BCI_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0a4e3cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from C:\\codebase\\mi_debug\\BCI_Data\\A01T.gdf...\n",
      "GDF file detected\n",
      "Setting channel info structure...\n",
      "Could not determine channel type of the following channels, they will be set as EEG:\n",
      "EEG-Fz, EEG, EEG, EEG, EEG, EEG, EEG, EEG-C3, EEG, EEG-Cz, EEG, EEG-C4, EEG, EEG, EEG, EEG, EEG, EEG, EEG, EEG-Pz, EEG, EEG, EOG-left, EOG-central, EOG-right\n",
      "Creating raw.info structure...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\mne\\io\\edf\\edf.py:1131: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
      "  etmode = np.fromstring(etmode, UINT8).tolist()[0]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\contextlib.py:120: RuntimeWarning: Channel names are not unique, found duplicates for: {'EEG'}. Applying running numbers for duplicates.\n",
      "  next(self.gen)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Info | 7 non-empty values\n",
      " bads: []\n",
      " ch_names: EEG-Fz, EEG-0, EEG-1, EEG-2, EEG-3, EEG-4, EEG-5, EEG-C3, EEG-6, ...\n",
      " chs: 25 EEG\n",
      " custom_ref_applied: False\n",
      " highpass: 0.5 Hz\n",
      " lowpass: 100.0 Hz\n",
      " meas_date: 2005-01-17 12:00:00 UTC\n",
      " nchan: 25\n",
      " projs: []\n",
      " sfreq: 250.0 Hz\n",
      ">\n",
      "['EEG-Fz', 'EEG-0', 'EEG-1', 'EEG-2', 'EEG-3', 'EEG-4', 'EEG-5', 'EEG-C3', 'EEG-6', 'EEG-Cz', 'EEG-7', 'EEG-C4', 'EEG-8', 'EEG-9', 'EEG-10', 'EEG-11', 'EEG-12', 'EEG-13', 'EEG-14', 'EEG-Pz', 'EEG-15', 'EEG-16', 'EOG-left', 'EOG-central', 'EOG-right']\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import mne\n",
    "import numpy as np\n",
    "\n",
    "# Mention the file path to the dataset\n",
    "filename = 'BCI_Data/A01T.gdf'\n",
    "\n",
    "raw = mne.io.read_raw_gdf(filename)\n",
    "\n",
    "print(raw.info)\n",
    "print(raw.ch_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0e5be20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Used Annotations descriptions: ['1023', '1072', '276', '277', '32766', '768', '769', '770', '771', '772']\n",
      "Reading 0 ... 672527  =      0.000 ...  2690.108 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 7 - 35 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 7.00\n",
      "- Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 6.00 Hz)\n",
      "- Upper passband edge: 35.00 Hz\n",
      "- Upper transition bandwidth: 8.75 Hz (-6 dB cutoff frequency: 39.38 Hz)\n",
      "- Filter length: 413 samples (1.652 sec)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "events, _ = mne.events_from_annotations(raw)\n",
    "\n",
    "raw.load_data()\n",
    "\n",
    "raw.filter(7., 35., fir_design='firwin')\n",
    "\n",
    "raw.info['bads'] += ['EOG-left', 'EOG-central', 'EOG-right']\n",
    "\n",
    "picks = mne.pick_types(raw.info, meg=False, eeg=True, eog=False, stim=False,\n",
    "                       exclude='bads')\n",
    "\n",
    "tmin, tmax = 1., 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d0dd292",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "Not setting metadata\n",
      "288 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Loading data for 288 events and 751 original time points ...\n",
      "0 bad epochs dropped\n"
     ]
    }
   ],
   "source": [
    "event_id = dict({'769': 7,'770': 8,'771': 9,'772': 10})\n",
    "epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,\n",
    "                baseline=None, preload=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67075ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting labels and changing labels from 7,8,9,10 -> 1,2,3,4\n",
    "y = epochs.events[:,-1] - 7 \n",
    "\n",
    "X = epochs.get_data()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1b65e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 22, 751)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd4a62c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4000\n",
      "9/9 - 1s - loss: 1.3866 - accuracy: 0.2292 - val_loss: 1.3866 - val_accuracy: 0.2222 - 907ms/epoch - 101ms/step\n",
      "Epoch 2/4000\n",
      "9/9 - 0s - loss: 1.3868 - accuracy: 0.2292 - val_loss: 1.3866 - val_accuracy: 0.2222 - 114ms/epoch - 13ms/step\n",
      "Epoch 3/4000\n",
      "9/9 - 0s - loss: 1.3864 - accuracy: 0.2500 - val_loss: 1.3865 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 4/4000\n",
      "9/9 - 0s - loss: 1.3856 - accuracy: 0.3194 - val_loss: 1.3865 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 5/4000\n",
      "9/9 - 0s - loss: 1.3858 - accuracy: 0.2569 - val_loss: 1.3864 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 6/4000\n",
      "9/9 - 0s - loss: 1.3857 - accuracy: 0.3194 - val_loss: 1.3863 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 7/4000\n",
      "9/9 - 0s - loss: 1.3860 - accuracy: 0.2500 - val_loss: 1.3862 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 8/4000\n",
      "9/9 - 0s - loss: 1.3854 - accuracy: 0.2708 - val_loss: 1.3863 - val_accuracy: 0.2917 - 74ms/epoch - 8ms/step\n",
      "Epoch 9/4000\n",
      "9/9 - 0s - loss: 1.3847 - accuracy: 0.3333 - val_loss: 1.3864 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 10/4000\n",
      "9/9 - 0s - loss: 1.3837 - accuracy: 0.3056 - val_loss: 1.3865 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 11/4000\n",
      "9/9 - 0s - loss: 1.3805 - accuracy: 0.3403 - val_loss: 1.3866 - val_accuracy: 0.2222 - 73ms/epoch - 8ms/step\n",
      "Epoch 12/4000\n",
      "9/9 - 0s - loss: 1.3743 - accuracy: 0.4375 - val_loss: 1.3865 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 13/4000\n",
      "9/9 - 0s - loss: 1.3685 - accuracy: 0.4375 - val_loss: 1.3864 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 14/4000\n",
      "9/9 - 0s - loss: 1.3543 - accuracy: 0.4653 - val_loss: 1.3865 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 15/4000\n",
      "9/9 - 0s - loss: 1.3312 - accuracy: 0.4167 - val_loss: 1.3863 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 16/4000\n",
      "9/9 - 0s - loss: 1.3123 - accuracy: 0.3958 - val_loss: 1.3860 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 17/4000\n",
      "9/9 - 0s - loss: 1.2876 - accuracy: 0.3889 - val_loss: 1.3858 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 18/4000\n",
      "9/9 - 0s - loss: 1.2748 - accuracy: 0.3889 - val_loss: 1.3858 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 19/4000\n",
      "9/9 - 0s - loss: 1.2464 - accuracy: 0.4444 - val_loss: 1.3863 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 20/4000\n",
      "9/9 - 0s - loss: 1.2283 - accuracy: 0.4583 - val_loss: 1.3869 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 21/4000\n",
      "9/9 - 0s - loss: 1.2153 - accuracy: 0.4792 - val_loss: 1.3885 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 22/4000\n",
      "9/9 - 0s - loss: 1.1940 - accuracy: 0.4792 - val_loss: 1.3924 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 23/4000\n",
      "9/9 - 0s - loss: 1.1815 - accuracy: 0.5694 - val_loss: 1.3954 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 24/4000\n",
      "9/9 - 0s - loss: 1.1708 - accuracy: 0.5625 - val_loss: 1.4024 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 25/4000\n",
      "9/9 - 0s - loss: 1.1638 - accuracy: 0.6111 - val_loss: 1.4071 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 26/4000\n",
      "9/9 - 0s - loss: 1.1441 - accuracy: 0.6250 - val_loss: 1.4151 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 27/4000\n",
      "9/9 - 0s - loss: 1.1454 - accuracy: 0.6458 - val_loss: 1.4259 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 28/4000\n",
      "9/9 - 0s - loss: 1.1177 - accuracy: 0.6736 - val_loss: 1.4347 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 29/4000\n",
      "9/9 - 0s - loss: 1.1135 - accuracy: 0.6806 - val_loss: 1.4502 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 30/4000\n",
      "9/9 - 0s - loss: 1.1107 - accuracy: 0.6736 - val_loss: 1.4625 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 31/4000\n",
      "9/9 - 0s - loss: 1.0950 - accuracy: 0.6875 - val_loss: 1.4788 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 32/4000\n",
      "9/9 - 0s - loss: 1.0834 - accuracy: 0.7500 - val_loss: 1.4912 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 33/4000\n",
      "9/9 - 0s - loss: 1.0956 - accuracy: 0.6667 - val_loss: 1.4988 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 34/4000\n",
      "9/9 - 0s - loss: 1.0678 - accuracy: 0.7292 - val_loss: 1.5131 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 35/4000\n",
      "9/9 - 0s - loss: 1.0615 - accuracy: 0.7014 - val_loss: 1.5372 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 36/4000\n",
      "9/9 - 0s - loss: 1.0629 - accuracy: 0.7222 - val_loss: 1.5497 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 37/4000\n",
      "9/9 - 0s - loss: 1.0394 - accuracy: 0.7153 - val_loss: 1.5658 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 38/4000\n",
      "9/9 - 0s - loss: 1.0476 - accuracy: 0.7292 - val_loss: 1.5890 - val_accuracy: 0.2917 - 76ms/epoch - 8ms/step\n",
      "Epoch 39/4000\n",
      "9/9 - 0s - loss: 1.0308 - accuracy: 0.7014 - val_loss: 1.6084 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 40/4000\n",
      "9/9 - 0s - loss: 1.0251 - accuracy: 0.7292 - val_loss: 1.6446 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 41/4000\n",
      "9/9 - 0s - loss: 1.0195 - accuracy: 0.7292 - val_loss: 1.6804 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 42/4000\n",
      "9/9 - 0s - loss: 1.0104 - accuracy: 0.6944 - val_loss: 1.7145 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 43/4000\n",
      "9/9 - 0s - loss: 1.0051 - accuracy: 0.7500 - val_loss: 1.7373 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 44/4000\n",
      "9/9 - 0s - loss: 1.0170 - accuracy: 0.7222 - val_loss: 1.7534 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 45/4000\n",
      "9/9 - 0s - loss: 0.9873 - accuracy: 0.7431 - val_loss: 1.7761 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 46/4000\n",
      "9/9 - 0s - loss: 1.0185 - accuracy: 0.7014 - val_loss: 1.7920 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 47/4000\n",
      "9/9 - 0s - loss: 0.9715 - accuracy: 0.7014 - val_loss: 1.7852 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 48/4000\n",
      "9/9 - 0s - loss: 0.9718 - accuracy: 0.7014 - val_loss: 1.7796 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 49/4000\n",
      "9/9 - 0s - loss: 0.9715 - accuracy: 0.7222 - val_loss: 1.7336 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 50/4000\n",
      "9/9 - 0s - loss: 0.9772 - accuracy: 0.7361 - val_loss: 1.7305 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 51/4000\n",
      "9/9 - 0s - loss: 0.9860 - accuracy: 0.6667 - val_loss: 1.7446 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 52/4000\n",
      "9/9 - 0s - loss: 0.9592 - accuracy: 0.6875 - val_loss: 1.7459 - val_accuracy: 0.2917 - 76ms/epoch - 8ms/step\n",
      "Epoch 53/4000\n",
      "9/9 - 0s - loss: 0.9562 - accuracy: 0.7222 - val_loss: 1.6811 - val_accuracy: 0.2917 - 76ms/epoch - 8ms/step\n",
      "Epoch 54/4000\n",
      "9/9 - 0s - loss: 0.9610 - accuracy: 0.7153 - val_loss: 1.6330 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 55/4000\n",
      "9/9 - 0s - loss: 0.9577 - accuracy: 0.7153 - val_loss: 1.6182 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 56/4000\n",
      "9/9 - 0s - loss: 0.9490 - accuracy: 0.6736 - val_loss: 1.6342 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 57/4000\n",
      "9/9 - 0s - loss: 0.9618 - accuracy: 0.6528 - val_loss: 1.6595 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 58/4000\n",
      "9/9 - 0s - loss: 0.9487 - accuracy: 0.6944 - val_loss: 1.7035 - val_accuracy: 0.2917 - 73ms/epoch - 8ms/step\n",
      "Epoch 59/4000\n",
      "9/9 - 0s - loss: 0.9572 - accuracy: 0.7569 - val_loss: 1.6918 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 60/4000\n",
      "9/9 - 0s - loss: 0.9296 - accuracy: 0.7014 - val_loss: 1.6666 - val_accuracy: 0.2917 - 75ms/epoch - 8ms/step\n",
      "Epoch 61/4000\n",
      "9/9 - 0s - loss: 0.9262 - accuracy: 0.6944 - val_loss: 1.6912 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 62/4000\n",
      "9/9 - 0s - loss: 0.9483 - accuracy: 0.6736 - val_loss: 1.6185 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 63/4000\n",
      "9/9 - 0s - loss: 0.9212 - accuracy: 0.6806 - val_loss: 1.5431 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 64/4000\n",
      "9/9 - 0s - loss: 0.9353 - accuracy: 0.7222 - val_loss: 1.6115 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 65/4000\n",
      "9/9 - 0s - loss: 0.9164 - accuracy: 0.7222 - val_loss: 1.6069 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 66/4000\n",
      "9/9 - 0s - loss: 0.9284 - accuracy: 0.7014 - val_loss: 1.6269 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 67/4000\n",
      "9/9 - 0s - loss: 0.9215 - accuracy: 0.7222 - val_loss: 1.6123 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 68/4000\n",
      "9/9 - 0s - loss: 0.9059 - accuracy: 0.7153 - val_loss: 1.5635 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 69/4000\n",
      "9/9 - 0s - loss: 0.8929 - accuracy: 0.7153 - val_loss: 1.6167 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 70/4000\n",
      "9/9 - 0s - loss: 0.9097 - accuracy: 0.7014 - val_loss: 1.6261 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 71/4000\n",
      "9/9 - 0s - loss: 0.9060 - accuracy: 0.6944 - val_loss: 1.6092 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 72/4000\n",
      "9/9 - 0s - loss: 0.8946 - accuracy: 0.7292 - val_loss: 1.5988 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 73/4000\n",
      "9/9 - 0s - loss: 0.9029 - accuracy: 0.7361 - val_loss: 1.6508 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 74/4000\n",
      "9/9 - 0s - loss: 0.8880 - accuracy: 0.7292 - val_loss: 1.5998 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 75/4000\n",
      "9/9 - 0s - loss: 0.8954 - accuracy: 0.7153 - val_loss: 1.5181 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 76/4000\n",
      "9/9 - 0s - loss: 0.8833 - accuracy: 0.7361 - val_loss: 1.4675 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 77/4000\n",
      "9/9 - 0s - loss: 0.8786 - accuracy: 0.7014 - val_loss: 1.4422 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 78/4000\n",
      "9/9 - 0s - loss: 0.8840 - accuracy: 0.7153 - val_loss: 1.3809 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 79/4000\n",
      "9/9 - 0s - loss: 0.8852 - accuracy: 0.6806 - val_loss: 1.4095 - val_accuracy: 0.2917 - 75ms/epoch - 8ms/step\n",
      "Epoch 80/4000\n",
      "9/9 - 0s - loss: 0.8602 - accuracy: 0.7361 - val_loss: 1.6468 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 81/4000\n",
      "9/9 - 0s - loss: 0.8855 - accuracy: 0.7292 - val_loss: 1.6622 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 82/4000\n",
      "9/9 - 0s - loss: 0.8738 - accuracy: 0.6944 - val_loss: 1.7263 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 83/4000\n",
      "9/9 - 0s - loss: 0.8665 - accuracy: 0.7292 - val_loss: 1.8985 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 84/4000\n",
      "9/9 - 0s - loss: 0.8657 - accuracy: 0.7361 - val_loss: 2.0959 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 85/4000\n",
      "9/9 - 0s - loss: 0.8725 - accuracy: 0.7222 - val_loss: 2.1543 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 86/4000\n",
      "9/9 - 0s - loss: 0.9135 - accuracy: 0.7153 - val_loss: 1.9652 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 87/4000\n",
      "9/9 - 0s - loss: 0.8657 - accuracy: 0.7222 - val_loss: 1.8729 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 88/4000\n",
      "9/9 - 0s - loss: 0.8487 - accuracy: 0.7361 - val_loss: 2.0684 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 89/4000\n",
      "9/9 - 0s - loss: 0.8493 - accuracy: 0.7500 - val_loss: 2.2839 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 90/4000\n",
      "9/9 - 0s - loss: 0.8594 - accuracy: 0.7153 - val_loss: 2.5014 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 91/4000\n",
      "9/9 - 0s - loss: 0.8514 - accuracy: 0.7292 - val_loss: 2.5740 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 92/4000\n",
      "9/9 - 0s - loss: 0.8481 - accuracy: 0.7500 - val_loss: 2.3713 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 93/4000\n",
      "9/9 - 0s - loss: 0.8279 - accuracy: 0.7361 - val_loss: 2.6796 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 94/4000\n",
      "9/9 - 0s - loss: 0.8405 - accuracy: 0.7431 - val_loss: 2.7004 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 95/4000\n",
      "9/9 - 0s - loss: 0.8540 - accuracy: 0.6944 - val_loss: 2.6571 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 96/4000\n",
      "9/9 - 0s - loss: 0.8303 - accuracy: 0.7361 - val_loss: 2.2787 - val_accuracy: 0.2917 - 100ms/epoch - 11ms/step\n",
      "Epoch 97/4000\n",
      "9/9 - 0s - loss: 0.8274 - accuracy: 0.7431 - val_loss: 1.8319 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 98/4000\n",
      "9/9 - 0s - loss: 0.8430 - accuracy: 0.7153 - val_loss: 1.8245 - val_accuracy: 0.2917 - 112ms/epoch - 12ms/step\n",
      "Epoch 99/4000\n",
      "9/9 - 0s - loss: 0.8482 - accuracy: 0.7292 - val_loss: 1.6721 - val_accuracy: 0.2917 - 128ms/epoch - 14ms/step\n",
      "Epoch 100/4000\n",
      "9/9 - 0s - loss: 0.8434 - accuracy: 0.7222 - val_loss: 1.8122 - val_accuracy: 0.2917 - 106ms/epoch - 12ms/step\n",
      "Epoch 101/4000\n",
      "9/9 - 0s - loss: 0.8321 - accuracy: 0.7569 - val_loss: 1.9210 - val_accuracy: 0.2917 - 111ms/epoch - 12ms/step\n",
      "Epoch 102/4000\n",
      "9/9 - 0s - loss: 0.8273 - accuracy: 0.7361 - val_loss: 2.3655 - val_accuracy: 0.2917 - 121ms/epoch - 13ms/step\n",
      "Epoch 103/4000\n",
      "9/9 - 0s - loss: 0.8386 - accuracy: 0.7431 - val_loss: 2.8328 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 104/4000\n",
      "9/9 - 0s - loss: 0.8123 - accuracy: 0.7639 - val_loss: 2.8927 - val_accuracy: 0.2917 - 140ms/epoch - 16ms/step\n",
      "Epoch 105/4000\n",
      "9/9 - 0s - loss: 0.8287 - accuracy: 0.7292 - val_loss: 3.0885 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 106/4000\n",
      "9/9 - 0s - loss: 0.8198 - accuracy: 0.7639 - val_loss: 2.9814 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 107/4000\n",
      "9/9 - 0s - loss: 0.8102 - accuracy: 0.7500 - val_loss: 2.6968 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 108/4000\n",
      "9/9 - 0s - loss: 0.8152 - accuracy: 0.7708 - val_loss: 2.6615 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 109/4000\n",
      "9/9 - 0s - loss: 0.8406 - accuracy: 0.7431 - val_loss: 2.4189 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 110/4000\n",
      "9/9 - 0s - loss: 0.8237 - accuracy: 0.7569 - val_loss: 2.2831 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 111/4000\n",
      "9/9 - 0s - loss: 0.8026 - accuracy: 0.7847 - val_loss: 2.3196 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 112/4000\n",
      "9/9 - 0s - loss: 0.8381 - accuracy: 0.7569 - val_loss: 2.3747 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 113/4000\n",
      "9/9 - 0s - loss: 0.8334 - accuracy: 0.7500 - val_loss: 2.4342 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 114/4000\n",
      "9/9 - 0s - loss: 0.8035 - accuracy: 0.7431 - val_loss: 2.4320 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 115/4000\n",
      "9/9 - 0s - loss: 0.8119 - accuracy: 0.7639 - val_loss: 2.3872 - val_accuracy: 0.2917 - 121ms/epoch - 13ms/step\n",
      "Epoch 116/4000\n",
      "9/9 - 0s - loss: 0.8113 - accuracy: 0.7431 - val_loss: 2.3304 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 117/4000\n",
      "9/9 - 0s - loss: 0.7927 - accuracy: 0.7639 - val_loss: 2.5351 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 118/4000\n",
      "9/9 - 0s - loss: 0.8423 - accuracy: 0.7292 - val_loss: 2.8043 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 119/4000\n",
      "9/9 - 0s - loss: 0.7942 - accuracy: 0.7500 - val_loss: 2.7321 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 120/4000\n",
      "9/9 - 0s - loss: 0.8052 - accuracy: 0.7569 - val_loss: 3.0277 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 121/4000\n",
      "9/9 - 0s - loss: 0.7890 - accuracy: 0.7431 - val_loss: 3.0001 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 122/4000\n",
      "9/9 - 0s - loss: 0.8098 - accuracy: 0.7153 - val_loss: 2.9443 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 123/4000\n",
      "9/9 - 0s - loss: 0.7744 - accuracy: 0.7986 - val_loss: 2.8732 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 124/4000\n",
      "9/9 - 0s - loss: 0.8039 - accuracy: 0.7500 - val_loss: 2.7841 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 125/4000\n",
      "9/9 - 0s - loss: 0.8019 - accuracy: 0.7500 - val_loss: 2.9620 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 126/4000\n",
      "9/9 - 0s - loss: 0.7838 - accuracy: 0.7639 - val_loss: 3.2074 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 127/4000\n",
      "9/9 - 0s - loss: 0.7921 - accuracy: 0.7639 - val_loss: 3.2255 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 128/4000\n",
      "9/9 - 0s - loss: 0.7737 - accuracy: 0.7778 - val_loss: 3.2229 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 129/4000\n",
      "9/9 - 0s - loss: 0.7942 - accuracy: 0.7639 - val_loss: 3.1505 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 130/4000\n",
      "9/9 - 0s - loss: 0.7934 - accuracy: 0.7778 - val_loss: 3.0954 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 131/4000\n",
      "9/9 - 0s - loss: 0.7979 - accuracy: 0.7708 - val_loss: 2.8119 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 132/4000\n",
      "9/9 - 0s - loss: 0.7732 - accuracy: 0.7639 - val_loss: 2.6617 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 133/4000\n",
      "9/9 - 0s - loss: 0.7598 - accuracy: 0.7639 - val_loss: 3.6691 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 134/4000\n",
      "9/9 - 0s - loss: 0.7674 - accuracy: 0.7708 - val_loss: 3.1341 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 135/4000\n",
      "9/9 - 0s - loss: 0.7641 - accuracy: 0.7917 - val_loss: 3.6623 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 136/4000\n",
      "9/9 - 0s - loss: 0.7795 - accuracy: 0.7847 - val_loss: 4.9618 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 137/4000\n",
      "9/9 - 0s - loss: 0.7625 - accuracy: 0.7500 - val_loss: 5.6511 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 138/4000\n",
      "9/9 - 0s - loss: 0.7663 - accuracy: 0.7986 - val_loss: 6.2759 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 139/4000\n",
      "9/9 - 0s - loss: 0.7762 - accuracy: 0.7778 - val_loss: 6.9772 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 140/4000\n",
      "9/9 - 0s - loss: 0.7844 - accuracy: 0.7639 - val_loss: 4.8650 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 141/4000\n",
      "9/9 - 0s - loss: 0.7737 - accuracy: 0.7639 - val_loss: 4.8212 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 142/4000\n",
      "9/9 - 0s - loss: 0.7655 - accuracy: 0.7639 - val_loss: 6.0728 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 143/4000\n",
      "9/9 - 0s - loss: 0.7612 - accuracy: 0.7986 - val_loss: 4.6660 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 144/4000\n",
      "9/9 - 0s - loss: 0.7501 - accuracy: 0.7778 - val_loss: 4.1544 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 145/4000\n",
      "9/9 - 0s - loss: 0.7598 - accuracy: 0.7708 - val_loss: 3.5863 - val_accuracy: 0.4583 - 82ms/epoch - 9ms/step\n",
      "Epoch 146/4000\n",
      "9/9 - 0s - loss: 0.7561 - accuracy: 0.7778 - val_loss: 3.4487 - val_accuracy: 0.3889 - 88ms/epoch - 10ms/step\n",
      "Epoch 147/4000\n",
      "9/9 - 0s - loss: 0.7784 - accuracy: 0.7569 - val_loss: 3.9825 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 148/4000\n",
      "9/9 - 0s - loss: 0.7679 - accuracy: 0.7500 - val_loss: 3.6816 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 149/4000\n",
      "9/9 - 0s - loss: 0.7590 - accuracy: 0.7708 - val_loss: 3.9351 - val_accuracy: 0.4167 - 93ms/epoch - 10ms/step\n",
      "Epoch 150/4000\n",
      "9/9 - 0s - loss: 0.7527 - accuracy: 0.7847 - val_loss: 3.6255 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 151/4000\n",
      "9/9 - 0s - loss: 0.7362 - accuracy: 0.8056 - val_loss: 3.6750 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 152/4000\n",
      "9/9 - 0s - loss: 0.7466 - accuracy: 0.7847 - val_loss: 3.8281 - val_accuracy: 0.2500 - 90ms/epoch - 10ms/step\n",
      "Epoch 153/4000\n",
      "9/9 - 0s - loss: 0.7664 - accuracy: 0.7986 - val_loss: 4.2601 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 154/4000\n",
      "9/9 - 0s - loss: 0.7520 - accuracy: 0.7986 - val_loss: 3.6411 - val_accuracy: 0.3333 - 94ms/epoch - 10ms/step\n",
      "Epoch 155/4000\n",
      "9/9 - 0s - loss: 0.7365 - accuracy: 0.7847 - val_loss: 3.4681 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 156/4000\n",
      "9/9 - 0s - loss: 0.7647 - accuracy: 0.7917 - val_loss: 2.9178 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 157/4000\n",
      "9/9 - 0s - loss: 0.7430 - accuracy: 0.7847 - val_loss: 2.5451 - val_accuracy: 0.3611 - 93ms/epoch - 10ms/step\n",
      "Epoch 158/4000\n",
      "9/9 - 0s - loss: 0.7478 - accuracy: 0.8125 - val_loss: 2.1026 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 159/4000\n",
      "9/9 - 0s - loss: 0.7552 - accuracy: 0.7778 - val_loss: 2.5602 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 160/4000\n",
      "9/9 - 0s - loss: 0.7621 - accuracy: 0.7708 - val_loss: 3.4124 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 161/4000\n",
      "9/9 - 0s - loss: 0.7306 - accuracy: 0.8125 - val_loss: 3.9026 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 162/4000\n",
      "9/9 - 0s - loss: 0.7438 - accuracy: 0.7986 - val_loss: 3.6713 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 163/4000\n",
      "9/9 - 0s - loss: 0.7379 - accuracy: 0.7847 - val_loss: 4.2591 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 164/4000\n",
      "9/9 - 0s - loss: 0.7348 - accuracy: 0.7708 - val_loss: 4.9703 - val_accuracy: 0.2361 - 83ms/epoch - 9ms/step\n",
      "Epoch 165/4000\n",
      "9/9 - 0s - loss: 0.7474 - accuracy: 0.7986 - val_loss: 5.2211 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 166/4000\n",
      "9/9 - 0s - loss: 0.7318 - accuracy: 0.7986 - val_loss: 4.9186 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 167/4000\n",
      "9/9 - 0s - loss: 0.7620 - accuracy: 0.7778 - val_loss: 4.7932 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 168/4000\n",
      "9/9 - 0s - loss: 0.7278 - accuracy: 0.7917 - val_loss: 4.4381 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 169/4000\n",
      "9/9 - 0s - loss: 0.7515 - accuracy: 0.7847 - val_loss: 4.2187 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 170/4000\n",
      "9/9 - 0s - loss: 0.7467 - accuracy: 0.7639 - val_loss: 4.2225 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 171/4000\n",
      "9/9 - 0s - loss: 0.7198 - accuracy: 0.8264 - val_loss: 4.8381 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 172/4000\n",
      "9/9 - 0s - loss: 0.7405 - accuracy: 0.7778 - val_loss: 5.2677 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 173/4000\n",
      "9/9 - 0s - loss: 0.7434 - accuracy: 0.8056 - val_loss: 5.1734 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 174/4000\n",
      "9/9 - 0s - loss: 0.7138 - accuracy: 0.8194 - val_loss: 4.2508 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 175/4000\n",
      "9/9 - 0s - loss: 0.7066 - accuracy: 0.7986 - val_loss: 4.2183 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 176/4000\n",
      "9/9 - 0s - loss: 0.7072 - accuracy: 0.8194 - val_loss: 3.9926 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 177/4000\n",
      "9/9 - 0s - loss: 0.7221 - accuracy: 0.7778 - val_loss: 4.7963 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 178/4000\n",
      "9/9 - 0s - loss: 0.7371 - accuracy: 0.8125 - val_loss: 5.1547 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 179/4000\n",
      "9/9 - 0s - loss: 0.7213 - accuracy: 0.7778 - val_loss: 5.4966 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 180/4000\n",
      "9/9 - 0s - loss: 0.7005 - accuracy: 0.8125 - val_loss: 6.0541 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 181/4000\n",
      "9/9 - 0s - loss: 0.7157 - accuracy: 0.7778 - val_loss: 5.8874 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 182/4000\n",
      "9/9 - 0s - loss: 0.6883 - accuracy: 0.8264 - val_loss: 5.3137 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 183/4000\n",
      "9/9 - 0s - loss: 0.7041 - accuracy: 0.7986 - val_loss: 6.3110 - val_accuracy: 0.2222 - 76ms/epoch - 8ms/step\n",
      "Epoch 184/4000\n",
      "9/9 - 0s - loss: 0.6973 - accuracy: 0.7847 - val_loss: 6.8093 - val_accuracy: 0.2222 - 75ms/epoch - 8ms/step\n",
      "Epoch 185/4000\n",
      "9/9 - 0s - loss: 0.7131 - accuracy: 0.7986 - val_loss: 6.5531 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 186/4000\n",
      "9/9 - 0s - loss: 0.7232 - accuracy: 0.7847 - val_loss: 5.8050 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 187/4000\n",
      "9/9 - 0s - loss: 0.7131 - accuracy: 0.7986 - val_loss: 6.1892 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 188/4000\n",
      "9/9 - 0s - loss: 0.7137 - accuracy: 0.8056 - val_loss: 6.0579 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 189/4000\n",
      "9/9 - 0s - loss: 0.7198 - accuracy: 0.7986 - val_loss: 5.4262 - val_accuracy: 0.2222 - 112ms/epoch - 12ms/step\n",
      "Epoch 190/4000\n",
      "9/9 - 0s - loss: 0.6967 - accuracy: 0.7917 - val_loss: 4.7864 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 191/4000\n",
      "9/9 - 0s - loss: 0.7071 - accuracy: 0.8056 - val_loss: 4.5009 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 192/4000\n",
      "9/9 - 0s - loss: 0.6894 - accuracy: 0.7986 - val_loss: 4.8995 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 193/4000\n",
      "9/9 - 0s - loss: 0.7168 - accuracy: 0.8056 - val_loss: 5.1285 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 194/4000\n",
      "9/9 - 0s - loss: 0.6976 - accuracy: 0.7986 - val_loss: 5.1501 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 195/4000\n",
      "9/9 - 0s - loss: 0.7322 - accuracy: 0.7847 - val_loss: 5.5885 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 196/4000\n",
      "9/9 - 0s - loss: 0.7169 - accuracy: 0.8056 - val_loss: 4.8721 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 197/4000\n",
      "9/9 - 0s - loss: 0.7039 - accuracy: 0.7708 - val_loss: 4.0895 - val_accuracy: 0.2222 - 76ms/epoch - 8ms/step\n",
      "Epoch 198/4000\n",
      "9/9 - 0s - loss: 0.7028 - accuracy: 0.8056 - val_loss: 3.8919 - val_accuracy: 0.2500 - 83ms/epoch - 9ms/step\n",
      "Epoch 199/4000\n",
      "9/9 - 0s - loss: 0.6798 - accuracy: 0.7917 - val_loss: 3.9993 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 200/4000\n",
      "9/9 - 0s - loss: 0.6827 - accuracy: 0.7986 - val_loss: 3.9243 - val_accuracy: 0.2500 - 81ms/epoch - 9ms/step\n",
      "Epoch 201/4000\n",
      "9/9 - 0s - loss: 0.6726 - accuracy: 0.8056 - val_loss: 3.6664 - val_accuracy: 0.2500 - 96ms/epoch - 11ms/step\n",
      "Epoch 202/4000\n",
      "9/9 - 0s - loss: 0.6783 - accuracy: 0.8194 - val_loss: 1.3408 - val_accuracy: 0.2361 - 90ms/epoch - 10ms/step\n",
      "Epoch 203/4000\n",
      "9/9 - 0s - loss: 0.6877 - accuracy: 0.8194 - val_loss: 4.2601 - val_accuracy: 0.2361 - 83ms/epoch - 9ms/step\n",
      "Epoch 204/4000\n",
      "9/9 - 0s - loss: 0.6847 - accuracy: 0.8056 - val_loss: 1.0708 - val_accuracy: 0.4028 - 92ms/epoch - 10ms/step\n",
      "Epoch 205/4000\n",
      "9/9 - 0s - loss: 0.6944 - accuracy: 0.8125 - val_loss: 2.0849 - val_accuracy: 0.2361 - 82ms/epoch - 9ms/step\n",
      "Epoch 206/4000\n",
      "9/9 - 0s - loss: 0.6945 - accuracy: 0.8056 - val_loss: 1.1732 - val_accuracy: 0.4306 - 79ms/epoch - 9ms/step\n",
      "Epoch 207/4000\n",
      "9/9 - 0s - loss: 0.6646 - accuracy: 0.8403 - val_loss: 4.5722 - val_accuracy: 0.2639 - 80ms/epoch - 9ms/step\n",
      "Epoch 208/4000\n",
      "9/9 - 0s - loss: 0.6837 - accuracy: 0.8125 - val_loss: 1.8458 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 209/4000\n",
      "9/9 - 0s - loss: 0.6832 - accuracy: 0.8056 - val_loss: 1.9613 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 210/4000\n",
      "9/9 - 0s - loss: 0.6786 - accuracy: 0.8333 - val_loss: 2.5969 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 211/4000\n",
      "9/9 - 0s - loss: 0.6670 - accuracy: 0.8194 - val_loss: 2.2378 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 212/4000\n",
      "9/9 - 0s - loss: 0.6689 - accuracy: 0.8472 - val_loss: 6.5696 - val_accuracy: 0.2639 - 91ms/epoch - 10ms/step\n",
      "Epoch 213/4000\n",
      "9/9 - 0s - loss: 0.6626 - accuracy: 0.8264 - val_loss: 18.4963 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 214/4000\n",
      "9/9 - 0s - loss: 0.6827 - accuracy: 0.8264 - val_loss: 23.7254 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 215/4000\n",
      "9/9 - 0s - loss: 0.6697 - accuracy: 0.8472 - val_loss: 23.7285 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 216/4000\n",
      "9/9 - 0s - loss: 0.6674 - accuracy: 0.8472 - val_loss: 20.9377 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 217/4000\n",
      "9/9 - 0s - loss: 0.6667 - accuracy: 0.8403 - val_loss: 18.3971 - val_accuracy: 0.2639 - 115ms/epoch - 13ms/step\n",
      "Epoch 218/4000\n",
      "9/9 - 0s - loss: 0.7014 - accuracy: 0.7986 - val_loss: 16.1208 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 219/4000\n",
      "9/9 - 0s - loss: 0.6621 - accuracy: 0.8472 - val_loss: 7.5260 - val_accuracy: 0.2639 - 126ms/epoch - 14ms/step\n",
      "Epoch 220/4000\n",
      "9/9 - 0s - loss: 0.6694 - accuracy: 0.8125 - val_loss: 2.0858 - val_accuracy: 0.2500 - 86ms/epoch - 10ms/step\n",
      "Epoch 221/4000\n",
      "9/9 - 0s - loss: 0.6646 - accuracy: 0.7986 - val_loss: 2.9195 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 222/4000\n",
      "9/9 - 0s - loss: 0.6530 - accuracy: 0.8194 - val_loss: 2.5088 - val_accuracy: 0.2500 - 89ms/epoch - 10ms/step\n",
      "Epoch 223/4000\n",
      "9/9 - 0s - loss: 0.6704 - accuracy: 0.8333 - val_loss: 3.2678 - val_accuracy: 0.2361 - 81ms/epoch - 9ms/step\n",
      "Epoch 224/4000\n",
      "9/9 - 0s - loss: 0.7009 - accuracy: 0.7917 - val_loss: 12.9285 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 225/4000\n",
      "9/9 - 0s - loss: 0.6718 - accuracy: 0.8056 - val_loss: 19.9570 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 226/4000\n",
      "9/9 - 0s - loss: 0.6504 - accuracy: 0.8333 - val_loss: 10.3213 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 227/4000\n",
      "9/9 - 0s - loss: 0.6496 - accuracy: 0.8403 - val_loss: 3.6863 - val_accuracy: 0.2639 - 91ms/epoch - 10ms/step\n",
      "Epoch 228/4000\n",
      "9/9 - 0s - loss: 0.6803 - accuracy: 0.8056 - val_loss: 2.8209 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 229/4000\n",
      "9/9 - 0s - loss: 0.6462 - accuracy: 0.8542 - val_loss: 2.5451 - val_accuracy: 0.2778 - 85ms/epoch - 9ms/step\n",
      "Epoch 230/4000\n",
      "9/9 - 0s - loss: 0.6404 - accuracy: 0.8472 - val_loss: 2.7327 - val_accuracy: 0.3333 - 85ms/epoch - 9ms/step\n",
      "Epoch 231/4000\n",
      "9/9 - 0s - loss: 0.6540 - accuracy: 0.8264 - val_loss: 3.1057 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 232/4000\n",
      "9/9 - 0s - loss: 0.6463 - accuracy: 0.8819 - val_loss: 1.0010 - val_accuracy: 0.5972 - 77ms/epoch - 9ms/step\n",
      "Epoch 233/4000\n",
      "9/9 - 0s - loss: 0.6325 - accuracy: 0.8542 - val_loss: 1.3556 - val_accuracy: 0.3333 - 78ms/epoch - 9ms/step\n",
      "Epoch 234/4000\n",
      "9/9 - 0s - loss: 0.6337 - accuracy: 0.8403 - val_loss: 2.1057 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 235/4000\n",
      "9/9 - 0s - loss: 0.6461 - accuracy: 0.8472 - val_loss: 3.3654 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 236/4000\n",
      "9/9 - 0s - loss: 0.6523 - accuracy: 0.8403 - val_loss: 3.7383 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 237/4000\n",
      "9/9 - 0s - loss: 0.6571 - accuracy: 0.8472 - val_loss: 3.7496 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 238/4000\n",
      "9/9 - 0s - loss: 0.6397 - accuracy: 0.8333 - val_loss: 3.7139 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 239/4000\n",
      "9/9 - 0s - loss: 0.6331 - accuracy: 0.8472 - val_loss: 3.6879 - val_accuracy: 0.3056 - 82ms/epoch - 9ms/step\n",
      "Epoch 240/4000\n",
      "9/9 - 0s - loss: 0.6491 - accuracy: 0.8472 - val_loss: 3.9663 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 241/4000\n",
      "9/9 - 0s - loss: 0.6640 - accuracy: 0.8194 - val_loss: 4.0883 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 242/4000\n",
      "9/9 - 0s - loss: 0.6434 - accuracy: 0.8333 - val_loss: 3.9529 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 243/4000\n",
      "9/9 - 0s - loss: 0.6561 - accuracy: 0.8750 - val_loss: 3.9886 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 244/4000\n",
      "9/9 - 0s - loss: 0.6210 - accuracy: 0.8542 - val_loss: 4.0468 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 245/4000\n",
      "9/9 - 0s - loss: 0.6460 - accuracy: 0.8542 - val_loss: 3.8728 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 246/4000\n",
      "9/9 - 0s - loss: 0.6402 - accuracy: 0.8542 - val_loss: 3.9176 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 247/4000\n",
      "9/9 - 0s - loss: 0.6320 - accuracy: 0.8750 - val_loss: 3.9179 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 248/4000\n",
      "9/9 - 0s - loss: 0.6386 - accuracy: 0.8542 - val_loss: 3.8900 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 249/4000\n",
      "9/9 - 0s - loss: 0.6347 - accuracy: 0.8611 - val_loss: 3.8849 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 250/4000\n",
      "9/9 - 0s - loss: 0.6333 - accuracy: 0.8333 - val_loss: 3.9678 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 251/4000\n",
      "9/9 - 0s - loss: 0.6229 - accuracy: 0.8125 - val_loss: 3.9547 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 252/4000\n",
      "9/9 - 0s - loss: 0.6319 - accuracy: 0.8542 - val_loss: 3.8967 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 253/4000\n",
      "9/9 - 0s - loss: 0.6244 - accuracy: 0.8472 - val_loss: 3.9088 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 254/4000\n",
      "9/9 - 0s - loss: 0.6119 - accuracy: 0.8819 - val_loss: 4.2347 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 255/4000\n",
      "9/9 - 0s - loss: 0.6147 - accuracy: 0.8264 - val_loss: 4.0923 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 256/4000\n",
      "9/9 - 0s - loss: 0.6322 - accuracy: 0.8264 - val_loss: 4.0265 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 257/4000\n",
      "9/9 - 0s - loss: 0.6026 - accuracy: 0.8681 - val_loss: 4.0674 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 258/4000\n",
      "9/9 - 0s - loss: 0.6825 - accuracy: 0.7847 - val_loss: 3.9807 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 259/4000\n",
      "9/9 - 0s - loss: 0.6405 - accuracy: 0.8333 - val_loss: 3.8853 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 260/4000\n",
      "9/9 - 0s - loss: 0.6164 - accuracy: 0.8542 - val_loss: 3.7765 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 261/4000\n",
      "9/9 - 0s - loss: 0.6123 - accuracy: 0.8542 - val_loss: 3.8764 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 262/4000\n",
      "9/9 - 0s - loss: 0.6195 - accuracy: 0.8542 - val_loss: 4.0073 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 263/4000\n",
      "9/9 - 0s - loss: 0.6135 - accuracy: 0.8681 - val_loss: 3.4314 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 264/4000\n",
      "9/9 - 0s - loss: 0.6183 - accuracy: 0.8611 - val_loss: 2.8061 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 265/4000\n",
      "9/9 - 0s - loss: 0.6322 - accuracy: 0.8611 - val_loss: 2.6311 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 266/4000\n",
      "9/9 - 0s - loss: 0.6208 - accuracy: 0.8542 - val_loss: 1.1785 - val_accuracy: 0.3472 - 82ms/epoch - 9ms/step\n",
      "Epoch 267/4000\n",
      "9/9 - 0s - loss: 0.5936 - accuracy: 0.8819 - val_loss: 7.3868 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 268/4000\n",
      "9/9 - 0s - loss: 0.6141 - accuracy: 0.8333 - val_loss: 1.0639 - val_accuracy: 0.4444 - 85ms/epoch - 9ms/step\n",
      "Epoch 269/4000\n",
      "9/9 - 0s - loss: 0.6360 - accuracy: 0.8264 - val_loss: 3.1689 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 270/4000\n",
      "9/9 - 0s - loss: 0.6200 - accuracy: 0.8472 - val_loss: 3.6767 - val_accuracy: 0.3056 - 90ms/epoch - 10ms/step\n",
      "Epoch 271/4000\n",
      "9/9 - 0s - loss: 0.6275 - accuracy: 0.8472 - val_loss: 3.4423 - val_accuracy: 0.2500 - 81ms/epoch - 9ms/step\n",
      "Epoch 272/4000\n",
      "9/9 - 0s - loss: 0.6147 - accuracy: 0.8681 - val_loss: 2.6546 - val_accuracy: 0.3056 - 80ms/epoch - 9ms/step\n",
      "Epoch 273/4000\n",
      "9/9 - 0s - loss: 0.6317 - accuracy: 0.8194 - val_loss: 1.2823 - val_accuracy: 0.3889 - 84ms/epoch - 9ms/step\n",
      "Epoch 274/4000\n",
      "9/9 - 0s - loss: 0.6010 - accuracy: 0.8889 - val_loss: 8.0785 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 275/4000\n",
      "9/9 - 0s - loss: 0.6035 - accuracy: 0.8611 - val_loss: 8.5735 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 276/4000\n",
      "9/9 - 0s - loss: 0.6298 - accuracy: 0.8611 - val_loss: 5.7421 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 277/4000\n",
      "9/9 - 0s - loss: 0.6023 - accuracy: 0.8333 - val_loss: 4.3997 - val_accuracy: 0.3056 - 95ms/epoch - 11ms/step\n",
      "Epoch 278/4000\n",
      "9/9 - 0s - loss: 0.6095 - accuracy: 0.8403 - val_loss: 4.0535 - val_accuracy: 0.3333 - 82ms/epoch - 9ms/step\n",
      "Epoch 279/4000\n",
      "9/9 - 0s - loss: 0.6051 - accuracy: 0.8819 - val_loss: 6.1966 - val_accuracy: 0.2500 - 85ms/epoch - 9ms/step\n",
      "Epoch 280/4000\n",
      "9/9 - 0s - loss: 0.6163 - accuracy: 0.8819 - val_loss: 7.2388 - val_accuracy: 0.2639 - 100ms/epoch - 11ms/step\n",
      "Epoch 281/4000\n",
      "9/9 - 0s - loss: 0.5920 - accuracy: 0.8750 - val_loss: 3.8632 - val_accuracy: 0.2639 - 95ms/epoch - 11ms/step\n",
      "Epoch 282/4000\n",
      "9/9 - 0s - loss: 0.5944 - accuracy: 0.8681 - val_loss: 1.1756 - val_accuracy: 0.4306 - 92ms/epoch - 10ms/step\n",
      "Epoch 283/4000\n",
      "9/9 - 0s - loss: 0.6087 - accuracy: 0.8472 - val_loss: 2.6256 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 284/4000\n",
      "9/9 - 0s - loss: 0.5947 - accuracy: 0.8611 - val_loss: 3.7903 - val_accuracy: 0.2361 - 84ms/epoch - 9ms/step\n",
      "Epoch 285/4000\n",
      "9/9 - 0s - loss: 0.5943 - accuracy: 0.8819 - val_loss: 4.8181 - val_accuracy: 0.3194 - 85ms/epoch - 9ms/step\n",
      "Epoch 286/4000\n",
      "9/9 - 0s - loss: 0.5976 - accuracy: 0.8750 - val_loss: 3.0793 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 287/4000\n",
      "9/9 - 0s - loss: 0.5942 - accuracy: 0.8750 - val_loss: 3.7393 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 288/4000\n",
      "9/9 - 0s - loss: 0.6151 - accuracy: 0.8264 - val_loss: 3.7765 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 289/4000\n",
      "9/9 - 0s - loss: 0.5912 - accuracy: 0.8542 - val_loss: 3.6603 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 290/4000\n",
      "9/9 - 0s - loss: 0.5956 - accuracy: 0.8819 - val_loss: 3.6425 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 291/4000\n",
      "9/9 - 0s - loss: 0.5873 - accuracy: 0.8681 - val_loss: 2.3143 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 292/4000\n",
      "9/9 - 0s - loss: 0.6008 - accuracy: 0.8472 - val_loss: 3.3228 - val_accuracy: 0.3056 - 92ms/epoch - 10ms/step\n",
      "Epoch 293/4000\n",
      "9/9 - 0s - loss: 0.6347 - accuracy: 0.8472 - val_loss: 6.7041 - val_accuracy: 0.2500 - 86ms/epoch - 10ms/step\n",
      "Epoch 294/4000\n",
      "9/9 - 0s - loss: 0.6018 - accuracy: 0.8542 - val_loss: 10.0977 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 295/4000\n",
      "9/9 - 0s - loss: 0.6071 - accuracy: 0.8611 - val_loss: 7.0982 - val_accuracy: 0.2778 - 87ms/epoch - 10ms/step\n",
      "Epoch 296/4000\n",
      "9/9 - 0s - loss: 0.5863 - accuracy: 0.8750 - val_loss: 4.4851 - val_accuracy: 0.2639 - 80ms/epoch - 9ms/step\n",
      "Epoch 297/4000\n",
      "9/9 - 0s - loss: 0.5912 - accuracy: 0.8542 - val_loss: 2.9088 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 298/4000\n",
      "9/9 - 0s - loss: 0.6093 - accuracy: 0.8194 - val_loss: 0.8440 - val_accuracy: 0.5833 - 88ms/epoch - 10ms/step\n",
      "Epoch 299/4000\n",
      "9/9 - 0s - loss: 0.6061 - accuracy: 0.8750 - val_loss: 2.1655 - val_accuracy: 0.3056 - 85ms/epoch - 9ms/step\n",
      "Epoch 300/4000\n",
      "9/9 - 0s - loss: 0.5898 - accuracy: 0.8542 - val_loss: 1.8736 - val_accuracy: 0.3056 - 75ms/epoch - 8ms/step\n",
      "Epoch 301/4000\n",
      "9/9 - 0s - loss: 0.5974 - accuracy: 0.8681 - val_loss: 2.8836 - val_accuracy: 0.3056 - 89ms/epoch - 10ms/step\n",
      "Epoch 302/4000\n",
      "9/9 - 0s - loss: 0.5873 - accuracy: 0.8681 - val_loss: 3.1745 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 303/4000\n",
      "9/9 - 0s - loss: 0.5977 - accuracy: 0.8611 - val_loss: 2.0485 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 304/4000\n",
      "9/9 - 0s - loss: 0.6151 - accuracy: 0.8542 - val_loss: 2.8662 - val_accuracy: 0.5139 - 79ms/epoch - 9ms/step\n",
      "Epoch 305/4000\n",
      "9/9 - 0s - loss: 0.5793 - accuracy: 0.8750 - val_loss: 2.2506 - val_accuracy: 0.3333 - 82ms/epoch - 9ms/step\n",
      "Epoch 306/4000\n",
      "9/9 - 0s - loss: 0.5659 - accuracy: 0.8750 - val_loss: 3.2560 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 307/4000\n",
      "9/9 - 0s - loss: 0.5996 - accuracy: 0.8472 - val_loss: 2.2711 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 308/4000\n",
      "9/9 - 0s - loss: 0.5974 - accuracy: 0.8750 - val_loss: 3.1029 - val_accuracy: 0.2778 - 88ms/epoch - 10ms/step\n",
      "Epoch 309/4000\n",
      "9/9 - 0s - loss: 0.5876 - accuracy: 0.8681 - val_loss: 9.1247 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 310/4000\n",
      "9/9 - 0s - loss: 0.5666 - accuracy: 0.9028 - val_loss: 8.6736 - val_accuracy: 0.2639 - 81ms/epoch - 9ms/step\n",
      "Epoch 311/4000\n",
      "9/9 - 0s - loss: 0.5776 - accuracy: 0.8611 - val_loss: 4.0245 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 312/4000\n",
      "9/9 - 0s - loss: 0.6213 - accuracy: 0.8333 - val_loss: 1.0096 - val_accuracy: 0.5278 - 83ms/epoch - 9ms/step\n",
      "Epoch 313/4000\n",
      "9/9 - 0s - loss: 0.5726 - accuracy: 0.8542 - val_loss: 1.8548 - val_accuracy: 0.3056 - 81ms/epoch - 9ms/step\n",
      "Epoch 314/4000\n",
      "9/9 - 0s - loss: 0.5854 - accuracy: 0.8958 - val_loss: 1.4601 - val_accuracy: 0.4861 - 84ms/epoch - 9ms/step\n",
      "Epoch 315/4000\n",
      "9/9 - 0s - loss: 0.6035 - accuracy: 0.8403 - val_loss: 2.3488 - val_accuracy: 0.2639 - 94ms/epoch - 10ms/step\n",
      "Epoch 316/4000\n",
      "9/9 - 0s - loss: 0.5898 - accuracy: 0.8194 - val_loss: 1.1097 - val_accuracy: 0.4167 - 78ms/epoch - 9ms/step\n",
      "Epoch 317/4000\n",
      "9/9 - 0s - loss: 0.5678 - accuracy: 0.8889 - val_loss: 1.0358 - val_accuracy: 0.4167 - 83ms/epoch - 9ms/step\n",
      "Epoch 318/4000\n",
      "9/9 - 0s - loss: 0.5771 - accuracy: 0.8750 - val_loss: 1.2767 - val_accuracy: 0.3750 - 85ms/epoch - 9ms/step\n",
      "Epoch 319/4000\n",
      "9/9 - 0s - loss: 0.5656 - accuracy: 0.8889 - val_loss: 1.4606 - val_accuracy: 0.4306 - 83ms/epoch - 9ms/step\n",
      "Epoch 320/4000\n",
      "9/9 - 0s - loss: 0.5792 - accuracy: 0.8750 - val_loss: 1.8683 - val_accuracy: 0.2500 - 81ms/epoch - 9ms/step\n",
      "Epoch 321/4000\n",
      "9/9 - 0s - loss: 0.5447 - accuracy: 0.9097 - val_loss: 2.1959 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 322/4000\n",
      "9/9 - 0s - loss: 0.5634 - accuracy: 0.8750 - val_loss: 1.8474 - val_accuracy: 0.3333 - 85ms/epoch - 9ms/step\n",
      "Epoch 323/4000\n",
      "9/9 - 0s - loss: 0.5718 - accuracy: 0.8819 - val_loss: 1.5336 - val_accuracy: 0.2778 - 94ms/epoch - 10ms/step\n",
      "Epoch 324/4000\n",
      "9/9 - 0s - loss: 0.5618 - accuracy: 0.8750 - val_loss: 1.5884 - val_accuracy: 0.3056 - 83ms/epoch - 9ms/step\n",
      "Epoch 325/4000\n",
      "9/9 - 0s - loss: 0.5874 - accuracy: 0.8542 - val_loss: 1.1968 - val_accuracy: 0.5972 - 87ms/epoch - 10ms/step\n",
      "Epoch 326/4000\n",
      "9/9 - 0s - loss: 0.5759 - accuracy: 0.9028 - val_loss: 1.9672 - val_accuracy: 0.3056 - 92ms/epoch - 10ms/step\n",
      "Epoch 327/4000\n",
      "9/9 - 0s - loss: 0.5563 - accuracy: 0.9028 - val_loss: 1.4077 - val_accuracy: 0.3333 - 79ms/epoch - 9ms/step\n",
      "Epoch 328/4000\n",
      "9/9 - 0s - loss: 0.5683 - accuracy: 0.8611 - val_loss: 1.2304 - val_accuracy: 0.5972 - 81ms/epoch - 9ms/step\n",
      "Epoch 329/4000\n",
      "9/9 - 0s - loss: 0.5685 - accuracy: 0.8472 - val_loss: 2.7681 - val_accuracy: 0.2778 - 78ms/epoch - 9ms/step\n",
      "Epoch 330/4000\n",
      "9/9 - 0s - loss: 0.5656 - accuracy: 0.8889 - val_loss: 1.2751 - val_accuracy: 0.5000 - 90ms/epoch - 10ms/step\n",
      "Epoch 331/4000\n",
      "9/9 - 0s - loss: 0.5751 - accuracy: 0.8750 - val_loss: 2.8097 - val_accuracy: 0.3056 - 86ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 332/4000\n",
      "9/9 - 0s - loss: 0.5658 - accuracy: 0.8472 - val_loss: 1.3664 - val_accuracy: 0.5000 - 88ms/epoch - 10ms/step\n",
      "Epoch 333/4000\n",
      "9/9 - 0s - loss: 0.5857 - accuracy: 0.8611 - val_loss: 2.8136 - val_accuracy: 0.2639 - 80ms/epoch - 9ms/step\n",
