Fibbonacci LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[0.0], [0.0], [0.6931471805599453], [1.0986122886681098]],\n",
       " [[0.0], [0.6931471805599453], [1.0986122886681098], [1.6094379124341003]],\n",
       " [[0.6931471805599453],\n",
       "  [1.0986122886681098],\n",
       "  [1.6094379124341003],\n",
       "  [2.0794415416798357]],\n",
       " [[1.0986122886681098],\n",
       "  [1.6094379124341003],\n",
       "  [2.0794415416798357],\n",
       "  [2.5649493574615367]],\n",
       " [[1.6094379124341003],\n",
       "  [2.0794415416798357],\n",
       "  [2.5649493574615367],\n",
       "  [3.044522437723423]],\n",
       " [[2.0794415416798357],\n",
       "  [2.5649493574615367],\n",
       "  [3.044522437723423],\n",
       "  [3.5263605246161616]],\n",
       " [[2.5649493574615367],\n",
       "  [3.044522437723423],\n",
       "  [3.5263605246161616],\n",
       "  [4.007333185232471]],\n",
       " [[3.044522437723423],\n",
       "  [3.5263605246161616],\n",
       "  [4.007333185232471],\n",
       "  [4.48863636973214]],\n",
       " [[3.5263605246161616],\n",
       "  [4.007333185232471],\n",
       "  [4.48863636973214],\n",
       "  [4.969813299576001]],\n",
       " [[4.007333185232471],\n",
       "  [4.48863636973214],\n",
       "  [4.969813299576001],\n",
       "  [5.4510384535657]],\n",
       " [[4.48863636973214],\n",
       "  [4.969813299576001],\n",
       "  [5.4510384535657],\n",
       "  [5.932245187448011]],\n",
       " [[4.969813299576001],\n",
       "  [5.4510384535657],\n",
       "  [5.932245187448011],\n",
       "  [6.413458957167357]],\n",
       " [[5.4510384535657],\n",
       "  [5.932245187448011],\n",
       "  [6.413458957167357],\n",
       "  [6.894670039433482]],\n",
       " [[5.932245187448011],\n",
       "  [6.413458957167357],\n",
       "  [6.894670039433482],\n",
       "  [7.3758821482150125]],\n",
       " [[6.413458957167357],\n",
       "  [6.894670039433482],\n",
       "  [7.3758821482150125],\n",
       "  [7.857093864902493]],\n",
       " [[6.894670039433482],\n",
       "  [7.3758821482150125],\n",
       "  [7.857093864902493],\n",
       "  [8.338305731356565]],\n",
       " [[7.3758821482150125],\n",
       "  [7.857093864902493],\n",
       "  [8.338305731356565],\n",
       "  [8.819517540604888]],\n",
       " [[7.857093864902493],\n",
       "  [8.338305731356565],\n",
       "  [8.819517540604888],\n",
       "  [9.300729371703863]],\n",
       " [[8.338305731356565],\n",
       "  [8.819517540604888],\n",
       "  [9.300729371703863],\n",
       "  [9.781941194456632]],\n",
       " [[8.819517540604888],\n",
       "  [9.300729371703863],\n",
       "  [9.781941194456632],\n",
       "  [10.263153020397368]],\n",
       " [[9.300729371703863],\n",
       "  [9.781941194456632],\n",
       "  [10.263153020397368],\n",
       "  [10.744364845120408]],\n",
       " [[9.781941194456632],\n",
       "  [10.263153020397368],\n",
       "  [10.744364845120408],\n",
       "  [11.225576670308568]],\n",
       " [[10.263153020397368],\n",
       "  [10.744364845120408],\n",
       "  [11.225576670308568],\n",
       "  [11.706788495319067]],\n",
       " [[10.744364845120408],\n",
       "  [11.225576670308568],\n",
       "  [11.706788495319067],\n",
       "  [12.188000320397427]],\n",
       " [[11.225576670308568],\n",
       "  [11.706788495319067],\n",
       "  [12.188000320397427],\n",
       "  [12.669212145449865]],\n",
       " [[11.706788495319067],\n",
       "  [12.188000320397427],\n",
       "  [12.669212145449865],\n",
       "  [13.150423970512206]],\n",
       " [[12.188000320397427],\n",
       "  [12.669212145449865],\n",
       "  [13.150423970512206],\n",
       "  [13.631635795570764]],\n",
       " [[12.669212145449865],\n",
       "  [13.150423970512206],\n",
       "  [13.631635795570764],\n",
       "  [14.112847620630767]],\n",
       " [[13.150423970512206],\n",
       "  [13.631635795570764],\n",
       "  [14.112847620630767],\n",
       "  [14.594059445690219]],\n",
       " [[13.631635795570764],\n",
       "  [14.112847620630767],\n",
       "  [14.594059445690219],\n",
       "  [15.07527127074988]],\n",
       " [[14.112847620630767],\n",
       "  [14.594059445690219],\n",
       "  [15.07527127074988],\n",
       "  [15.556483095809462]],\n",
       " [[14.594059445690219],\n",
       "  [15.07527127074988],\n",
       "  [15.556483095809462],\n",
       "  [16.037694920869072]],\n",
       " [[15.07527127074988],\n",
       "  [15.556483095809462],\n",
       "  [16.037694920869072],\n",
       "  [16.518906745928675]],\n",
       " [[15.556483095809462],\n",
       "  [16.037694920869072],\n",
       "  [16.518906745928675],\n",
       "  [17.000118570988278]],\n",
       " [[16.037694920869072],\n",
       "  [16.518906745928675],\n",
       "  [17.000118570988278],\n",
       "  [17.48133039604788]],\n",
       " [[16.518906745928675],\n",
       "  [17.000118570988278],\n",
       "  [17.48133039604788],\n",
       "  [17.962542221107483]],\n",
       " [[17.000118570988278],\n",
       "  [17.48133039604788],\n",
       "  [17.962542221107483],\n",
       "  [18.443754046167086]],\n",
       " [[17.48133039604788],\n",
       "  [17.962542221107483],\n",
       "  [18.443754046167086],\n",
       "  [18.924965871226693]],\n",
       " [[17.962542221107483],\n",
       "  [18.443754046167086],\n",
       "  [18.924965871226693],\n",
       "  [19.406177696286296]],\n",
       " [[18.443754046167086],\n",
       "  [18.924965871226693],\n",
       "  [19.406177696286296],\n",
       "  [19.8873895213459]],\n",
       " [[18.924965871226693],\n",
       "  [19.406177696286296],\n",
       "  [19.8873895213459],\n",
       "  [20.3686013464055]],\n",
       " [[19.406177696286296],\n",
       "  [19.8873895213459],\n",
       "  [20.3686013464055],\n",
       "  [20.849813171465104]],\n",
       " [[19.8873895213459],\n",
       "  [20.3686013464055],\n",
       "  [20.849813171465104],\n",
       "  [21.331024996524707]],\n",
       " [[20.3686013464055],\n",
       "  [20.849813171465104],\n",
       "  [21.331024996524707],\n",
       "  [21.81223682158431]],\n",
       " [[20.849813171465104],\n",
       "  [21.331024996524707],\n",
       "  [21.81223682158431],\n",
       "  [22.293448646643917]],\n",
       " [[21.331024996524707],\n",
       "  [21.81223682158431],\n",
       "  [22.293448646643917],\n",
       "  [22.77466047170352]],\n",
       " [[21.81223682158431],\n",
       "  [22.293448646643917],\n",
       "  [22.77466047170352],\n",
       "  [23.255872296763123]],\n",
       " [[22.293448646643917],\n",
       "  [22.77466047170352],\n",
       "  [23.255872296763123],\n",
       "  [23.737084121822726]],\n",
       " [[22.77466047170352],\n",
       "  [23.255872296763123],\n",
       "  [23.737084121822726],\n",
       "  [24.21829594688233]],\n",
       " [[23.255872296763123],\n",
       "  [23.737084121822726],\n",
       "  [24.21829594688233],\n",
       "  [24.69950777194193]],\n",
       " [[23.737084121822726],\n",
       "  [24.21829594688233],\n",
       "  [24.69950777194193],\n",
       "  [25.180719597001534]],\n",
       " [[24.21829594688233],\n",
       "  [24.69950777194193],\n",
       "  [25.180719597001534],\n",
       "  [25.66193142206114]],\n",
       " [[24.69950777194193],\n",
       "  [25.180719597001534],\n",
       "  [25.66193142206114],\n",
       "  [26.143143247120744]],\n",
       " [[25.180719597001534],\n",
       "  [25.66193142206114],\n",
       "  [26.143143247120744],\n",
       "  [26.624355072180347]],\n",
       " [[25.66193142206114],\n",
       "  [26.143143247120744],\n",
       "  [26.624355072180347],\n",
       "  [27.10556689723995]],\n",
       " [[26.143143247120744],\n",
       "  [26.624355072180347],\n",
       "  [27.10556689723995],\n",
       "  [27.586778722299552]],\n",
       " [[26.624355072180347],\n",
       "  [27.10556689723995],\n",
       "  [27.586778722299552],\n",
       "  [28.067990547359155]],\n",
       " [[27.10556689723995],\n",
       "  [27.586778722299552],\n",
       "  [28.067990547359155],\n",
       "  [28.549202372418762]],\n",
       " [[27.586778722299552],\n",
       "  [28.067990547359155],\n",
       "  [28.549202372418762],\n",
       "  [29.030414197478365]],\n",
       " [[28.067990547359155],\n",
       "  [28.549202372418762],\n",
       "  [29.030414197478365],\n",
       "  [29.511626022537968]],\n",
       " [[28.549202372418762],\n",
       "  [29.030414197478365],\n",
       "  [29.511626022537968],\n",
       "  [29.99283784759757]],\n",
       " [[29.030414197478365],\n",
       "  [29.511626022537968],\n",
       "  [29.99283784759757],\n",
       "  [30.474049672657173]],\n",
       " [[29.511626022537968],\n",
       "  [29.99283784759757],\n",
       "  [30.474049672657173],\n",
       "  [30.955261497716776]],\n",
       " [[29.99283784759757],\n",
       "  [30.474049672657173],\n",
       "  [30.955261497716776],\n",
       "  [31.43647332277638]],\n",
       " [[30.474049672657173],\n",
       "  [30.955261497716776],\n",
       "  [31.43647332277638],\n",
       "  [31.917685147835986]],\n",
       " [[30.955261497716776],\n",
       "  [31.43647332277638],\n",
       "  [31.917685147835986],\n",
       "  [32.398896972895585]],\n",
       " [[31.43647332277638],\n",
       "  [31.917685147835986],\n",
       "  [32.398896972895585],\n",
       "  [32.88010879795519]],\n",
       " [[31.917685147835986],\n",
       "  [32.398896972895585],\n",
       "  [32.88010879795519],\n",
       "  [33.3613206230148]],\n",
       " [[32.398896972895585],\n",
       "  [32.88010879795519],\n",
       "  [33.3613206230148],\n",
       "  [33.8425324480744]],\n",
       " [[32.88010879795519],\n",
       "  [33.3613206230148],\n",
       "  [33.8425324480744],\n",
       "  [34.323744273134004]],\n",
       " [[33.3613206230148],\n",
       "  [33.8425324480744],\n",
       "  [34.323744273134004],\n",
       "  [34.80495609819361]],\n",
       " [[33.8425324480744],\n",
       "  [34.323744273134004],\n",
       "  [34.80495609819361],\n",
       "  [35.28616792325321]],\n",
       " [[34.323744273134004],\n",
       "  [34.80495609819361],\n",
       "  [35.28616792325321],\n",
       "  [35.76737974831281]],\n",
       " [[34.80495609819361],\n",
       "  [35.28616792325321],\n",
       "  [35.76737974831281],\n",
       "  [36.248591573372416]],\n",
       " [[35.28616792325321],\n",
       "  [35.76737974831281],\n",
       "  [36.248591573372416],\n",
       "  [36.72980339843202]],\n",
       " [[35.76737974831281],\n",
       "  [36.248591573372416],\n",
       "  [36.72980339843202],\n",
       "  [37.21101522349162]],\n",
       " [[36.248591573372416],\n",
       "  [36.72980339843202],\n",
       "  [37.21101522349162],\n",
