Task 2 new.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eGP-BSmIV919"
   },
   "source": [
    "**Hand Gesture Detection assignment**\n",
    "\n",
    "\n",
    "\n",
    "   Instruction/Hints are mentioned, to run the cell you have to do shift+enter\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "q6kVvrvqBNmi"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opendatasets in c:\\users\\neeld\\anaconda3\\lib\\site-packages (0.1.22)\n",
      "Requirement already satisfied: tqdm in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from opendatasets) (4.65.0)\n",
      "Requirement already satisfied: kaggle in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from opendatasets) (1.5.16)\n",
      "Requirement already satisfied: click in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from opendatasets) (8.1.7)\n",
      "Requirement already satisfied: colorama in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from click->opendatasets) (0.4.6)\n",
      "Requirement already satisfied: six>=1.10 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (1.16.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (2023.11.17)\n",
      "Requirement already satisfied: python-dateutil in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (2.8.2)\n",
      "Requirement already satisfied: requests in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (2.31.0)\n",
      "Requirement already satisfied: python-slugify in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (5.0.2)\n",
      "Requirement already satisfied: urllib3 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (1.26.18)\n",
      "Requirement already satisfied: bleach in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (4.1.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from bleach->kaggle->opendatasets) (23.1)\n",
      "Requirement already satisfied: webencodings in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from bleach->kaggle->opendatasets) (0.5.1)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from requests->kaggle->opendatasets) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from requests->kaggle->opendatasets) (3.4)\n",
      "Requirement already satisfied: keras in c:\\users\\neeld\\anaconda3\\lib\\site-packages (2.15.0)\n",
      "Requirement already satisfied: tensorflow in c:\\users\\neeld\\anaconda3\\lib\\site-packages (2.15.0)\n",
      "Requirement already satisfied: tensorflow-intel==2.15.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow) (2.15.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (2.0.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers>=23.5.26 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (23.5.26)\n",
      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (0.5.4)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (0.2.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (3.9.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (16.0.6)\n",
      "Requirement already satisfied: ml-dtypes~=0.2.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (0.2.0)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (1.26.2)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (3.3.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (23.1)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (4.23.4)\n",
      "Requirement already satisfied: setuptools in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (68.0.0)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (1.16.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (2.4.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (4.7.1)\n",
      "Requirement already satisfied: wrapt<1.15,>=1.11.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (1.14.1)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (0.31.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (1.60.0)\n",
      "Requirement already satisfied: tensorboard<2.16,>=2.15 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (2.15.1)\n",
      "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (2.15.0)\n",
      "Requirement already satisfied: keras<2.16,>=2.15.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (2.15.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.15.0->tensorflow) (0.38.4)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2.25.2)\n",
      "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (1.2.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (3.4.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2.31.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2.2.3)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (5.3.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (4.9)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2023.11.17)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2.1.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (3.2.2)\n",
      "Collecting rasa==1.1.4\n",
      "  Using cached rasa-1.1.4-py3-none-any.whl (447 kB)\n",
      "Requirement already satisfied: requests~=2.22 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from rasa==1.1.4) (2.31.0)\n",
      "Collecting boto3~=1.9 (from rasa==1.1.4)\n",
      "  Obtaining dependency information for boto3~=1.9 from https://files.pythonhosted.org/packages/d4/e8/3ccebf1cb78702beb8bb2e535525ff350d0f597ff794baeeab5206800a2c/boto3-1.34.1-py3-none-any.whl.metadata\n",
      "  Using cached boto3-1.34.1-py3-none-any.whl.metadata (6.6 kB)\n",
      "Requirement already satisfied: matplotlib~=3.0 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from rasa==1.1.4) (3.8.0)\n",
      "Collecting simplejson~=3.16 (from rasa==1.1.4)\n",
      "  Obtaining dependency information for simplejson~=3.16 from https://files.pythonhosted.org/packages/b6/8e/3e12d122dfdf549a8d12eaf39954ee39f2027060aa38b63430f8ab3244e7/simplejson-3.19.2-cp311-cp311-win_amd64.whl.metadata\n",
      "  Using cached simplejson-3.19.2-cp311-cp311-win_amd64.whl.metadata (3.2 kB)\n",
      "Requirement already satisfied: attrs>=18 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from rasa==1.1.4) (23.1.0)\n",
      "Collecting jsonpickle~=1.1 (from rasa==1.1.4)\n",
      "  Using cached jsonpickle-1.5.2-py2.py3-none-any.whl (37 kB)\n",
      "Collecting redis~=3.2 (from rasa==1.1.4)\n",
      "  Using cached redis-3.5.3-py2.py3-none-any.whl (72 kB)\n",
      "Collecting fakeredis~=1.0 (from rasa==1.1.4)\n",
      "  Using cached fakeredis-1.10.2-py3-none-any.whl (43 kB)\n",
      "Collecting pymongo~=3.8 (from rasa==1.1.4)\n",
      "  Using cached pymongo-3.13.0-cp311-cp311-win_amd64.whl (394 kB)\n",
      "Requirement already satisfied: numpy~=1.16 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from rasa==1.1.4) (1.26.2)\n",
      "Requirement already satisfied: scipy~=1.2 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from rasa==1.1.4) (1.11.4)\n",
      "INFO: pip is looking at multiple versions of rasa to determine which version is compatible with other requirements. This could take a while.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Ignored the following versions that require a different python version: 1.10.10 Requires-Python >=3.6,<3.8; 1.10.11 Requires-Python >=3.6,<3.8; 1.10.12 Requires-Python >=3.6,<3.8; 1.10.13 Requires-Python >=3.6,<3.8; 1.10.14 Requires-Python >=3.6,<3.8; 1.10.15 Requires-Python >=3.6,<3.8; 1.10.16 Requires-Python >=3.6,<3.8; 1.10.17 Requires-Python >=3.6,<3.8; 1.10.18 Requires-Python >=3.6,<3.8; 1.10.19 Requires-Python >=3.6,<3.8; 1.10.20 Requires-Python >=3.6,<3.8; 1.10.21 Requires-Python >=3.6,<3.8; 1.10.22 Requires-Python >=3.6,<3.8; 1.10.23 Requires-Python >=3.6,<3.8; 1.10.24 Requires-Python >=3.6,<3.8; 1.10.25 Requires-Python >=3.6,<3.8; 1.10.26 Requires-Python >=3.6,<3.8; 1.10.3 Requires-Python >=3.6,<3.8; 1.10.4 Requires-Python >=3.6,<3.8; 1.10.5 Requires-Python >=3.6,<3.8; 1.10.6 Requires-Python >=3.6,<3.8; 1.10.7 Requires-Python >=3.6,<3.8; 1.10.8 Requires-Python >=3.6,<3.8; 1.10.9 Requires-Python >=3.6,<3.8; 2.0.0 Requires-Python >=3.6,<3.9; 2.0.0a1 Requires-Python >=3.6,<3.9; 2.0.0a2 Requires-Python >=3.6,<3.9; 2.0.0a3 Requires-Python >=3.6,<3.9; 2.0.0a4 Requires-Python >=3.6,<3.9; 2.0.0a5 Requires-Python >=3.6,<3.9; 2.0.0a6 Requires-Python >=3.6,<3.9; 2.0.0rc1 Requires-Python >=3.6,<3.9; 2.0.0rc2 Requires-Python >=3.6,<3.9; 2.0.0rc3 Requires-Python >=3.6,<3.9; 2.0.0rc4 Requires-Python >=3.6,<3.9; 2.0.1 Requires-Python >=3.6,<3.9; 2.0.2 Requires-Python >=3.6,<3.9; 2.0.3 Requires-Python >=3.6,<3.9; 2.0.4 Requires-Python >=3.6,<3.9; 2.0.5 Requires-Python >=3.6,<3.9; 2.0.6 Requires-Python >=3.6,<3.9; 2.0.7 Requires-Python >=3.6,<3.9; 2.0.8 Requires-Python >=3.6,<3.9; 2.1.0 Requires-Python >=3.6,<3.9; 2.1.1 Requires-Python >=3.6,<3.9; 2.1.2 Requires-Python >=3.6,<3.9; 2.1.3 Requires-Python >=3.6,<3.9; 2.2.0 Requires-Python >=3.6,<3.9; 2.2.0a1 Requires-Python >=3.6,<3.9; 2.2.1 Requires-Python >=3.6,<3.9; 2.2.10 Requires-Python >=3.6,<3.9; 2.2.2 Requires-Python >=3.6,<3.9; 2.2.3 Requires-Python >=3.6,<3.9; 2.2.4 Requires-Python >=3.6,<3.9; 2.2.5 Requires-Python >=3.6,<3.9; 2.2.6 Requires-Python >=3.6,<3.9; 2.2.7 Requires-Python >=3.6,<3.9; 2.2.8 Requires-Python >=3.6,<3.9; 2.2.9 Requires-Python >=3.6,<3.9; 2.3.0 Requires-Python >=3.6,<3.9; 2.3.1 Requires-Python >=3.6,<3.9; 2.3.2 Requires-Python >=3.6,<3.9; 2.3.3 Requires-Python >=3.6,<3.9; 2.3.4 Requires-Python >=3.6,<3.9; 2.3.5 Requires-Python >=3.6,<3.9; 2.4.0 Requires-Python >=3.6,<3.9; 2.4.1 Requires-Python >=3.6,<3.9; 2.4.2 Requires-Python >=3.6,<3.9; 2.4.3 Requires-Python >=3.6,<3.9; 2.5.0 Requires-Python >=3.6,<3.9; 2.5.1 Requires-Python >=3.6,<3.9; 2.5.2 Requires-Python >=3.6,<3.9; 2.6.0 Requires-Python >=3.6,<3.9; 2.6.1 Requires-Python >=3.6,<3.9; 2.6.2 Requires-Python >=3.6,<3.9; 2.6.3 Requires-Python >=3.6,<3.9; 2.7.0 Requires-Python >=3.6,<3.9; 2.7.1 Requires-Python >=3.6,<3.9; 2.7.2 Requires-Python >=3.6,<3.9; 2.8.0 Requires-Python >=3.6,<3.9; 2.8.1 Requires-Python >=3.6,<3.9; 2.8.10 Requires-Python >=3.6,<3.9; 2.8.11 Requires-Python >=3.6,<3.9; 2.8.12 Requires-Python >=3.6,<3.9; 2.8.13 Requires-Python >=3.6,<3.9; 2.8.14 Requires-Python >=3.6,<3.9; 2.8.15 Requires-Python >=3.6,<3.9; 2.8.16 Requires-Python >=3.6,<3.9; 2.8.17 Requires-Python >=3.6,<3.9; 2.8.18 Requires-Python >=3.6,<3.9; 2.8.19 Requires-Python >=3.6,<3.9; 2.8.2 Requires-Python >=3.6,<3.9; 2.8.20 Requires-Python >=3.6,<3.9; 2.8.21 Requires-Python >=3.6,<3.9; 2.8.22 Requires-Python >=3.6,<3.9; 2.8.23 Requires-Python >=3.6,<3.9; 2.8.24 Requires-Python >=3.6,<3.9; 2.8.25 Requires-Python >=3.6,<3.9; 2.8.26 Requires-Python >=3.6,<3.9; 2.8.27 Requires-Python >=3.7,<3.9; 2.8.28 Requires-Python >=3.7,<3.9; 2.8.29 Requires-Python >=3.7,<3.9; 2.8.3 Requires-Python >=3.6,<3.9; 2.8.30 Requires-Python >=3.7,<3.9; 2.8.31 Requires-Python >=3.7,<3.9; 2.8.32 Requires-Python >=3.7,<3.9; 2.8.33 Requires-Python >=3.7,<3.9; 2.8.34 Requires-Python >=3.7,<3.9; 2.8.4 Requires-Python >=3.6,<3.9; 2.8.5 Requires-Python >=3.6,<3.9; 2.8.6 Requires-Python >=3.6,<3.9; 2.8.7 Requires-Python >=3.6,<3.9; 2.8.8 Requires-Python >=3.6,<3.9; 2.8.9 Requires-Python >=3.6,<3.9; 3.0.0 Requires-Python >=3.7,<3.9; 3.0.0rc1 Requires-Python >=3.7,<3.9; 3.0.0rc2 Requires-Python >=3.7,<3.9; 3.0.0rc3 Requires-Python >=3.7,<3.9; 3.0.1 Requires-Python >=3.7,<3.9; 3.0.11 Requires-Python >=3.7,<3.9; 3.0.12 Requires-Python >=3.7,<3.9; 3.0.13 Requires-Python >=3.7,<3.9; 3.0.2 Requires-Python >=3.7,<3.9; 3.0.3 Requires-Python >=3.7,<3.9; 3.0.4 Requires-Python >=3.7,<3.9; 3.0.5 Requires-Python >=3.7,<3.9; 3.0.6 Requires-Python >=3.7,<3.9; 3.0.7 Requires-Python >=3.7,<3.9; 3.0.8 Requires-Python >=3.7,<3.9; 3.0.9 Requires-Python >=3.7,<3.9; 3.1.0 Requires-Python >=3.7,<3.10; 3.1.1 Requires-Python >=3.7,<3.10; 3.1.2 Requires-Python >=3.7,<3.10; 3.1.3 Requires-Python >=3.7,<3.10; 3.1.4 Requires-Python >=3.7,<3.10; 3.1.5 Requires-Python >=3.7,<3.10; 3.1.6 Requires-Python >=3.7,<3.10; 3.1.7 Requires-Python >=3.7,<3.10; 3.2.0 Requires-Python >=3.7,<3.10; 3.2.1 Requires-Python >=3.7,<3.10; 3.2.10 Requires-Python >=3.7,<3.10; 3.2.11 Requires-Python >=3.7,<3.10; 3.2.12 Requires-Python >=3.7,<3.10; 3.2.13 Requires-Python >=3.7,<3.10; 3.2.2 Requires-Python >=3.7,<3.10; 3.2.4 Requires-Python >=3.7,<3.10; 3.2.5 Requires-Python >=3.7,<3.10; 3.2.6 Requires-Python >=3.7,<3.10; 3.2.7 Requires-Python >=3.7,<3.10; 3.2.8 Requires-Python >=3.7,<3.10; 3.3.0 Requires-Python >=3.7,<3.10; 3.3.0a1 Requires-Python >=3.7,<3.10; 3.3.1 Requires-Python >=3.7,<3.10; 3.3.10 Requires-Python >=3.7,<3.10; 3.3.11 Requires-Python >=3.7,<3.10; 3.3.12 Requires-Python >=3.7,<3.10; 3.3.2 Requires-Python >=3.7,<3.10; 3.3.3 Requires-Python >=3.7,<3.10; 3.3.4 Requires-Python >=3.7,<3.10; 3.3.5 Requires-Python >=3.7,<3.10; 3.3.6 Requires-Python >=3.7,<3.10; 3.3.7 Requires-Python >=3.7,<3.10; 3.3.8 Requires-Python >=3.7,<3.10; 3.3.9 Requires-Python >=3.7,<3.10; 3.4.0 Requires-Python >=3.7,<3.11; 3.4.1 Requires-Python >=3.7,<3.11; 3.4.10 Requires-Python >=3.7,<3.11; 3.4.11 Requires-Python >=3.7,<3.11; 3.4.12 Requires-Python >=3.7,<3.11; 3.4.13 Requires-Python >=3.7,<3.11; 3.4.14 Requires-Python >=3.7,<3.11; 3.4.15 Requires-Python >=3.7,<3.11; 3.4.16 Requires-Python >=3.7,<3.11; 3.4.17 Requires-Python >=3.7,<3.11; 3.4.18 Requires-Python >=3.7,<3.11; 3.4.2 Requires-Python >=3.7,<3.11; 3.4.3 Requires-Python >=3.7,<3.11; 3.4.4 Requires-Python >=3.7,<3.11; 3.4.5 Requires-Python >=3.7,<3.11; 3.4.6 Requires-Python >=3.7,<3.11; 3.4.7 Requires-Python >=3.7,<3.11; 3.4.8 Requires-Python >=3.7,<3.11; 3.4.9 Requires-Python >=3.7,<3.11; 3.5.0 Requires-Python >=3.7,<3.11; 3.5.0a1.dev1 Requires-Python >=3.7,<3.11; 3.5.0a2.dev1 Requires-Python >=3.7,<3.11; 3.5.0a2.dev2 Requires-Python >=3.7,<3.11; 3.5.0b1 Requires-Python >=3.7,<3.11; 3.5.1 Requires-Python >=3.7,<3.11; 3.5.10 Requires-Python >=3.7,<3.11; 3.5.11 Requires-Python >=3.7,<3.11; 3.5.12 Requires-Python >=3.7,<3.11; 3.5.13 Requires-Python >=3.7,<3.11; 3.5.14 Requires-Python >=3.7,<3.11; 3.5.15 Requires-Python >=3.7,<3.11; 3.5.16 Requires-Python >=3.7,<3.11; 3.5.17 Requires-Python >=3.7,<3.11; 3.5.2 Requires-Python >=3.7,<3.11; 3.5.3 Requires-Python >=3.7,<3.11; 3.5.4 Requires-Python >=3.7,<3.11; 3.5.5 Requires-Python >=3.7,<3.11; 3.5.6 Requires-Python >=3.7,<3.11; 3.5.7 Requires-Python >=3.7,<3.11; 3.5.8 Requires-Python >=3.7,<3.11; 3.5.9 Requires-Python >=3.7,<3.11; 3.6.0 Requires-Python >=3.8,<3.11; 3.6.0a1 Requires-Python >=3.8,<3.11; 3.6.1 Requires-Python >=3.8,<3.11; 3.6.10 Requires-Python >=3.8,<3.11; 3.6.11 Requires-Python >=3.8,<3.11; 3.6.12 Requires-Python >=3.8,<3.11; 3.6.13 Requires-Python >=3.8,<3.11; 3.6.14 Requires-Python >=3.8,<3.11; 3.6.15 Requires-Python >=3.8,<3.11; 3.6.1a1 Requires-Python >=3.8,<3.11; 3.6.2 Requires-Python >=3.8,<3.11; 3.6.3 Requires-Python >=3.8,<3.11; 3.6.4 Requires-Python >=3.8,<3.11; 3.6.5 Requires-Python >=3.8,<3.11; 3.6.6 Requires-Python >=3.8,<3.11; 3.6.7 Requires-Python >=3.8,<3.11; 3.6.8 Requires-Python >=3.8,<3.11; 3.6.9 Requires-Python >=3.8,<3.11; 3.7.0a1 Requires-Python >=3.8,<3.11; 3.7.0b1 Requires-Python >=3.8,<3.11; 3.7.0b2 Requires-Python >=3.8,<3.11; 3.8.0a1 Requires-Python >=3.8.1,<3.11; 3.8.0a10 Requires-Python >=3.8.1,<3.11; 3.8.0a11 Requires-Python >=3.8.1,<3.11; 3.8.0a12 Requires-Python >=3.8.1,<3.11; 3.8.0a2 Requires-Python >=3.8.1,<3.11; 3.8.0a3 Requires-Python >=3.8.1,<3.11; 3.8.0a4 Requires-Python >=3.8.1,<3.11; 3.8.0a5 Requires-Python >=3.8.1,<3.11; 3.8.0a6 Requires-Python >=3.8.1,<3.11; 3.8.0a7 Requires-Python >=3.8.1,<3.11; 3.8.0a7.dev2 Requires-Python >=3.8.1,<3.11; 3.8.0a9 Requires-Python >=3.8.1,<3.11\n",
      "ERROR: Could not find a version that satisfies the requirement tensorflow~=1.13.0 (from rasa) (from versions: 2.12.0rc0, 2.12.0rc1, 2.12.0, 2.12.1, 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0, 2.14.1, 2.15.0rc0, 2.15.0rc1, 2.15.0)\n",
      "ERROR: No matching distribution found for tensorflow~=1.13.0\n"
     ]
    }
   ],
   "source": [
    "!pip install opendatasets\n",
    "!pip install keras\n",
    "!pip install tensorflow\n",
    "!pip install rasa==1.1.4;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "dUOO_1rLBUyz"
   },
   "outputs": [],
   "source": [
    "# Import all the necessary dependcies\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imread\n",
    "import scipy\n",
    "from PIL import Image\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.layers as tfl\n",
    "from tensorflow.python.framework import ops\n",
    "tf.keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J5VzIMhTXZ3U"
   },
   "source": [
    "You will have to make an account on kaggle and will have to use that 'username' and key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "id": "mrNGblj5BcsB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping, found downloaded files in \".\\fingers\" (use force=True to force download)\n"
     ]
    }
   ],
   "source": [
    "# Download the dataset from kaggle\n",
    "\n",
    "\n",
    "import opendatasets as od\n",
    "import pandas as pd\n",
    "od.download(\"https://www.kaggle.com/datasets/koryakinp/fingers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in c:\\users\\neeld\\anaconda3\\lib\\site-packages (4.8.1.78)\n",
      "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\neeld\\anaconda3\\lib\\site-packages (from opencv-python) (1.26.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "KSshdfM7BjEe"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Path to the dataset directory\n",
    "dataset_dir = 'C:/Users/neeld/Downloads/fingers/test'\n",
    "\n",
    "# Initialize lists to store images and labels\n",
    "images = []\n",
    "labels = []\n",
    "\n",
    "# Iterate through each image file\n",
    "for filename in os.listdir(dataset_dir):\n",
    "    # Load the image using OpenCV\n",
    "    img_path = os.path.join(dataset_dir, filename)\n",
    "    img = cv2.imread(img_path)\n",
    "\n",
    "    # Resize the image to a desired size (e.g., 64x64)\n",
    "    img = cv2.resize(img, (64, 64))\n",
    "\n",
    "    # Append the image to the images list\n",
    "    images.append(img)\n",
    "\n",
    "    # Get the label from the filename (the first character)\n",
    "    label = filename[0]  \n",
    "    labels.append(label)\n",
    "\n",
    "# Convert the lists to NumPy arrays\n",
    "images = np.array(images)\n",
    "labels = np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (18000, 64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "image_dir = 'C:/Users/neeld/Downloads/fingers/train'\n",
    "\n",
    "# List to store image data\n",
    "x_train = []\n",
    "\n",
    "# Iterate through each image file in the directory\n",
    "for filename in os.listdir(image_dir):\n",
    "    if filename.endswith(\".png\") or filename.endswith(\".jpg\"):\n",
    "        # Construct the full file path\n",
    "        file_path = os.path.join(image_dir, filename)\n",
    "\n",
    "        # Load the image using OpenCV\n",
    "        img = cv2.imread(file_path)\n",
    "\n",
    "        # Resize the image if needed\n",
    "        img = cv2.resize(img, (64, 64))\n",
    "\n",
    "        # Append the image to the x_train list\n",
    "        x_train.append(img)\n",
    "\n",
    "# Convert the list to a NumPy array\n",
    "x_train = np.array(x_train)\n",
    "\n",
    "# Display the shape of x_train\n",
    "print(\"x_train shape:\", x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_test shape: (3600, 64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "image_dir = 'C:/Users/neeld/Downloads/fingers/test'\n",
    "\n",
    "# List to store image data\n",
    "x_test = []\n",
    "\n",
    "# Iterate through each image file in the directory\n",
    "for filename in os.listdir(image_dir):\n",
    "    if filename.endswith(\".png\") or filename.endswith(\".jpg\"):\n",
    "        # Construct the full file path\n",
    "        file_path = os.path.join(image_dir, filename)\n",
    "\n",
    "        # Load the image using OpenCV\n",
    "        img = cv2.imread(file_path)\n",
    "\n",
    "        # Resize the image if needed\n",
    "        img = cv2.resize(img, (64, 64))\n",
    "\n",
    "        # Append the image to the x_train list\n",
    "        x_test.append(img)\n",
    "\n",
    "# Convert the list to a NumPy array\n",
    "x_test = np.array(x_test)\n",
    "\n",
    "# Display the shape of x_train\n",
    "print(\"x_test shape:\", x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape images to add a channel dimension\n",
    "x_train = x_train.reshape((-1, 64, 64, 1))\n",
    "x_test = x_test.reshape((-1, 64, 64, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "eyO82oK4bzct"
   },
   "outputs": [],
   "source": [
    "x_train = np.expand_dims(x_train, axis=-1)\n",
    "x_test = np.expand_dims(x_test, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to the dataset directory\n",
    "dataset_dir = 'C:/Users/neeld/Downloads/fingers/train'\n",
    "\n",
    "y_train = []\n",
    "\n",
    "# Iterate through each image file\n",
    "for filename in os.listdir(dataset_dir):\n",
    "\n",
    " # Split the filename by underscores ('_')\n",
    "    parts = filename.split('_')\n",
    "    \n",
    "    # Extract the label from the last part (assuming it's a number)\n",
    "    label = int(parts[-1][0])\n",
    "    y_train.append(label)\n",
    "# Convert the lists to NumPy arrays\n",
    "y_train = np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to the dataset directory\n",
    "dataset_dir = 'C:/Users/neeld/Downloads/fingers/test'\n",
    "\n",
    "y_test = []\n",
    "\n",
    "# Iterate through each image file\n",
    "for filename in os.listdir(dataset_dir):\n",
    "\n",
    " # Split the filename by underscores ('_')\n",
    "    parts = filename.split('_')\n",
    "    \n",
    "    # Extract the label from the last part (assuming it's a number)\n",
    "    label = int(parts[-1][0])\n",
    "    y_test.append(label)\n",
    "# Convert the lists to NumPy arrays\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "LalU-m8ed9tI"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "\n",
    "Y_train = to_categorical(y_train, num_classes=6)\n",
    "\n",
    "Y_test = to_categorical(y_test, num_classes=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "eGg9wTjWZJnO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y_train shape: (18000, 6)\n",
      "Y_test shape: (3600, 6)\n"
     ]
    }
   ],
   "source": [
    "print (\"Y_train shape: \" + str(Y_train.shape))\n",
    "print (\"Y_test shape: \" + str(Y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "JIMLdalLXtPl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 64, 64, 8)         136       \n",
      "                                                                 \n",
      " activation (Activation)     (None, 64, 64, 8)         0         \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 8, 8, 8)           0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 8, 8, 16)          528       \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 8, 8, 16)          0         \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 2, 2, 16)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 64)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 6)                 390       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1054 (4.12 KB)\n",
      "Trainable params: 1054 (4.12 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# MAKE YOUR CNN MODEL HERE\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "\n",
    "def convolutional_model(input_shape):\n",
    "    model = models.Sequential()\n",
    "\n",
    "    # First Convolutional Layer\n",
    "    model.add(layers.Conv2D(8, (4, 4), strides=(1, 1), padding='SAME', input_shape=input_shape))\n",
    "    model.add(layers.Activation('relu'))\n",
    "    model.add(layers.MaxPooling2D((8, 8), strides=(8, 8), padding='SAME'))\n",
    "\n",
    "    # Second Convolutional Layer\n",
    "    model.add(layers.Conv2D(16, (2, 2), strides=(1, 1), padding='SAME'))\n",
    "    model.add(layers.Activation('relu'))\n",
    "    model.add(layers.MaxPooling2D((4, 4), strides=(4, 4), padding='SAME'))\n",
    "\n",
    "    # Flatten Layer\n",
    "    model.add(layers.Flatten())\n",
    "\n",
    "    # Fully Connected (Dense) Layer\n",
    "    model.add(layers.Dense(6, activation='softmax'))\n",
    "\n",
    "    return model\n",
    "\n",
    "# Example usage\n",
    "input_shape = (64, 64, 1)\n",
    "model = convolutional_model(input_shape)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "8BdaQ2alTT9v"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_2 (Conv2D)           (None, 64, 64, 8)         136       \n",
      "                                                                 \n",
      " activation_2 (Activation)   (None, 64, 64, 8)         0         \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPoolin  (None, 8, 8, 8)           0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 8, 8, 16)          528       \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 8, 8, 16)          0         \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPoolin  (None, 2, 2, 16)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 6)                 390       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1054 (4.12 KB)\n",
      "Trainable params: 1054 (4.12 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.losses import CategoricalCrossentropy\n",
    "from tensorflow.keras.metrics import Accuracy\n",
    "\n",
    "# Create your model\n",
    "conv_model = convolutional_model((64, 64, 1))\n",
    "\n",
    "# Compile the model\n",
    "conv_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "                   loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "                   metrics=[tf.keras.metrics.CategoricalAccuracy()])\n",
    "\n",
    "# Print a summary of the compiled model\n",
    "conv_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "eQBPWlDATXQI"
   },
   "outputs": [],
   "source": [
    "# SLICE THE DATASET FOR 1800 IMAGES FOR TRAINING AND 300 FOR TESTING, ALSO MENTION THE BATCH SIZE\n",
    "\n",
    "# Slicing the dataset for training\n",
    "x_train_slice = x_train[:1800]\n",
    "Y_train_slice = Y_train[:1800]\n",
    "\n",
    "# Slicing the dataset for testing\n",
    "x_test_slice = x_test[:300]\n",
    "Y_test_slice = Y_test[:300]\n",
    "\n",
    "# Batch size\n",
    "batch_size = 32\n",
    "\n",
    "# Create TensorFlow datasets\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((x_train_slice, Y_train_slice))\n",
    "train_dataset = train_dataset.batch(batch_size)\n",
    "\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((x_test_slice, Y_test_slice))\n",
    "test_dataset = test_dataset.batch(batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "id": "w0tKznpya0im",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.6564 - categorical_accuracy: 0.3039 - val_loss: 1.9905 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6509 - categorical_accuracy: 0.2989 - val_loss: 1.9913 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6506 - categorical_accuracy: 0.2994 - val_loss: 2.0004 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6443 - categorical_accuracy: 0.3028 - val_loss: 2.0109 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6438 - categorical_accuracy: 0.3089 - val_loss: 2.0000 - val_categorical_accuracy: 0.1700\n",
      "Epoch 6/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6527 - categorical_accuracy: 0.3000 - val_loss: 1.9921 - val_categorical_accuracy: 0.1567\n",
      "Epoch 7/10000\n",
      "57/57 [==============================] - 1s 14ms/step - loss: 1.6454 - categorical_accuracy: 0.3072 - val_loss: 2.0069 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6464 - categorical_accuracy: 0.3172 - val_loss: 2.0208 - val_categorical_accuracy: 0.1433\n",
      "Epoch 9/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6426 - categorical_accuracy: 0.3167 - val_loss: 2.0253 - val_categorical_accuracy: 0.1600\n",
      "Epoch 10/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6396 - categorical_accuracy: 0.3028 - val_loss: 2.0221 - val_categorical_accuracy: 0.1533\n",
      "Epoch 11/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6393 - categorical_accuracy: 0.3089 - val_loss: 2.0204 - val_categorical_accuracy: 0.1633\n",
      "Epoch 12/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6337 - categorical_accuracy: 0.3172 - val_loss: 2.0112 - val_categorical_accuracy: 0.1500\n",
      "Epoch 13/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6318 - categorical_accuracy: 0.3194 - val_loss: 2.0304 - val_categorical_accuracy: 0.1567\n",
      "Epoch 14/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.6346 - categorical_accuracy: 0.3172 - val_loss: 2.0276 - val_categorical_accuracy: 0.1500\n",
      "Epoch 15/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6402 - categorical_accuracy: 0.3067 - val_loss: 2.0001 - val_categorical_accuracy: 0.1367\n",
      "Epoch 16/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6456 - categorical_accuracy: 0.3100 - val_loss: 2.0150 - val_categorical_accuracy: 0.1433\n",
      "Epoch 17/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6448 - categorical_accuracy: 0.3150 - val_loss: 2.0115 - val_categorical_accuracy: 0.1500\n",
      "Epoch 18/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.6508 - categorical_accuracy: 0.3139 - val_loss: 2.0197 - val_categorical_accuracy: 0.1533\n",
      "Epoch 19/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6645 - categorical_accuracy: 0.3039 - val_loss: 2.0300 - val_categorical_accuracy: 0.1333\n",
      "Epoch 20/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6428 - categorical_accuracy: 0.3128 - val_loss: 2.0111 - val_categorical_accuracy: 0.1400\n",
      "Epoch 21/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6424 - categorical_accuracy: 0.3172 - val_loss: 2.0335 - val_categorical_accuracy: 0.1467\n",
      "Epoch 22/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.6468 - categorical_accuracy: 0.3228 - val_loss: 2.0516 - val_categorical_accuracy: 0.1567\n",
      "Epoch 23/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.6350 - categorical_accuracy: 0.3156 - val_loss: 2.0295 - val_categorical_accuracy: 0.1400\n",
      "Epoch 24/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.6348 - categorical_accuracy: 0.3250 - val_loss: 2.0397 - val_categorical_accuracy: 0.1567\n",
      "Epoch 25/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6331 - categorical_accuracy: 0.3217 - val_loss: 2.0315 - val_categorical_accuracy: 0.1467\n",
      "Epoch 26/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6404 - categorical_accuracy: 0.3261 - val_loss: 2.0774 - val_categorical_accuracy: 0.1467\n",
      "Epoch 27/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.6377 - categorical_accuracy: 0.3111 - val_loss: 2.0714 - val_categorical_accuracy: 0.1500\n",
      "Epoch 28/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6314 - categorical_accuracy: 0.3144 - val_loss: 2.0808 - val_categorical_accuracy: 0.1467\n",
      "Epoch 29/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6241 - categorical_accuracy: 0.3144 - val_loss: 2.0894 - val_categorical_accuracy: 0.1500\n",
      "Epoch 30/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6228 - categorical_accuracy: 0.3122 - val_loss: 2.0902 - val_categorical_accuracy: 0.1300\n",
      "Epoch 31/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6257 - categorical_accuracy: 0.3133 - val_loss: 2.1009 - val_categorical_accuracy: 0.1467\n",
      "Epoch 32/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6267 - categorical_accuracy: 0.3083 - val_loss: 2.0947 - val_categorical_accuracy: 0.1500\n",
      "Epoch 33/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6227 - categorical_accuracy: 0.3172 - val_loss: 2.1047 - val_categorical_accuracy: 0.1467\n",
      "Epoch 34/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6177 - categorical_accuracy: 0.3178 - val_loss: 2.0973 - val_categorical_accuracy: 0.1467\n",
      "Epoch 35/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6122 - categorical_accuracy: 0.3294 - val_loss: 2.0914 - val_categorical_accuracy: 0.1400\n",
      "Epoch 36/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6075 - categorical_accuracy: 0.3294 - val_loss: 2.1146 - val_categorical_accuracy: 0.1467\n",
      "Epoch 37/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6135 - categorical_accuracy: 0.3172 - val_loss: 2.1226 - val_categorical_accuracy: 0.1567\n",
      "Epoch 38/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.6101 - categorical_accuracy: 0.3322 - val_loss: 2.1221 - val_categorical_accuracy: 0.1500\n",
      "Epoch 39/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.6140 - categorical_accuracy: 0.3378 - val_loss: 2.1166 - val_categorical_accuracy: 0.1567\n",
      "Epoch 40/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6184 - categorical_accuracy: 0.3367 - val_loss: 2.0953 - val_categorical_accuracy: 0.1467\n",
      "Epoch 41/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6176 - categorical_accuracy: 0.3311 - val_loss: 2.0986 - val_categorical_accuracy: 0.1567\n",
      "Epoch 42/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6229 - categorical_accuracy: 0.3333 - val_loss: 2.0999 - val_categorical_accuracy: 0.1300\n",
      "Epoch 43/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6179 - categorical_accuracy: 0.3333 - val_loss: 2.0993 - val_categorical_accuracy: 0.1267\n",
      "Epoch 44/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6104 - categorical_accuracy: 0.3289 - val_loss: 2.1055 - val_categorical_accuracy: 0.1300\n",
      "Epoch 45/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6127 - categorical_accuracy: 0.3344 - val_loss: 2.1413 - val_categorical_accuracy: 0.1667\n",
      "Epoch 46/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.6120 - categorical_accuracy: 0.3361 - val_loss: 2.1739 - val_categorical_accuracy: 0.1500\n",
      "Epoch 47/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.6181 - categorical_accuracy: 0.3333 - val_loss: 2.1763 - val_categorical_accuracy: 0.1700\n",
      "Epoch 48/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6196 - categorical_accuracy: 0.3183 - val_loss: 2.1641 - val_categorical_accuracy: 0.1667\n",
      "Epoch 49/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6157 - categorical_accuracy: 0.3306 - val_loss: 2.1551 - val_categorical_accuracy: 0.1600\n",
      "Epoch 50/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6046 - categorical_accuracy: 0.3344 - val_loss: 2.1860 - val_categorical_accuracy: 0.1600\n",
      "Epoch 51/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6135 - categorical_accuracy: 0.3267 - val_loss: 2.1544 - val_categorical_accuracy: 0.1733\n",
      "Epoch 52/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6125 - categorical_accuracy: 0.3278 - val_loss: 2.1682 - val_categorical_accuracy: 0.1667\n",
      "Epoch 53/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6135 - categorical_accuracy: 0.3250 - val_loss: 2.1409 - val_categorical_accuracy: 0.1767\n",
      "Epoch 54/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6177 - categorical_accuracy: 0.3233 - val_loss: 2.1165 - val_categorical_accuracy: 0.1800\n",
      "Epoch 55/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.6179 - categorical_accuracy: 0.3256 - val_loss: 2.0529 - val_categorical_accuracy: 0.1767\n",
      "Epoch 56/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.6259 - categorical_accuracy: 0.3278 - val_loss: 2.0275 - val_categorical_accuracy: 0.1733\n",
      "Epoch 57/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6205 - categorical_accuracy: 0.3244 - val_loss: 2.0447 - val_categorical_accuracy: 0.1767\n",
      "Epoch 58/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.6257 - categorical_accuracy: 0.3211 - val_loss: 1.9998 - val_categorical_accuracy: 0.1800\n",
      "Epoch 59/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6254 - categorical_accuracy: 0.3128 - val_loss: 2.0338 - val_categorical_accuracy: 0.1733\n",
      "Epoch 60/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.6172 - categorical_accuracy: 0.3178 - val_loss: 2.0176 - val_categorical_accuracy: 0.1833\n",
      "Epoch 61/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6063 - categorical_accuracy: 0.3328 - val_loss: 2.0477 - val_categorical_accuracy: 0.1933\n",
      "Epoch 62/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.6084 - categorical_accuracy: 0.3294 - val_loss: 2.0433 - val_categorical_accuracy: 0.1967\n",
      "Epoch 63/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5985 - categorical_accuracy: 0.3400 - val_loss: 2.0529 - val_categorical_accuracy: 0.1800\n",
      "Epoch 64/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5945 - categorical_accuracy: 0.3322 - val_loss: 2.0586 - val_categorical_accuracy: 0.1833\n",
      "Epoch 65/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5906 - categorical_accuracy: 0.3367 - val_loss: 2.0726 - val_categorical_accuracy: 0.1867\n",
      "Epoch 66/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5885 - categorical_accuracy: 0.3378 - val_loss: 2.0508 - val_categorical_accuracy: 0.1900\n",
      "Epoch 67/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5826 - categorical_accuracy: 0.3467 - val_loss: 2.0526 - val_categorical_accuracy: 0.1700\n",
      "Epoch 68/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5790 - categorical_accuracy: 0.3572 - val_loss: 2.0570 - val_categorical_accuracy: 0.1800\n",
      "Epoch 69/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5841 - categorical_accuracy: 0.3333 - val_loss: 2.0705 - val_categorical_accuracy: 0.1833\n",
      "Epoch 70/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5808 - categorical_accuracy: 0.3411 - val_loss: 2.0658 - val_categorical_accuracy: 0.2167\n",
      "Epoch 71/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5779 - categorical_accuracy: 0.3478 - val_loss: 2.0789 - val_categorical_accuracy: 0.2000\n",
      "Epoch 72/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5779 - categorical_accuracy: 0.3383 - val_loss: 2.0800 - val_categorical_accuracy: 0.1900\n",
      "Epoch 73/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5775 - categorical_accuracy: 0.3372 - val_loss: 2.0973 - val_categorical_accuracy: 0.1933\n",
      "Epoch 74/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5759 - categorical_accuracy: 0.3461 - val_loss: 2.0971 - val_categorical_accuracy: 0.1900\n",
      "Epoch 75/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5794 - categorical_accuracy: 0.3450 - val_loss: 2.0968 - val_categorical_accuracy: 0.1867\n",
      "Epoch 76/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5760 - categorical_accuracy: 0.3461 - val_loss: 2.1096 - val_categorical_accuracy: 0.1667\n",
      "Epoch 77/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5891 - categorical_accuracy: 0.3361 - val_loss: 2.1021 - val_categorical_accuracy: 0.1900\n",
      "Epoch 78/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5849 - categorical_accuracy: 0.3228 - val_loss: 2.0910 - val_categorical_accuracy: 0.1800\n",
      "Epoch 79/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5773 - categorical_accuracy: 0.3272 - val_loss: 2.0765 - val_categorical_accuracy: 0.2000\n",
      "Epoch 80/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5728 - categorical_accuracy: 0.3400 - val_loss: 2.0884 - val_categorical_accuracy: 0.1800\n",
      "Epoch 81/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5780 - categorical_accuracy: 0.3289 - val_loss: 2.0805 - val_categorical_accuracy: 0.1667\n",
      "Epoch 82/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5730 - categorical_accuracy: 0.3372 - val_loss: 2.1018 - val_categorical_accuracy: 0.1833\n",
      "Epoch 83/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5806 - categorical_accuracy: 0.3317 - val_loss: 2.1147 - val_categorical_accuracy: 0.1700\n",
      "Epoch 84/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5681 - categorical_accuracy: 0.3478 - val_loss: 2.1144 - val_categorical_accuracy: 0.1733\n",
      "Epoch 85/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5706 - categorical_accuracy: 0.3467 - val_loss: 2.1187 - val_categorical_accuracy: 0.1867\n",
      "Epoch 86/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5756 - categorical_accuracy: 0.3444 - val_loss: 2.1411 - val_categorical_accuracy: 0.2000\n",
      "Epoch 87/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5741 - categorical_accuracy: 0.3439 - val_loss: 2.1537 - val_categorical_accuracy: 0.1767\n",
      "Epoch 88/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5730 - categorical_accuracy: 0.3517 - val_loss: 2.1579 - val_categorical_accuracy: 0.1767\n",
      "Epoch 89/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5622 - categorical_accuracy: 0.3517 - val_loss: 2.1483 - val_categorical_accuracy: 0.1800\n",
      "Epoch 90/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5612 - categorical_accuracy: 0.3489 - val_loss: 2.1679 - val_categorical_accuracy: 0.1867\n",
      "Epoch 91/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5658 - categorical_accuracy: 0.3472 - val_loss: 2.1939 - val_categorical_accuracy: 0.1833\n",
      "Epoch 92/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5664 - categorical_accuracy: 0.3400 - val_loss: 2.2238 - val_categorical_accuracy: 0.1800\n",
      "Epoch 93/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5622 - categorical_accuracy: 0.3372 - val_loss: 2.2229 - val_categorical_accuracy: 0.1800\n",
      "Epoch 94/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5615 - categorical_accuracy: 0.3400 - val_loss: 2.2165 - val_categorical_accuracy: 0.1567\n",
      "Epoch 95/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5561 - categorical_accuracy: 0.3456 - val_loss: 2.2182 - val_categorical_accuracy: 0.1667\n",
      "Epoch 96/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.5622 - categorical_accuracy: 0.3456 - val_loss: 2.2024 - val_categorical_accuracy: 0.1600\n",
      "Epoch 97/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5586 - categorical_accuracy: 0.3444 - val_loss: 2.1884 - val_categorical_accuracy: 0.1633\n",
      "Epoch 98/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5568 - categorical_accuracy: 0.3500 - val_loss: 2.1952 - val_categorical_accuracy: 0.1700\n",
      "Epoch 99/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5594 - categorical_accuracy: 0.3489 - val_loss: 2.1786 - val_categorical_accuracy: 0.1600\n",
      "Epoch 100/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5584 - categorical_accuracy: 0.3450 - val_loss: 2.1575 - val_categorical_accuracy: 0.1767\n",
      "Epoch 101/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5602 - categorical_accuracy: 0.3433 - val_loss: 2.1363 - val_categorical_accuracy: 0.1633\n",
      "Epoch 102/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5510 - categorical_accuracy: 0.3606 - val_loss: 2.1358 - val_categorical_accuracy: 0.1700\n",
      "Epoch 103/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5426 - categorical_accuracy: 0.3600 - val_loss: 2.1525 - val_categorical_accuracy: 0.1900\n",
      "Epoch 104/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5410 - categorical_accuracy: 0.3617 - val_loss: 2.1531 - val_categorical_accuracy: 0.1800\n",
      "Epoch 105/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5375 - categorical_accuracy: 0.3667 - val_loss: 2.1475 - val_categorical_accuracy: 0.1900\n",
      "Epoch 106/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5356 - categorical_accuracy: 0.3617 - val_loss: 2.1706 - val_categorical_accuracy: 0.1833\n",
      "Epoch 107/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5346 - categorical_accuracy: 0.3611 - val_loss: 2.1503 - val_categorical_accuracy: 0.1867\n",
      "Epoch 108/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5318 - categorical_accuracy: 0.3589 - val_loss: 2.1657 - val_categorical_accuracy: 0.1933\n",
      "Epoch 109/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5316 - categorical_accuracy: 0.3628 - val_loss: 2.1722 - val_categorical_accuracy: 0.1933\n",
      "Epoch 110/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5280 - categorical_accuracy: 0.3661 - val_loss: 2.1745 - val_categorical_accuracy: 0.1867\n",
      "Epoch 111/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5293 - categorical_accuracy: 0.3672 - val_loss: 2.1487 - val_categorical_accuracy: 0.1967\n",
      "Epoch 112/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5242 - categorical_accuracy: 0.3678 - val_loss: 2.1825 - val_categorical_accuracy: 0.1900\n",
      "Epoch 113/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5254 - categorical_accuracy: 0.3622 - val_loss: 2.1666 - val_categorical_accuracy: 0.1967\n",
      "Epoch 114/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5274 - categorical_accuracy: 0.3650 - val_loss: 2.1761 - val_categorical_accuracy: 0.1867\n",
      "Epoch 115/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5327 - categorical_accuracy: 0.3572 - val_loss: 2.2119 - val_categorical_accuracy: 0.1733\n",
      "Epoch 116/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5247 - categorical_accuracy: 0.3611 - val_loss: 2.1521 - val_categorical_accuracy: 0.1767\n",
      "Epoch 117/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5319 - categorical_accuracy: 0.3650 - val_loss: 2.1856 - val_categorical_accuracy: 0.2000\n",
      "Epoch 118/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5325 - categorical_accuracy: 0.3594 - val_loss: 2.2047 - val_categorical_accuracy: 0.1933\n",
      "Epoch 119/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5346 - categorical_accuracy: 0.3628 - val_loss: 2.2331 - val_categorical_accuracy: 0.1867\n",
      "Epoch 120/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5240 - categorical_accuracy: 0.3711 - val_loss: 2.2051 - val_categorical_accuracy: 0.1833\n",
      "Epoch 121/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5233 - categorical_accuracy: 0.3683 - val_loss: 2.2173 - val_categorical_accuracy: 0.1800\n",
      "Epoch 122/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5151 - categorical_accuracy: 0.3767 - val_loss: 2.1946 - val_categorical_accuracy: 0.1767\n",
      "Epoch 123/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5162 - categorical_accuracy: 0.3750 - val_loss: 2.2032 - val_categorical_accuracy: 0.1933\n",
      "Epoch 124/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5188 - categorical_accuracy: 0.3711 - val_loss: 2.2283 - val_categorical_accuracy: 0.1933\n",
      "Epoch 125/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5313 - categorical_accuracy: 0.3728 - val_loss: 2.2230 - val_categorical_accuracy: 0.1900\n",
      "Epoch 126/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5363 - categorical_accuracy: 0.3694 - val_loss: 2.2415 - val_categorical_accuracy: 0.1733\n",
      "Epoch 127/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5327 - categorical_accuracy: 0.3806 - val_loss: 2.2177 - val_categorical_accuracy: 0.1600\n",
      "Epoch 128/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5243 - categorical_accuracy: 0.3739 - val_loss: 2.2329 - val_categorical_accuracy: 0.1667\n",
      "Epoch 129/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5275 - categorical_accuracy: 0.3700 - val_loss: 2.2443 - val_categorical_accuracy: 0.1667\n",
      "Epoch 130/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5264 - categorical_accuracy: 0.3694 - val_loss: 2.2316 - val_categorical_accuracy: 0.1700\n",
      "Epoch 131/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5179 - categorical_accuracy: 0.3717 - val_loss: 2.2316 - val_categorical_accuracy: 0.1633\n",
      "Epoch 132/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5168 - categorical_accuracy: 0.3706 - val_loss: 2.2469 - val_categorical_accuracy: 0.1667\n",
      "Epoch 133/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5181 - categorical_accuracy: 0.3683 - val_loss: 2.2369 - val_categorical_accuracy: 0.1633\n",
      "Epoch 134/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.5210 - categorical_accuracy: 0.3572 - val_loss: 2.2179 - val_categorical_accuracy: 0.1600\n",
      "Epoch 135/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5310 - categorical_accuracy: 0.3628 - val_loss: 2.2115 - val_categorical_accuracy: 0.1633\n",
      "Epoch 136/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5318 - categorical_accuracy: 0.3528 - val_loss: 2.2091 - val_categorical_accuracy: 0.1533\n",
      "Epoch 137/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5290 - categorical_accuracy: 0.3600 - val_loss: 2.2297 - val_categorical_accuracy: 0.1567\n",
      "Epoch 138/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5277 - categorical_accuracy: 0.3639 - val_loss: 2.2195 - val_categorical_accuracy: 0.1600\n",
      "Epoch 139/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5324 - categorical_accuracy: 0.3633 - val_loss: 2.2412 - val_categorical_accuracy: 0.1833\n",
      "Epoch 140/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5238 - categorical_accuracy: 0.3806 - val_loss: 2.1928 - val_categorical_accuracy: 0.1800\n",
      "Epoch 141/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5138 - categorical_accuracy: 0.3706 - val_loss: 2.2501 - val_categorical_accuracy: 0.1633\n",
      "Epoch 142/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5102 - categorical_accuracy: 0.3744 - val_loss: 2.2551 - val_categorical_accuracy: 0.1800\n",
      "Epoch 143/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5119 - categorical_accuracy: 0.3750 - val_loss: 2.2393 - val_categorical_accuracy: 0.1767\n",
      "Epoch 144/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5061 - categorical_accuracy: 0.3756 - val_loss: 2.2308 - val_categorical_accuracy: 0.1867\n",
      "Epoch 145/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5035 - categorical_accuracy: 0.3700 - val_loss: 2.2706 - val_categorical_accuracy: 0.1733\n",
      "Epoch 146/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5038 - categorical_accuracy: 0.3689 - val_loss: 2.2590 - val_categorical_accuracy: 0.1767\n",
      "Epoch 147/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5028 - categorical_accuracy: 0.3678 - val_loss: 2.2479 - val_categorical_accuracy: 0.1867\n",
      "Epoch 148/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5051 - categorical_accuracy: 0.3778 - val_loss: 2.2823 - val_categorical_accuracy: 0.1933\n",
      "Epoch 149/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4970 - categorical_accuracy: 0.3778 - val_loss: 2.2796 - val_categorical_accuracy: 0.1967\n",
      "Epoch 150/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4941 - categorical_accuracy: 0.3750 - val_loss: 2.3036 - val_categorical_accuracy: 0.2033\n",
      "Epoch 151/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4992 - categorical_accuracy: 0.3683 - val_loss: 2.2772 - val_categorical_accuracy: 0.2000\n",
      "Epoch 152/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4949 - categorical_accuracy: 0.3778 - val_loss: 2.3123 - val_categorical_accuracy: 0.1933\n",
      "Epoch 153/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4929 - categorical_accuracy: 0.3761 - val_loss: 2.3258 - val_categorical_accuracy: 0.1900\n",
      "Epoch 154/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4898 - categorical_accuracy: 0.3750 - val_loss: 2.3198 - val_categorical_accuracy: 0.1933\n",
      "Epoch 155/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4887 - categorical_accuracy: 0.3856 - val_loss: 2.3653 - val_categorical_accuracy: 0.1800\n",
      "Epoch 156/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4929 - categorical_accuracy: 0.3689 - val_loss: 2.4005 - val_categorical_accuracy: 0.1800\n",
      "Epoch 157/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4892 - categorical_accuracy: 0.3706 - val_loss: 2.3428 - val_categorical_accuracy: 0.1733\n",
      "Epoch 158/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4893 - categorical_accuracy: 0.3750 - val_loss: 2.3972 - val_categorical_accuracy: 0.1767\n",
      "Epoch 159/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4855 - categorical_accuracy: 0.3672 - val_loss: 2.3472 - val_categorical_accuracy: 0.1733\n",
      "Epoch 160/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5003 - categorical_accuracy: 0.3628 - val_loss: 2.4455 - val_categorical_accuracy: 0.1467\n",
      "Epoch 161/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4982 - categorical_accuracy: 0.3578 - val_loss: 2.3581 - val_categorical_accuracy: 0.1733\n",
      "Epoch 162/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4964 - categorical_accuracy: 0.3650 - val_loss: 2.4605 - val_categorical_accuracy: 0.1533\n",
      "Epoch 163/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4941 - categorical_accuracy: 0.3594 - val_loss: 2.3771 - val_categorical_accuracy: 0.1733\n",
      "Epoch 164/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4977 - categorical_accuracy: 0.3589 - val_loss: 2.5007 - val_categorical_accuracy: 0.1600\n",
      "Epoch 165/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4952 - categorical_accuracy: 0.3706 - val_loss: 2.3994 - val_categorical_accuracy: 0.1467\n",
      "Epoch 166/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4994 - categorical_accuracy: 0.3706 - val_loss: 2.4143 - val_categorical_accuracy: 0.1333\n",
      "Epoch 167/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4944 - categorical_accuracy: 0.3617 - val_loss: 2.4414 - val_categorical_accuracy: 0.1400\n",
      "Epoch 168/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4967 - categorical_accuracy: 0.3628 - val_loss: 2.4309 - val_categorical_accuracy: 0.1467\n",
      "Epoch 169/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5009 - categorical_accuracy: 0.3672 - val_loss: 2.4112 - val_categorical_accuracy: 0.1433\n",
      "Epoch 170/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5082 - categorical_accuracy: 0.3678 - val_loss: 2.4129 - val_categorical_accuracy: 0.1467\n",
      "Epoch 171/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5080 - categorical_accuracy: 0.3711 - val_loss: 2.3808 - val_categorical_accuracy: 0.1433\n",
      "Epoch 172/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4981 - categorical_accuracy: 0.3783 - val_loss: 2.4154 - val_categorical_accuracy: 0.1333\n",
      "Epoch 173/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5054 - categorical_accuracy: 0.3744 - val_loss: 2.3924 - val_categorical_accuracy: 0.1233\n",
      "Epoch 174/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5173 - categorical_accuracy: 0.3706 - val_loss: 2.4233 - val_categorical_accuracy: 0.1367\n",
      "Epoch 175/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5152 - categorical_accuracy: 0.3717 - val_loss: 2.4048 - val_categorical_accuracy: 0.1433\n",
      "Epoch 176/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5225 - categorical_accuracy: 0.3600 - val_loss: 2.3952 - val_categorical_accuracy: 0.1467\n",
      "Epoch 177/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5281 - categorical_accuracy: 0.3689 - val_loss: 2.3269 - val_categorical_accuracy: 0.1433\n",
      "Epoch 178/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5332 - categorical_accuracy: 0.3617 - val_loss: 2.4151 - val_categorical_accuracy: 0.1433\n",
      "Epoch 179/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5406 - categorical_accuracy: 0.3483 - val_loss: 2.3606 - val_categorical_accuracy: 0.1300\n",
      "Epoch 180/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5200 - categorical_accuracy: 0.3744 - val_loss: 2.3644 - val_categorical_accuracy: 0.1433\n",
      "Epoch 181/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5246 - categorical_accuracy: 0.3600 - val_loss: 2.4011 - val_categorical_accuracy: 0.1433\n",
      "Epoch 182/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5183 - categorical_accuracy: 0.3689 - val_loss: 2.4252 - val_categorical_accuracy: 0.1500\n",
      "Epoch 183/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5126 - categorical_accuracy: 0.3711 - val_loss: 2.4158 - val_categorical_accuracy: 0.1400\n",
      "Epoch 184/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5086 - categorical_accuracy: 0.3794 - val_loss: 2.3846 - val_categorical_accuracy: 0.1467\n",
      "Epoch 185/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5092 - categorical_accuracy: 0.3706 - val_loss: 2.3785 - val_categorical_accuracy: 0.1467\n",
      "Epoch 186/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5045 - categorical_accuracy: 0.3783 - val_loss: 2.3977 - val_categorical_accuracy: 0.1400\n",
      "Epoch 187/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5034 - categorical_accuracy: 0.3756 - val_loss: 2.4507 - val_categorical_accuracy: 0.1467\n",
      "Epoch 188/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5165 - categorical_accuracy: 0.3628 - val_loss: 2.3893 - val_categorical_accuracy: 0.1533\n",
      "Epoch 189/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.5011 - categorical_accuracy: 0.3778 - val_loss: 2.4240 - val_categorical_accuracy: 0.1400\n",
      "Epoch 190/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4945 - categorical_accuracy: 0.3744 - val_loss: 2.4319 - val_categorical_accuracy: 0.1333\n",
      "Epoch 191/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4941 - categorical_accuracy: 0.3739 - val_loss: 2.4541 - val_categorical_accuracy: 0.1367\n",
      "Epoch 192/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4920 - categorical_accuracy: 0.3783 - val_loss: 2.4242 - val_categorical_accuracy: 0.1467\n",
      "Epoch 193/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5018 - categorical_accuracy: 0.3761 - val_loss: 2.4341 - val_categorical_accuracy: 0.1533\n",
      "Epoch 194/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5021 - categorical_accuracy: 0.3683 - val_loss: 2.4715 - val_categorical_accuracy: 0.1567\n",
      "Epoch 195/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5048 - categorical_accuracy: 0.3794 - val_loss: 2.3943 - val_categorical_accuracy: 0.1633\n",
      "Epoch 196/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4983 - categorical_accuracy: 0.3739 - val_loss: 2.4539 - val_categorical_accuracy: 0.1500\n",
      "Epoch 197/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5021 - categorical_accuracy: 0.3767 - val_loss: 2.4379 - val_categorical_accuracy: 0.1533\n",
      "Epoch 198/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5079 - categorical_accuracy: 0.3689 - val_loss: 2.4436 - val_categorical_accuracy: 0.1733\n",
      "Epoch 199/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5084 - categorical_accuracy: 0.3706 - val_loss: 2.4183 - val_categorical_accuracy: 0.1733\n",
      "Epoch 200/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5115 - categorical_accuracy: 0.3778 - val_loss: 2.4223 - val_categorical_accuracy: 0.1700\n",
      "Epoch 201/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5129 - categorical_accuracy: 0.3761 - val_loss: 2.4431 - val_categorical_accuracy: 0.1800\n",
      "Epoch 202/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5144 - categorical_accuracy: 0.3661 - val_loss: 2.4018 - val_categorical_accuracy: 0.1767\n",
      "Epoch 203/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5129 - categorical_accuracy: 0.3722 - val_loss: 2.4157 - val_categorical_accuracy: 0.1733\n",
      "Epoch 204/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.5180 - categorical_accuracy: 0.3822 - val_loss: 2.4331 - val_categorical_accuracy: 0.1633\n",
      "Epoch 205/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5166 - categorical_accuracy: 0.3722 - val_loss: 2.4256 - val_categorical_accuracy: 0.1800\n",
      "Epoch 206/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5158 - categorical_accuracy: 0.3844 - val_loss: 2.4303 - val_categorical_accuracy: 0.1700\n",
      "Epoch 207/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5066 - categorical_accuracy: 0.3861 - val_loss: 2.4399 - val_categorical_accuracy: 0.1800\n",
      "Epoch 208/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5072 - categorical_accuracy: 0.3739 - val_loss: 2.4197 - val_categorical_accuracy: 0.1667\n",
      "Epoch 209/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5063 - categorical_accuracy: 0.3717 - val_loss: 2.4770 - val_categorical_accuracy: 0.1567\n",
      "Epoch 210/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5054 - categorical_accuracy: 0.3761 - val_loss: 2.4495 - val_categorical_accuracy: 0.1500\n",
      "Epoch 211/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5022 - categorical_accuracy: 0.3694 - val_loss: 2.4608 - val_categorical_accuracy: 0.1533\n",
      "Epoch 212/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5061 - categorical_accuracy: 0.3744 - val_loss: 2.5035 - val_categorical_accuracy: 0.1500\n",
      "Epoch 213/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4949 - categorical_accuracy: 0.3761 - val_loss: 2.4733 - val_categorical_accuracy: 0.1500\n",
      "Epoch 214/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4969 - categorical_accuracy: 0.3678 - val_loss: 2.4633 - val_categorical_accuracy: 0.1533\n",
      "Epoch 215/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4983 - categorical_accuracy: 0.3722 - val_loss: 2.4698 - val_categorical_accuracy: 0.1433\n",
      "Epoch 216/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4933 - categorical_accuracy: 0.3844 - val_loss: 2.4804 - val_categorical_accuracy: 0.1367\n",
      "Epoch 217/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5000 - categorical_accuracy: 0.3822 - val_loss: 2.4935 - val_categorical_accuracy: 0.1600\n",
      "Epoch 218/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4984 - categorical_accuracy: 0.3728 - val_loss: 2.4627 - val_categorical_accuracy: 0.1333\n",
      "Epoch 219/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5081 - categorical_accuracy: 0.3656 - val_loss: 2.4586 - val_categorical_accuracy: 0.1600\n",
      "Epoch 220/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5048 - categorical_accuracy: 0.3739 - val_loss: 2.4666 - val_categorical_accuracy: 0.1400\n",
      "Epoch 221/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4978 - categorical_accuracy: 0.3772 - val_loss: 2.4583 - val_categorical_accuracy: 0.1467\n",
      "Epoch 222/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4989 - categorical_accuracy: 0.3817 - val_loss: 2.4624 - val_categorical_accuracy: 0.1633\n",
      "Epoch 223/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5017 - categorical_accuracy: 0.3728 - val_loss: 2.4779 - val_categorical_accuracy: 0.1500\n",
      "Epoch 224/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5002 - categorical_accuracy: 0.3772 - val_loss: 2.4786 - val_categorical_accuracy: 0.1533\n",
      "Epoch 225/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4991 - categorical_accuracy: 0.3883 - val_loss: 2.5130 - val_categorical_accuracy: 0.1400\n",
      "Epoch 226/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4931 - categorical_accuracy: 0.3833 - val_loss: 2.4623 - val_categorical_accuracy: 0.1500\n",
      "Epoch 227/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5001 - categorical_accuracy: 0.3750 - val_loss: 2.4406 - val_categorical_accuracy: 0.1500\n",
      "Epoch 228/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4954 - categorical_accuracy: 0.3778 - val_loss: 2.4841 - val_categorical_accuracy: 0.1400\n",
      "Epoch 229/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4968 - categorical_accuracy: 0.3800 - val_loss: 2.4769 - val_categorical_accuracy: 0.1533\n",
      "Epoch 230/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4971 - categorical_accuracy: 0.3794 - val_loss: 2.4447 - val_categorical_accuracy: 0.1467\n",
      "Epoch 231/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5005 - categorical_accuracy: 0.3811 - val_loss: 2.4233 - val_categorical_accuracy: 0.1467\n",
      "Epoch 232/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5058 - categorical_accuracy: 0.3767 - val_loss: 2.4375 - val_categorical_accuracy: 0.1533\n",
      "Epoch 233/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5011 - categorical_accuracy: 0.3722 - val_loss: 2.4859 - val_categorical_accuracy: 0.1433\n",
      "Epoch 234/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.5001 - categorical_accuracy: 0.3722 - val_loss: 2.5271 - val_categorical_accuracy: 0.1567\n",
      "Epoch 235/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5075 - categorical_accuracy: 0.3711 - val_loss: 2.5608 - val_categorical_accuracy: 0.1600\n",
      "Epoch 236/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5075 - categorical_accuracy: 0.3794 - val_loss: 2.5596 - val_categorical_accuracy: 0.1567\n",
      "Epoch 237/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5059 - categorical_accuracy: 0.3711 - val_loss: 2.5515 - val_categorical_accuracy: 0.1667\n",
      "Epoch 238/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5078 - categorical_accuracy: 0.3733 - val_loss: 2.5604 - val_categorical_accuracy: 0.1733\n",
      "Epoch 239/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5141 - categorical_accuracy: 0.3678 - val_loss: 2.5619 - val_categorical_accuracy: 0.1600\n",
      "Epoch 240/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5118 - categorical_accuracy: 0.3750 - val_loss: 2.5334 - val_categorical_accuracy: 0.1733\n",
      "Epoch 241/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5149 - categorical_accuracy: 0.3672 - val_loss: 2.5278 - val_categorical_accuracy: 0.1533\n",
      "Epoch 242/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5128 - categorical_accuracy: 0.3639 - val_loss: 2.5366 - val_categorical_accuracy: 0.1567\n",
      "Epoch 243/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5152 - categorical_accuracy: 0.3589 - val_loss: 2.5403 - val_categorical_accuracy: 0.1667\n",
      "Epoch 244/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5145 - categorical_accuracy: 0.3700 - val_loss: 2.5086 - val_categorical_accuracy: 0.1800\n",
      "Epoch 245/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5141 - categorical_accuracy: 0.3667 - val_loss: 2.5134 - val_categorical_accuracy: 0.1867\n",
      "Epoch 246/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5168 - categorical_accuracy: 0.3578 - val_loss: 2.5039 - val_categorical_accuracy: 0.1800\n",
      "Epoch 247/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5204 - categorical_accuracy: 0.3544 - val_loss: 2.4861 - val_categorical_accuracy: 0.1633\n",
      "Epoch 248/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5231 - categorical_accuracy: 0.3622 - val_loss: 2.5076 - val_categorical_accuracy: 0.1533\n",
      "Epoch 249/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5225 - categorical_accuracy: 0.3606 - val_loss: 2.4962 - val_categorical_accuracy: 0.1567\n",
      "Epoch 250/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5258 - categorical_accuracy: 0.3589 - val_loss: 2.4727 - val_categorical_accuracy: 0.1600\n",
      "Epoch 251/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5327 - categorical_accuracy: 0.3578 - val_loss: 2.4769 - val_categorical_accuracy: 0.1367\n",
      "Epoch 252/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5244 - categorical_accuracy: 0.3550 - val_loss: 2.4513 - val_categorical_accuracy: 0.1433\n",
      "Epoch 253/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5329 - categorical_accuracy: 0.3639 - val_loss: 2.4431 - val_categorical_accuracy: 0.1533\n",
      "Epoch 254/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.5375 - categorical_accuracy: 0.3522 - val_loss: 2.4125 - val_categorical_accuracy: 0.1367\n",
      "Epoch 255/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5274 - categorical_accuracy: 0.3539 - val_loss: 2.4576 - val_categorical_accuracy: 0.1400\n",
      "Epoch 256/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5253 - categorical_accuracy: 0.3544 - val_loss: 2.4451 - val_categorical_accuracy: 0.1300\n",
      "Epoch 257/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.5171 - categorical_accuracy: 0.3578 - val_loss: 2.5028 - val_categorical_accuracy: 0.1467\n",
      "Epoch 258/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5148 - categorical_accuracy: 0.3606 - val_loss: 2.4894 - val_categorical_accuracy: 0.1367\n",
      "Epoch 259/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5111 - categorical_accuracy: 0.3661 - val_loss: 2.4738 - val_categorical_accuracy: 0.1500\n",
      "Epoch 260/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5077 - categorical_accuracy: 0.3689 - val_loss: 2.4721 - val_categorical_accuracy: 0.1500\n",
      "Epoch 261/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5063 - categorical_accuracy: 0.3728 - val_loss: 2.4439 - val_categorical_accuracy: 0.1367\n",
      "Epoch 262/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.5016 - categorical_accuracy: 0.3722 - val_loss: 2.5001 - val_categorical_accuracy: 0.1267\n",
      "Epoch 263/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5015 - categorical_accuracy: 0.3750 - val_loss: 2.5041 - val_categorical_accuracy: 0.1333\n",
      "Epoch 264/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.5066 - categorical_accuracy: 0.3761 - val_loss: 2.4984 - val_categorical_accuracy: 0.1300\n",
      "Epoch 265/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4952 - categorical_accuracy: 0.3800 - val_loss: 2.5012 - val_categorical_accuracy: 0.1300\n",
      "Epoch 266/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4948 - categorical_accuracy: 0.3794 - val_loss: 2.5376 - val_categorical_accuracy: 0.1333\n",
      "Epoch 267/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4968 - categorical_accuracy: 0.3839 - val_loss: 2.5316 - val_categorical_accuracy: 0.1433\n",
      "Epoch 268/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4939 - categorical_accuracy: 0.3789 - val_loss: 2.5442 - val_categorical_accuracy: 0.1367\n",
      "Epoch 269/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4872 - categorical_accuracy: 0.3844 - val_loss: 2.5473 - val_categorical_accuracy: 0.1333\n",
      "Epoch 270/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4860 - categorical_accuracy: 0.3833 - val_loss: 2.5702 - val_categorical_accuracy: 0.1267\n",
      "Epoch 271/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4873 - categorical_accuracy: 0.3817 - val_loss: 2.5624 - val_categorical_accuracy: 0.1200\n",
      "Epoch 272/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4838 - categorical_accuracy: 0.3922 - val_loss: 2.5824 - val_categorical_accuracy: 0.1300\n",
      "Epoch 273/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4806 - categorical_accuracy: 0.3933 - val_loss: 2.5667 - val_categorical_accuracy: 0.1167\n",
      "Epoch 274/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4798 - categorical_accuracy: 0.3944 - val_loss: 2.5850 - val_categorical_accuracy: 0.1233\n",
      "Epoch 275/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4789 - categorical_accuracy: 0.3933 - val_loss: 2.5954 - val_categorical_accuracy: 0.1233\n",
      "Epoch 276/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4778 - categorical_accuracy: 0.3928 - val_loss: 2.5952 - val_categorical_accuracy: 0.1267\n",
      "Epoch 277/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4769 - categorical_accuracy: 0.3911 - val_loss: 2.6076 - val_categorical_accuracy: 0.1300\n",
      "Epoch 278/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4725 - categorical_accuracy: 0.3956 - val_loss: 2.5636 - val_categorical_accuracy: 0.1333\n",
      "Epoch 279/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4775 - categorical_accuracy: 0.3867 - val_loss: 2.5847 - val_categorical_accuracy: 0.1267\n",
      "Epoch 280/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4753 - categorical_accuracy: 0.3967 - val_loss: 2.5931 - val_categorical_accuracy: 0.1267\n",
      "Epoch 281/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4720 - categorical_accuracy: 0.3950 - val_loss: 2.5890 - val_categorical_accuracy: 0.1367\n",
      "Epoch 282/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4703 - categorical_accuracy: 0.3928 - val_loss: 2.5866 - val_categorical_accuracy: 0.1333\n",
      "Epoch 283/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4646 - categorical_accuracy: 0.4011 - val_loss: 2.5995 - val_categorical_accuracy: 0.1333\n",
      "Epoch 284/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4781 - categorical_accuracy: 0.3933 - val_loss: 2.6137 - val_categorical_accuracy: 0.1533\n",
      "Epoch 285/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4776 - categorical_accuracy: 0.3900 - val_loss: 2.6036 - val_categorical_accuracy: 0.1533\n",
      "Epoch 286/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4706 - categorical_accuracy: 0.3850 - val_loss: 2.6221 - val_categorical_accuracy: 0.1567\n",
      "Epoch 287/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4681 - categorical_accuracy: 0.3933 - val_loss: 2.6204 - val_categorical_accuracy: 0.1667\n",
      "Epoch 288/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4680 - categorical_accuracy: 0.3961 - val_loss: 2.6642 - val_categorical_accuracy: 0.1500\n",
      "Epoch 289/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4702 - categorical_accuracy: 0.3978 - val_loss: 2.6617 - val_categorical_accuracy: 0.1300\n",
      "Epoch 290/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4690 - categorical_accuracy: 0.3956 - val_loss: 2.6576 - val_categorical_accuracy: 0.1400\n",
      "Epoch 291/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4717 - categorical_accuracy: 0.3894 - val_loss: 2.6957 - val_categorical_accuracy: 0.1267\n",
      "Epoch 292/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4729 - categorical_accuracy: 0.3933 - val_loss: 2.6721 - val_categorical_accuracy: 0.1267\n",
      "Epoch 293/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4765 - categorical_accuracy: 0.3872 - val_loss: 2.6852 - val_categorical_accuracy: 0.1333\n",
      "Epoch 294/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4780 - categorical_accuracy: 0.3894 - val_loss: 2.6653 - val_categorical_accuracy: 0.1200\n",
      "Epoch 295/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4728 - categorical_accuracy: 0.3983 - val_loss: 2.6664 - val_categorical_accuracy: 0.1300\n",
      "Epoch 296/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4774 - categorical_accuracy: 0.3844 - val_loss: 2.6323 - val_categorical_accuracy: 0.1367\n",
      "Epoch 297/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4855 - categorical_accuracy: 0.3889 - val_loss: 2.6125 - val_categorical_accuracy: 0.1233\n",
      "Epoch 298/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4765 - categorical_accuracy: 0.3844 - val_loss: 2.5932 - val_categorical_accuracy: 0.1267\n",
      "Epoch 299/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4833 - categorical_accuracy: 0.3850 - val_loss: 2.5954 - val_categorical_accuracy: 0.1300\n",
      "Epoch 300/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4780 - categorical_accuracy: 0.3894 - val_loss: 2.6005 - val_categorical_accuracy: 0.1267\n",
      "Epoch 301/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4747 - categorical_accuracy: 0.3889 - val_loss: 2.5845 - val_categorical_accuracy: 0.1200\n",
      "Epoch 302/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4735 - categorical_accuracy: 0.4000 - val_loss: 2.5862 - val_categorical_accuracy: 0.1367\n",
      "Epoch 303/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4747 - categorical_accuracy: 0.3928 - val_loss: 2.6056 - val_categorical_accuracy: 0.1333\n",
      "Epoch 304/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4748 - categorical_accuracy: 0.3867 - val_loss: 2.5821 - val_categorical_accuracy: 0.1267\n",
      "Epoch 305/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4711 - categorical_accuracy: 0.3911 - val_loss: 2.5983 - val_categorical_accuracy: 0.1333\n",
      "Epoch 306/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4667 - categorical_accuracy: 0.3972 - val_loss: 2.5701 - val_categorical_accuracy: 0.1367\n",
      "Epoch 307/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4672 - categorical_accuracy: 0.4006 - val_loss: 2.5944 - val_categorical_accuracy: 0.1167\n",
      "Epoch 308/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4662 - categorical_accuracy: 0.4039 - val_loss: 2.5915 - val_categorical_accuracy: 0.1167\n",
      "Epoch 309/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4655 - categorical_accuracy: 0.3978 - val_loss: 2.5951 - val_categorical_accuracy: 0.1133\n",
      "Epoch 310/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4655 - categorical_accuracy: 0.3972 - val_loss: 2.6004 - val_categorical_accuracy: 0.1033\n",
      "Epoch 311/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4665 - categorical_accuracy: 0.4061 - val_loss: 2.6768 - val_categorical_accuracy: 0.1067\n",
      "Epoch 312/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4666 - categorical_accuracy: 0.3911 - val_loss: 2.5967 - val_categorical_accuracy: 0.1067\n",
      "Epoch 313/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4696 - categorical_accuracy: 0.4039 - val_loss: 2.6001 - val_categorical_accuracy: 0.1233\n",
      "Epoch 314/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4604 - categorical_accuracy: 0.3922 - val_loss: 2.6702 - val_categorical_accuracy: 0.1200\n",
      "Epoch 315/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4556 - categorical_accuracy: 0.4056 - val_loss: 2.6923 - val_categorical_accuracy: 0.1200\n",
      "Epoch 316/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4581 - categorical_accuracy: 0.4056 - val_loss: 2.6752 - val_categorical_accuracy: 0.1167\n",
      "Epoch 317/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4594 - categorical_accuracy: 0.4006 - val_loss: 2.7077 - val_categorical_accuracy: 0.1167\n",
      "Epoch 318/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4560 - categorical_accuracy: 0.4000 - val_loss: 2.7064 - val_categorical_accuracy: 0.1200\n",
      "Epoch 319/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.4588 - categorical_accuracy: 0.3928 - val_loss: 2.6515 - val_categorical_accuracy: 0.1167\n",
      "Epoch 320/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4552 - categorical_accuracy: 0.3956 - val_loss: 2.6763 - val_categorical_accuracy: 0.1167\n",
      "Epoch 321/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4537 - categorical_accuracy: 0.4044 - val_loss: 2.7004 - val_categorical_accuracy: 0.1000\n",
      "Epoch 322/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4523 - categorical_accuracy: 0.3972 - val_loss: 2.6847 - val_categorical_accuracy: 0.1067\n",
      "Epoch 323/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4591 - categorical_accuracy: 0.3967 - val_loss: 2.6641 - val_categorical_accuracy: 0.1167\n",
      "Epoch 324/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4604 - categorical_accuracy: 0.3994 - val_loss: 2.6943 - val_categorical_accuracy: 0.1067\n",
      "Epoch 325/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4520 - categorical_accuracy: 0.4011 - val_loss: 2.7120 - val_categorical_accuracy: 0.1133\n",
      "Epoch 326/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4499 - categorical_accuracy: 0.4067 - val_loss: 2.6964 - val_categorical_accuracy: 0.1033\n",
      "Epoch 327/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4491 - categorical_accuracy: 0.4078 - val_loss: 2.7120 - val_categorical_accuracy: 0.1000\n",
      "Epoch 328/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4507 - categorical_accuracy: 0.4128 - val_loss: 2.7080 - val_categorical_accuracy: 0.1067\n",
      "Epoch 329/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4530 - categorical_accuracy: 0.4083 - val_loss: 2.7310 - val_categorical_accuracy: 0.1100\n",
      "Epoch 330/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4501 - categorical_accuracy: 0.4067 - val_loss: 2.7568 - val_categorical_accuracy: 0.1000\n",
      "Epoch 331/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4541 - categorical_accuracy: 0.3972 - val_loss: 2.7348 - val_categorical_accuracy: 0.1067\n",
      "Epoch 332/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4561 - categorical_accuracy: 0.4017 - val_loss: 2.7153 - val_categorical_accuracy: 0.1067\n",
      "Epoch 333/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4529 - categorical_accuracy: 0.4050 - val_loss: 2.7566 - val_categorical_accuracy: 0.1033\n",
      "Epoch 334/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4549 - categorical_accuracy: 0.4067 - val_loss: 2.7142 - val_categorical_accuracy: 0.1067\n",
      "Epoch 335/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4433 - categorical_accuracy: 0.4150 - val_loss: 2.7434 - val_categorical_accuracy: 0.1133\n",
      "Epoch 336/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4473 - categorical_accuracy: 0.4061 - val_loss: 2.7087 - val_categorical_accuracy: 0.1100\n",
      "Epoch 337/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4427 - categorical_accuracy: 0.4100 - val_loss: 2.6956 - val_categorical_accuracy: 0.1000\n",
      "Epoch 338/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4488 - categorical_accuracy: 0.4039 - val_loss: 2.7203 - val_categorical_accuracy: 0.0967\n",
      "Epoch 339/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4434 - categorical_accuracy: 0.4183 - val_loss: 2.7421 - val_categorical_accuracy: 0.1000\n",
      "Epoch 340/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4497 - categorical_accuracy: 0.4022 - val_loss: 2.7212 - val_categorical_accuracy: 0.1033\n",
      "Epoch 341/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4441 - categorical_accuracy: 0.4078 - val_loss: 2.7061 - val_categorical_accuracy: 0.1033\n",
      "Epoch 342/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4438 - categorical_accuracy: 0.4083 - val_loss: 2.7241 - val_categorical_accuracy: 0.1067\n",
      "Epoch 343/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4357 - categorical_accuracy: 0.4150 - val_loss: 2.7224 - val_categorical_accuracy: 0.1067\n",
      "Epoch 344/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4416 - categorical_accuracy: 0.4161 - val_loss: 2.7120 - val_categorical_accuracy: 0.1033\n",
      "Epoch 345/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4350 - categorical_accuracy: 0.4261 - val_loss: 2.7044 - val_categorical_accuracy: 0.1133\n",
      "Epoch 346/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4372 - categorical_accuracy: 0.4206 - val_loss: 2.7194 - val_categorical_accuracy: 0.1167\n",
      "Epoch 347/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4377 - categorical_accuracy: 0.4161 - val_loss: 2.6927 - val_categorical_accuracy: 0.1333\n",
      "Epoch 348/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4421 - categorical_accuracy: 0.4217 - val_loss: 2.7085 - val_categorical_accuracy: 0.1267\n",
      "Epoch 349/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4334 - categorical_accuracy: 0.4178 - val_loss: 2.6866 - val_categorical_accuracy: 0.1400\n",
      "Epoch 350/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4351 - categorical_accuracy: 0.4156 - val_loss: 2.6737 - val_categorical_accuracy: 0.1233\n",
      "Epoch 351/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4406 - categorical_accuracy: 0.4144 - val_loss: 2.6695 - val_categorical_accuracy: 0.1267\n",
      "Epoch 352/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4428 - categorical_accuracy: 0.4072 - val_loss: 2.6874 - val_categorical_accuracy: 0.1300\n",
      "Epoch 353/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4509 - categorical_accuracy: 0.4056 - val_loss: 2.6832 - val_categorical_accuracy: 0.1367\n",
      "Epoch 354/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4661 - categorical_accuracy: 0.4006 - val_loss: 2.6977 - val_categorical_accuracy: 0.1233\n",
      "Epoch 355/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4509 - categorical_accuracy: 0.4089 - val_loss: 2.6777 - val_categorical_accuracy: 0.1433\n",
      "Epoch 356/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4567 - categorical_accuracy: 0.4061 - val_loss: 2.6787 - val_categorical_accuracy: 0.1167\n",
      "Epoch 357/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4483 - categorical_accuracy: 0.4089 - val_loss: 2.6649 - val_categorical_accuracy: 0.1167\n",
      "Epoch 358/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4460 - categorical_accuracy: 0.4194 - val_loss: 2.6580 - val_categorical_accuracy: 0.1233\n",
      "Epoch 359/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4521 - categorical_accuracy: 0.4056 - val_loss: 2.6793 - val_categorical_accuracy: 0.1433\n",
      "Epoch 360/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4473 - categorical_accuracy: 0.4089 - val_loss: 2.6242 - val_categorical_accuracy: 0.1400\n",
      "Epoch 361/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.4383 - categorical_accuracy: 0.4156 - val_loss: 2.6612 - val_categorical_accuracy: 0.1333\n",
      "Epoch 362/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4344 - categorical_accuracy: 0.4111 - val_loss: 2.6693 - val_categorical_accuracy: 0.1367\n",
      "Epoch 363/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4362 - categorical_accuracy: 0.4072 - val_loss: 2.6791 - val_categorical_accuracy: 0.1200\n",
      "Epoch 364/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4303 - categorical_accuracy: 0.4156 - val_loss: 2.6482 - val_categorical_accuracy: 0.1233\n",
      "Epoch 365/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4319 - categorical_accuracy: 0.4156 - val_loss: 2.6579 - val_categorical_accuracy: 0.1233\n",
      "Epoch 366/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4305 - categorical_accuracy: 0.4122 - val_loss: 2.6416 - val_categorical_accuracy: 0.1233\n",
      "Epoch 367/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4325 - categorical_accuracy: 0.4100 - val_loss: 2.5744 - val_categorical_accuracy: 0.1167\n",
      "Epoch 368/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4326 - categorical_accuracy: 0.4106 - val_loss: 2.5853 - val_categorical_accuracy: 0.1267\n",
      "Epoch 369/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4329 - categorical_accuracy: 0.4094 - val_loss: 2.5821 - val_categorical_accuracy: 0.1167\n",
      "Epoch 370/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4316 - categorical_accuracy: 0.4089 - val_loss: 2.5807 - val_categorical_accuracy: 0.1267\n",
      "Epoch 371/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4274 - categorical_accuracy: 0.4106 - val_loss: 2.5923 - val_categorical_accuracy: 0.1367\n",
      "Epoch 372/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4259 - categorical_accuracy: 0.4100 - val_loss: 2.6132 - val_categorical_accuracy: 0.1067\n",
      "Epoch 373/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4280 - categorical_accuracy: 0.4172 - val_loss: 2.6221 - val_categorical_accuracy: 0.1200\n",
      "Epoch 374/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4314 - categorical_accuracy: 0.4078 - val_loss: 2.6129 - val_categorical_accuracy: 0.1233\n",
      "Epoch 375/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4194 - categorical_accuracy: 0.4172 - val_loss: 2.6076 - val_categorical_accuracy: 0.1133\n",
      "Epoch 376/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4224 - categorical_accuracy: 0.4144 - val_loss: 2.6145 - val_categorical_accuracy: 0.1167\n",
      "Epoch 377/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4259 - categorical_accuracy: 0.4106 - val_loss: 2.6175 - val_categorical_accuracy: 0.1167\n",
      "Epoch 378/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4319 - categorical_accuracy: 0.4067 - val_loss: 2.6343 - val_categorical_accuracy: 0.1200\n",
      "Epoch 379/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4251 - categorical_accuracy: 0.4150 - val_loss: 2.5943 - val_categorical_accuracy: 0.1200\n",
      "Epoch 380/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4160 - categorical_accuracy: 0.4194 - val_loss: 2.6410 - val_categorical_accuracy: 0.1167\n",
      "Epoch 381/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4173 - categorical_accuracy: 0.4272 - val_loss: 2.5995 - val_categorical_accuracy: 0.1100\n",
      "Epoch 382/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4124 - categorical_accuracy: 0.4222 - val_loss: 2.6356 - val_categorical_accuracy: 0.1300\n",
      "Epoch 383/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4129 - categorical_accuracy: 0.4222 - val_loss: 2.6512 - val_categorical_accuracy: 0.1167\n",
      "Epoch 384/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4115 - categorical_accuracy: 0.4194 - val_loss: 2.6846 - val_categorical_accuracy: 0.1167\n",
      "Epoch 385/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4098 - categorical_accuracy: 0.4211 - val_loss: 2.6761 - val_categorical_accuracy: 0.1167\n",
      "Epoch 386/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4124 - categorical_accuracy: 0.4178 - val_loss: 2.7016 - val_categorical_accuracy: 0.1300\n",
      "Epoch 387/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4112 - categorical_accuracy: 0.4256 - val_loss: 2.6969 - val_categorical_accuracy: 0.1133\n",
      "Epoch 388/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4121 - categorical_accuracy: 0.4194 - val_loss: 2.7502 - val_categorical_accuracy: 0.1167\n",
      "Epoch 389/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4098 - categorical_accuracy: 0.4228 - val_loss: 2.7498 - val_categorical_accuracy: 0.1067\n",
      "Epoch 390/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4084 - categorical_accuracy: 0.4256 - val_loss: 2.7374 - val_categorical_accuracy: 0.1200\n",
      "Epoch 391/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4032 - categorical_accuracy: 0.4328 - val_loss: 2.7414 - val_categorical_accuracy: 0.1200\n",
      "Epoch 392/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4062 - categorical_accuracy: 0.4261 - val_loss: 2.7272 - val_categorical_accuracy: 0.1067\n",
      "Epoch 393/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4025 - categorical_accuracy: 0.4339 - val_loss: 2.7432 - val_categorical_accuracy: 0.1233\n",
      "Epoch 394/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4038 - categorical_accuracy: 0.4317 - val_loss: 2.7202 - val_categorical_accuracy: 0.1233\n",
      "Epoch 395/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4033 - categorical_accuracy: 0.4256 - val_loss: 2.7706 - val_categorical_accuracy: 0.1000\n",
      "Epoch 396/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4010 - categorical_accuracy: 0.4283 - val_loss: 2.7572 - val_categorical_accuracy: 0.1067\n",
      "Epoch 397/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4093 - categorical_accuracy: 0.4244 - val_loss: 2.7541 - val_categorical_accuracy: 0.1100\n",
      "Epoch 398/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4036 - categorical_accuracy: 0.4294 - val_loss: 2.7359 - val_categorical_accuracy: 0.1133\n",
      "Epoch 399/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3973 - categorical_accuracy: 0.4339 - val_loss: 2.7587 - val_categorical_accuracy: 0.1167\n",
      "Epoch 400/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3952 - categorical_accuracy: 0.4272 - val_loss: 2.7705 - val_categorical_accuracy: 0.1100\n",
      "Epoch 401/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3907 - categorical_accuracy: 0.4300 - val_loss: 2.7750 - val_categorical_accuracy: 0.1167\n",
      "Epoch 402/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3939 - categorical_accuracy: 0.4300 - val_loss: 2.7664 - val_categorical_accuracy: 0.1133\n",
      "Epoch 403/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3961 - categorical_accuracy: 0.4211 - val_loss: 2.8045 - val_categorical_accuracy: 0.1133\n",
      "Epoch 404/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3910 - categorical_accuracy: 0.4272 - val_loss: 2.7502 - val_categorical_accuracy: 0.1200\n",
      "Epoch 405/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3882 - categorical_accuracy: 0.4211 - val_loss: 2.7518 - val_categorical_accuracy: 0.1267\n",
      "Epoch 406/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3904 - categorical_accuracy: 0.4233 - val_loss: 2.7459 - val_categorical_accuracy: 0.1133\n",
      "Epoch 407/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3919 - categorical_accuracy: 0.4294 - val_loss: 2.7637 - val_categorical_accuracy: 0.1200\n",
      "Epoch 408/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3891 - categorical_accuracy: 0.4239 - val_loss: 2.8111 - val_categorical_accuracy: 0.1267\n",
      "Epoch 409/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3875 - categorical_accuracy: 0.4272 - val_loss: 2.7618 - val_categorical_accuracy: 0.1200\n",
      "Epoch 410/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3859 - categorical_accuracy: 0.4289 - val_loss: 2.7683 - val_categorical_accuracy: 0.1267\n",
      "Epoch 411/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3891 - categorical_accuracy: 0.4306 - val_loss: 2.8059 - val_categorical_accuracy: 0.1267\n",
      "Epoch 412/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3915 - categorical_accuracy: 0.4306 - val_loss: 2.7940 - val_categorical_accuracy: 0.1200\n",
      "Epoch 413/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3923 - categorical_accuracy: 0.4328 - val_loss: 2.7785 - val_categorical_accuracy: 0.1300\n",
      "Epoch 414/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3819 - categorical_accuracy: 0.4344 - val_loss: 2.7808 - val_categorical_accuracy: 0.1233\n",
      "Epoch 415/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3840 - categorical_accuracy: 0.4306 - val_loss: 2.8064 - val_categorical_accuracy: 0.1333\n",
      "Epoch 416/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3870 - categorical_accuracy: 0.4250 - val_loss: 2.7807 - val_categorical_accuracy: 0.1367\n",
      "Epoch 417/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3838 - categorical_accuracy: 0.4306 - val_loss: 2.8143 - val_categorical_accuracy: 0.1267\n",
      "Epoch 418/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3791 - categorical_accuracy: 0.4417 - val_loss: 2.8140 - val_categorical_accuracy: 0.1200\n",
      "Epoch 419/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3860 - categorical_accuracy: 0.4350 - val_loss: 2.7742 - val_categorical_accuracy: 0.1267\n",
      "Epoch 420/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3824 - categorical_accuracy: 0.4356 - val_loss: 2.7966 - val_categorical_accuracy: 0.1367\n",
      "Epoch 421/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3830 - categorical_accuracy: 0.4306 - val_loss: 2.8140 - val_categorical_accuracy: 0.1300\n",
      "Epoch 422/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3872 - categorical_accuracy: 0.4267 - val_loss: 2.8318 - val_categorical_accuracy: 0.1267\n",
      "Epoch 423/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3755 - categorical_accuracy: 0.4383 - val_loss: 2.8192 - val_categorical_accuracy: 0.1267\n",
      "Epoch 424/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3804 - categorical_accuracy: 0.4339 - val_loss: 2.7993 - val_categorical_accuracy: 0.1367\n",
      "Epoch 425/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3807 - categorical_accuracy: 0.4394 - val_loss: 2.8389 - val_categorical_accuracy: 0.1200\n",
      "Epoch 426/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3760 - categorical_accuracy: 0.4417 - val_loss: 2.8270 - val_categorical_accuracy: 0.1467\n",
      "Epoch 427/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3917 - categorical_accuracy: 0.4294 - val_loss: 2.8323 - val_categorical_accuracy: 0.1300\n",
      "Epoch 428/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3817 - categorical_accuracy: 0.4278 - val_loss: 2.8993 - val_categorical_accuracy: 0.1267\n",