      "Epoch 334/4000\n",
      "9/9 - 0s - loss: 0.5782 - accuracy: 0.8542 - val_loss: 2.6457 - val_accuracy: 0.2500 - 93ms/epoch - 10ms/step\n",
      "Epoch 335/4000\n",
      "9/9 - 0s - loss: 0.5754 - accuracy: 0.8819 - val_loss: 2.5517 - val_accuracy: 0.2500 - 83ms/epoch - 9ms/step\n",
      "Epoch 336/4000\n",
      "9/9 - 0s - loss: 0.5564 - accuracy: 0.8472 - val_loss: 1.4658 - val_accuracy: 0.3472 - 81ms/epoch - 9ms/step\n",
      "Epoch 337/4000\n",
      "9/9 - 0s - loss: 0.5548 - accuracy: 0.8889 - val_loss: 3.8328 - val_accuracy: 0.3056 - 81ms/epoch - 9ms/step\n",
      "Epoch 338/4000\n",
      "9/9 - 0s - loss: 0.5668 - accuracy: 0.8472 - val_loss: 1.8681 - val_accuracy: 0.3056 - 92ms/epoch - 10ms/step\n",
      "Epoch 339/4000\n",
      "9/9 - 0s - loss: 0.5595 - accuracy: 0.8611 - val_loss: 3.4062 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 340/4000\n",
      "9/9 - 0s - loss: 0.5531 - accuracy: 0.8542 - val_loss: 3.6422 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 341/4000\n",
      "9/9 - 0s - loss: 0.5563 - accuracy: 0.8889 - val_loss: 3.8302 - val_accuracy: 0.2361 - 86ms/epoch - 10ms/step\n",
      "Epoch 342/4000\n",
      "9/9 - 0s - loss: 0.5862 - accuracy: 0.8264 - val_loss: 3.4745 - val_accuracy: 0.2778 - 81ms/epoch - 9ms/step\n",
      "Epoch 343/4000\n",
      "9/9 - 0s - loss: 0.5493 - accuracy: 0.8819 - val_loss: 3.1769 - val_accuracy: 0.3194 - 85ms/epoch - 9ms/step\n",
      "Epoch 344/4000\n",
      "9/9 - 0s - loss: 0.5754 - accuracy: 0.8889 - val_loss: 3.1318 - val_accuracy: 0.3472 - 79ms/epoch - 9ms/step\n",
      "Epoch 345/4000\n",
      "9/9 - 0s - loss: 0.5595 - accuracy: 0.8681 - val_loss: 2.9752 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 346/4000\n",
      "9/9 - 0s - loss: 0.5527 - accuracy: 0.8681 - val_loss: 2.5486 - val_accuracy: 0.3194 - 83ms/epoch - 9ms/step\n",
      "Epoch 347/4000\n",
      "9/9 - 0s - loss: 0.5414 - accuracy: 0.9236 - val_loss: 0.8028 - val_accuracy: 0.6111 - 93ms/epoch - 10ms/step\n",
      "Epoch 348/4000\n",
      "9/9 - 0s - loss: 0.5604 - accuracy: 0.8819 - val_loss: 0.8742 - val_accuracy: 0.5278 - 75ms/epoch - 8ms/step\n",
      "Epoch 349/4000\n",
      "9/9 - 0s - loss: 0.5248 - accuracy: 0.9097 - val_loss: 1.4123 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 350/4000\n",
      "9/9 - 0s - loss: 0.5460 - accuracy: 0.8819 - val_loss: 2.5427 - val_accuracy: 0.2778 - 83ms/epoch - 9ms/step\n",
      "Epoch 351/4000\n",
      "9/9 - 0s - loss: 0.5558 - accuracy: 0.8750 - val_loss: 4.7867 - val_accuracy: 0.2639 - 78ms/epoch - 9ms/step\n",
      "Epoch 352/4000\n",
      "9/9 - 0s - loss: 0.5402 - accuracy: 0.8958 - val_loss: 7.1289 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 353/4000\n",
      "9/9 - 0s - loss: 0.5481 - accuracy: 0.9236 - val_loss: 7.8186 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 354/4000\n",
      "9/9 - 0s - loss: 0.5783 - accuracy: 0.8681 - val_loss: 4.7830 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 355/4000\n",
      "9/9 - 0s - loss: 0.5771 - accuracy: 0.8750 - val_loss: 4.8950 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 356/4000\n",
      "9/9 - 0s - loss: 0.5316 - accuracy: 0.8611 - val_loss: 8.9241 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 357/4000\n",
      "9/9 - 0s - loss: 0.5536 - accuracy: 0.8889 - val_loss: 9.5093 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 358/4000\n",
      "9/9 - 0s - loss: 0.5365 - accuracy: 0.9028 - val_loss: 10.6632 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 359/4000\n",
      "9/9 - 0s - loss: 0.5540 - accuracy: 0.8750 - val_loss: 12.2824 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 360/4000\n",
      "9/9 - 0s - loss: 0.5472 - accuracy: 0.8681 - val_loss: 13.5414 - val_accuracy: 0.2639 - 78ms/epoch - 9ms/step\n",
      "Epoch 361/4000\n",
      "9/9 - 0s - loss: 0.5407 - accuracy: 0.8958 - val_loss: 11.7218 - val_accuracy: 0.2500 - 89ms/epoch - 10ms/step\n",
      "Epoch 362/4000\n",
      "9/9 - 0s - loss: 0.5360 - accuracy: 0.8611 - val_loss: 12.6083 - val_accuracy: 0.2639 - 81ms/epoch - 9ms/step\n",
      "Epoch 363/4000\n",
      "9/9 - 0s - loss: 0.5632 - accuracy: 0.8611 - val_loss: 15.2550 - val_accuracy: 0.3194 - 87ms/epoch - 10ms/step\n",
      "Epoch 364/4000\n",
      "9/9 - 0s - loss: 0.5257 - accuracy: 0.9028 - val_loss: 15.3239 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 365/4000\n",
      "9/9 - 0s - loss: 0.5539 - accuracy: 0.8819 - val_loss: 16.3087 - val_accuracy: 0.2639 - 77ms/epoch - 9ms/step\n",
      "Epoch 366/4000\n",
      "9/9 - 0s - loss: 0.5353 - accuracy: 0.8750 - val_loss: 19.2482 - val_accuracy: 0.2639 - 81ms/epoch - 9ms/step\n",
      "Epoch 367/4000\n",
      "9/9 - 0s - loss: 0.5323 - accuracy: 0.8889 - val_loss: 20.4314 - val_accuracy: 0.2639 - 80ms/epoch - 9ms/step\n",
      "Epoch 368/4000\n",
      "9/9 - 0s - loss: 0.5275 - accuracy: 0.8889 - val_loss: 21.2629 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 369/4000\n",
      "9/9 - 0s - loss: 0.5257 - accuracy: 0.9028 - val_loss: 17.3012 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 370/4000\n",
      "9/9 - 0s - loss: 0.5560 - accuracy: 0.8403 - val_loss: 15.4731 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 371/4000\n",
      "9/9 - 0s - loss: 0.5529 - accuracy: 0.8958 - val_loss: 16.6775 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 372/4000\n",
      "9/9 - 0s - loss: 0.5455 - accuracy: 0.8819 - val_loss: 18.9299 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 373/4000\n",
      "9/9 - 0s - loss: 0.5609 - accuracy: 0.8750 - val_loss: 20.8977 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 374/4000\n",
      "9/9 - 0s - loss: 0.5303 - accuracy: 0.8750 - val_loss: 19.0432 - val_accuracy: 0.2639 - 81ms/epoch - 9ms/step\n",
      "Epoch 375/4000\n",
      "9/9 - 0s - loss: 0.5365 - accuracy: 0.9236 - val_loss: 22.0502 - val_accuracy: 0.2639 - 112ms/epoch - 12ms/step\n",
      "Epoch 376/4000\n",
      "9/9 - 0s - loss: 0.5307 - accuracy: 0.9167 - val_loss: 18.2686 - val_accuracy: 0.2639 - 122ms/epoch - 14ms/step\n",
      "Epoch 377/4000\n",
      "9/9 - 0s - loss: 0.5577 - accuracy: 0.8403 - val_loss: 10.7479 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 378/4000\n",
      "9/9 - 0s - loss: 0.5305 - accuracy: 0.8889 - val_loss: 8.0337 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 379/4000\n",
      "9/9 - 0s - loss: 0.5654 - accuracy: 0.8750 - val_loss: 6.9489 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 380/4000\n",
      "9/9 - 0s - loss: 0.5307 - accuracy: 0.8611 - val_loss: 5.1108 - val_accuracy: 0.2917 - 112ms/epoch - 12ms/step\n",
      "Epoch 381/4000\n",
      "9/9 - 0s - loss: 0.5408 - accuracy: 0.8681 - val_loss: 7.5052 - val_accuracy: 0.2639 - 74ms/epoch - 8ms/step\n",
      "Epoch 382/4000\n",
      "9/9 - 0s - loss: 0.5190 - accuracy: 0.8889 - val_loss: 12.4363 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 383/4000\n",
      "9/9 - 0s - loss: 0.5327 - accuracy: 0.9028 - val_loss: 15.0449 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 384/4000\n",
      "9/9 - 0s - loss: 0.5157 - accuracy: 0.8889 - val_loss: 13.5631 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 385/4000\n",
      "9/9 - 0s - loss: 0.5386 - accuracy: 0.8958 - val_loss: 18.1545 - val_accuracy: 0.2639 - 90ms/epoch - 10ms/step\n",
      "Epoch 386/4000\n",
      "9/9 - 0s - loss: 0.5069 - accuracy: 0.8958 - val_loss: 17.4338 - val_accuracy: 0.2639 - 134ms/epoch - 15ms/step\n",
      "Epoch 387/4000\n",
      "9/9 - 0s - loss: 0.5021 - accuracy: 0.9097 - val_loss: 12.6479 - val_accuracy: 0.2639 - 91ms/epoch - 10ms/step\n",
      "Epoch 388/4000\n",
      "9/9 - 0s - loss: 0.5058 - accuracy: 0.9167 - val_loss: 13.1480 - val_accuracy: 0.3056 - 91ms/epoch - 10ms/step\n",
      "Epoch 389/4000\n",
      "9/9 - 0s - loss: 0.5274 - accuracy: 0.9167 - val_loss: 15.1022 - val_accuracy: 0.2639 - 109ms/epoch - 12ms/step\n",
      "Epoch 390/4000\n",
      "9/9 - 0s - loss: 0.5121 - accuracy: 0.8889 - val_loss: 15.6594 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 391/4000\n",
      "9/9 - 0s - loss: 0.5278 - accuracy: 0.8889 - val_loss: 13.8472 - val_accuracy: 0.2639 - 81ms/epoch - 9ms/step\n",
      "Epoch 392/4000\n",
      "9/9 - 0s - loss: 0.5143 - accuracy: 0.9028 - val_loss: 12.1536 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 393/4000\n",
      "9/9 - 0s - loss: 0.5169 - accuracy: 0.8611 - val_loss: 13.8224 - val_accuracy: 0.3194 - 89ms/epoch - 10ms/step\n",
      "Epoch 394/4000\n",
      "9/9 - 0s - loss: 0.5369 - accuracy: 0.8611 - val_loss: 16.0847 - val_accuracy: 0.2778 - 92ms/epoch - 10ms/step\n",
      "Epoch 395/4000\n",
      "9/9 - 0s - loss: 0.5236 - accuracy: 0.8819 - val_loss: 17.3801 - val_accuracy: 0.3056 - 85ms/epoch - 9ms/step\n",
      "Epoch 396/4000\n",
      "9/9 - 0s - loss: 0.5325 - accuracy: 0.8958 - val_loss: 15.4427 - val_accuracy: 0.2500 - 85ms/epoch - 9ms/step\n",
      "Epoch 397/4000\n",
      "9/9 - 0s - loss: 0.5068 - accuracy: 0.8542 - val_loss: 13.1139 - val_accuracy: 0.2639 - 137ms/epoch - 15ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 398/4000\n",
      "9/9 - 0s - loss: 0.5116 - accuracy: 0.8681 - val_loss: 22.4398 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 399/4000\n",
      "9/9 - 0s - loss: 0.5088 - accuracy: 0.8889 - val_loss: 30.7276 - val_accuracy: 0.2639 - 90ms/epoch - 10ms/step\n",
      "Epoch 400/4000\n",
      "9/9 - 0s - loss: 0.4952 - accuracy: 0.9236 - val_loss: 19.7659 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 401/4000\n",
      "9/9 - 0s - loss: 0.5027 - accuracy: 0.9097 - val_loss: 13.5806 - val_accuracy: 0.3333 - 91ms/epoch - 10ms/step\n",
      "Epoch 402/4000\n",
      "9/9 - 0s - loss: 0.5176 - accuracy: 0.8681 - val_loss: 9.4894 - val_accuracy: 0.3056 - 84ms/epoch - 9ms/step\n",
      "Epoch 403/4000\n",
      "9/9 - 0s - loss: 0.5169 - accuracy: 0.8750 - val_loss: 9.6246 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 404/4000\n",
      "9/9 - 0s - loss: 0.5114 - accuracy: 0.8889 - val_loss: 10.4319 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 405/4000\n",
      "9/9 - 0s - loss: 0.5174 - accuracy: 0.8958 - val_loss: 12.4366 - val_accuracy: 0.2639 - 91ms/epoch - 10ms/step\n",
      "Epoch 406/4000\n",
      "9/9 - 0s - loss: 0.5038 - accuracy: 0.9097 - val_loss: 13.9552 - val_accuracy: 0.2500 - 78ms/epoch - 9ms/step\n",
      "Epoch 407/4000\n",
      "9/9 - 0s - loss: 0.4956 - accuracy: 0.9028 - val_loss: 10.3799 - val_accuracy: 0.2778 - 89ms/epoch - 10ms/step\n",
      "Epoch 408/4000\n",
      "9/9 - 0s - loss: 0.4968 - accuracy: 0.9097 - val_loss: 11.2250 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 409/4000\n",
      "9/9 - 0s - loss: 0.4939 - accuracy: 0.8889 - val_loss: 8.9493 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 410/4000\n",
      "9/9 - 0s - loss: 0.5139 - accuracy: 0.8542 - val_loss: 6.1927 - val_accuracy: 0.3056 - 84ms/epoch - 9ms/step\n",
      "Epoch 411/4000\n",
      "9/9 - 0s - loss: 0.5151 - accuracy: 0.8750 - val_loss: 7.9207 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 412/4000\n",
      "9/9 - 0s - loss: 0.5076 - accuracy: 0.8889 - val_loss: 11.8857 - val_accuracy: 0.2639 - 78ms/epoch - 9ms/step\n",
      "Epoch 413/4000\n",
      "9/9 - 0s - loss: 0.4713 - accuracy: 0.9236 - val_loss: 17.5521 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 414/4000\n",
      "9/9 - 0s - loss: 0.4990 - accuracy: 0.9028 - val_loss: 23.1881 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 415/4000\n",
      "9/9 - 0s - loss: 0.5014 - accuracy: 0.8681 - val_loss: 32.7772 - val_accuracy: 0.2639 - 76ms/epoch - 8ms/step\n",
      "Epoch 416/4000\n",
      "9/9 - 0s - loss: 0.4890 - accuracy: 0.8958 - val_loss: 36.6415 - val_accuracy: 0.2639 - 91ms/epoch - 10ms/step\n",
      "Epoch 417/4000\n",
      "9/9 - 0s - loss: 0.4873 - accuracy: 0.8819 - val_loss: 30.2565 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 418/4000\n",
      "9/9 - 0s - loss: 0.5034 - accuracy: 0.8681 - val_loss: 24.9315 - val_accuracy: 0.2639 - 76ms/epoch - 8ms/step\n",
      "Epoch 419/4000\n",
      "9/9 - 0s - loss: 0.4961 - accuracy: 0.8958 - val_loss: 20.7075 - val_accuracy: 0.2639 - 80ms/epoch - 9ms/step\n",
      "Epoch 420/4000\n",
      "9/9 - 0s - loss: 0.4809 - accuracy: 0.8889 - val_loss: 16.4140 - val_accuracy: 0.2639 - 94ms/epoch - 10ms/step\n",
      "Epoch 421/4000\n",
      "9/9 - 0s - loss: 0.4879 - accuracy: 0.8889 - val_loss: 6.8421 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 422/4000\n",
      "9/9 - 0s - loss: 0.4983 - accuracy: 0.8819 - val_loss: 7.1322 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 423/4000\n",
      "9/9 - 0s - loss: 0.4835 - accuracy: 0.8889 - val_loss: 11.1550 - val_accuracy: 0.2639 - 81ms/epoch - 9ms/step\n",
      "Epoch 424/4000\n",
      "9/9 - 0s - loss: 0.4769 - accuracy: 0.9375 - val_loss: 13.2789 - val_accuracy: 0.2639 - 80ms/epoch - 9ms/step\n",
      "Epoch 425/4000\n",
      "9/9 - 0s - loss: 0.4914 - accuracy: 0.8958 - val_loss: 13.4533 - val_accuracy: 0.2778 - 79ms/epoch - 9ms/step\n",
      "Epoch 426/4000\n",
      "9/9 - 0s - loss: 0.5221 - accuracy: 0.8958 - val_loss: 15.0677 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 427/4000\n",
      "9/9 - 0s - loss: 0.5663 - accuracy: 0.8194 - val_loss: 16.7205 - val_accuracy: 0.2639 - 77ms/epoch - 9ms/step\n",
      "Epoch 428/4000\n",
      "9/9 - 0s - loss: 0.4825 - accuracy: 0.9236 - val_loss: 11.8190 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 429/4000\n",
      "9/9 - 0s - loss: 0.4907 - accuracy: 0.9028 - val_loss: 8.6769 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 430/4000\n",
      "9/9 - 0s - loss: 0.5032 - accuracy: 0.8819 - val_loss: 13.8710 - val_accuracy: 0.2639 - 115ms/epoch - 13ms/step\n",
      "Epoch 431/4000\n",
      "9/9 - 0s - loss: 0.4782 - accuracy: 0.9097 - val_loss: 10.4172 - val_accuracy: 0.2639 - 94ms/epoch - 10ms/step\n",
      "Epoch 432/4000\n",
      "9/9 - 0s - loss: 0.5012 - accuracy: 0.9306 - val_loss: 6.2873 - val_accuracy: 0.2639 - 110ms/epoch - 12ms/step\n",
      "Epoch 433/4000\n",
      "9/9 - 0s - loss: 0.4989 - accuracy: 0.8750 - val_loss: 3.0876 - val_accuracy: 0.5833 - 98ms/epoch - 11ms/step\n",
      "Epoch 434/4000\n",
      "9/9 - 0s - loss: 0.4623 - accuracy: 0.9236 - val_loss: 2.1965 - val_accuracy: 0.4306 - 88ms/epoch - 10ms/step\n",
      "Epoch 435/4000\n",
      "9/9 - 0s - loss: 0.4966 - accuracy: 0.8958 - val_loss: 2.0753 - val_accuracy: 0.3472 - 87ms/epoch - 10ms/step\n",
      "Epoch 436/4000\n",
      "9/9 - 0s - loss: 0.4693 - accuracy: 0.8819 - val_loss: 5.8681 - val_accuracy: 0.2778 - 76ms/epoch - 8ms/step\n",
      "Epoch 437/4000\n",
      "9/9 - 0s - loss: 0.4653 - accuracy: 0.9167 - val_loss: 11.2903 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 438/4000\n",
      "9/9 - 0s - loss: 0.4826 - accuracy: 0.9167 - val_loss: 14.6120 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 439/4000\n",
      "9/9 - 0s - loss: 0.4796 - accuracy: 0.8889 - val_loss: 18.0592 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 440/4000\n",
      "9/9 - 0s - loss: 0.4870 - accuracy: 0.8958 - val_loss: 23.5975 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 441/4000\n",
      "9/9 - 0s - loss: 0.4595 - accuracy: 0.9167 - val_loss: 23.0218 - val_accuracy: 0.2639 - 94ms/epoch - 10ms/step\n",
      "Epoch 442/4000\n",
      "9/9 - 0s - loss: 0.4922 - accuracy: 0.8750 - val_loss: 19.0218 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 443/4000\n",
      "9/9 - 0s - loss: 0.5067 - accuracy: 0.8681 - val_loss: 9.7908 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 444/4000\n",
      "9/9 - 0s - loss: 0.4758 - accuracy: 0.9097 - val_loss: 5.6428 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 445/4000\n",
      "9/9 - 0s - loss: 0.4564 - accuracy: 0.9167 - val_loss: 11.8540 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 446/4000\n",
      "9/9 - 0s - loss: 0.4571 - accuracy: 0.9444 - val_loss: 11.7560 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 447/4000\n",
      "9/9 - 0s - loss: 0.4935 - accuracy: 0.8750 - val_loss: 17.4128 - val_accuracy: 0.2639 - 93ms/epoch - 10ms/step\n",
      "Epoch 448/4000\n",
      "9/9 - 0s - loss: 0.4485 - accuracy: 0.9167 - val_loss: 18.8176 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 449/4000\n",
      "9/9 - 0s - loss: 0.5148 - accuracy: 0.8819 - val_loss: 19.5504 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 450/4000\n",
      "9/9 - 0s - loss: 0.4870 - accuracy: 0.8889 - val_loss: 13.7771 - val_accuracy: 0.2639 - 94ms/epoch - 10ms/step\n",
      "Epoch 451/4000\n",
      "9/9 - 0s - loss: 0.4797 - accuracy: 0.8819 - val_loss: 8.0460 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 452/4000\n",
      "9/9 - 0s - loss: 0.4796 - accuracy: 0.9167 - val_loss: 6.7282 - val_accuracy: 0.3194 - 90ms/epoch - 10ms/step\n",
      "Epoch 453/4000\n",
      "9/9 - 0s - loss: 0.4811 - accuracy: 0.8958 - val_loss: 7.3437 - val_accuracy: 0.3056 - 88ms/epoch - 10ms/step\n",
      "Epoch 454/4000\n",
      "9/9 - 0s - loss: 0.4621 - accuracy: 0.9167 - val_loss: 6.4723 - val_accuracy: 0.3056 - 79ms/epoch - 9ms/step\n",
      "Epoch 455/4000\n",
      "9/9 - 0s - loss: 0.4697 - accuracy: 0.9167 - val_loss: 5.5606 - val_accuracy: 0.3056 - 170ms/epoch - 19ms/step\n",
      "Epoch 456/4000\n",
      "9/9 - 0s - loss: 0.4770 - accuracy: 0.8819 - val_loss: 6.5119 - val_accuracy: 0.3056 - 87ms/epoch - 10ms/step\n",
      "Epoch 457/4000\n",
      "9/9 - 0s - loss: 0.4954 - accuracy: 0.8958 - val_loss: 1.9908 - val_accuracy: 0.4444 - 97ms/epoch - 11ms/step\n",
      "Epoch 458/4000\n",
      "9/9 - 0s - loss: 0.4568 - accuracy: 0.9028 - val_loss: 1.7230 - val_accuracy: 0.3194 - 87ms/epoch - 10ms/step\n",
      "Epoch 459/4000\n",
      "9/9 - 0s - loss: 0.4836 - accuracy: 0.9097 - val_loss: 2.1754 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 460/4000\n",
      "9/9 - 0s - loss: 0.4669 - accuracy: 0.9028 - val_loss: 2.9214 - val_accuracy: 0.4306 - 91ms/epoch - 10ms/step\n",
      "Epoch 461/4000\n",
      "9/9 - 0s - loss: 0.4401 - accuracy: 0.9444 - val_loss: 10.9803 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 462/4000\n",
      "9/9 - 0s - loss: 0.4639 - accuracy: 0.8889 - val_loss: 13.2373 - val_accuracy: 0.3056 - 91ms/epoch - 10ms/step\n",
      "Epoch 463/4000\n",
      "9/9 - 0s - loss: 0.4940 - accuracy: 0.8542 - val_loss: 10.8700 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 464/4000\n",
      "9/9 - 0s - loss: 0.4473 - accuracy: 0.8958 - val_loss: 10.1097 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 465/4000\n",
      "9/9 - 0s - loss: 0.4294 - accuracy: 0.9097 - val_loss: 10.0494 - val_accuracy: 0.3194 - 91ms/epoch - 10ms/step\n",
      "Epoch 466/4000\n",
      "9/9 - 0s - loss: 0.5030 - accuracy: 0.8958 - val_loss: 11.6893 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 467/4000\n",
      "9/9 - 0s - loss: 0.4447 - accuracy: 0.9375 - val_loss: 12.0725 - val_accuracy: 0.3056 - 79ms/epoch - 9ms/step\n",
      "Epoch 468/4000\n",
      "9/9 - 0s - loss: 0.4800 - accuracy: 0.9028 - val_loss: 13.4246 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 469/4000\n",
      "9/9 - 0s - loss: 0.4623 - accuracy: 0.8889 - val_loss: 12.2431 - val_accuracy: 0.3333 - 87ms/epoch - 10ms/step\n",
      "Epoch 470/4000\n",
      "9/9 - 0s - loss: 0.4627 - accuracy: 0.8819 - val_loss: 13.6827 - val_accuracy: 0.3472 - 92ms/epoch - 10ms/step\n",
      "Epoch 471/4000\n",
      "9/9 - 0s - loss: 0.4344 - accuracy: 0.9236 - val_loss: 13.2493 - val_accuracy: 0.3333 - 96ms/epoch - 11ms/step\n",
      "Epoch 472/4000\n",
      "9/9 - 0s - loss: 0.4527 - accuracy: 0.9306 - val_loss: 12.1509 - val_accuracy: 0.3333 - 85ms/epoch - 9ms/step\n",
      "Epoch 473/4000\n",
      "9/9 - 0s - loss: 0.4622 - accuracy: 0.9236 - val_loss: 13.0405 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 474/4000\n",
      "9/9 - 0s - loss: 0.4638 - accuracy: 0.8958 - val_loss: 13.0718 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 475/4000\n",
      "9/9 - 0s - loss: 0.4433 - accuracy: 0.9167 - val_loss: 15.0738 - val_accuracy: 0.2778 - 81ms/epoch - 9ms/step\n",
      "Epoch 476/4000\n",
      "9/9 - 0s - loss: 0.4391 - accuracy: 0.9236 - val_loss: 11.5567 - val_accuracy: 0.2778 - 84ms/epoch - 9ms/step\n",
      "Epoch 477/4000\n",
      "9/9 - 0s - loss: 0.4734 - accuracy: 0.8681 - val_loss: 9.4608 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 478/4000\n",
      "9/9 - 0s - loss: 0.4422 - accuracy: 0.9097 - val_loss: 9.7261 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 479/4000\n",
      "9/9 - 0s - loss: 0.4500 - accuracy: 0.9236 - val_loss: 8.4547 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 480/4000\n",
      "9/9 - 0s - loss: 0.4433 - accuracy: 0.9444 - val_loss: 7.5877 - val_accuracy: 0.3056 - 81ms/epoch - 9ms/step\n",
      "Epoch 481/4000\n",
      "9/9 - 0s - loss: 0.4690 - accuracy: 0.9097 - val_loss: 8.9163 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 482/4000\n",
      "9/9 - 0s - loss: 0.4486 - accuracy: 0.8958 - val_loss: 8.5131 - val_accuracy: 0.3333 - 85ms/epoch - 9ms/step\n",
      "Epoch 483/4000\n",
      "9/9 - 0s - loss: 0.4701 - accuracy: 0.8819 - val_loss: 8.8487 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 484/4000\n",
      "9/9 - 0s - loss: 0.4478 - accuracy: 0.9097 - val_loss: 6.0510 - val_accuracy: 0.3056 - 78ms/epoch - 9ms/step\n",
      "Epoch 485/4000\n",
      "9/9 - 0s - loss: 0.4612 - accuracy: 0.9306 - val_loss: 2.7200 - val_accuracy: 0.4444 - 78ms/epoch - 9ms/step\n",
      "Epoch 486/4000\n",
      "9/9 - 0s - loss: 0.4547 - accuracy: 0.9097 - val_loss: 1.9838 - val_accuracy: 0.3056 - 92ms/epoch - 10ms/step\n",
      "Epoch 487/4000\n",
      "9/9 - 0s - loss: 0.4608 - accuracy: 0.8750 - val_loss: 2.4768 - val_accuracy: 0.3056 - 82ms/epoch - 9ms/step\n",
      "Epoch 488/4000\n",
      "9/9 - 0s - loss: 0.4355 - accuracy: 0.9306 - val_loss: 4.0343 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 489/4000\n",
      "9/9 - 0s - loss: 0.4540 - accuracy: 0.8819 - val_loss: 5.2157 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 490/4000\n",
      "9/9 - 0s - loss: 0.4288 - accuracy: 0.9444 - val_loss: 5.5281 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 491/4000\n",
      "9/9 - 0s - loss: 0.4472 - accuracy: 0.9097 - val_loss: 4.5207 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 492/4000\n",
      "9/9 - 0s - loss: 0.4433 - accuracy: 0.9236 - val_loss: 3.4300 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 493/4000\n",
      "9/9 - 0s - loss: 0.4881 - accuracy: 0.8819 - val_loss: 3.9773 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 494/4000\n",
      "9/9 - 0s - loss: 0.4465 - accuracy: 0.8889 - val_loss: 3.5981 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 495/4000\n",
      "9/9 - 0s - loss: 0.4178 - accuracy: 0.9306 - val_loss: 3.1196 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 496/4000\n",
      "9/9 - 0s - loss: 0.4498 - accuracy: 0.9167 - val_loss: 3.9062 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 497/4000\n",
      "9/9 - 0s - loss: 0.4147 - accuracy: 0.9444 - val_loss: 3.7320 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 498/4000\n",
      "9/9 - 0s - loss: 0.4524 - accuracy: 0.9028 - val_loss: 3.3637 - val_accuracy: 0.3333 - 78ms/epoch - 9ms/step\n",
      "Epoch 499/4000\n",
      "9/9 - 0s - loss: 0.4531 - accuracy: 0.9375 - val_loss: 3.6540 - val_accuracy: 0.3056 - 85ms/epoch - 9ms/step\n",
      "Epoch 500/4000\n",
      "9/9 - 0s - loss: 0.4497 - accuracy: 0.9306 - val_loss: 8.0274 - val_accuracy: 0.2639 - 107ms/epoch - 12ms/step\n",
      "Epoch 501/4000\n",
      "9/9 - 0s - loss: 0.3969 - accuracy: 0.9583 - val_loss: 10.6526 - val_accuracy: 0.2639 - 101ms/epoch - 11ms/step\n",
      "Epoch 502/4000\n",
      "9/9 - 0s - loss: 0.4449 - accuracy: 0.9236 - val_loss: 12.0459 - val_accuracy: 0.2639 - 101ms/epoch - 11ms/step\n",
      "Epoch 503/4000\n",
      "9/9 - 0s - loss: 0.4320 - accuracy: 0.8958 - val_loss: 13.8754 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 504/4000\n",
      "9/9 - 0s - loss: 0.4171 - accuracy: 0.9375 - val_loss: 13.0091 - val_accuracy: 0.2639 - 93ms/epoch - 10ms/step\n",
      "Epoch 505/4000\n",
      "9/9 - 0s - loss: 0.4569 - accuracy: 0.9028 - val_loss: 12.2033 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 506/4000\n",
      "9/9 - 0s - loss: 0.4444 - accuracy: 0.9306 - val_loss: 8.9122 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 507/4000\n",
      "9/9 - 0s - loss: 0.4194 - accuracy: 0.9236 - val_loss: 4.4200 - val_accuracy: 0.2639 - 78ms/epoch - 9ms/step\n",
      "Epoch 508/4000\n",
      "9/9 - 0s - loss: 0.4194 - accuracy: 0.9306 - val_loss: 4.7307 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 509/4000\n",
      "9/9 - 0s - loss: 0.4308 - accuracy: 0.9097 - val_loss: 9.5828 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 510/4000\n",
      "9/9 - 0s - loss: 0.4484 - accuracy: 0.8958 - val_loss: 6.9217 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 511/4000\n",
      "9/9 - 0s - loss: 0.4304 - accuracy: 0.9236 - val_loss: 10.7886 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 512/4000\n",
      "9/9 - 0s - loss: 0.4419 - accuracy: 0.9028 - val_loss: 8.9378 - val_accuracy: 0.2639 - 74ms/epoch - 8ms/step\n",
      "Epoch 513/4000\n",
      "9/9 - 0s - loss: 0.4142 - accuracy: 0.9514 - val_loss: 3.9454 - val_accuracy: 0.3472 - 82ms/epoch - 9ms/step\n",
      "Epoch 514/4000\n",
      "9/9 - 0s - loss: 0.4461 - accuracy: 0.8958 - val_loss: 1.1780 - val_accuracy: 0.4444 - 85ms/epoch - 9ms/step\n",
      "Epoch 515/4000\n",
      "9/9 - 0s - loss: 0.4143 - accuracy: 0.9097 - val_loss: 3.1222 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 516/4000\n",
      "9/9 - 0s - loss: 0.4238 - accuracy: 0.9236 - val_loss: 4.0676 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 517/4000\n",
      "9/9 - 0s - loss: 0.4270 - accuracy: 0.9028 - val_loss: 3.3055 - val_accuracy: 0.2361 - 88ms/epoch - 10ms/step\n",
      "Epoch 518/4000\n",
      "9/9 - 0s - loss: 0.4202 - accuracy: 0.9375 - val_loss: 3.8474 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 519/4000\n",
      "9/9 - 0s - loss: 0.4576 - accuracy: 0.9236 - val_loss: 4.2689 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 520/4000\n",
      "9/9 - 0s - loss: 0.4150 - accuracy: 0.9375 - val_loss: 1.8872 - val_accuracy: 0.4583 - 80ms/epoch - 9ms/step\n",
      "Epoch 521/4000\n",
      "9/9 - 0s - loss: 0.4265 - accuracy: 0.8958 - val_loss: 5.7673 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 522/4000\n",
      "9/9 - 0s - loss: 0.4559 - accuracy: 0.8958 - val_loss: 9.4956 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 523/4000\n",
      "9/9 - 0s - loss: 0.3938 - accuracy: 0.9514 - val_loss: 10.2217 - val_accuracy: 0.3056 - 80ms/epoch - 9ms/step\n",
      "Epoch 524/4000\n",
      "9/9 - 0s - loss: 0.3819 - accuracy: 0.9583 - val_loss: 8.0470 - val_accuracy: 0.3333 - 83ms/epoch - 9ms/step\n",
      "Epoch 525/4000\n",
      "9/9 - 0s - loss: 0.4307 - accuracy: 0.9167 - val_loss: 3.6009 - val_accuracy: 0.3194 - 86ms/epoch - 10ms/step\n",
      "Epoch 526/4000\n",
      "9/9 - 0s - loss: 0.4517 - accuracy: 0.8889 - val_loss: 3.3420 - val_accuracy: 0.3056 - 88ms/epoch - 10ms/step\n",
      "Epoch 527/4000\n",
      "9/9 - 0s - loss: 0.4308 - accuracy: 0.9306 - val_loss: 1.1131 - val_accuracy: 0.3750 - 86ms/epoch - 10ms/step\n",
      "Epoch 528/4000\n",
      "9/9 - 0s - loss: 0.4387 - accuracy: 0.9028 - val_loss: 1.7978 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 529/4000\n",
      "9/9 - 0s - loss: 0.4580 - accuracy: 0.8889 - val_loss: 1.1638 - val_accuracy: 0.3750 - 86ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 530/4000\n",
      "9/9 - 0s - loss: 0.4220 - accuracy: 0.9236 - val_loss: 4.2646 - val_accuracy: 0.3056 - 80ms/epoch - 9ms/step\n",
      "Epoch 531/4000\n",
      "9/9 - 0s - loss: 0.4128 - accuracy: 0.9375 - val_loss: 1.7052 - val_accuracy: 0.6111 - 83ms/epoch - 9ms/step\n",
      "Epoch 532/4000\n",
      "9/9 - 0s - loss: 0.4153 - accuracy: 0.9375 - val_loss: 2.5683 - val_accuracy: 0.4722 - 79ms/epoch - 9ms/step\n",
      "Epoch 533/4000\n",
      "9/9 - 0s - loss: 0.4499 - accuracy: 0.9097 - val_loss: 2.9712 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 534/4000\n",
      "9/9 - 0s - loss: 0.4157 - accuracy: 0.9097 - val_loss: 4.3280 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 535/4000\n",
      "9/9 - 0s - loss: 0.4240 - accuracy: 0.9167 - val_loss: 3.2971 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 536/4000\n",
      "9/9 - 0s - loss: 0.4193 - accuracy: 0.9097 - val_loss: 4.0512 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 537/4000\n",
      "9/9 - 0s - loss: 0.4085 - accuracy: 0.9306 - val_loss: 2.9960 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 538/4000\n",
      "9/9 - 0s - loss: 0.4377 - accuracy: 0.8958 - val_loss: 0.8897 - val_accuracy: 0.5972 - 84ms/epoch - 9ms/step\n",
      "Epoch 539/4000\n",
      "9/9 - 0s - loss: 0.4497 - accuracy: 0.9167 - val_loss: 7.7308 - val_accuracy: 0.2361 - 93ms/epoch - 10ms/step\n",
      "Epoch 540/4000\n",
      "9/9 - 0s - loss: 0.4181 - accuracy: 0.9236 - val_loss: 11.2683 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 541/4000\n",
      "9/9 - 0s - loss: 0.4109 - accuracy: 0.9236 - val_loss: 7.5579 - val_accuracy: 0.2500 - 85ms/epoch - 9ms/step\n",
      "Epoch 542/4000\n",
      "9/9 - 0s - loss: 0.4166 - accuracy: 0.9444 - val_loss: 5.7203 - val_accuracy: 0.3056 - 89ms/epoch - 10ms/step\n",
      "Epoch 543/4000\n",
      "9/9 - 0s - loss: 0.4050 - accuracy: 0.9306 - val_loss: 4.0161 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 544/4000\n",
      "9/9 - 0s - loss: 0.4222 - accuracy: 0.9167 - val_loss: 3.7867 - val_accuracy: 0.3194 - 79ms/epoch - 9ms/step\n",
      "Epoch 545/4000\n",
      "9/9 - 0s - loss: 0.4251 - accuracy: 0.9167 - val_loss: 1.1987 - val_accuracy: 0.3889 - 94ms/epoch - 10ms/step\n",
      "Epoch 546/4000\n",
      "9/9 - 0s - loss: 0.4279 - accuracy: 0.9236 - val_loss: 5.9554 - val_accuracy: 0.2361 - 84ms/epoch - 9ms/step\n",
      "Epoch 547/4000\n",
      "9/9 - 0s - loss: 0.4304 - accuracy: 0.9444 - val_loss: 8.2028 - val_accuracy: 0.2500 - 79ms/epoch - 9ms/step\n",
      "Epoch 548/4000\n",
      "9/9 - 0s - loss: 0.4080 - accuracy: 0.9236 - val_loss: 2.5241 - val_accuracy: 0.4028 - 86ms/epoch - 10ms/step\n",
      "Epoch 549/4000\n",
      "9/9 - 0s - loss: 0.4017 - accuracy: 0.9306 - val_loss: 2.6550 - val_accuracy: 0.3333 - 91ms/epoch - 10ms/step\n",
      "Epoch 550/4000\n",
      "9/9 - 0s - loss: 0.3783 - accuracy: 0.9583 - val_loss: 1.4480 - val_accuracy: 0.4583 - 99ms/epoch - 11ms/step\n",
      "Epoch 551/4000\n",
      "9/9 - 0s - loss: 0.4263 - accuracy: 0.9236 - val_loss: 2.4988 - val_accuracy: 0.2778 - 99ms/epoch - 11ms/step\n",
      "Epoch 552/4000\n",
      "9/9 - 0s - loss: 0.4033 - accuracy: 0.9444 - val_loss: 5.4468 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 553/4000\n",
      "9/9 - 0s - loss: 0.4347 - accuracy: 0.9097 - val_loss: 6.1214 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 554/4000\n",
      "9/9 - 0s - loss: 0.4193 - accuracy: 0.8889 - val_loss: 5.8384 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 555/4000\n",
      "9/9 - 0s - loss: 0.4446 - accuracy: 0.9097 - val_loss: 7.9248 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 556/4000\n",
      "9/9 - 0s - loss: 0.3856 - accuracy: 0.9375 - val_loss: 9.9589 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 557/4000\n",
      "9/9 - 0s - loss: 0.3935 - accuracy: 0.9306 - val_loss: 9.9064 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 558/4000\n",
      "9/9 - 0s - loss: 0.4182 - accuracy: 0.9028 - val_loss: 6.4392 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 559/4000\n",
      "9/9 - 0s - loss: 0.4074 - accuracy: 0.9236 - val_loss: 6.2573 - val_accuracy: 0.2917 - 99ms/epoch - 11ms/step\n",
      "Epoch 560/4000\n",
      "9/9 - 0s - loss: 0.4077 - accuracy: 0.9444 - val_loss: 6.6974 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 561/4000\n",
      "9/9 - 0s - loss: 0.4018 - accuracy: 0.9375 - val_loss: 5.8484 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 562/4000\n",
      "9/9 - 0s - loss: 0.4133 - accuracy: 0.9306 - val_loss: 3.8075 - val_accuracy: 0.3333 - 89ms/epoch - 10ms/step\n",
      "Epoch 563/4000\n",
      "9/9 - 0s - loss: 0.4025 - accuracy: 0.9236 - val_loss: 3.4314 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 564/4000\n",
      "9/9 - 0s - loss: 0.4217 - accuracy: 0.9306 - val_loss: 4.8680 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 565/4000\n",
      "9/9 - 0s - loss: 0.3835 - accuracy: 0.9444 - val_loss: 6.3150 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 566/4000\n",
      "9/9 - 0s - loss: 0.4076 - accuracy: 0.8889 - val_loss: 5.2992 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 567/4000\n",
      "9/9 - 0s - loss: 0.3979 - accuracy: 0.9167 - val_loss: 4.3110 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 568/4000\n",
      "9/9 - 0s - loss: 0.4150 - accuracy: 0.9306 - val_loss: 4.6714 - val_accuracy: 0.2222 - 76ms/epoch - 8ms/step\n",
      "Epoch 569/4000\n",
      "9/9 - 0s - loss: 0.3901 - accuracy: 0.9583 - val_loss: 4.5055 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 570/4000\n",
      "9/9 - 0s - loss: 0.3845 - accuracy: 0.9375 - val_loss: 6.9257 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 571/4000\n",
      "9/9 - 0s - loss: 0.4091 - accuracy: 0.9236 - val_loss: 9.7802 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 572/4000\n",
      "9/9 - 0s - loss: 0.4093 - accuracy: 0.9375 - val_loss: 12.5304 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 573/4000\n",
      "9/9 - 0s - loss: 0.3834 - accuracy: 0.9375 - val_loss: 10.7398 - val_accuracy: 0.3333 - 86ms/epoch - 10ms/step\n",
      "Epoch 574/4000\n",
      "9/9 - 0s - loss: 0.3975 - accuracy: 0.9236 - val_loss: 8.6990 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 575/4000\n",
      "9/9 - 0s - loss: 0.3984 - accuracy: 0.9306 - val_loss: 7.3592 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 576/4000\n",
      "9/9 - 0s - loss: 0.3991 - accuracy: 0.9306 - val_loss: 7.1030 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 577/4000\n",
      "9/9 - 0s - loss: 0.4284 - accuracy: 0.9375 - val_loss: 6.5018 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 578/4000\n",
      "9/9 - 0s - loss: 0.4019 - accuracy: 0.9097 - val_loss: 5.3020 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 579/4000\n",
      "9/9 - 0s - loss: 0.3852 - accuracy: 0.9444 - val_loss: 4.3756 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 580/4000\n",
      "9/9 - 0s - loss: 0.4030 - accuracy: 0.9375 - val_loss: 4.6621 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 581/4000\n",
      "9/9 - 0s - loss: 0.3866 - accuracy: 0.9444 - val_loss: 4.7283 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 582/4000\n",
      "9/9 - 0s - loss: 0.3723 - accuracy: 0.9583 - val_loss: 6.5499 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 583/4000\n",
      "9/9 - 0s - loss: 0.4238 - accuracy: 0.9375 - val_loss: 8.5540 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 584/4000\n",
      "9/9 - 0s - loss: 0.3849 - accuracy: 0.9306 - val_loss: 9.9716 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 585/4000\n",
      "9/9 - 0s - loss: 0.4054 - accuracy: 0.9306 - val_loss: 10.7986 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 586/4000\n",
      "9/9 - 0s - loss: 0.3929 - accuracy: 0.9514 - val_loss: 10.7074 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 587/4000\n",
      "9/9 - 0s - loss: 0.4132 - accuracy: 0.9167 - val_loss: 14.2893 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 588/4000\n",
      "9/9 - 0s - loss: 0.4266 - accuracy: 0.9097 - val_loss: 13.7077 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 589/4000\n",
      "9/9 - 0s - loss: 0.3822 - accuracy: 0.9097 - val_loss: 11.9922 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 590/4000\n",
      "9/9 - 0s - loss: 0.3588 - accuracy: 0.9375 - val_loss: 9.3619 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 591/4000\n",
      "9/9 - 0s - loss: 0.4119 - accuracy: 0.9167 - val_loss: 9.2134 - val_accuracy: 0.2083 - 85ms/epoch - 9ms/step\n",
      "Epoch 592/4000\n",
      "9/9 - 0s - loss: 0.3859 - accuracy: 0.9375 - val_loss: 10.3155 - val_accuracy: 0.2361 - 94ms/epoch - 10ms/step\n",
      "Epoch 593/4000\n",
      "9/9 - 0s - loss: 0.3935 - accuracy: 0.9514 - val_loss: 14.1047 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 594/4000\n",
      "9/9 - 0s - loss: 0.3818 - accuracy: 0.9375 - val_loss: 14.7450 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 595/4000\n",
      "9/9 - 0s - loss: 0.4071 - accuracy: 0.9028 - val_loss: 10.8763 - val_accuracy: 0.2778 - 90ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 596/4000\n",
      "9/9 - 0s - loss: 0.4035 - accuracy: 0.9375 - val_loss: 11.7248 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 597/4000\n",
      "9/9 - 0s - loss: 0.4084 - accuracy: 0.9097 - val_loss: 9.2595 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 598/4000\n",
      "9/9 - 0s - loss: 0.3690 - accuracy: 0.9583 - val_loss: 10.4849 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 599/4000\n",
      "9/9 - 0s - loss: 0.3750 - accuracy: 0.9583 - val_loss: 9.4110 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 600/4000\n",
      "9/9 - 0s - loss: 0.3664 - accuracy: 0.9514 - val_loss: 5.8254 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 601/4000\n",
      "9/9 - 0s - loss: 0.3975 - accuracy: 0.9306 - val_loss: 0.8176 - val_accuracy: 0.5833 - 92ms/epoch - 10ms/step\n",
      "Epoch 602/4000\n",
      "9/9 - 0s - loss: 0.4004 - accuracy: 0.9306 - val_loss: 2.0642 - val_accuracy: 0.5833 - 94ms/epoch - 10ms/step\n",
      "Epoch 603/4000\n",
      "9/9 - 0s - loss: 0.3929 - accuracy: 0.9236 - val_loss: 1.6561 - val_accuracy: 0.3194 - 82ms/epoch - 9ms/step\n",
      "Epoch 604/4000\n",
      "9/9 - 0s - loss: 0.4142 - accuracy: 0.9236 - val_loss: 4.6840 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 605/4000\n",
      "9/9 - 0s - loss: 0.3745 - accuracy: 0.9097 - val_loss: 9.7444 - val_accuracy: 0.2778 - 100ms/epoch - 11ms/step\n",
      "Epoch 606/4000\n",
      "9/9 - 0s - loss: 0.4086 - accuracy: 0.9167 - val_loss: 12.8205 - val_accuracy: 0.2639 - 92ms/epoch - 10ms/step\n",
      "Epoch 607/4000\n",
      "9/9 - 0s - loss: 0.4143 - accuracy: 0.9167 - val_loss: 12.7520 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 608/4000\n",
      "9/9 - 0s - loss: 0.4020 - accuracy: 0.9167 - val_loss: 15.8929 - val_accuracy: 0.2639 - 93ms/epoch - 10ms/step\n",
      "Epoch 609/4000\n",
      "9/9 - 0s - loss: 0.3612 - accuracy: 0.9444 - val_loss: 17.7751 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 610/4000\n",
      "9/9 - 0s - loss: 0.3835 - accuracy: 0.9236 - val_loss: 18.4728 - val_accuracy: 0.3333 - 91ms/epoch - 10ms/step\n",
      "Epoch 611/4000\n",
      "9/9 - 0s - loss: 0.3714 - accuracy: 0.9514 - val_loss: 6.8741 - val_accuracy: 0.3333 - 88ms/epoch - 10ms/step\n",
      "Epoch 612/4000\n",
      "9/9 - 0s - loss: 0.3698 - accuracy: 0.9375 - val_loss: 5.8383 - val_accuracy: 0.4583 - 82ms/epoch - 9ms/step\n",
      "Epoch 613/4000\n",
      "9/9 - 0s - loss: 0.3818 - accuracy: 0.9375 - val_loss: 5.1349 - val_accuracy: 0.3056 - 91ms/epoch - 10ms/step\n",
      "Epoch 614/4000\n",
      "9/9 - 0s - loss: 0.4012 - accuracy: 0.9306 - val_loss: 2.6357 - val_accuracy: 0.4722 - 84ms/epoch - 9ms/step\n",
      "Epoch 615/4000\n",
      "9/9 - 0s - loss: 0.3621 - accuracy: 0.9792 - val_loss: 1.7352 - val_accuracy: 0.3056 - 84ms/epoch - 9ms/step\n",
      "Epoch 616/4000\n",
      "9/9 - 0s - loss: 0.3774 - accuracy: 0.9444 - val_loss: 1.5900 - val_accuracy: 0.3889 - 85ms/epoch - 9ms/step\n",
      "Epoch 617/4000\n",
      "9/9 - 0s - loss: 0.3901 - accuracy: 0.9375 - val_loss: 4.0016 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 618/4000\n",
      "9/9 - 0s - loss: 0.3779 - accuracy: 0.9236 - val_loss: 5.0535 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 619/4000\n",
      "9/9 - 0s - loss: 0.3862 - accuracy: 0.9375 - val_loss: 6.4597 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 620/4000\n",
      "9/9 - 0s - loss: 0.4165 - accuracy: 0.9097 - val_loss: 3.2088 - val_accuracy: 0.3750 - 86ms/epoch - 10ms/step\n",
      "Epoch 621/4000\n",
      "9/9 - 0s - loss: 0.3779 - accuracy: 0.9236 - val_loss: 4.4683 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 622/4000\n",
      "9/9 - 0s - loss: 0.4287 - accuracy: 0.9028 - val_loss: 3.1886 - val_accuracy: 0.3194 - 94ms/epoch - 10ms/step\n",
      "Epoch 623/4000\n",
      "9/9 - 0s - loss: 0.3727 - accuracy: 0.9444 - val_loss: 1.1699 - val_accuracy: 0.5139 - 87ms/epoch - 10ms/step\n",
      "Epoch 624/4000\n",
      "9/9 - 0s - loss: 0.3922 - accuracy: 0.9236 - val_loss: 3.1851 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 625/4000\n",
      "9/9 - 0s - loss: 0.3651 - accuracy: 0.9444 - val_loss: 7.5859 - val_accuracy: 0.3056 - 94ms/epoch - 10ms/step\n",
      "Epoch 626/4000\n",
      "9/9 - 0s - loss: 0.3621 - accuracy: 0.9375 - val_loss: 7.0443 - val_accuracy: 0.2778 - 87ms/epoch - 10ms/step\n",
      "Epoch 627/4000\n",
      "9/9 - 0s - loss: 0.3848 - accuracy: 0.9306 - val_loss: 5.5475 - val_accuracy: 0.3056 - 85ms/epoch - 9ms/step\n",
      "Epoch 628/4000\n",
      "9/9 - 0s - loss: 0.3732 - accuracy: 0.9306 - val_loss: 3.1809 - val_accuracy: 0.2778 - 82ms/epoch - 9ms/step\n",
      "Epoch 629/4000\n",
      "9/9 - 0s - loss: 0.3659 - accuracy: 0.9514 - val_loss: 3.2845 - val_accuracy: 0.2361 - 86ms/epoch - 10ms/step\n",
      "Epoch 630/4000\n",
      "9/9 - 0s - loss: 0.3786 - accuracy: 0.9375 - val_loss: 3.1422 - val_accuracy: 0.5000 - 91ms/epoch - 10ms/step\n",
      "Epoch 631/4000\n",
      "9/9 - 0s - loss: 0.3485 - accuracy: 0.9861 - val_loss: 2.9452 - val_accuracy: 0.2778 - 96ms/epoch - 11ms/step\n",
      "Epoch 632/4000\n",
      "9/9 - 0s - loss: 0.3850 - accuracy: 0.9306 - val_loss: 5.9067 - val_accuracy: 0.3056 - 97ms/epoch - 11ms/step\n",
      "Epoch 633/4000\n",
      "9/9 - 0s - loss: 0.3526 - accuracy: 0.9444 - val_loss: 1.4459 - val_accuracy: 0.4583 - 94ms/epoch - 10ms/step\n",
      "Epoch 634/4000\n",
      "9/9 - 0s - loss: 0.3755 - accuracy: 0.9514 - val_loss: 5.0844 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 635/4000\n",
      "9/9 - 0s - loss: 0.3578 - accuracy: 0.9306 - val_loss: 4.4869 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 636/4000\n",
      "9/9 - 0s - loss: 0.3755 - accuracy: 0.9583 - val_loss: 3.0557 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 637/4000\n",
      "9/9 - 0s - loss: 0.3626 - accuracy: 0.9514 - val_loss: 3.0569 - val_accuracy: 0.3194 - 79ms/epoch - 9ms/step\n",
      "Epoch 638/4000\n",
      "9/9 - 0s - loss: 0.3465 - accuracy: 0.9583 - val_loss: 3.7702 - val_accuracy: 0.5000 - 83ms/epoch - 9ms/step\n",
      "Epoch 639/4000\n",
      "9/9 - 0s - loss: 0.3809 - accuracy: 0.9028 - val_loss: 7.9951 - val_accuracy: 0.3056 - 99ms/epoch - 11ms/step\n",
      "Epoch 640/4000\n",
      "9/9 - 0s - loss: 0.3772 - accuracy: 0.9444 - val_loss: 11.3280 - val_accuracy: 0.2778 - 89ms/epoch - 10ms/step\n",
      "Epoch 641/4000\n",
      "9/9 - 0s - loss: 0.3745 - accuracy: 0.9583 - val_loss: 4.5073 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 642/4000\n",
      "9/9 - 0s - loss: 0.3475 - accuracy: 0.9375 - val_loss: 9.6340 - val_accuracy: 0.2639 - 93ms/epoch - 10ms/step\n",
      "Epoch 643/4000\n",
      "9/9 - 0s - loss: 0.3695 - accuracy: 0.9236 - val_loss: 7.8658 - val_accuracy: 0.3056 - 84ms/epoch - 9ms/step\n",
      "Epoch 644/4000\n",
      "9/9 - 0s - loss: 0.3875 - accuracy: 0.9167 - val_loss: 6.1798 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 645/4000\n",
      "9/9 - 0s - loss: 0.3615 - accuracy: 0.9444 - val_loss: 7.1577 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 646/4000\n",
      "9/9 - 0s - loss: 0.3590 - accuracy: 0.9375 - val_loss: 6.8662 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 647/4000\n",
      "9/9 - 0s - loss: 0.3732 - accuracy: 0.9306 - val_loss: 8.1527 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 648/4000\n",
      "9/9 - 0s - loss: 0.3543 - accuracy: 0.9375 - val_loss: 6.8125 - val_accuracy: 0.2500 - 85ms/epoch - 9ms/step\n",
      "Epoch 649/4000\n",
      "9/9 - 0s - loss: 0.3259 - accuracy: 0.9514 - val_loss: 7.5396 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 650/4000\n",
      "9/9 - 0s - loss: 0.3656 - accuracy: 0.9375 - val_loss: 7.6400 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 651/4000\n",
      "9/9 - 0s - loss: 0.3672 - accuracy: 0.9722 - val_loss: 7.0482 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 652/4000\n",
      "9/9 - 0s - loss: 0.3454 - accuracy: 0.9444 - val_loss: 9.0616 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 653/4000\n",
      "9/9 - 0s - loss: 0.3838 - accuracy: 0.9375 - val_loss: 10.5514 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 654/4000\n",
      "9/9 - 0s - loss: 0.3748 - accuracy: 0.9028 - val_loss: 9.4568 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 655/4000\n",
      "9/9 - 0s - loss: 0.3507 - accuracy: 0.9514 - val_loss: 5.7279 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 656/4000\n",
      "9/9 - 0s - loss: 0.3679 - accuracy: 0.9097 - val_loss: 4.1845 - val_accuracy: 0.4167 - 86ms/epoch - 10ms/step\n",
      "Epoch 657/4000\n",
      "9/9 - 0s - loss: 0.3569 - accuracy: 0.9444 - val_loss: 5.8300 - val_accuracy: 0.2361 - 81ms/epoch - 9ms/step\n",
      "Epoch 658/4000\n",
      "9/9 - 0s - loss: 0.3583 - accuracy: 0.9306 - val_loss: 4.6736 - val_accuracy: 0.2500 - 81ms/epoch - 9ms/step\n",
      "Epoch 659/4000\n",
      "9/9 - 0s - loss: 0.3661 - accuracy: 0.9514 - val_loss: 4.2369 - val_accuracy: 0.2361 - 80ms/epoch - 9ms/step\n",
      "Epoch 660/4000\n",
      "9/9 - 0s - loss: 0.3804 - accuracy: 0.9028 - val_loss: 2.5462 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 661/4000\n",
      "9/9 - 0s - loss: 0.3434 - accuracy: 0.9444 - val_loss: 4.8622 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 662/4000\n",
      "9/9 - 0s - loss: 0.3590 - accuracy: 0.9514 - val_loss: 3.2472 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 663/4000\n",
      "9/9 - 0s - loss: 0.3492 - accuracy: 0.9514 - val_loss: 4.2312 - val_accuracy: 0.2361 - 82ms/epoch - 9ms/step\n",
      "Epoch 664/4000\n",
      "9/9 - 0s - loss: 0.3735 - accuracy: 0.9514 - val_loss: 1.5174 - val_accuracy: 0.5556 - 80ms/epoch - 9ms/step\n",
      "Epoch 665/4000\n",
      "9/9 - 0s - loss: 0.3273 - accuracy: 0.9722 - val_loss: 3.2194 - val_accuracy: 0.4167 - 81ms/epoch - 9ms/step\n",
      "Epoch 666/4000\n",
      "9/9 - 0s - loss: 0.3422 - accuracy: 0.9653 - val_loss: 12.0711 - val_accuracy: 0.2778 - 83ms/epoch - 9ms/step\n",
      "Epoch 667/4000\n",
      "9/9 - 0s - loss: 0.3448 - accuracy: 0.9444 - val_loss: 13.9835 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 668/4000\n",
      "9/9 - 0s - loss: 0.3602 - accuracy: 0.9306 - val_loss: 8.3337 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 669/4000\n",
      "9/9 - 0s - loss: 0.3570 - accuracy: 0.9722 - val_loss: 16.0264 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 670/4000\n",
      "9/9 - 0s - loss: 0.3505 - accuracy: 0.9306 - val_loss: 16.6377 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 671/4000\n",
      "9/9 - 0s - loss: 0.3600 - accuracy: 0.9514 - val_loss: 18.3756 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 672/4000\n",
      "9/9 - 0s - loss: 0.3847 - accuracy: 0.9236 - val_loss: 12.6547 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 673/4000\n",
      "9/9 - 0s - loss: 0.3565 - accuracy: 0.9722 - val_loss: 8.4531 - val_accuracy: 0.2500 - 78ms/epoch - 9ms/step\n",
      "Epoch 674/4000\n",
      "9/9 - 0s - loss: 0.3971 - accuracy: 0.9167 - val_loss: 12.0153 - val_accuracy: 0.2222 - 77ms/epoch - 9ms/step\n",
      "Epoch 675/4000\n",
      "9/9 - 0s - loss: 0.3666 - accuracy: 0.9444 - val_loss: 9.3792 - val_accuracy: 0.2222 - 76ms/epoch - 8ms/step\n",
      "Epoch 676/4000\n",
      "9/9 - 0s - loss: 0.3629 - accuracy: 0.9444 - val_loss: 5.9204 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 677/4000\n",
      "9/9 - 0s - loss: 0.3661 - accuracy: 0.9306 - val_loss: 7.3237 - val_accuracy: 0.2500 - 87ms/epoch - 10ms/step\n",
      "Epoch 678/4000\n",
      "9/9 - 0s - loss: 0.3650 - accuracy: 0.9375 - val_loss: 3.9163 - val_accuracy: 0.2500 - 89ms/epoch - 10ms/step\n",
      "Epoch 679/4000\n",
      "9/9 - 0s - loss: 0.3337 - accuracy: 0.9583 - val_loss: 6.4967 - val_accuracy: 0.3472 - 93ms/epoch - 10ms/step\n",
      "Epoch 680/4000\n",
      "9/9 - 0s - loss: 0.3353 - accuracy: 0.9583 - val_loss: 5.7881 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 681/4000\n",
      "9/9 - 0s - loss: 0.3771 - accuracy: 0.9306 - val_loss: 8.3175 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 682/4000\n",
      "9/9 - 0s - loss: 0.3469 - accuracy: 0.9583 - val_loss: 9.8201 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 683/4000\n",
      "9/9 - 0s - loss: 0.4100 - accuracy: 0.8958 - val_loss: 3.7367 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 684/4000\n",
      "9/9 - 0s - loss: 0.3554 - accuracy: 0.9722 - val_loss: 6.1828 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 685/4000\n",
      "9/9 - 0s - loss: 0.3698 - accuracy: 0.9514 - val_loss: 4.8220 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 686/4000\n",
      "9/9 - 0s - loss: 0.3459 - accuracy: 0.9444 - val_loss: 6.2129 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 687/4000\n",
      "9/9 - 0s - loss: 0.3567 - accuracy: 0.9722 - val_loss: 6.3859 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 688/4000\n",