       "  [37.692227048551224]],\n",
       " [[36.72980339843202],\n",
       "  [37.21101522349162],\n",
       "  [37.692227048551224],\n",
       "  [38.17343887361083]],\n",
       " [[37.21101522349162],\n",
       "  [37.692227048551224],\n",
       "  [38.17343887361083],\n",
       "  [38.65465069867043]],\n",
       " [[37.692227048551224],\n",
       "  [38.17343887361083],\n",
       "  [38.65465069867043],\n",
       "  [39.13586252373003]],\n",
       " [[38.17343887361083],\n",
       "  [38.65465069867043],\n",
       "  [39.13586252373003],\n",
       "  [39.617074348789636]],\n",
       " [[38.65465069867043],\n",
       "  [39.13586252373003],\n",
       "  [39.617074348789636],\n",
       "  [40.098286173849246]],\n",
       " [[39.13586252373003],\n",
       "  [39.617074348789636],\n",
       "  [40.098286173849246],\n",
       "  [40.57949799890885]],\n",
       " [[39.617074348789636],\n",
       "  [40.098286173849246],\n",
       "  [40.57949799890885],\n",
       "  [41.06070982396845]],\n",
       " [[40.098286173849246],\n",
       "  [40.57949799890885],\n",
       "  [41.06070982396845],\n",
       "  [41.541921649028055]],\n",
       " [[40.57949799890885],\n",
       "  [41.06070982396845],\n",
       "  [41.541921649028055],\n",
       "  [42.02313347408766]],\n",
       " [[41.06070982396845],\n",
       "  [41.541921649028055],\n",
       "  [42.02313347408766],\n",
       "  [42.50434529914726]],\n",
       " [[41.541921649028055],\n",
       "  [42.02313347408766],\n",
       "  [42.50434529914726],\n",
       "  [42.98555712420686]],\n",
       " [[42.02313347408766],\n",
       "  [42.50434529914726],\n",
       "  [42.98555712420686],\n",
       "  [43.466768949266466]],\n",
       " [[42.50434529914726],\n",
       "  [42.98555712420686],\n",
       "  [43.466768949266466],\n",
       "  [43.94798077432607]],\n",
       " [[42.98555712420686],\n",
       "  [43.466768949266466],\n",
       "  [43.94798077432607],\n",
       "  [44.42919259938567]],\n",
       " [[43.466768949266466],\n",
       "  [43.94798077432607],\n",
       "  [44.42919259938567],\n",
       "  [44.910404424445275]],\n",
       " [[43.94798077432607],\n",
       "  [44.42919259938567],\n",
       "  [44.910404424445275],\n",
       "  [45.39161624950488]],\n",
       " [[44.42919259938567],\n",
       "  [44.910404424445275],\n",
       "  [45.39161624950488],\n",
       "  [45.87282807456448]],\n",
       " [[44.910404424445275],\n",
       "  [45.39161624950488],\n",
       "  [45.87282807456448],\n",
       "  [46.35403989962409]],\n",
       " [[45.39161624950488],\n",
       "  [45.87282807456448],\n",
       "  [46.35403989962409],\n",
       "  [46.835251724683694]],\n",
       " [[45.87282807456448],\n",
       "  [46.35403989962409],\n",
       "  [46.835251724683694],\n",
       "  [47.3164635497433]],\n",
       " [[46.35403989962409],\n",
       "  [46.835251724683694],\n",
       "  [47.3164635497433],\n",
       "  [47.7976753748029]],\n",
       " [[46.835251724683694],\n",
       "  [47.3164635497433],\n",
       "  [47.7976753748029],\n",
       "  [48.2788871998625]],\n",
       " [[47.3164635497433],\n",
       "  [47.7976753748029],\n",
       "  [48.2788871998625],\n",
       "  [48.760099024922106]]]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For predicting the sequence [1,2,3,4 ...]\n",
    "#Data = [[[(i+j)/100] for i in range(5)] for j in range(100)]\n",
    "#Target = [(i+5)/100 for i in range(1,101)]\n",
    "\n",
    "Data = []\n",
    "Target = []\n",
    "\n",
    "a = 1\n",
    "b = 1\n",
    "c = a+b\n",
    "norm_value = 6.624090658521993e+19\n",
    "log_value = 10\n",
    "for x in range (0, 100):\n",
    "    c = a+b\n",
    "    \n",
    "    Data.append([[math.log(a)], [math.log(b)], [math.log(c)], [math.log(b+c)]])\n",
    "    Target.append(math.log(c+b+c))\n",
    "    \n",
    "    a = b\n",
    "    b = c\n",
    "\n",
    "\n",
    "    \n",
    "#Normalization\n",
    "\n",
    "\n",
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array(Data, dtype=float)\n",
    "target = np.array(Target, dtype=float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 4, 1), (100,))"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape, target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(data, target, test_size = 0.2, random_state = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM((1), batch_input_shape = (None, 4, 1), return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#model.add(LSTM(1, activation='relu'))\n",
    "\n",
    "\n",
    "\n",
    "model.compile(loss=\"mean_absolute_error\", optimizer=\"adam\", metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_29\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_35 (LSTM)               (None, 1)                 12        \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 12\n",
      "Trainable params: 12\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "3/3 [==============================] - 1s 168ms/step - loss: 27.2991 - accuracy: 0.0000e+00 - val_loss: 22.0668 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 27.2995 - accuracy: 0.0000e+00 - val_loss: 22.0633 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 27.3173 - accuracy: 0.0000e+00 - val_loss: 22.0599 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 27.2682 - accuracy: 0.0000e+00 - val_loss: 22.0565 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 27.2508 - accuracy: 0.0000e+00 - val_loss: 22.0532 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 27.2518 - accuracy: 0.0000e+00 - val_loss: 22.0499 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 27.3282 - accuracy: 0.0000e+00 - val_loss: 22.0467 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 27.2520 - accuracy: 0.0000e+00 - val_loss: 22.0434 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 27.3500 - accuracy: 0.0000e+00 - val_loss: 22.0402 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 27.2928 - accuracy: 0.0000e+00 - val_loss: 22.0368 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 27.2618 - accuracy: 0.0000e+00 - val_loss: 22.0335 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 27.3518 - accuracy: 0.0000e+00 - val_loss: 22.0301 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 27.2630 - accuracy: 0.0000e+00 - val_loss: 22.0267 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 27.1265 - accuracy: 0.0000e+00 - val_loss: 22.0234 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 27.2249 - accuracy: 0.0000e+00 - val_loss: 22.0200 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 27.2718 - accuracy: 0.0000e+00 - val_loss: 22.0165 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 27.2001 - accuracy: 0.0000e+00 - val_loss: 22.0128 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 27.1800 - accuracy: 0.0000e+00 - val_loss: 22.0092 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 27.2665 - accuracy: 0.0000e+00 - val_loss: 22.0054 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 27.2791 - accuracy: 0.0000e+00 - val_loss: 22.0013 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 27.1941 - accuracy: 0.0000e+00 - val_loss: 21.9968 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 27.1987 - accuracy: 0.0000e+00 - val_loss: 21.9923 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 27.2239 - accuracy: 0.0000e+00 - val_loss: 21.9873 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 27.2528 - accuracy: 0.0000e+00 - val_loss: 21.9821 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 27.2412 - accuracy: 0.0000e+00 - val_loss: 21.9768 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/500\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 27.2728 - accuracy: 0.0000e+00 - val_loss: 21.9711 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/500\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 27.2290 - accuracy: 0.0000e+00 - val_loss: 21.9648 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/500\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 27.2059 - accuracy: 0.0000e+00 - val_loss: 21.9579 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 27.2093 - accuracy: 0.0000e+00 - val_loss: 21.9500 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/500\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 27.2337 - accuracy: 0.0000e+00 - val_loss: 21.9412 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/500\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 27.2579 - accuracy: 0.0000e+00 - val_loss: 21.9318 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/500\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 27.1834 - accuracy: 0.0000e+00 - val_loss: 21.9214 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 27.1718 - accuracy: 0.0000e+00 - val_loss: 21.9100 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 27.1921 - accuracy: 0.0000e+00 - val_loss: 21.8973 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 27.2007 - accuracy: 0.0000e+00 - val_loss: 21.8830 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 27.1741 - accuracy: 0.0000e+00 - val_loss: 21.8674 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 27.1495 - accuracy: 0.0000e+00 - val_loss: 21.8501 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 27.0619 - accuracy: 0.0000e+00 - val_loss: 21.8304 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 27.1288 - accuracy: 0.0000e+00 - val_loss: 21.8080 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 27.0706 - accuracy: 0.0000e+00 - val_loss: 21.7822 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 27.0914 - accuracy: 0.0000e+00 - val_loss: 21.7529 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 27.0874 - accuracy: 0.0000e+00 - val_loss: 21.7179 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 26.9924 - accuracy: 0.0000e+00 - val_loss: 21.6765 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 26.9550 - accuracy: 0.0000e+00 - val_loss: 21.6267 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 26.9404 - accuracy: 0.0000e+00 - val_loss: 21.5659 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 26.8628 - accuracy: 0.0000e+00 - val_loss: 21.4915 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 26.7741 - accuracy: 0.0000e+00 - val_loss: 21.4006 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 26.7169 - accuracy: 0.0000e+00 - val_loss: 21.2930 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/500\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 26.5656 - accuracy: 0.0000e+00 - val_loss: 21.1751 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 26.4186 - accuracy: 0.0000e+00 - val_loss: 21.0575 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 26.2686 - accuracy: 0.0000e+00 - val_loss: 20.9468 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 26.1066 - accuracy: 0.0000e+00 - val_loss: 20.8524 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 26.0854 - accuracy: 0.0000e+00 - val_loss: 20.7811 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 26.0198 - accuracy: 0.0000e+00 - val_loss: 20.7292 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 17ms/step - loss: 25.9210 - accuracy: 0.0000e+00 - val_loss: 20.6910 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.9000 - accuracy: 0.0000e+00 - val_loss: 20.6620 - val_accuracy: 0.0000e+00\n",