      "Epoch 429/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3773 - categorical_accuracy: 0.4350 - val_loss: 2.8821 - val_categorical_accuracy: 0.1333\n",
      "Epoch 430/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3752 - categorical_accuracy: 0.4367 - val_loss: 2.9149 - val_categorical_accuracy: 0.1333\n",
      "Epoch 431/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3821 - categorical_accuracy: 0.4350 - val_loss: 2.8871 - val_categorical_accuracy: 0.1267\n",
      "Epoch 432/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3918 - categorical_accuracy: 0.4317 - val_loss: 2.8656 - val_categorical_accuracy: 0.1400\n",
      "Epoch 433/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3832 - categorical_accuracy: 0.4322 - val_loss: 2.9638 - val_categorical_accuracy: 0.1367\n",
      "Epoch 434/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3880 - categorical_accuracy: 0.4306 - val_loss: 2.9347 - val_categorical_accuracy: 0.1333\n",
      "Epoch 435/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3797 - categorical_accuracy: 0.4372 - val_loss: 2.9321 - val_categorical_accuracy: 0.1367\n",
      "Epoch 436/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3886 - categorical_accuracy: 0.4339 - val_loss: 2.8994 - val_categorical_accuracy: 0.1433\n",
      "Epoch 437/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3856 - categorical_accuracy: 0.4333 - val_loss: 2.9830 - val_categorical_accuracy: 0.1333\n",
      "Epoch 438/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3880 - categorical_accuracy: 0.4417 - val_loss: 2.9490 - val_categorical_accuracy: 0.1300\n",
      "Epoch 439/10000\n",
      "57/57 [==============================] - 1s 14ms/step - loss: 1.3792 - categorical_accuracy: 0.4361 - val_loss: 2.9412 - val_categorical_accuracy: 0.1367\n",
      "Epoch 440/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3819 - categorical_accuracy: 0.4350 - val_loss: 2.9091 - val_categorical_accuracy: 0.1300\n",
      "Epoch 441/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3872 - categorical_accuracy: 0.4356 - val_loss: 2.9375 - val_categorical_accuracy: 0.1333\n",
      "Epoch 442/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3860 - categorical_accuracy: 0.4378 - val_loss: 2.9505 - val_categorical_accuracy: 0.1333\n",
      "Epoch 443/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3752 - categorical_accuracy: 0.4489 - val_loss: 2.9566 - val_categorical_accuracy: 0.1300\n",
      "Epoch 444/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3796 - categorical_accuracy: 0.4394 - val_loss: 2.9463 - val_categorical_accuracy: 0.1400\n",
      "Epoch 445/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3769 - categorical_accuracy: 0.4394 - val_loss: 2.9478 - val_categorical_accuracy: 0.1400\n",
      "Epoch 446/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3780 - categorical_accuracy: 0.4500 - val_loss: 2.9815 - val_categorical_accuracy: 0.1267\n",
      "Epoch 447/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3933 - categorical_accuracy: 0.4289 - val_loss: 2.8978 - val_categorical_accuracy: 0.1333\n",
      "Epoch 448/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3904 - categorical_accuracy: 0.4339 - val_loss: 2.9237 - val_categorical_accuracy: 0.1400\n",
      "Epoch 449/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3824 - categorical_accuracy: 0.4528 - val_loss: 2.9505 - val_categorical_accuracy: 0.1133\n",
      "Epoch 450/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4006 - categorical_accuracy: 0.4383 - val_loss: 2.9033 - val_categorical_accuracy: 0.1267\n",
      "Epoch 451/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4091 - categorical_accuracy: 0.4328 - val_loss: 2.8740 - val_categorical_accuracy: 0.1267\n",
      "Epoch 452/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4045 - categorical_accuracy: 0.4322 - val_loss: 2.9198 - val_categorical_accuracy: 0.1200\n",
      "Epoch 453/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4065 - categorical_accuracy: 0.4250 - val_loss: 2.8745 - val_categorical_accuracy: 0.1200\n",
      "Epoch 454/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3929 - categorical_accuracy: 0.4383 - val_loss: 2.8897 - val_categorical_accuracy: 0.1233\n",
      "Epoch 455/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3992 - categorical_accuracy: 0.4322 - val_loss: 2.8633 - val_categorical_accuracy: 0.1433\n",
      "Epoch 456/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4044 - categorical_accuracy: 0.4350 - val_loss: 2.7985 - val_categorical_accuracy: 0.1333\n",
      "Epoch 457/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4030 - categorical_accuracy: 0.4278 - val_loss: 2.7913 - val_categorical_accuracy: 0.1300\n",
      "Epoch 458/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3971 - categorical_accuracy: 0.4278 - val_loss: 2.7874 - val_categorical_accuracy: 0.1500\n",
      "Epoch 459/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3967 - categorical_accuracy: 0.4317 - val_loss: 2.8730 - val_categorical_accuracy: 0.1267\n",
      "Epoch 460/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3947 - categorical_accuracy: 0.4294 - val_loss: 2.8368 - val_categorical_accuracy: 0.1333\n",
      "Epoch 461/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3922 - categorical_accuracy: 0.4328 - val_loss: 2.8606 - val_categorical_accuracy: 0.1267\n",
      "Epoch 462/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4080 - categorical_accuracy: 0.4183 - val_loss: 2.8047 - val_categorical_accuracy: 0.1367\n",
      "Epoch 463/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4037 - categorical_accuracy: 0.4178 - val_loss: 2.7817 - val_categorical_accuracy: 0.1500\n",
      "Epoch 464/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4041 - categorical_accuracy: 0.4306 - val_loss: 2.8001 - val_categorical_accuracy: 0.1433\n",
      "Epoch 465/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4017 - categorical_accuracy: 0.4256 - val_loss: 2.7668 - val_categorical_accuracy: 0.1433\n",
      "Epoch 466/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4046 - categorical_accuracy: 0.4256 - val_loss: 2.7632 - val_categorical_accuracy: 0.1400\n",
      "Epoch 467/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4118 - categorical_accuracy: 0.4233 - val_loss: 2.7401 - val_categorical_accuracy: 0.1333\n",
      "Epoch 468/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4062 - categorical_accuracy: 0.4228 - val_loss: 2.7614 - val_categorical_accuracy: 0.1400\n",
      "Epoch 469/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4035 - categorical_accuracy: 0.4233 - val_loss: 2.7503 - val_categorical_accuracy: 0.1433\n",
      "Epoch 470/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4030 - categorical_accuracy: 0.4183 - val_loss: 2.7936 - val_categorical_accuracy: 0.1267\n",
      "Epoch 471/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3973 - categorical_accuracy: 0.4311 - val_loss: 2.7721 - val_categorical_accuracy: 0.1433\n",
      "Epoch 472/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4008 - categorical_accuracy: 0.4217 - val_loss: 2.7641 - val_categorical_accuracy: 0.1400\n",
      "Epoch 473/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4052 - categorical_accuracy: 0.4194 - val_loss: 2.7816 - val_categorical_accuracy: 0.1400\n",
      "Epoch 474/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4053 - categorical_accuracy: 0.4256 - val_loss: 2.7568 - val_categorical_accuracy: 0.1367\n",
      "Epoch 475/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4013 - categorical_accuracy: 0.4244 - val_loss: 2.7886 - val_categorical_accuracy: 0.1467\n",
      "Epoch 476/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3909 - categorical_accuracy: 0.4267 - val_loss: 2.8043 - val_categorical_accuracy: 0.1300\n",
      "Epoch 477/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3945 - categorical_accuracy: 0.4306 - val_loss: 2.7708 - val_categorical_accuracy: 0.1433\n",
      "Epoch 478/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3879 - categorical_accuracy: 0.4322 - val_loss: 2.8210 - val_categorical_accuracy: 0.1400\n",
      "Epoch 479/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3882 - categorical_accuracy: 0.4339 - val_loss: 2.8083 - val_categorical_accuracy: 0.1400\n",
      "Epoch 480/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3816 - categorical_accuracy: 0.4422 - val_loss: 2.8171 - val_categorical_accuracy: 0.1400\n",
      "Epoch 481/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3993 - categorical_accuracy: 0.4217 - val_loss: 2.8635 - val_categorical_accuracy: 0.1300\n",
      "Epoch 482/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3958 - categorical_accuracy: 0.4317 - val_loss: 2.8117 - val_categorical_accuracy: 0.1400\n",
      "Epoch 483/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3927 - categorical_accuracy: 0.4356 - val_loss: 2.8066 - val_categorical_accuracy: 0.1333\n",
      "Epoch 484/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3834 - categorical_accuracy: 0.4439 - val_loss: 2.7869 - val_categorical_accuracy: 0.1333\n",
      "Epoch 485/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3893 - categorical_accuracy: 0.4383 - val_loss: 2.8223 - val_categorical_accuracy: 0.1467\n",
      "Epoch 486/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3813 - categorical_accuracy: 0.4400 - val_loss: 2.8552 - val_categorical_accuracy: 0.1300\n",
      "Epoch 487/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3777 - categorical_accuracy: 0.4444 - val_loss: 2.8684 - val_categorical_accuracy: 0.1300\n",
      "Epoch 488/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3804 - categorical_accuracy: 0.4411 - val_loss: 2.8557 - val_categorical_accuracy: 0.1333\n",
      "Epoch 489/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3722 - categorical_accuracy: 0.4456 - val_loss: 2.8832 - val_categorical_accuracy: 0.1300\n",
      "Epoch 490/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3725 - categorical_accuracy: 0.4478 - val_loss: 2.8565 - val_categorical_accuracy: 0.1367\n",
      "Epoch 491/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3818 - categorical_accuracy: 0.4350 - val_loss: 2.8996 - val_categorical_accuracy: 0.1367\n",
      "Epoch 492/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3830 - categorical_accuracy: 0.4422 - val_loss: 2.8645 - val_categorical_accuracy: 0.1367\n",
      "Epoch 493/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3818 - categorical_accuracy: 0.4439 - val_loss: 2.8683 - val_categorical_accuracy: 0.1300\n",
      "Epoch 494/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3791 - categorical_accuracy: 0.4428 - val_loss: 2.9178 - val_categorical_accuracy: 0.1367\n",
      "Epoch 495/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3786 - categorical_accuracy: 0.4456 - val_loss: 2.8747 - val_categorical_accuracy: 0.1333\n",
      "Epoch 496/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3849 - categorical_accuracy: 0.4433 - val_loss: 2.9085 - val_categorical_accuracy: 0.1267\n",
      "Epoch 497/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3834 - categorical_accuracy: 0.4472 - val_loss: 2.9091 - val_categorical_accuracy: 0.1300\n",
      "Epoch 498/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3813 - categorical_accuracy: 0.4483 - val_loss: 2.9253 - val_categorical_accuracy: 0.1367\n",
      "Epoch 499/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3831 - categorical_accuracy: 0.4389 - val_loss: 2.9595 - val_categorical_accuracy: 0.1333\n",
      "Epoch 500/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3820 - categorical_accuracy: 0.4439 - val_loss: 2.9208 - val_categorical_accuracy: 0.1333\n",
      "Epoch 501/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3861 - categorical_accuracy: 0.4450 - val_loss: 2.9214 - val_categorical_accuracy: 0.1200\n",
      "Epoch 502/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3889 - categorical_accuracy: 0.4422 - val_loss: 2.8965 - val_categorical_accuracy: 0.1233\n",
      "Epoch 503/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3842 - categorical_accuracy: 0.4500 - val_loss: 2.8809 - val_categorical_accuracy: 0.1267\n",
      "Epoch 504/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3802 - categorical_accuracy: 0.4444 - val_loss: 2.8379 - val_categorical_accuracy: 0.1300\n",
      "Epoch 505/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3940 - categorical_accuracy: 0.4339 - val_loss: 2.8374 - val_categorical_accuracy: 0.1233\n",
      "Epoch 506/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3912 - categorical_accuracy: 0.4339 - val_loss: 2.8773 - val_categorical_accuracy: 0.1200\n",
      "Epoch 507/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3771 - categorical_accuracy: 0.4433 - val_loss: 2.7897 - val_categorical_accuracy: 0.1333\n",
      "Epoch 508/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3874 - categorical_accuracy: 0.4367 - val_loss: 2.8344 - val_categorical_accuracy: 0.1367\n",
      "Epoch 509/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3880 - categorical_accuracy: 0.4367 - val_loss: 2.8430 - val_categorical_accuracy: 0.1267\n",
      "Epoch 510/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3796 - categorical_accuracy: 0.4428 - val_loss: 2.8707 - val_categorical_accuracy: 0.1200\n",
      "Epoch 511/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3782 - categorical_accuracy: 0.4394 - val_loss: 2.8535 - val_categorical_accuracy: 0.1400\n",
      "Epoch 512/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3829 - categorical_accuracy: 0.4444 - val_loss: 2.8344 - val_categorical_accuracy: 0.1200\n",
      "Epoch 513/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3791 - categorical_accuracy: 0.4433 - val_loss: 2.8506 - val_categorical_accuracy: 0.1267\n",
      "Epoch 514/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3805 - categorical_accuracy: 0.4461 - val_loss: 2.8616 - val_categorical_accuracy: 0.1267\n",
      "Epoch 515/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3745 - categorical_accuracy: 0.4411 - val_loss: 2.8018 - val_categorical_accuracy: 0.1433\n",
      "Epoch 516/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3860 - categorical_accuracy: 0.4394 - val_loss: 2.8595 - val_categorical_accuracy: 0.1333\n",
      "Epoch 517/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3759 - categorical_accuracy: 0.4422 - val_loss: 2.8636 - val_categorical_accuracy: 0.1400\n",
      "Epoch 518/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3761 - categorical_accuracy: 0.4422 - val_loss: 2.8917 - val_categorical_accuracy: 0.1233\n",
      "Epoch 519/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3732 - categorical_accuracy: 0.4400 - val_loss: 2.8437 - val_categorical_accuracy: 0.1467\n",
      "Epoch 520/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3751 - categorical_accuracy: 0.4339 - val_loss: 2.8799 - val_categorical_accuracy: 0.1333\n",
      "Epoch 521/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3721 - categorical_accuracy: 0.4433 - val_loss: 2.9087 - val_categorical_accuracy: 0.1433\n",
      "Epoch 522/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3676 - categorical_accuracy: 0.4467 - val_loss: 2.8741 - val_categorical_accuracy: 0.1467\n",
      "Epoch 523/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3715 - categorical_accuracy: 0.4506 - val_loss: 2.9165 - val_categorical_accuracy: 0.1333\n",
      "Epoch 524/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3709 - categorical_accuracy: 0.4517 - val_loss: 2.9085 - val_categorical_accuracy: 0.1400\n",
      "Epoch 525/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3667 - categorical_accuracy: 0.4417 - val_loss: 2.8787 - val_categorical_accuracy: 0.1367\n",
      "Epoch 526/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3765 - categorical_accuracy: 0.4456 - val_loss: 2.9125 - val_categorical_accuracy: 0.1400\n",
      "Epoch 527/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3799 - categorical_accuracy: 0.4417 - val_loss: 2.8739 - val_categorical_accuracy: 0.1500\n",
      "Epoch 528/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3855 - categorical_accuracy: 0.4389 - val_loss: 2.9757 - val_categorical_accuracy: 0.1300\n",
      "Epoch 529/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3716 - categorical_accuracy: 0.4478 - val_loss: 2.9122 - val_categorical_accuracy: 0.1367\n",
      "Epoch 530/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3707 - categorical_accuracy: 0.4517 - val_loss: 2.9090 - val_categorical_accuracy: 0.1433\n",
      "Epoch 531/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3706 - categorical_accuracy: 0.4494 - val_loss: 2.9344 - val_categorical_accuracy: 0.1467\n",
      "Epoch 532/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3686 - categorical_accuracy: 0.4528 - val_loss: 2.9291 - val_categorical_accuracy: 0.1433\n",
      "Epoch 533/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3692 - categorical_accuracy: 0.4489 - val_loss: 2.9336 - val_categorical_accuracy: 0.1267\n",
      "Epoch 534/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3639 - categorical_accuracy: 0.4567 - val_loss: 2.9632 - val_categorical_accuracy: 0.1533\n",
      "Epoch 535/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3741 - categorical_accuracy: 0.4494 - val_loss: 2.9897 - val_categorical_accuracy: 0.1533\n",
      "Epoch 536/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3718 - categorical_accuracy: 0.4572 - val_loss: 2.9990 - val_categorical_accuracy: 0.1433\n",
      "Epoch 537/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3754 - categorical_accuracy: 0.4439 - val_loss: 2.9630 - val_categorical_accuracy: 0.1500\n",
      "Epoch 538/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3720 - categorical_accuracy: 0.4461 - val_loss: 2.9439 - val_categorical_accuracy: 0.1333\n",
      "Epoch 539/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3696 - categorical_accuracy: 0.4517 - val_loss: 2.9911 - val_categorical_accuracy: 0.1467\n",
      "Epoch 540/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3646 - categorical_accuracy: 0.4494 - val_loss: 3.0071 - val_categorical_accuracy: 0.1467\n",
      "Epoch 541/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3647 - categorical_accuracy: 0.4494 - val_loss: 3.0602 - val_categorical_accuracy: 0.1433\n",
      "Epoch 542/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3614 - categorical_accuracy: 0.4617 - val_loss: 3.0012 - val_categorical_accuracy: 0.1433\n",
      "Epoch 543/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3612 - categorical_accuracy: 0.4561 - val_loss: 3.0194 - val_categorical_accuracy: 0.1500\n",
      "Epoch 544/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3629 - categorical_accuracy: 0.4544 - val_loss: 3.0418 - val_categorical_accuracy: 0.1533\n",
      "Epoch 545/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3614 - categorical_accuracy: 0.4456 - val_loss: 3.0646 - val_categorical_accuracy: 0.1400\n",
      "Epoch 546/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3636 - categorical_accuracy: 0.4494 - val_loss: 2.9760 - val_categorical_accuracy: 0.1300\n",
      "Epoch 547/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3705 - categorical_accuracy: 0.4506 - val_loss: 3.0192 - val_categorical_accuracy: 0.1400\n",
      "Epoch 548/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3688 - categorical_accuracy: 0.4461 - val_loss: 3.0152 - val_categorical_accuracy: 0.1400\n",
      "Epoch 549/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3715 - categorical_accuracy: 0.4489 - val_loss: 3.0497 - val_categorical_accuracy: 0.1367\n",
      "Epoch 550/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3630 - categorical_accuracy: 0.4522 - val_loss: 3.0484 - val_categorical_accuracy: 0.1433\n",
      "Epoch 551/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3642 - categorical_accuracy: 0.4539 - val_loss: 3.0437 - val_categorical_accuracy: 0.1467\n",
      "Epoch 552/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3646 - categorical_accuracy: 0.4533 - val_loss: 3.0361 - val_categorical_accuracy: 0.1433\n",
      "Epoch 553/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3637 - categorical_accuracy: 0.4539 - val_loss: 3.0747 - val_categorical_accuracy: 0.1433\n",
      "Epoch 554/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3648 - categorical_accuracy: 0.4483 - val_loss: 3.0931 - val_categorical_accuracy: 0.1367\n",
      "Epoch 555/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3585 - categorical_accuracy: 0.4506 - val_loss: 3.0477 - val_categorical_accuracy: 0.1400\n",
      "Epoch 556/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3643 - categorical_accuracy: 0.4517 - val_loss: 3.0804 - val_categorical_accuracy: 0.1400\n",
      "Epoch 557/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3653 - categorical_accuracy: 0.4550 - val_loss: 3.1179 - val_categorical_accuracy: 0.1267\n",
      "Epoch 558/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3651 - categorical_accuracy: 0.4483 - val_loss: 3.0554 - val_categorical_accuracy: 0.1267\n",
      "Epoch 559/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3661 - categorical_accuracy: 0.4461 - val_loss: 3.0898 - val_categorical_accuracy: 0.1333\n",
      "Epoch 560/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3712 - categorical_accuracy: 0.4528 - val_loss: 3.0925 - val_categorical_accuracy: 0.1400\n",
      "Epoch 561/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3713 - categorical_accuracy: 0.4433 - val_loss: 3.0820 - val_categorical_accuracy: 0.1400\n",
      "Epoch 562/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3694 - categorical_accuracy: 0.4506 - val_loss: 3.0836 - val_categorical_accuracy: 0.1433\n",
      "Epoch 563/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3748 - categorical_accuracy: 0.4567 - val_loss: 3.1113 - val_categorical_accuracy: 0.1300\n",
      "Epoch 564/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3759 - categorical_accuracy: 0.4450 - val_loss: 3.0852 - val_categorical_accuracy: 0.1367\n",
      "Epoch 565/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3811 - categorical_accuracy: 0.4433 - val_loss: 3.0895 - val_categorical_accuracy: 0.1433\n",
      "Epoch 566/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3842 - categorical_accuracy: 0.4428 - val_loss: 3.1241 - val_categorical_accuracy: 0.1400\n",
      "Epoch 567/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3837 - categorical_accuracy: 0.4417 - val_loss: 3.0724 - val_categorical_accuracy: 0.1300\n",
      "Epoch 568/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3732 - categorical_accuracy: 0.4550 - val_loss: 3.0810 - val_categorical_accuracy: 0.1467\n",
      "Epoch 569/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3759 - categorical_accuracy: 0.4533 - val_loss: 3.1022 - val_categorical_accuracy: 0.1400\n",
      "Epoch 570/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3851 - categorical_accuracy: 0.4483 - val_loss: 3.0291 - val_categorical_accuracy: 0.1300\n",
      "Epoch 571/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3845 - categorical_accuracy: 0.4517 - val_loss: 2.9799 - val_categorical_accuracy: 0.1400\n",
      "Epoch 572/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3928 - categorical_accuracy: 0.4506 - val_loss: 2.9243 - val_categorical_accuracy: 0.1433\n",
      "Epoch 573/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3787 - categorical_accuracy: 0.4533 - val_loss: 2.9631 - val_categorical_accuracy: 0.1467\n",
      "Epoch 574/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3802 - categorical_accuracy: 0.4456 - val_loss: 3.0164 - val_categorical_accuracy: 0.1433\n",
      "Epoch 575/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3777 - categorical_accuracy: 0.4456 - val_loss: 2.9990 - val_categorical_accuracy: 0.1333\n",
      "Epoch 576/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3724 - categorical_accuracy: 0.4444 - val_loss: 2.9942 - val_categorical_accuracy: 0.1367\n",
      "Epoch 577/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3745 - categorical_accuracy: 0.4489 - val_loss: 2.9558 - val_categorical_accuracy: 0.1467\n",
      "Epoch 578/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3661 - categorical_accuracy: 0.4506 - val_loss: 3.0178 - val_categorical_accuracy: 0.1367\n",
      "Epoch 579/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3731 - categorical_accuracy: 0.4517 - val_loss: 2.9762 - val_categorical_accuracy: 0.1300\n",
      "Epoch 580/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3745 - categorical_accuracy: 0.4517 - val_loss: 2.9648 - val_categorical_accuracy: 0.1400\n",
      "Epoch 581/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3741 - categorical_accuracy: 0.4444 - val_loss: 2.9566 - val_categorical_accuracy: 0.1400\n",
      "Epoch 582/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3765 - categorical_accuracy: 0.4444 - val_loss: 2.9805 - val_categorical_accuracy: 0.1367\n",
      "Epoch 583/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3697 - categorical_accuracy: 0.4489 - val_loss: 3.0009 - val_categorical_accuracy: 0.1333\n",
      "Epoch 584/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3702 - categorical_accuracy: 0.4528 - val_loss: 2.9987 - val_categorical_accuracy: 0.1400\n",
      "Epoch 585/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3724 - categorical_accuracy: 0.4489 - val_loss: 2.9862 - val_categorical_accuracy: 0.1500\n",
      "Epoch 586/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3726 - categorical_accuracy: 0.4472 - val_loss: 2.9959 - val_categorical_accuracy: 0.1300\n",
      "Epoch 587/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3677 - categorical_accuracy: 0.4472 - val_loss: 2.9416 - val_categorical_accuracy: 0.1300\n",
      "Epoch 588/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3688 - categorical_accuracy: 0.4461 - val_loss: 3.0419 - val_categorical_accuracy: 0.1300\n",
      "Epoch 589/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3789 - categorical_accuracy: 0.4433 - val_loss: 2.9608 - val_categorical_accuracy: 0.1367\n",
      "Epoch 590/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3674 - categorical_accuracy: 0.4483 - val_loss: 3.0252 - val_categorical_accuracy: 0.1500\n",
      "Epoch 591/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3699 - categorical_accuracy: 0.4494 - val_loss: 3.0192 - val_categorical_accuracy: 0.1500\n",
      "Epoch 592/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3667 - categorical_accuracy: 0.4494 - val_loss: 3.0107 - val_categorical_accuracy: 0.1500\n",
      "Epoch 593/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3732 - categorical_accuracy: 0.4539 - val_loss: 3.0062 - val_categorical_accuracy: 0.1433\n",
      "Epoch 594/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3763 - categorical_accuracy: 0.4428 - val_loss: 2.9839 - val_categorical_accuracy: 0.1467\n",
      "Epoch 595/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3793 - categorical_accuracy: 0.4322 - val_loss: 3.0329 - val_categorical_accuracy: 0.1567\n",
      "Epoch 596/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3918 - categorical_accuracy: 0.4300 - val_loss: 3.0048 - val_categorical_accuracy: 0.1400\n",
      "Epoch 597/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3872 - categorical_accuracy: 0.4394 - val_loss: 3.0349 - val_categorical_accuracy: 0.1400\n",
      "Epoch 598/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3885 - categorical_accuracy: 0.4456 - val_loss: 3.0224 - val_categorical_accuracy: 0.1467\n",
      "Epoch 599/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3922 - categorical_accuracy: 0.4328 - val_loss: 3.0400 - val_categorical_accuracy: 0.1467\n",
      "Epoch 600/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4051 - categorical_accuracy: 0.4256 - val_loss: 2.9655 - val_categorical_accuracy: 0.1467\n",
      "Epoch 601/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3886 - categorical_accuracy: 0.4400 - val_loss: 3.0350 - val_categorical_accuracy: 0.1400\n",
      "Epoch 602/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3998 - categorical_accuracy: 0.4344 - val_loss: 3.1012 - val_categorical_accuracy: 0.1267\n",
      "Epoch 603/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4048 - categorical_accuracy: 0.4306 - val_loss: 3.0068 - val_categorical_accuracy: 0.1400\n",
      "Epoch 604/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4117 - categorical_accuracy: 0.4222 - val_loss: 3.0756 - val_categorical_accuracy: 0.1200\n",
      "Epoch 605/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4432 - categorical_accuracy: 0.4156 - val_loss: 3.0290 - val_categorical_accuracy: 0.1267\n",
      "Epoch 606/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4204 - categorical_accuracy: 0.4150 - val_loss: 3.0076 - val_categorical_accuracy: 0.1333\n",
      "Epoch 607/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3965 - categorical_accuracy: 0.4344 - val_loss: 3.0561 - val_categorical_accuracy: 0.1200\n",
      "Epoch 608/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3929 - categorical_accuracy: 0.4300 - val_loss: 3.0450 - val_categorical_accuracy: 0.1267\n",
      "Epoch 609/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3910 - categorical_accuracy: 0.4333 - val_loss: 3.0969 - val_categorical_accuracy: 0.1300\n",
      "Epoch 610/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3850 - categorical_accuracy: 0.4361 - val_loss: 3.0977 - val_categorical_accuracy: 0.1333\n",
      "Epoch 611/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3851 - categorical_accuracy: 0.4411 - val_loss: 3.0817 - val_categorical_accuracy: 0.1367\n",
      "Epoch 612/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3869 - categorical_accuracy: 0.4294 - val_loss: 3.0792 - val_categorical_accuracy: 0.1400\n",
      "Epoch 613/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3921 - categorical_accuracy: 0.4300 - val_loss: 3.1105 - val_categorical_accuracy: 0.1400\n",
      "Epoch 614/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3896 - categorical_accuracy: 0.4444 - val_loss: 3.0501 - val_categorical_accuracy: 0.1400\n",
      "Epoch 615/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3807 - categorical_accuracy: 0.4389 - val_loss: 3.0552 - val_categorical_accuracy: 0.1433\n",
      "Epoch 616/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3769 - categorical_accuracy: 0.4411 - val_loss: 3.0765 - val_categorical_accuracy: 0.1300\n",
      "Epoch 617/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3798 - categorical_accuracy: 0.4389 - val_loss: 3.0141 - val_categorical_accuracy: 0.1433\n",
      "Epoch 618/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3831 - categorical_accuracy: 0.4394 - val_loss: 3.0046 - val_categorical_accuracy: 0.1500\n",
      "Epoch 619/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3817 - categorical_accuracy: 0.4417 - val_loss: 2.9775 - val_categorical_accuracy: 0.1433\n",
      "Epoch 620/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3724 - categorical_accuracy: 0.4456 - val_loss: 3.0518 - val_categorical_accuracy: 0.1500\n",
      "Epoch 621/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3788 - categorical_accuracy: 0.4428 - val_loss: 2.9585 - val_categorical_accuracy: 0.1400\n",
      "Epoch 622/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3853 - categorical_accuracy: 0.4294 - val_loss: 2.9914 - val_categorical_accuracy: 0.1367\n",
      "Epoch 623/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3800 - categorical_accuracy: 0.4333 - val_loss: 3.0403 - val_categorical_accuracy: 0.1400\n",
      "Epoch 624/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3709 - categorical_accuracy: 0.4389 - val_loss: 3.0495 - val_categorical_accuracy: 0.1433\n",
      "Epoch 625/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3795 - categorical_accuracy: 0.4383 - val_loss: 2.9186 - val_categorical_accuracy: 0.1433\n",
      "Epoch 626/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3874 - categorical_accuracy: 0.4367 - val_loss: 3.0294 - val_categorical_accuracy: 0.1433\n",
      "Epoch 627/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3770 - categorical_accuracy: 0.4417 - val_loss: 3.0453 - val_categorical_accuracy: 0.1533\n",
      "Epoch 628/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3753 - categorical_accuracy: 0.4356 - val_loss: 2.9582 - val_categorical_accuracy: 0.1500\n",
      "Epoch 629/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3664 - categorical_accuracy: 0.4389 - val_loss: 3.0106 - val_categorical_accuracy: 0.1533\n",
      "Epoch 630/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3730 - categorical_accuracy: 0.4344 - val_loss: 2.9895 - val_categorical_accuracy: 0.1333\n",
      "Epoch 631/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3874 - categorical_accuracy: 0.4294 - val_loss: 2.9813 - val_categorical_accuracy: 0.1500\n",
      "Epoch 632/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3748 - categorical_accuracy: 0.4356 - val_loss: 2.9317 - val_categorical_accuracy: 0.1500\n",
      "Epoch 633/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3788 - categorical_accuracy: 0.4383 - val_loss: 2.9827 - val_categorical_accuracy: 0.1533\n",
      "Epoch 634/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3825 - categorical_accuracy: 0.4383 - val_loss: 2.9234 - val_categorical_accuracy: 0.1333\n",
      "Epoch 635/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3857 - categorical_accuracy: 0.4383 - val_loss: 2.9965 - val_categorical_accuracy: 0.1467\n",
      "Epoch 636/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3745 - categorical_accuracy: 0.4428 - val_loss: 3.0460 - val_categorical_accuracy: 0.1533\n",
      "Epoch 637/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3768 - categorical_accuracy: 0.4367 - val_loss: 2.9729 - val_categorical_accuracy: 0.1633\n",
      "Epoch 638/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3754 - categorical_accuracy: 0.4378 - val_loss: 3.0430 - val_categorical_accuracy: 0.1500\n",
      "Epoch 639/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3745 - categorical_accuracy: 0.4472 - val_loss: 3.0567 - val_categorical_accuracy: 0.1567\n",
      "Epoch 640/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3708 - categorical_accuracy: 0.4367 - val_loss: 3.0995 - val_categorical_accuracy: 0.1600\n",
      "Epoch 641/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3667 - categorical_accuracy: 0.4472 - val_loss: 3.0752 - val_categorical_accuracy: 0.1633\n",
      "Epoch 642/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3697 - categorical_accuracy: 0.4378 - val_loss: 3.0710 - val_categorical_accuracy: 0.1800\n",
      "Epoch 643/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3671 - categorical_accuracy: 0.4494 - val_loss: 2.9238 - val_categorical_accuracy: 0.1567\n",
      "Epoch 644/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3792 - categorical_accuracy: 0.4400 - val_loss: 3.0664 - val_categorical_accuracy: 0.1633\n",
      "Epoch 645/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3724 - categorical_accuracy: 0.4422 - val_loss: 2.9176 - val_categorical_accuracy: 0.1500\n",
      "Epoch 646/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3875 - categorical_accuracy: 0.4400 - val_loss: 3.0216 - val_categorical_accuracy: 0.1667\n",
      "Epoch 647/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3709 - categorical_accuracy: 0.4428 - val_loss: 3.0575 - val_categorical_accuracy: 0.1667\n",
      "Epoch 648/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4281 - categorical_accuracy: 0.4272 - val_loss: 2.8651 - val_categorical_accuracy: 0.1800\n",
      "Epoch 649/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4332 - categorical_accuracy: 0.4322 - val_loss: 2.9712 - val_categorical_accuracy: 0.1800\n",
      "Epoch 650/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3769 - categorical_accuracy: 0.4400 - val_loss: 2.9987 - val_categorical_accuracy: 0.1667\n",
      "Epoch 651/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3715 - categorical_accuracy: 0.4417 - val_loss: 3.0030 - val_categorical_accuracy: 0.1567\n",
      "Epoch 652/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3827 - categorical_accuracy: 0.4372 - val_loss: 3.0387 - val_categorical_accuracy: 0.1600\n",
      "Epoch 653/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3869 - categorical_accuracy: 0.4367 - val_loss: 3.0221 - val_categorical_accuracy: 0.1533\n",
      "Epoch 654/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3768 - categorical_accuracy: 0.4422 - val_loss: 3.0139 - val_categorical_accuracy: 0.1800\n",
      "Epoch 655/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3834 - categorical_accuracy: 0.4328 - val_loss: 2.9879 - val_categorical_accuracy: 0.1567\n",
      "Epoch 656/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3870 - categorical_accuracy: 0.4317 - val_loss: 3.0261 - val_categorical_accuracy: 0.1600\n",
      "Epoch 657/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3704 - categorical_accuracy: 0.4400 - val_loss: 3.0331 - val_categorical_accuracy: 0.1700\n",
      "Epoch 658/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3703 - categorical_accuracy: 0.4533 - val_loss: 3.0295 - val_categorical_accuracy: 0.1633\n",
      "Epoch 659/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3815 - categorical_accuracy: 0.4372 - val_loss: 3.0758 - val_categorical_accuracy: 0.1367\n",
      "Epoch 660/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3746 - categorical_accuracy: 0.4428 - val_loss: 3.0822 - val_categorical_accuracy: 0.1900\n",
      "Epoch 661/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3746 - categorical_accuracy: 0.4472 - val_loss: 2.9825 - val_categorical_accuracy: 0.1767\n",
      "Epoch 662/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3706 - categorical_accuracy: 0.4422 - val_loss: 3.0691 - val_categorical_accuracy: 0.1533\n",
      "Epoch 663/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3728 - categorical_accuracy: 0.4394 - val_loss: 3.0352 - val_categorical_accuracy: 0.1600\n",
      "Epoch 664/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3795 - categorical_accuracy: 0.4311 - val_loss: 3.0502 - val_categorical_accuracy: 0.1667\n",
      "Epoch 665/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3982 - categorical_accuracy: 0.4311 - val_loss: 3.0620 - val_categorical_accuracy: 0.1700\n",
      "Epoch 666/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3918 - categorical_accuracy: 0.4289 - val_loss: 3.0223 - val_categorical_accuracy: 0.1733\n",
      "Epoch 667/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3971 - categorical_accuracy: 0.4417 - val_loss: 2.9346 - val_categorical_accuracy: 0.1700\n",
      "Epoch 668/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3949 - categorical_accuracy: 0.4239 - val_loss: 3.1419 - val_categorical_accuracy: 0.1767\n",
      "Epoch 669/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3943 - categorical_accuracy: 0.4333 - val_loss: 3.0745 - val_categorical_accuracy: 0.1767\n",
      "Epoch 670/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3841 - categorical_accuracy: 0.4311 - val_loss: 3.0922 - val_categorical_accuracy: 0.1700\n",
      "Epoch 671/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3896 - categorical_accuracy: 0.4294 - val_loss: 3.1491 - val_categorical_accuracy: 0.1567\n",
      "Epoch 672/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3901 - categorical_accuracy: 0.4344 - val_loss: 3.0382 - val_categorical_accuracy: 0.1700\n",
      "Epoch 673/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3777 - categorical_accuracy: 0.4400 - val_loss: 3.1484 - val_categorical_accuracy: 0.1667\n",
      "Epoch 674/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3769 - categorical_accuracy: 0.4306 - val_loss: 3.1429 - val_categorical_accuracy: 0.1733\n",
      "Epoch 675/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3733 - categorical_accuracy: 0.4400 - val_loss: 3.0934 - val_categorical_accuracy: 0.1733\n",
      "Epoch 676/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3862 - categorical_accuracy: 0.4272 - val_loss: 3.0226 - val_categorical_accuracy: 0.1733\n",
      "Epoch 677/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3912 - categorical_accuracy: 0.4272 - val_loss: 3.0786 - val_categorical_accuracy: 0.1700\n",
      "Epoch 678/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3655 - categorical_accuracy: 0.4444 - val_loss: 3.1182 - val_categorical_accuracy: 0.1767\n",
      "Epoch 679/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3679 - categorical_accuracy: 0.4411 - val_loss: 3.0909 - val_categorical_accuracy: 0.1800\n",
      "Epoch 680/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3782 - categorical_accuracy: 0.4389 - val_loss: 3.0928 - val_categorical_accuracy: 0.1667\n",
      "Epoch 681/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3815 - categorical_accuracy: 0.4356 - val_loss: 3.1403 - val_categorical_accuracy: 0.1767\n",
      "Epoch 682/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3750 - categorical_accuracy: 0.4344 - val_loss: 3.1147 - val_categorical_accuracy: 0.1733\n",
      "Epoch 683/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3709 - categorical_accuracy: 0.4428 - val_loss: 3.0781 - val_categorical_accuracy: 0.1600\n",
      "Epoch 684/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3734 - categorical_accuracy: 0.4422 - val_loss: 3.1317 - val_categorical_accuracy: 0.1567\n",
      "Epoch 685/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3678 - categorical_accuracy: 0.4322 - val_loss: 3.1198 - val_categorical_accuracy: 0.1633\n",
      "Epoch 686/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3855 - categorical_accuracy: 0.4278 - val_loss: 2.9324 - val_categorical_accuracy: 0.1567\n",
      "Epoch 687/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3880 - categorical_accuracy: 0.4244 - val_loss: 3.0766 - val_categorical_accuracy: 0.1600\n",
      "Epoch 688/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3814 - categorical_accuracy: 0.4361 - val_loss: 2.9175 - val_categorical_accuracy: 0.1600\n",
      "Epoch 689/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3926 - categorical_accuracy: 0.4244 - val_loss: 2.8715 - val_categorical_accuracy: 0.1667\n",
      "Epoch 690/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4008 - categorical_accuracy: 0.4244 - val_loss: 2.8744 - val_categorical_accuracy: 0.1600\n",
      "Epoch 691/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3827 - categorical_accuracy: 0.4322 - val_loss: 2.9050 - val_categorical_accuracy: 0.1667\n",
      "Epoch 692/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3904 - categorical_accuracy: 0.4333 - val_loss: 2.8930 - val_categorical_accuracy: 0.1633\n",
      "Epoch 693/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3882 - categorical_accuracy: 0.4289 - val_loss: 2.9052 - val_categorical_accuracy: 0.1600\n",
      "Epoch 694/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3824 - categorical_accuracy: 0.4356 - val_loss: 2.9188 - val_categorical_accuracy: 0.1667\n",
      "Epoch 695/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3837 - categorical_accuracy: 0.4356 - val_loss: 2.9507 - val_categorical_accuracy: 0.1667\n",
      "Epoch 696/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3862 - categorical_accuracy: 0.4256 - val_loss: 2.9440 - val_categorical_accuracy: 0.1633\n",
      "Epoch 697/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3811 - categorical_accuracy: 0.4328 - val_loss: 2.9679 - val_categorical_accuracy: 0.1600\n",
      "Epoch 698/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3842 - categorical_accuracy: 0.4283 - val_loss: 2.9834 - val_categorical_accuracy: 0.1467\n",
      "Epoch 699/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3956 - categorical_accuracy: 0.4372 - val_loss: 2.8639 - val_categorical_accuracy: 0.1667\n",
      "Epoch 700/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3848 - categorical_accuracy: 0.4267 - val_loss: 2.8806 - val_categorical_accuracy: 0.1733\n",
      "Epoch 701/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3718 - categorical_accuracy: 0.4328 - val_loss: 2.9499 - val_categorical_accuracy: 0.1633\n",
      "Epoch 702/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3778 - categorical_accuracy: 0.4389 - val_loss: 2.9475 - val_categorical_accuracy: 0.1700\n",
      "Epoch 703/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3824 - categorical_accuracy: 0.4306 - val_loss: 2.8985 - val_categorical_accuracy: 0.1633\n",
      "Epoch 704/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3867 - categorical_accuracy: 0.4289 - val_loss: 2.9563 - val_categorical_accuracy: 0.1700\n",
      "Epoch 705/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3763 - categorical_accuracy: 0.4328 - val_loss: 2.9549 - val_categorical_accuracy: 0.1700\n",
      "Epoch 706/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3735 - categorical_accuracy: 0.4272 - val_loss: 2.9241 - val_categorical_accuracy: 0.1767\n",
      "Epoch 707/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3771 - categorical_accuracy: 0.4261 - val_loss: 2.9367 - val_categorical_accuracy: 0.1567\n",
      "Epoch 708/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3752 - categorical_accuracy: 0.4272 - val_loss: 2.9716 - val_categorical_accuracy: 0.1667\n",
      "Epoch 709/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3699 - categorical_accuracy: 0.4389 - val_loss: 2.9385 - val_categorical_accuracy: 0.1567\n",
      "Epoch 710/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3682 - categorical_accuracy: 0.4339 - val_loss: 2.9566 - val_categorical_accuracy: 0.1700\n",
      "Epoch 711/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3698 - categorical_accuracy: 0.4317 - val_loss: 2.9600 - val_categorical_accuracy: 0.1600\n",
      "Epoch 712/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3652 - categorical_accuracy: 0.4422 - val_loss: 3.0020 - val_categorical_accuracy: 0.1700\n",
      "Epoch 713/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3644 - categorical_accuracy: 0.4411 - val_loss: 3.1238 - val_categorical_accuracy: 0.1533\n",
      "Epoch 714/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3649 - categorical_accuracy: 0.4339 - val_loss: 3.0318 - val_categorical_accuracy: 0.1533\n",
      "Epoch 715/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3637 - categorical_accuracy: 0.4389 - val_loss: 3.0727 - val_categorical_accuracy: 0.1600\n",
      "Epoch 716/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3704 - categorical_accuracy: 0.4317 - val_loss: 3.0810 - val_categorical_accuracy: 0.1533\n",
      "Epoch 717/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3565 - categorical_accuracy: 0.4461 - val_loss: 3.1343 - val_categorical_accuracy: 0.1667\n",
      "Epoch 718/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3500 - categorical_accuracy: 0.4506 - val_loss: 3.1125 - val_categorical_accuracy: 0.1667\n",
      "Epoch 719/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3564 - categorical_accuracy: 0.4483 - val_loss: 3.0395 - val_categorical_accuracy: 0.1833\n",
      "Epoch 720/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3497 - categorical_accuracy: 0.4450 - val_loss: 3.0806 - val_categorical_accuracy: 0.1567\n",
      "Epoch 721/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3546 - categorical_accuracy: 0.4394 - val_loss: 3.0824 - val_categorical_accuracy: 0.1467\n",
      "Epoch 722/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3510 - categorical_accuracy: 0.4439 - val_loss: 3.0982 - val_categorical_accuracy: 0.1567\n",
      "Epoch 723/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3447 - categorical_accuracy: 0.4467 - val_loss: 3.0870 - val_categorical_accuracy: 0.1500\n",
      "Epoch 724/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3438 - categorical_accuracy: 0.4456 - val_loss: 3.0990 - val_categorical_accuracy: 0.1500\n",
      "Epoch 725/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3466 - categorical_accuracy: 0.4472 - val_loss: 3.1453 - val_categorical_accuracy: 0.1567\n",
      "Epoch 726/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3489 - categorical_accuracy: 0.4428 - val_loss: 3.1546 - val_categorical_accuracy: 0.1700\n",
      "Epoch 727/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3512 - categorical_accuracy: 0.4422 - val_loss: 3.0375 - val_categorical_accuracy: 0.1667\n",
      "Epoch 728/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3539 - categorical_accuracy: 0.4272 - val_loss: 3.0580 - val_categorical_accuracy: 0.1667\n",
      "Epoch 729/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3636 - categorical_accuracy: 0.4317 - val_loss: 3.0867 - val_categorical_accuracy: 0.1467\n",
      "Epoch 730/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3620 - categorical_accuracy: 0.4222 - val_loss: 3.1126 - val_categorical_accuracy: 0.1567\n",
      "Epoch 731/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3497 - categorical_accuracy: 0.4439 - val_loss: 3.1332 - val_categorical_accuracy: 0.1667\n",
      "Epoch 732/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3531 - categorical_accuracy: 0.4394 - val_loss: 3.1976 - val_categorical_accuracy: 0.1633\n",
      "Epoch 733/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3508 - categorical_accuracy: 0.4433 - val_loss: 3.1163 - val_categorical_accuracy: 0.1567\n",
      "Epoch 734/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3507 - categorical_accuracy: 0.4433 - val_loss: 3.1835 - val_categorical_accuracy: 0.1600\n",
      "Epoch 735/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3483 - categorical_accuracy: 0.4428 - val_loss: 3.1593 - val_categorical_accuracy: 0.1633\n",
      "Epoch 736/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3703 - categorical_accuracy: 0.4328 - val_loss: 3.0374 - val_categorical_accuracy: 0.1500\n",
      "Epoch 737/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3604 - categorical_accuracy: 0.4333 - val_loss: 3.2019 - val_categorical_accuracy: 0.1633\n",
      "Epoch 738/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3556 - categorical_accuracy: 0.4322 - val_loss: 3.1404 - val_categorical_accuracy: 0.1667\n",
      "Epoch 739/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3651 - categorical_accuracy: 0.4328 - val_loss: 3.2526 - val_categorical_accuracy: 0.1733\n",
      "Epoch 740/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3581 - categorical_accuracy: 0.4483 - val_loss: 3.2461 - val_categorical_accuracy: 0.1433\n",
      "Epoch 741/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3547 - categorical_accuracy: 0.4478 - val_loss: 3.2442 - val_categorical_accuracy: 0.1633\n",
      "Epoch 742/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3498 - categorical_accuracy: 0.4433 - val_loss: 3.2568 - val_categorical_accuracy: 0.1533\n",
      "Epoch 743/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3481 - categorical_accuracy: 0.4483 - val_loss: 3.2337 - val_categorical_accuracy: 0.1500\n",
      "Epoch 744/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3498 - categorical_accuracy: 0.4411 - val_loss: 3.2298 - val_categorical_accuracy: 0.1500\n",
      "Epoch 745/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3491 - categorical_accuracy: 0.4406 - val_loss: 3.1429 - val_categorical_accuracy: 0.1600\n",
      "Epoch 746/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3531 - categorical_accuracy: 0.4422 - val_loss: 3.2482 - val_categorical_accuracy: 0.1600\n",
      "Epoch 747/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3559 - categorical_accuracy: 0.4450 - val_loss: 3.3000 - val_categorical_accuracy: 0.1567\n",
      "Epoch 748/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3594 - categorical_accuracy: 0.4444 - val_loss: 3.2562 - val_categorical_accuracy: 0.1567\n",
      "Epoch 749/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3592 - categorical_accuracy: 0.4444 - val_loss: 3.2783 - val_categorical_accuracy: 0.1567\n",
      "Epoch 750/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3529 - categorical_accuracy: 0.4450 - val_loss: 3.3553 - val_categorical_accuracy: 0.1667\n",
      "Epoch 751/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3541 - categorical_accuracy: 0.4322 - val_loss: 3.2609 - val_categorical_accuracy: 0.1667\n",
      "Epoch 752/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3408 - categorical_accuracy: 0.4550 - val_loss: 3.3208 - val_categorical_accuracy: 0.1567\n",
      "Epoch 753/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3477 - categorical_accuracy: 0.4489 - val_loss: 3.2564 - val_categorical_accuracy: 0.1667\n",
      "Epoch 754/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3441 - categorical_accuracy: 0.4489 - val_loss: 3.2877 - val_categorical_accuracy: 0.1633\n",
      "Epoch 755/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3486 - categorical_accuracy: 0.4433 - val_loss: 3.2992 - val_categorical_accuracy: 0.1733\n",
      "Epoch 756/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3611 - categorical_accuracy: 0.4367 - val_loss: 3.2331 - val_categorical_accuracy: 0.1633\n",
      "Epoch 757/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3442 - categorical_accuracy: 0.4472 - val_loss: 3.3718 - val_categorical_accuracy: 0.1700\n",
      "Epoch 758/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3506 - categorical_accuracy: 0.4439 - val_loss: 3.3496 - val_categorical_accuracy: 0.1667\n",
      "Epoch 759/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3495 - categorical_accuracy: 0.4494 - val_loss: 3.2829 - val_categorical_accuracy: 0.1633\n",
      "Epoch 760/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3581 - categorical_accuracy: 0.4444 - val_loss: 3.2206 - val_categorical_accuracy: 0.1767\n",
      "Epoch 761/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3697 - categorical_accuracy: 0.4317 - val_loss: 3.2857 - val_categorical_accuracy: 0.1767\n",
      "Epoch 762/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3557 - categorical_accuracy: 0.4356 - val_loss: 3.2345 - val_categorical_accuracy: 0.1633\n",
      "Epoch 763/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3527 - categorical_accuracy: 0.4494 - val_loss: 3.2236 - val_categorical_accuracy: 0.1700\n",
      "Epoch 764/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3556 - categorical_accuracy: 0.4417 - val_loss: 3.2497 - val_categorical_accuracy: 0.1667\n",
      "Epoch 765/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3543 - categorical_accuracy: 0.4361 - val_loss: 3.2118 - val_categorical_accuracy: 0.1633\n",
      "Epoch 766/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3494 - categorical_accuracy: 0.4522 - val_loss: 3.2217 - val_categorical_accuracy: 0.1633\n",
      "Epoch 767/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3486 - categorical_accuracy: 0.4489 - val_loss: 3.2480 - val_categorical_accuracy: 0.1567\n",
      "Epoch 768/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3403 - categorical_accuracy: 0.4533 - val_loss: 3.2785 - val_categorical_accuracy: 0.1533\n",
      "Epoch 769/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3434 - categorical_accuracy: 0.4522 - val_loss: 3.2870 - val_categorical_accuracy: 0.1633\n",
      "Epoch 770/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3394 - categorical_accuracy: 0.4494 - val_loss: 3.2759 - val_categorical_accuracy: 0.1667\n",
      "Epoch 771/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3435 - categorical_accuracy: 0.4494 - val_loss: 3.2929 - val_categorical_accuracy: 0.1633\n",
      "Epoch 772/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3434 - categorical_accuracy: 0.4472 - val_loss: 3.2773 - val_categorical_accuracy: 0.1600\n",
      "Epoch 773/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3398 - categorical_accuracy: 0.4517 - val_loss: 3.2284 - val_categorical_accuracy: 0.1600\n",
      "Epoch 774/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3431 - categorical_accuracy: 0.4511 - val_loss: 3.2321 - val_categorical_accuracy: 0.1600\n",
      "Epoch 775/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3436 - categorical_accuracy: 0.4444 - val_loss: 3.3113 - val_categorical_accuracy: 0.1633\n",
      "Epoch 776/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3537 - categorical_accuracy: 0.4400 - val_loss: 3.1440 - val_categorical_accuracy: 0.1667\n",
      "Epoch 777/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3551 - categorical_accuracy: 0.4411 - val_loss: 3.2803 - val_categorical_accuracy: 0.1600\n",
      "Epoch 778/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3538 - categorical_accuracy: 0.4422 - val_loss: 3.2303 - val_categorical_accuracy: 0.1633\n",
      "Epoch 779/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3497 - categorical_accuracy: 0.4394 - val_loss: 3.2743 - val_categorical_accuracy: 0.1600\n",
      "Epoch 780/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3484 - categorical_accuracy: 0.4433 - val_loss: 3.1845 - val_categorical_accuracy: 0.1600\n",
      "Epoch 781/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3446 - categorical_accuracy: 0.4483 - val_loss: 3.2291 - val_categorical_accuracy: 0.1767\n",
      "Epoch 782/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3457 - categorical_accuracy: 0.4433 - val_loss: 3.1800 - val_categorical_accuracy: 0.1633\n",
      "Epoch 783/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3566 - categorical_accuracy: 0.4400 - val_loss: 3.1636 - val_categorical_accuracy: 0.1633\n",
      "Epoch 784/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3560 - categorical_accuracy: 0.4383 - val_loss: 3.1732 - val_categorical_accuracy: 0.1700\n",
      "Epoch 785/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3509 - categorical_accuracy: 0.4444 - val_loss: 3.1702 - val_categorical_accuracy: 0.1700\n",
      "Epoch 786/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3567 - categorical_accuracy: 0.4411 - val_loss: 3.1874 - val_categorical_accuracy: 0.1667\n",
      "Epoch 787/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3470 - categorical_accuracy: 0.4383 - val_loss: 3.2067 - val_categorical_accuracy: 0.1700\n",
      "Epoch 788/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3576 - categorical_accuracy: 0.4372 - val_loss: 3.1884 - val_categorical_accuracy: 0.1633\n",
      "Epoch 789/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3628 - categorical_accuracy: 0.4406 - val_loss: 3.1608 - val_categorical_accuracy: 0.1733\n",
      "Epoch 790/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3523 - categorical_accuracy: 0.4361 - val_loss: 3.2061 - val_categorical_accuracy: 0.1633\n",
      "Epoch 791/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3614 - categorical_accuracy: 0.4356 - val_loss: 3.1226 - val_categorical_accuracy: 0.1667\n",
      "Epoch 792/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3541 - categorical_accuracy: 0.4489 - val_loss: 3.1085 - val_categorical_accuracy: 0.1733\n",
      "Epoch 793/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3647 - categorical_accuracy: 0.4444 - val_loss: 3.0884 - val_categorical_accuracy: 0.1633\n",
      "Epoch 794/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3610 - categorical_accuracy: 0.4467 - val_loss: 3.2157 - val_categorical_accuracy: 0.1633\n",
      "Epoch 795/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3493 - categorical_accuracy: 0.4422 - val_loss: 3.1591 - val_categorical_accuracy: 0.1733\n",
      "Epoch 796/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3528 - categorical_accuracy: 0.4461 - val_loss: 3.2057 - val_categorical_accuracy: 0.1733\n",
      "Epoch 797/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3519 - categorical_accuracy: 0.4411 - val_loss: 3.1828 - val_categorical_accuracy: 0.1767\n",
      "Epoch 798/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3472 - categorical_accuracy: 0.4422 - val_loss: 3.2031 - val_categorical_accuracy: 0.1700\n",
      "Epoch 799/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3479 - categorical_accuracy: 0.4500 - val_loss: 3.1363 - val_categorical_accuracy: 0.1700\n",
      "Epoch 800/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3422 - categorical_accuracy: 0.4483 - val_loss: 3.1352 - val_categorical_accuracy: 0.1833\n",
      "Epoch 801/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3466 - categorical_accuracy: 0.4483 - val_loss: 3.1520 - val_categorical_accuracy: 0.1800\n",
      "Epoch 802/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3383 - categorical_accuracy: 0.4506 - val_loss: 3.1900 - val_categorical_accuracy: 0.1767\n",
      "Epoch 803/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3403 - categorical_accuracy: 0.4556 - val_loss: 3.1791 - val_categorical_accuracy: 0.1800\n",
      "Epoch 804/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3477 - categorical_accuracy: 0.4472 - val_loss: 3.1958 - val_categorical_accuracy: 0.1800\n",
      "Epoch 805/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3411 - categorical_accuracy: 0.4561 - val_loss: 3.1501 - val_categorical_accuracy: 0.1867\n",
      "Epoch 806/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3402 - categorical_accuracy: 0.4550 - val_loss: 3.1650 - val_categorical_accuracy: 0.1833\n",
      "Epoch 807/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3379 - categorical_accuracy: 0.4511 - val_loss: 3.2229 - val_categorical_accuracy: 0.1800\n",
      "Epoch 808/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3412 - categorical_accuracy: 0.4522 - val_loss: 3.2044 - val_categorical_accuracy: 0.1733\n",
      "Epoch 809/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3508 - categorical_accuracy: 0.4461 - val_loss: 3.1271 - val_categorical_accuracy: 0.1733\n",
      "Epoch 810/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3405 - categorical_accuracy: 0.4489 - val_loss: 3.2141 - val_categorical_accuracy: 0.1700\n",
      "Epoch 811/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3360 - categorical_accuracy: 0.4522 - val_loss: 3.1815 - val_categorical_accuracy: 0.1667\n",
      "Epoch 812/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3340 - categorical_accuracy: 0.4556 - val_loss: 3.2134 - val_categorical_accuracy: 0.1700\n",
      "Epoch 813/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3361 - categorical_accuracy: 0.4500 - val_loss: 3.1806 - val_categorical_accuracy: 0.1633\n",
      "Epoch 814/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3363 - categorical_accuracy: 0.4500 - val_loss: 3.2139 - val_categorical_accuracy: 0.1667\n",
      "Epoch 815/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3375 - categorical_accuracy: 0.4578 - val_loss: 3.2036 - val_categorical_accuracy: 0.1567\n",
      "Epoch 816/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3439 - categorical_accuracy: 0.4517 - val_loss: 3.2247 - val_categorical_accuracy: 0.1633\n",
      "Epoch 817/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3385 - categorical_accuracy: 0.4472 - val_loss: 3.2034 - val_categorical_accuracy: 0.1633\n",
      "Epoch 818/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3362 - categorical_accuracy: 0.4528 - val_loss: 3.1868 - val_categorical_accuracy: 0.1667\n",
      "Epoch 819/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3423 - categorical_accuracy: 0.4478 - val_loss: 3.2047 - val_categorical_accuracy: 0.1633\n",
      "Epoch 820/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3334 - categorical_accuracy: 0.4528 - val_loss: 3.1554 - val_categorical_accuracy: 0.1700\n",
      "Epoch 821/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3332 - categorical_accuracy: 0.4506 - val_loss: 3.2391 - val_categorical_accuracy: 0.1667\n",
      "Epoch 822/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3296 - categorical_accuracy: 0.4478 - val_loss: 3.2505 - val_categorical_accuracy: 0.1767\n",
      "Epoch 823/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3272 - categorical_accuracy: 0.4583 - val_loss: 3.2402 - val_categorical_accuracy: 0.1733\n",
      "Epoch 824/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3320 - categorical_accuracy: 0.4561 - val_loss: 3.2179 - val_categorical_accuracy: 0.1700\n",
      "Epoch 825/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3286 - categorical_accuracy: 0.4517 - val_loss: 3.2236 - val_categorical_accuracy: 0.1800\n",
      "Epoch 826/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3280 - categorical_accuracy: 0.4500 - val_loss: 3.2845 - val_categorical_accuracy: 0.1800\n",
      "Epoch 827/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3279 - categorical_accuracy: 0.4561 - val_loss: 3.2441 - val_categorical_accuracy: 0.1733\n",
      "Epoch 828/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3401 - categorical_accuracy: 0.4422 - val_loss: 3.2014 - val_categorical_accuracy: 0.1767\n",
      "Epoch 829/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3314 - categorical_accuracy: 0.4522 - val_loss: 3.3052 - val_categorical_accuracy: 0.1700\n",
      "Epoch 830/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3209 - categorical_accuracy: 0.4539 - val_loss: 3.2800 - val_categorical_accuracy: 0.1733\n",
      "Epoch 831/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3325 - categorical_accuracy: 0.4500 - val_loss: 3.3342 - val_categorical_accuracy: 0.1700\n",
      "Epoch 832/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3212 - categorical_accuracy: 0.4572 - val_loss: 3.3097 - val_categorical_accuracy: 0.1700\n",
      "Epoch 833/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3230 - categorical_accuracy: 0.4533 - val_loss: 3.3182 - val_categorical_accuracy: 0.1600\n",
      "Epoch 834/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3458 - categorical_accuracy: 0.4417 - val_loss: 3.2489 - val_categorical_accuracy: 0.1800\n",
      "Epoch 835/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3570 - categorical_accuracy: 0.4417 - val_loss: 3.3488 - val_categorical_accuracy: 0.1700\n",
      "Epoch 836/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3335 - categorical_accuracy: 0.4511 - val_loss: 3.2948 - val_categorical_accuracy: 0.1700\n",
      "Epoch 837/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3217 - categorical_accuracy: 0.4533 - val_loss: 3.3078 - val_categorical_accuracy: 0.1733\n",
      "Epoch 838/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3186 - categorical_accuracy: 0.4622 - val_loss: 3.3377 - val_categorical_accuracy: 0.1667\n",
      "Epoch 839/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3262 - categorical_accuracy: 0.4589 - val_loss: 3.4557 - val_categorical_accuracy: 0.1600\n",
      "Epoch 840/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3259 - categorical_accuracy: 0.4594 - val_loss: 3.2657 - val_categorical_accuracy: 0.1633\n",
      "Epoch 841/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3184 - categorical_accuracy: 0.4544 - val_loss: 3.3056 - val_categorical_accuracy: 0.1667\n",
      "Epoch 842/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3193 - categorical_accuracy: 0.4594 - val_loss: 3.3015 - val_categorical_accuracy: 0.1633\n",
      "Epoch 843/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3176 - categorical_accuracy: 0.4650 - val_loss: 3.3537 - val_categorical_accuracy: 0.1667\n",
      "Epoch 844/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3203 - categorical_accuracy: 0.4600 - val_loss: 3.3226 - val_categorical_accuracy: 0.1667\n",
      "Epoch 845/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3246 - categorical_accuracy: 0.4628 - val_loss: 3.3391 - val_categorical_accuracy: 0.1700\n",
      "Epoch 846/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3132 - categorical_accuracy: 0.4589 - val_loss: 3.3210 - val_categorical_accuracy: 0.1700\n",
      "Epoch 847/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3136 - categorical_accuracy: 0.4650 - val_loss: 3.3743 - val_categorical_accuracy: 0.1633\n",
      "Epoch 848/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3193 - categorical_accuracy: 0.4611 - val_loss: 3.4736 - val_categorical_accuracy: 0.1533\n",
      "Epoch 849/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3210 - categorical_accuracy: 0.4594 - val_loss: 3.3332 - val_categorical_accuracy: 0.1633\n",
      "Epoch 850/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3238 - categorical_accuracy: 0.4544 - val_loss: 3.2814 - val_categorical_accuracy: 0.1633\n",
      "Epoch 851/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3091 - categorical_accuracy: 0.4628 - val_loss: 3.3447 - val_categorical_accuracy: 0.1600\n",
      "Epoch 852/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3113 - categorical_accuracy: 0.4633 - val_loss: 3.3807 - val_categorical_accuracy: 0.1567\n",
      "Epoch 853/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3251 - categorical_accuracy: 0.4561 - val_loss: 3.3594 - val_categorical_accuracy: 0.1633\n",
      "Epoch 854/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3352 - categorical_accuracy: 0.4556 - val_loss: 3.3862 - val_categorical_accuracy: 0.1567\n",
      "Epoch 855/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3113 - categorical_accuracy: 0.4628 - val_loss: 3.2923 - val_categorical_accuracy: 0.1633\n",
      "Epoch 856/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3212 - categorical_accuracy: 0.4539 - val_loss: 3.4230 - val_categorical_accuracy: 0.1533\n",
      "Epoch 857/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3187 - categorical_accuracy: 0.4600 - val_loss: 3.3744 - val_categorical_accuracy: 0.1600\n",
      "Epoch 858/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3173 - categorical_accuracy: 0.4583 - val_loss: 3.4141 - val_categorical_accuracy: 0.1533\n",
      "Epoch 859/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3173 - categorical_accuracy: 0.4561 - val_loss: 3.4513 - val_categorical_accuracy: 0.1500\n",
      "Epoch 860/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3264 - categorical_accuracy: 0.4522 - val_loss: 3.3586 - val_categorical_accuracy: 0.1600\n",
      "Epoch 861/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3166 - categorical_accuracy: 0.4622 - val_loss: 3.4303 - val_categorical_accuracy: 0.1567\n",
      "Epoch 862/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3095 - categorical_accuracy: 0.4689 - val_loss: 3.4413 - val_categorical_accuracy: 0.1567\n",
      "Epoch 863/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3192 - categorical_accuracy: 0.4633 - val_loss: 3.2589 - val_categorical_accuracy: 0.1600\n",
      "Epoch 864/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3313 - categorical_accuracy: 0.4489 - val_loss: 3.3591 - val_categorical_accuracy: 0.1567\n",
      "Epoch 865/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3228 - categorical_accuracy: 0.4556 - val_loss: 3.4128 - val_categorical_accuracy: 0.1500\n",
      "Epoch 866/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3125 - categorical_accuracy: 0.4644 - val_loss: 3.3924 - val_categorical_accuracy: 0.1533\n",
      "Epoch 867/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3107 - categorical_accuracy: 0.4661 - val_loss: 3.4485 - val_categorical_accuracy: 0.1533\n",
      "Epoch 868/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3141 - categorical_accuracy: 0.4672 - val_loss: 3.3947 - val_categorical_accuracy: 0.1500\n",
      "Epoch 869/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3110 - categorical_accuracy: 0.4561 - val_loss: 3.4215 - val_categorical_accuracy: 0.1533\n",
      "Epoch 870/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3364 - categorical_accuracy: 0.4478 - val_loss: 3.5169 - val_categorical_accuracy: 0.1433\n",
      "Epoch 871/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3270 - categorical_accuracy: 0.4517 - val_loss: 3.4478 - val_categorical_accuracy: 0.1533\n",
      "Epoch 872/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3057 - categorical_accuracy: 0.4672 - val_loss: 3.2921 - val_categorical_accuracy: 0.1567\n",
      "Epoch 873/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3124 - categorical_accuracy: 0.4656 - val_loss: 3.3815 - val_categorical_accuracy: 0.1600\n",
      "Epoch 874/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3134 - categorical_accuracy: 0.4600 - val_loss: 3.4356 - val_categorical_accuracy: 0.1533\n",
      "Epoch 875/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3125 - categorical_accuracy: 0.4633 - val_loss: 3.3800 - val_categorical_accuracy: 0.1533\n",
      "Epoch 876/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3199 - categorical_accuracy: 0.4567 - val_loss: 3.4430 - val_categorical_accuracy: 0.1500\n",
      "Epoch 877/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3118 - categorical_accuracy: 0.4544 - val_loss: 3.4553 - val_categorical_accuracy: 0.1533\n",
      "Epoch 878/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3069 - categorical_accuracy: 0.4667 - val_loss: 3.3862 - val_categorical_accuracy: 0.1500\n",
      "Epoch 879/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3085 - categorical_accuracy: 0.4578 - val_loss: 3.3567 - val_categorical_accuracy: 0.1700\n",
      "Epoch 880/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3263 - categorical_accuracy: 0.4478 - val_loss: 3.3806 - val_categorical_accuracy: 0.1500\n",
      "Epoch 881/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3252 - categorical_accuracy: 0.4606 - val_loss: 3.4447 - val_categorical_accuracy: 0.1600\n",
      "Epoch 882/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3050 - categorical_accuracy: 0.4617 - val_loss: 3.4196 - val_categorical_accuracy: 0.1567\n",
      "Epoch 883/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3034 - categorical_accuracy: 0.4633 - val_loss: 3.3958 - val_categorical_accuracy: 0.1533\n",
      "Epoch 884/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3087 - categorical_accuracy: 0.4600 - val_loss: 3.3594 - val_categorical_accuracy: 0.1633\n",
      "Epoch 885/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3006 - categorical_accuracy: 0.4672 - val_loss: 3.4328 - val_categorical_accuracy: 0.1533\n",
      "Epoch 886/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3005 - categorical_accuracy: 0.4722 - val_loss: 3.3765 - val_categorical_accuracy: 0.1533\n",
      "Epoch 887/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3086 - categorical_accuracy: 0.4656 - val_loss: 3.3549 - val_categorical_accuracy: 0.1500\n",
      "Epoch 888/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3197 - categorical_accuracy: 0.4494 - val_loss: 3.3875 - val_categorical_accuracy: 0.1500\n",
      "Epoch 889/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3021 - categorical_accuracy: 0.4628 - val_loss: 3.4620 - val_categorical_accuracy: 0.1567\n",
      "Epoch 890/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2979 - categorical_accuracy: 0.4694 - val_loss: 3.4499 - val_categorical_accuracy: 0.1467\n",
      "Epoch 891/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3074 - categorical_accuracy: 0.4589 - val_loss: 3.4646 - val_categorical_accuracy: 0.1533\n",
      "Epoch 892/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3098 - categorical_accuracy: 0.4567 - val_loss: 3.3535 - val_categorical_accuracy: 0.1600\n",
      "Epoch 893/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3123 - categorical_accuracy: 0.4556 - val_loss: 3.4293 - val_categorical_accuracy: 0.1567\n",
      "Epoch 894/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3117 - categorical_accuracy: 0.4572 - val_loss: 3.3892 - val_categorical_accuracy: 0.1567\n",
      "Epoch 895/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3189 - categorical_accuracy: 0.4578 - val_loss: 3.3292 - val_categorical_accuracy: 0.1567\n",
      "Epoch 896/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3272 - categorical_accuracy: 0.4500 - val_loss: 3.3951 - val_categorical_accuracy: 0.1700\n",
      "Epoch 897/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3177 - categorical_accuracy: 0.4583 - val_loss: 3.4190 - val_categorical_accuracy: 0.1500\n",
      "Epoch 898/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3161 - categorical_accuracy: 0.4506 - val_loss: 3.4478 - val_categorical_accuracy: 0.1500\n",
      "Epoch 899/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3106 - categorical_accuracy: 0.4550 - val_loss: 3.3529 - val_categorical_accuracy: 0.1600\n",
      "Epoch 900/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3148 - categorical_accuracy: 0.4517 - val_loss: 3.3461 - val_categorical_accuracy: 0.1567\n",
      "Epoch 901/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3093 - categorical_accuracy: 0.4583 - val_loss: 3.3512 - val_categorical_accuracy: 0.1633\n",
      "Epoch 902/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3162 - categorical_accuracy: 0.4567 - val_loss: 3.4008 - val_categorical_accuracy: 0.1533\n",
      "Epoch 903/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3553 - categorical_accuracy: 0.4350 - val_loss: 3.4996 - val_categorical_accuracy: 0.1633\n",
      "Epoch 904/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3215 - categorical_accuracy: 0.4483 - val_loss: 3.3524 - val_categorical_accuracy: 0.1667\n",
      "Epoch 905/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3140 - categorical_accuracy: 0.4572 - val_loss: 3.3524 - val_categorical_accuracy: 0.1567\n",
      "Epoch 906/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3226 - categorical_accuracy: 0.4472 - val_loss: 3.3639 - val_categorical_accuracy: 0.1800\n",
      "Epoch 907/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3100 - categorical_accuracy: 0.4589 - val_loss: 3.3600 - val_categorical_accuracy: 0.1700\n",
      "Epoch 908/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3096 - categorical_accuracy: 0.4544 - val_loss: 3.3473 - val_categorical_accuracy: 0.1567\n",
      "Epoch 909/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3084 - categorical_accuracy: 0.4561 - val_loss: 3.4038 - val_categorical_accuracy: 0.1667\n",
      "Epoch 910/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3518 - categorical_accuracy: 0.4422 - val_loss: 3.3338 - val_categorical_accuracy: 0.1667\n",
      "Epoch 911/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3352 - categorical_accuracy: 0.4489 - val_loss: 3.3244 - val_categorical_accuracy: 0.1700\n",
      "Epoch 912/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3304 - categorical_accuracy: 0.4511 - val_loss: 3.3826 - val_categorical_accuracy: 0.1633\n",
      "Epoch 913/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3217 - categorical_accuracy: 0.4544 - val_loss: 3.3293 - val_categorical_accuracy: 0.1633\n",
      "Epoch 914/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3064 - categorical_accuracy: 0.4583 - val_loss: 3.3760 - val_categorical_accuracy: 0.1700\n",
      "Epoch 915/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3162 - categorical_accuracy: 0.4494 - val_loss: 3.4178 - val_categorical_accuracy: 0.1800\n",
      "Epoch 916/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3156 - categorical_accuracy: 0.4450 - val_loss: 3.3498 - val_categorical_accuracy: 0.1733\n",
      "Epoch 917/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3037 - categorical_accuracy: 0.4572 - val_loss: 3.3809 - val_categorical_accuracy: 0.1700\n",
      "Epoch 918/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3026 - categorical_accuracy: 0.4561 - val_loss: 3.4152 - val_categorical_accuracy: 0.1733\n",
      "Epoch 919/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3089 - categorical_accuracy: 0.4594 - val_loss: 3.3559 - val_categorical_accuracy: 0.1733\n",
      "Epoch 920/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3035 - categorical_accuracy: 0.4583 - val_loss: 3.3843 - val_categorical_accuracy: 0.1600\n",
      "Epoch 921/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3026 - categorical_accuracy: 0.4650 - val_loss: 3.4025 - val_categorical_accuracy: 0.1733\n",
      "Epoch 922/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3022 - categorical_accuracy: 0.4578 - val_loss: 3.4297 - val_categorical_accuracy: 0.1667\n",
      "Epoch 923/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3006 - categorical_accuracy: 0.4589 - val_loss: 3.4001 - val_categorical_accuracy: 0.1667\n",
      "Epoch 924/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3054 - categorical_accuracy: 0.4589 - val_loss: 3.3651 - val_categorical_accuracy: 0.1733\n",
      "Epoch 925/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3064 - categorical_accuracy: 0.4561 - val_loss: 3.4050 - val_categorical_accuracy: 0.1800\n",
      "Epoch 926/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3044 - categorical_accuracy: 0.4594 - val_loss: 3.3470 - val_categorical_accuracy: 0.1733\n",
      "Epoch 927/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3040 - categorical_accuracy: 0.4556 - val_loss: 3.3769 - val_categorical_accuracy: 0.1800\n",
      "Epoch 928/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3155 - categorical_accuracy: 0.4539 - val_loss: 3.4660 - val_categorical_accuracy: 0.1633\n",
      "Epoch 929/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3272 - categorical_accuracy: 0.4450 - val_loss: 3.3264 - val_categorical_accuracy: 0.1800\n",
      "Epoch 930/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3137 - categorical_accuracy: 0.4539 - val_loss: 3.3054 - val_categorical_accuracy: 0.1800\n",
      "Epoch 931/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3024 - categorical_accuracy: 0.4606 - val_loss: 3.3780 - val_categorical_accuracy: 0.1867\n",
      "Epoch 932/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3172 - categorical_accuracy: 0.4544 - val_loss: 3.4019 - val_categorical_accuracy: 0.1800\n",
      "Epoch 933/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3280 - categorical_accuracy: 0.4417 - val_loss: 3.4806 - val_categorical_accuracy: 0.1633\n",
      "Epoch 934/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3194 - categorical_accuracy: 0.4539 - val_loss: 3.4480 - val_categorical_accuracy: 0.1667\n",
      "Epoch 935/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3103 - categorical_accuracy: 0.4533 - val_loss: 3.3574 - val_categorical_accuracy: 0.1800\n",
      "Epoch 936/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2965 - categorical_accuracy: 0.4650 - val_loss: 3.4643 - val_categorical_accuracy: 0.1833\n",
      "Epoch 937/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3018 - categorical_accuracy: 0.4567 - val_loss: 3.3761 - val_categorical_accuracy: 0.1767\n",
      "Epoch 938/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2914 - categorical_accuracy: 0.4589 - val_loss: 3.4154 - val_categorical_accuracy: 0.1833\n",
      "Epoch 939/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3002 - categorical_accuracy: 0.4683 - val_loss: 3.3384 - val_categorical_accuracy: 0.1900\n",
      "Epoch 940/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2895 - categorical_accuracy: 0.4628 - val_loss: 3.3708 - val_categorical_accuracy: 0.1767\n",
      "Epoch 941/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2970 - categorical_accuracy: 0.4567 - val_loss: 3.4049 - val_categorical_accuracy: 0.1733\n",
      "Epoch 942/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2990 - categorical_accuracy: 0.4628 - val_loss: 3.3558 - val_categorical_accuracy: 0.1767\n",
      "Epoch 943/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2957 - categorical_accuracy: 0.4650 - val_loss: 3.4102 - val_categorical_accuracy: 0.1633\n",
      "Epoch 944/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3038 - categorical_accuracy: 0.4678 - val_loss: 3.4584 - val_categorical_accuracy: 0.1767\n",
      "Epoch 945/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3374 - categorical_accuracy: 0.4461 - val_loss: 3.4093 - val_categorical_accuracy: 0.1767\n",
      "Epoch 946/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3151 - categorical_accuracy: 0.4544 - val_loss: 3.4248 - val_categorical_accuracy: 0.1733\n",
      "Epoch 947/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3045 - categorical_accuracy: 0.4511 - val_loss: 3.3994 - val_categorical_accuracy: 0.1633\n",
      "Epoch 948/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2989 - categorical_accuracy: 0.4650 - val_loss: 3.3344 - val_categorical_accuracy: 0.1867\n",
      "Epoch 949/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3105 - categorical_accuracy: 0.4561 - val_loss: 3.3911 - val_categorical_accuracy: 0.1800\n",
      "Epoch 950/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2989 - categorical_accuracy: 0.4550 - val_loss: 3.4217 - val_categorical_accuracy: 0.1733\n",
      "Epoch 951/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2885 - categorical_accuracy: 0.4611 - val_loss: 3.4820 - val_categorical_accuracy: 0.1633\n",
      "Epoch 952/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2980 - categorical_accuracy: 0.4572 - val_loss: 3.3530 - val_categorical_accuracy: 0.1733\n",
      "Epoch 953/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2958 - categorical_accuracy: 0.4589 - val_loss: 3.3752 - val_categorical_accuracy: 0.1800\n",
      "Epoch 954/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2911 - categorical_accuracy: 0.4606 - val_loss: 3.3947 - val_categorical_accuracy: 0.1800\n",
      "Epoch 955/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2932 - categorical_accuracy: 0.4656 - val_loss: 3.3599 - val_categorical_accuracy: 0.1867\n",
      "Epoch 956/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3031 - categorical_accuracy: 0.4617 - val_loss: 3.3704 - val_categorical_accuracy: 0.1667\n",
      "Epoch 957/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3122 - categorical_accuracy: 0.4606 - val_loss: 3.5133 - val_categorical_accuracy: 0.1767\n",
      "Epoch 958/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3106 - categorical_accuracy: 0.4500 - val_loss: 3.3917 - val_categorical_accuracy: 0.1800\n",
      "Epoch 959/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2962 - categorical_accuracy: 0.4706 - val_loss: 3.3444 - val_categorical_accuracy: 0.1900\n",
      "Epoch 960/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2913 - categorical_accuracy: 0.4578 - val_loss: 3.3920 - val_categorical_accuracy: 0.1800\n",
      "Epoch 961/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2955 - categorical_accuracy: 0.4633 - val_loss: 3.4909 - val_categorical_accuracy: 0.1733\n",
      "Epoch 962/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2919 - categorical_accuracy: 0.4589 - val_loss: 3.4592 - val_categorical_accuracy: 0.1867\n",
      "Epoch 963/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2872 - categorical_accuracy: 0.4661 - val_loss: 3.4661 - val_categorical_accuracy: 0.1667\n",
      "Epoch 964/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2809 - categorical_accuracy: 0.4689 - val_loss: 3.4168 - val_categorical_accuracy: 0.1867\n",
      "Epoch 965/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2881 - categorical_accuracy: 0.4661 - val_loss: 3.3946 - val_categorical_accuracy: 0.1667\n",
      "Epoch 966/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2882 - categorical_accuracy: 0.4667 - val_loss: 3.3213 - val_categorical_accuracy: 0.1833\n",
      "Epoch 967/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2910 - categorical_accuracy: 0.4706 - val_loss: 3.3609 - val_categorical_accuracy: 0.1900\n",
      "Epoch 968/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2857 - categorical_accuracy: 0.4689 - val_loss: 3.3358 - val_categorical_accuracy: 0.1900\n",
      "Epoch 969/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2893 - categorical_accuracy: 0.4711 - val_loss: 3.3571 - val_categorical_accuracy: 0.1900\n",
      "Epoch 970/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2873 - categorical_accuracy: 0.4689 - val_loss: 3.3030 - val_categorical_accuracy: 0.2033\n",
      "Epoch 971/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2885 - categorical_accuracy: 0.4672 - val_loss: 3.3969 - val_categorical_accuracy: 0.1933\n",
      "Epoch 972/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2882 - categorical_accuracy: 0.4683 - val_loss: 3.4170 - val_categorical_accuracy: 0.1833\n",
      "Epoch 973/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2929 - categorical_accuracy: 0.4806 - val_loss: 3.4811 - val_categorical_accuracy: 0.1800\n",
      "Epoch 974/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2823 - categorical_accuracy: 0.4694 - val_loss: 3.4282 - val_categorical_accuracy: 0.1867\n",
      "Epoch 975/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3114 - categorical_accuracy: 0.4539 - val_loss: 3.3127 - val_categorical_accuracy: 0.1900\n",
      "Epoch 976/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3564 - categorical_accuracy: 0.4472 - val_loss: 3.4374 - val_categorical_accuracy: 0.1933\n",
      "Epoch 977/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3222 - categorical_accuracy: 0.4467 - val_loss: 3.3910 - val_categorical_accuracy: 0.1900\n",
      "Epoch 978/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3016 - categorical_accuracy: 0.4678 - val_loss: 3.4355 - val_categorical_accuracy: 0.1867\n",
      "Epoch 979/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2862 - categorical_accuracy: 0.4756 - val_loss: 3.4126 - val_categorical_accuracy: 0.1833\n",
      "Epoch 980/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2848 - categorical_accuracy: 0.4700 - val_loss: 3.3978 - val_categorical_accuracy: 0.1833\n",
      "Epoch 981/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3064 - categorical_accuracy: 0.4622 - val_loss: 3.3610 - val_categorical_accuracy: 0.1833\n",
      "Epoch 982/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2886 - categorical_accuracy: 0.4656 - val_loss: 3.3091 - val_categorical_accuracy: 0.1867\n",
      "Epoch 983/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2885 - categorical_accuracy: 0.4672 - val_loss: 3.3536 - val_categorical_accuracy: 0.1900\n",
      "Epoch 984/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2830 - categorical_accuracy: 0.4728 - val_loss: 3.3167 - val_categorical_accuracy: 0.1967\n",
      "Epoch 985/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2930 - categorical_accuracy: 0.4706 - val_loss: 3.4241 - val_categorical_accuracy: 0.1833\n",
      "Epoch 986/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2854 - categorical_accuracy: 0.4644 - val_loss: 3.3561 - val_categorical_accuracy: 0.1933\n",
      "Epoch 987/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2877 - categorical_accuracy: 0.4689 - val_loss: 3.3466 - val_categorical_accuracy: 0.1867\n",
      "Epoch 988/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2997 - categorical_accuracy: 0.4650 - val_loss: 3.4795 - val_categorical_accuracy: 0.1633\n",
      "Epoch 989/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2928 - categorical_accuracy: 0.4583 - val_loss: 3.3510 - val_categorical_accuracy: 0.1867\n",
      "Epoch 990/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2984 - categorical_accuracy: 0.4661 - val_loss: 3.5028 - val_categorical_accuracy: 0.1767\n",
      "Epoch 991/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3027 - categorical_accuracy: 0.4544 - val_loss: 3.4524 - val_categorical_accuracy: 0.1833\n",
      "Epoch 992/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2987 - categorical_accuracy: 0.4689 - val_loss: 3.3487 - val_categorical_accuracy: 0.1867\n",
      "Epoch 993/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2957 - categorical_accuracy: 0.4667 - val_loss: 3.3578 - val_categorical_accuracy: 0.1833\n",
      "Epoch 994/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2785 - categorical_accuracy: 0.4789 - val_loss: 3.3497 - val_categorical_accuracy: 0.1933\n",
      "Epoch 995/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3021 - categorical_accuracy: 0.4650 - val_loss: 3.3597 - val_categorical_accuracy: 0.1867\n",
      "Epoch 996/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3036 - categorical_accuracy: 0.4661 - val_loss: 3.2871 - val_categorical_accuracy: 0.1933\n",
      "Epoch 997/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3373 - categorical_accuracy: 0.4472 - val_loss: 3.4907 - val_categorical_accuracy: 0.1833\n",
      "Epoch 998/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2983 - categorical_accuracy: 0.4656 - val_loss: 3.4352 - val_categorical_accuracy: 0.1833\n",
      "Epoch 999/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2892 - categorical_accuracy: 0.4672 - val_loss: 3.3967 - val_categorical_accuracy: 0.1967\n",
      "Epoch 1000/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2838 - categorical_accuracy: 0.4717 - val_loss: 3.3724 - val_categorical_accuracy: 0.1933\n",
      "Epoch 1001/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2982 - categorical_accuracy: 0.4644 - val_loss: 3.3274 - val_categorical_accuracy: 0.2000\n",
      "Epoch 1002/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3110 - categorical_accuracy: 0.4594 - val_loss: 3.3214 - val_categorical_accuracy: 0.2067\n",
      "Epoch 1003/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3054 - categorical_accuracy: 0.4650 - val_loss: 3.4233 - val_categorical_accuracy: 0.1900\n",
      "Epoch 1004/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3176 - categorical_accuracy: 0.4567 - val_loss: 3.3830 - val_categorical_accuracy: 0.1967\n",
      "Epoch 1005/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3064 - categorical_accuracy: 0.4567 - val_loss: 3.3942 - val_categorical_accuracy: 0.1867\n",
      "Epoch 1006/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3125 - categorical_accuracy: 0.4544 - val_loss: 3.3685 - val_categorical_accuracy: 0.1900\n",
      "Epoch 1007/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2995 - categorical_accuracy: 0.4628 - val_loss: 3.4147 - val_categorical_accuracy: 0.1933\n",
      "Epoch 1008/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3007 - categorical_accuracy: 0.4667 - val_loss: 3.3376 - val_categorical_accuracy: 0.2000\n",
      "Epoch 1009/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3080 - categorical_accuracy: 0.4644 - val_loss: 3.3728 - val_categorical_accuracy: 0.1933\n",
      "Epoch 1010/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3022 - categorical_accuracy: 0.4661 - val_loss: 3.4216 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1011/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3195 - categorical_accuracy: 0.4567 - val_loss: 3.3717 - val_categorical_accuracy: 0.1900\n",
      "Epoch 1012/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3270 - categorical_accuracy: 0.4539 - val_loss: 3.4038 - val_categorical_accuracy: 0.1867\n",
      "Epoch 1013/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2919 - categorical_accuracy: 0.4700 - val_loss: 3.4185 - val_categorical_accuracy: 0.1867\n",
      "Epoch 1014/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3008 - categorical_accuracy: 0.4644 - val_loss: 3.4391 - val_categorical_accuracy: 0.1833\n",
      "Epoch 1015/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2980 - categorical_accuracy: 0.4683 - val_loss: 3.4305 - val_categorical_accuracy: 0.1867\n",
      "Epoch 1016/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3006 - categorical_accuracy: 0.4606 - val_loss: 3.4874 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1017/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3017 - categorical_accuracy: 0.4544 - val_loss: 3.4516 - val_categorical_accuracy: 0.1833\n",
      "Epoch 1018/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3280 - categorical_accuracy: 0.4483 - val_loss: 3.4407 - val_categorical_accuracy: 0.1867\n",