      "9/9 - 0s - loss: 0.3428 - accuracy: 0.9375 - val_loss: 3.0961 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 689/4000\n",
      "9/9 - 0s - loss: 0.3605 - accuracy: 0.9236 - val_loss: 2.3507 - val_accuracy: 0.5000 - 85ms/epoch - 9ms/step\n",
      "Epoch 690/4000\n",
      "9/9 - 0s - loss: 0.3375 - accuracy: 0.9583 - val_loss: 2.4635 - val_accuracy: 0.5139 - 74ms/epoch - 8ms/step\n",
      "Epoch 691/4000\n",
      "9/9 - 0s - loss: 0.3366 - accuracy: 0.9722 - val_loss: 5.0243 - val_accuracy: 0.4861 - 85ms/epoch - 9ms/step\n",
      "Epoch 692/4000\n",
      "9/9 - 0s - loss: 0.3428 - accuracy: 0.9444 - val_loss: 7.9883 - val_accuracy: 0.2778 - 86ms/epoch - 10ms/step\n",
      "Epoch 693/4000\n",
      "9/9 - 0s - loss: 0.3654 - accuracy: 0.9375 - val_loss: 2.6998 - val_accuracy: 0.3056 - 87ms/epoch - 10ms/step\n",
      "Epoch 694/4000\n",
      "9/9 - 0s - loss: 0.3511 - accuracy: 0.9514 - val_loss: 6.7567 - val_accuracy: 0.2361 - 86ms/epoch - 10ms/step\n",
      "Epoch 695/4000\n",
      "9/9 - 0s - loss: 0.3827 - accuracy: 0.9236 - val_loss: 7.7109 - val_accuracy: 0.2361 - 95ms/epoch - 11ms/step\n",
      "Epoch 696/4000\n",
      "9/9 - 0s - loss: 0.3623 - accuracy: 0.9167 - val_loss: 3.6107 - val_accuracy: 0.5000 - 85ms/epoch - 9ms/step\n",
      "Epoch 697/4000\n",
      "9/9 - 0s - loss: 0.3437 - accuracy: 0.9583 - val_loss: 5.4877 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 698/4000\n",
      "9/9 - 0s - loss: 0.3322 - accuracy: 0.9653 - val_loss: 3.2891 - val_accuracy: 0.2500 - 88ms/epoch - 10ms/step\n",
      "Epoch 699/4000\n",
      "9/9 - 0s - loss: 0.3201 - accuracy: 0.9514 - val_loss: 0.8852 - val_accuracy: 0.5694 - 83ms/epoch - 9ms/step\n",
      "Epoch 700/4000\n",
      "9/9 - 0s - loss: 0.3418 - accuracy: 0.9306 - val_loss: 1.3728 - val_accuracy: 0.5278 - 89ms/epoch - 10ms/step\n",
      "Epoch 701/4000\n",
      "9/9 - 0s - loss: 0.3122 - accuracy: 0.9722 - val_loss: 5.9893 - val_accuracy: 0.2778 - 97ms/epoch - 11ms/step\n",
      "Epoch 702/4000\n",
      "9/9 - 0s - loss: 0.3455 - accuracy: 0.9444 - val_loss: 2.3652 - val_accuracy: 0.5139 - 86ms/epoch - 10ms/step\n",
      "Epoch 703/4000\n",
      "9/9 - 0s - loss: 0.3421 - accuracy: 0.9306 - val_loss: 1.5867 - val_accuracy: 0.6111 - 87ms/epoch - 10ms/step\n",
      "Epoch 704/4000\n",
      "9/9 - 0s - loss: 0.3477 - accuracy: 0.9375 - val_loss: 13.6384 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 705/4000\n",
      "9/9 - 0s - loss: 0.3310 - accuracy: 0.9583 - val_loss: 12.4213 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 706/4000\n",
      "9/9 - 0s - loss: 0.3416 - accuracy: 0.9306 - val_loss: 3.7993 - val_accuracy: 0.2500 - 85ms/epoch - 9ms/step\n",
      "Epoch 707/4000\n",
      "9/9 - 0s - loss: 0.3327 - accuracy: 0.9514 - val_loss: 1.7786 - val_accuracy: 0.4028 - 86ms/epoch - 10ms/step\n",
      "Epoch 708/4000\n",
      "9/9 - 0s - loss: 0.3357 - accuracy: 0.9375 - val_loss: 7.3243 - val_accuracy: 0.2500 - 76ms/epoch - 8ms/step\n",
      "Epoch 709/4000\n",
      "9/9 - 0s - loss: 0.3226 - accuracy: 0.9722 - val_loss: 2.6721 - val_accuracy: 0.2361 - 82ms/epoch - 9ms/step\n",
      "Epoch 710/4000\n",
      "9/9 - 0s - loss: 0.3661 - accuracy: 0.9514 - val_loss: 2.2862 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 711/4000\n",
      "9/9 - 0s - loss: 0.3306 - accuracy: 0.9583 - val_loss: 7.2211 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 712/4000\n",
      "9/9 - 0s - loss: 0.3664 - accuracy: 0.9167 - val_loss: 17.1378 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 713/4000\n",
      "9/9 - 0s - loss: 0.3120 - accuracy: 0.9444 - val_loss: 21.8401 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 714/4000\n",
      "9/9 - 0s - loss: 0.3324 - accuracy: 0.9722 - val_loss: 23.9219 - val_accuracy: 0.2361 - 78ms/epoch - 9ms/step\n",
      "Epoch 715/4000\n",
      "9/9 - 0s - loss: 0.3209 - accuracy: 0.9583 - val_loss: 29.1656 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 716/4000\n",
      "9/9 - 0s - loss: 0.3375 - accuracy: 0.9583 - val_loss: 25.6731 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 717/4000\n",
      "9/9 - 0s - loss: 0.3643 - accuracy: 0.9167 - val_loss: 21.7542 - val_accuracy: 0.2361 - 95ms/epoch - 11ms/step\n",
      "Epoch 718/4000\n",
      "9/9 - 0s - loss: 0.3489 - accuracy: 0.9375 - val_loss: 14.7779 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 719/4000\n",
      "9/9 - 0s - loss: 0.3497 - accuracy: 0.9583 - val_loss: 16.4731 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 720/4000\n",
      "9/9 - 0s - loss: 0.3241 - accuracy: 0.9722 - val_loss: 15.8966 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 721/4000\n",
      "9/9 - 0s - loss: 0.3105 - accuracy: 0.9583 - val_loss: 15.6449 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 722/4000\n",
      "9/9 - 0s - loss: 0.3331 - accuracy: 0.9514 - val_loss: 23.5279 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 723/4000\n",
      "9/9 - 0s - loss: 0.3179 - accuracy: 0.9653 - val_loss: 21.2181 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 724/4000\n",
      "9/9 - 0s - loss: 0.2991 - accuracy: 0.9653 - val_loss: 23.0153 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 725/4000\n",
      "9/9 - 0s - loss: 0.3292 - accuracy: 0.9444 - val_loss: 24.4221 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 726/4000\n",
      "9/9 - 0s - loss: 0.3222 - accuracy: 0.9583 - val_loss: 22.2372 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 727/4000\n",
      "9/9 - 0s - loss: 0.3649 - accuracy: 0.9444 - val_loss: 26.1710 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 728/4000\n",
      "9/9 - 0s - loss: 0.3488 - accuracy: 0.9236 - val_loss: 18.3250 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 729/4000\n",
      "9/9 - 0s - loss: 0.3541 - accuracy: 0.9375 - val_loss: 10.1888 - val_accuracy: 0.2361 - 95ms/epoch - 11ms/step\n",
      "Epoch 730/4000\n",
      "9/9 - 0s - loss: 0.3276 - accuracy: 0.9722 - val_loss: 11.0177 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 731/4000\n",
      "9/9 - 0s - loss: 0.3460 - accuracy: 0.9444 - val_loss: 15.4221 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 732/4000\n",
      "9/9 - 0s - loss: 0.3466 - accuracy: 0.9444 - val_loss: 19.8231 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 733/4000\n",
      "9/9 - 0s - loss: 0.3407 - accuracy: 0.9583 - val_loss: 15.9779 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 734/4000\n",
      "9/9 - 0s - loss: 0.3141 - accuracy: 0.9722 - val_loss: 5.7432 - val_accuracy: 0.2500 - 82ms/epoch - 9ms/step\n",
      "Epoch 735/4000\n",
      "9/9 - 0s - loss: 0.3092 - accuracy: 0.9722 - val_loss: 0.9133 - val_accuracy: 0.5139 - 89ms/epoch - 10ms/step\n",
      "Epoch 736/4000\n",
      "9/9 - 0s - loss: 0.3144 - accuracy: 0.9375 - val_loss: 5.4308 - val_accuracy: 0.3056 - 95ms/epoch - 11ms/step\n",
      "Epoch 737/4000\n",
      "9/9 - 0s - loss: 0.3399 - accuracy: 0.9722 - val_loss: 6.7087 - val_accuracy: 0.3056 - 82ms/epoch - 9ms/step\n",
      "Epoch 738/4000\n",
      "9/9 - 0s - loss: 0.3488 - accuracy: 0.9583 - val_loss: 12.8326 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 739/4000\n",
      "9/9 - 0s - loss: 0.3250 - accuracy: 0.9236 - val_loss: 10.3293 - val_accuracy: 0.2778 - 93ms/epoch - 10ms/step\n",
      "Epoch 740/4000\n",
      "9/9 - 0s - loss: 0.3205 - accuracy: 0.9444 - val_loss: 16.1502 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 741/4000\n",
      "9/9 - 0s - loss: 0.3150 - accuracy: 0.9375 - val_loss: 16.4660 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 742/4000\n",
      "9/9 - 0s - loss: 0.3237 - accuracy: 0.9583 - val_loss: 20.7720 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 743/4000\n",
      "9/9 - 0s - loss: 0.3298 - accuracy: 0.9514 - val_loss: 18.8708 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 744/4000\n",
      "9/9 - 0s - loss: 0.3299 - accuracy: 0.9375 - val_loss: 13.3414 - val_accuracy: 0.2500 - 86ms/epoch - 10ms/step\n",
      "Epoch 745/4000\n",
      "9/9 - 0s - loss: 0.3297 - accuracy: 0.9514 - val_loss: 16.2807 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 746/4000\n",
      "9/9 - 0s - loss: 0.3424 - accuracy: 0.9444 - val_loss: 14.5202 - val_accuracy: 0.2500 - 89ms/epoch - 10ms/step\n",
      "Epoch 747/4000\n",
      "9/9 - 0s - loss: 0.3068 - accuracy: 0.9792 - val_loss: 16.7617 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 748/4000\n",
      "9/9 - 0s - loss: 0.3151 - accuracy: 0.9583 - val_loss: 20.6393 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 749/4000\n",
      "9/9 - 0s - loss: 0.3277 - accuracy: 0.9583 - val_loss: 18.5059 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 750/4000\n",
      "9/9 - 0s - loss: 0.3312 - accuracy: 0.9444 - val_loss: 9.6101 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 751/4000\n",
      "9/9 - 0s - loss: 0.3334 - accuracy: 0.9306 - val_loss: 4.3207 - val_accuracy: 0.3056 - 96ms/epoch - 11ms/step\n",
      "Epoch 752/4000\n",
      "9/9 - 0s - loss: 0.3072 - accuracy: 0.9583 - val_loss: 3.5269 - val_accuracy: 0.4722 - 88ms/epoch - 10ms/step\n",
      "Epoch 753/4000\n",
      "9/9 - 0s - loss: 0.2908 - accuracy: 0.9722 - val_loss: 5.8267 - val_accuracy: 0.2361 - 96ms/epoch - 11ms/step\n",
      "Epoch 754/4000\n",
      "9/9 - 0s - loss: 0.3013 - accuracy: 0.9792 - val_loss: 14.2736 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 755/4000\n",
      "9/9 - 0s - loss: 0.3454 - accuracy: 0.9514 - val_loss: 20.6935 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 756/4000\n",
      "9/9 - 0s - loss: 0.3284 - accuracy: 0.9583 - val_loss: 18.3807 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 757/4000\n",
      "9/9 - 0s - loss: 0.3407 - accuracy: 0.9375 - val_loss: 14.8761 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 758/4000\n",
      "9/9 - 0s - loss: 0.3423 - accuracy: 0.9514 - val_loss: 13.9350 - val_accuracy: 0.3750 - 91ms/epoch - 10ms/step\n",
      "Epoch 759/4000\n",
      "9/9 - 0s - loss: 0.3234 - accuracy: 0.9583 - val_loss: 11.2877 - val_accuracy: 0.3056 - 85ms/epoch - 9ms/step\n",
      "Epoch 760/4000\n",
      "9/9 - 0s - loss: 0.3221 - accuracy: 0.9583 - val_loss: 15.6512 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 761/4000\n",
      "9/9 - 0s - loss: 0.2959 - accuracy: 0.9722 - val_loss: 25.2484 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 762/4000\n",
      "9/9 - 0s - loss: 0.3599 - accuracy: 0.9375 - val_loss: 32.0696 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 763/4000\n",
      "9/9 - 0s - loss: 0.3350 - accuracy: 0.9444 - val_loss: 30.5555 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 764/4000\n",
      "9/9 - 0s - loss: 0.3113 - accuracy: 0.9653 - val_loss: 32.6335 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 765/4000\n",
      "9/9 - 0s - loss: 0.3342 - accuracy: 0.9444 - val_loss: 20.6634 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 766/4000\n",
      "9/9 - 0s - loss: 0.3498 - accuracy: 0.9375 - val_loss: 9.0174 - val_accuracy: 0.3472 - 89ms/epoch - 10ms/step\n",
      "Epoch 767/4000\n",
      "9/9 - 0s - loss: 0.3349 - accuracy: 0.9653 - val_loss: 4.6994 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 768/4000\n",
      "9/9 - 0s - loss: 0.3320 - accuracy: 0.9514 - val_loss: 4.0940 - val_accuracy: 0.3056 - 88ms/epoch - 10ms/step\n",
      "Epoch 769/4000\n",
      "9/9 - 0s - loss: 0.3224 - accuracy: 0.9514 - val_loss: 4.7947 - val_accuracy: 0.3333 - 89ms/epoch - 10ms/step\n",
      "Epoch 770/4000\n",
      "9/9 - 0s - loss: 0.3254 - accuracy: 0.9583 - val_loss: 9.2399 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 771/4000\n",
      "9/9 - 0s - loss: 0.3049 - accuracy: 0.9722 - val_loss: 1.8355 - val_accuracy: 0.5139 - 95ms/epoch - 11ms/step\n",
      "Epoch 772/4000\n",
      "9/9 - 0s - loss: 0.3033 - accuracy: 0.9583 - val_loss: 9.9774 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 773/4000\n",
      "9/9 - 0s - loss: 0.2907 - accuracy: 0.9722 - val_loss: 10.0517 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 774/4000\n",
      "9/9 - 0s - loss: 0.3108 - accuracy: 0.9653 - val_loss: 10.8812 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 775/4000\n",
      "9/9 - 0s - loss: 0.3250 - accuracy: 0.9583 - val_loss: 8.4270 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 776/4000\n",
      "9/9 - 0s - loss: 0.3351 - accuracy: 0.9653 - val_loss: 6.7463 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 777/4000\n",
      "9/9 - 0s - loss: 0.3097 - accuracy: 0.9583 - val_loss: 5.8335 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 778/4000\n",
      "9/9 - 0s - loss: 0.3119 - accuracy: 0.9722 - val_loss: 1.2986 - val_accuracy: 0.4583 - 82ms/epoch - 9ms/step\n",
      "Epoch 779/4000\n",
      "9/9 - 0s - loss: 0.2987 - accuracy: 0.9653 - val_loss: 3.6192 - val_accuracy: 0.5556 - 83ms/epoch - 9ms/step\n",
      "Epoch 780/4000\n",
      "9/9 - 0s - loss: 0.3166 - accuracy: 0.9306 - val_loss: 12.4755 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 781/4000\n",
      "9/9 - 0s - loss: 0.3024 - accuracy: 0.9583 - val_loss: 18.6155 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 782/4000\n",
      "9/9 - 0s - loss: 0.3179 - accuracy: 0.9653 - val_loss: 29.7641 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 783/4000\n",
      "9/9 - 0s - loss: 0.3340 - accuracy: 0.9444 - val_loss: 31.2343 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 784/4000\n",
      "9/9 - 0s - loss: 0.3252 - accuracy: 0.9514 - val_loss: 21.1781 - val_accuracy: 0.2222 - 116ms/epoch - 13ms/step\n",
      "Epoch 785/4000\n",
      "9/9 - 0s - loss: 0.3260 - accuracy: 0.9583 - val_loss: 17.9369 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 786/4000\n",
      "9/9 - 0s - loss: 0.2952 - accuracy: 0.9722 - val_loss: 16.1261 - val_accuracy: 0.2361 - 105ms/epoch - 12ms/step\n",
      "Epoch 787/4000\n",
      "9/9 - 0s - loss: 0.3073 - accuracy: 0.9583 - val_loss: 20.4516 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 788/4000\n",
      "9/9 - 0s - loss: 0.3081 - accuracy: 0.9792 - val_loss: 25.5538 - val_accuracy: 0.2222 - 117ms/epoch - 13ms/step\n",
      "Epoch 789/4000\n",
      "9/9 - 0s - loss: 0.2867 - accuracy: 0.9653 - val_loss: 22.9963 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 790/4000\n",
      "9/9 - 0s - loss: 0.3047 - accuracy: 0.9653 - val_loss: 24.7528 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 791/4000\n",
      "9/9 - 0s - loss: 0.3171 - accuracy: 0.9514 - val_loss: 24.1999 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 792/4000\n",
      "9/9 - 0s - loss: 0.3221 - accuracy: 0.9653 - val_loss: 24.7865 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 793/4000\n",
      "9/9 - 0s - loss: 0.3050 - accuracy: 0.9722 - val_loss: 23.8800 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 794/4000\n",
      "9/9 - 0s - loss: 0.3017 - accuracy: 0.9722 - val_loss: 25.0179 - val_accuracy: 0.2222 - 124ms/epoch - 14ms/step\n",
      "Epoch 795/4000\n",
      "9/9 - 0s - loss: 0.3072 - accuracy: 0.9514 - val_loss: 27.3147 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 796/4000\n",
      "9/9 - 0s - loss: 0.3107 - accuracy: 0.9514 - val_loss: 15.8668 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 797/4000\n",
      "9/9 - 0s - loss: 0.3017 - accuracy: 0.9792 - val_loss: 13.4164 - val_accuracy: 0.2222 - 122ms/epoch - 14ms/step\n",
      "Epoch 798/4000\n",
      "9/9 - 0s - loss: 0.3013 - accuracy: 0.9375 - val_loss: 19.5302 - val_accuracy: 0.2222 - 119ms/epoch - 13ms/step\n",
      "Epoch 799/4000\n",
      "9/9 - 0s - loss: 0.3122 - accuracy: 0.9653 - val_loss: 19.6632 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 800/4000\n",
      "9/9 - 0s - loss: 0.3076 - accuracy: 0.9653 - val_loss: 14.9067 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 801/4000\n",
      "9/9 - 0s - loss: 0.2995 - accuracy: 0.9583 - val_loss: 9.5745 - val_accuracy: 0.3194 - 96ms/epoch - 11ms/step\n",
      "Epoch 802/4000\n",
      "9/9 - 0s - loss: 0.3168 - accuracy: 0.9514 - val_loss: 13.0482 - val_accuracy: 0.3056 - 99ms/epoch - 11ms/step\n",
      "Epoch 803/4000\n",
      "9/9 - 0s - loss: 0.2948 - accuracy: 0.9653 - val_loss: 25.6526 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 804/4000\n",
      "9/9 - 0s - loss: 0.3041 - accuracy: 0.9444 - val_loss: 23.5238 - val_accuracy: 0.2361 - 84ms/epoch - 9ms/step\n",
      "Epoch 805/4000\n",
      "9/9 - 0s - loss: 0.3072 - accuracy: 0.9722 - val_loss: 21.9090 - val_accuracy: 0.2222 - 135ms/epoch - 15ms/step\n",
      "Epoch 806/4000\n",
      "9/9 - 0s - loss: 0.2968 - accuracy: 0.9653 - val_loss: 11.3124 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 807/4000\n",
      "9/9 - 0s - loss: 0.2990 - accuracy: 0.9722 - val_loss: 0.7655 - val_accuracy: 0.6528 - 94ms/epoch - 10ms/step\n",
      "Epoch 808/4000\n",
      "9/9 - 0s - loss: 0.2948 - accuracy: 0.9514 - val_loss: 5.3838 - val_accuracy: 0.5278 - 87ms/epoch - 10ms/step\n",
      "Epoch 809/4000\n",
      "9/9 - 0s - loss: 0.2948 - accuracy: 0.9653 - val_loss: 6.7472 - val_accuracy: 0.4583 - 79ms/epoch - 9ms/step\n",
      "Epoch 810/4000\n",
      "9/9 - 0s - loss: 0.3242 - accuracy: 0.9583 - val_loss: 13.6635 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 811/4000\n",
      "9/9 - 0s - loss: 0.2972 - accuracy: 0.9722 - val_loss: 18.6751 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 812/4000\n",
      "9/9 - 0s - loss: 0.2934 - accuracy: 0.9583 - val_loss: 17.5293 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 813/4000\n",
      "9/9 - 0s - loss: 0.3027 - accuracy: 0.9653 - val_loss: 20.6155 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 814/4000\n",
      "9/9 - 0s - loss: 0.2866 - accuracy: 0.9722 - val_loss: 23.2022 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 815/4000\n",
      "9/9 - 0s - loss: 0.3173 - accuracy: 0.9444 - val_loss: 30.5812 - val_accuracy: 0.2222 - 114ms/epoch - 13ms/step\n",
      "Epoch 816/4000\n",
      "9/9 - 0s - loss: 0.2732 - accuracy: 0.9792 - val_loss: 26.6307 - val_accuracy: 0.2222 - 147ms/epoch - 16ms/step\n",
      "Epoch 817/4000\n",
      "9/9 - 0s - loss: 0.2781 - accuracy: 0.9514 - val_loss: 23.9982 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 818/4000\n",
      "9/9 - 0s - loss: 0.2943 - accuracy: 0.9653 - val_loss: 27.1271 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 819/4000\n",
      "9/9 - 0s - loss: 0.2835 - accuracy: 0.9583 - val_loss: 32.1840 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 820/4000\n",
      "9/9 - 0s - loss: 0.2843 - accuracy: 0.9653 - val_loss: 37.6603 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 821/4000\n",
      "9/9 - 0s - loss: 0.3173 - accuracy: 0.9653 - val_loss: 17.4191 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 822/4000\n",
      "9/9 - 0s - loss: 0.3042 - accuracy: 0.9514 - val_loss: 6.4487 - val_accuracy: 0.3472 - 88ms/epoch - 10ms/step\n",
      "Epoch 823/4000\n",
      "9/9 - 0s - loss: 0.3100 - accuracy: 0.9444 - val_loss: 14.2536 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 824/4000\n",
      "9/9 - 0s - loss: 0.3246 - accuracy: 0.9306 - val_loss: 19.9185 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 825/4000\n",
      "9/9 - 0s - loss: 0.2824 - accuracy: 0.9514 - val_loss: 38.4065 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 826/4000\n",
      "9/9 - 0s - loss: 0.3029 - accuracy: 0.9722 - val_loss: 27.4578 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 827/4000\n",
      "9/9 - 0s - loss: 0.2911 - accuracy: 0.9653 - val_loss: 23.9822 - val_accuracy: 0.2222 - 76ms/epoch - 8ms/step\n",
      "Epoch 828/4000\n",
      "9/9 - 0s - loss: 0.2768 - accuracy: 0.9792 - val_loss: 25.0456 - val_accuracy: 0.2222 - 132ms/epoch - 15ms/step\n",
      "Epoch 829/4000\n",
      "9/9 - 0s - loss: 0.2722 - accuracy: 0.9861 - val_loss: 18.1413 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n",
      "Epoch 830/4000\n",
      "9/9 - 0s - loss: 0.3012 - accuracy: 0.9653 - val_loss: 17.9244 - val_accuracy: 0.2222 - 128ms/epoch - 14ms/step\n",
      "Epoch 831/4000\n",
      "9/9 - 0s - loss: 0.3243 - accuracy: 0.9375 - val_loss: 4.7063 - val_accuracy: 0.4306 - 104ms/epoch - 12ms/step\n",
      "Epoch 832/4000\n",
      "9/9 - 0s - loss: 0.2930 - accuracy: 0.9653 - val_loss: 10.4221 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 833/4000\n",
      "9/9 - 0s - loss: 0.2656 - accuracy: 0.9792 - val_loss: 16.6803 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 834/4000\n",
      "9/9 - 0s - loss: 0.3037 - accuracy: 0.9653 - val_loss: 1.1212 - val_accuracy: 0.5972 - 94ms/epoch - 10ms/step\n",
      "Epoch 835/4000\n",
      "9/9 - 0s - loss: 0.2794 - accuracy: 0.9792 - val_loss: 12.2864 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 836/4000\n",
      "9/9 - 0s - loss: 0.3276 - accuracy: 0.9444 - val_loss: 22.2971 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 837/4000\n",
      "9/9 - 0s - loss: 0.2847 - accuracy: 0.9722 - val_loss: 4.0482 - val_accuracy: 0.2778 - 82ms/epoch - 9ms/step\n",
      "Epoch 838/4000\n",
      "9/9 - 0s - loss: 0.2909 - accuracy: 0.9514 - val_loss: 3.5427 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 839/4000\n",
      "9/9 - 0s - loss: 0.2975 - accuracy: 0.9583 - val_loss: 4.8696 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 840/4000\n",
      "9/9 - 0s - loss: 0.3101 - accuracy: 0.9375 - val_loss: 5.8638 - val_accuracy: 0.3194 - 86ms/epoch - 10ms/step\n",
      "Epoch 841/4000\n",
      "9/9 - 0s - loss: 0.2882 - accuracy: 0.9583 - val_loss: 12.7848 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 842/4000\n",
      "9/9 - 0s - loss: 0.2850 - accuracy: 0.9792 - val_loss: 16.4043 - val_accuracy: 0.2639 - 82ms/epoch - 9ms/step\n",
      "Epoch 843/4000\n",
      "9/9 - 0s - loss: 0.2737 - accuracy: 0.9722 - val_loss: 12.5782 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 844/4000\n",
      "9/9 - 0s - loss: 0.2864 - accuracy: 0.9583 - val_loss: 19.0005 - val_accuracy: 0.2222 - 124ms/epoch - 14ms/step\n",
      "Epoch 845/4000\n",
      "9/9 - 0s - loss: 0.2758 - accuracy: 0.9722 - val_loss: 31.7934 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 846/4000\n",
      "9/9 - 0s - loss: 0.2760 - accuracy: 0.9792 - val_loss: 18.7205 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 847/4000\n",
      "9/9 - 0s - loss: 0.2611 - accuracy: 0.9861 - val_loss: 18.1552 - val_accuracy: 0.2222 - 128ms/epoch - 14ms/step\n",
      "Epoch 848/4000\n",
      "9/9 - 0s - loss: 0.2913 - accuracy: 0.9792 - val_loss: 25.4593 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 849/4000\n",
      "9/9 - 0s - loss: 0.2857 - accuracy: 0.9653 - val_loss: 26.3617 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 850/4000\n",
      "9/9 - 0s - loss: 0.2648 - accuracy: 0.9792 - val_loss: 22.1789 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 851/4000\n",
      "9/9 - 0s - loss: 0.2782 - accuracy: 0.9722 - val_loss: 18.0096 - val_accuracy: 0.2222 - 141ms/epoch - 16ms/step\n",
      "Epoch 852/4000\n",
      "9/9 - 0s - loss: 0.2759 - accuracy: 0.9792 - val_loss: 22.7231 - val_accuracy: 0.3056 - 81ms/epoch - 9ms/step\n",
      "Epoch 853/4000\n",
      "9/9 - 0s - loss: 0.2859 - accuracy: 0.9792 - val_loss: 37.8879 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 854/4000\n",
      "9/9 - 0s - loss: 0.2695 - accuracy: 0.9861 - val_loss: 21.1083 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 855/4000\n",
      "9/9 - 0s - loss: 0.2526 - accuracy: 0.9722 - val_loss: 22.7839 - val_accuracy: 0.2222 - 119ms/epoch - 13ms/step\n",
      "Epoch 856/4000\n",
      "9/9 - 0s - loss: 0.2799 - accuracy: 0.9722 - val_loss: 27.2548 - val_accuracy: 0.2222 - 107ms/epoch - 12ms/step\n",
      "Epoch 857/4000\n",
      "9/9 - 0s - loss: 0.3056 - accuracy: 0.9514 - val_loss: 23.3518 - val_accuracy: 0.2222 - 121ms/epoch - 13ms/step\n",
      "Epoch 858/4000\n",
      "9/9 - 0s - loss: 0.2707 - accuracy: 0.9722 - val_loss: 27.7298 - val_accuracy: 0.2222 - 138ms/epoch - 15ms/step\n",
      "Epoch 859/4000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 - 0s - loss: 0.3084 - accuracy: 0.9583 - val_loss: 7.5551 - val_accuracy: 0.3056 - 79ms/epoch - 9ms/step\n",
      "Epoch 860/4000\n",
      "9/9 - 0s - loss: 0.3014 - accuracy: 0.9514 - val_loss: 7.4707 - val_accuracy: 0.2917 - 129ms/epoch - 14ms/step\n",
      "Epoch 861/4000\n",
      "9/9 - 0s - loss: 0.2928 - accuracy: 0.9653 - val_loss: 7.7688 - val_accuracy: 0.2917 - 111ms/epoch - 12ms/step\n",
      "Epoch 862/4000\n",
      "9/9 - 0s - loss: 0.2810 - accuracy: 0.9722 - val_loss: 6.2492 - val_accuracy: 0.3194 - 87ms/epoch - 10ms/step\n",
      "Epoch 863/4000\n",
      "9/9 - 0s - loss: 0.3067 - accuracy: 0.9375 - val_loss: 14.4787 - val_accuracy: 0.2222 - 148ms/epoch - 16ms/step\n",
      "Epoch 864/4000\n",
      "9/9 - 0s - loss: 0.2794 - accuracy: 0.9653 - val_loss: 9.8203 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 865/4000\n",
      "9/9 - 0s - loss: 0.2507 - accuracy: 0.9931 - val_loss: 17.8923 - val_accuracy: 0.2222 - 107ms/epoch - 12ms/step\n",
      "Epoch 866/4000\n",
      "9/9 - 0s - loss: 0.2767 - accuracy: 0.9792 - val_loss: 15.3929 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 867/4000\n",
      "9/9 - 0s - loss: 0.2631 - accuracy: 0.9861 - val_loss: 12.0772 - val_accuracy: 0.2500 - 82ms/epoch - 9ms/step\n",
      "Epoch 868/4000\n",
      "9/9 - 0s - loss: 0.2725 - accuracy: 0.9722 - val_loss: 20.3264 - val_accuracy: 0.3194 - 123ms/epoch - 14ms/step\n",
      "Epoch 869/4000\n",
      "9/9 - 0s - loss: 0.2812 - accuracy: 0.9792 - val_loss: 15.5618 - val_accuracy: 0.3333 - 102ms/epoch - 11ms/step\n",
      "Epoch 870/4000\n",
      "9/9 - 0s - loss: 0.2838 - accuracy: 0.9653 - val_loss: 22.3429 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 871/4000\n",
      "9/9 - 0s - loss: 0.2788 - accuracy: 0.9444 - val_loss: 26.8003 - val_accuracy: 0.2639 - 127ms/epoch - 14ms/step\n",
      "Epoch 872/4000\n",
      "9/9 - 0s - loss: 0.2539 - accuracy: 0.9861 - val_loss: 27.7373 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 873/4000\n",
      "9/9 - 0s - loss: 0.2951 - accuracy: 0.9792 - val_loss: 33.0617 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 874/4000\n",
      "9/9 - 0s - loss: 0.2903 - accuracy: 0.9653 - val_loss: 26.1468 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 875/4000\n",
      "9/9 - 0s - loss: 0.2782 - accuracy: 0.9583 - val_loss: 28.1048 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 876/4000\n",
      "9/9 - 0s - loss: 0.2762 - accuracy: 0.9722 - val_loss: 33.5662 - val_accuracy: 0.2222 - 107ms/epoch - 12ms/step\n",
      "Epoch 877/4000\n",
      "9/9 - 0s - loss: 0.2755 - accuracy: 0.9653 - val_loss: 26.4558 - val_accuracy: 0.2222 - 152ms/epoch - 17ms/step\n",
      "Epoch 878/4000\n",
      "9/9 - 0s - loss: 0.2848 - accuracy: 0.9722 - val_loss: 30.3340 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 879/4000\n",
      "9/9 - 0s - loss: 0.2673 - accuracy: 0.9722 - val_loss: 27.5939 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 880/4000\n",
      "9/9 - 0s - loss: 0.2705 - accuracy: 0.9722 - val_loss: 35.0845 - val_accuracy: 0.2222 - 129ms/epoch - 14ms/step\n",
      "Epoch 881/4000\n",
      "9/9 - 0s - loss: 0.2816 - accuracy: 0.9444 - val_loss: 29.5079 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 882/4000\n",
      "9/9 - 0s - loss: 0.2967 - accuracy: 0.9583 - val_loss: 27.2719 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 883/4000\n",
      "9/9 - 0s - loss: 0.2744 - accuracy: 0.9861 - val_loss: 36.2355 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 884/4000\n",
      "9/9 - 0s - loss: 0.2850 - accuracy: 0.9583 - val_loss: 27.8946 - val_accuracy: 0.3056 - 98ms/epoch - 11ms/step\n",
      "Epoch 885/4000\n",
      "9/9 - 0s - loss: 0.2600 - accuracy: 1.0000 - val_loss: 27.8671 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 886/4000\n",
      "9/9 - 0s - loss: 0.2540 - accuracy: 0.9722 - val_loss: 30.7674 - val_accuracy: 0.2222 - 143ms/epoch - 16ms/step\n",
      "Epoch 887/4000\n",
      "9/9 - 0s - loss: 0.2525 - accuracy: 0.9861 - val_loss: 30.9950 - val_accuracy: 0.2222 - 126ms/epoch - 14ms/step\n",
      "Epoch 888/4000\n",
      "9/9 - 0s - loss: 0.2548 - accuracy: 0.9722 - val_loss: 19.0311 - val_accuracy: 0.2222 - 110ms/epoch - 12ms/step\n",
      "Epoch 889/4000\n",
      "9/9 - 0s - loss: 0.2781 - accuracy: 0.9514 - val_loss: 12.6191 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 890/4000\n",
      "9/9 - 0s - loss: 0.2861 - accuracy: 0.9583 - val_loss: 19.9376 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 891/4000\n",
      "9/9 - 0s - loss: 0.2689 - accuracy: 0.9653 - val_loss: 35.2684 - val_accuracy: 0.2222 - 128ms/epoch - 14ms/step\n",
      "Epoch 892/4000\n",
      "9/9 - 0s - loss: 0.2670 - accuracy: 0.9722 - val_loss: 27.4693 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 893/4000\n",
      "9/9 - 0s - loss: 0.2786 - accuracy: 0.9583 - val_loss: 7.5729 - val_accuracy: 0.2222 - 122ms/epoch - 14ms/step\n",
      "Epoch 894/4000\n",
      "9/9 - 0s - loss: 0.2662 - accuracy: 0.9722 - val_loss: 12.5591 - val_accuracy: 0.2222 - 142ms/epoch - 16ms/step\n",
      "Epoch 895/4000\n",
      "9/9 - 0s - loss: 0.2934 - accuracy: 0.9444 - val_loss: 12.2860 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 896/4000\n",
      "9/9 - 0s - loss: 0.2672 - accuracy: 0.9653 - val_loss: 24.8929 - val_accuracy: 0.2222 - 109ms/epoch - 12ms/step\n",
      "Epoch 897/4000\n",
      "9/9 - 0s - loss: 0.2575 - accuracy: 0.9722 - val_loss: 14.8841 - val_accuracy: 0.2222 - 120ms/epoch - 13ms/step\n",
      "Epoch 898/4000\n",
      "9/9 - 0s - loss: 0.2543 - accuracy: 0.9931 - val_loss: 7.0909 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 899/4000\n",
      "9/9 - 0s - loss: 0.2444 - accuracy: 0.9653 - val_loss: 15.5890 - val_accuracy: 0.2222 - 115ms/epoch - 13ms/step\n",
      "Epoch 900/4000\n",
      "9/9 - 0s - loss: 0.2421 - accuracy: 0.9861 - val_loss: 23.8456 - val_accuracy: 0.2222 - 107ms/epoch - 12ms/step\n",
      "Epoch 901/4000\n",
      "9/9 - 0s - loss: 0.2776 - accuracy: 0.9653 - val_loss: 33.5388 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 902/4000\n",
      "9/9 - 0s - loss: 0.2574 - accuracy: 0.9861 - val_loss: 48.2964 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 903/4000\n",
      "9/9 - 0s - loss: 0.2639 - accuracy: 0.9722 - val_loss: 42.7015 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 904/4000\n",
      "9/9 - 0s - loss: 0.2391 - accuracy: 0.9792 - val_loss: 25.9747 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 905/4000\n",
      "9/9 - 0s - loss: 0.2521 - accuracy: 0.9792 - val_loss: 39.0942 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 906/4000\n",
      "9/9 - 0s - loss: 0.3408 - accuracy: 0.9167 - val_loss: 22.1674 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 907/4000\n",
      "9/9 - 0s - loss: 0.3201 - accuracy: 0.9444 - val_loss: 11.4545 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 908/4000\n",
      "9/9 - 0s - loss: 0.2695 - accuracy: 0.9861 - val_loss: 28.3913 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 909/4000\n",
      "9/9 - 0s - loss: 0.2528 - accuracy: 0.9653 - val_loss: 33.6358 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 910/4000\n",
      "9/9 - 0s - loss: 0.2874 - accuracy: 0.9583 - val_loss: 23.0298 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 911/4000\n",
      "9/9 - 0s - loss: 0.2508 - accuracy: 0.9792 - val_loss: 36.3603 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 912/4000\n",
      "9/9 - 0s - loss: 0.2523 - accuracy: 0.9792 - val_loss: 28.1711 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 913/4000\n",
      "9/9 - 0s - loss: 0.2381 - accuracy: 0.9792 - val_loss: 22.1650 - val_accuracy: 0.4306 - 100ms/epoch - 11ms/step\n",
      "Epoch 914/4000\n",
      "9/9 - 0s - loss: 0.2488 - accuracy: 0.9792 - val_loss: 30.3184 - val_accuracy: 0.3194 - 114ms/epoch - 13ms/step\n",
      "Epoch 915/4000\n",
      "9/9 - 0s - loss: 0.2603 - accuracy: 0.9653 - val_loss: 33.8787 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 916/4000\n",
      "9/9 - 0s - loss: 0.2954 - accuracy: 0.9306 - val_loss: 36.3845 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 917/4000\n",
      "9/9 - 0s - loss: 0.2794 - accuracy: 0.9653 - val_loss: 44.0807 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 918/4000\n",
      "9/9 - 0s - loss: 0.2484 - accuracy: 0.9722 - val_loss: 31.9570 - val_accuracy: 0.2222 - 116ms/epoch - 13ms/step\n",
      "Epoch 919/4000\n",
      "9/9 - 0s - loss: 0.2854 - accuracy: 0.9444 - val_loss: 29.2485 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 920/4000\n",
      "9/9 - 0s - loss: 0.2906 - accuracy: 0.9722 - val_loss: 18.5296 - val_accuracy: 0.2222 - 112ms/epoch - 12ms/step\n",
      "Epoch 921/4000\n",
      "9/9 - 0s - loss: 0.2370 - accuracy: 0.9792 - val_loss: 2.6570 - val_accuracy: 0.5139 - 125ms/epoch - 14ms/step\n",
      "Epoch 922/4000\n",
      "9/9 - 0s - loss: 0.2518 - accuracy: 0.9792 - val_loss: 8.5748 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 923/4000\n",
      "9/9 - 0s - loss: 0.2498 - accuracy: 0.9653 - val_loss: 23.7894 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 924/4000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 - 0s - loss: 0.2464 - accuracy: 1.0000 - val_loss: 28.0184 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 925/4000\n",
      "9/9 - 0s - loss: 0.2395 - accuracy: 0.9931 - val_loss: 11.0474 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 926/4000\n",
      "9/9 - 0s - loss: 0.2540 - accuracy: 0.9722 - val_loss: 1.0712 - val_accuracy: 0.5000 - 109ms/epoch - 12ms/step\n",
      "Epoch 927/4000\n",
      "9/9 - 0s - loss: 0.2730 - accuracy: 0.9583 - val_loss: 3.7339 - val_accuracy: 0.5278 - 116ms/epoch - 13ms/step\n",
      "Epoch 928/4000\n",
      "9/9 - 0s - loss: 0.2952 - accuracy: 0.9375 - val_loss: 1.8008 - val_accuracy: 0.4306 - 102ms/epoch - 11ms/step\n",
      "Epoch 929/4000\n",
      "9/9 - 0s - loss: 0.2434 - accuracy: 0.9861 - val_loss: 2.6103 - val_accuracy: 0.4306 - 100ms/epoch - 11ms/step\n",
      "Epoch 930/4000\n",
      "9/9 - 0s - loss: 0.2661 - accuracy: 0.9722 - val_loss: 1.9555 - val_accuracy: 0.3333 - 88ms/epoch - 10ms/step\n",
      "Epoch 931/4000\n",
      "9/9 - 0s - loss: 0.2538 - accuracy: 0.9792 - val_loss: 4.2645 - val_accuracy: 0.4028 - 87ms/epoch - 10ms/step\n",
      "Epoch 932/4000\n",
      "9/9 - 0s - loss: 0.2218 - accuracy: 0.9931 - val_loss: 16.3829 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 933/4000\n",
      "9/9 - 0s - loss: 0.2658 - accuracy: 0.9792 - val_loss: 9.6533 - val_accuracy: 0.2222 - 117ms/epoch - 13ms/step\n",
      "Epoch 934/4000\n",
      "9/9 - 0s - loss: 0.2776 - accuracy: 0.9514 - val_loss: 16.2501 - val_accuracy: 0.2222 - 123ms/epoch - 14ms/step\n",
      "Epoch 935/4000\n",
      "9/9 - 0s - loss: 0.2591 - accuracy: 0.9722 - val_loss: 15.6265 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 936/4000\n",
      "9/9 - 0s - loss: 0.2346 - accuracy: 1.0000 - val_loss: 5.2217 - val_accuracy: 0.3889 - 96ms/epoch - 11ms/step\n",
      "Epoch 937/4000\n",
      "9/9 - 0s - loss: 0.2524 - accuracy: 0.9861 - val_loss: 15.8074 - val_accuracy: 0.3889 - 101ms/epoch - 11ms/step\n",
      "Epoch 938/4000\n",
      "9/9 - 0s - loss: 0.2452 - accuracy: 0.9722 - val_loss: 2.8111 - val_accuracy: 0.4861 - 118ms/epoch - 13ms/step\n",
      "Epoch 939/4000\n",
      "9/9 - 0s - loss: 0.2684 - accuracy: 0.9653 - val_loss: 3.3627 - val_accuracy: 0.2222 - 117ms/epoch - 13ms/step\n",
      "Epoch 940/4000\n",
      "9/9 - 0s - loss: 0.3105 - accuracy: 0.9236 - val_loss: 15.6770 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 941/4000\n",
      "9/9 - 0s - loss: 0.3116 - accuracy: 0.9514 - val_loss: 18.4754 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 942/4000\n",
      "9/9 - 0s - loss: 0.2573 - accuracy: 0.9792 - val_loss: 29.4885 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 943/4000\n",
      "9/9 - 0s - loss: 0.2354 - accuracy: 0.9792 - val_loss: 39.1533 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 944/4000\n",
      "9/9 - 0s - loss: 0.2164 - accuracy: 0.9722 - val_loss: 39.2171 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 945/4000\n",
      "9/9 - 0s - loss: 0.2407 - accuracy: 0.9792 - val_loss: 42.8510 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 946/4000\n",
      "9/9 - 0s - loss: 0.2610 - accuracy: 0.9861 - val_loss: 41.8646 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 947/4000\n",
      "9/9 - 0s - loss: 0.2699 - accuracy: 0.9583 - val_loss: 45.2194 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 948/4000\n",
      "9/9 - 0s - loss: 0.2300 - accuracy: 0.9861 - val_loss: 42.2039 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 949/4000\n",
      "9/9 - 0s - loss: 0.2624 - accuracy: 0.9653 - val_loss: 40.8821 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 950/4000\n",
      "9/9 - 0s - loss: 0.2737 - accuracy: 0.9583 - val_loss: 39.8193 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 951/4000\n",
      "9/9 - 0s - loss: 0.2869 - accuracy: 0.9514 - val_loss: 37.0638 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 952/4000\n",
      "9/9 - 0s - loss: 0.2353 - accuracy: 0.9722 - val_loss: 34.4368 - val_accuracy: 0.2361 - 89ms/epoch - 10ms/step\n",
      "Epoch 953/4000\n",
      "9/9 - 0s - loss: 0.2416 - accuracy: 0.9792 - val_loss: 41.7143 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 954/4000\n",
      "9/9 - 0s - loss: 0.2355 - accuracy: 0.9653 - val_loss: 52.8537 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 955/4000\n",
      "9/9 - 0s - loss: 0.2309 - accuracy: 0.9792 - val_loss: 45.0310 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 956/4000\n",
      "9/9 - 0s - loss: 0.2366 - accuracy: 0.9861 - val_loss: 33.2302 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 957/4000\n",
      "9/9 - 0s - loss: 0.2664 - accuracy: 0.9653 - val_loss: 32.7833 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 958/4000\n",
      "9/9 - 0s - loss: 0.2440 - accuracy: 0.9722 - val_loss: 14.6894 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 959/4000\n",
      "9/9 - 0s - loss: 0.2685 - accuracy: 0.9722 - val_loss: 16.5459 - val_accuracy: 0.2778 - 80ms/epoch - 9ms/step\n",
      "Epoch 960/4000\n",
      "9/9 - 0s - loss: 0.2627 - accuracy: 0.9514 - val_loss: 35.8929 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 961/4000\n",
      "9/9 - 0s - loss: 0.2670 - accuracy: 0.9653 - val_loss: 38.0730 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 962/4000\n",
      "9/9 - 0s - loss: 0.2583 - accuracy: 0.9653 - val_loss: 68.1884 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 963/4000\n",
      "9/9 - 0s - loss: 0.2511 - accuracy: 0.9792 - val_loss: 103.6523 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 964/4000\n",
      "9/9 - 0s - loss: 0.2457 - accuracy: 0.9722 - val_loss: 121.8822 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 965/4000\n",
      "9/9 - 0s - loss: 0.2546 - accuracy: 0.9792 - val_loss: 92.2302 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 966/4000\n",
      "9/9 - 0s - loss: 0.2430 - accuracy: 0.9792 - val_loss: 75.7501 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 967/4000\n",
      "9/9 - 0s - loss: 0.2427 - accuracy: 0.9722 - val_loss: 30.2520 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 968/4000\n",
      "9/9 - 0s - loss: 0.2423 - accuracy: 0.9792 - val_loss: 29.8865 - val_accuracy: 0.3056 - 93ms/epoch - 10ms/step\n",
      "Epoch 969/4000\n",
      "9/9 - 0s - loss: 0.2370 - accuracy: 0.9722 - val_loss: 35.7978 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 970/4000\n",
      "9/9 - 0s - loss: 0.2429 - accuracy: 0.9861 - val_loss: 67.9743 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 971/4000\n",
      "9/9 - 0s - loss: 0.2328 - accuracy: 0.9861 - val_loss: 73.0461 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 972/4000\n",
      "9/9 - 0s - loss: 0.2232 - accuracy: 0.9861 - val_loss: 71.6623 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 973/4000\n",
      "9/9 - 0s - loss: 0.2497 - accuracy: 0.9861 - val_loss: 40.3289 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 974/4000\n",
      "9/9 - 0s - loss: 0.2665 - accuracy: 0.9653 - val_loss: 29.4976 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 975/4000\n",
      "9/9 - 0s - loss: 0.2443 - accuracy: 0.9722 - val_loss: 50.1661 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 976/4000\n",
      "9/9 - 0s - loss: 0.2479 - accuracy: 0.9792 - val_loss: 11.3657 - val_accuracy: 0.5139 - 84ms/epoch - 9ms/step\n",
      "Epoch 977/4000\n",
      "9/9 - 0s - loss: 0.2596 - accuracy: 0.9653 - val_loss: 23.4854 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 978/4000\n",
      "9/9 - 0s - loss: 0.2370 - accuracy: 0.9792 - val_loss: 34.3011 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 979/4000\n",
      "9/9 - 0s - loss: 0.2326 - accuracy: 0.9792 - val_loss: 62.9600 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 980/4000\n",
      "9/9 - 0s - loss: 0.2320 - accuracy: 0.9792 - val_loss: 96.4467 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 981/4000\n",
      "9/9 - 0s - loss: 0.2521 - accuracy: 0.9722 - val_loss: 88.2528 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 982/4000\n",
      "9/9 - 0s - loss: 0.2327 - accuracy: 0.9722 - val_loss: 103.7150 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 983/4000\n",
      "9/9 - 0s - loss: 0.2726 - accuracy: 0.9514 - val_loss: 90.8705 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 984/4000\n",
      "9/9 - 0s - loss: 0.2246 - accuracy: 0.9861 - val_loss: 57.4071 - val_accuracy: 0.2222 - 113ms/epoch - 13ms/step\n",
      "Epoch 985/4000\n",
      "9/9 - 0s - loss: 0.2427 - accuracy: 0.9722 - val_loss: 33.9446 - val_accuracy: 0.2222 - 110ms/epoch - 12ms/step\n",
      "Epoch 986/4000\n",
      "9/9 - 0s - loss: 0.2361 - accuracy: 0.9722 - val_loss: 9.8677 - val_accuracy: 0.4167 - 112ms/epoch - 12ms/step\n",
      "Epoch 987/4000\n",
      "9/9 - 0s - loss: 0.2433 - accuracy: 0.9792 - val_loss: 20.3222 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 988/4000\n",
      "9/9 - 0s - loss: 0.2379 - accuracy: 0.9792 - val_loss: 28.1748 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 989/4000\n",
      "9/9 - 0s - loss: 0.2428 - accuracy: 0.9722 - val_loss: 34.4581 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 990/4000\n",
      "9/9 - 0s - loss: 0.2714 - accuracy: 0.9861 - val_loss: 32.5348 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 991/4000\n",
      "9/9 - 0s - loss: 0.2333 - accuracy: 0.9931 - val_loss: 39.6797 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 992/4000\n",
      "9/9 - 0s - loss: 0.2314 - accuracy: 0.9792 - val_loss: 36.2872 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 993/4000\n",
      "9/9 - 0s - loss: 0.2125 - accuracy: 0.9931 - val_loss: 30.2739 - val_accuracy: 0.2222 - 136ms/epoch - 15ms/step\n",
      "Epoch 994/4000\n",
      "9/9 - 0s - loss: 0.2297 - accuracy: 0.9792 - val_loss: 28.6236 - val_accuracy: 0.2222 - 115ms/epoch - 13ms/step\n",
      "Epoch 995/4000\n",
      "9/9 - 0s - loss: 0.2177 - accuracy: 0.9792 - val_loss: 5.9134 - val_accuracy: 0.4167 - 99ms/epoch - 11ms/step\n",
      "Epoch 996/4000\n",
      "9/9 - 0s - loss: 0.2475 - accuracy: 0.9583 - val_loss: 4.9940 - val_accuracy: 0.3056 - 98ms/epoch - 11ms/step\n",
      "Epoch 997/4000\n",
      "9/9 - 0s - loss: 0.2186 - accuracy: 1.0000 - val_loss: 36.7824 - val_accuracy: 0.2361 - 98ms/epoch - 11ms/step\n",
      "Epoch 998/4000\n",
      "9/9 - 0s - loss: 0.2463 - accuracy: 0.9722 - val_loss: 31.5548 - val_accuracy: 0.2222 - 107ms/epoch - 12ms/step\n",
      "Epoch 999/4000\n",
      "9/9 - 0s - loss: 0.2313 - accuracy: 0.9792 - val_loss: 38.9173 - val_accuracy: 0.2222 - 114ms/epoch - 13ms/step\n",
      "Epoch 1000/4000\n",
      "9/9 - 0s - loss: 0.2268 - accuracy: 0.9653 - val_loss: 43.6871 - val_accuracy: 0.2222 - 194ms/epoch - 22ms/step\n",
      "Epoch 1001/4000\n",
      "9/9 - 0s - loss: 0.2497 - accuracy: 0.9722 - val_loss: 45.1979 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1002/4000\n",
      "9/9 - 0s - loss: 0.2402 - accuracy: 0.9722 - val_loss: 39.2616 - val_accuracy: 0.3194 - 87ms/epoch - 10ms/step\n",
      "Epoch 1003/4000\n",
      "9/9 - 0s - loss: 0.2539 - accuracy: 0.9722 - val_loss: 44.1720 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 1004/4000\n",
      "9/9 - 0s - loss: 0.2307 - accuracy: 0.9931 - val_loss: 37.6320 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1005/4000\n",
      "9/9 - 0s - loss: 0.2363 - accuracy: 0.9861 - val_loss: 38.8893 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1006/4000\n",
      "9/9 - 0s - loss: 0.2479 - accuracy: 0.9792 - val_loss: 17.8823 - val_accuracy: 0.3472 - 92ms/epoch - 10ms/step\n",
      "Epoch 1007/4000\n",
      "9/9 - 0s - loss: 0.2304 - accuracy: 0.9792 - val_loss: 17.7162 - val_accuracy: 0.2083 - 84ms/epoch - 9ms/step\n",
      "Epoch 1008/4000\n",
      "9/9 - 0s - loss: 0.2335 - accuracy: 0.9722 - val_loss: 12.6514 - val_accuracy: 0.3889 - 85ms/epoch - 9ms/step\n",
      "Epoch 1009/4000\n",
      "9/9 - 0s - loss: 0.2184 - accuracy: 0.9792 - val_loss: 7.3565 - val_accuracy: 0.4167 - 82ms/epoch - 9ms/step\n",
      "Epoch 1010/4000\n",
      "9/9 - 0s - loss: 0.2470 - accuracy: 0.9722 - val_loss: 13.0267 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1011/4000\n",
      "9/9 - 0s - loss: 0.2293 - accuracy: 0.9792 - val_loss: 4.0778 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1012/4000\n",
      "9/9 - 0s - loss: 0.2257 - accuracy: 0.9931 - val_loss: 4.8736 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1013/4000\n",
      "9/9 - 0s - loss: 0.2303 - accuracy: 0.9861 - val_loss: 11.4786 - val_accuracy: 0.3472 - 91ms/epoch - 10ms/step\n",
      "Epoch 1014/4000\n",
      "9/9 - 0s - loss: 0.2421 - accuracy: 0.9792 - val_loss: 10.6772 - val_accuracy: 0.2917 - 103ms/epoch - 11ms/step\n",
      "Epoch 1015/4000\n",
      "9/9 - 0s - loss: 0.2610 - accuracy: 0.9375 - val_loss: 2.7670 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1016/4000\n",
      "9/9 - 0s - loss: 0.2659 - accuracy: 0.9583 - val_loss: 36.2007 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1017/4000\n",
      "9/9 - 0s - loss: 0.2616 - accuracy: 0.9653 - val_loss: 18.2473 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1018/4000\n",
      "9/9 - 0s - loss: 0.2279 - accuracy: 0.9653 - val_loss: 6.1339 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1019/4000\n",
      "9/9 - 0s - loss: 0.2271 - accuracy: 0.9792 - val_loss: 5.5713 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1020/4000\n",
      "9/9 - 0s - loss: 0.2307 - accuracy: 0.9792 - val_loss: 20.0901 - val_accuracy: 0.2222 - 142ms/epoch - 16ms/step\n",
      "Epoch 1021/4000\n",
      "9/9 - 0s - loss: 0.2512 - accuracy: 0.9792 - val_loss: 32.6715 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1022/4000\n",
      "9/9 - 0s - loss: 0.2387 - accuracy: 0.9653 - val_loss: 43.1625 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1023/4000\n",
      "9/9 - 0s - loss: 0.2294 - accuracy: 0.9792 - val_loss: 24.1208 - val_accuracy: 0.2222 - 209ms/epoch - 23ms/step\n",
      "Epoch 1024/4000\n",
      "9/9 - 0s - loss: 0.2015 - accuracy: 0.9931 - val_loss: 29.6161 - val_accuracy: 0.2222 - 138ms/epoch - 15ms/step\n",
      "Epoch 1025/4000\n",
      "9/9 - 0s - loss: 0.2947 - accuracy: 0.9583 - val_loss: 38.7993 - val_accuracy: 0.2222 - 118ms/epoch - 13ms/step\n",
      "Epoch 1026/4000\n",
      "9/9 - 0s - loss: 0.2445 - accuracy: 0.9722 - val_loss: 44.5701 - val_accuracy: 0.2917 - 117ms/epoch - 13ms/step\n",
      "Epoch 1027/4000\n",
      "9/9 - 0s - loss: 0.2495 - accuracy: 0.9722 - val_loss: 39.5670 - val_accuracy: 0.2917 - 102ms/epoch - 11ms/step\n",
      "Epoch 1028/4000\n",
      "9/9 - 0s - loss: 0.2293 - accuracy: 0.9653 - val_loss: 10.4583 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 1029/4000\n",
      "9/9 - 0s - loss: 0.2362 - accuracy: 0.9653 - val_loss: 27.8026 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 1030/4000\n",
      "9/9 - 0s - loss: 0.2232 - accuracy: 0.9931 - val_loss: 58.2690 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 1031/4000\n",
      "9/9 - 0s - loss: 0.2106 - accuracy: 0.9861 - val_loss: 89.1519 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n",
      "Epoch 1032/4000\n",
      "9/9 - 0s - loss: 0.2232 - accuracy: 0.9861 - val_loss: 78.1204 - val_accuracy: 0.2222 - 145ms/epoch - 16ms/step\n",
      "Epoch 1033/4000\n",
      "9/9 - 0s - loss: 0.2447 - accuracy: 0.9653 - val_loss: 35.4514 - val_accuracy: 0.2222 - 132ms/epoch - 15ms/step\n",
      "Epoch 1034/4000\n",
      "9/9 - 0s - loss: 0.2376 - accuracy: 0.9514 - val_loss: 29.4583 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n",
      "Epoch 1035/4000\n",
      "9/9 - 0s - loss: 0.2488 - accuracy: 0.9792 - val_loss: 11.9295 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 1036/4000\n",
      "9/9 - 0s - loss: 0.2322 - accuracy: 0.9792 - val_loss: 5.1876 - val_accuracy: 0.2500 - 90ms/epoch - 10ms/step\n",
      "Epoch 1037/4000\n",
      "9/9 - 0s - loss: 0.2248 - accuracy: 0.9792 - val_loss: 2.3984 - val_accuracy: 0.3472 - 112ms/epoch - 12ms/step\n",
      "Epoch 1038/4000\n",
      "9/9 - 0s - loss: 0.2278 - accuracy: 0.9861 - val_loss: 12.6146 - val_accuracy: 0.2222 - 117ms/epoch - 13ms/step\n",
      "Epoch 1039/4000\n",
      "9/9 - 0s - loss: 0.2188 - accuracy: 0.9861 - val_loss: 31.9049 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n",
      "Epoch 1040/4000\n",
      "9/9 - 0s - loss: 0.2315 - accuracy: 0.9792 - val_loss: 1.8074 - val_accuracy: 0.5139 - 119ms/epoch - 13ms/step\n",
      "Epoch 1041/4000\n",
      "9/9 - 0s - loss: 0.2473 - accuracy: 0.9583 - val_loss: 12.2411 - val_accuracy: 0.3194 - 112ms/epoch - 12ms/step\n",
      "Epoch 1042/4000\n",
      "9/9 - 0s - loss: 0.2424 - accuracy: 0.9653 - val_loss: 23.1102 - val_accuracy: 0.3056 - 103ms/epoch - 11ms/step\n",
