Sign_lang_main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sign Language using MediaPipe and LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in ./.conda/lib/python3.11/site-packages (2.15.0)\n",
      "Requirement already satisfied: opencv-python in ./.conda/lib/python3.11/site-packages (4.9.0.80)\n",
      "Requirement already satisfied: mediapipe in ./.conda/lib/python3.11/site-packages (0.10.9)\n",
      "Requirement already satisfied: scikit-learn in ./.conda/lib/python3.11/site-packages (1.4.0)\n",
      "Requirement already satisfied: matplotlib in ./.conda/lib/python3.11/site-packages (3.8.2)\n",
      "Requirement already satisfied: tensorflow-macos==2.15.0 in ./.conda/lib/python3.11/site-packages (from tensorflow) (2.15.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (2.1.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers>=23.5.26 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==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 ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (0.5.4)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (0.2.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (3.10.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (16.0.6)\n",
      "Requirement already satisfied: ml-dtypes~=0.2.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (0.2.0)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (1.26.3)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (3.3.0)\n",
      "Requirement already satisfied: packaging in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (23.2)\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 ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (3.20.3)\n",
      "Requirement already satisfied: setuptools in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (68.2.2)\n",
      "Requirement already satisfied: six>=1.12.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (1.16.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (2.4.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (4.9.0)\n",
      "Requirement already satisfied: wrapt<1.15,>=1.11.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (1.14.1)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (0.34.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (1.60.1)\n",
      "Requirement already satisfied: tensorboard<2.16,>=2.15 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (2.15.1)\n",
      "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (2.15.0)\n",
      "Requirement already satisfied: keras<2.16,>=2.15.0 in ./.conda/lib/python3.11/site-packages (from tensorflow-macos==2.15.0->tensorflow) (2.15.0)\n",
      "Requirement already satisfied: attrs>=19.1.0 in ./.conda/lib/python3.11/site-packages (from mediapipe) (23.2.0)\n",
      "Requirement already satisfied: opencv-contrib-python in ./.conda/lib/python3.11/site-packages (from mediapipe) (4.9.0.80)\n",
      "Requirement already satisfied: sounddevice>=0.4.4 in ./.conda/lib/python3.11/site-packages (from mediapipe) (0.4.6)\n",
      "Requirement already satisfied: scipy>=1.6.0 in ./.conda/lib/python3.11/site-packages (from scikit-learn) (1.12.0)\n",
      "Requirement already satisfied: joblib>=1.2.0 in ./.conda/lib/python3.11/site-packages (from scikit-learn) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in ./.conda/lib/python3.11/site-packages (from scikit-learn) (3.2.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in ./.conda/lib/python3.11/site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in ./.conda/lib/python3.11/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in ./.conda/lib/python3.11/site-packages (from matplotlib) (4.47.2)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in ./.conda/lib/python3.11/site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: pillow>=8 in ./.conda/lib/python3.11/site-packages (from matplotlib) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in ./.conda/lib/python3.11/site-packages (from matplotlib) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in ./.conda/lib/python3.11/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: CFFI>=1.0 in ./.conda/lib/python3.11/site-packages (from sounddevice>=0.4.4->mediapipe) (1.16.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in ./.conda/lib/python3.11/site-packages (from astunparse>=1.6.0->tensorflow-macos==2.15.0->tensorflow) (0.41.2)\n",
      "Requirement already satisfied: pycparser in ./.conda/lib/python3.11/site-packages (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe) (2.21)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in ./.conda/lib/python3.11/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (2.27.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in ./.conda/lib/python3.11/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (1.2.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in ./.conda/lib/python3.11/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (3.5.2)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in ./.conda/lib/python3.11/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (2.31.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in ./.conda/lib/python3.11/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in ./.conda/lib/python3.11/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (3.0.1)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in ./.conda/lib/python3.11/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (5.3.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in ./.conda/lib/python3.11/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (0.3.0)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in ./.conda/lib/python3.11/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (4.9)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in ./.conda/lib/python3.11/site-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in ./.conda/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in ./.conda/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in ./.conda/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (2.2.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in ./.conda/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (2024.2.2)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in ./.conda/lib/python3.11/site-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (2.1.5)\n",
      "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in ./.conda/lib/python3.11/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (0.5.1)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in ./.conda/lib/python3.11/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow) (3.2.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow opencv-python mediapipe scikit-learn matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "import time\n",
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_holistic = mp.solutions.mp_holistic\n",
    "mp_drawing = mp.solutions.drawing_utils \n",
    "\n",
    "def mediapipe_detection(image, model):\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) \n",
    "    image.flags.writeable = False                  \n",
    "    results = model.process(image)                \n",
    "    image.flags.writeable = True                   \n",
    "    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) \n",
    "    return image, results\n",
    "\n",
    "def draw_landmarks(image, results):\n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION) \n",
    "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) \n",
    "    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) \n",
    "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_styled_landmarks(image, results):\n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, \n",
    "                             mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1), \n",
    "                             mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "                             ) \n",
    "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,\n",
    "                             mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4), \n",
    "                             mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n",
    "                             ) \n",
    "    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                             mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4), \n",
    "                             mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
    "                             ) \n",
    "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                             mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4), \n",
    "                             mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n",
    "                             ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1707062735.362418       1 gl_context.cc:344] GL version: 2.1 (2.1 Metal - 88), renderer: Apple M2 Pro\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "        draw_styled_landmarks(image, results)\n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "  \n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_landmarks(frame, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2f5c4bb50>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "pose = []\n",
    "for res in results.pose_landmarks.landmark:\n",
    "    test = np.array([res.x, res.y, res.z, res.visibility])\n",
    "    pose.append(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(132)\n",
    "face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(1404)\n",
    "lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
    "rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "if results.face_landmarks:\n",
    "    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten()\n",
    "else:\n",
    "    face = np.zeros(1404)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_keypoints(results):\n",
    "    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)\n",
    "    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)\n",
    "    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
    "    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)\n",
    "    return np.concatenate([pose, face, lh, rh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_test = extract_keypoints(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.52246398,  0.36239022, -0.68394452, ...,  0.        ,\n",
       "        0.        ,  0.        ])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1662"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "468*3+33*4+21*3+21*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('0', result_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.52246398,  0.36239022, -0.68394452, ...,  0.        ,\n",
       "        0.        ,  0.        ])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load('0.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = os.path.join('MP_Data') \n",
    "actions = np.array(['hello', 'call', 'doctor'])\n",
    "no_sequences = 30\n",
    "sequence_length = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "for action in actions: \n",
    "    for sequence in range(no_sequences):\n",
    "        try: \n",
    "            os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1707062740.194703       1 gl_context.cc:344] GL version: 2.1 (2.1 Metal - 88), renderer: Apple M2 Pro\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    for action in actions:\n",
    "        for sequence in range(no_sequences):\n",
    "            for frame_num in range(sequence_length):\n",
    "                ret, frame = cap.read()\n",
    "\n",
    "                image, results = mediapipe_detection(frame, holistic)\n",
    "                draw_styled_landmarks(image, results)\n",
    "                \n",
    "                if frame_num == 0: \n",
    "                    cv2.putText(image, 'STARTING COLLECTION', (120,200), \n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255, 0), 4, cv2.LINE_AA)\n",
    "                    cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), \n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n",
    "                    cv2.imshow('OpenCV Feed', image)\n",
    "                    cv2.waitKey(2000)\n",
    "                else: \n",
    "                    cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), \n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n",
    "                    cv2.imshow('OpenCV Feed', image)\n",
    "                \n",
    "                keypoints = extract_keypoints(results)\n",
    "                npy_path = os.path.join(DATA_PATH, action, str(sequence), str(frame_num))\n",
    "                np.save(npy_path, keypoints)\n",
    "                if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "                    break\n",
    "                    \n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = {label:num for num, label in enumerate(actions)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences, labels = [], []\n",
    "for action in actions:\n",
    "    for sequence in range(no_sequences):\n",
    "        window = []\n",
    "        for frame_num in range(sequence_length):\n",
    "            res = np.load(os.path.join(DATA_PATH, action, str(sequence), \"{}.npy\".format(frame_num)))\n",
    "            window.append(res)\n",
    "        sequences.append(window)\n",
    "        labels.append(label_map[action])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "from keras.models import load_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = os.path.join('Logs')\n",
    "tb_callback = TensorBoard(log_dir=log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30,1662)))\n",
    "model.add(LSTM(128, return_sequences=True, activation='relu'))\n",
    "model.add(LSTM(64, return_sequences=False, activation='relu'))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(actions.shape[0], activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = [.7, 0.2, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(res)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "3/3 [==============================] - 2s 27ms/step - loss: 1.1385 - categorical_accuracy: 0.3176\n",
      "Epoch 2/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 2.0410 - categorical_accuracy: 0.2941\n",
      "Epoch 3/2000\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 1.1310 - categorical_accuracy: 0.2235\n",
      "Epoch 4/2000\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 1.0039 - categorical_accuracy: 0.5529\n",
      "Epoch 5/2000\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 1.9087 - categorical_accuracy: 0.4353\n",
      "Epoch 6/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.1544 - categorical_accuracy: 0.3059\n",
      "Epoch 7/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0995 - categorical_accuracy: 0.3529\n",
      "Epoch 8/2000\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 1.0961 - categorical_accuracy: 0.4118\n",
      "Epoch 9/2000\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 1.0924 - categorical_accuracy: 0.6824\n",
      "Epoch 10/2000\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 1.0905 - categorical_accuracy: 0.6706\n",
      "Epoch 11/2000\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 1.0869 - categorical_accuracy: 0.4588\n",
      "Epoch 12/2000\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 1.0827 - categorical_accuracy: 0.3412\n",
      "Epoch 13/2000\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 1.0763 - categorical_accuracy: 0.3412\n",
      "Epoch 14/2000\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 1.0624 - categorical_accuracy: 0.6118\n",
      "Epoch 15/2000\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 1.0391 - categorical_accuracy: 0.5176\n",
      "Epoch 16/2000\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.9958 - categorical_accuracy: 0.6118\n",
      "Epoch 17/2000\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 1.0318 - categorical_accuracy: 0.4588\n",
      "Epoch 18/2000\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 0.9953 - categorical_accuracy: 0.4353\n",
      "Epoch 19/2000\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 1.0713 - categorical_accuracy: 0.5294\n",
      "Epoch 20/2000\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 1.0479 - categorical_accuracy: 0.5059\n",
      "Epoch 21/2000\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 1.0434 - categorical_accuracy: 0.3412\n",
      "Epoch 22/2000\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 0.9758 - categorical_accuracy: 0.3412\n",
      "Epoch 23/2000\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.9837 - categorical_accuracy: 0.4235\n",
      "Epoch 24/2000\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.8650 - categorical_accuracy: 0.6353\n",
      "Epoch 25/2000\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 0.9823 - categorical_accuracy: 0.5529\n",
      "Epoch 26/2000\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 0.9684 - categorical_accuracy: 0.4235\n",
      "Epoch 27/2000\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.7411 - categorical_accuracy: 0.6706\n",
      "Epoch 28/2000\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 0.6930 - categorical_accuracy: 0.6824\n",
      "Epoch 29/2000\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 0.4854 - categorical_accuracy: 0.6706\n",
      "Epoch 30/2000\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 1.7249 - categorical_accuracy: 0.6353\n",
      "Epoch 31/2000\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 0.8865 - categorical_accuracy: 0.4706\n",
      "Epoch 32/2000\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 0.9522 - categorical_accuracy: 0.3412\n",
      "Epoch 33/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.9040 - categorical_accuracy: 0.3412\n",
      "Epoch 34/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.8645 - categorical_accuracy: 0.3412\n",
      "Epoch 35/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.8095 - categorical_accuracy: 0.3412\n",
      "Epoch 36/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.7669 - categorical_accuracy: 0.3412\n",
      "Epoch 37/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.7666 - categorical_accuracy: 0.5176\n",
      "Epoch 38/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.8414 - categorical_accuracy: 0.5059\n",
      "Epoch 39/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0918 - categorical_accuracy: 0.3176\n",
      "Epoch 40/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0968 - categorical_accuracy: 0.3176\n",
      "Epoch 41/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0983 - categorical_accuracy: 0.3176\n",
      "Epoch 42/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0986 - categorical_accuracy: 0.3176\n",
      "Epoch 43/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0983 - categorical_accuracy: 0.3176\n",
      "Epoch 44/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0976 - categorical_accuracy: 0.3176\n",
      "Epoch 45/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0973 - categorical_accuracy: 0.3176\n",
      "Epoch 46/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0968 - categorical_accuracy: 0.3176\n",
      "Epoch 47/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.0959 - categorical_accuracy: 0.3176\n",
      "Epoch 48/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0952 - categorical_accuracy: 0.5294\n",
      "Epoch 49/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.0946 - categorical_accuracy: 0.3412\n",
      "Epoch 50/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0941 - categorical_accuracy: 0.3412\n",
      "Epoch 51/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0934 - categorical_accuracy: 0.3412\n",
      "Epoch 52/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0926 - categorical_accuracy: 0.3412\n",
      "Epoch 53/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0918 - categorical_accuracy: 0.3412\n",
      "Epoch 54/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0909 - categorical_accuracy: 0.4000\n",
      "Epoch 55/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0898 - categorical_accuracy: 0.5647\n",
      "Epoch 56/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0884 - categorical_accuracy: 0.6588\n",
      "Epoch 57/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0865 - categorical_accuracy: 0.6706\n",
      "Epoch 58/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0846 - categorical_accuracy: 0.7059\n",
      "Epoch 59/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0820 - categorical_accuracy: 0.8941\n",
      "Epoch 60/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0786 - categorical_accuracy: 0.9647\n",
      "Epoch 61/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0731 - categorical_accuracy: 0.9882\n",
      "Epoch 62/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0648 - categorical_accuracy: 0.7765\n",
      "Epoch 63/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0530 - categorical_accuracy: 0.5294\n",
      "Epoch 64/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0319 - categorical_accuracy: 0.3529\n",
      "Epoch 65/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.9901 - categorical_accuracy: 0.3412\n",
      "Epoch 66/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.8958 - categorical_accuracy: 0.3412\n",
      "Epoch 67/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.8199 - categorical_accuracy: 0.8471\n",
      "Epoch 68/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.7970 - categorical_accuracy: 0.6588\n",
      "Epoch 69/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.9248 - categorical_accuracy: 0.6235\n",
      "Epoch 70/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.7069 - categorical_accuracy: 0.7647\n",
      "Epoch 71/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.7025 - categorical_accuracy: 0.6118\n",
      "Epoch 72/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.7060 - categorical_accuracy: 0.6118\n",
      "Epoch 73/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.5957 - categorical_accuracy: 0.6588\n",
      "Epoch 74/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0132 - categorical_accuracy: 0.5529\n",
      "Epoch 75/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.2742 - categorical_accuracy: 0.3412\n",
      "Epoch 76/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.1289 - categorical_accuracy: 0.3412\n",
      "Epoch 77/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1125 - categorical_accuracy: 0.3412\n",
      "Epoch 78/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.1055 - categorical_accuracy: 0.3412\n",
      "Epoch 79/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1052 - categorical_accuracy: 0.3412\n",
      "Epoch 80/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.1037 - categorical_accuracy: 0.3412\n",
      "Epoch 81/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.1029 - categorical_accuracy: 0.3412\n",
      "Epoch 82/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.1024 - categorical_accuracy: 0.3412\n",
      "Epoch 83/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.1015 - categorical_accuracy: 0.3412\n",
      "Epoch 84/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.1013 - categorical_accuracy: 0.3412\n",
      "Epoch 85/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1007 - categorical_accuracy: 0.3412\n",
      "Epoch 86/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.1004 - categorical_accuracy: 0.3412\n",
      "Epoch 87/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0999 - categorical_accuracy: 0.3412\n",
      "Epoch 88/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0995 - categorical_accuracy: 0.3412\n",
      "Epoch 89/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0990 - categorical_accuracy: 0.3412\n",
      "Epoch 90/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0989 - categorical_accuracy: 0.3412\n",
      "Epoch 91/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0986 - categorical_accuracy: 0.3412\n",
      "Epoch 92/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0984 - categorical_accuracy: 0.3412\n",
      "Epoch 93/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0979 - categorical_accuracy: 0.3412\n",
      "Epoch 94/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0976 - categorical_accuracy: 0.3412\n",
      "Epoch 95/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0975 - categorical_accuracy: 0.3412\n",
      "Epoch 96/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0968 - categorical_accuracy: 0.3412\n",
      "Epoch 97/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0962 - categorical_accuracy: 0.3412\n",
      "Epoch 98/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0954 - categorical_accuracy: 0.3412\n",
      "Epoch 99/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0949 - categorical_accuracy: 0.3412\n",
      "Epoch 100/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0943 - categorical_accuracy: 0.3412\n",
      "Epoch 101/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0932 - categorical_accuracy: 0.3412\n",
      "Epoch 102/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0919 - categorical_accuracy: 0.3412\n",
      "Epoch 103/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0903 - categorical_accuracy: 0.4235\n",
      "Epoch 104/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0881 - categorical_accuracy: 0.6353\n",
      "Epoch 105/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0847 - categorical_accuracy: 0.6588\n",
      "Epoch 106/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0793 - categorical_accuracy: 0.6824\n",
      "Epoch 107/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0698 - categorical_accuracy: 0.6824\n",
      "Epoch 108/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 1.0556 - categorical_accuracy: 0.6706\n",
      "Epoch 109/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0181 - categorical_accuracy: 0.6235\n",
      "Epoch 110/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1327 - categorical_accuracy: 0.4000\n",
      "Epoch 111/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1082 - categorical_accuracy: 0.3412\n",
      "Epoch 112/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.1108 - categorical_accuracy: 0.3412\n",
      "Epoch 113/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1073 - categorical_accuracy: 0.3412\n",
      "Epoch 114/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1070 - categorical_accuracy: 0.3412\n",
      "Epoch 115/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1093 - categorical_accuracy: 0.3412\n",
      "Epoch 116/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.1052 - categorical_accuracy: 0.3412\n",
      "Epoch 117/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0986 - categorical_accuracy: 0.3412\n",
      "Epoch 118/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0896 - categorical_accuracy: 0.3412\n",
      "Epoch 119/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 1.0914 - categorical_accuracy: 0.1647\n",
      "Epoch 120/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0692 - categorical_accuracy: 0.3412\n",
      "Epoch 121/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0409 - categorical_accuracy: 0.3412\n",
      "Epoch 122/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.9742 - categorical_accuracy: 0.6353\n",
      "Epoch 123/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.8646 - categorical_accuracy: 0.4706\n",
      "Epoch 124/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.8389 - categorical_accuracy: 0.4824\n",
      "Epoch 125/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.1958 - categorical_accuracy: 0.4471\n",
      "Epoch 126/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.8382 - categorical_accuracy: 0.6824\n",
      "Epoch 127/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 1.0032 - categorical_accuracy: 0.4000\n",
      "Epoch 128/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.8451 - categorical_accuracy: 0.6471\n",
      "Epoch 129/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.8202 - categorical_accuracy: 0.5882\n",
      "Epoch 130/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.7056 - categorical_accuracy: 0.7059\n",
      "Epoch 131/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.6135 - categorical_accuracy: 0.6824\n",
      "Epoch 132/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.5096 - categorical_accuracy: 0.8000\n",
      "Epoch 133/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.4598 - categorical_accuracy: 0.7529\n",
      "Epoch 134/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.4913 - categorical_accuracy: 0.5882\n",
      "Epoch 135/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.6771 - categorical_accuracy: 0.5882\n",
      "Epoch 136/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.6458 - categorical_accuracy: 0.5647\n",
      "Epoch 137/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.6520 - categorical_accuracy: 0.7647\n",
      "Epoch 138/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.7123 - categorical_accuracy: 0.6471\n",
      "Epoch 139/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.5611 - categorical_accuracy: 0.7412\n",
      "Epoch 140/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.5348 - categorical_accuracy: 0.5176\n",
      "Epoch 141/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 3.1517 - categorical_accuracy: 0.2000\n",
      "Epoch 142/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 1.6408 - categorical_accuracy: 0.4353\n",
      "Epoch 143/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.2012 - categorical_accuracy: 0.3412\n",
      "Epoch 144/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.1627 - categorical_accuracy: 0.3412\n",
      "Epoch 145/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.0761 - categorical_accuracy: 0.6118\n",
      "Epoch 146/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.0050 - categorical_accuracy: 0.4588\n",
      "Epoch 147/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.9537 - categorical_accuracy: 0.3412\n",
      "Epoch 148/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.9163 - categorical_accuracy: 0.3412\n",
      "Epoch 149/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.8674 - categorical_accuracy: 0.4353\n",
      "Epoch 150/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.8109 - categorical_accuracy: 0.6588\n",
      "Epoch 151/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.7387 - categorical_accuracy: 0.6588\n",
      "Epoch 152/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.7450 - categorical_accuracy: 0.6941\n",
      "Epoch 153/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.0207 - categorical_accuracy: 0.4941\n",