      "Epoch 57/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.9615 - accuracy: 0.0000e+00 - val_loss: 20.6402 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.8446 - accuracy: 0.0000e+00 - val_loss: 20.6233 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.8364 - accuracy: 0.0000e+00 - val_loss: 20.6096 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.8051 - accuracy: 0.0000e+00 - val_loss: 20.5983 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.8377 - accuracy: 0.0000e+00 - val_loss: 20.5890 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.7954 - accuracy: 0.0000e+00 - val_loss: 20.5810 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/500\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 25.8582 - accuracy: 0.0000e+00 - val_loss: 20.5740 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.7465 - accuracy: 0.0000e+00 - val_loss: 20.5678 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.8536 - accuracy: 0.0000e+00 - val_loss: 20.5623 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.7774 - accuracy: 0.0000e+00 - val_loss: 20.5574 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.7760 - accuracy: 0.0000e+00 - val_loss: 20.5530 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.7689 - accuracy: 0.0000e+00 - val_loss: 20.5487 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.7547 - accuracy: 0.0000e+00 - val_loss: 20.5447 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.7593 - accuracy: 0.0000e+00 - val_loss: 20.5407 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.8655 - accuracy: 0.0000e+00 - val_loss: 20.5371 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.7152 - accuracy: 0.0000e+00 - val_loss: 20.5335 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.8590 - accuracy: 0.0000e+00 - val_loss: 20.5301 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.7766 - accuracy: 0.0000e+00 - val_loss: 20.5269 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.7466 - accuracy: 0.0000e+00 - val_loss: 20.5238 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.7436 - accuracy: 0.0000e+00 - val_loss: 20.5207 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.8145 - accuracy: 0.0000e+00 - val_loss: 20.5177 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.7499 - accuracy: 0.0000e+00 - val_loss: 20.5148 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.8123 - accuracy: 0.0000e+00 - val_loss: 20.5120 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.7249 - accuracy: 0.0000e+00 - val_loss: 20.5093 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.7780 - accuracy: 0.0000e+00 - val_loss: 20.5066 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.8461 - accuracy: 0.0000e+00 - val_loss: 20.5039 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.6894 - accuracy: 0.0000e+00 - val_loss: 20.5013 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.6957 - accuracy: 0.0000e+00 - val_loss: 20.4987 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.6908 - accuracy: 0.0000e+00 - val_loss: 20.4961 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.7448 - accuracy: 0.0000e+00 - val_loss: 20.4934 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.7089 - accuracy: 0.0000e+00 - val_loss: 20.4907 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.7402 - accuracy: 0.0000e+00 - val_loss: 20.4880 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.7328 - accuracy: 0.0000e+00 - val_loss: 20.4853 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.6807 - accuracy: 0.0000e+00 - val_loss: 20.4826 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.7601 - accuracy: 0.0000e+00 - val_loss: 20.4799 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.8101 - accuracy: 0.0000e+00 - val_loss: 20.4773 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.7522 - accuracy: 0.0000e+00 - val_loss: 20.4747 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.7748 - accuracy: 0.0000e+00 - val_loss: 20.4721 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.6897 - accuracy: 0.0000e+00 - val_loss: 20.4694 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.6649 - accuracy: 0.0000e+00 - val_loss: 20.4666 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.6960 - accuracy: 0.0000e+00 - val_loss: 20.4638 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.7313 - accuracy: 0.0000e+00 - val_loss: 20.4609 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.7302 - accuracy: 0.0000e+00 - val_loss: 20.4579 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.7422 - accuracy: 0.0000e+00 - val_loss: 20.4548 - val_accuracy: 0.0000e+00\n",
      "Epoch 101/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.7237 - accuracy: 0.0000e+00 - val_loss: 20.4516 - val_accuracy: 0.0000e+00\n",
      "Epoch 102/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.7220 - accuracy: 0.0000e+00 - val_loss: 20.4481 - val_accuracy: 0.0000e+00\n",
      "Epoch 103/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.7289 - accuracy: 0.0000e+00 - val_loss: 20.4445 - val_accuracy: 0.0000e+00\n",
      "Epoch 104/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.7091 - accuracy: 0.0000e+00 - val_loss: 20.4405 - val_accuracy: 0.0000e+00\n",
      "Epoch 105/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.7418 - accuracy: 0.0000e+00 - val_loss: 20.4364 - val_accuracy: 0.0000e+00\n",
      "Epoch 106/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.7400 - accuracy: 0.0000e+00 - val_loss: 20.4321 - val_accuracy: 0.0000e+00\n",
      "Epoch 107/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.7338 - accuracy: 0.0000e+00 - val_loss: 20.4277 - val_accuracy: 0.0000e+00\n",
      "Epoch 108/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.7431 - accuracy: 0.0000e+00 - val_loss: 20.4230 - val_accuracy: 0.0000e+00\n",
      "Epoch 109/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 17ms/step - loss: 25.6811 - accuracy: 0.0000e+00 - val_loss: 20.4180 - val_accuracy: 0.0000e+00\n",
      "Epoch 110/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.7218 - accuracy: 0.0000e+00 - val_loss: 20.4127 - val_accuracy: 0.0000e+00\n",
      "Epoch 111/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.7146 - accuracy: 0.0000e+00 - val_loss: 20.4069 - val_accuracy: 0.0000e+00\n",
      "Epoch 112/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.6807 - accuracy: 0.0000e+00 - val_loss: 20.4006 - val_accuracy: 0.0000e+00\n",
      "Epoch 113/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.6148 - accuracy: 0.0000e+00 - val_loss: 20.3936 - val_accuracy: 0.0000e+00\n",
      "Epoch 114/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5990 - accuracy: 0.0000e+00 - val_loss: 20.3856 - val_accuracy: 0.0000e+00\n",
      "Epoch 115/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.6854 - accuracy: 0.0000e+00 - val_loss: 20.3768 - val_accuracy: 0.0000e+00\n",
      "Epoch 116/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.5875 - accuracy: 0.0000e+00 - val_loss: 20.3670 - val_accuracy: 0.0000e+00\n",
      "Epoch 117/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5602 - accuracy: 0.0000e+00 - val_loss: 20.3560 - val_accuracy: 0.0000e+00\n",
      "Epoch 118/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.7320 - accuracy: 0.0000e+00 - val_loss: 20.3441 - val_accuracy: 0.0000e+00\n",
      "Epoch 119/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.7186 - accuracy: 0.0000e+00 - val_loss: 20.3315 - val_accuracy: 0.0000e+00\n",
      "Epoch 120/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.6809 - accuracy: 0.0000e+00 - val_loss: 20.3180 - val_accuracy: 0.0000e+00\n",
      "Epoch 121/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.5984 - accuracy: 0.0000e+00 - val_loss: 20.3037 - val_accuracy: 0.0000e+00\n",
      "Epoch 122/500\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 25.5521 - accuracy: 0.0000e+00 - val_loss: 20.2894 - val_accuracy: 0.0000e+00\n",
      "Epoch 123/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.5343 - accuracy: 0.0000e+00 - val_loss: 20.2765 - val_accuracy: 0.0000e+00\n",
      "Epoch 124/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.5410 - accuracy: 0.0000e+00 - val_loss: 20.2664 - val_accuracy: 0.0000e+00\n",
      "Epoch 125/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.6587 - accuracy: 0.0000e+00 - val_loss: 20.2592 - val_accuracy: 0.0000e+00\n",
      "Epoch 126/500\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 25.6032 - accuracy: 0.0000e+00 - val_loss: 20.2543 - val_accuracy: 0.0000e+00\n",
      "Epoch 127/500\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 25.5745 - accuracy: 0.0000e+00 - val_loss: 20.2509 - val_accuracy: 0.0000e+00\n",
      "Epoch 128/500\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 25.4538 - accuracy: 0.0000e+00 - val_loss: 20.2486 - val_accuracy: 0.0000e+00\n",
      "Epoch 129/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.4721 - accuracy: 0.0000e+00 - val_loss: 20.2468 - val_accuracy: 0.0000e+00\n",
      "Epoch 130/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.5450 - accuracy: 0.0000e+00 - val_loss: 20.2454 - val_accuracy: 0.0000e+00\n",
      "Epoch 131/500\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 25.5056 - accuracy: 0.0000e+00 - val_loss: 20.2443 - val_accuracy: 0.0000e+00\n",
      "Epoch 132/500\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 25.4427 - accuracy: 0.0000e+00 - val_loss: 20.2434 - val_accuracy: 0.0000e+00\n",
      "Epoch 133/500\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 25.4666 - accuracy: 0.0000e+00 - val_loss: 20.2426 - val_accuracy: 0.0000e+00\n",
      "Epoch 134/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.4360 - accuracy: 0.0000e+00 - val_loss: 20.2419 - val_accuracy: 0.0000e+00\n",
      "Epoch 135/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.5236 - accuracy: 0.0000e+00 - val_loss: 20.2413 - val_accuracy: 0.0000e+00\n",
      "Epoch 136/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.4410 - accuracy: 0.0000e+00 - val_loss: 20.2407 - val_accuracy: 0.0000e+00\n",
      "Epoch 137/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.5014 - accuracy: 0.0000e+00 - val_loss: 20.2402 - val_accuracy: 0.0000e+00\n",
      "Epoch 138/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4763 - accuracy: 0.0000e+00 - val_loss: 20.2398 - val_accuracy: 0.0000e+00\n",
      "Epoch 139/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.6326 - accuracy: 0.0000e+00 - val_loss: 20.2394 - val_accuracy: 0.0000e+00\n",
