Action_identification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import and Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pip==21.3.1\n",
      "  Downloading pip-21.3.1-py3-none-any.whl.metadata (4.2 kB)\n",
      "Downloading pip-21.3.1-py3-none-any.whl (1.7 MB)\n",
      "   ---------------------------------------- 0.0/1.7 MB ? eta -:--:--\n",
      "   - -------------------------------------- 0.1/1.7 MB 1.9 MB/s eta 0:00:01\n",
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      "   ---------------------------------------- 1.7/1.7 MB 3.2 MB/s eta 0:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: To modify pip, please run the following command:\n",
      "C:\\Users\\Administrator\\Desktop\\Action_Analyzer\\venv\\Scripts\\python.exe -m pip install pip==21.3.1\n"
     ]
    }
   ],
   "source": [
    "!pip install pip==21.3.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install tensorflow opencv-python mediapipe scikit-learn matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "import time\n",
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Keypoints using MP Holistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_holistic = mp.solutions.holistic \n",
    "mp_drawing = mp.solutions.drawing_utils "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_landmarks(image, results):\n",
    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION) # 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": 6,
   "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_TESSELATION, \n",
    "                             mp_drawing.DrawingSpec(color=(0,255,102), thickness=1, circle_radius=1), \n",
    "                             mp_drawing.DrawingSpec(color=(0,0,0), 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": 53,
   "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",
    "    while cap.isOpened():\n",
    "\n",
    "        # Read feed\n",
    "        ret, frame = cap.read()\n",
    "        inverted_frame = cv2.flip(frame,1)\n",
    "                \n",
    "        # Make detections\n",
    "        image, results = mediapipe_detection(inverted_frame, holistic)\n",
    "        \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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "if (results.left_hand_landmarks):\n",
    "    print(len(results.left_hand_landmarks.landmark))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'inverted_image' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[63], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m draw_styled_landmarks(\u001b[43minverted_image\u001b[49m, results)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'inverted_image' is not defined"
     ]
    }
   ],
   "source": [
    "draw_styled_landmarks(inverted_image, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x28119ecba10>"
      ]
     },
     "execution_count": 64,
     "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(inverted_frame, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Extract Keypoint Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'landmark'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[21], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mlen\u001b[39m(\u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mleft_hand_landmarks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlandmark\u001b[49m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'landmark'"
     ]
    }
   ],
   "source": [
    "len(results.left_hand_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing Error Because in the above Image No left hand is detected in order to capture landmarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separating the Pose,Face,LeftHand,RightHand Landmarks into 4 numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### Below Function Extracts KeyPoints from the given results and returns a concatenated array of pose,face,lefthand and righthand arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_test = extract_keypoints(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.50570261,  0.42038119, -1.1342752 , ...,  0.        ,\n",
       "        0.        ,  0.        ])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1662"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "468*3+33*4+21*3+21*3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Setup Folders for Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path for exported data, numpy arrays\n",
    "DATA_PATH = os.path.join('Project_Data') \n",
    "\n",
    "# Actions that we try to detect\n",
    "actions = np.array(['good_work','peace','heart'])\n",
    "\n",
    "# forty 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": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "for action in actions: \n",
    "    for i in range(2):\n",
    "        if i == 0:\n",
    "            try:\n",
    "                os.makedirs(os.path.join(DATA_PATH,action,\"left\"))\n",
    "                for sequence in range(no_sequences):\n",
    "                    try:\n",
    "                        os.makedirs(os.path.join(DATA_PATH, action,'left',str(sequence)))\n",
    "                    except:\n",
    "                        pass\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "        if i == 1:\n",
    "            try:\n",
    "                os.makedirs(os.path.join(DATA_PATH,action,\"right\"))\n",
    "                for sequence in range(no_sequences):\n",
    "                    try:\n",
    "                        os.makedirs(os.path.join(DATA_PATH, action,'right',str(sequence)))\n",
    "                    except:\n",
    "                        pass\n",
    "            except:\n",
    "                pass\n",
    "                        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Collect Keypoint Values for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\google\\protobuf\\symbol_database.py:55: UserWarning: SymbolDatabase.GetPrototype() is deprecated. Please use message_factory.GetMessageClass() instead. SymbolDatabase.GetPrototype() will be removed soon.\n",
      "  warnings.warn('SymbolDatabase.GetPrototype() is deprecated. Please '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "<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",
    "    \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",
    "\n",
    "                # Read feed\n",
    "                ret, frame = cap.read()\n",
    "                inverted_frame = cv2.flip(frame,1)\n",
    "                \n",
    "                # Make detections\n",
    "                \n",
    "                image, results = mediapipe_detection(frame, holistic)\n",
    "                inverted_image,results_inverted = mediapipe_detection(inverted_frame,holistic)\n",
    "                print(results)\n",
    "\n",
    "                # Draw landmarks\n",
    "                draw_styled_landmarks(image, results)\n",
    "                #draw_styled_landmarks(inverted_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",
    "                inverted_keypoints = extract_keypoints(results_inverted)\n",
    "                npy_path0 = os.path.join(DATA_PATH, action,'right',str(sequence), str(frame_num))\n",
    "                npy_path1 = os.path.join(DATA_PATH, action,'left',str(sequence), str(frame_num))\n",
    "                np.save(npy_path0, keypoints)\n",
    "                np.save(npy_path1,inverted_keypoints)\n",
    "\n",
    "                # Break gracefully\n",
    "                if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "                    break\n",
    "                    \n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Preprocess Data and Create Labels and Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = {label:num for num, label in enumerate(actions)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'good_work': 0, 'peace': 1, 'heart': 2}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Project_Data\\\\good_work\\\\left\\\\2\\\\27.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[97], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m window \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame_num \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(sequence_length):\n\u001b[1;32m----> 6\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mDATA_PATH\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mleft\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msequence\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m.npy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframe_num\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      7\u001b[0m     window\u001b[38;5;241m.\u001b[39mappend(res)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame_num \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(sequence_length):\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\numpy\\lib\\npyio.py:427\u001b[0m, in \u001b[0;36mload\u001b[1;34m(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)\u001b[0m\n\u001b[0;32m    425\u001b[0m     own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    426\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 427\u001b[0m     fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28mopen\u001b[39m(os_fspath(file), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m    428\u001b[0m     own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    430\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Project_Data\\\\good_work\\\\left\\\\2\\\\27.npy'"
