/500\n",
      "104/104 [==============================] - 0s 447us/step - loss: 2.2200\n",
      "Epoch 433/500\n",
      "104/104 [==============================] - 0s 307us/step - loss: 2.2199\n",
      "Epoch 434/500\n",
      "104/104 [==============================] - 0s 280us/step - loss: 2.2198\n",
      "Epoch 435/500\n",
      "104/104 [==============================] - 0s 319us/step - loss: 2.2197\n",
      "Epoch 436/500\n",
      "104/104 [==============================] - 0s 331us/step - loss: 2.2196\n",
      "Epoch 437/500\n",
      "104/104 [==============================] - 0s 348us/step - loss: 2.2195\n",
      "Epoch 438/500\n",
      "104/104 [==============================] - 0s 304us/step - loss: 2.2194\n",
      "Epoch 439/500\n",
      "104/104 [==============================] - 0s 327us/step - loss: 2.2193\n",
      "Epoch 440/500\n",
      "104/104 [==============================] - 0s 437us/step - loss: 2.2192\n",
      "Epoch 441/500\n",
      "104/104 [==============================] - 0s 388us/step - loss: 2.2192\n",
      "Epoch 442/500\n",
      "104/104 [==============================] - 0s 318us/step - loss: 2.2191\n",
      "Epoch 443/500\n",
      "104/104 [==============================] - 0s 280us/step - loss: 2.2190\n",
      "Epoch 444/500\n",
      "104/104 [==============================] - 0s 290us/step - loss: 2.2189\n",
      "Epoch 445/500\n",
      "104/104 [==============================] - 0s 271us/step - loss: 2.2188\n",
      "Epoch 446/500\n",
      "104/104 [==============================] - 0s 286us/step - loss: 2.2186\n",
      "Epoch 447/500\n",
      "104/104 [==============================] - 0s 264us/step - loss: 2.2185\n",
      "Epoch 448/500\n",
      "104/104 [==============================] - 0s 265us/step - loss: 2.2184\n",
      "Epoch 449/500\n",
      "104/104 [==============================] - 0s 475us/step - loss: 2.2183\n",
      "Epoch 450/500\n",
      "104/104 [==============================] - 0s 908us/step - loss: 2.2182\n",
      "Epoch 451/500\n",
      "104/104 [==============================] - 0s 276us/step - loss: 2.2181\n",
      "Epoch 452/500\n",
      "104/104 [==============================] - 0s 315us/step - loss: 2.2180\n",
      "Epoch 453/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 1.293 - 0s 341us/step - loss: 2.2179\n",
      "Epoch 454/500\n",
      "104/104 [==============================] - 0s 358us/step - loss: 2.2178\n",
      "Epoch 455/500\n",
      "104/104 [==============================] - 0s 400us/step - loss: 2.2177\n",
      "Epoch 456/500\n",
      "104/104 [==============================] - 0s 400us/step - loss: 2.2176\n",
      "Epoch 457/500\n",
      "104/104 [==============================] - 0s 622us/step - loss: 2.2175\n",
      "Epoch 458/500\n",
      "104/104 [==============================] - 0s 311us/step - loss: 2.2174\n",
      "Epoch 459/500\n",
      "104/104 [==============================] - 0s 225us/step - loss: 2.2173\n",
      "Epoch 460/500\n",
      "104/104 [==============================] - 0s 271us/step - loss: 2.2172\n",
      "Epoch 461/500\n",
      "104/104 [==============================] - 0s 328us/step - loss: 2.2171\n",
      "Epoch 462/500\n",
      "104/104 [==============================] - 0s 316us/step - loss: 2.2170\n",
      "Epoch 463/500\n",
      "104/104 [==============================] - 0s 273us/step - loss: 2.2169\n",
      "Epoch 464/500\n",
      "104/104 [==============================] - 0s 307us/step - loss: 2.2168\n",
      "Epoch 465/500\n",
      "104/104 [==============================] - 0s 323us/step - loss: 2.2167\n",
      "Epoch 466/500\n",
      "104/104 [==============================] - 0s 233us/step - loss: 2.2166\n",
      "Epoch 467/500\n",
      "104/104 [==============================] - 0s 279us/step - loss: 2.2165\n",
      "Epoch 468/500\n",
      "104/104 [==============================] - 0s 452us/step - loss: 2.2164\n",
      "Epoch 469/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 2.2163\n",
      "Epoch 470/500\n",
      "104/104 [==============================] - 0s 296us/step - loss: 2.2162\n",
      "Epoch 471/500\n",
      "104/104 [==============================] - 0s 346us/step - loss: 2.2161\n",
      "Epoch 472/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 1.291 - 0s 364us/step - loss: 2.2160\n",
      "Epoch 473/500\n",
      "104/104 [==============================] - 0s 567us/step - loss: 2.2159\n",
      "Epoch 474/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 400us/step - loss: 2.2158\n",
      "Epoch 475/500\n",
      "104/104 [==============================] - 0s 281us/step - loss: 2.2157\n",
      "Epoch 476/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 2.2156\n",
      "Epoch 477/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 2.2155\n",
      "Epoch 478/500\n",
      "104/104 [==============================] - 0s 239us/step - loss: 2.2154\n",
      "Epoch 479/500\n",
      "104/104 [==============================] - 0s 225us/step - loss: 2.2153\n",
      "Epoch 480/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 2.2152\n",
      "Epoch 481/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 2.2151\n",
      "Epoch 482/500\n",
      "104/104 [==============================] - 0s 217us/step - loss: 2.2150\n",
      "Epoch 483/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 2.2149\n",
      "Epoch 484/500\n",
      "104/104 [==============================] - 0s 210us/step - loss: 2.2148\n",
      "Epoch 485/500\n",
      "104/104 [==============================] - 0s 693us/step - loss: 2.2148\n",
      "Epoch 486/500\n",
      "104/104 [==============================] - 0s 265us/step - loss: 2.2147\n",
      "Epoch 487/500\n",
      "104/104 [==============================] - 0s 410us/step - loss: 2.2146\n",
      "Epoch 488/500\n",
      "104/104 [==============================] - 0s 652us/step - loss: 2.2145\n",
      "Epoch 489/500\n",
      "104/104 [==============================] - 0s 382us/step - loss: 2.2144\n",
      "Epoch 490/500\n",
      "104/104 [==============================] - 0s 305us/step - loss: 2.2143\n",
      "Epoch 491/500\n",
      "104/104 [==============================] - 0s 302us/step - loss: 2.2142\n",
      "Epoch 492/500\n",
      "104/104 [==============================] - 0s 493us/step - loss: 2.2141\n",
      "Epoch 493/500\n",
      "104/104 [==============================] - 0s 295us/step - loss: 2.2140\n",
      "Epoch 494/500\n",
      "104/104 [==============================] - 0s 283us/step - loss: 2.2139\n",
      "Epoch 495/500\n",
      "104/104 [==============================] - 0s 501us/step - loss: 2.2139\n",
      "Epoch 496/500\n",
      "104/104 [==============================] - 0s 301us/step - loss: 2.2138\n",
      "Epoch 497/500\n",
      "104/104 [==============================] - 0s 274us/step - loss: 2.2137\n",
      "Epoch 498/500\n",
      "104/104 [==============================] - 0s 408us/step - loss: 2.2136\n",
      "Epoch 499/500\n",
      "104/104 [==============================] - 0s 263us/step - loss: 2.2135\n",
      "Epoch 500/500\n",
      "104/104 [==============================] - 0s 326us/step - loss: 2.2134\n"
     ]
    }
   ],
   "source": [
    "encoding_dim = 5 # 5 neurons\n",
    "num_stock = len(stock_lp.columns) # Use 83 stocks as features\n",
    "\n",
    "# connect all layers\n",
    "input_img = Input(shape=(num_stock, ))\n",
    "encoded = Dense(encoding_dim, activation='relu', kernel_regularizer=regularizers.l2(0.01))(input_img)\n",
    "decoded = Dense(num_stock, activation= 'linear', kernel_regularizer=regularizers.l2(0.01))(encoded) # see 'Stacked Auto-Encoders' in paper\n",
    "\n",
    "# construct and compile AE model\n",
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.compile(optimizer='sgd', loss='mean_squared_error')\n",
    "\n",
    "# train autoencoder\n",
    "data = stock['calibrate']['net']\n",
    "autoencoder.fit(data, data, shuffle=False, epochs=500, batch_size = 10)\n",
    "autoencoder.save('model/beat_autoencoder.h5')\n",
    "\n",
    "# test/reconstruct market information matrix\n",
    "reconstruct = autoencoder.predict(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Communal Information **\n",
    "\n",
    "- Compute $||X - \\hat{X}||$ as 2 norm difference\n",
    "- The least 2 norm difference a stock has, the more important role it plays in the compressed market auto-encoder\n",
    "- The paper says that, AMGN should be the best, and BCRX should be the worst. (But I didn't achieve this result.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stock #, 2-norm, stock name\n",
      "68 1.288070797920227 TLGT    US Equity\n",
      "65 1.321529507637024 INO     US Equity\n",
      "38 1.6224092245101929 LXRX    US Equity\n",
      "76 1.8336045742034912 NVAX    US Equity\n",
      "66 2.0743954181671143 CERS    US Equity\n",
      "79 2.180755138397217 ZGNX    US Equity\n",
      "81 2.4205305576324463 MNOV    US Equity\n",
      "77 2.4227540493011475 PDLI    US Equity\n",
      "71 2.5004076957702637 CRIS    US Equity\n",
      "52 2.7980635166168213 IMMU    US Equity\n",
      "17 2.858445644378662 EXEL    US Equity\n",
      "75 3.101935386657715 GERN    US Equity\n",
      "49 3.104473352432251 DEPO    US Equity\n",
      "37 3.432741641998291 ARRY    US Equity\n",
      "73 3.605278491973877 AVDL    US Equity\n",
      "67 3.6483685970306396 PACB    US Equity\n",
      "48 3.875211000442505 SGYP    US Equity\n",
      "19 4.003232479095459 OPK     US Equity\n",
      "42 4.117496967315674 RGEN    US Equity\n",
      "55 4.188165187835693 BCRX    US Equity\n",
      "31 4.208094596862793 HZNP    US Equity\n",
      "72 4.267415523529053 PRTK    US Equity\n",
      "82 4.347030162811279 ARWR    US Equity\n",
      "80 4.5980401039123535 CGEN    US Equity\n",
      "69 4.860776424407959 RIGL    US Equity\n",
      "44 4.875702857971191 FOLD    US Equity\n",
      "64 5.046663761138916 SCMP    US Equity\n",
      "78 5.592516899108887 SGMO    US Equity\n",
      "59 5.841182231903076 AMRI    US Equity\n",
      "15 5.9435858726501465 QGEN    US Equity\n",
      "26 6.252754211425781 AKRX    US Equity\n",
      "74 6.464998722076416 ARNA    US Equity\n",
      "23 6.582633018493652 NKTR    US Equity\n",
      "62 7.102594375610352 SPPI    US Equity\n",
      "45 7.111962795257568 INSM    US Equity\n",
      "25 7.400853633880615 NBIX    US Equity\n",
      "54 7.716067314147949 PGNX    US Equity\n",
      "32 7.9678497314453125 IRWD    US Equity\n",
      "60 8.033944129943848 ANIP    US Equity\n",
      "7 8.294389724731445 MYL     US Equity\n",
      "57 8.34908676147461 OMER    US Equity\n",
      "46 8.573929786682129 MNTA    US Equity\n",
      "50 8.666171073913574 AMRN    US Equity\n",
      "28 8.932644844055176 CBPO    US Equity\n",
      "34 8.982589721679688 HALO    US Equity\n",
      "53 9.538496017456055 LMNX    US Equity\n",
      "61 9.713685035705566 ACHN    US Equity\n",
      "47 9.862861633300781 IPXL    US Equity\n",
      "63 10.075468063354492 CYTK    US Equity\n",
      "58 10.148923873901367 NLNK    US Equity\n",
      "13 10.349818229675293 ALKS    US Equity\n",
      "21 10.764059066772461 ACAD    US Equity\n",
      "56 11.010350227355957 VNDA    US Equity\n",
      "51 11.043745994567871 AMAG    US Equity\n",
      "24 11.310149192810059 MDCO    US Equity\n",
      "18 11.3660249710083 IONS    US Equity\n",
      "40 12.866910934448242 MYGN    US Equity\n",
      "29 13.550722122192383 GRFS    US Equity\n",
      "70 14.15591049194336 CLDX    US Equity\n",
      "35 14.543994903564453 PCRX    US Equity\n",
      "4 14.601616859436035 REGN    US Equity\n",
      "41 14.650755882263184 ACOR    US Equity\n",
      "12 14.715482711791992 SGEN    US Equity\n",
      "43 14.815167427062988 GHDX    US Equity\n",
      "27 15.164388656616211 CLVS    US Equity\n",
      "33 17.312023162841797 LGND    US Equity\n",
      "5 17.9119930267334 INCY    US Equity\n",
      "16 17.924509048461914 UTHR    US Equity\n",
      "22 17.99226188659668 TECH    US Equity\n",
      "39 18.304092407226562 INVA    US Equity\n",
      "20 20.009620666503906 ALNY    US Equity\n",
      "30 20.401140213012695 ENDP    US Equity\n",
      "10 21.267717361450195 BMRN    US Equity\n",
      "3 22.449106216430664 AMGN    US Equity\n",
      "2 24.339393615722656 BIIB    US Equity\n",
      "14 27.020423889160156 JAZZ    US Equity\n",
      "6 27.044326782226562 ILMN    US Equity\n",
      "0 28.11642074584961 CELG    US Equity\n",
      "11 29.251052856445312 SHPG    US Equity\n",
      "36 35.49846267700195 SRPT    US Equity\n",
      "9 35.913639068603516 ALXN    US Equity\n",
      "1 43.21410369873047 GILD    US Equity\n",
      "8 45.488643646240234 VRTX    US Equity\n"
     ]
    }
   ],
   "source": [
    "communal_information = []\n",
    "\n",
    "for i in range(0,83):\n",
    "    diff = np.linalg.norm((data.iloc[:,i] - reconstruct[:,i])) # 2 norm difference\n",
    "    communal_information.append(float(diff))\n",
    " \n",
    "print(\"stock #, 2-norm, stock name\")\n",
    "ranking = np.array(communal_information).argsort()\n",
    "for stock_index in ranking:\n",
    "    print(stock_index, communal_information[stock_index], stock['calibrate']['net'].iloc[:,stock_index].name) # print stock name from lowest different to highest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look on how well this auto-encoder works on a certain stock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2a890780>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEHCAYAAABV4gY/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a29ae7160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which_stock = 65\n",
    "\n",
    "# now decoded last price plot\n",
    "stock_autoencoder = copy.deepcopy(reconstruct[:, which_stock])\n",
    "stock_autoencoder[0] = 0\n",
    "stock_autoencoder = stock_autoencoder.cumsum()\n",
    "stock_autoencoder += (stock['calibrate']['lp'].iloc[0, which_stock])\n",
    "\n",
    "## plot for comparison\n",
    "pd.Series(stock['calibrate']['lp'].iloc[:, which_stock].as_matrix(), index=pd.date_range(start='01/06/2012', periods=104, freq='W')).plot(label='stock original', legend=True)\n",
    "pd.Series(stock_autoencoder, index=pd.date_range(start='01/06/2012', periods = 104,freq='W')).plot(label='stock autoencoded', legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 2: Calibrating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the application of beating the IBB index, we will amend the target data during the calibration phase by replacing all returns smaller than -5% by exactly 5%, which aims to create an index tracker with anti-correlation in periods of large drawdowns.\n",
    "\n",
    "The comparison is shown as bellow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a28caa630>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0VwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a28c8e4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from -5% to 5%\n",
    "y_amended = ibb['calibrate']['percentage']\n",
    "y_amended[y_amended < -5] = 5\n",
    "\n",
    "# re-calculate the last price\n",
    "y_amended[0] = 0\n",
    "relative_percentage = (y_amended /100) + 1\n",
    "lp_amended = ibb['calibrate']['lp'][0] * (relative_percentage.cumprod()) \n",
    "\n",
    "# plot comparison\n",
    "pd.Series(ibb['calibrate']['lp'].as_matrix(), index=pd.date_range(start='01/06/2012', periods=104, freq='W')).plot(label='ibb original', legend=True) \n",
    "pd.Series(lp_amended.as_matrix(), index=pd.date_range(start='01/06/2012', periods=104, freq='W')).plot(label='ibb amended', legend=True)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "104/104 [==============================] - 1s 13ms/step - loss: 9.1094\n",
      "Epoch 2/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 5.1081\n",
      "Epoch 3/500\n",
      "104/104 [==============================] - 0s 151us/step - loss: 3.7867\n",
      "Epoch 4/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 3.0469\n",
      "Epoch 5/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 2.5905\n",
      "Epoch 6/500\n",
      "104/104 [==============================] - 0s 294us/step - loss: 2.2992\n",
      "Epoch 7/500\n",
      "104/104 [==============================] - 0s 494us/step - loss: 2.0913\n",
      "Epoch 8/500\n",
      "104/104 [==============================] - 0s 259us/step - loss: 1.9365\n",
      "Epoch 9/500\n",
      "104/104 [==============================] - 0s 303us/step - loss: 1.7954\n",
      "Epoch 10/500\n",
      "104/104 [==============================] - 0s 324us/step - loss: 1.6589\n",
      "Epoch 11/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 1.5455\n",
      "Epoch 12/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 1.4542\n",
      "Epoch 13/500\n",
      "104/104 [==============================] - 0s 273us/step - loss: 1.3603\n",
      "Epoch 14/500\n",
      "104/104 [==============================] - 0s 340us/step - loss: 1.2836\n",
      "Epoch 15/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 1.2217\n",
      "Epoch 16/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 1.1646\n",
      "Epoch 17/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 1.1154\n",
      "Epoch 18/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 1.0781\n",
      "Epoch 19/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 1.0399\n",
      "Epoch 20/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 1.0071\n",
      "Epoch 21/500\n",
      "104/104 [==============================] - 0s 210us/step - loss: 0.9776\n",
      "Epoch 22/500\n",
      "104/104 [==============================] - 0s 214us/step - loss: 0.9556\n",
      "Epoch 23/500\n",
      "104/104 [==============================] - 0s 263us/step - loss: 0.9293\n",
      "Epoch 24/500\n",
      "104/104 [==============================] - 0s 259us/step - loss: 0.9098\n",
      "Epoch 25/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.8937\n",
      "Epoch 26/500\n",
      "104/104 [==============================] - 0s 238us/step - loss: 0.8720\n",
      "Epoch 27/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.803 - 0s 188us/step - loss: 0.8562\n",
      "Epoch 28/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.8464\n",
      "Epoch 29/500\n",
      "104/104 [==============================] - 0s 212us/step - loss: 0.8227\n",
      "Epoch 30/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.8152\n",
      "Epoch 31/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 0.7969\n",
      "Epoch 32/500\n",
      "104/104 [==============================] - 0s 295us/step - loss: 0.7886\n",
      "Epoch 33/500\n",
      "104/104 [==============================] - 0s 323us/step - loss: 0.7745\n",
      "Epoch 34/500\n",
      "104/104 [==============================] - 0s 299us/step - loss: 0.7652\n",
