CM_VGG16_RNN_Test.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CM_VGG16_RNN_Test.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TruongGiangVu/VGG16_RNN_LSTM_SignLanguageRecognition/blob/main/CM_VGG16_RNN_Test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcOqjcCwbm0m"
      },
      "source": [
        "# Import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BpEWvZHrVFA-",
        "outputId": "b2aa5b97-e895-4849-fc28-545152d5d40f"
      },
      "source": [
        "!pip uninstall tensorflow\n",
        "!pip install --no-cache-dir tensorflow==2.2.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling tensorflow-2.2.0:\n",
            "  Would remove:\n",
            "    /usr/local/bin/estimator_ckpt_converter\n",
            "    /usr/local/bin/saved_model_cli\n",
            "    /usr/local/bin/tensorboard\n",
            "    /usr/local/bin/tf_upgrade_v2\n",
            "    /usr/local/bin/tflite_convert\n",
            "    /usr/local/bin/toco\n",
            "    /usr/local/bin/toco_from_protos\n",
            "    /usr/local/lib/python3.7/dist-packages/tensorflow-2.2.0.dist-info/*\n",
            "    /usr/local/lib/python3.7/dist-packages/tensorflow/*\n",
            "Proceed (y/n)? y\n",
            "  Successfully uninstalled tensorflow-2.2.0\n",
            "y\n",
            "Collecting tensorflow==2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4c/1a/0d79814736cfecc825ab8094b39648cc9c46af7af1bae839928acb73b4dd/tensorflow-2.2.0-cp37-cp37m-manylinux2010_x86_64.whl (516.2MB)\n",
            "\u001b[K     |████████████████████████████████| 516.2MB 1.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (1.19.5)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (0.2.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (1.1.2)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (0.12.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.3.0,>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (2.2.0)\n",
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            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (1.32.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (1.12.1)\n",
            "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (1.4.1)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (3.12.4)\n",
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            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (1.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (1.15.0)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (0.36.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (3.3.0)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2.0) (0.3.3)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (56.1.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (0.4.4)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.3.4)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.30.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.8.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (2.23.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (4.0.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (4.7.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (4.2.2)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.0.4)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.7.4.3)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (0.4.8)\n",
            "Installing collected packages: tensorflow\n",
            "Successfully installed tensorflow-2.2.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tensorflow"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fqXERKxbaaX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6c32e24-7e67-4cd1-87f0-6fc119981dc0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Ge2FCREbqke"
      },
      "source": [
        "# common library \n",
        "import numpy as np\n",
        "import os\n",
        "import time\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "# tensorflow library\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, GRU, LSTM, Flatten, TimeDistributed, Flatten, BatchNormalization, Activation, Dropout, SimpleRNN\n",
        "from keras.layers import Conv2D, BatchNormalization, MaxPool2D, GlobalMaxPool2D, MaxPooling2D\n",
        "# from keras.layers import TimeDistributed, GRU, Dense, Dropout\n",
        "from keras.layers.convolutional import Conv3D, MaxPooling3D\n",
        "from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
        "from keras.losses import categorical_crossentropy\n",
        "from keras.optimizers import Adam\n",
        "\n",