      "Epoch 1019/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3216 - categorical_accuracy: 0.4456 - val_loss: 3.4435 - val_categorical_accuracy: 0.1800\n",
      "Epoch 1020/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3164 - categorical_accuracy: 0.4589 - val_loss: 3.4792 - val_categorical_accuracy: 0.1833\n",
      "Epoch 1021/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3097 - categorical_accuracy: 0.4489 - val_loss: 3.3964 - val_categorical_accuracy: 0.1800\n",
      "Epoch 1022/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3045 - categorical_accuracy: 0.4567 - val_loss: 3.4983 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1023/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3058 - categorical_accuracy: 0.4544 - val_loss: 3.5554 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1024/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3185 - categorical_accuracy: 0.4467 - val_loss: 3.4985 - val_categorical_accuracy: 0.1933\n",
      "Epoch 1025/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3200 - categorical_accuracy: 0.4483 - val_loss: 3.5895 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1026/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3043 - categorical_accuracy: 0.4594 - val_loss: 3.5378 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1027/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2984 - categorical_accuracy: 0.4589 - val_loss: 3.5094 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1028/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3024 - categorical_accuracy: 0.4589 - val_loss: 3.5500 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1029/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3226 - categorical_accuracy: 0.4461 - val_loss: 3.5391 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1030/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3148 - categorical_accuracy: 0.4567 - val_loss: 3.5269 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1031/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3151 - categorical_accuracy: 0.4556 - val_loss: 3.5001 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1032/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3073 - categorical_accuracy: 0.4561 - val_loss: 3.4795 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1033/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3103 - categorical_accuracy: 0.4578 - val_loss: 3.5418 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1034/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3403 - categorical_accuracy: 0.4472 - val_loss: 3.5036 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1035/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3234 - categorical_accuracy: 0.4500 - val_loss: 3.3865 - val_categorical_accuracy: 0.1867\n",
      "Epoch 1036/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3133 - categorical_accuracy: 0.4567 - val_loss: 3.5063 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1037/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3068 - categorical_accuracy: 0.4544 - val_loss: 3.4174 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1038/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3132 - categorical_accuracy: 0.4556 - val_loss: 3.6140 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1039/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3136 - categorical_accuracy: 0.4500 - val_loss: 3.4736 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1040/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3131 - categorical_accuracy: 0.4578 - val_loss: 3.5515 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1041/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3108 - categorical_accuracy: 0.4644 - val_loss: 3.5047 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1042/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3147 - categorical_accuracy: 0.4556 - val_loss: 3.5322 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1043/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3092 - categorical_accuracy: 0.4672 - val_loss: 3.5157 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1044/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3159 - categorical_accuracy: 0.4533 - val_loss: 3.5220 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1045/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3113 - categorical_accuracy: 0.4606 - val_loss: 3.5109 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1046/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3112 - categorical_accuracy: 0.4583 - val_loss: 3.4846 - val_categorical_accuracy: 0.1900\n",
      "Epoch 1047/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3200 - categorical_accuracy: 0.4589 - val_loss: 3.5553 - val_categorical_accuracy: 0.1867\n",
      "Epoch 1048/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3163 - categorical_accuracy: 0.4561 - val_loss: 3.4243 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1049/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3191 - categorical_accuracy: 0.4489 - val_loss: 3.5721 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1050/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3430 - categorical_accuracy: 0.4478 - val_loss: 3.6549 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1051/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3040 - categorical_accuracy: 0.4639 - val_loss: 3.5347 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1052/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3054 - categorical_accuracy: 0.4533 - val_loss: 3.5672 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1053/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3045 - categorical_accuracy: 0.4639 - val_loss: 3.5693 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1054/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3078 - categorical_accuracy: 0.4611 - val_loss: 3.5033 - val_categorical_accuracy: 0.1800\n",
      "Epoch 1055/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3097 - categorical_accuracy: 0.4528 - val_loss: 3.6224 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1056/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3052 - categorical_accuracy: 0.4667 - val_loss: 3.5569 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1057/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3165 - categorical_accuracy: 0.4522 - val_loss: 3.5014 - val_categorical_accuracy: 0.1800\n",
      "Epoch 1058/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3597 - categorical_accuracy: 0.4372 - val_loss: 3.5301 - val_categorical_accuracy: 0.1800\n",
      "Epoch 1059/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3306 - categorical_accuracy: 0.4589 - val_loss: 3.6139 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1060/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3079 - categorical_accuracy: 0.4556 - val_loss: 3.5098 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1061/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3226 - categorical_accuracy: 0.4583 - val_loss: 3.5323 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1062/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3410 - categorical_accuracy: 0.4478 - val_loss: 3.7417 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1063/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3370 - categorical_accuracy: 0.4561 - val_loss: 3.5808 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1064/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3157 - categorical_accuracy: 0.4544 - val_loss: 3.7161 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1065/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3058 - categorical_accuracy: 0.4617 - val_loss: 3.7241 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1066/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2980 - categorical_accuracy: 0.4611 - val_loss: 3.6891 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1067/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3003 - categorical_accuracy: 0.4572 - val_loss: 3.5533 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1068/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3059 - categorical_accuracy: 0.4628 - val_loss: 3.6263 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1069/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3110 - categorical_accuracy: 0.4600 - val_loss: 3.6661 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1070/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3132 - categorical_accuracy: 0.4567 - val_loss: 3.7075 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1071/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3053 - categorical_accuracy: 0.4533 - val_loss: 3.6508 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1072/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3057 - categorical_accuracy: 0.4656 - val_loss: 3.6696 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1073/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3036 - categorical_accuracy: 0.4611 - val_loss: 3.5128 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1074/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3003 - categorical_accuracy: 0.4783 - val_loss: 3.5363 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1075/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3107 - categorical_accuracy: 0.4650 - val_loss: 3.5214 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1076/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3082 - categorical_accuracy: 0.4589 - val_loss: 3.6597 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1077/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3148 - categorical_accuracy: 0.4606 - val_loss: 3.6419 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1078/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3233 - categorical_accuracy: 0.4539 - val_loss: 3.7441 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1079/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3052 - categorical_accuracy: 0.4633 - val_loss: 3.6961 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1080/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3224 - categorical_accuracy: 0.4556 - val_loss: 3.6564 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1081/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3038 - categorical_accuracy: 0.4672 - val_loss: 3.6028 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1082/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3014 - categorical_accuracy: 0.4661 - val_loss: 3.6048 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1083/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3185 - categorical_accuracy: 0.4606 - val_loss: 3.5598 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1084/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3130 - categorical_accuracy: 0.4700 - val_loss: 3.7170 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1085/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2971 - categorical_accuracy: 0.4706 - val_loss: 3.6344 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1086/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3190 - categorical_accuracy: 0.4522 - val_loss: 3.5576 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1087/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3206 - categorical_accuracy: 0.4600 - val_loss: 3.5391 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1088/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3096 - categorical_accuracy: 0.4572 - val_loss: 3.5866 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1089/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3038 - categorical_accuracy: 0.4611 - val_loss: 3.6094 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1090/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3236 - categorical_accuracy: 0.4606 - val_loss: 3.6473 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1091/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3026 - categorical_accuracy: 0.4656 - val_loss: 3.6785 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1092/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3020 - categorical_accuracy: 0.4728 - val_loss: 3.5972 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1093/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3066 - categorical_accuracy: 0.4656 - val_loss: 3.6193 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1094/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3159 - categorical_accuracy: 0.4617 - val_loss: 3.5942 - val_categorical_accuracy: 0.1833\n",
      "Epoch 1095/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3214 - categorical_accuracy: 0.4622 - val_loss: 3.6414 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1096/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3039 - categorical_accuracy: 0.4694 - val_loss: 3.6473 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1097/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3346 - categorical_accuracy: 0.4561 - val_loss: 3.6597 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1098/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3179 - categorical_accuracy: 0.4528 - val_loss: 3.7141 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1099/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3059 - categorical_accuracy: 0.4556 - val_loss: 3.7135 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1100/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3017 - categorical_accuracy: 0.4700 - val_loss: 3.5606 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1101/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3010 - categorical_accuracy: 0.4650 - val_loss: 3.6209 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1102/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2984 - categorical_accuracy: 0.4656 - val_loss: 3.6875 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1103/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2981 - categorical_accuracy: 0.4700 - val_loss: 3.6654 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1104/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2980 - categorical_accuracy: 0.4606 - val_loss: 3.6638 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1105/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2994 - categorical_accuracy: 0.4667 - val_loss: 3.6361 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1106/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3003 - categorical_accuracy: 0.4661 - val_loss: 3.5524 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1107/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2988 - categorical_accuracy: 0.4717 - val_loss: 3.6260 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1108/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3031 - categorical_accuracy: 0.4633 - val_loss: 3.6049 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1109/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3007 - categorical_accuracy: 0.4644 - val_loss: 3.6833 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1110/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2959 - categorical_accuracy: 0.4700 - val_loss: 3.5613 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1111/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3006 - categorical_accuracy: 0.4678 - val_loss: 3.6130 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1112/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2895 - categorical_accuracy: 0.4717 - val_loss: 3.5449 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1113/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2945 - categorical_accuracy: 0.4733 - val_loss: 3.5684 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1114/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2959 - categorical_accuracy: 0.4756 - val_loss: 3.5501 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1115/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3057 - categorical_accuracy: 0.4667 - val_loss: 3.5463 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1116/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3077 - categorical_accuracy: 0.4650 - val_loss: 3.4620 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1117/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3081 - categorical_accuracy: 0.4600 - val_loss: 3.5708 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1118/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3101 - categorical_accuracy: 0.4528 - val_loss: 3.4473 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1119/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3054 - categorical_accuracy: 0.4594 - val_loss: 3.4638 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1120/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3065 - categorical_accuracy: 0.4644 - val_loss: 3.4759 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1121/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2972 - categorical_accuracy: 0.4694 - val_loss: 3.5919 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1122/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3139 - categorical_accuracy: 0.4594 - val_loss: 3.5777 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1123/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3060 - categorical_accuracy: 0.4578 - val_loss: 3.5506 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1124/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3271 - categorical_accuracy: 0.4622 - val_loss: 3.5892 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1125/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3379 - categorical_accuracy: 0.4517 - val_loss: 3.5863 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1126/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3142 - categorical_accuracy: 0.4572 - val_loss: 3.5265 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1127/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3208 - categorical_accuracy: 0.4522 - val_loss: 3.5360 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1128/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3084 - categorical_accuracy: 0.4611 - val_loss: 3.5670 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1129/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2900 - categorical_accuracy: 0.4711 - val_loss: 3.6132 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1130/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2895 - categorical_accuracy: 0.4700 - val_loss: 3.5934 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1131/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2940 - categorical_accuracy: 0.4706 - val_loss: 3.5948 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1132/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2944 - categorical_accuracy: 0.4700 - val_loss: 3.5862 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1133/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2887 - categorical_accuracy: 0.4722 - val_loss: 3.6581 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1134/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2860 - categorical_accuracy: 0.4733 - val_loss: 3.6746 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1135/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3065 - categorical_accuracy: 0.4611 - val_loss: 3.5447 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1136/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2907 - categorical_accuracy: 0.4672 - val_loss: 3.6398 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1137/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3032 - categorical_accuracy: 0.4689 - val_loss: 3.5449 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1138/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3071 - categorical_accuracy: 0.4611 - val_loss: 3.6873 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1139/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3100 - categorical_accuracy: 0.4772 - val_loss: 3.6284 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1140/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3062 - categorical_accuracy: 0.4678 - val_loss: 3.6134 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1141/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3257 - categorical_accuracy: 0.4489 - val_loss: 3.6484 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1142/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3093 - categorical_accuracy: 0.4622 - val_loss: 3.6236 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1143/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2985 - categorical_accuracy: 0.4639 - val_loss: 3.6546 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1144/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3018 - categorical_accuracy: 0.4694 - val_loss: 3.6325 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1145/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3060 - categorical_accuracy: 0.4683 - val_loss: 3.6303 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1146/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2964 - categorical_accuracy: 0.4628 - val_loss: 3.6578 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1147/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2898 - categorical_accuracy: 0.4744 - val_loss: 3.5780 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1148/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2878 - categorical_accuracy: 0.4783 - val_loss: 3.6335 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1149/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2993 - categorical_accuracy: 0.4656 - val_loss: 3.5892 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1150/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3111 - categorical_accuracy: 0.4622 - val_loss: 3.5493 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1151/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3052 - categorical_accuracy: 0.4611 - val_loss: 3.6378 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1152/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3178 - categorical_accuracy: 0.4600 - val_loss: 3.6876 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1153/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3129 - categorical_accuracy: 0.4639 - val_loss: 3.6828 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1154/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3256 - categorical_accuracy: 0.4483 - val_loss: 3.5759 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1155/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2928 - categorical_accuracy: 0.4706 - val_loss: 3.6952 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1156/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2840 - categorical_accuracy: 0.4794 - val_loss: 3.6690 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1157/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2894 - categorical_accuracy: 0.4783 - val_loss: 3.7014 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1158/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3041 - categorical_accuracy: 0.4722 - val_loss: 3.6531 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1159/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3421 - categorical_accuracy: 0.4544 - val_loss: 3.4785 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1160/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3251 - categorical_accuracy: 0.4572 - val_loss: 3.6929 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1161/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3039 - categorical_accuracy: 0.4639 - val_loss: 3.6736 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1162/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3041 - categorical_accuracy: 0.4678 - val_loss: 3.7451 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1163/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3009 - categorical_accuracy: 0.4644 - val_loss: 3.6604 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1164/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2967 - categorical_accuracy: 0.4694 - val_loss: 3.6573 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1165/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3181 - categorical_accuracy: 0.4539 - val_loss: 3.5150 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1166/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2986 - categorical_accuracy: 0.4572 - val_loss: 3.5374 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1167/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3099 - categorical_accuracy: 0.4600 - val_loss: 3.7232 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1168/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3013 - categorical_accuracy: 0.4600 - val_loss: 3.7429 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1169/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3083 - categorical_accuracy: 0.4689 - val_loss: 3.7705 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1170/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3007 - categorical_accuracy: 0.4689 - val_loss: 3.6898 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1171/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2929 - categorical_accuracy: 0.4678 - val_loss: 3.7085 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1172/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2977 - categorical_accuracy: 0.4667 - val_loss: 3.6405 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1173/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2966 - categorical_accuracy: 0.4767 - val_loss: 3.6933 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1174/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2867 - categorical_accuracy: 0.4639 - val_loss: 3.6159 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1175/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2998 - categorical_accuracy: 0.4672 - val_loss: 3.6828 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1176/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3246 - categorical_accuracy: 0.4567 - val_loss: 3.7341 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1177/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3206 - categorical_accuracy: 0.4583 - val_loss: 3.8096 - val_categorical_accuracy: 0.1300\n",
      "Epoch 1178/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2989 - categorical_accuracy: 0.4644 - val_loss: 3.6518 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1179/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2919 - categorical_accuracy: 0.4706 - val_loss: 3.7465 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1180/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3016 - categorical_accuracy: 0.4667 - val_loss: 3.6920 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1181/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3072 - categorical_accuracy: 0.4500 - val_loss: 3.6313 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1182/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2865 - categorical_accuracy: 0.4806 - val_loss: 3.6425 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1183/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3030 - categorical_accuracy: 0.4567 - val_loss: 3.5498 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1184/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3037 - categorical_accuracy: 0.4650 - val_loss: 3.6748 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1185/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3126 - categorical_accuracy: 0.4572 - val_loss: 3.6753 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1186/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3071 - categorical_accuracy: 0.4672 - val_loss: 3.7603 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1187/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2959 - categorical_accuracy: 0.4650 - val_loss: 3.7223 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1188/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2848 - categorical_accuracy: 0.4706 - val_loss: 3.6749 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1189/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2961 - categorical_accuracy: 0.4683 - val_loss: 3.6068 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1190/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2949 - categorical_accuracy: 0.4661 - val_loss: 3.6858 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1191/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2850 - categorical_accuracy: 0.4761 - val_loss: 3.7372 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1192/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2814 - categorical_accuracy: 0.4772 - val_loss: 3.6964 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1193/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2773 - categorical_accuracy: 0.4739 - val_loss: 3.6459 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1194/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2923 - categorical_accuracy: 0.4756 - val_loss: 3.6790 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1195/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2809 - categorical_accuracy: 0.4717 - val_loss: 3.6845 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1196/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2895 - categorical_accuracy: 0.4700 - val_loss: 3.7232 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1197/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2840 - categorical_accuracy: 0.4706 - val_loss: 3.7243 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1198/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2772 - categorical_accuracy: 0.4728 - val_loss: 3.6958 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1199/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3014 - categorical_accuracy: 0.4672 - val_loss: 3.6634 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1200/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3134 - categorical_accuracy: 0.4583 - val_loss: 3.7574 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1201/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3281 - categorical_accuracy: 0.4533 - val_loss: 3.7624 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1202/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3124 - categorical_accuracy: 0.4644 - val_loss: 3.7206 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1203/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2945 - categorical_accuracy: 0.4650 - val_loss: 3.7440 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1204/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2882 - categorical_accuracy: 0.4756 - val_loss: 3.7664 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1205/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2916 - categorical_accuracy: 0.4700 - val_loss: 3.7439 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1206/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3123 - categorical_accuracy: 0.4644 - val_loss: 3.7972 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1207/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3068 - categorical_accuracy: 0.4678 - val_loss: 3.6597 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1208/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3121 - categorical_accuracy: 0.4617 - val_loss: 3.7338 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1209/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2948 - categorical_accuracy: 0.4700 - val_loss: 3.7580 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1210/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2839 - categorical_accuracy: 0.4744 - val_loss: 3.7333 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1211/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3231 - categorical_accuracy: 0.4561 - val_loss: 3.6902 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1212/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2856 - categorical_accuracy: 0.4722 - val_loss: 3.7742 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1213/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2998 - categorical_accuracy: 0.4728 - val_loss: 3.6768 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1214/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2841 - categorical_accuracy: 0.4761 - val_loss: 3.7973 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1215/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2893 - categorical_accuracy: 0.4783 - val_loss: 3.7165 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1216/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3120 - categorical_accuracy: 0.4628 - val_loss: 3.6461 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1217/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3041 - categorical_accuracy: 0.4661 - val_loss: 3.8094 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1218/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3601 - categorical_accuracy: 0.4389 - val_loss: 3.6142 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1219/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3452 - categorical_accuracy: 0.4489 - val_loss: 3.5714 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1220/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3493 - categorical_accuracy: 0.4433 - val_loss: 3.6484 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1221/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3126 - categorical_accuracy: 0.4583 - val_loss: 3.6158 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1222/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3066 - categorical_accuracy: 0.4633 - val_loss: 3.7830 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1223/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3273 - categorical_accuracy: 0.4500 - val_loss: 3.6459 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1224/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2958 - categorical_accuracy: 0.4667 - val_loss: 3.6376 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1225/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3059 - categorical_accuracy: 0.4639 - val_loss: 3.5747 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1226/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3195 - categorical_accuracy: 0.4589 - val_loss: 3.6831 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1227/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3149 - categorical_accuracy: 0.4467 - val_loss: 3.6851 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1228/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3144 - categorical_accuracy: 0.4494 - val_loss: 3.6744 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1229/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3200 - categorical_accuracy: 0.4600 - val_loss: 3.7104 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1230/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3259 - categorical_accuracy: 0.4567 - val_loss: 3.6414 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1231/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3011 - categorical_accuracy: 0.4644 - val_loss: 3.6801 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1232/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2869 - categorical_accuracy: 0.4678 - val_loss: 3.6300 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1233/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2998 - categorical_accuracy: 0.4617 - val_loss: 3.6618 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1234/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3173 - categorical_accuracy: 0.4611 - val_loss: 3.6412 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1235/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3045 - categorical_accuracy: 0.4600 - val_loss: 3.7623 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1236/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3144 - categorical_accuracy: 0.4550 - val_loss: 3.7029 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1237/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3049 - categorical_accuracy: 0.4689 - val_loss: 3.6311 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1238/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3062 - categorical_accuracy: 0.4544 - val_loss: 3.6850 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1239/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3008 - categorical_accuracy: 0.4594 - val_loss: 3.7069 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1240/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3321 - categorical_accuracy: 0.4539 - val_loss: 3.6429 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1241/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3416 - categorical_accuracy: 0.4433 - val_loss: 3.6290 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1242/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3262 - categorical_accuracy: 0.4400 - val_loss: 3.7617 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1243/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4271 - categorical_accuracy: 0.4200 - val_loss: 3.4834 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1244/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3436 - categorical_accuracy: 0.4328 - val_loss: 3.7219 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1245/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3219 - categorical_accuracy: 0.4561 - val_loss: 3.6630 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1246/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3104 - categorical_accuracy: 0.4533 - val_loss: 3.6289 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1247/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3286 - categorical_accuracy: 0.4506 - val_loss: 3.6697 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1248/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3325 - categorical_accuracy: 0.4444 - val_loss: 3.6871 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1249/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3111 - categorical_accuracy: 0.4644 - val_loss: 3.6814 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1250/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3030 - categorical_accuracy: 0.4594 - val_loss: 3.6911 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1251/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3125 - categorical_accuracy: 0.4528 - val_loss: 3.7189 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1252/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3280 - categorical_accuracy: 0.4444 - val_loss: 3.6754 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1253/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3385 - categorical_accuracy: 0.4456 - val_loss: 3.7192 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1254/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3362 - categorical_accuracy: 0.4400 - val_loss: 3.6041 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1255/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3271 - categorical_accuracy: 0.4528 - val_loss: 3.7233 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1256/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3470 - categorical_accuracy: 0.4378 - val_loss: 3.6235 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1257/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3228 - categorical_accuracy: 0.4500 - val_loss: 3.7070 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1258/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3060 - categorical_accuracy: 0.4567 - val_loss: 3.6922 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1259/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3100 - categorical_accuracy: 0.4544 - val_loss: 3.6549 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1260/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3103 - categorical_accuracy: 0.4594 - val_loss: 3.6427 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1261/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2976 - categorical_accuracy: 0.4650 - val_loss: 3.6841 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1262/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3037 - categorical_accuracy: 0.4622 - val_loss: 3.6477 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1263/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2953 - categorical_accuracy: 0.4694 - val_loss: 3.6980 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1264/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2953 - categorical_accuracy: 0.4706 - val_loss: 3.6439 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1265/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2934 - categorical_accuracy: 0.4678 - val_loss: 3.7173 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1266/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3017 - categorical_accuracy: 0.4639 - val_loss: 3.7149 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1267/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3080 - categorical_accuracy: 0.4561 - val_loss: 3.7125 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1268/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3097 - categorical_accuracy: 0.4617 - val_loss: 3.7222 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1269/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2991 - categorical_accuracy: 0.4617 - val_loss: 3.7061 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1270/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3081 - categorical_accuracy: 0.4561 - val_loss: 3.7639 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1271/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3020 - categorical_accuracy: 0.4611 - val_loss: 3.7567 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1272/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2997 - categorical_accuracy: 0.4633 - val_loss: 3.7278 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1273/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3032 - categorical_accuracy: 0.4622 - val_loss: 3.7251 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1274/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3158 - categorical_accuracy: 0.4572 - val_loss: 3.7137 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1275/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3683 - categorical_accuracy: 0.4267 - val_loss: 3.7028 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1276/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4046 - categorical_accuracy: 0.4228 - val_loss: 3.5960 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1277/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3686 - categorical_accuracy: 0.4250 - val_loss: 3.6382 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1278/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3902 - categorical_accuracy: 0.4178 - val_loss: 3.7273 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1279/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3460 - categorical_accuracy: 0.4406 - val_loss: 3.8581 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1280/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4096 - categorical_accuracy: 0.4206 - val_loss: 3.4470 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1281/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3692 - categorical_accuracy: 0.4228 - val_loss: 3.5313 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1282/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3368 - categorical_accuracy: 0.4350 - val_loss: 3.6502 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1283/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3928 - categorical_accuracy: 0.4056 - val_loss: 3.4535 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1284/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3467 - categorical_accuracy: 0.4367 - val_loss: 3.4978 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1285/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3283 - categorical_accuracy: 0.4372 - val_loss: 3.5602 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1286/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3375 - categorical_accuracy: 0.4372 - val_loss: 3.5532 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1287/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3374 - categorical_accuracy: 0.4367 - val_loss: 3.5826 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1288/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3272 - categorical_accuracy: 0.4439 - val_loss: 3.5953 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1289/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3182 - categorical_accuracy: 0.4428 - val_loss: 3.5567 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1290/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3173 - categorical_accuracy: 0.4489 - val_loss: 3.6025 - val_categorical_accuracy: 0.1900\n",
      "Epoch 1291/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3122 - categorical_accuracy: 0.4444 - val_loss: 3.5583 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1292/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3152 - categorical_accuracy: 0.4472 - val_loss: 3.6595 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1293/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3111 - categorical_accuracy: 0.4511 - val_loss: 3.5496 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1294/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3164 - categorical_accuracy: 0.4483 - val_loss: 3.6647 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1295/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3163 - categorical_accuracy: 0.4472 - val_loss: 3.6568 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1296/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3135 - categorical_accuracy: 0.4467 - val_loss: 3.6916 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1297/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3206 - categorical_accuracy: 0.4467 - val_loss: 3.7285 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1298/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3182 - categorical_accuracy: 0.4511 - val_loss: 3.6614 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1299/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3523 - categorical_accuracy: 0.4344 - val_loss: 3.5877 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1300/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3318 - categorical_accuracy: 0.4422 - val_loss: 3.5018 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1301/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3271 - categorical_accuracy: 0.4456 - val_loss: 3.7398 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1302/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3286 - categorical_accuracy: 0.4461 - val_loss: 3.5754 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1303/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3180 - categorical_accuracy: 0.4444 - val_loss: 3.6581 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1304/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3240 - categorical_accuracy: 0.4383 - val_loss: 3.5559 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1305/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3364 - categorical_accuracy: 0.4356 - val_loss: 3.4165 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1306/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3409 - categorical_accuracy: 0.4394 - val_loss: 3.6891 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1307/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3271 - categorical_accuracy: 0.4350 - val_loss: 3.6048 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1308/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3222 - categorical_accuracy: 0.4378 - val_loss: 3.6269 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1309/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3175 - categorical_accuracy: 0.4478 - val_loss: 3.6345 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1310/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3179 - categorical_accuracy: 0.4511 - val_loss: 3.6596 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1311/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3126 - categorical_accuracy: 0.4544 - val_loss: 3.6701 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1312/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3060 - categorical_accuracy: 0.4622 - val_loss: 3.7217 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1313/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3170 - categorical_accuracy: 0.4511 - val_loss: 3.6431 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1314/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3121 - categorical_accuracy: 0.4550 - val_loss: 3.6478 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1315/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3092 - categorical_accuracy: 0.4606 - val_loss: 3.7376 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1316/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3222 - categorical_accuracy: 0.4450 - val_loss: 3.6967 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1317/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3735 - categorical_accuracy: 0.4278 - val_loss: 3.8230 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1318/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3474 - categorical_accuracy: 0.4433 - val_loss: 3.8512 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1319/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3521 - categorical_accuracy: 0.4367 - val_loss: 3.6490 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1320/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3369 - categorical_accuracy: 0.4406 - val_loss: 3.5147 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1321/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3124 - categorical_accuracy: 0.4544 - val_loss: 3.7047 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1322/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2999 - categorical_accuracy: 0.4622 - val_loss: 3.6556 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1323/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2993 - categorical_accuracy: 0.4611 - val_loss: 3.7075 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1324/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2975 - categorical_accuracy: 0.4650 - val_loss: 3.6588 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1325/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2980 - categorical_accuracy: 0.4628 - val_loss: 3.6732 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1326/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3072 - categorical_accuracy: 0.4511 - val_loss: 3.6185 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1327/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3084 - categorical_accuracy: 0.4511 - val_loss: 3.6781 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1328/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3095 - categorical_accuracy: 0.4578 - val_loss: 3.7180 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1329/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3024 - categorical_accuracy: 0.4539 - val_loss: 3.7030 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1330/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3015 - categorical_accuracy: 0.4622 - val_loss: 3.7515 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1331/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3375 - categorical_accuracy: 0.4328 - val_loss: 3.7257 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1332/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3106 - categorical_accuracy: 0.4539 - val_loss: 3.6954 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1333/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2920 - categorical_accuracy: 0.4628 - val_loss: 3.7580 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1334/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2885 - categorical_accuracy: 0.4683 - val_loss: 3.6832 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1335/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3195 - categorical_accuracy: 0.4500 - val_loss: 3.7086 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1336/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3262 - categorical_accuracy: 0.4461 - val_loss: 3.6748 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1337/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3237 - categorical_accuracy: 0.4544 - val_loss: 3.6079 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1338/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3269 - categorical_accuracy: 0.4539 - val_loss: 3.6732 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1339/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3150 - categorical_accuracy: 0.4578 - val_loss: 3.5977 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1340/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3204 - categorical_accuracy: 0.4511 - val_loss: 3.6765 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1341/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3180 - categorical_accuracy: 0.4600 - val_loss: 3.6436 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1342/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3316 - categorical_accuracy: 0.4478 - val_loss: 3.6882 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1343/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3070 - categorical_accuracy: 0.4544 - val_loss: 3.7448 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1344/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2944 - categorical_accuracy: 0.4683 - val_loss: 3.6933 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1345/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3080 - categorical_accuracy: 0.4661 - val_loss: 3.7560 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1346/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3812 - categorical_accuracy: 0.4306 - val_loss: 3.6775 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1347/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3069 - categorical_accuracy: 0.4583 - val_loss: 3.8031 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1348/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2898 - categorical_accuracy: 0.4678 - val_loss: 3.7857 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1349/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2810 - categorical_accuracy: 0.4711 - val_loss: 3.7900 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1350/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2840 - categorical_accuracy: 0.4761 - val_loss: 3.7673 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1351/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3177 - categorical_accuracy: 0.4511 - val_loss: 3.8496 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1352/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3589 - categorical_accuracy: 0.4411 - val_loss: 3.7231 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1353/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3414 - categorical_accuracy: 0.4511 - val_loss: 3.6131 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1354/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3240 - categorical_accuracy: 0.4478 - val_loss: 3.6630 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1355/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3151 - categorical_accuracy: 0.4572 - val_loss: 3.5211 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1356/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3234 - categorical_accuracy: 0.4567 - val_loss: 3.6950 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1357/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2906 - categorical_accuracy: 0.4656 - val_loss: 3.8516 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1358/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2915 - categorical_accuracy: 0.4694 - val_loss: 3.7030 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1359/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3020 - categorical_accuracy: 0.4689 - val_loss: 3.6282 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1360/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3236 - categorical_accuracy: 0.4589 - val_loss: 3.7866 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1361/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3072 - categorical_accuracy: 0.4656 - val_loss: 3.6848 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1362/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2994 - categorical_accuracy: 0.4539 - val_loss: 3.6794 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1363/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2999 - categorical_accuracy: 0.4644 - val_loss: 3.6038 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1364/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3061 - categorical_accuracy: 0.4556 - val_loss: 3.6494 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1365/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2937 - categorical_accuracy: 0.4717 - val_loss: 3.7183 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1366/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2883 - categorical_accuracy: 0.4728 - val_loss: 3.7258 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1367/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2837 - categorical_accuracy: 0.4733 - val_loss: 3.7854 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1368/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2914 - categorical_accuracy: 0.4694 - val_loss: 3.7818 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1369/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3085 - categorical_accuracy: 0.4556 - val_loss: 3.7857 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1370/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2984 - categorical_accuracy: 0.4717 - val_loss: 3.7481 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1371/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2925 - categorical_accuracy: 0.4689 - val_loss: 3.7442 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1372/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2902 - categorical_accuracy: 0.4756 - val_loss: 3.7627 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1373/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2827 - categorical_accuracy: 0.4706 - val_loss: 3.7590 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1374/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3097 - categorical_accuracy: 0.4561 - val_loss: 3.7117 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1375/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3860 - categorical_accuracy: 0.4344 - val_loss: 3.8140 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1376/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3239 - categorical_accuracy: 0.4500 - val_loss: 3.7140 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1377/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3015 - categorical_accuracy: 0.4694 - val_loss: 3.6990 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1378/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3312 - categorical_accuracy: 0.4406 - val_loss: 3.7096 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1379/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3018 - categorical_accuracy: 0.4594 - val_loss: 3.6985 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1380/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3061 - categorical_accuracy: 0.4672 - val_loss: 3.7804 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1381/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2908 - categorical_accuracy: 0.4683 - val_loss: 3.7953 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1382/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2937 - categorical_accuracy: 0.4700 - val_loss: 3.7965 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1383/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2983 - categorical_accuracy: 0.4661 - val_loss: 3.7152 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1384/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3210 - categorical_accuracy: 0.4522 - val_loss: 3.6240 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1385/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2922 - categorical_accuracy: 0.4678 - val_loss: 3.7771 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1386/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2910 - categorical_accuracy: 0.4683 - val_loss: 3.7892 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1387/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2910 - categorical_accuracy: 0.4744 - val_loss: 3.8180 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1388/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2882 - categorical_accuracy: 0.4778 - val_loss: 3.7794 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1389/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3084 - categorical_accuracy: 0.4578 - val_loss: 3.7479 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1390/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3300 - categorical_accuracy: 0.4433 - val_loss: 3.6516 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1391/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3408 - categorical_accuracy: 0.4522 - val_loss: 3.7726 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1392/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3030 - categorical_accuracy: 0.4528 - val_loss: 3.8510 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1393/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3072 - categorical_accuracy: 0.4594 - val_loss: 3.9712 - val_categorical_accuracy: 0.1367\n",
      "Epoch 1394/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3270 - categorical_accuracy: 0.4467 - val_loss: 3.7572 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1395/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3046 - categorical_accuracy: 0.4633 - val_loss: 3.8613 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1396/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2947 - categorical_accuracy: 0.4772 - val_loss: 3.8375 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1397/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3204 - categorical_accuracy: 0.4556 - val_loss: 3.7782 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1398/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3021 - categorical_accuracy: 0.4611 - val_loss: 3.8370 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1399/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2942 - categorical_accuracy: 0.4672 - val_loss: 3.8755 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1400/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2865 - categorical_accuracy: 0.4700 - val_loss: 3.8413 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1401/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3374 - categorical_accuracy: 0.4600 - val_loss: 3.7185 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1402/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3160 - categorical_accuracy: 0.4550 - val_loss: 3.7729 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1403/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2992 - categorical_accuracy: 0.4611 - val_loss: 3.8329 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1404/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2868 - categorical_accuracy: 0.4650 - val_loss: 3.8892 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1405/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2848 - categorical_accuracy: 0.4667 - val_loss: 3.8467 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1406/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2946 - categorical_accuracy: 0.4678 - val_loss: 3.9203 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1407/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3003 - categorical_accuracy: 0.4722 - val_loss: 3.8567 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1408/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2946 - categorical_accuracy: 0.4672 - val_loss: 3.8303 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1409/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2859 - categorical_accuracy: 0.4689 - val_loss: 3.9434 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1410/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2828 - categorical_accuracy: 0.4767 - val_loss: 3.9090 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1411/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2900 - categorical_accuracy: 0.4711 - val_loss: 3.8303 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1412/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3169 - categorical_accuracy: 0.4600 - val_loss: 3.7806 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1413/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3017 - categorical_accuracy: 0.4633 - val_loss: 3.8359 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1414/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3184 - categorical_accuracy: 0.4594 - val_loss: 3.7844 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1415/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3268 - categorical_accuracy: 0.4467 - val_loss: 3.8301 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1416/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3157 - categorical_accuracy: 0.4689 - val_loss: 3.9476 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1417/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3113 - categorical_accuracy: 0.4467 - val_loss: 3.7610 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1418/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3043 - categorical_accuracy: 0.4611 - val_loss: 3.8864 - val_categorical_accuracy: 0.1300\n",
      "Epoch 1419/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3259 - categorical_accuracy: 0.4589 - val_loss: 3.8701 - val_categorical_accuracy: 0.1333\n",
      "Epoch 1420/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2911 - categorical_accuracy: 0.4633 - val_loss: 3.8194 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1421/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2818 - categorical_accuracy: 0.4778 - val_loss: 3.8596 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1422/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2801 - categorical_accuracy: 0.4711 - val_loss: 3.9509 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1423/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2769 - categorical_accuracy: 0.4711 - val_loss: 3.7859 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1424/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2757 - categorical_accuracy: 0.4739 - val_loss: 3.8584 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1425/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3058 - categorical_accuracy: 0.4594 - val_loss: 3.6520 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1426/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3361 - categorical_accuracy: 0.4456 - val_loss: 3.7693 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1427/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3213 - categorical_accuracy: 0.4550 - val_loss: 3.8613 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1428/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2934 - categorical_accuracy: 0.4678 - val_loss: 3.8768 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1429/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3095 - categorical_accuracy: 0.4583 - val_loss: 3.8209 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1430/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2873 - categorical_accuracy: 0.4700 - val_loss: 3.8359 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1431/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2851 - categorical_accuracy: 0.4728 - val_loss: 3.8110 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1432/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2784 - categorical_accuracy: 0.4711 - val_loss: 3.8305 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1433/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2795 - categorical_accuracy: 0.4667 - val_loss: 3.8486 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1434/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2961 - categorical_accuracy: 0.4656 - val_loss: 3.8097 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1435/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2844 - categorical_accuracy: 0.4850 - val_loss: 3.8661 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1436/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2763 - categorical_accuracy: 0.4750 - val_loss: 3.9003 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1437/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2768 - categorical_accuracy: 0.4800 - val_loss: 3.9032 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1438/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2796 - categorical_accuracy: 0.4728 - val_loss: 3.9076 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1439/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2789 - categorical_accuracy: 0.4761 - val_loss: 3.8362 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1440/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2974 - categorical_accuracy: 0.4694 - val_loss: 3.9993 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1441/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2825 - categorical_accuracy: 0.4728 - val_loss: 3.9410 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1442/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2808 - categorical_accuracy: 0.4822 - val_loss: 3.9256 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1443/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2867 - categorical_accuracy: 0.4683 - val_loss: 3.9194 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1444/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3030 - categorical_accuracy: 0.4694 - val_loss: 3.7955 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1445/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2851 - categorical_accuracy: 0.4706 - val_loss: 3.8786 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1446/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3081 - categorical_accuracy: 0.4617 - val_loss: 4.0184 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1447/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3083 - categorical_accuracy: 0.4617 - val_loss: 3.9660 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1448/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2992 - categorical_accuracy: 0.4722 - val_loss: 4.0241 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1449/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2956 - categorical_accuracy: 0.4706 - val_loss: 3.8721 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1450/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3135 - categorical_accuracy: 0.4561 - val_loss: 4.0397 - val_categorical_accuracy: 0.1367\n",
      "Epoch 1451/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2924 - categorical_accuracy: 0.4661 - val_loss: 3.8761 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1452/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2855 - categorical_accuracy: 0.4739 - val_loss: 3.8939 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1453/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2801 - categorical_accuracy: 0.4750 - val_loss: 4.0533 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1454/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2876 - categorical_accuracy: 0.4800 - val_loss: 3.9401 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1455/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3354 - categorical_accuracy: 0.4572 - val_loss: 3.8939 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1456/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4214 - categorical_accuracy: 0.4178 - val_loss: 3.9666 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1457/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3085 - categorical_accuracy: 0.4644 - val_loss: 3.9430 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1458/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2781 - categorical_accuracy: 0.4717 - val_loss: 4.0132 - val_categorical_accuracy: 0.1333\n",
      "Epoch 1459/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2785 - categorical_accuracy: 0.4711 - val_loss: 4.1375 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1460/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2819 - categorical_accuracy: 0.4694 - val_loss: 3.9148 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1461/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2821 - categorical_accuracy: 0.4761 - val_loss: 3.9475 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1462/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2787 - categorical_accuracy: 0.4767 - val_loss: 3.9600 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1463/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2701 - categorical_accuracy: 0.4772 - val_loss: 3.9810 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1464/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3011 - categorical_accuracy: 0.4628 - val_loss: 3.8360 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1465/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3103 - categorical_accuracy: 0.4622 - val_loss: 3.9079 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1466/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2889 - categorical_accuracy: 0.4772 - val_loss: 3.9272 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1467/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2844 - categorical_accuracy: 0.4711 - val_loss: 4.0625 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1468/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2863 - categorical_accuracy: 0.4661 - val_loss: 4.0548 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1469/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3179 - categorical_accuracy: 0.4572 - val_loss: 3.9160 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1470/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3073 - categorical_accuracy: 0.4633 - val_loss: 3.9234 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1471/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2829 - categorical_accuracy: 0.4806 - val_loss: 3.9150 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1472/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2873 - categorical_accuracy: 0.4689 - val_loss: 3.8510 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1473/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2874 - categorical_accuracy: 0.4722 - val_loss: 3.8923 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1474/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2924 - categorical_accuracy: 0.4683 - val_loss: 3.9947 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1475/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2958 - categorical_accuracy: 0.4633 - val_loss: 4.0100 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1476/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2996 - categorical_accuracy: 0.4689 - val_loss: 3.8495 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1477/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2811 - categorical_accuracy: 0.4689 - val_loss: 4.0418 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1478/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2954 - categorical_accuracy: 0.4678 - val_loss: 3.9425 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1479/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3114 - categorical_accuracy: 0.4606 - val_loss: 3.7700 - val_categorical_accuracy: 0.1800\n",
      "Epoch 1480/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3025 - categorical_accuracy: 0.4606 - val_loss: 3.9663 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1481/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2744 - categorical_accuracy: 0.4761 - val_loss: 3.9658 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1482/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2833 - categorical_accuracy: 0.4733 - val_loss: 4.0447 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1483/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2942 - categorical_accuracy: 0.4683 - val_loss: 4.0377 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1484/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2816 - categorical_accuracy: 0.4739 - val_loss: 3.9090 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1485/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3347 - categorical_accuracy: 0.4550 - val_loss: 3.8754 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1486/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3027 - categorical_accuracy: 0.4739 - val_loss: 3.8642 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1487/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2884 - categorical_accuracy: 0.4772 - val_loss: 3.9708 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1488/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2820 - categorical_accuracy: 0.4800 - val_loss: 4.0845 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1489/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2775 - categorical_accuracy: 0.4728 - val_loss: 4.2197 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1490/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2797 - categorical_accuracy: 0.4717 - val_loss: 3.9772 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1491/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3058 - categorical_accuracy: 0.4656 - val_loss: 3.9112 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1492/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2920 - categorical_accuracy: 0.4761 - val_loss: 4.0073 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1493/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2768 - categorical_accuracy: 0.4761 - val_loss: 4.1469 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1494/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2832 - categorical_accuracy: 0.4683 - val_loss: 4.0887 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1495/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2952 - categorical_accuracy: 0.4667 - val_loss: 4.0367 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1496/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2947 - categorical_accuracy: 0.4817 - val_loss: 4.0770 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1497/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2813 - categorical_accuracy: 0.4778 - val_loss: 4.0913 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1498/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2740 - categorical_accuracy: 0.4811 - val_loss: 4.0198 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1499/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2858 - categorical_accuracy: 0.4667 - val_loss: 4.0747 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1500/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2824 - categorical_accuracy: 0.4728 - val_loss: 4.0405 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1501/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2831 - categorical_accuracy: 0.4728 - val_loss: 4.0462 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1502/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3250 - categorical_accuracy: 0.4561 - val_loss: 3.8822 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1503/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3245 - categorical_accuracy: 0.4500 - val_loss: 4.0141 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1504/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3291 - categorical_accuracy: 0.4544 - val_loss: 4.0676 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1505/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3163 - categorical_accuracy: 0.4494 - val_loss: 4.0250 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1506/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2841 - categorical_accuracy: 0.4656 - val_loss: 3.9589 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1507/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2939 - categorical_accuracy: 0.4717 - val_loss: 4.0437 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1508/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2820 - categorical_accuracy: 0.4778 - val_loss: 4.0026 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1509/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2738 - categorical_accuracy: 0.4772 - val_loss: 4.0026 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1510/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2865 - categorical_accuracy: 0.4778 - val_loss: 4.2041 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1511/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2696 - categorical_accuracy: 0.4733 - val_loss: 4.0568 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1512/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2763 - categorical_accuracy: 0.4822 - val_loss: 4.1602 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1513/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2862 - categorical_accuracy: 0.4694 - val_loss: 3.9250 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1514/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3035 - categorical_accuracy: 0.4644 - val_loss: 4.0978 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1515/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2769 - categorical_accuracy: 0.4861 - val_loss: 4.1061 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1516/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2830 - categorical_accuracy: 0.4750 - val_loss: 4.0677 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1517/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2904 - categorical_accuracy: 0.4689 - val_loss: 3.9900 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1518/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2840 - categorical_accuracy: 0.4778 - val_loss: 3.8887 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1519/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3012 - categorical_accuracy: 0.4750 - val_loss: 3.9800 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1520/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3091 - categorical_accuracy: 0.4617 - val_loss: 4.1453 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1521/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2994 - categorical_accuracy: 0.4628 - val_loss: 4.1394 - val_categorical_accuracy: 0.1367\n",
      "Epoch 1522/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2958 - categorical_accuracy: 0.4628 - val_loss: 4.0006 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1523/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3243 - categorical_accuracy: 0.4561 - val_loss: 4.0430 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1524/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3410 - categorical_accuracy: 0.4389 - val_loss: 4.1209 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1525/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2836 - categorical_accuracy: 0.4722 - val_loss: 4.0503 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1526/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2808 - categorical_accuracy: 0.4811 - val_loss: 4.0972 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1527/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2843 - categorical_accuracy: 0.4761 - val_loss: 4.1421 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1528/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2752 - categorical_accuracy: 0.4828 - val_loss: 4.1532 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1529/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2693 - categorical_accuracy: 0.4833 - val_loss: 4.1921 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1530/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2750 - categorical_accuracy: 0.4844 - val_loss: 4.1978 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1531/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2893 - categorical_accuracy: 0.4739 - val_loss: 3.9817 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1532/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3243 - categorical_accuracy: 0.4617 - val_loss: 4.1956 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1533/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3100 - categorical_accuracy: 0.4656 - val_loss: 4.0055 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1534/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3150 - categorical_accuracy: 0.4606 - val_loss: 4.1255 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1535/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2941 - categorical_accuracy: 0.4650 - val_loss: 4.0866 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1536/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3400 - categorical_accuracy: 0.4467 - val_loss: 4.1160 - val_categorical_accuracy: 0.1300\n",
      "Epoch 1537/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3449 - categorical_accuracy: 0.4533 - val_loss: 4.2165 - val_categorical_accuracy: 0.1367\n",
      "Epoch 1538/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2941 - categorical_accuracy: 0.4683 - val_loss: 4.1852 - val_categorical_accuracy: 0.1267\n",
      "Epoch 1539/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2869 - categorical_accuracy: 0.4794 - val_loss: 4.1390 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1540/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2830 - categorical_accuracy: 0.4756 - val_loss: 4.1907 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1541/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2762 - categorical_accuracy: 0.4850 - val_loss: 4.0147 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1542/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3107 - categorical_accuracy: 0.4656 - val_loss: 4.1655 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1543/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3059 - categorical_accuracy: 0.4633 - val_loss: 4.0141 - val_categorical_accuracy: 0.1300\n",
      "Epoch 1544/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2911 - categorical_accuracy: 0.4750 - val_loss: 4.1965 - val_categorical_accuracy: 0.1367\n",
      "Epoch 1545/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3240 - categorical_accuracy: 0.4572 - val_loss: 4.2098 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1546/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3449 - categorical_accuracy: 0.4489 - val_loss: 4.1722 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1547/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2925 - categorical_accuracy: 0.4678 - val_loss: 4.0042 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1548/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3256 - categorical_accuracy: 0.4656 - val_loss: 4.1379 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1549/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3018 - categorical_accuracy: 0.4717 - val_loss: 4.0438 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1550/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2941 - categorical_accuracy: 0.4733 - val_loss: 4.2369 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1551/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2810 - categorical_accuracy: 0.4811 - val_loss: 4.2046 - val_categorical_accuracy: 0.1267\n",
      "Epoch 1552/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2900 - categorical_accuracy: 0.4717 - val_loss: 4.3183 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1553/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2825 - categorical_accuracy: 0.4772 - val_loss: 4.1444 - val_categorical_accuracy: 0.1300\n",
      "Epoch 1554/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3274 - categorical_accuracy: 0.4567 - val_loss: 4.1988 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1555/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3922 - categorical_accuracy: 0.4356 - val_loss: 4.1257 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1556/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3185 - categorical_accuracy: 0.4628 - val_loss: 4.2156 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1557/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2981 - categorical_accuracy: 0.4778 - val_loss: 4.1430 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1558/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2842 - categorical_accuracy: 0.4806 - val_loss: 4.2389 - val_categorical_accuracy: 0.1367\n",
      "Epoch 1559/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3137 - categorical_accuracy: 0.4600 - val_loss: 4.3074 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1560/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2979 - categorical_accuracy: 0.4667 - val_loss: 4.1268 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1561/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3014 - categorical_accuracy: 0.4700 - val_loss: 4.2435 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1562/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2886 - categorical_accuracy: 0.4656 - val_loss: 4.4122 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1563/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2858 - categorical_accuracy: 0.4733 - val_loss: 4.2284 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1564/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2837 - categorical_accuracy: 0.4689 - val_loss: 4.1841 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1565/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2995 - categorical_accuracy: 0.4633 - val_loss: 4.4374 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1566/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2957 - categorical_accuracy: 0.4700 - val_loss: 4.3223 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1567/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2884 - categorical_accuracy: 0.4717 - val_loss: 4.1515 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1568/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2969 - categorical_accuracy: 0.4661 - val_loss: 4.2660 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1569/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3200 - categorical_accuracy: 0.4528 - val_loss: 4.2631 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1570/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2910 - categorical_accuracy: 0.4728 - val_loss: 4.2854 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1571/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3128 - categorical_accuracy: 0.4644 - val_loss: 4.2598 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1572/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3215 - categorical_accuracy: 0.4622 - val_loss: 4.2523 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1573/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3038 - categorical_accuracy: 0.4739 - val_loss: 4.1164 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1574/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3201 - categorical_accuracy: 0.4567 - val_loss: 4.0456 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1575/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3072 - categorical_accuracy: 0.4672 - val_loss: 4.2315 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1576/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2959 - categorical_accuracy: 0.4700 - val_loss: 4.2583 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1577/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3259 - categorical_accuracy: 0.4622 - val_loss: 4.1081 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1578/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3763 - categorical_accuracy: 0.4383 - val_loss: 4.3917 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1579/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3096 - categorical_accuracy: 0.4606 - val_loss: 4.2688 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1580/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2969 - categorical_accuracy: 0.4689 - val_loss: 4.2749 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1581/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3112 - categorical_accuracy: 0.4578 - val_loss: 4.4036 - val_categorical_accuracy: 0.1333\n",
      "Epoch 1582/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2937 - categorical_accuracy: 0.4700 - val_loss: 4.4611 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1583/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3241 - categorical_accuracy: 0.4422 - val_loss: 4.2675 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1584/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3070 - categorical_accuracy: 0.4611 - val_loss: 4.2359 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1585/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3019 - categorical_accuracy: 0.4550 - val_loss: 4.4389 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1586/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2883 - categorical_accuracy: 0.4689 - val_loss: 4.4777 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1587/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2852 - categorical_accuracy: 0.4750 - val_loss: 4.2998 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1588/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2805 - categorical_accuracy: 0.4728 - val_loss: 4.4787 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1589/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3264 - categorical_accuracy: 0.4539 - val_loss: 4.2901 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1590/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3635 - categorical_accuracy: 0.4400 - val_loss: 4.4009 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1591/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2974 - categorical_accuracy: 0.4800 - val_loss: 4.1159 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1592/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2908 - categorical_accuracy: 0.4756 - val_loss: 4.4491 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1593/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2845 - categorical_accuracy: 0.4694 - val_loss: 4.4340 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1594/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2872 - categorical_accuracy: 0.4744 - val_loss: 4.4032 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1595/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2934 - categorical_accuracy: 0.4733 - val_loss: 4.3564 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1596/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2850 - categorical_accuracy: 0.4744 - val_loss: 4.2864 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1597/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3088 - categorical_accuracy: 0.4628 - val_loss: 4.2139 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1598/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2968 - categorical_accuracy: 0.4672 - val_loss: 4.3649 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1599/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3063 - categorical_accuracy: 0.4683 - val_loss: 4.4792 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1600/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3031 - categorical_accuracy: 0.4661 - val_loss: 4.4113 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1601/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2941 - categorical_accuracy: 0.4678 - val_loss: 4.4812 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1602/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2808 - categorical_accuracy: 0.4656 - val_loss: 4.1971 - val_categorical_accuracy: 0.1367\n",