      "Epoch 35/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.7538\n",
      "Epoch 36/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.694 - 0s 172us/step - loss: 0.7439\n",
      "Epoch 37/500\n",
      "104/104 [==============================] - 0s 225us/step - loss: 0.7413\n",
      "Epoch 38/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.7230\n",
      "Epoch 39/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 0.7137\n",
      "Epoch 40/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.7079\n",
      "Epoch 41/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.6921\n",
      "Epoch 42/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.6843\n",
      "Epoch 43/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.6785\n",
      "Epoch 44/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 0.6655\n",
      "Epoch 45/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.6585\n",
      "Epoch 46/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.6532\n",
      "Epoch 47/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.6416\n",
      "Epoch 48/500\n",
      "104/104 [==============================] - 0s 214us/step - loss: 0.6356\n",
      "Epoch 49/500\n",
      "104/104 [==============================] - 0s 282us/step - loss: 0.6286\n",
      "Epoch 50/500\n",
      "104/104 [==============================] - 0s 599us/step - loss: 0.6249\n",
      "Epoch 51/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.6146\n",
      "Epoch 52/500\n",
      "104/104 [==============================] - 0s 259us/step - loss: 0.6099\n",
      "Epoch 53/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.6044\n",
      "Epoch 54/500\n",
      "104/104 [==============================] - 0s 262us/step - loss: 0.5974\n",
      "Epoch 55/500\n",
      "104/104 [==============================] - 0s 213us/step - loss: 0.5929\n",
      "Epoch 56/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 0.5883\n",
      "Epoch 57/500\n",
      "104/104 [==============================] - 0s 229us/step - loss: 0.5809\n",
      "Epoch 58/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 0.5773\n",
      "Epoch 59/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 0.5719\n",
      "Epoch 60/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 0.5690\n",
      "Epoch 61/500\n",
      "104/104 [==============================] - 0s 277us/step - loss: 0.5615\n",
      "Epoch 62/500\n",
      "104/104 [==============================] - 0s 317us/step - loss: 0.5575\n",
      "Epoch 63/500\n",
      "104/104 [==============================] - 0s 326us/step - loss: 0.5528\n",
      "Epoch 64/500\n",
      "104/104 [==============================] - 0s 298us/step - loss: 0.5501\n",
      "Epoch 65/500\n",
      "104/104 [==============================] - 0s 331us/step - loss: 0.5420\n",
      "Epoch 66/500\n",
      "104/104 [==============================] - 0s 298us/step - loss: 0.5381\n",
      "Epoch 67/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.5356\n",
      "Epoch 68/500\n",
      "104/104 [==============================] - 0s 373us/step - loss: 0.5292\n",
      "Epoch 69/500\n",
      "104/104 [==============================] - 0s 259us/step - loss: 0.5256\n",
      "Epoch 70/500\n",
      "104/104 [==============================] - 0s 226us/step - loss: 0.5226\n",
      "Epoch 71/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 0.5170\n",
      "Epoch 72/500\n",
      "104/104 [==============================] - 0s 226us/step - loss: 0.5142\n",
      "Epoch 73/500\n",
      "104/104 [==============================] - 0s 276us/step - loss: 0.5102\n",
      "Epoch 74/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 0.5059\n",
      "Epoch 75/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.5021\n",
      "Epoch 76/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.4988\n",
      "Epoch 77/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.4937\n",
      "Epoch 78/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.4906\n",
      "Epoch 79/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.4862\n",
      "Epoch 80/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.4849\n",
      "Epoch 81/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.4777\n",
      "Epoch 82/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.4745\n",
      "Epoch 83/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.4725\n",
      "Epoch 84/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.4679\n",
      "Epoch 85/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.4638\n",
      "Epoch 86/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.4626\n",
      "Epoch 87/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 0.4567\n",
      "Epoch 88/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 0.4540\n",
      "Epoch 89/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.4519\n",
      "Epoch 90/500\n",
      "104/104 [==============================] - 0s 274us/step - loss: 0.4463\n",
      "Epoch 91/500\n",
      "104/104 [==============================] - 0s 257us/step - loss: 0.4437\n",
      "Epoch 92/500\n",
      "104/104 [==============================] - 0s 232us/step - loss: 0.4422\n",
      "Epoch 93/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.4365\n",
      "Epoch 94/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.4341\n",
      "Epoch 95/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.4338\n",
      "Epoch 96/500\n",
      "104/104 [==============================] - 0s 217us/step - loss: 0.4272\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/500\n",
      "104/104 [==============================] - 0s 210us/step - loss: 0.4265\n",
      "Epoch 98/500\n",
      "104/104 [==============================] - 0s 278us/step - loss: 0.4251\n",
      "Epoch 99/500\n",
      "104/104 [==============================] - 0s 422us/step - loss: 0.4189\n",
      "Epoch 100/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.4191\n",
      "Epoch 101/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.4172\n",
      "Epoch 102/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.4127\n",
      "Epoch 103/500\n",
      "104/104 [==============================] - 0s 276us/step - loss: 0.4120\n",
      "Epoch 104/500\n",
      "104/104 [==============================] - 0s 352us/step - loss: 0.4077\n",
      "Epoch 105/500\n",
      "104/104 [==============================] - 0s 398us/step - loss: 0.4060\n",
      "Epoch 106/500\n",
      "104/104 [==============================] - 0s 290us/step - loss: 0.4058\n",
      "Epoch 107/500\n",
      "104/104 [==============================] - 0s 525us/step - loss: 0.4001\n",
      "Epoch 108/500\n",
      "104/104 [==============================] - 0s 294us/step - loss: 0.3999\n",
      "Epoch 109/500\n",
      "104/104 [==============================] - 0s 259us/step - loss: 0.3987\n",
      "Epoch 110/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 0.3934\n",
      "Epoch 111/500\n",
      "104/104 [==============================] - 0s 377us/step - loss: 0.3928\n",
      "Epoch 112/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.3877\n",
      "Epoch 113/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.3868\n",
      "Epoch 114/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.3850\n",
      "Epoch 115/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 0.3809\n",
      "Epoch 116/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.3797\n",
      "Epoch 117/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.3749\n",
      "Epoch 118/500\n",
      "104/104 [==============================] - 0s 255us/step - loss: 0.3749\n",
      "Epoch 119/500\n",
      "104/104 [==============================] - 0s 215us/step - loss: 0.3730\n",
      "Epoch 120/500\n",
      "104/104 [==============================] - 0s 149us/step - loss: 0.3689\n",
      "Epoch 121/500\n",
      "104/104 [==============================] - 0s 263us/step - loss: 0.3684\n",
      "Epoch 122/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.3641\n",
      "Epoch 123/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.3637\n",
      "Epoch 124/500\n",
      "104/104 [==============================] - 0s 229us/step - loss: 0.3599\n",
      "Epoch 125/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 0.3601\n",
      "Epoch 126/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.3588\n",
      "Epoch 127/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.3539\n",
      "Epoch 128/500\n",
      "104/104 [==============================] - 0s 169us/step - loss: 0.3529\n",
      "Epoch 129/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.3513\n",
      "Epoch 130/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.3489\n",
      "Epoch 131/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.3493\n",
      "Epoch 132/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.3440\n",
      "Epoch 133/500\n",
      "104/104 [==============================] - 0s 169us/step - loss: 0.3449\n",
      "Epoch 134/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.3437\n",
      "Epoch 135/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.389 - 0s 373us/step - loss: 0.3386\n",
      "Epoch 136/500\n",
      "104/104 [==============================] - 0s 268us/step - loss: 0.3402\n",
      "Epoch 137/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.3355\n",
      "Epoch 138/500\n",
      "104/104 [==============================] - 0s 267us/step - loss: 0.3362\n",
      "Epoch 139/500\n",
      "104/104 [==============================] - 0s 391us/step - loss: 0.3336\n",
      "Epoch 140/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.3323\n",
      "Epoch 141/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.3321\n",
      "Epoch 142/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.3281\n",
      "Epoch 143/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 0.3282\n",
      "Epoch 144/500\n",
      "104/104 [==============================] - 0s 384us/step - loss: 0.3252\n",
      "Epoch 145/500\n",
      "104/104 [==============================] - 0s 288us/step - loss: 0.3243\n",
      "Epoch 146/500\n",
      "104/104 [==============================] - 0s 226us/step - loss: 0.3243\n",
      "Epoch 147/500\n",
      "104/104 [==============================] - 0s 292us/step - loss: 0.3201\n",
      "Epoch 148/500\n",
      "104/104 [==============================] - 0s 301us/step - loss: 0.3203\n",
      "Epoch 149/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 0.3184\n",
      "Epoch 150/500\n",
      "104/104 [==============================] - 0s 255us/step - loss: 0.3171\n",
      "Epoch 151/500\n",
      "104/104 [==============================] - 0s 296us/step - loss: 0.3130\n",
      "Epoch 152/500\n",
      "104/104 [==============================] - 0s 327us/step - loss: 0.3142\n",
      "Epoch 153/500\n",
      "104/104 [==============================] - 0s 450us/step - loss: 0.3107\n",
      "Epoch 154/500\n",
      "104/104 [==============================] - 0s 273us/step - loss: 0.3088\n",
      "Epoch 155/500\n",
      "104/104 [==============================] - 0s 323us/step - loss: 0.3079\n",
      "Epoch 156/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.3068\n",
      "Epoch 157/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.3044\n",
      "Epoch 158/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.3030\n",
      "Epoch 159/500\n",
      "104/104 [==============================] - 0s 271us/step - loss: 0.3034\n",
      "Epoch 160/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 0.2991\n",
      "Epoch 161/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.2993\n",
      "Epoch 162/500\n",
      "104/104 [==============================] - 0s 237us/step - loss: 0.2964\n",
      "Epoch 163/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 0.2974\n",
      "Epoch 164/500\n",
      "104/104 [==============================] - 0s 239us/step - loss: 0.2937\n",
      "Epoch 165/500\n",
      "104/104 [==============================] - 0s 247us/step - loss: 0.2937\n",
      "Epoch 166/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 0.2917\n",
      "Epoch 167/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.2915\n",
      "Epoch 168/500\n",
      "104/104 [==============================] - 0s 390us/step - loss: 0.2871\n",
      "Epoch 169/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.2890\n",
      "Epoch 170/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2850\n",
      "Epoch 171/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.2848\n",
      "Epoch 172/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2832\n",
      "Epoch 173/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.2826\n",
      "Epoch 174/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2787\n",
      "Epoch 175/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2808\n",
      "Epoch 176/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.2774\n",
      "Epoch 177/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2767\n",
      "Epoch 178/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.2754\n",
      "Epoch 179/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 0.2750\n",
      "Epoch 180/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2712\n",
      "Epoch 181/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2728\n",
      "Epoch 182/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2692\n",
      "Epoch 183/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2709\n",
      "Epoch 184/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2649\n",
      "Epoch 185/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2690\n",
      "Epoch 186/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2648\n",
      "Epoch 187/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2658\n",
      "Epoch 188/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2625\n",
      "Epoch 189/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.2625\n",
      "Epoch 190/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2609\n",
      "Epoch 191/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.2617\n",
      "Epoch 192/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 233us/step - loss: 0.2583\n",
      "Epoch 193/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2596\n",
      "Epoch 194/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2562\n",
      "Epoch 195/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.2575\n",
      "Epoch 196/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.2548\n",
      "Epoch 197/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.2559\n",
      "Epoch 198/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2523\n",
      "Epoch 199/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2548\n",
      "Epoch 200/500\n",
      "104/104 [==============================] - 0s 158us/step - loss: 0.2516\n",
      "Epoch 201/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.2518\n",
      "Epoch 202/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2496\n",
      "Epoch 203/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2507\n",
      "Epoch 204/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.2500\n",
      "Epoch 205/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2489\n",
      "Epoch 206/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2471\n",
      "Epoch 207/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2477\n",
      "Epoch 208/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2455\n",
      "Epoch 209/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2459\n",
      "Epoch 210/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.2454\n",
      "Epoch 211/500\n",
      "104/104 [==============================] - 0s 286us/step - loss: 0.2450\n",
      "Epoch 212/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.2434\n",
      "Epoch 213/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.2436\n",
      "Epoch 214/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.2413\n",
      "Epoch 215/500\n",
      "104/104 [==============================] - 0s 226us/step - loss: 0.2420\n",
      "Epoch 216/500\n",
      "104/104 [==============================] - 0s 252us/step - loss: 0.2425\n",
      "Epoch 217/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 0.2402\n",
      "Epoch 218/500\n",
      "104/104 [==============================] - 0s 226us/step - loss: 0.2414\n",
      "Epoch 219/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.2375\n",
      "Epoch 220/500\n",
      "104/104 [==============================] - 0s 602us/step - loss: 0.2408\n",
      "Epoch 221/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.2374\n",
      "Epoch 222/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.2392\n",
      "Epoch 223/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 0.2364\n",
      "Epoch 224/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.2372\n",
      "Epoch 225/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.2356\n",
      "Epoch 226/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2357\n",
      "Epoch 227/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 0.2345\n",
      "Epoch 228/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.2351\n",
      "Epoch 229/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.2335\n",
      "Epoch 230/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.2326\n",
      "Epoch 231/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.2329\n",
      "Epoch 232/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.2310\n",
      "Epoch 233/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2310\n",
      "Epoch 234/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2306\n",
      "Epoch 235/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.2296\n",
      "Epoch 236/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.2294\n",
      "Epoch 237/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 0.2271\n",
      "Epoch 238/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 0.2272\n",
      "Epoch 239/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 0.2259\n",
      "Epoch 240/500\n",
      "104/104 [==============================] - 0s 296us/step - loss: 0.2255\n",
      "Epoch 241/500\n",
      "104/104 [==============================] - 0s 363us/step - loss: 0.2258\n",
      "Epoch 242/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2236\n",
      "Epoch 243/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.196 - 0s 174us/step - loss: 0.2235\n",
      "Epoch 244/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.2225\n",
      "Epoch 245/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2218\n",
      "Epoch 246/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.2210\n",
      "Epoch 247/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.2210\n",