        "from keras.applications.vgg16 import VGG16\n",
        "\n",
        "# reading data\n",
        "import csv\n",
        "import threading\n",
        "\n",
        "# Label encoder\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.utils import np_utils"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IQwb27bbvUl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43835fb9-d7f6-41f7-a8e7-b28a3f05a7f5"
      },
      "source": [
        "# change path\n",
        "import os\n",
        "os.chdir('/content/drive/MyDrive/VSLProject')\n",
        "print(os.getcwd())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1FHuZ4ytzi1pRYSLpL7aaEmpdDa1jCION/VSLProject\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ur7qEYnfbxdl"
      },
      "source": [
        "# Global params"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAZ50D6pbz_u"
      },
      "source": [
        "# some global params\n",
        "SIZE = (64, 64) \n",
        "\n",
        "# SIZE = (224, 224) \n",
        "CHANNELS = 3\n",
        "NBFRAME = 10\n",
        "BS = 20\n",
        "\n",
        "NUM_THREADS = 8\n",
        "\n",
        "#preprocessing\n",
        "LENGTH_TRIM = 130\n",
        "classes_file = 'classes.npy'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3H20UoCVb30u"
      },
      "source": [
        "#Funtion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0ihc1ijcNT0"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GwEKz4I4b5Et"
      },
      "source": [
        "# resize image by dsize = (128, 128)\n",
        "def resizeImage(img, dsize):\n",
        "  sizeImg = (img.shape[0],img.shape[1])\n",
        "  if sizeImg != dsize and dsize != (None, None):\n",
        "    img = cv2.resize(img,dsize)\n",
        "  return img\n",
        "\n",
        "# remove left right background\n",
        "def removeTrimBackground(img, length = 120):\n",
        "  img = img[:,length:-1*length]\n",
        "  return img\n",
        "\n",
        "def resizeScale(img, scale = 30):\n",
        "  scale_percent = scale # percent of original size\n",
        "  width = int(img.shape[1] * scale_percent / 100)\n",
        "  height = int(img.shape[0] * scale_percent / 100)\n",
        "  dim = (width, height)\n",
        "    \n",
        "  # resize image\n",
        "  resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n",
        "  return resized\n",
        "  \n",
        "def preprocessingImg(img, dsize = (128,128)):\n",
        "  # img = human_detection(img)\n",
        "  img = removeTrimBackground(img, LENGTH_TRIM)\n",
        "  img = resizeImage(img, dsize)\n",
        "  return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6dxT7dWcPzN"
      },
      "source": [
        "## Video to frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-42njMG7cMok"
      },
      "source": [
        "class MyVideo:\n",
        "  def __init__(self, path='', root_path=''):\n",
        "    self.path = path\n",
        "    self.root = root_path\n",
        "    self.video = cv2.VideoCapture(path)\n",
        "    self.name = path.split(\"/\")[-1]\n",
        "    self.label = path.split(\"/\")[-2]\n",
        "  def getVideo(self):\n",
        "    return self.video\n",
        "  def getFullPath(self):\n",
        "    return self.root + '/' + self.path\n",
        "  def getFileName(self):\n",
        "    return self.name\n",
        "  def getFrameCount(self):\n",
        "    return int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))\n",
        "  def getLabel(self):\n",
        "    return self.label\n",
        "  def release(self):\n",
        "    self.video.release()\n",
        "    del self\n",
        "\n",
        "\n",
        "def videotoframe(myVideo, dsize=(128,128) , numframe=5, start=0, stop=0):\n",
        "  listframe = []\n",
        "  length = myVideo.getFrameCount()\n",
        "\n",
        "  currentFrame = -1\n",
        "  skipframe = int((length - start - stop)/numframe)\n",
        "  takeframe = skipframe + start\n",
        "  taken = 0\n",
        "  # print('video',video,'length', length, 'skip', skipframe)\n",
        "  video = myVideo.getVideo()\n",
        "  while(True):\n",
        "      # Capture frame-by-frame\n",
        "      ret, frame = video.read()\n",
        "      currentFrame += 1\n",
        "\n",
        "      if ret == False:\n",
        "          break\n",
        "      if currentFrame < takeframe:\n",
        "          continue\n",
        "      if taken >= numframe:\n",
        "          break\n",
        "      frame = resizeScale(frame)\n",
