training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_holistic = mp.solutions.holistic # Holistic model\n",
    "mp_drawing = mp.solutions.drawing_utils # Drawing utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mediapipe_detection(image, model):\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB\n",
    "    image.flags.writeable = False                  # Image is no longer writeable\n",
    "    results = model.process(image)                 # Make prediction\n",
    "    image.flags.writeable = True                   # Image is now writeable \n",
    "    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR\n",
    "    return image, results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_landmarks(image, results):\n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS) # Draw face connections\n",
    "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) # Draw pose connections\n",
    "    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw left hand connections\n",
    "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw right hand connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_styled_landmarks(image, results):\n",
    "    # Draw face connections\n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, \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",
    "    # Draw pose connections\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",
    "    # Draw left hand connections\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",
    "    # Draw right hand connections  \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": 43,
   "metadata": {},
   "outputs": [
    {
     "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",
      "<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",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "\n",
    "        # Read feed\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        # Make detections\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "        \n",
    "        # Draw landmarks\n",
    "        draw_styled_landmarks(image, results)\n",
    "\n",
    "        # Show to screen\n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results.left_hand_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mediapipe.python.solution_base.SolutionOutputs"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_landmarks(frame, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1de9f82f310>"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results.left_hand_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 52,
   "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": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_test = extract_keypoints(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.58936989,  0.46833208, -1.01303601, ...,  0.        ,\n",
       "        0.        ,  0.        ])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path for exported data, numpy arrays\n",
    "DATA_PATH = os.path.join('data') \n",
    "\n",
    "# Actions that we try to detect\n",
    "actions = np.array(['hello', 'thanks', 'iloveyou'])\n",
    "\n",
    "# Thirty videos worth of data\n",
    "no_sequences = 30\n",
    "\n",
    "# Videos are going to be 30 frames in length\n",
    "sequence_length = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    # NEW LOOP\n",
    "    # Loop through actions\n",
    "    for action in actions:\n",
    "        # Loop through sequences aka videos\n",
    "        for sequence in range(no_sequences):\n",
    "            # Loop through video length aka sequence length\n",
    "            for frame_num in range(sequence_length):\n",
    "                # Read feed\n",
    "                ret, frame = cap.read()\n",
    "\n",
    "                # Make detections\n",
    "                image, results = mediapipe_detection(frame, holistic)\n",
    "                # print(results)\n",
    "\n",
    "                # Draw landmarks\n",
    "                draw_styled_landmarks(image, results)\n",
    "                \n",
    "                # NEW Apply wait logic\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",
    "                    # Show to screen\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",
    "                    # Show to screen\n",
    "                    cv2.imshow('OpenCV Feed', image)\n",
    "                \n",
    "                # NEW Export keypoints\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",
    "\n",
    "                # Break gracefully\n",
    "                if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "                    break\n",
    "                    \n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = {label:num for num, label in enumerate(actions)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hello': 0, 'thanks': 1, 'iloveyou': 2}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 30, 1662)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(sequences).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90,)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(labels).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 30, 1662)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "from tensorflow.keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = os.path.join('Logs')\n",
    "tb_callback = TensorBoard(log_dir=log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    }
   ],
   "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": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = [.7, 0.2, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(res)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "3/3 [==============================] - 9s 757ms/step - loss: 15.3633 - categorical_accuracy: 0.2706\n",
      "Epoch 2/2000\n",
      "3/3 [==============================] - 2s 688ms/step - loss: 25.3119 - categorical_accuracy: 0.3294\n",
      "Epoch 3/2000\n",
      "3/3 [==============================] - 2s 551ms/step - loss: 23.3747 - categorical_accuracy: 0.4235\n",
      "Epoch 4/2000\n",
      "3/3 [==============================] - 2s 593ms/step - loss: 21.2691 - categorical_accuracy: 0.4235\n",
      "Epoch 5/2000\n",
      "3/3 [==============================] - 2s 587ms/step - loss: 13.0778 - categorical_accuracy: 0.2353\n",
      "Epoch 6/2000\n",
      "3/3 [==============================] - 2s 584ms/step - loss: 13.5334 - categorical_accuracy: 0.4118\n",
      "Epoch 7/2000\n",
      "3/3 [==============================] - 2s 622ms/step - loss: 25.8851 - categorical_accuracy: 0.3294\n",
      "Epoch 8/2000\n",
      "3/3 [==============================] - 2s 618ms/step - loss: 21.1273 - categorical_accuracy: 0.3176\n",
      "Epoch 9/2000\n",
      "3/3 [==============================] - 2s 792ms/step - loss: 29.0058 - categorical_accuracy: 0.3529\n",
      "Epoch 10/2000\n",
      "3/3 [==============================] - 2s 587ms/step - loss: 18.2796 - categorical_accuracy: 0.3647\n",
      "Epoch 11/2000\n",
      "3/3 [==============================] - 2s 599ms/step - loss: 45.8054 - categorical_accuracy: 0.3529\n",
      "Epoch 12/2000\n",
      "3/3 [==============================] - 2s 687ms/step - loss: 70.4138 - categorical_accuracy: 0.2588\n",
      "Epoch 13/2000\n",
      "3/3 [==============================] - 2s 609ms/step - loss: 38.8611 - categorical_accuracy: 0.4588\n",
      "Epoch 14/2000\n",
      "3/3 [==============================] - 2s 633ms/step - loss: 17.8224 - categorical_accuracy: 0.4235\n",
      "Epoch 15/2000\n",
      "3/3 [==============================] - 2s 526ms/step - loss: 12.6983 - categorical_accuracy: 0.4235\n",
      "Epoch 16/2000\n",
      "3/3 [==============================] - 2s 532ms/step - loss: 11.1965 - categorical_accuracy: 0.2941\n",
      "Epoch 17/2000\n",
      "3/3 [==============================] - 2s 560ms/step - loss: 11.8613 - categorical_accuracy: 0.4235\n",
      "Epoch 18/2000\n",
      "3/3 [==============================] - 2s 558ms/step - loss: 22.1940 - categorical_accuracy: 0.3059\n",
      "Epoch 19/2000\n",
      "3/3 [==============================] - 2s 533ms/step - loss: 68.9884 - categorical_accuracy: 0.3765\n",
      "Epoch 20/2000\n",
      "3/3 [==============================] - 2s 541ms/step - loss: 134.1778 - categorical_accuracy: 0.2588\n",
      "Epoch 21/2000\n",
      "3/3 [==============================] - 2s 589ms/step - loss: 114.0905 - categorical_accuracy: 0.4000\n",
      "Epoch 22/2000\n",
      "3/3 [==============================] - 2s 533ms/step - loss: 109.0124 - categorical_accuracy: 0.3294\n",
      "Epoch 23/2000\n",
      "3/3 [==============================] - 2s 532ms/step - loss: 72.3664 - categorical_accuracy: 0.3294\n",
      "Epoch 24/2000\n",
      "3/3 [==============================] - 2s 533ms/step - loss: 122.7217 - categorical_accuracy: 0.3529\n",
      "Epoch 25/2000\n",
      "3/3 [==============================] - 2s 565ms/step - loss: 77.2062 - categorical_accuracy: 0.2824\n",
      "Epoch 26/2000\n",
      "3/3 [==============================] - 2s 836ms/step - loss: 102.4873 - categorical_accuracy: 0.3059\n",
      "Epoch 27/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 205.7061 - categorical_accuracy: 0.2824\n",
      "Epoch 28/2000\n",
      "3/3 [==============================] - 3s 965ms/step - loss: 261.0636 - categorical_accuracy: 0.4000\n",
      "Epoch 29/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 209.0920 - categorical_accuracy: 0.3294\n",
      "Epoch 30/2000\n",
      "3/3 [==============================] - 4s 1s/step - loss: 309.3786 - categorical_accuracy: 0.3412\n",
      "Epoch 31/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 288.0939 - categorical_accuracy: 0.3647\n",
      "Epoch 32/2000\n",
      "3/3 [==============================] - 4s 1s/step - loss: 185.2907 - categorical_accuracy: 0.3765\n",
      "Epoch 33/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 61.5850 - categorical_accuracy: 0.3294\n",
      "Epoch 34/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 20.3819 - categorical_accuracy: 0.2941\n",
      "Epoch 35/2000\n",
      "3/3 [==============================] - 3s 877ms/step - loss: 19.7030 - categorical_accuracy: 0.3647\n",
      "Epoch 36/2000\n",
      "3/3 [==============================] - 2s 865ms/step - loss: 27.6719 - categorical_accuracy: 0.3647\n",
      "Epoch 37/2000\n",
      "3/3 [==============================] - 2s 623ms/step - loss: 42.5840 - categorical_accuracy: 0.2588\n",
      "Epoch 38/2000\n",
      "3/3 [==============================] - 2s 694ms/step - loss: 24.6537 - categorical_accuracy: 0.1882\n",
      "Epoch 39/2000\n",
      "3/3 [==============================] - 2s 769ms/step - loss: 8.3389 - categorical_accuracy: 0.1882\n",
      "Epoch 40/2000\n",
      "3/3 [==============================] - 5s 2s/step - loss: 8.0615 - categorical_accuracy: 0.3294\n",
      "Epoch 41/2000\n",
      "3/3 [==============================] - 5s 2s/step - loss: 6.4805 - categorical_accuracy: 0.2353\n",
      "Epoch 42/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 8.0089 - categorical_accuracy: 0.3412\n",
      "Epoch 43/2000\n",
      "3/3 [==============================] - 4s 1s/step - loss: 4.3031 - categorical_accuracy: 0.2353\n",
      "Epoch 44/2000\n",
      "3/3 [==============================] - 4s 1s/step - loss: 5.0368 - categorical_accuracy: 0.2588\n",
      "Epoch 45/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 4.2603 - categorical_accuracy: 0.2941\n",
      "Epoch 46/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 4.2032 - categorical_accuracy: 0.2706\n",
      "Epoch 47/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 3.2452 - categorical_accuracy: 0.3059\n",
      "Epoch 48/2000\n",
      "3/3 [==============================] - 2s 684ms/step - loss: 3.0998 - categorical_accuracy: 0.2941\n",
      "Epoch 49/2000\n",
      "3/3 [==============================] - 2s 667ms/step - loss: 2.9864 - categorical_accuracy: 0.3294\n",
      "Epoch 50/2000\n",
      "3/3 [==============================] - 2s 636ms/step - loss: 2.8027 - categorical_accuracy: 0.2353\n",
      "Epoch 51/2000\n",
      "3/3 [==============================] - 2s 738ms/step - loss: 3.1956 - categorical_accuracy: 0.3059\n",
      "Epoch 52/2000\n",
      "3/3 [==============================] - 4s 2s/step - loss: 2.6766 - categorical_accuracy: 0.3176\n",
      "Epoch 53/2000\n",