      "Epoch 248/500\n",
      "104/104 [==============================] - 0s 169us/step - loss: 0.2204\n",
      "Epoch 249/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2188\n",
      "Epoch 250/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2194\n",
      "Epoch 251/500\n",
      "104/104 [==============================] - 0s 158us/step - loss: 0.2183\n",
      "Epoch 252/500\n",
      "104/104 [==============================] - 0s 157us/step - loss: 0.2175\n",
      "Epoch 253/500\n",
      "104/104 [==============================] - 0s 157us/step - loss: 0.2172\n",
      "Epoch 254/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.2166\n",
      "Epoch 255/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2164\n",
      "Epoch 256/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 0.2156\n",
      "Epoch 257/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2148\n",
      "Epoch 258/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2153\n",
      "Epoch 259/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2137\n",
      "Epoch 260/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2139\n",
      "Epoch 261/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2140\n",
      "Epoch 262/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.2138\n",
      "Epoch 263/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2115\n",
      "Epoch 264/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.2132\n",
      "Epoch 265/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2108\n",
      "Epoch 266/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2118\n",
      "Epoch 267/500\n",
      "104/104 [==============================] - 0s 155us/step - loss: 0.2107\n",
      "Epoch 268/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.2096\n",
      "Epoch 269/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2100\n",
      "Epoch 270/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2090\n",
      "Epoch 271/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.2088\n",
      "Epoch 272/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2086\n",
      "Epoch 273/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 0.2065\n",
      "Epoch 274/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.2076\n",
      "Epoch 275/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.2070\n",
      "Epoch 276/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2064\n",
      "Epoch 277/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2059\n",
      "Epoch 278/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2047\n",
      "Epoch 279/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2051\n",
      "Epoch 280/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2044\n",
      "Epoch 281/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2033\n",
      "Epoch 282/500\n",
      "104/104 [==============================] - 0s 223us/step - loss: 0.2039\n",
      "Epoch 283/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2027\n",
      "Epoch 284/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2029\n",
      "Epoch 285/500\n",
      "104/104 [==============================] - 0s 212us/step - loss: 0.2025\n",
      "Epoch 286/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2014\n",
      "Epoch 287/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 177us/step - loss: 0.2019\n",
      "Epoch 288/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 0.2006\n",
      "Epoch 289/500\n",
      "104/104 [==============================] - 0s 229us/step - loss: 0.2002\n",
      "Epoch 290/500\n",
      "104/104 [==============================] - 0s 271us/step - loss: 0.1995\n",
      "Epoch 291/500\n",
      "104/104 [==============================] - 0s 157us/step - loss: 0.2005\n",
      "Epoch 292/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.1998\n",
      "Epoch 293/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.1993\n",
      "Epoch 294/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.1983\n",
      "Epoch 295/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.1986\n",
      "Epoch 296/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.1975\n",
      "Epoch 297/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.1977\n",
      "Epoch 298/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.1968\n",
      "Epoch 299/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.1971\n",
      "Epoch 300/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.1970\n",
      "Epoch 301/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.1961\n",
      "Epoch 302/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.1958\n",
      "Epoch 303/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.1957\n",
      "Epoch 304/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.1954\n",
      "Epoch 305/500\n",
      "104/104 [==============================] - 0s 210us/step - loss: 0.1949\n",
      "Epoch 306/500\n",
      "104/104 [==============================] - 0s 317us/step - loss: 0.1949\n",
      "Epoch 307/500\n",
      "104/104 [==============================] - 0s 385us/step - loss: 0.1951\n",
      "Epoch 308/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.1938\n",
      "Epoch 309/500\n",
      "104/104 [==============================] - 0s 299us/step - loss: 0.1944\n",
      "Epoch 310/500\n",
      "104/104 [==============================] - 0s 212us/step - loss: 0.1941\n",
      "Epoch 311/500\n",
      "104/104 [==============================] - 0s 224us/step - loss: 0.1940\n",
      "Epoch 312/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.1926\n",
      "Epoch 313/500\n",
      "104/104 [==============================] - 0s 260us/step - loss: 0.1931\n",
      "Epoch 314/500\n",
      "104/104 [==============================] - 0s 287us/step - loss: 0.1925\n",
      "Epoch 315/500\n",
      "104/104 [==============================] - 0s 215us/step - loss: 0.1923\n",
      "Epoch 316/500\n",
      "104/104 [==============================] - 0s 240us/step - loss: 0.1926\n",
      "Epoch 317/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.1910\n",
      "Epoch 318/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.1919\n",
      "Epoch 319/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 0.1910\n",
      "Epoch 320/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 0.1915\n",
      "Epoch 321/500\n",
      "104/104 [==============================] - 0s 239us/step - loss: 0.1903\n",
      "Epoch 322/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.156 - 0s 221us/step - loss: 0.1904\n",
      "Epoch 323/500\n",
      "104/104 [==============================] - 0s 278us/step - loss: 0.1905\n",
      "Epoch 324/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.1904\n",
      "Epoch 325/500\n",
      "104/104 [==============================] - 0s 332us/step - loss: 0.1899\n",
      "Epoch 326/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.1891\n",
      "Epoch 327/500\n",
      "104/104 [==============================] - 0s 261us/step - loss: 0.1891\n",
      "Epoch 328/500\n",
      "104/104 [==============================] - 0s 225us/step - loss: 0.1884\n",
      "Epoch 329/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.1889\n",
      "Epoch 330/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.1884\n",
      "Epoch 331/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.1876\n",
      "Epoch 332/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.1882\n",
      "Epoch 333/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.1880\n",
      "Epoch 334/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.1867\n",
      "Epoch 335/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.1865\n",
      "Epoch 336/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.1873\n",
      "Epoch 337/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.1857\n",
      "Epoch 338/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.1868\n",
      "Epoch 339/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.1863\n",
      "Epoch 340/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.1847\n",
      "Epoch 341/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 0.1854\n",
      "Epoch 342/500\n",
      "104/104 [==============================] - 0s 231us/step - loss: 0.1855\n",
      "Epoch 343/500\n",
      "104/104 [==============================] - 0s 226us/step - loss: 0.1843\n",
      "Epoch 344/500\n",
      "104/104 [==============================] - 0s 237us/step - loss: 0.1850\n",
      "Epoch 345/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.1848\n",
      "Epoch 346/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 0.1837\n",
      "Epoch 347/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 0.1843\n",
      "Epoch 348/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.1829\n",
      "Epoch 349/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.1836\n",
      "Epoch 350/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.1826\n",
      "Epoch 351/500\n",
      "104/104 [==============================] - 0s 213us/step - loss: 0.1826\n",
      "Epoch 352/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.1827\n",
      "Epoch 353/500\n",
      "104/104 [==============================] - 0s 223us/step - loss: 0.1825\n",
      "Epoch 354/500\n",
      "104/104 [==============================] - 0s 286us/step - loss: 0.1822\n",
      "Epoch 355/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.1809\n",
      "Epoch 356/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 0.1815\n",
      "Epoch 357/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.1814\n",
      "Epoch 358/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.1807\n",
      "Epoch 359/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.1802\n",
      "Epoch 360/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.1808\n",
      "Epoch 361/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 0.1791\n",
      "Epoch 362/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.1804\n",
      "Epoch 363/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.1796\n",
      "Epoch 364/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.1796\n",
      "Epoch 365/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.1791\n",
      "Epoch 366/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.1786\n",
      "Epoch 367/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.1791\n",
      "Epoch 368/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.1785\n",
      "Epoch 369/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.1778\n",
      "Epoch 370/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.1780\n",
      "Epoch 371/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.1779\n",
      "Epoch 372/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.1777\n",
      "Epoch 373/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.1769\n",
      "Epoch 374/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 0.1772\n",
      "Epoch 375/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.1770\n",
      "Epoch 376/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.1765\n",
      "Epoch 377/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.1764\n",
      "Epoch 378/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.1765\n",
      "Epoch 379/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.1760\n",
      "Epoch 380/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.1765\n",
      "Epoch 381/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.1750\n",
      "Epoch 382/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 180us/step - loss: 0.1760\n",
      "Epoch 383/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.1755\n",
      "Epoch 384/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.1753\n",
      "Epoch 385/500\n",
      "104/104 [==============================] - 0s 214us/step - loss: 0.1745\n",
      "Epoch 386/500\n",
      "104/104 [==============================] - 0s 244us/step - loss: 0.1747\n",
      "Epoch 387/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 0.1748\n",
      "Epoch 388/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.1739\n",
      "Epoch 389/500\n",
      "104/104 [==============================] - 0s 276us/step - loss: 0.1738\n",
      "Epoch 390/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 0.1742\n",
      "Epoch 391/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.1739\n",
      "Epoch 392/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.1731\n",
      "Epoch 393/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.1739\n",
      "Epoch 394/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.1732\n",
      "Epoch 395/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 0.1731\n",
      "Epoch 396/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 0.1726\n",
      "Epoch 397/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.1722\n",
      "Epoch 398/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 0.1724\n",
      "Epoch 399/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 0.1722\n",
      "Epoch 400/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.1722\n",
      "Epoch 401/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.1722\n",
      "Epoch 402/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.1714\n",
      "Epoch 403/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.1717\n",
      "Epoch 404/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.1712\n",
      "Epoch 405/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.1716\n",
      "Epoch 406/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.1707\n",
      "Epoch 407/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.1709\n",
      "Epoch 408/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.1709\n",
      "Epoch 409/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.1703\n",
      "Epoch 410/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.1701\n",
      "Epoch 411/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.1704\n",
      "Epoch 412/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.1705\n",
      "Epoch 413/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.1696\n",
      "Epoch 414/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.1700\n",
      "Epoch 415/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.1691\n",
      "Epoch 416/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.1698\n",
      "Epoch 417/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.1691\n",
      "Epoch 418/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.1694\n",
      "Epoch 419/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.1687\n",
      "Epoch 420/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.1686\n",
      "Epoch 421/500\n",
      "104/104 [==============================] - 0s 158us/step - loss: 0.1677\n",
      "Epoch 422/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.1680\n",
      "Epoch 423/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.1680\n",
      "Epoch 424/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.1677\n",
      "Epoch 425/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.1674\n",
      "Epoch 426/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.1667\n",
      "Epoch 427/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.1666\n",
      "Epoch 428/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.1667\n",
      "Epoch 429/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.1663\n",
      "Epoch 430/500\n",
      "104/104 [==============================] - 0s 212us/step - loss: 0.1659\n",
      "Epoch 431/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.1655\n",
      "Epoch 432/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.1650\n",
      "Epoch 433/500\n",
      "104/104 [==============================] - 0s 298us/step - loss: 0.1654\n",
      "Epoch 434/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.1647\n",
      "Epoch 435/500\n",
      "104/104 [==============================] - 0s 310us/step - loss: 0.1644\n",
      "Epoch 436/500\n",
      "104/104 [==============================] - 0s 254us/step - loss: 0.1644\n",
      "Epoch 437/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.1640\n",
      "Epoch 438/500\n",
      "104/104 [==============================] - 0s 240us/step - loss: 0.1642\n",
      "Epoch 439/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.1637\n",
      "Epoch 440/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.1630\n",
      "Epoch 441/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.1631\n",
      "Epoch 442/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 0.1632\n",
      "Epoch 443/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.1629\n",
      "Epoch 444/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.1626\n",
      "Epoch 445/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.1621\n",
      "Epoch 446/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.1624\n",
      "Epoch 447/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.1618\n",
      "Epoch 448/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.135 - 0s 189us/step - loss: 0.1618\n",
      "Epoch 449/500\n",
      "104/104 [==============================] - 0s 238us/step - loss: 0.1615\n",
      "Epoch 450/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.1613\n",
      "Epoch 451/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 0.1611\n",
      "Epoch 452/500\n",
      "104/104 [==============================] - 0s 286us/step - loss: 0.1611\n",
      "Epoch 453/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 0.1610\n",
      "Epoch 454/500\n",
      "104/104 [==============================] - 0s 212us/step - loss: 0.1608\n",
      "Epoch 455/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.1599\n",
      "Epoch 456/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.1605\n",
      "Epoch 457/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.1598\n",
      "Epoch 458/500\n",
      "104/104 [==============================] - 0s 235us/step - loss: 0.1599\n",
      "Epoch 459/500\n",
      "104/104 [==============================] - 0s 215us/step - loss: 0.1592\n",
      "Epoch 460/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.1597\n",
      "Epoch 461/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.1593\n",
      "Epoch 462/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.1592\n",
      "Epoch 463/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.1590\n",
      "Epoch 464/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.1585\n",
      "Epoch 465/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.1590\n",
      "Epoch 466/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.1581\n",
      "Epoch 467/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.1585\n",
      "Epoch 468/500\n",
      "104/104 [==============================] - 0s 247us/step - loss: 0.1580\n",