      "Epoch 1603/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2965 - categorical_accuracy: 0.4650 - val_loss: 4.3902 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1604/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3359 - categorical_accuracy: 0.4511 - val_loss: 4.3607 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1605/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2942 - categorical_accuracy: 0.4767 - val_loss: 4.4105 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1606/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2958 - categorical_accuracy: 0.4633 - val_loss: 4.3675 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1607/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2785 - categorical_accuracy: 0.4739 - val_loss: 4.4144 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1608/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2777 - categorical_accuracy: 0.4739 - val_loss: 4.4418 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1609/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2757 - categorical_accuracy: 0.4811 - val_loss: 4.4228 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1610/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2921 - categorical_accuracy: 0.4728 - val_loss: 4.4778 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1611/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2915 - categorical_accuracy: 0.4717 - val_loss: 4.2323 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1612/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3337 - categorical_accuracy: 0.4539 - val_loss: 4.3579 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1613/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3709 - categorical_accuracy: 0.4333 - val_loss: 4.4174 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1614/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3163 - categorical_accuracy: 0.4611 - val_loss: 4.4402 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1615/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2948 - categorical_accuracy: 0.4756 - val_loss: 4.4511 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1616/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2859 - categorical_accuracy: 0.4689 - val_loss: 4.2360 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1617/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2924 - categorical_accuracy: 0.4728 - val_loss: 4.5245 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1618/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2885 - categorical_accuracy: 0.4722 - val_loss: 4.4478 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1619/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2781 - categorical_accuracy: 0.4800 - val_loss: 4.3441 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1620/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3022 - categorical_accuracy: 0.4728 - val_loss: 4.3315 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1621/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3167 - categorical_accuracy: 0.4700 - val_loss: 4.2582 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1622/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3114 - categorical_accuracy: 0.4622 - val_loss: 4.3941 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1623/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3502 - categorical_accuracy: 0.4556 - val_loss: 4.6740 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1624/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3206 - categorical_accuracy: 0.4544 - val_loss: 4.4365 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1625/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2915 - categorical_accuracy: 0.4756 - val_loss: 4.4274 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1626/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2972 - categorical_accuracy: 0.4728 - val_loss: 4.4026 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1627/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3180 - categorical_accuracy: 0.4589 - val_loss: 4.3861 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1628/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2979 - categorical_accuracy: 0.4689 - val_loss: 4.5108 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1629/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2956 - categorical_accuracy: 0.4817 - val_loss: 4.3027 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1630/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3392 - categorical_accuracy: 0.4561 - val_loss: 4.4098 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1631/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3031 - categorical_accuracy: 0.4661 - val_loss: 4.4056 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1632/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2897 - categorical_accuracy: 0.4783 - val_loss: 4.4083 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1633/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2779 - categorical_accuracy: 0.4789 - val_loss: 4.3819 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1634/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2787 - categorical_accuracy: 0.4772 - val_loss: 4.4801 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1635/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2702 - categorical_accuracy: 0.4961 - val_loss: 4.4621 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1636/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2794 - categorical_accuracy: 0.4800 - val_loss: 4.4274 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1637/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2894 - categorical_accuracy: 0.4744 - val_loss: 4.4433 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1638/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2933 - categorical_accuracy: 0.4789 - val_loss: 4.4115 - val_categorical_accuracy: 0.1367\n",
      "Epoch 1639/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3022 - categorical_accuracy: 0.4683 - val_loss: 4.3740 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1640/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3110 - categorical_accuracy: 0.4711 - val_loss: 4.6154 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1641/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3171 - categorical_accuracy: 0.4661 - val_loss: 4.6440 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1642/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2909 - categorical_accuracy: 0.4822 - val_loss: 4.3975 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1643/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2987 - categorical_accuracy: 0.4756 - val_loss: 4.5777 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1644/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2720 - categorical_accuracy: 0.4889 - val_loss: 4.5096 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1645/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2877 - categorical_accuracy: 0.4772 - val_loss: 4.5108 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1646/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3032 - categorical_accuracy: 0.4700 - val_loss: 4.5113 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1647/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2877 - categorical_accuracy: 0.4844 - val_loss: 4.4488 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1648/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2797 - categorical_accuracy: 0.4878 - val_loss: 4.6433 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1649/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2820 - categorical_accuracy: 0.4828 - val_loss: 4.3871 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1650/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3034 - categorical_accuracy: 0.4683 - val_loss: 4.4726 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1651/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2848 - categorical_accuracy: 0.4861 - val_loss: 4.5517 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1652/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2778 - categorical_accuracy: 0.4833 - val_loss: 4.5822 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1653/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2670 - categorical_accuracy: 0.4894 - val_loss: 4.3556 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1654/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2855 - categorical_accuracy: 0.4761 - val_loss: 4.3909 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1655/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3067 - categorical_accuracy: 0.4667 - val_loss: 4.5987 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1656/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2707 - categorical_accuracy: 0.4928 - val_loss: 4.4962 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1657/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2845 - categorical_accuracy: 0.4822 - val_loss: 4.5121 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1658/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2897 - categorical_accuracy: 0.4861 - val_loss: 4.1688 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1659/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3213 - categorical_accuracy: 0.4650 - val_loss: 4.3106 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1660/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3014 - categorical_accuracy: 0.4672 - val_loss: 4.5150 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1661/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2792 - categorical_accuracy: 0.4828 - val_loss: 4.5752 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1662/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2736 - categorical_accuracy: 0.4856 - val_loss: 4.7028 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1663/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2754 - categorical_accuracy: 0.4922 - val_loss: 4.6089 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1664/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2877 - categorical_accuracy: 0.4778 - val_loss: 4.6846 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1665/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2708 - categorical_accuracy: 0.4817 - val_loss: 4.6385 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1666/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2854 - categorical_accuracy: 0.4756 - val_loss: 4.5567 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1667/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2755 - categorical_accuracy: 0.4900 - val_loss: 4.6616 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1668/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2946 - categorical_accuracy: 0.4806 - val_loss: 4.4006 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1669/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2983 - categorical_accuracy: 0.4806 - val_loss: 4.7259 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1670/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3106 - categorical_accuracy: 0.4711 - val_loss: 4.7036 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1671/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3265 - categorical_accuracy: 0.4583 - val_loss: 4.4089 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1672/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2825 - categorical_accuracy: 0.4883 - val_loss: 4.5326 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1673/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2690 - categorical_accuracy: 0.4922 - val_loss: 4.3323 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1674/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2703 - categorical_accuracy: 0.4761 - val_loss: 4.6161 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1675/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2790 - categorical_accuracy: 0.4867 - val_loss: 4.6169 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1676/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2914 - categorical_accuracy: 0.4733 - val_loss: 4.5549 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1677/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2864 - categorical_accuracy: 0.4833 - val_loss: 4.6954 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1678/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2731 - categorical_accuracy: 0.4850 - val_loss: 4.7130 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1679/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2807 - categorical_accuracy: 0.4794 - val_loss: 4.6545 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1680/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2684 - categorical_accuracy: 0.5000 - val_loss: 4.2598 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1681/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2915 - categorical_accuracy: 0.4867 - val_loss: 4.5512 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1682/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2742 - categorical_accuracy: 0.4889 - val_loss: 4.6741 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1683/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2603 - categorical_accuracy: 0.4928 - val_loss: 4.5929 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1684/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2612 - categorical_accuracy: 0.4928 - val_loss: 4.5277 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1685/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2736 - categorical_accuracy: 0.4861 - val_loss: 4.7288 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1686/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2611 - categorical_accuracy: 0.4950 - val_loss: 4.6322 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1687/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2520 - categorical_accuracy: 0.4922 - val_loss: 4.6866 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1688/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2558 - categorical_accuracy: 0.4889 - val_loss: 4.6996 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1689/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2610 - categorical_accuracy: 0.4961 - val_loss: 4.6595 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1690/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2768 - categorical_accuracy: 0.4900 - val_loss: 4.9841 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1691/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2672 - categorical_accuracy: 0.4883 - val_loss: 4.6091 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1692/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2524 - categorical_accuracy: 0.4983 - val_loss: 4.6286 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1693/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2817 - categorical_accuracy: 0.4889 - val_loss: 4.7200 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1694/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2864 - categorical_accuracy: 0.4822 - val_loss: 4.6287 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1695/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2980 - categorical_accuracy: 0.4811 - val_loss: 4.5853 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1696/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2963 - categorical_accuracy: 0.4839 - val_loss: 4.5746 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1697/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3176 - categorical_accuracy: 0.4700 - val_loss: 4.6214 - val_categorical_accuracy: 0.1333\n",
      "Epoch 1698/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3021 - categorical_accuracy: 0.4756 - val_loss: 4.6538 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1699/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2694 - categorical_accuracy: 0.4900 - val_loss: 4.7131 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1700/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2677 - categorical_accuracy: 0.4911 - val_loss: 4.6466 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1701/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2684 - categorical_accuracy: 0.4878 - val_loss: 4.2850 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1702/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3007 - categorical_accuracy: 0.4717 - val_loss: 4.7025 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1703/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2665 - categorical_accuracy: 0.4956 - val_loss: 4.6737 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1704/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2734 - categorical_accuracy: 0.4783 - val_loss: 4.5774 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1705/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2826 - categorical_accuracy: 0.4856 - val_loss: 4.6898 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1706/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3061 - categorical_accuracy: 0.4717 - val_loss: 4.7891 - val_categorical_accuracy: 0.1367\n",
      "Epoch 1707/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2698 - categorical_accuracy: 0.4906 - val_loss: 4.7087 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1708/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2599 - categorical_accuracy: 0.4928 - val_loss: 4.6688 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1709/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2670 - categorical_accuracy: 0.4867 - val_loss: 5.0052 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1710/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2718 - categorical_accuracy: 0.4906 - val_loss: 4.4530 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1711/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2851 - categorical_accuracy: 0.4800 - val_loss: 4.7412 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1712/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2734 - categorical_accuracy: 0.4883 - val_loss: 4.7655 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1713/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2782 - categorical_accuracy: 0.4867 - val_loss: 4.7000 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1714/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2644 - categorical_accuracy: 0.4922 - val_loss: 4.6941 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1715/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2860 - categorical_accuracy: 0.4767 - val_loss: 4.6543 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1716/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2915 - categorical_accuracy: 0.4783 - val_loss: 4.6282 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1717/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2694 - categorical_accuracy: 0.4839 - val_loss: 4.6051 - val_categorical_accuracy: 0.1400\n",
      "Epoch 1718/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2876 - categorical_accuracy: 0.4789 - val_loss: 4.5804 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1719/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3007 - categorical_accuracy: 0.4639 - val_loss: 4.4193 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1720/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2613 - categorical_accuracy: 0.4917 - val_loss: 4.6020 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1721/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2577 - categorical_accuracy: 0.4950 - val_loss: 4.5767 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1722/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2665 - categorical_accuracy: 0.4883 - val_loss: 4.5666 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1723/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2642 - categorical_accuracy: 0.4928 - val_loss: 4.6556 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1724/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2724 - categorical_accuracy: 0.4900 - val_loss: 4.6305 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1725/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2647 - categorical_accuracy: 0.4878 - val_loss: 4.6539 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1726/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2873 - categorical_accuracy: 0.4906 - val_loss: 4.5676 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1727/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2623 - categorical_accuracy: 0.4950 - val_loss: 4.4245 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1728/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2501 - categorical_accuracy: 0.4972 - val_loss: 4.6769 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1729/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2515 - categorical_accuracy: 0.4922 - val_loss: 4.7177 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1730/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2732 - categorical_accuracy: 0.4883 - val_loss: 4.4496 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1731/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2503 - categorical_accuracy: 0.4950 - val_loss: 4.6230 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1732/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2779 - categorical_accuracy: 0.4861 - val_loss: 4.5048 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1733/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2619 - categorical_accuracy: 0.4883 - val_loss: 4.4893 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1734/10000\n",
      "57/57 [==============================] - 1151s 21s/step - loss: 1.2206 - categorical_accuracy: 0.5144 - val_loss: 4.9094 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1888/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2241 - categorical_accuracy: 0.5072 - val_loss: 4.9650 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1889/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.2426 - categorical_accuracy: 0.5022 - val_loss: 5.2266 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1890/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2801 - categorical_accuracy: 0.4883 - val_loss: 4.9299 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1891/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.2752 - categorical_accuracy: 0.4911 - val_loss: 4.6587 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1892/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.2635 - categorical_accuracy: 0.4894 - val_loss: 4.6509 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1893/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2594 - categorical_accuracy: 0.4872 - val_loss: 4.7885 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1894/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.2557 - categorical_accuracy: 0.4961 - val_loss: 4.6340 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1895/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.2886 - categorical_accuracy: 0.4794 - val_loss: 4.7161 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1896/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2485 - categorical_accuracy: 0.4989 - val_loss: 4.7601 - val_categorical_accuracy: 0.1833\n",
      "Epoch 1897/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2433 - categorical_accuracy: 0.5000 - val_loss: 4.8660 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1898/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2285 - categorical_accuracy: 0.5061 - val_loss: 4.8803 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1899/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2226 - categorical_accuracy: 0.5094 - val_loss: 4.8312 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1900/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2296 - categorical_accuracy: 0.5089 - val_loss: 4.8972 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1901/10000\n",
      "57/57 [==============================] - 1s 24ms/step - loss: 1.2295 - categorical_accuracy: 0.4983 - val_loss: 5.0651 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1902/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2273 - categorical_accuracy: 0.5089 - val_loss: 4.8377 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1903/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2534 - categorical_accuracy: 0.4906 - val_loss: 4.8050 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1904/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2918 - categorical_accuracy: 0.4856 - val_loss: 4.8040 - val_categorical_accuracy: 0.1900\n",
      "Epoch 1905/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2624 - categorical_accuracy: 0.4911 - val_loss: 4.7402 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1906/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2516 - categorical_accuracy: 0.4922 - val_loss: 4.8081 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1907/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2505 - categorical_accuracy: 0.4894 - val_loss: 4.6965 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1908/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2450 - categorical_accuracy: 0.4917 - val_loss: 4.8341 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1909/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2371 - categorical_accuracy: 0.4944 - val_loss: 4.9124 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1910/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2235 - categorical_accuracy: 0.5150 - val_loss: 4.9154 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1911/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2245 - categorical_accuracy: 0.5050 - val_loss: 4.8998 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1912/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2558 - categorical_accuracy: 0.4906 - val_loss: 4.7995 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1913/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2439 - categorical_accuracy: 0.4922 - val_loss: 4.7209 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1914/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2340 - categorical_accuracy: 0.4978 - val_loss: 5.0003 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1915/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2501 - categorical_accuracy: 0.4911 - val_loss: 4.8082 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1916/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2350 - categorical_accuracy: 0.5028 - val_loss: 4.7491 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1917/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2289 - categorical_accuracy: 0.5000 - val_loss: 5.0294 - val_categorical_accuracy: 0.1800\n",
      "Epoch 1918/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2340 - categorical_accuracy: 0.5022 - val_loss: 4.8907 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1919/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2279 - categorical_accuracy: 0.4989 - val_loss: 4.9440 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1920/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2598 - categorical_accuracy: 0.4889 - val_loss: 4.9433 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1921/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2940 - categorical_accuracy: 0.4839 - val_loss: 4.4108 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1922/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3042 - categorical_accuracy: 0.4600 - val_loss: 4.8411 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1923/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3066 - categorical_accuracy: 0.4689 - val_loss: 4.8246 - val_categorical_accuracy: 0.1833\n",
      "Epoch 1924/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2789 - categorical_accuracy: 0.4883 - val_loss: 4.7482 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1925/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2465 - categorical_accuracy: 0.4933 - val_loss: 4.7542 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1926/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2542 - categorical_accuracy: 0.5033 - val_loss: 4.5643 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1927/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3293 - categorical_accuracy: 0.4556 - val_loss: 4.5031 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1928/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2799 - categorical_accuracy: 0.4789 - val_loss: 4.7929 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1929/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2518 - categorical_accuracy: 0.4917 - val_loss: 4.7545 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1930/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2557 - categorical_accuracy: 0.4889 - val_loss: 4.7623 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1931/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2545 - categorical_accuracy: 0.4839 - val_loss: 4.7038 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1932/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2524 - categorical_accuracy: 0.4928 - val_loss: 4.6592 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1933/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2725 - categorical_accuracy: 0.4950 - val_loss: 4.8118 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1934/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2514 - categorical_accuracy: 0.4911 - val_loss: 4.6957 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1935/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2464 - categorical_accuracy: 0.5039 - val_loss: 4.7058 - val_categorical_accuracy: 0.1333\n",
      "Epoch 1936/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2489 - categorical_accuracy: 0.4939 - val_loss: 4.7595 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1937/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2849 - categorical_accuracy: 0.4783 - val_loss: 4.4631 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1938/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2953 - categorical_accuracy: 0.4661 - val_loss: 4.6594 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1939/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2361 - categorical_accuracy: 0.5011 - val_loss: 4.8819 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1940/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2320 - categorical_accuracy: 0.5011 - val_loss: 4.8974 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1941/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2611 - categorical_accuracy: 0.4911 - val_loss: 4.6645 - val_categorical_accuracy: 0.1433\n",
      "Epoch 1942/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2919 - categorical_accuracy: 0.4783 - val_loss: 4.6602 - val_categorical_accuracy: 0.1867\n",
      "Epoch 1943/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2396 - categorical_accuracy: 0.4939 - val_loss: 4.9417 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1944/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2261 - categorical_accuracy: 0.5056 - val_loss: 4.8292 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1945/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2397 - categorical_accuracy: 0.4878 - val_loss: 4.8922 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1946/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2276 - categorical_accuracy: 0.4961 - val_loss: 4.8978 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1947/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2697 - categorical_accuracy: 0.4894 - val_loss: 4.4372 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1948/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2457 - categorical_accuracy: 0.4883 - val_loss: 4.8803 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1949/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2276 - categorical_accuracy: 0.5006 - val_loss: 4.9639 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1950/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2254 - categorical_accuracy: 0.5033 - val_loss: 4.8231 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1951/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2511 - categorical_accuracy: 0.4867 - val_loss: 4.9309 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1952/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2663 - categorical_accuracy: 0.4794 - val_loss: 4.8386 - val_categorical_accuracy: 0.1800\n",
      "Epoch 1953/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2563 - categorical_accuracy: 0.4872 - val_loss: 4.7779 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1954/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2576 - categorical_accuracy: 0.4844 - val_loss: 4.6499 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1955/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2371 - categorical_accuracy: 0.4994 - val_loss: 4.5559 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1956/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2447 - categorical_accuracy: 0.4889 - val_loss: 4.8684 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1957/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2225 - categorical_accuracy: 0.5056 - val_loss: 4.7033 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1958/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2314 - categorical_accuracy: 0.5000 - val_loss: 5.0120 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1959/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2350 - categorical_accuracy: 0.4994 - val_loss: 4.9345 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1960/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2385 - categorical_accuracy: 0.4928 - val_loss: 5.0567 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1961/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2274 - categorical_accuracy: 0.5017 - val_loss: 5.0458 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1962/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2473 - categorical_accuracy: 0.4806 - val_loss: 4.9247 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1963/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2506 - categorical_accuracy: 0.4956 - val_loss: 4.5943 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1964/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2576 - categorical_accuracy: 0.4872 - val_loss: 4.8062 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1965/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2560 - categorical_accuracy: 0.4922 - val_loss: 4.7389 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1966/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2501 - categorical_accuracy: 0.4878 - val_loss: 4.9870 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1967/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2823 - categorical_accuracy: 0.4872 - val_loss: 4.7086 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1968/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2325 - categorical_accuracy: 0.5067 - val_loss: 4.8366 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1969/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2536 - categorical_accuracy: 0.4961 - val_loss: 4.7111 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1970/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2422 - categorical_accuracy: 0.4872 - val_loss: 4.5414 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1971/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2626 - categorical_accuracy: 0.4933 - val_loss: 4.8204 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1972/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2461 - categorical_accuracy: 0.4944 - val_loss: 4.7828 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1973/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2445 - categorical_accuracy: 0.4933 - val_loss: 4.7629 - val_categorical_accuracy: 0.1667\n",
      "Epoch 1974/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2358 - categorical_accuracy: 0.4989 - val_loss: 4.8895 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1975/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2299 - categorical_accuracy: 0.5044 - val_loss: 4.8704 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1976/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2270 - categorical_accuracy: 0.5111 - val_loss: 4.9618 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1977/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2286 - categorical_accuracy: 0.5144 - val_loss: 4.9082 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1978/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2499 - categorical_accuracy: 0.4894 - val_loss: 4.7473 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1979/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2689 - categorical_accuracy: 0.4828 - val_loss: 5.1292 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1980/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3160 - categorical_accuracy: 0.4778 - val_loss: 4.4174 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1981/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2629 - categorical_accuracy: 0.4928 - val_loss: 4.5945 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1982/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2610 - categorical_accuracy: 0.4978 - val_loss: 4.5658 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1983/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2358 - categorical_accuracy: 0.5050 - val_loss: 4.7750 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1984/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2298 - categorical_accuracy: 0.5089 - val_loss: 4.6841 - val_categorical_accuracy: 0.1633\n",
      "Epoch 1985/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2510 - categorical_accuracy: 0.4994 - val_loss: 4.7392 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1986/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2384 - categorical_accuracy: 0.5006 - val_loss: 4.7752 - val_categorical_accuracy: 0.1700\n",
      "Epoch 1987/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3003 - categorical_accuracy: 0.4794 - val_loss: 4.7384 - val_categorical_accuracy: 0.1733\n",
      "Epoch 1988/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2609 - categorical_accuracy: 0.4889 - val_loss: 4.5302 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1989/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2682 - categorical_accuracy: 0.4956 - val_loss: 4.6754 - val_categorical_accuracy: 0.1533\n",
      "Epoch 1990/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2533 - categorical_accuracy: 0.5006 - val_loss: 4.7384 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1991/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2561 - categorical_accuracy: 0.4950 - val_loss: 4.2834 - val_categorical_accuracy: 0.1467\n",
      "Epoch 1992/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3092 - categorical_accuracy: 0.4656 - val_loss: 4.7125 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1993/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2568 - categorical_accuracy: 0.4933 - val_loss: 4.6573 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1994/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2472 - categorical_accuracy: 0.4933 - val_loss: 4.7815 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1995/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2356 - categorical_accuracy: 0.5022 - val_loss: 4.6199 - val_categorical_accuracy: 0.1567\n",
      "Epoch 1996/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2329 - categorical_accuracy: 0.5094 - val_loss: 4.8788 - val_categorical_accuracy: 0.1500\n",
      "Epoch 1997/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2308 - categorical_accuracy: 0.5083 - val_loss: 4.7529 - val_categorical_accuracy: 0.1600\n",
      "Epoch 1998/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2363 - categorical_accuracy: 0.4983 - val_loss: 4.9700 - val_categorical_accuracy: 0.1767\n",
      "Epoch 1999/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2513 - categorical_accuracy: 0.5011 - val_loss: 4.6565 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2000/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2451 - categorical_accuracy: 0.4894 - val_loss: 4.6936 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2001/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2417 - categorical_accuracy: 0.4944 - val_loss: 5.0071 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2002/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2504 - categorical_accuracy: 0.4911 - val_loss: 4.7269 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2003/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2766 - categorical_accuracy: 0.4917 - val_loss: 4.8236 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2004/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2568 - categorical_accuracy: 0.4900 - val_loss: 4.6643 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2005/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2476 - categorical_accuracy: 0.4978 - val_loss: 4.7660 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2006/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2503 - categorical_accuracy: 0.4917 - val_loss: 4.9110 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2007/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2524 - categorical_accuracy: 0.4933 - val_loss: 4.7149 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2008/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2395 - categorical_accuracy: 0.5050 - val_loss: 4.7405 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2009/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2546 - categorical_accuracy: 0.4944 - val_loss: 4.6686 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2010/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2460 - categorical_accuracy: 0.4939 - val_loss: 4.9429 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2011/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2342 - categorical_accuracy: 0.5017 - val_loss: 4.8224 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2012/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2359 - categorical_accuracy: 0.4967 - val_loss: 4.9922 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2013/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2479 - categorical_accuracy: 0.5044 - val_loss: 4.6361 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2014/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2642 - categorical_accuracy: 0.4917 - val_loss: 4.9041 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2015/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3056 - categorical_accuracy: 0.4822 - val_loss: 4.3816 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2016/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2983 - categorical_accuracy: 0.4689 - val_loss: 4.3545 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2017/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2765 - categorical_accuracy: 0.4872 - val_loss: 4.5199 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2018/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2583 - categorical_accuracy: 0.4872 - val_loss: 4.8724 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2019/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2617 - categorical_accuracy: 0.4817 - val_loss: 4.7639 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2020/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2602 - categorical_accuracy: 0.4950 - val_loss: 4.9204 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2021/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2544 - categorical_accuracy: 0.5011 - val_loss: 4.4414 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2022/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2951 - categorical_accuracy: 0.4650 - val_loss: 4.8217 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2023/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2539 - categorical_accuracy: 0.4933 - val_loss: 4.7877 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2024/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2472 - categorical_accuracy: 0.5022 - val_loss: 4.5981 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2025/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2522 - categorical_accuracy: 0.4933 - val_loss: 4.5444 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2026/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2576 - categorical_accuracy: 0.4911 - val_loss: 4.7678 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2027/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2776 - categorical_accuracy: 0.4906 - val_loss: 4.1228 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2028/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3234 - categorical_accuracy: 0.4611 - val_loss: 4.6294 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2029/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2591 - categorical_accuracy: 0.4983 - val_loss: 4.5792 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2030/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2565 - categorical_accuracy: 0.4933 - val_loss: 4.6606 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2031/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2485 - categorical_accuracy: 0.4917 - val_loss: 4.7765 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2032/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2527 - categorical_accuracy: 0.4922 - val_loss: 4.7826 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2033/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2625 - categorical_accuracy: 0.4883 - val_loss: 4.6527 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2034/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2733 - categorical_accuracy: 0.4844 - val_loss: 4.4300 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2035/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2567 - categorical_accuracy: 0.4894 - val_loss: 4.6871 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2036/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2729 - categorical_accuracy: 0.4883 - val_loss: 4.4846 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2037/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2536 - categorical_accuracy: 0.4989 - val_loss: 4.5324 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2038/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2790 - categorical_accuracy: 0.4861 - val_loss: 4.6618 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2039/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2764 - categorical_accuracy: 0.4839 - val_loss: 4.5039 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2040/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2524 - categorical_accuracy: 0.5056 - val_loss: 4.4268 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2041/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2933 - categorical_accuracy: 0.4794 - val_loss: 4.4287 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2042/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2783 - categorical_accuracy: 0.4856 - val_loss: 4.8106 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2043/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2591 - categorical_accuracy: 0.5000 - val_loss: 4.4899 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2044/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2686 - categorical_accuracy: 0.4867 - val_loss: 4.5324 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2045/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2631 - categorical_accuracy: 0.4806 - val_loss: 4.5949 - val_categorical_accuracy: 0.1333\n",
      "Epoch 2046/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2613 - categorical_accuracy: 0.4811 - val_loss: 5.0033 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2047/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3366 - categorical_accuracy: 0.4572 - val_loss: 4.6982 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2048/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2689 - categorical_accuracy: 0.4917 - val_loss: 4.7503 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2049/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2472 - categorical_accuracy: 0.5028 - val_loss: 4.8030 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2050/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2488 - categorical_accuracy: 0.4972 - val_loss: 4.5777 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2051/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2455 - categorical_accuracy: 0.5050 - val_loss: 4.7261 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2052/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2525 - categorical_accuracy: 0.4994 - val_loss: 4.7443 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2053/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2433 - categorical_accuracy: 0.4950 - val_loss: 5.0436 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2054/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2564 - categorical_accuracy: 0.4894 - val_loss: 4.7286 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2055/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2890 - categorical_accuracy: 0.4811 - val_loss: 4.7205 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2056/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2984 - categorical_accuracy: 0.4806 - val_loss: 4.8279 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2057/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2928 - categorical_accuracy: 0.4783 - val_loss: 4.4769 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2058/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2578 - categorical_accuracy: 0.4967 - val_loss: 4.7491 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2059/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2409 - categorical_accuracy: 0.4961 - val_loss: 4.8316 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2060/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2657 - categorical_accuracy: 0.4900 - val_loss: 4.5033 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2061/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2658 - categorical_accuracy: 0.4928 - val_loss: 4.4486 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2062/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2867 - categorical_accuracy: 0.4767 - val_loss: 4.6498 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2063/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3838 - categorical_accuracy: 0.4511 - val_loss: 4.7711 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2064/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2708 - categorical_accuracy: 0.4889 - val_loss: 4.6329 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2065/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2550 - categorical_accuracy: 0.5017 - val_loss: 4.6096 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2066/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2564 - categorical_accuracy: 0.4861 - val_loss: 4.5971 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2067/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2515 - categorical_accuracy: 0.4978 - val_loss: 4.7676 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2068/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2618 - categorical_accuracy: 0.4972 - val_loss: 4.3533 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2069/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2631 - categorical_accuracy: 0.4939 - val_loss: 4.7039 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2070/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2772 - categorical_accuracy: 0.4917 - val_loss: 4.4953 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2071/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2675 - categorical_accuracy: 0.4839 - val_loss: 4.4470 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2072/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2693 - categorical_accuracy: 0.4944 - val_loss: 4.3984 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2073/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2683 - categorical_accuracy: 0.4950 - val_loss: 4.4978 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2074/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2712 - categorical_accuracy: 0.4811 - val_loss: 4.3025 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2075/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2671 - categorical_accuracy: 0.4856 - val_loss: 4.6580 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2076/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2612 - categorical_accuracy: 0.4861 - val_loss: 4.5920 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2077/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2669 - categorical_accuracy: 0.4878 - val_loss: 4.4626 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2078/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2774 - categorical_accuracy: 0.4861 - val_loss: 4.6655 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2079/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2974 - categorical_accuracy: 0.4722 - val_loss: 4.2786 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2080/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2853 - categorical_accuracy: 0.4739 - val_loss: 4.7247 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2081/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2855 - categorical_accuracy: 0.4739 - val_loss: 4.5651 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2082/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2838 - categorical_accuracy: 0.4817 - val_loss: 4.7747 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2083/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2751 - categorical_accuracy: 0.4861 - val_loss: 4.4207 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2084/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2975 - categorical_accuracy: 0.4683 - val_loss: 4.4834 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2085/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3039 - categorical_accuracy: 0.4750 - val_loss: 4.6972 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2086/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2925 - categorical_accuracy: 0.4711 - val_loss: 4.8342 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2087/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2651 - categorical_accuracy: 0.4861 - val_loss: 4.5918 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2088/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2596 - categorical_accuracy: 0.4939 - val_loss: 4.7851 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2089/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2736 - categorical_accuracy: 0.4872 - val_loss: 4.5257 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2090/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2727 - categorical_accuracy: 0.4856 - val_loss: 4.7140 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2091/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2550 - categorical_accuracy: 0.5033 - val_loss: 4.8474 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2092/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2741 - categorical_accuracy: 0.4878 - val_loss: 4.5813 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2093/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2697 - categorical_accuracy: 0.4817 - val_loss: 4.6184 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2094/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2607 - categorical_accuracy: 0.4900 - val_loss: 4.9134 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2095/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2729 - categorical_accuracy: 0.4872 - val_loss: 4.8929 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2096/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3077 - categorical_accuracy: 0.4639 - val_loss: 4.7597 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2097/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2913 - categorical_accuracy: 0.4722 - val_loss: 4.6387 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2098/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2762 - categorical_accuracy: 0.4822 - val_loss: 4.3971 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2099/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2768 - categorical_accuracy: 0.4889 - val_loss: 5.1079 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2100/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2841 - categorical_accuracy: 0.4728 - val_loss: 4.8687 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2101/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2785 - categorical_accuracy: 0.4761 - val_loss: 4.9626 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2102/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2871 - categorical_accuracy: 0.4678 - val_loss: 5.0359 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2103/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2732 - categorical_accuracy: 0.4844 - val_loss: 4.9854 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2104/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2901 - categorical_accuracy: 0.4750 - val_loss: 5.0584 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2105/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2816 - categorical_accuracy: 0.4744 - val_loss: 4.9409 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2106/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2638 - categorical_accuracy: 0.4911 - val_loss: 4.7868 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2107/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2676 - categorical_accuracy: 0.4850 - val_loss: 4.9892 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2108/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2817 - categorical_accuracy: 0.4772 - val_loss: 4.9645 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2109/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2631 - categorical_accuracy: 0.4894 - val_loss: 5.0163 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2110/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2595 - categorical_accuracy: 0.4833 - val_loss: 5.1988 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2111/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2874 - categorical_accuracy: 0.4789 - val_loss: 5.3495 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2112/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2955 - categorical_accuracy: 0.4894 - val_loss: 5.0671 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2113/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2967 - categorical_accuracy: 0.4744 - val_loss: 5.0851 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2114/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3078 - categorical_accuracy: 0.4767 - val_loss: 4.9345 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2115/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3162 - categorical_accuracy: 0.4606 - val_loss: 5.0765 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2116/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3013 - categorical_accuracy: 0.4706 - val_loss: 4.9446 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2117/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2944 - categorical_accuracy: 0.4772 - val_loss: 4.9308 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2118/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3187 - categorical_accuracy: 0.4656 - val_loss: 4.7857 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2119/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3079 - categorical_accuracy: 0.4639 - val_loss: 4.9584 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2120/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3014 - categorical_accuracy: 0.4761 - val_loss: 4.9283 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2121/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2923 - categorical_accuracy: 0.4800 - val_loss: 4.9813 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2122/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3089 - categorical_accuracy: 0.4694 - val_loss: 4.9950 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2123/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3019 - categorical_accuracy: 0.4728 - val_loss: 5.1526 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2124/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2991 - categorical_accuracy: 0.4750 - val_loss: 4.6985 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2125/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2882 - categorical_accuracy: 0.4878 - val_loss: 4.9449 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2126/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2958 - categorical_accuracy: 0.4772 - val_loss: 4.8042 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2127/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3129 - categorical_accuracy: 0.4722 - val_loss: 4.9295 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2128/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3229 - categorical_accuracy: 0.4611 - val_loss: 4.5674 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2129/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3345 - categorical_accuracy: 0.4617 - val_loss: 4.4097 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2130/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4025 - categorical_accuracy: 0.4422 - val_loss: 4.4565 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2131/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3101 - categorical_accuracy: 0.4744 - val_loss: 4.7759 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2132/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2985 - categorical_accuracy: 0.4733 - val_loss: 5.0941 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2133/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2756 - categorical_accuracy: 0.4933 - val_loss: 4.9039 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2134/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2764 - categorical_accuracy: 0.4967 - val_loss: 4.9849 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2135/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2740 - categorical_accuracy: 0.4906 - val_loss: 4.9748 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2136/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2790 - categorical_accuracy: 0.4894 - val_loss: 4.9933 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2137/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2708 - categorical_accuracy: 0.4906 - val_loss: 4.9953 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2138/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2661 - categorical_accuracy: 0.4911 - val_loss: 4.9570 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2139/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2660 - categorical_accuracy: 0.4944 - val_loss: 5.0020 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2140/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2668 - categorical_accuracy: 0.4967 - val_loss: 5.1571 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2141/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2639 - categorical_accuracy: 0.4883 - val_loss: 5.0991 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2142/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2596 - categorical_accuracy: 0.5006 - val_loss: 5.1207 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2143/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2768 - categorical_accuracy: 0.4844 - val_loss: 5.0522 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2144/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3126 - categorical_accuracy: 0.4739 - val_loss: 5.0548 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2145/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2718 - categorical_accuracy: 0.4894 - val_loss: 5.0887 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2146/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2614 - categorical_accuracy: 0.4867 - val_loss: 5.0105 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2147/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2943 - categorical_accuracy: 0.4833 - val_loss: 4.7014 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2148/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2833 - categorical_accuracy: 0.4828 - val_loss: 5.2208 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2149/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2865 - categorical_accuracy: 0.4756 - val_loss: 5.0193 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2150/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2755 - categorical_accuracy: 0.4822 - val_loss: 5.0229 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2151/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2780 - categorical_accuracy: 0.4917 - val_loss: 5.2240 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2152/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2821 - categorical_accuracy: 0.4844 - val_loss: 5.1911 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2153/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2998 - categorical_accuracy: 0.4817 - val_loss: 4.6777 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2154/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2957 - categorical_accuracy: 0.4817 - val_loss: 4.8299 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2155/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2943 - categorical_accuracy: 0.4783 - val_loss: 5.1495 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2156/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3114 - categorical_accuracy: 0.4639 - val_loss: 4.8886 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2157/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2830 - categorical_accuracy: 0.4878 - val_loss: 4.9725 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2158/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2685 - categorical_accuracy: 0.4922 - val_loss: 5.0209 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2159/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2725 - categorical_accuracy: 0.4972 - val_loss: 4.8813 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2160/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2981 - categorical_accuracy: 0.4806 - val_loss: 4.9015 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2161/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3502 - categorical_accuracy: 0.4544 - val_loss: 4.8814 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2162/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2911 - categorical_accuracy: 0.4822 - val_loss: 4.8640 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2163/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3139 - categorical_accuracy: 0.4778 - val_loss: 5.1209 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2164/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3046 - categorical_accuracy: 0.4794 - val_loss: 4.9706 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2165/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2752 - categorical_accuracy: 0.4878 - val_loss: 5.0606 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2166/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2709 - categorical_accuracy: 0.4894 - val_loss: 5.0166 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2167/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2702 - categorical_accuracy: 0.4917 - val_loss: 4.8687 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2168/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2895 - categorical_accuracy: 0.4878 - val_loss: 5.0680 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2169/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2882 - categorical_accuracy: 0.4800 - val_loss: 4.8036 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2170/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2730 - categorical_accuracy: 0.5000 - val_loss: 4.8336 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2171/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2683 - categorical_accuracy: 0.4839 - val_loss: 5.0321 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2172/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2529 - categorical_accuracy: 0.5000 - val_loss: 4.9821 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2173/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2897 - categorical_accuracy: 0.4828 - val_loss: 4.6709 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2174/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3085 - categorical_accuracy: 0.4717 - val_loss: 5.0289 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2175/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2596 - categorical_accuracy: 0.4911 - val_loss: 4.7362 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2176/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2873 - categorical_accuracy: 0.4806 - val_loss: 4.8952 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2177/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3258 - categorical_accuracy: 0.4661 - val_loss: 4.9241 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2178/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2856 - categorical_accuracy: 0.4800 - val_loss: 4.7739 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2179/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2839 - categorical_accuracy: 0.4867 - val_loss: 5.0231 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2180/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2815 - categorical_accuracy: 0.4767 - val_loss: 5.0580 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2181/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2765 - categorical_accuracy: 0.4800 - val_loss: 5.0744 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2182/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2689 - categorical_accuracy: 0.4911 - val_loss: 5.0007 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2183/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2586 - categorical_accuracy: 0.4933 - val_loss: 4.9870 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2184/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2513 - categorical_accuracy: 0.4967 - val_loss: 4.9902 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2185/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2689 - categorical_accuracy: 0.4939 - val_loss: 5.2229 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2186/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2906 - categorical_accuracy: 0.4856 - val_loss: 4.7799 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2187/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2892 - categorical_accuracy: 0.4861 - val_loss: 4.9986 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2188/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3266 - categorical_accuracy: 0.4606 - val_loss: 4.8278 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2189/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3579 - categorical_accuracy: 0.4483 - val_loss: 4.8880 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2190/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3067 - categorical_accuracy: 0.4694 - val_loss: 4.7131 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2191/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3003 - categorical_accuracy: 0.4672 - val_loss: 4.9752 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2192/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2733 - categorical_accuracy: 0.4722 - val_loss: 4.8996 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2193/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3018 - categorical_accuracy: 0.4717 - val_loss: 4.8541 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2194/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3053 - categorical_accuracy: 0.4633 - val_loss: 4.9265 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2195/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3177 - categorical_accuracy: 0.4583 - val_loss: 5.0653 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2196/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2821 - categorical_accuracy: 0.4794 - val_loss: 5.0745 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2197/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2703 - categorical_accuracy: 0.4689 - val_loss: 5.0177 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2198/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2727 - categorical_accuracy: 0.4800 - val_loss: 4.7354 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2199/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3173 - categorical_accuracy: 0.4594 - val_loss: 4.7297 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2200/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3077 - categorical_accuracy: 0.4672 - val_loss: 4.6538 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2201/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3069 - categorical_accuracy: 0.4578 - val_loss: 5.2667 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2202/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2855 - categorical_accuracy: 0.4761 - val_loss: 4.9549 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2203/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2684 - categorical_accuracy: 0.4800 - val_loss: 4.9870 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2204/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2908 - categorical_accuracy: 0.4633 - val_loss: 5.0138 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2205/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2767 - categorical_accuracy: 0.4761 - val_loss: 4.8553 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2206/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2798 - categorical_accuracy: 0.4750 - val_loss: 5.0874 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2207/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2627 - categorical_accuracy: 0.4767 - val_loss: 5.0836 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2208/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2550 - categorical_accuracy: 0.4844 - val_loss: 4.9952 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2209/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2875 - categorical_accuracy: 0.4783 - val_loss: 4.8926 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2210/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2877 - categorical_accuracy: 0.4711 - val_loss: 4.9933 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2211/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2694 - categorical_accuracy: 0.4778 - val_loss: 5.8681 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2212/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3494 - categorical_accuracy: 0.4672 - val_loss: 4.8008 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2213/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2678 - categorical_accuracy: 0.4806 - val_loss: 5.0019 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2214/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2675 - categorical_accuracy: 0.4783 - val_loss: 4.8417 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2215/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3103 - categorical_accuracy: 0.4767 - val_loss: 5.0600 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2216/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2764 - categorical_accuracy: 0.4856 - val_loss: 4.9591 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2217/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2575 - categorical_accuracy: 0.4828 - val_loss: 5.2819 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2218/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2723 - categorical_accuracy: 0.4861 - val_loss: 5.1194 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2219/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2631 - categorical_accuracy: 0.4933 - val_loss: 5.1034 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2220/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2685 - categorical_accuracy: 0.4811 - val_loss: 5.0100 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2221/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2549 - categorical_accuracy: 0.4922 - val_loss: 5.1531 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2222/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2805 - categorical_accuracy: 0.4717 - val_loss: 5.2090 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2223/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2703 - categorical_accuracy: 0.4861 - val_loss: 5.1019 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2224/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2608 - categorical_accuracy: 0.4861 - val_loss: 5.1526 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2225/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2604 - categorical_accuracy: 0.4806 - val_loss: 5.2777 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2226/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2755 - categorical_accuracy: 0.4706 - val_loss: 4.9802 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2227/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2685 - categorical_accuracy: 0.4783 - val_loss: 4.9298 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2228/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2540 - categorical_accuracy: 0.4917 - val_loss: 5.2443 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2229/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2589 - categorical_accuracy: 0.4817 - val_loss: 5.0451 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2230/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2398 - categorical_accuracy: 0.4906 - val_loss: 5.0528 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2231/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2674 - categorical_accuracy: 0.4856 - val_loss: 4.7049 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2232/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3533 - categorical_accuracy: 0.4461 - val_loss: 4.7287 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2233/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2853 - categorical_accuracy: 0.4683 - val_loss: 5.2640 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2234/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2531 - categorical_accuracy: 0.4894 - val_loss: 5.0728 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2235/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2720 - categorical_accuracy: 0.4822 - val_loss: 4.9863 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2236/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2666 - categorical_accuracy: 0.4822 - val_loss: 4.9948 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2237/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2417 - categorical_accuracy: 0.4911 - val_loss: 5.0355 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2238/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2522 - categorical_accuracy: 0.4861 - val_loss: 4.9956 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2239/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2745 - categorical_accuracy: 0.4750 - val_loss: 5.2508 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2240/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2770 - categorical_accuracy: 0.4722 - val_loss: 5.0359 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2241/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2557 - categorical_accuracy: 0.4889 - val_loss: 5.1547 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2242/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2741 - categorical_accuracy: 0.4844 - val_loss: 5.2365 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2243/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2580 - categorical_accuracy: 0.4922 - val_loss: 5.0588 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2244/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2404 - categorical_accuracy: 0.4978 - val_loss: 5.0132 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2245/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2544 - categorical_accuracy: 0.4917 - val_loss: 5.0306 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2246/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2561 - categorical_accuracy: 0.4906 - val_loss: 5.0363 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2247/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2645 - categorical_accuracy: 0.4817 - val_loss: 5.0660 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2248/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2675 - categorical_accuracy: 0.4889 - val_loss: 5.1013 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2249/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2647 - categorical_accuracy: 0.4789 - val_loss: 5.0465 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2250/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2473 - categorical_accuracy: 0.4939 - val_loss: 5.2919 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2251/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2544 - categorical_accuracy: 0.4878 - val_loss: 5.0948 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2252/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2582 - categorical_accuracy: 0.4856 - val_loss: 4.8877 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2253/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2727 - categorical_accuracy: 0.4872 - val_loss: 5.2108 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2254/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2521 - categorical_accuracy: 0.4867 - val_loss: 4.9990 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2255/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2538 - categorical_accuracy: 0.4833 - val_loss: 5.1706 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2256/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2462 - categorical_accuracy: 0.4906 - val_loss: 5.2442 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2257/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2622 - categorical_accuracy: 0.4772 - val_loss: 4.8375 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2258/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2529 - categorical_accuracy: 0.4878 - val_loss: 5.1704 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2259/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3149 - categorical_accuracy: 0.4667 - val_loss: 5.6224 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2260/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3357 - categorical_accuracy: 0.4667 - val_loss: 4.8001 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2261/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2977 - categorical_accuracy: 0.4628 - val_loss: 4.8601 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2262/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2742 - categorical_accuracy: 0.4728 - val_loss: 5.2210 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2263/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2608 - categorical_accuracy: 0.4844 - val_loss: 5.2458 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2264/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2524 - categorical_accuracy: 0.4972 - val_loss: 5.2449 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2265/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2413 - categorical_accuracy: 0.4978 - val_loss: 5.2218 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2266/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2611 - categorical_accuracy: 0.4878 - val_loss: 5.1480 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2267/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2583 - categorical_accuracy: 0.4983 - val_loss: 5.2405 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2268/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2464 - categorical_accuracy: 0.4944 - val_loss: 5.3503 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2269/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2370 - categorical_accuracy: 0.5028 - val_loss: 5.3874 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2270/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2444 - categorical_accuracy: 0.4944 - val_loss: 5.3921 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2271/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2366 - categorical_accuracy: 0.4967 - val_loss: 5.5815 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2272/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2602 - categorical_accuracy: 0.4906 - val_loss: 5.3380 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2273/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2623 - categorical_accuracy: 0.4856 - val_loss: 5.3151 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2274/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2545 - categorical_accuracy: 0.4867 - val_loss: 5.2729 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2275/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2824 - categorical_accuracy: 0.4733 - val_loss: 5.4060 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2276/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2650 - categorical_accuracy: 0.4844 - val_loss: 5.2938 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2277/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2559 - categorical_accuracy: 0.4867 - val_loss: 5.4644 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2278/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2556 - categorical_accuracy: 0.4917 - val_loss: 5.3137 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2279/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2441 - categorical_accuracy: 0.4972 - val_loss: 5.3814 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2280/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2427 - categorical_accuracy: 0.4956 - val_loss: 5.3766 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2281/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2549 - categorical_accuracy: 0.4878 - val_loss: 5.5729 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2282/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2573 - categorical_accuracy: 0.4961 - val_loss: 5.0739 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2283/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2344 - categorical_accuracy: 0.4922 - val_loss: 5.4395 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2284/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2444 - categorical_accuracy: 0.4967 - val_loss: 5.2948 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2285/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2448 - categorical_accuracy: 0.4911 - val_loss: 5.4030 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2286/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2456 - categorical_accuracy: 0.4939 - val_loss: 5.5025 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2287/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2389 - categorical_accuracy: 0.4989 - val_loss: 5.3707 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2288/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2525 - categorical_accuracy: 0.4906 - val_loss: 5.3863 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2289/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2407 - categorical_accuracy: 0.5011 - val_loss: 5.1209 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2290/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2346 - categorical_accuracy: 0.5006 - val_loss: 5.3964 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2291/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2412 - categorical_accuracy: 0.5072 - val_loss: 5.0813 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2292/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2347 - categorical_accuracy: 0.4994 - val_loss: 5.4521 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2293/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2238 - categorical_accuracy: 0.5083 - val_loss: 5.5620 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2294/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2402 - categorical_accuracy: 0.5028 - val_loss: 5.5675 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2295/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2449 - categorical_accuracy: 0.4944 - val_loss: 5.5944 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2296/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2497 - categorical_accuracy: 0.4961 - val_loss: 5.6554 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2297/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2481 - categorical_accuracy: 0.4956 - val_loss: 5.2517 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2298/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2504 - categorical_accuracy: 0.4889 - val_loss: 5.4246 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2299/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2730 - categorical_accuracy: 0.4789 - val_loss: 5.6448 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2300/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2410 - categorical_accuracy: 0.4950 - val_loss: 5.3537 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2301/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2244 - categorical_accuracy: 0.5100 - val_loss: 5.6837 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2302/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2740 - categorical_accuracy: 0.4811 - val_loss: 4.9601 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2303/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3064 - categorical_accuracy: 0.4694 - val_loss: 5.5966 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2304/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2532 - categorical_accuracy: 0.4839 - val_loss: 5.4947 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2305/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2437 - categorical_accuracy: 0.4983 - val_loss: 5.5179 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2306/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2663 - categorical_accuracy: 0.4817 - val_loss: 5.5817 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2307/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2334 - categorical_accuracy: 0.5000 - val_loss: 5.3699 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2308/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2386 - categorical_accuracy: 0.4994 - val_loss: 5.4665 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2309/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2292 - categorical_accuracy: 0.4967 - val_loss: 5.5772 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2310/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2277 - categorical_accuracy: 0.4989 - val_loss: 5.5650 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2311/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2228 - categorical_accuracy: 0.5022 - val_loss: 5.7446 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2312/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2424 - categorical_accuracy: 0.4978 - val_loss: 5.7430 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2313/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2385 - categorical_accuracy: 0.4900 - val_loss: 5.8575 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2314/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2889 - categorical_accuracy: 0.4733 - val_loss: 5.8501 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2315/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2577 - categorical_accuracy: 0.4806 - val_loss: 5.6675 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2316/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2325 - categorical_accuracy: 0.5044 - val_loss: 5.9447 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2317/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2496 - categorical_accuracy: 0.4906 - val_loss: 5.5537 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2318/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2240 - categorical_accuracy: 0.5106 - val_loss: 5.7292 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2319/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2371 - categorical_accuracy: 0.5006 - val_loss: 5.8448 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2320/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2431 - categorical_accuracy: 0.4900 - val_loss: 5.4550 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2321/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2324 - categorical_accuracy: 0.5011 - val_loss: 5.9784 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2322/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2707 - categorical_accuracy: 0.4856 - val_loss: 5.8138 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2323/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2607 - categorical_accuracy: 0.4839 - val_loss: 5.5871 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2324/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2400 - categorical_accuracy: 0.4906 - val_loss: 5.4987 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2325/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2340 - categorical_accuracy: 0.4911 - val_loss: 5.8391 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2326/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2475 - categorical_accuracy: 0.4872 - val_loss: 5.7025 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2327/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2405 - categorical_accuracy: 0.4972 - val_loss: 5.6068 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2328/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2442 - categorical_accuracy: 0.4956 - val_loss: 5.6525 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2329/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2401 - categorical_accuracy: 0.4911 - val_loss: 5.5552 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2330/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2259 - categorical_accuracy: 0.5078 - val_loss: 5.9389 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2331/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2356 - categorical_accuracy: 0.4983 - val_loss: 5.7060 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2332/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2409 - categorical_accuracy: 0.4933 - val_loss: 5.6584 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2333/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2583 - categorical_accuracy: 0.4839 - val_loss: 5.6265 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2334/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2432 - categorical_accuracy: 0.4950 - val_loss: 5.8676 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2335/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2443 - categorical_accuracy: 0.4906 - val_loss: 5.4860 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2336/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2181 - categorical_accuracy: 0.5089 - val_loss: 5.7686 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2337/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2243 - categorical_accuracy: 0.5028 - val_loss: 5.6495 