      "Epoch 1043/4000\n",
      "9/9 - 0s - loss: 0.2476 - accuracy: 0.9653 - val_loss: 26.5272 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1044/4000\n",
      "9/9 - 0s - loss: 0.2387 - accuracy: 0.9792 - val_loss: 43.6104 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1045/4000\n",
      "9/9 - 0s - loss: 0.2372 - accuracy: 0.9722 - val_loss: 44.5356 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 1046/4000\n",
      "9/9 - 0s - loss: 0.2356 - accuracy: 0.9722 - val_loss: 38.3069 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1047/4000\n",
      "9/9 - 0s - loss: 0.2309 - accuracy: 0.9583 - val_loss: 40.6790 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 1048/4000\n",
      "9/9 - 0s - loss: 0.2277 - accuracy: 0.9722 - val_loss: 39.5001 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1049/4000\n",
      "9/9 - 0s - loss: 0.2491 - accuracy: 0.9583 - val_loss: 27.9978 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1050/4000\n",
      "9/9 - 0s - loss: 0.2668 - accuracy: 0.9514 - val_loss: 31.4620 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1051/4000\n",
      "9/9 - 0s - loss: 0.2103 - accuracy: 0.9792 - val_loss: 7.3258 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1052/4000\n",
      "9/9 - 0s - loss: 0.2224 - accuracy: 0.9653 - val_loss: 15.5322 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1053/4000\n",
      "9/9 - 0s - loss: 0.2388 - accuracy: 0.9653 - val_loss: 27.8497 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1054/4000\n",
      "9/9 - 0s - loss: 0.2123 - accuracy: 0.9861 - val_loss: 30.3079 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1055/4000\n",
      "9/9 - 0s - loss: 0.2411 - accuracy: 0.9722 - val_loss: 2.5846 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1056/4000\n",
      "9/9 - 0s - loss: 0.2407 - accuracy: 0.9722 - val_loss: 42.3177 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1057/4000\n",
      "9/9 - 0s - loss: 0.2390 - accuracy: 0.9792 - val_loss: 34.2704 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1058/4000\n",
      "9/9 - 0s - loss: 0.2332 - accuracy: 0.9792 - val_loss: 4.4859 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 1059/4000\n",
      "9/9 - 0s - loss: 0.2305 - accuracy: 0.9792 - val_loss: 35.2362 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1060/4000\n",
      "9/9 - 0s - loss: 0.1993 - accuracy: 0.9861 - val_loss: 48.0421 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1061/4000\n",
      "9/9 - 0s - loss: 0.2173 - accuracy: 0.9792 - val_loss: 45.6745 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1062/4000\n",
      "9/9 - 0s - loss: 0.2124 - accuracy: 0.9722 - val_loss: 43.8644 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1063/4000\n",
      "9/9 - 0s - loss: 0.2255 - accuracy: 0.9861 - val_loss: 57.1376 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1064/4000\n",
      "9/9 - 0s - loss: 0.2255 - accuracy: 0.9792 - val_loss: 52.8408 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 1065/4000\n",
      "9/9 - 0s - loss: 0.2167 - accuracy: 0.9792 - val_loss: 55.0205 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1066/4000\n",
      "9/9 - 0s - loss: 0.2110 - accuracy: 0.9931 - val_loss: 46.3501 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1067/4000\n",
      "9/9 - 0s - loss: 0.2047 - accuracy: 0.9792 - val_loss: 43.6399 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1068/4000\n",
      "9/9 - 0s - loss: 0.2140 - accuracy: 0.9861 - val_loss: 31.6753 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1069/4000\n",
      "9/9 - 0s - loss: 0.2064 - accuracy: 0.9792 - val_loss: 11.4716 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1070/4000\n",
      "9/9 - 0s - loss: 0.2126 - accuracy: 0.9861 - val_loss: 59.6937 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1071/4000\n",
      "9/9 - 0s - loss: 0.2177 - accuracy: 0.9792 - val_loss: 52.3962 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1072/4000\n",
      "9/9 - 0s - loss: 0.2189 - accuracy: 0.9861 - val_loss: 54.8267 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1073/4000\n",
      "9/9 - 0s - loss: 0.2153 - accuracy: 0.9792 - val_loss: 31.5762 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1074/4000\n",
      "9/9 - 0s - loss: 0.2511 - accuracy: 0.9653 - val_loss: 12.6045 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1075/4000\n",
      "9/9 - 0s - loss: 0.2735 - accuracy: 0.9514 - val_loss: 4.0561 - val_accuracy: 0.2500 - 99ms/epoch - 11ms/step\n",
      "Epoch 1076/4000\n",
      "9/9 - 0s - loss: 0.2284 - accuracy: 0.9861 - val_loss: 13.2361 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1077/4000\n",
      "9/9 - 0s - loss: 0.2355 - accuracy: 0.9653 - val_loss: 6.5710 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1078/4000\n",
      "9/9 - 0s - loss: 0.2198 - accuracy: 0.9931 - val_loss: 9.4764 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1079/4000\n",
      "9/9 - 0s - loss: 0.2068 - accuracy: 0.9861 - val_loss: 48.7493 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1080/4000\n",
      "9/9 - 0s - loss: 0.2321 - accuracy: 0.9722 - val_loss: 32.0872 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1081/4000\n",
      "9/9 - 0s - loss: 0.2065 - accuracy: 0.9792 - val_loss: 56.5548 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1082/4000\n",
      "9/9 - 0s - loss: 0.2174 - accuracy: 0.9861 - val_loss: 63.2518 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1083/4000\n",
      "9/9 - 0s - loss: 0.2015 - accuracy: 0.9861 - val_loss: 105.5834 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1084/4000\n",
      "9/9 - 0s - loss: 0.2033 - accuracy: 0.9792 - val_loss: 148.8986 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1085/4000\n",
      "9/9 - 0s - loss: 0.2056 - accuracy: 0.9931 - val_loss: 138.1038 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1086/4000\n",
      "9/9 - 0s - loss: 0.2479 - accuracy: 0.9722 - val_loss: 47.2165 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1087/4000\n",
      "9/9 - 0s - loss: 0.2266 - accuracy: 0.9583 - val_loss: 5.2842 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1088/4000\n",
      "9/9 - 0s - loss: 0.1997 - accuracy: 0.9861 - val_loss: 4.3061 - val_accuracy: 0.3333 - 87ms/epoch - 10ms/step\n",
      "Epoch 1089/4000\n",
      "9/9 - 0s - loss: 0.2159 - accuracy: 0.9931 - val_loss: 45.9842 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1090/4000\n",
      "9/9 - 0s - loss: 0.2150 - accuracy: 0.9931 - val_loss: 37.7855 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1091/4000\n",
      "9/9 - 0s - loss: 0.2326 - accuracy: 0.9792 - val_loss: 44.4090 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1092/4000\n",
      "9/9 - 0s - loss: 0.2028 - accuracy: 0.9722 - val_loss: 60.5327 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1093/4000\n",
      "9/9 - 0s - loss: 0.2178 - accuracy: 0.9861 - val_loss: 54.4583 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1094/4000\n",
      "9/9 - 0s - loss: 0.2047 - accuracy: 0.9861 - val_loss: 53.4283 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1095/4000\n",
      "9/9 - 0s - loss: 0.2323 - accuracy: 0.9583 - val_loss: 29.0839 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1096/4000\n",
      "9/9 - 0s - loss: 0.2081 - accuracy: 0.9861 - val_loss: 3.2471 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1097/4000\n",
      "9/9 - 0s - loss: 0.2313 - accuracy: 0.9583 - val_loss: 51.1471 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1098/4000\n",
      "9/9 - 0s - loss: 0.2156 - accuracy: 0.9931 - val_loss: 89.1493 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1099/4000\n",
      "9/9 - 0s - loss: 0.1978 - accuracy: 0.9931 - val_loss: 135.2993 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1100/4000\n",
      "9/9 - 0s - loss: 0.2088 - accuracy: 0.9792 - val_loss: 95.9363 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1101/4000\n",
      "9/9 - 0s - loss: 0.2079 - accuracy: 1.0000 - val_loss: 37.0451 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1102/4000\n",
      "9/9 - 0s - loss: 0.2133 - accuracy: 0.9722 - val_loss: 26.8712 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1103/4000\n",
      "9/9 - 0s - loss: 0.2093 - accuracy: 0.9861 - val_loss: 30.7695 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1104/4000\n",
      "9/9 - 0s - loss: 0.1832 - accuracy: 0.9792 - val_loss: 65.0932 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1105/4000\n",
      "9/9 - 0s - loss: 0.1866 - accuracy: 0.9931 - val_loss: 98.4341 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1106/4000\n",
      "9/9 - 0s - loss: 0.2224 - accuracy: 0.9861 - val_loss: 142.8658 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1107/4000\n",
      "9/9 - 0s - loss: 0.2282 - accuracy: 0.9722 - val_loss: 134.5164 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1108/4000\n",
      "9/9 - 0s - loss: 0.1986 - accuracy: 0.9861 - val_loss: 115.7947 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1109/4000\n",
      "9/9 - 0s - loss: 0.2066 - accuracy: 0.9792 - val_loss: 71.4912 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1110/4000\n",
      "9/9 - 0s - loss: 0.2223 - accuracy: 0.9792 - val_loss: 40.0657 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1111/4000\n",
      "9/9 - 0s - loss: 0.2108 - accuracy: 0.9861 - val_loss: 3.2357 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1112/4000\n",
      "9/9 - 0s - loss: 0.2003 - accuracy: 0.9861 - val_loss: 30.1887 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1113/4000\n",
      "9/9 - 0s - loss: 0.2213 - accuracy: 0.9653 - val_loss: 46.7494 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1114/4000\n",
      "9/9 - 0s - loss: 0.2198 - accuracy: 0.9861 - val_loss: 5.9953 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 1115/4000\n",
      "9/9 - 0s - loss: 0.2066 - accuracy: 0.9861 - val_loss: 25.7870 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 1116/4000\n",
      "9/9 - 0s - loss: 0.2098 - accuracy: 0.9792 - val_loss: 14.7612 - val_accuracy: 0.2500 - 84ms/epoch - 9ms/step\n",
      "Epoch 1117/4000\n",
      "9/9 - 0s - loss: 0.2179 - accuracy: 0.9792 - val_loss: 14.4891 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1118/4000\n",
      "9/9 - 0s - loss: 0.2296 - accuracy: 0.9792 - val_loss: 38.7105 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1119/4000\n",
      "9/9 - 0s - loss: 0.1867 - accuracy: 0.9931 - val_loss: 76.5643 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1120/4000\n",
      "9/9 - 0s - loss: 0.1932 - accuracy: 0.9931 - val_loss: 125.3221 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1121/4000\n",
      "9/9 - 0s - loss: 0.1939 - accuracy: 0.9931 - val_loss: 125.5939 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1122/4000\n",
      "9/9 - 0s - loss: 0.1863 - accuracy: 0.9931 - val_loss: 133.9315 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1123/4000\n",
      "9/9 - 0s - loss: 0.1980 - accuracy: 0.9931 - val_loss: 91.6284 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1124/4000\n",
      "9/9 - 0s - loss: 0.1944 - accuracy: 0.9861 - val_loss: 2.7231 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1125/4000\n",
      "9/9 - 0s - loss: 0.2217 - accuracy: 0.9931 - val_loss: 44.9619 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 1126/4000\n",
      "9/9 - 0s - loss: 0.2212 - accuracy: 0.9861 - val_loss: 62.0639 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1127/4000\n",
      "9/9 - 0s - loss: 0.1910 - accuracy: 0.9931 - val_loss: 63.3529 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1128/4000\n",
      "9/9 - 0s - loss: 0.1930 - accuracy: 0.9792 - val_loss: 57.9002 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1129/4000\n",
      "9/9 - 0s - loss: 0.2197 - accuracy: 0.9861 - val_loss: 58.4200 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1130/4000\n",
      "9/9 - 0s - loss: 0.2026 - accuracy: 0.9861 - val_loss: 57.5131 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1131/4000\n",
      "9/9 - 0s - loss: 0.1790 - accuracy: 0.9931 - val_loss: 59.6094 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1132/4000\n",
      "9/9 - 0s - loss: 0.1952 - accuracy: 1.0000 - val_loss: 77.4412 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1133/4000\n",
      "9/9 - 0s - loss: 0.1737 - accuracy: 0.9861 - val_loss: 81.3623 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1134/4000\n",
      "9/9 - 0s - loss: 0.2478 - accuracy: 0.9792 - val_loss: 62.9925 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1135/4000\n",
      "9/9 - 0s - loss: 0.1889 - accuracy: 0.9792 - val_loss: 46.9717 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1136/4000\n",
      "9/9 - 0s - loss: 0.2059 - accuracy: 0.9931 - val_loss: 57.8214 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1137/4000\n",
      "9/9 - 0s - loss: 0.2142 - accuracy: 0.9861 - val_loss: 44.4443 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1138/4000\n",
      "9/9 - 0s - loss: 0.1996 - accuracy: 0.9861 - val_loss: 20.4589 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1139/4000\n",
      "9/9 - 0s - loss: 0.2347 - accuracy: 0.9722 - val_loss: 24.2274 - val_accuracy: 0.3472 - 81ms/epoch - 9ms/step\n",
      "Epoch 1140/4000\n",
      "9/9 - 0s - loss: 0.1979 - accuracy: 0.9792 - val_loss: 30.3032 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1141/4000\n",
      "9/9 - 0s - loss: 0.1687 - accuracy: 1.0000 - val_loss: 25.4638 - val_accuracy: 0.3889 - 85ms/epoch - 9ms/step\n",
      "Epoch 1142/4000\n",
      "9/9 - 0s - loss: 0.1808 - accuracy: 0.9792 - val_loss: 11.8262 - val_accuracy: 0.3194 - 90ms/epoch - 10ms/step\n",
      "Epoch 1143/4000\n",
      "9/9 - 0s - loss: 0.2183 - accuracy: 0.9792 - val_loss: 15.0572 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1144/4000\n",
      "9/9 - 0s - loss: 0.1884 - accuracy: 0.9931 - val_loss: 66.2296 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1145/4000\n",
      "9/9 - 0s - loss: 0.1823 - accuracy: 1.0000 - val_loss: 61.9701 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1146/4000\n",
      "9/9 - 0s - loss: 0.1967 - accuracy: 0.9931 - val_loss: 15.9471 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1147/4000\n",
      "9/9 - 0s - loss: 0.2138 - accuracy: 0.9792 - val_loss: 24.2246 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1148/4000\n",
      "9/9 - 0s - loss: 0.2100 - accuracy: 0.9861 - val_loss: 46.2442 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1149/4000\n",
      "9/9 - 0s - loss: 0.1963 - accuracy: 0.9792 - val_loss: 50.5808 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1150/4000\n",
      "9/9 - 0s - loss: 0.2107 - accuracy: 0.9583 - val_loss: 48.4294 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1151/4000\n",
      "9/9 - 0s - loss: 0.2417 - accuracy: 0.9583 - val_loss: 51.3442 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1152/4000\n",
      "9/9 - 0s - loss: 0.2083 - accuracy: 0.9792 - val_loss: 54.4668 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1153/4000\n",
      "9/9 - 0s - loss: 0.2033 - accuracy: 0.9792 - val_loss: 52.1064 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1154/4000\n",
      "9/9 - 0s - loss: 0.2175 - accuracy: 0.9931 - val_loss: 49.8505 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1155/4000\n",
      "9/9 - 0s - loss: 0.1765 - accuracy: 1.0000 - val_loss: 60.6648 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1156/4000\n",
      "9/9 - 0s - loss: 0.1976 - accuracy: 0.9931 - val_loss: 51.3853 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1157/4000\n",
      "9/9 - 0s - loss: 0.1871 - accuracy: 0.9792 - val_loss: 46.2630 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1158/4000\n",
      "9/9 - 0s - loss: 0.2201 - accuracy: 0.9722 - val_loss: 53.1971 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1159/4000\n",
      "9/9 - 0s - loss: 0.2246 - accuracy: 0.9583 - val_loss: 70.3601 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1160/4000\n",
      "9/9 - 0s - loss: 0.1762 - accuracy: 0.9931 - val_loss: 67.4961 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1161/4000\n",
      "9/9 - 0s - loss: 0.1866 - accuracy: 0.9792 - val_loss: 61.4605 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1162/4000\n",
      "9/9 - 0s - loss: 0.1923 - accuracy: 0.9861 - val_loss: 53.5084 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1163/4000\n",
      "9/9 - 0s - loss: 0.1790 - accuracy: 0.9792 - val_loss: 60.6195 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1164/4000\n",
      "9/9 - 0s - loss: 0.1980 - accuracy: 0.9931 - val_loss: 62.8976 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 1165/4000\n",
      "9/9 - 0s - loss: 0.1766 - accuracy: 0.9861 - val_loss: 27.3260 - val_accuracy: 0.2083 - 89ms/epoch - 10ms/step\n",
      "Epoch 1166/4000\n",
      "9/9 - 0s - loss: 0.2149 - accuracy: 0.9861 - val_loss: 38.6851 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1167/4000\n",
      "9/9 - 0s - loss: 0.2094 - accuracy: 0.9792 - val_loss: 101.0092 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 1168/4000\n",
      "9/9 - 0s - loss: 0.1834 - accuracy: 0.9861 - val_loss: 77.9476 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1169/4000\n",
      "9/9 - 0s - loss: 0.1721 - accuracy: 1.0000 - val_loss: 31.0470 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1170/4000\n",
      "9/9 - 0s - loss: 0.1933 - accuracy: 0.9792 - val_loss: 24.3839 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1171/4000\n",
      "9/9 - 0s - loss: 0.1792 - accuracy: 0.9861 - val_loss: 38.7504 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1172/4000\n",
      "9/9 - 0s - loss: 0.2001 - accuracy: 0.9792 - val_loss: 57.3526 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1173/4000\n",
      "9/9 - 0s - loss: 0.2044 - accuracy: 0.9792 - val_loss: 49.5751 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1174/4000\n",
      "9/9 - 0s - loss: 0.1776 - accuracy: 0.9931 - val_loss: 39.4689 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1175/4000\n",
      "9/9 - 0s - loss: 0.2120 - accuracy: 0.9861 - val_loss: 17.7179 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1176/4000\n",
      "9/9 - 0s - loss: 0.1806 - accuracy: 0.9931 - val_loss: 2.4722 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 1177/4000\n",
      "9/9 - 0s - loss: 0.1797 - accuracy: 0.9861 - val_loss: 39.2974 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1178/4000\n",
      "9/9 - 0s - loss: 0.1851 - accuracy: 0.9861 - val_loss: 45.4924 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1179/4000\n",
      "9/9 - 0s - loss: 0.1980 - accuracy: 0.9792 - val_loss: 17.3830 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 1180/4000\n",
      "9/9 - 0s - loss: 0.1783 - accuracy: 1.0000 - val_loss: 8.9332 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1181/4000\n",
      "9/9 - 0s - loss: 0.1912 - accuracy: 0.9931 - val_loss: 10.1878 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 1182/4000\n",
      "9/9 - 0s - loss: 0.1784 - accuracy: 0.9861 - val_loss: 36.3657 - val_accuracy: 0.4167 - 91ms/epoch - 10ms/step\n",
      "Epoch 1183/4000\n",
      "9/9 - 0s - loss: 0.2024 - accuracy: 0.9792 - val_loss: 52.5457 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1184/4000\n",
      "9/9 - 0s - loss: 0.2069 - accuracy: 0.9792 - val_loss: 44.5936 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1185/4000\n",
      "9/9 - 0s - loss: 0.1853 - accuracy: 0.9861 - val_loss: 57.5798 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1186/4000\n",
      "9/9 - 0s - loss: 0.2161 - accuracy: 0.9722 - val_loss: 102.2355 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 1187/4000\n",
      "9/9 - 0s - loss: 0.2266 - accuracy: 0.9722 - val_loss: 110.2026 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 1188/4000\n",
      "9/9 - 0s - loss: 0.1881 - accuracy: 0.9792 - val_loss: 61.0140 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1189/4000\n",
      "9/9 - 0s - loss: 0.2407 - accuracy: 0.9722 - val_loss: 93.2784 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1190/4000\n",
      "9/9 - 0s - loss: 0.2008 - accuracy: 0.9653 - val_loss: 133.1293 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1191/4000\n",
      "9/9 - 0s - loss: 0.1893 - accuracy: 0.9722 - val_loss: 169.4190 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1192/4000\n",
      "9/9 - 0s - loss: 0.2136 - accuracy: 0.9722 - val_loss: 154.8484 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1193/4000\n",
      "9/9 - 0s - loss: 0.1944 - accuracy: 0.9792 - val_loss: 75.9922 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1194/4000\n",
      "9/9 - 0s - loss: 0.2159 - accuracy: 0.9653 - val_loss: 51.1740 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1195/4000\n",
      "9/9 - 0s - loss: 0.1966 - accuracy: 0.9931 - val_loss: 7.1757 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1196/4000\n",
      "9/9 - 0s - loss: 0.2089 - accuracy: 0.9653 - val_loss: 3.4358 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1197/4000\n",
      "9/9 - 0s - loss: 0.1927 - accuracy: 0.9861 - val_loss: 22.6901 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1198/4000\n",
      "9/9 - 0s - loss: 0.2127 - accuracy: 0.9931 - val_loss: 42.1491 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1199/4000\n",
      "9/9 - 0s - loss: 0.2067 - accuracy: 1.0000 - val_loss: 49.3183 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1200/4000\n",
      "9/9 - 0s - loss: 0.1809 - accuracy: 0.9861 - val_loss: 44.4191 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1201/4000\n",
      "9/9 - 0s - loss: 0.1987 - accuracy: 0.9861 - val_loss: 33.2717 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1202/4000\n",
      "9/9 - 0s - loss: 0.1852 - accuracy: 0.9861 - val_loss: 26.7922 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1203/4000\n",
      "9/9 - 0s - loss: 0.1886 - accuracy: 1.0000 - val_loss: 1.7658 - val_accuracy: 0.3472 - 88ms/epoch - 10ms/step\n",
      "Epoch 1204/4000\n",
      "9/9 - 0s - loss: 0.2442 - accuracy: 0.9583 - val_loss: 36.5293 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1205/4000\n",
      "9/9 - 0s - loss: 0.2083 - accuracy: 0.9722 - val_loss: 58.5854 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1206/4000\n",
      "9/9 - 0s - loss: 0.1841 - accuracy: 0.9792 - val_loss: 78.3604 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1207/4000\n",
      "9/9 - 0s - loss: 0.1856 - accuracy: 0.9792 - val_loss: 60.9536 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1208/4000\n",
      "9/9 - 0s - loss: 0.2055 - accuracy: 0.9722 - val_loss: 73.5924 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1209/4000\n",
      "9/9 - 0s - loss: 0.1950 - accuracy: 0.9861 - val_loss: 57.5102 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1210/4000\n",
      "9/9 - 0s - loss: 0.2173 - accuracy: 0.9792 - val_loss: 53.0158 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1211/4000\n",
      "9/9 - 0s - loss: 0.1932 - accuracy: 0.9861 - val_loss: 4.7515 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1212/4000\n",
      "9/9 - 0s - loss: 0.1815 - accuracy: 0.9861 - val_loss: 27.5611 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1213/4000\n",
      "9/9 - 0s - loss: 0.2069 - accuracy: 0.9861 - val_loss: 55.0426 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1214/4000\n",
      "9/9 - 0s - loss: 0.1854 - accuracy: 0.9931 - val_loss: 58.7772 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1215/4000\n",
      "9/9 - 0s - loss: 0.1969 - accuracy: 0.9861 - val_loss: 72.1352 - val_accuracy: 0.2222 - 75ms/epoch - 8ms/step\n",
      "Epoch 1216/4000\n",
      "9/9 - 0s - loss: 0.2380 - accuracy: 0.9583 - val_loss: 57.3646 - val_accuracy: 0.2222 - 74ms/epoch - 8ms/step\n",
      "Epoch 1217/4000\n",
      "9/9 - 0s - loss: 0.2056 - accuracy: 0.9792 - val_loss: 37.7892 - val_accuracy: 0.3889 - 94ms/epoch - 10ms/step\n",
      "Epoch 1218/4000\n",
      "9/9 - 0s - loss: 0.2062 - accuracy: 0.9792 - val_loss: 14.6707 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1219/4000\n",
      "9/9 - 0s - loss: 0.1974 - accuracy: 0.9792 - val_loss: 7.0198 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1220/4000\n",
      "9/9 - 0s - loss: 0.1781 - accuracy: 0.9931 - val_loss: 28.3159 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1221/4000\n",
      "9/9 - 0s - loss: 0.1870 - accuracy: 0.9861 - val_loss: 62.9242 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1222/4000\n",
      "9/9 - 0s - loss: 0.1820 - accuracy: 0.9931 - val_loss: 62.5878 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1223/4000\n",
      "9/9 - 0s - loss: 0.1918 - accuracy: 0.9931 - val_loss: 32.3746 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1224/4000\n",
      "9/9 - 0s - loss: 0.2005 - accuracy: 0.9861 - val_loss: 32.0586 - val_accuracy: 0.2083 - 84ms/epoch - 9ms/step\n",
      "Epoch 1225/4000\n",
      "9/9 - 0s - loss: 0.1835 - accuracy: 0.9861 - val_loss: 52.3164 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1226/4000\n",
      "9/9 - 0s - loss: 0.2022 - accuracy: 0.9722 - val_loss: 50.1208 - val_accuracy: 0.3472 - 95ms/epoch - 11ms/step\n",
      "Epoch 1227/4000\n",
      "9/9 - 0s - loss: 0.2055 - accuracy: 0.9653 - val_loss: 54.8078 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1228/4000\n",
      "9/9 - 0s - loss: 0.2050 - accuracy: 0.9931 - val_loss: 54.2762 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1229/4000\n",
      "9/9 - 0s - loss: 0.1682 - accuracy: 0.9931 - val_loss: 25.1186 - val_accuracy: 0.2083 - 90ms/epoch - 10ms/step\n",
      "Epoch 1230/4000\n",
      "9/9 - 0s - loss: 0.1725 - accuracy: 0.9931 - val_loss: 14.5198 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1231/4000\n",
      "9/9 - 0s - loss: 0.1855 - accuracy: 0.9861 - val_loss: 30.4384 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1232/4000\n",
      "9/9 - 0s - loss: 0.2277 - accuracy: 0.9722 - val_loss: 50.2585 - val_accuracy: 0.2083 - 88ms/epoch - 10ms/step\n",
      "Epoch 1233/4000\n",
      "9/9 - 0s - loss: 0.1947 - accuracy: 0.9861 - val_loss: 30.9474 - val_accuracy: 0.4028 - 84ms/epoch - 9ms/step\n",
      "Epoch 1234/4000\n",
      "9/9 - 0s - loss: 0.1760 - accuracy: 0.9931 - val_loss: 52.4338 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1235/4000\n",
      "9/9 - 0s - loss: 0.2132 - accuracy: 0.9792 - val_loss: 111.3365 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1236/4000\n",
      "9/9 - 0s - loss: 0.1572 - accuracy: 1.0000 - val_loss: 169.0726 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1237/4000\n",
      "9/9 - 0s - loss: 0.1743 - accuracy: 0.9931 - val_loss: 102.3777 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1238/4000\n",
      "9/9 - 0s - loss: 0.1981 - accuracy: 0.9861 - val_loss: 133.4826 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1239/4000\n",
      "9/9 - 0s - loss: 0.1761 - accuracy: 0.9931 - val_loss: 100.6367 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1240/4000\n",
      "9/9 - 0s - loss: 0.1737 - accuracy: 0.9931 - val_loss: 47.3871 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1241/4000\n",
      "9/9 - 0s - loss: 0.1890 - accuracy: 0.9722 - val_loss: 13.8261 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1242/4000\n",
      "9/9 - 0s - loss: 0.1855 - accuracy: 0.9931 - val_loss: 10.2263 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1243/4000\n",
      "9/9 - 0s - loss: 0.1864 - accuracy: 0.9931 - val_loss: 35.4367 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 1244/4000\n",
      "9/9 - 0s - loss: 0.1970 - accuracy: 0.9653 - val_loss: 60.2463 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1245/4000\n",
      "9/9 - 0s - loss: 0.1563 - accuracy: 1.0000 - val_loss: 52.0496 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1246/4000\n",
      "9/9 - 0s - loss: 0.1606 - accuracy: 1.0000 - val_loss: 24.3345 - val_accuracy: 0.2083 - 82ms/epoch - 9ms/step\n",
      "Epoch 1247/4000\n",
      "9/9 - 0s - loss: 0.1920 - accuracy: 1.0000 - val_loss: 25.6508 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1248/4000\n",
      "9/9 - 0s - loss: 0.1856 - accuracy: 1.0000 - val_loss: 28.9239 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1249/4000\n",
      "9/9 - 0s - loss: 0.1998 - accuracy: 0.9722 - val_loss: 21.5087 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1250/4000\n",
      "9/9 - 0s - loss: 0.1693 - accuracy: 0.9931 - val_loss: 44.9278 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1251/4000\n",
      "9/9 - 0s - loss: 0.1729 - accuracy: 0.9722 - val_loss: 32.7993 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1252/4000\n",
      "9/9 - 0s - loss: 0.1852 - accuracy: 0.9861 - val_loss: 9.4909 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1253/4000\n",
      "9/9 - 0s - loss: 0.1640 - accuracy: 0.9931 - val_loss: 13.6036 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1254/4000\n",
      "9/9 - 0s - loss: 0.1913 - accuracy: 0.9931 - val_loss: 26.6444 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1255/4000\n",
      "9/9 - 0s - loss: 0.1747 - accuracy: 1.0000 - val_loss: 15.2403 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1256/4000\n",
      "9/9 - 0s - loss: 0.2053 - accuracy: 0.9792 - val_loss: 13.1066 - val_accuracy: 0.2222 - 77ms/epoch - 9ms/step\n",
      "Epoch 1257/4000\n",
      "9/9 - 0s - loss: 0.1757 - accuracy: 0.9931 - val_loss: 11.5635 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1258/4000\n",
      "9/9 - 0s - loss: 0.1907 - accuracy: 0.9722 - val_loss: 4.6934 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1259/4000\n",
      "9/9 - 0s - loss: 0.1812 - accuracy: 0.9792 - val_loss: 45.4746 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1260/4000\n",
      "9/9 - 0s - loss: 0.1640 - accuracy: 0.9861 - val_loss: 56.0872 - val_accuracy: 0.2083 - 85ms/epoch - 9ms/step\n",
      "Epoch 1261/4000\n",
      "9/9 - 0s - loss: 0.1686 - accuracy: 0.9931 - val_loss: 47.3879 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1262/4000\n",
      "9/9 - 0s - loss: 0.1814 - accuracy: 0.9931 - val_loss: 44.0232 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1263/4000\n",
      "9/9 - 0s - loss: 0.1995 - accuracy: 0.9792 - val_loss: 41.6811 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1264/4000\n",
      "9/9 - 0s - loss: 0.2161 - accuracy: 0.9861 - val_loss: 53.3982 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1265/4000\n",
      "9/9 - 0s - loss: 0.1894 - accuracy: 0.9792 - val_loss: 57.8999 - val_accuracy: 0.3056 - 89ms/epoch - 10ms/step\n",
      "Epoch 1266/4000\n",
      "9/9 - 0s - loss: 0.1951 - accuracy: 0.9861 - val_loss: 54.9421 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1267/4000\n",
      "9/9 - 0s - loss: 0.1909 - accuracy: 0.9861 - val_loss: 60.7138 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1268/4000\n",
      "9/9 - 0s - loss: 0.1706 - accuracy: 0.9931 - val_loss: 56.4511 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1269/4000\n",
      "9/9 - 0s - loss: 0.1833 - accuracy: 0.9792 - val_loss: 54.9087 - val_accuracy: 0.4028 - 82ms/epoch - 9ms/step\n",
      "Epoch 1270/4000\n",
      "9/9 - 0s - loss: 0.1664 - accuracy: 0.9861 - val_loss: 46.2919 - val_accuracy: 0.3333 - 96ms/epoch - 11ms/step\n",
      "Epoch 1271/4000\n",
      "9/9 - 0s - loss: 0.1763 - accuracy: 0.9931 - val_loss: 31.8381 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1272/4000\n",
      "9/9 - 0s - loss: 0.1842 - accuracy: 0.9792 - val_loss: 17.1057 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1273/4000\n",
      "9/9 - 0s - loss: 0.1649 - accuracy: 0.9931 - val_loss: 3.3015 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1274/4000\n",
      "9/9 - 0s - loss: 0.2023 - accuracy: 0.9861 - val_loss: 9.3582 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 1275/4000\n",
      "9/9 - 0s - loss: 0.1829 - accuracy: 0.9861 - val_loss: 8.5123 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1276/4000\n",
      "9/9 - 0s - loss: 0.1677 - accuracy: 0.9861 - val_loss: 40.4466 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1277/4000\n",
      "9/9 - 0s - loss: 0.1907 - accuracy: 0.9861 - val_loss: 107.4482 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1278/4000\n",
      "9/9 - 0s - loss: 0.1982 - accuracy: 0.9792 - val_loss: 135.0925 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1279/4000\n",
      "9/9 - 0s - loss: 0.1767 - accuracy: 0.9861 - val_loss: 176.5881 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1280/4000\n",
      "9/9 - 0s - loss: 0.1779 - accuracy: 0.9792 - val_loss: 88.5010 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1281/4000\n",
      "9/9 - 0s - loss: 0.1996 - accuracy: 0.9792 - val_loss: 92.8157 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1282/4000\n",
      "9/9 - 0s - loss: 0.1702 - accuracy: 0.9861 - val_loss: 161.5151 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1283/4000\n",
      "9/9 - 0s - loss: 0.1797 - accuracy: 0.9861 - val_loss: 234.0243 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1284/4000\n",
      "9/9 - 0s - loss: 0.1923 - accuracy: 0.9861 - val_loss: 130.5659 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1285/4000\n",
      "9/9 - 0s - loss: 0.1750 - accuracy: 0.9792 - val_loss: 123.1962 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1286/4000\n",
      "9/9 - 0s - loss: 0.1692 - accuracy: 0.9931 - val_loss: 129.7577 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1287/4000\n",
      "9/9 - 0s - loss: 0.1793 - accuracy: 1.0000 - val_loss: 100.4083 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1288/4000\n",
      "9/9 - 0s - loss: 0.1831 - accuracy: 0.9792 - val_loss: 99.3164 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1289/4000\n",
      "9/9 - 0s - loss: 0.1611 - accuracy: 1.0000 - val_loss: 103.9717 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1290/4000\n",
      "9/9 - 0s - loss: 0.1659 - accuracy: 0.9861 - val_loss: 132.5385 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1291/4000\n",
      "9/9 - 0s - loss: 0.1579 - accuracy: 0.9861 - val_loss: 61.4852 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1292/4000\n",
      "9/9 - 0s - loss: 0.1571 - accuracy: 0.9931 - val_loss: 23.7402 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1293/4000\n",
      "9/9 - 0s - loss: 0.1803 - accuracy: 0.9931 - val_loss: 18.0614 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1294/4000\n",
      "9/9 - 0s - loss: 0.1620 - accuracy: 0.9792 - val_loss: 44.6593 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1295/4000\n",
      "9/9 - 0s - loss: 0.1722 - accuracy: 0.9931 - val_loss: 49.8938 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1296/4000\n",
      "9/9 - 0s - loss: 0.1779 - accuracy: 0.9792 - val_loss: 53.7880 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 1297/4000\n",
      "9/9 - 0s - loss: 0.1851 - accuracy: 0.9861 - val_loss: 91.3835 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1298/4000\n",
      "9/9 - 0s - loss: 0.1737 - accuracy: 0.9792 - val_loss: 177.8206 - val_accuracy: 0.2639 - 90ms/epoch - 10ms/step\n",
      "Epoch 1299/4000\n",
      "9/9 - 0s - loss: 0.1814 - accuracy: 0.9931 - val_loss: 125.5410 - val_accuracy: 0.2639 - 90ms/epoch - 10ms/step\n",
      "Epoch 1300/4000\n",
      "9/9 - 0s - loss: 0.1688 - accuracy: 0.9861 - val_loss: 45.0937 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1301/4000\n",
      "9/9 - 0s - loss: 0.1869 - accuracy: 0.9583 - val_loss: 32.0676 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1302/4000\n",
      "9/9 - 0s - loss: 0.1633 - accuracy: 0.9861 - val_loss: 30.7535 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1303/4000\n",
      "9/9 - 0s - loss: 0.1634 - accuracy: 1.0000 - val_loss: 143.6337 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1304/4000\n",
      "9/9 - 0s - loss: 0.1593 - accuracy: 0.9792 - val_loss: 198.9357 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1305/4000\n",
      "9/9 - 0s - loss: 0.1774 - accuracy: 0.9722 - val_loss: 175.0061 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 1306/4000\n",
      "9/9 - 0s - loss: 0.1997 - accuracy: 0.9722 - val_loss: 111.3097 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1307/4000\n",
      "9/9 - 0s - loss: 0.1595 - accuracy: 0.9861 - val_loss: 37.5024 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1308/4000\n",
      "9/9 - 0s - loss: 0.1468 - accuracy: 0.9931 - val_loss: 31.2411 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1309/4000\n",
      "9/9 - 0s - loss: 0.1570 - accuracy: 0.9931 - val_loss: 24.6795 - val_accuracy: 0.3194 - 91ms/epoch - 10ms/step\n",
      "Epoch 1310/4000\n",
      "9/9 - 0s - loss: 0.1623 - accuracy: 1.0000 - val_loss: 22.9035 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1311/4000\n",
      "9/9 - 0s - loss: 0.2079 - accuracy: 0.9653 - val_loss: 33.2774 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1312/4000\n",
      "9/9 - 0s - loss: 0.1904 - accuracy: 0.9792 - val_loss: 46.5506 - val_accuracy: 0.2083 - 84ms/epoch - 9ms/step\n",
      "Epoch 1313/4000\n",
      "9/9 - 0s - loss: 0.1660 - accuracy: 0.9931 - val_loss: 37.2099 - val_accuracy: 0.2500 - 84ms/epoch - 9ms/step\n",
      "Epoch 1314/4000\n",
      "9/9 - 0s - loss: 0.1758 - accuracy: 0.9931 - val_loss: 27.7273 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1315/4000\n",
      "9/9 - 0s - loss: 0.1484 - accuracy: 0.9861 - val_loss: 3.5332 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1316/4000\n",
      "9/9 - 0s - loss: 0.1953 - accuracy: 0.9861 - val_loss: 91.1991 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1317/4000\n",
      "9/9 - 0s - loss: 0.1537 - accuracy: 1.0000 - val_loss: 108.9142 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1318/4000\n",
      "9/9 - 0s - loss: 0.1812 - accuracy: 0.9931 - val_loss: 85.0322 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1319/4000\n",
      "9/9 - 0s - loss: 0.2055 - accuracy: 0.9653 - val_loss: 72.7254 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1320/4000\n",
      "9/9 - 0s - loss: 0.1729 - accuracy: 0.9931 - val_loss: 48.3188 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1321/4000\n",
      "9/9 - 0s - loss: 0.1757 - accuracy: 0.9931 - val_loss: 49.2505 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1322/4000\n",
      "9/9 - 0s - loss: 0.1758 - accuracy: 0.9931 - val_loss: 16.9707 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1323/4000\n",
      "9/9 - 0s - loss: 0.1787 - accuracy: 0.9861 - val_loss: 18.2920 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1324/4000\n",
      "9/9 - 0s - loss: 0.1890 - accuracy: 0.9861 - val_loss: 41.0398 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1325/4000\n",
      "9/9 - 0s - loss: 0.1780 - accuracy: 0.9653 - val_loss: 16.0098 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1326/4000\n",
      "9/9 - 0s - loss: 0.1703 - accuracy: 0.9861 - val_loss: 45.4906 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 1327/4000\n",
      "9/9 - 0s - loss: 0.1611 - accuracy: 1.0000 - val_loss: 92.2629 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1328/4000\n",
      "9/9 - 0s - loss: 0.1477 - accuracy: 0.9931 - val_loss: 137.6384 - val_accuracy: 0.2639 - 90ms/epoch - 10ms/step\n",
      "Epoch 1329/4000\n",
      "9/9 - 0s - loss: 0.1654 - accuracy: 0.9931 - val_loss: 151.1034 - val_accuracy: 0.2778 - 97ms/epoch - 11ms/step\n",
      "Epoch 1330/4000\n",
      "9/9 - 0s - loss: 0.2006 - accuracy: 0.9722 - val_loss: 144.2274 - val_accuracy: 0.2639 - 96ms/epoch - 11ms/step\n",
      "Epoch 1331/4000\n",
      "9/9 - 0s - loss: 0.1935 - accuracy: 0.9792 - val_loss: 137.8202 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1332/4000\n",
      "9/9 - 0s - loss: 0.1725 - accuracy: 0.9931 - val_loss: 125.3287 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 1333/4000\n",
      "9/9 - 0s - loss: 0.2430 - accuracy: 0.9653 - val_loss: 34.5686 - val_accuracy: 0.2639 - 81ms/epoch - 9ms/step\n",
      "Epoch 1334/4000\n",
      "9/9 - 0s - loss: 0.1928 - accuracy: 0.9931 - val_loss: 96.6248 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1335/4000\n",
      "9/9 - 0s - loss: 0.1649 - accuracy: 0.9722 - val_loss: 128.9263 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 1336/4000\n",
      "9/9 - 0s - loss: 0.1550 - accuracy: 0.9931 - val_loss: 160.4333 - val_accuracy: 0.3056 - 86ms/epoch - 10ms/step\n",
      "Epoch 1337/4000\n",
      "9/9 - 0s - loss: 0.1816 - accuracy: 0.9722 - val_loss: 115.3698 - val_accuracy: 0.2639 - 102ms/epoch - 11ms/step\n",
      "Epoch 1338/4000\n",
      "9/9 - 0s - loss: 0.1712 - accuracy: 1.0000 - val_loss: 74.4347 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1339/4000\n",
      "9/9 - 0s - loss: 0.1773 - accuracy: 0.9861 - val_loss: 59.3136 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1340/4000\n",
      "9/9 - 0s - loss: 0.1719 - accuracy: 0.9653 - val_loss: 24.7428 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1341/4000\n",
      "9/9 - 0s - loss: 0.1780 - accuracy: 0.9931 - val_loss: 24.3503 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1342/4000\n",
      "9/9 - 0s - loss: 0.1734 - accuracy: 0.9792 - val_loss: 54.3132 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1343/4000\n",
      "9/9 - 0s - loss: 0.1789 - accuracy: 0.9861 - val_loss: 61.1248 - val_accuracy: 0.2222 - 77ms/epoch - 9ms/step\n",
      "Epoch 1344/4000\n",
      "9/9 - 0s - loss: 0.1561 - accuracy: 0.9931 - val_loss: 65.6537 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1345/4000\n",
      "9/9 - 0s - loss: 0.1666 - accuracy: 0.9931 - val_loss: 59.3597 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 1346/4000\n",
      "9/9 - 0s - loss: 0.2035 - accuracy: 0.9792 - val_loss: 69.7253 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1347/4000\n",
      "9/9 - 0s - loss: 0.1890 - accuracy: 1.0000 - val_loss: 54.7272 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1348/4000\n",
      "9/9 - 0s - loss: 0.1753 - accuracy: 1.0000 - val_loss: 28.3831 - val_accuracy: 0.2083 - 88ms/epoch - 10ms/step\n",
      "Epoch 1349/4000\n",
      "9/9 - 0s - loss: 0.1568 - accuracy: 0.9931 - val_loss: 5.9889 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1350/4000\n",
      "9/9 - 0s - loss: 0.1687 - accuracy: 0.9931 - val_loss: 23.0265 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1351/4000\n",
      "9/9 - 0s - loss: 0.1713 - accuracy: 0.9792 - val_loss: 54.3886 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1352/4000\n",
      "9/9 - 0s - loss: 0.1506 - accuracy: 0.9931 - val_loss: 86.0205 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1353/4000\n",
      "9/9 - 0s - loss: 0.1685 - accuracy: 0.9792 - val_loss: 87.6481 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1354/4000\n",
      "9/9 - 0s - loss: 0.1501 - accuracy: 0.9861 - val_loss: 126.8237 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1355/4000\n",
      "9/9 - 0s - loss: 0.1582 - accuracy: 0.9931 - val_loss: 71.1140 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1356/4000\n",
      "9/9 - 0s - loss: 0.1417 - accuracy: 1.0000 - val_loss: 34.1502 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1357/4000\n",
      "9/9 - 0s - loss: 0.1593 - accuracy: 0.9931 - val_loss: 39.8348 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1358/4000\n",
      "9/9 - 0s - loss: 0.1671 - accuracy: 0.9861 - val_loss: 29.9375 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1359/4000\n",
      "9/9 - 0s - loss: 0.1492 - accuracy: 0.9931 - val_loss: 55.7808 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 1360/4000\n",
      "9/9 - 0s - loss: 0.1436 - accuracy: 1.0000 - val_loss: 45.7365 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 1361/4000\n",
      "9/9 - 0s - loss: 0.1774 - accuracy: 0.9792 - val_loss: 5.4521 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 1362/4000\n",
      "9/9 - 0s - loss: 0.2661 - accuracy: 0.9236 - val_loss: 30.6251 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1363/4000\n",
      "9/9 - 0s - loss: 0.1944 - accuracy: 0.9722 - val_loss: 32.0010 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1364/4000\n",
      "9/9 - 0s - loss: 0.1789 - accuracy: 0.9722 - val_loss: 15.0784 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1365/4000\n",
      "9/9 - 0s - loss: 0.1702 - accuracy: 0.9792 - val_loss: 4.1181 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1366/4000\n",
      "9/9 - 0s - loss: 0.1809 - accuracy: 0.9861 - val_loss: 29.4478 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1367/4000\n",
      "9/9 - 0s - loss: 0.1834 - accuracy: 0.9722 - val_loss: 152.2577 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1368/4000\n",
      "9/9 - 0s - loss: 0.1848 - accuracy: 0.9861 - val_loss: 185.2219 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1369/4000\n",
      "9/9 - 0s - loss: 0.1993 - accuracy: 0.9653 - val_loss: 146.4301 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1370/4000\n",
      "9/9 - 0s - loss: 0.1877 - accuracy: 0.9722 - val_loss: 208.6978 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1371/4000\n",
      "9/9 - 0s - loss: 0.1802 - accuracy: 0.9792 - val_loss: 124.2961 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1372/4000\n",
      "9/9 - 0s - loss: 0.1762 - accuracy: 0.9792 - val_loss: 47.6656 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1373/4000\n",
      "9/9 - 0s - loss: 0.1442 - accuracy: 0.9861 - val_loss: 27.3883 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 1374/4000\n",
      "9/9 - 0s - loss: 0.1588 - accuracy: 0.9861 - val_loss: 49.8320 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1375/4000\n",
      "9/9 - 0s - loss: 0.1625 - accuracy: 0.9931 - val_loss: 8.9688 - val_accuracy: 0.2778 - 90ms/epoch - 10ms/step\n",
      "Epoch 1376/4000\n",
      "9/9 - 0s - loss: 0.1621 - accuracy: 0.9931 - val_loss: 36.0059 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1377/4000\n",
      "9/9 - 0s - loss: 0.1538 - accuracy: 0.9792 - val_loss: 4.8309 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1378/4000\n",
      "9/9 - 0s - loss: 0.1546 - accuracy: 1.0000 - val_loss: 79.6204 - val_accuracy: 0.3056 - 79ms/epoch - 9ms/step\n",
      "Epoch 1379/4000\n",
      "9/9 - 0s - loss: 0.1429 - accuracy: 0.9861 - val_loss: 88.0008 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1380/4000\n",
      "9/9 - 0s - loss: 0.1564 - accuracy: 0.9792 - val_loss: 103.8329 - val_accuracy: 0.2639 - 78ms/epoch - 9ms/step\n",
      "Epoch 1381/4000\n",
      "9/9 - 0s - loss: 0.1587 - accuracy: 0.9861 - val_loss: 177.4304 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1382/4000\n",
      "9/9 - 0s - loss: 0.1545 - accuracy: 0.9931 - val_loss: 181.2985 - val_accuracy: 0.2222 - 115ms/epoch - 13ms/step\n",
      "Epoch 1383/4000\n",
      "9/9 - 0s - loss: 0.1450 - accuracy: 1.0000 - val_loss: 194.4376 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1384/4000\n",
      "9/9 - 0s - loss: 0.1501 - accuracy: 1.0000 - val_loss: 177.1462 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1385/4000\n",
      "9/9 - 0s - loss: 0.1653 - accuracy: 0.9861 - val_loss: 181.4431 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1386/4000\n",
      "9/9 - 0s - loss: 0.1839 - accuracy: 0.9583 - val_loss: 63.0890 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1387/4000\n",
      "9/9 - 0s - loss: 0.1876 - accuracy: 0.9861 - val_loss: 155.2018 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1388/4000\n",
      "9/9 - 0s - loss: 0.1660 - accuracy: 0.9861 - val_loss: 163.7319 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1389/4000\n",
      "9/9 - 0s - loss: 0.1515 - accuracy: 1.0000 - val_loss: 73.5264 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1390/4000\n",
      "9/9 - 0s - loss: 0.1722 - accuracy: 0.9792 - val_loss: 44.7123 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 1391/4000\n",
      "9/9 - 0s - loss: 0.1421 - accuracy: 0.9931 - val_loss: 50.5889 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 1392/4000\n",
      "9/9 - 0s - loss: 0.1474 - accuracy: 0.9931 - val_loss: 51.3046 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1393/4000\n",
      "9/9 - 0s - loss: 0.1881 - accuracy: 0.9722 - val_loss: 65.8978 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1394/4000\n",
      "9/9 - 0s - loss: 0.1490 - accuracy: 0.9861 - val_loss: 81.8946 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1395/4000\n",
      "9/9 - 0s - loss: 0.1669 - accuracy: 0.9931 - val_loss: 101.4095 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 1396/4000\n",
      "9/9 - 0s - loss: 0.1783 - accuracy: 0.9792 - val_loss: 91.1184 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1397/4000\n",
      "9/9 - 0s - loss: 0.1612 - accuracy: 1.0000 - val_loss: 89.4550 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1398/4000\n",
      "9/9 - 0s - loss: 0.1721 - accuracy: 0.9653 - val_loss: 213.6086 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1399/4000\n",
      "9/9 - 0s - loss: 0.1582 - accuracy: 1.0000 - val_loss: 81.7739 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1400/4000\n",
      "9/9 - 0s - loss: 0.1545 - accuracy: 0.9861 - val_loss: 106.1832 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1401/4000\n",
      "9/9 - 0s - loss: 0.1408 - accuracy: 0.9931 - val_loss: 196.7007 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1402/4000\n",
      "9/9 - 0s - loss: 0.1588 - accuracy: 0.9931 - val_loss: 83.5052 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1403/4000\n",
      "9/9 - 0s - loss: 0.1776 - accuracy: 0.9931 - val_loss: 150.6091 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1404/4000\n",
      "9/9 - 0s - loss: 0.1629 - accuracy: 0.9861 - val_loss: 209.1650 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 1405/4000\n",
      "9/9 - 0s - loss: 0.1445 - accuracy: 1.0000 - val_loss: 106.8979 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1406/4000\n",