      "3/3 [==============================] - 3s 709ms/step - loss: 3.8528 - categorical_accuracy: 0.1765\n",
      "Epoch 54/2000\n",
      "3/3 [==============================] - 2s 861ms/step - loss: 2.6618 - categorical_accuracy: 0.4235\n",
      "Epoch 55/2000\n",
      "3/3 [==============================] - 2s 701ms/step - loss: 2.6955 - categorical_accuracy: 0.3882\n",
      "Epoch 56/2000\n",
      "3/3 [==============================] - 2s 722ms/step - loss: 4.5287 - categorical_accuracy: 0.3529\n",
      "Epoch 57/2000\n",
      "3/3 [==============================] - 2s 667ms/step - loss: 2.8898 - categorical_accuracy: 0.2588\n",
      "Epoch 58/2000\n",
      "3/3 [==============================] - 2s 824ms/step - loss: 2.7022 - categorical_accuracy: 0.4000\n",
      "Epoch 59/2000\n",
      "3/3 [==============================] - 3s 977ms/step - loss: 2.7295 - categorical_accuracy: 0.3529\n",
      "Epoch 60/2000\n",
      "3/3 [==============================] - 3s 902ms/step - loss: 2.2126 - categorical_accuracy: 0.3412\n",
      "Epoch 61/2000\n",
      "3/3 [==============================] - 2s 671ms/step - loss: 1.7455 - categorical_accuracy: 0.2471\n",
      "Epoch 62/2000\n",
      "3/3 [==============================] - 2s 880ms/step - loss: 1.7427 - categorical_accuracy: 0.2941\n",
      "Epoch 63/2000\n",
      "3/3 [==============================] - 2s 694ms/step - loss: 2.0622 - categorical_accuracy: 0.2824\n",
      "Epoch 64/2000\n",
      "3/3 [==============================] - 2s 877ms/step - loss: 2.1637 - categorical_accuracy: 0.3529\n",
      "Epoch 65/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 1.3733 - categorical_accuracy: 0.4824\n",
      "Epoch 66/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 1.7343 - categorical_accuracy: 0.2353\n",
      "Epoch 67/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 2.3278 - categorical_accuracy: 0.4706\n",
      "Epoch 68/2000\n",
      "3/3 [==============================] - 2s 661ms/step - loss: 1.5907 - categorical_accuracy: 0.3647\n",
      "Epoch 69/2000\n",
      "3/3 [==============================] - 2s 716ms/step - loss: 1.2616 - categorical_accuracy: 0.5176\n",
      "Epoch 70/2000\n",
      "3/3 [==============================] - 2s 745ms/step - loss: 1.3887 - categorical_accuracy: 0.2824\n",
      "Epoch 71/2000\n",
      "3/3 [==============================] - 2s 791ms/step - loss: 1.4554 - categorical_accuracy: 0.4235\n",
      "Epoch 72/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 1.5882 - categorical_accuracy: 0.4118\n",
      "Epoch 73/2000\n",
      "3/3 [==============================] - 3s 955ms/step - loss: 1.5965 - categorical_accuracy: 0.3059\n",
      "Epoch 74/2000\n",
      "3/3 [==============================] - 2s 680ms/step - loss: 1.7690 - categorical_accuracy: 0.4235\n",
      "Epoch 75/2000\n",
      "3/3 [==============================] - 2s 606ms/step - loss: 1.5927 - categorical_accuracy: 0.3882\n",
      "Epoch 76/2000\n",
      "3/3 [==============================] - 2s 618ms/step - loss: 1.5123 - categorical_accuracy: 0.4353\n",
      "Epoch 77/2000\n",
      "3/3 [==============================] - 2s 624ms/step - loss: 1.7939 - categorical_accuracy: 0.3294\n",
      "Epoch 78/2000\n",
      "3/3 [==============================] - 2s 607ms/step - loss: 1.7559 - categorical_accuracy: 0.3529\n",
      "Epoch 79/2000\n",
      "3/3 [==============================] - 2s 602ms/step - loss: 1.6579 - categorical_accuracy: 0.3529\n",
      "Epoch 80/2000\n",
      "3/3 [==============================] - 2s 586ms/step - loss: 1.2492 - categorical_accuracy: 0.3529\n",
      "Epoch 81/2000\n",
      "3/3 [==============================] - 2s 742ms/step - loss: 1.0828 - categorical_accuracy: 0.4706\n",
      "Epoch 82/2000\n",
      "3/3 [==============================] - 2s 579ms/step - loss: 1.0705 - categorical_accuracy: 0.4941\n",
      "Epoch 83/2000\n",
      "3/3 [==============================] - 2s 635ms/step - loss: 0.9864 - categorical_accuracy: 0.5176\n",
      "Epoch 84/2000\n",
      "3/3 [==============================] - 2s 574ms/step - loss: 1.0983 - categorical_accuracy: 0.5294\n",
      "Epoch 85/2000\n",
      "3/3 [==============================] - 2s 711ms/step - loss: 1.1874 - categorical_accuracy: 0.4471\n",
      "Epoch 86/2000\n",
      "3/3 [==============================] - 2s 602ms/step - loss: 0.9967 - categorical_accuracy: 0.5176\n",
      "Epoch 87/2000\n",
      "3/3 [==============================] - 2s 621ms/step - loss: 0.8675 - categorical_accuracy: 0.5882\n",
      "Epoch 88/2000\n",
      "3/3 [==============================] - 2s 613ms/step - loss: 0.9636 - categorical_accuracy: 0.6353\n",
      "Epoch 89/2000\n",
      "3/3 [==============================] - 2s 616ms/step - loss: 0.9341 - categorical_accuracy: 0.5176\n",
      "Epoch 90/2000\n",
      "3/3 [==============================] - 2s 514ms/step - loss: 0.8869 - categorical_accuracy: 0.5765\n",
      "Epoch 91/2000\n",
      "3/3 [==============================] - 2s 533ms/step - loss: 0.7993 - categorical_accuracy: 0.6941\n",
      "Epoch 92/2000\n",
      "3/3 [==============================] - 2s 566ms/step - loss: 0.8505 - categorical_accuracy: 0.5529\n",
      "Epoch 93/2000\n",
      "3/3 [==============================] - 2s 566ms/step - loss: 0.8993 - categorical_accuracy: 0.6353\n",
      "Epoch 94/2000\n",
      "3/3 [==============================] - 2s 568ms/step - loss: 0.8227 - categorical_accuracy: 0.6353\n",
      "Epoch 95/2000\n",
      "3/3 [==============================] - 2s 611ms/step - loss: 0.9029 - categorical_accuracy: 0.5412\n",
      "Epoch 96/2000\n",
      "3/3 [==============================] - 2s 536ms/step - loss: 0.9352 - categorical_accuracy: 0.5882\n",
      "Epoch 97/2000\n",
      "3/3 [==============================] - 2s 532ms/step - loss: 0.9119 - categorical_accuracy: 0.5176\n",
      "Epoch 98/2000\n",
      "3/3 [==============================] - 2s 583ms/step - loss: 0.8668 - categorical_accuracy: 0.6353\n",
      "Epoch 99/2000\n",
      "3/3 [==============================] - 2s 557ms/step - loss: 0.8553 - categorical_accuracy: 0.6235\n",
      "Epoch 100/2000\n",
      "3/3 [==============================] - 2s 557ms/step - loss: 0.8361 - categorical_accuracy: 0.5529\n",
      "Epoch 101/2000\n",
      "3/3 [==============================] - 2s 575ms/step - loss: 0.7758 - categorical_accuracy: 0.6824\n",
      "Epoch 102/2000\n",
      "3/3 [==============================] - 2s 541ms/step - loss: 0.7742 - categorical_accuracy: 0.6353\n",
      "Epoch 103/2000\n",
      "3/3 [==============================] - 2s 529ms/step - loss: 0.8659 - categorical_accuracy: 0.6000\n",
      "Epoch 104/2000\n",
      "3/3 [==============================] - 2s 549ms/step - loss: 0.8407 - categorical_accuracy: 0.6588\n",
      "Epoch 105/2000\n",
      "3/3 [==============================] - 2s 660ms/step - loss: 0.7152 - categorical_accuracy: 0.6588\n",
      "Epoch 106/2000\n",
      "3/3 [==============================] - 2s 558ms/step - loss: 0.7822 - categorical_accuracy: 0.6118\n",
      "Epoch 107/2000\n",
      "3/3 [==============================] - 2s 564ms/step - loss: 0.7421 - categorical_accuracy: 0.7176\n",
      "Epoch 108/2000\n",
      "3/3 [==============================] - 2s 545ms/step - loss: 0.7111 - categorical_accuracy: 0.6471\n",
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      "3/3 [==============================] - 2s 604ms/step - loss: 1.0335 - categorical_accuracy: 0.4118\n",
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      "3/3 [==============================] - 2s 610ms/step - loss: 2.2104 - categorical_accuracy: 0.3412\n",
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      "3/3 [==============================] - 2s 581ms/step - loss: 1.1005 - categorical_accuracy: 0.3765\n",
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      "3/3 [==============================] - 2s 589ms/step - loss: 1.2583 - categorical_accuracy: 0.3647\n",
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      "3/3 [==============================] - 2s 613ms/step - loss: 1.3236 - categorical_accuracy: 0.4000\n",
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      "3/3 [==============================] - 2s 602ms/step - loss: 1.2010 - categorical_accuracy: 0.4471\n",
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      "3/3 [==============================] - 2s 586ms/step - loss: 1.1728 - categorical_accuracy: 0.3647\n",
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      "3/3 [==============================] - 2s 611ms/step - loss: 2.4390 - categorical_accuracy: 0.4118\n",
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      "3/3 [==============================] - 2s 595ms/step - loss: 1.4632 - categorical_accuracy: 0.4000\n",
      "Epoch 700/2000\n",
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      "3/3 [==============================] - 2s 603ms/step - loss: 1.1845 - categorical_accuracy: 0.5059\n",
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      "3/3 [==============================] - 2s 588ms/step - loss: 1.0970 - categorical_accuracy: 0.4353\n",
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      "3/3 [==============================] - 2s 604ms/step - loss: 1.0606 - categorical_accuracy: 0.4471\n",
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      "3/3 [==============================] - 2s 596ms/step - loss: 1.0994 - categorical_accuracy: 0.4118\n",
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      "3/3 [==============================] - 3s 1s/step - loss: 0.9120 - categorical_accuracy: 0.6000\n",
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      "3/3 [==============================] - 4s 1s/step - loss: 0.8908 - categorical_accuracy: 0.6471\n",
      "Epoch 711/2000\n",
      "3/3 [==============================] - 4s 1s/step - loss: 0.9871 - categorical_accuracy: 0.4235\n",
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      "3/3 [==============================] - 4s 1s/step - loss: 0.8869 - categorical_accuracy: 0.6000\n",
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      "Epoch 714/2000\n",
      "3/3 [==============================] - 2s 707ms/step - loss: 0.8730 - categorical_accuracy: 0.6588\n",
      "Epoch 715/2000\n",
      "3/3 [==============================] - 2s 649ms/step - loss: 0.9018 - categorical_accuracy: 0.6235\n",
      "Epoch 716/2000\n",
      "3/3 [==============================] - 2s 696ms/step - loss: 0.8998 - categorical_accuracy: 0.5412\n",
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      "3/3 [==============================] - 2s 814ms/step - loss: 0.9082 - categorical_accuracy: 0.5529\n",
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      "3/3 [==============================] - 3s 868ms/step - loss: 0.8730 - categorical_accuracy: 0.7529\n",
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      "3/3 [==============================] - 5s 2s/step - loss: 0.9526 - categorical_accuracy: 0.4824\n",
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      "3/3 [==============================] - 4s 1s/step - loss: 0.9255 - categorical_accuracy: 0.6471\n",
      "Epoch 721/2000\n",
      "3/3 [==============================] - 3s 998ms/step - loss: 0.8927 - categorical_accuracy: 0.6588\n",
      "Epoch 722/2000\n",
      "3/3 [==============================] - 3s 863ms/step - loss: 0.8621 - categorical_accuracy: 0.7647\n",
      "Epoch 723/2000\n",
      "3/3 [==============================] - 2s 722ms/step - loss: 0.8768 - categorical_accuracy: 0.7059\n",
      "Epoch 724/2000\n",
      "3/3 [==============================] - 3s 893ms/step - loss: 0.8858 - categorical_accuracy: 0.6824\n",
      "Epoch 725/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.8647 - categorical_accuracy: 0.6235\n",
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      "3/3 [==============================] - 4s 1s/step - loss: 0.8510 - categorical_accuracy: 0.6471\n",
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      "3/3 [==============================] - 3s 1s/step - loss: 0.8881 - categorical_accuracy: 0.6706\n",
      "Epoch 728/2000\n",
      "3/3 [==============================] - 4s 1s/step - loss: 0.8672 - categorical_accuracy: 0.6471\n",
      "Epoch 729/2000\n",