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2338/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2229 - categorical_accuracy: 0.5011 - val_loss: 5.6129 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2339/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2326 - categorical_accuracy: 0.5011 - val_loss: 5.8395 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2340/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2303 - categorical_accuracy: 0.5006 - val_loss: 5.7399 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2341/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2250 - categorical_accuracy: 0.5017 - val_loss: 5.6007 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2342/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2307 - categorical_accuracy: 0.5017 - val_loss: 5.6072 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2343/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2211 - categorical_accuracy: 0.4989 - val_loss: 5.7486 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2344/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2165 - categorical_accuracy: 0.5106 - val_loss: 5.7223 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2345/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2291 - categorical_accuracy: 0.5050 - val_loss: 5.8315 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2346/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3118 - categorical_accuracy: 0.4700 - val_loss: 5.8041 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2347/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2846 - categorical_accuracy: 0.4850 - val_loss: 5.9381 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2348/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2308 - categorical_accuracy: 0.4922 - val_loss: 6.0223 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2349/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2629 - categorical_accuracy: 0.4811 - val_loss: 5.3121 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2350/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2477 - categorical_accuracy: 0.4967 - val_loss: 5.6360 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2351/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2277 - categorical_accuracy: 0.4894 - val_loss: 5.6410 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2352/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2397 - categorical_accuracy: 0.4950 - val_loss: 5.5564 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2353/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2519 - categorical_accuracy: 0.4867 - val_loss: 6.0546 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2354/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3085 - categorical_accuracy: 0.4711 - val_loss: 5.1404 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2355/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2728 - categorical_accuracy: 0.4861 - val_loss: 6.0027 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2356/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2350 - categorical_accuracy: 0.4933 - val_loss: 5.7218 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2357/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2179 - categorical_accuracy: 0.5017 - val_loss: 5.9434 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2358/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2526 - categorical_accuracy: 0.4872 - val_loss: 5.7231 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2359/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2385 - categorical_accuracy: 0.4956 - val_loss: 5.6630 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2360/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2339 - categorical_accuracy: 0.5056 - val_loss: 5.8655 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2361/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2336 - categorical_accuracy: 0.4961 - val_loss: 5.9055 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2362/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2290 - categorical_accuracy: 0.5050 - val_loss: 5.7103 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2363/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2654 - categorical_accuracy: 0.4933 - val_loss: 5.9397 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2364/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2473 - categorical_accuracy: 0.4933 - val_loss: 5.4685 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2365/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2306 - categorical_accuracy: 0.5150 - val_loss: 5.9067 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2366/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2441 - categorical_accuracy: 0.4933 - val_loss: 5.5502 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2367/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2903 - categorical_accuracy: 0.4767 - val_loss: 5.6052 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2368/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2344 - categorical_accuracy: 0.4928 - val_loss: 5.5925 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2369/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2408 - categorical_accuracy: 0.4917 - val_loss: 5.5108 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2370/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2414 - categorical_accuracy: 0.5011 - val_loss: 5.6185 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2371/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2508 - categorical_accuracy: 0.5006 - val_loss: 5.4252 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2372/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2473 - categorical_accuracy: 0.5028 - val_loss: 5.5421 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2373/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2469 - categorical_accuracy: 0.4850 - val_loss: 5.7790 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2374/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2262 - categorical_accuracy: 0.5000 - val_loss: 5.5424 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2375/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3395 - categorical_accuracy: 0.4672 - val_loss: 5.2855 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2376/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2727 - categorical_accuracy: 0.4772 - val_loss: 5.4984 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2377/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2445 - categorical_accuracy: 0.4889 - val_loss: 5.9737 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2378/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2392 - categorical_accuracy: 0.5056 - val_loss: 5.6272 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2379/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2407 - categorical_accuracy: 0.4939 - val_loss: 5.5849 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2380/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2848 - categorical_accuracy: 0.4706 - val_loss: 5.5584 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2381/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2365 - categorical_accuracy: 0.5039 - val_loss: 5.4664 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2382/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2415 - categorical_accuracy: 0.4928 - val_loss: 5.9459 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2383/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2396 - categorical_accuracy: 0.5022 - val_loss: 5.4077 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2384/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2402 - categorical_accuracy: 0.5011 - val_loss: 5.6035 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2385/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2332 - categorical_accuracy: 0.5022 - val_loss: 5.6318 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2386/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2311 - categorical_accuracy: 0.5017 - val_loss: 5.8141 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2387/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2346 - categorical_accuracy: 0.5044 - val_loss: 5.6548 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2388/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2581 - categorical_accuracy: 0.4900 - val_loss: 5.6771 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2389/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2280 - categorical_accuracy: 0.5000 - val_loss: 5.6305 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2390/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2282 - categorical_accuracy: 0.5039 - val_loss: 5.7172 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2391/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2148 - categorical_accuracy: 0.5194 - val_loss: 5.8186 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2392/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2817 - categorical_accuracy: 0.4839 - val_loss: 5.8610 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2393/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2423 - categorical_accuracy: 0.4867 - val_loss: 5.8517 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2394/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2186 - categorical_accuracy: 0.5089 - val_loss: 5.7801 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2395/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2127 - categorical_accuracy: 0.5083 - val_loss: 5.7944 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2396/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2114 - categorical_accuracy: 0.5206 - val_loss: 6.0232 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2397/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2181 - categorical_accuracy: 0.5128 - val_loss: 5.9786 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2398/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2197 - categorical_accuracy: 0.5028 - val_loss: 5.6750 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2399/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2068 - categorical_accuracy: 0.5139 - val_loss: 5.8147 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2400/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2103 - categorical_accuracy: 0.5094 - val_loss: 5.7036 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2401/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2103 - categorical_accuracy: 0.5200 - val_loss: 5.7482 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2402/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2454 - categorical_accuracy: 0.5078 - val_loss: 6.3759 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2403/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2796 - categorical_accuracy: 0.4911 - val_loss: 5.8661 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2404/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2319 - categorical_accuracy: 0.5078 - val_loss: 5.5437 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2405/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2343 - categorical_accuracy: 0.5089 - val_loss: 5.7554 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2406/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2446 - categorical_accuracy: 0.4956 - val_loss: 5.8446 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2407/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2340 - categorical_accuracy: 0.5011 - val_loss: 5.3147 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2408/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2918 - categorical_accuracy: 0.4861 - val_loss: 5.4669 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2409/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2605 - categorical_accuracy: 0.4883 - val_loss: 5.9671 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2410/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3099 - categorical_accuracy: 0.4689 - val_loss: 5.5670 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2411/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2573 - categorical_accuracy: 0.4922 - val_loss: 5.9623 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2412/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2442 - categorical_accuracy: 0.4928 - val_loss: 5.7521 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2413/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2731 - categorical_accuracy: 0.5067 - val_loss: 6.0418 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2414/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2585 - categorical_accuracy: 0.4856 - val_loss: 5.7895 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2415/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2381 - categorical_accuracy: 0.4983 - val_loss: 5.7116 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2416/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2176 - categorical_accuracy: 0.5078 - val_loss: 5.7454 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2417/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2129 - categorical_accuracy: 0.5067 - val_loss: 5.7070 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2418/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2133 - categorical_accuracy: 0.5072 - val_loss: 5.8298 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2419/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2070 - categorical_accuracy: 0.5228 - val_loss: 5.7350 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2420/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2037 - categorical_accuracy: 0.5228 - val_loss: 5.8652 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2421/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2091 - categorical_accuracy: 0.5211 - val_loss: 5.7309 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2422/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2193 - categorical_accuracy: 0.5061 - val_loss: 5.8315 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2423/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2384 - categorical_accuracy: 0.4956 - val_loss: 5.8199 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2424/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2129 - categorical_accuracy: 0.5178 - val_loss: 5.8989 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2425/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2070 - categorical_accuracy: 0.5150 - val_loss: 5.9734 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2426/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2071 - categorical_accuracy: 0.5161 - val_loss: 5.8763 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2427/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2111 - categorical_accuracy: 0.5211 - val_loss: 5.9060 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2428/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2248 - categorical_accuracy: 0.5083 - val_loss: 5.7977 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2429/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2133 - categorical_accuracy: 0.5022 - val_loss: 5.8045 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2430/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2143 - categorical_accuracy: 0.5122 - val_loss: 6.1154 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2431/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2216 - categorical_accuracy: 0.5017 - val_loss: 5.9009 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2432/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2181 - categorical_accuracy: 0.5050 - val_loss: 5.6030 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2433/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2274 - categorical_accuracy: 0.4994 - val_loss: 5.9255 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2434/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2207 - categorical_accuracy: 0.4989 - val_loss: 5.9916 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2435/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2344 - categorical_accuracy: 0.5061 - val_loss: 5.6645 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2436/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2371 - categorical_accuracy: 0.5006 - val_loss: 5.7542 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2437/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2376 - categorical_accuracy: 0.4989 - val_loss: 5.8644 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2438/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2493 - categorical_accuracy: 0.4956 - val_loss: 5.8718 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2439/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2145 - categorical_accuracy: 0.5133 - val_loss: 5.6735 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2440/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2395 - categorical_accuracy: 0.5039 - val_loss: 5.7260 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2441/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2705 - categorical_accuracy: 0.4756 - val_loss: 6.2602 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2442/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2930 - categorical_accuracy: 0.4739 - val_loss: 5.7412 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2443/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2820 - categorical_accuracy: 0.4750 - val_loss: 6.3358 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2444/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2992 - categorical_accuracy: 0.4806 - val_loss: 6.1515 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2445/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2627 - categorical_accuracy: 0.4817 - val_loss: 5.8459 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2446/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2398 - categorical_accuracy: 0.4900 - val_loss: 5.7603 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2447/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2287 - categorical_accuracy: 0.5011 - val_loss: 5.7980 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2448/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2432 - categorical_accuracy: 0.4989 - val_loss: 6.0355 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2449/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2443 - categorical_accuracy: 0.4928 - val_loss: 6.3324 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2450/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2364 - categorical_accuracy: 0.4978 - val_loss: 6.0966 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2451/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2412 - categorical_accuracy: 0.4967 - val_loss: 6.2474 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2452/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2175 - categorical_accuracy: 0.4994 - val_loss: 6.0632 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2453/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2066 - categorical_accuracy: 0.5111 - val_loss: 5.9534 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2454/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2100 - categorical_accuracy: 0.5094 - val_loss: 5.9696 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2455/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2339 - categorical_accuracy: 0.5050 - val_loss: 6.0900 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2456/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2844 - categorical_accuracy: 0.4700 - val_loss: 5.8809 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2457/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2375 - categorical_accuracy: 0.4911 - val_loss: 5.9828 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2458/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2253 - categorical_accuracy: 0.5089 - val_loss: 5.9793 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2459/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2372 - categorical_accuracy: 0.4944 - val_loss: 6.2273 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2460/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2129 - categorical_accuracy: 0.5061 - val_loss: 5.9691 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2461/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2040 - categorical_accuracy: 0.5228 - val_loss: 6.3326 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2462/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2041 - categorical_accuracy: 0.5172 - val_loss: 6.0439 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2463/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2039 - categorical_accuracy: 0.5150 - val_loss: 6.0865 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2464/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2125 - categorical_accuracy: 0.5156 - val_loss: 6.1357 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2465/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2259 - categorical_accuracy: 0.5094 - val_loss: 6.0440 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2466/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2354 - categorical_accuracy: 0.4939 - val_loss: 5.9557 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2467/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2257 - categorical_accuracy: 0.5044 - val_loss: 5.9042 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2468/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2111 - categorical_accuracy: 0.5172 - val_loss: 5.7805 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2469/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2011 - categorical_accuracy: 0.5144 - val_loss: 5.9097 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2470/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2216 - categorical_accuracy: 0.5067 - val_loss: 5.8935 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2471/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2249 - categorical_accuracy: 0.5000 - val_loss: 5.8717 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2472/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2131 - categorical_accuracy: 0.5056 - val_loss: 6.0389 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2473/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2107 - categorical_accuracy: 0.5144 - val_loss: 6.1510 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2474/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2268 - categorical_accuracy: 0.5056 - val_loss: 5.7998 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2475/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2678 - categorical_accuracy: 0.4789 - val_loss: 5.8072 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2476/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2260 - categorical_accuracy: 0.4972 - val_loss: 6.0468 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2477/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2765 - categorical_accuracy: 0.4906 - val_loss: 5.6081 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2478/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2495 - categorical_accuracy: 0.4856 - val_loss: 5.7095 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2479/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2128 - categorical_accuracy: 0.5000 - val_loss: 6.0717 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2480/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2124 - categorical_accuracy: 0.5106 - val_loss: 6.0826 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2481/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2171 - categorical_accuracy: 0.5061 - val_loss: 6.2581 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2482/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2370 - categorical_accuracy: 0.4967 - val_loss: 5.9062 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2483/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2398 - categorical_accuracy: 0.4850 - val_loss: 6.1772 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2484/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2449 - categorical_accuracy: 0.4839 - val_loss: 5.9656 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2485/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2635 - categorical_accuracy: 0.4717 - val_loss: 6.1395 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2486/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2223 - categorical_accuracy: 0.5000 - val_loss: 6.2072 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2487/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2079 - categorical_accuracy: 0.5094 - val_loss: 6.0709 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2488/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2061 - categorical_accuracy: 0.5061 - val_loss: 5.9443 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2489/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2051 - categorical_accuracy: 0.5117 - val_loss: 6.0213 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2490/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2089 - categorical_accuracy: 0.5156 - val_loss: 6.0038 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2491/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2181 - categorical_accuracy: 0.5033 - val_loss: 5.9423 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2492/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2595 - categorical_accuracy: 0.4794 - val_loss: 6.2534 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2493/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2235 - categorical_accuracy: 0.5011 - val_loss: 5.9762 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2494/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2431 - categorical_accuracy: 0.5017 - val_loss: 6.0880 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2495/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2128 - categorical_accuracy: 0.5061 - val_loss: 6.0557 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2496/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2022 - categorical_accuracy: 0.5139 - val_loss: 6.0659 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2497/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2364 - categorical_accuracy: 0.4956 - val_loss: 5.7769 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2498/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2798 - categorical_accuracy: 0.4778 - val_loss: 5.5696 - val_categorical_accuracy: 0.1300\n",
      "Epoch 2499/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2531 - categorical_accuracy: 0.4856 - val_loss: 6.0768 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2500/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2104 - categorical_accuracy: 0.5122 - val_loss: 6.1514 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2501/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2078 - categorical_accuracy: 0.5078 - val_loss: 6.3410 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2502/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2396 - categorical_accuracy: 0.5000 - val_loss: 5.9256 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2503/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2144 - categorical_accuracy: 0.5033 - val_loss: 6.0826 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2504/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1970 - categorical_accuracy: 0.5111 - val_loss: 6.2768 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2505/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2163 - categorical_accuracy: 0.5061 - val_loss: 6.1340 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2506/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2198 - categorical_accuracy: 0.5022 - val_loss: 6.0651 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2507/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1979 - categorical_accuracy: 0.5117 - val_loss: 6.2034 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2508/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1975 - categorical_accuracy: 0.5067 - val_loss: 6.1950 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2509/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1961 - categorical_accuracy: 0.5161 - val_loss: 6.2042 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2510/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2041 - categorical_accuracy: 0.5156 - val_loss: 6.0706 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2511/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2049 - categorical_accuracy: 0.5033 - val_loss: 6.2885 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2512/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1973 - categorical_accuracy: 0.5167 - val_loss: 6.3659 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2513/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1997 - categorical_accuracy: 0.5189 - val_loss: 6.2755 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2514/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2071 - categorical_accuracy: 0.5144 - val_loss: 6.3883 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2515/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2176 - categorical_accuracy: 0.5039 - val_loss: 6.2476 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2516/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2745 - categorical_accuracy: 0.4889 - val_loss: 5.2888 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2517/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2829 - categorical_accuracy: 0.4800 - val_loss: 6.1483 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2518/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2767 - categorical_accuracy: 0.4839 - val_loss: 6.3239 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2519/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2387 - categorical_accuracy: 0.4989 - val_loss: 6.2627 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2520/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2627 - categorical_accuracy: 0.4889 - val_loss: 6.2965 - val_categorical_accuracy: 0.1333\n",
      "Epoch 2521/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2439 - categorical_accuracy: 0.4844 - val_loss: 6.4243 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2522/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2318 - categorical_accuracy: 0.5083 - val_loss: 6.2854 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2523/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2213 - categorical_accuracy: 0.5067 - val_loss: 6.1137 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2524/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2077 - categorical_accuracy: 0.5122 - val_loss: 6.1944 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2525/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2059 - categorical_accuracy: 0.5206 - val_loss: 6.0510 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2526/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1949 - categorical_accuracy: 0.5106 - val_loss: 6.3142 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2527/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1986 - categorical_accuracy: 0.5139 - val_loss: 6.5037 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2528/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2268 - categorical_accuracy: 0.5050 - val_loss: 6.0263 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2529/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2141 - categorical_accuracy: 0.5100 - val_loss: 6.4246 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2530/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2053 - categorical_accuracy: 0.5117 - val_loss: 6.2375 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2531/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1915 - categorical_accuracy: 0.5256 - val_loss: 6.3015 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2532/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2018 - categorical_accuracy: 0.5150 - val_loss: 6.3288 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2533/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1992 - categorical_accuracy: 0.5178 - val_loss: 6.3189 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2534/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2158 - categorical_accuracy: 0.5033 - val_loss: 6.3908 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2535/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2344 - categorical_accuracy: 0.4961 - val_loss: 6.3101 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2536/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2344 - categorical_accuracy: 0.5061 - val_loss: 6.1150 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2537/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3158 - categorical_accuracy: 0.4772 - val_loss: 6.1487 - val_categorical_accuracy: 0.1200\n",
      "Epoch 2538/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2801 - categorical_accuracy: 0.4683 - val_loss: 6.2712 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2539/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2689 - categorical_accuracy: 0.4850 - val_loss: 6.3580 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2540/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3101 - categorical_accuracy: 0.4678 - val_loss: 5.8963 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2541/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2696 - categorical_accuracy: 0.4744 - val_loss: 6.1353 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2542/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2324 - categorical_accuracy: 0.5044 - val_loss: 6.0379 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2543/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2034 - categorical_accuracy: 0.5100 - val_loss: 6.1522 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2544/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2016 - categorical_accuracy: 0.5128 - val_loss: 6.4130 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2545/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1985 - categorical_accuracy: 0.5200 - val_loss: 6.3645 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2546/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2035 - categorical_accuracy: 0.5144 - val_loss: 6.2798 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2547/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2142 - categorical_accuracy: 0.5122 - val_loss: 6.1721 - val_categorical_accuracy: 0.1233\n",
      "Epoch 2548/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2208 - categorical_accuracy: 0.5039 - val_loss: 6.2478 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2549/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2671 - categorical_accuracy: 0.4872 - val_loss: 6.2056 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2550/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2518 - categorical_accuracy: 0.4756 - val_loss: 6.4100 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2551/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2153 - categorical_accuracy: 0.5039 - val_loss: 6.3690 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2552/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2115 - categorical_accuracy: 0.5100 - val_loss: 6.2475 - val_categorical_accuracy: 0.1300\n",
      "Epoch 2553/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2119 - categorical_accuracy: 0.5078 - val_loss: 6.2229 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2554/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2206 - categorical_accuracy: 0.5133 - val_loss: 6.3665 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2555/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2129 - categorical_accuracy: 0.5078 - val_loss: 6.3910 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2556/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2058 - categorical_accuracy: 0.5133 - val_loss: 6.2417 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2557/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2114 - categorical_accuracy: 0.5061 - val_loss: 6.4543 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2558/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2173 - categorical_accuracy: 0.5122 - val_loss: 6.3779 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2559/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2152 - categorical_accuracy: 0.5078 - val_loss: 6.4612 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2560/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2262 - categorical_accuracy: 0.5011 - val_loss: 6.2106 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2561/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3029 - categorical_accuracy: 0.4717 - val_loss: 6.3892 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2562/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3449 - categorical_accuracy: 0.4667 - val_loss: 6.0086 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2563/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2475 - categorical_accuracy: 0.4822 - val_loss: 6.5945 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2564/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2143 - categorical_accuracy: 0.5100 - val_loss: 6.5756 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2565/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2091 - categorical_accuracy: 0.5078 - val_loss: 6.3485 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2566/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2218 - categorical_accuracy: 0.5000 - val_loss: 6.4585 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2567/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2027 - categorical_accuracy: 0.5172 - val_loss: 6.3259 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2568/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2067 - categorical_accuracy: 0.5183 - val_loss: 6.3982 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2569/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2473 - categorical_accuracy: 0.5000 - val_loss: 6.2371 - val_categorical_accuracy: 0.1300\n",
      "Epoch 2570/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2574 - categorical_accuracy: 0.4783 - val_loss: 6.9133 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2571/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2969 - categorical_accuracy: 0.4794 - val_loss: 5.7001 - val_categorical_accuracy: 0.1333\n",
      "Epoch 2572/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2662 - categorical_accuracy: 0.4867 - val_loss: 5.9696 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2573/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2420 - categorical_accuracy: 0.4883 - val_loss: 6.5814 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2574/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2313 - categorical_accuracy: 0.5039 - val_loss: 6.6031 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2575/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2179 - categorical_accuracy: 0.5072 - val_loss: 6.2150 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2576/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2028 - categorical_accuracy: 0.5111 - val_loss: 6.3628 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2577/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2098 - categorical_accuracy: 0.5083 - val_loss: 6.3161 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2578/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2271 - categorical_accuracy: 0.5056 - val_loss: 6.5048 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2579/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2355 - categorical_accuracy: 0.4933 - val_loss: 6.2917 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2580/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2398 - categorical_accuracy: 0.4944 - val_loss: 6.4462 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2581/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2218 - categorical_accuracy: 0.5100 - val_loss: 6.3560 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2582/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2462 - categorical_accuracy: 0.4989 - val_loss: 6.4972 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2583/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2147 - categorical_accuracy: 0.4994 - val_loss: 6.2075 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2584/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2073 - categorical_accuracy: 0.5150 - val_loss: 6.5854 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2585/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2013 - categorical_accuracy: 0.5111 - val_loss: 6.3776 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2586/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2047 - categorical_accuracy: 0.5167 - val_loss: 6.5297 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2587/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2057 - categorical_accuracy: 0.5089 - val_loss: 6.4053 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2588/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2214 - categorical_accuracy: 0.5183 - val_loss: 6.3688 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2589/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2172 - categorical_accuracy: 0.5144 - val_loss: 6.4392 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2590/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2089 - categorical_accuracy: 0.5122 - val_loss: 6.3673 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2591/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1961 - categorical_accuracy: 0.5233 - val_loss: 6.3904 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2592/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1970 - categorical_accuracy: 0.5194 - val_loss: 6.4055 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2593/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2143 - categorical_accuracy: 0.5161 - val_loss: 6.5865 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2594/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2503 - categorical_accuracy: 0.4917 - val_loss: 5.8696 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2595/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2335 - categorical_accuracy: 0.4994 - val_loss: 6.4714 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2596/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2470 - categorical_accuracy: 0.4978 - val_loss: 6.2457 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2597/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2093 - categorical_accuracy: 0.5078 - val_loss: 6.3440 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2598/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2133 - categorical_accuracy: 0.5111 - val_loss: 6.4554 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2599/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2095 - categorical_accuracy: 0.5067 - val_loss: 5.9375 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2600/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2801 - categorical_accuracy: 0.4856 - val_loss: 6.1823 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2601/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2898 - categorical_accuracy: 0.4728 - val_loss: 6.2111 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2602/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2567 - categorical_accuracy: 0.4878 - val_loss: 6.7232 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2603/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2162 - categorical_accuracy: 0.5144 - val_loss: 6.6010 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2604/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2078 - categorical_accuracy: 0.5156 - val_loss: 6.3858 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2605/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2181 - categorical_accuracy: 0.5244 - val_loss: 6.4697 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2606/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2091 - categorical_accuracy: 0.5094 - val_loss: 6.2376 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2607/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2244 - categorical_accuracy: 0.5028 - val_loss: 6.8113 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2608/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2885 - categorical_accuracy: 0.4806 - val_loss: 6.3775 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2609/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2207 - categorical_accuracy: 0.5072 - val_loss: 6.0952 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2610/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2125 - categorical_accuracy: 0.5128 - val_loss: 6.1233 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2611/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2182 - categorical_accuracy: 0.5200 - val_loss: 6.2783 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2612/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2196 - categorical_accuracy: 0.5128 - val_loss: 6.0572 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2613/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1996 - categorical_accuracy: 0.5167 - val_loss: 6.1915 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2614/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2591 - categorical_accuracy: 0.4867 - val_loss: 6.1257 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2615/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2572 - categorical_accuracy: 0.4922 - val_loss: 6.1645 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2616/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2523 - categorical_accuracy: 0.4922 - val_loss: 6.1416 - val_categorical_accuracy: 0.1333\n",
      "Epoch 2617/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2377 - categorical_accuracy: 0.4900 - val_loss: 6.5952 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2618/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2703 - categorical_accuracy: 0.4900 - val_loss: 6.3201 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2619/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2386 - categorical_accuracy: 0.5022 - val_loss: 6.2403 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2620/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2458 - categorical_accuracy: 0.4939 - val_loss: 6.5035 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2621/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2199 - categorical_accuracy: 0.5094 - val_loss: 6.4645 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2622/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2143 - categorical_accuracy: 0.5083 - val_loss: 6.2531 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2623/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2073 - categorical_accuracy: 0.5144 - val_loss: 6.7027 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2624/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2108 - categorical_accuracy: 0.5078 - val_loss: 6.7386 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2625/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2398 - categorical_accuracy: 0.5011 - val_loss: 6.3584 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2626/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2368 - categorical_accuracy: 0.4922 - val_loss: 6.4943 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2627/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2315 - categorical_accuracy: 0.4983 - val_loss: 6.4680 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2628/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2203 - categorical_accuracy: 0.5128 - val_loss: 6.4311 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2629/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2023 - categorical_accuracy: 0.5117 - val_loss: 6.3620 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2630/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2054 - categorical_accuracy: 0.5094 - val_loss: 6.7043 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2631/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2000 - categorical_accuracy: 0.5172 - val_loss: 6.5281 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2632/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2028 - categorical_accuracy: 0.5111 - val_loss: 6.5455 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2633/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1978 - categorical_accuracy: 0.5122 - val_loss: 6.5923 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2634/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1933 - categorical_accuracy: 0.5183 - val_loss: 6.6756 - val_categorical_accuracy: 0.1333\n",
      "Epoch 2635/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2129 - categorical_accuracy: 0.5144 - val_loss: 6.1024 - val_categorical_accuracy: 0.1267\n",
      "Epoch 2636/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2278 - categorical_accuracy: 0.5017 - val_loss: 6.7025 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2637/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2227 - categorical_accuracy: 0.4978 - val_loss: 6.2518 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2638/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2729 - categorical_accuracy: 0.4794 - val_loss: 6.3279 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2639/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.2167 - categorical_accuracy: 0.4956 - val_loss: 6.4505 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2640/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2002 - categorical_accuracy: 0.5139 - val_loss: 6.4156 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2641/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1995 - categorical_accuracy: 0.5183 - val_loss: 6.4656 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2642/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2260 - categorical_accuracy: 0.5122 - val_loss: 6.1225 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2643/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.2328 - categorical_accuracy: 0.4967 - val_loss: 6.5720 - val_categorical_accuracy: 0.1333\n",
      "Epoch 2644/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1987 - categorical_accuracy: 0.5139 - val_loss: 6.4860 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2645/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2256 - categorical_accuracy: 0.5000 - val_loss: 6.2174 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2646/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2117 - categorical_accuracy: 0.5106 - val_loss: 6.5647 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2647/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2275 - categorical_accuracy: 0.4883 - val_loss: 6.6140 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2648/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2733 - categorical_accuracy: 0.4883 - val_loss: 6.4481 - val_categorical_accuracy: 0.1300\n",
      "Epoch 2649/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2505 - categorical_accuracy: 0.4878 - val_loss: 6.7509 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2650/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2820 - categorical_accuracy: 0.4972 - val_loss: 6.3500 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2651/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.2276 - categorical_accuracy: 0.5017 - val_loss: 6.3014 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2652/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2141 - categorical_accuracy: 0.5056 - val_loss: 6.4010 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2653/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2002 - categorical_accuracy: 0.5217 - val_loss: 6.7215 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2654/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2467 - categorical_accuracy: 0.5022 - val_loss: 6.6034 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2655/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.2335 - categorical_accuracy: 0.5017 - val_loss: 6.5872 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2656/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2195 - categorical_accuracy: 0.5128 - val_loss: 6.6406 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2657/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2281 - categorical_accuracy: 0.4944 - val_loss: 6.5956 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2658/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.2148 - categorical_accuracy: 0.5050 - val_loss: 6.4616 - val_categorical_accuracy: 0.1333\n",
      "Epoch 2659/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2371 - categorical_accuracy: 0.5067 - val_loss: 6.1036 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2660/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2535 - categorical_accuracy: 0.4939 - val_loss: 6.5580 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2661/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2462 - categorical_accuracy: 0.4911 - val_loss: 6.4304 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2662/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.2166 - categorical_accuracy: 0.5100 - val_loss: 6.4370 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2663/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2335 - categorical_accuracy: 0.5106 - val_loss: 6.4203 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2664/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2659 - categorical_accuracy: 0.4839 - val_loss: 5.8713 - val_categorical_accuracy: 0.1233\n",
      "Epoch 2665/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.2918 - categorical_accuracy: 0.4722 - val_loss: 6.6134 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2666/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2330 - categorical_accuracy: 0.4944 - val_loss: 6.1799 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2667/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2327 - categorical_accuracy: 0.4889 - val_loss: 6.6664 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2668/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2137 - categorical_accuracy: 0.5078 - val_loss: 6.5528 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2669/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2226 - categorical_accuracy: 0.5017 - val_loss: 6.3580 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2670/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2518 - categorical_accuracy: 0.4906 - val_loss: 6.3461 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2671/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2555 - categorical_accuracy: 0.4839 - val_loss: 6.5342 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2672/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.2302 - categorical_accuracy: 0.4944 - val_loss: 6.7713 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2673/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2179 - categorical_accuracy: 0.5128 - val_loss: 6.8006 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2674/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2214 - categorical_accuracy: 0.5111 - val_loss: 6.5013 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2675/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2127 - categorical_accuracy: 0.5089 - val_loss: 6.4070 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2676/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2094 - categorical_accuracy: 0.5078 - val_loss: 6.2890 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2677/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2038 - categorical_accuracy: 0.5211 - val_loss: 6.4050 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2678/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2245 - categorical_accuracy: 0.5161 - val_loss: 6.6201 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2679/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2445 - categorical_accuracy: 0.4950 - val_loss: 6.2265 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2680/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2373 - categorical_accuracy: 0.4944 - val_loss: 6.4034 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2681/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2279 - categorical_accuracy: 0.5050 - val_loss: 6.4094 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2682/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2262 - categorical_accuracy: 0.4967 - val_loss: 6.3784 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2683/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2168 - categorical_accuracy: 0.5028 - val_loss: 6.2874 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2684/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2034 - categorical_accuracy: 0.5094 - val_loss: 6.0537 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2685/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2130 - categorical_accuracy: 0.4967 - val_loss: 6.4093 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2686/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2164 - categorical_accuracy: 0.5100 - val_loss: 6.3003 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2687/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2169 - categorical_accuracy: 0.5033 - val_loss: 5.9638 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2688/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2146 - categorical_accuracy: 0.5022 - val_loss: 5.9924 - val_categorical_accuracy: 0.1333\n",
      "Epoch 2689/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2478 - categorical_accuracy: 0.4906 - val_loss: 5.9951 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2690/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2593 - categorical_accuracy: 0.4783 - val_loss: 6.7837 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2691/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2318 - categorical_accuracy: 0.5017 - val_loss: 6.6301 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2692/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2147 - categorical_accuracy: 0.5078 - val_loss: 6.2182 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2693/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2062 - categorical_accuracy: 0.5067 - val_loss: 6.4991 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2694/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2324 - categorical_accuracy: 0.4944 - val_loss: 6.2000 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2695/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2542 - categorical_accuracy: 0.4850 - val_loss: 6.0925 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2696/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2708 - categorical_accuracy: 0.4806 - val_loss: 6.1475 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2697/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2341 - categorical_accuracy: 0.5017 - val_loss: 6.2775 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2698/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2473 - categorical_accuracy: 0.4911 - val_loss: 6.5392 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2699/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2365 - categorical_accuracy: 0.5078 - val_loss: 6.6405 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2700/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2167 - categorical_accuracy: 0.5172 - val_loss: 5.8027 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2701/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2899 - categorical_accuracy: 0.4872 - val_loss: 6.1141 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2702/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2661 - categorical_accuracy: 0.4844 - val_loss: 6.3674 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2703/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2208 - categorical_accuracy: 0.5122 - val_loss: 6.8872 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2704/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2136 - categorical_accuracy: 0.5122 - val_loss: 6.4423 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2705/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2077 - categorical_accuracy: 0.5189 - val_loss: 6.4002 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2706/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2143 - categorical_accuracy: 0.5106 - val_loss: 6.5149 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2707/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2053 - categorical_accuracy: 0.5161 - val_loss: 6.5554 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2708/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2445 - categorical_accuracy: 0.5089 - val_loss: 5.9561 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2709/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2585 - categorical_accuracy: 0.4822 - val_loss: 6.4869 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2710/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2391 - categorical_accuracy: 0.4950 - val_loss: 6.5903 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2711/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2428 - categorical_accuracy: 0.4944 - val_loss: 6.3711 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2712/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2281 - categorical_accuracy: 0.5083 - val_loss: 6.5101 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2713/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2793 - categorical_accuracy: 0.4794 - val_loss: 6.0377 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2714/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2378 - categorical_accuracy: 0.4983 - val_loss: 6.4131 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2715/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2397 - categorical_accuracy: 0.4972 - val_loss: 6.9078 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2716/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2185 - categorical_accuracy: 0.5117 - val_loss: 6.4861 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2717/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2211 - categorical_accuracy: 0.5133 - val_loss: 6.3795 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2718/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2503 - categorical_accuracy: 0.4967 - val_loss: 6.0252 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2719/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2975 - categorical_accuracy: 0.4639 - val_loss: 6.2313 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2720/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2452 - categorical_accuracy: 0.4894 - val_loss: 6.2863 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2721/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2287 - categorical_accuracy: 0.5028 - val_loss: 6.5956 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2722/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2124 - categorical_accuracy: 0.5111 - val_loss: 6.7094 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2723/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2057 - categorical_accuracy: 0.5156 - val_loss: 6.5715 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2724/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2054 - categorical_accuracy: 0.5150 - val_loss: 6.3828 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2725/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2031 - categorical_accuracy: 0.5144 - val_loss: 6.6906 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2726/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2078 - categorical_accuracy: 0.5222 - val_loss: 6.5374 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2727/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2057 - categorical_accuracy: 0.5156 - val_loss: 6.5220 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2728/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2373 - categorical_accuracy: 0.5011 - val_loss: 6.7464 - val_categorical_accuracy: 0.1367\n",
      "Epoch 2729/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2793 - categorical_accuracy: 0.4744 - val_loss: 6.6834 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2730/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2113 - categorical_accuracy: 0.5044 - val_loss: 6.6392 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2731/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2008 - categorical_accuracy: 0.5261 - val_loss: 6.6656 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2732/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2114 - categorical_accuracy: 0.5189 - val_loss: 6.6361 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2733/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2405 - categorical_accuracy: 0.4911 - val_loss: 6.5526 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2734/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2318 - categorical_accuracy: 0.5128 - val_loss: 6.2533 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2735/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2228 - categorical_accuracy: 0.5050 - val_loss: 6.2404 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2736/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2745 - categorical_accuracy: 0.4872 - val_loss: 6.4127 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2737/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2349 - categorical_accuracy: 0.5000 - val_loss: 6.6406 - val_categorical_accuracy: 0.1467\n",
      "Epoch 2738/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1959 - categorical_accuracy: 0.5178 - val_loss: 6.6484 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2739/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2196 - categorical_accuracy: 0.5133 - val_loss: 6.1644 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2740/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2313 - categorical_accuracy: 0.5017 - val_loss: 6.2436 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2741/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2144 - categorical_accuracy: 0.5156 - val_loss: 6.4584 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2742/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2223 - categorical_accuracy: 0.5044 - val_loss: 6.5839 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2743/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2132 - categorical_accuracy: 0.5144 - val_loss: 6.6964 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2744/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2111 - categorical_accuracy: 0.5144 - val_loss: 6.6556 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2745/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2034 - categorical_accuracy: 0.5144 - val_loss: 6.5958 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2746/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2042 - categorical_accuracy: 0.5150 - val_loss: 6.5532 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2747/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2058 - categorical_accuracy: 0.5172 - val_loss: 6.9302 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2748/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2252 - categorical_accuracy: 0.5089 - val_loss: 6.5903 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2749/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2221 - categorical_accuracy: 0.5167 - val_loss: 6.4185 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2750/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2531 - categorical_accuracy: 0.5033 - val_loss: 6.2888 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2751/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2399 - categorical_accuracy: 0.4961 - val_loss: 6.5900 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2752/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2062 - categorical_accuracy: 0.5167 - val_loss: 6.5997 - val_categorical_accuracy: 0.1400\n",
      "Epoch 2753/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2182 - categorical_accuracy: 0.5206 - val_loss: 6.7385 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2754/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2094 - categorical_accuracy: 0.5128 - val_loss: 6.5295 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2755/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1972 - categorical_accuracy: 0.5233 - val_loss: 6.7338 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2756/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2562 - categorical_accuracy: 0.4944 - val_loss: 6.5468 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2757/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2912 - categorical_accuracy: 0.4728 - val_loss: 6.3650 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2758/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2570 - categorical_accuracy: 0.4822 - val_loss: 6.5172 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2759/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2356 - categorical_accuracy: 0.5106 - val_loss: 6.2631 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2760/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2360 - categorical_accuracy: 0.5161 - val_loss: 6.7801 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2761/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2287 - categorical_accuracy: 0.5094 - val_loss: 6.5524 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2762/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2277 - categorical_accuracy: 0.5017 - val_loss: 6.5815 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2763/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2399 - categorical_accuracy: 0.5022 - val_loss: 6.5643 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2764/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2505 - categorical_accuracy: 0.4889 - val_loss: 6.5339 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2765/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2332 - categorical_accuracy: 0.5078 - val_loss: 6.7849 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2766/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2165 - categorical_accuracy: 0.5106 - val_loss: 6.7614 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2767/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2066 - categorical_accuracy: 0.5228 - val_loss: 6.7938 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2768/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2024 - categorical_accuracy: 0.5133 - val_loss: 6.7120 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2769/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2299 - categorical_accuracy: 0.5078 - val_loss: 6.5380 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2770/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2093 - categorical_accuracy: 0.5100 - val_loss: 6.7942 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2771/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2055 - categorical_accuracy: 0.5139 - val_loss: 6.4696 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2772/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2300 - categorical_accuracy: 0.5106 - val_loss: 6.3951 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2773/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2391 - categorical_accuracy: 0.5044 - val_loss: 6.3066 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2774/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2738 - categorical_accuracy: 0.4878 - val_loss: 6.1577 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2775/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2609 - categorical_accuracy: 0.4861 - val_loss: 6.4301 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2776/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2113 - categorical_accuracy: 0.5089 - val_loss: 6.8142 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2777/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1998 - categorical_accuracy: 0.5167 - val_loss: 6.6652 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2778/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2027 - categorical_accuracy: 0.5233 - val_loss: 6.5735 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2779/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2544 - categorical_accuracy: 0.4956 - val_loss: 6.3274 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2780/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2407 - categorical_accuracy: 0.5028 - val_loss: 6.6552 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2781/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2960 - categorical_accuracy: 0.4678 - val_loss: 5.6116 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2782/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3291 - categorical_accuracy: 0.4550 - val_loss: 6.5944 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2783/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2503 - categorical_accuracy: 0.5028 - val_loss: 6.7522 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2784/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2253 - categorical_accuracy: 0.5022 - val_loss: 6.8319 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2785/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2090 - categorical_accuracy: 0.5072 - val_loss: 6.6897 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2786/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2003 - categorical_accuracy: 0.5189 - val_loss: 6.7712 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2787/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2078 - categorical_accuracy: 0.5106 - val_loss: 6.9230 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2788/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1978 - categorical_accuracy: 0.5244 - val_loss: 6.8660 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2789/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1963 - categorical_accuracy: 0.5244 - val_loss: 6.7788 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2790/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1968 - categorical_accuracy: 0.5267 - val_loss: 6.7643 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2791/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1983 - categorical_accuracy: 0.5194 - val_loss: 6.9864 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2792/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2129 - categorical_accuracy: 0.5128 - val_loss: 6.3775 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2793/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2224 - categorical_accuracy: 0.5039 - val_loss: 6.7360 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2794/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1950 - categorical_accuracy: 0.5244 - val_loss: 6.9161 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2795/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1972 - categorical_accuracy: 0.5172 - val_loss: 6.9198 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2796/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2540 - categorical_accuracy: 0.4911 - val_loss: 6.3752 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2797/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2309 - categorical_accuracy: 0.5083 - val_loss: 6.6729 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2798/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2135 - categorical_accuracy: 0.5100 - val_loss: 6.5477 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2799/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2134 - categorical_accuracy: 0.5122 - val_loss: 6.5885 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2800/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2073 - categorical_accuracy: 0.5178 - val_loss: 6.7097 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2801/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2028 - categorical_accuracy: 0.5167 - val_loss: 6.7184 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2802/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1965 - categorical_accuracy: 0.5233 - val_loss: 6.6879 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2803/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1888 - categorical_accuracy: 0.5278 - val_loss: 6.8421 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2804/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1999 - categorical_accuracy: 0.5244 - val_loss: 7.0395 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2805/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2572 - categorical_accuracy: 0.4928 - val_loss: 6.6286 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2806/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2293 - categorical_accuracy: 0.5133 - val_loss: 6.6526 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2807/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2430 - categorical_accuracy: 0.5050 - val_loss: 6.5626 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2808/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2604 - categorical_accuracy: 0.4972 - val_loss: 6.9110 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2809/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2635 - categorical_accuracy: 0.4922 - val_loss: 6.6030 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2810/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2461 - categorical_accuracy: 0.5050 - val_loss: 6.5433 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2811/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2202 - categorical_accuracy: 0.5094 - val_loss: 6.7735 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2812/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2311 - categorical_accuracy: 0.5022 - val_loss: 6.5650 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2813/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2174 - categorical_accuracy: 0.5106 - val_loss: 6.5826 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2814/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1999 - categorical_accuracy: 0.5228 - val_loss: 6.8170 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2815/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2045 - categorical_accuracy: 0.5206 - val_loss: 6.9408 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2816/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2182 - categorical_accuracy: 0.5100 - val_loss: 6.7757 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2817/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2179 - categorical_accuracy: 0.5111 - val_loss: 6.7608 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2818/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2150 - categorical_accuracy: 0.5122 - val_loss: 6.6692 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2819/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2219 - categorical_accuracy: 0.5061 - val_loss: 5.8427 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2820/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3324 - categorical_accuracy: 0.4678 - val_loss: 6.3094 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2821/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2598 - categorical_accuracy: 0.4833 - val_loss: 6.6677 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2822/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2369 - categorical_accuracy: 0.5089 - val_loss: 6.3711 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2823/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2582 - categorical_accuracy: 0.4894 - val_loss: 6.8801 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2824/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2504 - categorical_accuracy: 0.4972 - val_loss: 6.5066 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2825/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2364 - categorical_accuracy: 0.4906 - val_loss: 6.7676 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2826/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2253 - categorical_accuracy: 0.5017 - val_loss: 6.6923 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2827/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2191 - categorical_accuracy: 0.5217 - val_loss: 6.4962 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2828/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2115 - categorical_accuracy: 0.5178 - val_loss: 6.6244 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2829/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2480 - categorical_accuracy: 0.5056 - val_loss: 6.4539 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2830/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2863 - categorical_accuracy: 0.4794 - val_loss: 6.6179 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2831/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2453 - categorical_accuracy: 0.4922 - val_loss: 6.0698 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2832/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2507 - categorical_accuracy: 0.4806 - val_loss: 6.6498 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2833/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2137 - categorical_accuracy: 0.5139 - val_loss: 6.8752 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2834/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2167 - categorical_accuracy: 0.5144 - val_loss: 6.6027 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2835/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2117 - categorical_accuracy: 0.5117 - val_loss: 6.8523 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2836/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2078 - categorical_accuracy: 0.5222 - val_loss: 6.7143 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2837/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2132 - categorical_accuracy: 0.5256 - val_loss: 6.6434 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2838/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2596 - categorical_accuracy: 0.5000 - val_loss: 6.6989 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2839/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2390 - categorical_accuracy: 0.5061 - val_loss: 6.6059 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2840/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2622 - categorical_accuracy: 0.4867 - val_loss: 5.9841 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2841/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2780 - categorical_accuracy: 0.4794 - val_loss: 6.4592 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2842/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2243 - categorical_accuracy: 0.5044 - val_loss: 6.6164 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2843/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2076 - categorical_accuracy: 0.5200 - val_loss: 6.8176 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2844/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2094 - categorical_accuracy: 0.5161 - val_loss: 6.6728 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2845/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2094 - categorical_accuracy: 0.5122 - val_loss: 6.6787 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2846/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2153 - categorical_accuracy: 0.5167 - val_loss: 6.7296 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2847/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2168 - categorical_accuracy: 0.5233 - val_loss: 6.6429 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2848/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2205 - categorical_accuracy: 0.5056 - val_loss: 6.5832 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2849/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2047 - categorical_accuracy: 0.5211 - val_loss: 6.7817 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2850/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2101 - categorical_accuracy: 0.5106 - val_loss: 6.5795 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2851/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2085 - categorical_accuracy: 0.5150 - val_loss: 6.6436 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2852/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2719 - categorical_accuracy: 0.4944 - val_loss: 6.4640 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2853/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2496 - categorical_accuracy: 0.4917 - val_loss: 7.0592 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2854/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2142 - categorical_accuracy: 0.5117 - val_loss: 6.6544 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2855/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2241 - categorical_accuracy: 0.5111 - val_loss: 5.9146 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2856/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2987 - categorical_accuracy: 0.4606 - val_loss: 6.5938 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2857/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2628 - categorical_accuracy: 0.4711 - val_loss: 6.6797 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2858/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2676 - categorical_accuracy: 0.4817 - val_loss: 6.0731 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2859/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3098 - categorical_accuracy: 0.4711 - val_loss: 6.6653 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2860/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2619 - categorical_accuracy: 0.4889 - val_loss: 6.7431 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2861/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2240 - categorical_accuracy: 0.5000 - val_loss: 6.8612 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2862/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2175 - categorical_accuracy: 0.5056 - val_loss: 6.9175 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2863/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2079 - categorical_accuracy: 0.5172 - val_loss: 6.9998 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2864/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2118 - categorical_accuracy: 0.5061 - val_loss: 6.9436 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2865/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2082 - categorical_accuracy: 0.5128 - val_loss: 6.9143 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2866/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2102 - categorical_accuracy: 0.5106 - val_loss: 6.7962 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2867/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2142 - categorical_accuracy: 0.5139 - val_loss: 6.5494 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2868/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2135 - categorical_accuracy: 0.5156 - val_loss: 6.8224 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2869/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2250 - categorical_accuracy: 0.5083 - val_loss: 6.6707 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2870/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2533 - categorical_accuracy: 0.4961 - val_loss: 6.3560 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2871/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2457 - categorical_accuracy: 0.4983 - val_loss: 6.0348 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2872/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2467 - categorical_accuracy: 0.4967 - val_loss: 6.8428 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2873/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2176 - categorical_accuracy: 0.5061 - val_loss: 6.7562 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2874/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2245 - categorical_accuracy: 0.5067 - val_loss: 6.6481 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2875/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2317 - categorical_accuracy: 0.4911 - val_loss: 6.5547 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2876/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2239 - categorical_accuracy: 0.5094 - val_loss: 7.0179 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2877/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2735 - categorical_accuracy: 0.4828 - val_loss: 5.8855 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2878/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2843 - categorical_accuracy: 0.4706 - val_loss: 6.1063 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2879/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2585 - categorical_accuracy: 0.4844 - val_loss: 7.0128 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2880/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2662 - categorical_accuracy: 0.4950 - val_loss: 6.4393 - val_categorical_accuracy: 0.1500\n",
      "Epoch 2881/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2375 - categorical_accuracy: 0.4994 - val_loss: 6.7526 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2882/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2299 - categorical_accuracy: 0.4961 - val_loss: 7.0126 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2883/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2178 - categorical_accuracy: 0.5050 - val_loss: 6.7927 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2884/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2265 - categorical_accuracy: 0.5033 - val_loss: 6.6571 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2885/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2566 - categorical_accuracy: 0.4867 - val_loss: 6.6157 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2886/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2879 - categorical_accuracy: 0.4672 - val_loss: 6.6218 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2887/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3070 - categorical_accuracy: 0.4622 - val_loss: 6.9329 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2888/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3431 - categorical_accuracy: 0.4578 - val_loss: 7.2505 - val_categorical_accuracy: 0.1533\n",
      "Epoch 2889/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2938 - categorical_accuracy: 0.4728 - val_loss: 6.2878 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2890/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2489 - categorical_accuracy: 0.4872 - val_loss: 6.8358 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2891/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2368 - categorical_accuracy: 0.4944 - val_loss: 6.5257 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2892/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2341 - categorical_accuracy: 0.5028 - val_loss: 6.7170 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2893/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2170 - categorical_accuracy: 0.5039 - val_loss: 6.7449 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2894/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2205 - categorical_accuracy: 0.5011 - val_loss: 6.6018 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2895/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2079 - categorical_accuracy: 0.5094 - val_loss: 6.9518 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2896/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2098 - categorical_accuracy: 0.5133 - val_loss: 6.7839 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2897/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2130 - categorical_accuracy: 0.5039 - val_loss: 6.7705 - val_categorical_accuracy: 0.2000\n",
      "Epoch 2898/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2071 - categorical_accuracy: 0.5117 - val_loss: 6.4231 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2899/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2201 - categorical_accuracy: 0.5056 - val_loss: 6.7519 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2900/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2493 - categorical_accuracy: 0.4861 - val_loss: 6.8230 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2901/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2610 - categorical_accuracy: 0.4850 - val_loss: 6.6109 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2902/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2206 - categorical_accuracy: 0.5056 - val_loss: 7.0482 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2903/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2397 - categorical_accuracy: 0.4961 - val_loss: 6.7662 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2904/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2568 - categorical_accuracy: 0.4878 - val_loss: 7.0096 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2905/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2234 - categorical_accuracy: 0.5011 - val_loss: 6.5304 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2906/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2517 - categorical_accuracy: 0.4922 - val_loss: 7.1761 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2907/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2333 - categorical_accuracy: 0.4928 - val_loss: 6.9908 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2908/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2308 - categorical_accuracy: 0.4939 - val_loss: 6.6945 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2909/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2422 - categorical_accuracy: 0.4967 - val_loss: 6.8201 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2910/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2732 - categorical_accuracy: 0.4828 - val_loss: 6.0623 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2911/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3075 - categorical_accuracy: 0.4694 - val_loss: 7.0502 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2912/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2853 - categorical_accuracy: 0.4733 - val_loss: 6.6822 - val_categorical_accuracy: 0.1433\n",
      "Epoch 2913/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2725 - categorical_accuracy: 0.4822 - val_loss: 7.0338 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2914/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2556 - categorical_accuracy: 0.4872 - val_loss: 6.8041 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2915/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2297 - categorical_accuracy: 0.5044 - val_loss: 7.0134 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2916/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2530 - categorical_accuracy: 0.4917 - val_loss: 6.9466 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2917/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2354 - categorical_accuracy: 0.4839 - val_loss: 6.8650 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2918/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2651 - categorical_accuracy: 0.4817 - val_loss: 6.5064 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2919/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2547 - categorical_accuracy: 0.4833 - val_loss: 6.9239 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2920/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2389 - categorical_accuracy: 0.4928 - val_loss: 7.0445 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2921/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2623 - categorical_accuracy: 0.4833 - val_loss: 6.9189 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2922/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2321 - categorical_accuracy: 0.4944 - val_loss: 7.2306 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2923/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2396 - categorical_accuracy: 0.4883 - val_loss: 6.9405 - val_categorical_accuracy: 0.1967\n",