     ]
    }
   ],
   "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,'left',str(sequence), \"{}.npy\".format(frame_num)))\n",
    "            window.append(res)\n",
    "        for frame_num in range(sequence_length):\n",
    "            res = np.load(os.path.join(DATA_PATH, action,'right',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": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 60, 1662)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(sequences).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(labels).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 30, 1662)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Build and Train LSTM Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = os.path.join('Logs')\n",
    "tb_callback = TensorBoard(log_dir=log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\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": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = [.7, 0.2, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(res)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 35ms/step - categorical_accuracy: 0.3642 - loss: 1.4367\n",
      "Epoch 2/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.3600 - loss: 1.1384\n",
      "Epoch 3/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.3447 - loss: 6.7745\n",
      "Epoch 4/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.3811 - loss: 1.0933\n",
      "Epoch 5/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.3704 - loss: 1.2094\n",
      "Epoch 6/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.3314 - loss: 1.1357\n",
      "Epoch 7/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.3476 - loss: 1.1014\n",
      "Epoch 8/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.3877 - loss: 1.0572\n",
      "Epoch 9/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.5239 - loss: 0.9381\n",
      "Epoch 10/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - categorical_accuracy: 0.4247 - loss: 2.3829\n",
      "Epoch 11/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - categorical_accuracy: 0.4891 - loss: 1.0182\n",
      "Epoch 12/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.6189 - loss: 0.8943\n",
      "Epoch 13/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.5138 - loss: 1.2142\n",
      "Epoch 14/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.5037 - loss: 0.9184\n",
      "Epoch 15/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - categorical_accuracy: 0.5714 - loss: 0.9296\n",
      "Epoch 16/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.5255 - loss: 1.0263\n",
      "Epoch 17/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.7935 - loss: 0.7373\n",
      "Epoch 18/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.7587 - loss: 0.6850\n",
      "Epoch 19/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.6267 - loss: 0.6231\n",
      "Epoch 20/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.7834 - loss: 0.5821\n",
      "Epoch 21/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.6514 - loss: 0.5644\n",
      "Epoch 22/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.6419 - loss: 0.6042\n",
      "Epoch 23/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.7912 - loss: 0.5329\n",
      "Epoch 24/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.7890 - loss: 0.4383\n",
      "Epoch 25/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.7275 - loss: 0.4510\n",
      "Epoch 26/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.8270 - loss: 0.4520\n",
      "Epoch 27/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.7382 - loss: 0.5616\n",
      "Epoch 28/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.8198 - loss: 0.3869\n",
      "Epoch 29/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.6380 - loss: 0.6517\n",
      "Epoch 30/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.7258 - loss: 0.6809\n",
      "Epoch 31/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.6481 - loss: 0.5210\n",
      "Epoch 32/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8478 - loss: 0.3524\n",
      "Epoch 33/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8338 - loss: 0.3559\n",
      "Epoch 34/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8612 - loss: 0.3242\n",
      "Epoch 35/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.8628 - loss: 0.3057\n",
      "Epoch 36/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.8556 - loss: 0.3381\n",
      "Epoch 37/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8959 - loss: 0.2394\n",
      "Epoch 38/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.8455 - loss: 0.3615\n",
      "Epoch 39/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.7528 - loss: 0.5382\n",
      "Epoch 40/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8361 - loss: 0.3466\n",
      "Epoch 41/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.7690 - loss: 0.4667\n",
      "Epoch 42/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.8680 - loss: 0.2868\n",
      "Epoch 43/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - categorical_accuracy: 0.9597 - loss: 0.2285\n",
      "Epoch 44/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - categorical_accuracy: 0.9418 - loss: 0.2018\n",
      "Epoch 45/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9060 - loss: 0.2324\n",
      "Epoch 46/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - categorical_accuracy: 0.8680 - loss: 0.3837\n",
      "Epoch 47/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8026 - loss: 0.3554\n",
      "Epoch 48/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8875 - loss: 0.2943\n",
      "Epoch 49/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9480 - loss: 0.1558\n",
      "Epoch 50/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9457 - loss: 0.2726\n",
      "Epoch 51/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.9597 - loss: 0.1702\n",
      "Epoch 52/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.9597 - loss: 0.1676\n",
      "Epoch 53/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9418 - loss: 0.1533\n",
      "Epoch 54/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8898 - loss: 0.2304\n",
      "Epoch 55/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8914 - loss: 0.2008\n",
      "Epoch 56/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9054 - loss: 0.2859\n",
      "Epoch 57/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.7678 - loss: 0.3589\n",
      "Epoch 58/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9093 - loss: 0.3104\n",
      "Epoch 59/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9899 - loss: 0.1440\n",
      "Epoch 60/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9005 - loss: 0.1913\n",
      "Epoch 61/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9005 - loss: 0.3134\n",
      "Epoch 62/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8797 - loss: 0.2746\n",
      "Epoch 63/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.9278 - loss: 0.2007\n",
      "Epoch 64/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - categorical_accuracy: 0.8859 - loss: 0.4254\n",
      "Epoch 65/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9558 - loss: 0.1782\n",
      "Epoch 66/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.9233 - loss: 0.2196\n",
      "Epoch 67/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9496 - loss: 0.1668\n",
      "Epoch 68/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.9535 - loss: 0.2006\n",
      "Epoch 69/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.9496 - loss: 0.1860\n",
      "Epoch 70/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9620 - loss: 0.0991\n",
      "Epoch 71/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.9541 - loss: 0.1256\n",
      "Epoch 72/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.9899 - loss: 0.0938\n",
      "Epoch 73/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9541 - loss: 0.0791\n",
      "Epoch 74/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9877 - loss: 0.0550\n",
      "Epoch 75/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9821 - loss: 0.0900\n",
      "Epoch 76/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9698 - loss: 0.0717\n",
      "Epoch 77/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.9362 - loss: 0.2372\n",
      "Epoch 78/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8937 - loss: 0.6822\n",
      "Epoch 79/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8959 - loss: 0.2775\n",
      "Epoch 80/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9116 - loss: 0.2085\n",
      "Epoch 81/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9083 - loss: 0.1884\n",
      "Epoch 82/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9759 - loss: 0.0791\n",
      "Epoch 83/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9698 - loss: 0.0956\n",