      "Epoch 140/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.4588 - accuracy: 0.0000e+00 - val_loss: 20.2390 - val_accuracy: 0.0000e+00\n",
      "Epoch 141/500\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 25.4364 - accuracy: 0.0000e+00 - val_loss: 20.2386 - val_accuracy: 0.0000e+00\n",
      "Epoch 142/500\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 25.4817 - accuracy: 0.0000e+00 - val_loss: 20.2383 - val_accuracy: 0.0000e+00\n",
      "Epoch 143/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.5084 - accuracy: 0.0000e+00 - val_loss: 20.2379 - val_accuracy: 0.0000e+00\n",
      "Epoch 144/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.5339 - accuracy: 0.0000e+00 - val_loss: 20.2376 - val_accuracy: 0.0000e+00\n",
      "Epoch 145/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.4738 - accuracy: 0.0000e+00 - val_loss: 20.2373 - val_accuracy: 0.0000e+00\n",
      "Epoch 146/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.5541 - accuracy: 0.0000e+00 - val_loss: 20.2370 - val_accuracy: 0.0000e+00\n",
      "Epoch 147/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4887 - accuracy: 0.0000e+00 - val_loss: 20.2367 - val_accuracy: 0.0000e+00\n",
      "Epoch 148/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4728 - accuracy: 0.0000e+00 - val_loss: 20.2364 - val_accuracy: 0.0000e+00\n",
      "Epoch 149/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5349 - accuracy: 0.0000e+00 - val_loss: 20.2361 - val_accuracy: 0.0000e+00\n",
      "Epoch 150/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.5572 - accuracy: 0.0000e+00 - val_loss: 20.2358 - val_accuracy: 0.0000e+00\n",
      "Epoch 151/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.4970 - accuracy: 0.0000e+00 - val_loss: 20.2356 - val_accuracy: 0.0000e+00\n",
      "Epoch 152/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4444 - accuracy: 0.0000e+00 - val_loss: 20.2354 - val_accuracy: 0.0000e+00\n",
      "Epoch 153/500\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 25.4411 - accuracy: 0.0000e+00 - val_loss: 20.2351 - val_accuracy: 0.0000e+00\n",
      "Epoch 154/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4996 - accuracy: 0.0000e+00 - val_loss: 20.2349 - val_accuracy: 0.0000e+00\n",
      "Epoch 155/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.4500 - accuracy: 0.0000e+00 - val_loss: 20.2347 - val_accuracy: 0.0000e+00\n",
      "Epoch 156/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4978 - accuracy: 0.0000e+00 - val_loss: 20.2344 - val_accuracy: 0.0000e+00\n",
      "Epoch 157/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4556 - accuracy: 0.0000e+00 - val_loss: 20.2342 - val_accuracy: 0.0000e+00\n",
      "Epoch 158/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.5179 - accuracy: 0.0000e+00 - val_loss: 20.2340 - val_accuracy: 0.0000e+00\n",
      "Epoch 159/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.5959 - accuracy: 0.0000e+00 - val_loss: 20.2337 - val_accuracy: 0.0000e+00\n",
      "Epoch 160/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4980 - accuracy: 0.0000e+00 - val_loss: 20.2335 - val_accuracy: 0.0000e+00\n",
      "Epoch 161/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.3835 - accuracy: 0.0000e+00 - val_loss: 20.2333 - val_accuracy: 0.0000e+00\n",
      "Epoch 162/500\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 25.5087 - accuracy: 0.0000e+00 - val_loss: 20.2331 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 163/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.5482 - accuracy: 0.0000e+00 - val_loss: 20.2329 - val_accuracy: 0.0000e+00\n",
      "Epoch 164/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4994 - accuracy: 0.0000e+00 - val_loss: 20.2327 - val_accuracy: 0.0000e+00\n",
      "Epoch 165/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.4804 - accuracy: 0.0000e+00 - val_loss: 20.2325 - val_accuracy: 0.0000e+00\n",
      "Epoch 166/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.5748 - accuracy: 0.0000e+00 - val_loss: 20.2323 - val_accuracy: 0.0000e+00\n",
      "Epoch 167/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4828 - accuracy: 0.0000e+00 - val_loss: 20.2321 - val_accuracy: 0.0000e+00\n",
      "Epoch 168/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4749 - accuracy: 0.0000e+00 - val_loss: 20.2319 - val_accuracy: 0.0000e+00\n",
      "Epoch 169/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.7048 - accuracy: 0.0000e+00 - val_loss: 20.2318 - val_accuracy: 0.0000e+00\n",
      "Epoch 170/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4151 - accuracy: 0.0000e+00 - val_loss: 20.2316 - val_accuracy: 0.0000e+00\n",
      "Epoch 171/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5262 - accuracy: 0.0000e+00 - val_loss: 20.2315 - val_accuracy: 0.0000e+00\n",
      "Epoch 172/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5829 - accuracy: 0.0000e+00 - val_loss: 20.2313 - val_accuracy: 0.0000e+00\n",
      "Epoch 173/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4519 - accuracy: 0.0000e+00 - val_loss: 20.2312 - val_accuracy: 0.0000e+00\n",
      "Epoch 174/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4544 - accuracy: 0.0000e+00 - val_loss: 20.2310 - val_accuracy: 0.0000e+00\n",
      "Epoch 175/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5415 - accuracy: 0.0000e+00 - val_loss: 20.2308 - val_accuracy: 0.0000e+00\n",
      "Epoch 176/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4668 - accuracy: 0.0000e+00 - val_loss: 20.2307 - val_accuracy: 0.0000e+00\n",
      "Epoch 177/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5310 - accuracy: 0.0000e+00 - val_loss: 20.2305 - val_accuracy: 0.0000e+00\n",
      "Epoch 178/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4948 - accuracy: 0.0000e+00 - val_loss: 20.2303 - val_accuracy: 0.0000e+00\n",
      "Epoch 179/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4954 - accuracy: 0.0000e+00 - val_loss: 20.2302 - val_accuracy: 0.0000e+00\n",
      "Epoch 180/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4062 - accuracy: 0.0000e+00 - val_loss: 20.2300 - val_accuracy: 0.0000e+00\n",
      "Epoch 181/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5696 - accuracy: 0.0000e+00 - val_loss: 20.2298 - val_accuracy: 0.0000e+00\n",
      "Epoch 182/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5746 - accuracy: 0.0000e+00 - val_loss: 20.2297 - val_accuracy: 0.0000e+00\n",
      "Epoch 183/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5448 - accuracy: 0.0000e+00 - val_loss: 20.2295 - val_accuracy: 0.0000e+00\n",
      "Epoch 184/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5588 - accuracy: 0.0000e+00 - val_loss: 20.2294 - val_accuracy: 0.0000e+00\n",
      "Epoch 185/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.5294 - accuracy: 0.0000e+00 - val_loss: 20.2292 - val_accuracy: 0.0000e+00\n",
      "Epoch 186/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.3851 - accuracy: 0.0000e+00 - val_loss: 20.2290 - val_accuracy: 0.0000e+00\n",
      "Epoch 187/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.4950 - accuracy: 0.0000e+00 - val_loss: 20.2289 - val_accuracy: 0.0000e+00\n",
      "Epoch 188/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4953 - accuracy: 0.0000e+00 - val_loss: 20.2287 - val_accuracy: 0.0000e+00\n",
      "Epoch 189/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.4503 - accuracy: 0.0000e+00 - val_loss: 20.2286 - val_accuracy: 0.0000e+00\n",
      "Epoch 190/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.4203 - accuracy: 0.0000e+00 - val_loss: 20.2284 - val_accuracy: 0.0000e+00\n",
      "Epoch 191/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4511 - accuracy: 0.0000e+00 - val_loss: 20.2283 - val_accuracy: 0.0000e+00\n",
      "Epoch 192/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4824 - accuracy: 0.0000e+00 - val_loss: 20.2281 - val_accuracy: 0.0000e+00\n",
      "Epoch 193/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.5105 - accuracy: 0.0000e+00 - val_loss: 20.2280 - val_accuracy: 0.0000e+00\n",
      "Epoch 194/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.4966 - accuracy: 0.0000e+00 - val_loss: 20.2279 - val_accuracy: 0.0000e+00\n",
      "Epoch 195/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.4752 - accuracy: 0.0000e+00 - val_loss: 20.2277 - val_accuracy: 0.0000e+00\n",
      "Epoch 196/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4174 - accuracy: 0.0000e+00 - val_loss: 20.2276 - val_accuracy: 0.0000e+00\n",
      "Epoch 197/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.5243 - accuracy: 0.0000e+00 - val_loss: 20.2275 - val_accuracy: 0.0000e+00\n",
      "Epoch 198/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.4647 - accuracy: 0.0000e+00 - val_loss: 20.2273 - val_accuracy: 0.0000e+00\n",
      "Epoch 199/500\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 25.4807 - accuracy: 0.0000e+00 - val_loss: 20.2272 - val_accuracy: 0.0000e+00\n",
      "Epoch 200/500\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 25.4794 - accuracy: 0.0000e+00 - val_loss: 20.2271 - val_accuracy: 0.0000e+00\n",
      "Epoch 201/500\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 25.5539 - accuracy: 0.0000e+00 - val_loss: 20.2270 - val_accuracy: 0.0000e+00\n",
      "Epoch 202/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.4651 - accuracy: 0.0000e+00 - val_loss: 20.2268 - val_accuracy: 0.0000e+00\n",
      "Epoch 203/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.4642 - accuracy: 0.0000e+00 - val_loss: 20.2267 - val_accuracy: 0.0000e+00\n",
      "Epoch 204/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.4777 - accuracy: 0.0000e+00 - val_loss: 20.2266 - val_accuracy: 0.0000e+00\n",
      "Epoch 205/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.5104 - accuracy: 0.0000e+00 - val_loss: 20.2265 - val_accuracy: 0.0000e+00\n",
      "Epoch 206/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.4627 - accuracy: 0.0000e+00 - val_loss: 20.2263 - val_accuracy: 0.0000e+00\n",
      "Epoch 207/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.4924 - accuracy: 0.0000e+00 - val_loss: 20.2262 - val_accuracy: 0.0000e+00\n",
      "Epoch 208/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.5704 - accuracy: 0.0000e+00 - val_loss: 20.2261 - val_accuracy: 0.0000e+00\n",
      "Epoch 209/500\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 25.5652 - accuracy: 0.0000e+00 - val_loss: 20.2260 - val_accuracy: 0.0000e+00\n",
      "Epoch 210/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.3702 - accuracy: 0.0000e+00 - val_loss: 20.2259 - val_accuracy: 0.0000e+00\n",
      "Epoch 211/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.5199 - accuracy: 0.0000e+00 - val_loss: 20.2258 - val_accuracy: 0.0000e+00\n",