      "Epoch 154/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.1081 - categorical_accuracy: 0.3412\n",
      "Epoch 155/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1040 - categorical_accuracy: 0.3412\n",
      "Epoch 156/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1038 - categorical_accuracy: 0.3412\n",
      "Epoch 157/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1010 - categorical_accuracy: 0.3412\n",
      "Epoch 158/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0974 - categorical_accuracy: 0.3412\n",
      "Epoch 159/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.0959 - categorical_accuracy: 0.3412\n",
      "Epoch 160/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0930 - categorical_accuracy: 0.3412\n",
      "Epoch 161/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0898 - categorical_accuracy: 0.3412\n",
      "Epoch 162/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0869 - categorical_accuracy: 0.3412\n",
      "Epoch 163/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0798 - categorical_accuracy: 0.3412\n",
      "Epoch 164/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0718 - categorical_accuracy: 0.3412\n",
      "Epoch 165/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4465 - categorical_accuracy: 0.3882\n",
      "Epoch 166/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 1.0999 - categorical_accuracy: 0.3412\n",
      "Epoch 167/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.0665 - categorical_accuracy: 0.3412\n",
      "Epoch 168/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.8731 - categorical_accuracy: 0.7412\n",
      "Epoch 169/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.8646 - categorical_accuracy: 0.4824\n",
      "Epoch 170/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.7931 - categorical_accuracy: 0.6000\n",
      "Epoch 171/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.8582 - categorical_accuracy: 0.6706\n",
      "Epoch 172/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.8338 - categorical_accuracy: 0.7412\n",
      "Epoch 173/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.7433 - categorical_accuracy: 0.6471\n",
      "Epoch 174/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.7668 - categorical_accuracy: 0.6353\n",
      "Epoch 175/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.7032 - categorical_accuracy: 0.6706\n",
      "Epoch 176/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.6833 - categorical_accuracy: 0.7412\n",
      "Epoch 177/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.6478 - categorical_accuracy: 0.6588\n",
      "Epoch 178/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.5991 - categorical_accuracy: 0.8118\n",
      "Epoch 179/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.5483 - categorical_accuracy: 0.8235\n",
      "Epoch 180/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 0.4869 - categorical_accuracy: 0.9059\n",
      "Epoch 181/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.4160 - categorical_accuracy: 0.9412\n",
      "Epoch 182/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.6519 - categorical_accuracy: 0.6941\n",
      "Epoch 183/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.6790 - categorical_accuracy: 0.7176\n",
      "Epoch 184/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.6159 - categorical_accuracy: 0.7176\n",
      "Epoch 185/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.8073 - categorical_accuracy: 0.7412\n",
      "Epoch 186/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.8211 - categorical_accuracy: 0.4000\n",
      "Epoch 187/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.4852 - categorical_accuracy: 0.8000\n",
      "Epoch 188/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.3850 - categorical_accuracy: 0.8118\n",
      "Epoch 189/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.3512 - categorical_accuracy: 0.8941\n",
      "Epoch 190/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.2221 - categorical_accuracy: 0.9412\n",
      "Epoch 191/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.1318 - categorical_accuracy: 0.9882\n",
      "Epoch 192/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 12.8288 - categorical_accuracy: 0.5176\n",
      "Epoch 193/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 16.6265 - categorical_accuracy: 0.3412\n",
      "Epoch 194/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 6.1591 - categorical_accuracy: 0.2941\n",
      "Epoch 195/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.1942 - categorical_accuracy: 0.4000\n",
      "Epoch 196/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.3777 - categorical_accuracy: 0.2588\n",
      "Epoch 197/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.2228 - categorical_accuracy: 0.4118\n",
      "Epoch 198/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0871 - categorical_accuracy: 0.2824\n",
      "Epoch 199/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.1010 - categorical_accuracy: 0.5765\n",
      "Epoch 200/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0824 - categorical_accuracy: 0.3412\n",
      "Epoch 201/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.0731 - categorical_accuracy: 0.3412\n",
      "Epoch 202/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0496 - categorical_accuracy: 0.3412\n",
      "Epoch 203/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0324 - categorical_accuracy: 0.3647\n",
      "Epoch 204/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.0170 - categorical_accuracy: 0.5765\n",
      "Epoch 205/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.9940 - categorical_accuracy: 0.5882\n",
      "Epoch 206/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.9571 - categorical_accuracy: 0.6471\n",
      "Epoch 207/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.9009 - categorical_accuracy: 0.6235\n",
      "Epoch 208/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.7759 - categorical_accuracy: 0.6588\n",
      "Epoch 209/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.8977 - categorical_accuracy: 0.4824\n",
      "Epoch 210/2000\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.7640 - categorical_accuracy: 0.6118\n",
      "Epoch 211/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.7559 - categorical_accuracy: 0.6353\n",
      "Epoch 212/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.6708 - categorical_accuracy: 0.6706\n",
      "Epoch 213/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.6631 - categorical_accuracy: 0.7059\n",
      "Epoch 214/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.6119 - categorical_accuracy: 0.6471\n",
      "Epoch 215/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.6069 - categorical_accuracy: 0.7529\n",
      "Epoch 216/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.5731 - categorical_accuracy: 0.6941\n",
      "Epoch 217/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.6477 - categorical_accuracy: 0.5647\n",
      "Epoch 218/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0498 - categorical_accuracy: 0.4824\n",
      "Epoch 219/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.8558 - categorical_accuracy: 0.5059\n",
      "Epoch 220/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.5924 - categorical_accuracy: 0.7412\n",
      "Epoch 221/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.6917 - categorical_accuracy: 0.6235\n",
      "Epoch 222/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.5390 - categorical_accuracy: 0.6941\n",
      "Epoch 223/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.5092 - categorical_accuracy: 0.7647\n",
      "Epoch 224/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.5499 - categorical_accuracy: 0.6588\n",
      "Epoch 225/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.4726 - categorical_accuracy: 0.7647\n",
      "Epoch 226/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.4684 - categorical_accuracy: 0.7294\n",
      "Epoch 227/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.4596 - categorical_accuracy: 0.7176\n",
      "Epoch 228/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 0.4655 - categorical_accuracy: 0.6588\n",
      "Epoch 229/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.4539 - categorical_accuracy: 0.7294\n",
      "Epoch 230/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.4228 - categorical_accuracy: 0.7294\n",
      "Epoch 231/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.4080 - categorical_accuracy: 0.8235\n",
      "Epoch 232/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.4054 - categorical_accuracy: 0.7647\n",
      "Epoch 233/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.3982 - categorical_accuracy: 0.7765\n",
      "Epoch 234/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.3744 - categorical_accuracy: 0.8000\n",
      "Epoch 235/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.4179 - categorical_accuracy: 0.8235\n",
      "Epoch 236/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.9457 - categorical_accuracy: 0.6118\n",
      "Epoch 237/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.7016 - categorical_accuracy: 0.6118\n",
      "Epoch 238/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.8675 - categorical_accuracy: 0.5765\n",
      "Epoch 239/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.5451 - categorical_accuracy: 0.6706\n",
      "Epoch 240/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.6216 - categorical_accuracy: 0.6118\n",
      "Epoch 241/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.5184 - categorical_accuracy: 0.6706\n",
      "Epoch 242/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.5074 - categorical_accuracy: 0.8706\n",
      "Epoch 243/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.4576 - categorical_accuracy: 0.8118\n",
      "Epoch 244/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.4410 - categorical_accuracy: 0.6941\n",
      "Epoch 245/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.4149 - categorical_accuracy: 0.7765\n",
      "Epoch 246/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.3917 - categorical_accuracy: 0.8471\n",
      "Epoch 247/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.3698 - categorical_accuracy: 0.8353\n",
      "Epoch 248/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.3484 - categorical_accuracy: 0.8353\n",
      "Epoch 249/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.3485 - categorical_accuracy: 0.8000\n",
      "Epoch 250/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.3468 - categorical_accuracy: 0.8353\n",
      "Epoch 251/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.6191 - categorical_accuracy: 0.6706\n",
      "Epoch 252/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.3599 - categorical_accuracy: 0.8118\n",
      "Epoch 253/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.4137 - categorical_accuracy: 0.7647\n",
      "Epoch 254/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.3829 - categorical_accuracy: 0.8471\n",
      "Epoch 255/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.3350 - categorical_accuracy: 0.8235\n",
      "Epoch 256/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.2860 - categorical_accuracy: 0.9294\n",
      "Epoch 257/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.2628 - categorical_accuracy: 0.8941\n",
      "Epoch 258/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.5929 - categorical_accuracy: 0.7176\n",
      "Epoch 259/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.7604 - categorical_accuracy: 0.6235\n",
      "Epoch 260/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.4870 - categorical_accuracy: 0.7294\n",
      "Epoch 261/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.4652 - categorical_accuracy: 0.6706\n",
      "Epoch 262/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.6344 - categorical_accuracy: 0.7882\n",
      "Epoch 263/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.5658 - categorical_accuracy: 0.8235\n",
      "Epoch 264/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.4203 - categorical_accuracy: 0.8588\n",
      "Epoch 265/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.4267 - categorical_accuracy: 0.8118\n",
      "Epoch 266/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.3766 - categorical_accuracy: 0.7765\n",
      "Epoch 267/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.3712 - categorical_accuracy: 0.7882\n",
      "Epoch 268/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.3247 - categorical_accuracy: 0.9176\n",
      "Epoch 269/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.3197 - categorical_accuracy: 0.8471\n",
      "Epoch 270/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.2717 - categorical_accuracy: 0.8941\n",
      "Epoch 271/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.3018 - categorical_accuracy: 0.8471\n",
      "Epoch 272/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.2215 - categorical_accuracy: 0.9647\n",
      "Epoch 273/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.1904 - categorical_accuracy: 0.9529\n",
      "Epoch 274/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.2184 - categorical_accuracy: 0.8824\n",
      "Epoch 275/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.1556 - categorical_accuracy: 0.9529\n",
      "Epoch 276/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.1666 - categorical_accuracy: 0.9176\n",
      "Epoch 277/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.4297 - categorical_accuracy: 0.8353\n",
      "Epoch 278/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.4522 - categorical_accuracy: 0.7882\n",
      "Epoch 279/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.5086 - categorical_accuracy: 0.7294\n",
      "Epoch 280/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.3379 - categorical_accuracy: 0.8235\n",
      "Epoch 281/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.4505 - categorical_accuracy: 0.6824\n",
      "Epoch 282/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 0.3667 - categorical_accuracy: 0.8941\n",
      "Epoch 283/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.4076 - categorical_accuracy: 0.7882\n",
      "Epoch 284/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 0.2978 - categorical_accuracy: 0.9647\n",
      "Epoch 285/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.3309 - categorical_accuracy: 0.8824\n",
      "Epoch 286/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.2518 - categorical_accuracy: 0.9765\n",
      "Epoch 287/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.2501 - categorical_accuracy: 0.8706\n",
      "Epoch 288/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.1803 - categorical_accuracy: 0.9882\n",
      "Epoch 289/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.1494 - categorical_accuracy: 0.9882\n",
      "Epoch 290/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.1284 - categorical_accuracy: 0.9882\n",
      "Epoch 291/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.1029 - categorical_accuracy: 1.0000\n",
      "Epoch 292/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0696 - categorical_accuracy: 1.0000\n",
      "Epoch 293/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.0571 - categorical_accuracy: 1.0000\n",
      "Epoch 294/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.0478 - categorical_accuracy: 1.0000\n",
      "Epoch 295/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0660 - categorical_accuracy: 0.9882\n",
      "Epoch 296/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.0249 - categorical_accuracy: 1.0000\n",
      "Epoch 297/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.0192 - categorical_accuracy: 1.0000\n",
      "Epoch 298/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0243 - categorical_accuracy: 0.9882\n",
      "Epoch 299/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.0211 - categorical_accuracy: 1.0000\n",
      "Epoch 300/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0147 - categorical_accuracy: 1.0000\n",
      "Epoch 301/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0480 - categorical_accuracy: 0.9882\n",
      "Epoch 302/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.0151 - categorical_accuracy: 1.0000\n",
      "Epoch 303/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0090 - categorical_accuracy: 1.0000\n",
      "Epoch 304/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.0062 - categorical_accuracy: 1.0000\n",
      "Epoch 305/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.0067 - categorical_accuracy: 1.0000\n",
      "Epoch 306/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 0.0073 - categorical_accuracy: 1.0000\n",
      "Epoch 307/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.0044 - categorical_accuracy: 1.0000\n",
      "Epoch 308/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.0037 - categorical_accuracy: 1.0000\n",
      "Epoch 309/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0041 - categorical_accuracy: 1.0000\n",
      "Epoch 310/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.0032 - categorical_accuracy: 1.0000\n",
      "Epoch 311/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.0023 - categorical_accuracy: 1.0000\n",
      "Epoch 312/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0031 - categorical_accuracy: 1.0000\n",
      "Epoch 313/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0021 - categorical_accuracy: 1.0000\n",
      "Epoch 314/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0029 - categorical_accuracy: 1.0000\n",
      "Epoch 315/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0029 - categorical_accuracy: 1.0000\n",
      "Epoch 316/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0016 - categorical_accuracy: 1.0000\n",
      "Epoch 317/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.0026 - categorical_accuracy: 1.0000\n",
      "Epoch 318/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.0091 - categorical_accuracy: 0.9882\n",
      "Epoch 319/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.0886 - categorical_accuracy: 0.7176\n",
      "Epoch 320/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.3661 - categorical_accuracy: 0.3882\n",
      "Epoch 321/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.6703 - categorical_accuracy: 0.3294\n",
      "Epoch 322/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0527 - categorical_accuracy: 0.5294\n",
      "Epoch 323/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.4875 - categorical_accuracy: 0.3882\n",
      "Epoch 324/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.4291 - categorical_accuracy: 0.3647\n",
      "Epoch 325/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.1764 - categorical_accuracy: 0.2588\n",
      "Epoch 326/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0642 - categorical_accuracy: 0.4706\n",
      "Epoch 327/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0678 - categorical_accuracy: 0.4824\n",
      "Epoch 328/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0318 - categorical_accuracy: 0.3412\n",
      "Epoch 329/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.9894 - categorical_accuracy: 0.3412\n",
      "Epoch 330/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.9086 - categorical_accuracy: 0.4471\n",
      "Epoch 331/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.8534 - categorical_accuracy: 0.5294\n",
      "Epoch 332/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.8160 - categorical_accuracy: 0.7412\n",
      "Epoch 333/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.7367 - categorical_accuracy: 0.8824\n",
      "Epoch 334/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.8031 - categorical_accuracy: 0.7882\n",
      "Epoch 335/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.6853 - categorical_accuracy: 0.7412\n",
      "Epoch 336/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.6420 - categorical_accuracy: 0.9059\n",
      "Epoch 337/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.6449 - categorical_accuracy: 0.7412\n",
      "Epoch 338/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.5770 - categorical_accuracy: 0.8588\n",
      "Epoch 339/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.5728 - categorical_accuracy: 0.8118\n",
      "Epoch 340/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.4900 - categorical_accuracy: 0.8235\n",
      "Epoch 341/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.8100 - categorical_accuracy: 0.5765\n",
      "Epoch 342/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.5923 - categorical_accuracy: 0.7765\n",
      "Epoch 343/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.5789 - categorical_accuracy: 0.7529\n",
      "Epoch 344/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.5117 - categorical_accuracy: 0.8471\n",
      "Epoch 345/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.4904 - categorical_accuracy: 0.8471\n",
      "Epoch 346/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.4769 - categorical_accuracy: 0.7647\n",
      "Epoch 347/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.3794 - categorical_accuracy: 0.8824\n",
      "Epoch 348/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.5775 - categorical_accuracy: 0.7059\n",
      "Epoch 349/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.4863 - categorical_accuracy: 0.7765\n",
      "Epoch 350/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.4536 - categorical_accuracy: 0.7647\n",
      "Epoch 351/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.3633 - categorical_accuracy: 0.8471\n",
      "Epoch 352/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.3458 - categorical_accuracy: 0.8588\n",
      "Epoch 353/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.2430 - categorical_accuracy: 0.9529\n",
      "Epoch 354/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.6013 - categorical_accuracy: 0.7059\n",
      "Epoch 355/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.8884 - categorical_accuracy: 0.6235\n",
      "Epoch 356/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.6599 - categorical_accuracy: 0.7176\n",
      "Epoch 357/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.4917 - categorical_accuracy: 0.8235\n",
      "Epoch 358/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.4287 - categorical_accuracy: 0.8588\n",
      "Epoch 359/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.4170 - categorical_accuracy: 0.9059\n",
      "Epoch 360/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.3898 - categorical_accuracy: 0.9176\n",
      "Epoch 361/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.3650 - categorical_accuracy: 0.9412\n",
      "Epoch 362/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.3540 - categorical_accuracy: 0.8941\n",
      "Epoch 363/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.2876 - categorical_accuracy: 0.9294\n",
      "Epoch 364/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.2553 - categorical_accuracy: 0.9647\n",
      "Epoch 365/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.2172 - categorical_accuracy: 0.9647\n",
      "Epoch 366/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.2171 - categorical_accuracy: 0.9647\n",
      "Epoch 367/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.1684 - categorical_accuracy: 0.9647\n",
      "Epoch 368/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.1345 - categorical_accuracy: 0.9765\n",
      "Epoch 369/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.1206 - categorical_accuracy: 0.9765\n",
      "Epoch 370/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.1832 - categorical_accuracy: 0.9412\n",
      "Epoch 371/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.1030 - categorical_accuracy: 0.9765\n",
      "Epoch 372/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0711 - categorical_accuracy: 0.9882\n",
      "Epoch 373/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0609 - categorical_accuracy: 0.9882\n",
      "Epoch 374/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0500 - categorical_accuracy: 0.9882\n",
      "Epoch 375/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0331 - categorical_accuracy: 1.0000\n",
      "Epoch 376/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.0397 - categorical_accuracy: 0.9882\n",
      "Epoch 377/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.0227 - categorical_accuracy: 1.0000\n",
      "Epoch 378/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.0164 - categorical_accuracy: 1.0000\n",
      "Epoch 379/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0333 - categorical_accuracy: 0.9882\n",
      "Epoch 380/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.4458 - categorical_accuracy: 0.8706\n",
      "Epoch 381/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.1233 - categorical_accuracy: 0.6000\n",
      "Epoch 382/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.0768 - categorical_accuracy: 0.3412\n",
      "Epoch 383/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.9801 - categorical_accuracy: 0.3412\n",
      "Epoch 384/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.3948 - categorical_accuracy: 0.3412\n",
      "Epoch 385/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2236 - categorical_accuracy: 0.2706\n",
      "Epoch 386/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1850 - categorical_accuracy: 0.3176\n",
      "Epoch 387/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1517 - categorical_accuracy: 0.3176\n",
      "Epoch 388/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1035 - categorical_accuracy: 0.3176\n",
      "Epoch 389/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0794 - categorical_accuracy: 0.3176\n",
      "Epoch 390/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0594 - categorical_accuracy: 0.3176\n",
      "Epoch 391/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0433 - categorical_accuracy: 0.3176\n",
      "Epoch 392/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0298 - categorical_accuracy: 0.4588\n",
      "Epoch 393/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0206 - categorical_accuracy: 0.6000\n",
      "Epoch 394/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0078 - categorical_accuracy: 0.6471\n",
      "Epoch 395/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.9941 - categorical_accuracy: 0.7294\n",
      "Epoch 396/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.9780 - categorical_accuracy: 0.8000\n",
      "Epoch 397/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.9630 - categorical_accuracy: 0.9176\n",
      "Epoch 398/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.9456 - categorical_accuracy: 0.9882\n",
      "Epoch 399/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.9258 - categorical_accuracy: 0.9647\n",
      "Epoch 400/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.9030 - categorical_accuracy: 0.8471\n",
      "Epoch 401/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.8755 - categorical_accuracy: 0.9176\n",
      "Epoch 402/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.8429 - categorical_accuracy: 1.0000\n",
      "Epoch 403/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.8075 - categorical_accuracy: 1.0000\n",
      "Epoch 404/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.7567 - categorical_accuracy: 1.0000\n",
      "Epoch 405/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.6978 - categorical_accuracy: 1.0000\n",
      "Epoch 406/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.6820 - categorical_accuracy: 0.8000\n",
      "Epoch 407/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.5810 - categorical_accuracy: 0.8824\n",
      "Epoch 408/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.5731 - categorical_accuracy: 0.6588\n",
      "Epoch 409/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.9000 - categorical_accuracy: 0.5176\n",
      "Epoch 410/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2082 - categorical_accuracy: 0.3412\n",
      "Epoch 411/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0822 - categorical_accuracy: 0.3765\n",
      "Epoch 412/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.9609 - categorical_accuracy: 0.6588\n",
      "Epoch 413/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.9214 - categorical_accuracy: 0.6824\n",
      "Epoch 414/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.8768 - categorical_accuracy: 0.6824\n",
      "Epoch 415/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.8378 - categorical_accuracy: 0.6824\n",
      "Epoch 416/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.8056 - categorical_accuracy: 0.6824\n",
      "Epoch 417/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.7637 - categorical_accuracy: 0.8000\n",
      "Epoch 418/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.7190 - categorical_accuracy: 0.8941\n",
      "Epoch 419/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.6757 - categorical_accuracy: 1.0000\n",
      "Epoch 420/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.6153 - categorical_accuracy: 0.9059\n",
      "Epoch 421/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.5629 - categorical_accuracy: 0.7412\n",
      "Epoch 422/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.5082 - categorical_accuracy: 0.8000\n",
      "Epoch 423/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.4233 - categorical_accuracy: 0.8824\n",
      "Epoch 424/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.3880 - categorical_accuracy: 0.8118\n",
      "Epoch 425/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.3740 - categorical_accuracy: 0.9294\n",
      "Epoch 426/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.3016 - categorical_accuracy: 0.9882\n",
      "Epoch 427/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.2699 - categorical_accuracy: 0.9412\n",
      "Epoch 428/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.4884 - categorical_accuracy: 0.7647\n",
      "Epoch 429/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.2689 - categorical_accuracy: 0.9059\n",
      "Epoch 430/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.3706 - categorical_accuracy: 0.7765\n",
      "Epoch 431/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.3101 - categorical_accuracy: 0.8353\n",
      "Epoch 432/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.2215 - categorical_accuracy: 0.9882\n",
      "Epoch 433/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.1926 - categorical_accuracy: 1.0000\n",
      "Epoch 434/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.2036 - categorical_accuracy: 0.9176\n",
      "Epoch 435/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.1636 - categorical_accuracy: 0.9765\n",