      "Epoch 84/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.8130 - loss: 0.4201\n",
      "Epoch 85/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8647 - loss: 0.3563\n",
      "Epoch 86/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.7756 - loss: 0.4496\n",
      "Epoch 87/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.8416 - loss: 0.3804\n",
      "Epoch 88/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.8842 - loss: 0.2532\n",
      "Epoch 89/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9821 - loss: 0.1515\n",
      "Epoch 90/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9642 - loss: 0.1434\n",
      "Epoch 91/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9558 - loss: 0.1501\n",
      "Epoch 92/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.9541 - loss: 0.1203\n",
      "Epoch 93/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9642 - loss: 0.0902\n",
      "Epoch 94/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9837 - loss: 0.0568\n",
      "Epoch 95/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9519 - loss: 0.0775\n",
      "Epoch 96/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9642 - loss: 0.0844\n",
      "Epoch 97/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9821 - loss: 0.0374\n",
      "Epoch 98/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9798 - loss: 0.0371\n",
      "Epoch 99/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9737 - loss: 0.0615\n",
      "Epoch 100/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.9200 - loss: 0.2035\n",
      "Epoch 101/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9138 - loss: 0.1971\n",
      "Epoch 102/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.8556 - loss: 0.2700\n",
      "Epoch 103/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8719 - loss: 0.2215\n",
      "Epoch 104/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - categorical_accuracy: 0.7551 - loss: 0.4027\n",
      "Epoch 105/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - categorical_accuracy: 0.9138 - loss: 0.2440\n",
      "Epoch 106/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9620 - loss: 0.1056\n",
      "Epoch 107/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9698 - loss: 0.1094\n",
      "Epoch 108/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.8998 - loss: 0.1629\n",
      "Epoch 109/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9698 - loss: 0.0805\n",
      "Epoch 110/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9899 - loss: 0.0379\n",
      "Epoch 111/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8826 - loss: 0.1384\n",
      "Epoch 112/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9021 - loss: 0.1701\n",
      "Epoch 113/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9580 - loss: 0.1161\n",
      "Epoch 114/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0431\n",
      "Epoch 115/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0724\n",
      "Epoch 116/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9821 - loss: 0.0418\n",
      "Epoch 117/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9821 - loss: 0.0307\n",
      "Epoch 118/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0172\n",
      "Epoch 119/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0072\n",
      "Epoch 120/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0078\n",
      "Epoch 121/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - categorical_accuracy: 1.0000 - loss: 0.0056\n",
      "Epoch 122/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 1.0000 - loss: 0.0061\n",
      "Epoch 123/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 1.0000 - loss: 0.0042\n",
      "Epoch 124/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 0.0037   \n",
      "Epoch 125/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0031\n",
      "Epoch 126/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0063   \n",
      "Epoch 127/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 1.0000 - loss: 9.0664e-04\n",
      "Epoch 128/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9899 - loss: 0.0102   \n",
      "Epoch 129/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0085\n",
      "Epoch 130/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 1.0000 - loss: 0.0015\n",
      "Epoch 131/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9899 - loss: 0.0319   \n",
      "Epoch 132/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 8.7095e-04\n",
      "Epoch 133/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0050\n",
      "Epoch 134/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 1.0000 - loss: 0.0017\n",
      "Epoch 135/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 0.0022\n",
      "Epoch 136/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9899 - loss: 0.0105\n",
      "Epoch 137/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9798 - loss: 0.0492\n",
      "Epoch 138/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9642 - loss: 0.0759\n",
      "Epoch 139/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9109 - loss: 0.3756\n",
      "Epoch 140/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.7639 - loss: 1.0364\n",
      "Epoch 141/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.7733 - loss: 0.9608\n",
      "Epoch 142/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.6143 - loss: 1.1334\n",
      "Epoch 143/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.6783 - loss: 0.6632\n",
      "Epoch 144/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.5613 - loss: 0.8372\n",
      "Epoch 145/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.5642 - loss: 0.8014\n",
      "Epoch 146/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8696 - loss: 0.5568\n",
      "Epoch 147/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.8069 - loss: 0.3609\n",
      "Epoch 148/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8293 - loss: 2.7547\n",
      "Epoch 149/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.8377 - loss: 0.3022\n",
      "Epoch 150/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8221 - loss: 0.4581\n",
      "Epoch 151/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8523 - loss: 0.3571\n",
      "Epoch 152/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9659 - loss: 0.2287\n",
      "Epoch 153/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9171 - loss: 0.8174\n",
      "Epoch 154/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8836 - loss: 0.3120\n",
      "Epoch 155/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8260 - loss: 0.4357\n",
      "Epoch 156/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8478 - loss: 0.3084\n",
      "Epoch 157/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.8624 - loss: 0.3529\n",
      "Epoch 158/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - categorical_accuracy: 0.8998 - loss: 0.2830\n",
      "Epoch 159/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9659 - loss: 0.1605\n",
      "Epoch 160/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9060 - loss: 0.2337\n",
      "Epoch 161/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8758 - loss: 0.2980\n",
      "Epoch 162/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8406 - loss: 0.3667\n",
      "Epoch 163/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8875 - loss: 0.2950\n",
      "Epoch 164/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9720 - loss: 0.1776\n",
      "Epoch 165/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9642 - loss: 0.1406\n",
      "Epoch 166/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9837 - loss: 0.0942\n",
      "Epoch 167/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.9821 - loss: 0.0769\n",
      "Epoch 168/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9698 - loss: 0.2046\n",
      "Epoch 169/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9340 - loss: 0.1538\n",
      "Epoch 170/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9021 - loss: 0.2489\n",
      "Epoch 171/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9362 - loss: 0.1602\n",
      "Epoch 172/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9877 - loss: 0.0935\n",
      "Epoch 173/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9480 - loss: 0.1234\n",
      "Epoch 174/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9938 - loss: 0.0682\n",
      "Epoch 175/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9837 - loss: 0.0793\n",
      "Epoch 176/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9821 - loss: 0.0423\n",
      "Epoch 177/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 0.0233\n",
      "Epoch 178/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.9798 - loss: 0.0490\n",
      "Epoch 179/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9821 - loss: 0.0551\n",
      "Epoch 180/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9821 - loss: 0.0504\n",
      "Epoch 181/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 1.0000 - loss: 0.0085\n",
      "Epoch 182/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9821 - loss: 0.0215\n",
      "Epoch 183/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0127\n",
