Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b5e79267",
   "metadata": {},
   "source": [
    "# 1. Import and Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0cdf55b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (4.7.0.72)\n",
      "Requirement already satisfied: mediapipe in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (0.9.3.0)\n",
      "Requirement already satisfied: opencv-contrib-python in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (4.7.0.72)\n",
      "Requirement already satisfied: scipy in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (1.10.1)\n",
      "Requirement already satisfied: scikit-learn in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (1.2.2)\n",
      "Requirement already satisfied: matplotlib in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (3.7.1)\n",
      "Requirement already satisfied: numpy>=1.21.2 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from opencv-python) (1.24.2)\n",
      "Requirement already satisfied: absl-py in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from mediapipe) (1.4.0)\n",
      "Requirement already satisfied: attrs>=19.1.0 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from mediapipe) (23.1.0)\n",
      "Requirement already satisfied: flatbuffers>=2.0 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from mediapipe) (23.3.3)\n",
      "Requirement already satisfied: protobuf<4,>=3.11 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from mediapipe) (3.20.3)\n",
      "Requirement already satisfied: sounddevice>=0.4.4 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from mediapipe) (0.4.6)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from scikit-learn) (3.1.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from matplotlib) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from matplotlib) (4.39.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from matplotlib) (9.5.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.14.0)\n",
      "Requirement already satisfied: CFFI>=1.0 in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from sounddevice>=0.4.4->mediapipe) (1.15.1)\n",
      "Requirement already satisfied: pycparser in /Users/tsaichinta/miniconda3/lib/python3.10/site-packages (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe) (2.21)\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-python mediapipe opencv-contrib-python scipy scikit-learn matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eaf77fdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (4.7.0.72)\n",
      "Requirement already satisfied: mediapipe in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (0.9.3.0)\n",
      "Requirement already satisfied: opencv-contrib-python in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (4.7.0.72)\n",
      "Requirement already satisfied: scipy in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (1.10.1)\n",
      "Requirement already satisfied: scikit-learn in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (1.2.2)\n",
      "Requirement already satisfied: matplotlib in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (3.7.1)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from opencv-python) (1.23.5)\n",
      "Requirement already satisfied: sounddevice>=0.4.4 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from mediapipe) (0.4.6)\n",
      "Requirement already satisfied: absl-py in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from mediapipe) (1.4.0)\n",
      "Requirement already satisfied: protobuf<4,>=3.11 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from mediapipe) (3.20.3)\n",
      "Requirement already satisfied: attrs>=19.1.0 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from mediapipe) (22.1.0)\n",
      "Requirement already satisfied: flatbuffers>=2.0 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from mediapipe) (23.3.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from scikit-learn) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from matplotlib) (5.12.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from matplotlib) (4.39.3)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from matplotlib) (9.5.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from matplotlib) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.15.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Requirement already satisfied: CFFI>=1.0 in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from sounddevice>=0.4.4->mediapipe) (1.15.1)\n",
      "Requirement already satisfied: pycparser in /Users/tsaichinta/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe) (2.21)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install opencv-python mediapipe opencv-contrib-python scipy scikit-learn matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d220d6e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a95437e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c904a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e07ddd5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03b194bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6b5413fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0dee5c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb331999",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff45ae2",
   "metadata": {},
   "source": [
    "# 2. Keypoints using MP Holistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d4fcbf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_holistic = mp.solutions.holistic # Holistic model\n",
    "mp_drawing = mp.solutions.drawing_utils # Drawing utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b24a1f0",
   "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": 11,
   "id": "dd4d83b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_landmarks(image, results):\n",
    "#     mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.) # 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": 12,
   "id": "8695024a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_styled_landmarks(image, results):\n",
    "    # Draw face connections\n",
    "#     mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS, \n",
    "#                              mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1), \n",
    "#                              mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "#                              ) \n",
    "    # Draw pose connections\n",
    "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,\n",
    "                             mp_drawing.DrawingSpec(color=(121,22,76), thickness=3, circle_radius=4), \n",
    "                             mp_drawing.DrawingSpec(color=(121,44,250), thickness=3, 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=3, circle_radius=4), \n",
    "                             mp_drawing.DrawingSpec(color=(121,44,250), thickness=3, 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=(121,22,76), thickness=3, circle_radius=4), \n",
    "                             mp_drawing.DrawingSpec(color=(121,44,250), thickness=3, circle_radius=2)\n",
    "                             ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "671bab47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
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      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[57], line 20\u001b[0m\n\u001b[1;32m     17\u001b[0m     cv2\u001b[38;5;241m.\u001b[39mimshow(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOpenCV Feed\u001b[39m\u001b[38;5;124m'\u001b[39m, image)\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;66;03m# Break gracefully\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitKey\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;241m&\u001b[39m \u001b[38;5;241m0xFF\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mord\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mq\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     21\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m     22\u001b[0m cap\u001b[38;5;241m.\u001b[39mrelease()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(1)\n",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "\n",
    "        # Read feed\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        # Make detections\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        print(results)\n",
    "\n",
    "        # Draw landmarks\n",
    "        draw_styled_landmarks(image, results)\n",
    "\n",
    "        # Show to screen\n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aedeb1e6",
   "metadata": {},
   "source": [
    "# 3. Extract Keypoint Values (put all keypoints into one array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fd2812f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999510645866394"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.pose_landmarks.landmark[0].visibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4678da05",
   "metadata": {},
   "outputs": [],
   "source": [
    "pose = []\n",
    "for res in results.pose_landmarks.landmark:\n",
    "    test = np.array([res.x, res.y, res.z, res.visibility])\n",
    "    pose.append(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e80d5f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(132)\n",
    "# face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(1404)\n",
    "lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
    "rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "336b9863",
   "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(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)\n",
    "    return np.concatenate([pose, lh, rh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6ddffd6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_keypoints(results).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "08c9c692",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_test = extract_keypoints(results);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6f094994",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('0', result_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40bdac27",
   "metadata": {},
   "source": [
    "# 4. Setup Folders for Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "58b36657",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path for exported data, numpy arrays\n",
    "DATA_PATH = os.path.join('MP_Data') \n",
    "\n",
    "# Actions that we try to detect\n",
    "actions = np.array(['no_action', 'right_hand_lift', 'left_hand_lift', 'wakanda'])\n",
    "\n",
    "# Thirty videos worth of data\n",
    "no_sequences = 30\n",
    "\n",
    "# Videos are going to be 30 frames in length\n",
    "sequence_length = 30\n",
    "\n",
    "# Folder start\n",
    "start_folder = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9ba209c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for action in actions: \n",
    "    for sequence in range(no_sequences):\n",
    "        try: \n",
    "            os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8152a5c3",
   "metadata": {},
   "source": [
