ISL_Translator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import mediapipe as mp\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "mp_hands = mp.solutions.hands\n",
    "\n",
    "# For static images:\n",
    "IMAGE_FILES = []\n",
    "with mp_hands.Hands(\n",
    "    static_image_mode=True,\n",
    "    max_num_hands=2,\n",
    "    min_detection_confidence=0.5) as hands:\n",
    "    file = \"ISL_CSLRT_Corpus/Frames_Word_Level/AFRAID/AFRAID (1).jpg\"\n",
    "\n",
    "    image = cv2.flip(cv2.imread(file), 1)\n",
    "    # Convert the BGR image to RGB before processing.\n",
    "    results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "    \n",
    "    print('Handedness:', results.multi_handedness)\n",
    "    if not results.multi_hand_landmarks:\n",
    "        pass\n",
    "    image_height, image_width, _ = image.shape\n",
    "    annotated_image = image.copy()\n",
    "    for hand_landmarks in results.multi_hand_landmarks:\n",
    "        print('hand_landmarks:', hand_landmarks)\n",
    "        print(\n",
    "          f'Index finger tip coordinates: (',\n",
    "          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '\n",
    "          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height})'\n",
    "      )\n",
    "        mp_drawing.draw_landmarks(\n",
    "          annotated_image,\n",
    "          hand_landmarks,\n",
    "          mp_hands.HAND_CONNECTIONS,\n",
    "          mp_drawing_styles.get_default_hand_landmarks_style(),\n",
    "          mp_drawing_styles.get_default_hand_connections_style())\n",
    "        # cv2.imshow(\"sdc\",annotated_image)\n",
    "    cv2.imwrite(\n",
    "        '/tmp/annotated_image' + '.jpg', cv2.flip(annotated_image, 1))\n",
    "    if not results.multi_hand_world_landmarks:\n",
    "        pass\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1500074e610>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(annotated_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run this 1st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import mediapipe as mp\n",
    "import PIL\n",
    "import matplotlib.pyplot as plt\n",
    "mp_holistic = mp.solutions.holistic\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "mp_hands = mp.solutions.hands\n",
    "\n",
    "def draw_landmarks(image,results):\n",
    "    mp_drawing.draw_landmarks(image,results.left_hand_landmarks,mp_holistic.HAND_CONNECTIONS)\n",
    "    mp_drawing.draw_landmarks(image,results.right_hand_landmarks,mp_holistic.HAND_CONNECTIONS)\n",
    "    \n",
    "def extract_keypoints(results):\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([lh,rh])\n",
    "\n",
    "with mp_holistic.Holistic(static_image_mode=True, min_detection_confidence=0.5) as hands:\n",
    "    image = cv2.imread(\"ISL_CSLRT_Corpus/Frames_Word_Level/AFRAID/AFRAID (1).jpg\")\n",
    "    results = hands.process(cv2.cvtColor(image,cv2.COLOR_BGR2RGB))\n",
    "    draw_landmarks(image,results)\n",
    "    plt.imshow(cv2.cvtColor(image,cv2.COLOR_RGB2BGR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "data_path = \"trial_data\"\n",
    "data = os.path.join(data_path)\n",
    "actions = [\"ANGRY\",\"AFRAID\",\"AGREE\",\"CLASS\",\"COLD\",\"COME\"]\n",
    "# actions = [\"CLASS\"]\n",
    "for action in actions:\n",
    "    os.makedirs(os.path.join(data,action))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "run this 2nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "actions = [\"are you free today\\nक्या तुम आज उपलब्ध हो\", \n",
    "            \"can i help you\\nक्या मैं आपकी मदद कर सकता हूं\", \n",
    "            \"go and sleep\\nजाओ सो जाओ\"]\n",
    "\n",
    "data = os.path.join(\"trial_sentence_data\")\n",
    "\n",
    "x = lambda a: a.split(\"\\n\")[0]\n",
    "\n",
    "for action in actions:\n",
    "    if not os.path.exists(os.path.join(data, x(action))):\n",
    "        os.makedirs(os.path.join(data, x(action)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['free 01.jpg', 'free 02.jpg', 'free 03.jpg', 'free 04.jpg', 'free 05.jpg', 'free 06.jpg', 'free 07.jpg', 'free 08.jpg', 'free 09.jpg', 'free 10.jpg', 'free 11.jpg', 'free 12.jpg', 'free 13.jpg', 'free 14.jpg', 'free 15.jpg', 'free 16.jpg', 'free 17.jpg', 'free 18.jpg', 'free 19.jpg', 'free 20.jpg', 'free 21.jpg', 'free 22.jpg', 'free 23.jpg', 'free 24.jpg', 'free 25.jpg', 'free 26.jpg', 'free 27.jpg', 'free 28.jpg', 'free 29.jpg', 'free 30.jpg', 'free 31.jpg', 'free 32.jpg', 'free 33.jpg', 'free 34.jpg', 'free 35.jpg', 'free 36.jpg', 'free 37.jpg', 'free 38.jpg', 'free 39.jpg', 'free 40.jpg', 'free 41.jpg', 'free 42.jpg', 'Thumbs.db']\n"
     ]
    }
   ],
   "source": [
    "img_lst = os.path.join(\"ISL_CSLRT_Corpus\",\"Frames_Sentence_Level\",\"are you free today\", \"3\")\n",