      "Epoch 184/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0023\n",
      "Epoch 185/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9821 - loss: 0.0368\n",
      "Epoch 186/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0151\n",
      "Epoch 187/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0020\n",
      "Epoch 188/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9821 - loss: 0.0399\n",
      "Epoch 189/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.9821 - loss: 0.0196\n",
      "Epoch 190/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 5.6524e-04\n",
      "Epoch 191/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0046   \n",
      "Epoch 192/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 3.3541e-04\n",
      "Epoch 193/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8996 - loss: 8.4845   \n",
      "Epoch 194/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - categorical_accuracy: 0.5724 - loss: 2.6664\n",
      "Epoch 195/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.5333 - loss: 1.8450\n",
      "Epoch 196/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.5479 - loss: 1.2088\n",
      "Epoch 197/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.4823 - loss: 0.9397\n",
      "Epoch 198/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.6296 - loss: 0.8003\n",
      "Epoch 199/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.6738 - loss: 0.7100\n",
      "Epoch 200/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.7040 - loss: 0.6361\n",
      "Epoch 201/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.7929 - loss: 0.5429\n",
      "Epoch 202/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.6458 - loss: 0.7148\n",
      "Epoch 203/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.7766 - loss: 0.5119\n",
      "Epoch 204/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.7158 - loss: 0.5901\n",
      "Epoch 205/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.7090 - loss: 0.6041\n",
      "Epoch 206/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.8332 - loss: 0.4904\n",
      "Epoch 207/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8247 - loss: 0.4635\n",
      "Epoch 208/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.7561 - loss: 0.5137\n",
      "Epoch 209/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.7987 - loss: 0.4868\n",
      "Epoch 210/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8013 - loss: 0.4285\n",
      "Epoch 211/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.7980 - loss: 0.4497\n",
      "Epoch 212/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8400 - loss: 0.4116\n",
      "Epoch 213/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - categorical_accuracy: 0.8449 - loss: 0.9969\n",
      "Epoch 214/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8198 - loss: 0.4312\n",
      "Epoch 215/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - categorical_accuracy: 0.7997 - loss: 0.9584\n",
      "Epoch 216/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.7740 - loss: 0.4561\n",
      "Epoch 217/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.8137 - loss: 0.3931\n",
      "Epoch 218/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8628 - loss: 0.3314\n",
      "Epoch 219/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - categorical_accuracy: 0.8377 - loss: 0.3614\n",
      "Epoch 220/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8299 - loss: 0.3575\n",
      "Epoch 221/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.8735 - loss: 0.3319\n",
      "Epoch 222/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.8601 - loss: 0.3291\n",
      "Epoch 223/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - categorical_accuracy: 0.8937 - loss: 0.3080\n",
      "Epoch 224/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8383 - loss: 0.3402\n",
      "Epoch 225/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - categorical_accuracy: 0.8641 - loss: 0.3268\n",
      "Epoch 226/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8433 - loss: 0.3673\n",
      "Epoch 227/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.7554 - loss: 0.5975\n",
      "Epoch 228/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.7912 - loss: 0.5093\n",
      "Epoch 229/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.8465 - loss: 0.4298\n",
      "Epoch 230/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.8573 - loss: 0.3411\n",
      "Epoch 231/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.7444 - loss: 0.4625\n",
      "Epoch 232/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.7610 - loss: 0.4355\n",
      "Epoch 233/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.8657 - loss: 0.3298\n",
      "Epoch 234/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8494 - loss: 0.4077\n",
      "Epoch 235/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.8768 - loss: 0.3389\n",
      "Epoch 236/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.8315 - loss: 0.3355\n",
      "Epoch 237/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8930 - loss: 0.3121\n",
      "Epoch 238/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8819 - loss: 0.4257\n",
      "Epoch 239/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.8595 - loss: 0.3738\n",
      "Epoch 240/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8920 - loss: 0.3633\n",
      "Epoch 241/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.8780 - loss: 0.2999\n",
      "Epoch 242/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9116 - loss: 0.2627\n",
      "Epoch 243/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9099 - loss: 0.2686\n",
      "Epoch 244/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - categorical_accuracy: 0.9161 - loss: 0.2765\n",
      "Epoch 245/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9099 - loss: 0.2417\n",
      "Epoch 246/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9278 - loss: 0.2386\n",
      "Epoch 247/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9239 - loss: 0.2072\n",
      "Epoch 248/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - categorical_accuracy: 0.9441 - loss: 0.2303\n",
      "Epoch 249/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9262 - loss: 0.2044\n",
      "Epoch 250/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.9418 - loss: 0.1623\n",
      "Epoch 251/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9379 - loss: 0.1729\n",
      "Epoch 252/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.7701 - loss: 0.5359\n",
      "Epoch 253/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9395 - loss: 0.1929\n",
      "Epoch 254/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.9262 - loss: 0.2296\n",
      "Epoch 255/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9037 - loss: 0.2724\n",
      "Epoch 256/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.9418 - loss: 0.2165\n",
      "Epoch 257/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9060 - loss: 0.2707\n",
      "Epoch 258/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.9015 - loss: 0.2977\n",
      "Epoch 259/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9177 - loss: 0.2580\n",
      "Epoch 260/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.9356 - loss: 0.1930\n",
      "Epoch 261/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9099 - loss: 0.2624\n",
      "Epoch 262/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9379 - loss: 0.2054\n",
      "Epoch 263/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9473 - loss: 0.1704\n",
      "Epoch 264/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - categorical_accuracy: 0.9480 - loss: 0.1845\n",
      "Epoch 265/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9496 - loss: 0.1796\n",
      "Epoch 266/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8959 - loss: 0.2049\n",
      "Epoch 267/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9060 - loss: 0.1959\n",
      "Epoch 268/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9379 - loss: 0.1641\n",
      "Epoch 269/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.8791 - loss: 0.2507\n",
      "Epoch 270/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.9177 - loss: 0.3003\n",
      "Epoch 271/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9278 - loss: 0.1754\n",
      "Epoch 272/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.9278 - loss: 0.1953\n",
      "Epoch 273/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9441 - loss: 0.1643\n",
      "Epoch 274/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.9340 - loss: 0.1318\n",
      "Epoch 275/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9938 - loss: 0.1164\n",
      "Epoch 276/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9698 - loss: 0.1228\n",
      "Epoch 277/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9642 - loss: 0.1067\n",
      "Epoch 278/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9620 - loss: 0.1299\n",
      "Epoch 279/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9441 - loss: 0.1103\n",
      "Epoch 280/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9899 - loss: 0.0730\n",