    "# 5. Collect Keypoint Values for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5926cdad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[59], line 18\u001b[0m\n\u001b[1;32m     15\u001b[0m ret, frame \u001b[38;5;241m=\u001b[39m cap\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# Make detections\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m image, results \u001b[38;5;241m=\u001b[39m \u001b[43mmediapipe_detection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mholistic\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28mprint\u001b[39m(results)\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# Draw landmarks\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[10], line 4\u001b[0m, in \u001b[0;36mmediapipe_detection\u001b[0;34m(image, model)\u001b[0m\n\u001b[1;32m      2\u001b[0m image \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mcvtColor(image, cv2\u001b[38;5;241m.\u001b[39mCOLOR_BGR2RGB) \u001b[38;5;66;03m# COLOR CONVERSION BGR 2 RGB\u001b[39;00m\n\u001b[1;32m      3\u001b[0m image\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m                  \u001b[38;5;66;03m# Image is no longer writeable\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m                 \u001b[38;5;66;03m# Make prediction\u001b[39;00m\n\u001b[1;32m      5\u001b[0m image\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m                   \u001b[38;5;66;03m# Image is now writeable \u001b[39;00m\n\u001b[1;32m      6\u001b[0m image \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mcvtColor(image, cv2\u001b[38;5;241m.\u001b[39mCOLOR_RGB2BGR) \u001b[38;5;66;03m# COLOR COVERSION RGB 2 BGR\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/mediapipe/python/solutions/holistic.py:160\u001b[0m, in \u001b[0;36mHolistic.process\u001b[0;34m(self, image)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NamedTuple:\n\u001b[1;32m    137\u001b[0m   \u001b[38;5;124;03m\"\"\"Processes an RGB image and returns the pose landmarks, left and right hand landmarks, and face landmarks on the most prominent person detected.\u001b[39;00m\n\u001b[1;32m    138\u001b[0m \n\u001b[1;32m    139\u001b[0m \u001b[38;5;124;03m  Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;124;03m         \"enable_segmentation\" is set to true.\u001b[39;00m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;124;03m  \"\"\"\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m   results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mimage\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    161\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m results\u001b[38;5;241m.\u001b[39mpose_landmarks:  \u001b[38;5;66;03m# pytype: disable=attribute-error\u001b[39;00m\n\u001b[1;32m    162\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m landmark \u001b[38;5;129;01min\u001b[39;00m results\u001b[38;5;241m.\u001b[39mpose_landmarks\u001b[38;5;241m.\u001b[39mlandmark:  \u001b[38;5;66;03m# pytype: disable=attribute-error\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/mediapipe/python/solution_base.py:365\u001b[0m, in \u001b[0;36mSolutionBase.process\u001b[0;34m(self, input_data)\u001b[0m\n\u001b[1;32m    359\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    360\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_graph\u001b[38;5;241m.\u001b[39madd_packet_to_input_stream(\n\u001b[1;32m    361\u001b[0m         stream\u001b[38;5;241m=\u001b[39mstream_name,\n\u001b[1;32m    362\u001b[0m         packet\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_packet(input_stream_type,\n\u001b[1;32m    363\u001b[0m                                  data)\u001b[38;5;241m.\u001b[39mat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_simulated_timestamp))\n\u001b[0;32m--> 365\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_graph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_until_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;66;03m# Create a NamedTuple object where the field names are mapping to the graph\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;66;03m# output stream names.\u001b[39;00m\n\u001b[1;32m    368\u001b[0m solution_outputs \u001b[38;5;241m=\u001b[39m collections\u001b[38;5;241m.\u001b[39mnamedtuple(\n\u001b[1;32m    369\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSolutionOutputs\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_stream_type_info\u001b[38;5;241m.\u001b[39mkeys())\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(1)\n",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \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",
    "\n",
    "                # Make detections\n",
    "                image, results = mediapipe_detection(frame, holistic)\n",
    "                print(results)\n",
    "\n",
    "                # Draw landmarks\n",
    "                draw_styled_landmarks(image, results)\n",
    "\n",
    "                # Show to screen\n",
    "                cv2.imshow('OpenCV Feed', image)\n",
    "                \n",
    "                \n",
    "                # NEW Apply wait logic\n",
    "                if frame_num == 0: \n",
    "                    cv2.putText(image, 'STARTING COLLECTION', (120,150), \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), (30,40), \n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3, cv2.LINE_AA)\n",
    "                    # Show to screen\n",
    "                    cv2.imshow('OpenCV Feed', image)\n",
    "                    cv2.waitKey(3000)\n",
    "                else: \n",
    "                    cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (30,40), \n",
    "                               cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3, cv2.LINE_AA)\n",
    "                    # Show to screen\n",
    "                    cv2.imshow('OpenCV Feed', image)\n",
    "                \n",
    "                # NEW Export keypoints\n",
    "                keypoints = extract_keypoints(results)\n",
    "                npy_path = os.path.join(DATA_PATH, action, str(sequence), str(frame_num))\n",
    "                np.save(npy_path, keypoints)\n",
    "\n",
    "                # Break gracefully\n",
    "                if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "                    break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2f73c179",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70387699",
   "metadata": {},
   "source": [
    "# 6. Preprocess Data and Create Labels and Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b8ce725c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8299ca30",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# def to_categorical(y, num_classes=None, dtype='float32'):\n",
    "#     y = np.array(y, dtype='int')\n",
    "#     input_shape = y.shape\n",
    "#     if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:\n",
    "#         input_shape = tuple(input_shape[:-1])\n",
    "#     y = y.ravel()\n",
    "#     if not num_classes:\n",
    "#         num_classes = np.max(y) + 1\n",
    "#     n = y.shape[0]\n",
    "#     categorical = np.zeros((n, num_classes), dtype=dtype)\n",
    "#     categorical[np.arange(n), y] = 1\n",
    "#     output_shape = input_shape + (num_classes,)\n",
    "#     categorical = np.reshape(categorical, output_shape)\n",
    "#     return categorical\n",
    "from tensorflow.keras.utils import to_categorical\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea9ad42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = {label:num for num, label in enumerate(actions)}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "38a8f3dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'no_action': 0, 'right_hand_lift': 1, 'left_hand_lift': 2, 'wakanda': 3}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5bff02c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences, labels = [], []\n",
    "for action in actions:\n",
    "    for sequence in range(no_sequences):\n",
    "        window = []\n",
    "        for frame_num in range(sequence_length):\n",
    "            res = np.load(os.path.join(DATA_PATH, action, str(sequence), \"{}.npy\".format(frame_num)))\n",
    "            window.append(res)\n",
    "        sequences.append(window)\n",
    "        labels.append(label_map[action])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "584d0ed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 30, 258)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(sequences).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c93ffbb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(labels).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a60c021c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "55e6fdf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "96882d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aa45e060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 4)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8fb9b0",
   "metadata": {},
   "source": [
    "# 7. Build and Train LSTM Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c5def8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0ecf4814",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import LSTM, Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b53a73e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d8a575b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = os.path.join('Logs')\n",
    "tb_callback = TensorBoard(log_dir=log_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e5c251bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M2 Pro\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30,258)))\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": 32,
   "id": "dcc950f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "447cdf9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-25 19:02:18.218267: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 12s 3s/step - loss: 1.3261 - categorical_accuracy: 0.2632\n",
      "Epoch 2/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 1.0974 - categorical_accuracy: 0.2982\n",
      "Epoch 3/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 1.0255 - categorical_accuracy: 0.5877\n",
      "Epoch 4/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.8722 - categorical_accuracy: 0.6579\n",
      "Epoch 5/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.7566 - categorical_accuracy: 0.6842\n",
      "Epoch 6/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.6461 - categorical_accuracy: 0.7193\n",
      "Epoch 7/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.6682 - categorical_accuracy: 0.7368\n",
      "Epoch 8/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.6763 - categorical_accuracy: 0.8246\n",
      "Epoch 9/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.5629 - categorical_accuracy: 0.9035\n",
      "Epoch 10/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.4260 - categorical_accuracy: 0.8421\n",
      "Epoch 11/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 1.3873 - categorical_accuracy: 0.7807\n",
      "Epoch 12/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.6603 - categorical_accuracy: 0.8509\n",
      "Epoch 13/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.7045 - categorical_accuracy: 0.8246\n",
      "Epoch 14/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.5449 - categorical_accuracy: 0.8947\n",
      "Epoch 15/2000\n",
      "4/4 [==============================] - 10s 2s/step - loss: 0.4854 - categorical_accuracy: 0.9211\n",
      "Epoch 16/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.3383 - categorical_accuracy: 0.9474\n",
      "Epoch 17/2000\n",
      "4/4 [==============================] - 10s 3s/step - loss: 0.2552 - categorical_accuracy: 0.9386\n",
      "Epoch 18/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.2774 - categorical_accuracy: 0.9386\n",
      "Epoch 19/2000\n",
      "4/4 [==============================] - 10s 3s/step - loss: 0.1630 - categorical_accuracy: 0.9386\n",
      "Epoch 20/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.1690 - categorical_accuracy: 0.9474\n",
      "Epoch 21/2000\n",
      "4/4 [==============================] - 10s 3s/step - loss: 0.1810 - categorical_accuracy: 0.9386\n",
      "Epoch 22/2000\n",
      "4/4 [==============================] - 10s 3s/step - loss: 0.1539 - categorical_accuracy: 0.9474\n",
      "Epoch 23/2000\n",
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      "Epoch 62/2000\n",
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      "4/4 [==============================] - 10s 3s/step - loss: 0.5017 - categorical_accuracy: 0.7982\n",
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      "4/4 [==============================] - 9s 2s/step - loss: 0.2725 - categorical_accuracy: 0.8947\n",
      "Epoch 124/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.3299 - categorical_accuracy: 0.8509\n",
      "Epoch 125/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.3640 - categorical_accuracy: 0.8246\n",
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      "4/4 [==============================] - 9s 2s/step - loss: 0.3305 - categorical_accuracy: 0.8333\n",
      "Epoch 128/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.3136 - categorical_accuracy: 0.8509\n",
      "Epoch 129/2000\n",
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      "Epoch 130/2000\n",
      "4/4 [==============================] - 10s 2s/step - loss: 0.3035 - categorical_accuracy: 0.9123\n",
      "Epoch 131/2000\n",
      "4/4 [==============================] - 10s 2s/step - loss: 0.3157 - categorical_accuracy: 0.9123\n",
      "Epoch 132/2000\n",
      "4/4 [==============================] - 11s 3s/step - loss: 0.2395 - categorical_accuracy: 0.9298\n",
      "Epoch 133/2000\n",
      "4/4 [==============================] - 11s 3s/step - loss: 0.2674 - categorical_accuracy: 0.9211\n",
      "Epoch 134/2000\n",