      "9/9 - 0s - loss: 0.1535 - accuracy: 0.9792 - val_loss: 110.7193 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1407/4000\n",
      "9/9 - 0s - loss: 0.1823 - accuracy: 0.9792 - val_loss: 108.5170 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1408/4000\n",
      "9/9 - 0s - loss: 0.1658 - accuracy: 0.9931 - val_loss: 6.8847 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 1409/4000\n",
      "9/9 - 0s - loss: 0.1681 - accuracy: 0.9792 - val_loss: 8.1956 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1410/4000\n",
      "9/9 - 0s - loss: 0.1672 - accuracy: 0.9931 - val_loss: 32.6869 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1411/4000\n",
      "9/9 - 0s - loss: 0.1749 - accuracy: 0.9861 - val_loss: 44.5934 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 1412/4000\n",
      "9/9 - 0s - loss: 0.1580 - accuracy: 1.0000 - val_loss: 55.3100 - val_accuracy: 0.2917 - 76ms/epoch - 8ms/step\n",
      "Epoch 1413/4000\n",
      "9/9 - 0s - loss: 0.1524 - accuracy: 0.9931 - val_loss: 67.4070 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1414/4000\n",
      "9/9 - 0s - loss: 0.1638 - accuracy: 0.9931 - val_loss: 69.5705 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1415/4000\n",
      "9/9 - 0s - loss: 0.1596 - accuracy: 0.9931 - val_loss: 57.8578 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1416/4000\n",
      "9/9 - 0s - loss: 0.1346 - accuracy: 0.9861 - val_loss: 10.1765 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1417/4000\n",
      "9/9 - 0s - loss: 0.1549 - accuracy: 1.0000 - val_loss: 85.7214 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1418/4000\n",
      "9/9 - 0s - loss: 0.1481 - accuracy: 0.9931 - val_loss: 20.5666 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1419/4000\n",
      "9/9 - 0s - loss: 0.1494 - accuracy: 0.9861 - val_loss: 68.3637 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1420/4000\n",
      "9/9 - 0s - loss: 0.1846 - accuracy: 0.9931 - val_loss: 70.7932 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1421/4000\n",
      "9/9 - 0s - loss: 0.1721 - accuracy: 0.9722 - val_loss: 60.4840 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1422/4000\n",
      "9/9 - 0s - loss: 0.1633 - accuracy: 0.9931 - val_loss: 8.4766 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1423/4000\n",
      "9/9 - 0s - loss: 0.1526 - accuracy: 0.9931 - val_loss: 78.0806 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1424/4000\n",
      "9/9 - 0s - loss: 0.1656 - accuracy: 0.9931 - val_loss: 65.8923 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1425/4000\n",
      "9/9 - 0s - loss: 0.1877 - accuracy: 0.9514 - val_loss: 81.2809 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1426/4000\n",
      "9/9 - 0s - loss: 0.1714 - accuracy: 0.9722 - val_loss: 118.6112 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1427/4000\n",
      "9/9 - 0s - loss: 0.1911 - accuracy: 0.9792 - val_loss: 8.6782 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1428/4000\n",
      "9/9 - 0s - loss: 0.1740 - accuracy: 0.9792 - val_loss: 5.6566 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 1429/4000\n",
      "9/9 - 0s - loss: 0.1641 - accuracy: 0.9792 - val_loss: 73.2165 - val_accuracy: 0.2222 - 77ms/epoch - 9ms/step\n",
      "Epoch 1430/4000\n",
      "9/9 - 0s - loss: 0.1821 - accuracy: 0.9861 - val_loss: 180.4495 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 1431/4000\n",
      "9/9 - 0s - loss: 0.1770 - accuracy: 0.9722 - val_loss: 227.0401 - val_accuracy: 0.2222 - 77ms/epoch - 9ms/step\n",
      "Epoch 1432/4000\n",
      "9/9 - 0s - loss: 0.1564 - accuracy: 0.9722 - val_loss: 239.9569 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1433/4000\n",
      "9/9 - 0s - loss: 0.1406 - accuracy: 1.0000 - val_loss: 187.5836 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1434/4000\n",
      "9/9 - 0s - loss: 0.1611 - accuracy: 0.9931 - val_loss: 68.9627 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1435/4000\n",
      "9/9 - 0s - loss: 0.1730 - accuracy: 0.9861 - val_loss: 43.6539 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1436/4000\n",
      "9/9 - 0s - loss: 0.1668 - accuracy: 0.9792 - val_loss: 124.1745 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1437/4000\n",
      "9/9 - 0s - loss: 0.1557 - accuracy: 0.9931 - val_loss: 79.8738 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1438/4000\n",
      "9/9 - 0s - loss: 0.1499 - accuracy: 0.9931 - val_loss: 28.7890 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1439/4000\n",
      "9/9 - 0s - loss: 0.1551 - accuracy: 0.9931 - val_loss: 29.3932 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 1440/4000\n",
      "9/9 - 0s - loss: 0.1419 - accuracy: 0.9861 - val_loss: 5.8476 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 1441/4000\n",
      "9/9 - 0s - loss: 0.1374 - accuracy: 1.0000 - val_loss: 3.1959 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1442/4000\n",
      "9/9 - 0s - loss: 0.1299 - accuracy: 1.0000 - val_loss: 3.9001 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 1443/4000\n",
      "9/9 - 0s - loss: 0.1597 - accuracy: 0.9861 - val_loss: 20.6956 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1444/4000\n",
      "9/9 - 0s - loss: 0.1411 - accuracy: 0.9931 - val_loss: 77.8534 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1445/4000\n",
      "9/9 - 0s - loss: 0.1363 - accuracy: 0.9931 - val_loss: 66.4799 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1446/4000\n",
      "9/9 - 0s - loss: 0.1495 - accuracy: 0.9931 - val_loss: 38.2650 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1447/4000\n",
      "9/9 - 0s - loss: 0.1553 - accuracy: 0.9931 - val_loss: 33.0049 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1448/4000\n",
      "9/9 - 0s - loss: 0.1550 - accuracy: 0.9861 - val_loss: 44.7691 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1449/4000\n",
      "9/9 - 0s - loss: 0.1586 - accuracy: 0.9861 - val_loss: 13.0393 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1450/4000\n",
      "9/9 - 0s - loss: 0.1523 - accuracy: 0.9792 - val_loss: 9.6669 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1451/4000\n",
      "9/9 - 0s - loss: 0.1542 - accuracy: 0.9931 - val_loss: 28.4373 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1452/4000\n",
      "9/9 - 0s - loss: 0.1249 - accuracy: 0.9931 - val_loss: 77.1305 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1453/4000\n",
      "9/9 - 0s - loss: 0.1333 - accuracy: 1.0000 - val_loss: 63.7782 - val_accuracy: 0.2778 - 88ms/epoch - 10ms/step\n",
      "Epoch 1454/4000\n",
      "9/9 - 0s - loss: 0.1721 - accuracy: 0.9861 - val_loss: 83.8639 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1455/4000\n",
      "9/9 - 0s - loss: 0.1626 - accuracy: 0.9861 - val_loss: 74.2639 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1456/4000\n",
      "9/9 - 0s - loss: 0.1419 - accuracy: 0.9931 - val_loss: 109.3300 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1457/4000\n",
      "9/9 - 0s - loss: 0.1536 - accuracy: 1.0000 - val_loss: 124.9262 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1458/4000\n",
      "9/9 - 0s - loss: 0.1517 - accuracy: 0.9931 - val_loss: 142.5799 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1459/4000\n",
      "9/9 - 0s - loss: 0.1460 - accuracy: 0.9931 - val_loss: 97.8761 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 1460/4000\n",
      "9/9 - 0s - loss: 0.1534 - accuracy: 0.9931 - val_loss: 22.6455 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1461/4000\n",
      "9/9 - 0s - loss: 0.1346 - accuracy: 0.9931 - val_loss: 11.4920 - val_accuracy: 0.3056 - 95ms/epoch - 11ms/step\n",
      "Epoch 1462/4000\n",
      "9/9 - 0s - loss: 0.1290 - accuracy: 1.0000 - val_loss: 32.0914 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 1463/4000\n",
      "9/9 - 0s - loss: 0.1306 - accuracy: 1.0000 - val_loss: 11.9640 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1464/4000\n",
      "9/9 - 0s - loss: 0.1301 - accuracy: 1.0000 - val_loss: 29.5684 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 1465/4000\n",
      "9/9 - 0s - loss: 0.1317 - accuracy: 1.0000 - val_loss: 16.3929 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1466/4000\n",
      "9/9 - 0s - loss: 0.1318 - accuracy: 0.9931 - val_loss: 5.1898 - val_accuracy: 0.6528 - 93ms/epoch - 10ms/step\n",
      "Epoch 1467/4000\n",
      "9/9 - 0s - loss: 0.1285 - accuracy: 1.0000 - val_loss: 92.6258 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1468/4000\n",
      "9/9 - 0s - loss: 0.1374 - accuracy: 0.9931 - val_loss: 93.1038 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 1469/4000\n",
      "9/9 - 0s - loss: 0.1458 - accuracy: 0.9861 - val_loss: 62.6994 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1470/4000\n",
      "9/9 - 0s - loss: 0.1432 - accuracy: 1.0000 - val_loss: 14.8356 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1471/4000\n",
      "9/9 - 0s - loss: 0.1992 - accuracy: 0.9514 - val_loss: 15.9860 - val_accuracy: 0.2639 - 92ms/epoch - 10ms/step\n",
      "Epoch 1472/4000\n",
      "9/9 - 0s - loss: 0.1577 - accuracy: 0.9861 - val_loss: 72.9262 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 1473/4000\n",
      "9/9 - 0s - loss: 0.1660 - accuracy: 0.9792 - val_loss: 72.3056 - val_accuracy: 0.2083 - 90ms/epoch - 10ms/step\n",
      "Epoch 1474/4000\n",
      "9/9 - 0s - loss: 0.1467 - accuracy: 1.0000 - val_loss: 90.1277 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1475/4000\n",
      "9/9 - 0s - loss: 0.1332 - accuracy: 1.0000 - val_loss: 69.2682 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1476/4000\n",
      "9/9 - 0s - loss: 0.1296 - accuracy: 1.0000 - val_loss: 21.1894 - val_accuracy: 0.3194 - 76ms/epoch - 8ms/step\n",
      "Epoch 1477/4000\n",
      "9/9 - 0s - loss: 0.1539 - accuracy: 1.0000 - val_loss: 144.8358 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1478/4000\n",
      "9/9 - 0s - loss: 0.1515 - accuracy: 1.0000 - val_loss: 164.1118 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1479/4000\n",
      "9/9 - 0s - loss: 0.1571 - accuracy: 0.9861 - val_loss: 4.5544 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1480/4000\n",
      "9/9 - 0s - loss: 0.1459 - accuracy: 0.9931 - val_loss: 82.1028 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1481/4000\n",
      "9/9 - 0s - loss: 0.1422 - accuracy: 0.9861 - val_loss: 87.8245 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1482/4000\n",
      "9/9 - 0s - loss: 0.1421 - accuracy: 0.9931 - val_loss: 91.6176 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1483/4000\n",
      "9/9 - 0s - loss: 0.1354 - accuracy: 0.9931 - val_loss: 91.7951 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1484/4000\n",
      "9/9 - 0s - loss: 0.1286 - accuracy: 1.0000 - val_loss: 124.4551 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1485/4000\n",
      "9/9 - 0s - loss: 0.1330 - accuracy: 0.9931 - val_loss: 129.4431 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1486/4000\n",
      "9/9 - 0s - loss: 0.1583 - accuracy: 0.9931 - val_loss: 97.1875 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1487/4000\n",
      "9/9 - 0s - loss: 0.1550 - accuracy: 0.9861 - val_loss: 69.9907 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1488/4000\n",
      "9/9 - 0s - loss: 0.1420 - accuracy: 1.0000 - val_loss: 61.3490 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1489/4000\n",
      "9/9 - 0s - loss: 0.1378 - accuracy: 0.9931 - val_loss: 10.6036 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1490/4000\n",
      "9/9 - 0s - loss: 0.1523 - accuracy: 0.9861 - val_loss: 8.0447 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1491/4000\n",
      "9/9 - 0s - loss: 0.1916 - accuracy: 0.9514 - val_loss: 33.2402 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 1492/4000\n",
      "9/9 - 0s - loss: 0.1706 - accuracy: 0.9792 - val_loss: 49.8973 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1493/4000\n",
      "9/9 - 0s - loss: 0.1674 - accuracy: 0.9722 - val_loss: 117.7841 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1494/4000\n",
      "9/9 - 0s - loss: 0.1427 - accuracy: 0.9931 - val_loss: 56.5888 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1495/4000\n",
      "9/9 - 0s - loss: 0.1623 - accuracy: 0.9722 - val_loss: 87.7081 - val_accuracy: 0.2639 - 84ms/epoch - 9ms/step\n",
      "Epoch 1496/4000\n",
      "9/9 - 0s - loss: 0.1265 - accuracy: 0.9931 - val_loss: 94.2559 - val_accuracy: 0.2639 - 97ms/epoch - 11ms/step\n",
      "Epoch 1497/4000\n",
      "9/9 - 0s - loss: 0.1423 - accuracy: 1.0000 - val_loss: 74.0472 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1498/4000\n",
      "9/9 - 0s - loss: 0.1479 - accuracy: 0.9931 - val_loss: 102.5721 - val_accuracy: 0.2639 - 79ms/epoch - 9ms/step\n",
      "Epoch 1499/4000\n",
      "9/9 - 0s - loss: 0.1391 - accuracy: 1.0000 - val_loss: 76.8178 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1500/4000\n",
      "9/9 - 0s - loss: 0.1426 - accuracy: 1.0000 - val_loss: 5.5828 - val_accuracy: 0.2361 - 84ms/epoch - 9ms/step\n",
      "Epoch 1501/4000\n",
      "9/9 - 0s - loss: 0.1282 - accuracy: 0.9861 - val_loss: 24.4094 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1502/4000\n",
      "9/9 - 0s - loss: 0.1418 - accuracy: 0.9861 - val_loss: 86.9401 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1503/4000\n",
      "9/9 - 0s - loss: 0.1337 - accuracy: 1.0000 - val_loss: 76.8840 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1504/4000\n",
      "9/9 - 0s - loss: 0.1433 - accuracy: 0.9931 - val_loss: 64.4596 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1505/4000\n",
      "9/9 - 0s - loss: 0.1948 - accuracy: 0.9722 - val_loss: 140.9329 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1506/4000\n",
      "9/9 - 0s - loss: 0.1531 - accuracy: 0.9792 - val_loss: 124.0390 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1507/4000\n",
      "9/9 - 0s - loss: 0.1599 - accuracy: 0.9861 - val_loss: 91.8975 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 1508/4000\n",
      "9/9 - 0s - loss: 0.1194 - accuracy: 1.0000 - val_loss: 45.6870 - val_accuracy: 0.3194 - 75ms/epoch - 8ms/step\n",
      "Epoch 1509/4000\n",
      "9/9 - 0s - loss: 0.1644 - accuracy: 0.9792 - val_loss: 82.1421 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1510/4000\n",
      "9/9 - 0s - loss: 0.1519 - accuracy: 0.9931 - val_loss: 39.8349 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1511/4000\n",
      "9/9 - 0s - loss: 0.1432 - accuracy: 0.9931 - val_loss: 18.1162 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1512/4000\n",
      "9/9 - 0s - loss: 0.1343 - accuracy: 0.9931 - val_loss: 62.7107 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1513/4000\n",
      "9/9 - 0s - loss: 0.1403 - accuracy: 0.9861 - val_loss: 87.7094 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1514/4000\n",
      "9/9 - 0s - loss: 0.1337 - accuracy: 1.0000 - val_loss: 64.3702 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1515/4000\n",
      "9/9 - 0s - loss: 0.1539 - accuracy: 1.0000 - val_loss: 4.2926 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1516/4000\n",
      "9/9 - 0s - loss: 0.1381 - accuracy: 1.0000 - val_loss: 11.2353 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1517/4000\n",
      "9/9 - 0s - loss: 0.1329 - accuracy: 0.9931 - val_loss: 3.2611 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1518/4000\n",
      "9/9 - 0s - loss: 0.1601 - accuracy: 0.9861 - val_loss: 22.9527 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1519/4000\n",
      "9/9 - 0s - loss: 0.1367 - accuracy: 1.0000 - val_loss: 65.1036 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1520/4000\n",
      "9/9 - 0s - loss: 0.1169 - accuracy: 0.9931 - val_loss: 78.0840 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1521/4000\n",
      "9/9 - 0s - loss: 0.1513 - accuracy: 0.9931 - val_loss: 66.8020 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1522/4000\n",
      "9/9 - 0s - loss: 0.1377 - accuracy: 0.9931 - val_loss: 9.3607 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 1523/4000\n",
      "9/9 - 0s - loss: 0.1564 - accuracy: 0.9861 - val_loss: 30.1993 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1524/4000\n",
      "9/9 - 0s - loss: 0.1538 - accuracy: 0.9861 - val_loss: 11.1123 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 1525/4000\n",
      "9/9 - 0s - loss: 0.1706 - accuracy: 0.9792 - val_loss: 63.0314 - val_accuracy: 0.2917 - 111ms/epoch - 12ms/step\n",
      "Epoch 1526/4000\n",
      "9/9 - 0s - loss: 0.1946 - accuracy: 0.9583 - val_loss: 66.3580 - val_accuracy: 0.2917 - 102ms/epoch - 11ms/step\n",
      "Epoch 1527/4000\n",
      "9/9 - 0s - loss: 0.1555 - accuracy: 0.9861 - val_loss: 70.4945 - val_accuracy: 0.2917 - 99ms/epoch - 11ms/step\n",
      "Epoch 1528/4000\n",
      "9/9 - 0s - loss: 0.1630 - accuracy: 0.9792 - val_loss: 15.9100 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1529/4000\n",
      "9/9 - 0s - loss: 0.1706 - accuracy: 1.0000 - val_loss: 2.9965 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1530/4000\n",
      "9/9 - 0s - loss: 0.1467 - accuracy: 0.9931 - val_loss: 29.8127 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1531/4000\n",
      "9/9 - 0s - loss: 0.1347 - accuracy: 0.9931 - val_loss: 26.0160 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1532/4000\n",
      "9/9 - 0s - loss: 0.1336 - accuracy: 0.9931 - val_loss: 12.5917 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1533/4000\n",
      "9/9 - 0s - loss: 0.1267 - accuracy: 0.9931 - val_loss: 48.1412 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1534/4000\n",
      "9/9 - 0s - loss: 0.1655 - accuracy: 0.9444 - val_loss: 7.4574 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1535/4000\n",
      "9/9 - 0s - loss: 0.1540 - accuracy: 1.0000 - val_loss: 80.5291 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1536/4000\n",
      "9/9 - 0s - loss: 0.1493 - accuracy: 0.9931 - val_loss: 95.6249 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1537/4000\n",
      "9/9 - 0s - loss: 0.1361 - accuracy: 0.9931 - val_loss: 134.7728 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1538/4000\n",
      "9/9 - 0s - loss: 0.1489 - accuracy: 0.9931 - val_loss: 157.7802 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1539/4000\n",
      "9/9 - 0s - loss: 0.1326 - accuracy: 1.0000 - val_loss: 110.8254 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1540/4000\n",
      "9/9 - 0s - loss: 0.1564 - accuracy: 0.9792 - val_loss: 129.1783 - val_accuracy: 0.2639 - 76ms/epoch - 8ms/step\n",
      "Epoch 1541/4000\n",
      "9/9 - 0s - loss: 0.1255 - accuracy: 1.0000 - val_loss: 124.1913 - val_accuracy: 0.2639 - 94ms/epoch - 10ms/step\n",
      "Epoch 1542/4000\n",
      "9/9 - 0s - loss: 0.1587 - accuracy: 0.9792 - val_loss: 202.1963 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1543/4000\n",
      "9/9 - 0s - loss: 0.1614 - accuracy: 0.9861 - val_loss: 276.8582 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1544/4000\n",
      "9/9 - 0s - loss: 0.1430 - accuracy: 0.9931 - val_loss: 255.6695 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1545/4000\n",
      "9/9 - 0s - loss: 0.1320 - accuracy: 0.9861 - val_loss: 336.4029 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1546/4000\n",
      "9/9 - 0s - loss: 0.1955 - accuracy: 0.9722 - val_loss: 265.7554 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1547/4000\n",
      "9/9 - 0s - loss: 0.1410 - accuracy: 0.9792 - val_loss: 264.4139 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1548/4000\n",
      "9/9 - 0s - loss: 0.1300 - accuracy: 0.9931 - val_loss: 193.8532 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1549/4000\n",
      "9/9 - 0s - loss: 0.1577 - accuracy: 0.9722 - val_loss: 5.7333 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1550/4000\n",
      "9/9 - 0s - loss: 0.1175 - accuracy: 1.0000 - val_loss: 2.3508 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1551/4000\n",
      "9/9 - 0s - loss: 0.1664 - accuracy: 0.9653 - val_loss: 4.8632 - val_accuracy: 0.2361 - 104ms/epoch - 12ms/step\n",
      "Epoch 1552/4000\n",
      "9/9 - 0s - loss: 0.1321 - accuracy: 0.9931 - val_loss: 6.0758 - val_accuracy: 0.2917 - 105ms/epoch - 12ms/step\n",
      "Epoch 1553/4000\n",
      "9/9 - 0s - loss: 0.1551 - accuracy: 0.9861 - val_loss: 57.1198 - val_accuracy: 0.2222 - 109ms/epoch - 12ms/step\n",
      "Epoch 1554/4000\n",
      "9/9 - 0s - loss: 0.1252 - accuracy: 1.0000 - val_loss: 50.6303 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1555/4000\n",
      "9/9 - 0s - loss: 0.1487 - accuracy: 0.9792 - val_loss: 21.3903 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 1556/4000\n",
      "9/9 - 0s - loss: 0.1387 - accuracy: 0.9931 - val_loss: 108.9764 - val_accuracy: 0.2222 - 127ms/epoch - 14ms/step\n",
      "Epoch 1557/4000\n",
      "9/9 - 0s - loss: 0.1391 - accuracy: 0.9931 - val_loss: 133.4368 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1558/4000\n",
      "9/9 - 0s - loss: 0.1258 - accuracy: 0.9931 - val_loss: 222.9811 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1559/4000\n",
      "9/9 - 0s - loss: 0.1242 - accuracy: 0.9931 - val_loss: 204.5809 - val_accuracy: 0.2222 - 109ms/epoch - 12ms/step\n",
      "Epoch 1560/4000\n",
      "9/9 - 0s - loss: 0.1293 - accuracy: 0.9931 - val_loss: 154.1297 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1561/4000\n",
      "9/9 - 0s - loss: 0.1250 - accuracy: 1.0000 - val_loss: 13.7382 - val_accuracy: 0.2222 - 116ms/epoch - 13ms/step\n",
      "Epoch 1562/4000\n",
      "9/9 - 0s - loss: 0.1400 - accuracy: 0.9931 - val_loss: 47.4257 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 1563/4000\n",
      "9/9 - 0s - loss: 0.1442 - accuracy: 0.9722 - val_loss: 58.2385 - val_accuracy: 0.3472 - 109ms/epoch - 12ms/step\n",
      "Epoch 1564/4000\n",
      "9/9 - 0s - loss: 0.1244 - accuracy: 1.0000 - val_loss: 43.2154 - val_accuracy: 0.3194 - 102ms/epoch - 11ms/step\n",
      "Epoch 1565/4000\n",
      "9/9 - 0s - loss: 0.1321 - accuracy: 0.9931 - val_loss: 68.4091 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 1566/4000\n",
      "9/9 - 0s - loss: 0.1419 - accuracy: 0.9861 - val_loss: 84.6728 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 1567/4000\n",
      "9/9 - 0s - loss: 0.1376 - accuracy: 0.9931 - val_loss: 113.8717 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 1568/4000\n",
      "9/9 - 0s - loss: 0.1683 - accuracy: 0.9722 - val_loss: 122.3251 - val_accuracy: 0.2222 - 113ms/epoch - 13ms/step\n",
      "Epoch 1569/4000\n",
      "9/9 - 0s - loss: 0.1485 - accuracy: 0.9722 - val_loss: 103.5416 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1570/4000\n",
      "9/9 - 0s - loss: 0.1441 - accuracy: 1.0000 - val_loss: 86.9924 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1571/4000\n",
      "9/9 - 0s - loss: 0.1482 - accuracy: 0.9931 - val_loss: 127.1621 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1572/4000\n",
      "9/9 - 0s - loss: 0.1324 - accuracy: 1.0000 - val_loss: 93.5353 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1573/4000\n",
      "9/9 - 0s - loss: 0.1445 - accuracy: 1.0000 - val_loss: 88.1347 - val_accuracy: 0.3333 - 85ms/epoch - 9ms/step\n",
      "Epoch 1574/4000\n",
      "9/9 - 0s - loss: 0.1335 - accuracy: 0.9861 - val_loss: 57.8185 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1575/4000\n",
      "9/9 - 0s - loss: 0.1455 - accuracy: 0.9931 - val_loss: 60.1660 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1576/4000\n",
      "9/9 - 0s - loss: 0.1168 - accuracy: 1.0000 - val_loss: 53.0705 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1577/4000\n",
      "9/9 - 0s - loss: 0.1337 - accuracy: 1.0000 - val_loss: 53.3209 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1578/4000\n",
      "9/9 - 0s - loss: 0.1396 - accuracy: 0.9861 - val_loss: 50.8189 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 1579/4000\n",
      "9/9 - 0s - loss: 0.1376 - accuracy: 1.0000 - val_loss: 59.5406 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 1580/4000\n",
      "9/9 - 0s - loss: 0.1208 - accuracy: 1.0000 - val_loss: 40.5528 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 1581/4000\n",
      "9/9 - 0s - loss: 0.1732 - accuracy: 0.9861 - val_loss: 68.4414 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1582/4000\n",
      "9/9 - 0s - loss: 0.1385 - accuracy: 0.9861 - val_loss: 73.9094 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1583/4000\n",
      "9/9 - 0s - loss: 0.1309 - accuracy: 0.9931 - val_loss: 54.2412 - val_accuracy: 0.3056 - 91ms/epoch - 10ms/step\n",
      "Epoch 1584/4000\n",
      "9/9 - 0s - loss: 0.1386 - accuracy: 0.9861 - val_loss: 60.5932 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 1585/4000\n",
      "9/9 - 0s - loss: 0.1389 - accuracy: 1.0000 - val_loss: 95.0388 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 1586/4000\n",
      "9/9 - 0s - loss: 0.1831 - accuracy: 0.9514 - val_loss: 176.0338 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1587/4000\n",
      "9/9 - 0s - loss: 0.1269 - accuracy: 1.0000 - val_loss: 51.5736 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1588/4000\n",
      "9/9 - 0s - loss: 0.1606 - accuracy: 0.9792 - val_loss: 41.6755 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 1589/4000\n",
      "9/9 - 0s - loss: 0.1344 - accuracy: 0.9861 - val_loss: 19.6978 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1590/4000\n",
      "9/9 - 0s - loss: 0.1551 - accuracy: 0.9861 - val_loss: 10.5468 - val_accuracy: 0.2222 - 164ms/epoch - 18ms/step\n",
      "Epoch 1591/4000\n",
      "9/9 - 0s - loss: 0.1244 - accuracy: 0.9931 - val_loss: 10.8117 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1592/4000\n",
      "9/9 - 0s - loss: 0.1271 - accuracy: 0.9931 - val_loss: 21.9326 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1593/4000\n",
      "9/9 - 0s - loss: 0.1319 - accuracy: 1.0000 - val_loss: 46.4568 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 1594/4000\n",
      "9/9 - 0s - loss: 0.1461 - accuracy: 0.9861 - val_loss: 88.3313 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1595/4000\n",
      "9/9 - 0s - loss: 0.1467 - accuracy: 1.0000 - val_loss: 62.2096 - val_accuracy: 0.2639 - 127ms/epoch - 14ms/step\n",
      "Epoch 1596/4000\n",
      "9/9 - 0s - loss: 0.1346 - accuracy: 0.9931 - val_loss: 82.5251 - val_accuracy: 0.2917 - 108ms/epoch - 12ms/step\n",
      "Epoch 1597/4000\n",
      "9/9 - 0s - loss: 0.1419 - accuracy: 0.9861 - val_loss: 81.7093 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1598/4000\n",
      "9/9 - 0s - loss: 0.1413 - accuracy: 0.9861 - val_loss: 97.5684 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1599/4000\n",
      "9/9 - 0s - loss: 0.1455 - accuracy: 0.9931 - val_loss: 39.7325 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1600/4000\n",
      "9/9 - 0s - loss: 0.1450 - accuracy: 0.9931 - val_loss: 118.4478 - val_accuracy: 0.2222 - 108ms/epoch - 12ms/step\n",
      "Epoch 1601/4000\n",
      "9/9 - 0s - loss: 0.1345 - accuracy: 0.9931 - val_loss: 61.9691 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1602/4000\n",
      "9/9 - 0s - loss: 0.1266 - accuracy: 0.9931 - val_loss: 115.9782 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 1603/4000\n",
      "9/9 - 0s - loss: 0.1143 - accuracy: 0.9861 - val_loss: 50.3231 - val_accuracy: 0.2222 - 110ms/epoch - 12ms/step\n",
      "Epoch 1604/4000\n",
      "9/9 - 0s - loss: 0.1371 - accuracy: 0.9861 - val_loss: 36.7495 - val_accuracy: 0.2222 - 127ms/epoch - 14ms/step\n",
      "Epoch 1605/4000\n",
      "9/9 - 0s - loss: 0.1352 - accuracy: 1.0000 - val_loss: 43.0396 - val_accuracy: 0.2917 - 131ms/epoch - 15ms/step\n",
      "Epoch 1606/4000\n",
      "9/9 - 0s - loss: 0.1189 - accuracy: 1.0000 - val_loss: 45.1892 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 1607/4000\n",
      "9/9 - 0s - loss: 0.1315 - accuracy: 0.9931 - val_loss: 15.7698 - val_accuracy: 0.2917 - 107ms/epoch - 12ms/step\n",
      "Epoch 1608/4000\n",
      "9/9 - 0s - loss: 0.1139 - accuracy: 1.0000 - val_loss: 13.6022 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1609/4000\n",
      "9/9 - 0s - loss: 0.1438 - accuracy: 1.0000 - val_loss: 73.4300 - val_accuracy: 0.2222 - 110ms/epoch - 12ms/step\n",
      "Epoch 1610/4000\n",
      "9/9 - 0s - loss: 0.1368 - accuracy: 0.9861 - val_loss: 100.7067 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 1611/4000\n",
      "9/9 - 0s - loss: 0.1343 - accuracy: 0.9792 - val_loss: 48.9593 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1612/4000\n",
      "9/9 - 0s - loss: 0.1516 - accuracy: 0.9861 - val_loss: 61.2202 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1613/4000\n",
      "9/9 - 0s - loss: 0.1468 - accuracy: 0.9931 - val_loss: 155.0544 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1614/4000\n",
      "9/9 - 0s - loss: 0.1406 - accuracy: 0.9861 - val_loss: 127.1808 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1615/4000\n",
      "9/9 - 0s - loss: 0.1413 - accuracy: 0.9861 - val_loss: 53.6729 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1616/4000\n",
      "9/9 - 0s - loss: 0.1679 - accuracy: 0.9792 - val_loss: 25.5570 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1617/4000\n",
      "9/9 - 0s - loss: 0.1294 - accuracy: 0.9931 - val_loss: 21.7038 - val_accuracy: 0.2222 - 107ms/epoch - 12ms/step\n",
      "Epoch 1618/4000\n",
      "9/9 - 0s - loss: 0.1280 - accuracy: 1.0000 - val_loss: 105.7023 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 1619/4000\n",
      "9/9 - 0s - loss: 0.1372 - accuracy: 0.9861 - val_loss: 96.1732 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 1620/4000\n",
      "9/9 - 0s - loss: 0.1455 - accuracy: 0.9931 - val_loss: 156.2261 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n",
      "Epoch 1621/4000\n",
      "9/9 - 0s - loss: 0.1228 - accuracy: 0.9931 - val_loss: 77.2650 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1622/4000\n",
      "9/9 - 0s - loss: 0.1377 - accuracy: 0.9861 - val_loss: 150.7400 - val_accuracy: 0.2639 - 95ms/epoch - 11ms/step\n",
      "Epoch 1623/4000\n",
      "9/9 - 0s - loss: 0.1128 - accuracy: 1.0000 - val_loss: 95.4262 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 1624/4000\n",
      "9/9 - 0s - loss: 0.1310 - accuracy: 0.9861 - val_loss: 58.0466 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1625/4000\n",
      "9/9 - 0s - loss: 0.1279 - accuracy: 1.0000 - val_loss: 6.6987 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1626/4000\n",
      "9/9 - 0s - loss: 0.1119 - accuracy: 0.9931 - val_loss: 43.8996 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1627/4000\n",
      "9/9 - 0s - loss: 0.1222 - accuracy: 1.0000 - val_loss: 63.6040 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1628/4000\n",
      "9/9 - 0s - loss: 0.1132 - accuracy: 1.0000 - val_loss: 37.4919 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1629/4000\n",
      "9/9 - 0s - loss: 0.1163 - accuracy: 0.9931 - val_loss: 22.3705 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1630/4000\n",
      "9/9 - 0s - loss: 0.1532 - accuracy: 0.9861 - val_loss: 45.1914 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 1631/4000\n",
      "9/9 - 0s - loss: 0.1555 - accuracy: 0.9861 - val_loss: 39.5269 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 1632/4000\n",
      "9/9 - 0s - loss: 0.1108 - accuracy: 1.0000 - val_loss: 4.6540 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1633/4000\n",
      "9/9 - 0s - loss: 0.1147 - accuracy: 1.0000 - val_loss: 31.3053 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1634/4000\n",
      "9/9 - 0s - loss: 0.1509 - accuracy: 0.9792 - val_loss: 109.4513 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1635/4000\n",
      "9/9 - 0s - loss: 0.1518 - accuracy: 1.0000 - val_loss: 73.2328 - val_accuracy: 0.2222 - 113ms/epoch - 13ms/step\n",
      "Epoch 1636/4000\n",
      "9/9 - 0s - loss: 0.1232 - accuracy: 1.0000 - val_loss: 110.0828 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1637/4000\n",
      "9/9 - 0s - loss: 0.1180 - accuracy: 1.0000 - val_loss: 171.8157 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1638/4000\n",
      "9/9 - 0s - loss: 0.1374 - accuracy: 0.9792 - val_loss: 45.7694 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1639/4000\n",
      "9/9 - 0s - loss: 0.1328 - accuracy: 0.9931 - val_loss: 85.0128 - val_accuracy: 0.2639 - 97ms/epoch - 11ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1640/4000\n",
      "9/9 - 0s - loss: 0.1248 - accuracy: 0.9931 - val_loss: 127.2420 - val_accuracy: 0.2639 - 100ms/epoch - 11ms/step\n",
      "Epoch 1641/4000\n",
      "9/9 - 0s - loss: 0.1399 - accuracy: 0.9792 - val_loss: 96.3393 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1642/4000\n",
      "9/9 - 0s - loss: 0.1156 - accuracy: 1.0000 - val_loss: 56.2932 - val_accuracy: 0.2639 - 97ms/epoch - 11ms/step\n",
      "Epoch 1643/4000\n",
      "9/9 - 0s - loss: 0.1350 - accuracy: 0.9931 - val_loss: 133.1153 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1644/4000\n",
      "9/9 - 0s - loss: 0.1499 - accuracy: 0.9792 - val_loss: 122.3264 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 1645/4000\n",
      "9/9 - 0s - loss: 0.1411 - accuracy: 0.9931 - val_loss: 81.8409 - val_accuracy: 0.2222 - 110ms/epoch - 12ms/step\n",
      "Epoch 1646/4000\n",
      "9/9 - 0s - loss: 0.1591 - accuracy: 0.9792 - val_loss: 98.6788 - val_accuracy: 0.2222 - 109ms/epoch - 12ms/step\n",
      "Epoch 1647/4000\n",
      "9/9 - 0s - loss: 0.1244 - accuracy: 1.0000 - val_loss: 84.7972 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 1648/4000\n",
      "9/9 - 0s - loss: 0.1276 - accuracy: 0.9861 - val_loss: 83.5956 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1649/4000\n",
      "9/9 - 0s - loss: 0.1446 - accuracy: 0.9931 - val_loss: 66.0381 - val_accuracy: 0.2222 - 114ms/epoch - 13ms/step\n",
      "Epoch 1650/4000\n",
      "9/9 - 0s - loss: 0.1070 - accuracy: 0.9931 - val_loss: 82.6204 - val_accuracy: 0.2222 - 114ms/epoch - 13ms/step\n",
      "Epoch 1651/4000\n",
      "9/9 - 0s - loss: 0.1342 - accuracy: 0.9861 - val_loss: 177.4112 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1652/4000\n",
      "9/9 - 0s - loss: 0.1179 - accuracy: 1.0000 - val_loss: 174.2824 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1653/4000\n",
      "9/9 - 0s - loss: 0.1227 - accuracy: 0.9931 - val_loss: 48.2689 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 1654/4000\n",
      "9/9 - 0s - loss: 0.1348 - accuracy: 0.9931 - val_loss: 24.1501 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1655/4000\n",
      "9/9 - 0s - loss: 0.1201 - accuracy: 0.9931 - val_loss: 2.9260 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1656/4000\n",
      "9/9 - 0s - loss: 0.1182 - accuracy: 0.9931 - val_loss: 2.6682 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 1657/4000\n",
      "9/9 - 0s - loss: 0.1057 - accuracy: 1.0000 - val_loss: 6.4551 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1658/4000\n",
      "9/9 - 0s - loss: 0.1350 - accuracy: 0.9931 - val_loss: 24.0167 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1659/4000\n",
      "9/9 - 0s - loss: 0.1133 - accuracy: 0.9931 - val_loss: 14.9361 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1660/4000\n",
      "9/9 - 0s - loss: 0.1205 - accuracy: 0.9931 - val_loss: 146.9375 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1661/4000\n",
      "9/9 - 0s - loss: 0.1388 - accuracy: 0.9861 - val_loss: 125.4262 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1662/4000\n",
      "9/9 - 0s - loss: 0.1098 - accuracy: 1.0000 - val_loss: 116.9768 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1663/4000\n",
      "9/9 - 0s - loss: 0.1278 - accuracy: 0.9931 - val_loss: 48.0470 - val_accuracy: 0.2639 - 90ms/epoch - 10ms/step\n",
      "Epoch 1664/4000\n",
      "9/9 - 0s - loss: 0.1272 - accuracy: 0.9861 - val_loss: 43.2406 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1665/4000\n",
      "9/9 - 0s - loss: 0.1600 - accuracy: 0.9653 - val_loss: 134.2863 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1666/4000\n",
      "9/9 - 0s - loss: 0.1233 - accuracy: 1.0000 - val_loss: 196.4058 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1667/4000\n",
      "9/9 - 0s - loss: 0.1243 - accuracy: 0.9931 - val_loss: 201.2525 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1668/4000\n",
      "9/9 - 0s - loss: 0.1018 - accuracy: 1.0000 - val_loss: 96.0914 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1669/4000\n",
      "9/9 - 0s - loss: 0.1177 - accuracy: 0.9931 - val_loss: 65.9820 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 1670/4000\n",
      "9/9 - 0s - loss: 0.1152 - accuracy: 0.9931 - val_loss: 78.4795 - val_accuracy: 0.2917 - 102ms/epoch - 11ms/step\n",
      "Epoch 1671/4000\n",
      "9/9 - 0s - loss: 0.1248 - accuracy: 0.9861 - val_loss: 64.6767 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1672/4000\n",
      "9/9 - 0s - loss: 0.1125 - accuracy: 0.9931 - val_loss: 80.0538 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1673/4000\n",
      "9/9 - 0s - loss: 0.1402 - accuracy: 0.9931 - val_loss: 94.6380 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 1674/4000\n",
      "9/9 - 0s - loss: 0.1358 - accuracy: 0.9861 - val_loss: 141.6079 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1675/4000\n",
      "9/9 - 0s - loss: 0.1292 - accuracy: 1.0000 - val_loss: 141.9365 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1676/4000\n",
      "9/9 - 0s - loss: 0.1256 - accuracy: 1.0000 - val_loss: 93.7557 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1677/4000\n",
      "9/9 - 0s - loss: 0.1353 - accuracy: 0.9792 - val_loss: 111.6460 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1678/4000\n",
      "9/9 - 0s - loss: 0.1313 - accuracy: 0.9931 - val_loss: 118.8658 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1679/4000\n",
      "9/9 - 0s - loss: 0.1180 - accuracy: 1.0000 - val_loss: 2.6003 - val_accuracy: 0.2639 - 92ms/epoch - 10ms/step\n",
      "Epoch 1680/4000\n",
      "9/9 - 0s - loss: 0.1249 - accuracy: 0.9931 - val_loss: 44.5673 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 1681/4000\n",
      "9/9 - 0s - loss: 0.1393 - accuracy: 0.9792 - val_loss: 47.3450 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1682/4000\n",
      "9/9 - 0s - loss: 0.1355 - accuracy: 0.9931 - val_loss: 12.1057 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 1683/4000\n",
      "9/9 - 0s - loss: 0.1302 - accuracy: 0.9931 - val_loss: 54.8724 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 1684/4000\n",
      "9/9 - 0s - loss: 0.1601 - accuracy: 0.9861 - val_loss: 49.4758 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 1685/4000\n",
      "9/9 - 0s - loss: 0.1007 - accuracy: 1.0000 - val_loss: 49.6624 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1686/4000\n",
      "9/9 - 0s - loss: 0.1130 - accuracy: 0.9931 - val_loss: 14.7910 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1687/4000\n",
      "9/9 - 0s - loss: 0.1178 - accuracy: 0.9931 - val_loss: 37.6666 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1688/4000\n",
      "9/9 - 0s - loss: 0.1279 - accuracy: 0.9861 - val_loss: 100.7523 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1689/4000\n",
      "9/9 - 0s - loss: 0.1272 - accuracy: 1.0000 - val_loss: 43.4577 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1690/4000\n",
      "9/9 - 0s - loss: 0.1320 - accuracy: 1.0000 - val_loss: 6.5836 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1691/4000\n",
      "9/9 - 0s - loss: 0.1174 - accuracy: 1.0000 - val_loss: 2.3847 - val_accuracy: 0.2917 - 107ms/epoch - 12ms/step\n",
      "Epoch 1692/4000\n",
      "9/9 - 0s - loss: 0.1220 - accuracy: 0.9861 - val_loss: 6.1682 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1693/4000\n",
      "9/9 - 0s - loss: 0.1096 - accuracy: 1.0000 - val_loss: 2.8274 - val_accuracy: 0.3056 - 101ms/epoch - 11ms/step\n",
      "Epoch 1694/4000\n",
      "9/9 - 0s - loss: 0.1272 - accuracy: 0.9861 - val_loss: 67.1062 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1695/4000\n",
      "9/9 - 0s - loss: 0.1290 - accuracy: 0.9861 - val_loss: 97.7319 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1696/4000\n",
      "9/9 - 0s - loss: 0.1635 - accuracy: 0.9861 - val_loss: 58.7097 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1697/4000\n",
      "9/9 - 0s - loss: 0.1435 - accuracy: 0.9861 - val_loss: 3.2756 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1698/4000\n",
      "9/9 - 0s - loss: 0.0963 - accuracy: 1.0000 - val_loss: 41.1380 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1699/4000\n",
      "9/9 - 0s - loss: 0.1029 - accuracy: 1.0000 - val_loss: 63.1139 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1700/4000\n",
      "9/9 - 0s - loss: 0.1158 - accuracy: 0.9931 - val_loss: 55.7046 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1701/4000\n",
      "9/9 - 0s - loss: 0.1373 - accuracy: 0.9792 - val_loss: 66.3944 - val_accuracy: 0.3056 - 98ms/epoch - 11ms/step\n",
      "Epoch 1702/4000\n",
      "9/9 - 0s - loss: 0.1312 - accuracy: 0.9861 - val_loss: 109.6280 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1703/4000\n",
      "9/9 - 0s - loss: 0.1165 - accuracy: 1.0000 - val_loss: 98.3420 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1704/4000\n",
      "9/9 - 0s - loss: 0.1272 - accuracy: 0.9931 - val_loss: 140.8628 - val_accuracy: 0.2639 - 92ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1705/4000\n",
      "9/9 - 0s - loss: 0.1309 - accuracy: 0.9931 - val_loss: 88.6355 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1706/4000\n",
      "9/9 - 0s - loss: 0.1376 - accuracy: 0.9861 - val_loss: 40.4450 - val_accuracy: 0.2639 - 97ms/epoch - 11ms/step\n",
      "Epoch 1707/4000\n",
      "9/9 - 0s - loss: 0.1220 - accuracy: 0.9931 - val_loss: 60.1631 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1708/4000\n",
      "9/9 - 0s - loss: 0.1357 - accuracy: 1.0000 - val_loss: 92.0799 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1709/4000\n",
      "9/9 - 0s - loss: 0.1301 - accuracy: 0.9931 - val_loss: 44.8229 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1710/4000\n",
      "9/9 - 0s - loss: 0.1166 - accuracy: 0.9931 - val_loss: 7.6163 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1711/4000\n",
      "9/9 - 0s - loss: 0.1325 - accuracy: 0.9861 - val_loss: 64.2824 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1712/4000\n",
      "9/9 - 0s - loss: 0.1174 - accuracy: 0.9931 - val_loss: 77.9226 - val_accuracy: 0.2917 - 111ms/epoch - 12ms/step\n",
      "Epoch 1713/4000\n",
      "9/9 - 0s - loss: 0.1518 - accuracy: 0.9653 - val_loss: 87.6396 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1714/4000\n",
      "9/9 - 0s - loss: 0.1001 - accuracy: 1.0000 - val_loss: 80.3643 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 1715/4000\n",
      "9/9 - 0s - loss: 0.1337 - accuracy: 0.9861 - val_loss: 82.5536 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1716/4000\n",
      "9/9 - 0s - loss: 0.1098 - accuracy: 0.9931 - val_loss: 102.0215 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 1717/4000\n",
      "9/9 - 0s - loss: 0.1203 - accuracy: 0.9861 - val_loss: 82.4530 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1718/4000\n",
      "9/9 - 0s - loss: 0.1418 - accuracy: 0.9861 - val_loss: 88.6661 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1719/4000\n",
      "9/9 - 0s - loss: 0.0973 - accuracy: 1.0000 - val_loss: 28.3464 - val_accuracy: 0.2778 - 85ms/epoch - 9ms/step\n",
      "Epoch 1720/4000\n",
      "9/9 - 0s - loss: 0.1176 - accuracy: 0.9931 - val_loss: 121.9319 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1721/4000\n",
      "9/9 - 0s - loss: 0.1129 - accuracy: 1.0000 - val_loss: 151.7403 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1722/4000\n",
      "9/9 - 0s - loss: 0.1248 - accuracy: 0.9931 - val_loss: 278.6084 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1723/4000\n",
      "9/9 - 0s - loss: 0.1483 - accuracy: 0.9861 - val_loss: 84.0829 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1724/4000\n",
      "9/9 - 0s - loss: 0.1114 - accuracy: 0.9861 - val_loss: 28.4506 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1725/4000\n",
      "9/9 - 0s - loss: 0.1273 - accuracy: 0.9792 - val_loss: 34.5884 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 1726/4000\n",
      "9/9 - 0s - loss: 0.1314 - accuracy: 0.9931 - val_loss: 55.9289 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 1727/4000\n",
      "9/9 - 0s - loss: 0.1062 - accuracy: 1.0000 - val_loss: 9.5907 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1728/4000\n",
      "9/9 - 0s - loss: 0.1035 - accuracy: 0.9931 - val_loss: 78.8755 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1729/4000\n",
      "9/9 - 0s - loss: 0.1149 - accuracy: 0.9931 - val_loss: 118.0093 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1730/4000\n",
      "9/9 - 0s - loss: 0.1162 - accuracy: 0.9931 - val_loss: 65.5413 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1731/4000\n",
      "9/9 - 0s - loss: 0.1341 - accuracy: 0.9931 - val_loss: 59.4212 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1732/4000\n",
      "9/9 - 0s - loss: 0.1375 - accuracy: 0.9861 - val_loss: 111.5462 - val_accuracy: 0.2639 - 96ms/epoch - 11ms/step\n",
      "Epoch 1733/4000\n",
      "9/9 - 0s - loss: 0.1186 - accuracy: 0.9931 - val_loss: 132.0810 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 1734/4000\n",
      "9/9 - 0s - loss: 0.1079 - accuracy: 1.0000 - val_loss: 60.5330 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1735/4000\n",
      "9/9 - 0s - loss: 0.1069 - accuracy: 0.9931 - val_loss: 36.1548 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1736/4000\n",
      "9/9 - 0s - loss: 0.1262 - accuracy: 1.0000 - val_loss: 24.7722 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1737/4000\n",
      "9/9 - 0s - loss: 0.1230 - accuracy: 0.9861 - val_loss: 92.9154 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1738/4000\n",
      "9/9 - 0s - loss: 0.1200 - accuracy: 0.9861 - val_loss: 53.3824 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1739/4000\n",
      "9/9 - 0s - loss: 0.1182 - accuracy: 0.9792 - val_loss: 75.1994 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1740/4000\n",
      "9/9 - 0s - loss: 0.1321 - accuracy: 0.9861 - val_loss: 129.0083 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 1741/4000\n",
      "9/9 - 0s - loss: 0.1176 - accuracy: 0.9931 - val_loss: 63.8888 - val_accuracy: 0.2917 - 102ms/epoch - 11ms/step\n",
      "Epoch 1742/4000\n",
      "9/9 - 0s - loss: 0.1287 - accuracy: 0.9931 - val_loss: 28.8442 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1743/4000\n",
      "9/9 - 0s - loss: 0.1225 - accuracy: 0.9931 - val_loss: 20.4475 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1744/4000\n",
      "9/9 - 0s - loss: 0.1388 - accuracy: 0.9931 - val_loss: 47.0676 - val_accuracy: 0.2917 - 109ms/epoch - 12ms/step\n",
      "Epoch 1745/4000\n",
      "9/9 - 0s - loss: 0.1477 - accuracy: 0.9792 - val_loss: 80.3048 - val_accuracy: 0.2500 - 108ms/epoch - 12ms/step\n",
      "Epoch 1746/4000\n",
      "9/9 - 0s - loss: 0.1114 - accuracy: 1.0000 - val_loss: 42.5090 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1747/4000\n",
      "9/9 - 0s - loss: 0.1220 - accuracy: 0.9931 - val_loss: 38.9192 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1748/4000\n",
      "9/9 - 0s - loss: 0.1049 - accuracy: 1.0000 - val_loss: 101.3102 - val_accuracy: 0.3056 - 90ms/epoch - 10ms/step\n",
      "Epoch 1749/4000\n",
      "9/9 - 0s - loss: 0.1290 - accuracy: 0.9931 - val_loss: 174.7970 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1750/4000\n",
      "9/9 - 0s - loss: 0.1121 - accuracy: 1.0000 - val_loss: 190.9934 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1751/4000\n",
      "9/9 - 0s - loss: 0.1214 - accuracy: 0.9931 - val_loss: 198.4196 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1752/4000\n",
      "9/9 - 0s - loss: 0.1508 - accuracy: 0.9653 - val_loss: 109.9298 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1753/4000\n",
      "9/9 - 0s - loss: 0.1245 - accuracy: 0.9861 - val_loss: 32.1902 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1754/4000\n",
      "9/9 - 0s - loss: 0.1090 - accuracy: 0.9931 - val_loss: 24.1612 - val_accuracy: 0.2361 - 83ms/epoch - 9ms/step\n",
      "Epoch 1755/4000\n",
      "9/9 - 0s - loss: 0.1268 - accuracy: 0.9931 - val_loss: 22.8214 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1756/4000\n",
      "9/9 - 0s - loss: 0.1187 - accuracy: 1.0000 - val_loss: 23.2497 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1757/4000\n",
      "9/9 - 0s - loss: 0.1139 - accuracy: 0.9931 - val_loss: 167.9499 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1758/4000\n",
      "9/9 - 0s - loss: 0.0927 - accuracy: 1.0000 - val_loss: 51.1523 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 1759/4000\n",
      "9/9 - 0s - loss: 0.0980 - accuracy: 1.0000 - val_loss: 38.4361 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 1760/4000\n",
      "9/9 - 0s - loss: 0.1169 - accuracy: 0.9931 - val_loss: 74.5297 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1761/4000\n",
      "9/9 - 0s - loss: 0.1035 - accuracy: 0.9931 - val_loss: 81.7486 - val_accuracy: 0.2917 - 105ms/epoch - 12ms/step\n",
      "Epoch 1762/4000\n",
      "9/9 - 0s - loss: 0.1000 - accuracy: 0.9931 - val_loss: 113.7105 - val_accuracy: 0.2639 - 94ms/epoch - 10ms/step\n",
      "Epoch 1763/4000\n",
      "9/9 - 0s - loss: 0.1173 - accuracy: 0.9931 - val_loss: 74.7382 - val_accuracy: 0.3056 - 101ms/epoch - 11ms/step\n",
      "Epoch 1764/4000\n",
      "9/9 - 0s - loss: 0.1058 - accuracy: 0.9931 - val_loss: 99.7627 - val_accuracy: 0.2639 - 98ms/epoch - 11ms/step\n",
      "Epoch 1765/4000\n",