      "Epoch 281/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9938 - loss: 0.0471\n",
      "Epoch 282/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.9558 - loss: 0.0936\n",
      "Epoch 283/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9278 - loss: 0.1224\n",
      "Epoch 284/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.9815 - loss: 0.0848\n",
      "Epoch 285/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9233 - loss: 0.1891\n",
      "Epoch 286/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.9815 - loss: 0.0948\n",
      "Epoch 287/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9574 - loss: 0.0882\n",
      "Epoch 288/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9642 - loss: 0.0959\n",
      "Epoch 289/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9620 - loss: 0.1037\n",
      "Epoch 290/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9698 - loss: 0.0888\n",
      "Epoch 291/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0472\n",
      "Epoch 292/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9284 - loss: 0.1733\n",
      "Epoch 293/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9362 - loss: 0.1665\n",
      "Epoch 294/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9837 - loss: 0.0512\n",
      "Epoch 295/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 0.0389\n",
      "Epoch 296/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9720 - loss: 0.0768\n",
      "Epoch 297/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 0.0421\n",
      "Epoch 298/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9821 - loss: 0.0349\n",
      "Epoch 299/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 0.9798 - loss: 0.0475\n",
      "Epoch 300/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 1.0000 - loss: 0.0270\n",
      "Epoch 301/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0140\n",
      "Epoch 302/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9899 - loss: 0.0186\n",
      "Epoch 303/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0174\n",
      "Epoch 304/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 0.0118\n",
      "Epoch 305/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 1.0000 - loss: 0.0130\n",
      "Epoch 306/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 0.0123\n",
      "Epoch 307/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0115\n",
      "Epoch 308/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.9938 - loss: 0.0150\n",
      "Epoch 309/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - categorical_accuracy: 1.0000 - loss: 0.0038\n",
      "Epoch 310/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - categorical_accuracy: 0.9821 - loss: 0.0254\n",
      "Epoch 311/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0114\n",
      "Epoch 312/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 0.0113\n",
      "Epoch 313/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0064\n",
      "Epoch 314/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 0.0202\n",
      "Epoch 315/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 1.0000 - loss: 0.0105\n",
      "Epoch 316/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0049\n",
      "Epoch 317/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0041   \n",
      "Epoch 318/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0042\n",
      "Epoch 319/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 0.0049\n",
      "Epoch 320/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0028\n",
      "Epoch 321/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 0.0027\n",
      "Epoch 322/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0063\n",
      "Epoch 323/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 1.0000 - loss: 0.0134\n",
      "Epoch 324/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0020\n",
      "Epoch 325/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 0.0027   \n",
      "Epoch 326/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 0.0022\n",
      "Epoch 327/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 1.0000 - loss: 0.0034   \n",
      "Epoch 328/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0019\n",
      "Epoch 329/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 0.0031   \n",
      "Epoch 330/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0024\n",
      "Epoch 331/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 1.0000 - loss: 0.0033\n",
      "Epoch 332/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0075\n",
      "Epoch 333/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9698 - loss: 0.0719\n",
      "Epoch 334/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.8836 - loss: 0.4643\n",
      "Epoch 335/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.7450 - loss: 1.1424\n",
      "Epoch 336/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9496 - loss: 0.1961\n",
      "Epoch 337/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 0.9301 - loss: 0.1961\n",
      "Epoch 338/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9720 - loss: 0.1411\n",
      "Epoch 339/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9821 - loss: 0.1615\n",
      "Epoch 340/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9720 - loss: 0.1845\n",
      "Epoch 341/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.9938 - loss: 0.1746\n",
      "Epoch 342/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9821 - loss: 0.1688\n",
      "Epoch 343/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.9938 - loss: 0.1369\n",
      "Epoch 344/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 0.9759 - loss: 0.1219\n",
      "Epoch 345/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9899 - loss: 0.1101\n",
      "Epoch 346/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0958\n",
      "Epoch 347/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - categorical_accuracy: 0.9642 - loss: 0.0900\n",
      "Epoch 348/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9837 - loss: 0.0726\n",
      "Epoch 349/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0658\n",
      "Epoch 350/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0564\n",
      "Epoch 351/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9620 - loss: 0.0731\n",
      "Epoch 352/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9899 - loss: 0.0494\n",
      "Epoch 353/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 0.0352\n",
      "Epoch 354/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 0.9821 - loss: 0.0498\n",
      "Epoch 355/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9877 - loss: 0.0402\n",
      "Epoch 356/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 0.9798 - loss: 0.0718\n",
      "Epoch 357/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 0.9642 - loss: 0.0482\n",
      "Epoch 358/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9821 - loss: 0.0605\n",
      "Epoch 359/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 0.0396\n",
      "Epoch 360/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 1.0000 - loss: 0.0235\n",
      "Epoch 361/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 0.9759 - loss: 0.0331\n",
      "Epoch 362/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9938 - loss: 0.0324\n",
      "Epoch 363/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 0.0194\n",
      "Epoch 364/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 0.9899 - loss: 0.0418\n",
      "Epoch 365/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 0.9798 - loss: 0.0461\n",
      "Epoch 366/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 0.0152\n",
      "Epoch 367/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 0.9642 - loss: 0.0397\n",
      "Epoch 368/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 0.9899 - loss: 0.0224\n",
      "Epoch 369/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0087\n",
      "Epoch 370/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 1.0000 - loss: 0.0141\n",
      "Epoch 371/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0118\n",
      "Epoch 372/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 0.0110\n",
      "Epoch 373/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 0.0072\n",
      "Epoch 374/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 0.0051\n",
      "Epoch 375/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 0.0079\n",
      "Epoch 376/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 0.0069\n",
      "Epoch 377/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0053\n",
      "Epoch 378/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 0.0036\n",
      "Epoch 379/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0040\n",
      "Epoch 380/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 0.0043\n",
      "Epoch 381/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0039\n",
      "Epoch 382/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 0.0026\n",
      "Epoch 383/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0032\n",
      "Epoch 384/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0030\n",