      "Epoch 469/500\n",
      "104/104 [==============================] - 0s 301us/step - loss: 0.1581\n",
      "Epoch 470/500\n",
      "104/104 [==============================] - 0s 276us/step - loss: 0.1575\n",
      "Epoch 471/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 0.1578\n",
      "Epoch 472/500\n",
      "104/104 [==============================] - 0s 244us/step - loss: 0.1577\n",
      "Epoch 473/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.1571\n",
      "Epoch 474/500\n",
      "104/104 [==============================] - 0s 223us/step - loss: 0.1571\n",
      "Epoch 475/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.133 - 0s 249us/step - loss: 0.1572\n",
      "Epoch 476/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 232us/step - loss: 0.1567\n",
      "Epoch 477/500\n",
      "104/104 [==============================] - 0s 232us/step - loss: 0.1568\n",
      "Epoch 478/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.1566\n",
      "Epoch 479/500\n",
      "104/104 [==============================] - 0s 305us/step - loss: 0.1564\n",
      "Epoch 480/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.1563\n",
      "Epoch 481/500\n",
      "104/104 [==============================] - 0s 213us/step - loss: 0.1560\n",
      "Epoch 482/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.1561\n",
      "Epoch 483/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.1560\n",
      "Epoch 484/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.1555\n",
      "Epoch 485/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.1556\n",
      "Epoch 486/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.1557\n",
      "Epoch 487/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.1551\n",
      "Epoch 488/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.1554\n",
      "Epoch 489/500\n",
      "104/104 [==============================] - 0s 244us/step - loss: 0.1549\n",
      "Epoch 490/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.1545\n",
      "Epoch 491/500\n",
      "104/104 [==============================] - 0s 157us/step - loss: 0.1548\n",
      "Epoch 492/500\n",
      "104/104 [==============================] - 0s 217us/step - loss: 0.1549\n",
      "Epoch 493/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.1544\n",
      "Epoch 494/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.1546\n",
      "Epoch 495/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.1541\n",
      "Epoch 496/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.1543\n",
      "Epoch 497/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.1541\n",
      "Epoch 498/500\n",
      "104/104 [==============================] - 0s 169us/step - loss: 0.1536\n",
      "Epoch 499/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.1540\n",
      "Epoch 500/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.1538\n",
      "Epoch 1/500\n",
      "104/104 [==============================] - 1s 11ms/step - loss: 5.5302\n",
      "Epoch 2/500\n",
      "104/104 [==============================] - 0s 152us/step - loss: 2.3768\n",
      "Epoch 3/500\n",
      "104/104 [==============================] - 0s 154us/step - loss: 1.7121\n",
      "Epoch 4/500\n",
      "104/104 [==============================] - 0s 158us/step - loss: 1.4215\n",
      "Epoch 5/500\n",
      "104/104 [==============================] - 0s 157us/step - loss: 1.2581\n",
      "Epoch 6/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 1.1446\n",
      "Epoch 7/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 1.0536\n",
      "Epoch 8/500\n",
      "104/104 [==============================] - 0s 151us/step - loss: 0.9746\n",
      "Epoch 9/500\n",
      "104/104 [==============================] - 0s 157us/step - loss: 0.9099\n",
      "Epoch 10/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.8554\n",
      "Epoch 11/500\n",
      "104/104 [==============================] - 0s 154us/step - loss: 0.8080\n",
      "Epoch 12/500\n",
      "104/104 [==============================] - 0s 158us/step - loss: 0.7648\n",
      "Epoch 13/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.7278\n",
      "Epoch 14/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.6978\n",
      "Epoch 15/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.6681\n",
      "Epoch 16/500\n",
      "104/104 [==============================] - 0s 156us/step - loss: 0.6431\n",
      "Epoch 17/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.6281\n",
      "Epoch 18/500\n",
      "104/104 [==============================] - 0s 149us/step - loss: 0.6097\n",
      "Epoch 19/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.5921\n",
      "Epoch 20/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.5810\n",
      "Epoch 21/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.5658\n",
      "Epoch 22/500\n",
      "104/104 [==============================] - 0s 252us/step - loss: 0.5563\n",
      "Epoch 23/500\n",
      "104/104 [==============================] - 0s 233us/step - loss: 0.5443\n",
      "Epoch 24/500\n",
      "104/104 [==============================] - 0s 284us/step - loss: 0.5292\n",
      "Epoch 25/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.260 - 0s 302us/step - loss: 0.5269\n",
      "Epoch 26/500\n",
      "104/104 [==============================] - 0s 238us/step - loss: 0.5107\n",
      "Epoch 27/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.5080\n",
      "Epoch 28/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.4972\n",
      "Epoch 29/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.4851\n",
      "Epoch 30/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.4851\n",
      "Epoch 31/500\n",
      "104/104 [==============================] - 0s 256us/step - loss: 0.4740\n",
      "Epoch 32/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.4635\n",
      "Epoch 33/500\n",
      "104/104 [==============================] - 0s 255us/step - loss: 0.4655\n",
      "Epoch 34/500\n",
      "104/104 [==============================] - 0s 226us/step - loss: 0.4556\n",
      "Epoch 35/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 0.4462\n",
      "Epoch 36/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.4473\n",
      "Epoch 37/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.4398\n",
      "Epoch 38/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.4308\n",
      "Epoch 39/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.4301\n",
      "Epoch 40/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.4261\n",
      "Epoch 41/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.4178\n",
      "Epoch 42/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.4131\n",
      "Epoch 43/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.4135\n",
      "Epoch 44/500\n",
      "104/104 [==============================] - 0s 158us/step - loss: 0.4077\n",
      "Epoch 45/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.4017\n",
      "Epoch 46/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.3977\n",
      "Epoch 47/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.3989\n",
      "Epoch 48/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.3944\n",
      "Epoch 49/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.3885\n",
      "Epoch 50/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.3852\n",
      "Epoch 51/500\n",
      "104/104 [==============================] - 0s 158us/step - loss: 0.3843\n",
      "Epoch 52/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.3829\n",
      "Epoch 53/500\n",
      "104/104 [==============================] - 0s 299us/step - loss: 0.3771\n",
      "Epoch 54/500\n",
      "104/104 [==============================] - 0s 225us/step - loss: 0.3735\n",
      "Epoch 55/500\n",
      "104/104 [==============================] - 0s 217us/step - loss: 0.3704\n",
      "Epoch 56/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.3714\n",
      "Epoch 57/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.3679\n",
      "Epoch 58/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 0.3631\n",
      "Epoch 59/500\n",
      "104/104 [==============================] - 0s 352us/step - loss: 0.3603\n",
      "Epoch 60/500\n",
      "104/104 [==============================] - 0s 469us/step - loss: 0.3577\n",
      "Epoch 61/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.3578\n",
      "Epoch 62/500\n",
      "104/104 [==============================] - 0s 273us/step - loss: 0.3558\n",
      "Epoch 63/500\n",
      "104/104 [==============================] - 0s 286us/step - loss: 0.3517\n",
      "Epoch 64/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 0.3492\n",
      "Epoch 65/500\n",
      "104/104 [==============================] - 0s 213us/step - loss: 0.3471\n",
      "Epoch 66/500\n",
      "104/104 [==============================] - 0s 284us/step - loss: 0.3442\n",
      "Epoch 67/500\n",
      "104/104 [==============================] - 0s 250us/step - loss: 0.3454\n",
      "Epoch 68/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.3430\n",
      "Epoch 69/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 0.3392\n",
      "Epoch 70/500\n",
      "104/104 [==============================] - 0s 279us/step - loss: 0.3383\n",
      "Epoch 71/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 0.3356\n",
      "Epoch 72/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 247us/step - loss: 0.3330\n",
      "Epoch 73/500\n",
      "104/104 [==============================] - 0s 226us/step - loss: 0.3319\n",
      "Epoch 74/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 0.3324\n",
      "Epoch 75/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.3299\n",
      "Epoch 76/500\n",
      "104/104 [==============================] - 0s 169us/step - loss: 0.3283\n",
      "Epoch 77/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.3260\n",
      "Epoch 78/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.3236\n",
      "Epoch 79/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.3218\n",
      "Epoch 80/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.3198\n",
      "Epoch 81/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.3193\n",
      "Epoch 82/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.3216\n",
      "Epoch 83/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.3191\n",
      "Epoch 84/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.3161\n",
      "Epoch 85/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.3140\n",
      "Epoch 86/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.3123\n",
      "Epoch 87/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.3105\n",
      "Epoch 88/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.3087\n",
      "Epoch 89/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.3071\n",
      "Epoch 90/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.3070\n",
      "Epoch 91/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.3138\n",
      "Epoch 92/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.3069\n",
      "Epoch 93/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.3044\n",
      "Epoch 94/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.3025\n",
      "Epoch 95/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.3009\n",
      "Epoch 96/500\n",
      "104/104 [==============================] - 0s 287us/step - loss: 0.2993\n",
      "Epoch 97/500\n",
      "104/104 [==============================] - 0s 319us/step - loss: 0.2981\n",
      "Epoch 98/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.2972\n",
      "Epoch 99/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.3027\n",
      "Epoch 100/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.2996\n",
      "Epoch 101/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.2964\n",
      "Epoch 102/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.2944\n",
      "Epoch 103/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.2931\n",
      "Epoch 104/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.2917\n",
      "Epoch 105/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2913\n",
      "Epoch 106/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.2907\n",
      "Epoch 107/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2899\n",
      "Epoch 108/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2908\n",
      "Epoch 109/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.2873\n",
      "Epoch 110/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.2854\n",
      "Epoch 111/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2854\n",
      "Epoch 112/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2850\n",
      "Epoch 113/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2830\n",
      "Epoch 114/500\n",
      "104/104 [==============================] - 0s 299us/step - loss: 0.2816\n",
      "Epoch 115/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.2802\n",
      "Epoch 116/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.2789\n",
      "Epoch 117/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 0.2778\n",
      "Epoch 118/500\n",
      "104/104 [==============================] - 0s 156us/step - loss: 0.2766\n",
      "Epoch 119/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2756\n",
      "Epoch 120/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2758\n",
      "Epoch 121/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2840\n",
      "Epoch 122/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2769\n",
      "Epoch 123/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2745\n",
      "Epoch 124/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2732\n",
      "Epoch 125/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.2720\n",
      "Epoch 126/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.2710\n",
      "Epoch 127/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.2700\n",
      "Epoch 128/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2711\n",
      "Epoch 129/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.2705\n",
      "Epoch 130/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2710\n",
      "Epoch 131/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2712\n",
      "Epoch 132/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.2684\n",
      "Epoch 133/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2673\n",
      "Epoch 134/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2678\n",
      "Epoch 135/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.2677\n",
      "Epoch 136/500\n",
      "104/104 [==============================] - 0s 259us/step - loss: 0.2663\n",
      "Epoch 137/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2651\n",
      "Epoch 138/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.2642\n",
      "Epoch 139/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2633\n",
      "Epoch 140/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 0.2625\n",
      "Epoch 141/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2617\n",
      "Epoch 142/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2610\n",
      "Epoch 143/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2603\n",
      "Epoch 144/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2596\n",
      "Epoch 145/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2601\n",
      "Epoch 146/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2661\n",
      "Epoch 147/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2628\n",
      "Epoch 148/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2598\n",
      "Epoch 149/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2586\n",
      "Epoch 150/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.2576\n",
      "Epoch 151/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2569\n",
      "Epoch 152/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2578\n",
      "Epoch 153/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2578\n",
      "Epoch 154/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2565\n",
      "Epoch 155/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 0.2556\n",
      "Epoch 156/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2548\n",
      "Epoch 157/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2541\n",
      "Epoch 158/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2534\n",
      "Epoch 159/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2528\n",
      "Epoch 160/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2536\n",
      "Epoch 161/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2589\n",
      "Epoch 162/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.2553\n",
      "Epoch 163/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.2533\n",
      "Epoch 164/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2522\n",
      "Epoch 165/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.2514\n",
      "Epoch 166/500\n",
      "104/104 [==============================] - 0s 214us/step - loss: 0.2507\n",
      "Epoch 167/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 287us/step - loss: 0.2500\n",
      "Epoch 168/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.2494\n",
      "Epoch 169/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.2488\n",
      "Epoch 170/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2483\n",
      "Epoch 171/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.2492\n",
      "Epoch 172/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.2515\n",
      "Epoch 173/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.2523\n",
      "Epoch 174/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2492\n",
      "Epoch 175/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.2480\n",
      "Epoch 176/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2472\n",
      "Epoch 177/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2467\n",
      "Epoch 178/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.080 - 0s 167us/step - loss: 0.2473\n",
      "Epoch 179/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2479\n",
      "Epoch 180/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2466\n",
      "Epoch 181/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2459\n",
      "Epoch 182/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.2452\n",
      "Epoch 183/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2447\n",
      "Epoch 184/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2441\n",
      "Epoch 185/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.2437\n",
      "Epoch 186/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2432\n",
      "Epoch 187/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2427\n",
      "Epoch 188/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.081 - 0s 188us/step - loss: 0.2422\n",