      "9/9 - 0s - loss: 0.1362 - accuracy: 0.9653 - val_loss: 100.4690 - val_accuracy: 0.2639 - 93ms/epoch - 10ms/step\n",
      "Epoch 1766/4000\n",
      "9/9 - 0s - loss: 0.1614 - accuracy: 0.9861 - val_loss: 75.6302 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1767/4000\n",
      "9/9 - 0s - loss: 0.1100 - accuracy: 1.0000 - val_loss: 56.4401 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1768/4000\n",
      "9/9 - 0s - loss: 0.1287 - accuracy: 0.9931 - val_loss: 6.9407 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1769/4000\n",
      "9/9 - 0s - loss: 0.1085 - accuracy: 1.0000 - val_loss: 5.9929 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1770/4000\n",
      "9/9 - 0s - loss: 0.1115 - accuracy: 1.0000 - val_loss: 13.9988 - val_accuracy: 0.2917 - 100ms/epoch - 11ms/step\n",
      "Epoch 1771/4000\n",
      "9/9 - 0s - loss: 0.0961 - accuracy: 1.0000 - val_loss: 54.3911 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1772/4000\n",
      "9/9 - 0s - loss: 0.1042 - accuracy: 1.0000 - val_loss: 40.2810 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1773/4000\n",
      "9/9 - 0s - loss: 0.1059 - accuracy: 0.9931 - val_loss: 2.6804 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 1774/4000\n",
      "9/9 - 0s - loss: 0.1176 - accuracy: 0.9931 - val_loss: 4.5529 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1775/4000\n",
      "9/9 - 0s - loss: 0.1404 - accuracy: 0.9931 - val_loss: 22.4253 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 1776/4000\n",
      "9/9 - 0s - loss: 0.1077 - accuracy: 1.0000 - val_loss: 47.2539 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 1777/4000\n",
      "9/9 - 0s - loss: 0.1276 - accuracy: 1.0000 - val_loss: 104.9622 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 1778/4000\n",
      "9/9 - 0s - loss: 0.1375 - accuracy: 0.9792 - val_loss: 75.8464 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1779/4000\n",
      "9/9 - 0s - loss: 0.1109 - accuracy: 0.9931 - val_loss: 67.9111 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1780/4000\n",
      "9/9 - 0s - loss: 0.1166 - accuracy: 0.9931 - val_loss: 56.2797 - val_accuracy: 0.3194 - 103ms/epoch - 11ms/step\n",
      "Epoch 1781/4000\n",
      "9/9 - 0s - loss: 0.1269 - accuracy: 0.9861 - val_loss: 13.3943 - val_accuracy: 0.3611 - 88ms/epoch - 10ms/step\n",
      "Epoch 1782/4000\n",
      "9/9 - 0s - loss: 0.1022 - accuracy: 1.0000 - val_loss: 18.2986 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1783/4000\n",
      "9/9 - 0s - loss: 0.1344 - accuracy: 0.9792 - val_loss: 79.2286 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 1784/4000\n",
      "9/9 - 0s - loss: 0.1049 - accuracy: 0.9861 - val_loss: 88.6868 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1785/4000\n",
      "9/9 - 0s - loss: 0.1336 - accuracy: 0.9931 - val_loss: 165.3045 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1786/4000\n",
      "9/9 - 0s - loss: 0.1255 - accuracy: 0.9861 - val_loss: 97.3013 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 1787/4000\n",
      "9/9 - 0s - loss: 0.1168 - accuracy: 0.9861 - val_loss: 40.9674 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1788/4000\n",
      "9/9 - 0s - loss: 0.1174 - accuracy: 1.0000 - val_loss: 79.7668 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1789/4000\n",
      "9/9 - 0s - loss: 0.1200 - accuracy: 0.9931 - val_loss: 94.7417 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1790/4000\n",
      "9/9 - 0s - loss: 0.1024 - accuracy: 0.9931 - val_loss: 109.1313 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 1791/4000\n",
      "9/9 - 0s - loss: 0.1267 - accuracy: 0.9722 - val_loss: 100.4892 - val_accuracy: 0.2222 - 109ms/epoch - 12ms/step\n",
      "Epoch 1792/4000\n",
      "9/9 - 0s - loss: 0.1071 - accuracy: 1.0000 - val_loss: 10.3624 - val_accuracy: 0.2917 - 114ms/epoch - 13ms/step\n",
      "Epoch 1793/4000\n",
      "9/9 - 0s - loss: 0.1256 - accuracy: 0.9931 - val_loss: 57.4621 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 1794/4000\n",
      "9/9 - 0s - loss: 0.1054 - accuracy: 1.0000 - val_loss: 48.2129 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1795/4000\n",
      "9/9 - 0s - loss: 0.1026 - accuracy: 0.9931 - val_loss: 53.4694 - val_accuracy: 0.2917 - 100ms/epoch - 11ms/step\n",
      "Epoch 1796/4000\n",
      "9/9 - 0s - loss: 0.1093 - accuracy: 0.9861 - val_loss: 32.6187 - val_accuracy: 0.2917 - 108ms/epoch - 12ms/step\n",
      "Epoch 1797/4000\n",
      "9/9 - 0s - loss: 0.1404 - accuracy: 0.9653 - val_loss: 15.2192 - val_accuracy: 0.4444 - 89ms/epoch - 10ms/step\n",
      "Epoch 1798/4000\n",
      "9/9 - 0s - loss: 0.1592 - accuracy: 0.9792 - val_loss: 63.5010 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1799/4000\n",
      "9/9 - 0s - loss: 0.1213 - accuracy: 0.9931 - val_loss: 39.7128 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1800/4000\n",
      "9/9 - 0s - loss: 0.0999 - accuracy: 0.9861 - val_loss: 89.4652 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1801/4000\n",
      "9/9 - 0s - loss: 0.1451 - accuracy: 0.9722 - val_loss: 89.8221 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1802/4000\n",
      "9/9 - 0s - loss: 0.1217 - accuracy: 1.0000 - val_loss: 85.3332 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1803/4000\n",
      "9/9 - 0s - loss: 0.1125 - accuracy: 1.0000 - val_loss: 62.3063 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1804/4000\n",
      "9/9 - 0s - loss: 0.1092 - accuracy: 0.9861 - val_loss: 53.7121 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 1805/4000\n",
      "9/9 - 0s - loss: 0.1050 - accuracy: 1.0000 - val_loss: 72.4983 - val_accuracy: 0.2917 - 99ms/epoch - 11ms/step\n",
      "Epoch 1806/4000\n",
      "9/9 - 0s - loss: 0.1122 - accuracy: 0.9792 - val_loss: 37.4569 - val_accuracy: 0.2917 - 105ms/epoch - 12ms/step\n",
      "Epoch 1807/4000\n",
      "9/9 - 0s - loss: 0.1396 - accuracy: 0.9792 - val_loss: 3.0474 - val_accuracy: 0.3611 - 95ms/epoch - 11ms/step\n",
      "Epoch 1808/4000\n",
      "9/9 - 0s - loss: 0.1330 - accuracy: 0.9792 - val_loss: 82.7045 - val_accuracy: 0.2917 - 107ms/epoch - 12ms/step\n",
      "Epoch 1809/4000\n",
      "9/9 - 0s - loss: 0.1081 - accuracy: 0.9931 - val_loss: 91.3116 - val_accuracy: 0.2917 - 110ms/epoch - 12ms/step\n",
      "Epoch 1810/4000\n",
      "9/9 - 0s - loss: 0.0874 - accuracy: 1.0000 - val_loss: 82.5122 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1811/4000\n",
      "9/9 - 0s - loss: 0.1144 - accuracy: 0.9861 - val_loss: 42.2123 - val_accuracy: 0.2917 - 99ms/epoch - 11ms/step\n",
      "Epoch 1812/4000\n",
      "9/9 - 0s - loss: 0.0953 - accuracy: 1.0000 - val_loss: 27.0431 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1813/4000\n",
      "9/9 - 0s - loss: 0.0970 - accuracy: 0.9931 - val_loss: 11.0265 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1814/4000\n",
      "9/9 - 0s - loss: 0.1149 - accuracy: 0.9931 - val_loss: 9.6795 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1815/4000\n",
      "9/9 - 0s - loss: 0.1329 - accuracy: 0.9792 - val_loss: 84.2675 - val_accuracy: 0.2917 - 111ms/epoch - 12ms/step\n",
      "Epoch 1816/4000\n",
      "9/9 - 0s - loss: 0.1300 - accuracy: 0.9861 - val_loss: 83.0108 - val_accuracy: 0.2917 - 107ms/epoch - 12ms/step\n",
      "Epoch 1817/4000\n",
      "9/9 - 0s - loss: 0.1031 - accuracy: 0.9931 - val_loss: 59.9739 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 1818/4000\n",
      "9/9 - 0s - loss: 0.0965 - accuracy: 1.0000 - val_loss: 58.3060 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 1819/4000\n",
      "9/9 - 0s - loss: 0.0920 - accuracy: 1.0000 - val_loss: 40.1709 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1820/4000\n",
      "9/9 - 0s - loss: 0.1194 - accuracy: 0.9931 - val_loss: 6.6046 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 1821/4000\n",
      "9/9 - 0s - loss: 0.1032 - accuracy: 0.9861 - val_loss: 45.1144 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1822/4000\n",
      "9/9 - 0s - loss: 0.1003 - accuracy: 1.0000 - val_loss: 60.0975 - val_accuracy: 0.4028 - 87ms/epoch - 10ms/step\n",
      "Epoch 1823/4000\n",
      "9/9 - 0s - loss: 0.0998 - accuracy: 1.0000 - val_loss: 49.6596 - val_accuracy: 0.4167 - 95ms/epoch - 11ms/step\n",
      "Epoch 1824/4000\n",
      "9/9 - 0s - loss: 0.0944 - accuracy: 1.0000 - val_loss: 60.0117 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 1825/4000\n",
      "9/9 - 0s - loss: 0.1105 - accuracy: 0.9931 - val_loss: 45.4110 - val_accuracy: 0.2917 - 106ms/epoch - 12ms/step\n",
      "Epoch 1826/4000\n",
      "9/9 - 0s - loss: 0.1161 - accuracy: 0.9792 - val_loss: 89.3912 - val_accuracy: 0.2917 - 123ms/epoch - 14ms/step\n",
      "Epoch 1827/4000\n",
      "9/9 - 0s - loss: 0.1199 - accuracy: 0.9861 - val_loss: 86.2731 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 1828/4000\n",
      "9/9 - 0s - loss: 0.1117 - accuracy: 1.0000 - val_loss: 138.5459 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1829/4000\n",
      "9/9 - 0s - loss: 0.0997 - accuracy: 1.0000 - val_loss: 110.0174 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 1830/4000\n",
      "9/9 - 0s - loss: 0.1009 - accuracy: 0.9931 - val_loss: 71.8561 - val_accuracy: 0.3194 - 103ms/epoch - 11ms/step\n",
      "Epoch 1831/4000\n",
      "9/9 - 0s - loss: 0.0893 - accuracy: 1.0000 - val_loss: 169.0598 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1832/4000\n",
      "9/9 - 0s - loss: 0.0981 - accuracy: 0.9931 - val_loss: 330.7288 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1833/4000\n",
      "9/9 - 0s - loss: 0.1174 - accuracy: 0.9861 - val_loss: 291.9126 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1834/4000\n",
      "9/9 - 0s - loss: 0.1314 - accuracy: 0.9792 - val_loss: 198.8630 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1835/4000\n",
      "9/9 - 0s - loss: 0.1232 - accuracy: 0.9931 - val_loss: 6.7469 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 1836/4000\n",
      "9/9 - 0s - loss: 0.1127 - accuracy: 0.9931 - val_loss: 64.6817 - val_accuracy: 0.2917 - 115ms/epoch - 13ms/step\n",
      "Epoch 1837/4000\n",
      "9/9 - 0s - loss: 0.1200 - accuracy: 0.9931 - val_loss: 35.3950 - val_accuracy: 0.2917 - 106ms/epoch - 12ms/step\n",
      "Epoch 1838/4000\n",
      "9/9 - 0s - loss: 0.0999 - accuracy: 1.0000 - val_loss: 29.1483 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1839/4000\n",
      "9/9 - 0s - loss: 0.0935 - accuracy: 0.9931 - val_loss: 73.8502 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1840/4000\n",
      "9/9 - 0s - loss: 0.0930 - accuracy: 0.9931 - val_loss: 68.0368 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1841/4000\n",
      "9/9 - 0s - loss: 0.1217 - accuracy: 0.9861 - val_loss: 44.7313 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 1842/4000\n",
      "9/9 - 0s - loss: 0.1083 - accuracy: 0.9861 - val_loss: 37.2613 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1843/4000\n",
      "9/9 - 0s - loss: 0.0968 - accuracy: 0.9931 - val_loss: 13.4534 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 1844/4000\n",
      "9/9 - 0s - loss: 0.1267 - accuracy: 0.9861 - val_loss: 71.2009 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 1845/4000\n",
      "9/9 - 0s - loss: 0.0982 - accuracy: 0.9931 - val_loss: 27.1743 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1846/4000\n",
      "9/9 - 0s - loss: 0.1032 - accuracy: 0.9931 - val_loss: 5.4504 - val_accuracy: 0.2917 - 99ms/epoch - 11ms/step\n",
      "Epoch 1847/4000\n",
      "9/9 - 0s - loss: 0.1163 - accuracy: 0.9861 - val_loss: 25.3437 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1848/4000\n",
      "9/9 - 0s - loss: 0.1127 - accuracy: 0.9931 - val_loss: 14.5532 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1849/4000\n",
      "9/9 - 0s - loss: 0.1096 - accuracy: 0.9931 - val_loss: 4.7397 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1850/4000\n",
      "9/9 - 0s - loss: 0.1063 - accuracy: 0.9931 - val_loss: 38.6433 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1851/4000\n",
      "9/9 - 0s - loss: 0.0972 - accuracy: 1.0000 - val_loss: 76.6743 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 1852/4000\n",
      "9/9 - 0s - loss: 0.1019 - accuracy: 0.9931 - val_loss: 86.5526 - val_accuracy: 0.2639 - 98ms/epoch - 11ms/step\n",
      "Epoch 1853/4000\n",
      "9/9 - 0s - loss: 0.0980 - accuracy: 1.0000 - val_loss: 37.5532 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1854/4000\n",
      "9/9 - 0s - loss: 0.0995 - accuracy: 1.0000 - val_loss: 12.3122 - val_accuracy: 0.4306 - 91ms/epoch - 10ms/step\n",
      "Epoch 1855/4000\n",
      "9/9 - 0s - loss: 0.0970 - accuracy: 1.0000 - val_loss: 5.1076 - val_accuracy: 0.2500 - 96ms/epoch - 11ms/step\n",
      "Epoch 1856/4000\n",
      "9/9 - 0s - loss: 0.1354 - accuracy: 0.9792 - val_loss: 73.1484 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 1857/4000\n",
      "9/9 - 0s - loss: 0.1163 - accuracy: 0.9931 - val_loss: 38.6088 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1858/4000\n",
      "9/9 - 0s - loss: 0.0936 - accuracy: 1.0000 - val_loss: 23.8252 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1859/4000\n",
      "9/9 - 0s - loss: 0.0871 - accuracy: 1.0000 - val_loss: 47.3747 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 1860/4000\n",
      "9/9 - 0s - loss: 0.0962 - accuracy: 0.9931 - val_loss: 23.8510 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1861/4000\n",
      "9/9 - 0s - loss: 0.1276 - accuracy: 0.9792 - val_loss: 18.5029 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1862/4000\n",
      "9/9 - 0s - loss: 0.1073 - accuracy: 1.0000 - val_loss: 52.8060 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 1863/4000\n",
      "9/9 - 0s - loss: 0.1545 - accuracy: 0.9792 - val_loss: 42.8506 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1864/4000\n",
      "9/9 - 0s - loss: 0.1405 - accuracy: 0.9861 - val_loss: 53.6345 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1865/4000\n",
      "9/9 - 0s - loss: 0.1030 - accuracy: 0.9931 - val_loss: 179.1108 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1866/4000\n",
      "9/9 - 0s - loss: 0.1208 - accuracy: 0.9861 - val_loss: 85.0676 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1867/4000\n",
      "9/9 - 0s - loss: 0.1174 - accuracy: 1.0000 - val_loss: 8.7006 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n",
      "Epoch 1868/4000\n",
      "9/9 - 0s - loss: 0.1045 - accuracy: 0.9931 - val_loss: 60.5058 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1869/4000\n",
      "9/9 - 0s - loss: 0.1174 - accuracy: 0.9931 - val_loss: 96.4416 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1870/4000\n",
      "9/9 - 0s - loss: 0.1213 - accuracy: 1.0000 - val_loss: 73.0338 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 1871/4000\n",
      "9/9 - 0s - loss: 0.1261 - accuracy: 0.9792 - val_loss: 60.2493 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1872/4000\n",
      "9/9 - 0s - loss: 0.1097 - accuracy: 0.9792 - val_loss: 23.9089 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1873/4000\n",
      "9/9 - 0s - loss: 0.0863 - accuracy: 1.0000 - val_loss: 25.5596 - val_accuracy: 0.3750 - 116ms/epoch - 13ms/step\n",
      "Epoch 1874/4000\n",
      "9/9 - 0s - loss: 0.0941 - accuracy: 1.0000 - val_loss: 96.2065 - val_accuracy: 0.2639 - 105ms/epoch - 12ms/step\n",
      "Epoch 1875/4000\n",
      "9/9 - 0s - loss: 0.1191 - accuracy: 0.9861 - val_loss: 109.2430 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 1876/4000\n",
      "9/9 - 0s - loss: 0.1077 - accuracy: 0.9931 - val_loss: 81.3370 - val_accuracy: 0.2639 - 96ms/epoch - 11ms/step\n",
      "Epoch 1877/4000\n",
      "9/9 - 0s - loss: 0.1167 - accuracy: 0.9861 - val_loss: 86.3768 - val_accuracy: 0.2639 - 109ms/epoch - 12ms/step\n",
      "Epoch 1878/4000\n",
      "9/9 - 0s - loss: 0.1198 - accuracy: 0.9861 - val_loss: 111.5520 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1879/4000\n",
      "9/9 - 0s - loss: 0.1090 - accuracy: 1.0000 - val_loss: 104.9942 - val_accuracy: 0.2222 - 110ms/epoch - 12ms/step\n",
      "Epoch 1880/4000\n",
      "9/9 - 0s - loss: 0.1144 - accuracy: 0.9931 - val_loss: 42.7094 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1881/4000\n",
      "9/9 - 0s - loss: 0.1175 - accuracy: 0.9931 - val_loss: 75.4802 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1882/4000\n",
      "9/9 - 0s - loss: 0.0855 - accuracy: 1.0000 - val_loss: 63.4527 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1883/4000\n",
      "9/9 - 0s - loss: 0.1120 - accuracy: 0.9861 - val_loss: 88.6524 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1884/4000\n",
      "9/9 - 0s - loss: 0.1181 - accuracy: 0.9722 - val_loss: 131.7744 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 1885/4000\n",
      "9/9 - 0s - loss: 0.1068 - accuracy: 1.0000 - val_loss: 96.4805 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1886/4000\n",
      "9/9 - 0s - loss: 0.0977 - accuracy: 1.0000 - val_loss: 95.8993 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1887/4000\n",
      "9/9 - 0s - loss: 0.1143 - accuracy: 1.0000 - val_loss: 88.4391 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1888/4000\n",
      "9/9 - 0s - loss: 0.0982 - accuracy: 0.9861 - val_loss: 159.3726 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1889/4000\n",
      "9/9 - 0s - loss: 0.0939 - accuracy: 0.9931 - val_loss: 129.8636 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 1890/4000\n",
      "9/9 - 0s - loss: 0.0930 - accuracy: 0.9931 - val_loss: 36.3295 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1891/4000\n",
      "9/9 - 0s - loss: 0.1029 - accuracy: 1.0000 - val_loss: 92.2114 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 1892/4000\n",
      "9/9 - 0s - loss: 0.0860 - accuracy: 1.0000 - val_loss: 97.0916 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1893/4000\n",
      "9/9 - 0s - loss: 0.0977 - accuracy: 0.9931 - val_loss: 86.7783 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1894/4000\n",
      "9/9 - 0s - loss: 0.0979 - accuracy: 1.0000 - val_loss: 3.5293 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1895/4000\n",
      "9/9 - 0s - loss: 0.0879 - accuracy: 1.0000 - val_loss: 85.3515 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 1896/4000\n",
      "9/9 - 0s - loss: 0.1057 - accuracy: 0.9861 - val_loss: 242.7059 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 1897/4000\n",
      "9/9 - 0s - loss: 0.1072 - accuracy: 1.0000 - val_loss: 302.4151 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1898/4000\n",
      "9/9 - 0s - loss: 0.1091 - accuracy: 0.9861 - val_loss: 160.7692 - val_accuracy: 0.2222 - 113ms/epoch - 13ms/step\n",
      "Epoch 1899/4000\n",
      "9/9 - 0s - loss: 0.0879 - accuracy: 1.0000 - val_loss: 115.5850 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1900/4000\n",
      "9/9 - 0s - loss: 0.1000 - accuracy: 0.9931 - val_loss: 145.5722 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1901/4000\n",
      "9/9 - 0s - loss: 0.1031 - accuracy: 1.0000 - val_loss: 45.0244 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1902/4000\n",
      "9/9 - 0s - loss: 0.1178 - accuracy: 0.9792 - val_loss: 85.9813 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1903/4000\n",
      "9/9 - 0s - loss: 0.1158 - accuracy: 0.9861 - val_loss: 46.7313 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1904/4000\n",
      "9/9 - 0s - loss: 0.1273 - accuracy: 0.9861 - val_loss: 69.1214 - val_accuracy: 0.2639 - 91ms/epoch - 10ms/step\n",
      "Epoch 1905/4000\n",
      "9/9 - 0s - loss: 0.1419 - accuracy: 0.9722 - val_loss: 135.2912 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1906/4000\n",
      "9/9 - 0s - loss: 0.1364 - accuracy: 0.9931 - val_loss: 89.6487 - val_accuracy: 0.2639 - 95ms/epoch - 11ms/step\n",
      "Epoch 1907/4000\n",
      "9/9 - 0s - loss: 0.1182 - accuracy: 1.0000 - val_loss: 58.1026 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 1908/4000\n",
      "9/9 - 0s - loss: 0.1258 - accuracy: 0.9931 - val_loss: 75.4176 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 1909/4000\n",
      "9/9 - 0s - loss: 0.0963 - accuracy: 1.0000 - val_loss: 109.9825 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 1910/4000\n",
      "9/9 - 0s - loss: 0.1309 - accuracy: 0.9792 - val_loss: 60.4304 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1911/4000\n",
      "9/9 - 0s - loss: 0.1314 - accuracy: 0.9931 - val_loss: 71.3274 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 1912/4000\n",
      "9/9 - 0s - loss: 0.1037 - accuracy: 1.0000 - val_loss: 81.9755 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1913/4000\n",
      "9/9 - 0s - loss: 0.0891 - accuracy: 1.0000 - val_loss: 82.7329 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1914/4000\n",
      "9/9 - 0s - loss: 0.1046 - accuracy: 0.9931 - val_loss: 48.8624 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 1915/4000\n",
      "9/9 - 0s - loss: 0.1190 - accuracy: 0.9931 - val_loss: 1.7244 - val_accuracy: 0.3611 - 89ms/epoch - 10ms/step\n",
      "Epoch 1916/4000\n",
      "9/9 - 0s - loss: 0.1114 - accuracy: 0.9792 - val_loss: 88.3376 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1917/4000\n",
      "9/9 - 0s - loss: 0.1013 - accuracy: 0.9931 - val_loss: 22.0259 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1918/4000\n",
      "9/9 - 0s - loss: 0.0965 - accuracy: 0.9931 - val_loss: 4.9427 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 1919/4000\n",
      "9/9 - 0s - loss: 0.0974 - accuracy: 1.0000 - val_loss: 6.6925 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 1920/4000\n",
      "9/9 - 0s - loss: 0.1009 - accuracy: 0.9861 - val_loss: 64.6899 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 1921/4000\n",
      "9/9 - 0s - loss: 0.1433 - accuracy: 0.9931 - val_loss: 74.5321 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 1922/4000\n",
      "9/9 - 0s - loss: 0.1139 - accuracy: 0.9931 - val_loss: 35.9703 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1923/4000\n",
      "9/9 - 0s - loss: 0.1128 - accuracy: 0.9931 - val_loss: 59.9923 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1924/4000\n",
      "9/9 - 0s - loss: 0.1138 - accuracy: 0.9722 - val_loss: 75.5688 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1925/4000\n",
      "9/9 - 0s - loss: 0.1117 - accuracy: 0.9931 - val_loss: 92.1685 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1926/4000\n",
      "9/9 - 0s - loss: 0.0984 - accuracy: 1.0000 - val_loss: 91.4362 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1927/4000\n",
      "9/9 - 0s - loss: 0.0993 - accuracy: 1.0000 - val_loss: 37.7284 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 1928/4000\n",
      "9/9 - 0s - loss: 0.1029 - accuracy: 0.9931 - val_loss: 27.0692 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 1929/4000\n",
      "9/9 - 0s - loss: 0.1479 - accuracy: 0.9722 - val_loss: 90.4869 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 1930/4000\n",
      "9/9 - 0s - loss: 0.1168 - accuracy: 1.0000 - val_loss: 81.0426 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 1931/4000\n",
      "9/9 - 0s - loss: 0.1074 - accuracy: 1.0000 - val_loss: 35.0436 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1932/4000\n",
      "9/9 - 0s - loss: 0.1095 - accuracy: 1.0000 - val_loss: 5.5472 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1933/4000\n",
      "9/9 - 0s - loss: 0.0979 - accuracy: 1.0000 - val_loss: 24.2757 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1934/4000\n",
      "9/9 - 0s - loss: 0.1062 - accuracy: 1.0000 - val_loss: 38.4033 - val_accuracy: 0.3611 - 84ms/epoch - 9ms/step\n",
      "Epoch 1935/4000\n",
      "9/9 - 0s - loss: 0.0828 - accuracy: 1.0000 - val_loss: 140.7320 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1936/4000\n",
      "9/9 - 0s - loss: 0.0851 - accuracy: 1.0000 - val_loss: 196.1539 - val_accuracy: 0.2639 - 78ms/epoch - 9ms/step\n",
      "Epoch 1937/4000\n",
      "9/9 - 0s - loss: 0.0894 - accuracy: 0.9931 - val_loss: 174.4372 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 1938/4000\n",
      "9/9 - 0s - loss: 0.1012 - accuracy: 0.9861 - val_loss: 128.4457 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1939/4000\n",
      "9/9 - 0s - loss: 0.0918 - accuracy: 1.0000 - val_loss: 81.2523 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1940/4000\n",
      "9/9 - 0s - loss: 0.0938 - accuracy: 1.0000 - val_loss: 51.4827 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1941/4000\n",
      "9/9 - 0s - loss: 0.0941 - accuracy: 1.0000 - val_loss: 52.2369 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 1942/4000\n",
      "9/9 - 0s - loss: 0.1271 - accuracy: 0.9792 - val_loss: 43.6344 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1943/4000\n",
      "9/9 - 0s - loss: 0.1116 - accuracy: 0.9931 - val_loss: 52.4335 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 1944/4000\n",
      "9/9 - 0s - loss: 0.1301 - accuracy: 0.9792 - val_loss: 8.8334 - val_accuracy: 0.2500 - 86ms/epoch - 10ms/step\n",
      "Epoch 1945/4000\n",
      "9/9 - 0s - loss: 0.1088 - accuracy: 0.9931 - val_loss: 22.3655 - val_accuracy: 0.2917 - 116ms/epoch - 13ms/step\n",
      "Epoch 1946/4000\n",
      "9/9 - 0s - loss: 0.1060 - accuracy: 0.9931 - val_loss: 42.9162 - val_accuracy: 0.2917 - 132ms/epoch - 15ms/step\n",
      "Epoch 1947/4000\n",
      "9/9 - 0s - loss: 0.1450 - accuracy: 0.9931 - val_loss: 13.9078 - val_accuracy: 0.2917 - 100ms/epoch - 11ms/step\n",
      "Epoch 1948/4000\n",
      "9/9 - 0s - loss: 0.0915 - accuracy: 1.0000 - val_loss: 48.7346 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 1949/4000\n",
      "9/9 - 0s - loss: 0.0850 - accuracy: 1.0000 - val_loss: 32.1211 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n",
      "Epoch 1950/4000\n",
      "9/9 - 0s - loss: 0.0867 - accuracy: 1.0000 - val_loss: 26.0246 - val_accuracy: 0.2222 - 107ms/epoch - 12ms/step\n",
      "Epoch 1951/4000\n",
      "9/9 - 0s - loss: 0.1039 - accuracy: 0.9861 - val_loss: 1.7480 - val_accuracy: 0.4167 - 95ms/epoch - 11ms/step\n",
      "Epoch 1952/4000\n",
      "9/9 - 0s - loss: 0.0977 - accuracy: 0.9861 - val_loss: 40.7833 - val_accuracy: 0.2917 - 106ms/epoch - 12ms/step\n",
      "Epoch 1953/4000\n",
      "9/9 - 0s - loss: 0.0817 - accuracy: 1.0000 - val_loss: 27.5488 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1954/4000\n",
      "9/9 - 0s - loss: 0.0868 - accuracy: 0.9931 - val_loss: 31.8025 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 1955/4000\n",
      "9/9 - 0s - loss: 0.0801 - accuracy: 1.0000 - val_loss: 31.1646 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1956/4000\n",
      "9/9 - 0s - loss: 0.1047 - accuracy: 0.9792 - val_loss: 12.6343 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 1957/4000\n",
      "9/9 - 0s - loss: 0.0961 - accuracy: 1.0000 - val_loss: 22.7036 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 1958/4000\n",
      "9/9 - 0s - loss: 0.0923 - accuracy: 1.0000 - val_loss: 14.1951 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 1959/4000\n",
      "9/9 - 0s - loss: 0.0922 - accuracy: 0.9931 - val_loss: 25.5601 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 1960/4000\n",
      "9/9 - 0s - loss: 0.0813 - accuracy: 1.0000 - val_loss: 12.2674 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 1961/4000\n",
      "9/9 - 0s - loss: 0.0877 - accuracy: 0.9931 - val_loss: 18.8743 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 1962/4000\n",
      "9/9 - 0s - loss: 0.1320 - accuracy: 0.9931 - val_loss: 2.7372 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1963/4000\n",
      "9/9 - 0s - loss: 0.0952 - accuracy: 1.0000 - val_loss: 6.2445 - val_accuracy: 0.3194 - 90ms/epoch - 10ms/step\n",
      "Epoch 1964/4000\n",
      "9/9 - 0s - loss: 0.1106 - accuracy: 1.0000 - val_loss: 46.6759 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1965/4000\n",
      "9/9 - 0s - loss: 0.1117 - accuracy: 0.9931 - val_loss: 16.3835 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 1966/4000\n",
      "9/9 - 0s - loss: 0.1114 - accuracy: 0.9931 - val_loss: 42.6757 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 1967/4000\n",
      "9/9 - 0s - loss: 0.0853 - accuracy: 0.9931 - val_loss: 56.4401 - val_accuracy: 0.2917 - 112ms/epoch - 12ms/step\n",
      "Epoch 1968/4000\n",
      "9/9 - 0s - loss: 0.1429 - accuracy: 0.9653 - val_loss: 6.0736 - val_accuracy: 0.2917 - 110ms/epoch - 12ms/step\n",
      "Epoch 1969/4000\n",
      "9/9 - 0s - loss: 0.1037 - accuracy: 0.9931 - val_loss: 44.0660 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 1970/4000\n",
      "9/9 - 0s - loss: 0.1091 - accuracy: 0.9861 - val_loss: 82.7036 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 1971/4000\n",
      "9/9 - 0s - loss: 0.1321 - accuracy: 0.9792 - val_loss: 38.1967 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 1972/4000\n",
      "9/9 - 0s - loss: 0.0991 - accuracy: 0.9861 - val_loss: 86.2075 - val_accuracy: 0.2917 - 99ms/epoch - 11ms/step\n",
      "Epoch 1973/4000\n",
      "9/9 - 0s - loss: 0.1105 - accuracy: 0.9931 - val_loss: 86.8348 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 1974/4000\n",
      "9/9 - 0s - loss: 0.0829 - accuracy: 1.0000 - val_loss: 87.8325 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 1975/4000\n",
      "9/9 - 0s - loss: 0.1046 - accuracy: 0.9861 - val_loss: 71.9425 - val_accuracy: 0.2639 - 106ms/epoch - 12ms/step\n",
      "Epoch 1976/4000\n",
      "9/9 - 0s - loss: 0.1043 - accuracy: 0.9861 - val_loss: 13.0536 - val_accuracy: 0.2917 - 114ms/epoch - 13ms/step\n",
      "Epoch 1977/4000\n",
      "9/9 - 0s - loss: 0.1851 - accuracy: 0.9514 - val_loss: 83.7111 - val_accuracy: 0.2222 - 110ms/epoch - 12ms/step\n",
      "Epoch 1978/4000\n",
      "9/9 - 0s - loss: 0.1625 - accuracy: 0.9722 - val_loss: 97.6472 - val_accuracy: 0.2222 - 117ms/epoch - 13ms/step\n",
      "Epoch 1979/4000\n",
      "9/9 - 0s - loss: 0.1255 - accuracy: 0.9792 - val_loss: 39.1498 - val_accuracy: 0.2222 - 114ms/epoch - 13ms/step\n",
      "Epoch 1980/4000\n",
      "9/9 - 0s - loss: 0.1054 - accuracy: 0.9931 - val_loss: 16.5139 - val_accuracy: 0.2917 - 105ms/epoch - 12ms/step\n",
      "Epoch 1981/4000\n",
      "9/9 - 0s - loss: 0.0841 - accuracy: 1.0000 - val_loss: 21.7044 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1982/4000\n",
      "9/9 - 0s - loss: 0.0943 - accuracy: 1.0000 - val_loss: 19.0035 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 1983/4000\n",
      "9/9 - 0s - loss: 0.0795 - accuracy: 1.0000 - val_loss: 7.2615 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 1984/4000\n",
      "9/9 - 0s - loss: 0.1178 - accuracy: 0.9861 - val_loss: 33.1541 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 1985/4000\n",
      "9/9 - 0s - loss: 0.0922 - accuracy: 0.9861 - val_loss: 44.5688 - val_accuracy: 0.2917 - 112ms/epoch - 12ms/step\n",
      "Epoch 1986/4000\n",
      "9/9 - 0s - loss: 0.0822 - accuracy: 1.0000 - val_loss: 58.5171 - val_accuracy: 0.2917 - 125ms/epoch - 14ms/step\n",
      "Epoch 1987/4000\n",
      "9/9 - 0s - loss: 0.0934 - accuracy: 0.9792 - val_loss: 57.5905 - val_accuracy: 0.2917 - 110ms/epoch - 12ms/step\n",
      "Epoch 1988/4000\n",
      "9/9 - 0s - loss: 0.1053 - accuracy: 1.0000 - val_loss: 51.0929 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 1989/4000\n",
      "9/9 - 0s - loss: 0.1072 - accuracy: 0.9931 - val_loss: 63.3775 - val_accuracy: 0.2222 - 103ms/epoch - 11ms/step\n",
      "Epoch 1990/4000\n",
      "9/9 - 0s - loss: 0.1277 - accuracy: 0.9861 - val_loss: 37.4279 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 1991/4000\n",
      "9/9 - 0s - loss: 0.1020 - accuracy: 0.9931 - val_loss: 16.6100 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 1992/4000\n",
      "9/9 - 0s - loss: 0.0873 - accuracy: 0.9931 - val_loss: 51.9004 - val_accuracy: 0.2917 - 103ms/epoch - 11ms/step\n",
      "Epoch 1993/4000\n",
      "9/9 - 0s - loss: 0.0792 - accuracy: 0.9861 - val_loss: 69.1234 - val_accuracy: 0.2917 - 132ms/epoch - 15ms/step\n",
      "Epoch 1994/4000\n",
      "9/9 - 0s - loss: 0.0932 - accuracy: 0.9931 - val_loss: 52.8534 - val_accuracy: 0.2917 - 105ms/epoch - 12ms/step\n",
      "Epoch 1995/4000\n",
      "9/9 - 0s - loss: 0.1070 - accuracy: 0.9931 - val_loss: 47.5283 - val_accuracy: 0.2917 - 121ms/epoch - 13ms/step\n",
      "Epoch 1996/4000\n",
      "9/9 - 0s - loss: 0.0965 - accuracy: 0.9931 - val_loss: 51.0119 - val_accuracy: 0.2917 - 103ms/epoch - 11ms/step\n",
      "Epoch 1997/4000\n",
      "9/9 - 0s - loss: 0.0840 - accuracy: 1.0000 - val_loss: 72.3054 - val_accuracy: 0.3056 - 119ms/epoch - 13ms/step\n",
      "Epoch 1998/4000\n",
      "9/9 - 0s - loss: 0.0970 - accuracy: 0.9861 - val_loss: 61.8042 - val_accuracy: 0.2222 - 158ms/epoch - 18ms/step\n",
      "Epoch 1999/4000\n",
      "9/9 - 0s - loss: 0.0871 - accuracy: 1.0000 - val_loss: 14.8717 - val_accuracy: 0.3333 - 100ms/epoch - 11ms/step\n",
      "Epoch 2000/4000\n",
      "9/9 - 0s - loss: 0.0931 - accuracy: 0.9931 - val_loss: 18.5919 - val_accuracy: 0.4167 - 95ms/epoch - 11ms/step\n",
      "Epoch 2001/4000\n",
      "9/9 - 0s - loss: 0.0827 - accuracy: 1.0000 - val_loss: 4.8043 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 2002/4000\n",
      "9/9 - 0s - loss: 0.0850 - accuracy: 1.0000 - val_loss: 52.9824 - val_accuracy: 0.2778 - 105ms/epoch - 12ms/step\n",
      "Epoch 2003/4000\n",
      "9/9 - 0s - loss: 0.0910 - accuracy: 1.0000 - val_loss: 95.3861 - val_accuracy: 0.2917 - 111ms/epoch - 12ms/step\n",
      "Epoch 2004/4000\n",
      "9/9 - 0s - loss: 0.0986 - accuracy: 0.9931 - val_loss: 128.4176 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2005/4000\n",
      "9/9 - 0s - loss: 0.0912 - accuracy: 0.9931 - val_loss: 97.4201 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2006/4000\n",
      "9/9 - 0s - loss: 0.1062 - accuracy: 0.9931 - val_loss: 107.0849 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2007/4000\n",
      "9/9 - 0s - loss: 0.1424 - accuracy: 0.9931 - val_loss: 61.7694 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2008/4000\n",
      "9/9 - 0s - loss: 0.0905 - accuracy: 1.0000 - val_loss: 50.3556 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2009/4000\n",
      "9/9 - 0s - loss: 0.0982 - accuracy: 0.9931 - val_loss: 26.4078 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2010/4000\n",
      "9/9 - 0s - loss: 0.0931 - accuracy: 0.9931 - val_loss: 81.5706 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2011/4000\n",
      "9/9 - 0s - loss: 0.0925 - accuracy: 1.0000 - val_loss: 99.6484 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2012/4000\n",
      "9/9 - 0s - loss: 0.0714 - accuracy: 1.0000 - val_loss: 61.7115 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 2013/4000\n",
      "9/9 - 0s - loss: 0.0803 - accuracy: 1.0000 - val_loss: 68.9101 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 2014/4000\n",
      "9/9 - 0s - loss: 0.1047 - accuracy: 0.9931 - val_loss: 99.9237 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2015/4000\n",
      "9/9 - 0s - loss: 0.0839 - accuracy: 1.0000 - val_loss: 92.1323 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2016/4000\n",
      "9/9 - 0s - loss: 0.1029 - accuracy: 0.9931 - val_loss: 70.2236 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2017/4000\n",
      "9/9 - 0s - loss: 0.1031 - accuracy: 0.9792 - val_loss: 34.0098 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2018/4000\n",
      "9/9 - 0s - loss: 0.1240 - accuracy: 0.9792 - val_loss: 51.1914 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2019/4000\n",
      "9/9 - 0s - loss: 0.1041 - accuracy: 1.0000 - val_loss: 29.0507 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2020/4000\n",
      "9/9 - 0s - loss: 0.1006 - accuracy: 0.9931 - val_loss: 43.1103 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2021/4000\n",
      "9/9 - 0s - loss: 0.1229 - accuracy: 0.9792 - val_loss: 97.2683 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2022/4000\n",
      "9/9 - 0s - loss: 0.1194 - accuracy: 0.9722 - val_loss: 2.5270 - val_accuracy: 0.3194 - 88ms/epoch - 10ms/step\n",
      "Epoch 2023/4000\n",
      "9/9 - 0s - loss: 0.1167 - accuracy: 0.9931 - val_loss: 54.6352 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2024/4000\n",
      "9/9 - 0s - loss: 0.1193 - accuracy: 0.9931 - val_loss: 80.7302 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2025/4000\n",
      "9/9 - 0s - loss: 0.1012 - accuracy: 0.9931 - val_loss: 81.5266 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2026/4000\n",
      "9/9 - 0s - loss: 0.0860 - accuracy: 0.9931 - val_loss: 17.2970 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 2027/4000\n",
      "9/9 - 0s - loss: 0.0893 - accuracy: 0.9931 - val_loss: 26.1330 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 2028/4000\n",
      "9/9 - 0s - loss: 0.0991 - accuracy: 1.0000 - val_loss: 7.7719 - val_accuracy: 0.2917 - 107ms/epoch - 12ms/step\n",
      "Epoch 2029/4000\n",
      "9/9 - 0s - loss: 0.0980 - accuracy: 0.9931 - val_loss: 1.5085 - val_accuracy: 0.3750 - 116ms/epoch - 13ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2030/4000\n",
      "9/9 - 0s - loss: 0.0903 - accuracy: 1.0000 - val_loss: 44.2818 - val_accuracy: 0.2917 - 103ms/epoch - 11ms/step\n",
      "Epoch 2031/4000\n",
      "9/9 - 0s - loss: 0.0965 - accuracy: 1.0000 - val_loss: 73.7573 - val_accuracy: 0.2917 - 100ms/epoch - 11ms/step\n",
      "Epoch 2032/4000\n",
      "9/9 - 0s - loss: 0.0834 - accuracy: 1.0000 - val_loss: 64.4215 - val_accuracy: 0.2917 - 100ms/epoch - 11ms/step\n",
      "Epoch 2033/4000\n",
      "9/9 - 0s - loss: 0.1190 - accuracy: 0.9861 - val_loss: 46.1692 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2034/4000\n",
      "9/9 - 0s - loss: 0.0912 - accuracy: 1.0000 - val_loss: 23.8095 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2035/4000\n",
      "9/9 - 0s - loss: 0.0753 - accuracy: 1.0000 - val_loss: 67.5852 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2036/4000\n",
      "9/9 - 0s - loss: 0.0800 - accuracy: 1.0000 - val_loss: 68.7880 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2037/4000\n",
      "9/9 - 0s - loss: 0.0733 - accuracy: 1.0000 - val_loss: 48.1223 - val_accuracy: 0.2917 - 105ms/epoch - 12ms/step\n",
      "Epoch 2038/4000\n",
      "9/9 - 0s - loss: 0.0885 - accuracy: 1.0000 - val_loss: 47.2026 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 2039/4000\n",
      "9/9 - 0s - loss: 0.0931 - accuracy: 1.0000 - val_loss: 56.4412 - val_accuracy: 0.2917 - 107ms/epoch - 12ms/step\n",
      "Epoch 2040/4000\n",
      "9/9 - 0s - loss: 0.1049 - accuracy: 0.9792 - val_loss: 3.1726 - val_accuracy: 0.2778 - 87ms/epoch - 10ms/step\n",
      "Epoch 2041/4000\n",
      "9/9 - 0s - loss: 0.0762 - accuracy: 1.0000 - val_loss: 70.8531 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2042/4000\n",
      "9/9 - 0s - loss: 0.0889 - accuracy: 0.9931 - val_loss: 67.1567 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2043/4000\n",
      "9/9 - 0s - loss: 0.0881 - accuracy: 1.0000 - val_loss: 39.7168 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2044/4000\n",
      "9/9 - 0s - loss: 0.0880 - accuracy: 1.0000 - val_loss: 13.8420 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2045/4000\n",
      "9/9 - 0s - loss: 0.0795 - accuracy: 1.0000 - val_loss: 4.9330 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2046/4000\n",
      "9/9 - 0s - loss: 0.1088 - accuracy: 0.9861 - val_loss: 25.2857 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2047/4000\n",
      "9/9 - 0s - loss: 0.0888 - accuracy: 1.0000 - val_loss: 6.8231 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2048/4000\n",
      "9/9 - 0s - loss: 0.0858 - accuracy: 1.0000 - val_loss: 3.1836 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2049/4000\n",
      "9/9 - 0s - loss: 0.0943 - accuracy: 0.9931 - val_loss: 27.9784 - val_accuracy: 0.2917 - 102ms/epoch - 11ms/step\n",
      "Epoch 2050/4000\n",
      "9/9 - 0s - loss: 0.1090 - accuracy: 0.9931 - val_loss: 55.4871 - val_accuracy: 0.2917 - 116ms/epoch - 13ms/step\n",
      "Epoch 2051/4000\n",
      "9/9 - 0s - loss: 0.1055 - accuracy: 0.9861 - val_loss: 29.6395 - val_accuracy: 0.2917 - 121ms/epoch - 13ms/step\n",
      "Epoch 2052/4000\n",
      "9/9 - 0s - loss: 0.1044 - accuracy: 0.9931 - val_loss: 45.3932 - val_accuracy: 0.2778 - 91ms/epoch - 10ms/step\n",
      "Epoch 2053/4000\n",
      "9/9 - 0s - loss: 0.1120 - accuracy: 0.9792 - val_loss: 66.2898 - val_accuracy: 0.3194 - 89ms/epoch - 10ms/step\n",
      "Epoch 2054/4000\n",
      "9/9 - 0s - loss: 0.1262 - accuracy: 0.9931 - val_loss: 45.9432 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 2055/4000\n",
      "9/9 - 0s - loss: 0.1192 - accuracy: 0.9722 - val_loss: 94.4851 - val_accuracy: 0.2778 - 89ms/epoch - 10ms/step\n",
      "Epoch 2056/4000\n",
      "9/9 - 0s - loss: 0.1122 - accuracy: 0.9792 - val_loss: 115.3801 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2057/4000\n",
      "9/9 - 0s - loss: 0.0981 - accuracy: 1.0000 - val_loss: 139.2367 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2058/4000\n",
      "9/9 - 0s - loss: 0.0844 - accuracy: 1.0000 - val_loss: 112.5212 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2059/4000\n",
      "9/9 - 0s - loss: 0.1108 - accuracy: 0.9931 - val_loss: 122.8896 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2060/4000\n",
      "9/9 - 0s - loss: 0.1080 - accuracy: 0.9931 - val_loss: 78.4531 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 2061/4000\n",
      "9/9 - 0s - loss: 0.0959 - accuracy: 1.0000 - val_loss: 67.9852 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2062/4000\n",
      "9/9 - 0s - loss: 0.1202 - accuracy: 0.9861 - val_loss: 63.3965 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2063/4000\n",
      "9/9 - 0s - loss: 0.0936 - accuracy: 1.0000 - val_loss: 151.3104 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2064/4000\n",
      "9/9 - 0s - loss: 0.1021 - accuracy: 0.9931 - val_loss: 81.5663 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2065/4000\n",
      "9/9 - 0s - loss: 0.0937 - accuracy: 0.9931 - val_loss: 53.5403 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2066/4000\n",
      "9/9 - 0s - loss: 0.0865 - accuracy: 1.0000 - val_loss: 32.5827 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 2067/4000\n",
      "9/9 - 0s - loss: 0.0881 - accuracy: 1.0000 - val_loss: 29.4617 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2068/4000\n",
      "9/9 - 0s - loss: 0.0840 - accuracy: 0.9931 - val_loss: 44.0380 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2069/4000\n",
      "9/9 - 0s - loss: 0.0822 - accuracy: 1.0000 - val_loss: 31.1439 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2070/4000\n",
      "9/9 - 0s - loss: 0.0855 - accuracy: 0.9931 - val_loss: 1.6761 - val_accuracy: 0.3194 - 81ms/epoch - 9ms/step\n",
      "Epoch 2071/4000\n",
      "9/9 - 0s - loss: 0.1084 - accuracy: 0.9861 - val_loss: 91.3795 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2072/4000\n",
      "9/9 - 0s - loss: 0.1259 - accuracy: 0.9722 - val_loss: 106.7766 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2073/4000\n",
      "9/9 - 0s - loss: 0.1097 - accuracy: 0.9861 - val_loss: 56.1356 - val_accuracy: 0.2222 - 76ms/epoch - 8ms/step\n",
      "Epoch 2074/4000\n",
      "9/9 - 0s - loss: 0.1483 - accuracy: 0.9861 - val_loss: 99.8245 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2075/4000\n",
      "9/9 - 0s - loss: 0.1566 - accuracy: 0.9722 - val_loss: 130.3648 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 2076/4000\n",
      "9/9 - 0s - loss: 0.1088 - accuracy: 1.0000 - val_loss: 103.6564 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2077/4000\n",
      "9/9 - 0s - loss: 0.0875 - accuracy: 0.9931 - val_loss: 151.8244 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2078/4000\n",
      "9/9 - 0s - loss: 0.0892 - accuracy: 1.0000 - val_loss: 53.7073 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 2079/4000\n",
      "9/9 - 0s - loss: 0.0790 - accuracy: 1.0000 - val_loss: 66.9147 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 2080/4000\n",
      "9/9 - 0s - loss: 0.0828 - accuracy: 1.0000 - val_loss: 88.5847 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2081/4000\n",
      "9/9 - 0s - loss: 0.0857 - accuracy: 1.0000 - val_loss: 92.1098 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2082/4000\n",
      "9/9 - 0s - loss: 0.0844 - accuracy: 0.9861 - val_loss: 21.0001 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2083/4000\n",
      "9/9 - 0s - loss: 0.0845 - accuracy: 1.0000 - val_loss: 26.5492 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2084/4000\n",
      "9/9 - 0s - loss: 0.1067 - accuracy: 1.0000 - val_loss: 67.8936 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 2085/4000\n",
      "9/9 - 0s - loss: 0.0982 - accuracy: 0.9861 - val_loss: 133.2021 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2086/4000\n",
      "9/9 - 0s - loss: 0.1075 - accuracy: 0.9931 - val_loss: 43.2688 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2087/4000\n",
      "9/9 - 0s - loss: 0.0931 - accuracy: 0.9931 - val_loss: 66.6319 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 2088/4000\n",
      "9/9 - 0s - loss: 0.0929 - accuracy: 0.9931 - val_loss: 78.9249 - val_accuracy: 0.4444 - 94ms/epoch - 10ms/step\n",
      "Epoch 2089/4000\n",
      "9/9 - 0s - loss: 0.0884 - accuracy: 0.9931 - val_loss: 86.1604 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2090/4000\n",
      "9/9 - 0s - loss: 0.0663 - accuracy: 1.0000 - val_loss: 62.9476 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2091/4000\n",
      "9/9 - 0s - loss: 0.0942 - accuracy: 1.0000 - val_loss: 22.2963 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2092/4000\n",
      "9/9 - 0s - loss: 0.0981 - accuracy: 1.0000 - val_loss: 25.7786 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2093/4000\n",
      "9/9 - 0s - loss: 0.0938 - accuracy: 1.0000 - val_loss: 4.7292 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2094/4000\n",
      "9/9 - 0s - loss: 0.0864 - accuracy: 0.9931 - val_loss: 30.0374 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2095/4000\n",
      "9/9 - 0s - loss: 0.1029 - accuracy: 0.9931 - val_loss: 57.1968 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2096/4000\n",
      "9/9 - 0s - loss: 0.1076 - accuracy: 0.9861 - val_loss: 2.4065 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 2097/4000\n",
      "9/9 - 0s - loss: 0.0937 - accuracy: 1.0000 - val_loss: 18.6474 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 2098/4000\n",
      "9/9 - 0s - loss: 0.0920 - accuracy: 1.0000 - val_loss: 58.2316 - val_accuracy: 0.2917 - 109ms/epoch - 12ms/step\n",
      "Epoch 2099/4000\n",
      "9/9 - 0s - loss: 0.0833 - accuracy: 0.9931 - val_loss: 63.7497 - val_accuracy: 0.2917 - 103ms/epoch - 11ms/step\n",
      "Epoch 2100/4000\n",
      "9/9 - 0s - loss: 0.1021 - accuracy: 1.0000 - val_loss: 74.6079 - val_accuracy: 0.2917 - 110ms/epoch - 12ms/step\n",
      "Epoch 2101/4000\n",
      "9/9 - 0s - loss: 0.1512 - accuracy: 0.9653 - val_loss: 105.0068 - val_accuracy: 0.2917 - 130ms/epoch - 14ms/step\n",
      "Epoch 2102/4000\n",
      "9/9 - 0s - loss: 0.1144 - accuracy: 0.9861 - val_loss: 77.6856 - val_accuracy: 0.2917 - 118ms/epoch - 13ms/step\n",
      "Epoch 2103/4000\n",
      "9/9 - 0s - loss: 0.1217 - accuracy: 0.9792 - val_loss: 23.8325 - val_accuracy: 0.2917 - 119ms/epoch - 13ms/step\n",
      "Epoch 2104/4000\n",
      "9/9 - 0s - loss: 0.0997 - accuracy: 0.9931 - val_loss: 7.2345 - val_accuracy: 0.2917 - 118ms/epoch - 13ms/step\n",
      "Epoch 2105/4000\n",
      "9/9 - 0s - loss: 0.0872 - accuracy: 1.0000 - val_loss: 10.3242 - val_accuracy: 0.2917 - 136ms/epoch - 15ms/step\n",
      "Epoch 2106/4000\n",
      "9/9 - 0s - loss: 0.1014 - accuracy: 0.9931 - val_loss: 47.1555 - val_accuracy: 0.2222 - 129ms/epoch - 14ms/step\n",
      "Epoch 2107/4000\n",
      "9/9 - 0s - loss: 0.0911 - accuracy: 0.9861 - val_loss: 20.1689 - val_accuracy: 0.2222 - 110ms/epoch - 12ms/step\n",
      "Epoch 2108/4000\n",
      "9/9 - 0s - loss: 0.0717 - accuracy: 1.0000 - val_loss: 34.4473 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 2109/4000\n",
      "9/9 - 0s - loss: 0.0937 - accuracy: 0.9931 - val_loss: 16.3448 - val_accuracy: 0.2222 - 108ms/epoch - 12ms/step\n",
      "Epoch 2110/4000\n",
      "9/9 - 0s - loss: 0.0919 - accuracy: 1.0000 - val_loss: 24.0104 - val_accuracy: 0.2222 - 117ms/epoch - 13ms/step\n",
      "Epoch 2111/4000\n",
      "9/9 - 0s - loss: 0.0946 - accuracy: 0.9931 - val_loss: 2.7645 - val_accuracy: 0.2917 - 123ms/epoch - 14ms/step\n",
      "Epoch 2112/4000\n",
      "9/9 - 0s - loss: 0.1016 - accuracy: 0.9931 - val_loss: 9.4616 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 2113/4000\n",
      "9/9 - 0s - loss: 0.0920 - accuracy: 1.0000 - val_loss: 1.4932 - val_accuracy: 0.5139 - 109ms/epoch - 12ms/step\n",
      "Epoch 2114/4000\n",
      "9/9 - 0s - loss: 0.0753 - accuracy: 1.0000 - val_loss: 1.3658 - val_accuracy: 0.3750 - 122ms/epoch - 14ms/step\n",
      "Epoch 2115/4000\n",
      "9/9 - 0s - loss: 0.0794 - accuracy: 1.0000 - val_loss: 16.4710 - val_accuracy: 0.2222 - 114ms/epoch - 13ms/step\n",
      "Epoch 2116/4000\n",
      "9/9 - 0s - loss: 0.0820 - accuracy: 1.0000 - val_loss: 9.6590 - val_accuracy: 0.3472 - 127ms/epoch - 14ms/step\n",
      "Epoch 2117/4000\n",
      "9/9 - 0s - loss: 0.0907 - accuracy: 0.9792 - val_loss: 50.4073 - val_accuracy: 0.2222 - 133ms/epoch - 15ms/step\n",
      "Epoch 2118/4000\n",
      "9/9 - 0s - loss: 0.1012 - accuracy: 0.9861 - val_loss: 44.2527 - val_accuracy: 0.2917 - 112ms/epoch - 12ms/step\n",
      "Epoch 2119/4000\n",
      "9/9 - 0s - loss: 0.1162 - accuracy: 0.9792 - val_loss: 22.8113 - val_accuracy: 0.2917 - 112ms/epoch - 12ms/step\n",
      "Epoch 2120/4000\n",
      "9/9 - 0s - loss: 0.0915 - accuracy: 0.9931 - val_loss: 10.0100 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2121/4000\n",