        "      frame = removeTrimBackground(frame, LENGTH_TRIM)\n",
        "      frame = resizeImage(frame, dsize)\n",
        "      listframe.append(frame)\n",
        "\n",
        "      takeframe += skipframe\n",
        "      taken += 1\n",
        "  # When everything done, release the capture\n",
        "  myVideo.release()\n",
        "  cv2.destroyAllWindows()\n",
        "  return listframe\n",
        "\n",
        "def preprocessingVideo(myVideo):\n",
        "  frames = videotoframe(myVideo, SIZE, NBFRAME, 10, 30)\n",
        "  frames = np.array(frames)\n",
        "  frames = frames / 255.\n",
        "  return frames\n",
        "\n",
        "def predictOneVideo(video, classes):\n",
        "  # preprocessing video path\n",
        "  if type(video) is not MyVideo:\n",
        "    video = MyVideo(video)\n",
        "  item = preprocessingVideo(video)\n",
        "  item = item[None,:]  # [640,10,64,64,3]\n",
        "  try:\n",
        "    # a = model.predict_classes(item) # old version\n",
        "    predicted = np.argmax(model.predict(item), axis=-1) # [10] [15,12]\n",
        "    return classes[predicted[0]]  # [16] accept\n",
        "  except: \n",
        "    print('Video', myVideo.name,'has error')\n",
        "    return 'Null'\n",
        "  # print(predicted)\n",
        "\n",
        "def preparePath(path, csv_file = ''):\n",
        "  paths = []\n",
        "  if csv_file == '':\n",
        "    videos = os.listdir(path)\n",
        "    for video in videos:\n",
        "        video_path = os.path.join(path, video)\n",
        "        paths.append(video_path)\n",
        "  else:\n",
        "    with open(csv_file) as csvfile: # read path from csv\n",
        "      reader = csv.reader(csvfile)\n",
        "      for row in reader:\n",
        "        video_path = os.path.join(path, row[0])\n",
        "        paths.append(video_path)\n",
        "\n",
        "  print(len(paths))\n",
        "  return paths"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Jf9osZQcW0k"
      },
      "source": [
        "## Predict video"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfAACwH2cVqs"
      },
      "source": [
        "def predictVideoOneTime(paths, classes, verbose = 0):\n",
        "  data = []\n",
        "  names = []\n",
        "  expecteds = []\n",
        "  n = len(videos)\n",
        "  for path in paths:\n",
        "    myVideo = MyVideo(path)\n",
        "    item = preprocessingVideo(myVideo)\n",
        "    data.append(item)\n",
        "    names.append(myVideo.getFileName())\n",
        "    expecteds.append(myVideo.getLabel())\n",
        "\n",
        "  data = np.array(data)\n",
        "  # print(data.shape)\n",
        "  predicted = np.argmax(model.predict(data), axis=-1)\n",
        "  # print(predicted)\n",
        "  # print(classes[predicted])\n",
        "  dic = {}\n",
        "  count = 0\n",
        "  for i in range(n):\n",
        "    expected = expecteds[i]\n",
        "    text = 'Video: '+ names[i] + ' Expected: '+ expected + ' Predicted: '+ classes[predicted[i]]\n",
        "    if expected not in dic:\n",
        "      dic[expected] = 0\n",
        "    if expected == classes[predicted[i]]:\n",
        "      count = count + 1\n",
        "      dic[expected] = dic[expected] + 1\n",
        "    else:\n",
        "      text = '\\x1b[31m'+ text + '\\x1b[0m'\n",
        "    \n",
        "    if verbose == 1 : \n",
        "      print(text)\n",
        "  print()\n",
        "  print('Accuray:',str(count)+'/'+str(n), ',',str(count/n))\n",
        "  return dic\n",
        "\n",
        "def predictVideoOneByOne(paths, classes, verbose = 0):\n",
        "  dic = {}\n",
        "\n",
        "  count = 0\n",
        "  n = len(paths)\n",
        "  index = 1\n",
        "  for video in paths:\n",
        "    myVideo = MyVideo(video)\n",
        "    predicted = predictOneVideo( myVideo, classes)\n",
        "    expected = myVideo.getLabel()\n",
        "    print(index, end=' ')\n",
        "    text = 'Video: '+ myVideo.getFileName() + ' Expected: '+  expected + ' Predicted: '+ predicted\n",
        "    if expected not in dic:\n",
        "      dic[expected] = 0\n",
        "    if expected == predicted:\n",
        "      count = count + 1\n",
        "      dic[expected] = dic[expected] + 1\n",
        "    else:\n",
        "      text = '\\x1b[31m'+ text + '\\x1b[0m'\n",
        "    index = index + 1\n",
        "    \n",
        "    if verbose == 1 : \n",
        "      print(text)\n",
        "    \n",
        "  print()\n",