      "Epoch 2924/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2177 - categorical_accuracy: 0.5067 - val_loss: 7.1492 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2925/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2099 - categorical_accuracy: 0.5033 - val_loss: 7.0600 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2926/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2238 - categorical_accuracy: 0.5033 - val_loss: 6.7103 - val_categorical_accuracy: 0.1967\n",
      "Epoch 2927/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2158 - categorical_accuracy: 0.4983 - val_loss: 7.0792 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2928/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2121 - categorical_accuracy: 0.5050 - val_loss: 7.0503 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2929/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2220 - categorical_accuracy: 0.4944 - val_loss: 6.9324 - val_categorical_accuracy: 0.1933\n",
      "Epoch 2930/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3012 - categorical_accuracy: 0.4694 - val_loss: 6.1823 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2931/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2906 - categorical_accuracy: 0.4706 - val_loss: 7.2435 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2932/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2522 - categorical_accuracy: 0.4889 - val_loss: 7.0036 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2933/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2858 - categorical_accuracy: 0.4744 - val_loss: 7.1214 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2934/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3511 - categorical_accuracy: 0.4567 - val_loss: 6.6223 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2935/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2586 - categorical_accuracy: 0.4789 - val_loss: 7.0016 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2936/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2283 - categorical_accuracy: 0.4944 - val_loss: 6.8965 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2937/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2344 - categorical_accuracy: 0.4900 - val_loss: 6.8284 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2938/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2195 - categorical_accuracy: 0.4978 - val_loss: 7.0441 - val_categorical_accuracy: 0.1933\n",
      "Epoch 2939/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2145 - categorical_accuracy: 0.5067 - val_loss: 7.0835 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2940/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2207 - categorical_accuracy: 0.5011 - val_loss: 7.2521 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2941/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2168 - categorical_accuracy: 0.5011 - val_loss: 7.0659 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2942/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2406 - categorical_accuracy: 0.4833 - val_loss: 7.3337 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2943/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2677 - categorical_accuracy: 0.4822 - val_loss: 7.1355 - val_categorical_accuracy: 0.1933\n",
      "Epoch 2944/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2561 - categorical_accuracy: 0.4878 - val_loss: 7.3238 - val_categorical_accuracy: 0.1567\n",
      "Epoch 2945/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2793 - categorical_accuracy: 0.4806 - val_loss: 6.6340 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2946/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2592 - categorical_accuracy: 0.4839 - val_loss: 6.9467 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2947/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2242 - categorical_accuracy: 0.5017 - val_loss: 7.1449 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2948/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2185 - categorical_accuracy: 0.5017 - val_loss: 7.1327 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2949/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2120 - categorical_accuracy: 0.5011 - val_loss: 6.9214 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2950/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2348 - categorical_accuracy: 0.4944 - val_loss: 7.0379 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2951/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2413 - categorical_accuracy: 0.4867 - val_loss: 7.1295 - val_categorical_accuracy: 0.1933\n",
      "Epoch 2952/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2407 - categorical_accuracy: 0.4928 - val_loss: 7.0490 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2953/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2335 - categorical_accuracy: 0.4911 - val_loss: 6.8841 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2954/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2352 - categorical_accuracy: 0.4983 - val_loss: 7.2293 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2955/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3248 - categorical_accuracy: 0.4656 - val_loss: 6.5676 - val_categorical_accuracy: 0.2000\n",
      "Epoch 2956/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2665 - categorical_accuracy: 0.4767 - val_loss: 7.0955 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2957/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2462 - categorical_accuracy: 0.4850 - val_loss: 6.7757 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2958/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2665 - categorical_accuracy: 0.4750 - val_loss: 7.2200 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2959/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2419 - categorical_accuracy: 0.4928 - val_loss: 7.2662 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2960/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2262 - categorical_accuracy: 0.5017 - val_loss: 7.1611 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2961/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2312 - categorical_accuracy: 0.4994 - val_loss: 7.1303 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2962/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2677 - categorical_accuracy: 0.4794 - val_loss: 6.8259 - val_categorical_accuracy: 0.1633\n",
      "Epoch 2963/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2308 - categorical_accuracy: 0.5006 - val_loss: 6.8315 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2964/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2251 - categorical_accuracy: 0.4883 - val_loss: 6.8724 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2965/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2511 - categorical_accuracy: 0.4861 - val_loss: 6.7414 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2966/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2310 - categorical_accuracy: 0.4900 - val_loss: 7.1426 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2967/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2282 - categorical_accuracy: 0.4900 - val_loss: 7.1937 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2968/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2440 - categorical_accuracy: 0.4933 - val_loss: 7.2365 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2969/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2472 - categorical_accuracy: 0.4961 - val_loss: 7.0067 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2970/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2299 - categorical_accuracy: 0.5050 - val_loss: 7.2309 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2971/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2219 - categorical_accuracy: 0.5017 - val_loss: 6.4976 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2972/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2297 - categorical_accuracy: 0.4961 - val_loss: 7.2335 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2973/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2575 - categorical_accuracy: 0.4806 - val_loss: 7.0685 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2974/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2448 - categorical_accuracy: 0.4900 - val_loss: 7.0166 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2975/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2458 - categorical_accuracy: 0.4817 - val_loss: 6.9592 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2976/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2316 - categorical_accuracy: 0.4922 - val_loss: 7.0216 - val_categorical_accuracy: 0.1667\n",
      "Epoch 2977/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2191 - categorical_accuracy: 0.5022 - val_loss: 7.1371 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2978/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2339 - categorical_accuracy: 0.4911 - val_loss: 7.1066 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2979/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2090 - categorical_accuracy: 0.4983 - val_loss: 7.1637 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2980/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1986 - categorical_accuracy: 0.5056 - val_loss: 7.0552 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2981/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2007 - categorical_accuracy: 0.5128 - val_loss: 7.0588 - val_categorical_accuracy: 0.1800\n",
      "Epoch 2982/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2228 - categorical_accuracy: 0.5022 - val_loss: 6.4485 - val_categorical_accuracy: 0.1600\n",
      "Epoch 2983/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2264 - categorical_accuracy: 0.4900 - val_loss: 7.1666 - val_categorical_accuracy: 0.1733\n",
      "Epoch 2984/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2745 - categorical_accuracy: 0.4800 - val_loss: 6.9655 - val_categorical_accuracy: 0.2033\n",
      "Epoch 2985/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2150 - categorical_accuracy: 0.5017 - val_loss: 7.1132 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2986/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2165 - categorical_accuracy: 0.5028 - val_loss: 7.3077 - val_categorical_accuracy: 0.1967\n",
      "Epoch 2987/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2879 - categorical_accuracy: 0.4700 - val_loss: 6.0106 - val_categorical_accuracy: 0.1900\n",
      "Epoch 2988/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3589 - categorical_accuracy: 0.4483 - val_loss: 6.9506 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2989/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2492 - categorical_accuracy: 0.4822 - val_loss: 7.1318 - val_categorical_accuracy: 0.1933\n",
      "Epoch 2990/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2172 - categorical_accuracy: 0.5072 - val_loss: 7.0924 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2991/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2146 - categorical_accuracy: 0.5028 - val_loss: 7.0280 - val_categorical_accuracy: 0.2000\n",
      "Epoch 2992/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2468 - categorical_accuracy: 0.4983 - val_loss: 6.9859 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2993/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2956 - categorical_accuracy: 0.4589 - val_loss: 7.1560 - val_categorical_accuracy: 0.1767\n",
      "Epoch 2994/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2557 - categorical_accuracy: 0.4828 - val_loss: 6.9997 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2995/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2218 - categorical_accuracy: 0.4961 - val_loss: 7.1118 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2996/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2275 - categorical_accuracy: 0.4911 - val_loss: 7.1273 - val_categorical_accuracy: 0.1833\n",
      "Epoch 2997/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2116 - categorical_accuracy: 0.5039 - val_loss: 7.2404 - val_categorical_accuracy: 0.1700\n",
      "Epoch 2998/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2077 - categorical_accuracy: 0.5083 - val_loss: 7.2598 - val_categorical_accuracy: 0.1867\n",
      "Epoch 2999/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2602 - categorical_accuracy: 0.4783 - val_loss: 6.6887 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3000/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2346 - categorical_accuracy: 0.4822 - val_loss: 6.8909 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3001/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2186 - categorical_accuracy: 0.4994 - val_loss: 6.8064 - val_categorical_accuracy: 0.1933\n",
      "Epoch 3002/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2123 - categorical_accuracy: 0.4944 - val_loss: 7.2318 - val_categorical_accuracy: 0.1700\n",
      "Epoch 3003/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2141 - categorical_accuracy: 0.5000 - val_loss: 7.2818 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3004/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2202 - categorical_accuracy: 0.4939 - val_loss: 7.2470 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3005/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2468 - categorical_accuracy: 0.4772 - val_loss: 6.7052 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3006/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3407 - categorical_accuracy: 0.4439 - val_loss: 7.0278 - val_categorical_accuracy: 0.1933\n",
      "Epoch 3007/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2374 - categorical_accuracy: 0.4839 - val_loss: 7.5528 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3008/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2666 - categorical_accuracy: 0.4800 - val_loss: 6.7515 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3009/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2362 - categorical_accuracy: 0.4961 - val_loss: 7.0683 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3010/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2153 - categorical_accuracy: 0.5022 - val_loss: 7.2238 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3011/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2111 - categorical_accuracy: 0.4983 - val_loss: 7.1908 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3012/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2137 - categorical_accuracy: 0.5006 - val_loss: 7.1911 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3013/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2112 - categorical_accuracy: 0.5006 - val_loss: 7.2518 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3014/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2112 - categorical_accuracy: 0.5050 - val_loss: 6.8510 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3015/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2330 - categorical_accuracy: 0.4922 - val_loss: 6.8593 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3016/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2254 - categorical_accuracy: 0.4956 - val_loss: 6.6370 - val_categorical_accuracy: 0.1700\n",
      "Epoch 3017/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2411 - categorical_accuracy: 0.4911 - val_loss: 7.4321 - val_categorical_accuracy: 0.1967\n",
      "Epoch 3018/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2954 - categorical_accuracy: 0.4611 - val_loss: 6.6616 - val_categorical_accuracy: 0.2033\n",
      "Epoch 3019/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2392 - categorical_accuracy: 0.4944 - val_loss: 7.3557 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3020/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2094 - categorical_accuracy: 0.5111 - val_loss: 6.9724 - val_categorical_accuracy: 0.1967\n",
      "Epoch 3021/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2297 - categorical_accuracy: 0.4950 - val_loss: 7.0000 - val_categorical_accuracy: 0.1933\n",
      "Epoch 3022/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2203 - categorical_accuracy: 0.4928 - val_loss: 6.9784 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3023/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2746 - categorical_accuracy: 0.4811 - val_loss: 6.2507 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3024/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2898 - categorical_accuracy: 0.4833 - val_loss: 6.7805 - val_categorical_accuracy: 0.1967\n",
      "Epoch 3025/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2295 - categorical_accuracy: 0.4894 - val_loss: 7.1689 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3026/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2229 - categorical_accuracy: 0.4944 - val_loss: 7.0820 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3027/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2267 - categorical_accuracy: 0.4833 - val_loss: 6.6420 - val_categorical_accuracy: 0.1967\n",
      "Epoch 3028/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2296 - categorical_accuracy: 0.4906 - val_loss: 7.0463 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3029/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2071 - categorical_accuracy: 0.5022 - val_loss: 7.0809 - val_categorical_accuracy: 0.1667\n",
      "Epoch 3030/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2070 - categorical_accuracy: 0.5011 - val_loss: 7.2909 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3031/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2429 - categorical_accuracy: 0.4828 - val_loss: 6.7488 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3032/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2143 - categorical_accuracy: 0.4994 - val_loss: 7.1283 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3033/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2072 - categorical_accuracy: 0.5017 - val_loss: 7.1176 - val_categorical_accuracy: 0.1667\n",
      "Epoch 3034/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1966 - categorical_accuracy: 0.5128 - val_loss: 7.1442 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3035/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1953 - categorical_accuracy: 0.5150 - val_loss: 7.0393 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3036/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1932 - categorical_accuracy: 0.5111 - val_loss: 7.0685 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3037/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1991 - categorical_accuracy: 0.5089 - val_loss: 7.0449 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3038/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1943 - categorical_accuracy: 0.5167 - val_loss: 7.2283 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3039/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2054 - categorical_accuracy: 0.5056 - val_loss: 7.1393 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3040/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2287 - categorical_accuracy: 0.4839 - val_loss: 6.8341 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3041/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2969 - categorical_accuracy: 0.4622 - val_loss: 7.2149 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3042/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2443 - categorical_accuracy: 0.4806 - val_loss: 7.0065 - val_categorical_accuracy: 0.1933\n",
      "Epoch 3043/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2336 - categorical_accuracy: 0.4906 - val_loss: 7.5099 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3044/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.4017 - categorical_accuracy: 0.4389 - val_loss: 6.4659 - val_categorical_accuracy: 0.1933\n",
      "Epoch 3045/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2925 - categorical_accuracy: 0.4661 - val_loss: 6.8623 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3046/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2513 - categorical_accuracy: 0.4811 - val_loss: 6.9892 - val_categorical_accuracy: 0.1967\n",
      "Epoch 3047/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2301 - categorical_accuracy: 0.4906 - val_loss: 7.2055 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3048/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2161 - categorical_accuracy: 0.4994 - val_loss: 7.0061 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3049/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.2264 - categorical_accuracy: 0.4961 - val_loss: 7.1996 - val_categorical_accuracy: 0.1933\n",
      "Epoch 3050/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2653 - categorical_accuracy: 0.4739 - val_loss: 6.6043 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3051/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3333 - categorical_accuracy: 0.4539 - val_loss: 6.8939 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3052/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2505 - categorical_accuracy: 0.4817 - val_loss: 7.1957 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3053/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2239 - categorical_accuracy: 0.4994 - val_loss: 7.1910 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3054/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2054 - categorical_accuracy: 0.5039 - val_loss: 7.2136 - val_categorical_accuracy: 0.2067\n",
      "Epoch 3055/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2179 - categorical_accuracy: 0.4939 - val_loss: 7.0317 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3056/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2099 - categorical_accuracy: 0.5044 - val_loss: 7.0096 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3057/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2092 - categorical_accuracy: 0.4967 - val_loss: 6.6706 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3058/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2240 - categorical_accuracy: 0.4961 - val_loss: 6.6668 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3059/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2209 - categorical_accuracy: 0.4983 - val_loss: 6.8664 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3060/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2101 - categorical_accuracy: 0.5006 - val_loss: 6.8929 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3061/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2822 - categorical_accuracy: 0.4761 - val_loss: 6.6758 - val_categorical_accuracy: 0.1467\n",
      "Epoch 3062/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2990 - categorical_accuracy: 0.4544 - val_loss: 6.9100 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3063/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2652 - categorical_accuracy: 0.4744 - val_loss: 7.0783 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3064/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2200 - categorical_accuracy: 0.4972 - val_loss: 6.7787 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3065/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2251 - categorical_accuracy: 0.4994 - val_loss: 6.6620 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3066/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2112 - categorical_accuracy: 0.4944 - val_loss: 6.8619 - val_categorical_accuracy: 0.1933\n",
      "Epoch 3067/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2046 - categorical_accuracy: 0.5017 - val_loss: 7.0150 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3068/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2016 - categorical_accuracy: 0.5039 - val_loss: 6.9280 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3069/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2093 - categorical_accuracy: 0.4950 - val_loss: 6.9064 - val_categorical_accuracy: 0.1633\n",
      "Epoch 3070/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2891 - categorical_accuracy: 0.4706 - val_loss: 6.4071 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3071/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2642 - categorical_accuracy: 0.4806 - val_loss: 6.7828 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3072/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3283 - categorical_accuracy: 0.4633 - val_loss: 7.3130 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3073/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2465 - categorical_accuracy: 0.4767 - val_loss: 7.1561 - val_categorical_accuracy: 0.1967\n",
      "Epoch 3074/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2197 - categorical_accuracy: 0.4972 - val_loss: 7.1589 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3075/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2232 - categorical_accuracy: 0.4978 - val_loss: 6.9955 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3076/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2303 - categorical_accuracy: 0.4789 - val_loss: 6.9902 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3077/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2377 - categorical_accuracy: 0.4872 - val_loss: 7.1336 - val_categorical_accuracy: 0.1700\n",
      "Epoch 3078/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2492 - categorical_accuracy: 0.4872 - val_loss: 6.9694 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3079/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2166 - categorical_accuracy: 0.4989 - val_loss: 6.9222 - val_categorical_accuracy: 0.1933\n",
      "Epoch 3080/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2084 - categorical_accuracy: 0.5050 - val_loss: 6.9639 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3081/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2076 - categorical_accuracy: 0.5033 - val_loss: 7.0303 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3082/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2111 - categorical_accuracy: 0.5044 - val_loss: 6.7295 - val_categorical_accuracy: 0.1500\n",
      "Epoch 3083/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2801 - categorical_accuracy: 0.4650 - val_loss: 6.7947 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3084/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2242 - categorical_accuracy: 0.4933 - val_loss: 7.1635 - val_categorical_accuracy: 0.2000\n",
      "Epoch 3085/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2156 - categorical_accuracy: 0.4972 - val_loss: 7.1418 - val_categorical_accuracy: 0.2067\n",
      "Epoch 3086/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2119 - categorical_accuracy: 0.5022 - val_loss: 7.3957 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3087/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2152 - categorical_accuracy: 0.4983 - val_loss: 7.3200 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3088/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2312 - categorical_accuracy: 0.4933 - val_loss: 6.6523 - val_categorical_accuracy: 0.1667\n",
      "Epoch 3089/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2333 - categorical_accuracy: 0.4967 - val_loss: 6.8719 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3090/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2541 - categorical_accuracy: 0.4633 - val_loss: 6.8407 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3091/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2299 - categorical_accuracy: 0.4883 - val_loss: 7.2619 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3092/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2456 - categorical_accuracy: 0.4911 - val_loss: 7.2327 - val_categorical_accuracy: 0.1700\n",
      "Epoch 3093/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3035 - categorical_accuracy: 0.4644 - val_loss: 7.0041 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3094/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2791 - categorical_accuracy: 0.4600 - val_loss: 7.2965 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3095/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2857 - categorical_accuracy: 0.4689 - val_loss: 6.7018 - val_categorical_accuracy: 0.1867\n",
      "Epoch 3096/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2609 - categorical_accuracy: 0.4761 - val_loss: 7.2620 - val_categorical_accuracy: 0.1967\n",
      "Epoch 3097/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2471 - categorical_accuracy: 0.4761 - val_loss: 6.9283 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3098/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2291 - categorical_accuracy: 0.4878 - val_loss: 7.2674 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3099/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2286 - categorical_accuracy: 0.4867 - val_loss: 6.9830 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3100/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2280 - categorical_accuracy: 0.4961 - val_loss: 7.3740 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3101/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2163 - categorical_accuracy: 0.4967 - val_loss: 6.9009 - val_categorical_accuracy: 0.1667\n",
      "Epoch 3102/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2231 - categorical_accuracy: 0.4911 - val_loss: 6.9944 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3103/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2308 - categorical_accuracy: 0.4883 - val_loss: 7.1460 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3104/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2175 - categorical_accuracy: 0.4883 - val_loss: 7.3245 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3105/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2379 - categorical_accuracy: 0.4789 - val_loss: 7.0204 - val_categorical_accuracy: 0.1933\n",
      "Epoch 3106/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2250 - categorical_accuracy: 0.4917 - val_loss: 6.7633 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3107/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2366 - categorical_accuracy: 0.4878 - val_loss: 6.7660 - val_categorical_accuracy: 0.1700\n",
      "Epoch 3108/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2116 - categorical_accuracy: 0.4978 - val_loss: 7.2779 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3109/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2106 - categorical_accuracy: 0.5033 - val_loss: 7.1952 - val_categorical_accuracy: 0.1667\n",
      "Epoch 3110/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2014 - categorical_accuracy: 0.5044 - val_loss: 6.9591 - val_categorical_accuracy: 0.1733\n",
      "Epoch 3111/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2090 - categorical_accuracy: 0.4950 - val_loss: 7.1753 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3112/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2208 - categorical_accuracy: 0.4967 - val_loss: 6.9846 - val_categorical_accuracy: 0.1600\n",
      "Epoch 3113/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2316 - categorical_accuracy: 0.4900 - val_loss: 7.4708 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3114/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2364 - categorical_accuracy: 0.4839 - val_loss: 7.1900 - val_categorical_accuracy: 0.1700\n",
      "Epoch 3115/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2246 - categorical_accuracy: 0.4933 - val_loss: 7.2600 - val_categorical_accuracy: 0.1833\n",
      "Epoch 3116/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2108 - categorical_accuracy: 0.4978 - val_loss: 7.3098 - val_categorical_accuracy: 0.1800\n",
      "Epoch 3117/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2122 - categorical_accuracy: 0.5028 - val_loss: 7.0262 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3118/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2010 - categorical_accuracy: 0.5050 - val_loss: 7.2777 - val_categorical_accuracy: 0.1767\n",
      "Epoch 3119/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2061 - categorical_accuracy: 0.5056 - val_loss: 7.2154 - val_categorical_accuracy: 0.1633\n",
      "Epoch 3120/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2287 - categorical_accuracy: 0.4933 - val_loss: 6.9904 - val_categorical_accuracy: 0.1900\n",
      "Epoch 3121/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1917 - categorical_accuracy: 0.5111 - val_loss: 8.2259 - val_categorical_accuracy: 0.1300\n",
      "Epoch 4381/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1915 - categorical_accuracy: 0.5094 - val_loss: 8.3420 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4382/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1821 - categorical_accuracy: 0.5122 - val_loss: 8.5518 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4383/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2042 - categorical_accuracy: 0.5050 - val_loss: 8.3861 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4384/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2273 - categorical_accuracy: 0.5000 - val_loss: 8.6822 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4385/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2276 - categorical_accuracy: 0.4894 - val_loss: 8.0345 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4386/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2643 - categorical_accuracy: 0.4817 - val_loss: 7.9145 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4387/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2718 - categorical_accuracy: 0.4778 - val_loss: 7.6780 - val_categorical_accuracy: 0.1167\n",
      "Epoch 4388/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2499 - categorical_accuracy: 0.4828 - val_loss: 8.3672 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4389/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2197 - categorical_accuracy: 0.4911 - val_loss: 8.2415 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4390/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2024 - categorical_accuracy: 0.5089 - val_loss: 8.0636 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4391/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2053 - categorical_accuracy: 0.5044 - val_loss: 8.6558 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4392/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2237 - categorical_accuracy: 0.5039 - val_loss: 8.6410 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4393/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2317 - categorical_accuracy: 0.5000 - val_loss: 8.3770 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4394/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2456 - categorical_accuracy: 0.4872 - val_loss: 8.3383 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4395/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1926 - categorical_accuracy: 0.5056 - val_loss: 8.5874 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4396/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1970 - categorical_accuracy: 0.5083 - val_loss: 8.2580 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4397/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1964 - categorical_accuracy: 0.5100 - val_loss: 8.1846 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4398/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2970 - categorical_accuracy: 0.4722 - val_loss: 8.3842 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4399/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2564 - categorical_accuracy: 0.4889 - val_loss: 8.4128 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4400/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2145 - categorical_accuracy: 0.4978 - val_loss: 8.7180 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4401/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2238 - categorical_accuracy: 0.4928 - val_loss: 8.7605 - val_categorical_accuracy: 0.1300\n",
      "Epoch 4402/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2874 - categorical_accuracy: 0.4744 - val_loss: 8.3870 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4403/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3645 - categorical_accuracy: 0.4511 - val_loss: 8.2879 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4404/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3199 - categorical_accuracy: 0.4678 - val_loss: 8.7645 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4405/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2570 - categorical_accuracy: 0.4867 - val_loss: 8.5274 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4406/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2026 - categorical_accuracy: 0.5039 - val_loss: 8.4757 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4407/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1925 - categorical_accuracy: 0.5094 - val_loss: 8.4815 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4408/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1920 - categorical_accuracy: 0.5128 - val_loss: 8.5597 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4409/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2212 - categorical_accuracy: 0.5011 - val_loss: 8.6193 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4410/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1939 - categorical_accuracy: 0.5083 - val_loss: 8.6541 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4411/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2019 - categorical_accuracy: 0.5083 - val_loss: 8.6009 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4412/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1994 - categorical_accuracy: 0.5022 - val_loss: 8.7445 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4413/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1897 - categorical_accuracy: 0.5094 - val_loss: 8.6569 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4414/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1960 - categorical_accuracy: 0.5072 - val_loss: 8.8309 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4415/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2130 - categorical_accuracy: 0.4944 - val_loss: 8.3347 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4416/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2155 - categorical_accuracy: 0.5078 - val_loss: 8.8603 - val_categorical_accuracy: 0.1700\n",
      "Epoch 4417/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2176 - categorical_accuracy: 0.4956 - val_loss: 8.1737 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4418/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2299 - categorical_accuracy: 0.4939 - val_loss: 8.6113 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4419/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1964 - categorical_accuracy: 0.5022 - val_loss: 8.6928 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4420/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2313 - categorical_accuracy: 0.4928 - val_loss: 8.0434 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4421/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3176 - categorical_accuracy: 0.4733 - val_loss: 8.4181 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4422/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2545 - categorical_accuracy: 0.4906 - val_loss: 8.5368 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4423/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2587 - categorical_accuracy: 0.4817 - val_loss: 8.6289 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4424/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2311 - categorical_accuracy: 0.4906 - val_loss: 8.5497 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4425/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2094 - categorical_accuracy: 0.4939 - val_loss: 8.5608 - val_categorical_accuracy: 0.1267\n",
      "Epoch 4426/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1994 - categorical_accuracy: 0.5033 - val_loss: 8.6780 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4427/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2130 - categorical_accuracy: 0.4956 - val_loss: 8.8119 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4428/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2649 - categorical_accuracy: 0.4878 - val_loss: 8.5925 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4429/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3391 - categorical_accuracy: 0.4539 - val_loss: 8.2478 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4430/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3026 - categorical_accuracy: 0.4728 - val_loss: 8.6797 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4431/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2152 - categorical_accuracy: 0.5017 - val_loss: 8.7095 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4432/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1995 - categorical_accuracy: 0.5033 - val_loss: 8.5672 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4433/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1940 - categorical_accuracy: 0.5189 - val_loss: 8.8004 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4434/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1937 - categorical_accuracy: 0.5106 - val_loss: 8.5611 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4435/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1838 - categorical_accuracy: 0.5139 - val_loss: 8.5968 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4436/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1937 - categorical_accuracy: 0.5117 - val_loss: 8.2597 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4437/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1916 - categorical_accuracy: 0.5050 - val_loss: 8.5339 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4438/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2060 - categorical_accuracy: 0.5006 - val_loss: 8.7175 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4439/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2171 - categorical_accuracy: 0.4944 - val_loss: 8.6040 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4440/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2079 - categorical_accuracy: 0.4983 - val_loss: 8.5994 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4441/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2330 - categorical_accuracy: 0.4933 - val_loss: 8.3191 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4442/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2376 - categorical_accuracy: 0.4978 - val_loss: 8.5006 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4443/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2015 - categorical_accuracy: 0.5106 - val_loss: 8.3774 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4444/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1955 - categorical_accuracy: 0.5100 - val_loss: 8.5939 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4445/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1955 - categorical_accuracy: 0.5044 - val_loss: 8.6511 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4446/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1961 - categorical_accuracy: 0.5150 - val_loss: 8.3552 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4447/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2068 - categorical_accuracy: 0.5139 - val_loss: 8.4686 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4448/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2163 - categorical_accuracy: 0.4950 - val_loss: 8.2693 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4449/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2646 - categorical_accuracy: 0.4922 - val_loss: 9.1825 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4450/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2426 - categorical_accuracy: 0.4972 - val_loss: 8.9376 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4451/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2138 - categorical_accuracy: 0.5033 - val_loss: 8.8151 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4452/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2165 - categorical_accuracy: 0.4906 - val_loss: 8.9420 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4453/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1941 - categorical_accuracy: 0.5072 - val_loss: 8.4128 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4454/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1863 - categorical_accuracy: 0.5139 - val_loss: 8.6775 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4455/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2081 - categorical_accuracy: 0.5017 - val_loss: 8.8155 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4456/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1961 - categorical_accuracy: 0.5100 - val_loss: 8.7179 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4457/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1913 - categorical_accuracy: 0.5044 - val_loss: 8.6871 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4458/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1899 - categorical_accuracy: 0.5094 - val_loss: 8.7057 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4459/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1927 - categorical_accuracy: 0.5089 - val_loss: 8.7830 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4460/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1846 - categorical_accuracy: 0.5161 - val_loss: 8.8569 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4461/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1796 - categorical_accuracy: 0.5200 - val_loss: 8.7626 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4462/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1762 - categorical_accuracy: 0.5206 - val_loss: 8.8403 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4463/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1866 - categorical_accuracy: 0.5211 - val_loss: 8.6202 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4464/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1798 - categorical_accuracy: 0.5178 - val_loss: 8.8274 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4465/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2140 - categorical_accuracy: 0.5061 - val_loss: 8.8473 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4466/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2266 - categorical_accuracy: 0.4917 - val_loss: 8.3026 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4467/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2792 - categorical_accuracy: 0.4850 - val_loss: 8.5326 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4468/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3289 - categorical_accuracy: 0.4678 - val_loss: 8.6712 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4469/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2503 - categorical_accuracy: 0.4967 - val_loss: 8.6375 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4470/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2323 - categorical_accuracy: 0.4967 - val_loss: 8.2142 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4471/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2477 - categorical_accuracy: 0.4900 - val_loss: 8.6031 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4472/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2083 - categorical_accuracy: 0.4978 - val_loss: 8.2498 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4473/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2438 - categorical_accuracy: 0.5000 - val_loss: 8.4197 - val_categorical_accuracy: 0.1333\n",
      "Epoch 4474/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2549 - categorical_accuracy: 0.4939 - val_loss: 8.6001 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4475/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2181 - categorical_accuracy: 0.4972 - val_loss: 8.4271 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4476/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2469 - categorical_accuracy: 0.4928 - val_loss: 8.4637 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4477/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2588 - categorical_accuracy: 0.4844 - val_loss: 8.3569 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4478/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2201 - categorical_accuracy: 0.5000 - val_loss: 8.9982 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4479/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2047 - categorical_accuracy: 0.5094 - val_loss: 8.5776 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4480/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3373 - categorical_accuracy: 0.4722 - val_loss: 8.5073 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4481/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2576 - categorical_accuracy: 0.4906 - val_loss: 8.4453 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4482/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2305 - categorical_accuracy: 0.5022 - val_loss: 8.3833 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4483/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2459 - categorical_accuracy: 0.4994 - val_loss: 8.0483 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4484/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2298 - categorical_accuracy: 0.5061 - val_loss: 8.5528 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4485/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2545 - categorical_accuracy: 0.4844 - val_loss: 8.6548 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4486/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2594 - categorical_accuracy: 0.4933 - val_loss: 8.4058 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4487/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2067 - categorical_accuracy: 0.5089 - val_loss: 8.8013 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4488/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2091 - categorical_accuracy: 0.5072 - val_loss: 8.7843 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4489/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1978 - categorical_accuracy: 0.5183 - val_loss: 8.6001 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4490/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2067 - categorical_accuracy: 0.5144 - val_loss: 8.5316 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4491/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1957 - categorical_accuracy: 0.5133 - val_loss: 8.7304 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4492/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2170 - categorical_accuracy: 0.5050 - val_loss: 8.8865 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4493/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2081 - categorical_accuracy: 0.5033 - val_loss: 9.1063 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4494/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2055 - categorical_accuracy: 0.5144 - val_loss: 8.9788 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4495/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1990 - categorical_accuracy: 0.5106 - val_loss: 8.7490 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4496/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1867 - categorical_accuracy: 0.5233 - val_loss: 8.6771 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4497/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1926 - categorical_accuracy: 0.5239 - val_loss: 8.2951 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4498/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2104 - categorical_accuracy: 0.5117 - val_loss: 8.7708 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4499/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1968 - categorical_accuracy: 0.5117 - val_loss: 8.5058 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4500/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1920 - categorical_accuracy: 0.5150 - val_loss: 8.8072 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4501/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1857 - categorical_accuracy: 0.5228 - val_loss: 8.7498 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4502/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1826 - categorical_accuracy: 0.5278 - val_loss: 8.6386 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4503/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1890 - categorical_accuracy: 0.5172 - val_loss: 8.5319 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4504/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3314 - categorical_accuracy: 0.4756 - val_loss: 8.5320 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4505/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2894 - categorical_accuracy: 0.4794 - val_loss: 8.9273 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4506/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2397 - categorical_accuracy: 0.4956 - val_loss: 8.5818 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4507/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2474 - categorical_accuracy: 0.4944 - val_loss: 8.7616 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4508/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2515 - categorical_accuracy: 0.4800 - val_loss: 8.3430 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4509/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2576 - categorical_accuracy: 0.4944 - val_loss: 8.7731 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4510/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2340 - categorical_accuracy: 0.4972 - val_loss: 7.9983 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4511/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2311 - categorical_accuracy: 0.4939 - val_loss: 8.2884 - val_categorical_accuracy: 0.1300\n",
      "Epoch 4512/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2474 - categorical_accuracy: 0.4906 - val_loss: 8.2120 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4513/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2177 - categorical_accuracy: 0.5078 - val_loss: 8.5658 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4514/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2397 - categorical_accuracy: 0.4928 - val_loss: 8.2217 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4515/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2967 - categorical_accuracy: 0.4883 - val_loss: 7.9419 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4516/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.3271 - categorical_accuracy: 0.4583 - val_loss: 8.3907 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4517/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2967 - categorical_accuracy: 0.4711 - val_loss: 8.0358 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4706/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2381 - categorical_accuracy: 0.4772 - val_loss: 7.6777 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4707/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2226 - categorical_accuracy: 0.4817 - val_loss: 8.0176 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4708/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2178 - categorical_accuracy: 0.4872 - val_loss: 8.2049 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4709/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2473 - categorical_accuracy: 0.4789 - val_loss: 7.6973 - val_categorical_accuracy: 0.1367\n",
      "Epoch 4710/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2722 - categorical_accuracy: 0.4656 - val_loss: 7.5969 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4711/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2252 - categorical_accuracy: 0.4917 - val_loss: 7.9131 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4712/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2508 - categorical_accuracy: 0.4783 - val_loss: 7.7480 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4713/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2191 - categorical_accuracy: 0.4983 - val_loss: 7.7110 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4714/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2015 - categorical_accuracy: 0.4972 - val_loss: 8.2198 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4715/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2069 - categorical_accuracy: 0.4972 - val_loss: 7.4287 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4716/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2215 - categorical_accuracy: 0.4894 - val_loss: 7.6790 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4717/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2888 - categorical_accuracy: 0.4617 - val_loss: 7.4930 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4718/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2370 - categorical_accuracy: 0.4889 - val_loss: 7.3066 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4719/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2391 - categorical_accuracy: 0.4811 - val_loss: 8.0673 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4720/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2114 - categorical_accuracy: 0.4917 - val_loss: 7.5268 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4721/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2059 - categorical_accuracy: 0.4878 - val_loss: 8.1954 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4722/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2028 - categorical_accuracy: 0.4933 - val_loss: 8.1302 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4723/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1927 - categorical_accuracy: 0.4983 - val_loss: 7.9412 - val_categorical_accuracy: 0.1700\n",
      "Epoch 4724/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2053 - categorical_accuracy: 0.4983 - val_loss: 7.8669 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4725/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.4403 - categorical_accuracy: 0.4233 - val_loss: 8.3799 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4726/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3066 - categorical_accuracy: 0.4644 - val_loss: 8.4263 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4727/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2947 - categorical_accuracy: 0.4589 - val_loss: 8.0602 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4728/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2913 - categorical_accuracy: 0.4617 - val_loss: 7.8860 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4729/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2789 - categorical_accuracy: 0.4650 - val_loss: 8.0721 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4730/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2241 - categorical_accuracy: 0.4883 - val_loss: 8.1422 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4731/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2078 - categorical_accuracy: 0.4922 - val_loss: 8.1537 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4732/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3190 - categorical_accuracy: 0.4578 - val_loss: 7.6727 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4733/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2310 - categorical_accuracy: 0.4839 - val_loss: 7.9869 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4734/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2055 - categorical_accuracy: 0.4917 - val_loss: 8.2425 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4735/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2178 - categorical_accuracy: 0.4917 - val_loss: 7.7820 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4736/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2151 - categorical_accuracy: 0.4933 - val_loss: 8.0155 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4737/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2125 - categorical_accuracy: 0.5000 - val_loss: 8.3729 - val_categorical_accuracy: 0.1700\n",
      "Epoch 4738/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2093 - categorical_accuracy: 0.5000 - val_loss: 7.8756 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4739/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2269 - categorical_accuracy: 0.4772 - val_loss: 8.1745 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4740/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1974 - categorical_accuracy: 0.4994 - val_loss: 8.3271 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4741/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1834 - categorical_accuracy: 0.5050 - val_loss: 8.3001 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4742/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2466 - categorical_accuracy: 0.4756 - val_loss: 8.1542 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4743/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2293 - categorical_accuracy: 0.4883 - val_loss: 7.2234 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4744/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2368 - categorical_accuracy: 0.4850 - val_loss: 7.9693 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4745/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2010 - categorical_accuracy: 0.5022 - val_loss: 8.2911 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4746/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2036 - categorical_accuracy: 0.4944 - val_loss: 8.1841 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4747/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1961 - categorical_accuracy: 0.5006 - val_loss: 7.8827 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4748/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2147 - categorical_accuracy: 0.4950 - val_loss: 7.9549 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4749/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2260 - categorical_accuracy: 0.4856 - val_loss: 8.1637 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4750/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1919 - categorical_accuracy: 0.5028 - val_loss: 7.6142 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4751/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2333 - categorical_accuracy: 0.4867 - val_loss: 8.4094 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4752/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2025 - categorical_accuracy: 0.4939 - val_loss: 8.2997 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4753/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2079 - categorical_accuracy: 0.4961 - val_loss: 8.4305 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4754/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2061 - categorical_accuracy: 0.5056 - val_loss: 7.6892 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4755/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2106 - categorical_accuracy: 0.4950 - val_loss: 8.0660 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4756/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1907 - categorical_accuracy: 0.5061 - val_loss: 8.4120 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4757/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1818 - categorical_accuracy: 0.5039 - val_loss: 8.3122 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4758/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1759 - categorical_accuracy: 0.5061 - val_loss: 8.2696 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4759/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1808 - categorical_accuracy: 0.5006 - val_loss: 7.9992 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4760/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1878 - categorical_accuracy: 0.5100 - val_loss: 8.2459 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4761/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2005 - categorical_accuracy: 0.4972 - val_loss: 8.4348 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4762/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1834 - categorical_accuracy: 0.5028 - val_loss: 8.1284 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4763/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1782 - categorical_accuracy: 0.5094 - val_loss: 8.1065 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4764/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1789 - categorical_accuracy: 0.5172 - val_loss: 8.2258 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4765/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1765 - categorical_accuracy: 0.5128 - val_loss: 8.3879 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4766/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1834 - categorical_accuracy: 0.5094 - val_loss: 8.2473 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4767/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2110 - categorical_accuracy: 0.4939 - val_loss: 7.7906 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4768/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2953 - categorical_accuracy: 0.4578 - val_loss: 8.3807 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4769/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3656 - categorical_accuracy: 0.4467 - val_loss: 7.8506 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4770/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2565 - categorical_accuracy: 0.4744 - val_loss: 7.9774 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4771/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2421 - categorical_accuracy: 0.4811 - val_loss: 8.1733 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4772/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2294 - categorical_accuracy: 0.4883 - val_loss: 8.1627 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4773/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2625 - categorical_accuracy: 0.4750 - val_loss: 8.2507 - val_categorical_accuracy: 0.1733\n",
      "Epoch 4774/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2524 - categorical_accuracy: 0.4783 - val_loss: 7.8902 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4775/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2051 - categorical_accuracy: 0.5022 - val_loss: 7.8916 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4776/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1913 - categorical_accuracy: 0.4989 - val_loss: 8.1996 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4777/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1918 - categorical_accuracy: 0.5033 - val_loss: 8.1272 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4778/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1778 - categorical_accuracy: 0.5078 - val_loss: 8.0163 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4779/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1885 - categorical_accuracy: 0.5044 - val_loss: 8.3389 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4780/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2097 - categorical_accuracy: 0.4911 - val_loss: 7.7816 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4781/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2122 - categorical_accuracy: 0.4961 - val_loss: 7.7471 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4782/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2111 - categorical_accuracy: 0.4911 - val_loss: 8.2669 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4783/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2036 - categorical_accuracy: 0.4922 - val_loss: 8.0874 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4784/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1964 - categorical_accuracy: 0.4933 - val_loss: 8.3026 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4785/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1870 - categorical_accuracy: 0.4944 - val_loss: 8.5384 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4786/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1919 - categorical_accuracy: 0.4928 - val_loss: 8.3318 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4787/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1804 - categorical_accuracy: 0.5072 - val_loss: 8.5553 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4788/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1774 - categorical_accuracy: 0.5028 - val_loss: 8.6306 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4789/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2547 - categorical_accuracy: 0.4767 - val_loss: 8.4184 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4790/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2105 - categorical_accuracy: 0.4933 - val_loss: 8.7416 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4791/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2462 - categorical_accuracy: 0.4844 - val_loss: 8.0175 - val_categorical_accuracy: 0.1433\n",
      "Epoch 4792/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2434 - categorical_accuracy: 0.4839 - val_loss: 8.1000 - val_categorical_accuracy: 0.1733\n",
      "Epoch 4793/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2172 - categorical_accuracy: 0.4917 - val_loss: 7.9470 - val_categorical_accuracy: 0.1800\n",
      "Epoch 4794/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2401 - categorical_accuracy: 0.4850 - val_loss: 8.0713 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4795/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2153 - categorical_accuracy: 0.4950 - val_loss: 8.0647 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4796/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2222 - categorical_accuracy: 0.4878 - val_loss: 8.1831 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4797/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1997 - categorical_accuracy: 0.5017 - val_loss: 8.4260 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4798/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1876 - categorical_accuracy: 0.5117 - val_loss: 8.3679 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4799/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1977 - categorical_accuracy: 0.5044 - val_loss: 7.8687 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4800/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2344 - categorical_accuracy: 0.4922 - val_loss: 8.0694 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4801/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2043 - categorical_accuracy: 0.4989 - val_loss: 8.7366 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4802/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1936 - categorical_accuracy: 0.4956 - val_loss: 8.2900 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4803/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1892 - categorical_accuracy: 0.4989 - val_loss: 8.5786 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4804/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1826 - categorical_accuracy: 0.5067 - val_loss: 8.2829 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4805/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1771 - categorical_accuracy: 0.5050 - val_loss: 8.3078 - val_categorical_accuracy: 0.1400\n",
      "Epoch 4806/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1720 - categorical_accuracy: 0.5078 - val_loss: 8.4105 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4807/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1786 - categorical_accuracy: 0.5061 - val_loss: 8.5723 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4808/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1790 - categorical_accuracy: 0.5033 - val_loss: 8.5215 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4809/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1709 - categorical_accuracy: 0.5106 - val_loss: 8.1853 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4810/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2081 - categorical_accuracy: 0.4978 - val_loss: 8.5414 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4811/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2609 - categorical_accuracy: 0.4772 - val_loss: 8.2357 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4812/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2067 - categorical_accuracy: 0.4933 - val_loss: 8.0771 - val_categorical_accuracy: 0.1733\n",
      "Epoch 4813/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1945 - categorical_accuracy: 0.4994 - val_loss: 8.3181 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4814/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1869 - categorical_accuracy: 0.5028 - val_loss: 8.3347 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4815/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1786 - categorical_accuracy: 0.5044 - val_loss: 8.3635 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4816/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1764 - categorical_accuracy: 0.5111 - val_loss: 8.3518 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4817/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1775 - categorical_accuracy: 0.5094 - val_loss: 8.3984 - val_categorical_accuracy: 0.1700\n",
      "Epoch 4818/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2094 - categorical_accuracy: 0.4911 - val_loss: 8.2613 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4819/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3085 - categorical_accuracy: 0.4606 - val_loss: 8.1374 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4820/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2929 - categorical_accuracy: 0.4544 - val_loss: 7.6727 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4821/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2562 - categorical_accuracy: 0.4778 - val_loss: 7.4880 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4822/10000\n",
      "57/57 [==============================] - 0s 7ms/step - loss: 1.2270 - categorical_accuracy: 0.4833 - val_loss: 8.1096 - val_categorical_accuracy: 0.1733\n",
      "Epoch 4823/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1913 - categorical_accuracy: 0.4972 - val_loss: 8.5079 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4824/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1875 - categorical_accuracy: 0.5017 - val_loss: 8.4876 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4825/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1860 - categorical_accuracy: 0.5028 - val_loss: 8.6184 - val_categorical_accuracy: 0.1700\n",
      "Epoch 4826/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1882 - categorical_accuracy: 0.5044 - val_loss: 8.4060 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4827/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1868 - categorical_accuracy: 0.5017 - val_loss: 8.0541 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4828/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2375 - categorical_accuracy: 0.4878 - val_loss: 8.1446 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4829/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1902 - categorical_accuracy: 0.4972 - val_loss: 8.0538 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4830/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1743 - categorical_accuracy: 0.5028 - val_loss: 8.3940 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4831/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1727 - categorical_accuracy: 0.5067 - val_loss: 8.4749 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4832/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1693 - categorical_accuracy: 0.5089 - val_loss: 8.4252 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4833/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1759 - categorical_accuracy: 0.5078 - val_loss: 8.6296 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4834/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1676 - categorical_accuracy: 0.5128 - val_loss: 8.4949 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4835/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1687 - categorical_accuracy: 0.5106 - val_loss: 8.6946 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4836/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1885 - categorical_accuracy: 0.4994 - val_loss: 8.6718 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4837/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2310 - categorical_accuracy: 0.4867 - val_loss: 8.2271 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4838/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1926 - categorical_accuracy: 0.4994 - val_loss: 8.4642 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4839/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2164 - categorical_accuracy: 0.4872 - val_loss: 8.1594 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4840/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2773 - categorical_accuracy: 0.4722 - val_loss: 7.5613 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4841/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2569 - categorical_accuracy: 0.4717 - val_loss: 8.2410 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4842/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2343 - categorical_accuracy: 0.4806 - val_loss: 8.0280 - val_categorical_accuracy: 0.1700\n",
      "Epoch 4843/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2770 - categorical_accuracy: 0.4683 - val_loss: 8.1339 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4844/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2630 - categorical_accuracy: 0.4761 - val_loss: 8.5159 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4845/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2148 - categorical_accuracy: 0.5000 - val_loss: 8.5783 - val_categorical_accuracy: 0.1733\n",
      "Epoch 4846/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1926 - categorical_accuracy: 0.4956 - val_loss: 8.3848 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4847/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3042 - categorical_accuracy: 0.4689 - val_loss: 7.1422 - val_categorical_accuracy: 0.1467\n",
      "Epoch 4848/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3058 - categorical_accuracy: 0.4583 - val_loss: 7.8349 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4849/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2165 - categorical_accuracy: 0.4939 - val_loss: 7.9872 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4850/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1843 - categorical_accuracy: 0.4989 - val_loss: 8.1026 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4851/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1780 - categorical_accuracy: 0.5083 - val_loss: 8.2643 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4852/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1850 - categorical_accuracy: 0.5022 - val_loss: 8.3991 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4853/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1764 - categorical_accuracy: 0.5028 - val_loss: 8.2813 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4854/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2061 - categorical_accuracy: 0.4878 - val_loss: 8.2444 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4855/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1835 - categorical_accuracy: 0.5106 - val_loss: 8.4021 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4856/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1752 - categorical_accuracy: 0.5056 - val_loss: 8.1588 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4857/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1719 - categorical_accuracy: 0.5083 - val_loss: 8.4147 - val_categorical_accuracy: 0.1700\n",
      "Epoch 4858/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1765 - categorical_accuracy: 0.5089 - val_loss: 8.1792 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4859/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1740 - categorical_accuracy: 0.5100 - val_loss: 8.2587 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4860/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2074 - categorical_accuracy: 0.4972 - val_loss: 7.9491 - val_categorical_accuracy: 0.1567\n",
      "Epoch 4861/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2042 - categorical_accuracy: 0.4911 - val_loss: 8.3665 - val_categorical_accuracy: 0.1533\n",
      "Epoch 4862/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1855 - categorical_accuracy: 0.5044 - val_loss: 8.2706 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4863/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1805 - categorical_accuracy: 0.5056 - val_loss: 8.4981 - val_categorical_accuracy: 0.1500\n",
      "Epoch 4864/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1824 - categorical_accuracy: 0.5022 - val_loss: 8.1548 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4865/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1730 - categorical_accuracy: 0.5050 - val_loss: 8.2923 - val_categorical_accuracy: 0.1633\n",
      "Epoch 4866/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2644 - categorical_accuracy: 0.4761 - val_loss: 8.2903 - val_categorical_accuracy: 0.1600\n",
      "Epoch 4867/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2550 - categorical_accuracy: 0.4756 - val_loss: 7.5737 - val_categorical_accuracy: 0.1667\n",
      "Epoch 4868/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2370 - categorical_accuracy: 0.4828 - val_loss: 8.3331 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5082/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2534 - categorical_accuracy: 0.4661 - val_loss: 8.0097 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5083/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1877 - categorical_accuracy: 0.4983 - val_loss: 7.9435 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5084/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1962 - categorical_accuracy: 0.4922 - val_loss: 8.1463 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5085/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1879 - categorical_accuracy: 0.4961 - val_loss: 8.4495 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5086/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1913 - categorical_accuracy: 0.5000 - val_loss: 8.6195 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5087/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1737 - categorical_accuracy: 0.4972 - val_loss: 8.2419 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5088/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1677 - categorical_accuracy: 0.5039 - val_loss: 8.3511 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5089/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1604 - categorical_accuracy: 0.5111 - val_loss: 8.5394 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5090/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1756 - categorical_accuracy: 0.5056 - val_loss: 8.3746 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5091/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1869 - categorical_accuracy: 0.5106 - val_loss: 8.1054 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5092/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2703 - categorical_accuracy: 0.4706 - val_loss: 7.9335 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5093/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2267 - categorical_accuracy: 0.4861 - val_loss: 8.3549 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5094/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1772 - categorical_accuracy: 0.5006 - val_loss: 8.5111 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5095/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1638 - categorical_accuracy: 0.5133 - val_loss: 8.3340 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5096/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1566 - categorical_accuracy: 0.5139 - val_loss: 8.4959 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5097/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1583 - categorical_accuracy: 0.5144 - val_loss: 8.5594 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5098/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1694 - categorical_accuracy: 0.5067 - val_loss: 8.4011 - val_categorical_accuracy: 0.1867\n",
      "Epoch 5099/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1798 - categorical_accuracy: 0.5033 - val_loss: 7.7725 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5100/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2271 - categorical_accuracy: 0.4872 - val_loss: 8.5165 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5101/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2537 - categorical_accuracy: 0.4833 - val_loss: 8.3444 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5102/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2033 - categorical_accuracy: 0.4906 - val_loss: 8.3406 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5103/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1772 - categorical_accuracy: 0.5100 - val_loss: 8.3919 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5104/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1618 - categorical_accuracy: 0.5117 - val_loss: 8.4754 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5105/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1519 - categorical_accuracy: 0.5194 - val_loss: 8.2972 - val_categorical_accuracy: 0.1900\n",
      "Epoch 5106/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1529 - categorical_accuracy: 0.5267 - val_loss: 8.3464 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5107/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1533 - categorical_accuracy: 0.5206 - val_loss: 8.3399 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5108/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2065 - categorical_accuracy: 0.4983 - val_loss: 8.4476 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5109/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2309 - categorical_accuracy: 0.4928 - val_loss: 8.0731 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5110/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1829 - categorical_accuracy: 0.5017 - val_loss: 8.5405 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5111/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1912 - categorical_accuracy: 0.5006 - val_loss: 8.2665 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5112/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1823 - categorical_accuracy: 0.4944 - val_loss: 8.1508 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5113/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1729 - categorical_accuracy: 0.5094 - val_loss: 8.0821 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5114/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1802 - categorical_accuracy: 0.5106 - val_loss: 8.3521 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5115/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1564 - categorical_accuracy: 0.5167 - val_loss: 8.3774 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5116/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1504 - categorical_accuracy: 0.5144 - val_loss: 8.3794 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5117/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1574 - categorical_accuracy: 0.5117 - val_loss: 8.2569 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5118/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1686 - categorical_accuracy: 0.5139 - val_loss: 8.4827 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5119/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1617 - categorical_accuracy: 0.5133 - val_loss: 8.3115 - val_categorical_accuracy: 0.1833\n",
      "Epoch 5120/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2726 - categorical_accuracy: 0.4761 - val_loss: 8.0944 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5121/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3605 - categorical_accuracy: 0.4444 - val_loss: 7.8843 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5122/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2623 - categorical_accuracy: 0.4756 - val_loss: 7.8871 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5123/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2065 - categorical_accuracy: 0.4906 - val_loss: 8.4699 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5124/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1898 - categorical_accuracy: 0.5028 - val_loss: 8.2580 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5125/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1722 - categorical_accuracy: 0.5067 - val_loss: 7.8711 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5126/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1914 - categorical_accuracy: 0.5017 - val_loss: 8.3318 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5127/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1980 - categorical_accuracy: 0.4894 - val_loss: 8.3577 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5128/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2209 - categorical_accuracy: 0.4906 - val_loss: 8.2499 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5129/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1860 - categorical_accuracy: 0.5022 - val_loss: 8.2649 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5130/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1744 - categorical_accuracy: 0.5050 - val_loss: 8.4468 - val_categorical_accuracy: 0.1833\n",
      "Epoch 5131/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1671 - categorical_accuracy: 0.5078 - val_loss: 8.2003 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5132/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1734 - categorical_accuracy: 0.5083 - val_loss: 8.5273 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5133/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1648 - categorical_accuracy: 0.5050 - val_loss: 8.4699 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5134/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1597 - categorical_accuracy: 0.5072 - val_loss: 8.3798 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5135/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1704 - categorical_accuracy: 0.5083 - val_loss: 8.2528 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5136/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1552 - categorical_accuracy: 0.5194 - val_loss: 8.2717 - val_categorical_accuracy: 0.1867\n",
      "Epoch 5137/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1647 - categorical_accuracy: 0.5089 - val_loss: 8.2282 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5138/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2162 - categorical_accuracy: 0.4867 - val_loss: 8.3244 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5139/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2331 - categorical_accuracy: 0.4833 - val_loss: 8.1149 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5140/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1900 - categorical_accuracy: 0.4889 - val_loss: 8.1564 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5141/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1833 - categorical_accuracy: 0.5061 - val_loss: 8.2062 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5142/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1641 - categorical_accuracy: 0.5100 - val_loss: 8.4059 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5143/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1723 - categorical_accuracy: 0.5106 - val_loss: 8.3424 - val_categorical_accuracy: 0.1833\n",
      "Epoch 5144/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1735 - categorical_accuracy: 0.5128 - val_loss: 8.3416 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5145/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3037 - categorical_accuracy: 0.4850 - val_loss: 7.5156 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5146/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3167 - categorical_accuracy: 0.4561 - val_loss: 8.9830 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5147/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2355 - categorical_accuracy: 0.4789 - val_loss: 8.2695 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5148/10000\n",
      "57/57 [==============================] - 1s 14ms/step - loss: 1.1949 - categorical_accuracy: 0.4944 - val_loss: 8.4624 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5149/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1849 - categorical_accuracy: 0.5006 - val_loss: 8.4272 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5150/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1807 - categorical_accuracy: 0.4972 - val_loss: 8.3887 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5151/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1651 - categorical_accuracy: 0.5078 - val_loss: 8.3970 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5152/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1860 - categorical_accuracy: 0.4967 - val_loss: 8.7341 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5153/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1991 - categorical_accuracy: 0.4867 - val_loss: 8.5757 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5154/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1728 - categorical_accuracy: 0.5122 - val_loss: 8.4239 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5155/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1633 - categorical_accuracy: 0.5150 - val_loss: 8.2850 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5156/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2069 - categorical_accuracy: 0.4956 - val_loss: 7.9498 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5157/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2651 - categorical_accuracy: 0.4694 - val_loss: 8.3357 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5158/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1716 - categorical_accuracy: 0.5050 - val_loss: 8.2864 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5159/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1637 - categorical_accuracy: 0.5200 - val_loss: 8.1631 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5160/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1689 - categorical_accuracy: 0.5128 - val_loss: 8.4312 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5161/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1508 - categorical_accuracy: 0.5106 - val_loss: 8.2730 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5162/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1575 - categorical_accuracy: 0.5122 - val_loss: 8.5389 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5163/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1534 - categorical_accuracy: 0.5128 - val_loss: 8.5712 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5164/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2216 - categorical_accuracy: 0.4844 - val_loss: 8.2207 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5165/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2167 - categorical_accuracy: 0.4961 - val_loss: 8.2953 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5166/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2170 - categorical_accuracy: 0.4839 - val_loss: 8.3248 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5167/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1791 - categorical_accuracy: 0.5006 - val_loss: 8.4068 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5168/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1592 - categorical_accuracy: 0.5133 - val_loss: 8.7692 - val_categorical_accuracy: 0.1867\n",