      "Epoch 436/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.1666 - categorical_accuracy: 0.9176\n",
      "Epoch 437/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.1425 - categorical_accuracy: 0.9529\n",
      "Epoch 438/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.1379 - categorical_accuracy: 0.9412\n",
      "Epoch 439/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.1041 - categorical_accuracy: 0.9647\n",
      "Epoch 440/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0620 - categorical_accuracy: 1.0000\n",
      "Epoch 441/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.0705 - categorical_accuracy: 0.9882\n",
      "Epoch 442/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.0848 - categorical_accuracy: 0.9765\n",
      "Epoch 443/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.1812 - categorical_accuracy: 0.8941\n",
      "Epoch 444/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.2484 - categorical_accuracy: 0.8706\n",
      "Epoch 445/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.1906 - categorical_accuracy: 0.9412\n",
      "Epoch 446/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.1868 - categorical_accuracy: 0.9176\n",
      "Epoch 447/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.1341 - categorical_accuracy: 0.9647\n",
      "Epoch 448/2000\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 0.1627 - categorical_accuracy: 0.9059\n",
      "Epoch 449/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.1316 - categorical_accuracy: 0.9882\n",
      "Epoch 450/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.1064 - categorical_accuracy: 1.0000\n",
      "Epoch 451/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.0702 - categorical_accuracy: 1.0000\n",
      "Epoch 452/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.0763 - categorical_accuracy: 1.0000\n",
      "Epoch 453/2000\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.0498 - categorical_accuracy: 1.0000\n",
      "Epoch 454/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.0465 - categorical_accuracy: 1.0000\n",
      "Epoch 455/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.0345 - categorical_accuracy: 1.0000\n",
      "Epoch 456/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.0200 - categorical_accuracy: 1.0000\n",
      "Epoch 457/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.0146 - categorical_accuracy: 1.0000\n",
      "Epoch 458/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0119 - categorical_accuracy: 1.0000\n",
      "Epoch 459/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.0077 - categorical_accuracy: 1.0000\n",
      "Epoch 460/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0063 - categorical_accuracy: 1.0000\n",
      "Epoch 461/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.0047 - categorical_accuracy: 1.0000\n",
      "Epoch 462/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.0041 - categorical_accuracy: 1.0000\n",
      "Epoch 463/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.0036 - categorical_accuracy: 1.0000\n",
      "Epoch 464/2000\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.0024 - categorical_accuracy: 1.0000\n",
      "Epoch 465/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.0025 - categorical_accuracy: 1.0000\n",
      "Epoch 466/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.0023 - categorical_accuracy: 1.0000\n",
      "Epoch 467/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0018 - categorical_accuracy: 1.0000\n",
      "Epoch 468/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.0017 - categorical_accuracy: 1.0000\n",
      "Epoch 469/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.0016 - categorical_accuracy: 1.0000\n",
      "Epoch 470/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.0014 - categorical_accuracy: 1.0000\n",
      "Epoch 471/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.0013 - categorical_accuracy: 1.0000\n",
      "Epoch 472/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.0013 - categorical_accuracy: 1.0000\n",
      "Epoch 473/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0012 - categorical_accuracy: 1.0000\n",
      "Epoch 474/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.0011 - categorical_accuracy: 1.0000\n",
      "Epoch 475/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.0011 - categorical_accuracy: 1.0000\n",
      "Epoch 476/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.0010 - categorical_accuracy: 1.0000\n",
      "Epoch 477/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.0010 - categorical_accuracy: 1.0000\n",
      "Epoch 478/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 9.7551e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 479/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.4050e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 480/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 8.8735e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 481/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 8.7898e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 482/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 8.1973e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 483/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.1039e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 484/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.7139e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 485/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.4505e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 486/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 7.1704e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 487/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.9548e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 488/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.8125e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 489/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.4736e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 490/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.3280e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 491/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.1762e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 492/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.0143e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 493/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.8071e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 494/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.6204e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 495/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.5535e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 496/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 5.3854e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 497/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.2324e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 498/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.1469e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 499/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.9738e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 500/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.8288e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 501/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 4.8384e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 502/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 4.6362e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 503/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.6193e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 504/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.3712e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 505/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.2924e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 506/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.2671e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 507/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.0466e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 508/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.9937e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 509/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.9221e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 510/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.8680e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 511/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.7988e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 512/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 3.6354e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 513/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.5296e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 514/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 3.4886e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 515/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.4450e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 516/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.3965e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 517/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.2599e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 518/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.1898e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 519/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.1628e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 520/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.0537e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 521/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.0236e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 522/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.9422e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 523/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 2.9457e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 524/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.8822e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 525/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.7951e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 526/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.8129e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 527/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.7032e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 528/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.6213e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 529/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.5789e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 530/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.5272e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 531/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.4831e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 532/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.4630e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 533/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.4173e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 534/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.3992e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 535/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 2.3670e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 536/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.3159e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 537/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.2621e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 538/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 2.2342e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 539/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.1996e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 540/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.1929e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 541/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.1310e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 542/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.1083e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 543/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.0706e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 544/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.0322e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 545/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 2.0049e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 546/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 1.9724e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 547/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.9618e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 548/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.9607e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 549/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.9566e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 550/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.8841e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 551/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.8430e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 552/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.8459e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 553/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.8499e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 554/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8105e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 555/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.7818e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 556/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.7378e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 557/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.7294e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 558/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.6882e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 559/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.6758e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 560/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.6612e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 561/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.6350e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 562/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.6076e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 563/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5958e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 564/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5627e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 565/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.5502e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 566/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.5385e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 567/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.5189e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 568/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.5125e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 569/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4851e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 570/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.4692e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 571/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.4436e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 572/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.4344e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 573/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.4178e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 574/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.3949e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 575/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.3859e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 576/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.3777e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 577/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3605e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 578/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.3436e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 579/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.3201e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 580/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.3138e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 581/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.2984e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 582/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2739e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 583/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2609e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 584/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 1.2641e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 585/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.2475e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 586/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 1.2361e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 587/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2168e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 588/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2149e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 589/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1833e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 590/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1854e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 591/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1860e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 592/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1484e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 593/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.1490e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 594/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.1277e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 595/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1168e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 596/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1079e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 597/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0910e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 598/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0786e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 599/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0679e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 600/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0569e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 601/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0589e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 602/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0346e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 603/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0239e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 604/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.0163e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 605/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.0147e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 606/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 9.9659e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 607/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 9.8730e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 608/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.8029e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 609/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 9.6515e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 610/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 9.5801e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 611/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 9.5330e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 612/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 9.3777e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 613/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 9.2936e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 614/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 9.2080e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 615/2000\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 9.1814e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 616/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 9.0370e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 617/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 8.9522e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 618/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 8.8224e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 619/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.7627e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 620/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 8.7336e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 621/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.6474e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 622/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.6052e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 623/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 8.4331e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 624/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 8.3534e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 625/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 8.2740e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 626/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 8.2150e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 627/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 8.1312e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 628/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.1204e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 629/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.1062e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 630/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 7.8941e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 631/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 7.8534e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 632/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.7730e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 633/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 7.7090e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 634/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 7.6240e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 635/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 7.6473e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 636/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 7.4978e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 637/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 7.4713e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 638/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 7.3724e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 639/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 7.2739e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 640/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 7.2596e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 641/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 7.2090e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 642/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 7.0861e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 643/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 7.0654e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 644/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.9496e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 645/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 6.8942e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 646/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 6.8312e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 647/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 6.8325e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 648/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 6.7298e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 649/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 6.6803e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 650/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 6.6254e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 651/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 6.6301e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 652/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.5752e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 653/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 6.4672e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 654/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 6.3975e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 655/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.3429e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 656/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.2992e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 657/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 6.2617e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 658/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 6.2078e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 659/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.1947e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 660/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 6.1044e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 661/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 6.0769e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 662/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.0201e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 663/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.9489e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 664/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 5.9185e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 665/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 5.8787e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 666/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.8097e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 667/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.7798e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 668/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 5.7980e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 669/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.6916e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 670/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.6328e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 671/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.5920e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 672/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 5.6198e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 673/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 5.5442e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 674/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 5.4862e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 675/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.4353e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 676/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 5.3777e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 677/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 5.3342e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 678/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 5.3046e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 679/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 5.2886e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 680/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.2565e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 681/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 5.1948e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 682/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 5.1494e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 683/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.1242e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 684/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 5.0819e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 685/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.0321e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 686/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.0070e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 687/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.9822e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 688/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.9824e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 689/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 4.9142e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 690/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 4.8847e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 691/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.8974e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 692/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.8326e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 693/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.7709e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 694/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 4.7534e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 695/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.6927e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 696/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.6647e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 697/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.6169e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 698/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.5929e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 699/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 4.5787e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 700/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.5458e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 701/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.5322e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 702/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.4749e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 703/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.4380e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 