    "print(os.listdir(img_lst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[WinError 123] The filename, directory name, or volume label syntax is incorrect: 'ISL_CSLRT_Corpus\\\\Frames_Sentence_Level\\\\are you free today\\nक्या तुम आज उपलब्ध हो'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-3259803fff64>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mimg_lst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ISL_CSLRT_Corpus\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Frames_Sentence_Level\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mcnt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mimage\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_lst\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mmp_holistic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHolistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatic_image_mode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_detection_confidence\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mhands\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m             \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_lst\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: [WinError 123] The filename, directory name, or volume label syntax is incorrect: 'ISL_CSLRT_Corpus\\\\Frames_Sentence_Level\\\\are you free today\\nक्या तुम आज उपलब्ध हो'"
     ]
    }
   ],
   "source": [
    "no_sequences = 7\n",
    "\n",
    "for action in actions:\n",
    "    img_lst = os.path.join(\"ISL_CSLRT_Corpus\",\"Frames_Sentence_Level\",action)\n",
    "    cnt=0\n",
    "    for image in os.listdir(img_lst):\n",
    "        with mp_holistic.Holistic(static_image_mode=True, min_detection_confidence=0.5) as hands:\n",
    "            image = cv2.imread(os.path.join(img_lst,image))\n",
    "            results = hands.process(cv2.cvtColor(image,cv2.COLOR_BGR2RGB))\n",
    "            draw_landmarks(image,results)\n",
    "        a = extract_keypoints(results)\n",
    "        path = os.path.join(\"trial_sentence_data\",action,str(cnt))\n",
    "        np.save(path,a)\n",
    "        cnt = cnt+1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[WinError 123] The filename, directory name, or volume label syntax is incorrect: 'ISL_CSLRT_Corpus\\\\Frames_Sentence_Level\\\\are you free today\\nक्या तुम आज उपलब्ध हो'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-d996ea52e24b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mclip_lst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ISL_CSLRT_Corpus\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Frames_Sentence_Level\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;31m# print(img_lst)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mclip_no\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclip_lst\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m         \u001b[0mimg_lst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclip_lst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip_no\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[0mcnt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: [WinError 123] The filename, directory name, or volume label syntax is incorrect: 'ISL_CSLRT_Corpus\\\\Frames_Sentence_Level\\\\are you free today\\nक्या तुम आज उपलब्ध हो'"
     ]
    }
   ],
   "source": [
    "for action in actions:\n",
    "    clip_lst = os.path.join(\"ISL_CSLRT_Corpus\",\"Frames_Sentence_Level\",action)\n",
    "    # print(img_lst)\n",
    "    for clip_no in os.listdir(clip_lst):\n",
    "        img_lst = os.path.join(clip_lst, clip_no)\n",
    "        cnt = 0\n",
    "        for image in os.listdir(img_lst):\n",
    "            if image.endswith(\"db\"):\n",
    "                continue\n",
    "            with mp_holistic.Holistic(static_image_mode=True, min_detection_confidence=0.5) as hands:\n",
    "                image = cv2.imread(os.path.join(img_lst,image))\n",
    "                results = hands.process(cv2.cvtColor(image,cv2.COLOR_BGR2RGB))\n",
    "                draw_landmarks(image,results)\n",
    "            a = extract_keypoints(results)\n",
    "            path = os.path.join(\"trial_sentence_data\",action,str(cnt))\n",
    "            np.save(path,a)\n",
    "            cnt = cnt+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'are you free today\\nक्या तुम आज उपलब्ध हो': 0,\n",
       " 'can i help you\\nक्या मैं आपकी मदद कर सकता हूं': 1,\n",
       " 'go and sleep\\nजाओ सो जाओ': 2}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "label_map = {label:num for num,label in enumerate(actions)}\n",
    "label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "sequence , labels = [],[]\n",
    "no_sequences = 30\n",
    "\n",
    "for action in actions:\n",
    "    file_name = x(action)\n",
    "    for frame_num in range(no_sequences):\n",
    "        res = np.load(os.path.join(\"trial_sentence_data\",file_name,\"{}.npy\".format(frame_num)))\n",
    "        sequence.append(res)\n",
    "        labels.append(label_map[action])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 126)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(sequence).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 126)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array(sequence)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 126)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_CNN = np.array(sequence)\n",
    "X_CNN.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 1, 126)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.reshape(X.shape[0],1,X.shape[1])\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Dropout, Conv2D\n",
    "from tensorflow.keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = os.path.join('Logs')\n",
    "tb_callback = TensorBoard(log_dir = log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(64,return_sequences=True,activation=\"relu\",input_shape=(1,126)))\n",