      "3/3 [==============================] - 4s 1s/step - loss: 0.8547 - categorical_accuracy: 0.7412\n",
      "Epoch 730/2000\n",
      "3/3 [==============================] - 2s 690ms/step - loss: 0.8469 - categorical_accuracy: 0.6588\n",
      "Epoch 731/2000\n",
      "3/3 [==============================] - 2s 674ms/step - loss: 0.8478 - categorical_accuracy: 0.6118\n",
      "Epoch 732/2000\n",
      "3/3 [==============================] - 2s 625ms/step - loss: 0.8633 - categorical_accuracy: 0.7176\n",
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      "3/3 [==============================] - 2s 624ms/step - loss: 0.8536 - categorical_accuracy: 0.7412\n",
      "Epoch 734/2000\n",
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      "Epoch 735/2000\n",
      "3/3 [==============================] - 2s 624ms/step - loss: 0.8872 - categorical_accuracy: 0.6588\n",
      "Epoch 736/2000\n",
      "3/3 [==============================] - 2s 699ms/step - loss: 0.9122 - categorical_accuracy: 0.6824\n",
      "Epoch 737/2000\n",
      "3/3 [==============================] - 2s 625ms/step - loss: 0.9550 - categorical_accuracy: 0.5412\n",
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      "3/3 [==============================] - 2s 627ms/step - loss: 0.9221 - categorical_accuracy: 0.6118\n",
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      "3/3 [==============================] - 2s 627ms/step - loss: 0.8626 - categorical_accuracy: 0.6000\n",
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      "3/3 [==============================] - 2s 627ms/step - loss: 0.8708 - categorical_accuracy: 0.7294\n",
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      "3/3 [==============================] - 2s 704ms/step - loss: 0.8433 - categorical_accuracy: 0.6824\n",
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      "3/3 [==============================] - 2s 687ms/step - loss: 0.8283 - categorical_accuracy: 0.7059\n",
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      "Epoch 749/2000\n",
      "3/3 [==============================] - 2s 628ms/step - loss: 0.8377 - categorical_accuracy: 0.7294\n",
      "Epoch 750/2000\n",
      "3/3 [==============================] - 2s 621ms/step - loss: 0.8374 - categorical_accuracy: 0.7294\n",
      "Epoch 751/2000\n",
      "3/3 [==============================] - 2s 608ms/step - loss: 0.8459 - categorical_accuracy: 0.5765\n",
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      "3/3 [==============================] - 2s 645ms/step - loss: 0.8077 - categorical_accuracy: 0.7059\n",
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      "3/3 [==============================] - 2s 628ms/step - loss: 0.8135 - categorical_accuracy: 0.5765\n",
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      "3/3 [==============================] - 2s 741ms/step - loss: 0.8352 - categorical_accuracy: 0.7412\n",
      "Epoch 755/2000\n",
      "3/3 [==============================] - 2s 627ms/step - loss: 0.8143 - categorical_accuracy: 0.6706\n",
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      "3/3 [==============================] - 2s 669ms/step - loss: 0.8162 - categorical_accuracy: 0.7529\n",
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      "3/3 [==============================] - 2s 634ms/step - loss: 0.7975 - categorical_accuracy: 0.7647\n",
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      "Epoch 760/2000\n",
      "3/3 [==============================] - 2s 710ms/step - loss: 0.8165 - categorical_accuracy: 0.7176\n",
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      "Epoch 800/2000\n",
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      "Epoch 820/2000\n",
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      "3/3 [==============================] - 2s 575ms/step - loss: 0.7459 - categorical_accuracy: 0.7176\n",
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      "3/3 [==============================] - 2s 548ms/step - loss: 0.7615 - categorical_accuracy: 0.6588\n",
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      "3/3 [==============================] - 2s 549ms/step - loss: 0.7775 - categorical_accuracy: 0.5882\n",
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      "3/3 [==============================] - 2s 560ms/step - loss: 0.7416 - categorical_accuracy: 0.6353\n",
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      "3/3 [==============================] - 2s 557ms/step - loss: 0.7639 - categorical_accuracy: 0.7529\n",
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      "3/3 [==============================] - 2s 595ms/step - loss: 0.7759 - categorical_accuracy: 0.7412\n",
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      "3/3 [==============================] - 2s 556ms/step - loss: 0.8655 - categorical_accuracy: 0.6471\n",
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      "3/3 [==============================] - 2s 545ms/step - loss: 0.8349 - categorical_accuracy: 0.6471\n",
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      "3/3 [==============================] - 2s 618ms/step - loss: 0.7882 - categorical_accuracy: 0.6235\n",
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      "3/3 [==============================] - 2s 593ms/step - loss: 0.7491 - categorical_accuracy: 0.6824\n",
      "Epoch 850/2000\n",
      "3/3 [==============================] - 2s 568ms/step - loss: 0.7435 - categorical_accuracy: 0.6941\n",
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      "3/3 [==============================] - 2s 560ms/step - loss: 0.7462 - categorical_accuracy: 0.7176\n",
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      "3/3 [==============================] - 2s 536ms/step - loss: 0.7649 - categorical_accuracy: 0.7294\n",
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      "3/3 [==============================] - 2s 604ms/step - loss: 0.7731 - categorical_accuracy: 0.6588\n",
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      "3/3 [==============================] - 2s 545ms/step - loss: 0.7431 - categorical_accuracy: 0.7176\n",
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      "3/3 [==============================] - 2s 556ms/step - loss: 0.7975 - categorical_accuracy: 0.7176\n",
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      "3/3 [==============================] - 2s 557ms/step - loss: 0.7353 - categorical_accuracy: 0.7059\n",
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      "3/3 [==============================] - 2s 580ms/step - loss: 0.7028 - categorical_accuracy: 0.7294\n",
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      "3/3 [==============================] - 2s 541ms/step - loss: 0.7439 - categorical_accuracy: 0.6706\n",
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      "3/3 [==============================] - 2s 576ms/step - loss: 0.6967 - categorical_accuracy: 0.7176\n",
      "Epoch 864/2000\n",
      "3/3 [==============================] - 2s 567ms/step - loss: 0.7355 - categorical_accuracy: 0.6706\n",
      "Epoch 865/2000\n",
      "3/3 [==============================] - 2s 567ms/step - loss: 0.7342 - categorical_accuracy: 0.7529\n",
      "Epoch 866/2000\n",
      "3/3 [==============================] - 2s 547ms/step - loss: 0.7275 - categorical_accuracy: 0.6941\n",
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      "3/3 [==============================] - 2s 554ms/step - loss: 0.7151 - categorical_accuracy: 0.7529\n",
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      "3/3 [==============================] - 2s 550ms/step - loss: 0.7019 - categorical_accuracy: 0.6941\n",
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      "3/3 [==============================] - 2s 561ms/step - loss: 0.7571 - categorical_accuracy: 0.6118\n",
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      "3/3 [==============================] - 2s 638ms/step - loss: 0.7081 - categorical_accuracy: 0.7294\n",
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      "3/3 [==============================] - 2s 633ms/step - loss: 0.7417 - categorical_accuracy: 0.6118\n",
      "Epoch 872/2000\n",
      "3/3 [==============================] - 2s 568ms/step - loss: 0.6886 - categorical_accuracy: 0.7647\n",
      "Epoch 873/2000\n",
      "3/3 [==============================] - 2s 556ms/step - loss: 0.7169 - categorical_accuracy: 0.7176\n",
      "Epoch 874/2000\n",
      "3/3 [==============================] - 2s 560ms/step - loss: 0.7122 - categorical_accuracy: 0.7176\n",
      "Epoch 875/2000\n",
      "3/3 [==============================] - 2s 551ms/step - loss: 0.7043 - categorical_accuracy: 0.6471\n",
      "Epoch 876/2000\n",
      "3/3 [==============================] - 2s 552ms/step - loss: 0.6947 - categorical_accuracy: 0.6824\n",
      "Epoch 877/2000\n",
      "3/3 [==============================] - 2s 550ms/step - loss: 0.7006 - categorical_accuracy: 0.6941\n",
      "Epoch 878/2000\n",
      "3/3 [==============================] - 2s 571ms/step - loss: 0.7007 - categorical_accuracy: 0.7647\n",
      "Epoch 879/2000\n",
      "3/3 [==============================] - 2s 629ms/step - loss: 0.6904 - categorical_accuracy: 0.7882\n",
      "Epoch 880/2000\n",
      "3/3 [==============================] - 2s 574ms/step - loss: 0.7087 - categorical_accuracy: 0.7059\n",
      "Epoch 881/2000\n",
      "3/3 [==============================] - 2s 567ms/step - loss: 0.7250 - categorical_accuracy: 0.7176\n",
      "Epoch 882/2000\n",
      "3/3 [==============================] - 2s 601ms/step - loss: 0.7658 - categorical_accuracy: 0.6588\n",
      "Epoch 883/2000\n",
      "3/3 [==============================] - 2s 560ms/step - loss: 0.6901 - categorical_accuracy: 0.7765\n",
      "Epoch 884/2000\n",
      "3/3 [==============================] - 2s 574ms/step - loss: 0.7019 - categorical_accuracy: 0.7412\n",
      "Epoch 885/2000\n",
      "3/3 [==============================] - 2s 537ms/step - loss: 0.7381 - categorical_accuracy: 0.6941\n",
      "Epoch 886/2000\n",
      "3/3 [==============================] - 2s 577ms/step - loss: 0.7195 - categorical_accuracy: 0.6824\n",
      "Epoch 887/2000\n",
      "3/3 [==============================] - 2s 577ms/step - loss: 0.6770 - categorical_accuracy: 0.7647\n",
      "Epoch 888/2000\n",
      "3/3 [==============================] - 2s 571ms/step - loss: 0.6856 - categorical_accuracy: 0.7882\n",
      "Epoch 889/2000\n",
      "3/3 [==============================] - 2s 575ms/step - loss: 0.7026 - categorical_accuracy: 0.7647\n",
      "Epoch 890/2000\n",
      "3/3 [==============================] - 2s 642ms/step - loss: 0.7370 - categorical_accuracy: 0.7176\n",
      "Epoch 891/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.7148 - categorical_accuracy: 0.6588\n",
      "Epoch 892/2000\n",
      "3/3 [==============================] - 2s 627ms/step - loss: 0.6911 - categorical_accuracy: 0.7294\n",
      "Epoch 893/2000\n",
      "3/3 [==============================] - 2s 653ms/step - loss: 0.7225 - categorical_accuracy: 0.7529\n",
      "Epoch 894/2000\n",
      "3/3 [==============================] - 2s 654ms/step - loss: 0.7193 - categorical_accuracy: 0.6824\n",
      "Epoch 895/2000\n",
      "3/3 [==============================] - 2s 635ms/step - loss: 0.7523 - categorical_accuracy: 0.7647\n",
      "Epoch 896/2000\n",
      "3/3 [==============================] - 2s 636ms/step - loss: 0.7147 - categorical_accuracy: 0.6706\n",
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      "3/3 [==============================] - 2s 632ms/step - loss: 0.7081 - categorical_accuracy: 0.7059\n",
      "Epoch 898/2000\n",
      "3/3 [==============================] - 2s 661ms/step - loss: 0.7184 - categorical_accuracy: 0.6941\n",
      "Epoch 899/2000\n",
      "3/3 [==============================] - 3s 909ms/step - loss: 0.7158 - categorical_accuracy: 0.6941\n",
      "Epoch 900/2000\n",
      "3/3 [==============================] - 2s 840ms/step - loss: 0.7199 - categorical_accuracy: 0.6000\n",
      "Epoch 901/2000\n",
      "3/3 [==============================] - 2s 796ms/step - loss: 0.6743 - categorical_accuracy: 0.7882\n",
      "Epoch 902/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.7465 - categorical_accuracy: 0.6471\n",
      "Epoch 903/2000\n",
      "3/3 [==============================] - 4s 1s/step - loss: 0.6882 - categorical_accuracy: 0.6588\n",