      "4/4 [==============================] - 10s 3s/step - loss: 2.5354 - categorical_accuracy: 0.9123\n",
      "Epoch 135/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.3841 - categorical_accuracy: 0.9298\n",
      "Epoch 136/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.5070 - categorical_accuracy: 0.9035\n",
      "Epoch 137/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.4329 - categorical_accuracy: 0.9035\n",
      "Epoch 138/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.4170 - categorical_accuracy: 0.8596\n",
      "Epoch 139/2000\n",
      "4/4 [==============================] - 10s 2s/step - loss: 0.3569 - categorical_accuracy: 0.8860\n",
      "Epoch 140/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.3331 - categorical_accuracy: 0.9298\n",
      "Epoch 141/2000\n",
      "4/4 [==============================] - 10s 2s/step - loss: 0.3007 - categorical_accuracy: 0.9211\n",
      "Epoch 142/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.2412 - categorical_accuracy: 0.9298\n",
      "Epoch 143/2000\n",
      "4/4 [==============================] - 9s 2s/step - loss: 0.1848 - categorical_accuracy: 0.9298\n",
      "Epoch 144/2000\n",
      "1/4 [======>.......................] - ETA: 7s - loss: 0.1318 - categorical_accuracy: 0.9375"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[33], line 1\u001b[0m\n\u001b[0;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;43m2000\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[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\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[1;32m     66\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[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/keras/engine/training.py:1685\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;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, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1677\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1678\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1679\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1682\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1683\u001b[0m ):\n\u001b[1;32m   1684\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1685\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1686\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1687\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\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[1;32m    151\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[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:894\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    891\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    893\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 894\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\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[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    896\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    897\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:926\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    923\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    924\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    925\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 926\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_fn\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[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    927\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_fn \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;32m    928\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    929\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    930\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:143\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m    141\u001b[0m   (concrete_function,\n\u001b[1;32m    142\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    144\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1757\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1753\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1754\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1755\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1756\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1758\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1759\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1760\u001b[0m     args,\n\u001b[1;32m   1761\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1762\u001b[0m     executing_eagerly)\n\u001b[1;32m   1763\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:381\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    379\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    380\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 381\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    384\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    385\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    386\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    387\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    388\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m    389\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m    390\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    393\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m    394\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
      "File \u001b[0;32m~/miniconda3/envs/env_tensorflow/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\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[1;32m     53\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[1;32m     54\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[1;32m     55\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[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "caae76d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 30, 64)            82688     \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 30, 128)           98816     \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 64)                49408     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 4)                 132       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 237,284\n",
      "Trainable params: 237,284\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a855dce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "87ceb8c2",
   "metadata": {},
   "source": [
    "# 8. Make Predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a171a7a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 418ms/step\n"
     ]
    }
   ],
   "source": [
    "res = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d7a4a1c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.13657045e-04, 4.72650951e-04, 2.98969820e-03, 9.96223927e-01],\n",
       "       [4.27545888e-10, 1.02823826e-07, 9.99999881e-01, 1.77754495e-11],\n",
       "       [1.05781384e-10, 5.22596988e-09, 1.00000000e+00, 5.13251462e-16],\n",
       "       [5.86086826e-04, 8.20936635e-04, 3.92313907e-03, 9.94669855e-01],\n",
       "       [5.11995256e-02, 4.74779010e-02, 6.27209991e-02, 8.38601530e-01],\n",
       "       [1.37928528e-05, 6.65483472e-04, 9.53087926e-01, 4.62328568e-02]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "699dc55f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea16b175",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0a922341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'left_hand_lift'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(res[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "928a1948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'left_hand_lift'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions[np.argmax(y_test[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6816e6c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 1],\n",
       "       [0, 0, 1, 0],\n",
       "       [0, 0, 1, 0],\n",
       "       [0, 0, 0, 1],\n",
       "       [0, 0, 0, 1],\n",
       "       [0, 0, 1, 0]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fed8e6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5ae538d6",
   "metadata": {},
   "source": [
    "# 9. Save Weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8334bc55",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('action.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180c2006",
   "metadata": {},
   "source": [
    "# 10. Evaluation using Confusion Matrix and Accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9cd85bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "10c136c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 166ms/step\n"
     ]
    }
   ],
   "source": [
    "yhat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4578bcba",
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = np.argmax(y_test, axis=1).tolist()\n",
    "yhat = np.argmax(yhat, axis=1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "068743ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[3, 0],\n",
       "        [0, 3]],\n",
       "\n",
       "       [[3, 0],\n",
       "        [0, 3]]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "985b2b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytrue, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45925370",
   "metadata": {},
   "source": [
    "# 11. Test in Real Time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "60cfa818",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [(255,0,0), (0,255,0), (0,0,255), (232, 23, 208)]\n",
    "def prob_viz(res, actions, input_frame, colors):\n",
    "    output_frame = input_frame.copy()\n",
    "    for num, prob in enumerate(res):\n",
    "        cv2.rectangle(output_frame, (10,100+num*60), (int(prob*100), 135+num*60 ), colors[num], -1)\n",
    "        cv2.putText(output_frame, actions[num], (10, 130+num*60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "        \n",
    "    return output_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67539ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d13acdc0>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,14))\n",
    "plt.imshow(prob_viz(res, actions, image, colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4602269c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd787ba5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "28e58fd5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 105ms/step\n",
      "[8.5938329e-01 1.4025164e-01 1.3190757e-04 2.3313405e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[8.6110812e-01 1.3855205e-01 1.2225594e-04 2.1753497e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[8.61311853e-01 1.38369888e-01 1.14134855e-04 2.04123411e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[8.6112696e-01 1.3857761e-01 1.0556905e-04 1.8991690e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[8.59359801e-01 1.40360102e-01 1.00128615e-04 1.80028233e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.5592759e-01 1.4381102e-01 9.3721414e-05 1.6765020e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[8.5154510e-01 1.4821382e-01 8.6971602e-05 1.5412303e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[8.4587932e-01 1.5389904e-01 8.0797079e-05 1.4082916e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 108ms/step\n",
      "[8.3790725e-01 1.6188903e-01 7.5209726e-05 1.2847966e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.2611501e-01 1.7369591e-01 7.1123672e-05 1.1794343e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.0931789e-01 1.9051500e-01 6.4596286e-05 1.0250974e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[7.8784084e-01 2.1201554e-01 5.7243047e-05 8.6366381e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[7.6204360e-01 2.3783566e-01 4.9753518e-05 7.0946473e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[7.3250157e-01 2.6739734e-01 4.3139302e-05 5.7967132e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[6.9874060e-01 3.0117372e-01 3.7742429e-05 4.7944581e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[6.6102678e-01 3.3889762e-01 3.4322082e-05 4.1285562e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[6.2184626e-01 3.7808383e-01 3.2685595e-05 3.7254551e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[5.8681983e-01 4.1311222e-01 3.2583699e-05 3.5355391e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[5.5677956e-01 4.4314909e-01 3.4683439e-05 3.6651814e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[5.3240710e-01 4.6751216e-01 3.9685481e-05 4.1063700e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 110ms/step\n",