704/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 4.4317e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 705/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 4.4161e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 706/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 4.3779e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 707/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 4.3177e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 708/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 4.2912e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 709/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 4.2736e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 710/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.2381e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 711/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 4.2231e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 712/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 4.1991e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 713/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 4.1561e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 714/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.1543e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 715/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.0933e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 716/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 4.0711e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 717/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 4.0436e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 718/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 4.0156e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 719/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 4.0034e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 720/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.9845e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 721/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.9427e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 722/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.9044e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 723/2000\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 3.8854e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 724/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 3.8684e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 725/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.8943e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 726/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.8561e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 727/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.8090e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 728/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.8004e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 729/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.7970e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 730/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.7310e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 731/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 3.7306e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 732/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 3.6718e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 733/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 3.6582e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 734/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.6247e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 735/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.6246e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 736/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.5883e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 737/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.5558e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 738/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.5413e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 739/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.5103e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 740/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 3.5029e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 741/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 3.4740e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 742/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 3.4515e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 743/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 3.4409e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 744/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 3.4084e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 745/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 3.3818e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 746/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 3.3659e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 747/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 3.3491e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 748/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 3.3175e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 749/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.2976e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 750/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.2805e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 751/2000\n",
      "3/3 [==============================] - 0s 63ms/step - loss: 3.2641e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 752/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.2233e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 753/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.2131e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 754/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.1738e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 755/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 3.1614e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 756/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.1457e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 757/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.1206e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 758/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.1000e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 759/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 3.0924e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 760/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.0540e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 761/2000\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 3.0421e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 762/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.0245e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 763/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.0036e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 764/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.9892e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 765/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.9797e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 766/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.9596e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 767/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.9362e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 768/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.9099e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 769/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.9171e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 770/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.8749e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 771/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 2.8860e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 772/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 2.8645e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 773/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.8250e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 774/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.8062e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 775/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.7898e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 776/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 2.7709e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 777/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.7671e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 778/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.7416e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 779/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.7239e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 780/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.7126e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 781/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.7033e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 782/2000\n",
      "3/3 [==============================] - 0s 61ms/step - loss: 2.6820e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 783/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.6663e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 784/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.6485e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 785/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.6311e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 786/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.6097e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 787/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.6000e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 788/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.5894e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 789/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 2.5725e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 790/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.5743e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 791/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.5695e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 792/2000\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 2.5369e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 793/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.5083e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 794/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.4928e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 795/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.4895e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 796/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 2.4666e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 797/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.4548e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 798/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.4418e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 799/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.4665e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 800/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.4176e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 801/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 2.4005e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 802/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.3951e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 803/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.3873e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 804/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.3533e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 805/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.3409e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 806/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.3240e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 807/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.3139e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 808/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 2.3047e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 809/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.2866e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 810/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 2.2712e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 811/2000\n",
      "3/3 [==============================] - 0s 63ms/step - loss: 2.2577e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 812/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 2.2527e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 813/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.2364e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 814/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.2224e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 815/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.2175e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 816/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.2067e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 817/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.1915e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 818/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.1772e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 819/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.1618e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 820/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.1495e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 821/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.1556e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 822/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 2.1352e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 823/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.1172e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 824/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.1202e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 825/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.0986e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 826/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.1060e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 827/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.0739e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 828/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.0579e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 829/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.0641e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 830/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 2.0534e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 831/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.0360e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 832/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.0258e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 833/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.0109e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 834/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.9993e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 835/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.0024e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 836/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.9792e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 837/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.9708e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 838/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.9714e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 839/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.9468e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 840/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.9354e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 841/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.9408e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 842/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.9219e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 843/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.9135e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 844/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.9093e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 845/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.8895e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 846/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.8995e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 847/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8739e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 848/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8634e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 849/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.8578e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 850/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 1.8494e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 851/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8445e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 852/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8323e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 853/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.8222e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 854/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.8101e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 855/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8028e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 856/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.7957e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 857/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.7919e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 858/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.7782e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 859/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 1.7668e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 860/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.7568e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 861/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.7602e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 862/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.7438e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 863/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.7350e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 864/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.7358e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 865/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.7267e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 866/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.7152e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 867/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.7041e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 868/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 1.7001e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 869/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.6883e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 870/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.6773e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 871/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.6730e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 872/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.6597e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 873/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.6520e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 874/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.6560e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 875/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.6364e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 876/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.6419e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 877/2000\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 1.6224e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 878/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.6105e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 879/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.6117e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 880/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.6004e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 881/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.5983e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 882/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.5849e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 883/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.5806e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 884/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.5677e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 885/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.5633e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 886/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5600e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 887/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.5590e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 888/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.5454e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 889/2000\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 1.5329e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 890/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.5440e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 891/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.5265e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 892/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.5143e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 893/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.5081e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 894/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.5019e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 895/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.4932e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 896/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.4892e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 897/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.4828e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 898/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.4757e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 899/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.4673e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 900/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.4632e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 901/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.4579e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 902/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.4499e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 903/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.4457e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 904/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4432e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 905/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.4311e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 906/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 1.4270e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 907/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.4202e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 908/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.4171e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 909/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.4085e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 910/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.4087e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 911/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.3966e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 912/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.3890e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 913/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.3854e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 914/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.3874e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 915/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.3826e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 916/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3662e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 917/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.3581e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 918/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.3607e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 919/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.3482e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 920/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.3440e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 921/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.3388e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 922/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.3341e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 923/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3321e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 924/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.3230e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 925/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.3153e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 926/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.3097e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 927/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.3034e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 928/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.3035e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 929/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.3028e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 930/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 1.2881e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 931/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.2837e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 932/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.2853e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 933/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 1.2745e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 934/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.2736e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 935/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.2621e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 936/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.2561e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 937/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2536e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 938/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.2473e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 939/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2426e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 940/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.2380e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 941/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.2353e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 942/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 1.2344e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 943/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.2255e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 944/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.2236e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 945/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.2174e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 946/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.2056e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 947/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2023e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 948/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.2014e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 949/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.1909e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 950/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 1.1983e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 951/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1891e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 952/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1832e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 953/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1755e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 954/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1744e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 955/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1631e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 956/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1579e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 957/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1547e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 958/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1494e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 959/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1466e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 960/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1403e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 961/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1369e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 962/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1305e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 963/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1255e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 964/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.1254e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 965/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1190e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 966/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1153e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 967/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1121e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 968/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1129e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 969/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1045e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 970/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1000e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 971/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0955e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 972/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0920e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 973/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0845e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 974/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.0821e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 975/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0793e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 976/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0751e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 977/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0745e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 978/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0678e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 979/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0709e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 980/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0574e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 981/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.0552e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 982/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0532e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 983/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0497e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 984/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0411e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 985/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.0396e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 986/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 1.0338e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 987/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0330e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 988/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0274e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 989/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.0251e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 990/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.0205e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 991/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0165e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 992/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0128e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 993/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0094e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 994/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 1.0055e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 995/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.0031e-05 - categorical_accuracy: 1.0000\n",