      "Epoch 904/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.6782 - categorical_accuracy: 0.8118\n",
      "Epoch 905/2000\n",
      "3/3 [==============================] - 3s 789ms/step - loss: 0.6651 - categorical_accuracy: 0.7647\n",
      "Epoch 906/2000\n",
      "3/3 [==============================] - 2s 794ms/step - loss: 0.6663 - categorical_accuracy: 0.7294\n",
      "Epoch 907/2000\n",
      "3/3 [==============================] - 2s 615ms/step - loss: 0.6979 - categorical_accuracy: 0.7765\n",
      "Epoch 908/2000\n",
      "3/3 [==============================] - 2s 674ms/step - loss: 0.7094 - categorical_accuracy: 0.7294\n",
      "Epoch 909/2000\n",
      "3/3 [==============================] - 2s 631ms/step - loss: 0.6886 - categorical_accuracy: 0.6588\n",
      "Epoch 910/2000\n",
      "3/3 [==============================] - 2s 654ms/step - loss: 0.6805 - categorical_accuracy: 0.7412\n",
      "Epoch 911/2000\n",
      "3/3 [==============================] - 2s 748ms/step - loss: 0.6632 - categorical_accuracy: 0.7647\n",
      "Epoch 912/2000\n",
      "3/3 [==============================] - 2s 629ms/step - loss: 0.6690 - categorical_accuracy: 0.6941\n",
      "Epoch 913/2000\n",
      "3/3 [==============================] - 2s 659ms/step - loss: 0.6684 - categorical_accuracy: 0.8000\n",
      "Epoch 914/2000\n",
      "3/3 [==============================] - 2s 706ms/step - loss: 0.7005 - categorical_accuracy: 0.7529\n",
      "Epoch 915/2000\n",
      "3/3 [==============================] - 2s 540ms/step - loss: 0.6835 - categorical_accuracy: 0.7412\n",
      "Epoch 916/2000\n",
      "3/3 [==============================] - 2s 569ms/step - loss: 0.6562 - categorical_accuracy: 0.6941\n",
      "Epoch 917/2000\n",
      "3/3 [==============================] - 2s 543ms/step - loss: 0.6583 - categorical_accuracy: 0.7765\n",
      "Epoch 918/2000\n",
      "3/3 [==============================] - 2s 592ms/step - loss: 0.6868 - categorical_accuracy: 0.7176\n",
      "Epoch 919/2000\n",
      "3/3 [==============================] - 2s 545ms/step - loss: 0.7157 - categorical_accuracy: 0.7059\n",
      "Epoch 920/2000\n",
      "3/3 [==============================] - 2s 555ms/step - loss: 0.6933 - categorical_accuracy: 0.7294\n",
      "Epoch 921/2000\n",
      "3/3 [==============================] - 2s 535ms/step - loss: 0.7196 - categorical_accuracy: 0.6235\n",
      "Epoch 922/2000\n",
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      "Epoch 923/2000\n",
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      "3/3 [==============================] - 2s 553ms/step - loss: 0.6281 - categorical_accuracy: 0.7412\n",
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      "3/3 [==============================] - 2s 539ms/step - loss: 0.6129 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 542ms/step - loss: 0.6105 - categorical_accuracy: 0.7765\n",
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      "3/3 [==============================] - 2s 553ms/step - loss: 0.5995 - categorical_accuracy: 0.8000\n",
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      "3/3 [==============================] - 2s 535ms/step - loss: 0.6472 - categorical_accuracy: 0.8000\n",
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      "3/3 [==============================] - 2s 542ms/step - loss: 0.6359 - categorical_accuracy: 0.7059\n",
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      "3/3 [==============================] - 2s 551ms/step - loss: 0.6174 - categorical_accuracy: 0.8235\n",
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      "3/3 [==============================] - 2s 535ms/step - loss: 0.5924 - categorical_accuracy: 0.7412\n",
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      "3/3 [==============================] - 2s 542ms/step - loss: 0.6377 - categorical_accuracy: 0.7412\n",
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      "Epoch 980/2000\n",
      "3/3 [==============================] - 2s 547ms/step - loss: 0.6444 - categorical_accuracy: 0.7059\n",
      "Epoch 981/2000\n",
      "3/3 [==============================] - 2s 643ms/step - loss: 0.5828 - categorical_accuracy: 0.8118\n",
      "Epoch 982/2000\n",
      "3/3 [==============================] - 2s 530ms/step - loss: 0.5978 - categorical_accuracy: 0.7882\n",
      "Epoch 983/2000\n",
      "3/3 [==============================] - 2s 547ms/step - loss: 0.6633 - categorical_accuracy: 0.7294\n",
      "Epoch 984/2000\n",
      "3/3 [==============================] - 2s 542ms/step - loss: 0.7827 - categorical_accuracy: 0.7412\n",
      "Epoch 985/2000\n",
      "3/3 [==============================] - 2s 550ms/step - loss: 0.6219 - categorical_accuracy: 0.7647\n",
      "Epoch 986/2000\n",
      "3/3 [==============================] - 2s 551ms/step - loss: 0.6103 - categorical_accuracy: 0.7647\n",
      "Epoch 987/2000\n",
      "3/3 [==============================] - 2s 543ms/step - loss: 0.6106 - categorical_accuracy: 0.8118\n",
      "Epoch 988/2000\n",
      "3/3 [==============================] - 2s 549ms/step - loss: 0.6215 - categorical_accuracy: 0.7647\n",
      "Epoch 989/2000\n",
      "3/3 [==============================] - 2s 538ms/step - loss: 0.5856 - categorical_accuracy: 0.8118\n",
      "Epoch 990/2000\n",
      "3/3 [==============================] - 2s 549ms/step - loss: 0.5785 - categorical_accuracy: 0.8000\n",
      "Epoch 991/2000\n",
      "3/3 [==============================] - 2s 820ms/step - loss: 0.6149 - categorical_accuracy: 0.7412\n",
      "Epoch 992/2000\n",
      "3/3 [==============================] - 2s 582ms/step - loss: 0.5792 - categorical_accuracy: 0.7882\n",
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      "3/3 [==============================] - 2s 553ms/step - loss: 0.5519 - categorical_accuracy: 0.8353\n",
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      "3/3 [==============================] - 2s 548ms/step - loss: 0.5634 - categorical_accuracy: 0.7647\n",
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      "3/3 [==============================] - 2s 554ms/step - loss: 0.5617 - categorical_accuracy: 0.8118\n",
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      "3/3 [==============================] - 2s 529ms/step - loss: 0.6138 - categorical_accuracy: 0.7059\n",
      "Epoch 997/2000\n",
      "3/3 [==============================] - 2s 539ms/step - loss: 0.5587 - categorical_accuracy: 0.7529\n",
      "Epoch 998/2000\n",
      "3/3 [==============================] - 2s 558ms/step - loss: 0.5989 - categorical_accuracy: 0.7882\n",
      "Epoch 999/2000\n",
      "3/3 [==============================] - 2s 546ms/step - loss: 0.6158 - categorical_accuracy: 0.7647\n",
      "Epoch 1000/2000\n",
      "3/3 [==============================] - 2s 547ms/step - loss: 0.6584 - categorical_accuracy: 0.6706\n",
      "Epoch 1001/2000\n",
      "3/3 [==============================] - 2s 585ms/step - loss: 0.6637 - categorical_accuracy: 0.7765\n",
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      "3/3 [==============================] - 2s 530ms/step - loss: 0.6139 - categorical_accuracy: 0.8118\n",
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      "3/3 [==============================] - 2s 548ms/step - loss: 0.5887 - categorical_accuracy: 0.7529\n",
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      "3/3 [==============================] - 2s 538ms/step - loss: 0.5598 - categorical_accuracy: 0.7529\n",
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      "3/3 [==============================] - 2s 574ms/step - loss: 0.5727 - categorical_accuracy: 0.8118\n",
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      "3/3 [==============================] - 2s 585ms/step - loss: 0.5293 - categorical_accuracy: 0.8588\n",
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      "3/3 [==============================] - 2s 544ms/step - loss: 0.5483 - categorical_accuracy: 0.7647\n",
      "Epoch 1008/2000\n",
      "3/3 [==============================] - 2s 545ms/step - loss: 0.5726 - categorical_accuracy: 0.8118\n",
      "Epoch 1009/2000\n",
      "3/3 [==============================] - 2s 535ms/step - loss: 0.5547 - categorical_accuracy: 0.7765\n",
      "Epoch 1010/2000\n",
      "3/3 [==============================] - 2s 533ms/step - loss: 0.5607 - categorical_accuracy: 0.7882\n",
      "Epoch 1011/2000\n",
      "3/3 [==============================] - 2s 572ms/step - loss: 0.5819 - categorical_accuracy: 0.8000\n",
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      "3/3 [==============================] - 2s 758ms/step - loss: 0.6436 - categorical_accuracy: 0.7882\n",
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      "3/3 [==============================] - 2s 552ms/step - loss: 0.6053 - categorical_accuracy: 0.7294\n",
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      "3/3 [==============================] - 2s 551ms/step - loss: 0.6062 - categorical_accuracy: 0.6588\n",
      "Epoch 1015/2000\n",
      "3/3 [==============================] - 2s 573ms/step - loss: 0.5852 - categorical_accuracy: 0.7882\n",
      "Epoch 1016/2000\n",
      "3/3 [==============================] - 2s 542ms/step - loss: 0.6191 - categorical_accuracy: 0.7412\n",
      "Epoch 1017/2000\n",
      "3/3 [==============================] - 2s 537ms/step - loss: 0.5467 - categorical_accuracy: 0.7647\n",
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      "3/3 [==============================] - 2s 538ms/step - loss: 0.5658 - categorical_accuracy: 0.8118\n",
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      "3/3 [==============================] - 2s 550ms/step - loss: 0.6737 - categorical_accuracy: 0.7412\n",
      "Epoch 1020/2000\n",
      "3/3 [==============================] - 2s 560ms/step - loss: 0.7026 - categorical_accuracy: 0.7765\n",
      "Epoch 1021/2000\n",
      "3/3 [==============================] - 2s 571ms/step - loss: 0.5953 - categorical_accuracy: 0.6941\n",
      "Epoch 1022/2000\n",
      "3/3 [==============================] - 2s 638ms/step - loss: 0.5341 - categorical_accuracy: 0.8235\n",
      "Epoch 1023/2000\n",
      "3/3 [==============================] - 2s 547ms/step - loss: 0.5629 - categorical_accuracy: 0.7294\n",
      "Epoch 1024/2000\n",
      "3/3 [==============================] - 2s 555ms/step - loss: 0.5049 - categorical_accuracy: 0.7882\n",
      "Epoch 1025/2000\n",
      "3/3 [==============================] - 2s 559ms/step - loss: 0.5167 - categorical_accuracy: 0.8471\n",
      "Epoch 1026/2000\n",
      "3/3 [==============================] - 2s 544ms/step - loss: 0.5126 - categorical_accuracy: 0.8471\n",
      "Epoch 1027/2000\n",
      "3/3 [==============================] - 2s 554ms/step - loss: 0.5061 - categorical_accuracy: 0.7529\n",
      "Epoch 1028/2000\n",
      "3/3 [==============================] - 2s 540ms/step - loss: 0.5041 - categorical_accuracy: 0.8824\n",
      "Epoch 1029/2000\n",
      "3/3 [==============================] - 2s 538ms/step - loss: 0.5013 - categorical_accuracy: 0.8824\n",
      "Epoch 1030/2000\n",
      "3/3 [==============================] - 2s 540ms/step - loss: 0.5014 - categorical_accuracy: 0.8235\n",
      "Epoch 1031/2000\n",
      "3/3 [==============================] - 2s 599ms/step - loss: 0.4960 - categorical_accuracy: 0.8353\n",
      "Epoch 1032/2000\n",
      "3/3 [==============================] - 2s 598ms/step - loss: 0.5310 - categorical_accuracy: 0.7294\n",
      "Epoch 1033/2000\n",
      "3/3 [==============================] - 2s 538ms/step - loss: 0.5272 - categorical_accuracy: 0.8000\n",
      "Epoch 1034/2000\n",
      "3/3 [==============================] - 2s 540ms/step - loss: 0.5101 - categorical_accuracy: 0.8235\n",
      "Epoch 1035/2000\n",
      "3/3 [==============================] - 2s 704ms/step - loss: 0.5748 - categorical_accuracy: 0.8118\n",
      "Epoch 1036/2000\n",
      "3/3 [==============================] - 2s 669ms/step - loss: 0.5530 - categorical_accuracy: 0.7647\n",
      "Epoch 1037/2000\n",
      "3/3 [==============================] - 2s 654ms/step - loss: 0.6217 - categorical_accuracy: 0.8235\n",
      "Epoch 1038/2000\n",
      "3/3 [==============================] - 2s 589ms/step - loss: 0.5301 - categorical_accuracy: 0.7412\n",
      "Epoch 1039/2000\n",
      "3/3 [==============================] - 2s 603ms/step - loss: 0.4624 - categorical_accuracy: 0.8941\n",