    "# model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(128,return_sequences=True,activation=\"relu\"))\n",
    "# model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(64,return_sequences=False,activation=\"relu\"))\n",
    "# model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(64,activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(32,activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(16,activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(3,activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"Adam\",loss=\"categorical_crossentropy\",metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "3/3 [==============================] - 5s 106ms/step - loss: 1.0986 - categorical_accuracy: 0.2778\n",
      "Epoch 2/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 1.0985 - categorical_accuracy: 0.3333\n",
      "Epoch 3/200\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 1.0983 - categorical_accuracy: 0.3333\n",
      "Epoch 4/200\n",
      "3/3 [==============================] - 0s 85ms/step - loss: 1.0977 - categorical_accuracy: 0.3333\n",
      "Epoch 5/200\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.0970 - categorical_accuracy: 0.3333\n",
      "Epoch 6/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 1.0954 - categorical_accuracy: 0.3333\n",
      "Epoch 7/200\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 1.0936 - categorical_accuracy: 0.3333\n",
      "Epoch 8/200\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 1.0898 - categorical_accuracy: 0.3333\n",
      "Epoch 9/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 1.0834 - categorical_accuracy: 0.3333\n",
      "Epoch 10/200\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.0763 - categorical_accuracy: 0.3444\n",
      "Epoch 11/200\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 1.0700 - categorical_accuracy: 0.3444\n",
      "Epoch 12/200\n",
      "3/3 [==============================] - 0s 110ms/step - loss: 1.0720 - categorical_accuracy: 0.4444\n",
      "Epoch 13/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 1.0695 - categorical_accuracy: 0.4333\n",
      "Epoch 14/200\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 1.0650 - categorical_accuracy: 0.4444\n",
      "Epoch 15/200\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 1.0624 - categorical_accuracy: 0.4444\n",
      "Epoch 16/200\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.0584 - categorical_accuracy: 0.4444\n",
      "Epoch 17/200\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.0550 - categorical_accuracy: 0.4444\n",
      "Epoch 18/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 1.0532 - categorical_accuracy: 0.4444\n",
      "Epoch 19/200\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 1.0480 - categorical_accuracy: 0.4444\n",
      "Epoch 20/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 1.0437 - categorical_accuracy: 0.4444\n",
      "Epoch 21/200\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 1.0382 - categorical_accuracy: 0.4667\n",
      "Epoch 22/200\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 1.0326 - categorical_accuracy: 0.4667\n",
      "Epoch 23/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 1.0266 - categorical_accuracy: 0.5111\n",
      "Epoch 24/200\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 1.0227 - categorical_accuracy: 0.5222\n",
      "Epoch 25/200\n",
      "3/3 [==============================] - 0s 100ms/step - loss: 1.0195 - categorical_accuracy: 0.4889\n",
      "Epoch 26/200\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 1.0102 - categorical_accuracy: 0.4889\n",
      "Epoch 27/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.9984 - categorical_accuracy: 0.5333\n",
      "Epoch 28/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.9915 - categorical_accuracy: 0.6222\n",
      "Epoch 29/200\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.9967 - categorical_accuracy: 0.5556\n",
      "Epoch 30/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.9832 - categorical_accuracy: 0.5444\n",
      "Epoch 31/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 0.9759 - categorical_accuracy: 0.5889\n",
      "Epoch 32/200\n",
      "3/3 [==============================] - 0s 68ms/step - loss: 0.9709 - categorical_accuracy: 0.5778\n",
      "Epoch 33/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.9692 - categorical_accuracy: 0.6000\n",
      "Epoch 34/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.9648 - categorical_accuracy: 0.5889\n",
      "Epoch 35/200\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.9668 - categorical_accuracy: 0.5667\n",
      "Epoch 36/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.9610 - categorical_accuracy: 0.5889\n",
      "Epoch 37/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.9604 - categorical_accuracy: 0.6111\n",
      "Epoch 38/200\n",
      "3/3 [==============================] - 0s 66ms/step - loss: 0.9551 - categorical_accuracy: 0.6333\n",
      "Epoch 39/200\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.9474 - categorical_accuracy: 0.6000\n",
      "Epoch 40/200\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.9476 - categorical_accuracy: 0.6000\n",
      "Epoch 41/200\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 0.9448 - categorical_accuracy: 0.5889\n",