      "Epoch 189/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.2418\n",
      "Epoch 190/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2413\n",
      "Epoch 191/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.2420\n",
      "Epoch 192/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.2509\n",
      "Epoch 193/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2456\n",
      "Epoch 194/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2424\n",
      "Epoch 195/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.2413\n",
      "Epoch 196/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2406\n",
      "Epoch 197/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2401\n",
      "Epoch 198/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2406\n",
      "Epoch 199/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.2415\n",
      "Epoch 200/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.2403\n",
      "Epoch 201/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2396\n",
      "Epoch 202/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 0.2389\n",
      "Epoch 203/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.2384\n",
      "Epoch 204/500\n",
      "104/104 [==============================] - 0s 212us/step - loss: 0.2394\n",
      "Epoch 205/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2414\n",
      "Epoch 206/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.2418\n",
      "Epoch 207/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2396\n",
      "Epoch 208/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2386\n",
      "Epoch 209/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2378\n",
      "Epoch 210/500\n",
      "104/104 [==============================] - 0s 240us/step - loss: 0.2372\n",
      "Epoch 211/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.2367\n",
      "Epoch 212/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.2363\n",
      "Epoch 213/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.077 - 0s 213us/step - loss: 0.2358\n",
      "Epoch 214/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.2353\n",
      "Epoch 215/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2350\n",
      "Epoch 216/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 0.2346\n",
      "Epoch 217/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.2342\n",
      "Epoch 218/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2354\n",
      "Epoch 219/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.2390\n",
      "Epoch 220/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.2388\n",
      "Epoch 221/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2357\n",
      "Epoch 222/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2347\n",
      "Epoch 223/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2340\n",
      "Epoch 224/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2334\n",
      "Epoch 225/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.2338\n",
      "Epoch 226/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.2352\n",
      "Epoch 227/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2339\n",
      "Epoch 228/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.2332\n",
      "Epoch 229/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.2327\n",
      "Epoch 230/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2322\n",
      "Epoch 231/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2318\n",
      "Epoch 232/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2314\n",
      "Epoch 233/500\n",
      "104/104 [==============================] - 0s 281us/step - loss: 0.2311\n",
      "Epoch 234/500\n",
      "104/104 [==============================] - 0s 281us/step - loss: 0.2308\n",
      "Epoch 235/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.2305\n",
      "Epoch 236/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2301\n",
      "Epoch 237/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2298\n",
      "Epoch 238/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2295\n",
      "Epoch 239/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.2302\n",
      "Epoch 240/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.2394\n",
      "Epoch 241/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2342\n",
      "Epoch 242/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.2311\n",
      "Epoch 243/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.2301\n",
      "Epoch 244/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2295\n",
      "Epoch 245/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.2290\n",
      "Epoch 246/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2287\n",
      "Epoch 247/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2295\n",
      "Epoch 248/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2306\n",
      "Epoch 249/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2294\n",
      "Epoch 250/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2288\n",
      "Epoch 251/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.074 - 0s 197us/step - loss: 0.2283\n",
      "Epoch 252/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.2279\n",
      "Epoch 253/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2275\n",
      "Epoch 254/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2288\n",
      "Epoch 255/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.2312\n",
      "Epoch 256/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.2313\n",
      "Epoch 257/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2291\n",
      "Epoch 258/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2282\n",
      "Epoch 259/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2275\n",
      "Epoch 260/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2271\n",
      "Epoch 261/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 182us/step - loss: 0.2267\n",
      "Epoch 262/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.2263\n",
      "Epoch 263/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2261\n",
      "Epoch 264/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.2258\n",
      "Epoch 265/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.2255\n",
      "Epoch 266/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2252\n",
      "Epoch 267/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2250\n",
      "Epoch 268/500\n",
      "104/104 [==============================] - 0s 156us/step - loss: 0.2247\n",
      "Epoch 269/500\n",
      "104/104 [==============================] - 0s 158us/step - loss: 0.2245\n",
      "Epoch 270/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.2256\n",
      "Epoch 271/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2308\n",
      "Epoch 272/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2293\n",
      "Epoch 273/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.2262\n",
      "Epoch 274/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.2253\n",
      "Epoch 275/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2247\n",
      "Epoch 276/500\n",
      "104/104 [==============================] - 0s 156us/step - loss: 0.2243\n",
      "Epoch 277/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2240\n",
      "Epoch 278/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2247\n",
      "Epoch 279/500\n",
      "104/104 [==============================] - 0s 255us/step - loss: 0.2259\n",
      "Epoch 280/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 0.2247\n",
      "Epoch 281/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.2241\n",
      "Epoch 282/500\n",
      "104/104 [==============================] - 0s 295us/step - loss: 0.2237\n",
      "Epoch 283/500\n",
      "104/104 [==============================] - 0s 304us/step - loss: 0.2233\n",
      "Epoch 284/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2230\n",
      "Epoch 285/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.2228\n",
      "Epoch 286/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 0.2225\n",
      "Epoch 287/500\n",
      "104/104 [==============================] - 0s 264us/step - loss: 0.2223\n",
      "Epoch 288/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.2221\n",
      "Epoch 289/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2218\n",
      "Epoch 290/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2216\n",
      "Epoch 291/500\n",
      "104/104 [==============================] - 0s 210us/step - loss: 0.2228\n",
      "Epoch 292/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.2311\n",
      "Epoch 293/500\n",
      "104/104 [==============================] - 0s 215us/step - loss: 0.2257\n",
      "Epoch 294/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.2234\n",
      "Epoch 295/500\n",
      "104/104 [==============================] - 0s 235us/step - loss: 0.2225\n",
      "Epoch 296/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.2219\n",
      "Epoch 297/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.2216\n",
      "Epoch 298/500\n",
      "104/104 [==============================] - 0s 233us/step - loss: 0.2213\n",
      "Epoch 299/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2210\n",
      "Epoch 300/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2207\n",
      "Epoch 301/500\n",
      "104/104 [==============================] - 0s 157us/step - loss: 0.2206\n",
      "Epoch 302/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2203\n",
      "Epoch 303/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 0.2201\n",
      "Epoch 304/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2212\n",
      "Epoch 305/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2257\n",
      "Epoch 306/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2252\n",
      "Epoch 307/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.2220\n",
      "Epoch 308/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.2211\n",
      "Epoch 309/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2205\n",
      "Epoch 310/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2201\n",
      "Epoch 311/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2199\n",
      "Epoch 312/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2203\n",
      "Epoch 313/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.2220\n",
      "Epoch 314/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2207\n",
      "Epoch 315/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2201\n",
      "Epoch 316/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2198\n",
      "Epoch 317/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2195\n",
      "Epoch 318/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2192\n",
      "Epoch 319/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2191\n",
      "Epoch 320/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2188\n",
      "Epoch 321/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 0.2186\n",
      "Epoch 322/500\n",
      "104/104 [==============================] - 0s 240us/step - loss: 0.2185\n",
      "Epoch 323/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.2182\n",
      "Epoch 324/500\n",
      "104/104 [==============================] - 0s 297us/step - loss: 0.2196\n",
      "Epoch 325/500\n",
      "104/104 [==============================] - 0s 510us/step - loss: 0.2258\n",
      "Epoch 326/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.2226\n",
      "Epoch 327/500\n",
      "104/104 [==============================] - 0s 212us/step - loss: 0.2201\n",
      "Epoch 328/500\n",
      "104/104 [==============================] - 0s 224us/step - loss: 0.2193\n",
      "Epoch 329/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2187\n",
      "Epoch 330/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.2184\n",
      "Epoch 331/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.2182\n",
      "Epoch 332/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2180\n",
      "Epoch 333/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2177\n",
      "Epoch 334/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.2175\n",
      "Epoch 335/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2174\n",
      "Epoch 336/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2172\n",
      "Epoch 337/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.2170\n",
      "Epoch 338/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.2169\n",
      "Epoch 339/500\n",
      "104/104 [==============================] - 0s 447us/step - loss: 0.2177\n",
      "Epoch 340/500\n",
      "104/104 [==============================] - 0s 469us/step - loss: 0.2238\n",
      "Epoch 341/500\n",
      "104/104 [==============================] - 0s 280us/step - loss: 0.2222\n",
      "Epoch 342/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.2189\n",
      "Epoch 343/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 0.2180\n",
      "Epoch 344/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 0.2175\n",
      "Epoch 345/500\n",
      "104/104 [==============================] - 0s 241us/step - loss: 0.2172\n",
      "Epoch 346/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 0.2169\n",
      "Epoch 347/500\n",
      "104/104 [==============================] - 0s 237us/step - loss: 0.2167\n",
      "Epoch 348/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.2175\n",
      "Epoch 349/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.2190\n",
      "Epoch 350/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.2177\n",
      "Epoch 351/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2172\n",
      "Epoch 352/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2168\n",
      "Epoch 353/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2166\n",
      "Epoch 354/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2164\n",
      "Epoch 355/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2162\n",
      "Epoch 356/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 188us/step - loss: 0.2176\n",
      "Epoch 357/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2198\n",
      "Epoch 358/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2207\n",
      "Epoch 359/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.2181\n",
      "Epoch 360/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2172\n",
      "Epoch 361/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2168\n",
      "Epoch 362/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2165\n",
      "Epoch 363/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.2163\n",
      "Epoch 364/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2160\n",
      "Epoch 365/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.2158\n",
      "Epoch 366/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.2156\n",
      "Epoch 367/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2155\n",
      "Epoch 368/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2154\n",
      "Epoch 369/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2152\n",
      "Epoch 370/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.2151\n",
      "Epoch 371/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.2149\n",
      "Epoch 372/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.2148\n",
      "Epoch 373/500\n",
      "104/104 [==============================] - 0s 244us/step - loss: 0.2147\n",
      "Epoch 374/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2145\n",
      "Epoch 375/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2144\n",
      "Epoch 376/500\n",
      "104/104 [==============================] - 0s 503us/step - loss: 0.2157\n",
      "Epoch 377/500\n",
      "104/104 [==============================] - 0s 273us/step - loss: 0.2237\n",
      "Epoch 378/500\n",
      "104/104 [==============================] - 0s 309us/step - loss: 0.2198\n",
      "Epoch 379/500\n",
      "104/104 [==============================] - 0s 229us/step - loss: 0.2165\n",
      "Epoch 380/500\n",
      "104/104 [==============================] - 0s 232us/step - loss: 0.2156\n",
      "Epoch 381/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 0.2151\n",
      "Epoch 382/500\n",
      "104/104 [==============================] - 0s 278us/step - loss: 0.2149\n",
      "Epoch 383/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.2146\n",
      "Epoch 384/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.2145\n",
      "Epoch 385/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2152\n",
      "Epoch 386/500\n",
      "104/104 [==============================] - 0s 147us/step - loss: 0.2168\n",
      "Epoch 387/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2166\n",
      "Epoch 388/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.2186\n",
      "Epoch 389/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2161\n",
      "Epoch 390/500\n",
      "104/104 [==============================] - 0s 285us/step - loss: 0.2153\n",
      "Epoch 391/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 0.2149\n",
      "Epoch 392/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2147\n",
      "Epoch 393/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2145\n",
      "Epoch 394/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.063 - 0s 199us/step - loss: 0.2150\n",
      "Epoch 395/500\n",
      "104/104 [==============================] - 0s 213us/step - loss: 0.2168\n",
      "Epoch 396/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.2154\n",
      "Epoch 397/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2149\n",
      "Epoch 398/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2146\n",
      "Epoch 399/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2143\n",
      "Epoch 400/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2141\n",
      "Epoch 401/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.2140\n",
      "Epoch 402/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.2138\n",
      "Epoch 403/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2137\n",
      "Epoch 404/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2135\n",
      "Epoch 405/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.2134\n",
      "Epoch 406/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.2133\n",
      "Epoch 407/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2132\n",
      "Epoch 408/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2130\n",
      "Epoch 409/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 0.2130\n",
      "Epoch 410/500\n",
      "104/104 [==============================] - 0s 265us/step - loss: 0.2128\n",
      "Epoch 411/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.2127\n",
      "Epoch 412/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2126\n",