      "9/9 - 0s - loss: 0.0875 - accuracy: 0.9931 - val_loss: 22.7117 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 2122/4000\n",
      "9/9 - 0s - loss: 0.0801 - accuracy: 0.9931 - val_loss: 92.2419 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 2123/4000\n",
      "9/9 - 0s - loss: 0.1001 - accuracy: 0.9861 - val_loss: 78.9100 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2124/4000\n",
      "9/9 - 0s - loss: 0.0965 - accuracy: 1.0000 - val_loss: 15.6331 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2125/4000\n",
      "9/9 - 0s - loss: 0.0873 - accuracy: 1.0000 - val_loss: 40.7539 - val_accuracy: 0.3611 - 85ms/epoch - 9ms/step\n",
      "Epoch 2126/4000\n",
      "9/9 - 0s - loss: 0.0951 - accuracy: 1.0000 - val_loss: 78.8676 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 2127/4000\n",
      "9/9 - 0s - loss: 0.0858 - accuracy: 0.9931 - val_loss: 79.1968 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2128/4000\n",
      "9/9 - 0s - loss: 0.1249 - accuracy: 0.9792 - val_loss: 139.8115 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2129/4000\n",
      "9/9 - 0s - loss: 0.0796 - accuracy: 0.9931 - val_loss: 187.1065 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2130/4000\n",
      "9/9 - 0s - loss: 0.0788 - accuracy: 0.9931 - val_loss: 47.8254 - val_accuracy: 0.2639 - 90ms/epoch - 10ms/step\n",
      "Epoch 2131/4000\n",
      "9/9 - 0s - loss: 0.0983 - accuracy: 0.9861 - val_loss: 84.2359 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2132/4000\n",
      "9/9 - 0s - loss: 0.0772 - accuracy: 0.9931 - val_loss: 85.9248 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 2133/4000\n",
      "9/9 - 0s - loss: 0.1006 - accuracy: 0.9861 - val_loss: 78.8144 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 2134/4000\n",
      "9/9 - 0s - loss: 0.0997 - accuracy: 0.9931 - val_loss: 1.2675 - val_accuracy: 0.5556 - 94ms/epoch - 10ms/step\n",
      "Epoch 2135/4000\n",
      "9/9 - 0s - loss: 0.0942 - accuracy: 0.9861 - val_loss: 23.5362 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2136/4000\n",
      "9/9 - 0s - loss: 0.0922 - accuracy: 1.0000 - val_loss: 50.8922 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2137/4000\n",
      "9/9 - 0s - loss: 0.1040 - accuracy: 0.9861 - val_loss: 54.4364 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2138/4000\n",
      "9/9 - 0s - loss: 0.1027 - accuracy: 0.9861 - val_loss: 26.0752 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2139/4000\n",
      "9/9 - 0s - loss: 0.1089 - accuracy: 0.9861 - val_loss: 4.1916 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2140/4000\n",
      "9/9 - 0s - loss: 0.1104 - accuracy: 0.9861 - val_loss: 53.7332 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 2141/4000\n",
      "9/9 - 0s - loss: 0.1104 - accuracy: 0.9861 - val_loss: 53.0774 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2142/4000\n",
      "9/9 - 0s - loss: 0.0851 - accuracy: 0.9931 - val_loss: 17.1580 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 2143/4000\n",
      "9/9 - 0s - loss: 0.0766 - accuracy: 0.9931 - val_loss: 66.2019 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2144/4000\n",
      "9/9 - 0s - loss: 0.0841 - accuracy: 0.9931 - val_loss: 115.3512 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2145/4000\n",
      "9/9 - 0s - loss: 0.0987 - accuracy: 1.0000 - val_loss: 96.6244 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2146/4000\n",
      "9/9 - 0s - loss: 0.1096 - accuracy: 0.9792 - val_loss: 29.3234 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2147/4000\n",
      "9/9 - 0s - loss: 0.0860 - accuracy: 1.0000 - val_loss: 6.6604 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 2148/4000\n",
      "9/9 - 0s - loss: 0.1064 - accuracy: 0.9931 - val_loss: 27.5169 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 2149/4000\n",
      "9/9 - 0s - loss: 0.0881 - accuracy: 0.9931 - val_loss: 66.5493 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2150/4000\n",
      "9/9 - 0s - loss: 0.1079 - accuracy: 0.9931 - val_loss: 57.1750 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2151/4000\n",
      "9/9 - 0s - loss: 0.0800 - accuracy: 0.9931 - val_loss: 70.4790 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 2152/4000\n",
      "9/9 - 0s - loss: 0.1186 - accuracy: 1.0000 - val_loss: 65.0286 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 2153/4000\n",
      "9/9 - 0s - loss: 0.0739 - accuracy: 1.0000 - val_loss: 79.6194 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 2154/4000\n",
      "9/9 - 0s - loss: 0.0688 - accuracy: 1.0000 - val_loss: 72.8407 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 2155/4000\n",
      "9/9 - 0s - loss: 0.0926 - accuracy: 0.9861 - val_loss: 81.0405 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 2156/4000\n",
      "9/9 - 0s - loss: 0.0682 - accuracy: 1.0000 - val_loss: 66.0600 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 2157/4000\n",
      "9/9 - 0s - loss: 0.0763 - accuracy: 1.0000 - val_loss: 48.6148 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2158/4000\n",
      "9/9 - 0s - loss: 0.1284 - accuracy: 0.9792 - val_loss: 115.4134 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2159/4000\n",
      "9/9 - 0s - loss: 0.0787 - accuracy: 1.0000 - val_loss: 70.9223 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2160/4000\n",
      "9/9 - 0s - loss: 0.0984 - accuracy: 1.0000 - val_loss: 48.3049 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 2161/4000\n",
      "9/9 - 0s - loss: 0.0983 - accuracy: 0.9931 - val_loss: 51.9400 - val_accuracy: 0.3194 - 83ms/epoch - 9ms/step\n",
      "Epoch 2162/4000\n",
      "9/9 - 0s - loss: 0.0787 - accuracy: 1.0000 - val_loss: 79.1327 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2163/4000\n",
      "9/9 - 0s - loss: 0.0855 - accuracy: 1.0000 - val_loss: 90.6686 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2164/4000\n",
      "9/9 - 0s - loss: 0.0776 - accuracy: 1.0000 - val_loss: 65.5805 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2165/4000\n",
      "9/9 - 0s - loss: 0.0821 - accuracy: 1.0000 - val_loss: 45.3465 - val_accuracy: 0.5000 - 91ms/epoch - 10ms/step\n",
      "Epoch 2166/4000\n",
      "9/9 - 0s - loss: 0.0834 - accuracy: 1.0000 - val_loss: 51.8703 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 2167/4000\n",
      "9/9 - 0s - loss: 0.0730 - accuracy: 1.0000 - val_loss: 68.6944 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2168/4000\n",
      "9/9 - 0s - loss: 0.0740 - accuracy: 1.0000 - val_loss: 13.5725 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2169/4000\n",
      "9/9 - 0s - loss: 0.0879 - accuracy: 1.0000 - val_loss: 55.2432 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 2170/4000\n",
      "9/9 - 0s - loss: 0.0887 - accuracy: 0.9931 - val_loss: 85.7862 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2171/4000\n",
      "9/9 - 0s - loss: 0.1030 - accuracy: 0.9861 - val_loss: 85.9681 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2172/4000\n",
      "9/9 - 0s - loss: 0.0769 - accuracy: 1.0000 - val_loss: 4.5738 - val_accuracy: 0.3611 - 83ms/epoch - 9ms/step\n",
      "Epoch 2173/4000\n",
      "9/9 - 0s - loss: 0.0813 - accuracy: 0.9931 - val_loss: 69.1352 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2174/4000\n",
      "9/9 - 0s - loss: 0.0814 - accuracy: 1.0000 - val_loss: 86.6721 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 2175/4000\n",
      "9/9 - 0s - loss: 0.1119 - accuracy: 0.9792 - val_loss: 45.8580 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2176/4000\n",
      "9/9 - 0s - loss: 0.1239 - accuracy: 0.9722 - val_loss: 94.2602 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2177/4000\n",
      "9/9 - 0s - loss: 0.0722 - accuracy: 1.0000 - val_loss: 64.0168 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 2178/4000\n",
      "9/9 - 0s - loss: 0.0884 - accuracy: 0.9792 - val_loss: 66.9882 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2179/4000\n",
      "9/9 - 0s - loss: 0.0841 - accuracy: 1.0000 - val_loss: 73.5418 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2180/4000\n",
      "9/9 - 0s - loss: 0.0955 - accuracy: 1.0000 - val_loss: 110.2771 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2181/4000\n",
      "9/9 - 0s - loss: 0.1096 - accuracy: 0.9931 - val_loss: 120.0415 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2182/4000\n",
      "9/9 - 0s - loss: 0.1107 - accuracy: 0.9861 - val_loss: 30.8580 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 2183/4000\n",
      "9/9 - 0s - loss: 0.1005 - accuracy: 0.9931 - val_loss: 1.7536 - val_accuracy: 0.3056 - 85ms/epoch - 9ms/step\n",
      "Epoch 2184/4000\n",
      "9/9 - 0s - loss: 0.0942 - accuracy: 0.9861 - val_loss: 37.3864 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2185/4000\n",
      "9/9 - 0s - loss: 0.0947 - accuracy: 1.0000 - val_loss: 109.5294 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2186/4000\n",
      "9/9 - 0s - loss: 0.0992 - accuracy: 0.9931 - val_loss: 57.9351 - val_accuracy: 0.2639 - 81ms/epoch - 9ms/step\n",
      "Epoch 2187/4000\n",
      "9/9 - 0s - loss: 0.0904 - accuracy: 0.9931 - val_loss: 37.9600 - val_accuracy: 0.2222 - 77ms/epoch - 9ms/step\n",
      "Epoch 2188/4000\n",
      "9/9 - 0s - loss: 0.0881 - accuracy: 1.0000 - val_loss: 54.2963 - val_accuracy: 0.2222 - 75ms/epoch - 8ms/step\n",
      "Epoch 2189/4000\n",
      "9/9 - 0s - loss: 0.1178 - accuracy: 0.9792 - val_loss: 68.3319 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2190/4000\n",
      "9/9 - 0s - loss: 0.0723 - accuracy: 1.0000 - val_loss: 7.6246 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2191/4000\n",
      "9/9 - 0s - loss: 0.0988 - accuracy: 1.0000 - val_loss: 14.6334 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 2192/4000\n",
      "9/9 - 0s - loss: 0.0796 - accuracy: 1.0000 - val_loss: 13.4777 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2193/4000\n",
      "9/9 - 0s - loss: 0.1045 - accuracy: 0.9861 - val_loss: 3.5218 - val_accuracy: 0.3472 - 88ms/epoch - 10ms/step\n",
      "Epoch 2194/4000\n",
      "9/9 - 0s - loss: 0.0801 - accuracy: 1.0000 - val_loss: 3.7252 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 2195/4000\n",
      "9/9 - 0s - loss: 0.0804 - accuracy: 0.9931 - val_loss: 46.6277 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 2196/4000\n",
      "9/9 - 0s - loss: 0.0967 - accuracy: 0.9861 - val_loss: 91.9930 - val_accuracy: 0.2917 - 99ms/epoch - 11ms/step\n",
      "Epoch 2197/4000\n",
      "9/9 - 0s - loss: 0.0680 - accuracy: 1.0000 - val_loss: 75.4212 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2198/4000\n",
      "9/9 - 0s - loss: 0.0737 - accuracy: 1.0000 - val_loss: 2.6529 - val_accuracy: 0.3194 - 85ms/epoch - 9ms/step\n",
      "Epoch 2199/4000\n",
      "9/9 - 0s - loss: 0.0921 - accuracy: 0.9931 - val_loss: 17.9450 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 2200/4000\n",
      "9/9 - 0s - loss: 0.0844 - accuracy: 1.0000 - val_loss: 90.5424 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2201/4000\n",
      "9/9 - 0s - loss: 0.0666 - accuracy: 1.0000 - val_loss: 58.9300 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2202/4000\n",
      "9/9 - 0s - loss: 0.0888 - accuracy: 0.9861 - val_loss: 3.9921 - val_accuracy: 0.2361 - 87ms/epoch - 10ms/step\n",
      "Epoch 2203/4000\n",
      "9/9 - 0s - loss: 0.0880 - accuracy: 0.9931 - val_loss: 17.5117 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 2204/4000\n",
      "9/9 - 0s - loss: 0.0820 - accuracy: 1.0000 - val_loss: 55.4399 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2205/4000\n",
      "9/9 - 0s - loss: 0.1064 - accuracy: 0.9792 - val_loss: 41.0317 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 2206/4000\n",
      "9/9 - 0s - loss: 0.0940 - accuracy: 1.0000 - val_loss: 44.3466 - val_accuracy: 0.3750 - 81ms/epoch - 9ms/step\n",
      "Epoch 2207/4000\n",
      "9/9 - 0s - loss: 0.1230 - accuracy: 0.9861 - val_loss: 53.8154 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2208/4000\n",
      "9/9 - 0s - loss: 0.1249 - accuracy: 0.9931 - val_loss: 31.3881 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2209/4000\n",
      "9/9 - 0s - loss: 0.1072 - accuracy: 0.9861 - val_loss: 44.3036 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 2210/4000\n",
      "9/9 - 0s - loss: 0.0814 - accuracy: 1.0000 - val_loss: 1.9449 - val_accuracy: 0.2361 - 80ms/epoch - 9ms/step\n",
      "Epoch 2211/4000\n",
      "9/9 - 0s - loss: 0.1154 - accuracy: 0.9792 - val_loss: 28.6758 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2212/4000\n",
      "9/9 - 0s - loss: 0.1055 - accuracy: 0.9931 - val_loss: 69.3080 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2213/4000\n",
      "9/9 - 0s - loss: 0.0795 - accuracy: 0.9931 - val_loss: 67.6646 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2214/4000\n",
      "9/9 - 0s - loss: 0.0944 - accuracy: 0.9931 - val_loss: 33.5569 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2215/4000\n",
      "9/9 - 0s - loss: 0.1117 - accuracy: 0.9792 - val_loss: 182.8517 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 2216/4000\n",
      "9/9 - 0s - loss: 0.1019 - accuracy: 0.9931 - val_loss: 232.7741 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2217/4000\n",
      "9/9 - 0s - loss: 0.1477 - accuracy: 0.9514 - val_loss: 340.7057 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 2218/4000\n",
      "9/9 - 0s - loss: 0.1075 - accuracy: 0.9792 - val_loss: 72.7822 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2219/4000\n",
      "9/9 - 0s - loss: 0.0986 - accuracy: 0.9861 - val_loss: 150.6038 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2220/4000\n",
      "9/9 - 0s - loss: 0.1043 - accuracy: 1.0000 - val_loss: 173.8987 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 2221/4000\n",
      "9/9 - 0s - loss: 0.1255 - accuracy: 0.9792 - val_loss: 64.3401 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2222/4000\n",
      "9/9 - 0s - loss: 0.0943 - accuracy: 1.0000 - val_loss: 86.5168 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2223/4000\n",
      "9/9 - 0s - loss: 0.0823 - accuracy: 0.9931 - val_loss: 38.2509 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2224/4000\n",
      "9/9 - 0s - loss: 0.0889 - accuracy: 1.0000 - val_loss: 63.0894 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2225/4000\n",
      "9/9 - 0s - loss: 0.0890 - accuracy: 1.0000 - val_loss: 60.0187 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 2226/4000\n",
      "9/9 - 0s - loss: 0.0839 - accuracy: 0.9792 - val_loss: 66.9888 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2227/4000\n",
      "9/9 - 0s - loss: 0.0816 - accuracy: 1.0000 - val_loss: 50.9284 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n",
      "Epoch 2228/4000\n",
      "9/9 - 0s - loss: 0.0765 - accuracy: 1.0000 - val_loss: 45.7554 - val_accuracy: 0.2639 - 93ms/epoch - 10ms/step\n",
      "Epoch 2229/4000\n",
      "9/9 - 0s - loss: 0.0848 - accuracy: 1.0000 - val_loss: 77.0138 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2230/4000\n",
      "9/9 - 0s - loss: 0.0726 - accuracy: 1.0000 - val_loss: 83.2121 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2231/4000\n",
      "9/9 - 0s - loss: 0.1040 - accuracy: 0.9931 - val_loss: 94.1395 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 2232/4000\n",
      "9/9 - 0s - loss: 0.0711 - accuracy: 1.0000 - val_loss: 98.2818 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2233/4000\n",
      "9/9 - 0s - loss: 0.0831 - accuracy: 0.9931 - val_loss: 72.9921 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2234/4000\n",
      "9/9 - 0s - loss: 0.0719 - accuracy: 1.0000 - val_loss: 8.9312 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2235/4000\n",
      "9/9 - 0s - loss: 0.0788 - accuracy: 0.9931 - val_loss: 66.4169 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2236/4000\n",
      "9/9 - 0s - loss: 0.0713 - accuracy: 0.9861 - val_loss: 81.9960 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2237/4000\n",
      "9/9 - 0s - loss: 0.0863 - accuracy: 1.0000 - val_loss: 96.9360 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2238/4000\n",
      "9/9 - 0s - loss: 0.1021 - accuracy: 0.9861 - val_loss: 110.4270 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2239/4000\n",
      "9/9 - 0s - loss: 0.0761 - accuracy: 1.0000 - val_loss: 123.6553 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2240/4000\n",
      "9/9 - 0s - loss: 0.0872 - accuracy: 0.9861 - val_loss: 85.0598 - val_accuracy: 0.3194 - 99ms/epoch - 11ms/step\n",
      "Epoch 2241/4000\n",
      "9/9 - 0s - loss: 0.0908 - accuracy: 1.0000 - val_loss: 49.6369 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2242/4000\n",
      "9/9 - 0s - loss: 0.0708 - accuracy: 1.0000 - val_loss: 34.4953 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2243/4000\n",
      "9/9 - 0s - loss: 0.0772 - accuracy: 1.0000 - val_loss: 69.4432 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2244/4000\n",
      "9/9 - 0s - loss: 0.0997 - accuracy: 1.0000 - val_loss: 80.4827 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2245/4000\n",
      "9/9 - 0s - loss: 0.0697 - accuracy: 1.0000 - val_loss: 50.7085 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2246/4000\n",
      "9/9 - 0s - loss: 0.0874 - accuracy: 0.9931 - val_loss: 14.1144 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2247/4000\n",
      "9/9 - 0s - loss: 0.0793 - accuracy: 0.9931 - val_loss: 77.5701 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 2248/4000\n",
      "9/9 - 0s - loss: 0.0752 - accuracy: 1.0000 - val_loss: 61.4019 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 2249/4000\n",
      "9/9 - 0s - loss: 0.0846 - accuracy: 0.9861 - val_loss: 36.0024 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2250/4000\n",
      "9/9 - 0s - loss: 0.0916 - accuracy: 0.9861 - val_loss: 1.3416 - val_accuracy: 0.3889 - 90ms/epoch - 10ms/step\n",
      "Epoch 2251/4000\n",
      "9/9 - 0s - loss: 0.0868 - accuracy: 1.0000 - val_loss: 28.6511 - val_accuracy: 0.2917 - 124ms/epoch - 14ms/step\n",
      "Epoch 2252/4000\n",
      "9/9 - 0s - loss: 0.0911 - accuracy: 0.9861 - val_loss: 26.6816 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2253/4000\n",
      "9/9 - 0s - loss: 0.0870 - accuracy: 1.0000 - val_loss: 20.2883 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 2254/4000\n",
      "9/9 - 0s - loss: 0.1122 - accuracy: 0.9861 - val_loss: 33.3489 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 2255/4000\n",
      "9/9 - 0s - loss: 0.1080 - accuracy: 0.9861 - val_loss: 58.3906 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 2256/4000\n",
      "9/9 - 0s - loss: 0.0945 - accuracy: 0.9931 - val_loss: 88.1507 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 2257/4000\n",
      "9/9 - 0s - loss: 0.0777 - accuracy: 0.9931 - val_loss: 46.1684 - val_accuracy: 0.2917 - 100ms/epoch - 11ms/step\n",
      "Epoch 2258/4000\n",
      "9/9 - 0s - loss: 0.0939 - accuracy: 0.9861 - val_loss: 31.4255 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 2259/4000\n",
      "9/9 - 0s - loss: 0.0748 - accuracy: 0.9931 - val_loss: 56.3810 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2260/4000\n",
      "9/9 - 0s - loss: 0.0900 - accuracy: 0.9931 - val_loss: 103.7046 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2261/4000\n",
      "9/9 - 0s - loss: 0.0837 - accuracy: 1.0000 - val_loss: 85.3990 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n",
      "Epoch 2262/4000\n",
      "9/9 - 0s - loss: 0.1000 - accuracy: 1.0000 - val_loss: 60.1142 - val_accuracy: 0.2222 - 108ms/epoch - 12ms/step\n",
      "Epoch 2263/4000\n",
      "9/9 - 0s - loss: 0.1053 - accuracy: 0.9931 - val_loss: 8.6379 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2264/4000\n",
      "9/9 - 0s - loss: 0.1386 - accuracy: 0.9653 - val_loss: 33.0403 - val_accuracy: 0.2222 - 109ms/epoch - 12ms/step\n",
      "Epoch 2265/4000\n",
      "9/9 - 0s - loss: 0.1034 - accuracy: 0.9861 - val_loss: 21.0657 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 2266/4000\n",
      "9/9 - 0s - loss: 0.1105 - accuracy: 0.9861 - val_loss: 71.4315 - val_accuracy: 0.2917 - 109ms/epoch - 12ms/step\n",
      "Epoch 2267/4000\n",
      "9/9 - 0s - loss: 0.0946 - accuracy: 0.9931 - val_loss: 122.9880 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 2268/4000\n",
      "9/9 - 0s - loss: 0.0979 - accuracy: 0.9792 - val_loss: 96.9298 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 2269/4000\n",
      "9/9 - 0s - loss: 0.1410 - accuracy: 0.9722 - val_loss: 6.5292 - val_accuracy: 0.3333 - 88ms/epoch - 10ms/step\n",
      "Epoch 2270/4000\n",
      "9/9 - 0s - loss: 0.0905 - accuracy: 1.0000 - val_loss: 33.2352 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2271/4000\n",
      "9/9 - 0s - loss: 0.0946 - accuracy: 1.0000 - val_loss: 39.2560 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2272/4000\n",
      "9/9 - 0s - loss: 0.0778 - accuracy: 1.0000 - val_loss: 35.2860 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2273/4000\n",
      "9/9 - 0s - loss: 0.0800 - accuracy: 1.0000 - val_loss: 42.7406 - val_accuracy: 0.2222 - 129ms/epoch - 14ms/step\n",
      "Epoch 2274/4000\n",
      "9/9 - 0s - loss: 0.0910 - accuracy: 0.9861 - val_loss: 52.7021 - val_accuracy: 0.2222 - 122ms/epoch - 14ms/step\n",
      "Epoch 2275/4000\n",
      "9/9 - 0s - loss: 0.0918 - accuracy: 0.9931 - val_loss: 76.5953 - val_accuracy: 0.2222 - 121ms/epoch - 13ms/step\n",
      "Epoch 2276/4000\n",
      "9/9 - 0s - loss: 0.0762 - accuracy: 1.0000 - val_loss: 54.5618 - val_accuracy: 0.2917 - 131ms/epoch - 15ms/step\n",
      "Epoch 2277/4000\n",
      "9/9 - 0s - loss: 0.0676 - accuracy: 1.0000 - val_loss: 27.4231 - val_accuracy: 0.4167 - 132ms/epoch - 15ms/step\n",
      "Epoch 2278/4000\n",
      "9/9 - 0s - loss: 0.0703 - accuracy: 1.0000 - val_loss: 119.9262 - val_accuracy: 0.2222 - 161ms/epoch - 18ms/step\n",
      "Epoch 2279/4000\n",
      "9/9 - 0s - loss: 0.0690 - accuracy: 1.0000 - val_loss: 150.6549 - val_accuracy: 0.2222 - 183ms/epoch - 20ms/step\n",
      "Epoch 2280/4000\n",
      "9/9 - 0s - loss: 0.0770 - accuracy: 1.0000 - val_loss: 200.6100 - val_accuracy: 0.2222 - 224ms/epoch - 25ms/step\n",
      "Epoch 2281/4000\n",
      "9/9 - 0s - loss: 0.0929 - accuracy: 1.0000 - val_loss: 119.3462 - val_accuracy: 0.2222 - 175ms/epoch - 19ms/step\n",
      "Epoch 2282/4000\n",
      "9/9 - 0s - loss: 0.0976 - accuracy: 0.9861 - val_loss: 154.4949 - val_accuracy: 0.2222 - 495ms/epoch - 55ms/step\n",
      "Epoch 2283/4000\n",
      "9/9 - 1s - loss: 0.0974 - accuracy: 0.9861 - val_loss: 238.2638 - val_accuracy: 0.2222 - 514ms/epoch - 57ms/step\n",
      "Epoch 2284/4000\n",
      "9/9 - 2s - loss: 0.1070 - accuracy: 0.9861 - val_loss: 47.8527 - val_accuracy: 0.2639 - 2s/epoch - 201ms/step\n",
      "Epoch 2285/4000\n",
      "9/9 - 1s - loss: 0.0811 - accuracy: 1.0000 - val_loss: 31.1528 - val_accuracy: 0.2917 - 757ms/epoch - 84ms/step\n",
      "Epoch 2286/4000\n",
      "9/9 - 0s - loss: 0.0778 - accuracy: 1.0000 - val_loss: 11.4009 - val_accuracy: 0.2917 - 107ms/epoch - 12ms/step\n",
      "Epoch 2287/4000\n",
      "9/9 - 0s - loss: 0.0828 - accuracy: 0.9931 - val_loss: 14.0175 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 2288/4000\n",
      "9/9 - 0s - loss: 0.0835 - accuracy: 1.0000 - val_loss: 3.5645 - val_accuracy: 0.2917 - 105ms/epoch - 12ms/step\n",
      "Epoch 2289/4000\n",
      "9/9 - 0s - loss: 0.0845 - accuracy: 0.9931 - val_loss: 6.2449 - val_accuracy: 0.2917 - 103ms/epoch - 11ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2290/4000\n",
      "9/9 - 0s - loss: 0.0977 - accuracy: 0.9792 - val_loss: 28.5090 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2291/4000\n",
      "9/9 - 0s - loss: 0.0800 - accuracy: 1.0000 - val_loss: 115.9403 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2292/4000\n",
      "9/9 - 0s - loss: 0.0923 - accuracy: 0.9931 - val_loss: 141.5559 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2293/4000\n",
      "9/9 - 2s - loss: 0.1089 - accuracy: 0.9792 - val_loss: 62.7175 - val_accuracy: 0.2222 - 2s/epoch - 207ms/step\n",
      "Epoch 2294/4000\n",
      "9/9 - 0s - loss: 0.0906 - accuracy: 0.9931 - val_loss: 51.7149 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 2295/4000\n",
      "9/9 - 0s - loss: 0.0839 - accuracy: 1.0000 - val_loss: 76.0770 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 2296/4000\n",
      "9/9 - 0s - loss: 0.0760 - accuracy: 1.0000 - val_loss: 76.8415 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2297/4000\n",
      "9/9 - 0s - loss: 0.0858 - accuracy: 0.9931 - val_loss: 71.8058 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2298/4000\n",
      "9/9 - 0s - loss: 0.0762 - accuracy: 1.0000 - val_loss: 46.9154 - val_accuracy: 0.2917 - 74ms/epoch - 8ms/step\n",
      "Epoch 2299/4000\n",
      "9/9 - 0s - loss: 0.0761 - accuracy: 1.0000 - val_loss: 91.5191 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 2300/4000\n",
      "9/9 - 0s - loss: 0.0604 - accuracy: 1.0000 - val_loss: 71.5234 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2301/4000\n",
      "9/9 - 0s - loss: 0.0774 - accuracy: 1.0000 - val_loss: 76.5716 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2302/4000\n",
      "9/9 - 0s - loss: 0.0660 - accuracy: 1.0000 - val_loss: 182.7293 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2303/4000\n",
      "9/9 - 0s - loss: 0.0641 - accuracy: 1.0000 - val_loss: 134.2328 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2304/4000\n",
      "9/9 - 0s - loss: 0.0780 - accuracy: 1.0000 - val_loss: 101.9584 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2305/4000\n",
      "9/9 - 3s - loss: 0.0656 - accuracy: 1.0000 - val_loss: 57.2480 - val_accuracy: 0.2222 - 3s/epoch - 345ms/step\n",
      "Epoch 2306/4000\n",
      "9/9 - 0s - loss: 0.0777 - accuracy: 1.0000 - val_loss: 84.5385 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2307/4000\n",
      "9/9 - 0s - loss: 0.0674 - accuracy: 1.0000 - val_loss: 36.9066 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 2308/4000\n",
      "9/9 - 0s - loss: 0.0843 - accuracy: 1.0000 - val_loss: 11.2221 - val_accuracy: 0.3194 - 103ms/epoch - 11ms/step\n",
      "Epoch 2309/4000\n",
      "9/9 - 0s - loss: 0.0752 - accuracy: 0.9931 - val_loss: 20.9713 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 2310/4000\n",
      "9/9 - 0s - loss: 0.0845 - accuracy: 0.9931 - val_loss: 1.4704 - val_accuracy: 0.4028 - 81ms/epoch - 9ms/step\n",
      "Epoch 2311/4000\n",
      "9/9 - 0s - loss: 0.0622 - accuracy: 1.0000 - val_loss: 65.4877 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 2312/4000\n",
      "9/9 - 0s - loss: 0.0582 - accuracy: 1.0000 - val_loss: 53.1680 - val_accuracy: 0.2639 - 94ms/epoch - 10ms/step\n",
      "Epoch 2313/4000\n",
      "9/9 - 0s - loss: 0.0860 - accuracy: 0.9931 - val_loss: 79.5280 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2314/4000\n",
      "9/9 - 0s - loss: 0.0758 - accuracy: 0.9931 - val_loss: 32.5535 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2315/4000\n",
      "9/9 - 0s - loss: 0.0708 - accuracy: 0.9931 - val_loss: 34.2947 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2316/4000\n",
      "9/9 - 3s - loss: 0.1183 - accuracy: 0.9722 - val_loss: 99.9927 - val_accuracy: 0.2917 - 3s/epoch - 293ms/step\n",
      "Epoch 2317/4000\n",
      "9/9 - 0s - loss: 0.0788 - accuracy: 0.9931 - val_loss: 69.0800 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2318/4000\n",
      "9/9 - 0s - loss: 0.0810 - accuracy: 1.0000 - val_loss: 84.6310 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2319/4000\n",
      "9/9 - 0s - loss: 0.0683 - accuracy: 1.0000 - val_loss: 154.1902 - val_accuracy: 0.2222 - 112ms/epoch - 12ms/step\n",
      "Epoch 2320/4000\n",
      "9/9 - 0s - loss: 0.0642 - accuracy: 1.0000 - val_loss: 107.1363 - val_accuracy: 0.2222 - 116ms/epoch - 13ms/step\n",
      "Epoch 2321/4000\n",
      "9/9 - 0s - loss: 0.0560 - accuracy: 1.0000 - val_loss: 61.4047 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 2322/4000\n",
      "9/9 - 0s - loss: 0.0716 - accuracy: 0.9931 - val_loss: 14.3962 - val_accuracy: 0.2917 - 121ms/epoch - 13ms/step\n",
      "Epoch 2323/4000\n",
      "9/9 - 0s - loss: 0.0925 - accuracy: 0.9931 - val_loss: 76.8168 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 2324/4000\n",
      "9/9 - 0s - loss: 0.0720 - accuracy: 0.9931 - val_loss: 97.6003 - val_accuracy: 0.2917 - 147ms/epoch - 16ms/step\n",
      "Epoch 2325/4000\n",
      "9/9 - 3s - loss: 0.0813 - accuracy: 0.9931 - val_loss: 88.7859 - val_accuracy: 0.2917 - 3s/epoch - 288ms/step\n",
      "Epoch 2326/4000\n",
      "9/9 - 0s - loss: 0.0642 - accuracy: 1.0000 - val_loss: 46.2010 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 2327/4000\n",
      "9/9 - 0s - loss: 0.0769 - accuracy: 0.9931 - val_loss: 67.8216 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2328/4000\n",
      "9/9 - 0s - loss: 0.1111 - accuracy: 0.9931 - val_loss: 44.7074 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2329/4000\n",
      "9/9 - 0s - loss: 0.0940 - accuracy: 0.9861 - val_loss: 121.6158 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2330/4000\n",
      "9/9 - 0s - loss: 0.0980 - accuracy: 0.9931 - val_loss: 111.1837 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 2331/4000\n",
      "9/9 - 0s - loss: 0.0683 - accuracy: 1.0000 - val_loss: 120.0170 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 2332/4000\n",
      "9/9 - 0s - loss: 0.0744 - accuracy: 1.0000 - val_loss: 104.1793 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 2333/4000\n",
      "9/9 - 0s - loss: 0.0920 - accuracy: 0.9931 - val_loss: 143.3853 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2334/4000\n",
      "9/9 - 0s - loss: 0.0695 - accuracy: 1.0000 - val_loss: 136.3533 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2335/4000\n",
      "9/9 - 3s - loss: 0.0734 - accuracy: 1.0000 - val_loss: 115.9662 - val_accuracy: 0.2222 - 3s/epoch - 330ms/step\n",
      "Epoch 2336/4000\n",
      "9/9 - 0s - loss: 0.0777 - accuracy: 0.9931 - val_loss: 123.3697 - val_accuracy: 0.2222 - 131ms/epoch - 15ms/step\n",
      "Epoch 2337/4000\n",
      "9/9 - 0s - loss: 0.0775 - accuracy: 1.0000 - val_loss: 97.6243 - val_accuracy: 0.2222 - 140ms/epoch - 16ms/step\n",
      "Epoch 2338/4000\n",
      "9/9 - 0s - loss: 0.0715 - accuracy: 1.0000 - val_loss: 64.1848 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 2339/4000\n",
      "9/9 - 0s - loss: 0.0778 - accuracy: 1.0000 - val_loss: 5.4720 - val_accuracy: 0.3472 - 77ms/epoch - 9ms/step\n",
      "Epoch 2340/4000\n",
      "9/9 - 0s - loss: 0.1018 - accuracy: 0.9861 - val_loss: 2.2730 - val_accuracy: 0.4306 - 79ms/epoch - 9ms/step\n",
      "Epoch 2341/4000\n",
      "9/9 - 0s - loss: 0.0794 - accuracy: 0.9931 - val_loss: 29.5145 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 2342/4000\n",
      "9/9 - 0s - loss: 0.1080 - accuracy: 0.9931 - val_loss: 29.3434 - val_accuracy: 0.2222 - 77ms/epoch - 9ms/step\n",
      "Epoch 2343/4000\n",
      "9/9 - 0s - loss: 0.0944 - accuracy: 0.9792 - val_loss: 10.8272 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2344/4000\n",
      "9/9 - 0s - loss: 0.0837 - accuracy: 0.9931 - val_loss: 11.9247 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 2345/4000\n",
      "9/9 - 0s - loss: 0.0778 - accuracy: 0.9931 - val_loss: 18.8227 - val_accuracy: 0.2917 - 76ms/epoch - 8ms/step\n",
      "Epoch 2346/4000\n",
      "9/9 - 3s - loss: 0.1161 - accuracy: 0.9792 - val_loss: 37.2795 - val_accuracy: 0.2639 - 3s/epoch - 292ms/step\n",
      "Epoch 2347/4000\n",
      "9/9 - 0s - loss: 0.0842 - accuracy: 1.0000 - val_loss: 89.9089 - val_accuracy: 0.2222 - 115ms/epoch - 13ms/step\n",
      "Epoch 2348/4000\n",
      "9/9 - 0s - loss: 0.1231 - accuracy: 0.9792 - val_loss: 125.2147 - val_accuracy: 0.2222 - 119ms/epoch - 13ms/step\n",
      "Epoch 2349/4000\n",
      "9/9 - 0s - loss: 0.1147 - accuracy: 0.9792 - val_loss: 212.6306 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2350/4000\n",
      "9/9 - 0s - loss: 0.0760 - accuracy: 1.0000 - val_loss: 112.4564 - val_accuracy: 0.2222 - 78ms/epoch - 9ms/step\n",
      "Epoch 2351/4000\n",
      "9/9 - 0s - loss: 0.0880 - accuracy: 1.0000 - val_loss: 96.7826 - val_accuracy: 0.2639 - 110ms/epoch - 12ms/step\n",
      "Epoch 2352/4000\n",
      "9/9 - 0s - loss: 0.1162 - accuracy: 0.9722 - val_loss: 27.5268 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2353/4000\n",
      "9/9 - 0s - loss: 0.0806 - accuracy: 0.9931 - val_loss: 30.0621 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2354/4000\n",
      "9/9 - 0s - loss: 0.0844 - accuracy: 1.0000 - val_loss: 5.8722 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2355/4000\n",
      "9/9 - 0s - loss: 0.0732 - accuracy: 1.0000 - val_loss: 27.1986 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2356/4000\n",
      "9/9 - 3s - loss: 0.0735 - accuracy: 1.0000 - val_loss: 16.5867 - val_accuracy: 0.2222 - 3s/epoch - 320ms/step\n",
      "Epoch 2357/4000\n",
      "9/9 - 0s - loss: 0.1057 - accuracy: 0.9861 - val_loss: 27.1721 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2358/4000\n",
      "9/9 - 0s - loss: 0.0833 - accuracy: 0.9931 - val_loss: 82.0227 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 2359/4000\n",
      "9/9 - 0s - loss: 0.0875 - accuracy: 0.9931 - val_loss: 58.2920 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 2360/4000\n",
      "9/9 - 0s - loss: 0.0881 - accuracy: 0.9861 - val_loss: 3.0398 - val_accuracy: 0.3889 - 83ms/epoch - 9ms/step\n",
      "Epoch 2361/4000\n",
      "9/9 - 0s - loss: 0.0766 - accuracy: 0.9861 - val_loss: 49.1369 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2362/4000\n",
      "9/9 - 0s - loss: 0.0885 - accuracy: 0.9931 - val_loss: 1.0990 - val_accuracy: 0.5833 - 88ms/epoch - 10ms/step\n",
      "Epoch 2363/4000\n",
      "9/9 - 0s - loss: 0.0973 - accuracy: 0.9722 - val_loss: 25.7791 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2364/4000\n",
      "9/9 - 0s - loss: 0.0824 - accuracy: 0.9931 - val_loss: 123.7617 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2365/4000\n",
      "9/9 - 0s - loss: 0.0728 - accuracy: 1.0000 - val_loss: 162.3081 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2366/4000\n",
      "9/9 - 3s - loss: 0.0823 - accuracy: 1.0000 - val_loss: 182.1588 - val_accuracy: 0.2222 - 3s/epoch - 320ms/step\n",
      "Epoch 2367/4000\n",
      "9/9 - 0s - loss: 0.0850 - accuracy: 0.9931 - val_loss: 88.0676 - val_accuracy: 0.2222 - 128ms/epoch - 14ms/step\n",
      "Epoch 2368/4000\n",
      "9/9 - 0s - loss: 0.0639 - accuracy: 1.0000 - val_loss: 42.9358 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2369/4000\n",
      "9/9 - 0s - loss: 0.0745 - accuracy: 0.9931 - val_loss: 68.4730 - val_accuracy: 0.2639 - 92ms/epoch - 10ms/step\n",
      "Epoch 2370/4000\n",
      "9/9 - 0s - loss: 0.0676 - accuracy: 1.0000 - val_loss: 129.8198 - val_accuracy: 0.2639 - 94ms/epoch - 10ms/step\n",
      "Epoch 2371/4000\n",
      "9/9 - 0s - loss: 0.0771 - accuracy: 0.9931 - val_loss: 167.6600 - val_accuracy: 0.2639 - 113ms/epoch - 13ms/step\n",
      "Epoch 2372/4000\n",
      "9/9 - 0s - loss: 0.0751 - accuracy: 1.0000 - val_loss: 116.8484 - val_accuracy: 0.2639 - 87ms/epoch - 10ms/step\n",
      "Epoch 2373/4000\n",
      "9/9 - 0s - loss: 0.0574 - accuracy: 1.0000 - val_loss: 137.1568 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2374/4000\n",
      "9/9 - 0s - loss: 0.0585 - accuracy: 1.0000 - val_loss: 78.1285 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2375/4000\n",
      "9/9 - 0s - loss: 0.0669 - accuracy: 1.0000 - val_loss: 69.2050 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 2376/4000\n",
      "9/9 - 0s - loss: 0.0822 - accuracy: 0.9861 - val_loss: 63.2717 - val_accuracy: 0.2639 - 85ms/epoch - 9ms/step\n",
      "Epoch 2377/4000\n",
      "9/9 - 3s - loss: 0.0686 - accuracy: 1.0000 - val_loss: 89.5661 - val_accuracy: 0.2639 - 3s/epoch - 315ms/step\n",
      "Epoch 2378/4000\n",
      "9/9 - 0s - loss: 0.0760 - accuracy: 1.0000 - val_loss: 150.0282 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2379/4000\n",
      "9/9 - 0s - loss: 0.0636 - accuracy: 1.0000 - val_loss: 83.0335 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2380/4000\n",
      "9/9 - 0s - loss: 0.0654 - accuracy: 1.0000 - val_loss: 49.1135 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2381/4000\n",
      "9/9 - 0s - loss: 0.0646 - accuracy: 1.0000 - val_loss: 53.1795 - val_accuracy: 0.2639 - 102ms/epoch - 11ms/step\n",
      "Epoch 2382/4000\n",
      "9/9 - 0s - loss: 0.0716 - accuracy: 1.0000 - val_loss: 49.0886 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2383/4000\n",
      "9/9 - 0s - loss: 0.0650 - accuracy: 1.0000 - val_loss: 58.7969 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2384/4000\n",
      "9/9 - 0s - loss: 0.0717 - accuracy: 1.0000 - val_loss: 82.1566 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2385/4000\n",
      "9/9 - 0s - loss: 0.0859 - accuracy: 1.0000 - val_loss: 71.7844 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 2386/4000\n",
      "9/9 - 0s - loss: 0.0756 - accuracy: 0.9931 - val_loss: 91.7130 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2387/4000\n",
      "9/9 - 0s - loss: 0.0657 - accuracy: 1.0000 - val_loss: 103.9682 - val_accuracy: 0.2917 - 99ms/epoch - 11ms/step\n",
      "Epoch 2388/4000\n",
      "9/9 - 3s - loss: 0.0778 - accuracy: 1.0000 - val_loss: 1.9946 - val_accuracy: 0.3333 - 3s/epoch - 280ms/step\n",
      "Epoch 2389/4000\n",
      "9/9 - 0s - loss: 0.0659 - accuracy: 0.9931 - val_loss: 53.5354 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2390/4000\n",
      "9/9 - 0s - loss: 0.0697 - accuracy: 1.0000 - val_loss: 218.9799 - val_accuracy: 0.2222 - 122ms/epoch - 14ms/step\n",
      "Epoch 2391/4000\n",
      "9/9 - 0s - loss: 0.0757 - accuracy: 0.9861 - val_loss: 143.2183 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 2392/4000\n",
      "9/9 - 0s - loss: 0.1014 - accuracy: 0.9931 - val_loss: 27.4350 - val_accuracy: 0.2639 - 114ms/epoch - 13ms/step\n",
      "Epoch 2393/4000\n",
      "9/9 - 0s - loss: 0.0849 - accuracy: 0.9931 - val_loss: 43.8422 - val_accuracy: 0.2917 - 107ms/epoch - 12ms/step\n",
      "Epoch 2394/4000\n",
      "9/9 - 0s - loss: 0.1073 - accuracy: 0.9861 - val_loss: 36.8610 - val_accuracy: 0.2917 - 120ms/epoch - 13ms/step\n",
      "Epoch 2395/4000\n",
      "9/9 - 0s - loss: 0.0670 - accuracy: 1.0000 - val_loss: 5.0416 - val_accuracy: 0.3056 - 108ms/epoch - 12ms/step\n",
      "Epoch 2396/4000\n",
      "9/9 - 0s - loss: 0.1008 - accuracy: 0.9792 - val_loss: 66.0906 - val_accuracy: 0.2917 - 99ms/epoch - 11ms/step\n",
      "Epoch 2397/4000\n",
      "9/9 - 2s - loss: 0.1014 - accuracy: 0.9931 - val_loss: 60.7285 - val_accuracy: 0.2222 - 2s/epoch - 266ms/step\n",
      "Epoch 2398/4000\n",
      "9/9 - 0s - loss: 0.0815 - accuracy: 0.9931 - val_loss: 259.3685 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2399/4000\n",
      "9/9 - 0s - loss: 0.0694 - accuracy: 0.9931 - val_loss: 307.7791 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 2400/4000\n",
      "9/9 - 0s - loss: 0.0675 - accuracy: 1.0000 - val_loss: 286.4345 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2401/4000\n",
      "9/9 - 0s - loss: 0.0740 - accuracy: 0.9931 - val_loss: 196.6329 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 2402/4000\n",
      "9/9 - 0s - loss: 0.1202 - accuracy: 0.9653 - val_loss: 183.5929 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2403/4000\n",
      "9/9 - 0s - loss: 0.0859 - accuracy: 0.9861 - val_loss: 16.4149 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2404/4000\n",
      "9/9 - 0s - loss: 0.0889 - accuracy: 1.0000 - val_loss: 51.8102 - val_accuracy: 0.2917 - 103ms/epoch - 11ms/step\n",
      "Epoch 2405/4000\n",
      "9/9 - 0s - loss: 0.0697 - accuracy: 0.9931 - val_loss: 46.2634 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2406/4000\n",
      "9/9 - 0s - loss: 0.0853 - accuracy: 1.0000 - val_loss: 86.4142 - val_accuracy: 0.2639 - 93ms/epoch - 10ms/step\n",
      "Epoch 2407/4000\n",
      "9/9 - 0s - loss: 0.0846 - accuracy: 0.9931 - val_loss: 61.9885 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 2408/4000\n",
      "9/9 - 0s - loss: 0.0738 - accuracy: 1.0000 - val_loss: 34.1411 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2409/4000\n",
      "9/9 - 0s - loss: 0.0810 - accuracy: 1.0000 - val_loss: 50.1213 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2410/4000\n",
      "9/9 - 0s - loss: 0.0778 - accuracy: 1.0000 - val_loss: 31.3312 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2411/4000\n",
      "9/9 - 0s - loss: 0.0622 - accuracy: 0.9931 - val_loss: 89.8515 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 2412/4000\n",
      "9/9 - 0s - loss: 0.1175 - accuracy: 0.9792 - val_loss: 25.0226 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 2413/4000\n",
      "9/9 - 0s - loss: 0.0683 - accuracy: 1.0000 - val_loss: 14.1477 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2414/4000\n",
      "9/9 - 0s - loss: 0.0712 - accuracy: 1.0000 - val_loss: 105.1562 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2415/4000\n",
      "9/9 - 0s - loss: 0.0600 - accuracy: 1.0000 - val_loss: 68.9039 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 2416/4000\n",
      "9/9 - 0s - loss: 0.0626 - accuracy: 1.0000 - val_loss: 46.5649 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2417/4000\n",
      "9/9 - 0s - loss: 0.0727 - accuracy: 0.9861 - val_loss: 3.4418 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2418/4000\n",
      "9/9 - 2s - loss: 0.0643 - accuracy: 1.0000 - val_loss: 7.4280 - val_accuracy: 0.2917 - 2s/epoch - 266ms/step\n",
      "Epoch 2419/4000\n",
      "9/9 - 0s - loss: 0.0660 - accuracy: 1.0000 - val_loss: 20.3406 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2420/4000\n",
      "9/9 - 0s - loss: 0.0658 - accuracy: 0.9931 - val_loss: 75.6791 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 2421/4000\n",
      "9/9 - 0s - loss: 0.0921 - accuracy: 0.9931 - val_loss: 78.3279 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2422/4000\n",
      "9/9 - 0s - loss: 0.0715 - accuracy: 1.0000 - val_loss: 72.5189 - val_accuracy: 0.2222 - 108ms/epoch - 12ms/step\n",
      "Epoch 2423/4000\n",
      "9/9 - 0s - loss: 0.0802 - accuracy: 1.0000 - val_loss: 7.5489 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2424/4000\n",
      "9/9 - 0s - loss: 0.0718 - accuracy: 1.0000 - val_loss: 4.3290 - val_accuracy: 0.3056 - 100ms/epoch - 11ms/step\n",
      "Epoch 2425/4000\n",
      "9/9 - 0s - loss: 0.0831 - accuracy: 0.9931 - val_loss: 1.2673 - val_accuracy: 0.3750 - 101ms/epoch - 11ms/step\n",
      "Epoch 2426/4000\n",
      "9/9 - 0s - loss: 0.0761 - accuracy: 1.0000 - val_loss: 18.3251 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 2427/4000\n",
      "9/9 - 0s - loss: 0.0772 - accuracy: 1.0000 - val_loss: 22.4736 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 2428/4000\n",
      "9/9 - 0s - loss: 0.0647 - accuracy: 1.0000 - val_loss: 25.6137 - val_accuracy: 0.2222 - 112ms/epoch - 12ms/step\n",
      "Epoch 2429/4000\n",
      "9/9 - 0s - loss: 0.0641 - accuracy: 1.0000 - val_loss: 122.8191 - val_accuracy: 0.2222 - 117ms/epoch - 13ms/step\n",
      "Epoch 2430/4000\n",
      "9/9 - 0s - loss: 0.0752 - accuracy: 1.0000 - val_loss: 43.3524 - val_accuracy: 0.2917 - 116ms/epoch - 13ms/step\n",
      "Epoch 2431/4000\n",
      "9/9 - 0s - loss: 0.0614 - accuracy: 1.0000 - val_loss: 52.5676 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2432/4000\n",
      "9/9 - 0s - loss: 0.0664 - accuracy: 1.0000 - val_loss: 14.8262 - val_accuracy: 0.4306 - 102ms/epoch - 11ms/step\n",
      "Epoch 2433/4000\n",
      "9/9 - 0s - loss: 0.0642 - accuracy: 1.0000 - val_loss: 124.6557 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 2434/4000\n",
      "9/9 - 0s - loss: 0.0693 - accuracy: 0.9931 - val_loss: 34.9057 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2435/4000\n",
      "9/9 - 0s - loss: 0.0719 - accuracy: 1.0000 - val_loss: 1.6921 - val_accuracy: 0.3611 - 93ms/epoch - 10ms/step\n",
      "Epoch 2436/4000\n",
      "9/9 - 0s - loss: 0.0777 - accuracy: 1.0000 - val_loss: 1.9082 - val_accuracy: 0.3333 - 93ms/epoch - 10ms/step\n",
      "Epoch 2437/4000\n",
      "9/9 - 0s - loss: 0.0698 - accuracy: 1.0000 - val_loss: 5.5318 - val_accuracy: 0.2361 - 90ms/epoch - 10ms/step\n",
      "Epoch 2438/4000\n",
      "9/9 - 0s - loss: 0.0646 - accuracy: 1.0000 - val_loss: 67.9288 - val_accuracy: 0.2917 - 108ms/epoch - 12ms/step\n",
      "Epoch 2439/4000\n",
      "9/9 - 0s - loss: 0.0624 - accuracy: 0.9931 - val_loss: 74.4320 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2440/4000\n",
      "9/9 - 0s - loss: 0.0717 - accuracy: 0.9931 - val_loss: 25.7384 - val_accuracy: 0.3056 - 92ms/epoch - 10ms/step\n",
      "Epoch 2441/4000\n",
      "9/9 - 0s - loss: 0.0747 - accuracy: 0.9931 - val_loss: 152.3964 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 2442/4000\n",
      "9/9 - 0s - loss: 0.0813 - accuracy: 0.9931 - val_loss: 276.1006 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2443/4000\n",
      "9/9 - 0s - loss: 0.0660 - accuracy: 1.0000 - val_loss: 42.0876 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 2444/4000\n",
      "9/9 - 0s - loss: 0.0721 - accuracy: 0.9931 - val_loss: 41.3579 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2445/4000\n",
      "9/9 - 0s - loss: 0.0668 - accuracy: 0.9931 - val_loss: 5.1453 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2446/4000\n",
      "9/9 - 0s - loss: 0.0629 - accuracy: 1.0000 - val_loss: 110.4587 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 2447/4000\n",
      "9/9 - 0s - loss: 0.0810 - accuracy: 0.9861 - val_loss: 43.2461 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2448/4000\n",
      "9/9 - 0s - loss: 0.0592 - accuracy: 1.0000 - val_loss: 27.4437 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2449/4000\n",
      "9/9 - 0s - loss: 0.0925 - accuracy: 0.9931 - val_loss: 79.0389 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2450/4000\n",
      "9/9 - 0s - loss: 0.0783 - accuracy: 1.0000 - val_loss: 56.6933 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 2451/4000\n",
      "9/9 - 0s - loss: 0.0796 - accuracy: 0.9931 - val_loss: 22.9144 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2452/4000\n",
      "9/9 - 0s - loss: 0.0640 - accuracy: 1.0000 - val_loss: 5.4019 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2453/4000\n",
      "9/9 - 0s - loss: 0.0728 - accuracy: 1.0000 - val_loss: 15.8033 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2454/4000\n",
      "9/9 - 0s - loss: 0.0681 - accuracy: 0.9861 - val_loss: 61.0215 - val_accuracy: 0.2917 - 103ms/epoch - 11ms/step\n",
      "Epoch 2455/4000\n",
      "9/9 - 0s - loss: 0.0894 - accuracy: 0.9792 - val_loss: 74.1312 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 2456/4000\n",
      "9/9 - 0s - loss: 0.0842 - accuracy: 0.9931 - val_loss: 2.5817 - val_accuracy: 0.2639 - 98ms/epoch - 11ms/step\n",
      "Epoch 2457/4000\n",
      "9/9 - 0s - loss: 0.0757 - accuracy: 0.9931 - val_loss: 139.3965 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2458/4000\n",
      "9/9 - 0s - loss: 0.0673 - accuracy: 1.0000 - val_loss: 42.9585 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2459/4000\n",
      "9/9 - 0s - loss: 0.0637 - accuracy: 0.9931 - val_loss: 21.9591 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2460/4000\n",
      "9/9 - 0s - loss: 0.0995 - accuracy: 0.9792 - val_loss: 16.9477 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2461/4000\n",
      "9/9 - 0s - loss: 0.1476 - accuracy: 0.9583 - val_loss: 41.5801 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2462/4000\n",
      "9/9 - 0s - loss: 0.2000 - accuracy: 0.9236 - val_loss: 278.0589 - val_accuracy: 0.2222 - 108ms/epoch - 12ms/step\n",
      "Epoch 2463/4000\n",
      "9/9 - 0s - loss: 0.2141 - accuracy: 0.9375 - val_loss: 432.8004 - val_accuracy: 0.2222 - 121ms/epoch - 13ms/step\n",
      "Epoch 2464/4000\n",
      "9/9 - 0s - loss: 0.1221 - accuracy: 0.9722 - val_loss: 35.6364 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 2465/4000\n",
      "9/9 - 0s - loss: 0.0945 - accuracy: 0.9861 - val_loss: 51.6566 - val_accuracy: 0.2222 - 120ms/epoch - 13ms/step\n",
      "Epoch 2466/4000\n",
      "9/9 - 0s - loss: 0.0776 - accuracy: 1.0000 - val_loss: 130.6264 - val_accuracy: 0.2917 - 119ms/epoch - 13ms/step\n",
      "Epoch 2467/4000\n",
      "9/9 - 0s - loss: 0.0823 - accuracy: 1.0000 - val_loss: 89.9907 - val_accuracy: 0.2917 - 109ms/epoch - 12ms/step\n",
      "Epoch 2468/4000\n",
      "9/9 - 0s - loss: 0.0807 - accuracy: 0.9931 - val_loss: 31.0016 - val_accuracy: 0.2917 - 120ms/epoch - 13ms/step\n",
      "Epoch 2469/4000\n",
      "9/9 - 0s - loss: 0.0764 - accuracy: 1.0000 - val_loss: 24.2262 - val_accuracy: 0.2222 - 121ms/epoch - 13ms/step\n",
      "Epoch 2470/4000\n",
      "9/9 - 0s - loss: 0.1022 - accuracy: 0.9931 - val_loss: 74.6018 - val_accuracy: 0.2222 - 111ms/epoch - 12ms/step\n",
      "Epoch 2471/4000\n",
      "9/9 - 0s - loss: 0.0682 - accuracy: 1.0000 - val_loss: 71.8067 - val_accuracy: 0.2222 - 135ms/epoch - 15ms/step\n",
      "Epoch 2472/4000\n",
      "9/9 - 0s - loss: 0.0713 - accuracy: 0.9931 - val_loss: 107.1340 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2473/4000\n",
      "9/9 - 0s - loss: 0.0732 - accuracy: 0.9861 - val_loss: 144.0194 - val_accuracy: 0.2222 - 112ms/epoch - 12ms/step\n",
      "Epoch 2474/4000\n",
      "9/9 - 0s - loss: 0.0549 - accuracy: 1.0000 - val_loss: 161.8140 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2475/4000\n",
      "9/9 - 0s - loss: 0.0628 - accuracy: 1.0000 - val_loss: 103.3998 - val_accuracy: 0.3750 - 109ms/epoch - 12ms/step\n",
      "Epoch 2476/4000\n",