      "Epoch 42/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.9483 - categorical_accuracy: 0.5889\n",
      "Epoch 43/200\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.9397 - categorical_accuracy: 0.5889\n",
      "Epoch 44/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.9361 - categorical_accuracy: 0.6222\n",
      "Epoch 45/200\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.9331 - categorical_accuracy: 0.6444\n",
      "Epoch 46/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 0.9354 - categorical_accuracy: 0.6333\n",
      "Epoch 47/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.9289 - categorical_accuracy: 0.6222\n",
      "Epoch 48/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.9214 - categorical_accuracy: 0.6222\n",
      "Epoch 49/200\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.9214 - categorical_accuracy: 0.6222\n",
      "Epoch 50/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.9242 - categorical_accuracy: 0.6111\n",
      "Epoch 51/200\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 0.9099 - categorical_accuracy: 0.6333\n",
      "Epoch 52/200\n",
      "3/3 [==============================] - 0s 64ms/step - loss: 0.9202 - categorical_accuracy: 0.6667\n",
      "Epoch 53/200\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 0.9079 - categorical_accuracy: 0.6444\n",
      "Epoch 54/200\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.9097 - categorical_accuracy: 0.5889\n",
      "Epoch 55/200\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 0.8954 - categorical_accuracy: 0.6000\n",
      "Epoch 56/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.8984 - categorical_accuracy: 0.6556\n",
      "Epoch 57/200\n",
      "3/3 [==============================] - 0s 74ms/step - loss: 0.8880 - categorical_accuracy: 0.6444\n",
      "Epoch 58/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.8935 - categorical_accuracy: 0.6111\n",
      "Epoch 59/200\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 0.8788 - categorical_accuracy: 0.6222\n",
      "Epoch 60/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.8728 - categorical_accuracy: 0.6556\n",
      "Epoch 61/200\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.8614 - categorical_accuracy: 0.6333\n",
      "Epoch 62/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 0.8563 - categorical_accuracy: 0.6333\n",
      "Epoch 63/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.8625 - categorical_accuracy: 0.6333\n",
      "Epoch 64/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.8557 - categorical_accuracy: 0.5778\n",
      "Epoch 65/200\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.8671 - categorical_accuracy: 0.6111\n",
      "Epoch 66/200\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.8109 - categorical_accuracy: 0.6556\n",
      "Epoch 67/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.8628 - categorical_accuracy: 0.6444\n",
      "Epoch 68/200\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.8226 - categorical_accuracy: 0.6444\n",
      "Epoch 69/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.8092 - categorical_accuracy: 0.6778\n",
      "Epoch 70/200\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.8732 - categorical_accuracy: 0.5778\n",
      "Epoch 71/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 0.8494 - categorical_accuracy: 0.6222\n",
      "Epoch 72/200\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.8177 - categorical_accuracy: 0.6111\n",
      "Epoch 73/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.8112 - categorical_accuracy: 0.6222\n",
      "Epoch 74/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.7719 - categorical_accuracy: 0.6778\n",
      "Epoch 75/200\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.8251 - categorical_accuracy: 0.6222\n",
      "Epoch 76/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 0.8169 - categorical_accuracy: 0.6444\n",
      "Epoch 77/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.7348 - categorical_accuracy: 0.7222\n",
      "Epoch 78/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.7869 - categorical_accuracy: 0.6111\n",
      "Epoch 79/200\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.7892 - categorical_accuracy: 0.6333\n",
      "Epoch 80/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 0.7633 - categorical_accuracy: 0.7000\n",
      "Epoch 81/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.7227 - categorical_accuracy: 0.7000\n",
      "Epoch 82/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.7354 - categorical_accuracy: 0.6667\n",
      "Epoch 83/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.7126 - categorical_accuracy: 0.7000\n",
      "Epoch 84/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.7117 - categorical_accuracy: 0.6778\n",
      "Epoch 85/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 0.6912 - categorical_accuracy: 0.7111\n",
      "Epoch 86/200\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.6971 - categorical_accuracy: 0.7111\n",
      "Epoch 87/200\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.7023 - categorical_accuracy: 0.6778\n",
      "Epoch 88/200\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 0.6756 - categorical_accuracy: 0.7000\n",