      "Epoch 1040/2000\n",
      "3/3 [==============================] - 2s 653ms/step - loss: 0.5063 - categorical_accuracy: 0.8471\n",
      "Epoch 1041/2000\n",
      "3/3 [==============================] - 2s 601ms/step - loss: 0.5779 - categorical_accuracy: 0.6706\n",
      "Epoch 1042/2000\n",
      "3/3 [==============================] - 2s 618ms/step - loss: 0.5728 - categorical_accuracy: 0.7412\n",
      "Epoch 1043/2000\n",
      "3/3 [==============================] - 2s 618ms/step - loss: 0.6592 - categorical_accuracy: 0.6941\n",
      "Epoch 1044/2000\n",
      "3/3 [==============================] - 3s 913ms/step - loss: 0.7744 - categorical_accuracy: 0.7529\n",
      "Epoch 1045/2000\n",
      "3/3 [==============================] - 3s 998ms/step - loss: 0.6308 - categorical_accuracy: 0.6824\n",
      "Epoch 1046/2000\n",
      "3/3 [==============================] - 2s 709ms/step - loss: 0.5545 - categorical_accuracy: 0.7294\n",
      "Epoch 1047/2000\n",
      "3/3 [==============================] - 2s 638ms/step - loss: 0.5407 - categorical_accuracy: 0.8118\n",
      "Epoch 1048/2000\n",
      "3/3 [==============================] - 2s 732ms/step - loss: 0.6062 - categorical_accuracy: 0.7294\n",
      "Epoch 1049/2000\n",
      "3/3 [==============================] - 2s 639ms/step - loss: 0.6530 - categorical_accuracy: 0.7059\n",
      "Epoch 1050/2000\n",
      "3/3 [==============================] - 2s 727ms/step - loss: 0.5543 - categorical_accuracy: 0.8000\n",
      "Epoch 1051/2000\n",
      "3/3 [==============================] - 2s 633ms/step - loss: 0.4911 - categorical_accuracy: 0.7059\n",
      "Epoch 1052/2000\n",
      "3/3 [==============================] - 2s 558ms/step - loss: 0.4855 - categorical_accuracy: 0.8118\n",
      "Epoch 1053/2000\n",
      "3/3 [==============================] - 2s 559ms/step - loss: 0.4751 - categorical_accuracy: 0.8118\n",
      "Epoch 1054/2000\n",
      "3/3 [==============================] - 2s 543ms/step - loss: 0.4414 - categorical_accuracy: 0.8353\n",
      "Epoch 1055/2000\n",
      "3/3 [==============================] - 2s 588ms/step - loss: 0.4644 - categorical_accuracy: 0.8118\n",
      "Epoch 1056/2000\n",
      "3/3 [==============================] - 2s 586ms/step - loss: 0.4324 - categorical_accuracy: 0.9294\n",
      "Epoch 1057/2000\n",
      "3/3 [==============================] - 2s 533ms/step - loss: 0.4460 - categorical_accuracy: 0.8235\n",
      "Epoch 1058/2000\n",
      "3/3 [==============================] - 2s 543ms/step - loss: 0.4224 - categorical_accuracy: 0.8941\n",
      "Epoch 1059/2000\n",
      "3/3 [==============================] - 2s 540ms/step - loss: 0.4620 - categorical_accuracy: 0.8235\n",
      "Epoch 1060/2000\n",
      "3/3 [==============================] - 2s 544ms/step - loss: 0.5165 - categorical_accuracy: 0.8000\n",
      "Epoch 1061/2000\n",
      "3/3 [==============================] - 2s 547ms/step - loss: 0.5340 - categorical_accuracy: 0.7529\n",
      "Epoch 1062/2000\n",
      "3/3 [==============================] - 2s 551ms/step - loss: 0.4855 - categorical_accuracy: 0.7647\n",
      "Epoch 1063/2000\n",
      "3/3 [==============================] - 2s 536ms/step - loss: 0.5520 - categorical_accuracy: 0.7882\n",
      "Epoch 1064/2000\n",
      "3/3 [==============================] - 2s 541ms/step - loss: 0.4711 - categorical_accuracy: 0.8706\n",
      "Epoch 1065/2000\n",
      "3/3 [==============================] - 2s 543ms/step - loss: 0.4355 - categorical_accuracy: 0.8235\n",
      "Epoch 1066/2000\n",
      "3/3 [==============================] - 2s 598ms/step - loss: 0.4482 - categorical_accuracy: 0.7882\n",
      "Epoch 1067/2000\n",
      "3/3 [==============================] - 2s 553ms/step - loss: 0.4236 - categorical_accuracy: 0.8706\n",
      "Epoch 1068/2000\n",
      "3/3 [==============================] - 2s 558ms/step - loss: 0.4294 - categorical_accuracy: 0.8471\n",
      "Epoch 1069/2000\n",
      "3/3 [==============================] - 2s 606ms/step - loss: 0.4236 - categorical_accuracy: 0.8471\n",
      "Epoch 1070/2000\n",
      "3/3 [==============================] - 2s 771ms/step - loss: 0.4402 - categorical_accuracy: 0.8706\n",
      "Epoch 1071/2000\n",
      "3/3 [==============================] - 2s 548ms/step - loss: 0.4131 - categorical_accuracy: 0.8824\n",
      "Epoch 1072/2000\n",
      "3/3 [==============================] - 2s 531ms/step - loss: 0.4118 - categorical_accuracy: 0.8941\n",
      "Epoch 1073/2000\n",
      "3/3 [==============================] - 2s 547ms/step - loss: 0.3959 - categorical_accuracy: 0.8824\n",
      "Epoch 1074/2000\n",
      "3/3 [==============================] - 2s 826ms/step - loss: 0.4000 - categorical_accuracy: 0.8941\n",
      "Epoch 1075/2000\n",
      "3/3 [==============================] - 2s 614ms/step - loss: 0.4078 - categorical_accuracy: 0.8588\n",
      "Epoch 1076/2000\n",
      "3/3 [==============================] - 2s 618ms/step - loss: 0.4269 - categorical_accuracy: 0.8588\n",
      "Epoch 1077/2000\n",
      "3/3 [==============================] - 2s 633ms/step - loss: 0.4582 - categorical_accuracy: 0.7765\n",
      "Epoch 1078/2000\n",
      "3/3 [==============================] - 2s 651ms/step - loss: 0.4155 - categorical_accuracy: 0.8235\n",
      "Epoch 1079/2000\n",
      "3/3 [==============================] - 2s 706ms/step - loss: 0.4082 - categorical_accuracy: 0.8235\n",
      "Epoch 1080/2000\n",
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      "Epoch 1081/2000\n",
      "3/3 [==============================] - 2s 632ms/step - loss: 0.4051 - categorical_accuracy: 0.8941\n",
      "Epoch 1082/2000\n",
      "3/3 [==============================] - 2s 626ms/step - loss: 0.3678 - categorical_accuracy: 0.9294\n",
      "Epoch 1083/2000\n",
      "3/3 [==============================] - 2s 681ms/step - loss: 0.3858 - categorical_accuracy: 0.8706\n",
      "Epoch 1084/2000\n",
      "3/3 [==============================] - 2s 593ms/step - loss: 0.3940 - categorical_accuracy: 0.8941\n",
      "Epoch 1085/2000\n",
      "3/3 [==============================] - 2s 604ms/step - loss: 0.3968 - categorical_accuracy: 0.8706\n",
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      "3/3 [==============================] - 2s 559ms/step - loss: 0.3594 - categorical_accuracy: 0.8706\n",
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      "3/3 [==============================] - 2s 553ms/step - loss: 0.3725 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 567ms/step - loss: 0.3540 - categorical_accuracy: 0.8824\n",
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      "3/3 [==============================] - 2s 551ms/step - loss: 0.3412 - categorical_accuracy: 0.8824\n",
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      "3/3 [==============================] - 2s 554ms/step - loss: 0.3608 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 571ms/step - loss: 0.3237 - categorical_accuracy: 0.9294\n",
      "Epoch 1111/2000\n",
      "3/3 [==============================] - 2s 575ms/step - loss: 0.3103 - categorical_accuracy: 0.9294\n",
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      "3/3 [==============================] - 2s 564ms/step - loss: 0.3334 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 559ms/step - loss: 0.3487 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 555ms/step - loss: 0.3724 - categorical_accuracy: 0.8588\n",
      "Epoch 1119/2000\n",
      "3/3 [==============================] - 2s 556ms/step - loss: 0.3336 - categorical_accuracy: 0.8588\n",
      "Epoch 1120/2000\n",
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      "Epoch 1121/2000\n",
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      "3/3 [==============================] - 2s 549ms/step - loss: 0.4901 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 547ms/step - loss: 0.5315 - categorical_accuracy: 0.7882\n",
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      "3/3 [==============================] - 2s 573ms/step - loss: 0.5505 - categorical_accuracy: 0.7765\n",
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      "3/3 [==============================] - 2s 556ms/step - loss: 0.5156 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 595ms/step - loss: 0.4981 - categorical_accuracy: 0.7765\n",
      "Epoch 1128/2000\n",
      "3/3 [==============================] - 2s 555ms/step - loss: 0.4671 - categorical_accuracy: 0.8235\n",
      "Epoch 1129/2000\n",
      "3/3 [==============================] - 2s 548ms/step - loss: 0.4620 - categorical_accuracy: 0.8706\n",
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      "3/3 [==============================] - 2s 554ms/step - loss: 0.3890 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 560ms/step - loss: 0.3827 - categorical_accuracy: 0.8706\n",
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      "3/3 [==============================] - 2s 560ms/step - loss: 0.3657 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 552ms/step - loss: 0.3302 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 560ms/step - loss: 0.3487 - categorical_accuracy: 0.8706\n",
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      "3/3 [==============================] - 2s 554ms/step - loss: 0.4079 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 565ms/step - loss: 0.5553 - categorical_accuracy: 0.7529\n",
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      "3/3 [==============================] - 2s 579ms/step - loss: 0.5289 - categorical_accuracy: 0.7647\n",
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      "3/3 [==============================] - 2s 559ms/step - loss: 0.4100 - categorical_accuracy: 0.8000\n",
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      "3/3 [==============================] - 2s 535ms/step - loss: 0.3939 - categorical_accuracy: 0.8706\n",
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      "3/3 [==============================] - 2s 588ms/step - loss: 0.3191 - categorical_accuracy: 0.9294\n",
      "Epoch 1150/2000\n",
      "3/3 [==============================] - 2s 618ms/step - loss: 0.3534 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 561ms/step - loss: 0.4185 - categorical_accuracy: 0.8353\n",
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      "3/3 [==============================] - 2s 545ms/step - loss: 0.3814 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 602ms/step - loss: 0.3993 - categorical_accuracy: 0.8235\n",
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      "3/3 [==============================] - 2s 602ms/step - loss: 0.3212 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 554ms/step - loss: 0.2880 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 558ms/step - loss: 0.3152 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 646ms/step - loss: 0.2829 - categorical_accuracy: 0.9176\n",
      "Epoch 1160/2000\n",
      "3/3 [==============================] - 2s 561ms/step - loss: 0.2898 - categorical_accuracy: 0.9294\n",
      "Epoch 1161/2000\n",
      "3/3 [==============================] - 2s 559ms/step - loss: 0.2654 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 553ms/step - loss: 0.2798 - categorical_accuracy: 0.9294\n",
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      "3/3 [==============================] - 2s 565ms/step - loss: 0.2455 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 543ms/step - loss: 0.2473 - categorical_accuracy: 0.9529\n",
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      "3/3 [==============================] - 2s 550ms/step - loss: 0.2763 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 558ms/step - loss: 0.2819 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 553ms/step - loss: 0.2757 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 566ms/step - loss: 0.2568 - categorical_accuracy: 0.9529\n",
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      "3/3 [==============================] - 2s 556ms/step - loss: 0.2591 - categorical_accuracy: 0.9529\n",
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      "3/3 [==============================] - 2s 551ms/step - loss: 0.2607 - categorical_accuracy: 0.9294\n",