      "[5.2544260e-01 4.7446236e-01 4.6867775e-05 4.8268197e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 109ms/step\n",
      "[5.3663045e-01 4.6326560e-01 5.0911141e-05 5.3105559e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 108ms/step\n",
      "[5.5244219e-01 4.4744796e-01 5.3390235e-05 5.6463436e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[5.6546396e-01 4.3440667e-01 6.2263825e-05 6.7109322e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[5.9136498e-01 4.0847668e-01 7.4831951e-05 8.3492720e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[6.0556281e-01 3.9424166e-01 9.1528571e-05 1.0406068e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[6.20817006e-01 3.78952682e-01 1.06643136e-04 1.23709469e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[6.4448076e-01 3.5525894e-01 1.1848454e-04 1.4178392e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[6.5926409e-01 3.4043291e-01 1.3623490e-04 1.6684794e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 111ms/step\n",
      "[6.7351568e-01 3.2613939e-01 1.5311484e-04 1.9183157e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 112ms/step\n",
      "[6.8662357e-01 3.1299108e-01 1.6904806e-04 2.1629244e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[6.9835800e-01 3.0122620e-01 1.8039238e-04 2.3539658e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 92ms/step\n",
      "[7.1033126e-01 2.8923336e-01 1.8647249e-04 2.4896362e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[7.2544229e-01 2.7412230e-01 1.8404983e-04 2.5139569e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[7.3718011e-01 2.6241019e-01 1.7145605e-04 2.3830979e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[7.4451196e-01 2.5511354e-01 1.5572087e-04 2.1878333e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 109ms/step\n",
      "[7.4754745e-01 2.5208247e-01 1.5337227e-04 2.1676840e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[7.5174284e-01 2.4788675e-01 1.5277599e-04 2.1762861e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[7.5547433e-01 2.4415380e-01 1.5293396e-04 2.1895752e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[7.5810081e-01 2.4152389e-01 1.5396702e-04 2.2135387e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[7.6023239e-01 2.3938300e-01 1.5729619e-04 2.2728613e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[7.6126832e-01 2.3833665e-01 1.6131543e-04 2.3369236e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[7.6194018e-01 2.3765224e-01 1.6631058e-04 2.4129302e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[7.6406568e-01 2.3552637e-01 1.6621541e-04 2.4176307e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[7.6569563e-01 2.3389393e-01 1.6698166e-04 2.4351096e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[7.6526546e-01 2.3431379e-01 1.7117095e-04 2.4959931e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[7.6405019e-01 2.3552014e-01 1.7485642e-04 2.5482816e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 108ms/step\n",
      "[7.6214004e-01 2.3742212e-01 1.7844322e-04 2.5940832e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[7.6031333e-01 2.3924124e-01 1.8182471e-04 2.6368746e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[7.5723851e-01 2.4229637e-01 1.9002930e-04 2.7508911e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 92ms/step\n",
      "[7.5524712e-01 2.4427120e-01 1.9698439e-04 2.8477237e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[7.5382781e-01 2.4568966e-01 1.9761034e-04 2.8490179e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[7.5243813e-01 2.4707513e-01 1.9961296e-04 2.8720961e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[7.5155038e-01 2.4795879e-01 2.0148698e-04 2.8933145e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[7.5046647e-01 2.4902245e-01 2.0993419e-04 3.0118143e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[7.4871445e-01 2.5076136e-01 2.1569777e-04 3.0848855e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[7.4564552e-01 2.5380936e-01 2.2483732e-04 3.2036300e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[7.4174863e-01 2.5767908e-01 2.3683925e-04 3.3546975e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[7.3923528e-01 2.6017654e-01 2.4435736e-04 3.4385175e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[7.3802084e-01 2.6137891e-01 2.4916424e-04 3.5114930e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[7.2136366e-01 2.7794275e-01 2.8806218e-04 4.0550748e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[6.9534737e-01 3.0385473e-01 3.4190385e-04 4.5597929e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[6.5800136e-01 3.4104553e-01 4.1766107e-04 5.3540373e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[6.0318595e-01 3.9570463e-01 5.0704996e-04 6.0240540e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.5334512  0.46529892 0.0005964  0.00065355]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.44624305 0.5524164  0.00067408 0.00066656]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[3.450995e-01 6.535658e-01 7.141509e-04 6.205319e-04]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[2.3024237e-01 7.6855642e-01 6.9966790e-04 5.0151843e-04]\n",
      "right_hand_lift\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 103ms/step\n",
      "[1.3805971e-01 8.6099976e-01 5.9424108e-04 3.4630229e-04]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[7.6124199e-02 9.2329472e-01 3.9182036e-04 1.8935712e-04]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[3.4698024e-02 9.6506077e-01 1.6892853e-04 7.2374081e-05]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[1.1558294e-02 9.8838544e-01 4.0840128e-05 1.5393092e-05]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 92ms/step\n",
      "[3.2969888e-03 9.9669397e-01 6.6440111e-06 2.3634325e-06]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[4.7848703e-04 9.9952054e-01 7.4527293e-07 2.0311434e-07]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[1.3975060e-04 9.9986017e-01 8.9966193e-08 2.0069091e-08]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[3.1619293e-05 9.9996841e-01 7.2733219e-09 1.5455468e-09]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[1.0589427e-05 9.9998939e-01 7.2259637e-10 1.4398834e-10]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[5.0263143e-06 9.9999499e-01 1.0610188e-10 1.9922348e-11]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[4.9572645e-06 9.9999499e-01 4.6321818e-11 8.6100354e-12]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[2.8594572e-05 9.9997139e-01 5.9059901e-10 1.3439333e-10]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[2.5479085e-04 9.9974519e-01 3.7618609e-08 1.2278188e-08]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[2.8194778e-03 9.9716741e-01 8.5733127e-06 4.4871672e-06]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 142ms/step\n",
      "[0.02686584 0.9704888  0.00149052 0.00115479]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[0.13082635 0.7246798  0.09183338 0.05266052]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.1461511  0.36111495 0.33990973 0.1528242 ]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.11862288 0.22898565 0.46098286 0.1914086 ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.10048269 0.19957227 0.5038789  0.19606614]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[0.0770374  0.19218397 0.5263626  0.20441602]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.05162623 0.17301951 0.57166666 0.20368765]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[0.03333333 0.14901954 0.6174964  0.20015083]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.02259199 0.12722765 0.65160054 0.19857986]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[0.01786807 0.11408731 0.6614017  0.20664287]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 107ms/step\n",
      "[0.01707434 0.11165109 0.66685367 0.2044209 ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.01696087 0.11116024 0.6649717  0.20690721]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[0.01655072 0.10978931 0.6662798  0.20738016]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.01619695 0.10858487 0.66732496 0.2078933 ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.01643395 0.10916415 0.66186637 0.21253549]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.01641948 0.10894933 0.65850186 0.21612929]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.01749805 0.10977156 0.6292176  0.24351273]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.01954689 0.11167919 0.5731257  0.29564816]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.02228334 0.11251578 0.49742883 0.36777207]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.02514942 0.10972121 0.4113505  0.45377892]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.0286114  0.10321688 0.32339573 0.544776  ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.03230992 0.09545402 0.25008893 0.6221472 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.03732219 0.08820897 0.19925682 0.6752121 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.04387557 0.0831842  0.16424306 0.7086972 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[0.05320388 0.08316433 0.14519739 0.7184344 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.06558728 0.08640654 0.13640943 0.7115968 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.08130495 0.09223175 0.13445778 0.69200546]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.09943778 0.10085726 0.13702112 0.6626839 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.11942808 0.1135162  0.14265184 0.6244039 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.1387329  0.12590913 0.14861354 0.5867445 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.15603395 0.13662261 0.15366153 0.55368197]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.17010532 0.14584282 0.15880843 0.5252435 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.18135317 0.15488572 0.1653575  0.49840367]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.18935867 0.16296832 0.17266539 0.47500762]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.19381464 0.16995803 0.1811857  0.45504165]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 91ms/step\n",
      "[0.19538885 0.17641455 0.19392964 0.43426695]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.19058885 0.18410152 0.21594502 0.40936458]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.18679658 0.19402368 0.24617966 0.37300017]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.18743402 0.20764144 0.2752408  0.3296838 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.2027313  0.2208833  0.29486144 0.28152403]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.22177064 0.24609204 0.28992024 0.24221712]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.23113425 0.2954895  0.26897827 0.20439807]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 120ms/step\n",
      "[0.22149543 0.35450393 0.23955931 0.18444131]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 107ms/step\n",
      "[0.22458756 0.42288324 0.19687244 0.15565675]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.23893623 0.5022373  0.141788   0.11703841]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.27085003 0.55164856 0.09795875 0.07954266]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.31923476 0.5725852  0.05898102 0.049199  ]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.39243218 0.5527963  0.02852102 0.02625054]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.48400506 0.4935831  0.01102796 0.01138383]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.5872027  0.40569162 0.00330726 0.00379831]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.70094365 0.29742268 0.00072343 0.00091027]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.7024724  0.29574257 0.00078626 0.00099871]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[0.70701575 0.29113275 0.00081014 0.00104143]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.7056825  0.29230803 0.00087676 0.00113276]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.703508   0.2943104  0.00095035 0.00123122]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.6997705  0.2978266  0.00104523 0.00135772]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.6992525  0.29824093 0.00108566 0.00142098]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.6999441  0.2975751  0.00106966 0.00141111]\n",