      "Epoch 89/200\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.6582 - categorical_accuracy: 0.7000\n",
      "Epoch 90/200\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 0.6575 - categorical_accuracy: 0.7111\n",
      "Epoch 91/200\n",
      "3/3 [==============================] - 0s 64ms/step - loss: 0.6564 - categorical_accuracy: 0.7111\n",
      "Epoch 92/200\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 0.6412 - categorical_accuracy: 0.7556\n",
      "Epoch 93/200\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 0.6436 - categorical_accuracy: 0.7111\n",
      "Epoch 94/200\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 0.7030 - categorical_accuracy: 0.6667\n",
      "Epoch 95/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.6645 - categorical_accuracy: 0.6889\n",
      "Epoch 96/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.6273 - categorical_accuracy: 0.7333\n",
      "Epoch 97/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.6735 - categorical_accuracy: 0.7222\n",
      "Epoch 98/200\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 0.6269 - categorical_accuracy: 0.7111\n",
      "Epoch 99/200\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 0.6095 - categorical_accuracy: 0.7556\n",
      "Epoch 100/200\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 0.6294 - categorical_accuracy: 0.6889\n",
      "Epoch 101/200\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.6517 - categorical_accuracy: 0.7222\n",
      "Epoch 102/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.5876 - categorical_accuracy: 0.7778\n",
      "Epoch 103/200\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 0.5909 - categorical_accuracy: 0.7222\n",
      "Epoch 104/200\n",
      "3/3 [==============================] - 0s 63ms/step - loss: 0.5992 - categorical_accuracy: 0.7222\n",
      "Epoch 105/200\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 0.5746 - categorical_accuracy: 0.7444\n",
      "Epoch 106/200\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 0.5921 - categorical_accuracy: 0.7444\n",
      "Epoch 107/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.5874 - categorical_accuracy: 0.7333\n",
      "Epoch 108/200\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.5855 - categorical_accuracy: 0.7556\n",
      "Epoch 109/200\n",
      "3/3 [==============================] - 0s 89ms/step - loss: 0.5922 - categorical_accuracy: 0.7222\n",
      "Epoch 110/200\n",
      "3/3 [==============================] - 0s 75ms/step - loss: 0.6068 - categorical_accuracy: 0.7444\n",
      "Epoch 111/200\n",
      "3/3 [==============================] - 0s 64ms/step - loss: 0.5924 - categorical_accuracy: 0.7222\n",
      "Epoch 112/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.5597 - categorical_accuracy: 0.7333\n",
      "Epoch 113/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.5592 - categorical_accuracy: 0.7444\n",
      "Epoch 114/200\n",
      "3/3 [==============================] - 0s 61ms/step - loss: 0.5533 - categorical_accuracy: 0.7778\n",
      "Epoch 115/200\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 0.6397 - categorical_accuracy: 0.7333\n",
      "Epoch 116/200\n",
      "3/3 [==============================] - 0s 66ms/step - loss: 0.7130 - categorical_accuracy: 0.6778\n",
      "Epoch 117/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.6715 - categorical_accuracy: 0.7222\n",
      "Epoch 118/200\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 0.7103 - categorical_accuracy: 0.6778\n",
      "Epoch 119/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.7261 - categorical_accuracy: 0.6556\n",
      "Epoch 120/200\n",
      "3/3 [==============================] - 0s 62ms/step - loss: 0.6416 - categorical_accuracy: 0.7222\n",
      "Epoch 121/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.5940 - categorical_accuracy: 0.7444\n",
      "Epoch 122/200\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 0.6607 - categorical_accuracy: 0.7222\n",
      "Epoch 123/200\n",
      "3/3 [==============================] - 0s 63ms/step - loss: 0.6166 - categorical_accuracy: 0.7222\n",
      "Epoch 124/200\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 0.5763 - categorical_accuracy: 0.7556\n",
      "Epoch 125/200\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.6045 - categorical_accuracy: 0.7222\n",
      "Epoch 126/200\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.5483 - categorical_accuracy: 0.7778\n",
      "Epoch 127/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.5751 - categorical_accuracy: 0.7333\n",
      "Epoch 128/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.5448 - categorical_accuracy: 0.7667\n",
      "Epoch 129/200\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 0.5639 - categorical_accuracy: 0.7667\n",
      "Epoch 130/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.5396 - categorical_accuracy: 0.7444\n",
      "Epoch 131/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.5407 - categorical_accuracy: 0.7667\n",
      "Epoch 132/200\n",
      "3/3 [==============================] - 0s 61ms/step - loss: 0.5231 - categorical_accuracy: 0.7556\n",
      "Epoch 133/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.5311 - categorical_accuracy: 0.7778\n",
      "Epoch 134/200\n",