      "Epoch 5169/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.1991 - categorical_accuracy: 0.5083 - val_loss: 8.1901 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5170/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1661 - categorical_accuracy: 0.5106 - val_loss: 8.5383 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5171/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1763 - categorical_accuracy: 0.5128 - val_loss: 8.5332 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5172/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1483 - categorical_accuracy: 0.5178 - val_loss: 8.6393 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5173/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1626 - categorical_accuracy: 0.5067 - val_loss: 8.4400 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5174/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1871 - categorical_accuracy: 0.5017 - val_loss: 8.2197 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5175/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1640 - categorical_accuracy: 0.5022 - val_loss: 8.4552 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5176/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1980 - categorical_accuracy: 0.4950 - val_loss: 9.1000 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5177/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2676 - categorical_accuracy: 0.4872 - val_loss: 8.1059 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5178/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1817 - categorical_accuracy: 0.5028 - val_loss: 8.5987 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5179/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1795 - categorical_accuracy: 0.5022 - val_loss: 8.1876 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5180/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1800 - categorical_accuracy: 0.4994 - val_loss: 8.2694 - val_categorical_accuracy: 0.1867\n",
      "Epoch 5181/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1778 - categorical_accuracy: 0.5133 - val_loss: 8.5227 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5182/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1799 - categorical_accuracy: 0.5133 - val_loss: 8.1762 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5183/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1644 - categorical_accuracy: 0.5194 - val_loss: 8.5133 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5184/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1478 - categorical_accuracy: 0.5222 - val_loss: 8.3673 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5185/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2293 - categorical_accuracy: 0.4928 - val_loss: 9.1185 - val_categorical_accuracy: 0.1900\n",
      "Epoch 5186/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2506 - categorical_accuracy: 0.4833 - val_loss: 8.3621 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5187/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3217 - categorical_accuracy: 0.4600 - val_loss: 8.2479 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5188/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2117 - categorical_accuracy: 0.4789 - val_loss: 8.3228 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5189/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1864 - categorical_accuracy: 0.5083 - val_loss: 8.5824 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5190/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1973 - categorical_accuracy: 0.5028 - val_loss: 8.4790 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5191/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.3726 - categorical_accuracy: 0.4528 - val_loss: 7.1151 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5192/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2427 - categorical_accuracy: 0.4706 - val_loss: 8.2661 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5193/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1905 - categorical_accuracy: 0.4928 - val_loss: 8.4360 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5194/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1641 - categorical_accuracy: 0.5100 - val_loss: 8.6981 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5558/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1778 - categorical_accuracy: 0.5100 - val_loss: 8.9319 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5559/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1580 - categorical_accuracy: 0.5133 - val_loss: 8.7794 - val_categorical_accuracy: 0.1867\n",
      "Epoch 5560/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1635 - categorical_accuracy: 0.4994 - val_loss: 8.4996 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5561/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1489 - categorical_accuracy: 0.5144 - val_loss: 8.9234 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5562/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1525 - categorical_accuracy: 0.5150 - val_loss: 8.8020 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5563/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1554 - categorical_accuracy: 0.5150 - val_loss: 8.6027 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5564/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1541 - categorical_accuracy: 0.5161 - val_loss: 8.5934 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5565/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1752 - categorical_accuracy: 0.4978 - val_loss: 8.6340 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5566/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1782 - categorical_accuracy: 0.5044 - val_loss: 8.7987 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5567/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1754 - categorical_accuracy: 0.5067 - val_loss: 8.8719 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5568/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1564 - categorical_accuracy: 0.5111 - val_loss: 8.8396 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5569/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1475 - categorical_accuracy: 0.5167 - val_loss: 8.8052 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5570/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1595 - categorical_accuracy: 0.5122 - val_loss: 8.8109 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5571/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1562 - categorical_accuracy: 0.5117 - val_loss: 8.7528 - val_categorical_accuracy: 0.1433\n",
      "Epoch 5572/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2484 - categorical_accuracy: 0.4828 - val_loss: 8.9635 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5573/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1844 - categorical_accuracy: 0.5011 - val_loss: 8.6308 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5574/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1755 - categorical_accuracy: 0.5039 - val_loss: 8.6518 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5575/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1688 - categorical_accuracy: 0.5167 - val_loss: 8.4936 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5576/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1656 - categorical_accuracy: 0.5133 - val_loss: 8.8760 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5577/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1548 - categorical_accuracy: 0.5161 - val_loss: 8.7762 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5578/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1437 - categorical_accuracy: 0.5139 - val_loss: 9.0303 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5579/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1445 - categorical_accuracy: 0.5189 - val_loss: 8.7224 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5580/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1670 - categorical_accuracy: 0.5089 - val_loss: 8.5590 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5581/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1573 - categorical_accuracy: 0.5139 - val_loss: 8.7651 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5582/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2021 - categorical_accuracy: 0.4911 - val_loss: 8.8029 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5583/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2146 - categorical_accuracy: 0.5022 - val_loss: 8.7117 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5584/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2019 - categorical_accuracy: 0.5044 - val_loss: 8.8311 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5585/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2047 - categorical_accuracy: 0.4933 - val_loss: 8.9497 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5586/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1566 - categorical_accuracy: 0.5122 - val_loss: 8.8069 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5587/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1519 - categorical_accuracy: 0.5122 - val_loss: 8.9350 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5588/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1564 - categorical_accuracy: 0.5122 - val_loss: 9.0438 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5589/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1665 - categorical_accuracy: 0.5189 - val_loss: 8.8292 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5590/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1602 - categorical_accuracy: 0.5172 - val_loss: 8.6354 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5591/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1641 - categorical_accuracy: 0.5094 - val_loss: 8.9451 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5592/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1580 - categorical_accuracy: 0.5189 - val_loss: 8.9734 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5593/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1616 - categorical_accuracy: 0.5156 - val_loss: 8.7922 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5594/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1485 - categorical_accuracy: 0.5194 - val_loss: 8.9873 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5595/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1605 - categorical_accuracy: 0.5100 - val_loss: 9.0131 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5596/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1744 - categorical_accuracy: 0.5050 - val_loss: 8.9145 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5597/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1569 - categorical_accuracy: 0.5206 - val_loss: 8.7685 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5598/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1964 - categorical_accuracy: 0.4978 - val_loss: 8.5589 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5599/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1920 - categorical_accuracy: 0.5033 - val_loss: 9.0943 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5600/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3246 - categorical_accuracy: 0.4783 - val_loss: 7.7220 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5601/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3759 - categorical_accuracy: 0.4500 - val_loss: 8.4479 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5602/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3783 - categorical_accuracy: 0.4461 - val_loss: 8.6024 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5603/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2052 - categorical_accuracy: 0.5050 - val_loss: 8.6803 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5604/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1746 - categorical_accuracy: 0.5044 - val_loss: 8.8132 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5605/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1713 - categorical_accuracy: 0.5011 - val_loss: 8.6936 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5606/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1690 - categorical_accuracy: 0.5061 - val_loss: 8.6931 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5607/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1551 - categorical_accuracy: 0.5117 - val_loss: 8.9145 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5608/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1562 - categorical_accuracy: 0.5061 - val_loss: 8.4345 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5609/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1755 - categorical_accuracy: 0.5106 - val_loss: 8.6715 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5610/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1605 - categorical_accuracy: 0.5033 - val_loss: 8.7371 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5611/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1444 - categorical_accuracy: 0.5178 - val_loss: 8.6273 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5612/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1474 - categorical_accuracy: 0.5150 - val_loss: 8.5720 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5613/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1550 - categorical_accuracy: 0.5189 - val_loss: 8.3904 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5614/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2184 - categorical_accuracy: 0.4928 - val_loss: 8.3603 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5615/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3344 - categorical_accuracy: 0.4644 - val_loss: 9.0955 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5616/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2451 - categorical_accuracy: 0.4761 - val_loss: 8.8932 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5617/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1819 - categorical_accuracy: 0.5022 - val_loss: 8.7004 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5618/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1672 - categorical_accuracy: 0.5067 - val_loss: 8.6888 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5619/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1522 - categorical_accuracy: 0.5117 - val_loss: 8.4638 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5620/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1593 - categorical_accuracy: 0.5156 - val_loss: 8.3720 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5621/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1783 - categorical_accuracy: 0.5050 - val_loss: 8.5657 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5622/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2000 - categorical_accuracy: 0.4922 - val_loss: 8.7561 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5623/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1819 - categorical_accuracy: 0.5044 - val_loss: 8.8949 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5624/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1592 - categorical_accuracy: 0.5161 - val_loss: 8.3356 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5625/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1578 - categorical_accuracy: 0.5072 - val_loss: 8.6020 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5626/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1761 - categorical_accuracy: 0.5000 - val_loss: 9.0591 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5627/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2804 - categorical_accuracy: 0.4756 - val_loss: 8.6568 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5628/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2285 - categorical_accuracy: 0.4789 - val_loss: 8.4956 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5629/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1865 - categorical_accuracy: 0.4978 - val_loss: 8.5570 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5630/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1575 - categorical_accuracy: 0.5128 - val_loss: 8.6383 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5631/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1543 - categorical_accuracy: 0.5144 - val_loss: 8.7670 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5632/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1441 - categorical_accuracy: 0.5217 - val_loss: 8.7654 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5633/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1375 - categorical_accuracy: 0.5289 - val_loss: 8.9216 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5634/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1454 - categorical_accuracy: 0.5239 - val_loss: 8.6533 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5635/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1716 - categorical_accuracy: 0.5022 - val_loss: 9.0009 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5636/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2156 - categorical_accuracy: 0.4961 - val_loss: 8.3851 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5637/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2462 - categorical_accuracy: 0.4856 - val_loss: 8.6017 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5638/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1805 - categorical_accuracy: 0.5156 - val_loss: 8.2899 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5639/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1793 - categorical_accuracy: 0.5078 - val_loss: 8.7637 - val_categorical_accuracy: 0.1733\n",
      "Epoch 5640/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2567 - categorical_accuracy: 0.4683 - val_loss: 8.6544 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5641/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1874 - categorical_accuracy: 0.5000 - val_loss: 8.3265 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5642/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2108 - categorical_accuracy: 0.4900 - val_loss: 8.1282 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5643/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2090 - categorical_accuracy: 0.4956 - val_loss: 8.7304 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5644/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1711 - categorical_accuracy: 0.5100 - val_loss: 8.4199 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5645/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1599 - categorical_accuracy: 0.5133 - val_loss: 8.7730 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5646/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1554 - categorical_accuracy: 0.5139 - val_loss: 8.7063 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5647/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2287 - categorical_accuracy: 0.4967 - val_loss: 8.8266 - val_categorical_accuracy: 0.1767\n",
      "Epoch 5648/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1771 - categorical_accuracy: 0.5072 - val_loss: 8.7469 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5649/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1737 - categorical_accuracy: 0.5067 - val_loss: 8.8261 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5650/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1557 - categorical_accuracy: 0.5150 - val_loss: 8.7374 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5651/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1439 - categorical_accuracy: 0.5178 - val_loss: 8.7625 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5652/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1439 - categorical_accuracy: 0.5183 - val_loss: 8.3851 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5653/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1562 - categorical_accuracy: 0.5089 - val_loss: 8.7418 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5654/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1488 - categorical_accuracy: 0.5217 - val_loss: 8.3929 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5655/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1592 - categorical_accuracy: 0.5094 - val_loss: 8.7460 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5656/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1635 - categorical_accuracy: 0.5117 - val_loss: 8.8492 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5657/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1411 - categorical_accuracy: 0.5244 - val_loss: 8.7634 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5658/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1482 - categorical_accuracy: 0.5267 - val_loss: 8.6380 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5659/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1764 - categorical_accuracy: 0.5100 - val_loss: 8.4613 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5660/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1921 - categorical_accuracy: 0.5006 - val_loss: 8.6827 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5661/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2217 - categorical_accuracy: 0.5022 - val_loss: 8.6924 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5662/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2071 - categorical_accuracy: 0.5000 - val_loss: 8.4789 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5663/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1518 - categorical_accuracy: 0.5233 - val_loss: 8.2887 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5664/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1842 - categorical_accuracy: 0.5083 - val_loss: 9.0168 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5665/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1862 - categorical_accuracy: 0.5039 - val_loss: 8.8661 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5666/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1734 - categorical_accuracy: 0.5028 - val_loss: 9.0976 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5667/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1532 - categorical_accuracy: 0.5183 - val_loss: 9.1001 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5668/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1587 - categorical_accuracy: 0.5161 - val_loss: 8.7019 - val_categorical_accuracy: 0.1600\n",
      "Epoch 5669/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1551 - categorical_accuracy: 0.5172 - val_loss: 8.6492 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5670/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1734 - categorical_accuracy: 0.5100 - val_loss: 8.3421 - val_categorical_accuracy: 0.1633\n",
      "Epoch 5671/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2748 - categorical_accuracy: 0.4789 - val_loss: 9.2936 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5672/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2481 - categorical_accuracy: 0.4817 - val_loss: 8.9659 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5673/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1883 - categorical_accuracy: 0.5017 - val_loss: 8.6469 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5674/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1720 - categorical_accuracy: 0.5056 - val_loss: 9.1585 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5675/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1778 - categorical_accuracy: 0.5094 - val_loss: 8.8008 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5676/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1614 - categorical_accuracy: 0.5139 - val_loss: 8.8347 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5677/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1926 - categorical_accuracy: 0.4967 - val_loss: 8.3785 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5678/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2137 - categorical_accuracy: 0.4794 - val_loss: 8.9656 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5679/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2807 - categorical_accuracy: 0.4800 - val_loss: 9.6123 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5680/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2589 - categorical_accuracy: 0.4717 - val_loss: 8.4366 - val_categorical_accuracy: 0.1433\n",
      "Epoch 5681/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3130 - categorical_accuracy: 0.4750 - val_loss: 8.6879 - val_categorical_accuracy: 0.1700\n",
      "Epoch 5682/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1681 - categorical_accuracy: 0.5167 - val_loss: 8.6927 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5683/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1594 - categorical_accuracy: 0.5111 - val_loss: 8.2646 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5684/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1511 - categorical_accuracy: 0.5144 - val_loss: 8.6553 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5685/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1414 - categorical_accuracy: 0.5283 - val_loss: 8.7258 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5686/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1454 - categorical_accuracy: 0.5161 - val_loss: 8.3682 - val_categorical_accuracy: 0.1433\n",
      "Epoch 5687/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1395 - categorical_accuracy: 0.5211 - val_loss: 8.6818 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5688/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1393 - categorical_accuracy: 0.5144 - val_loss: 8.9218 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5689/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1363 - categorical_accuracy: 0.5200 - val_loss: 8.8716 - val_categorical_accuracy: 0.1433\n",
      "Epoch 5690/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1394 - categorical_accuracy: 0.5272 - val_loss: 8.9190 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5691/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1647 - categorical_accuracy: 0.5072 - val_loss: 9.1654 - val_categorical_accuracy: 0.1800\n",
      "Epoch 5692/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1441 - categorical_accuracy: 0.5183 - val_loss: 8.8784 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5693/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1443 - categorical_accuracy: 0.5250 - val_loss: 8.5967 - val_categorical_accuracy: 0.1433\n",
      "Epoch 5694/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1497 - categorical_accuracy: 0.5183 - val_loss: 9.2673 - val_categorical_accuracy: 0.1333\n",
      "Epoch 5947/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1284 - categorical_accuracy: 0.5278 - val_loss: 9.2142 - val_categorical_accuracy: 0.1300\n",
      "Epoch 5948/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1272 - categorical_accuracy: 0.5306 - val_loss: 9.2698 - val_categorical_accuracy: 0.1333\n",
      "Epoch 5949/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1391 - categorical_accuracy: 0.5267 - val_loss: 9.3607 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5950/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1418 - categorical_accuracy: 0.5239 - val_loss: 9.2609 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5951/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1315 - categorical_accuracy: 0.5344 - val_loss: 9.3665 - val_categorical_accuracy: 0.1300\n",
      "Epoch 5952/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1335 - categorical_accuracy: 0.5256 - val_loss: 9.4576 - val_categorical_accuracy: 0.1233\n",
      "Epoch 5953/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1365 - categorical_accuracy: 0.5261 - val_loss: 9.3498 - val_categorical_accuracy: 0.1367\n",
      "Epoch 5954/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1276 - categorical_accuracy: 0.5311 - val_loss: 9.3511 - val_categorical_accuracy: 0.1267\n",
      "Epoch 5955/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1344 - categorical_accuracy: 0.5294 - val_loss: 8.9785 - val_categorical_accuracy: 0.1333\n",
      "Epoch 5956/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1755 - categorical_accuracy: 0.5072 - val_loss: 9.7909 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5957/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3021 - categorical_accuracy: 0.4778 - val_loss: 9.3888 - val_categorical_accuracy: 0.1333\n",
      "Epoch 5958/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3489 - categorical_accuracy: 0.4683 - val_loss: 8.5988 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5959/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3998 - categorical_accuracy: 0.4622 - val_loss: 9.2174 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5960/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2460 - categorical_accuracy: 0.4811 - val_loss: 9.4007 - val_categorical_accuracy: 0.1333\n",
      "Epoch 5961/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2952 - categorical_accuracy: 0.4739 - val_loss: 8.8337 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5962/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1665 - categorical_accuracy: 0.5122 - val_loss: 8.9753 - val_categorical_accuracy: 0.1333\n",
      "Epoch 5963/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1770 - categorical_accuracy: 0.4956 - val_loss: 9.2267 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5964/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1445 - categorical_accuracy: 0.5106 - val_loss: 8.9464 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5965/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1589 - categorical_accuracy: 0.5133 - val_loss: 9.0864 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5966/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1417 - categorical_accuracy: 0.5156 - val_loss: 9.1361 - val_categorical_accuracy: 0.1300\n",
      "Epoch 5967/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1311 - categorical_accuracy: 0.5267 - val_loss: 8.9812 - val_categorical_accuracy: 0.1233\n",
      "Epoch 5968/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1278 - categorical_accuracy: 0.5300 - val_loss: 9.2388 - val_categorical_accuracy: 0.1267\n",
      "Epoch 5969/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1212 - categorical_accuracy: 0.5361 - val_loss: 9.1010 - val_categorical_accuracy: 0.1300\n",
      "Epoch 5970/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1229 - categorical_accuracy: 0.5300 - val_loss: 9.1176 - val_categorical_accuracy: 0.1267\n",
      "Epoch 5971/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1223 - categorical_accuracy: 0.5322 - val_loss: 9.1365 - val_categorical_accuracy: 0.1233\n",
      "Epoch 5972/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1204 - categorical_accuracy: 0.5367 - val_loss: 9.1973 - val_categorical_accuracy: 0.1333\n",
      "Epoch 5973/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1197 - categorical_accuracy: 0.5356 - val_loss: 9.2430 - val_categorical_accuracy: 0.1300\n",
      "Epoch 5974/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1405 - categorical_accuracy: 0.5222 - val_loss: 9.1007 - val_categorical_accuracy: 0.1300\n",
      "Epoch 5975/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1512 - categorical_accuracy: 0.5089 - val_loss: 9.1703 - val_categorical_accuracy: 0.1267\n",
      "Epoch 5976/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1539 - categorical_accuracy: 0.5089 - val_loss: 9.3851 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5977/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1497 - categorical_accuracy: 0.5211 - val_loss: 9.0683 - val_categorical_accuracy: 0.1333\n",
      "Epoch 5978/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2422 - categorical_accuracy: 0.4956 - val_loss: 8.8792 - val_categorical_accuracy: 0.1333\n",
      "Epoch 5979/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2042 - categorical_accuracy: 0.5022 - val_loss: 8.9225 - val_categorical_accuracy: 0.1433\n",
      "Epoch 5980/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1916 - categorical_accuracy: 0.5106 - val_loss: 8.5973 - val_categorical_accuracy: 0.1467\n",
      "Epoch 5981/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1926 - categorical_accuracy: 0.5033 - val_loss: 8.7716 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5982/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2580 - categorical_accuracy: 0.4789 - val_loss: 8.7000 - val_categorical_accuracy: 0.1367\n",
      "Epoch 5983/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2102 - categorical_accuracy: 0.4850 - val_loss: 8.8131 - val_categorical_accuracy: 0.1667\n",
      "Epoch 5984/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2606 - categorical_accuracy: 0.4817 - val_loss: 8.8175 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5985/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2112 - categorical_accuracy: 0.4911 - val_loss: 8.8029 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5986/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2065 - categorical_accuracy: 0.4983 - val_loss: 8.9542 - val_categorical_accuracy: 0.1500\n",
      "Epoch 5987/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2068 - categorical_accuracy: 0.4950 - val_loss: 9.1925 - val_categorical_accuracy: 0.1400\n",
      "Epoch 5988/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1600 - categorical_accuracy: 0.5194 - val_loss: 9.1043 - val_categorical_accuracy: 0.1567\n",
      "Epoch 5989/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1480 - categorical_accuracy: 0.5194 - val_loss: 9.1362 - val_categorical_accuracy: 0.1533\n",
      "Epoch 5990/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1404 - categorical_accuracy: 0.5261 - val_loss: 9.0378 - val_categorical_accuracy: 0.1367\n",
      "Epoch 5991/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1310 - categorical_accuracy: 0.5256 - val_loss: 9.1022 - val_categorical_accuracy: 0.1300\n",
      "Epoch 5992/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1357 - categorical_accuracy: 0.5294 - val_loss: 9.4663 - val_categorical_accuracy: 0.1367\n",
      "Epoch 5993/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1637 - categorical_accuracy: 0.5172 - val_loss: 9.4698 - val_categorical_accuracy: 0.1433\n",
      "Epoch 5994/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1476 - categorical_accuracy: 0.5167 - val_loss: 9.0648 - val_categorical_accuracy: 0.1367\n",
      "Epoch 5995/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1392 - categorical_accuracy: 0.5261 - val_loss: 9.2893 - val_categorical_accuracy: 0.1433\n",
      "Epoch 5996/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1371 - categorical_accuracy: 0.5261 - val_loss: 9.2004 - val_categorical_accuracy: 0.1300\n",
      "Epoch 5997/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1398 - categorical_accuracy: 0.5200 - val_loss: 9.2195 - val_categorical_accuracy: 0.1367\n",
      "Epoch 5998/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1346 - categorical_accuracy: 0.5283 - val_loss: 8.9024 - val_categorical_accuracy: 0.1300\n",
      "Epoch 5999/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1768 - categorical_accuracy: 0.5161 - val_loss: 8.8886 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6000/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2424 - categorical_accuracy: 0.4789 - val_loss: 9.3544 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6001/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1935 - categorical_accuracy: 0.4978 - val_loss: 9.3883 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6002/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1979 - categorical_accuracy: 0.5011 - val_loss: 8.9609 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6003/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1609 - categorical_accuracy: 0.5089 - val_loss: 9.0331 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6004/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1502 - categorical_accuracy: 0.5211 - val_loss: 9.2567 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6005/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1534 - categorical_accuracy: 0.5139 - val_loss: 9.3252 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6006/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1413 - categorical_accuracy: 0.5217 - val_loss: 9.0277 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6007/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1569 - categorical_accuracy: 0.5133 - val_loss: 8.9877 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6008/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2862 - categorical_accuracy: 0.4644 - val_loss: 8.1557 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6009/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1951 - categorical_accuracy: 0.5056 - val_loss: 9.0635 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6010/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1471 - categorical_accuracy: 0.5089 - val_loss: 9.1501 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6011/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1348 - categorical_accuracy: 0.5233 - val_loss: 9.0886 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6012/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1370 - categorical_accuracy: 0.5206 - val_loss: 9.1779 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6013/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1543 - categorical_accuracy: 0.5200 - val_loss: 9.1992 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6014/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1476 - categorical_accuracy: 0.5161 - val_loss: 9.3452 - val_categorical_accuracy: 0.1300\n",
      "Epoch 6015/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1387 - categorical_accuracy: 0.5283 - val_loss: 9.1064 - val_categorical_accuracy: 0.1267\n",
      "Epoch 6016/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1980 - categorical_accuracy: 0.4944 - val_loss: 8.6236 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6017/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2445 - categorical_accuracy: 0.4800 - val_loss: 9.1926 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6018/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2077 - categorical_accuracy: 0.4950 - val_loss: 9.3176 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6019/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1757 - categorical_accuracy: 0.4972 - val_loss: 8.4342 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6020/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1983 - categorical_accuracy: 0.4883 - val_loss: 8.8774 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6021/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1796 - categorical_accuracy: 0.5061 - val_loss: 8.6205 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6022/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1464 - categorical_accuracy: 0.5206 - val_loss: 9.0273 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6023/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1344 - categorical_accuracy: 0.5294 - val_loss: 9.0951 - val_categorical_accuracy: 0.1233\n",
      "Epoch 6024/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1328 - categorical_accuracy: 0.5244 - val_loss: 9.0482 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6025/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1354 - categorical_accuracy: 0.5283 - val_loss: 9.2895 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6026/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1350 - categorical_accuracy: 0.5272 - val_loss: 9.2296 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6027/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1261 - categorical_accuracy: 0.5228 - val_loss: 9.0032 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6028/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1275 - categorical_accuracy: 0.5222 - val_loss: 9.0540 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6029/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1245 - categorical_accuracy: 0.5317 - val_loss: 9.2189 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6030/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1212 - categorical_accuracy: 0.5328 - val_loss: 9.0384 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6031/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1226 - categorical_accuracy: 0.5317 - val_loss: 9.1806 - val_categorical_accuracy: 0.1267\n",
      "Epoch 6032/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1383 - categorical_accuracy: 0.5294 - val_loss: 9.6204 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6033/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2941 - categorical_accuracy: 0.4867 - val_loss: 9.1078 - val_categorical_accuracy: 0.1300\n",
      "Epoch 6034/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1959 - categorical_accuracy: 0.5022 - val_loss: 8.9922 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6035/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2584 - categorical_accuracy: 0.4839 - val_loss: 8.8765 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6036/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2842 - categorical_accuracy: 0.4644 - val_loss: 8.0077 - val_categorical_accuracy: 0.1233\n",
      "Epoch 6037/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2103 - categorical_accuracy: 0.4928 - val_loss: 8.9146 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6038/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1768 - categorical_accuracy: 0.5006 - val_loss: 8.9228 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6039/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1710 - categorical_accuracy: 0.5050 - val_loss: 8.9305 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6040/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1945 - categorical_accuracy: 0.5056 - val_loss: 9.0087 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6041/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1579 - categorical_accuracy: 0.5100 - val_loss: 8.8938 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6042/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1807 - categorical_accuracy: 0.5078 - val_loss: 9.2146 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6043/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2191 - categorical_accuracy: 0.4928 - val_loss: 9.0789 - val_categorical_accuracy: 0.1267\n",
      "Epoch 6044/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1563 - categorical_accuracy: 0.5128 - val_loss: 8.9831 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6045/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2159 - categorical_accuracy: 0.4833 - val_loss: 8.9850 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6046/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1501 - categorical_accuracy: 0.5194 - val_loss: 8.9555 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6047/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1445 - categorical_accuracy: 0.5211 - val_loss: 9.0862 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6048/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1366 - categorical_accuracy: 0.5272 - val_loss: 8.7978 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6049/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1303 - categorical_accuracy: 0.5172 - val_loss: 9.1506 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6050/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1420 - categorical_accuracy: 0.5261 - val_loss: 8.8622 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6051/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1611 - categorical_accuracy: 0.5133 - val_loss: 9.4471 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6052/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1860 - categorical_accuracy: 0.5050 - val_loss: 8.9464 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6053/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1628 - categorical_accuracy: 0.5178 - val_loss: 8.7601 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6054/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1452 - categorical_accuracy: 0.5211 - val_loss: 9.1363 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6055/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1373 - categorical_accuracy: 0.5217 - val_loss: 8.6546 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6056/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1370 - categorical_accuracy: 0.5233 - val_loss: 8.9208 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6057/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1593 - categorical_accuracy: 0.5139 - val_loss: 8.7698 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6058/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1548 - categorical_accuracy: 0.5167 - val_loss: 9.2186 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6059/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1716 - categorical_accuracy: 0.5056 - val_loss: 8.6638 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6060/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2191 - categorical_accuracy: 0.4878 - val_loss: 8.7828 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6061/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1827 - categorical_accuracy: 0.5061 - val_loss: 8.7198 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6062/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2683 - categorical_accuracy: 0.4850 - val_loss: 9.5554 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6063/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2607 - categorical_accuracy: 0.4856 - val_loss: 8.7385 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6064/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1537 - categorical_accuracy: 0.5094 - val_loss: 9.3590 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6483/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1972 - categorical_accuracy: 0.4978 - val_loss: 9.8096 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6484/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2183 - categorical_accuracy: 0.4889 - val_loss: 8.7037 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6485/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1869 - categorical_accuracy: 0.4950 - val_loss: 9.2915 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6486/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1647 - categorical_accuracy: 0.4972 - val_loss: 9.3496 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6487/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1382 - categorical_accuracy: 0.5156 - val_loss: 9.4308 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6488/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1339 - categorical_accuracy: 0.5178 - val_loss: 9.3713 - val_categorical_accuracy: 0.1300\n",
      "Epoch 6489/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1303 - categorical_accuracy: 0.5194 - val_loss: 9.3535 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6490/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1275 - categorical_accuracy: 0.5172 - val_loss: 9.3238 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6491/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1249 - categorical_accuracy: 0.5167 - val_loss: 9.4416 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6492/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1309 - categorical_accuracy: 0.5183 - val_loss: 9.4675 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6493/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1335 - categorical_accuracy: 0.5078 - val_loss: 9.3045 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6494/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1279 - categorical_accuracy: 0.5139 - val_loss: 9.2521 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6495/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1252 - categorical_accuracy: 0.5156 - val_loss: 9.4652 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6496/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1219 - categorical_accuracy: 0.5189 - val_loss: 9.4171 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6497/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1293 - categorical_accuracy: 0.5144 - val_loss: 9.5031 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6498/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1311 - categorical_accuracy: 0.5161 - val_loss: 9.4349 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6499/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1433 - categorical_accuracy: 0.5111 - val_loss: 9.5686 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6500/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2448 - categorical_accuracy: 0.4717 - val_loss: 9.1081 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6501/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2894 - categorical_accuracy: 0.4656 - val_loss: 8.8896 - val_categorical_accuracy: 0.1833\n",
      "Epoch 6502/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2551 - categorical_accuracy: 0.4839 - val_loss: 9.1511 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6503/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2082 - categorical_accuracy: 0.4894 - val_loss: 9.7485 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6504/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1565 - categorical_accuracy: 0.5178 - val_loss: 9.2421 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6505/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1487 - categorical_accuracy: 0.5117 - val_loss: 9.4790 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6506/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1553 - categorical_accuracy: 0.5106 - val_loss: 9.4396 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6507/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2074 - categorical_accuracy: 0.4928 - val_loss: 9.2062 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6508/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1620 - categorical_accuracy: 0.5017 - val_loss: 9.5858 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6509/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1568 - categorical_accuracy: 0.5139 - val_loss: 9.3329 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6510/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1391 - categorical_accuracy: 0.5056 - val_loss: 9.6047 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6511/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1593 - categorical_accuracy: 0.5039 - val_loss: 9.5061 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6512/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1442 - categorical_accuracy: 0.5089 - val_loss: 9.5090 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6513/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1266 - categorical_accuracy: 0.5206 - val_loss: 9.6865 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6514/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1275 - categorical_accuracy: 0.5167 - val_loss: 9.5690 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6515/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1230 - categorical_accuracy: 0.5189 - val_loss: 9.6759 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6516/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1189 - categorical_accuracy: 0.5222 - val_loss: 9.6043 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6517/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1321 - categorical_accuracy: 0.5183 - val_loss: 9.6025 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6518/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2456 - categorical_accuracy: 0.4833 - val_loss: 9.8329 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6519/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2381 - categorical_accuracy: 0.4778 - val_loss: 9.6288 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6520/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2419 - categorical_accuracy: 0.4800 - val_loss: 9.5131 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6521/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.1614 - categorical_accuracy: 0.5022 - val_loss: 9.3339 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6522/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1541 - categorical_accuracy: 0.5078 - val_loss: 9.4506 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6523/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1426 - categorical_accuracy: 0.5067 - val_loss: 9.5139 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6524/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1330 - categorical_accuracy: 0.5106 - val_loss: 9.3556 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6525/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1316 - categorical_accuracy: 0.5100 - val_loss: 9.4710 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6526/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1989 - categorical_accuracy: 0.4961 - val_loss: 8.7522 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6527/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2115 - categorical_accuracy: 0.4939 - val_loss: 9.8149 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6528/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3180 - categorical_accuracy: 0.4683 - val_loss: 9.3650 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6529/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1774 - categorical_accuracy: 0.4994 - val_loss: 9.4040 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6530/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2131 - categorical_accuracy: 0.4889 - val_loss: 9.1481 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6531/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1921 - categorical_accuracy: 0.4889 - val_loss: 9.1765 - val_categorical_accuracy: 0.1700\n",
      "Epoch 6532/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1708 - categorical_accuracy: 0.5006 - val_loss: 9.2924 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6533/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1558 - categorical_accuracy: 0.5122 - val_loss: 9.1762 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6534/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1549 - categorical_accuracy: 0.5128 - val_loss: 8.8792 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6535/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2159 - categorical_accuracy: 0.5017 - val_loss: 9.2907 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6536/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3309 - categorical_accuracy: 0.4689 - val_loss: 9.2047 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6537/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2012 - categorical_accuracy: 0.4861 - val_loss: 9.0018 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6538/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1610 - categorical_accuracy: 0.5011 - val_loss: 9.1338 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6539/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1400 - categorical_accuracy: 0.5100 - val_loss: 9.3068 - val_categorical_accuracy: 0.1633\n",
      "Epoch 6540/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1390 - categorical_accuracy: 0.5167 - val_loss: 8.9674 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6541/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1387 - categorical_accuracy: 0.5133 - val_loss: 9.0934 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6542/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1272 - categorical_accuracy: 0.5133 - val_loss: 9.4126 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6543/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1217 - categorical_accuracy: 0.5222 - val_loss: 9.3110 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6544/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1177 - categorical_accuracy: 0.5233 - val_loss: 9.3550 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6545/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1221 - categorical_accuracy: 0.5256 - val_loss: 9.4180 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6546/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1277 - categorical_accuracy: 0.5183 - val_loss: 9.3555 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6547/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3073 - categorical_accuracy: 0.4744 - val_loss: 9.6665 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6548/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1931 - categorical_accuracy: 0.4961 - val_loss: 8.9168 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6549/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1528 - categorical_accuracy: 0.5033 - val_loss: 9.1381 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6550/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1363 - categorical_accuracy: 0.5078 - val_loss: 9.0990 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6551/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.1355 - categorical_accuracy: 0.5133 - val_loss: 9.2961 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6552/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1194 - categorical_accuracy: 0.5278 - val_loss: 9.3349 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6553/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1177 - categorical_accuracy: 0.5261 - val_loss: 9.3739 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6554/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1186 - categorical_accuracy: 0.5222 - val_loss: 9.4305 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6555/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1448 - categorical_accuracy: 0.5139 - val_loss: 9.5843 - val_categorical_accuracy: 0.1333\n",
      "Epoch 6556/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1252 - categorical_accuracy: 0.5167 - val_loss: 9.3130 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6557/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1300 - categorical_accuracy: 0.5161 - val_loss: 9.3105 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6558/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1276 - categorical_accuracy: 0.5161 - val_loss: 9.6446 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6559/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1225 - categorical_accuracy: 0.5272 - val_loss: 9.4823 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6560/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1246 - categorical_accuracy: 0.5233 - val_loss: 9.5025 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6561/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1342 - categorical_accuracy: 0.5144 - val_loss: 9.8407 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6562/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2301 - categorical_accuracy: 0.4794 - val_loss: 9.1191 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6563/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1714 - categorical_accuracy: 0.5028 - val_loss: 9.2416 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6564/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1424 - categorical_accuracy: 0.5117 - val_loss: 9.3916 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6565/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1366 - categorical_accuracy: 0.5161 - val_loss: 9.3786 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6566/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1679 - categorical_accuracy: 0.5000 - val_loss: 9.6593 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6567/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1425 - categorical_accuracy: 0.5211 - val_loss: 9.4433 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6568/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1662 - categorical_accuracy: 0.5067 - val_loss: 9.1729 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6569/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1590 - categorical_accuracy: 0.5083 - val_loss: 9.0913 - val_categorical_accuracy: 0.1700\n",
      "Epoch 6570/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1544 - categorical_accuracy: 0.5106 - val_loss: 9.5538 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6571/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2414 - categorical_accuracy: 0.4811 - val_loss: 8.5025 - val_categorical_accuracy: 0.1700\n",
      "Epoch 6572/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1584 - categorical_accuracy: 0.5083 - val_loss: 9.2653 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6573/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1593 - categorical_accuracy: 0.5067 - val_loss: 9.0905 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6574/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1331 - categorical_accuracy: 0.5183 - val_loss: 9.6015 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6575/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1461 - categorical_accuracy: 0.5144 - val_loss: 9.5646 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6576/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2136 - categorical_accuracy: 0.4917 - val_loss: 9.6244 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6577/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1991 - categorical_accuracy: 0.4978 - val_loss: 9.5472 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6578/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2012 - categorical_accuracy: 0.4889 - val_loss: 9.4012 - val_categorical_accuracy: 0.1733\n",
      "Epoch 6579/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1532 - categorical_accuracy: 0.5206 - val_loss: 9.5007 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6580/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1400 - categorical_accuracy: 0.5133 - val_loss: 9.6399 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6581/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1268 - categorical_accuracy: 0.5217 - val_loss: 9.6470 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6582/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1211 - categorical_accuracy: 0.5244 - val_loss: 9.5073 - val_categorical_accuracy: 0.1633\n",
      "Epoch 6583/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1674 - categorical_accuracy: 0.5094 - val_loss: 9.1932 - val_categorical_accuracy: 0.1700\n",
      "Epoch 6584/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2041 - categorical_accuracy: 0.5089 - val_loss: 9.2992 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6585/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1514 - categorical_accuracy: 0.5083 - val_loss: 9.5806 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6586/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1376 - categorical_accuracy: 0.5156 - val_loss: 9.3465 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6587/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1278 - categorical_accuracy: 0.5233 - val_loss: 9.6130 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6588/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1361 - categorical_accuracy: 0.5239 - val_loss: 9.8036 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6589/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1646 - categorical_accuracy: 0.4994 - val_loss: 9.7176 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6590/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1410 - categorical_accuracy: 0.5106 - val_loss: 9.7052 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6591/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1446 - categorical_accuracy: 0.5172 - val_loss: 9.4851 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6592/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1406 - categorical_accuracy: 0.5144 - val_loss: 9.5351 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6593/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1376 - categorical_accuracy: 0.5167 - val_loss: 9.5703 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6594/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1657 - categorical_accuracy: 0.5117 - val_loss: 9.4027 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6595/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2118 - categorical_accuracy: 0.4950 - val_loss: 9.8741 - val_categorical_accuracy: 0.1733\n",
      "Epoch 6596/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2107 - categorical_accuracy: 0.4994 - val_loss: 9.6901 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6597/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2056 - categorical_accuracy: 0.5006 - val_loss: 9.6409 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6598/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1830 - categorical_accuracy: 0.4967 - val_loss: 9.0484 - val_categorical_accuracy: 0.1733\n",
      "Epoch 6599/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1414 - categorical_accuracy: 0.5100 - val_loss: 9.5418 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6600/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1040 - categorical_accuracy: 0.5300 - val_loss: 9.8802 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6904/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1080 - categorical_accuracy: 0.5272 - val_loss: 9.8479 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6905/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1183 - categorical_accuracy: 0.5206 - val_loss: 9.8239 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6906/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1110 - categorical_accuracy: 0.5361 - val_loss: 10.0302 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6907/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1165 - categorical_accuracy: 0.5261 - val_loss: 9.8346 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6908/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1093 - categorical_accuracy: 0.5350 - val_loss: 9.9365 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6909/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1243 - categorical_accuracy: 0.5211 - val_loss: 9.6638 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6910/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1689 - categorical_accuracy: 0.5061 - val_loss: 9.9371 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6911/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2503 - categorical_accuracy: 0.4922 - val_loss: 9.8931 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6912/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1775 - categorical_accuracy: 0.5067 - val_loss: 9.5802 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6913/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1446 - categorical_accuracy: 0.5167 - val_loss: 10.3137 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6914/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1672 - categorical_accuracy: 0.5106 - val_loss: 9.6894 - val_categorical_accuracy: 0.1633\n",
      "Epoch 6915/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1507 - categorical_accuracy: 0.5144 - val_loss: 9.6187 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6916/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1800 - categorical_accuracy: 0.5006 - val_loss: 9.2976 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6917/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2309 - categorical_accuracy: 0.5044 - val_loss: 10.4580 - val_categorical_accuracy: 0.1633\n",
      "Epoch 6918/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2687 - categorical_accuracy: 0.4739 - val_loss: 9.9048 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6919/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1808 - categorical_accuracy: 0.4994 - val_loss: 9.6336 - val_categorical_accuracy: 0.1633\n",
      "Epoch 6920/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1444 - categorical_accuracy: 0.5128 - val_loss: 9.8334 - val_categorical_accuracy: 0.1367\n",
      "Epoch 6921/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1309 - categorical_accuracy: 0.5189 - val_loss: 9.9676 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6922/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1361 - categorical_accuracy: 0.5150 - val_loss: 10.2462 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6923/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1367 - categorical_accuracy: 0.5161 - val_loss: 9.5685 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6924/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1127 - categorical_accuracy: 0.5294 - val_loss: 9.9525 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6925/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1210 - categorical_accuracy: 0.5261 - val_loss: 9.8290 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6926/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1187 - categorical_accuracy: 0.5267 - val_loss: 10.0096 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6927/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1139 - categorical_accuracy: 0.5333 - val_loss: 10.0972 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6928/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1182 - categorical_accuracy: 0.5267 - val_loss: 10.0105 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6929/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1327 - categorical_accuracy: 0.5244 - val_loss: 9.6718 - val_categorical_accuracy: 0.1467\n",
      "Epoch 6930/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1204 - categorical_accuracy: 0.5228 - val_loss: 10.0512 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6931/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1208 - categorical_accuracy: 0.5167 - val_loss: 10.1265 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6932/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1235 - categorical_accuracy: 0.5233 - val_loss: 10.0298 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6933/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1443 - categorical_accuracy: 0.5217 - val_loss: 9.8130 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6934/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1536 - categorical_accuracy: 0.5117 - val_loss: 9.7082 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6935/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1528 - categorical_accuracy: 0.5044 - val_loss: 9.5294 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6936/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1295 - categorical_accuracy: 0.5211 - val_loss: 9.8777 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6937/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1238 - categorical_accuracy: 0.5239 - val_loss: 9.9644 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6938/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1154 - categorical_accuracy: 0.5283 - val_loss: 10.0582 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6939/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1133 - categorical_accuracy: 0.5311 - val_loss: 10.0541 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6940/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1117 - categorical_accuracy: 0.5322 - val_loss: 10.0014 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6941/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1311 - categorical_accuracy: 0.5156 - val_loss: 10.2564 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6942/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1491 - categorical_accuracy: 0.5072 - val_loss: 10.0674 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6943/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2351 - categorical_accuracy: 0.4850 - val_loss: 10.0611 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6944/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1892 - categorical_accuracy: 0.5061 - val_loss: 10.2907 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6945/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1856 - categorical_accuracy: 0.5017 - val_loss: 10.2015 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6946/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1426 - categorical_accuracy: 0.5100 - val_loss: 10.0656 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6947/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1294 - categorical_accuracy: 0.5239 - val_loss: 10.0447 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6948/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1273 - categorical_accuracy: 0.5144 - val_loss: 10.1283 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6949/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1259 - categorical_accuracy: 0.5233 - val_loss: 10.2001 - val_categorical_accuracy: 0.1500\n",
      "Epoch 6950/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1491 - categorical_accuracy: 0.5150 - val_loss: 10.3705 - val_categorical_accuracy: 0.1400\n",
      "Epoch 6951/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1847 - categorical_accuracy: 0.4944 - val_loss: 10.1412 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6952/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1567 - categorical_accuracy: 0.5033 - val_loss: 10.3626 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6953/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1300 - categorical_accuracy: 0.5117 - val_loss: 10.2905 - val_categorical_accuracy: 0.1567\n",
      "Epoch 6954/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1291 - categorical_accuracy: 0.5233 - val_loss: 10.2610 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6955/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1224 - categorical_accuracy: 0.5222 - val_loss: 10.2551 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6956/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1167 - categorical_accuracy: 0.5244 - val_loss: 9.9027 - val_categorical_accuracy: 0.1633\n",
      "Epoch 6957/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1170 - categorical_accuracy: 0.5228 - val_loss: 10.1013 - val_categorical_accuracy: 0.1600\n",
      "Epoch 6958/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1086 - categorical_accuracy: 0.5228 - val_loss: 10.5376 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6959/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1553 - categorical_accuracy: 0.5128 - val_loss: 10.0639 - val_categorical_accuracy: 0.1633\n",
      "Epoch 6960/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2739 - categorical_accuracy: 0.4728 - val_loss: 10.1888 - val_categorical_accuracy: 0.1867\n",
      "Epoch 6961/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2347 - categorical_accuracy: 0.4883 - val_loss: 9.9314 - val_categorical_accuracy: 0.1667\n",
      "Epoch 6962/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2004 - categorical_accuracy: 0.4983 - val_loss: 10.1420 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6963/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1766 - categorical_accuracy: 0.4978 - val_loss: 10.5613 - val_categorical_accuracy: 0.1433\n",
      "Epoch 6964/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1879 - categorical_accuracy: 0.5011 - val_loss: 9.9159 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6965/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1886 - categorical_accuracy: 0.4944 - val_loss: 10.0099 - val_categorical_accuracy: 0.1533\n",
      "Epoch 6966/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1473 - categorical_accuracy: 0.5256 - val_loss: 11.1176 - val_categorical_accuracy: 0.1333\n",
      "Epoch 7617/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1380 - categorical_accuracy: 0.5261 - val_loss: 11.4717 - val_categorical_accuracy: 0.1367\n",
      "Epoch 7618/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1284 - categorical_accuracy: 0.5183 - val_loss: 11.2026 - val_categorical_accuracy: 0.1400\n",
      "Epoch 7619/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1129 - categorical_accuracy: 0.5394 - val_loss: 11.1843 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7620/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1201 - categorical_accuracy: 0.5406 - val_loss: 10.9034 - val_categorical_accuracy: 0.1300\n",
      "Epoch 7621/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1126 - categorical_accuracy: 0.5367 - val_loss: 10.9010 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7622/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1454 - categorical_accuracy: 0.5172 - val_loss: 10.9014 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7623/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3445 - categorical_accuracy: 0.4789 - val_loss: 9.9289 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7624/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3905 - categorical_accuracy: 0.4478 - val_loss: 10.7292 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7625/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1830 - categorical_accuracy: 0.5122 - val_loss: 11.0013 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7626/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1680 - categorical_accuracy: 0.5072 - val_loss: 11.0241 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7627/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1574 - categorical_accuracy: 0.5106 - val_loss: 10.7519 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7628/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1283 - categorical_accuracy: 0.5261 - val_loss: 11.2695 - val_categorical_accuracy: 0.1367\n",
      "Epoch 7629/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1324 - categorical_accuracy: 0.5261 - val_loss: 11.0730 - val_categorical_accuracy: 0.1400\n",
      "Epoch 7630/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1154 - categorical_accuracy: 0.5344 - val_loss: 10.8499 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7631/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1142 - categorical_accuracy: 0.5328 - val_loss: 11.0068 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7632/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1180 - categorical_accuracy: 0.5356 - val_loss: 11.0069 - val_categorical_accuracy: 0.1400\n",
      "Epoch 7633/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0993 - categorical_accuracy: 0.5400 - val_loss: 11.1253 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7634/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0933 - categorical_accuracy: 0.5444 - val_loss: 11.1358 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7635/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1033 - categorical_accuracy: 0.5422 - val_loss: 11.0071 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7636/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1095 - categorical_accuracy: 0.5378 - val_loss: 10.9630 - val_categorical_accuracy: 0.1367\n",
      "Epoch 7637/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0998 - categorical_accuracy: 0.5317 - val_loss: 11.0276 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7638/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1108 - categorical_accuracy: 0.5367 - val_loss: 10.7620 - val_categorical_accuracy: 0.1533\n",
      "Epoch 7639/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1284 - categorical_accuracy: 0.5283 - val_loss: 10.9623 - val_categorical_accuracy: 0.1367\n",
      "Epoch 7640/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1577 - categorical_accuracy: 0.5139 - val_loss: 11.4004 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7641/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2399 - categorical_accuracy: 0.4861 - val_loss: 10.8445 - val_categorical_accuracy: 0.1400\n",
      "Epoch 7642/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1363 - categorical_accuracy: 0.5206 - val_loss: 11.2270 - val_categorical_accuracy: 0.1400\n",
      "Epoch 7643/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1205 - categorical_accuracy: 0.5239 - val_loss: 11.2515 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7644/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1154 - categorical_accuracy: 0.5283 - val_loss: 11.1454 - val_categorical_accuracy: 0.1367\n",
      "Epoch 7645/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1043 - categorical_accuracy: 0.5339 - val_loss: 11.1534 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7646/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1016 - categorical_accuracy: 0.5394 - val_loss: 11.1500 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7647/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.0974 - categorical_accuracy: 0.5428 - val_loss: 11.1731 - val_categorical_accuracy: 0.1400\n",
      "Epoch 7648/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1011 - categorical_accuracy: 0.5417 - val_loss: 10.9855 - val_categorical_accuracy: 0.1567\n",
      "Epoch 7649/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1407 - categorical_accuracy: 0.5183 - val_loss: 11.0216 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7650/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2171 - categorical_accuracy: 0.5011 - val_loss: 10.7804 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7651/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1653 - categorical_accuracy: 0.5200 - val_loss: 11.6218 - val_categorical_accuracy: 0.1267\n",
      "Epoch 7652/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1312 - categorical_accuracy: 0.5211 - val_loss: 11.0514 - val_categorical_accuracy: 0.1300\n",
      "Epoch 7653/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1829 - categorical_accuracy: 0.5006 - val_loss: 11.2473 - val_categorical_accuracy: 0.1367\n",
      "Epoch 7654/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2135 - categorical_accuracy: 0.4978 - val_loss: 11.1874 - val_categorical_accuracy: 0.1400\n",
      "Epoch 7655/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.1235 - categorical_accuracy: 0.5261 - val_loss: 11.3754 - val_categorical_accuracy: 0.1100\n",
      "Epoch 7656/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3272 - categorical_accuracy: 0.4706 - val_loss: 11.2104 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7657/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2287 - categorical_accuracy: 0.4950 - val_loss: 10.9744 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7658/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1900 - categorical_accuracy: 0.5017 - val_loss: 10.6843 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7659/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1527 - categorical_accuracy: 0.5178 - val_loss: 10.7962 - val_categorical_accuracy: 0.1333\n",
      "Epoch 7660/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1142 - categorical_accuracy: 0.5383 - val_loss: 10.7379 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7661/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0982 - categorical_accuracy: 0.5356 - val_loss: 10.6316 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7662/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0999 - categorical_accuracy: 0.5394 - val_loss: 11.1528 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7663/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1004 - categorical_accuracy: 0.5322 - val_loss: 10.8798 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7664/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0971 - categorical_accuracy: 0.5428 - val_loss: 10.9464 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7665/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1773 - categorical_accuracy: 0.5022 - val_loss: 10.6863 - val_categorical_accuracy: 0.1700\n",
      "Epoch 7666/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1234 - categorical_accuracy: 0.5211 - val_loss: 10.6165 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7667/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0964 - categorical_accuracy: 0.5389 - val_loss: 10.6740 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7668/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0927 - categorical_accuracy: 0.5450 - val_loss: 10.7499 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7669/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0934 - categorical_accuracy: 0.5439 - val_loss: 10.7224 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7670/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0901 - categorical_accuracy: 0.5478 - val_loss: 10.7591 - val_categorical_accuracy: 0.1400\n",
      "Epoch 7671/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0966 - categorical_accuracy: 0.5389 - val_loss: 11.1553 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7672/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1327 - categorical_accuracy: 0.5300 - val_loss: 10.8676 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7673/10000\n",
      "57/57 [==============================] - 0s 8ms/step - loss: 1.1163 - categorical_accuracy: 0.5361 - val_loss: 10.7936 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7674/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1508 - categorical_accuracy: 0.5300 - val_loss: 9.8842 - val_categorical_accuracy: 0.1367\n",
      "Epoch 7675/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2063 - categorical_accuracy: 0.5094 - val_loss: 10.3772 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7676/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1517 - categorical_accuracy: 0.5239 - val_loss: 10.8245 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7677/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1204 - categorical_accuracy: 0.5339 - val_loss: 10.7807 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7678/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1280 - categorical_accuracy: 0.5272 - val_loss: 10.8542 - val_categorical_accuracy: 0.1400\n",
      "Epoch 7679/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1016 - categorical_accuracy: 0.5400 - val_loss: 10.9259 - val_categorical_accuracy: 0.1300\n",
      "Epoch 7680/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1172 - categorical_accuracy: 0.5333 - val_loss: 10.5402 - val_categorical_accuracy: 0.1367\n",
      "Epoch 7681/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1240 - categorical_accuracy: 0.5372 - val_loss: 10.8435 - val_categorical_accuracy: 0.1633\n",
      "Epoch 7682/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3208 - categorical_accuracy: 0.4733 - val_loss: 10.4059 - val_categorical_accuracy: 0.1333\n",
      "Epoch 7683/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2225 - categorical_accuracy: 0.5033 - val_loss: 11.1781 - val_categorical_accuracy: 0.1500\n",
      "Epoch 7684/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1697 - categorical_accuracy: 0.5100 - val_loss: 10.6348 - val_categorical_accuracy: 0.1267\n",
      "Epoch 7685/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1376 - categorical_accuracy: 0.5222 - val_loss: 10.6067 - val_categorical_accuracy: 0.1300\n",
      "Epoch 7686/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1122 - categorical_accuracy: 0.5283 - val_loss: 10.6182 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7687/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1114 - categorical_accuracy: 0.5283 - val_loss: 10.8939 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7688/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1087 - categorical_accuracy: 0.5339 - val_loss: 10.8515 - val_categorical_accuracy: 0.1333\n",
      "Epoch 7689/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1049 - categorical_accuracy: 0.5378 - val_loss: 10.7637 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7690/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0941 - categorical_accuracy: 0.5456 - val_loss: 10.8169 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7691/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0934 - categorical_accuracy: 0.5444 - val_loss: 10.9796 - val_categorical_accuracy: 0.1333\n",
      "Epoch 7692/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0950 - categorical_accuracy: 0.5444 - val_loss: 10.8088 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7693/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0974 - categorical_accuracy: 0.5406 - val_loss: 11.2223 - val_categorical_accuracy: 0.1567\n",
      "Epoch 7694/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1551 - categorical_accuracy: 0.5228 - val_loss: 10.4112 - val_categorical_accuracy: 0.1600\n",
      "Epoch 7695/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1564 - categorical_accuracy: 0.5133 - val_loss: 10.8595 - val_categorical_accuracy: 0.1367\n",
      "Epoch 7696/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1113 - categorical_accuracy: 0.5322 - val_loss: 10.9709 - val_categorical_accuracy: 0.1333\n",
      "Epoch 7697/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1080 - categorical_accuracy: 0.5439 - val_loss: 11.1388 - val_categorical_accuracy: 0.1267\n",
      "Epoch 7698/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1236 - categorical_accuracy: 0.5261 - val_loss: 10.8531 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7699/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1534 - categorical_accuracy: 0.5200 - val_loss: 11.3113 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7700/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1179 - categorical_accuracy: 0.5372 - val_loss: 11.0240 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7701/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1133 - categorical_accuracy: 0.5289 - val_loss: 11.2440 - val_categorical_accuracy: 0.1333\n",
      "Epoch 7702/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1328 - categorical_accuracy: 0.5283 - val_loss: 11.1746 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7703/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1271 - categorical_accuracy: 0.5250 - val_loss: 11.0662 - val_categorical_accuracy: 0.1467\n",
      "Epoch 7704/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1135 - categorical_accuracy: 0.5356 - val_loss: 10.8417 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7705/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0968 - categorical_accuracy: 0.5372 - val_loss: 11.2550 - val_categorical_accuracy: 0.1433\n",
      "Epoch 7706/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1287 - categorical_accuracy: 0.5228 - val_loss: 11.1415 - val_categorical_accuracy: 0.1433\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1064 - categorical_accuracy: 0.5267 - val_loss: 11.4515 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8120/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1009 - categorical_accuracy: 0.5350 - val_loss: 11.4842 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8121/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0959 - categorical_accuracy: 0.5428 - val_loss: 11.4300 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8122/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0972 - categorical_accuracy: 0.5367 - val_loss: 11.5247 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8123/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0998 - categorical_accuracy: 0.5394 - val_loss: 11.4473 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8124/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1377 - categorical_accuracy: 0.5250 - val_loss: 10.8390 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8125/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1457 - categorical_accuracy: 0.5106 - val_loss: 11.5416 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8126/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1581 - categorical_accuracy: 0.5133 - val_loss: 12.2531 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8127/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2779 - categorical_accuracy: 0.4778 - val_loss: 11.1531 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8128/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2391 - categorical_accuracy: 0.4844 - val_loss: 11.5500 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8129/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2719 - categorical_accuracy: 0.4811 - val_loss: 9.9414 - val_categorical_accuracy: 0.1267\n",
      "Epoch 8130/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2329 - categorical_accuracy: 0.4922 - val_loss: 10.8254 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8131/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1499 - categorical_accuracy: 0.5150 - val_loss: 11.4344 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8132/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1385 - categorical_accuracy: 0.5156 - val_loss: 11.5379 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8133/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1341 - categorical_accuracy: 0.5194 - val_loss: 11.4819 - val_categorical_accuracy: 0.1267\n",
      "Epoch 8134/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1187 - categorical_accuracy: 0.5306 - val_loss: 11.6818 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8135/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1559 - categorical_accuracy: 0.5083 - val_loss: 11.1907 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8136/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1123 - categorical_accuracy: 0.5317 - val_loss: 11.3818 - val_categorical_accuracy: 0.1300\n",
      "Epoch 8137/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1199 - categorical_accuracy: 0.5289 - val_loss: 11.7308 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8138/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1316 - categorical_accuracy: 0.5206 - val_loss: 11.6935 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8139/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1400 - categorical_accuracy: 0.5194 - val_loss: 11.4121 - val_categorical_accuracy: 0.1267\n",
      "Epoch 8140/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1066 - categorical_accuracy: 0.5394 - val_loss: 11.5352 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8141/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1143 - categorical_accuracy: 0.5283 - val_loss: 11.4166 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8142/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1262 - categorical_accuracy: 0.5311 - val_loss: 11.4936 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8143/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1197 - categorical_accuracy: 0.5356 - val_loss: 11.9047 - val_categorical_accuracy: 0.1300\n",