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      "3/3 [==============================] - 2s 579ms/step - loss: 0.2412 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 573ms/step - loss: 0.2366 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 567ms/step - loss: 0.2280 - categorical_accuracy: 0.9529\n",
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      "3/3 [==============================] - 2s 565ms/step - loss: 0.2453 - categorical_accuracy: 0.9529\n",
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      "3/3 [==============================] - 2s 558ms/step - loss: 0.2703 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 565ms/step - loss: 0.3496 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 559ms/step - loss: 0.3153 - categorical_accuracy: 0.8353\n",
      "Epoch 1180/2000\n",
      "3/3 [==============================] - 2s 545ms/step - loss: 0.3085 - categorical_accuracy: 0.8706\n",
      "Epoch 1181/2000\n",
      "3/3 [==============================] - 2s 705ms/step - loss: 0.2558 - categorical_accuracy: 0.9294\n",
      "Epoch 1182/2000\n",
      "3/3 [==============================] - 2s 553ms/step - loss: 0.3021 - categorical_accuracy: 0.8353\n",
      "Epoch 1183/2000\n",
      "3/3 [==============================] - 2s 558ms/step - loss: 0.3073 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 612ms/step - loss: 0.3370 - categorical_accuracy: 0.8118\n",
      "Epoch 1185/2000\n",
      "3/3 [==============================] - 2s 558ms/step - loss: 0.3063 - categorical_accuracy: 0.8706\n",
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      "3/3 [==============================] - 2s 555ms/step - loss: 0.2616 - categorical_accuracy: 0.9294\n",
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      "3/3 [==============================] - 2s 577ms/step - loss: 0.2716 - categorical_accuracy: 0.8706\n",
      "Epoch 1188/2000\n",
      "3/3 [==============================] - 2s 553ms/step - loss: 0.2825 - categorical_accuracy: 0.9176\n",
      "Epoch 1189/2000\n",
      "3/3 [==============================] - 2s 543ms/step - loss: 0.3130 - categorical_accuracy: 0.8706\n",
      "Epoch 1190/2000\n",
      "3/3 [==============================] - 2s 560ms/step - loss: 0.2657 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 564ms/step - loss: 0.3162 - categorical_accuracy: 0.8941\n",
      "Epoch 1192/2000\n",
      "3/3 [==============================] - 2s 559ms/step - loss: 0.2399 - categorical_accuracy: 0.9294\n",
      "Epoch 1193/2000\n",
      "3/3 [==============================] - 2s 639ms/step - loss: 0.2567 - categorical_accuracy: 0.9176\n",
      "Epoch 1194/2000\n",
      "3/3 [==============================] - 2s 545ms/step - loss: 0.3052 - categorical_accuracy: 0.8824\n",
      "Epoch 1195/2000\n",
      "3/3 [==============================] - 2s 542ms/step - loss: 0.2617 - categorical_accuracy: 0.9176\n",
      "Epoch 1196/2000\n",
      "3/3 [==============================] - 2s 544ms/step - loss: 0.2670 - categorical_accuracy: 0.8824\n",
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      "3/3 [==============================] - 2s 553ms/step - loss: 0.2480 - categorical_accuracy: 0.8941\n",
      "Epoch 1198/2000\n",
      "3/3 [==============================] - 2s 553ms/step - loss: 0.2459 - categorical_accuracy: 0.9176\n",
      "Epoch 1199/2000\n",
      "3/3 [==============================] - 2s 639ms/step - loss: 0.2921 - categorical_accuracy: 0.8941\n",
      "Epoch 1200/2000\n",
      "3/3 [==============================] - 2s 612ms/step - loss: 0.2369 - categorical_accuracy: 0.9529\n",
      "Epoch 1201/2000\n",
      "3/3 [==============================] - 2s 538ms/step - loss: 0.2520 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 550ms/step - loss: 0.2386 - categorical_accuracy: 0.9294\n",
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      "3/3 [==============================] - 2s 561ms/step - loss: 0.2722 - categorical_accuracy: 0.9176\n",
      "Epoch 1204/2000\n",
      "3/3 [==============================] - 2s 558ms/step - loss: 0.2826 - categorical_accuracy: 0.8941\n",
      "Epoch 1205/2000\n",
      "3/3 [==============================] - 2s 562ms/step - loss: 0.2390 - categorical_accuracy: 0.9176\n",
      "Epoch 1206/2000\n",
      "3/3 [==============================] - 2s 563ms/step - loss: 0.2752 - categorical_accuracy: 0.9412\n",
      "Epoch 1207/2000\n",
      "3/3 [==============================] - 2s 589ms/step - loss: 0.2309 - categorical_accuracy: 0.9529\n",
      "Epoch 1208/2000\n",
      "3/3 [==============================] - 2s 569ms/step - loss: 0.2312 - categorical_accuracy: 0.9529\n",
      "Epoch 1209/2000\n",
      "3/3 [==============================] - 2s 587ms/step - loss: 0.2247 - categorical_accuracy: 0.9529\n",
      "Epoch 1210/2000\n",
      "3/3 [==============================] - 2s 604ms/step - loss: 0.2271 - categorical_accuracy: 0.9647\n",
      "Epoch 1211/2000\n",
      "3/3 [==============================] - 2s 566ms/step - loss: 0.2285 - categorical_accuracy: 0.9647\n",
      "Epoch 1212/2000\n",
      "3/3 [==============================] - 2s 550ms/step - loss: 0.2272 - categorical_accuracy: 0.9294\n",
      "Epoch 1213/2000\n",
      "3/3 [==============================] - 2s 571ms/step - loss: 0.2252 - categorical_accuracy: 0.9529\n",
      "Epoch 1214/2000\n",
      "3/3 [==============================] - 2s 556ms/step - loss: 0.2231 - categorical_accuracy: 0.9529\n",
      "Epoch 1215/2000\n",
      "3/3 [==============================] - 2s 580ms/step - loss: 0.2427 - categorical_accuracy: 0.9294\n",
      "Epoch 1216/2000\n",
      "3/3 [==============================] - 2s 560ms/step - loss: 0.2583 - categorical_accuracy: 0.9294\n",
      "Epoch 1217/2000\n",
      "3/3 [==============================] - 2s 546ms/step - loss: 0.2751 - categorical_accuracy: 0.8824\n",
      "Epoch 1218/2000\n",
      "3/3 [==============================] - 2s 545ms/step - loss: 0.2093 - categorical_accuracy: 0.9529\n",
      "Epoch 1219/2000\n",
      "3/3 [==============================] - 2s 669ms/step - loss: 0.2286 - categorical_accuracy: 0.9412\n",
      "Epoch 1220/2000\n",
      "3/3 [==============================] - 2s 567ms/step - loss: 0.2351 - categorical_accuracy: 0.9294\n",
      "Epoch 1221/2000\n",
      "3/3 [==============================] - 2s 588ms/step - loss: 0.2555 - categorical_accuracy: 0.9294\n",
      "Epoch 1222/2000\n",
      "3/3 [==============================] - 2s 558ms/step - loss: 0.2459 - categorical_accuracy: 0.8824\n",
      "Epoch 1223/2000\n",
      "3/3 [==============================] - 2s 808ms/step - loss: 0.2810 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 581ms/step - loss: 0.2709 - categorical_accuracy: 0.9176\n",
      "Epoch 1225/2000\n",
      "3/3 [==============================] - 2s 556ms/step - loss: 0.2079 - categorical_accuracy: 0.9529\n",
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      "3/3 [==============================] - 2s 555ms/step - loss: 0.2060 - categorical_accuracy: 0.9529\n",
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      "3/3 [==============================] - 2s 574ms/step - loss: 0.2176 - categorical_accuracy: 0.9529\n",
      "Epoch 1228/2000\n",
      "3/3 [==============================] - 2s 557ms/step - loss: 0.2110 - categorical_accuracy: 0.9529\n",
      "Epoch 1229/2000\n",
      "3/3 [==============================] - 2s 548ms/step - loss: 0.1920 - categorical_accuracy: 0.9529\n",
      "Epoch 1230/2000\n",
      "3/3 [==============================] - 2s 572ms/step - loss: 0.1964 - categorical_accuracy: 0.9529\n",
      "Epoch 1231/2000\n",
      "3/3 [==============================] - 2s 565ms/step - loss: 0.2028 - categorical_accuracy: 0.9529\n",
      "Epoch 1232/2000\n",
      "3/3 [==============================] - 2s 561ms/step - loss: 0.2227 - categorical_accuracy: 0.9529\n",
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      "3/3 [==============================] - 2s 543ms/step - loss: 0.2114 - categorical_accuracy: 0.9412\n",
      "Epoch 1234/2000\n",
      "3/3 [==============================] - 2s 740ms/step - loss: 0.2230 - categorical_accuracy: 0.9294\n",
      "Epoch 1235/2000\n",
      "3/3 [==============================] - 2s 724ms/step - loss: 0.2139 - categorical_accuracy: 0.9529\n",
      "Epoch 1236/2000\n",
      "3/3 [==============================] - 2s 568ms/step - loss: 0.2191 - categorical_accuracy: 0.9529\n",
      "Epoch 1237/2000\n",
      "3/3 [==============================] - 2s 588ms/step - loss: 0.1907 - categorical_accuracy: 0.9647\n",
      "Epoch 1238/2000\n",
      "3/3 [==============================] - 2s 575ms/step - loss: 0.1877 - categorical_accuracy: 0.9647\n",
      "Epoch 1239/2000\n",
      "3/3 [==============================] - 2s 581ms/step - loss: 0.1835 - categorical_accuracy: 0.9529\n",
      "Epoch 1240/2000\n",
      "3/3 [==============================] - 2s 609ms/step - loss: 0.2137 - categorical_accuracy: 0.9412\n",
      "Epoch 1241/2000\n",
      "3/3 [==============================] - 2s 569ms/step - loss: 0.2149 - categorical_accuracy: 0.9294\n",
      "Epoch 1242/2000\n",
      "3/3 [==============================] - 2s 575ms/step - loss: 0.1818 - categorical_accuracy: 0.9647\n",
      "Epoch 1243/2000\n",
      "3/3 [==============================] - 2s 568ms/step - loss: 0.1824 - categorical_accuracy: 0.9647\n",
      "Epoch 1244/2000\n",
      "3/3 [==============================] - 2s 560ms/step - loss: 0.1779 - categorical_accuracy: 0.9647\n",
      "Epoch 1245/2000\n",
      "3/3 [==============================] - 2s 552ms/step - loss: 0.1820 - categorical_accuracy: 0.9647\n",
      "Epoch 1246/2000\n",
      "3/3 [==============================] - 2s 544ms/step - loss: 0.1849 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 561ms/step - loss: 0.2017 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 561ms/step - loss: 0.2711 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 553ms/step - loss: 0.4862 - categorical_accuracy: 0.8824\n",
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      "3/3 [==============================] - 2s 559ms/step - loss: 0.3678 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 552ms/step - loss: 0.2770 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 554ms/step - loss: 0.2002 - categorical_accuracy: 0.9647\n",
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      "3/3 [==============================] - 2s 549ms/step - loss: 0.2425 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 558ms/step - loss: 0.2150 - categorical_accuracy: 0.9529\n",
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      "3/3 [==============================] - 2s 549ms/step - loss: 0.2285 - categorical_accuracy: 0.9294\n",
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      "3/3 [==============================] - 2s 585ms/step - loss: 0.1991 - categorical_accuracy: 0.9647\n",
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      "3/3 [==============================] - 2s 546ms/step - loss: 0.2558 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 702ms/step - loss: 0.2649 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 626ms/step - loss: 0.2032 - categorical_accuracy: 0.9529\n",
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      "Epoch 1600/2000\n",
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      "3/3 [==============================] - 2s 594ms/step - loss: 0.4409 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 587ms/step - loss: 0.4104 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 585ms/step - loss: 0.4297 - categorical_accuracy: 0.8588\n",
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      "3/3 [==============================] - 2s 612ms/step - loss: 0.3560 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 603ms/step - loss: 0.4027 - categorical_accuracy: 0.8353\n",
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      "3/3 [==============================] - 2s 636ms/step - loss: 0.4179 - categorical_accuracy: 0.8235\n",
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      "3/3 [==============================] - 2s 603ms/step - loss: 0.4364 - categorical_accuracy: 0.8235\n",
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      "3/3 [==============================] - 2s 586ms/step - loss: 0.4174 - categorical_accuracy: 0.8706\n",