      "no_action\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.6995088  0.29804462 0.00105171 0.00139492]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.6996234  0.2980075  0.00101355 0.00135551]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.66461974 0.33237147 0.0012983  0.00171047]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.5916467  0.40377513 0.00211901 0.00245926]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.5191594  0.47355196 0.00358812 0.00370064]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.4541991  0.5328932  0.00666728 0.00624042]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.40028423 0.5724612  0.01431568 0.01293893]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.35321358 0.5786336  0.03533093 0.03282189]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 111ms/step\n",
      "[0.2992833  0.5373586  0.08246917 0.08088893]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.26738563 0.43061513 0.14146705 0.1605323 ]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.24510603 0.3157243  0.20365149 0.23551814]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.21221808 0.24358065 0.2421964  0.30200487]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19211127 0.20683163 0.2420725  0.35898465]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.18767099 0.1935249  0.22639927 0.39240482]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.19243658 0.18425554 0.20856567 0.4147422 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.19873324 0.17857207 0.19495067 0.4277441 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.2015234  0.17729205 0.18884602 0.43233857]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.20314066 0.17521907 0.18379934 0.4378409 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.2033119  0.1727677  0.17939816 0.4445222 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.20184226 0.16990674 0.17549467 0.45275626]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.19828092 0.1659394  0.1714145  0.46436515]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.1949678  0.16237904 0.16761498 0.47503817]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.1943532  0.16183794 0.16712192 0.47668698]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.19342485 0.16109252 0.16651888 0.47896376]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19251233 0.1603482  0.16589497 0.48124442]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.19167143 0.15963568 0.16526574 0.48342717]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[0.19143547 0.15935957 0.16491754 0.48428738]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.19151592 0.15933436 0.1647566  0.48439315]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19233637 0.1598852  0.16502014 0.48275825]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.1937407  0.1606895  0.16525689 0.48031288]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19350147 0.1605183  0.16514073 0.48083958]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.19328591 0.1603566  0.16502166 0.48133588]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.19315778 0.1602668  0.16496995 0.48160547]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.19300045 0.1601744  0.16495617 0.481869  ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.19295956 0.1601693  0.16500448 0.48186663]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.19289692 0.1601448  0.16503525 0.48192298]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19262083 0.15996553 0.16496667 0.48244706]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.19233404 0.15977076 0.16487406 0.4830212 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.19207695 0.15958558 0.16476582 0.4835717 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[0.19195396 0.15949607 0.1647116  0.48383844]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.19180626 0.15939672 0.16465601 0.48414108]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.19180843 0.15940712 0.16467239 0.48411202]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.19200815 0.15956506 0.16478798 0.48363876]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.19225986 0.15976521 0.16493815 0.4830368 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.19241238 0.15990068 0.16505565 0.48263133]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.19254106 0.16001889 0.16515659 0.48228353]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.19264027 0.16011912 0.16524835 0.48199221]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.19280435 0.16026482 0.16536728 0.48156363]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.19295652 0.1603988  0.16547576 0.48116896]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.19309546 0.16052164 0.165578   0.48080486]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[0.19317672 0.16059577 0.16563964 0.48058784]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19331113 0.16070215 0.16571763 0.4802691 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19344808 0.16080411 0.16578493 0.4799629 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19352883 0.16086559 0.16584449 0.47976112]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[0.19393995 0.16209163 0.16727541 0.476693  ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.1942258  0.16447148 0.17087296 0.47042978]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19405003 0.16803896 0.17698306 0.460928  ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.19334225 0.17261827 0.1867323  0.44730723]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.19080317 0.17898826 0.20541812 0.4247905 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.18565044 0.18877743 0.23557203 0.39000013]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.18475774 0.20171775 0.26764858 0.34587598]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.19658338 0.2132675  0.2926611  0.29748806]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.2204919  0.22854581 0.2957349  0.2552274 ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.24181014 0.2716682  0.2734826  0.21303906]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.2386775  0.32625538 0.24367708 0.19139004]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.24577886 0.38689256 0.20197356 0.16535506]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 106ms/step\n",
      "[0.26314643 0.43290126 0.16575457 0.13819766]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.2756523  0.46779102 0.13867652 0.11788016]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.2870087  0.4933324  0.11908429 0.10057461]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.29832625 0.5173962  0.10003261 0.08424493]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.31661674 0.52865714 0.08252817 0.07219794]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.33581838 0.53449506 0.06769674 0.06198987]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.35604167 0.5356528  0.05506519 0.0532404 ]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.2879031  0.47734064 0.11621876 0.11853746]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.25299785 0.36881143 0.17849414 0.19969654]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 119ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.217677   0.2708481  0.24656886 0.26490617]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.18513428 0.21911035 0.25976476 0.33599058]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.17404382 0.19826156 0.24341278 0.3842819 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.17486697 0.18288602 0.22191496 0.420332  ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.18199891 0.17347188 0.20052171 0.44400755]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.18666914 0.16908619 0.18751512 0.45672953]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.18886942 0.16644685 0.18049069 0.4641931 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 107ms/step\n",
      "[0.18930195 0.16380207 0.17512846 0.47176754]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[0.18709755 0.15990908 0.16978474 0.4832085 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[0.18231341 0.1546117  0.16408752 0.49898735]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.17460285 0.14731395 0.15701248 0.5210708 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.17025355 0.14324181 0.15303543 0.5334692 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.16962703 0.14277042 0.15269595 0.5349066 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.16898994 0.14228313 0.15233648 0.5363905 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.16786595 0.14144081 0.15173352 0.53895974]\n",
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      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.16701537 0.14082497 0.15132576 0.54083395]\n",
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      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.16630602 0.140321   0.15100919 0.5423638 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[0.16554211 0.13977797 0.15066926 0.54401076]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.16483645 0.13928896 0.15038015 0.54549444]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[0.16418763 0.13884306 0.1501275  0.54684186]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 115ms/step\n",
      "[0.16357936 0.13842823 0.14989519 0.5480973 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.1630484  0.13805066 0.1496635  0.5492375 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[0.16247328 0.13762437 0.14938144 0.5505209 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.16184652 0.13714842 0.14905035 0.5519547 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.16139981 0.1367994  0.14880434 0.5529965 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.16092557 0.13642198 0.1485298  0.5541226 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.1603621  0.1359681  0.14818265 0.5554871 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.15963884 0.13538799 0.14772774 0.55724555]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.15909277 0.13494124 0.14737017 0.5585959 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.15847626 0.13444398 0.14697087 0.56010896]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.15805666 0.13410215 0.14669102 0.5611502 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.15766403 0.13378757 0.14643708 0.56211126]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.15729181 0.1334835  0.14619164 0.56303304]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.15694883 0.13330048 0.14600642 0.56374437]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.15709262 0.13455638 0.14784038 0.56051064]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.15779194 0.13767746 0.15255778 0.5519728 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.15913059 0.14262792 0.16024676 0.5379948 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.16101162 0.14945386 0.1714071  0.51812744]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.163843   0.15741646 0.18757561 0.49116495]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.16336074 0.17105365 0.21455185 0.45103374]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.1671129  0.18815328 0.24694528 0.3977886 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.17579494 0.21057655 0.270553   0.34307557]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.20257065 0.23686711 0.27892426 0.28163794]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.22069018 0.2913131  0.2535636  0.23443307]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.21969019 0.3619631  0.21545404 0.20289269]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 106ms/step\n",
      "[0.19851302 0.46376565 0.16721235 0.17050906]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.16793582 0.59000623 0.11957864 0.12247932]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.12398582 0.7330818  0.07218704 0.07074542]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.0818902  0.8469481  0.03730544 0.03385628]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.04854613 0.923988   0.01525901 0.01220687]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 106ms/step\n",