      "Epoch 413/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 0.2125\n",
      "Epoch 414/500\n",
      "104/104 [==============================] - 0s 226us/step - loss: 0.2124\n",
      "Epoch 415/500\n",
      "104/104 [==============================] - 0s 224us/step - loss: 0.2135\n",
      "Epoch 416/500\n",
      "104/104 [==============================] - 0s 257us/step - loss: 0.2240\n",
      "Epoch 417/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2179\n",
      "Epoch 418/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.2146\n",
      "Epoch 419/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.2136\n",
      "Epoch 420/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2131\n",
      "Epoch 421/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2129\n",
      "Epoch 422/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.2127\n",
      "Epoch 423/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.2125\n",
      "Epoch 424/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2125\n",
      "Epoch 425/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.2136\n",
      "Epoch 426/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2169\n",
      "Epoch 427/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.2178\n",
      "Epoch 428/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2146\n",
      "Epoch 429/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2136\n",
      "Epoch 430/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.2132\n",
      "Epoch 431/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2127\n",
      "Epoch 432/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.2124\n",
      "Epoch 433/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2121\n",
      "Epoch 434/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 0.2119\n",
      "Epoch 435/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2127\n",
      "Epoch 436/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2141\n",
      "Epoch 437/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2126\n",
      "Epoch 438/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.2121\n",
      "Epoch 439/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2117\n",
      "Epoch 440/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2115\n",
      "Epoch 441/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2128\n",
      "Epoch 442/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2144\n",
      "Epoch 443/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.2123\n",
      "Epoch 444/500\n",
      "104/104 [==============================] - 0s 214us/step - loss: 0.2122\n",
      "Epoch 445/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2140\n",
      "Epoch 446/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2124\n",
      "Epoch 447/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.2118\n",
      "Epoch 448/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2115\n",
      "Epoch 449/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2112\n",
      "Epoch 450/500\n",
      "104/104 [==============================] - 0s 169us/step - loss: 0.2110\n",
      "Epoch 451/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 185us/step - loss: 0.2108\n",
      "Epoch 452/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2106\n",
      "Epoch 453/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.2104\n",
      "Epoch 454/500\n",
      "104/104 [==============================] - 0s 158us/step - loss: 0.2103\n",
      "Epoch 455/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.2101\n",
      "Epoch 456/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.062 - 0s 176us/step - loss: 0.2100\n",
      "Epoch 457/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.2099\n",
      "Epoch 458/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.2097\n",
      "Epoch 459/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2096\n",
      "Epoch 460/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.2095\n",
      "Epoch 461/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.2094\n",
      "Epoch 462/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.2093\n",
      "Epoch 463/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2092\n",
      "Epoch 464/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.061 - 0s 179us/step - loss: 0.2091\n",
      "Epoch 465/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.2090\n",
      "Epoch 466/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2104\n",
      "Epoch 467/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.2210\n",
      "Epoch 468/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2136\n",
      "Epoch 469/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2109\n",
      "Epoch 470/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2101\n",
      "Epoch 471/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.2097\n",
      "Epoch 472/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.2094\n",
      "Epoch 473/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2093\n",
      "Epoch 474/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2091\n",
      "Epoch 475/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.2089\n",
      "Epoch 476/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2088\n",
      "Epoch 477/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.2087\n",
      "Epoch 478/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.2086\n",
      "Epoch 479/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2085\n",
      "Epoch 480/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.2084\n",
      "Epoch 481/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.2083\n",
      "Epoch 482/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.2096\n",
      "Epoch 483/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.2179\n",
      "Epoch 484/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.2137\n",
      "Epoch 485/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.2104\n",
      "Epoch 486/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2095\n",
      "Epoch 487/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.2091\n",
      "Epoch 488/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.2088\n",
      "Epoch 489/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.060 - 0s 191us/step - loss: 0.2086\n",
      "Epoch 490/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.2085\n",
      "Epoch 491/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.2083\n",
      "Epoch 492/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.2082\n",
      "Epoch 493/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.2092\n",
      "Epoch 494/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.2122\n",
      "Epoch 495/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2136\n",
      "Epoch 496/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.2104\n",
      "Epoch 497/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.2094\n",
      "Epoch 498/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.2090\n",
      "Epoch 499/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.2087\n",
      "Epoch 500/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.2085\n",
      "Epoch 1/500\n",
      "104/104 [==============================] - 1s 11ms/step - loss: 5.6066\n",
      "Epoch 2/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 3.2553\n",
      "Epoch 3/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 2.4867\n",
      "Epoch 4/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 2.0236\n",
      "Epoch 5/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 1.7506\n",
      "Epoch 6/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 1.5574\n",
      "Epoch 7/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 1.4053\n",
      "Epoch 8/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 1.2808\n",
      "Epoch 9/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 1.1756\n",
      "Epoch 10/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 1.0820\n",
      "Epoch 11/500\n",
      "104/104 [==============================] - 0s 164us/step - loss: 0.9985\n",
      "Epoch 12/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.9257\n",
      "Epoch 13/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.8576\n",
      "Epoch 14/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.7956\n",
      "Epoch 15/500\n",
      "104/104 [==============================] - 0s 169us/step - loss: 0.7401\n",
      "Epoch 16/500\n",
      "104/104 [==============================] - 0s 155us/step - loss: 0.6876\n",
      "Epoch 17/500\n",
      "104/104 [==============================] - 0s 144us/step - loss: 0.6415\n",
      "Epoch 18/500\n",
      "104/104 [==============================] - 0s 161us/step - loss: 0.5989\n",
      "Epoch 19/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.5586\n",
      "Epoch 20/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.5217\n",
      "Epoch 21/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.5007\n",
      "Epoch 22/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.4702\n",
      "Epoch 23/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.4597\n",
      "Epoch 24/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.4329\n",
      "Epoch 25/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.4312\n",
      "Epoch 26/500\n",
      "104/104 [==============================] - 0s 166us/step - loss: 0.4072\n",
      "Epoch 27/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.3976\n",
      "Epoch 28/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.3962\n",
      "Epoch 29/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.3787\n",
      "Epoch 30/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.3680\n",
      "Epoch 31/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.3678\n",
      "Epoch 32/500\n",
      "104/104 [==============================] - 0s 270us/step - loss: 0.3638\n",
      "Epoch 33/500\n",
      "104/104 [==============================] - 0s 220us/step - loss: 0.3508\n",
      "Epoch 34/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.3426\n",
      "Epoch 35/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 0.3409\n",
      "Epoch 36/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.3400\n",
      "Epoch 37/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.3290\n",
      "Epoch 38/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.3218\n",
      "Epoch 39/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.3151\n",
      "Epoch 40/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.3154\n",
      "Epoch 41/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.3123\n",
      "Epoch 42/500\n",
      "104/104 [==============================] - 0s 212us/step - loss: 0.3018\n",
      "Epoch 43/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.2945\n",
      "Epoch 44/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.2872\n",
      "Epoch 45/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.2882\n",
      "Epoch 46/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 339us/step - loss: 0.2831\n",
      "Epoch 47/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 0.2729\n",
      "Epoch 48/500\n",
      "104/104 [==============================] - 0s 224us/step - loss: 0.2653\n",
      "Epoch 49/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.2614\n",
      "Epoch 50/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.2604\n",
      "Epoch 51/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.2507\n",
      "Epoch 52/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.2430\n",
      "Epoch 53/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.2376\n",
      "Epoch 54/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.2375\n",
      "Epoch 55/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 0.2282\n",
      "Epoch 56/500\n",
      "104/104 [==============================] - 0s 447us/step - loss: 0.2208\n",
      "Epoch 57/500\n",
      "104/104 [==============================] - 0s 365us/step - loss: 0.2167\n",
      "Epoch 58/500\n",
      "104/104 [==============================] - 0s 212us/step - loss: 0.2158\n",
      "Epoch 59/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.2072\n",
      "Epoch 60/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.268 - 0s 162us/step - loss: 0.2002\n",
      "Epoch 61/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.1989\n",
      "Epoch 62/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.1954\n",
      "Epoch 63/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.1878\n",
      "Epoch 64/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.1849\n",
      "Epoch 65/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.1831\n",
      "Epoch 66/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.1762\n",
      "Epoch 67/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.1737\n",
      "Epoch 68/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.1721\n",
      "Epoch 69/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 0.1661\n",
      "Epoch 70/500\n",
      "104/104 [==============================] - 0s 237us/step - loss: 0.1645\n",
      "Epoch 71/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.1624\n",
      "Epoch 72/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.1573\n",
      "Epoch 73/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.1568\n",
      "Epoch 74/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.1540\n",
      "Epoch 75/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.1502\n",
      "Epoch 76/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.1502\n",
      "Epoch 77/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.267 - 0s 180us/step - loss: 0.1460\n",
      "Epoch 78/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.1449\n",
      "Epoch 79/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.1432\n",
      "Epoch 80/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.1397\n",
      "Epoch 81/500\n",
      "104/104 [==============================] - 0s 260us/step - loss: 0.1398\n",
      "Epoch 82/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.1372\n",
      "Epoch 83/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.1353\n",
      "Epoch 84/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.1345\n",
      "Epoch 85/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.1313\n",
      "Epoch 86/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.1312\n",
      "Epoch 87/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.1291\n",
      "Epoch 88/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.1275\n",
      "Epoch 89/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.1266\n",
      "Epoch 90/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.1243\n",
      "Epoch 91/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.1241\n",
      "Epoch 92/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.1216\n",
      "Epoch 93/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.1212\n",
      "Epoch 94/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.1196\n",
      "Epoch 95/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 0.1183\n",
      "Epoch 96/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.1175\n",
      "Epoch 97/500\n",
      "104/104 [==============================] - 0s 236us/step - loss: 0.1159\n",
      "Epoch 98/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.1156\n",
      "Epoch 99/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.1136\n",
      "Epoch 100/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.1135\n",
      "Epoch 101/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 0.1119\n",
      "Epoch 102/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.1113\n",
      "Epoch 103/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.1104\n",
      "Epoch 104/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.1095\n",
      "Epoch 105/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.1089\n",
      "Epoch 106/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.186 - 0s 201us/step - loss: 0.1078\n",
      "Epoch 107/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.1074\n",
      "Epoch 108/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.1061\n",
      "Epoch 109/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.1057\n",
      "Epoch 110/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.1048\n",
      "Epoch 111/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.1039\n",
      "Epoch 112/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 0.1035\n",
      "Epoch 113/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.1024\n",
      "Epoch 114/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.1022\n",
      "Epoch 115/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 0.1013\n",
      "Epoch 116/500\n",
      "104/104 [==============================] - 0s 309us/step - loss: 0.1006\n",
      "Epoch 117/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.1002\n",
      "Epoch 118/500\n",
      "104/104 [==============================] - 0s 258us/step - loss: 0.0992\n",
      "Epoch 119/500\n",
      "104/104 [==============================] - 0s 272us/step - loss: 0.0991\n",
      "Epoch 120/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.159 - 0s 317us/step - loss: 0.0982\n",
      "Epoch 121/500\n",
      "104/104 [==============================] - 0s 333us/step - loss: 0.0978\n",
      "Epoch 122/500\n",
      "104/104 [==============================] - 0s 281us/step - loss: 0.0972\n",
      "Epoch 123/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.155 - 0s 202us/step - loss: 0.0965\n",
      "Epoch 124/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.0963\n",
      "Epoch 125/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.0954\n",
      "Epoch 126/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.151 - 0s 177us/step - loss: 0.0953\n",
      "Epoch 127/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.0946\n",
      "Epoch 128/500\n",
      "104/104 [==============================] - 0s 224us/step - loss: 0.0942\n",
      "Epoch 129/500\n",
      "104/104 [==============================] - 0s 340us/step - loss: 0.0937\n",
      "Epoch 130/500\n",
      "104/104 [==============================] - 0s 217us/step - loss: 0.0932\n",
      "Epoch 131/500\n",
      "104/104 [==============================] - 0s 239us/step - loss: 0.0929\n",
      "Epoch 132/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.0922\n",
      "Epoch 133/500\n",
      "104/104 [==============================] - 0s 403us/step - loss: 0.0922\n",
      "Epoch 134/500\n",
      "104/104 [==============================] - 0s 256us/step - loss: 0.0915\n",
      "Epoch 135/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0912\n",
      "Epoch 136/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 0.0908\n",