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      "3/3 [==============================] - 2s 622ms/step - loss: 0.3725 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 605ms/step - loss: 0.3292 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 595ms/step - loss: 0.3876 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 577ms/step - loss: 0.4409 - categorical_accuracy: 0.8118\n",
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      "3/3 [==============================] - 2s 609ms/step - loss: 0.5053 - categorical_accuracy: 0.7882\n",
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      "3/3 [==============================] - 2s 596ms/step - loss: 0.3617 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 605ms/step - loss: 0.4034 - categorical_accuracy: 0.8353\n",
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      "3/3 [==============================] - 3s 1s/step - loss: 0.3618 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 600ms/step - loss: 0.3200 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 599ms/step - loss: 0.3135 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 596ms/step - loss: 0.3313 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 592ms/step - loss: 0.3174 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 637ms/step - loss: 0.3151 - categorical_accuracy: 0.9294\n",
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      "3/3 [==============================] - 2s 580ms/step - loss: 0.3262 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 622ms/step - loss: 0.3177 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 693ms/step - loss: 0.3308 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 610ms/step - loss: 0.3169 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 691ms/step - loss: 0.3384 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 617ms/step - loss: 0.3320 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 600ms/step - loss: 0.3093 - categorical_accuracy: 0.9294\n",
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      "3/3 [==============================] - 2s 569ms/step - loss: 0.3254 - categorical_accuracy: 0.9176\n",
      "Epoch 1690/2000\n",
      "3/3 [==============================] - 2s 576ms/step - loss: 0.3147 - categorical_accuracy: 0.9294\n",
      "Epoch 1691/2000\n",
      "3/3 [==============================] - 2s 596ms/step - loss: 0.3184 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 610ms/step - loss: 0.3553 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 603ms/step - loss: 0.3480 - categorical_accuracy: 0.8941\n",
      "Epoch 1694/2000\n",
      "3/3 [==============================] - 2s 581ms/step - loss: 0.3125 - categorical_accuracy: 0.9176\n",
      "Epoch 1695/2000\n",
      "3/3 [==============================] - 2s 586ms/step - loss: 0.3530 - categorical_accuracy: 0.8706\n",
      "Epoch 1696/2000\n",
      "3/3 [==============================] - 2s 574ms/step - loss: 0.3055 - categorical_accuracy: 0.9294\n",
      "Epoch 1697/2000\n",
      "3/3 [==============================] - 2s 589ms/step - loss: 0.3029 - categorical_accuracy: 0.9412\n",
      "Epoch 1698/2000\n",
      "3/3 [==============================] - 2s 589ms/step - loss: 0.3159 - categorical_accuracy: 0.9059\n",
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      "3/3 [==============================] - 2s 590ms/step - loss: 0.3452 - categorical_accuracy: 0.9176\n",
      "Epoch 1700/2000\n",
      "3/3 [==============================] - 2s 589ms/step - loss: 0.3237 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 589ms/step - loss: 0.3344 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 579ms/step - loss: 0.3074 - categorical_accuracy: 0.9294\n",
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      "3/3 [==============================] - 2s 594ms/step - loss: 0.3352 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 600ms/step - loss: 0.3792 - categorical_accuracy: 0.8824\n",
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      "3/3 [==============================] - 2s 647ms/step - loss: 0.3940 - categorical_accuracy: 0.8235\n",
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      "3/3 [==============================] - 2s 734ms/step - loss: 0.4990 - categorical_accuracy: 0.8706\n",
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      "3/3 [==============================] - 2s 627ms/step - loss: 0.4242 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 584ms/step - loss: 0.3081 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 628ms/step - loss: 0.3537 - categorical_accuracy: 0.9176\n",
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      "3/3 [==============================] - 2s 600ms/step - loss: 0.4509 - categorical_accuracy: 0.8471\n",
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      "3/3 [==============================] - 2s 598ms/step - loss: 0.4285 - categorical_accuracy: 0.8353\n",
      "Epoch 1713/2000\n",
      "3/3 [==============================] - 2s 694ms/step - loss: 0.3811 - categorical_accuracy: 0.8824\n",
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      "3/3 [==============================] - 2s 600ms/step - loss: 0.3279 - categorical_accuracy: 0.9412\n",
      "Epoch 1715/2000\n",
      "3/3 [==============================] - 2s 627ms/step - loss: 0.3215 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 587ms/step - loss: 0.3466 - categorical_accuracy: 0.8941\n",
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      "3/3 [==============================] - 2s 576ms/step - loss: 0.3998 - categorical_accuracy: 0.8824\n",
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      "3/3 [==============================] - 2s 567ms/step - loss: 0.4263 - categorical_accuracy: 0.8353\n",
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      "3/3 [==============================] - 2s 595ms/step - loss: 0.4120 - categorical_accuracy: 0.8941\n",
      "Epoch 1721/2000\n",
      "3/3 [==============================] - 2s 580ms/step - loss: 0.3141 - categorical_accuracy: 0.9529\n",
      "Epoch 1722/2000\n",
      "3/3 [==============================] - 2s 605ms/step - loss: 0.3208 - categorical_accuracy: 0.9294\n",
      "Epoch 1723/2000\n",
      "3/3 [==============================] - 2s 571ms/step - loss: 0.3262 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 599ms/step - loss: 0.3053 - categorical_accuracy: 0.9412\n",
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      "3/3 [==============================] - 2s 605ms/step - loss: 0.2962 - categorical_accuracy: 0.9294\n",
      "Epoch 1726/2000\n",
      "3/3 [==============================] - 2s 615ms/step - loss: 0.3048 - categorical_accuracy: 0.9412\n",
      "Epoch 1727/2000\n",
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      "Epoch 1728/2000\n",
      "3/3 [==============================] - 2s 672ms/step - loss: 0.2990 - categorical_accuracy: 0.9176\n",
      "Epoch 1729/2000\n",
      "3/3 [==============================] - 2s 589ms/step - loss: 0.2665 - categorical_accuracy: 0.9529\n",
      "Epoch 1730/2000\n",
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      "Epoch 1731/2000\n",
      "3/3 [==============================] - 2s 595ms/step - loss: 0.2735 - categorical_accuracy: 0.9529\n",
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      "Epoch 1894/2000\n",
      "3/3 [==============================] - 2s 576ms/step - loss: 0.3320 - categorical_accuracy: 0.9059\n",
      "Epoch 1895/2000\n",
      "3/3 [==============================] - 2s 569ms/step - loss: 0.3044 - categorical_accuracy: 0.9176\n",
      "Epoch 1896/2000\n",
      "3/3 [==============================] - 2s 608ms/step - loss: 0.3222 - categorical_accuracy: 0.9059\n",
      "Epoch 1897/2000\n",
      "3/3 [==============================] - 2s 549ms/step - loss: 0.2173 - categorical_accuracy: 0.9294\n",
      "Epoch 1898/2000\n",
      "3/3 [==============================] - 2s 603ms/step - loss: 0.2245 - categorical_accuracy: 0.9294\n",
      "Epoch 1899/2000\n",
      "3/3 [==============================] - 2s 566ms/step - loss: 0.2739 - categorical_accuracy: 0.9294\n",
      "Epoch 1900/2000\n",
      "3/3 [==============================] - 2s 584ms/step - loss: 0.2788 - categorical_accuracy: 0.9412\n",
      "Epoch 1901/2000\n",
      "3/3 [==============================] - 2s 594ms/step - loss: 0.2457 - categorical_accuracy: 0.9412\n",
      "Epoch 1902/2000\n",
      "3/3 [==============================] - 2s 556ms/step - loss: 0.2265 - categorical_accuracy: 0.9294\n",
      "Epoch 1903/2000\n",
      "3/3 [==============================] - 2s 564ms/step - loss: 0.2634 - categorical_accuracy: 0.9176\n",
      "Epoch 1904/2000\n",
      "3/3 [==============================] - 2s 667ms/step - loss: 0.2207 - categorical_accuracy: 0.9412\n",
      "Epoch 1905/2000\n",
      "3/3 [==============================] - 2s 563ms/step - loss: 0.2268 - categorical_accuracy: 0.9412\n",
      "Epoch 1906/2000\n",
      "3/3 [==============================] - 2s 576ms/step - loss: 0.1970 - categorical_accuracy: 0.9412\n",
      "Epoch 1907/2000\n",
      "3/3 [==============================] - 2s 583ms/step - loss: 0.2038 - categorical_accuracy: 0.9529\n",
      "Epoch 1908/2000\n",
      "3/3 [==============================] - 2s 565ms/step - loss: 0.2590 - categorical_accuracy: 0.9176\n",
      "Epoch 1909/2000\n",
      "3/3 [==============================] - 2s 584ms/step - loss: 0.3660 - categorical_accuracy: 0.8824\n",
      "Epoch 1910/2000\n",
      "3/3 [==============================] - 2s 566ms/step - loss: 0.3022 - categorical_accuracy: 0.8941\n",
      "Epoch 1911/2000\n",
      "3/3 [==============================] - 2s 577ms/step - loss: 0.4455 - categorical_accuracy: 0.8588\n",
      "Epoch 1912/2000\n",
      "3/3 [==============================] - 2s 617ms/step - loss: 0.5468 - categorical_accuracy: 0.7882\n",
      "Epoch 1913/2000\n",
      "3/3 [==============================] - 2s 554ms/step - loss: 0.7254 - categorical_accuracy: 0.7647\n",
      "Epoch 1914/2000\n",
      "3/3 [==============================] - 2s 553ms/step - loss: 0.6549 - categorical_accuracy: 0.7294\n",
      "Epoch 1915/2000\n",
      "3/3 [==============================] - 2s 632ms/step - loss: 0.5987 - categorical_accuracy: 0.8118\n",
      "Epoch 1916/2000\n",
      "3/3 [==============================] - 2s 584ms/step - loss: 0.4299 - categorical_accuracy: 0.8588\n",
      "Epoch 1917/2000\n",
      "3/3 [==============================] - 2s 617ms/step - loss: 0.3787 - categorical_accuracy: 0.8941\n",
      "Epoch 1918/2000\n",
      "3/3 [==============================] - 2s 582ms/step - loss: 0.2694 - categorical_accuracy: 0.9412\n",
      "Epoch 1919/2000\n",
      "3/3 [==============================] - 2s 563ms/step - loss: 0.3414 - categorical_accuracy: 0.8824\n",
      "Epoch 1920/2000\n",
      "3/3 [==============================] - 2s 594ms/step - loss: 0.2827 - categorical_accuracy: 0.9176\n",
      "Epoch 1921/2000\n",
      "3/3 [==============================] - 2s 569ms/step - loss: 0.3163 - categorical_accuracy: 0.8824\n",
      "Epoch 1922/2000\n",
      "3/3 [==============================] - 2s 604ms/step - loss: 0.2148 - categorical_accuracy: 0.9294\n",
      "Epoch 1923/2000\n",
      "3/3 [==============================] - 2s 561ms/step - loss: 0.2867 - categorical_accuracy: 0.9059\n",
      "Epoch 1924/2000\n",
      "3/3 [==============================] - 2s 633ms/step - loss: 0.3127 - categorical_accuracy: 0.9176\n",
      "Epoch 1925/2000\n",
      "3/3 [==============================] - 2s 602ms/step - loss: 0.2748 - categorical_accuracy: 0.9294\n",
      "Epoch 1926/2000\n",
      "3/3 [==============================] - 2s 561ms/step - loss: 0.2037 - categorical_accuracy: 0.9529\n",
      "Epoch 1927/2000\n",
      "3/3 [==============================] - 2s 591ms/step - loss: 0.2735 - categorical_accuracy: 0.9176\n",
      "Epoch 1928/2000\n",
      "3/3 [==============================] - 2s 561ms/step - loss: 0.2470 - categorical_accuracy: 0.9412\n",
      "Epoch 1929/2000\n",
      "3/3 [==============================] - 2s 561ms/step - loss: 0.2232 - categorical_accuracy: 0.9294\n",
      "Epoch 1930/2000\n",
      "3/3 [==============================] - 2s 591ms/step - loss: 0.2362 - categorical_accuracy: 0.9412\n",
      "Epoch 1931/2000\n",
      "3/3 [==============================] - 2s 562ms/step - loss: 0.2087 - categorical_accuracy: 0.9529\n",