      "Epoch 212/500\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 25.5362 - accuracy: 0.0000e+00 - val_loss: 20.2257 - val_accuracy: 0.0000e+00\n",
      "Epoch 213/500\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 25.5412 - accuracy: 0.0000e+00 - val_loss: 20.2256 - val_accuracy: 0.0000e+00\n",
      "Epoch 214/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.6163 - accuracy: 0.0000e+00 - val_loss: 20.2255 - val_accuracy: 0.0000e+00\n",
      "Epoch 215/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5528 - accuracy: 0.0000e+00 - val_loss: 20.2254 - val_accuracy: 0.0000e+00\n",
      "Epoch 216/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4909 - accuracy: 0.0000e+00 - val_loss: 20.2253 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 217/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4856 - accuracy: 0.0000e+00 - val_loss: 20.2252 - val_accuracy: 0.0000e+00\n",
      "Epoch 218/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4921 - accuracy: 0.0000e+00 - val_loss: 20.2251 - val_accuracy: 0.0000e+00\n",
      "Epoch 219/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.5659 - accuracy: 0.0000e+00 - val_loss: 20.2250 - val_accuracy: 0.0000e+00\n",
      "Epoch 220/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.5025 - accuracy: 0.0000e+00 - val_loss: 20.2250 - val_accuracy: 0.0000e+00\n",
      "Epoch 221/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.5962 - accuracy: 0.0000e+00 - val_loss: 20.2249 - val_accuracy: 0.0000e+00\n",
      "Epoch 222/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.5434 - accuracy: 0.0000e+00 - val_loss: 20.2248 - val_accuracy: 0.0000e+00\n",
      "Epoch 223/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4768 - accuracy: 0.0000e+00 - val_loss: 20.2247 - val_accuracy: 0.0000e+00\n",
      "Epoch 224/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4933 - accuracy: 0.0000e+00 - val_loss: 20.2246 - val_accuracy: 0.0000e+00\n",
      "Epoch 225/500\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 25.4753 - accuracy: 0.0000e+00 - val_loss: 20.2246 - val_accuracy: 0.0000e+00\n",
      "Epoch 226/500\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 25.4603 - accuracy: 0.0000e+00 - val_loss: 20.2245 - val_accuracy: 0.0000e+00\n",
      "Epoch 227/500\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 25.5066 - accuracy: 0.0000e+00 - val_loss: 20.2244 - val_accuracy: 0.0000e+00\n",
      "Epoch 228/500\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 25.4457 - accuracy: 0.0000e+00 - val_loss: 20.2243 - val_accuracy: 0.0000e+00\n",
      "Epoch 229/500\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 25.4764 - accuracy: 0.0000e+00 - val_loss: 20.2242 - val_accuracy: 0.0000e+00\n",
      "Epoch 230/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.5072 - accuracy: 0.0000e+00 - val_loss: 20.2241 - val_accuracy: 0.0000e+00\n",
      "Epoch 231/500\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 25.4765 - accuracy: 0.0000e+00 - val_loss: 20.2240 - val_accuracy: 0.0000e+00\n",
      "Epoch 232/500\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 25.4758 - accuracy: 0.0000e+00 - val_loss: 20.2239 - val_accuracy: 0.0000e+00\n",
      "Epoch 233/500\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 25.4560 - accuracy: 0.0000e+00 - val_loss: 20.2238 - val_accuracy: 0.0000e+00\n",
      "Epoch 234/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.4750 - accuracy: 0.0000e+00 - val_loss: 20.2237 - val_accuracy: 0.0000e+00\n",
      "Epoch 235/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5069 - accuracy: 0.0000e+00 - val_loss: 20.2236 - val_accuracy: 0.0000e+00\n",
      "Epoch 236/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4426 - accuracy: 0.0000e+00 - val_loss: 20.2236 - val_accuracy: 0.0000e+00\n",
      "Epoch 237/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4433 - accuracy: 0.0000e+00 - val_loss: 20.2235 - val_accuracy: 0.0000e+00\n",
      "Epoch 238/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4597 - accuracy: 0.0000e+00 - val_loss: 20.2234 - val_accuracy: 0.0000e+00\n",
      "Epoch 239/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5052 - accuracy: 0.0000e+00 - val_loss: 20.2233 - val_accuracy: 0.0000e+00\n",
      "Epoch 240/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5073 - accuracy: 0.0000e+00 - val_loss: 20.2232 - val_accuracy: 0.0000e+00\n",
      "Epoch 241/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.5175 - accuracy: 0.0000e+00 - val_loss: 20.2231 - val_accuracy: 0.0000e+00\n",
      "Epoch 242/500\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 25.4911 - accuracy: 0.0000e+00 - val_loss: 20.2230 - val_accuracy: 0.0000e+00\n",
      "Epoch 243/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.5065 - accuracy: 0.0000e+00 - val_loss: 20.2229 - val_accuracy: 0.0000e+00\n",
      "Epoch 244/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.5463 - accuracy: 0.0000e+00 - val_loss: 20.2229 - val_accuracy: 0.0000e+00\n",
      "Epoch 245/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4224 - accuracy: 0.0000e+00 - val_loss: 20.2228 - val_accuracy: 0.0000e+00\n",
      "Epoch 246/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4442 - accuracy: 0.0000e+00 - val_loss: 20.2227 - val_accuracy: 0.0000e+00\n",
      "Epoch 247/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4229 - accuracy: 0.0000e+00 - val_loss: 20.2226 - val_accuracy: 0.0000e+00\n",
      "Epoch 248/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5512 - accuracy: 0.0000e+00 - val_loss: 20.2226 - val_accuracy: 0.0000e+00\n",
      "Epoch 249/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5039 - accuracy: 0.0000e+00 - val_loss: 20.2225 - val_accuracy: 0.0000e+00\n",
      "Epoch 250/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4999 - accuracy: 0.0000e+00 - val_loss: 20.2224 - val_accuracy: 0.0000e+00\n",
      "Epoch 251/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.6257 - accuracy: 0.0000e+00 - val_loss: 20.2223 - val_accuracy: 0.0000e+00\n",
      "Epoch 252/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.4945 - accuracy: 0.0000e+00 - val_loss: 20.2223 - val_accuracy: 0.0000e+00\n",
      "Epoch 253/500\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 25.5657 - accuracy: 0.0000e+00 - val_loss: 20.2222 - val_accuracy: 0.0000e+00\n",
      "Epoch 254/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.5366 - accuracy: 0.0000e+00 - val_loss: 20.2222 - val_accuracy: 0.0000e+00\n",
      "Epoch 255/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5474 - accuracy: 0.0000e+00 - val_loss: 20.2221 - val_accuracy: 0.0000e+00\n",
      "Epoch 256/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.3940 - accuracy: 0.0000e+00 - val_loss: 20.2220 - val_accuracy: 0.0000e+00\n",
      "Epoch 257/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5213 - accuracy: 0.0000e+00 - val_loss: 20.2220 - val_accuracy: 0.0000e+00\n",
      "Epoch 258/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5784 - accuracy: 0.0000e+00 - val_loss: 20.2219 - val_accuracy: 0.0000e+00\n",
      "Epoch 259/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4378 - accuracy: 0.0000e+00 - val_loss: 20.2218 - val_accuracy: 0.0000e+00\n",
      "Epoch 260/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5208 - accuracy: 0.0000e+00 - val_loss: 20.2217 - val_accuracy: 0.0000e+00\n",
      "Epoch 261/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5049 - accuracy: 0.0000e+00 - val_loss: 20.2217 - val_accuracy: 0.0000e+00\n",
      "Epoch 262/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5208 - accuracy: 0.0000e+00 - val_loss: 20.2216 - val_accuracy: 0.0000e+00\n",
      "Epoch 263/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4107 - accuracy: 0.0000e+00 - val_loss: 20.2215 - val_accuracy: 0.0000e+00\n",
      "Epoch 264/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4567 - accuracy: 0.0000e+00 - val_loss: 20.2215 - val_accuracy: 0.0000e+00\n",
      "Epoch 265/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4835 - accuracy: 0.0000e+00 - val_loss: 20.2214 - val_accuracy: 0.0000e+00\n",
      "Epoch 266/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.4269 - accuracy: 0.0000e+00 - val_loss: 20.2213 - val_accuracy: 0.0000e+00\n",
      "Epoch 267/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5653 - accuracy: 0.0000e+00 - val_loss: 20.2213 - val_accuracy: 0.0000e+00\n",
      "Epoch 268/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5506 - accuracy: 0.0000e+00 - val_loss: 20.2212 - val_accuracy: 0.0000e+00\n",
      "Epoch 269/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5898 - accuracy: 0.0000e+00 - val_loss: 20.2211 - val_accuracy: 0.0000e+00\n",
      "Epoch 270/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4680 - accuracy: 0.0000e+00 - val_loss: 20.2211 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 271/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4702 - accuracy: 0.0000e+00 - val_loss: 20.2210 - val_accuracy: 0.0000e+00\n",
      "Epoch 272/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5154 - accuracy: 0.0000e+00 - val_loss: 20.2210 - val_accuracy: 0.0000e+00\n",
      "Epoch 273/500\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 25.5161 - accuracy: 0.0000e+00 - val_loss: 20.2209 - val_accuracy: 0.0000e+00\n",
      "Epoch 274/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4522 - accuracy: 0.0000e+00 - val_loss: 20.2209 - val_accuracy: 0.0000e+00\n",
      "Epoch 275/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4372 - accuracy: 0.0000e+00 - val_loss: 20.2208 - val_accuracy: 0.0000e+00\n",
      "Epoch 276/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.5483 - accuracy: 0.0000e+00 - val_loss: 20.2208 - val_accuracy: 0.0000e+00\n",
      "Epoch 277/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5506 - accuracy: 0.0000e+00 - val_loss: 20.2207 - val_accuracy: 0.0000e+00\n",
      "Epoch 278/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5186 - accuracy: 0.0000e+00 - val_loss: 20.2207 - val_accuracy: 0.0000e+00\n",
      "Epoch 279/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4716 - accuracy: 0.0000e+00 - val_loss: 20.2206 - val_accuracy: 0.0000e+00\n",
      "Epoch 280/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5492 - accuracy: 0.0000e+00 - val_loss: 20.2206 - val_accuracy: 0.0000e+00\n",
      "Epoch 281/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5754 - accuracy: 0.0000e+00 - val_loss: 20.2205 - val_accuracy: 0.0000e+00\n",
      "Epoch 282/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4713 - accuracy: 0.0000e+00 - val_loss: 20.2205 - val_accuracy: 0.0000e+00\n",
      "Epoch 283/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4407 - accuracy: 0.0000e+00 - val_loss: 20.2204 - val_accuracy: 0.0000e+00\n",