      "Epoch 996/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.9948e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 997/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.9780e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 998/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.9065e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 999/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.8995e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1000/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.8406e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1001/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.8139e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1002/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.7536e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1003/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.7733e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1004/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 9.6919e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1005/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.6779e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1006/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.6162e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1007/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.5769e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1008/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.5615e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1009/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.5405e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1010/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.4984e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1011/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.4591e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1012/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 9.4227e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1013/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.3750e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1014/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 9.3652e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1015/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.3035e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1016/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.2558e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1017/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.2236e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1018/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.2222e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1019/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 9.1745e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1020/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 9.1310e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1021/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 9.1114e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1022/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 9.0651e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1023/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 9.0300e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1024/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 8.9992e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1025/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 8.9725e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1026/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 8.9276e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1027/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.9347e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1028/2000\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 8.8996e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1029/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.8673e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1030/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.7930e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1031/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.8028e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1032/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.7355e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1033/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 8.7061e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1034/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 8.7047e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1035/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 8.6696e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1036/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 8.6388e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1037/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 8.6037e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1038/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 8.5743e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1039/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 8.5126e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1040/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.4761e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1041/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.4817e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1042/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.4312e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1043/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.3905e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1044/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.3709e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1045/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 8.3639e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1046/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 8.3807e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1047/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.3358e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1048/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 8.2559e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1049/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 8.2405e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1050/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.2363e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1051/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.1437e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1052/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.1199e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1053/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.1746e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1054/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 8.0961e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1055/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.0863e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1056/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.0498e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1057/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.9951e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1058/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.9895e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1059/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 7.9180e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1060/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.8913e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1061/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.8675e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1062/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.8535e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1063/2000\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 7.8282e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1064/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 7.8002e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1065/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 7.7960e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1066/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.7651e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1067/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 7.7539e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1068/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.7160e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1069/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.6753e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1070/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.6515e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1071/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.6347e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1072/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.5646e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1073/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 7.6291e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1074/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 7.5576e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1075/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.5281e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1076/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.4917e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1077/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 7.4468e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1078/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.4117e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1079/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 7.3809e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1080/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.4482e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1081/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.3780e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1082/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.3430e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1083/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.3135e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1084/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.3192e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1085/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.2729e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1086/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.2364e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1087/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 7.2000e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1088/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.1677e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1089/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.1355e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1090/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.1158e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1091/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 7.1004e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1092/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 7.0962e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1093/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.0555e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1094/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.0457e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1095/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 7.0331e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1096/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 6.9952e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1097/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.9840e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1098/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.9854e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1099/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.9293e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1100/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.9265e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1101/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.8788e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1102/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.8634e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1103/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.8171e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1104/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 6.8227e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1105/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 6.7751e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1106/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.7568e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1107/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.7330e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1108/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 6.7119e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1109/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 6.6853e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1110/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 6.6671e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1111/2000\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 6.6531e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1112/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.6474e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1113/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 6.6362e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1114/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 6.5998e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1115/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 6.5703e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1116/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 6.5310e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1117/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 6.5100e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1118/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 6.5002e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1119/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 6.4553e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1120/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.4679e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1121/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.5030e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1122/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.4609e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1123/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.3922e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1124/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.3754e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1125/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.3656e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1126/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.3319e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1127/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 6.3291e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1128/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 6.2814e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1129/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.2618e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1130/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.2464e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1131/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.2211e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1132/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.2071e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1133/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.1748e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1134/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.1762e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1135/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 6.1524e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1136/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.1328e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1137/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 6.0991e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1138/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.0837e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1139/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 6.0655e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1140/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.0528e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1141/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.0402e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1142/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.0122e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1143/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.9981e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1144/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.9715e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1145/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 5.9477e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1146/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.9322e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1147/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 5.9701e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1148/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.9098e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1149/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 5.8817e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1150/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.8817e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1151/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.8439e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1152/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.8172e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1153/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.8060e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1154/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.7892e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1155/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.7681e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1156/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 5.7513e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1157/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 5.7373e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1158/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.7078e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1159/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 5.7317e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1160/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.6854e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1161/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.6728e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1162/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 5.6742e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1163/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.6293e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1164/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.6055e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1165/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.5971e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1166/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.5802e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1167/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.5438e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1168/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 5.5340e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1169/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.5115e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1170/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 5.5227e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1171/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.4863e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1172/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.4680e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1173/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.4751e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1174/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 5.4302e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1175/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.4568e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1176/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.4063e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1177/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.3923e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1178/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.3797e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1179/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.3727e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1180/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 5.3264e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1181/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.3152e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1182/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 5.3026e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1183/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.2619e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1184/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 5.2591e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1185/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 5.2633e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1186/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.2296e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1187/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 5.2184e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1188/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.2114e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1189/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.1623e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1190/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 5.1679e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1191/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.1483e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1192/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 5.1273e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1193/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.1301e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1194/2000\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 5.1006e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1195/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 5.0838e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1196/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 5.0684e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1197/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 5.0431e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1198/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 5.0221e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1199/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 5.0179e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1200/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.9982e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1201/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 4.9912e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1202/2000\n",