      "Epoch 1932/2000\n",
      "3/3 [==============================] - 2s 757ms/step - loss: 0.2015 - categorical_accuracy: 0.9529\n",
      "Epoch 1933/2000\n",
      "3/3 [==============================] - 2s 577ms/step - loss: 0.2377 - categorical_accuracy: 0.9412\n",
      "Epoch 1934/2000\n",
      "3/3 [==============================] - 2s 591ms/step - loss: 0.1979 - categorical_accuracy: 0.9529\n",
      "Epoch 1935/2000\n",
      "3/3 [==============================] - 2s 591ms/step - loss: 0.1979 - categorical_accuracy: 0.9529\n",
      "Epoch 1936/2000\n",
      "3/3 [==============================] - 2s 570ms/step - loss: 0.1926 - categorical_accuracy: 0.9529\n",
      "Epoch 1937/2000\n",
      "3/3 [==============================] - 2s 583ms/step - loss: 0.1940 - categorical_accuracy: 0.9529\n",
      "Epoch 1938/2000\n",
      "3/3 [==============================] - 2s 611ms/step - loss: 0.1952 - categorical_accuracy: 0.9529\n",
      "Epoch 1939/2000\n",
      "3/3 [==============================] - 2s 571ms/step - loss: 0.1979 - categorical_accuracy: 0.9529\n",
      "Epoch 1940/2000\n",
      "3/3 [==============================] - 2s 579ms/step - loss: 0.1903 - categorical_accuracy: 0.9529\n",
      "Epoch 1941/2000\n",
      "3/3 [==============================] - 2s 565ms/step - loss: 0.1979 - categorical_accuracy: 0.9529\n",
      "Epoch 1942/2000\n",
      "3/3 [==============================] - 2s 576ms/step - loss: 0.1866 - categorical_accuracy: 0.9529\n",
      "Epoch 1943/2000\n",
      "3/3 [==============================] - 2s 581ms/step - loss: 0.2002 - categorical_accuracy: 0.9529\n",
      "Epoch 1944/2000\n",
      "3/3 [==============================] - 2s 640ms/step - loss: 0.1969 - categorical_accuracy: 0.9412\n",
      "Epoch 1945/2000\n",
      "3/3 [==============================] - 2s 759ms/step - loss: 0.1941 - categorical_accuracy: 0.9412\n",
      "Epoch 1946/2000\n",
      "3/3 [==============================] - 2s 575ms/step - loss: 0.2052 - categorical_accuracy: 0.9412\n",
      "Epoch 1947/2000\n",
      "3/3 [==============================] - 2s 556ms/step - loss: 0.1979 - categorical_accuracy: 0.9529\n",
      "Epoch 1948/2000\n",
      "3/3 [==============================] - 2s 539ms/step - loss: 0.1922 - categorical_accuracy: 0.9529\n",
      "Epoch 1949/2000\n",
      "3/3 [==============================] - 2s 576ms/step - loss: 0.1903 - categorical_accuracy: 0.9529\n",
      "Epoch 1950/2000\n",
      "3/3 [==============================] - 2s 603ms/step - loss: 0.1837 - categorical_accuracy: 0.9529\n",
      "Epoch 1951/2000\n",
      "3/3 [==============================] - 2s 579ms/step - loss: 0.1985 - categorical_accuracy: 0.9529\n",
      "Epoch 1952/2000\n",
      "3/3 [==============================] - 2s 560ms/step - loss: 0.1917 - categorical_accuracy: 0.9412\n",
      "Epoch 1953/2000\n",
      "3/3 [==============================] - 2s 563ms/step - loss: 0.1934 - categorical_accuracy: 0.9412\n",
      "Epoch 1954/2000\n",
      "3/3 [==============================] - 2s 555ms/step - loss: 0.2069 - categorical_accuracy: 0.9412\n",
      "Epoch 1955/2000\n",
      "3/3 [==============================] - 2s 616ms/step - loss: 0.1915 - categorical_accuracy: 0.9529\n",
      "Epoch 1956/2000\n",
      "3/3 [==============================] - 2s 561ms/step - loss: 0.1932 - categorical_accuracy: 0.9412\n",
      "Epoch 1957/2000\n",
      "3/3 [==============================] - 2s 570ms/step - loss: 0.1885 - categorical_accuracy: 0.9412\n",
      "Epoch 1958/2000\n",
      "3/3 [==============================] - 2s 567ms/step - loss: 0.2036 - categorical_accuracy: 0.9529\n",
      "Epoch 1959/2000\n",
      "3/3 [==============================] - 2s 568ms/step - loss: 0.1819 - categorical_accuracy: 0.9529\n",
      "Epoch 1960/2000\n",
      "3/3 [==============================] - 2s 641ms/step - loss: 0.1847 - categorical_accuracy: 0.9529\n",
      "Epoch 1961/2000\n",
      "3/3 [==============================] - 2s 576ms/step - loss: 0.1814 - categorical_accuracy: 0.9529\n",
      "Epoch 1962/2000\n",
      "3/3 [==============================] - 2s 563ms/step - loss: 0.1851 - categorical_accuracy: 0.9412\n",
      "Epoch 1963/2000\n",
      "3/3 [==============================] - 2s 734ms/step - loss: 0.1781 - categorical_accuracy: 0.9529\n",
      "Epoch 1964/2000\n",
      "3/3 [==============================] - 3s 947ms/step - loss: 0.1845 - categorical_accuracy: 0.9529\n",
      "Epoch 1965/2000\n",
      "3/3 [==============================] - 2s 767ms/step - loss: 0.1800 - categorical_accuracy: 0.9529\n",
      "Epoch 1966/2000\n",
      "3/3 [==============================] - 2s 674ms/step - loss: 0.1837 - categorical_accuracy: 0.9529\n",
      "Epoch 1967/2000\n",
      "3/3 [==============================] - 2s 602ms/step - loss: 0.1909 - categorical_accuracy: 0.9529\n",
      "Epoch 1968/2000\n",
      "3/3 [==============================] - 2s 580ms/step - loss: 0.1861 - categorical_accuracy: 0.9529\n",
      "Epoch 1969/2000\n",
      "3/3 [==============================] - 2s 596ms/step - loss: 0.1815 - categorical_accuracy: 0.9412\n",
      "Epoch 1970/2000\n",
      "3/3 [==============================] - 2s 609ms/step - loss: 0.1779 - categorical_accuracy: 0.9529\n",
      "Epoch 1971/2000\n",
      "3/3 [==============================] - 2s 611ms/step - loss: 0.1965 - categorical_accuracy: 0.9529\n",
      "Epoch 1972/2000\n",
      "3/3 [==============================] - 2s 656ms/step - loss: 0.1788 - categorical_accuracy: 0.9412\n",
      "Epoch 1973/2000\n",
      "3/3 [==============================] - 2s 744ms/step - loss: 0.1967 - categorical_accuracy: 0.9412\n",
      "Epoch 1974/2000\n",
      "3/3 [==============================] - 2s 688ms/step - loss: 0.1869 - categorical_accuracy: 0.9529\n",
      "Epoch 1975/2000\n",
      "3/3 [==============================] - 2s 596ms/step - loss: 0.1759 - categorical_accuracy: 0.9529\n",
      "Epoch 1976/2000\n",
      "3/3 [==============================] - 2s 676ms/step - loss: 0.1799 - categorical_accuracy: 0.9529\n",
      "Epoch 1977/2000\n",
      "3/3 [==============================] - 2s 699ms/step - loss: 0.1845 - categorical_accuracy: 0.9412\n",
      "Epoch 1978/2000\n",
      "3/3 [==============================] - 2s 750ms/step - loss: 0.1721 - categorical_accuracy: 0.9529\n",
      "Epoch 1979/2000\n",
      "3/3 [==============================] - 4s 2s/step - loss: 0.2134 - categorical_accuracy: 0.9412\n",
      "Epoch 1980/2000\n",
      "3/3 [==============================] - 5s 1s/step - loss: 0.2345 - categorical_accuracy: 0.9412\n",
      "Epoch 1981/2000\n",
      "3/3 [==============================] - 4s 1s/step - loss: 0.2137 - categorical_accuracy: 0.9412\n",
      "Epoch 1982/2000\n",
      "3/3 [==============================] - 3s 967ms/step - loss: 0.2048 - categorical_accuracy: 0.9294\n",
      "Epoch 1983/2000\n",
      "3/3 [==============================] - 3s 870ms/step - loss: 0.1805 - categorical_accuracy: 0.9529\n",
      "Epoch 1984/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1650 - categorical_accuracy: 0.9529\n",
      "Epoch 1985/2000\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1824 - categorical_accuracy: 0.9412\n",
      "Epoch 1986/2000\n",
      "3/3 [==============================] - 3s 897ms/step - loss: 0.1821 - categorical_accuracy: 0.9529\n",
      "Epoch 1987/2000\n",
      "3/3 [==============================] - 2s 574ms/step - loss: 0.1681 - categorical_accuracy: 0.9529\n",
      "Epoch 1988/2000\n",
      "3/3 [==============================] - 2s 583ms/step - loss: 0.1663 - categorical_accuracy: 0.9529\n",
      "Epoch 1989/2000\n",
      "3/3 [==============================] - 2s 579ms/step - loss: 0.2179 - categorical_accuracy: 0.9294\n",
      "Epoch 1990/2000\n",
      "3/3 [==============================] - 2s 570ms/step - loss: 0.2162 - categorical_accuracy: 0.9294\n",
      "Epoch 1991/2000\n",
      "3/3 [==============================] - 2s 586ms/step - loss: 0.1884 - categorical_accuracy: 0.9412\n",
      "Epoch 1992/2000\n",
      "3/3 [==============================] - 2s 576ms/step - loss: 0.1816 - categorical_accuracy: 0.9529\n",
      "Epoch 1993/2000\n",
      "3/3 [==============================] - 2s 703ms/step - loss: 0.1782 - categorical_accuracy: 0.9412\n",
      "Epoch 1994/2000\n",
      "3/3 [==============================] - 2s 685ms/step - loss: 0.1715 - categorical_accuracy: 0.9529\n",
      "Epoch 1995/2000\n",
      "3/3 [==============================] - 2s 603ms/step - loss: 0.1713 - categorical_accuracy: 0.9412\n",
      "Epoch 1996/2000\n",
      "3/3 [==============================] - 2s 562ms/step - loss: 0.1757 - categorical_accuracy: 0.9529\n",
      "Epoch 1997/2000\n",
      "3/3 [==============================] - 2s 594ms/step - loss: 0.1710 - categorical_accuracy: 0.9529\n",
      "Epoch 1998/2000\n",
      "3/3 [==============================] - 2s 591ms/step - loss: 0.1667 - categorical_accuracy: 0.9412\n",
      "Epoch 1999/2000\n",
      "3/3 [==============================] - 2s 628ms/step - loss: 0.1687 - categorical_accuracy: 0.9412\n",
      "Epoch 2000/2000\n",
      "3/3 [==============================] - 2s 633ms/step - loss: 0.1642 - categorical_accuracy: 0.9529\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1dea1f1f610>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 30, 64)            442112    \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 30, 128)           98816     \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 64)                49408     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 99        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 596,675\n",
      "Trainable params: 596,675\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 2s 2s/step\n"
     ]
    }
   ],
   "source": [
    "res = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'thanks'"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(res[4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'thanks'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(y_test[4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 122ms/step\n"
     ]
    }
   ],
   "source": [
    "yhat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_test, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[3, 0],\n",
       "        [0, 2]],\n",
       "\n",
       "       [[4, 0],\n",
       "        [0, 1]],\n",
       "\n",
       "       [[3, 0],\n",
       "        [0, 2]]], dtype=int64)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [(245,117,16), (117,245,16), (16,117,245)]\n",
    "def prob_viz(res, actions, input_frame, colors):\n",
    "    output_frame = input_frame.copy()\n",
    "    for num, prob in enumerate(res):\n",
    "        cv2.rectangle(output_frame, (0,60+num*40), (int(prob*100), 90+num*40), colors[num], -1)\n",
    "        cv2.putText(output_frame, actions[num], (0, 85+num*40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "        \n",
    "    return output_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(18,18))\n",
    "plt.imshow(prob_viz(res, actions, image, colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(\"model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "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",
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      "1/1 [==============================] - 0s 101ms/step\n",
      "thanks\n"
     ]
    }
   ],
   "source": [
    "sequence = []\n",
    "sentence = []\n",
    "predictions = []\n",
    "threshold = 0.5\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "\n",
    "        # Read feed\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        # Make detections\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "        \n",
    "        # Draw landmarks\n",
    "        draw_styled_landmarks(image, results)\n",
    "        \n",
    "        # 2. Prediction logic\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",
    "            \n",
    "        #3. Viz logic\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",
    "            # Viz probabilities\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",
    "        # Show to screen\n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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