      "Epoch 385/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0023\n",
      "Epoch 386/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 1.0000 - loss: 0.0024\n",
      "Epoch 387/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0025\n",
      "Epoch 388/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 0.0025\n",
      "Epoch 389/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0018   \n",
      "Epoch 390/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0020\n",
      "Epoch 391/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0016   \n",
      "Epoch 392/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 0.0024\n",
      "Epoch 393/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0017\n",
      "Epoch 394/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 0.0016\n",
      "Epoch 395/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0020\n",
      "Epoch 396/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0017\n",
      "Epoch 397/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0018\n",
      "Epoch 398/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 0.0016\n",
      "Epoch 399/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0014\n",
      "Epoch 400/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 0.0013\n",
      "Epoch 401/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 9.0209e-04\n",
      "Epoch 402/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 1.0000 - loss: 9.8190e-04\n",
      "Epoch 403/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0016\n",
      "Epoch 404/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 0.0011   \n",
      "Epoch 405/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 0.0010   \n",
      "Epoch 406/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0012\n",
      "Epoch 407/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 9.6285e-04\n",
      "Epoch 408/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 0.0010\n",
      "Epoch 409/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 8.2017e-04\n",
      "Epoch 410/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 9.6520e-04\n",
      "Epoch 411/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 9.0471e-04\n",
      "Epoch 412/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 8.7586e-04\n",
      "Epoch 413/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 7.7588e-04\n",
      "Epoch 414/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 8.1520e-04\n",
      "Epoch 415/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 8.1613e-04\n",
      "Epoch 416/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 6.7305e-04\n",
      "Epoch 417/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 7.0888e-04\n",
      "Epoch 418/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 6.6400e-04\n",
      "Epoch 419/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 5.2948e-04\n",
      "Epoch 420/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 5.4343e-04\n",
      "Epoch 421/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 6.1994e-04\n",
      "Epoch 422/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 6.0291e-04\n",
      "Epoch 423/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 5.0582e-04\n",
      "Epoch 424/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 7.0749e-04\n",
      "Epoch 425/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 4.1515e-04\n",
      "Epoch 426/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 5.6637e-04\n",
      "Epoch 427/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 4.0879e-04\n",
      "Epoch 428/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 4.0928e-04\n",
      "Epoch 429/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 4.3868e-04\n",
      "Epoch 430/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 4.7839e-04\n",
      "Epoch 431/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 4.8784e-04\n",
      "Epoch 432/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 5.0557e-04\n",
      "Epoch 433/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 4.3835e-04\n",
      "Epoch 434/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 3.1438e-04\n",
      "Epoch 435/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 5.0009e-04\n",
      "Epoch 436/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 3.8751e-04\n",
      "Epoch 437/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 3.8772e-04\n",
      "Epoch 438/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 3.8580e-04\n",
      "Epoch 439/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 2.8892e-04\n",
      "Epoch 440/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 4.3209e-04\n",
      "Epoch 441/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 2.7014e-04\n",
      "Epoch 442/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - categorical_accuracy: 1.0000 - loss: 2.5226e-04\n",
      "Epoch 443/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 2.7921e-04\n",
      "Epoch 444/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 3.2546e-04\n",
      "Epoch 445/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 3.7724e-04\n",
      "Epoch 446/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 3.1619e-04\n",
      "Epoch 447/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 2.7184e-04\n",
      "Epoch 448/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.9227e-04\n",
      "Epoch 449/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 1.0000 - loss: 2.1212e-04\n",
      "Epoch 450/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 2.5551e-04\n",
      "Epoch 451/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 2.2809e-04\n",
      "Epoch 452/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 2.7366e-04\n",
      "Epoch 453/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 2.5232e-04\n",
      "Epoch 454/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 2.1460e-04\n",
      "Epoch 455/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 1.8794e-04\n",
      "Epoch 456/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 2.6329e-04\n",
      "Epoch 457/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 2.5789e-04\n",
      "Epoch 458/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 2.3910e-04\n",
      "Epoch 459/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.6183e-04\n",
      "Epoch 460/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.6822e-04\n",
      "Epoch 461/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 2.1437e-04\n",
      "Epoch 462/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 2.0119e-04\n",
      "Epoch 463/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.8576e-04\n",
      "Epoch 464/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 1.8239e-04\n",
      "Epoch 465/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 1.9758e-04\n",
      "Epoch 466/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - categorical_accuracy: 1.0000 - loss: 2.0049e-04\n",
      "Epoch 467/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.7494e-04\n",
      "Epoch 468/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 1.6915e-04\n",
      "Epoch 469/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 1.5691e-04\n",
      "Epoch 470/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 1.0000 - loss: 1.4681e-04\n",
      "Epoch 471/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 1.4020e-04\n",
      "Epoch 472/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - categorical_accuracy: 1.0000 - loss: 1.5399e-04\n",
      "Epoch 473/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 1.3822e-04\n",
      "Epoch 474/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 1.5439e-04\n",
      "Epoch 475/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.3784e-04\n",
      "Epoch 476/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.0746e-04\n",
      "Epoch 477/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 1.1035e-04\n",
      "Epoch 478/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 1.1522e-04\n",
      "Epoch 479/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 1.0427e-04\n",
      "Epoch 480/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 1.0205e-04\n",
      "Epoch 481/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - categorical_accuracy: 1.0000 - loss: 1.2120e-04\n",
      "Epoch 482/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.3428e-04\n",
      "Epoch 483/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - categorical_accuracy: 1.0000 - loss: 1.2565e-04\n",
      "Epoch 484/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.2429e-04\n",
      "Epoch 485/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - categorical_accuracy: 1.0000 - loss: 1.0075e-04\n",
      "Epoch 486/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.0742e-04\n",
      "Epoch 487/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 1.3200e-04\n",
      "Epoch 488/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - categorical_accuracy: 1.0000 - loss: 1.0186e-04\n",
      "Epoch 489/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - categorical_accuracy: 1.0000 - loss: 9.5713e-05\n",