      "3/3 [==============================] - 0s 55ms/step - loss: 0.5293 - categorical_accuracy: 0.7667\n",
      "Epoch 135/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.5164 - categorical_accuracy: 0.7667\n",
      "Epoch 136/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.5078 - categorical_accuracy: 0.7667\n",
      "Epoch 137/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.5093 - categorical_accuracy: 0.7667\n",
      "Epoch 138/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.4996 - categorical_accuracy: 0.7778\n",
      "Epoch 139/200\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.4994 - categorical_accuracy: 0.7778\n",
      "Epoch 140/200\n",
      "3/3 [==============================] - 0s 61ms/step - loss: 0.5036 - categorical_accuracy: 0.7667\n",
      "Epoch 141/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.4927 - categorical_accuracy: 0.7778\n",
      "Epoch 142/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.4929 - categorical_accuracy: 0.7778\n",
      "Epoch 143/200\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 0.4851 - categorical_accuracy: 0.7667\n",
      "Epoch 144/200\n",
      "3/3 [==============================] - 0s 65ms/step - loss: 0.4852 - categorical_accuracy: 0.7778\n",
      "Epoch 145/200\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 0.4902 - categorical_accuracy: 0.7667\n",
      "Epoch 146/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.5415 - categorical_accuracy: 0.7556\n",
      "Epoch 147/200\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.5192 - categorical_accuracy: 0.7444\n",
      "Epoch 148/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.5462 - categorical_accuracy: 0.7556\n",
      "Epoch 149/200\n",
      "3/3 [==============================] - 0s 82ms/step - loss: 0.5323 - categorical_accuracy: 0.7556\n",
      "Epoch 150/200\n",
      "3/3 [==============================] - 0s 63ms/step - loss: 0.4813 - categorical_accuracy: 0.7778\n",
      "Epoch 151/200\n",
      "3/3 [==============================] - 0s 62ms/step - loss: 0.4919 - categorical_accuracy: 0.7556\n",
      "Epoch 152/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.5118 - categorical_accuracy: 0.7556\n",
      "Epoch 153/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.4932 - categorical_accuracy: 0.7778\n",
      "Epoch 154/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.5010 - categorical_accuracy: 0.7778\n",
      "Epoch 155/200\n",
      "3/3 [==============================] - 0s 63ms/step - loss: 0.4706 - categorical_accuracy: 0.7778\n",
      "Epoch 156/200\n",
      "3/3 [==============================] - 0s 70ms/step - loss: 0.4840 - categorical_accuracy: 0.7889\n",
      "Epoch 157/200\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 0.4630 - categorical_accuracy: 0.7667\n",
      "Epoch 158/200\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 0.5019 - categorical_accuracy: 0.7444\n",
      "Epoch 159/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.5109 - categorical_accuracy: 0.7556\n",
      "Epoch 160/200\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 0.5057 - categorical_accuracy: 0.7556\n",
      "Epoch 161/200\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.5024 - categorical_accuracy: 0.7667\n",
      "Epoch 162/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.4851 - categorical_accuracy: 0.7667\n",
      "Epoch 163/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.4632 - categorical_accuracy: 0.7778\n",
      "Epoch 164/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.4539 - categorical_accuracy: 0.7778\n",
      "Epoch 165/200\n",
      "3/3 [==============================] - 0s 63ms/step - loss: 0.4562 - categorical_accuracy: 0.7778\n",
      "Epoch 166/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.4589 - categorical_accuracy: 0.7778\n",
      "Epoch 167/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.4431 - categorical_accuracy: 0.7889\n",
      "Epoch 168/200\n",
      "3/3 [==============================] - 0s 66ms/step - loss: 0.4684 - categorical_accuracy: 0.7444\n",
      "Epoch 169/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.4501 - categorical_accuracy: 0.7667\n",
      "Epoch 170/200\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 0.4618 - categorical_accuracy: 0.7889\n",
      "Epoch 171/200\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 0.4588 - categorical_accuracy: 0.7778\n",
      "Epoch 172/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.4452 - categorical_accuracy: 0.7889\n",
      "Epoch 173/200\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 0.4430 - categorical_accuracy: 0.7889\n",
      "Epoch 174/200\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 0.4352 - categorical_accuracy: 0.7667\n",
      "Epoch 175/200\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 0.4208 - categorical_accuracy: 0.7778\n",
      "Epoch 176/200\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.4220 - categorical_accuracy: 0.7889\n",
      "Epoch 177/200\n",
      "3/3 [==============================] - 0s 63ms/step - loss: 0.4153 - categorical_accuracy: 0.7889\n",
      "Epoch 178/200\n",
      "3/3 [==============================] - 0s 62ms/step - loss: 0.4173 - categorical_accuracy: 0.7778\n",