      "Epoch 284/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4366 - accuracy: 0.0000e+00 - val_loss: 20.2204 - val_accuracy: 0.0000e+00\n",
      "Epoch 285/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.5025 - accuracy: 0.0000e+00 - val_loss: 20.2203 - val_accuracy: 0.0000e+00\n",
      "Epoch 286/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4820 - accuracy: 0.0000e+00 - val_loss: 20.2202 - val_accuracy: 0.0000e+00\n",
      "Epoch 287/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5183 - accuracy: 0.0000e+00 - val_loss: 20.2202 - val_accuracy: 0.0000e+00\n",
      "Epoch 288/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4822 - accuracy: 0.0000e+00 - val_loss: 20.2201 - val_accuracy: 0.0000e+00\n",
      "Epoch 289/500\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 25.4401 - accuracy: 0.0000e+00 - val_loss: 20.2201 - val_accuracy: 0.0000e+00\n",
      "Epoch 290/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4673 - accuracy: 0.0000e+00 - val_loss: 20.2201 - val_accuracy: 0.0000e+00\n",
      "Epoch 291/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.5136 - accuracy: 0.0000e+00 - val_loss: 20.2200 - val_accuracy: 0.0000e+00\n",
      "Epoch 292/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4233 - accuracy: 0.0000e+00 - val_loss: 20.2200 - val_accuracy: 0.0000e+00\n",
      "Epoch 293/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4554 - accuracy: 0.0000e+00 - val_loss: 20.2199 - val_accuracy: 0.0000e+00\n",
      "Epoch 294/500\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 25.4562 - accuracy: 0.0000e+00 - val_loss: 20.2199 - val_accuracy: 0.0000e+00\n",
      "Epoch 295/500\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 25.5642 - accuracy: 0.0000e+00 - val_loss: 20.2198 - val_accuracy: 0.0000e+00\n",
      "Epoch 296/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.5589 - accuracy: 0.0000e+00 - val_loss: 20.2198 - val_accuracy: 0.0000e+00\n",
      "Epoch 297/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4713 - accuracy: 0.0000e+00 - val_loss: 20.2197 - val_accuracy: 0.0000e+00\n",
      "Epoch 298/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5894 - accuracy: 0.0000e+00 - val_loss: 20.2197 - val_accuracy: 0.0000e+00\n",
      "Epoch 299/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.4999 - accuracy: 0.0000e+00 - val_loss: 20.2196 - val_accuracy: 0.0000e+00\n",
      "Epoch 300/500\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 25.4825 - accuracy: 0.0000e+00 - val_loss: 20.2196 - val_accuracy: 0.0000e+00\n",
      "Epoch 301/500\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 25.4055 - accuracy: 0.0000e+00 - val_loss: 20.2196 - val_accuracy: 0.0000e+00\n",
      "Epoch 302/500\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 25.3925 - accuracy: 0.0000e+00 - val_loss: 20.2195 - val_accuracy: 0.0000e+00\n",
      "Epoch 303/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4383 - accuracy: 0.0000e+00 - val_loss: 20.2195 - val_accuracy: 0.0000e+00\n",
      "Epoch 304/500\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 25.4987 - accuracy: 0.0000e+00 - val_loss: 20.2194 - val_accuracy: 0.0000e+00\n",
      "Epoch 305/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.6259 - accuracy: 0.0000e+00 - val_loss: 20.2194 - val_accuracy: 0.0000e+00\n",
      "Epoch 306/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.5178 - accuracy: 0.0000e+00 - val_loss: 20.2193 - val_accuracy: 0.0000e+00\n",
      "Epoch 307/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4522 - accuracy: 0.0000e+00 - val_loss: 20.2193 - val_accuracy: 0.0000e+00\n",
      "Epoch 308/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4856 - accuracy: 0.0000e+00 - val_loss: 20.2193 - val_accuracy: 0.0000e+00\n",
      "Epoch 309/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4860 - accuracy: 0.0000e+00 - val_loss: 20.2192 - val_accuracy: 0.0000e+00\n",
      "Epoch 310/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4695 - accuracy: 0.0000e+00 - val_loss: 20.2192 - val_accuracy: 0.0000e+00\n",
      "Epoch 311/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4702 - accuracy: 0.0000e+00 - val_loss: 20.2191 - val_accuracy: 0.0000e+00\n",
      "Epoch 312/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4806 - accuracy: 0.0000e+00 - val_loss: 20.2191 - val_accuracy: 0.0000e+00\n",
      "Epoch 313/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.4823 - accuracy: 0.0000e+00 - val_loss: 20.2190 - val_accuracy: 0.0000e+00\n",
      "Epoch 314/500\n",
      "3/3 [==============================] - ETA: 0s - loss: 25.6341 - accuracy: 0.0000e+0 - 0s 21ms/step - loss: 25.4543 - accuracy: 0.0000e+00 - val_loss: 20.2190 - val_accuracy: 0.0000e+00\n",
      "Epoch 315/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.5317 - accuracy: 0.0000e+00 - val_loss: 20.2190 - val_accuracy: 0.0000e+00\n",
      "Epoch 316/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.4523 - accuracy: 0.0000e+00 - val_loss: 20.2189 - val_accuracy: 0.0000e+00\n",
      "Epoch 317/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5781 - accuracy: 0.0000e+00 - val_loss: 20.2189 - val_accuracy: 0.0000e+00\n",
      "Epoch 318/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4853 - accuracy: 0.0000e+00 - val_loss: 20.2188 - val_accuracy: 0.0000e+00\n",
      "Epoch 319/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.5412 - accuracy: 0.0000e+00 - val_loss: 20.2188 - val_accuracy: 0.0000e+00\n",
      "Epoch 320/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4663 - accuracy: 0.0000e+00 - val_loss: 20.2188 - val_accuracy: 0.0000e+00\n",
      "Epoch 321/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4974 - accuracy: 0.0000e+00 - val_loss: 20.2187 - val_accuracy: 0.0000e+00\n",
      "Epoch 322/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4531 - accuracy: 0.0000e+00 - val_loss: 20.2187 - val_accuracy: 0.0000e+00\n",
      "Epoch 323/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.4474 - accuracy: 0.0000e+00 - val_loss: 20.2187 - val_accuracy: 0.0000e+00\n",
      "Epoch 324/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5782 - accuracy: 0.0000e+00 - val_loss: 20.2186 - val_accuracy: 0.0000e+00\n",
      "Epoch 325/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.3605 - accuracy: 0.0000e+00 - val_loss: 20.2186 - val_accuracy: 0.0000e+00\n",
      "Epoch 326/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4207 - accuracy: 0.0000e+00 - val_loss: 20.2186 - val_accuracy: 0.0000e+00\n",
      "Epoch 327/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4050 - accuracy: 0.0000e+00 - val_loss: 20.2185 - val_accuracy: 0.0000e+00\n",
      "Epoch 328/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4690 - accuracy: 0.0000e+00 - val_loss: 20.2185 - val_accuracy: 0.0000e+00\n",
      "Epoch 329/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4851 - accuracy: 0.0000e+00 - val_loss: 20.2184 - val_accuracy: 0.0000e+00\n",
      "Epoch 330/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5275 - accuracy: 0.0000e+00 - val_loss: 20.2184 - val_accuracy: 0.0000e+00\n",
      "Epoch 331/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5740 - accuracy: 0.0000e+00 - val_loss: 20.2184 - val_accuracy: 0.0000e+00\n",
      "Epoch 332/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4840 - accuracy: 0.0000e+00 - val_loss: 20.2183 - val_accuracy: 0.0000e+00\n",
      "Epoch 333/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4220 - accuracy: 0.0000e+00 - val_loss: 20.2183 - val_accuracy: 0.0000e+00\n",
      "Epoch 334/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.3911 - accuracy: 0.0000e+00 - val_loss: 20.2183 - val_accuracy: 0.0000e+00\n",
      "Epoch 335/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4690 - accuracy: 0.0000e+00 - val_loss: 20.2182 - val_accuracy: 0.0000e+00\n",
      "Epoch 336/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5563 - accuracy: 0.0000e+00 - val_loss: 20.2182 - val_accuracy: 0.0000e+00\n",
      "Epoch 337/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5139 - accuracy: 0.0000e+00 - val_loss: 20.2182 - val_accuracy: 0.0000e+00\n",
      "Epoch 338/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4218 - accuracy: 0.0000e+00 - val_loss: 20.2181 - val_accuracy: 0.0000e+00\n",
      "Epoch 339/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.5139 - accuracy: 0.0000e+00 - val_loss: 20.2181 - val_accuracy: 0.0000e+00\n",
      "Epoch 340/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4660 - accuracy: 0.0000e+00 - val_loss: 20.2181 - val_accuracy: 0.0000e+00\n",
      "Epoch 341/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4220 - accuracy: 0.0000e+00 - val_loss: 20.2180 - val_accuracy: 0.0000e+00\n",
      "Epoch 342/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.5738 - accuracy: 0.0000e+00 - val_loss: 20.2180 - val_accuracy: 0.0000e+00\n",
      "Epoch 343/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4817 - accuracy: 0.0000e+00 - val_loss: 20.2180 - val_accuracy: 0.0000e+00\n",
      "Epoch 344/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4198 - accuracy: 0.0000e+00 - val_loss: 20.2179 - val_accuracy: 0.0000e+00\n",
      "Epoch 345/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5455 - accuracy: 0.0000e+00 - val_loss: 20.2179 - val_accuracy: 0.0000e+00\n",
      "Epoch 346/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5613 - accuracy: 0.0000e+00 - val_loss: 20.2179 - val_accuracy: 0.0000e+00\n",
      "Epoch 347/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5002 - accuracy: 0.0000e+00 - val_loss: 20.2178 - val_accuracy: 0.0000e+00\n",
      "Epoch 348/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4826 - accuracy: 0.0000e+00 - val_loss: 20.2178 - val_accuracy: 0.0000e+00\n",
      "Epoch 349/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4374 - accuracy: 0.0000e+00 - val_loss: 20.2178 - val_accuracy: 0.0000e+00\n",
      "Epoch 350/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4841 - accuracy: 0.0000e+00 - val_loss: 20.2177 - val_accuracy: 0.0000e+00\n",
      "Epoch 351/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4800 - accuracy: 0.0000e+00 - val_loss: 20.2177 - val_accuracy: 0.0000e+00\n",
      "Epoch 352/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4986 - accuracy: 0.0000e+00 - val_loss: 20.2177 - val_accuracy: 0.0000e+00\n",
      "Epoch 353/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4995 - accuracy: 0.0000e+00 - val_loss: 20.2176 - val_accuracy: 0.0000e+00\n",
      "Epoch 354/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4217 - accuracy: 0.0000e+00 - val_loss: 20.2176 - val_accuracy: 0.0000e+00\n",
      "Epoch 355/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4974 - accuracy: 0.0000e+00 - val_loss: 20.2176 - val_accuracy: 0.0000e+00\n",