      "Epoch 8144/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1551 - categorical_accuracy: 0.5217 - val_loss: 11.2560 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8145/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1314 - categorical_accuracy: 0.5256 - val_loss: 11.5935 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8146/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1172 - categorical_accuracy: 0.5261 - val_loss: 11.4184 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8147/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1064 - categorical_accuracy: 0.5350 - val_loss: 11.7287 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8148/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1009 - categorical_accuracy: 0.5406 - val_loss: 11.4533 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8149/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1211 - categorical_accuracy: 0.5272 - val_loss: 11.1959 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8150/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0996 - categorical_accuracy: 0.5378 - val_loss: 11.5154 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8151/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0984 - categorical_accuracy: 0.5250 - val_loss: 11.4724 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8152/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0978 - categorical_accuracy: 0.5411 - val_loss: 11.4634 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8153/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1085 - categorical_accuracy: 0.5322 - val_loss: 11.4892 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8154/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1120 - categorical_accuracy: 0.5267 - val_loss: 11.4471 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8155/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0976 - categorical_accuracy: 0.5389 - val_loss: 11.5798 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8156/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0974 - categorical_accuracy: 0.5322 - val_loss: 11.6843 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8157/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1244 - categorical_accuracy: 0.5317 - val_loss: 10.7387 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8158/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2336 - categorical_accuracy: 0.4978 - val_loss: 11.6846 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8159/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1843 - categorical_accuracy: 0.5089 - val_loss: 11.3027 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8160/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1992 - categorical_accuracy: 0.5006 - val_loss: 11.6390 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8161/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2768 - categorical_accuracy: 0.4789 - val_loss: 11.5153 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8162/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2387 - categorical_accuracy: 0.4917 - val_loss: 11.6373 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8163/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1983 - categorical_accuracy: 0.4972 - val_loss: 11.6123 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8164/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1210 - categorical_accuracy: 0.5272 - val_loss: 11.7520 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8165/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1228 - categorical_accuracy: 0.5317 - val_loss: 11.9113 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8166/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2297 - categorical_accuracy: 0.4933 - val_loss: 11.6461 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8167/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1497 - categorical_accuracy: 0.5172 - val_loss: 11.5567 - val_categorical_accuracy: 0.1267\n",
      "Epoch 8168/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1396 - categorical_accuracy: 0.5250 - val_loss: 11.5231 - val_categorical_accuracy: 0.1233\n",
      "Epoch 8169/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2586 - categorical_accuracy: 0.4833 - val_loss: 11.4417 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8170/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2696 - categorical_accuracy: 0.4678 - val_loss: 11.3616 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8171/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1696 - categorical_accuracy: 0.5094 - val_loss: 11.7539 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8172/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1290 - categorical_accuracy: 0.5172 - val_loss: 11.6830 - val_categorical_accuracy: 0.1300\n",
      "Epoch 8173/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1075 - categorical_accuracy: 0.5267 - val_loss: 11.7711 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8174/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0981 - categorical_accuracy: 0.5422 - val_loss: 11.7419 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8175/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0962 - categorical_accuracy: 0.5339 - val_loss: 11.6700 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8176/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1349 - categorical_accuracy: 0.5200 - val_loss: 11.5351 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8177/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1099 - categorical_accuracy: 0.5322 - val_loss: 11.5336 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8178/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0990 - categorical_accuracy: 0.5383 - val_loss: 11.4940 - val_categorical_accuracy: 0.1267\n",
      "Epoch 8179/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1107 - categorical_accuracy: 0.5317 - val_loss: 11.7284 - val_categorical_accuracy: 0.1267\n",
      "Epoch 8180/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1306 - categorical_accuracy: 0.5256 - val_loss: 11.7223 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8181/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1134 - categorical_accuracy: 0.5356 - val_loss: 11.5299 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8182/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0980 - categorical_accuracy: 0.5406 - val_loss: 11.4728 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8183/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1045 - categorical_accuracy: 0.5400 - val_loss: 11.4553 - val_categorical_accuracy: 0.1300\n",
      "Epoch 8184/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1206 - categorical_accuracy: 0.5250 - val_loss: 11.4966 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8185/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1072 - categorical_accuracy: 0.5389 - val_loss: 11.9887 - val_categorical_accuracy: 0.1267\n",
      "Epoch 8186/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1178 - categorical_accuracy: 0.5244 - val_loss: 11.4864 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8187/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1016 - categorical_accuracy: 0.5311 - val_loss: 11.4268 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8188/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1042 - categorical_accuracy: 0.5389 - val_loss: 11.5801 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8189/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0962 - categorical_accuracy: 0.5467 - val_loss: 11.5544 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8190/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0904 - categorical_accuracy: 0.5400 - val_loss: 11.6411 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8191/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0857 - categorical_accuracy: 0.5478 - val_loss: 11.6028 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8192/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.0933 - categorical_accuracy: 0.5444 - val_loss: 11.6425 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8193/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0994 - categorical_accuracy: 0.5467 - val_loss: 11.5326 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8194/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1879 - categorical_accuracy: 0.5011 - val_loss: 11.4528 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8195/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1458 - categorical_accuracy: 0.5183 - val_loss: 11.4040 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8196/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1247 - categorical_accuracy: 0.5311 - val_loss: 11.3765 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8197/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1769 - categorical_accuracy: 0.5211 - val_loss: 12.0914 - val_categorical_accuracy: 0.1300\n",
      "Epoch 8198/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.2335 - categorical_accuracy: 0.4978 - val_loss: 11.6754 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8199/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2989 - categorical_accuracy: 0.4739 - val_loss: 12.0121 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8200/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2863 - categorical_accuracy: 0.4761 - val_loss: 11.7252 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8201/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2004 - categorical_accuracy: 0.4967 - val_loss: 10.3066 - val_categorical_accuracy: 0.1267\n",
      "Epoch 8202/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2067 - categorical_accuracy: 0.4867 - val_loss: 11.0393 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8203/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1629 - categorical_accuracy: 0.5117 - val_loss: 11.5233 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8204/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1412 - categorical_accuracy: 0.5128 - val_loss: 11.8620 - val_categorical_accuracy: 0.1267\n",
      "Epoch 8205/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2543 - categorical_accuracy: 0.4806 - val_loss: 11.2476 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8206/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1259 - categorical_accuracy: 0.5228 - val_loss: 11.4372 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8207/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1098 - categorical_accuracy: 0.5283 - val_loss: 11.3303 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8208/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1046 - categorical_accuracy: 0.5322 - val_loss: 11.2359 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8209/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1054 - categorical_accuracy: 0.5350 - val_loss: 11.7007 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8210/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1060 - categorical_accuracy: 0.5283 - val_loss: 11.5428 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8211/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0936 - categorical_accuracy: 0.5444 - val_loss: 11.4811 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8212/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0936 - categorical_accuracy: 0.5472 - val_loss: 11.5505 - val_categorical_accuracy: 0.1300\n",
      "Epoch 8213/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.0958 - categorical_accuracy: 0.5417 - val_loss: 11.5406 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8214/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1122 - categorical_accuracy: 0.5311 - val_loss: 11.4635 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8215/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1060 - categorical_accuracy: 0.5411 - val_loss: 11.2038 - val_categorical_accuracy: 0.1300\n",
      "Epoch 8216/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1093 - categorical_accuracy: 0.5356 - val_loss: 11.9261 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8217/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1560 - categorical_accuracy: 0.5133 - val_loss: 11.4200 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8218/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1133 - categorical_accuracy: 0.5378 - val_loss: 11.4523 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8219/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1109 - categorical_accuracy: 0.5350 - val_loss: 11.6122 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8220/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1159 - categorical_accuracy: 0.5394 - val_loss: 11.3652 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8221/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0974 - categorical_accuracy: 0.5383 - val_loss: 11.5742 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8222/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0952 - categorical_accuracy: 0.5433 - val_loss: 11.6586 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8223/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0920 - categorical_accuracy: 0.5411 - val_loss: 11.5079 - val_categorical_accuracy: 0.1367\n",
      "Epoch 8224/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.0934 - categorical_accuracy: 0.5444 - val_loss: 11.7403 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8225/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0911 - categorical_accuracy: 0.5439 - val_loss: 11.5547 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8226/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1004 - categorical_accuracy: 0.5383 - val_loss: 11.7756 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8227/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1808 - categorical_accuracy: 0.5094 - val_loss: 10.9899 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8228/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.4000 - categorical_accuracy: 0.4406 - val_loss: 11.9910 - val_categorical_accuracy: 0.1233\n",
      "Epoch 8229/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1145 - categorical_accuracy: 0.5350 - val_loss: 11.6942 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8780/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1255 - categorical_accuracy: 0.5306 - val_loss: 11.9222 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8781/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2061 - categorical_accuracy: 0.5028 - val_loss: 11.5824 - val_categorical_accuracy: 0.1667\n",
      "Epoch 8782/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1389 - categorical_accuracy: 0.5283 - val_loss: 12.0854 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8783/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1237 - categorical_accuracy: 0.5278 - val_loss: 11.4867 - val_categorical_accuracy: 0.1700\n",
      "Epoch 8784/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1124 - categorical_accuracy: 0.5372 - val_loss: 11.5928 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8785/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1857 - categorical_accuracy: 0.5150 - val_loss: 11.4231 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8786/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2535 - categorical_accuracy: 0.4844 - val_loss: 11.4843 - val_categorical_accuracy: 0.1700\n",
      "Epoch 8787/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1695 - categorical_accuracy: 0.5194 - val_loss: 11.6092 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8788/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1283 - categorical_accuracy: 0.5311 - val_loss: 11.0469 - val_categorical_accuracy: 0.1667\n",
      "Epoch 8789/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1105 - categorical_accuracy: 0.5344 - val_loss: 11.0936 - val_categorical_accuracy: 0.1733\n",
      "Epoch 8790/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0940 - categorical_accuracy: 0.5439 - val_loss: 11.2674 - val_categorical_accuracy: 0.1700\n",
      "Epoch 8791/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0921 - categorical_accuracy: 0.5478 - val_loss: 11.5590 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8792/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0946 - categorical_accuracy: 0.5450 - val_loss: 11.3875 - val_categorical_accuracy: 0.1667\n",
      "Epoch 8793/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0857 - categorical_accuracy: 0.5522 - val_loss: 11.5035 - val_categorical_accuracy: 0.1667\n",
      "Epoch 8794/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0845 - categorical_accuracy: 0.5467 - val_loss: 11.8554 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8795/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1007 - categorical_accuracy: 0.5361 - val_loss: 11.4883 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8796/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0947 - categorical_accuracy: 0.5406 - val_loss: 11.3548 - val_categorical_accuracy: 0.1767\n",
      "Epoch 8797/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0847 - categorical_accuracy: 0.5550 - val_loss: 11.3593 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8798/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0918 - categorical_accuracy: 0.5461 - val_loss: 11.9617 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8799/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2081 - categorical_accuracy: 0.4950 - val_loss: 11.7450 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8800/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2486 - categorical_accuracy: 0.4889 - val_loss: 11.4193 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8801/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1707 - categorical_accuracy: 0.5078 - val_loss: 11.9423 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8802/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1532 - categorical_accuracy: 0.5150 - val_loss: 11.8656 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8803/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1642 - categorical_accuracy: 0.5161 - val_loss: 11.5057 - val_categorical_accuracy: 0.1667\n",
      "Epoch 8804/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1837 - categorical_accuracy: 0.5033 - val_loss: 11.7158 - val_categorical_accuracy: 0.1700\n",
      "Epoch 8805/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1697 - categorical_accuracy: 0.5111 - val_loss: 11.1644 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8806/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1427 - categorical_accuracy: 0.5189 - val_loss: 10.7542 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8807/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1641 - categorical_accuracy: 0.5200 - val_loss: 11.3221 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8808/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1123 - categorical_accuracy: 0.5272 - val_loss: 11.5528 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8809/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1012 - categorical_accuracy: 0.5389 - val_loss: 11.5829 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8810/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1291 - categorical_accuracy: 0.5239 - val_loss: 11.3204 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8811/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1306 - categorical_accuracy: 0.5244 - val_loss: 11.4804 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8812/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1006 - categorical_accuracy: 0.5422 - val_loss: 11.4782 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8813/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1014 - categorical_accuracy: 0.5383 - val_loss: 11.4166 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8814/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0935 - categorical_accuracy: 0.5472 - val_loss: 11.6323 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8815/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1074 - categorical_accuracy: 0.5400 - val_loss: 11.7714 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8816/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0929 - categorical_accuracy: 0.5383 - val_loss: 11.5447 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8817/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0909 - categorical_accuracy: 0.5400 - val_loss: 11.5546 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8818/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0965 - categorical_accuracy: 0.5461 - val_loss: 11.6681 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8819/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1102 - categorical_accuracy: 0.5367 - val_loss: 11.6532 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8820/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1080 - categorical_accuracy: 0.5333 - val_loss: 11.8689 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8821/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1019 - categorical_accuracy: 0.5333 - val_loss: 11.9112 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8822/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1285 - categorical_accuracy: 0.5256 - val_loss: 11.4586 - val_categorical_accuracy: 0.1667\n",
      "Epoch 8823/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1444 - categorical_accuracy: 0.5189 - val_loss: 11.5947 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8824/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1088 - categorical_accuracy: 0.5300 - val_loss: 11.6479 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8825/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1883 - categorical_accuracy: 0.5111 - val_loss: 11.5612 - val_categorical_accuracy: 0.1333\n",
      "Epoch 8826/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1962 - categorical_accuracy: 0.5083 - val_loss: 12.0656 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8827/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2399 - categorical_accuracy: 0.4894 - val_loss: 11.6761 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8828/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1860 - categorical_accuracy: 0.4972 - val_loss: 11.3109 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8829/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1825 - categorical_accuracy: 0.5139 - val_loss: 11.7589 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8830/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1388 - categorical_accuracy: 0.5278 - val_loss: 11.7952 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8831/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1215 - categorical_accuracy: 0.5300 - val_loss: 11.8780 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8832/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1123 - categorical_accuracy: 0.5311 - val_loss: 11.6181 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8833/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1613 - categorical_accuracy: 0.5244 - val_loss: 11.6884 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8834/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1311 - categorical_accuracy: 0.5244 - val_loss: 11.6593 - val_categorical_accuracy: 0.1700\n",
      "Epoch 8835/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1260 - categorical_accuracy: 0.5256 - val_loss: 11.5498 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8836/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1073 - categorical_accuracy: 0.5294 - val_loss: 11.7688 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8837/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1228 - categorical_accuracy: 0.5289 - val_loss: 11.2700 - val_categorical_accuracy: 0.1700\n",
      "Epoch 8838/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2650 - categorical_accuracy: 0.5017 - val_loss: 11.1711 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8839/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2312 - categorical_accuracy: 0.4967 - val_loss: 11.5037 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8840/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1327 - categorical_accuracy: 0.5217 - val_loss: 11.2756 - val_categorical_accuracy: 0.1700\n",
      "Epoch 8841/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1026 - categorical_accuracy: 0.5350 - val_loss: 11.4205 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8842/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0971 - categorical_accuracy: 0.5450 - val_loss: 11.6389 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8843/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1032 - categorical_accuracy: 0.5411 - val_loss: 11.9818 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8844/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1458 - categorical_accuracy: 0.5217 - val_loss: 11.6365 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8845/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1122 - categorical_accuracy: 0.5367 - val_loss: 11.8346 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8846/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.0918 - categorical_accuracy: 0.5422 - val_loss: 11.6942 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8847/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.0878 - categorical_accuracy: 0.5483 - val_loss: 11.6443 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8848/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1042 - categorical_accuracy: 0.5378 - val_loss: 11.5133 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8849/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0878 - categorical_accuracy: 0.5467 - val_loss: 11.6413 - val_categorical_accuracy: 0.1633\n",
      "Epoch 8850/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0829 - categorical_accuracy: 0.5506 - val_loss: 11.6703 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8851/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0904 - categorical_accuracy: 0.5522 - val_loss: 11.8362 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8852/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1249 - categorical_accuracy: 0.5322 - val_loss: 11.9236 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8853/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1740 - categorical_accuracy: 0.5117 - val_loss: 11.5790 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8854/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1100 - categorical_accuracy: 0.5333 - val_loss: 11.4348 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8855/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1088 - categorical_accuracy: 0.5383 - val_loss: 11.3189 - val_categorical_accuracy: 0.1700\n",
      "Epoch 8856/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1804 - categorical_accuracy: 0.5072 - val_loss: 11.5432 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8857/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2106 - categorical_accuracy: 0.5022 - val_loss: 11.8624 - val_categorical_accuracy: 0.1667\n",
      "Epoch 8858/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1675 - categorical_accuracy: 0.5078 - val_loss: 11.2759 - val_categorical_accuracy: 0.1833\n",
      "Epoch 8859/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2254 - categorical_accuracy: 0.4939 - val_loss: 10.9976 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8860/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1736 - categorical_accuracy: 0.5050 - val_loss: 11.7873 - val_categorical_accuracy: 0.1733\n",
      "Epoch 8861/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.2241 - categorical_accuracy: 0.4956 - val_loss: 12.2318 - val_categorical_accuracy: 0.1400\n",
      "Epoch 8862/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2072 - categorical_accuracy: 0.5050 - val_loss: 11.6081 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8863/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1912 - categorical_accuracy: 0.5106 - val_loss: 10.5894 - val_categorical_accuracy: 0.1433\n",
      "Epoch 8864/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2100 - categorical_accuracy: 0.4961 - val_loss: 11.0330 - val_categorical_accuracy: 0.1600\n",
      "Epoch 8865/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1505 - categorical_accuracy: 0.5150 - val_loss: 11.8180 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8866/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1362 - categorical_accuracy: 0.5239 - val_loss: 11.5434 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8867/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1113 - categorical_accuracy: 0.5422 - val_loss: 11.9713 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8868/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1128 - categorical_accuracy: 0.5394 - val_loss: 11.6794 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8869/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1034 - categorical_accuracy: 0.5361 - val_loss: 11.9016 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8870/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1154 - categorical_accuracy: 0.5278 - val_loss: 11.7362 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8871/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1074 - categorical_accuracy: 0.5367 - val_loss: 12.0149 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8872/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1291 - categorical_accuracy: 0.5267 - val_loss: 11.6810 - val_categorical_accuracy: 0.1533\n",
      "Epoch 8873/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1093 - categorical_accuracy: 0.5400 - val_loss: 11.6073 - val_categorical_accuracy: 0.1467\n",
      "Epoch 8874/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1021 - categorical_accuracy: 0.5456 - val_loss: 11.6133 - val_categorical_accuracy: 0.1500\n",
      "Epoch 8875/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0893 - categorical_accuracy: 0.5489 - val_loss: 11.3943 - val_categorical_accuracy: 0.1700\n",
      "Epoch 8876/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1068 - categorical_accuracy: 0.5433 - val_loss: 11.6590 - val_categorical_accuracy: 0.1567\n",
      "Epoch 8877/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1317 - categorical_accuracy: 0.5244 - val_loss: 11.7869 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9213/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1164 - categorical_accuracy: 0.5317 - val_loss: 11.6809 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9214/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1171 - categorical_accuracy: 0.5328 - val_loss: 11.7183 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9215/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1479 - categorical_accuracy: 0.5244 - val_loss: 10.7211 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9216/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2412 - categorical_accuracy: 0.4867 - val_loss: 10.4256 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9217/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1723 - categorical_accuracy: 0.5089 - val_loss: 11.3014 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9218/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1952 - categorical_accuracy: 0.4967 - val_loss: 11.8129 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9219/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1298 - categorical_accuracy: 0.5261 - val_loss: 11.8323 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9220/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1255 - categorical_accuracy: 0.5394 - val_loss: 11.7068 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9221/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1091 - categorical_accuracy: 0.5344 - val_loss: 11.9563 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9222/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1058 - categorical_accuracy: 0.5306 - val_loss: 11.6815 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9223/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2157 - categorical_accuracy: 0.4972 - val_loss: 12.2509 - val_categorical_accuracy: 0.1433\n",
      "Epoch 9224/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1989 - categorical_accuracy: 0.4928 - val_loss: 11.8390 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9225/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1286 - categorical_accuracy: 0.5261 - val_loss: 12.3656 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9226/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1359 - categorical_accuracy: 0.5233 - val_loss: 11.7039 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9227/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1045 - categorical_accuracy: 0.5339 - val_loss: 11.7084 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9228/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0872 - categorical_accuracy: 0.5478 - val_loss: 11.9139 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9229/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0906 - categorical_accuracy: 0.5467 - val_loss: 11.8661 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9230/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0854 - categorical_accuracy: 0.5450 - val_loss: 11.9004 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9231/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0893 - categorical_accuracy: 0.5483 - val_loss: 11.7018 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9232/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0876 - categorical_accuracy: 0.5500 - val_loss: 11.8264 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9233/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0900 - categorical_accuracy: 0.5450 - val_loss: 11.7061 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9234/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0826 - categorical_accuracy: 0.5500 - val_loss: 11.7828 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9235/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0881 - categorical_accuracy: 0.5467 - val_loss: 11.8641 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9236/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0988 - categorical_accuracy: 0.5444 - val_loss: 11.6570 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9237/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0919 - categorical_accuracy: 0.5417 - val_loss: 11.8410 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9238/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1729 - categorical_accuracy: 0.5183 - val_loss: 11.8558 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9239/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2136 - categorical_accuracy: 0.4989 - val_loss: 11.9400 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9240/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1590 - categorical_accuracy: 0.5178 - val_loss: 11.7437 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9241/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1024 - categorical_accuracy: 0.5328 - val_loss: 11.8196 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9242/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0913 - categorical_accuracy: 0.5439 - val_loss: 11.8628 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9243/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1004 - categorical_accuracy: 0.5394 - val_loss: 11.8698 - val_categorical_accuracy: 0.1867\n",
      "Epoch 9244/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0944 - categorical_accuracy: 0.5450 - val_loss: 11.8967 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9245/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0862 - categorical_accuracy: 0.5467 - val_loss: 11.8206 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9246/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0893 - categorical_accuracy: 0.5461 - val_loss: 11.8461 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9247/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0986 - categorical_accuracy: 0.5433 - val_loss: 11.8363 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9248/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0892 - categorical_accuracy: 0.5511 - val_loss: 11.9341 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9249/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1245 - categorical_accuracy: 0.5250 - val_loss: 11.8585 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9250/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0855 - categorical_accuracy: 0.5511 - val_loss: 12.0166 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9251/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0841 - categorical_accuracy: 0.5461 - val_loss: 12.2549 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9252/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1316 - categorical_accuracy: 0.5306 - val_loss: 11.7422 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9253/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1490 - categorical_accuracy: 0.5217 - val_loss: 11.7685 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9254/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2317 - categorical_accuracy: 0.5028 - val_loss: 12.0897 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9255/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3741 - categorical_accuracy: 0.4617 - val_loss: 11.5423 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9256/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2713 - categorical_accuracy: 0.4794 - val_loss: 11.5598 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9257/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1997 - categorical_accuracy: 0.4967 - val_loss: 11.6049 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9258/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1642 - categorical_accuracy: 0.5144 - val_loss: 11.0055 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9259/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2174 - categorical_accuracy: 0.4989 - val_loss: 12.0122 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9260/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1459 - categorical_accuracy: 0.5256 - val_loss: 11.9729 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9261/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2157 - categorical_accuracy: 0.4872 - val_loss: 11.2906 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9262/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1261 - categorical_accuracy: 0.5278 - val_loss: 11.6606 - val_categorical_accuracy: 0.1833\n",
      "Epoch 9263/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1157 - categorical_accuracy: 0.5322 - val_loss: 11.8202 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9264/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0989 - categorical_accuracy: 0.5422 - val_loss: 11.8043 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9265/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0986 - categorical_accuracy: 0.5428 - val_loss: 11.8225 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9266/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1037 - categorical_accuracy: 0.5344 - val_loss: 12.0799 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9267/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0891 - categorical_accuracy: 0.5422 - val_loss: 11.7487 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9268/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0903 - categorical_accuracy: 0.5494 - val_loss: 12.2248 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9269/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1238 - categorical_accuracy: 0.5328 - val_loss: 11.7077 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9270/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1219 - categorical_accuracy: 0.5328 - val_loss: 11.6312 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9271/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1048 - categorical_accuracy: 0.5428 - val_loss: 12.0896 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9272/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1245 - categorical_accuracy: 0.5344 - val_loss: 12.1485 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9273/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1008 - categorical_accuracy: 0.5411 - val_loss: 11.8928 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9274/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1092 - categorical_accuracy: 0.5328 - val_loss: 11.7304 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9275/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1673 - categorical_accuracy: 0.5222 - val_loss: 12.3280 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9276/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1873 - categorical_accuracy: 0.5139 - val_loss: 11.6839 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9277/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2125 - categorical_accuracy: 0.5000 - val_loss: 11.7405 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9278/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2187 - categorical_accuracy: 0.5056 - val_loss: 11.9960 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9279/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2442 - categorical_accuracy: 0.4883 - val_loss: 11.7356 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9280/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1438 - categorical_accuracy: 0.5228 - val_loss: 11.8770 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9281/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1052 - categorical_accuracy: 0.5356 - val_loss: 11.8912 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9282/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0958 - categorical_accuracy: 0.5478 - val_loss: 11.7205 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9283/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1080 - categorical_accuracy: 0.5417 - val_loss: 11.4230 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9284/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1067 - categorical_accuracy: 0.5406 - val_loss: 11.9366 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9285/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1044 - categorical_accuracy: 0.5400 - val_loss: 11.8687 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9286/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1134 - categorical_accuracy: 0.5344 - val_loss: 12.1461 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9287/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1157 - categorical_accuracy: 0.5311 - val_loss: 11.7325 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9288/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1389 - categorical_accuracy: 0.5289 - val_loss: 10.5353 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9289/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2177 - categorical_accuracy: 0.4967 - val_loss: 11.5399 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9290/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1193 - categorical_accuracy: 0.5294 - val_loss: 12.0182 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9291/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1030 - categorical_accuracy: 0.5356 - val_loss: 11.9630 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9292/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1087 - categorical_accuracy: 0.5328 - val_loss: 11.9198 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9293/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0891 - categorical_accuracy: 0.5506 - val_loss: 11.8275 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9294/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0920 - categorical_accuracy: 0.5456 - val_loss: 11.9918 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9295/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0899 - categorical_accuracy: 0.5506 - val_loss: 11.9828 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9296/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.0900 - categorical_accuracy: 0.5483 - val_loss: 11.7665 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9297/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0906 - categorical_accuracy: 0.5556 - val_loss: 11.8853 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9298/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.0874 - categorical_accuracy: 0.5489 - val_loss: 12.3252 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9299/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1539 - categorical_accuracy: 0.5178 - val_loss: 11.3483 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9300/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1704 - categorical_accuracy: 0.5161 - val_loss: 11.9515 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9301/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1303 - categorical_accuracy: 0.5289 - val_loss: 11.7421 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9302/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.0916 - categorical_accuracy: 0.5456 - val_loss: 11.7091 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9303/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0873 - categorical_accuracy: 0.5478 - val_loss: 11.8474 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9304/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.0914 - categorical_accuracy: 0.5444 - val_loss: 11.8830 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9305/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0837 - categorical_accuracy: 0.5533 - val_loss: 11.9055 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9306/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1434 - categorical_accuracy: 0.5300 - val_loss: 12.1740 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9307/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.2886 - categorical_accuracy: 0.4744 - val_loss: 11.5057 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9308/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2269 - categorical_accuracy: 0.5006 - val_loss: 11.7798 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9309/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1614 - categorical_accuracy: 0.5139 - val_loss: 11.9218 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9310/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1336 - categorical_accuracy: 0.5278 - val_loss: 11.2717 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9311/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1158 - categorical_accuracy: 0.5294 - val_loss: 11.5661 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9312/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0964 - categorical_accuracy: 0.5383 - val_loss: 11.8068 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9313/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0888 - categorical_accuracy: 0.5467 - val_loss: 11.7446 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9314/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0910 - categorical_accuracy: 0.5456 - val_loss: 11.5501 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9315/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0934 - categorical_accuracy: 0.5444 - val_loss: 11.9079 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9316/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0859 - categorical_accuracy: 0.5467 - val_loss: 12.2502 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9317/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0909 - categorical_accuracy: 0.5428 - val_loss: 11.9768 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9318/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1128 - categorical_accuracy: 0.5367 - val_loss: 12.3852 - val_categorical_accuracy: 0.1433\n",
      "Epoch 9319/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1953 - categorical_accuracy: 0.5050 - val_loss: 11.5391 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9320/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2227 - categorical_accuracy: 0.4933 - val_loss: 11.9907 - val_categorical_accuracy: 0.1400\n",
      "Epoch 9321/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2525 - categorical_accuracy: 0.4889 - val_loss: 11.8847 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9322/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2023 - categorical_accuracy: 0.5017 - val_loss: 11.7018 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9323/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1758 - categorical_accuracy: 0.5172 - val_loss: 10.8408 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9324/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1947 - categorical_accuracy: 0.5056 - val_loss: 11.7650 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9325/10000\n",
      "57/57 [==============================] - 0s 9ms/step - loss: 1.1473 - categorical_accuracy: 0.5172 - val_loss: 11.8288 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9326/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1616 - categorical_accuracy: 0.5161 - val_loss: 11.8190 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9327/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1382 - categorical_accuracy: 0.5206 - val_loss: 11.9741 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9328/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1060 - categorical_accuracy: 0.5389 - val_loss: 11.9352 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9329/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0909 - categorical_accuracy: 0.5439 - val_loss: 12.0168 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9330/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0878 - categorical_accuracy: 0.5450 - val_loss: 11.9945 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9331/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0901 - categorical_accuracy: 0.5450 - val_loss: 11.9901 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9332/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0831 - categorical_accuracy: 0.5517 - val_loss: 11.9854 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9333/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0836 - categorical_accuracy: 0.5506 - val_loss: 11.9299 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9334/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0857 - categorical_accuracy: 0.5478 - val_loss: 12.0895 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9335/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1107 - categorical_accuracy: 0.5389 - val_loss: 11.7247 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9336/10000\n",
      "57/57 [==============================] - 1s 13ms/step - loss: 1.1092 - categorical_accuracy: 0.5372 - val_loss: 11.9894 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9337/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1344 - categorical_accuracy: 0.5317 - val_loss: 11.8127 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9338/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1298 - categorical_accuracy: 0.5306 - val_loss: 12.0836 - val_categorical_accuracy: 0.1433\n",
      "Epoch 9339/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1214 - categorical_accuracy: 0.5261 - val_loss: 11.5891 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9340/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1005 - categorical_accuracy: 0.5400 - val_loss: 11.9663 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9341/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.0926 - categorical_accuracy: 0.5411 - val_loss: 12.0551 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9342/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1316 - categorical_accuracy: 0.5256 - val_loss: 11.8419 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9343/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1981 - categorical_accuracy: 0.5011 - val_loss: 11.4570 - val_categorical_accuracy: 0.1867\n",
      "Epoch 9344/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1603 - categorical_accuracy: 0.4978 - val_loss: 11.5889 - val_categorical_accuracy: 0.1967\n",
      "Epoch 9345/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1576 - categorical_accuracy: 0.5183 - val_loss: 11.6384 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9346/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3495 - categorical_accuracy: 0.4750 - val_loss: 11.4764 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9347/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1903 - categorical_accuracy: 0.5056 - val_loss: 11.8554 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9348/10000\n",
      "57/57 [==============================] - 1s 12ms/step - loss: 1.1600 - categorical_accuracy: 0.5050 - val_loss: 11.7322 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9349/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1069 - categorical_accuracy: 0.5389 - val_loss: 12.5978 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9749/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2006 - categorical_accuracy: 0.5044 - val_loss: 12.2647 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9750/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2785 - categorical_accuracy: 0.4789 - val_loss: 12.0261 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9751/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2240 - categorical_accuracy: 0.4911 - val_loss: 12.3617 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9752/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2180 - categorical_accuracy: 0.4844 - val_loss: 11.9177 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9753/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1670 - categorical_accuracy: 0.5033 - val_loss: 11.9229 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9754/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1204 - categorical_accuracy: 0.5283 - val_loss: 12.1454 - val_categorical_accuracy: 0.1833\n",
      "Epoch 9755/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1369 - categorical_accuracy: 0.5267 - val_loss: 12.1168 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9756/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1137 - categorical_accuracy: 0.5322 - val_loss: 12.1691 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9757/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1052 - categorical_accuracy: 0.5394 - val_loss: 12.3268 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9758/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0890 - categorical_accuracy: 0.5439 - val_loss: 12.2365 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9759/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0854 - categorical_accuracy: 0.5467 - val_loss: 12.4254 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9760/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0975 - categorical_accuracy: 0.5428 - val_loss: 12.2244 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9761/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0890 - categorical_accuracy: 0.5478 - val_loss: 12.2886 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9762/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0961 - categorical_accuracy: 0.5450 - val_loss: 12.2704 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9763/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0861 - categorical_accuracy: 0.5472 - val_loss: 12.3780 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9764/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0926 - categorical_accuracy: 0.5433 - val_loss: 12.3014 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9765/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1031 - categorical_accuracy: 0.5406 - val_loss: 12.4155 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9766/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1035 - categorical_accuracy: 0.5361 - val_loss: 12.2596 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9767/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1403 - categorical_accuracy: 0.5211 - val_loss: 12.3697 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9768/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1446 - categorical_accuracy: 0.5217 - val_loss: 11.7820 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9769/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2349 - categorical_accuracy: 0.4889 - val_loss: 11.9430 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9770/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1979 - categorical_accuracy: 0.5172 - val_loss: 12.2587 - val_categorical_accuracy: 0.1400\n",
      "Epoch 9771/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1799 - categorical_accuracy: 0.5028 - val_loss: 12.2989 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9772/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1165 - categorical_accuracy: 0.5294 - val_loss: 12.3264 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9773/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1032 - categorical_accuracy: 0.5350 - val_loss: 12.1468 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9774/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1042 - categorical_accuracy: 0.5389 - val_loss: 11.8977 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9775/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0933 - categorical_accuracy: 0.5411 - val_loss: 12.3258 - val_categorical_accuracy: 0.1433\n",
      "Epoch 9776/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0837 - categorical_accuracy: 0.5539 - val_loss: 12.1739 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9777/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0841 - categorical_accuracy: 0.5528 - val_loss: 12.2447 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9778/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0827 - categorical_accuracy: 0.5528 - val_loss: 12.1024 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9779/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0813 - categorical_accuracy: 0.5483 - val_loss: 12.2659 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9780/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0848 - categorical_accuracy: 0.5500 - val_loss: 12.1731 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9781/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0844 - categorical_accuracy: 0.5439 - val_loss: 12.2318 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9782/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0853 - categorical_accuracy: 0.5411 - val_loss: 12.3314 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9783/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1022 - categorical_accuracy: 0.5433 - val_loss: 12.6972 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9784/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2320 - categorical_accuracy: 0.5039 - val_loss: 11.7462 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9785/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.4243 - categorical_accuracy: 0.4450 - val_loss: 11.8569 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9786/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1820 - categorical_accuracy: 0.5011 - val_loss: 12.1654 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9787/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.3659 - categorical_accuracy: 0.4833 - val_loss: 12.6914 - val_categorical_accuracy: 0.1433\n",
      "Epoch 9788/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3451 - categorical_accuracy: 0.4589 - val_loss: 12.2446 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9789/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1662 - categorical_accuracy: 0.5078 - val_loss: 11.7223 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9790/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1536 - categorical_accuracy: 0.5167 - val_loss: 12.0052 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9791/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1304 - categorical_accuracy: 0.5250 - val_loss: 11.8132 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9792/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1615 - categorical_accuracy: 0.5156 - val_loss: 10.4709 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9793/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2383 - categorical_accuracy: 0.4917 - val_loss: 11.5877 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9794/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1284 - categorical_accuracy: 0.5200 - val_loss: 11.9068 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9795/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1047 - categorical_accuracy: 0.5394 - val_loss: 11.8923 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9796/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1007 - categorical_accuracy: 0.5317 - val_loss: 12.4898 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9797/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1286 - categorical_accuracy: 0.5244 - val_loss: 11.9825 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9798/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0930 - categorical_accuracy: 0.5439 - val_loss: 12.3858 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9799/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1032 - categorical_accuracy: 0.5372 - val_loss: 12.1748 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9800/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0954 - categorical_accuracy: 0.5344 - val_loss: 12.2637 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9801/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0848 - categorical_accuracy: 0.5361 - val_loss: 12.2299 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9802/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0887 - categorical_accuracy: 0.5428 - val_loss: 12.3108 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9803/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1063 - categorical_accuracy: 0.5394 - val_loss: 12.0948 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9804/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0867 - categorical_accuracy: 0.5428 - val_loss: 12.5805 - val_categorical_accuracy: 0.1833\n",
      "Epoch 9805/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1214 - categorical_accuracy: 0.5233 - val_loss: 12.2996 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9806/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1111 - categorical_accuracy: 0.5278 - val_loss: 12.4634 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9807/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1320 - categorical_accuracy: 0.5228 - val_loss: 12.1152 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9808/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1103 - categorical_accuracy: 0.5322 - val_loss: 12.0130 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9809/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1184 - categorical_accuracy: 0.5250 - val_loss: 12.0079 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9810/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0983 - categorical_accuracy: 0.5367 - val_loss: 12.4263 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9811/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0962 - categorical_accuracy: 0.5367 - val_loss: 12.2407 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9812/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0919 - categorical_accuracy: 0.5472 - val_loss: 12.2362 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9813/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0853 - categorical_accuracy: 0.5467 - val_loss: 12.0292 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9814/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0822 - categorical_accuracy: 0.5489 - val_loss: 12.2806 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9815/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0847 - categorical_accuracy: 0.5456 - val_loss: 12.1541 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9816/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0806 - categorical_accuracy: 0.5478 - val_loss: 12.1189 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9817/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0842 - categorical_accuracy: 0.5494 - val_loss: 12.1990 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9818/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1194 - categorical_accuracy: 0.5306 - val_loss: 12.2067 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9819/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1440 - categorical_accuracy: 0.5311 - val_loss: 12.6436 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9820/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1039 - categorical_accuracy: 0.5389 - val_loss: 12.1280 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9821/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1170 - categorical_accuracy: 0.5306 - val_loss: 12.0969 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9822/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1102 - categorical_accuracy: 0.5344 - val_loss: 12.3621 - val_categorical_accuracy: 0.1933\n",
      "Epoch 9823/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1250 - categorical_accuracy: 0.5300 - val_loss: 11.9574 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9824/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1098 - categorical_accuracy: 0.5350 - val_loss: 12.0501 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9825/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1172 - categorical_accuracy: 0.5333 - val_loss: 12.0591 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9826/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1147 - categorical_accuracy: 0.5300 - val_loss: 12.0589 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9827/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1172 - categorical_accuracy: 0.5294 - val_loss: 12.4012 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9828/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1344 - categorical_accuracy: 0.5128 - val_loss: 12.2080 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9829/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1155 - categorical_accuracy: 0.5244 - val_loss: 12.2507 - val_categorical_accuracy: 0.1933\n",
      "Epoch 9830/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1730 - categorical_accuracy: 0.5122 - val_loss: 12.7326 - val_categorical_accuracy: 0.1867\n",
      "Epoch 9831/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2154 - categorical_accuracy: 0.4956 - val_loss: 11.8297 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9832/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1982 - categorical_accuracy: 0.5022 - val_loss: 12.3956 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9833/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1311 - categorical_accuracy: 0.5250 - val_loss: 12.3979 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9834/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2051 - categorical_accuracy: 0.4983 - val_loss: 12.2099 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9835/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1812 - categorical_accuracy: 0.5000 - val_loss: 12.8558 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9836/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1244 - categorical_accuracy: 0.5289 - val_loss: 12.4357 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9837/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1098 - categorical_accuracy: 0.5356 - val_loss: 12.1878 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9838/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0922 - categorical_accuracy: 0.5394 - val_loss: 12.3645 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9839/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0948 - categorical_accuracy: 0.5417 - val_loss: 12.4593 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9840/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0839 - categorical_accuracy: 0.5472 - val_loss: 12.1740 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9841/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0965 - categorical_accuracy: 0.5389 - val_loss: 12.1290 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9842/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1115 - categorical_accuracy: 0.5339 - val_loss: 12.3918 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9843/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1711 - categorical_accuracy: 0.5172 - val_loss: 11.5541 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9844/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1626 - categorical_accuracy: 0.5050 - val_loss: 12.0252 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9845/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1844 - categorical_accuracy: 0.5022 - val_loss: 12.5693 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9846/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1646 - categorical_accuracy: 0.5161 - val_loss: 12.5751 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9847/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1315 - categorical_accuracy: 0.5278 - val_loss: 11.9743 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9848/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1851 - categorical_accuracy: 0.5039 - val_loss: 12.8865 - val_categorical_accuracy: 0.1300\n",
      "Epoch 9849/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2472 - categorical_accuracy: 0.5056 - val_loss: 11.8718 - val_categorical_accuracy: 0.1900\n",
      "Epoch 9850/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1321 - categorical_accuracy: 0.5144 - val_loss: 12.6055 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9851/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1665 - categorical_accuracy: 0.5133 - val_loss: 12.1776 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9852/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1252 - categorical_accuracy: 0.5228 - val_loss: 12.4851 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9853/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1328 - categorical_accuracy: 0.5272 - val_loss: 12.2983 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9854/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0983 - categorical_accuracy: 0.5411 - val_loss: 12.2662 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9855/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0900 - categorical_accuracy: 0.5450 - val_loss: 12.2316 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9856/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0863 - categorical_accuracy: 0.5450 - val_loss: 12.0931 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9857/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0822 - categorical_accuracy: 0.5506 - val_loss: 12.1763 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9858/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0824 - categorical_accuracy: 0.5567 - val_loss: 12.1771 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9859/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0804 - categorical_accuracy: 0.5494 - val_loss: 12.1943 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9860/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0935 - categorical_accuracy: 0.5383 - val_loss: 12.2279 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9861/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0851 - categorical_accuracy: 0.5511 - val_loss: 12.2418 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9862/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0926 - categorical_accuracy: 0.5467 - val_loss: 12.4976 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9863/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1502 - categorical_accuracy: 0.5306 - val_loss: 11.9160 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9864/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1293 - categorical_accuracy: 0.5283 - val_loss: 12.1628 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9865/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1312 - categorical_accuracy: 0.5250 - val_loss: 12.1174 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9866/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1050 - categorical_accuracy: 0.5411 - val_loss: 12.1927 - val_categorical_accuracy: 0.1867\n",
      "Epoch 9867/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1052 - categorical_accuracy: 0.5356 - val_loss: 12.4456 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9868/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0970 - categorical_accuracy: 0.5322 - val_loss: 12.3245 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9869/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0875 - categorical_accuracy: 0.5472 - val_loss: 12.2932 - val_categorical_accuracy: 0.1833\n",
      "Epoch 9870/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1090 - categorical_accuracy: 0.5300 - val_loss: 12.2196 - val_categorical_accuracy: 0.1900\n",
      "Epoch 9871/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1460 - categorical_accuracy: 0.5256 - val_loss: 11.7257 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9872/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3079 - categorical_accuracy: 0.4811 - val_loss: 12.1599 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9873/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.2017 - categorical_accuracy: 0.4994 - val_loss: 12.1465 - val_categorical_accuracy: 0.1433\n",
      "Epoch 9874/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1504 - categorical_accuracy: 0.5183 - val_loss: 12.2454 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9875/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1195 - categorical_accuracy: 0.5322 - val_loss: 12.2019 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9876/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1008 - categorical_accuracy: 0.5372 - val_loss: 12.2032 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9877/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0926 - categorical_accuracy: 0.5444 - val_loss: 12.3757 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9878/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1016 - categorical_accuracy: 0.5450 - val_loss: 11.8781 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9879/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1210 - categorical_accuracy: 0.5283 - val_loss: 12.4933 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9880/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1285 - categorical_accuracy: 0.5267 - val_loss: 12.3437 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9881/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3454 - categorical_accuracy: 0.4600 - val_loss: 13.0387 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9882/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3383 - categorical_accuracy: 0.4639 - val_loss: 12.0310 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9883/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1942 - categorical_accuracy: 0.4922 - val_loss: 12.3007 - val_categorical_accuracy: 0.1867\n",
      "Epoch 9884/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1206 - categorical_accuracy: 0.5189 - val_loss: 12.2804 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9885/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1085 - categorical_accuracy: 0.5367 - val_loss: 12.1307 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9886/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0949 - categorical_accuracy: 0.5367 - val_loss: 12.2353 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9887/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0895 - categorical_accuracy: 0.5467 - val_loss: 12.2935 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9888/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0911 - categorical_accuracy: 0.5494 - val_loss: 12.3282 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9889/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0954 - categorical_accuracy: 0.5439 - val_loss: 12.5915 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9890/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1569 - categorical_accuracy: 0.5094 - val_loss: 12.2978 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9891/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1144 - categorical_accuracy: 0.5400 - val_loss: 12.5596 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9892/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1129 - categorical_accuracy: 0.5350 - val_loss: 12.3758 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9893/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1186 - categorical_accuracy: 0.5322 - val_loss: 12.3962 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9894/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1043 - categorical_accuracy: 0.5328 - val_loss: 12.5185 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9895/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0976 - categorical_accuracy: 0.5456 - val_loss: 12.2537 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9896/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0869 - categorical_accuracy: 0.5450 - val_loss: 12.4579 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9897/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0863 - categorical_accuracy: 0.5461 - val_loss: 12.3907 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9898/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0875 - categorical_accuracy: 0.5472 - val_loss: 12.4664 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9899/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0839 - categorical_accuracy: 0.5528 - val_loss: 12.3299 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9900/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0779 - categorical_accuracy: 0.5494 - val_loss: 12.4320 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9901/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0814 - categorical_accuracy: 0.5483 - val_loss: 12.4956 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9902/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0903 - categorical_accuracy: 0.5456 - val_loss: 12.5381 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9903/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1162 - categorical_accuracy: 0.5300 - val_loss: 11.8801 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9904/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1182 - categorical_accuracy: 0.5228 - val_loss: 13.0273 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9905/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2204 - categorical_accuracy: 0.4933 - val_loss: 12.0314 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9906/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1207 - categorical_accuracy: 0.5283 - val_loss: 11.8758 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9907/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1275 - categorical_accuracy: 0.5317 - val_loss: 12.4387 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9908/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1451 - categorical_accuracy: 0.5256 - val_loss: 12.2492 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9909/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1754 - categorical_accuracy: 0.5150 - val_loss: 12.8180 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9910/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1559 - categorical_accuracy: 0.5172 - val_loss: 11.8623 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9911/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1706 - categorical_accuracy: 0.5161 - val_loss: 11.6312 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9912/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1933 - categorical_accuracy: 0.5044 - val_loss: 12.9290 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9913/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2148 - categorical_accuracy: 0.4956 - val_loss: 12.2221 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9914/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1247 - categorical_accuracy: 0.5261 - val_loss: 12.4675 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9915/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1106 - categorical_accuracy: 0.5311 - val_loss: 12.2091 - val_categorical_accuracy: 0.1400\n",
      "Epoch 9916/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0956 - categorical_accuracy: 0.5383 - val_loss: 12.2436 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9917/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0861 - categorical_accuracy: 0.5478 - val_loss: 12.2667 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9918/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0796 - categorical_accuracy: 0.5478 - val_loss: 12.2675 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9919/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0856 - categorical_accuracy: 0.5467 - val_loss: 12.3230 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9920/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0810 - categorical_accuracy: 0.5456 - val_loss: 12.4073 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9921/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1045 - categorical_accuracy: 0.5367 - val_loss: 12.4192 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9922/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1001 - categorical_accuracy: 0.5417 - val_loss: 12.3857 - val_categorical_accuracy: 0.1833\n",
      "Epoch 9923/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1036 - categorical_accuracy: 0.5411 - val_loss: 12.4229 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9924/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1037 - categorical_accuracy: 0.5417 - val_loss: 12.5444 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9925/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1284 - categorical_accuracy: 0.5211 - val_loss: 12.4526 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9926/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2367 - categorical_accuracy: 0.4983 - val_loss: 11.8436 - val_categorical_accuracy: 0.1833\n",
      "Epoch 9927/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2013 - categorical_accuracy: 0.5089 - val_loss: 12.2496 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9928/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1757 - categorical_accuracy: 0.5106 - val_loss: 12.1733 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9929/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1907 - categorical_accuracy: 0.4956 - val_loss: 12.4755 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9930/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.2549 - categorical_accuracy: 0.4878 - val_loss: 12.5545 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9931/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1862 - categorical_accuracy: 0.5089 - val_loss: 11.8862 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9932/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1611 - categorical_accuracy: 0.5244 - val_loss: 11.9483 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9933/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1035 - categorical_accuracy: 0.5378 - val_loss: 12.3115 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9934/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1086 - categorical_accuracy: 0.5400 - val_loss: 12.9220 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9935/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1329 - categorical_accuracy: 0.5228 - val_loss: 12.3527 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9936/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1092 - categorical_accuracy: 0.5289 - val_loss: 12.4359 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9937/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1031 - categorical_accuracy: 0.5461 - val_loss: 12.3286 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9938/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0984 - categorical_accuracy: 0.5378 - val_loss: 12.4397 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9939/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0922 - categorical_accuracy: 0.5556 - val_loss: 13.0755 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9940/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1927 - categorical_accuracy: 0.5094 - val_loss: 12.5362 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9941/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1912 - categorical_accuracy: 0.5050 - val_loss: 12.5199 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9942/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1475 - categorical_accuracy: 0.5222 - val_loss: 12.0486 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9943/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0987 - categorical_accuracy: 0.5394 - val_loss: 12.2565 - val_categorical_accuracy: 0.1833\n",
      "Epoch 9944/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0924 - categorical_accuracy: 0.5417 - val_loss: 12.3186 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9945/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0801 - categorical_accuracy: 0.5556 - val_loss: 12.4113 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9946/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0785 - categorical_accuracy: 0.5517 - val_loss: 12.3830 - val_categorical_accuracy: 0.1867\n",
      "Epoch 9947/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0814 - categorical_accuracy: 0.5506 - val_loss: 12.3534 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9948/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1101 - categorical_accuracy: 0.5389 - val_loss: 11.8522 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9949/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1092 - categorical_accuracy: 0.5400 - val_loss: 12.6196 - val_categorical_accuracy: 0.1933\n",
      "Epoch 9950/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0908 - categorical_accuracy: 0.5467 - val_loss: 12.2188 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9951/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0843 - categorical_accuracy: 0.5511 - val_loss: 12.2669 - val_categorical_accuracy: 0.1900\n",
      "Epoch 9952/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1086 - categorical_accuracy: 0.5378 - val_loss: 12.2720 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9953/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1597 - categorical_accuracy: 0.5111 - val_loss: 12.0190 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9954/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.4921 - categorical_accuracy: 0.4522 - val_loss: 12.5209 - val_categorical_accuracy: 0.1500\n",
      "Epoch 9955/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.3805 - categorical_accuracy: 0.4683 - val_loss: 12.5782 - val_categorical_accuracy: 0.1433\n",
      "Epoch 9956/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2428 - categorical_accuracy: 0.4850 - val_loss: 12.4378 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9957/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1435 - categorical_accuracy: 0.5211 - val_loss: 12.4666 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9958/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1090 - categorical_accuracy: 0.5317 - val_loss: 12.3465 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9959/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0960 - categorical_accuracy: 0.5378 - val_loss: 12.3847 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9960/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1016 - categorical_accuracy: 0.5356 - val_loss: 12.3377 - val_categorical_accuracy: 0.1933\n",
      "Epoch 9961/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1122 - categorical_accuracy: 0.5317 - val_loss: 12.4261 - val_categorical_accuracy: 0.1867\n",
      "Epoch 9962/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0909 - categorical_accuracy: 0.5478 - val_loss: 12.4958 - val_categorical_accuracy: 0.1667\n",
      "Epoch 9963/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0856 - categorical_accuracy: 0.5494 - val_loss: 12.5633 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9964/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1099 - categorical_accuracy: 0.5344 - val_loss: 12.5250 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9965/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0868 - categorical_accuracy: 0.5417 - val_loss: 12.3950 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9966/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0816 - categorical_accuracy: 0.5506 - val_loss: 12.5663 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9967/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1041 - categorical_accuracy: 0.5350 - val_loss: 12.4385 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9968/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1663 - categorical_accuracy: 0.5139 - val_loss: 12.8655 - val_categorical_accuracy: 0.1900\n",
      "Epoch 9969/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1639 - categorical_accuracy: 0.5044 - val_loss: 12.1897 - val_categorical_accuracy: 0.1900\n",
      "Epoch 9970/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1411 - categorical_accuracy: 0.5211 - val_loss: 12.0436 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9971/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1129 - categorical_accuracy: 0.5356 - val_loss: 12.4312 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9972/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1025 - categorical_accuracy: 0.5444 - val_loss: 12.9861 - val_categorical_accuracy: 0.1467\n",
      "Epoch 9973/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1170 - categorical_accuracy: 0.5294 - val_loss: 12.4728 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9974/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0866 - categorical_accuracy: 0.5456 - val_loss: 12.5132 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9975/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0800 - categorical_accuracy: 0.5528 - val_loss: 12.6394 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9976/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0818 - categorical_accuracy: 0.5483 - val_loss: 12.9184 - val_categorical_accuracy: 0.1833\n",
      "Epoch 9977/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1406 - categorical_accuracy: 0.5156 - val_loss: 12.4922 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9978/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1019 - categorical_accuracy: 0.5406 - val_loss: 12.5548 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9979/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1222 - categorical_accuracy: 0.5339 - val_loss: 12.3754 - val_categorical_accuracy: 0.1867\n",
      "Epoch 9980/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1067 - categorical_accuracy: 0.5450 - val_loss: 12.4625 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9981/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1165 - categorical_accuracy: 0.5383 - val_loss: 12.7806 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9982/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.1069 - categorical_accuracy: 0.5400 - val_loss: 12.3381 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9983/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1007 - categorical_accuracy: 0.5406 - val_loss: 12.2183 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9984/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.0792 - categorical_accuracy: 0.5561 - val_loss: 12.4438 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9985/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.0955 - categorical_accuracy: 0.5467 - val_loss: 12.6224 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9986/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1237 - categorical_accuracy: 0.5339 - val_loss: 12.4048 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9987/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1328 - categorical_accuracy: 0.5239 - val_loss: 12.2842 - val_categorical_accuracy: 0.1767\n",
      "Epoch 9988/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1372 - categorical_accuracy: 0.5161 - val_loss: 12.6672 - val_categorical_accuracy: 0.1833\n",
      "Epoch 9989/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1312 - categorical_accuracy: 0.5222 - val_loss: 12.3974 - val_categorical_accuracy: 0.1700\n",
      "Epoch 9990/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1051 - categorical_accuracy: 0.5467 - val_loss: 13.0116 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9991/10000\n",
      "57/57 [==============================] - 1s 11ms/step - loss: 1.0964 - categorical_accuracy: 0.5361 - val_loss: 12.4817 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9992/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1304 - categorical_accuracy: 0.5367 - val_loss: 11.3089 - val_categorical_accuracy: 0.1567\n",
      "Epoch 9993/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.3470 - categorical_accuracy: 0.4656 - val_loss: 12.1415 - val_categorical_accuracy: 0.1633\n",
      "Epoch 9994/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.2257 - categorical_accuracy: 0.4939 - val_loss: 12.6544 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9995/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1355 - categorical_accuracy: 0.5233 - val_loss: 12.1048 - val_categorical_accuracy: 0.1800\n",
      "Epoch 9996/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1147 - categorical_accuracy: 0.5294 - val_loss: 12.5691 - val_categorical_accuracy: 0.1733\n",
      "Epoch 9997/10000\n",
      "57/57 [==============================] - 1s 10ms/step - loss: 1.1119 - categorical_accuracy: 0.5400 - val_loss: 12.6622 - val_categorical_accuracy: 0.1600\n",
      "Epoch 9998/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1215 - categorical_accuracy: 0.5344 - val_loss: 12.6473 - val_categorical_accuracy: 0.1533\n",
      "Epoch 9999/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1044 - categorical_accuracy: 0.5450 - val_loss: 12.8459 - val_categorical_accuracy: 0.1600\n",
      "Epoch 10000/10000\n",
      "57/57 [==============================] - 1s 9ms/step - loss: 1.1067 - categorical_accuracy: 0.5428 - val_loss: 12.3926 - val_categorical_accuracy: 0.1533\n"
     ]
    }
   ],
   "source": [
    "# TWEAK THE NUMBER OF IMAGES TO GET BETTER RESULTS\n",
    "# Specify the number of epochs\n",
    "num_epochs = 10000 \n",
    "\n",
    "# Train the model\n",
    "history = conv_model.fit(train_dataset, epochs=num_epochs, validation_data=test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Nm9s2JxiJAK"
   },
   "source": [
    "PLOT YOUR RESULTS HERE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "id": "cssoYMXsbi4X",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "numbers = list(range(1, 1001))\n",
    "random_array = random.sample(numbers, 10)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 7))  # Increase the width for better visualization\n",
    "\n",
    "for i, idx in enumerate(random_array):\n",
    "    img_resized = cv2.resize(x_test[idx], (64, 64))\n",
    "    y_pred = np.argmax(conv_model.predict(np.expand_dims(x_test[idx], axis=0), verbose=0))\n",
    "    ax = fig.add_subplot(1, 10, i + 1)\n",
    "    ax.imshow(img_resized, cmap='gray')\n",
    "    ax.axis('off')\n",
    "    ax.set_title(\"pred = \" + str(y_pred))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}