      "Epoch 179/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.4174 - categorical_accuracy: 0.8000\n",
      "Epoch 180/200\n",
      "3/3 [==============================] - 0s 78ms/step - loss: 0.4098 - categorical_accuracy: 0.8000\n",
      "Epoch 181/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.4158 - categorical_accuracy: 0.7778\n",
      "Epoch 182/200\n",
      "3/3 [==============================] - 0s 61ms/step - loss: 0.4114 - categorical_accuracy: 0.8000\n",
      "Epoch 183/200\n",
      "3/3 [==============================] - 0s 62ms/step - loss: 0.4134 - categorical_accuracy: 0.7667\n",
      "Epoch 184/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.3971 - categorical_accuracy: 0.8111\n",
      "Epoch 185/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.4207 - categorical_accuracy: 0.7667\n",
      "Epoch 186/200\n",
      "3/3 [==============================] - 0s 62ms/step - loss: 0.3971 - categorical_accuracy: 0.7889\n",
      "Epoch 187/200\n",
      "3/3 [==============================] - 0s 62ms/step - loss: 0.4058 - categorical_accuracy: 0.8000\n",
      "Epoch 188/200\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 0.3984 - categorical_accuracy: 0.8000\n",
      "Epoch 189/200\n",
      "3/3 [==============================] - 0s 54ms/step - loss: 0.3997 - categorical_accuracy: 0.8222\n",
      "Epoch 190/200\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 0.3922 - categorical_accuracy: 0.8333\n",
      "Epoch 191/200\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 0.3963 - categorical_accuracy: 0.8111\n",
      "Epoch 192/200\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.3826 - categorical_accuracy: 0.8444\n",
      "Epoch 193/200\n",
      "3/3 [==============================] - 0s 58ms/step - loss: 0.3803 - categorical_accuracy: 0.8333\n",
      "Epoch 194/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.3867 - categorical_accuracy: 0.8556\n",
      "Epoch 195/200\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 0.3785 - categorical_accuracy: 0.8556\n",
      "Epoch 196/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.3706 - categorical_accuracy: 0.8556\n",
      "Epoch 197/200\n",
      "3/3 [==============================] - 0s 59ms/step - loss: 0.3826 - categorical_accuracy: 0.8556\n",
      "Epoch 198/200\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 0.3985 - categorical_accuracy: 0.8444\n",
      "Epoch 199/200\n",
      "3/3 [==============================] - 0s 96ms/step - loss: 0.3891 - categorical_accuracy: 0.8333\n",
      "Epoch 200/200\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 0.3693 - categorical_accuracy: 0.8556\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1d59cb06f70>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y,epochs=200,callbacks = [tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 1, 64)             48896     \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 1, 128)            98816     \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 64)                49408     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 16)                528       \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 3)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 203,939\n",
      "Trainable params: 203,939\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('ISL_Translator_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_CNN = Sequential()\n",
    "\n",
    "model_CNN.add(Conv2D(64,kernel_size=(3,3),activation=\"relu\",padding='same',input_shape= (126,90,1)))\n",
    "# model.add(Dropout(0.2))\n",
    "\n",
    "model_CNN.add(Conv2D(128,kernel_size=(3,3),activation=\"relu\", padding='same'))\n",
    "# model.add(Dropout(0.2))\n",
    "\n",
    "model_CNN.add(Conv2D(64,kernel_size=(3,3),activation=\"relu\", padding='same'))\n",
    "# model.add(Dropout(0.2))\n",
    "\n",
    "model_CNN.add(Dense(64,activation=\"relu\"))\n",
    "\n",
    "model_CNN.add(Dense(32,activation=\"relu\"))\n",
    "\n",
    "model_CNN.add(Dense(16,activation=\"relu\"))\n",
    "\n",
    "model_CNN.add(Dense(3,activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_CNN.compile(optimizer=\"Adam\",loss=\"categorical_crossentropy\",metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126, 90, 1)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_CNN.reshape(126,90,1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_22\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_28 (Conv2D)          (None, 126, 90, 64)       640       \n",
      "                                                                 \n",
      " conv2d_29 (Conv2D)          (None, 126, 90, 128)      73856     \n",
      "                                                                 \n",
      " conv2d_30 (Conv2D)          (None, 126, 90, 64)       73792     \n",
      "                                                                 \n",
      " dense_36 (Dense)            (None, 126, 90, 64)       4160      \n",
      "                                                                 \n",
      " dense_37 (Dense)            (None, 126, 90, 32)       2080      \n",
      "                                                                 \n",
      " dense_38 (Dense)            (None, 126, 90, 16)       528       \n",
      "                                                                 \n",