      "Epoch 137/500\n",
      "104/104 [==============================] - 0s 251us/step - loss: 0.0903\n",
      "Epoch 138/500\n",
      "104/104 [==============================] - 0s 246us/step - loss: 0.0901\n",
      "Epoch 139/500\n",
      "104/104 [==============================] - 0s 333us/step - loss: 0.0895\n",
      "Epoch 140/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 296us/step - loss: 0.0894\n",
      "Epoch 141/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 0.0888\n",
      "Epoch 142/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 0.0887\n",
      "Epoch 143/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.0881\n",
      "Epoch 144/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 0.0879\n",
      "Epoch 145/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.0876\n",
      "Epoch 146/500\n",
      "104/104 [==============================] - 0s 239us/step - loss: 0.0873\n",
      "Epoch 147/500\n",
      "104/104 [==============================] - 0s 209us/step - loss: 0.0870\n",
      "Epoch 148/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.0866\n",
      "Epoch 149/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.0864\n",
      "Epoch 150/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.0860\n",
      "Epoch 151/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.0859\n",
      "Epoch 152/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.0854\n",
      "Epoch 153/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.0854\n",
      "Epoch 154/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 0.0849\n",
      "Epoch 155/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.0848\n",
      "Epoch 156/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.0845\n",
      "Epoch 157/500\n",
      "104/104 [==============================] - 0s 270us/step - loss: 0.0843\n",
      "Epoch 158/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.0841\n",
      "Epoch 159/500\n",
      "104/104 [==============================] - 0s 253us/step - loss: 0.0838\n",
      "Epoch 160/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.0836\n",
      "Epoch 161/500\n",
      "104/104 [==============================] - 0s 183us/step - loss: 0.0834\n",
      "Epoch 162/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.0832\n",
      "Epoch 163/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.0829\n",
      "Epoch 164/500\n",
      "104/104 [==============================] - 0s 285us/step - loss: 0.0828\n",
      "Epoch 165/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.0825\n",
      "Epoch 166/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.0824\n",
      "Epoch 167/500\n",
      "104/104 [==============================] - 0s 539us/step - loss: 0.0821\n",
      "Epoch 168/500\n",
      "104/104 [==============================] - 0s 277us/step - loss: 0.0821\n",
      "Epoch 169/500\n",
      "104/104 [==============================] - 0s 341us/step - loss: 0.0817\n",
      "Epoch 170/500\n",
      "104/104 [==============================] - 0s 482us/step - loss: 0.0817\n",
      "Epoch 171/500\n",
      "104/104 [==============================] - 0s 258us/step - loss: 0.0814\n",
      "Epoch 172/500\n",
      "104/104 [==============================] - 0s 270us/step - loss: 0.0813\n",
      "Epoch 173/500\n",
      "104/104 [==============================] - 0s 290us/step - loss: 0.0811\n",
      "Epoch 174/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 0.0810\n",
      "Epoch 175/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.0808\n",
      "Epoch 176/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.0807\n",
      "Epoch 177/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.0805\n",
      "Epoch 178/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.0803\n",
      "Epoch 179/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.0802\n",
      "Epoch 180/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.0800\n",
      "Epoch 181/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.0799\n",
      "Epoch 182/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.0798\n",
      "Epoch 183/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.0796\n",
      "Epoch 184/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.0795\n",
      "Epoch 185/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.0794\n",
      "Epoch 186/500\n",
      "104/104 [==============================] - 0s 210us/step - loss: 0.0792\n",
      "Epoch 187/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.0792\n",
      "Epoch 188/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.0790\n",
      "Epoch 189/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.0789\n",
      "Epoch 190/500\n",
      "104/104 [==============================] - 0s 274us/step - loss: 0.0787\n",
      "Epoch 191/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0787\n",
      "Epoch 192/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.0785\n",
      "Epoch 193/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.0784\n",
      "Epoch 194/500\n",
      "104/104 [==============================] - 0s 235us/step - loss: 0.0783\n",
      "Epoch 195/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 0.0782\n",
      "Epoch 196/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.0781\n",
      "Epoch 197/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.0780\n",
      "Epoch 198/500\n",
      "104/104 [==============================] - 0s 210us/step - loss: 0.0779\n",
      "Epoch 199/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.0778\n",
      "Epoch 200/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.0777\n",
      "Epoch 201/500\n",
      "104/104 [==============================] - 0s 286us/step - loss: 0.0776\n",
      "Epoch 202/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.0775\n",
      "Epoch 203/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.0774\n",
      "Epoch 204/500\n",
      "104/104 [==============================] - 0s 169us/step - loss: 0.0773\n",
      "Epoch 205/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.0772\n",
      "Epoch 206/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0771\n",
      "Epoch 207/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.0770\n",
      "Epoch 208/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 0.0769\n",
      "Epoch 209/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.0769\n",
      "Epoch 210/500\n",
      "104/104 [==============================] - 0s 159us/step - loss: 0.0768\n",
      "Epoch 211/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.0767\n",
      "Epoch 212/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.0766\n",
      "Epoch 213/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 0.0765\n",
      "Epoch 214/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.0764\n",
      "Epoch 215/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.0763\n",
      "Epoch 216/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.0763\n",
      "Epoch 217/500\n",
      "104/104 [==============================] - 0s 240us/step - loss: 0.0762\n",
      "Epoch 218/500\n",
      "104/104 [==============================] - 0s 233us/step - loss: 0.0761\n",
      "Epoch 219/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0760\n",
      "Epoch 220/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.0760\n",
      "Epoch 221/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 0.0759\n",
      "Epoch 222/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.0758\n",
      "Epoch 223/500\n",
      "104/104 [==============================] - 0s 194us/step - loss: 0.0757\n",
      "Epoch 224/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.099 - 0s 198us/step - loss: 0.0757\n",
      "Epoch 225/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.0756\n",
      "Epoch 226/500\n",
      "104/104 [==============================] - 0s 214us/step - loss: 0.0755\n",
      "Epoch 227/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.0754\n",
      "Epoch 228/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.0754\n",
      "Epoch 229/500\n",
      "104/104 [==============================] - 0s 213us/step - loss: 0.0753\n",
      "Epoch 230/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.0753\n",
      "Epoch 231/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0752\n",
      "Epoch 232/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.098 - 0s 191us/step - loss: 0.0751\n",
      "Epoch 233/500\n",
      "104/104 [==============================] - 0s 289us/step - loss: 0.0750\n",
      "Epoch 234/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 214us/step - loss: 0.0750\n",
      "Epoch 235/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.0749\n",
      "Epoch 236/500\n",
      "104/104 [==============================] - 0s 213us/step - loss: 0.0749\n",
      "Epoch 237/500\n",
      "104/104 [==============================] - 0s 154us/step - loss: 0.0748\n",
      "Epoch 238/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.0747\n",
      "Epoch 239/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.0746\n",
      "Epoch 240/500\n",
      "104/104 [==============================] - 0s 222us/step - loss: 0.0746\n",
      "Epoch 241/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.0745\n",
      "Epoch 242/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 0.0745\n",
      "Epoch 243/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.0744\n",
      "Epoch 244/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.0744\n",
      "Epoch 245/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.0743\n",
      "Epoch 246/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.0743\n",
      "Epoch 247/500\n",
      "104/104 [==============================] - 0s 252us/step - loss: 0.0741\n",
      "Epoch 248/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.0741\n",
      "Epoch 249/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.0740\n",
      "Epoch 250/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0740\n",
      "Epoch 251/500\n",
      "104/104 [==============================] - 0s 157us/step - loss: 0.0739\n",
      "Epoch 252/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.0739\n",
      "Epoch 253/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.0738\n",
      "Epoch 254/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0738\n",
      "Epoch 255/500\n",
      "104/104 [==============================] - 0s 237us/step - loss: 0.0737\n",
      "Epoch 256/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 0.0736\n",
      "Epoch 257/500\n",
      "104/104 [==============================] - 0s 174us/step - loss: 0.0736\n",
      "Epoch 258/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.096 - 0s 211us/step - loss: 0.0735\n",
      "Epoch 259/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.0735\n",
      "Epoch 260/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.0734\n",
      "Epoch 261/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0734\n",
      "Epoch 262/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.0733\n",
      "Epoch 263/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.096 - 0s 188us/step - loss: 0.0733\n",
      "Epoch 264/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.095 - 0s 168us/step - loss: 0.0732\n",
      "Epoch 265/500\n",
      "104/104 [==============================] - 0s 214us/step - loss: 0.0732\n",
      "Epoch 266/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.0731\n",
      "Epoch 267/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.0731\n",
      "Epoch 268/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.0730\n",
      "Epoch 269/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.0730\n",
      "Epoch 270/500\n",
      "104/104 [==============================] - 0s 162us/step - loss: 0.0729\n",
      "Epoch 271/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0729\n",
      "Epoch 272/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.0728\n",
      "Epoch 273/500\n",
      "104/104 [==============================] - 0s 235us/step - loss: 0.0727\n",
      "Epoch 274/500\n",
      "104/104 [==============================] - 0s 248us/step - loss: 0.0727\n",
      "Epoch 275/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.0726\n",
      "Epoch 276/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.0726\n",
      "Epoch 277/500\n",
      "104/104 [==============================] - 0s 169us/step - loss: 0.0725\n",
      "Epoch 278/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0725\n",
      "Epoch 279/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.0724\n",
      "Epoch 280/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 0.0724\n",
      "Epoch 281/500\n",
      "104/104 [==============================] - 0s 228us/step - loss: 0.0723\n",
      "Epoch 282/500\n",
      "104/104 [==============================] - 0s 213us/step - loss: 0.0723\n",
      "Epoch 283/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.0723\n",
      "Epoch 284/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0722\n",
      "Epoch 285/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.0722\n",
      "Epoch 286/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.0721\n",
      "Epoch 287/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.0721\n",
      "Epoch 288/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.0720\n",
      "Epoch 289/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.0720\n",
      "Epoch 290/500\n",
      "104/104 [==============================] - 0s 237us/step - loss: 0.0719\n",
      "Epoch 291/500\n",
      "104/104 [==============================] - 0s 214us/step - loss: 0.0719\n",
      "Epoch 292/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.0718\n",
      "Epoch 293/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.0718\n",
      "Epoch 294/500\n",
      "104/104 [==============================] - 0s 284us/step - loss: 0.0717\n",
      "Epoch 295/500\n",
      "104/104 [==============================] - 0s 281us/step - loss: 0.0717\n",
      "Epoch 296/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.0716\n",
      "Epoch 297/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.0716\n",
      "Epoch 298/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0716\n",
      "Epoch 299/500\n",
      "104/104 [==============================] - 0s 215us/step - loss: 0.0715\n",
      "Epoch 300/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.0715\n",
      "Epoch 301/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.0714\n",
      "Epoch 302/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.0714\n",
      "Epoch 303/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.0713\n",
      "Epoch 304/500\n",
      "104/104 [==============================] - 0s 210us/step - loss: 0.0713\n",
      "Epoch 305/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.0712\n",
      "Epoch 306/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.0712\n",
      "Epoch 307/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.0711\n",
      "Epoch 308/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0711\n",
      "Epoch 309/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.093 - 0s 193us/step - loss: 0.0711\n",
      "Epoch 310/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.0710\n",
      "Epoch 311/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0710\n",
      "Epoch 312/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.0709\n",
      "Epoch 313/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.0709\n",
      "Epoch 314/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.0708\n",
      "Epoch 315/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.0708\n",
      "Epoch 316/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.0707\n",
      "Epoch 317/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.0707\n",
      "Epoch 318/500\n",
      "104/104 [==============================] - 0s 246us/step - loss: 0.0707\n",
      "Epoch 319/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.0706\n",
      "Epoch 320/500\n",
      "104/104 [==============================] - 0s 216us/step - loss: 0.0706\n",
      "Epoch 321/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0705\n",
      "Epoch 322/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0705\n",
      "Epoch 323/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.092 - 0s 262us/step - loss: 0.0704\n",
      "Epoch 324/500\n",
      "104/104 [==============================] - 0s 218us/step - loss: 0.0704\n",
      "Epoch 325/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.0703\n",
      "Epoch 326/500\n",
      "104/104 [==============================] - 0s 261us/step - loss: 0.0703\n",
      "Epoch 327/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.0703\n",
      "Epoch 328/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 198us/step - loss: 0.0702\n",
      "Epoch 329/500\n",
      "104/104 [==============================] - 0s 244us/step - loss: 0.0702\n",
      "Epoch 330/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.0701\n",
      "Epoch 331/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.0701\n",
      "Epoch 332/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.0700\n",
      "Epoch 333/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.091 - 0s 182us/step - loss: 0.0700\n",
      "Epoch 334/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.0699\n",
      "Epoch 335/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.0699\n",
      "Epoch 336/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.0699\n",
      "Epoch 337/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.0698\n",
      "Epoch 338/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.0698\n",
      "Epoch 339/500\n",
      "104/104 [==============================] - 0s 230us/step - loss: 0.0698\n",
      "Epoch 340/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.0697\n",
      "Epoch 341/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.0697\n",
      "Epoch 342/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.0697\n",
      "Epoch 343/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0696\n",
      "Epoch 344/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.0696\n",
      "Epoch 345/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.0695\n",
      "Epoch 346/500\n",
      "104/104 [==============================] - 0s 168us/step - loss: 0.0695\n",
      "Epoch 347/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.0695\n",
      "Epoch 348/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.0694\n",
      "Epoch 349/500\n",
      "104/104 [==============================] - 0s 165us/step - loss: 0.0694\n",
      "Epoch 350/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0693\n",