      "Epoch 356/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5274 - accuracy: 0.0000e+00 - val_loss: 20.2175 - val_accuracy: 0.0000e+00\n",
      "Epoch 357/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.3896 - accuracy: 0.0000e+00 - val_loss: 20.2175 - val_accuracy: 0.0000e+00\n",
      "Epoch 358/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.6232 - accuracy: 0.0000e+00 - val_loss: 20.2175 - val_accuracy: 0.0000e+00\n",
      "Epoch 359/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4524 - accuracy: 0.0000e+00 - val_loss: 20.2174 - val_accuracy: 0.0000e+00\n",
      "Epoch 360/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5268 - accuracy: 0.0000e+00 - val_loss: 20.2174 - val_accuracy: 0.0000e+00\n",
      "Epoch 361/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4790 - accuracy: 0.0000e+00 - val_loss: 20.2174 - val_accuracy: 0.0000e+00\n",
      "Epoch 362/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4834 - accuracy: 0.0000e+00 - val_loss: 20.2173 - val_accuracy: 0.0000e+00\n",
      "Epoch 363/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4801 - accuracy: 0.0000e+00 - val_loss: 20.2173 - val_accuracy: 0.0000e+00\n",
      "Epoch 364/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4638 - accuracy: 0.0000e+00 - val_loss: 20.2173 - val_accuracy: 0.0000e+00\n",
      "Epoch 365/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4368 - accuracy: 0.0000e+00 - val_loss: 20.2173 - val_accuracy: 0.0000e+00\n",
      "Epoch 366/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5140 - accuracy: 0.0000e+00 - val_loss: 20.2172 - val_accuracy: 0.0000e+00\n",
      "Epoch 367/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4662 - accuracy: 0.0000e+00 - val_loss: 20.2172 - val_accuracy: 0.0000e+00\n",
      "Epoch 368/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4993 - accuracy: 0.0000e+00 - val_loss: 20.2172 - val_accuracy: 0.0000e+00\n",
      "Epoch 369/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4981 - accuracy: 0.0000e+00 - val_loss: 20.2172 - val_accuracy: 0.0000e+00\n",
      "Epoch 370/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.5593 - accuracy: 0.0000e+00 - val_loss: 20.2171 - val_accuracy: 0.0000e+00\n",
      "Epoch 371/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4990 - accuracy: 0.0000e+00 - val_loss: 20.2171 - val_accuracy: 0.0000e+00\n",
      "Epoch 372/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4482 - accuracy: 0.0000e+00 - val_loss: 20.2171 - val_accuracy: 0.0000e+00\n",
      "Epoch 373/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4357 - accuracy: 0.0000e+00 - val_loss: 20.2171 - val_accuracy: 0.0000e+00\n",
      "Epoch 374/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4050 - accuracy: 0.0000e+00 - val_loss: 20.2170 - val_accuracy: 0.0000e+00\n",
      "Epoch 375/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4471 - accuracy: 0.0000e+00 - val_loss: 20.2170 - val_accuracy: 0.0000e+00\n",
      "Epoch 376/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4676 - accuracy: 0.0000e+00 - val_loss: 20.2170 - val_accuracy: 0.0000e+00\n",
      "Epoch 377/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.3583 - accuracy: 0.0000e+00 - val_loss: 20.2169 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 378/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.3890 - accuracy: 0.0000e+00 - val_loss: 20.2169 - val_accuracy: 0.0000e+00\n",
      "Epoch 379/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4803 - accuracy: 0.0000e+00 - val_loss: 20.2169 - val_accuracy: 0.0000e+00\n",
      "Epoch 380/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.5600 - accuracy: 0.0000e+00 - val_loss: 20.2169 - val_accuracy: 0.0000e+00\n",
      "Epoch 381/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5143 - accuracy: 0.0000e+00 - val_loss: 20.2168 - val_accuracy: 0.0000e+00\n",
      "Epoch 382/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5293 - accuracy: 0.0000e+00 - val_loss: 20.2168 - val_accuracy: 0.0000e+00\n",
      "Epoch 383/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5441 - accuracy: 0.0000e+00 - val_loss: 20.2168 - val_accuracy: 0.0000e+00\n",
      "Epoch 384/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4516 - accuracy: 0.0000e+00 - val_loss: 20.2168 - val_accuracy: 0.0000e+00\n",
      "Epoch 385/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4788 - accuracy: 0.0000e+00 - val_loss: 20.2167 - val_accuracy: 0.0000e+00\n",
      "Epoch 386/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5103 - accuracy: 0.0000e+00 - val_loss: 20.2167 - val_accuracy: 0.0000e+00\n",
      "Epoch 387/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5282 - accuracy: 0.0000e+00 - val_loss: 20.2167 - val_accuracy: 0.0000e+00\n",
      "Epoch 388/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.5907 - accuracy: 0.0000e+00 - val_loss: 20.2167 - val_accuracy: 0.0000e+00\n",
      "Epoch 389/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4821 - accuracy: 0.0000e+00 - val_loss: 20.2166 - val_accuracy: 0.0000e+00\n",
      "Epoch 390/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4786 - accuracy: 0.0000e+00 - val_loss: 20.2166 - val_accuracy: 0.0000e+00\n",
      "Epoch 391/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.5107 - accuracy: 0.0000e+00 - val_loss: 20.2166 - val_accuracy: 0.0000e+00\n",
      "Epoch 392/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4340 - accuracy: 0.0000e+00 - val_loss: 20.2166 - val_accuracy: 0.0000e+00\n",
      "Epoch 393/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5708 - accuracy: 0.0000e+00 - val_loss: 20.2166 - val_accuracy: 0.0000e+00\n",
      "Epoch 394/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4806 - accuracy: 0.0000e+00 - val_loss: 20.2165 - val_accuracy: 0.0000e+00\n",
      "Epoch 395/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4825 - accuracy: 0.0000e+00 - val_loss: 20.2165 - val_accuracy: 0.0000e+00\n",
      "Epoch 396/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5131 - accuracy: 0.0000e+00 - val_loss: 20.2165 - val_accuracy: 0.0000e+00\n",
      "Epoch 397/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4503 - accuracy: 0.0000e+00 - val_loss: 20.2165 - val_accuracy: 0.0000e+00\n",
      "Epoch 398/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4774 - accuracy: 0.0000e+00 - val_loss: 20.2164 - val_accuracy: 0.0000e+00\n",
      "Epoch 399/500\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 25.4816 - accuracy: 0.0000e+00 - val_loss: 20.2164 - val_accuracy: 0.0000e+00\n",
      "Epoch 400/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.5598 - accuracy: 0.0000e+00 - val_loss: 20.2164 - val_accuracy: 0.0000e+00\n",
      "Epoch 401/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.3732 - accuracy: 0.0000e+00 - val_loss: 20.2164 - val_accuracy: 0.0000e+00\n",
      "Epoch 402/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5124 - accuracy: 0.0000e+00 - val_loss: 20.2164 - val_accuracy: 0.0000e+00\n",
      "Epoch 403/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4971 - accuracy: 0.0000e+00 - val_loss: 20.2163 - val_accuracy: 0.0000e+00\n",
      "Epoch 404/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5261 - accuracy: 0.0000e+00 - val_loss: 20.2163 - val_accuracy: 0.0000e+00\n",
      "Epoch 405/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4197 - accuracy: 0.0000e+00 - val_loss: 20.2163 - val_accuracy: 0.0000e+00\n",
      "Epoch 406/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5757 - accuracy: 0.0000e+00 - val_loss: 20.2163 - val_accuracy: 0.0000e+00\n",
      "Epoch 407/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4952 - accuracy: 0.0000e+00 - val_loss: 20.2163 - val_accuracy: 0.0000e+00\n",
      "Epoch 408/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5123 - accuracy: 0.0000e+00 - val_loss: 20.2162 - val_accuracy: 0.0000e+00\n",
      "Epoch 409/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.5127 - accuracy: 0.0000e+00 - val_loss: 20.2162 - val_accuracy: 0.0000e+00\n",
      "Epoch 410/500\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 25.4041 - accuracy: 0.0000e+00 - val_loss: 20.2162 - val_accuracy: 0.0000e+00\n",
      "Epoch 411/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4336 - accuracy: 0.0000e+00 - val_loss: 20.2162 - val_accuracy: 0.0000e+00\n",
      "Epoch 412/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4952 - accuracy: 0.0000e+00 - val_loss: 20.2162 - val_accuracy: 0.0000e+00\n",
      "Epoch 413/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4650 - accuracy: 0.0000e+00 - val_loss: 20.2161 - val_accuracy: 0.0000e+00\n",
      "Epoch 414/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4348 - accuracy: 0.0000e+00 - val_loss: 20.2161 - val_accuracy: 0.0000e+00\n",
      "Epoch 415/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4468 - accuracy: 0.0000e+00 - val_loss: 20.2161 - val_accuracy: 0.0000e+00\n",
      "Epoch 416/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.3879 - accuracy: 0.0000e+00 - val_loss: 20.2161 - val_accuracy: 0.0000e+00\n",
      "Epoch 417/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5417 - accuracy: 0.0000e+00 - val_loss: 20.2161 - val_accuracy: 0.0000e+00\n",
      "Epoch 418/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5590 - accuracy: 0.0000e+00 - val_loss: 20.2161 - val_accuracy: 0.0000e+00\n",
      "Epoch 419/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.3876 - accuracy: 0.0000e+00 - val_loss: 20.2160 - val_accuracy: 0.0000e+00\n",
      "Epoch 420/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4966 - accuracy: 0.0000e+00 - val_loss: 20.2160 - val_accuracy: 0.0000e+00\n",
      "Epoch 421/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5112 - accuracy: 0.0000e+00 - val_loss: 20.2160 - val_accuracy: 0.0000e+00\n",
      "Epoch 422/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4635 - accuracy: 0.0000e+00 - val_loss: 20.2160 - val_accuracy: 0.0000e+00\n",
      "Epoch 423/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4038 - accuracy: 0.0000e+00 - val_loss: 20.2160 - val_accuracy: 0.0000e+00\n",
      "Epoch 424/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4496 - accuracy: 0.0000e+00 - val_loss: 20.2159 - val_accuracy: 0.0000e+00\n",
      "Epoch 425/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5113 - accuracy: 0.0000e+00 - val_loss: 20.2159 - val_accuracy: 0.0000e+00\n",
      "Epoch 426/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.3251 - accuracy: 0.0000e+00 - val_loss: 20.2159 - val_accuracy: 0.0000e+00\n",
      "Epoch 427/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5430 - accuracy: 0.0000e+00 - val_loss: 20.2159 - val_accuracy: 0.0000e+00\n",
      "Epoch 428/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4962 - accuracy: 0.0000e+00 - val_loss: 20.2158 - val_accuracy: 0.0000e+00\n",