      " dense_39 (Dense)            (None, 126, 90, 3)        51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 155,107\n",
      "Trainable params: 155,107\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_CNN.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "WARNING:tensorflow:Model was constructed with shape (None, 126, 90, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 126, 90, 1), dtype=tf.float32, name='conv2d_28_input'), name='conv2d_28_input', description=\"created by layer 'conv2d_28_input'\"), but it was called on an input with incompatible shape (None, 126).\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\training.py\", line 808, in train_step\n        y_pred = self(x, training=True)\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 227, in assert_input_compatibility\n        raise ValueError(f'Input {input_index} of layer \"{layer_name}\" '\n\n    ValueError: Exception encountered when calling layer \"sequential_22\" (type Sequential).\n    \n    Input 0 of layer \"conv2d_28\" is incompatible with the layer: expected min_ndim=4, found ndim=2. Full shape received: (None, 126)\n    \n    Call arguments received:\n      • inputs=tf.Tensor(shape=(None, 126), dtype=float32)\n      • training=True\n      • mask=None\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-02a58827b8a3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel_CNN\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_CNN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcallbacks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtb_callback\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mautograph_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1127\u001b[0m           \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint:disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1128\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ag_error_metadata\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1129\u001b[1;33m               \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1130\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1131\u001b[0m               \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\training.py\", line 808, in train_step\n        y_pred = self(x, training=True)\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"c:\\Users\\krish\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 227, in assert_input_compatibility\n        raise ValueError(f'Input {input_index} of layer \"{layer_name}\" '\n\n    ValueError: Exception encountered when calling layer \"sequential_22\" (type Sequential).\n    \n    Input 0 of layer \"conv2d_28\" is incompatible with the layer: expected min_ndim=4, found ndim=2. Full shape received: (None, 126)\n    \n    Call arguments received:\n      • inputs=tf.Tensor(shape=(None, 126), dtype=float32)\n      • training=True\n      • mask=None\n"
     ]
    }
   ],
   "source": [
    "model_CNN.fit(X_CNN,y,epochs=200,callbacks = [tb_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "run this last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "from tensorflow.keras.models import load_model\n",
    "import mediapipe as mp\n",
    "from PIL import ImageDraw, ImageFont, Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mp_holistic = mp.solutions.holistic\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "mp_hands = mp.solutions.hands\n",
    "\n",
    "sequence=[]\n",
    "sentence=[]\n",
    "threshold = 0.4\n",
    "\n",
    "hindi_font = ImageFont.truetype('ARIALUNI.TTF', 26)\n",
    "\n",
    "def mediapipe_detection(image,model):\n",
    "    image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\n",
    "    image.flags.writeable = False\n",
    "    results = model.process(image)\n",
    "    image.flags.writeable = True\n",
    "    image = cv2.cvtColor(image,cv2.COLOR_RGB2BGR)\n",
    "    return image,results\n",
    "\n",
    "model = load_model('ISL_Translator_model.h5')\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "with mp_holistic.Holistic(min_detection_confidence = 0.5, min_tracking_confidence = 0.5) as holistic:\n",
    "    while(True):\n",
    "        ret, frame = cap.read()\n",
    "        image,results = mediapipe_detection(frame,holistic)\n",
    "        \n",
    "        draw_landmarks(image,results)\n",
    "        keypoints = extract_keypoints(results)\n",
    "        sequence.append(keypoints)\n",
    "        sequence = sequence[-1:]\n",
    "        if len(sequence) == 1:\n",
    "            res = model.predict(np.expand_dims(sequence,axis=0))[0]\n",
    "            # print(actions[np.argmax(res)])\n",
    "        \n",
    "#         cv2.putText(img=image, text=actions[np.argmax(res)], fontFace = cv2.FONT_HERSHEY_SIMPLEX, org=(40,70),thickness=2,fontScale = 1.0,\n",
    "#   color = (255, 0, 0))\n",
    "        pil_image = Image.fromarray(image)\n",
    "        draw = ImageDraw.Draw(pil_image)\n",
    "        draw.text((40, 70), text=actions[np.argmax(res)], font=hindi_font, fill=(153,0,255,1))\n",
    "        concat = cv2.hconcat([frame, image, np.array(pil_image)])\n",
    "        cv2.namedWindow('Sign Language Detection', cv2.WINDOW_NORMAL)\n",
    "        cv2.imshow('Sign Language Detection', concat)\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  }
 ],
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