      "[0.02882027 0.96275914 0.00503869 0.00338191]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[1.8522838e-02 9.7926420e-01 1.4146817e-03 7.9826190e-04]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[1.5890405e-02 9.8286539e-01 8.1674138e-04 4.2742077e-04]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[1.4168449e-02 9.8494762e-01 5.8762956e-04 2.9628121e-04]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[1.9494174e-02 9.7948635e-01 6.6795846e-04 3.5162838e-04]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[2.5110805e-02 9.7335809e-01 9.7059656e-04 5.6058721e-04]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.03969583 0.95682126 0.00211203 0.00137096]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.06484292 0.9247442  0.00599792 0.00441497]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.11076252 0.85362667 0.01934699 0.01626385]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.17406964 0.71578515 0.05664468 0.05350056]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.22496127 0.53069043 0.12199503 0.12235328]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.22122437 0.3844783  0.20669626 0.18760109]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.20852695 0.28296548 0.27311414 0.23539348]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.17825669 0.22991379 0.31077424 0.28105533]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.1554271  0.22047268 0.31639293 0.30770728]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.13549806 0.21435146 0.3285613  0.32158914]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.12035953 0.2066113  0.34062517 0.33240402]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.10522597 0.20008764 0.351911   0.34277537]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.09079757 0.1922625  0.36470783 0.3522321 ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 92ms/step\n",
      "[0.07705408 0.18302071 0.37856534 0.36135986]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.06368928 0.17174934 0.39311266 0.37144876]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.05130554 0.1585264  0.4067681  0.38339996]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.04114557 0.14576381 0.4225735  0.39051712]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.03193127 0.13624929 0.47556517 0.35625425]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.02671151 0.12856226 0.5135212  0.3312051 ]\n",
      "left_hand_lift\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.02287044 0.12143136 0.5436913  0.31200698]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.02281543 0.12123837 0.54334396 0.31260222]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.02277013 0.12108885 0.5432131  0.31292793]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.0225036  0.12045881 0.5443569  0.31268072]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.02209901 0.11951138 0.5463099  0.3120797 ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[0.02171623 0.11857103 0.5478758  0.31183693]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.0214512  0.11785762 0.54838854 0.31230268]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.02128597 0.11738952 0.5483539  0.31297064]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.02119018 0.11710716 0.5480046  0.313698  ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.02107841 0.11724239 0.5512569  0.31042236]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[0.02108198 0.11748827 0.55578214 0.30564764]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.02080312 0.11780514 0.5661928  0.295199  ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.02020898 0.11764296 0.5819064  0.28024173]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.01947224 0.11720073 0.60168654 0.26164043]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.01875489 0.11650021 0.62197053 0.24277434]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.01781413 0.11484342 0.6442788  0.22306365]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.01680429 0.11265837 0.6669344  0.203603  ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.01574252 0.10991463 0.6893159  0.1850269 ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 106ms/step\n",
      "[0.01468195 0.1067142  0.71073085 0.16787302]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.01359098 0.10299942 0.7314838  0.15192582]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.01249664 0.09889809 0.75132537 0.13727993]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.01161069 0.09489828 0.7685214  0.12496955]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 106ms/step\n",
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      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.01005629 0.08769277 0.7993414  0.10290959]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.00915052 0.08309934 0.81597286 0.09177729]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.00838329 0.07803676 0.8312668  0.08231313]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.00845428 0.07773907 0.82763004 0.08617659]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.00921858 0.08242659 0.80482036 0.10353442]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[0.0122244  0.09432366 0.7435307  0.14992127]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.01489322 0.10128213 0.65672696 0.22709769]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.01949843 0.10915045 0.54334587 0.32800525]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.02463516 0.11178558 0.42761558 0.43596363]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.03014163 0.10777432 0.32470077 0.5373833 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.03633562 0.10146211 0.24812749 0.6140748 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.04447334 0.09572931 0.19795655 0.66184086]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.05543172 0.09376387 0.16757269 0.6832318 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.0698195  0.0965132  0.15322201 0.6804453 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.08689237 0.1014649  0.1480144  0.6636284 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.1056352  0.10837016 0.14670672 0.63928795]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.12405559 0.11872172 0.14885214 0.60837054]\n",
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      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.13094793 0.12035323 0.14483523 0.6038636 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[0.13684608 0.12194739 0.14222756 0.59897894]\n",
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      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.14888412 0.12936567 0.14564838 0.57610184]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[0.1568253  0.13637264 0.15147094 0.5553312 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.16000597 0.14252485 0.15971266 0.5377566 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.15739185 0.1462083  0.16853963 0.5278602 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "[0.15465757 0.1512496  0.18112928 0.51296365]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.14748065 0.15892762 0.1997823  0.4938094 ]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 106ms/step\n",
      "[0.13816415 0.16785562 0.22463551 0.46934465]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "[0.12905914 0.1787034  0.25211245 0.44012505]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[0.11898959 0.18871269 0.28777635 0.40452144]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.1093528  0.1960662  0.32957813 0.36500287]\n",
      "wakanda\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.09924305 0.20057033 0.37978813 0.3203985 ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.08760599 0.20193176 0.438087   0.27237526]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 113ms/step\n",
      "[0.0761283  0.1991349  0.50109816 0.22363873]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.06570552 0.19074689 0.56292987 0.18061785]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[0.05863013 0.1767363  0.61841613 0.14621747]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.05526574 0.17169143 0.65438485 0.11865789]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.05439132 0.16652626 0.68218493 0.09689752]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[0.05631484 0.1651394  0.6975266  0.08101919]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[0.06555451 0.17165436 0.68956745 0.07322375]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.06924158 0.17648774 0.6838534  0.0704173 ]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.09381954 0.2130096  0.6115818  0.08158912]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[0.15197083 0.28741384 0.46614513 0.09447019]\n",
      "left_hand_lift\n",
      "1/1 [==============================] - 0s 92ms/step\n",
      "[0.20664765 0.44355235 0.2633924  0.08640767]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[0.2879129  0.53092104 0.11925004 0.06191606]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[0.4079068  0.5137274  0.04499307 0.03337272]\n",
      "right_hand_lift\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[0.53118855 0.4399242  0.01485872 0.01402855]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[0.64279854 0.34833547 0.0041134  0.00475273]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 180ms/step\n",
      "[0.744845   0.25324932 0.00080205 0.00110363]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 108ms/step\n",
      "[7.8309822e-01 2.1609943e-01 3.2982932e-04 4.7257214e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[8.1085640e-01 1.8878001e-01 1.4747493e-04 2.1607203e-04]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.3577430e-01 1.6405405e-01 6.8537971e-05 1.0309819e-04]\n",
      "no_action\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.5095501e-01 1.4894873e-01 3.8356386e-05 5.7975547e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[8.6308366e-01 1.3685955e-01 2.2858074e-05 3.3926728e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "[8.6631590e-01 1.3364215e-01 1.7187038e-05 2.4750871e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "[8.6800802e-01 1.3196073e-01 1.3096709e-05 1.8197439e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 111ms/step\n",
      "[8.7004191e-01 1.2993529e-01 9.7386446e-06 1.3024779e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[8.7083364e-01 1.2914877e-01 7.6885108e-06 9.9403869e-06]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 108ms/step\n",
      "[8.7286144e-01 1.2712468e-01 6.1180217e-06 7.7185477e-06]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[8.7547994e-01 1.2450861e-01 5.0653130e-06 6.3165407e-06]\n",
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      "1/1 [==============================] - 0s 101ms/step\n",
      "[8.76508951e-01 1.23480834e-01 4.54965902e-06 5.65753453e-06]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "[8.7608081e-01 1.2390804e-01 4.9148407e-06 6.2121167e-06]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "[8.75664711e-01 1.24322325e-01 5.64208085e-06 7.28537680e-06]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "[8.758171e-01 1.241689e-01 6.072957e-06 7.952291e-06]\n",
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      "1/1 [==============================] - 0s 100ms/step\n",
      "[8.75871897e-01 1.24112956e-01 6.52311292e-06 8.62535308e-06]\n",
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      "1/1 [==============================] - 0s 98ms/step\n",
      "[8.7772018e-01 1.2226416e-01 6.6723296e-06 8.9081359e-06]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[8.8009286e-01 1.1989024e-01 7.1057548e-06 9.6903141e-06]\n",
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      "1/1 [==============================] - 0s 96ms/step\n",
      "[8.8152963e-01 1.1845146e-01 7.8774583e-06 1.0949430e-05]\n",
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      "1/1 [==============================] - 0s 100ms/step\n",
      "[8.8352948e-01 1.1645017e-01 8.4473304e-06 1.1901417e-05]\n",
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      "1/1 [==============================] - 0s 105ms/step\n",
      "[8.8563061e-01 1.1434817e-01 8.7390208e-06 1.2429958e-05]\n",
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      "1/1 [==============================] - 0s 109ms/step\n",
      "[8.8768059e-01 1.1229787e-01 8.8608731e-06 1.2688506e-05]\n",
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      "1/1 [==============================] - 0s 107ms/step\n",
      "[8.89883041e-01 1.10095166e-01 8.89498551e-06 1.28839401e-05]\n",
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      "1/1 [==============================] - 0s 104ms/step\n",