      "3/3 [==============================] - 0s 62ms/step - loss: 4.9926e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1203/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 4.9842e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1204/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 4.9492e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1205/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.9421e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1206/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.9435e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1207/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.9015e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1208/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 4.8931e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1209/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.8804e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1210/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.8524e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1211/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.8454e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1212/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.8215e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1213/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 4.8145e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1214/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 4.8187e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1215/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.7977e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1216/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.7837e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1217/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.7556e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1218/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.7346e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1219/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.7164e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1220/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.7023e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1221/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.7472e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1222/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.7009e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1223/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.6743e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1224/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.6532e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1225/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.6434e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1226/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.6294e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1227/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.6098e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1228/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.5901e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1229/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.6014e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1230/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.5607e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1231/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.5537e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1232/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.5397e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1233/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.5228e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1234/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.5158e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1235/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.5088e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1236/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.4850e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1237/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.4808e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1238/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.4611e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1239/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.4443e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1240/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.4443e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1241/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.4177e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1242/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.3966e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1243/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.3854e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1244/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.3714e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1245/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.3559e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1246/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.3489e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1247/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.3391e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1248/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 4.3321e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1249/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 4.3181e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1250/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 4.3026e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1251/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 4.3041e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1252/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 4.2830e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1253/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 4.2718e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1254/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 4.2409e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1255/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 4.2269e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1256/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 4.2283e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1257/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 4.2087e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1258/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.2003e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1259/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 4.2031e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1260/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.1778e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1261/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 4.1722e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1262/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 4.1540e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1263/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 4.1330e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1264/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.1316e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1265/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.1077e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1266/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.0909e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1267/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 4.0881e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1268/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 4.0699e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1269/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 4.0685e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1270/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 4.0516e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1271/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.0390e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1272/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 4.0180e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1273/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 4.0110e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1274/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 4.0082e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1275/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.9913e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1276/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 3.9717e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1277/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 3.9773e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1278/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 3.9577e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1279/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.9422e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1280/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.9254e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1281/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.9422e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1282/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 3.9142e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1283/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.8960e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1284/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.8805e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1285/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 3.8637e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1286/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.8539e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1287/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.8469e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1288/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.8300e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1289/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.8258e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1290/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 3.8258e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1291/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 3.8076e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1292/2000\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 3.7908e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1293/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.7810e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1294/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.7641e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1295/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.7529e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1296/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.7571e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1297/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.7361e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1298/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.7165e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1299/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.7094e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1300/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.7052e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1301/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.7010e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1302/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.6786e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1303/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.6674e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1304/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.6660e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1305/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.6519e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1306/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.6477e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1307/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.6351e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1308/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.6253e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1309/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.6000e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1310/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.5944e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1311/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.5762e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1312/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.5916e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1313/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.5650e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1314/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.5580e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1315/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.5608e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1316/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.5327e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1317/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 3.5299e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1318/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.5033e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1319/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 3.4907e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1320/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.5005e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1321/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.4879e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1322/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.4654e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1323/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.4542e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1324/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.4570e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1325/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.4360e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1326/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 3.4233e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1327/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 3.4135e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1328/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.4009e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1329/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.4037e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1330/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.3883e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1331/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.3785e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1332/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 3.3757e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1333/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 3.3715e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1334/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.3518e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1335/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.3490e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1336/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 3.3266e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1337/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.3210e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1338/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.3041e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1339/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.3013e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1340/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.2957e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1341/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.2817e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1342/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.2747e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1343/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.2691e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1344/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 3.2579e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1345/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.2424e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1346/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.2326e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1347/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 3.2270e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1348/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.2200e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1349/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.2256e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1350/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.2032e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1351/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.1905e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1352/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.1905e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1353/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.1737e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1354/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.1653e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1355/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.1583e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1356/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.1513e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1357/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.1387e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1358/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.1344e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1359/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.1232e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1360/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.1162e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1361/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 3.1148e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1362/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.1008e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1363/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.0910e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1364/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.0868e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1365/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.0727e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1366/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.0573e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1367/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.0531e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1368/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 3.0545e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1369/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.0363e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1370/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 3.0307e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1371/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.0307e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1372/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.0251e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1373/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 3.0138e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1374/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 3.0096e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1375/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 2.9858e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1376/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 2.9760e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1377/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.9648e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1378/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.9676e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1379/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.9591e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1380/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.9437e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1381/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.9465e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1382/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.9381e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1383/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.9241e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1384/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.9171e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1385/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.9171e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1386/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.8932e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1387/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.8918e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1388/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.8932e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1389/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.8708e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1390/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.8680e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1391/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.8582e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1392/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.8484e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1393/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.8470e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1394/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.8329e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1395/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.8329e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1396/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 2.8245e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1397/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.8147e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1398/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.8133e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1399/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 2.7937e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1400/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.7979e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1401/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.7838e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1402/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.7824e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1403/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.7684e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1404/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.7558e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1405/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.7558e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1406/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.7488e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1407/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.7432e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1408/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.7263e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1409/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.7235e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1410/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.7263e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1411/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.7039e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1412/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.7081e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1413/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 2.6997e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1414/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.6885e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1415/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.6815e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1416/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.6843e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1417/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.6688e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1418/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.6562e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1419/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.6548e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1420/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.6534e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1421/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.6492e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1422/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.6380e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1423/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.6380e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1424/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.6198e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1425/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.6240e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1426/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.6085e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1427/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 2.5945e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1428/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.5777e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1429/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.5707e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1430/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.5693e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1431/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 2.5581e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1432/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.5538e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1433/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.5398e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1434/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.5398e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1435/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.5314e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1436/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.5202e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1437/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.5174e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1438/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.5090e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1439/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.5076e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1440/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.5076e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1441/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.5048e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1442/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.4992e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1443/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.4781e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1444/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.4753e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1445/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.4669e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1446/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.4571e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1447/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.4515e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1448/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.4459e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1449/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.4445e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1450/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 2.4374e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1451/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.4318e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1452/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.4276e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1453/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.4304e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1454/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.4192e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1455/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.4276e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1456/2000\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 2.4094e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1457/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.3912e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1458/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.3842e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1459/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.3785e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1460/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.3715e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1461/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 2.3715e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1462/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.3715e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1463/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 2.3491e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1464/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.3519e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1465/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.3477e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1466/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.3337e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1467/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.3309e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1468/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 2.3281e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1469/2000\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 2.3182e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1470/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.3112e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1471/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.3042e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1472/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 2.2986e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1473/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.3042e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1474/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.2902e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1475/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.2818e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1476/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.2720e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1477/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.2692e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1478/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 2.2635e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1479/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.2635e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1480/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.2481e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1481/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.2397e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1482/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.2439e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1483/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.2341e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1484/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 2.2257e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1485/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.2285e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1486/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.2145e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1487/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.2103e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1488/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.2018e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1489/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 2.1892e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1490/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.1892e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1491/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.1850e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1492/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.1724e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1493/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.1724e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1494/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.1668e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1495/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 