      "Epoch 490/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - categorical_accuracy: 1.0000 - loss: 8.9687e-05\n",
      "Epoch 491/1000\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - categorical_accuracy: 1.0000 - loss: 1.3066e-04"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[30], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mtb_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:354\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m    349\u001b[0m     val_logs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    350\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m name: val \u001b[38;5;28;01mfor\u001b[39;00m name, val \u001b[38;5;129;01min\u001b[39;00m val_logs\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m    351\u001b[0m     }\n\u001b[0;32m    352\u001b[0m     epoch_logs\u001b[38;5;241m.\u001b[39mupdate(val_logs)\n\u001b[1;32m--> 354\u001b[0m \u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mon_epoch_end\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch_logs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    355\u001b[0m training_logs \u001b[38;5;241m=\u001b[39m epoch_logs\n\u001b[0;32m    356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\keras\\src\\callbacks\\callback_list.py:96\u001b[0m, in \u001b[0;36mCallbackList.on_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m     94\u001b[0m logs \u001b[38;5;241m=\u001b[39m logs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks:\n\u001b[1;32m---> 96\u001b[0m     \u001b[43mcallback\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mon_epoch_end\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\keras\\src\\callbacks\\tensorboard.py:474\u001b[0m, in \u001b[0;36mTensorBoard.on_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m    472\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mon_epoch_end\u001b[39m(\u001b[38;5;28mself\u001b[39m, epoch, logs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    473\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Runs metrics and histogram summaries at epoch end.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 474\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_epoch_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    476\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhistogram_freq \u001b[38;5;129;01mand\u001b[39;00m epoch \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhistogram_freq \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    477\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_weights(epoch)\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\keras\\src\\callbacks\\tensorboard.py:539\u001b[0m, in \u001b[0;36mTensorBoard._log_epoch_metrics\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m    537\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_train_writer\u001b[38;5;241m.\u001b[39mas_default():\n\u001b[0;32m    538\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m train_logs\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m--> 539\u001b[0m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscalar\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepoch_\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    540\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m val_logs:\n\u001b[0;32m    541\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_val_writer\u001b[38;5;241m.\u001b[39mas_default():\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\tensorflow\\python\\summary\\tb_summary.py:303\u001b[0m, in \u001b[0;36mscalar\u001b[1;34m(name, data, step, description)\u001b[0m\n\u001b[0;32m    301\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m    302\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m TBNotInstalledError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf.summary.scalar\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m--> 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mscalar_v2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdescription\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdescription\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\tensorboard\\plugins\\scalar\\summary_v2.py:89\u001b[0m, in \u001b[0;36mscalar\u001b[1;34m(name, data, step, description)\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m summary_scope(name, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscalar_summary\u001b[39m\u001b[38;5;124m\"\u001b[39m, values\u001b[38;5;241m=\u001b[39m[data, step]) \u001b[38;5;28;01mas\u001b[39;00m (tag, _):\n\u001b[0;32m     88\u001b[0m     tf\u001b[38;5;241m.\u001b[39mdebugging\u001b[38;5;241m.\u001b[39massert_scalar(data)\n\u001b[1;32m---> 89\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     90\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtag\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     91\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtensor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     93\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msummary_metadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     94\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\tensorflow\\python\\ops\\summary_ops_v2.py:802\u001b[0m, in \u001b[0;36mwrite\u001b[1;34m(tag, tensor, step, metadata, name)\u001b[0m\n\u001b[0;32m    799\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mcontrol_dependencies([write_summary_op]):\n\u001b[0;32m    800\u001b[0m       \u001b[38;5;28;01mreturn\u001b[39;00m constant_op\u001b[38;5;241m.\u001b[39mconstant(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 802\u001b[0m op \u001b[38;5;241m=\u001b[39m \u001b[43msmart_cond\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msmart_cond\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    803\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshould_record_summaries\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrecord\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_nothing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msummary_cond\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    804\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m    805\u001b[0m   ops\u001b[38;5;241m.\u001b[39madd_to_collection(ops\u001b[38;5;241m.\u001b[39mGraphKeys\u001b[38;5;241m.\u001b[39m_SUMMARY_COLLECTION, op)  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\tensorflow\\python\\framework\\smart_cond.py:53\u001b[0m, in \u001b[0;36msmart_cond\u001b[1;34m(pred, true_fn, false_fn, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pred_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m pred_value:\n\u001b[1;32m---> 53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrue_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     54\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     55\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m false_fn()\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\tensorflow\\python\\ops\\summary_ops_v2.py:791\u001b[0m, in \u001b[0;36mwrite.<locals>.record\u001b[1;34m()\u001b[0m\n\u001b[0;32m    788\u001b[0m summary_value \u001b[38;5;241m=\u001b[39m _maybe_convert_tensor_to_dtensor(writer, summary_tensor)\n\u001b[0;32m    789\u001b[0m step_value \u001b[38;5;241m=\u001b[39m _maybe_convert_tensor_to_dtensor(writer, step)\n\u001b[1;32m--> 791\u001b[0m write_summary_op \u001b[38;5;241m=\u001b[39m \u001b[43mgen_summary_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_summary\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    792\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_resource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m    793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstep_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43msummary_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtag\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mserialized_metadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscope\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    798\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    799\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mcontrol_dependencies([write_summary_op]):\n\u001b[0;32m    800\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m constant_op\u001b[38;5;241m.\u001b[39mconstant(\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\tensorflow\\python\\ops\\gen_summary_ops.py:709\u001b[0m, in \u001b[0;36mwrite_summary\u001b[1;34m(writer, step, tensor, tag, summary_metadata, name)\u001b[0m\n\u001b[0;32m    707\u001b[0m   \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m    708\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 709\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwrite_summary_eager_fallback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    710\u001b[0m \u001b[43m      \u001b[49m\u001b[43mwriter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtag\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msummary_metadata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_ctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    711\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_SymbolicException:\n\u001b[0;32m    712\u001b[0m   \u001b[38;5;28;01mpass\u001b[39;00m  \u001b[38;5;66;03m# Add nodes to the TensorFlow graph.\u001b[39;00m\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\tensorflow\\python\\ops\\gen_summary_ops.py:729\u001b[0m, in \u001b[0;36mwrite_summary_eager_fallback\u001b[1;34m(writer, step, tensor, tag, summary_metadata, name, ctx)\u001b[0m\n\u001b[0;32m    727\u001b[0m _inputs_flat \u001b[38;5;241m=\u001b[39m [writer, step, tensor, tag, summary_metadata]\n\u001b[0;32m    728\u001b[0m _attrs \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mT\u001b[39m\u001b[38;5;124m\"\u001b[39m, _attr_T)\n\u001b[1;32m--> 729\u001b[0m _result \u001b[38;5;241m=\u001b[39m \u001b[43m_execute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mWriteSummary\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_inputs_flat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    730\u001b[0m \u001b[43m                           \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_attrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    731\u001b[0m _result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    732\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _result\n",