      "[8.91705513e-01 1.08271964e-01 9.11614643e-06 1.33597350e-05]\n",
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      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.93128932e-01 1.06847025e-01 9.70183191e-06 1.44143296e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[8.9405370e-01 1.0591986e-01 1.0549444e-05 1.5900692e-05]\n",
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      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.9392233e-01 1.0604750e-01 1.1927727e-05 1.8276676e-05]\n",
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      "1/1 [==============================] - 0s 104ms/step\n",
      "[8.9350116e-01 1.0646518e-01 1.3190176e-05 2.0486599e-05]\n",
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      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.9237928e-01 1.0758386e-01 1.4380665e-05 2.2544935e-05]\n",
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      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.9078581e-01 1.0917606e-01 1.4871682e-05 2.3281327e-05]\n",
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      "[8.89361978e-01 1.10599153e-01 1.51740305e-05 2.37187942e-05]\n",
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      "[8.8766718e-01 1.1229337e-01 1.5417576e-05 2.4030302e-05]\n",
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      "[8.8602722e-01 1.1393400e-01 1.5203046e-05 2.3579163e-05]\n",
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      "[8.84273291e-01 1.15688436e-01 1.50366095e-05 2.32408947e-05]\n",
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      "[8.82520139e-01 1.17442615e-01 1.46739767e-05 2.25932381e-05]\n",
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      "[8.80107105e-01 1.19855925e-01 1.45692147e-05 2.23508978e-05]\n",
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      "[8.75684023e-01 1.24278255e-01 1.49472899e-05 2.27582605e-05]\n",
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      "[8.7215334e-01 1.2780780e-01 1.5461597e-05 2.3380011e-05]\n",
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      "[8.7029660e-01 1.2966423e-01 1.5616073e-05 2.3547975e-05]\n",
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      "[8.6621010e-01 1.3374949e-01 1.6239976e-05 2.4212351e-05]\n",
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      "[8.6305654e-01 1.3690494e-01 1.5639704e-05 2.2914435e-05]\n",
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      "[8.5982656e-01 1.4013618e-01 1.5260905e-05 2.2010519e-05]\n",
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      "[8.5770386e-01 1.4226040e-01 1.4750190e-05 2.0973823e-05]\n",
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      "[8.5583031e-01 1.4413498e-01 1.4435402e-05 2.0334697e-05]\n",
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      "[8.5409963e-01 1.4586675e-01 1.4008992e-05 1.9597570e-05]\n",
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      "[8.52302372e-01 1.47664592e-01 1.38015475e-05 1.92046100e-05]\n",
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      "[8.4938079e-01 1.5058663e-01 1.3676150e-05 1.8920206e-05]\n",
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      "[8.4603238e-01 1.5393525e-01 1.3626766e-05 1.8751463e-05]\n",
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      "[8.4146947e-01 1.5849644e-01 1.4394001e-05 1.9745779e-05]\n",
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      "[8.3632004e-01 1.6364507e-01 1.4781221e-05 2.0123409e-05]\n",
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      "[8.3325750e-01 1.6670881e-01 1.4306041e-05 1.9381529e-05]\n",
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      "[8.2980186e-01 1.7016535e-01 1.3974226e-05 1.8852279e-05]\n",
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      "[8.2712471e-01 1.7284410e-01 1.3354498e-05 1.7879367e-05]\n",
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      "[8.2697666e-01 1.7299409e-01 1.2563910e-05 1.6755543e-05]\n",
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      "[8.2885748e-01 1.7111529e-01 1.1680836e-05 1.5555224e-05]\n",
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      "[8.3373469e-01 1.6624117e-01 1.0380309e-05 1.3835468e-05]\n",
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      "[8.4066743e-01 1.5931197e-01 8.8071665e-06 1.1787138e-05]\n",
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      "[8.4674758e-01 1.5323395e-01 7.8803005e-06 1.0640397e-05]\n",
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      "[8.5491455e-01 1.4506732e-01 7.6865299e-06 1.0528708e-05]\n",
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      "[8.6272156e-01 1.3725927e-01 8.0024274e-06 1.1186903e-05]\n",
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      "[8.69920611e-01 1.30058244e-01 8.70184158e-06 1.24746575e-05]\n",
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      "[8.7570238e-01 1.2427344e-01 9.7790544e-06 1.4380689e-05]\n",
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      "[8.8074213e-01 1.1922904e-01 1.1474925e-05 1.7377719e-05]\n",
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      "[8.85101140e-01 1.14865504e-01 1.30362869e-05 2.02791416e-05]\n",
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      "[8.8813132e-01 1.1182909e-01 1.5217075e-05 2.4306282e-05]\n",
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      "1/1 [==============================] - 0s 107ms/step\n",
      "[8.9069462e-01 1.0925897e-01 1.7645247e-05 2.8827462e-05]\n",
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      "1/1 [==============================] - 0s 105ms/step\n",
      "[8.9226550e-01 1.0768132e-01 1.9967274e-05 3.3168770e-05]\n",
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      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.9390415e-01 1.0603662e-01 2.2078035e-05 3.7219739e-05]\n",
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      "1/1 [==============================] - 0s 94ms/step\n",
      "[8.9449185e-01 1.0544160e-01 2.4609770e-05 4.1924854e-05]\n",
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      "1/1 [==============================] - 0s 104ms/step\n",
      "[8.9419085e-01 1.0573715e-01 2.6548842e-05 4.5456745e-05]\n",
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      "1/1 [==============================] - 0s 96ms/step\n",
      "[8.95420492e-01 1.04508534e-01 2.61273399e-05 4.48184655e-05]\n",
      "no_action\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 98ms/step\n",
      "[8.97071302e-01 1.02858886e-01 2.56557996e-05 4.41391567e-05]\n",
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      "[8.9892691e-01 1.0100523e-01 2.4890962e-05 4.2942811e-05]\n",
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      "[9.0033787e-01 9.9594809e-02 2.4688577e-05 4.2631596e-05]\n",
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      "1/1 [==============================] - 0s 105ms/step\n",
      "[9.0190583e-01 9.8026782e-02 2.4707275e-05 4.2713658e-05]\n",
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      "[9.0291017e-01 9.7021475e-02 2.5048719e-05 4.3282591e-05]\n",
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      "[9.0425169e-01 9.5678851e-02 2.5445295e-05 4.4030294e-05]\n",
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      "[9.0479738e-01 9.5131084e-02 2.6171703e-05 4.5285564e-05]\n",
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      "[9.0515500e-01 9.4772272e-02 2.6690850e-05 4.6081354e-05]\n",
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      "[9.0494543e-01 9.4980530e-02 2.7214855e-05 4.6867422e-05]\n",
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      "[9.0444869e-01 9.5475823e-02 2.7781227e-05 4.7685011e-05]\n",
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      "[9.0341419e-01 9.6507907e-02 2.8750377e-05 4.9215920e-05]\n",
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      "[9.0183055e-01 9.8088555e-02 2.9896553e-05 5.0970080e-05]\n",
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      "[9.0015537e-01 9.9761568e-02 3.0780640e-05 5.2289211e-05]\n",
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      "[8.9829063e-01 1.0162449e-01 3.1507589e-05 5.3365573e-05]\n",
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      "[8.9637250e-01 1.0354176e-01 3.1855488e-05 5.3890948e-05]\n",
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      "[8.9463836e-01 1.0527761e-01 3.1301235e-05 5.2768977e-05]\n",
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      "[8.9347273e-01 1.0644746e-01 2.9819495e-05 4.9994975e-05]\n",
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      "[8.9219016e-01 1.0773481e-01 2.8152426e-05 4.6908201e-05]\n",
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      "[8.9141876e-01 1.0851201e-01 2.6054526e-05 4.3170156e-05]\n",
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      "[8.90793085e-01 1.09143145e-01 2.40952922e-05 3.96694595e-05]\n",
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      "[8.9043772e-01 1.0950354e-01 2.2285371e-05 3.6467110e-05]\n",
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      "[8.9040774e-01 1.0953930e-01 2.0191683e-05 3.2771484e-05]\n",
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      "1/1 [==============================] - 0s 93ms/step\n",
      "[8.9000076e-01 1.0995013e-01 1.8788303e-05 3.0268027e-05]\n",
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      "[8.8908362e-01 1.1086930e-01 1.8114317e-05 2.8934459e-05]\n",
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      "1/1 [==============================] - 0s 95ms/step\n",
      "[8.8813263e-01 1.1182134e-01 1.7768411e-05 2.8248587e-05]\n",
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      "1/1 [==============================] - 0s 94ms/step\n",
      "[8.8701051e-01 1.1294338e-01 1.7818627e-05 2.8229744e-05]\n",
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      "1/1 [==============================] - 0s 99ms/step\n",
      "[8.86265457e-01 1.13689065e-01 1.76745798e-05 2.78819880e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 112ms/step\n",
      "[8.8522172e-01 1.1473252e-01 1.7784103e-05 2.7896025e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "[8.8413602e-01 1.1581852e-01 1.7774004e-05 2.7685855e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[8.83118927e-01 1.16834864e-01 1.81280193e-05 2.81272278e-05]\n",
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      "1/1 [==============================] - 0s 104ms/step\n",
      "[8.81922603e-01 1.18029356e-01 1.88430495e-05 2.91672077e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "[8.80425692e-01 1.19522296e-01 2.04104635e-05 3.16530131e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 107ms/step\n",
      "[8.7868243e-01 1.2126112e-01 2.2105694e-05 3.4351724e-05]\n",
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      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.7673378e-01 1.2320505e-01 2.3956225e-05 3.7299254e-05]\n",
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      "1/1 [==============================] - 0s 103ms/step\n",
      "[8.7479430e-01 1.2513979e-01 2.5715633e-05 4.0121035e-05]\n",
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      "1/1 [==============================] - 0s 99ms/step\n",
      "[8.7286484e-01 1.2706451e-01 2.7545599e-05 4.3107961e-05]\n",
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      "1/1 [==============================] - 0s 95ms/step\n",
      "[8.7105864e-01 1.2886611e-01 2.9241150e-05 4.5955392e-05]\n",
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      "[8.6896867e-01 1.3095166e-01 3.0912506e-05 4.8762558e-05]\n",
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      "1/1 [==============================] - 0s 112ms/step\n",
      "[8.6670011e-01 1.3321592e-01 3.2551558e-05 5.1433599e-05]\n",
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      "[8.6425453e-01 1.3565758e-01 3.4073597e-05 5.3807293e-05]\n",
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      "[8.6211932e-01 1.3779043e-01 3.5027046e-05 5.5200457e-05]\n",
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      "[8.6048239e-01 1.3942771e-01 3.5004312e-05 5.4951794e-05]\n",
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      "[8.5934371e-01 1.4056899e-01 3.4102301e-05 5.3247903e-05]\n",
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      "1/1 [==============================] - 0s 94ms/step\n",
      "[8.5817015e-01 1.4174539e-01 3.3101445e-05 5.1375788e-05]\n",