2.1598e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1496/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.1471e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1497/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.1499e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1498/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.1471e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1499/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.1429e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1500/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.1345e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1501/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.1219e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1502/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.1233e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1503/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.1205e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1504/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.1149e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1505/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.1065e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1506/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.0995e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1507/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.0953e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1508/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.0840e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1509/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.0798e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1510/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 2.0770e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1511/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 2.0672e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1512/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 2.0588e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1513/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 2.0602e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1514/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 2.0616e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1515/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 2.0546e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1516/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.0448e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1517/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.0406e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1518/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.0349e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1519/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.0364e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1520/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.0279e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1521/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 2.0181e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1522/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 2.0195e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1523/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 2.0097e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1524/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 2.0069e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1525/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.9999e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1526/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.9971e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1527/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.9985e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1528/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.9831e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1529/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.9775e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1530/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.9704e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1531/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.9690e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1532/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.9662e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1533/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 1.9536e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1534/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.9522e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1535/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.9452e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1536/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.9410e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1537/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.9368e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1538/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.9312e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1539/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 1.9284e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1540/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.9242e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1541/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.9171e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1542/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.9143e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1543/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.9059e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1544/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.9017e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1545/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 1.9017e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1546/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.8947e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1547/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.8989e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1548/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.8835e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1549/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.8779e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1550/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.8779e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1551/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8681e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1552/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.8653e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1553/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.8540e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1554/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.8456e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1555/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8484e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1556/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8428e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1557/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.8498e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1558/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8400e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1559/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.8302e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1560/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.8358e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1561/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.8176e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1562/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.8134e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1563/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.8078e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1564/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.8035e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1565/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.7951e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1566/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.7937e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1567/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.7909e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1568/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.7839e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1569/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.7741e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1570/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 1.7895e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1571/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.7713e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1572/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.7643e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1573/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.7587e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1574/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.7587e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1575/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.7573e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1576/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 1.7601e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1577/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.7475e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1578/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.7376e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1579/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.7334e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1580/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.7264e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1581/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.7250e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1582/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.7222e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1583/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 1.7152e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1584/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.7138e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1585/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.7082e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1586/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.7012e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1587/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 1.6984e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1588/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.6928e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1589/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 1.6900e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1590/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.6900e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1591/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.6984e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1592/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.6815e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1593/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.6829e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1594/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.6717e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1595/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.6717e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1596/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.6633e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1597/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.6535e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1598/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 1.6493e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1599/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.6493e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1600/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.6451e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1601/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 1.6395e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1602/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.6367e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1603/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.6296e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1604/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.6339e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1605/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.6282e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1606/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.6212e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1607/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.6156e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1608/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 1.6072e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1609/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.6016e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1610/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5974e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1611/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.5960e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1612/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.6002e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1613/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5918e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1614/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.5890e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1615/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.5792e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1616/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.5778e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1617/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.5806e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1618/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.5665e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1619/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5623e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1620/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.5595e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1621/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5539e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1622/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5497e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1623/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.5441e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1624/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.5497e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1625/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 1.5371e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1626/2000\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 1.5357e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1627/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.5385e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1628/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5329e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1629/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5287e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1630/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.5231e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1631/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.5217e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1632/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 1.5160e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1633/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.5118e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1634/2000\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 1.5062e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1635/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.5090e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1636/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 1.4992e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1637/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.5048e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1638/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.4964e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1639/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4950e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1640/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.4838e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1641/2000\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 1.4796e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1642/2000\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 1.4768e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1643/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.4754e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1644/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.4712e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1645/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4684e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1646/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4614e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1647/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4768e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1648/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4571e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1649/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.4585e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1650/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4445e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1651/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.4431e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1652/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4403e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1653/2000\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 1.4417e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1654/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.4403e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1655/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.4347e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1656/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.4347e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1657/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.4291e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1658/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4277e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1659/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.4193e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1660/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.4207e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1661/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.4277e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1662/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.4123e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1663/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.4053e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1664/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.4025e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1665/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3996e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1666/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.3954e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1667/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.3884e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1668/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.3842e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1669/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3786e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1670/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.3744e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1671/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.3744e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1672/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.3730e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1673/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3660e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1674/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.3618e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1675/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.3604e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1676/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.3660e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1677/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.3590e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1678/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3562e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1679/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.3464e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1680/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3520e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1681/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.3351e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1682/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.3421e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1683/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3393e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1684/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.3267e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1685/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.3267e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1686/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.3225e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1687/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.3197e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1688/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.3239e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1689/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.3155e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1690/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.3127e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1691/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.3113e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1692/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.3043e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1693/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.3029e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1694/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.3001e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1695/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.2945e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1696/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.2959e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1697/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.2945e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1698/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2903e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1699/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.2832e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1700/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.2790e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1701/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2748e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1702/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2720e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1703/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2692e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1704/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2678e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1705/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2650e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1706/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2608e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1707/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2608e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1708/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2566e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1709/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.2608e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1710/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.2496e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1711/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2426e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1712/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2454e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1713/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2398e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1714/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.2398e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1715/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.2370e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1716/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.2370e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1717/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.2300e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1718/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2243e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1719/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.2187e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1720/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2201e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1721/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.2201e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1722/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2103e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1723/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2131e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1724/2000\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 1.2061e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1725/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.2019e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1726/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.2061e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1727/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.2047e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1728/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1949e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1729/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1907e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1730/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.1865e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1731/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1865e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1732/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1767e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1733/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1767e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1734/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1739e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1735/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1710e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1736/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 1.1668e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1737/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1640e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1738/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1654e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1739/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.1570e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1740/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.1542e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1741/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1486e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1742/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.1472e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1743/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.1430e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1744/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.1374e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1745/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.1360e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1746/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.1360e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1747/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 1.1290e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1748/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1332e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1749/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1318e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1750/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.1220e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1751/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.1290e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1752/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1206e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1753/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.1192e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1754/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1178e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1755/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.1150e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1756/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.1051e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1757/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.1093e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1758/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.1037e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1759/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 1.0967e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1760/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.0939e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1761/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.0897e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1762/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0897e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1763/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.0925e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1764/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0869e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1765/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 1.0897e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1766/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0869e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1767/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0799e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1768/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0785e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1769/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0757e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1770/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 1.0757e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1771/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0701e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1772/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 1.0687e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1773/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0645e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1774/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0659e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1775/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 1.0589e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1776/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.0589e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1777/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0617e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1778/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0575e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1779/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0546e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1780/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.0546e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1781/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0518e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1782/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.0462e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1783/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.0434e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1784/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 1.0406e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1785/2000\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 1.0406e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1786/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0350e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1787/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0420e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1788/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0322e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1789/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0266e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1790/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0308e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1791/2000\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.0224e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1792/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.0196e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1793/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.0210e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1794/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.0168e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1795/2000\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 1.0112e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1796/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.0084e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1797/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 1.0070e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1798/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 1.0112e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1799/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0042e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1800/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 1.0028e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1801/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 1.0056e-06 - categorical_accuracy: 1.0000\n",