      "File \u001b[1;32m~\\Desktop\\Action_Analyzer\\venv\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=1000, callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">442,112</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">49,408</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">99</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m64\u001b[0m)              │         \u001b[38;5;34m442,112\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m128\u001b[0m)             │          \u001b[38;5;34m98,816\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │          \u001b[38;5;34m49,408\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m2,080\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │              \u001b[38;5;34m99\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,790,027</span> (6.83 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,790,027\u001b[0m (6.83 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">596,675</span> (2.28 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m596,675\u001b[0m (2.28 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,193,352</span> (4.55 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m1,193,352\u001b[0m (4.55 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[9.9972266e-01, 5.0235208e-06, 2.7228118e-04],\n",
       "       [3.8979871e-03, 9.9610198e-01, 2.1428981e-09],\n",
       "       [1.8901886e-06, 9.9999809e-01, 3.1966603e-13],\n",
       "       [9.9997854e-01, 1.7552467e-05, 3.9463935e-06],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00],\n",
       "       [9.9994683e-01, 5.0618801e-05, 2.5298543e-06],\n",
       "       [6.6314050e-07, 9.9999928e-01, 8.1258527e-14],\n",
       "       [2.1720440e-12, 1.0272922e-18, 1.0000000e+00],\n",
       "       [9.9975437e-01, 4.2573774e-06, 2.4134104e-04]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = model.predict(X_test)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'heart'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(res[4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'heart'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(y_test[4])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Save Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('Weigths_new.keras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Evaluation using Confusion Matrix and Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m yhat \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(\u001b[43mX_test\u001b[49m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X_test' is not defined"
     ]
    }
   ],
   "source": [
    "yhat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_test, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[6, 0],\n",
       "        [0, 3]],\n",
       "\n",
       "       [[4, 0],\n",
       "        [0, 5]],\n",
       "\n",
       "       [[8, 0],\n",
       "        [0, 1]]], dtype=int64)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Test in Real Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.9999464e-01, 3.2442888e-06, 2.2008010e-06],\n",
       "       [9.9999177e-01, 5.1481029e-06, 3.0944677e-06],\n",
       "       [4.9239346e-09, 1.0000000e+00, 0.0000000e+00],\n",
       "       [2.3337508e-08, 1.0000000e+00, 9.5057912e-35],\n",
       "       [4.5706718e-09, 1.0000000e+00, 6.0529210e-38],\n",
       "       [2.8830260e-10, 5.0856118e-11, 1.0000000e+00],\n",
       "       [6.0633041e-09, 1.0000000e+00, 3.1843160e-38],\n",
       "       [1.3909173e-08, 1.0000000e+00, 7.3678686e-36],\n",
       "       [9.9999535e-01, 2.8724910e-06, 1.7495431e-06]], dtype=float32)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
    "        print(num,prob)\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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [9.9972266e-01 5.0235208e-06 2.7228118e-04]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "only length-1 arrays can be converted to Python scalars",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mprob_viz\u001b[49m\u001b[43m(\u001b[49m\u001b[43mres\u001b[49m\u001b[43m,\u001b[49m\u001b[43mactions\u001b[49m\u001b[43m,\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m))\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(prob_viz(res, actions, image, colors))\n",
      "Cell \u001b[1;32mIn[35], line 6\u001b[0m, in \u001b[0;36mprob_viz\u001b[1;34m(res, actions, input_frame, colors)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num, prob \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(res):\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;28mprint\u001b[39m(num,prob)\n\u001b[1;32m----> 6\u001b[0m     cv2\u001b[38;5;241m.\u001b[39mrectangle(output_frame, (\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m60\u001b[39m\u001b[38;5;241m+\u001b[39mnum\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m40\u001b[39m), (\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprob\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;241m90\u001b[39m\u001b[38;5;241m+\u001b[39mnum\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m40\u001b[39m), colors[num], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      7\u001b[0m     cv2\u001b[38;5;241m.\u001b[39mputText(output_frame, actions[num], (\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m85\u001b[39m\u001b[38;5;241m+\u001b[39mnum\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m40\u001b[39m), cv2\u001b[38;5;241m.\u001b[39mFONT_HERSHEY_SIMPLEX, \u001b[38;5;241m1\u001b[39m, (\u001b[38;5;241m255\u001b[39m,\u001b[38;5;241m255\u001b[39m,\u001b[38;5;241m255\u001b[39m), \u001b[38;5;241m2\u001b[39m, cv2\u001b[38;5;241m.\u001b[39mLINE_AA)\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output_frame\n",
      "\u001b[1;31mTypeError\u001b[0m: only length-1 arrays can be converted to Python scalars"
     ]
    }
   ],
   "source": [
    "prob_viz(res,actions,image,colors)\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(prob_viz(res, actions, image, colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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",
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      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
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   ],
   "source": [
    "# 1. New detection variables\n",
    "sequence = []\n",
    "sentence = []\n",
    "threshold = 0.80\n",
    "predictions = []\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.75, min_tracking_confidence=0.75) 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",
    "        prob = []\n",
    "        if len(sequence) == 30:\n",
    "            res = model.predict(np.expand_dims(sequence, axis=0))[0]\n",
    "            print(np.argmax(res))\n",
    "            print(actions[np.argmax(res)])\n",
    "            predictions.append(np.argmax(res))\n",
    "            prob = res\n",
    "            \n",
    "        #3. Viz logic\n",
    "            if np.unique(predictions[-10:])[0] == np.argmax(res):\n",
    "                if res[np.argmax(res)] > threshold: \n",
    "                    if len(sentence) > 0: \n",
    "                        if actions[np.argmax(res)] != sentence[-1]:\n",
    "                            sentence.append(actions[np.argmax(res)])\n",
    "                    else:\n",
    "                        sentence.append(actions[np.argmax(res)])\n",
    "\n",
    "            if len(sentence) > 5: \n",
    "                sentence = sentence[-5:]\n",
    "                \n",
    "            image = prob_viz(res, actions, image, colors)\n",
    "            \n",
    "        cv2.rectangle(image, (0,0), (640, 40), (245, 117, 16), -1)\n",
    "        cv2.putText(image, ' '.join(sentence), (3,30), \n",
    "                       cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        if len(prob)!=0:\n",
    "            cv2.putText(image,'{}'.format(prob[np.argmax(prob)]),(120,80),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2,cv2.LINE_AA)\n",
    "        \n",
    "        cv2.imshow('OpenCV Feed', image)\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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