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      "[8.5678911e-01 1.4312947e-01 3.2024451e-05 4.9353603e-05]\n",
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      "[8.5537469e-01 1.4454725e-01 3.0872019e-05 4.7203179e-05]\n",
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      "[8.5401845e-01 1.4590678e-01 2.9719673e-05 4.5106655e-05]\n",
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      "[8.5246962e-01 1.4745775e-01 2.8945537e-05 4.3675285e-05]\n",
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      "[8.5073096e-01 1.4919803e-01 2.8394461e-05 4.2624426e-05]\n",
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      "1/1 [==============================] - 0s 103ms/step\n",
      "[8.4881854e-01 1.5111075e-01 2.8335744e-05 4.2397860e-05]\n",
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      "[8.4696805e-01 1.5296072e-01 2.8576469e-05 4.2682397e-05]\n",
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      "[8.4563518e-01 1.5429361e-01 2.8561009e-05 4.2654825e-05]\n",
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      "[8.4504491e-01 1.5488443e-01 2.8297083e-05 4.2360760e-05]\n",
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      "[8.4432572e-01 1.5560362e-01 2.8253495e-05 4.2403008e-05]\n",
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      "[8.4442949e-01 1.5550064e-01 2.7908327e-05 4.1996733e-05]\n",
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      "[8.4499890e-01 1.5493244e-01 2.7416392e-05 4.1342377e-05]\n",
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      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.4625250e-01 1.5367991e-01 2.6885808e-05 4.0644740e-05]\n",
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      "1/1 [==============================] - 0s 99ms/step\n",
      "[8.4803200e-01 1.5190180e-01 2.6300342e-05 3.9893501e-05]\n",
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      "1/1 [==============================] - 0s 99ms/step\n",
      "[8.4986961e-01 1.5006529e-01 2.5802798e-05 3.9261296e-05]\n",
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      "1/1 [==============================] - 0s 97ms/step\n",
      "[8.5192895e-01 1.4800745e-01 2.5203492e-05 3.8437109e-05]\n",
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      "1/1 [==============================] - 0s 101ms/step\n",
      "[8.5383469e-01 1.4610279e-01 2.4734378e-05 3.7783942e-05]\n",
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      "1/1 [==============================] - 0s 102ms/step\n",
      "[8.5548443e-01 1.4445326e-01 2.4640576e-05 3.7677171e-05]\n",
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      "1/1 [==============================] - 0s 100ms/step\n",
      "[8.5741371e-01 1.4252469e-01 2.4347864e-05 3.7255591e-05]\n",
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      "1/1 [==============================] - 0s 106ms/step\n",
      "[8.5956663e-01 1.4037211e-01 2.4187219e-05 3.7091831e-05]\n",
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      "1/1 [==============================] - 0s 100ms/step\n",
      "[8.6131150e-01 1.3862766e-01 2.4008183e-05 3.6847221e-05]\n",
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      "1/1 [==============================] - 0s 100ms/step\n",
      "[8.6256409e-01 1.3737528e-01 2.3927136e-05 3.6723995e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "[8.6323255e-01 1.3670668e-01 2.4016046e-05 3.6838097e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 105ms/step\n",
      "[8.6327565e-01 1.3666219e-01 2.4553783e-05 3.7666265e-05]\n",
      "no_action\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 98ms/step\n",
      "[8.6298245e-01 1.3695416e-01 2.4999226e-05 3.8344569e-05]\n",
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      "[8.6100042e-01 1.3894035e-01 2.3527580e-05 3.5791207e-05]\n",
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      "[8.4278059e-01 1.5716253e-01 2.2693779e-05 3.4220244e-05]\n",
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      "[8.3874279e-01 1.6120209e-01 2.2076030e-05 3.3023531e-05]\n",
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      "[8.3130360e-01 1.6864268e-01 2.1684491e-05 3.2054337e-05]\n",
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      "[8.2855475e-01 1.7139679e-01 1.9768990e-05 2.8770179e-05]\n",
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      "[8.2730144e-01 1.7265683e-01 1.7176493e-05 2.4589823e-05]\n",
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      "1/1 [==============================] - 0s 96ms/step\n",
      "[8.2816374e-01 1.7180021e-01 1.4939116e-05 2.1141561e-05]\n",
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      "1/1 [==============================] - 0s 98ms/step\n",
      "[8.2940239e-01 1.7056596e-01 1.3192910e-05 1.8503462e-05]\n",
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      "1/1 [==============================] - 0s 106ms/step\n",
      "[8.30927134e-01 1.69043526e-01 1.22528545e-05 1.70734256e-05]\n",
      "no_action\n",
      "1/1 [==============================] - 0s 106ms/step\n",
      "[8.3227813e-01 1.6769484e-01 1.1322207e-05 1.5656873e-05]\n",
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      "1/1 [==============================] - 0s 109ms/step\n",
      "[8.3280176e-01 1.6717236e-01 1.0914333e-05 1.5007555e-05]\n",
      "no_action\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[56], line 58\u001b[0m\n\u001b[1;32m     55\u001b[0m     cv2\u001b[38;5;241m.\u001b[39mimshow(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOpenCV Feed\u001b[39m\u001b[38;5;124m'\u001b[39m, image)\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;66;03m# Break gracefully\u001b[39;00m\n\u001b[0;32m---> 58\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitKey\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;241m&\u001b[39m \u001b[38;5;241m0xFF\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mord\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mq\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     59\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m     60\u001b[0m cap\u001b[38;5;241m.\u001b[39mrelease()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# 1. New detection variables\n",
    "sequence = []\n",
    "sentence = []\n",
    "predictions = []\n",
    "threshold = 0.65\n",
    "\n",
    "cap = cv2.VideoCapture(1)\n",
    "# Set mediapipe model \n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "\n",
    "        # Read feed\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        # Make detections\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "#         print(results)\n",
    "\n",
    "        # Draw landmarks\n",
    "        draw_styled_landmarks(image, results)\n",
    "        \n",
    "        # 2. Prediction logic\n",
    "        keypoints = extract_keypoints(results)\n",
    "        sequence.append(keypoints)\n",
    "        sequence = sequence[-19:]\n",
    "        \n",
    "        if len(sequence) == 19:\n",
    "            res = model.predict(np.expand_dims(sequence, axis=0))\n",
    "            res = res[0]\n",
    "            print(res)\n",
    "            print(actions[np.argmax(res)])\n",
    "            #predictions.append(np.argmax(res))\n",
    "            \n",
    "        #3. Viz logic\n",
    "            #if np.unique(predictions[-10:])[0]==np.argmax(res): \n",
    "            if res[np.argmax(res)] > threshold: \n",
    "\n",
    "                if len(sentence) > 0: \n",
    "                    if actions[np.argmax(res)] != sentence[-1]:\n",
    "                        sentence.append(actions[np.argmax(res)])\n",
    "                else:\n",
    "                    sentence.append(actions[np.argmax(res)])\n",
    "\n",
    "            if len(sentence) > 5: \n",
    "                sentence = sentence[-5:]\n",
    "\n",
    "            # Viz probabilities\n",
    "            image = prob_viz(res, actions, image, colors)\n",
    "            \n",
    "        cv2.rectangle(image, (0,0), (2000, 80), (245, 117, 16), -1)\n",
    "        cv2.putText(image, ' '.join(sentence), (45,40), \n",
    "                       cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        # Show to screen\n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85e170ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unique(predictions[-10:])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "46774f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "2d850ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e06cca43",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "25b0b792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.8983761e-08, 4.5698980e-07, 3.1607230e-03, 9.9683869e-01],\n",
       "       [3.9344290e-03, 3.4481210e-03, 4.3152526e-01, 5.6109220e-01],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c80a25b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6bcd5cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a61294e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a806367",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5ef52a39",
   "metadata": {},
   "source": [
    "# code for socket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b73e7a25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "686e206b",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[34], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m out \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mVideoWriter(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutput.mp4\u001b[39m\u001b[38;5;124m'\u001b[39m, fourcc, \u001b[38;5;241m20.0\u001b[39m, (\u001b[38;5;241m640\u001b[39m, \u001b[38;5;241m480\u001b[39m))\n\u001b[1;32m     10\u001b[0m s \u001b[38;5;241m=\u001b[39m socket\u001b[38;5;241m.\u001b[39msocket(socket\u001b[38;5;241m.\u001b[39mAF_INET, socket\u001b[38;5;241m.\u001b[39mSOCK_STREAM)\n\u001b[0;32m---> 11\u001b[0m \u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mHOST\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mPORT\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConnected to server at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mHOST\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mPORT\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m mp_holistic\u001b[38;5;241m.\u001b[39mHolistic(min_detection_confidence\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, min_tracking_confidence\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m holistic:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import socket\n",
    "import struct\n",
    "\n",
    "HOST = '172.20.10.14'\n",
    "PORT = 8080\n",
    "\n",
    "fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
    "out = cv2.VideoWriter('output.mp4', fourcc, 20.0, (640, 480))\n",
    "\n",
    "s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
    "s.connect((HOST, PORT))\n",
    "print(f'Connected to server at {HOST}:{PORT}')\n",
    "\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while True:\n",
    "        # Receive size of encoded image data as 4-byte binary string\n",
    "        data_size = s.recv(4)\n",
    "\n",
    "        # Unpack size of encoded image data as unsigned integer\n",
    "        data_size = struct.unpack(\"!I\", data_size)[0]\n",
    "\n",
    "        # Receive encoded image data\n",
    "        img_data = b''\n",
    "        while len(img_data) < data_size:\n",
    "            chunk = s.recv(data_size - len(img_data))\n",
    "            if not chunk:\n",
    "                break\n",
    "            img_data += chunk\n",
    "\n",
    "        # Decode image data\n",
    "        img = cv2.imdecode(np.frombuffer(img_data, np.uint8), cv2.IMREAD_COLOR)\n",
    "        \n",
    "        image, results = mediapipe_detection(img, holistic)\n",
    "        \n",
    "        print(results)\n",
    "\n",
    "        # Draw landmarks\n",
    "        draw_styled_landmarks(image, results)\n",
    "\n",
    "        # Display image\n",
    "        cv2.imshow('Client', image)\n",
    "\n",
    "        # Save the frame to video\n",
    "        out.write(img)\n",
    "\n",
    "        # Exit loop on 'q' key press\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "    # Release the video writer\n",
    "    out.release()\n",
    "\n",
    "    cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bcccb55",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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