      "Epoch 1802/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.9855e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1803/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.9715e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1804/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.9294e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1805/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.9294e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1806/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.8733e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1807/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.8172e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1808/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 9.8312e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1809/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 9.7891e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1810/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.7471e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1811/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.7471e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1812/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 9.7330e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1813/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.7330e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1814/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.6629e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1815/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.6769e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1816/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.6208e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1817/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 9.7190e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1818/2000\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 9.6208e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1819/2000\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 9.6349e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1820/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 9.5507e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1821/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 9.5227e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1822/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.4946e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1823/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 9.4946e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1824/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 9.4245e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1825/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.4105e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1826/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.3824e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1827/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.4105e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1828/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 9.3544e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1829/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 9.3544e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1830/2000\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 9.3544e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1831/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 9.2562e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1832/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 9.2983e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1833/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.2422e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1834/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.2282e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1835/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 9.2001e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1836/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 9.1721e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1837/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 9.1440e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1838/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 9.1580e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1839/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 9.0739e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1840/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 9.0879e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1841/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 9.0739e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1842/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 9.0739e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1843/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 9.0038e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1844/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.9617e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1845/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.9757e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1846/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.9617e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1847/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.9336e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1848/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.9336e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1849/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.9477e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1850/2000\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 8.9196e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1851/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.9196e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1852/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.8916e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1853/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.8495e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1854/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.8074e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1855/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 8.8214e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1856/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 8.7653e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1857/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.7653e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1858/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.7092e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1859/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.6952e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1860/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.7092e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1861/2000\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 8.6672e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1862/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 8.6111e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1863/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 8.6111e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1864/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.6391e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1865/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.6111e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1866/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.5269e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1867/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.5550e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1868/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.5129e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1869/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.4568e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1870/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.4288e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1871/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 8.4428e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1872/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.4568e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1873/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.4568e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1874/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.4288e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1875/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.3727e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1876/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.3867e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1877/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.3446e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1878/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 8.3306e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1879/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 8.3166e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1880/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.3446e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1881/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.3446e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1882/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 8.2745e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1883/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.2885e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1884/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.2464e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1885/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.2044e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1886/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 8.1763e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1887/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.1483e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1888/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.1903e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1889/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 8.1623e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1890/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.1062e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1891/2000\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 8.1483e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1892/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.0781e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1893/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 8.0641e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1894/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.0361e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1895/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 8.0361e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1896/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 8.0220e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1897/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.9940e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1898/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 7.9800e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1899/2000\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 7.9660e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1900/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.9519e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1901/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 7.9239e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1902/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.9099e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1903/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 7.8818e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1904/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.9099e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1905/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 7.8818e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1906/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 7.8538e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1907/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.8538e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1908/2000\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 7.7696e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1909/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.7556e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1910/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 7.7275e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1911/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.6855e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1912/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.6714e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1913/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 7.6434e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1914/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.6434e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1915/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.6714e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1916/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 7.6294e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1917/2000\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 7.6434e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1918/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.5733e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1919/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 7.5312e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1920/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 7.5733e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1921/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 7.5452e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1922/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.5312e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1923/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.5031e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1924/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.4751e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1925/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.4751e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1926/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.4751e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1927/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.4611e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1928/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 7.4050e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1929/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.4050e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1930/2000\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 7.4611e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1931/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.4050e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1932/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.3629e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1933/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.3489e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1934/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.3208e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1935/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.3208e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1936/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.3068e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1937/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 7.2787e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1938/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.2647e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1939/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.1946e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1940/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.2647e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1941/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.1806e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1942/2000\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 7.1806e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1943/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.1666e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1944/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 7.1666e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1945/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 7.1385e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1946/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 7.0964e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1947/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 7.0403e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1948/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.0684e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1949/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 7.0964e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1950/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 7.0544e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1951/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 7.0403e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1952/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 7.0544e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1953/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.9281e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1954/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.9141e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1955/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 6.9141e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1956/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 6.9141e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1957/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.8720e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1958/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.8440e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1959/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.9001e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1960/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.8440e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1961/2000\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 6.8159e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1962/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.8019e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1963/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.8159e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1964/2000\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 6.7739e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1965/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.7739e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1966/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 6.7318e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1967/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.7318e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1968/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.7037e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1969/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.7458e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1970/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 6.6757e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1971/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.6617e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1972/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 6.6617e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1973/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 6.6476e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1974/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 6.6476e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1975/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.6056e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1976/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 6.5775e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1977/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 6.5635e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1978/2000\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 6.5495e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1979/2000\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 6.6056e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1980/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 6.5495e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1981/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.5214e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1982/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 6.5074e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1983/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 6.4934e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1984/2000\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 6.4934e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1985/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.4653e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1986/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.4233e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1987/2000\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 6.4233e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1988/2000\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 6.4092e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1989/2000\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 6.3952e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1990/2000\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 6.3531e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1991/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.3812e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1992/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.3812e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1993/2000\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 6.3391e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1994/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.3251e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1995/2000\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 6.3111e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1996/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.3111e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1997/2000\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 6.2830e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1998/2000\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 6.3251e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 1999/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 6.2409e-07 - categorical_accuracy: 1.0000\n",
      "Epoch 2000/2000\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 6.2409e-07 - categorical_accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x327c3fad0>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_6 (LSTM)               (None, 30, 64)            442112    \n",
      "                                                                 \n",
      " lstm_7 (LSTM)               (None, 30, 128)           98816     \n",
      "                                                                 \n",
      " lstm_8 (LSTM)               (None, 64)                49408     \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 3)                 99        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 596675 (2.28 MB)\n",
      "Trainable params: 596675 (2.28 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 207ms/step\n"
     ]
    }
   ],
   "source": [
    "res = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'call'"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(res[4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'call'"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(y_test[4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maction.h5\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "model.save('action.h5')\n",
    "#Don't worry this will work for you"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('action.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 17ms/step\n"
     ]
    }
   ],
   "source": [
    "yhat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_test, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[4, 0],\n",
       "        [0, 1]],\n",
       "\n",
       "       [[2, 0],\n",
       "        [0, 3]],\n",
       "\n",
       "       [[4, 0],\n",
       "        [0, 1]]])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1707064200.200006       1 gl_context.cc:344] GL version: 2.1 (2.1 Metal - 88), renderer: Apple M2 Pro\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 41ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "doctor\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "call\n",
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "sequence = []\n",
    "sentence = []\n",
    "predictions = []\n",
    "threshold = 0.5\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "        \n",
    "        draw_styled_landmarks(image, results)\n",
    "        \n",
    "        keypoints = extract_keypoints(results)\n",
    "        sequence.append(keypoints)\n",
    "        sequence = sequence[-30:]\n",
    "        \n",
    "        if len(sequence) == 30:\n",
    "            res = model.predict(np.expand_dims(sequence, axis=0))[0]\n",
    "            print(actions[np.argmax(res)])\n",
    "            predictions.append(np.argmax(res))\n",
    "            \n",
    "            if np.unique(predictions[-10:])[0]==np.argmax(res): \n",
    "                if res[np.argmax(res)] > threshold: \n",
    "                    \n",
    "                    if len(sentence) > 0: \n",
    "                        if actions[np.argmax(res)] != sentence[-1]:\n",
    "                            sentence.append(actions[np.argmax(res)])\n",
    "                    else:\n",
    "                        sentence.append(actions[np.argmax(res)])\n",
    "\n",
    "            if len(sentence) > 5: \n",
    "                sentence = sentence[-5:]\n",
    "\n",
    "            image = prob_viz(res, actions, image, colors)\n",
    "            \n",
    "        cv2.rectangle(image, (0,0), (640, 40), (245, 117, 16), -1)\n",
    "        cv2.putText(image, ' '.join(sentence), (3,30), \n",
    "                       cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}