      "Epoch 351/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.0693\n",
      "Epoch 352/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.0692\n",
      "Epoch 353/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0692\n",
      "Epoch 354/500\n",
      "104/104 [==============================] - 0s 198us/step - loss: 0.0692\n",
      "Epoch 355/500\n",
      "104/104 [==============================] - 0s 155us/step - loss: 0.0691\n",
      "Epoch 356/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.0691\n",
      "Epoch 357/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.0690\n",
      "Epoch 358/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.0690\n",
      "Epoch 359/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.0689\n",
      "Epoch 360/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.0689\n",
      "Epoch 361/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.0689\n",
      "Epoch 362/500\n",
      "104/104 [==============================] - 0s 258us/step - loss: 0.0688\n",
      "Epoch 363/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0688\n",
      "Epoch 364/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.0688\n",
      "Epoch 365/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.0688\n",
      "Epoch 366/500\n",
      "104/104 [==============================] - 0s 191us/step - loss: 0.0687\n",
      "Epoch 367/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.0687\n",
      "Epoch 368/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.090 - 0s 181us/step - loss: 0.0686\n",
      "Epoch 369/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.0686\n",
      "Epoch 370/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.090 - 0s 182us/step - loss: 0.0686\n",
      "Epoch 371/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.0685\n",
      "Epoch 372/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.0685\n",
      "Epoch 373/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.0684\n",
      "Epoch 374/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.0684\n",
      "Epoch 375/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.0683\n",
      "Epoch 376/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.0683\n",
      "Epoch 377/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.089 - 0s 175us/step - loss: 0.0683\n",
      "Epoch 378/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0682\n",
      "Epoch 379/500\n",
      "104/104 [==============================] - 0s 167us/step - loss: 0.0682\n",
      "Epoch 380/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.0682\n",
      "Epoch 381/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0682\n",
      "Epoch 382/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.0681\n",
      "Epoch 383/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.0681\n",
      "Epoch 384/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.0680\n",
      "Epoch 385/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.0680\n",
      "Epoch 386/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.0680\n",
      "Epoch 387/500\n",
      "104/104 [==============================] - 0s 224us/step - loss: 0.0679\n",
      "Epoch 388/500\n",
      "104/104 [==============================] - 0s 234us/step - loss: 0.0679\n",
      "Epoch 389/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.0678\n",
      "Epoch 390/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.0678\n",
      "Epoch 391/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.0678\n",
      "Epoch 392/500\n",
      "104/104 [==============================] - 0s 160us/step - loss: 0.0677\n",
      "Epoch 393/500\n",
      "104/104 [==============================] - 0s 238us/step - loss: 0.0677\n",
      "Epoch 394/500\n",
      "104/104 [==============================] - 0s 266us/step - loss: 0.0677\n",
      "Epoch 395/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.0676\n",
      "Epoch 396/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.0676\n",
      "Epoch 397/500\n",
      "104/104 [==============================] - 0s 173us/step - loss: 0.0676\n",
      "Epoch 398/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.0675\n",
      "Epoch 399/500\n",
      "104/104 [==============================] - 0s 193us/step - loss: 0.0675\n",
      "Epoch 400/500\n",
      "104/104 [==============================] - 0s 196us/step - loss: 0.0675\n",
      "Epoch 401/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.0674\n",
      "Epoch 402/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.0674\n",
      "Epoch 403/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 0.0673\n",
      "Epoch 404/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.088 - 0s 212us/step - loss: 0.0673\n",
      "Epoch 405/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.0673\n",
      "Epoch 406/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0673\n",
      "Epoch 407/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.0672\n",
      "Epoch 408/500\n",
      "104/104 [==============================] - 0s 178us/step - loss: 0.0672\n",
      "Epoch 409/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.0671\n",
      "Epoch 410/500\n",
      "104/104 [==============================] - 0s 265us/step - loss: 0.0671\n",
      "Epoch 411/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.0671\n",
      "Epoch 412/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0670\n",
      "Epoch 413/500\n",
      "104/104 [==============================] - 0s 204us/step - loss: 0.0670\n",
      "Epoch 414/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0670\n",
      "Epoch 415/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.0669\n",
      "Epoch 416/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0669\n",
      "Epoch 417/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.0669\n",
      "Epoch 418/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.0668\n",
      "Epoch 419/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0668\n",
      "Epoch 420/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0668\n",
      "Epoch 421/500\n",
      "104/104 [==============================] - 0s 246us/step - loss: 0.0667\n",
      "Epoch 422/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104/104 [==============================] - 0s 253us/step - loss: 0.0667\n",
      "Epoch 423/500\n",
      "104/104 [==============================] - 0s 235us/step - loss: 0.0666\n",
      "Epoch 424/500\n",
      "104/104 [==============================] - 0s 199us/step - loss: 0.0666\n",
      "Epoch 425/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.0666\n",
      "Epoch 426/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.0666\n",
      "Epoch 427/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.0665\n",
      "Epoch 428/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.0665\n",
      "Epoch 429/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.0665\n",
      "Epoch 430/500\n",
      "104/104 [==============================] - 0s 231us/step - loss: 0.0664\n",
      "Epoch 431/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.0664\n",
      "Epoch 432/500\n",
      "104/104 [==============================] - 0s 176us/step - loss: 0.0664\n",
      "Epoch 433/500\n",
      "104/104 [==============================] - 0s 206us/step - loss: 0.0663\n",
      "Epoch 434/500\n",
      "104/104 [==============================] - 0s 214us/step - loss: 0.0663\n",
      "Epoch 435/500\n",
      "104/104 [==============================] - 0s 186us/step - loss: 0.0663\n",
      "Epoch 436/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.0663\n",
      "Epoch 437/500\n",
      "104/104 [==============================] - 0s 171us/step - loss: 0.0662\n",
      "Epoch 438/500\n",
      "104/104 [==============================] - 0s 249us/step - loss: 0.0662\n",
      "Epoch 439/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.0661\n",
      "Epoch 440/500\n",
      "104/104 [==============================] - ETA: 0s - loss: 0.087 - 0s 172us/step - loss: 0.0661\n",
      "Epoch 441/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0661\n",
      "Epoch 442/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.0660\n",
      "Epoch 443/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.0660\n",
      "Epoch 444/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.0660\n",
      "Epoch 445/500\n",
      "104/104 [==============================] - 0s 242us/step - loss: 0.0660\n",
      "Epoch 446/500\n",
      "104/104 [==============================] - 0s 257us/step - loss: 0.0659\n",
      "Epoch 447/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.0659\n",
      "Epoch 448/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.0659\n",
      "Epoch 449/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.0658\n",
      "Epoch 450/500\n",
      "104/104 [==============================] - 0s 170us/step - loss: 0.0653\n",
      "Epoch 451/500\n",
      "104/104 [==============================] - 0s 175us/step - loss: 0.0646\n",
      "Epoch 452/500\n",
      "104/104 [==============================] - 0s 184us/step - loss: 0.0642\n",
      "Epoch 453/500\n",
      "104/104 [==============================] - 0s 245us/step - loss: 0.0637\n",
      "Epoch 454/500\n",
      "104/104 [==============================] - 0s 203us/step - loss: 0.0633\n",
      "Epoch 455/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.0631\n",
      "Epoch 456/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 0.0628\n",
      "Epoch 457/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.0625\n",
      "Epoch 458/500\n",
      "104/104 [==============================] - 0s 195us/step - loss: 0.0624\n",
      "Epoch 459/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.0621\n",
      "Epoch 460/500\n",
      "104/104 [==============================] - 0s 181us/step - loss: 0.0619\n",
      "Epoch 461/500\n",
      "104/104 [==============================] - 0s 221us/step - loss: 0.0617\n",
      "Epoch 462/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.0616\n",
      "Epoch 463/500\n",
      "104/104 [==============================] - 0s 177us/step - loss: 0.0614\n",
      "Epoch 464/500\n",
      "104/104 [==============================] - 0s 227us/step - loss: 0.0613\n",
      "Epoch 465/500\n",
      "104/104 [==============================] - 0s 182us/step - loss: 0.0612\n",
      "Epoch 466/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.0611\n",
      "Epoch 467/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.0610\n",
      "Epoch 468/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.0608\n",
      "Epoch 469/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0608\n",
      "Epoch 470/500\n",
      "104/104 [==============================] - 0s 163us/step - loss: 0.0607\n",
      "Epoch 471/500\n",
      "104/104 [==============================] - 0s 185us/step - loss: 0.0606\n",
      "Epoch 472/500\n",
      "104/104 [==============================] - 0s 202us/step - loss: 0.0606\n",
      "Epoch 473/500\n",
      "104/104 [==============================] - 0s 208us/step - loss: 0.0604\n",
      "Epoch 474/500\n",
      "104/104 [==============================] - 0s 172us/step - loss: 0.0604\n",
      "Epoch 475/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.0603\n",
      "Epoch 476/500\n",
      "104/104 [==============================] - 0s 179us/step - loss: 0.0603\n",
      "Epoch 477/500\n",
      "104/104 [==============================] - 0s 217us/step - loss: 0.0602\n",
      "Epoch 478/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.0601\n",
      "Epoch 479/500\n",
      "104/104 [==============================] - 0s 207us/step - loss: 0.0601\n",
      "Epoch 480/500\n",
      "104/104 [==============================] - 0s 238us/step - loss: 0.0600\n",
      "Epoch 481/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.0600\n",
      "Epoch 482/500\n",
      "104/104 [==============================] - 0s 211us/step - loss: 0.0599\n",
      "Epoch 483/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0599\n",
      "Epoch 484/500\n",
      "104/104 [==============================] - 0s 192us/step - loss: 0.0598\n",
      "Epoch 485/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0598\n",
      "Epoch 486/500\n",
      "104/104 [==============================] - 0s 180us/step - loss: 0.0597\n",
      "Epoch 487/500\n",
      "104/104 [==============================] - 0s 201us/step - loss: 0.0597\n",
      "Epoch 488/500\n",
      "104/104 [==============================] - 0s 197us/step - loss: 0.0596\n",
      "Epoch 489/500\n",
      "104/104 [==============================] - 0s 188us/step - loss: 0.0596\n",
      "Epoch 490/500\n",
      "104/104 [==============================] - 0s 235us/step - loss: 0.0595\n",
      "Epoch 491/500\n",
      "104/104 [==============================] - 0s 205us/step - loss: 0.0595\n",
      "Epoch 492/500\n",
      "104/104 [==============================] - 0s 187us/step - loss: 0.0594\n",
      "Epoch 493/500\n",
      "104/104 [==============================] - 0s 189us/step - loss: 0.0594\n",
      "Epoch 494/500\n",
      "104/104 [==============================] - 0s 210us/step - loss: 0.0594\n",
      "Epoch 495/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.0593\n",
      "Epoch 496/500\n",
      "104/104 [==============================] - 0s 219us/step - loss: 0.0593\n",
      "Epoch 497/500\n",
      "104/104 [==============================] - 0s 238us/step - loss: 0.0592\n",
      "Epoch 498/500\n",
      "104/104 [==============================] - 0s 190us/step - loss: 0.0592\n",
      "Epoch 499/500\n",
      "104/104 [==============================] - 0s 215us/step - loss: 0.0591\n",
      "Epoch 500/500\n",
      "104/104 [==============================] - 0s 200us/step - loss: 0.0591\n"
     ]
    }
   ],
   "source": [
    "ibb_predict = defaultdict(defaultdict)\n",
    "total_2_norm_diff = defaultdict(defaultdict)\n",
    "dl_scaler = defaultdict(StandardScaler)\n",
    "\n",
    "for non_communal in [15, 35, 55]:  \n",
    "    # some numerical values\n",
    "    encoding_dim = 5\n",
    "    s = 10 + non_communal\n",
    "    stock_index = np.concatenate((ranking[0:10], ranking[-non_communal:])) # portfolio index\n",
    "    \n",
    "    \n",
    "    # connect all layers\n",
    "    input_img = Input(shape=(s,))\n",
    "    encoded = Dense(encoding_dim, activation='relu', kernel_regularizer=regularizers.l2(0.005))(input_img)\n",
    "    decoded = Dense(1, activation= 'linear', kernel_regularizer=regularizers.l2(0.005))(encoded)\n",
    "    \n",
    "    \n",
    "    # construct and compile deep learning routine\n",
    "    deep_learner = Model(input_img, decoded)\n",
    "    deep_learner.compile(optimizer='sgd', loss='mean_squared_error')\n",
    "    \n",
    "    x = stock['calibrate']['percentage'].iloc[:, stock_index]\n",
    "    y = y_amended # amended percentage\n",
    "    \n",
    "    dl_scaler[s] = StandardScaler()       # Multi-layer Perceptron is sensitive to feature scaling, so it is highly recommended to scale your data\n",
    "    dl_scaler[s].fit(x)\n",
    "    x = dl_scaler[s].transform(x)  \n",
    "    \n",
    "    deep_learner.fit(x, y, shuffle=False, epochs=500, batch_size = 10)    # fit the model\n",
    "    deep_learner.save('model/beat_s' + str(s) + '.h5') # for validation phase use\n",
    "    \n",
    "    \n",
    "    # is it good?\n",
    "    relative_percentage = copy.deepcopy(deep_learner.predict(x))\n",
    "    relative_percentage[0] = 0\n",
    "    relative_percentage = (relative_percentage/100) + 1\n",
    "    \n",
    "    ibb_predict['calibrate'][s] = ibb['calibrate']['lp'][0] * (relative_percentage.cumprod())          \n",
    "    total_2_norm_diff['calibrate'][s] = np.linalg.norm((ibb_predict['calibrate'][s] - lp_amended)) # compare with amended last price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we would like to see how similar these 3 portfolios perfom as the modified(amended) IBB Index does on training dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S25 2-norm difference:  10.0282\n",
      "S45 2-norm difference:  35.3057\n",
      "S65 2-norm difference:  14.6669\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntqYvVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2258e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot results and 2-norm differences \n",
    "pd.Series(ibb['calibrate']['lp'].as_matrix(), index=pd.date_range(start='01/06/2012', periods=104, freq='W')).plot(label='ibb original', legend=True) \n",
    "pd.Series(lp_amended.as_matrix(), index=pd.date_range(start='01/06/2012', periods=104, freq='W')).plot(label='IBB Amended', legend=True)\n",
    "\n",
    "for s in [25, 45, 65]:\n",
    "    pd.Series(ibb_predict['calibrate'][s], index=pd.date_range(start='01/06/2012', periods = 104,freq='W')).plot(label='IBB S'+str(s), legend=True)\n",
    "    print(\"S\" +str(s) + \" 2-norm difference: \", total_2_norm_diff['calibrate'][s])    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 3: Validating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for non_communal in [15, 35, 55]:  \n",
    "    # some numerical values\n",
    "    encoding_dim = 5\n",
    "    s = 10 + non_communal\n",
    "    stock_index = np.concatenate((ranking[0:10], ranking[-non_communal:])) # portfolio index\n",
    "    \n",
    "    # load our trained models\n",
    "    deep_learner = load_model('model/beat_s' + str(s) + '.h5')\n",
    "    \n",
    "    x = stock['validate']['percentage'].iloc[:, stock_index]\n",
    "    x = dl_scaler[s].transform(x)  \n",
    "    \n",
    "    # is it good?\n",
    "    relative_percentage = copy.deepcopy(deep_learner.predict(x))\n",
    "    relative_percentage[0] = 0\n",
    "    relative_percentage = (relative_percentage/100) + 1\n",
    "    \n",
    "    ibb_predict['validate'][s] = ibb['validate']['lp'][0] * (relative_percentage.cumprod())          \n",
    "#     total_2_norm_diff['validate'][s] = np.linalg.norm((ibb_predict['validate'][s] - ibb['validate']['lp']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we would like to see how similar these 3 portfolios perform as the original IBB Index does, but on test dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15e5bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot original IBB last price\n",
    "pd.Series(ibb['validate']['lp'].as_matrix(), index=pd.date_range(start='01/03/2014', periods=122, freq='W')).plot(label='IBB original', legend=True) \n",
    "\n",
    "# 2-norm difference is now meaningless to compare\n",
    "for s in [25, 45, 65]:\n",
    "    pd.Series(ibb_predict['validate'][s], index=pd.date_range(start='01/03/2014', periods = 122,freq='W')).plot(label='IBB S'+str(s), legend=True)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}