      "Epoch 429/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5565 - accuracy: 0.0000e+00 - val_loss: 20.2158 - val_accuracy: 0.0000e+00\n",
      "Epoch 430/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4638 - accuracy: 0.0000e+00 - val_loss: 20.2158 - val_accuracy: 0.0000e+00\n",
      "Epoch 431/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4804 - accuracy: 0.0000e+00 - val_loss: 20.2158 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 432/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5128 - accuracy: 0.0000e+00 - val_loss: 20.2158 - val_accuracy: 0.0000e+00\n",
      "Epoch 433/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4503 - accuracy: 0.0000e+00 - val_loss: 20.2158 - val_accuracy: 0.0000e+00\n",
      "Epoch 434/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5250 - accuracy: 0.0000e+00 - val_loss: 20.2157 - val_accuracy: 0.0000e+00\n",
      "Epoch 435/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4960 - accuracy: 0.0000e+00 - val_loss: 20.2157 - val_accuracy: 0.0000e+00\n",
      "Epoch 436/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4657 - accuracy: 0.0000e+00 - val_loss: 20.2157 - val_accuracy: 0.0000e+00\n",
      "Epoch 437/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4494 - accuracy: 0.0000e+00 - val_loss: 20.2157 - val_accuracy: 0.0000e+00\n",
      "Epoch 438/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.3563 - accuracy: 0.0000e+00 - val_loss: 20.2157 - val_accuracy: 0.0000e+00\n",
      "Epoch 439/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.3716 - accuracy: 0.0000e+00 - val_loss: 20.2157 - val_accuracy: 0.0000e+00\n",
      "Epoch 440/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5094 - accuracy: 0.0000e+00 - val_loss: 20.2156 - val_accuracy: 0.0000e+00\n",
      "Epoch 441/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5257 - accuracy: 0.0000e+00 - val_loss: 20.2156 - val_accuracy: 0.0000e+00\n",
      "Epoch 442/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4303 - accuracy: 0.0000e+00 - val_loss: 20.2156 - val_accuracy: 0.0000e+00\n",
      "Epoch 443/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4809 - accuracy: 0.0000e+00 - val_loss: 20.2156 - val_accuracy: 0.0000e+00\n",
      "Epoch 444/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4797 - accuracy: 0.0000e+00 - val_loss: 20.2156 - val_accuracy: 0.0000e+00\n",
      "Epoch 445/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4807 - accuracy: 0.0000e+00 - val_loss: 20.2156 - val_accuracy: 0.0000e+00\n",
      "Epoch 446/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5390 - accuracy: 0.0000e+00 - val_loss: 20.2155 - val_accuracy: 0.0000e+00\n",
      "Epoch 447/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4331 - accuracy: 0.0000e+00 - val_loss: 20.2155 - val_accuracy: 0.0000e+00\n",
      "Epoch 448/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5427 - accuracy: 0.0000e+00 - val_loss: 20.2155 - val_accuracy: 0.0000e+00\n",
      "Epoch 449/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4178 - accuracy: 0.0000e+00 - val_loss: 20.2155 - val_accuracy: 0.0000e+00\n",
      "Epoch 450/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.5118 - accuracy: 0.0000e+00 - val_loss: 20.2155 - val_accuracy: 0.0000e+00\n",
      "Epoch 451/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4804 - accuracy: 0.0000e+00 - val_loss: 20.2155 - val_accuracy: 0.0000e+00\n",
      "Epoch 452/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5589 - accuracy: 0.0000e+00 - val_loss: 20.2154 - val_accuracy: 0.0000e+00\n",
      "Epoch 453/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4964 - accuracy: 0.0000e+00 - val_loss: 20.2154 - val_accuracy: 0.0000e+00\n",
      "Epoch 454/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4321 - accuracy: 0.0000e+00 - val_loss: 20.2154 - val_accuracy: 0.0000e+00\n",
      "Epoch 455/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4317 - accuracy: 0.0000e+00 - val_loss: 20.2154 - val_accuracy: 0.0000e+00\n",
      "Epoch 456/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4956 - accuracy: 0.0000e+00 - val_loss: 20.2154 - val_accuracy: 0.0000e+00\n",
      "Epoch 457/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4320 - accuracy: 0.0000e+00 - val_loss: 20.2154 - val_accuracy: 0.0000e+00\n",
      "Epoch 458/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4806 - accuracy: 0.0000e+00 - val_loss: 20.2153 - val_accuracy: 0.0000e+00\n",
      "Epoch 459/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5408 - accuracy: 0.0000e+00 - val_loss: 20.2153 - val_accuracy: 0.0000e+00\n",
      "Epoch 460/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4027 - accuracy: 0.0000e+00 - val_loss: 20.2153 - val_accuracy: 0.0000e+00\n",
      "Epoch 461/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4647 - accuracy: 0.0000e+00 - val_loss: 20.2153 - val_accuracy: 0.0000e+00\n",
      "Epoch 462/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5416 - accuracy: 0.0000e+00 - val_loss: 20.2153 - val_accuracy: 0.0000e+00\n",
      "Epoch 463/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4652 - accuracy: 0.0000e+00 - val_loss: 20.2153 - val_accuracy: 0.0000e+00\n",
      "Epoch 464/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4485 - accuracy: 0.0000e+00 - val_loss: 20.2152 - val_accuracy: 0.0000e+00\n",
      "Epoch 465/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5266 - accuracy: 0.0000e+00 - val_loss: 20.2152 - val_accuracy: 0.0000e+00\n",
      "Epoch 466/500\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 25.4792 - accuracy: 0.0000e+00 - val_loss: 20.2152 - val_accuracy: 0.0000e+00\n",
      "Epoch 467/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5416 - accuracy: 0.0000e+00 - val_loss: 20.2152 - val_accuracy: 0.0000e+00\n",
      "Epoch 468/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4493 - accuracy: 0.0000e+00 - val_loss: 20.2152 - val_accuracy: 0.0000e+00\n",
      "Epoch 469/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4152 - accuracy: 0.0000e+00 - val_loss: 20.2152 - val_accuracy: 0.0000e+00\n",
      "Epoch 470/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4778 - accuracy: 0.0000e+00 - val_loss: 20.2152 - val_accuracy: 0.0000e+00\n",
      "Epoch 471/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.5118 - accuracy: 0.0000e+00 - val_loss: 20.2151 - val_accuracy: 0.0000e+00\n",
      "Epoch 472/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4484 - accuracy: 0.0000e+00 - val_loss: 20.2151 - val_accuracy: 0.0000e+00\n",
      "Epoch 473/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4955 - accuracy: 0.0000e+00 - val_loss: 20.2151 - val_accuracy: 0.0000e+00\n",
      "Epoch 474/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4336 - accuracy: 0.0000e+00 - val_loss: 20.2151 - val_accuracy: 0.0000e+00\n",
      "Epoch 475/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5717 - accuracy: 0.0000e+00 - val_loss: 20.2151 - val_accuracy: 0.0000e+00\n",
      "Epoch 476/500\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 25.4780 - accuracy: 0.0000e+00 - val_loss: 20.2151 - val_accuracy: 0.0000e+00\n",
      "Epoch 477/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5272 - accuracy: 0.0000e+00 - val_loss: 20.2151 - val_accuracy: 0.0000e+00\n",
      "Epoch 478/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5403 - accuracy: 0.0000e+00 - val_loss: 20.2150 - val_accuracy: 0.0000e+00\n",
      "Epoch 479/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5427 - accuracy: 0.0000e+00 - val_loss: 20.2150 - val_accuracy: 0.0000e+00\n",
      "Epoch 480/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4960 - accuracy: 0.0000e+00 - val_loss: 20.2150 - val_accuracy: 0.0000e+00\n",
      "Epoch 481/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5580 - accuracy: 0.0000e+00 - val_loss: 20.2150 - val_accuracy: 0.0000e+00\n",
      "Epoch 482/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4798 - accuracy: 0.0000e+00 - val_loss: 20.2150 - val_accuracy: 0.0000e+00\n",
      "Epoch 483/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4608 - accuracy: 0.0000e+00 - val_loss: 20.2150 - val_accuracy: 0.0000e+00\n",
      "Epoch 484/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4331 - accuracy: 0.0000e+00 - val_loss: 20.2150 - val_accuracy: 0.0000e+00\n",
      "Epoch 485/500\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 25.4788 - accuracy: 0.0000e+00 - val_loss: 20.2150 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 486/500\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 25.4173 - accuracy: 0.0000e+00 - val_loss: 20.2149 - val_accuracy: 0.0000e+00\n",
      "Epoch 487/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4022 - accuracy: 0.0000e+00 - val_loss: 20.2149 - val_accuracy: 0.0000e+00\n",
      "Epoch 488/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5268 - accuracy: 0.0000e+00 - val_loss: 20.2149 - val_accuracy: 0.0000e+00\n",
      "Epoch 489/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5114 - accuracy: 0.0000e+00 - val_loss: 20.2149 - val_accuracy: 0.0000e+00\n",
      "Epoch 490/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4942 - accuracy: 0.0000e+00 - val_loss: 20.2149 - val_accuracy: 0.0000e+00\n",
      "Epoch 491/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.5733 - accuracy: 0.0000e+00 - val_loss: 20.2149 - val_accuracy: 0.0000e+00\n",
      "Epoch 492/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4781 - accuracy: 0.0000e+00 - val_loss: 20.2149 - val_accuracy: 0.0000e+00\n",
      "Epoch 493/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4017 - accuracy: 0.0000e+00 - val_loss: 20.2148 - val_accuracy: 0.0000e+00\n",
      "Epoch 494/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.3862 - accuracy: 0.0000e+00 - val_loss: 20.2148 - val_accuracy: 0.0000e+00\n",
      "Epoch 495/500\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 25.4016 - accuracy: 0.0000e+00 - val_loss: 20.2148 - val_accuracy: 0.0000e+00\n",
      "Epoch 496/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.5886 - accuracy: 0.0000e+00 - val_loss: 20.2148 - val_accuracy: 0.0000e+00\n",
      "Epoch 497/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4487 - accuracy: 0.0000e+00 - val_loss: 20.2148 - val_accuracy: 0.0000e+00\n",
      "Epoch 498/500\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 25.4794 - accuracy: 0.0000e+00 - val_loss: 20.2148 - val_accuracy: 0.0000e+00\n",
      "Epoch 499/500\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 25.4310 - accuracy: 0.0000e+00 - val_loss: 20.2148 - val_accuracy: 0.0000e+00\n",
      "Epoch 500/500\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 25.4943 - accuracy: 0.0000e+00 - val_loss: 20.2148 - val_accuracy: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=500, validation_data = (x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}