      "9/9 - 0s - loss: 0.0678 - accuracy: 1.0000 - val_loss: 57.3585 - val_accuracy: 0.2917 - 115ms/epoch - 13ms/step\n",
      "Epoch 2477/4000\n",
      "9/9 - 0s - loss: 0.0675 - accuracy: 0.9861 - val_loss: 28.7465 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2478/4000\n",
      "9/9 - 0s - loss: 0.0789 - accuracy: 0.9931 - val_loss: 20.5127 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2479/4000\n",
      "9/9 - 0s - loss: 0.0550 - accuracy: 1.0000 - val_loss: 1.6037 - val_accuracy: 0.4167 - 86ms/epoch - 10ms/step\n",
      "Epoch 2480/4000\n",
      "9/9 - 0s - loss: 0.0583 - accuracy: 1.0000 - val_loss: 0.9928 - val_accuracy: 0.5972 - 94ms/epoch - 10ms/step\n",
      "Epoch 2481/4000\n",
      "9/9 - 0s - loss: 0.0855 - accuracy: 0.9861 - val_loss: 57.7170 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2482/4000\n",
      "9/9 - 0s - loss: 0.0597 - accuracy: 1.0000 - val_loss: 51.4552 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 2483/4000\n",
      "9/9 - 0s - loss: 0.0732 - accuracy: 0.9931 - val_loss: 69.6295 - val_accuracy: 0.2917 - 100ms/epoch - 11ms/step\n",
      "Epoch 2484/4000\n",
      "9/9 - 0s - loss: 0.0604 - accuracy: 1.0000 - val_loss: 66.1106 - val_accuracy: 0.2639 - 89ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2485/4000\n",
      "9/9 - 0s - loss: 0.0671 - accuracy: 1.0000 - val_loss: 173.2761 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2486/4000\n",
      "9/9 - 0s - loss: 0.0873 - accuracy: 0.9861 - val_loss: 196.3105 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2487/4000\n",
      "9/9 - 0s - loss: 0.0738 - accuracy: 1.0000 - val_loss: 116.6993 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2488/4000\n",
      "9/9 - 0s - loss: 0.0542 - accuracy: 1.0000 - val_loss: 99.3940 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2489/4000\n",
      "9/9 - 0s - loss: 0.0609 - accuracy: 0.9931 - val_loss: 106.4081 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2490/4000\n",
      "9/9 - 0s - loss: 0.0672 - accuracy: 1.0000 - val_loss: 130.5763 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2491/4000\n",
      "9/9 - 0s - loss: 0.0730 - accuracy: 1.0000 - val_loss: 115.0501 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 2492/4000\n",
      "9/9 - 0s - loss: 0.0722 - accuracy: 1.0000 - val_loss: 104.8087 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2493/4000\n",
      "9/9 - 0s - loss: 0.0902 - accuracy: 0.9931 - val_loss: 56.6051 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 2494/4000\n",
      "9/9 - 0s - loss: 0.0750 - accuracy: 1.0000 - val_loss: 36.1559 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2495/4000\n",
      "9/9 - 0s - loss: 0.0598 - accuracy: 1.0000 - val_loss: 61.6332 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2496/4000\n",
      "9/9 - 0s - loss: 0.0686 - accuracy: 0.9931 - val_loss: 35.3996 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2497/4000\n",
      "9/9 - 0s - loss: 0.0694 - accuracy: 1.0000 - val_loss: 51.2706 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 2498/4000\n",
      "9/9 - 0s - loss: 0.0627 - accuracy: 1.0000 - val_loss: 3.7174 - val_accuracy: 0.3056 - 112ms/epoch - 12ms/step\n",
      "Epoch 2499/4000\n",
      "9/9 - 0s - loss: 0.0561 - accuracy: 1.0000 - val_loss: 51.1205 - val_accuracy: 0.2222 - 106ms/epoch - 12ms/step\n",
      "Epoch 2500/4000\n",
      "9/9 - 0s - loss: 0.0743 - accuracy: 1.0000 - val_loss: 28.6092 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2501/4000\n",
      "9/9 - 0s - loss: 0.0614 - accuracy: 1.0000 - val_loss: 1.4368 - val_accuracy: 0.5139 - 89ms/epoch - 10ms/step\n",
      "Epoch 2502/4000\n",
      "9/9 - 0s - loss: 0.0641 - accuracy: 1.0000 - val_loss: 76.8160 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 2503/4000\n",
      "9/9 - 0s - loss: 0.0692 - accuracy: 0.9931 - val_loss: 109.6388 - val_accuracy: 0.2917 - 82ms/epoch - 9ms/step\n",
      "Epoch 2504/4000\n",
      "9/9 - 0s - loss: 0.0716 - accuracy: 0.9931 - val_loss: 121.0575 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2505/4000\n",
      "9/9 - 0s - loss: 0.0617 - accuracy: 1.0000 - val_loss: 93.7482 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2506/4000\n",
      "9/9 - 0s - loss: 0.0677 - accuracy: 0.9861 - val_loss: 79.3092 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2507/4000\n",
      "9/9 - 0s - loss: 0.0797 - accuracy: 0.9931 - val_loss: 92.9673 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2508/4000\n",
      "9/9 - 0s - loss: 0.1087 - accuracy: 0.9653 - val_loss: 146.9037 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 2509/4000\n",
      "9/9 - 0s - loss: 0.0914 - accuracy: 0.9861 - val_loss: 155.1479 - val_accuracy: 0.2222 - 119ms/epoch - 13ms/step\n",
      "Epoch 2510/4000\n",
      "9/9 - 0s - loss: 0.0832 - accuracy: 0.9931 - val_loss: 35.7961 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2511/4000\n",
      "9/9 - 0s - loss: 0.0656 - accuracy: 1.0000 - val_loss: 7.3317 - val_accuracy: 0.2917 - 102ms/epoch - 11ms/step\n",
      "Epoch 2512/4000\n",
      "9/9 - 0s - loss: 0.0838 - accuracy: 1.0000 - val_loss: 97.2202 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2513/4000\n",
      "9/9 - 0s - loss: 0.0670 - accuracy: 0.9931 - val_loss: 120.5307 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2514/4000\n",
      "9/9 - 0s - loss: 0.0724 - accuracy: 0.9861 - val_loss: 126.6773 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2515/4000\n",
      "9/9 - 0s - loss: 0.0787 - accuracy: 1.0000 - val_loss: 111.8156 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2516/4000\n",
      "9/9 - 0s - loss: 0.0738 - accuracy: 1.0000 - val_loss: 78.8001 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 2517/4000\n",
      "9/9 - 0s - loss: 0.0692 - accuracy: 0.9931 - val_loss: 69.2157 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 2518/4000\n",
      "9/9 - 0s - loss: 0.0607 - accuracy: 0.9931 - val_loss: 194.6617 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2519/4000\n",
      "9/9 - 0s - loss: 0.0614 - accuracy: 1.0000 - val_loss: 93.8702 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2520/4000\n",
      "9/9 - 0s - loss: 0.0724 - accuracy: 1.0000 - val_loss: 82.3771 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2521/4000\n",
      "9/9 - 0s - loss: 0.0615 - accuracy: 1.0000 - val_loss: 25.3129 - val_accuracy: 0.2222 - 100ms/epoch - 11ms/step\n",
      "Epoch 2522/4000\n",
      "9/9 - 0s - loss: 0.0558 - accuracy: 1.0000 - val_loss: 71.5579 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2523/4000\n",
      "9/9 - 0s - loss: 0.0604 - accuracy: 1.0000 - val_loss: 107.9204 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2524/4000\n",
      "9/9 - 0s - loss: 0.0686 - accuracy: 1.0000 - val_loss: 50.0731 - val_accuracy: 0.3472 - 103ms/epoch - 11ms/step\n",
      "Epoch 2525/4000\n",
      "9/9 - 0s - loss: 0.0591 - accuracy: 1.0000 - val_loss: 97.3639 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2526/4000\n",
      "9/9 - 0s - loss: 0.0622 - accuracy: 1.0000 - val_loss: 56.6158 - val_accuracy: 0.3056 - 91ms/epoch - 10ms/step\n",
      "Epoch 2527/4000\n",
      "9/9 - 0s - loss: 0.0458 - accuracy: 1.0000 - val_loss: 52.7981 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2528/4000\n",
      "9/9 - 0s - loss: 0.0635 - accuracy: 0.9931 - val_loss: 70.8420 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2529/4000\n",
      "9/9 - 0s - loss: 0.0713 - accuracy: 1.0000 - val_loss: 52.5971 - val_accuracy: 0.3611 - 88ms/epoch - 10ms/step\n",
      "Epoch 2530/4000\n",
      "9/9 - 0s - loss: 0.0610 - accuracy: 1.0000 - val_loss: 26.2465 - val_accuracy: 0.3750 - 99ms/epoch - 11ms/step\n",
      "Epoch 2531/4000\n",
      "9/9 - 0s - loss: 0.0532 - accuracy: 1.0000 - val_loss: 109.3559 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2532/4000\n",
      "9/9 - 0s - loss: 0.0841 - accuracy: 0.9931 - val_loss: 132.1579 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2533/4000\n",
      "9/9 - 0s - loss: 0.0730 - accuracy: 1.0000 - val_loss: 54.4489 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2534/4000\n",
      "9/9 - 0s - loss: 0.0683 - accuracy: 0.9861 - val_loss: 1.3939 - val_accuracy: 0.4167 - 87ms/epoch - 10ms/step\n",
      "Epoch 2535/4000\n",
      "9/9 - 0s - loss: 0.0601 - accuracy: 1.0000 - val_loss: 4.9538 - val_accuracy: 0.2361 - 83ms/epoch - 9ms/step\n",
      "Epoch 2536/4000\n",
      "9/9 - 0s - loss: 0.0583 - accuracy: 1.0000 - val_loss: 15.6907 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2537/4000\n",
      "9/9 - 0s - loss: 0.0680 - accuracy: 0.9931 - val_loss: 5.8229 - val_accuracy: 0.3056 - 90ms/epoch - 10ms/step\n",
      "Epoch 2538/4000\n",
      "9/9 - 0s - loss: 0.0615 - accuracy: 1.0000 - val_loss: 88.9252 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 2539/4000\n",
      "9/9 - 0s - loss: 0.0664 - accuracy: 1.0000 - val_loss: 111.3851 - val_accuracy: 0.2917 - 112ms/epoch - 12ms/step\n",
      "Epoch 2540/4000\n",
      "9/9 - 0s - loss: 0.0638 - accuracy: 1.0000 - val_loss: 32.9580 - val_accuracy: 0.2222 - 109ms/epoch - 12ms/step\n",
      "Epoch 2541/4000\n",
      "9/9 - 0s - loss: 0.0782 - accuracy: 0.9931 - val_loss: 153.0842 - val_accuracy: 0.2222 - 109ms/epoch - 12ms/step\n",
      "Epoch 2542/4000\n",
      "9/9 - 0s - loss: 0.0688 - accuracy: 1.0000 - val_loss: 113.8304 - val_accuracy: 0.2639 - 102ms/epoch - 11ms/step\n",
      "Epoch 2543/4000\n",
      "9/9 - 0s - loss: 0.0631 - accuracy: 1.0000 - val_loss: 91.7691 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2544/4000\n",
      "9/9 - 0s - loss: 0.0715 - accuracy: 0.9931 - val_loss: 78.8148 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2545/4000\n",
      "9/9 - 0s - loss: 0.0815 - accuracy: 0.9931 - val_loss: 63.2467 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2546/4000\n",
      "9/9 - 0s - loss: 0.0813 - accuracy: 0.9931 - val_loss: 1.8413 - val_accuracy: 0.5417 - 87ms/epoch - 10ms/step\n",
      "Epoch 2547/4000\n",
      "9/9 - 0s - loss: 0.0736 - accuracy: 1.0000 - val_loss: 10.7436 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2548/4000\n",
      "9/9 - 0s - loss: 0.0581 - accuracy: 1.0000 - val_loss: 99.0969 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2549/4000\n",
      "9/9 - 0s - loss: 0.0461 - accuracy: 1.0000 - val_loss: 87.4797 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2550/4000\n",
      "9/9 - 0s - loss: 0.0549 - accuracy: 1.0000 - val_loss: 7.5196 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 2551/4000\n",
      "9/9 - 0s - loss: 0.0733 - accuracy: 1.0000 - val_loss: 36.8639 - val_accuracy: 0.3056 - 93ms/epoch - 10ms/step\n",
      "Epoch 2552/4000\n",
      "9/9 - 0s - loss: 0.0736 - accuracy: 0.9931 - val_loss: 118.9725 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 2553/4000\n",
      "9/9 - 0s - loss: 0.0805 - accuracy: 0.9722 - val_loss: 78.3328 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2554/4000\n",
      "9/9 - 0s - loss: 0.0564 - accuracy: 1.0000 - val_loss: 87.7464 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2555/4000\n",
      "9/9 - 0s - loss: 0.0679 - accuracy: 0.9931 - val_loss: 84.9136 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2556/4000\n",
      "9/9 - 0s - loss: 0.0580 - accuracy: 1.0000 - val_loss: 29.4705 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2557/4000\n",
      "9/9 - 0s - loss: 0.0681 - accuracy: 1.0000 - val_loss: 22.0194 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2558/4000\n",
      "9/9 - 0s - loss: 0.0896 - accuracy: 0.9792 - val_loss: 34.3275 - val_accuracy: 0.2222 - 82ms/epoch - 9ms/step\n",
      "Epoch 2559/4000\n",
      "9/9 - 0s - loss: 0.0665 - accuracy: 1.0000 - val_loss: 30.2088 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2560/4000\n",
      "9/9 - 0s - loss: 0.0789 - accuracy: 0.9792 - val_loss: 2.7701 - val_accuracy: 0.2778 - 87ms/epoch - 10ms/step\n",
      "Epoch 2561/4000\n",
      "9/9 - 0s - loss: 0.0798 - accuracy: 0.9931 - val_loss: 29.3514 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2562/4000\n",
      "9/9 - 0s - loss: 0.0908 - accuracy: 0.9931 - val_loss: 63.0984 - val_accuracy: 0.2222 - 95ms/epoch - 11ms/step\n",
      "Epoch 2563/4000\n",
      "9/9 - 0s - loss: 0.0647 - accuracy: 1.0000 - val_loss: 102.8494 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 2564/4000\n",
      "9/9 - 0s - loss: 0.0521 - accuracy: 1.0000 - val_loss: 103.9934 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 2565/4000\n",
      "9/9 - 0s - loss: 0.0702 - accuracy: 1.0000 - val_loss: 82.5946 - val_accuracy: 0.2639 - 88ms/epoch - 10ms/step\n",
      "Epoch 2566/4000\n",
      "9/9 - 0s - loss: 0.0659 - accuracy: 1.0000 - val_loss: 60.5717 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2567/4000\n",
      "9/9 - 0s - loss: 0.0745 - accuracy: 1.0000 - val_loss: 118.0252 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2568/4000\n",
      "9/9 - 0s - loss: 0.0621 - accuracy: 1.0000 - val_loss: 87.8719 - val_accuracy: 0.3056 - 80ms/epoch - 9ms/step\n",
      "Epoch 2569/4000\n",
      "9/9 - 0s - loss: 0.0675 - accuracy: 1.0000 - val_loss: 87.8510 - val_accuracy: 0.2222 - 79ms/epoch - 9ms/step\n",
      "Epoch 2570/4000\n",
      "9/9 - 0s - loss: 0.0744 - accuracy: 1.0000 - val_loss: 75.8616 - val_accuracy: 0.3194 - 81ms/epoch - 9ms/step\n",
      "Epoch 2571/4000\n",
      "9/9 - 0s - loss: 0.0602 - accuracy: 1.0000 - val_loss: 78.3742 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2572/4000\n",
      "9/9 - 0s - loss: 0.0626 - accuracy: 0.9931 - val_loss: 105.0307 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2573/4000\n",
      "9/9 - 0s - loss: 0.0787 - accuracy: 0.9931 - val_loss: 88.1671 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2574/4000\n",
      "9/9 - 0s - loss: 0.0750 - accuracy: 1.0000 - val_loss: 63.9007 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2575/4000\n",
      "9/9 - 0s - loss: 0.0691 - accuracy: 0.9931 - val_loss: 47.4847 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2576/4000\n",
      "9/9 - 0s - loss: 0.0995 - accuracy: 0.9792 - val_loss: 30.0826 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2577/4000\n",
      "9/9 - 0s - loss: 0.0975 - accuracy: 1.0000 - val_loss: 79.9504 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 2578/4000\n",
      "9/9 - 0s - loss: 0.0845 - accuracy: 0.9861 - val_loss: 166.7788 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2579/4000\n",
      "9/9 - 0s - loss: 0.0827 - accuracy: 0.9931 - val_loss: 122.8125 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2580/4000\n",
      "9/9 - 0s - loss: 0.0783 - accuracy: 0.9792 - val_loss: 106.4896 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2581/4000\n",
      "9/9 - 0s - loss: 0.0877 - accuracy: 0.9792 - val_loss: 208.1930 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2582/4000\n",
      "9/9 - 0s - loss: 0.0924 - accuracy: 0.9861 - val_loss: 94.1648 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2583/4000\n",
      "9/9 - 0s - loss: 0.0771 - accuracy: 0.9931 - val_loss: 111.8615 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2584/4000\n",
      "9/9 - 0s - loss: 0.1038 - accuracy: 0.9722 - val_loss: 215.2073 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 2585/4000\n",
      "9/9 - 0s - loss: 0.0959 - accuracy: 0.9861 - val_loss: 320.7387 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 2586/4000\n",
      "9/9 - 0s - loss: 0.1451 - accuracy: 0.9514 - val_loss: 87.6837 - val_accuracy: 0.2222 - 124ms/epoch - 14ms/step\n",
      "Epoch 2587/4000\n",
      "9/9 - 0s - loss: 0.1308 - accuracy: 0.9514 - val_loss: 231.7635 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 2588/4000\n",
      "9/9 - 0s - loss: 0.0799 - accuracy: 1.0000 - val_loss: 346.2811 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2589/4000\n",
      "9/9 - 0s - loss: 0.0781 - accuracy: 0.9931 - val_loss: 415.4861 - val_accuracy: 0.2222 - 90ms/epoch - 10ms/step\n",
      "Epoch 2590/4000\n",
      "9/9 - 0s - loss: 0.0758 - accuracy: 1.0000 - val_loss: 216.8428 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2591/4000\n",
      "9/9 - 0s - loss: 0.0884 - accuracy: 1.0000 - val_loss: 122.8178 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 2592/4000\n",
      "9/9 - 0s - loss: 0.0765 - accuracy: 1.0000 - val_loss: 144.4605 - val_accuracy: 0.2639 - 86ms/epoch - 10ms/step\n",
      "Epoch 2593/4000\n",
      "9/9 - 0s - loss: 0.0756 - accuracy: 0.9861 - val_loss: 100.6476 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2594/4000\n",
      "9/9 - 0s - loss: 0.0797 - accuracy: 0.9931 - val_loss: 97.6895 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2595/4000\n",
      "9/9 - 0s - loss: 0.0769 - accuracy: 0.9931 - val_loss: 49.4069 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2596/4000\n",
      "9/9 - 0s - loss: 0.0819 - accuracy: 0.9931 - val_loss: 6.4007 - val_accuracy: 0.2361 - 95ms/epoch - 11ms/step\n",
      "Epoch 2597/4000\n",
      "9/9 - 0s - loss: 0.0653 - accuracy: 1.0000 - val_loss: 70.6178 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 2598/4000\n",
      "9/9 - 0s - loss: 0.0680 - accuracy: 0.9931 - val_loss: 93.6187 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 2599/4000\n",
      "9/9 - 0s - loss: 0.0625 - accuracy: 0.9931 - val_loss: 38.4368 - val_accuracy: 0.2917 - 88ms/epoch - 10ms/step\n",
      "Epoch 2600/4000\n",
      "9/9 - 0s - loss: 0.0649 - accuracy: 1.0000 - val_loss: 88.2547 - val_accuracy: 0.2639 - 83ms/epoch - 9ms/step\n",
      "Epoch 2601/4000\n",
      "9/9 - 0s - loss: 0.0687 - accuracy: 0.9931 - val_loss: 90.9322 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 2602/4000\n",
      "9/9 - 0s - loss: 0.0676 - accuracy: 0.9861 - val_loss: 67.8865 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 2603/4000\n",
      "9/9 - 0s - loss: 0.0776 - accuracy: 0.9931 - val_loss: 49.1273 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 2604/4000\n",
      "9/9 - 0s - loss: 0.0476 - accuracy: 1.0000 - val_loss: 47.5972 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2605/4000\n",
      "9/9 - 0s - loss: 0.0794 - accuracy: 0.9861 - val_loss: 19.4225 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2606/4000\n",
      "9/9 - 0s - loss: 0.0638 - accuracy: 0.9931 - val_loss: 105.4016 - val_accuracy: 0.2222 - 99ms/epoch - 11ms/step\n",
      "Epoch 2607/4000\n",
      "9/9 - 0s - loss: 0.0638 - accuracy: 0.9931 - val_loss: 146.6171 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2608/4000\n",
      "9/9 - 0s - loss: 0.0631 - accuracy: 1.0000 - val_loss: 60.3340 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2609/4000\n",
      "9/9 - 0s - loss: 0.0706 - accuracy: 1.0000 - val_loss: 43.3027 - val_accuracy: 0.2917 - 81ms/epoch - 9ms/step\n",
      "Epoch 2610/4000\n",
      "9/9 - 0s - loss: 0.0891 - accuracy: 0.9861 - val_loss: 82.3777 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2611/4000\n",
      "9/9 - 0s - loss: 0.0902 - accuracy: 0.9792 - val_loss: 118.9786 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 2612/4000\n",
      "9/9 - 0s - loss: 0.0702 - accuracy: 1.0000 - val_loss: 118.1146 - val_accuracy: 0.2917 - 80ms/epoch - 9ms/step\n",
      "Epoch 2613/4000\n",
      "9/9 - 0s - loss: 0.0932 - accuracy: 0.9931 - val_loss: 141.8543 - val_accuracy: 0.2917 - 78ms/epoch - 9ms/step\n",
      "Epoch 2614/4000\n",
      "9/9 - 0s - loss: 0.0885 - accuracy: 0.9861 - val_loss: 139.4158 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2615/4000\n",
      "9/9 - 0s - loss: 0.0729 - accuracy: 1.0000 - val_loss: 129.0246 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2616/4000\n",
      "9/9 - 0s - loss: 0.0904 - accuracy: 0.9722 - val_loss: 60.6880 - val_accuracy: 0.2917 - 77ms/epoch - 9ms/step\n",
      "Epoch 2617/4000\n",
      "9/9 - 0s - loss: 0.0677 - accuracy: 1.0000 - val_loss: 54.9987 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2618/4000\n",
      "9/9 - 0s - loss: 0.0731 - accuracy: 0.9931 - val_loss: 96.8542 - val_accuracy: 0.2222 - 80ms/epoch - 9ms/step\n",
      "Epoch 2619/4000\n",
      "9/9 - 0s - loss: 0.0835 - accuracy: 0.9931 - val_loss: 135.2648 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2620/4000\n",
      "9/9 - 0s - loss: 0.0740 - accuracy: 1.0000 - val_loss: 44.1985 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2621/4000\n",
      "9/9 - 0s - loss: 0.0636 - accuracy: 1.0000 - val_loss: 132.4061 - val_accuracy: 0.2917 - 79ms/epoch - 9ms/step\n",
      "Epoch 2622/4000\n",
      "9/9 - 0s - loss: 0.0809 - accuracy: 0.9861 - val_loss: 135.0121 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2623/4000\n",
      "9/9 - 0s - loss: 0.0672 - accuracy: 1.0000 - val_loss: 33.9567 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2624/4000\n",
      "9/9 - 0s - loss: 0.0811 - accuracy: 0.9931 - val_loss: 38.0664 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2625/4000\n",
      "9/9 - 0s - loss: 0.1275 - accuracy: 0.9722 - val_loss: 126.3769 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2626/4000\n",
      "9/9 - 0s - loss: 0.0636 - accuracy: 1.0000 - val_loss: 130.6375 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2627/4000\n",
      "9/9 - 0s - loss: 0.0717 - accuracy: 0.9861 - val_loss: 90.8087 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2628/4000\n",
      "9/9 - 0s - loss: 0.0528 - accuracy: 1.0000 - val_loss: 50.1319 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2629/4000\n",
      "9/9 - 0s - loss: 0.1019 - accuracy: 0.9861 - val_loss: 121.3480 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2630/4000\n",
      "9/9 - 0s - loss: 0.0805 - accuracy: 1.0000 - val_loss: 86.6879 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2631/4000\n",
      "9/9 - 0s - loss: 0.1036 - accuracy: 0.9792 - val_loss: 2.1327 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2632/4000\n",
      "9/9 - 0s - loss: 0.0839 - accuracy: 0.9931 - val_loss: 47.1963 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2633/4000\n",
      "9/9 - 0s - loss: 0.0815 - accuracy: 0.9931 - val_loss: 81.4492 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2634/4000\n",
      "9/9 - 0s - loss: 0.0744 - accuracy: 1.0000 - val_loss: 105.1317 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 2635/4000\n",
      "9/9 - 0s - loss: 0.0692 - accuracy: 0.9931 - val_loss: 95.7702 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2636/4000\n",
      "9/9 - 0s - loss: 0.0691 - accuracy: 1.0000 - val_loss: 135.0367 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 2637/4000\n",
      "9/9 - 0s - loss: 0.0697 - accuracy: 1.0000 - val_loss: 101.0760 - val_accuracy: 0.2222 - 101ms/epoch - 11ms/step\n",
      "Epoch 2638/4000\n",
      "9/9 - 0s - loss: 0.0993 - accuracy: 0.9722 - val_loss: 113.7086 - val_accuracy: 0.2639 - 109ms/epoch - 12ms/step\n",
      "Epoch 2639/4000\n",
      "9/9 - 0s - loss: 0.0946 - accuracy: 0.9792 - val_loss: 71.1405 - val_accuracy: 0.3333 - 88ms/epoch - 10ms/step\n",
      "Epoch 2640/4000\n",
      "9/9 - 0s - loss: 0.0781 - accuracy: 0.9792 - val_loss: 249.8395 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2641/4000\n",
      "9/9 - 0s - loss: 0.0684 - accuracy: 1.0000 - val_loss: 296.3165 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2642/4000\n",
      "9/9 - 0s - loss: 0.0648 - accuracy: 1.0000 - val_loss: 207.5324 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2643/4000\n",
      "9/9 - 0s - loss: 0.0667 - accuracy: 0.9931 - val_loss: 73.9578 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2644/4000\n",
      "9/9 - 0s - loss: 0.0699 - accuracy: 1.0000 - val_loss: 104.6269 - val_accuracy: 0.2917 - 87ms/epoch - 10ms/step\n",
      "Epoch 2645/4000\n",
      "9/9 - 0s - loss: 0.0837 - accuracy: 0.9931 - val_loss: 53.7751 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 2646/4000\n",
      "9/9 - 0s - loss: 0.0556 - accuracy: 1.0000 - val_loss: 67.6916 - val_accuracy: 0.2917 - 102ms/epoch - 11ms/step\n",
      "Epoch 2647/4000\n",
      "9/9 - 0s - loss: 0.0523 - accuracy: 1.0000 - val_loss: 26.4499 - val_accuracy: 0.2917 - 95ms/epoch - 11ms/step\n",
      "Epoch 2648/4000\n",
      "9/9 - 0s - loss: 0.0528 - accuracy: 1.0000 - val_loss: 4.9585 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2649/4000\n",
      "9/9 - 0s - loss: 0.0479 - accuracy: 1.0000 - val_loss: 1.8470 - val_accuracy: 0.5278 - 92ms/epoch - 10ms/step\n",
      "Epoch 2650/4000\n",
      "9/9 - 0s - loss: 0.0568 - accuracy: 1.0000 - val_loss: 9.8464 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2651/4000\n",
      "9/9 - 0s - loss: 0.0629 - accuracy: 0.9931 - val_loss: 124.0980 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2652/4000\n",
      "9/9 - 0s - loss: 0.0678 - accuracy: 0.9931 - val_loss: 46.5913 - val_accuracy: 0.2917 - 104ms/epoch - 12ms/step\n",
      "Epoch 2653/4000\n",
      "9/9 - 0s - loss: 0.0609 - accuracy: 0.9931 - val_loss: 2.7135 - val_accuracy: 0.2778 - 112ms/epoch - 12ms/step\n",
      "Epoch 2654/4000\n",
      "9/9 - 0s - loss: 0.0622 - accuracy: 1.0000 - val_loss: 1.0929 - val_accuracy: 0.3889 - 105ms/epoch - 12ms/step\n",
      "Epoch 2655/4000\n",
      "9/9 - 0s - loss: 0.0572 - accuracy: 1.0000 - val_loss: 3.4262 - val_accuracy: 0.2778 - 105ms/epoch - 12ms/step\n",
      "Epoch 2656/4000\n",
      "9/9 - 0s - loss: 0.0451 - accuracy: 1.0000 - val_loss: 39.0281 - val_accuracy: 0.2917 - 97ms/epoch - 11ms/step\n",
      "Epoch 2657/4000\n",
      "9/9 - 0s - loss: 0.0560 - accuracy: 0.9931 - val_loss: 0.9934 - val_accuracy: 0.6389 - 96ms/epoch - 11ms/step\n",
      "Epoch 2658/4000\n",
      "9/9 - 0s - loss: 0.0567 - accuracy: 0.9931 - val_loss: 121.0733 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2659/4000\n",
      "9/9 - 0s - loss: 0.0608 - accuracy: 1.0000 - val_loss: 81.6376 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2660/4000\n",
      "9/9 - 0s - loss: 0.0599 - accuracy: 0.9931 - val_loss: 92.4988 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2661/4000\n",
      "9/9 - 0s - loss: 0.0760 - accuracy: 0.9931 - val_loss: 6.9199 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2662/4000\n",
      "9/9 - 0s - loss: 0.0497 - accuracy: 1.0000 - val_loss: 107.9978 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 2663/4000\n",
      "9/9 - 0s - loss: 0.0454 - accuracy: 1.0000 - val_loss: 79.4601 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2664/4000\n",
      "9/9 - 0s - loss: 0.0829 - accuracy: 0.9792 - val_loss: 83.8571 - val_accuracy: 0.2222 - 94ms/epoch - 10ms/step\n",
      "Epoch 2665/4000\n",
      "9/9 - 0s - loss: 0.0646 - accuracy: 1.0000 - val_loss: 237.1967 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2666/4000\n",
      "9/9 - 0s - loss: 0.0518 - accuracy: 1.0000 - val_loss: 271.1156 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2667/4000\n",
      "9/9 - 0s - loss: 0.0553 - accuracy: 1.0000 - val_loss: 179.2008 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2668/4000\n",
      "9/9 - 0s - loss: 0.0539 - accuracy: 1.0000 - val_loss: 126.4735 - val_accuracy: 0.2222 - 83ms/epoch - 9ms/step\n",
      "Epoch 2669/4000\n",
      "9/9 - 0s - loss: 0.0690 - accuracy: 0.9931 - val_loss: 32.5937 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2670/4000\n",
      "9/9 - 0s - loss: 0.0692 - accuracy: 0.9931 - val_loss: 85.1748 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2671/4000\n",
      "9/9 - 0s - loss: 0.0564 - accuracy: 1.0000 - val_loss: 1.3764 - val_accuracy: 0.3194 - 89ms/epoch - 10ms/step\n",
      "Epoch 2672/4000\n",
      "9/9 - 0s - loss: 0.0541 - accuracy: 1.0000 - val_loss: 18.0451 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 2673/4000\n",
      "9/9 - 0s - loss: 0.0765 - accuracy: 0.9931 - val_loss: 81.9680 - val_accuracy: 0.2917 - 92ms/epoch - 10ms/step\n",
      "Epoch 2674/4000\n",
      "9/9 - 0s - loss: 0.0512 - accuracy: 0.9931 - val_loss: 73.5328 - val_accuracy: 0.2917 - 91ms/epoch - 10ms/step\n",
      "Epoch 2675/4000\n",
      "9/9 - 0s - loss: 0.0701 - accuracy: 0.9931 - val_loss: 44.6510 - val_accuracy: 0.4583 - 91ms/epoch - 10ms/step\n",
      "Epoch 2676/4000\n",
      "9/9 - 0s - loss: 0.0722 - accuracy: 0.9931 - val_loss: 50.5970 - val_accuracy: 0.2639 - 100ms/epoch - 11ms/step\n",
      "Epoch 2677/4000\n",
      "9/9 - 0s - loss: 0.1126 - accuracy: 0.9792 - val_loss: 28.4332 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2678/4000\n",
      "9/9 - 0s - loss: 0.0716 - accuracy: 0.9861 - val_loss: 142.7040 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2679/4000\n",
      "9/9 - 0s - loss: 0.0634 - accuracy: 1.0000 - val_loss: 130.7469 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2680/4000\n",
      "9/9 - 0s - loss: 0.0794 - accuracy: 0.9931 - val_loss: 82.2078 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2681/4000\n",
      "9/9 - 0s - loss: 0.0711 - accuracy: 0.9861 - val_loss: 9.8364 - val_accuracy: 0.2500 - 93ms/epoch - 10ms/step\n",
      "Epoch 2682/4000\n",
      "9/9 - 0s - loss: 0.0553 - accuracy: 1.0000 - val_loss: 39.3994 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2683/4000\n",
      "9/9 - 0s - loss: 0.0587 - accuracy: 1.0000 - val_loss: 29.9835 - val_accuracy: 0.2361 - 96ms/epoch - 11ms/step\n",
      "Epoch 2684/4000\n",
      "9/9 - 0s - loss: 0.0585 - accuracy: 1.0000 - val_loss: 65.0519 - val_accuracy: 0.2917 - 96ms/epoch - 11ms/step\n",
      "Epoch 2685/4000\n",
      "9/9 - 0s - loss: 0.0641 - accuracy: 1.0000 - val_loss: 21.4989 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2686/4000\n",
      "9/9 - 0s - loss: 0.0633 - accuracy: 0.9931 - val_loss: 10.2289 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2687/4000\n",
      "9/9 - 0s - loss: 0.0494 - accuracy: 1.0000 - val_loss: 41.3451 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2688/4000\n",
      "9/9 - 0s - loss: 0.0523 - accuracy: 1.0000 - val_loss: 9.2541 - val_accuracy: 0.3333 - 87ms/epoch - 10ms/step\n",
      "Epoch 2689/4000\n",
      "9/9 - 0s - loss: 0.0530 - accuracy: 0.9931 - val_loss: 22.9440 - val_accuracy: 0.2917 - 86ms/epoch - 10ms/step\n",
      "Epoch 2690/4000\n",
      "9/9 - 0s - loss: 0.0621 - accuracy: 1.0000 - val_loss: 2.5390 - val_accuracy: 0.3194 - 85ms/epoch - 9ms/step\n",
      "Epoch 2691/4000\n",
      "9/9 - 0s - loss: 0.0645 - accuracy: 1.0000 - val_loss: 101.3017 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 2692/4000\n",
      "9/9 - 0s - loss: 0.0716 - accuracy: 0.9861 - val_loss: 31.3997 - val_accuracy: 0.2222 - 91ms/epoch - 10ms/step\n",
      "Epoch 2693/4000\n",
      "9/9 - 0s - loss: 0.0861 - accuracy: 0.9861 - val_loss: 223.7997 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2694/4000\n",
      "9/9 - 0s - loss: 0.0768 - accuracy: 0.9861 - val_loss: 10.1650 - val_accuracy: 0.2222 - 92ms/epoch - 10ms/step\n",
      "Epoch 2695/4000\n",
      "9/9 - 0s - loss: 0.0664 - accuracy: 1.0000 - val_loss: 112.3476 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2696/4000\n",
      "9/9 - 0s - loss: 0.0579 - accuracy: 1.0000 - val_loss: 80.6742 - val_accuracy: 0.3056 - 85ms/epoch - 9ms/step\n",
      "Epoch 2697/4000\n",
      "9/9 - 0s - loss: 0.0649 - accuracy: 0.9861 - val_loss: 177.0461 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 2698/4000\n",
      "9/9 - 0s - loss: 0.0763 - accuracy: 0.9931 - val_loss: 102.1212 - val_accuracy: 0.2917 - 89ms/epoch - 10ms/step\n",
      "Epoch 2699/4000\n",
      "9/9 - 0s - loss: 0.0837 - accuracy: 0.9792 - val_loss: 31.7743 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2700/4000\n",
      "9/9 - 0s - loss: 0.0683 - accuracy: 1.0000 - val_loss: 154.1277 - val_accuracy: 0.2222 - 87ms/epoch - 10ms/step\n",
      "Epoch 2701/4000\n",
      "9/9 - 0s - loss: 0.0534 - accuracy: 1.0000 - val_loss: 142.1809 - val_accuracy: 0.2222 - 93ms/epoch - 10ms/step\n",
      "Epoch 2702/4000\n",
      "9/9 - 0s - loss: 0.0601 - accuracy: 0.9931 - val_loss: 129.3667 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2703/4000\n",
      "9/9 - 0s - loss: 0.0757 - accuracy: 0.9861 - val_loss: 134.7350 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2704/4000\n",
      "9/9 - 0s - loss: 0.0555 - accuracy: 1.0000 - val_loss: 141.3789 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2705/4000\n",
      "9/9 - 0s - loss: 0.0503 - accuracy: 1.0000 - val_loss: 142.0804 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2706/4000\n",
      "9/9 - 0s - loss: 0.0730 - accuracy: 0.9861 - val_loss: 140.6527 - val_accuracy: 0.2222 - 84ms/epoch - 9ms/step\n",
      "Epoch 2707/4000\n",
      "9/9 - 0s - loss: 0.0693 - accuracy: 1.0000 - val_loss: 58.4549 - val_accuracy: 0.2222 - 81ms/epoch - 9ms/step\n",
      "Epoch 2708/4000\n",
      "9/9 - 0s - loss: 0.0664 - accuracy: 0.9931 - val_loss: 21.0523 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2709/4000\n",
      "9/9 - 0s - loss: 0.0575 - accuracy: 1.0000 - val_loss: 70.1565 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2710/4000\n",
      "9/9 - 0s - loss: 0.0503 - accuracy: 1.0000 - val_loss: 21.2528 - val_accuracy: 0.2917 - 94ms/epoch - 10ms/step\n",
      "Epoch 2711/4000\n",
      "9/9 - 0s - loss: 0.0575 - accuracy: 1.0000 - val_loss: 90.1578 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2712/4000\n",
      "9/9 - 0s - loss: 0.0582 - accuracy: 0.9931 - val_loss: 81.6479 - val_accuracy: 0.2917 - 98ms/epoch - 11ms/step\n",
      "Epoch 2713/4000\n",
      "9/9 - 0s - loss: 0.0545 - accuracy: 0.9931 - val_loss: 79.6167 - val_accuracy: 0.2222 - 138ms/epoch - 15ms/step\n",
      "Epoch 2714/4000\n",
      "9/9 - 0s - loss: 0.0822 - accuracy: 0.9931 - val_loss: 137.3746 - val_accuracy: 0.2222 - 110ms/epoch - 12ms/step\n",
      "Epoch 2715/4000\n",
      "9/9 - 0s - loss: 0.0561 - accuracy: 1.0000 - val_loss: 210.1895 - val_accuracy: 0.2639 - 131ms/epoch - 15ms/step\n",
      "Epoch 2716/4000\n",
      "9/9 - 0s - loss: 0.0558 - accuracy: 1.0000 - val_loss: 257.7128 - val_accuracy: 0.2222 - 89ms/epoch - 10ms/step\n",
      "Epoch 2717/4000\n",
      "9/9 - 0s - loss: 0.0567 - accuracy: 1.0000 - val_loss: 169.7250 - val_accuracy: 0.2222 - 140ms/epoch - 16ms/step\n",
      "Epoch 2718/4000\n",
      "9/9 - 0s - loss: 0.0531 - accuracy: 0.9931 - val_loss: 99.8726 - val_accuracy: 0.2222 - 112ms/epoch - 12ms/step\n",
      "Epoch 2719/4000\n",
      "9/9 - 0s - loss: 0.0677 - accuracy: 1.0000 - val_loss: 91.2557 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 2720/4000\n",
      "9/9 - 0s - loss: 0.0531 - accuracy: 0.9931 - val_loss: 3.5532 - val_accuracy: 0.3472 - 250ms/epoch - 28ms/step\n",
      "Epoch 2721/4000\n",
      "9/9 - 2s - loss: 0.0541 - accuracy: 1.0000 - val_loss: 66.1005 - val_accuracy: 0.2917 - 2s/epoch - 189ms/step\n",
      "Epoch 2722/4000\n",
      "9/9 - 0s - loss: 0.0478 - accuracy: 1.0000 - val_loss: 58.0849 - val_accuracy: 0.2917 - 93ms/epoch - 10ms/step\n",
      "Epoch 2723/4000\n",
      "9/9 - 0s - loss: 0.0585 - accuracy: 1.0000 - val_loss: 48.3288 - val_accuracy: 0.2917 - 103ms/epoch - 11ms/step\n",
      "Epoch 2724/4000\n",
      "9/9 - 0s - loss: 0.0672 - accuracy: 0.9931 - val_loss: 3.4484 - val_accuracy: 0.2361 - 106ms/epoch - 12ms/step\n",
      "Epoch 2725/4000\n",
      "9/9 - 0s - loss: 0.0451 - accuracy: 1.0000 - val_loss: 67.8966 - val_accuracy: 0.2222 - 105ms/epoch - 12ms/step\n",
      "Epoch 2726/4000\n",
      "9/9 - 0s - loss: 0.0593 - accuracy: 0.9931 - val_loss: 95.7073 - val_accuracy: 0.2222 - 96ms/epoch - 11ms/step\n",
      "Epoch 2727/4000\n",
      "9/9 - 0s - loss: 0.0624 - accuracy: 0.9931 - val_loss: 76.7389 - val_accuracy: 0.2222 - 107ms/epoch - 12ms/step\n",
      "Epoch 2728/4000\n",
      "9/9 - 0s - loss: 0.0630 - accuracy: 0.9931 - val_loss: 86.4971 - val_accuracy: 0.2639 - 91ms/epoch - 10ms/step\n",
      "Epoch 2729/4000\n",
      "9/9 - 0s - loss: 0.0578 - accuracy: 0.9931 - val_loss: 75.6916 - val_accuracy: 0.4444 - 96ms/epoch - 11ms/step\n",
      "Epoch 2730/4000\n",
      "9/9 - 0s - loss: 0.0755 - accuracy: 1.0000 - val_loss: 52.9654 - val_accuracy: 0.2222 - 97ms/epoch - 11ms/step\n",
      "Epoch 2731/4000\n",
      "9/9 - 2s - loss: 0.0606 - accuracy: 1.0000 - val_loss: 62.3768 - val_accuracy: 0.2917 - 2s/epoch - 270ms/step\n",
      "Epoch 2732/4000\n",
      "9/9 - 0s - loss: 0.0670 - accuracy: 0.9931 - val_loss: 2.0846 - val_accuracy: 0.2639 - 127ms/epoch - 14ms/step\n",
      "Epoch 2733/4000\n",
      "9/9 - 0s - loss: 0.0525 - accuracy: 1.0000 - val_loss: 124.8246 - val_accuracy: 0.2222 - 124ms/epoch - 14ms/step\n",
      "Epoch 2734/4000\n",
      "9/9 - 0s - loss: 0.0528 - accuracy: 1.0000 - val_loss: 118.7447 - val_accuracy: 0.2222 - 126ms/epoch - 14ms/step\n",
      "Epoch 2735/4000\n",
      "9/9 - 0s - loss: 0.0660 - accuracy: 1.0000 - val_loss: 74.8051 - val_accuracy: 0.2222 - 125ms/epoch - 14ms/step\n",
      "Epoch 2736/4000\n",
      "9/9 - 0s - loss: 0.0732 - accuracy: 0.9931 - val_loss: 5.7615 - val_accuracy: 0.2917 - 124ms/epoch - 14ms/step\n",
      "Epoch 2737/4000\n",
      "9/9 - 0s - loss: 0.0795 - accuracy: 0.9931 - val_loss: 54.7676 - val_accuracy: 0.2639 - 125ms/epoch - 14ms/step\n",
      "Epoch 2738/4000\n",
      "9/9 - 3s - loss: 0.0711 - accuracy: 1.0000 - val_loss: 39.0903 - val_accuracy: 0.2222 - 3s/epoch - 278ms/step\n",
      "Epoch 2739/4000\n",
      "9/9 - 0s - loss: 0.0539 - accuracy: 1.0000 - val_loss: 29.9878 - val_accuracy: 0.2917 - 119ms/epoch - 13ms/step\n",
      "Epoch 2740/4000\n",
      "9/9 - 0s - loss: 0.0594 - accuracy: 1.0000 - val_loss: 67.6112 - val_accuracy: 0.2917 - 126ms/epoch - 14ms/step\n",
      "Epoch 2741/4000\n",
      "9/9 - 0s - loss: 0.0542 - accuracy: 1.0000 - val_loss: 64.9287 - val_accuracy: 0.2917 - 150ms/epoch - 17ms/step\n",
      "Epoch 2742/4000\n",
      "9/9 - 0s - loss: 0.0734 - accuracy: 0.9931 - val_loss: 20.5412 - val_accuracy: 0.2222 - 137ms/epoch - 15ms/step\n",
      "Epoch 2743/4000\n",
      "9/9 - 0s - loss: 0.0795 - accuracy: 0.9931 - val_loss: 28.0564 - val_accuracy: 0.2639 - 141ms/epoch - 16ms/step\n",
      "Epoch 2744/4000\n",
      "9/9 - 0s - loss: 0.0466 - accuracy: 1.0000 - val_loss: 62.6312 - val_accuracy: 0.2222 - 125ms/epoch - 14ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2745/4000\n",
      "9/9 - 0s - loss: 0.0711 - accuracy: 1.0000 - val_loss: 208.9599 - val_accuracy: 0.2222 - 135ms/epoch - 15ms/step\n",
      "Epoch 2746/4000\n",
      "9/9 - 3s - loss: 0.0519 - accuracy: 1.0000 - val_loss: 199.6234 - val_accuracy: 0.2222 - 3s/epoch - 278ms/step\n",
      "Epoch 2747/4000\n",
      "9/9 - 0s - loss: 0.0665 - accuracy: 1.0000 - val_loss: 97.0499 - val_accuracy: 0.2222 - 118ms/epoch - 13ms/step\n",
      "Epoch 2748/4000\n",
      "9/9 - 0s - loss: 0.0896 - accuracy: 0.9861 - val_loss: 114.0482 - val_accuracy: 0.2917 - 101ms/epoch - 11ms/step\n",
      "Epoch 2749/4000\n",
      "9/9 - 0s - loss: 0.0886 - accuracy: 0.9861 - val_loss: 155.6877 - val_accuracy: 0.2917 - 119ms/epoch - 13ms/step\n",
      "Epoch 2750/4000\n",
      "9/9 - 0s - loss: 0.0904 - accuracy: 0.9861 - val_loss: 4.6742 - val_accuracy: 0.2917 - 100ms/epoch - 11ms/step\n",
      "Epoch 2751/4000\n",
      "9/9 - 0s - loss: 0.0704 - accuracy: 1.0000 - val_loss: 26.8991 - val_accuracy: 0.2222 - 104ms/epoch - 12ms/step\n",
      "Epoch 2752/4000\n",
      "9/9 - 0s - loss: 0.0787 - accuracy: 0.9931 - val_loss: 124.3551 - val_accuracy: 0.2222 - 113ms/epoch - 13ms/step\n",
      "Epoch 2753/4000\n",
      "9/9 - 0s - loss: 0.0703 - accuracy: 1.0000 - val_loss: 102.7142 - val_accuracy: 0.2222 - 102ms/epoch - 11ms/step\n",
      "Epoch 2754/4000\n",
      "9/9 - 3s - loss: 0.0612 - accuracy: 1.0000 - val_loss: 37.4384 - val_accuracy: 0.2222 - 3s/epoch - 284ms/step\n",
      "Epoch 2755/4000\n",
      "9/9 - 0s - loss: 0.0601 - accuracy: 1.0000 - val_loss: 46.4662 - val_accuracy: 0.2222 - 131ms/epoch - 15ms/step\n",
      "Epoch 2756/4000\n",
      "9/9 - 0s - loss: 0.0522 - accuracy: 1.0000 - val_loss: 23.6854 - val_accuracy: 0.2222 - 86ms/epoch - 10ms/step\n",
      "Epoch 2757/4000\n",
      "9/9 - 0s - loss: 0.1089 - accuracy: 0.9861 - val_loss: 46.4802 - val_accuracy: 0.2917 - 85ms/epoch - 9ms/step\n",
      "Epoch 2758/4000\n",
      "9/9 - 0s - loss: 0.0681 - accuracy: 0.9931 - val_loss: 1.7799 - val_accuracy: 0.3889 - 85ms/epoch - 9ms/step\n",
      "Epoch 2759/4000\n",
      "9/9 - 0s - loss: 0.0681 - accuracy: 1.0000 - val_loss: 47.8025 - val_accuracy: 0.2222 - 88ms/epoch - 10ms/step\n",
      "Epoch 2760/4000\n",
      "9/9 - 0s - loss: 0.0572 - accuracy: 1.0000 - val_loss: 170.2229 - val_accuracy: 0.2917 - 83ms/epoch - 9ms/step\n",
      "Epoch 2761/4000\n",
      "9/9 - 0s - loss: 0.0801 - accuracy: 0.9861 - val_loss: 153.1116 - val_accuracy: 0.2917 - 90ms/epoch - 10ms/step\n",
      "Epoch 2762/4000\n",
      "9/9 - 0s - loss: 0.0546 - accuracy: 1.0000 - val_loss: 16.6891 - val_accuracy: 0.2917 - 84ms/epoch - 9ms/step\n",
      "Epoch 2763/4000\n",
      "9/9 - 0s - loss: 0.0728 - accuracy: 0.9931 - val_loss: 114.8769 - val_accuracy: 0.2222 - 98ms/epoch - 11ms/step\n",
      "Epoch 2764/4000\n",
      "9/9 - 0s - loss: 0.0656 - accuracy: 1.0000 - val_loss: 196.9272 - val_accuracy: 0.2222 - 85ms/epoch - 9ms/step\n",
      "Epoch 2765/4000\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import datetime\n",
    "# mne imports\n",
    "import mne\n",
    "from mne import io\n",
    "from mne.datasets import sample\n",
    "\n",
    "# EEGNet-specific imports\n",
    "from Algorithm.EEGModels import *\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import utils as np_utils\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "# PyRiemann imports\n",
    "\n",
    "# tools for plotting confusion matrices\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "kernels, chans, samples = 1, 22, 751\n",
    "\n",
    "X_train      = X[0:144,]\n",
    "Y_train      = y[0:144]\n",
    "X_validate   = X[144:216,]\n",
    "Y_validate   = y[144:216]\n",
    "X_test       = X[216:,]\n",
    "Y_test       = y[216:]\n",
    "\n",
    "Y_train      = np_utils.to_categorical(Y_train-1)\n",
    "Y_validate   = np_utils.to_categorical(Y_validate-1)\n",
    "Y_test       = np_utils.to_categorical(Y_test-1)\n",
    "\n",
    "X_train      = X_train.reshape(X_train.shape[0], chans, samples, kernels)\n",
    "X_validate   = X_validate.reshape(X_validate.shape[0], chans, samples, kernels)\n",
    "X_test       = X_test.reshape(X_test.shape[0], chans, samples, kernels)\n",
    "   \n",
    "model = EEGNet(nb_classes = 4, Chans = chans, Samples = samples,\n",
    "               dropoutRate = 0.25)\n",
    "\n",
    "# compile the model and set the optimizers\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', \n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "fittedModel = model.fit(X_train, Y_train, batch_size = 16,epochs = 4000,\n",
    "                        verbose = 2, validation_data=(X_validate, Y_validate))\n",
    "\n",
    "probs       = model.predict(X_test)\n",
    "preds       = probs.argmax(axis = -1)  \n",
    "acc         = np.mean(preds == Y_test.argmax(axis=-1))\n",
    "print(\"Classification accuracy: %f \" % (acc))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ed1ac046",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mne.decoding import CSP # Common Spatial Pattern Filtering\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras import regularizers\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "# OneHotEncoding Labels\n",
    "enc = OneHotEncoder()\n",
    "X_out = enc.fit_transform(labels.reshape(-1,1)).toarray()\n",
    "\n",
    "# Cross Validation Split\n",
    "cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "acc = []\n",
    "ka = []\n",
    "prec = []\n",
    "recall = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "417ae562",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ebfa10a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.1e-05 (2.2e-16 eps * 22 dim * 2.3e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.1e-05 (2.2e-16 eps * 22 dim * 2.2e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.2e-05 (2.2e-16 eps * 22 dim * 2.5e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.1e-05 (2.2e-16 eps * 22 dim * 2.3e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1e-05 (2.2e-16 eps * 22 dim * 2.1e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1e-05 (2.2e-16 eps * 22 dim * 2.1e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1e-05 (2.2e-16 eps * 22 dim * 2.1e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.1e-05 (2.2e-16 eps * 22 dim * 2.2e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.4e-05 (2.2e-16 eps * 22 dim * 2.9e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.3e-05 (2.2e-16 eps * 22 dim * 2.7e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.6e-05 (2.2e-16 eps * 22 dim * 3.2e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.5e-05 (2.2e-16 eps * 22 dim * 3.1e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 6.2e-06 (2.2e-16 eps * 22 dim * 1.3e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 5.4e-06 (2.2e-16 eps * 22 dim * 1.1e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 6e-06 (2.2e-16 eps * 22 dim * 1.2e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 5.9e-06 (2.2e-16 eps * 22 dim * 1.2e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 6.6e-06 (2.2e-16 eps * 22 dim * 1.4e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 6.3e-06 (2.2e-16 eps * 22 dim * 1.3e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 6.8e-06 (2.2e-16 eps * 22 dim * 1.4e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 6.6e-06 (2.2e-16 eps * 22 dim * 1.3e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 9.2e-06 (2.2e-16 eps * 22 dim * 1.9e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 8.5e-06 (2.2e-16 eps * 22 dim * 1.7e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 8.9e-06 (2.2e-16 eps * 22 dim * 1.8e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 9.1e-06 (2.2e-16 eps * 22 dim * 1.9e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 7.3e-06 (2.2e-16 eps * 22 dim * 1.5e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 6.8e-06 (2.2e-16 eps * 22 dim * 1.4e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 7.5e-06 (2.2e-16 eps * 22 dim * 1.5e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 7.7e-06 (2.2e-16 eps * 22 dim * 1.6e+09  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing rank from data with rank=None\n",
      "    Using tolerance 8.7e-07 (2.2e-16 eps * 22 dim * 1.8e+08  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 7e-07 (2.2e-16 eps * 22 dim * 1.4e+08  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 8.1e-07 (2.2e-16 eps * 22 dim * 1.7e+08  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 7e-07 (2.2e-16 eps * 22 dim * 1.4e+08  max singular value)\n",
      "    Estimated rank (mag): 22\n",
      "    MAG: rank 22 computed from 22 data channels with 0 projectors\n",
      "Reducing data rank from 22 -> 22\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "(230, 32)\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\training.py\", line 889, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\input_spec.py\", line 264, in assert_input_compatibility\n        raise ValueError(f'Input {input_index} of layer \"{layer_name}\" is '\n\n    ValueError: Input 0 of layer \"model_2\" is incompatible with the layer: expected shape=(None, 64, 751, 1), found shape=(None, 32)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-c2e6f76140ba>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     24\u001b[0m               metrics = ['accuracy'])\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m     fittedModel = model.fit(X_train, y_train, batch_size = 16,epochs = 20,\n\u001b[0m\u001b[0;32m     27\u001b[0m                         verbose = 1)\n\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m                     \u001b[0mretval_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m                 \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\training.py\", line 889, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python38\\site-packages\\keras\\engine\\input_spec.py\", line 264, in assert_input_compatibility\n        raise ValueError(f'Input {input_index} of layer \"{layer_name}\" is '\n\n    ValueError: Input 0 of layer \"model_2\" is incompatible with the layer: expected shape=(None, 64, 751, 1), found shape=(None, 32)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f61def2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Annotations | 595 segments: 1023 (7), 1072 (1), 276 (1), 277 (1), 32766 ...>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1237013d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Used Annotations descriptions: ['1023', '1072', '276', '277', '32766', '768', '783']\n"
     ]
    }
   ],
   "source": [
    "events, event_ids = mne.events_from_annotations(raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dcdbf93c",
   "metadata": {},
   "outputs": [],
   "source": [
    "stims = [value for key, value in event_ids.items() if key == \"783\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "410ef6e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "Not setting metadata\n",
      "288 matching events found\n",
      "No baseline correction applied\n",
      "Loading data for 288 events and 1001 original time points ...\n",
      "0 bad epochs dropped\n"
     ]
    }
   ],
   "source": [
    "epochs = mne.Epochs(raw, events, event_id=stims, tmin=0, tmax=4,\n",
    "                            baseline=None, preload=True, proj=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21e487db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'log this'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"log %s\" %('this')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ee99b53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 25, 1001)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29f5fc25",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.info['bads'] += ['EOG-left', 'EOG-central', 'EOG-right']\n"
   ]
  }
 ],
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