        "  print('Accuray:',str(count)+'/'+str(n), ',',str(count/n))\n",
        "  return dic\n",
        "\n",
        "def predictVideoOneByOneReturn2dDict(paths, classes, verbose = 0):\n",
        "  dic = {}    # to make a confusion maxtrix, im use a 2D dictionary\n",
        "  count = 0\n",
        "  n = len(paths)\n",
        "  index = 0\n",
        "  for video in paths:\n",
        "    myVideo = MyVideo(video)\n",
        "    predicted = predictOneVideo( myVideo, classes)\n",
        "    expected = myVideo.getLabel()\n",
        "    print(index+1, end=' ')\n",
        "    text = 'Video: '+ myVideo.getFileName() + ' Expected: '+  expected + ' Predicted: '+ predicted\n",
        "    if expected not in dic:\n",
        "      dic[expected] = dict()\n",
        "    if predicted not in dic[expected]:\n",
        "      dic[expected][predicted] = 0\n",
        "    if expected == predicted:\n",
        "      count = count + 1\n",
        "      # dic[expected] = dic[expected] + 1\n",
        "    else:\n",
        "      text = '\\x1b[31m'+ text + '\\x1b[0m'\n",
        "    \n",
        "    # process onfusion matrix array\n",
        "    dic[expected][predicted] += 1\n",
        "\n",
        "\n",
        "    index = index + 1\n",
        "    if verbose == 1 : \n",
        "      print(text)\n",
        "    \n",
        "  print()\n",
        "  print('Accuray:',str(count)+'/'+str(n), ',',str(count/n))\n",
        "  return dic\n",
        "\n",
        "def predictVideo(path, csv_file, classes, verbose = 0 ,isOneTime = False):\n",
        "  paths = preparePath(path, csv_file)\n",
        "  \n",
        "  if isOneTime == True:\n",
        "    dic = predictVideoOneTime(paths, classes, verbose)\n",
        "  else:\n",
        "    dic = predictVideoOneByOneReturn2dDict(paths, classes, verbose)\n",
        "  return dic"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkH-BQ4rcEj0"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osyGIM78b85p"
      },
      "source": [
        "def load_VGG16_model(shape=(112, 112, 3)):\n",
        "  base_model = VGG16(weights='imagenet', include_top=False, input_shape=shape)\n",
        "  print(\"Model loaded..!\")\n",
        "  base_model.summary()\n",
        "\n",
        "  # model.add(Flatten())\n",
        "  output1 = GlobalMaxPool2D()\n",
        "  output2 = Flatten()\n",
        "  module = keras.Sequential([base_model, output1, output2])\n",
        "  module.summary()\n",
        "  return module\n",
        "\n",
        "def LSTMModel(shape=(5, 112, 112, 3), nbout=3):\n",
        "  model = Sequential()\n",
        "  convnet = load_VGG16_model(shape[1:])\n",
        "  # convnet = VGG16_small(shape[1:])\n",
        "  model.add(TimeDistributed(convnet, input_shape=shape))\n",
        "\n",
        "  model.add(SimpleRNN(256, dropout=0.2))\n",
        "  model.add(Dense(1024, activation='relu'))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Dense(nbout, activation='softmax'))\n",
        "  sgd = keras.optimizers.SGD(lr=0.00005, decay = 1e-6, momentum=0.9, nesterov=True)\n",
        "  # sgd = keras.optimizers.SGD( momentum=0.9, nesterov=True)\n",
        "  model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbBbkrelcaZF"
      },
      "source": [
        "# Main"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgqpS_RZcb_F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c1c4fa7-1a0a-4710-dc87-17879adf45bb"
      },
      "source": [
        "# read classes from file\n",
        "classes_file = 'classes.npy'\n",
        "encoder = LabelEncoder()\n",
        "encoder.classes_ = np.load(classes_file)\n",
        "classes = np.array(encoder.classes_)\n",
        "num_classes = len(classes)\n",
        "INSHAPE=(NBFRAME,) + SIZE + (CHANNELS,) # (2560, 5, 112, 112, 3)\n",
        "print(classes)\n",
        "# print(INSHAPE) \n",
        "model = LSTMModel(INSHAPE, num_classes)\n",
        "model.load_weights('chkp/weights-RNN-10_05.hdf5')\n",
        "# model.load_weights('chkp/weights-LSTM-095625-08_05.hdf5')\n",
        "\n",
        "print(num_classes,classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Accept' 'Appear' 'Argentina' 'Away' 'Barbecue' 'Bathe' 'Birthday'\n",
            " 'Bitter' 'Born' 'Breakfast' 'Bright' 'Buy' 'Call' 'Candy' 'Catch'\n",
            " 'Chewing-gum' 'Coin' 'Colors' 'Copy' 'Country' 'Dance' 'Deaf' 'Drawer'\n",
            " 'Enemy' 'Find' 'Food' 'Give' 'Green' 'Help' 'Hungry' 'Last name' 'Learn'\n",
            " 'Light-blue' 'Man' 'Map' 'Milk' 'Mock' 'Music' 'Name' 'None' 'Opaque'\n",
            " 'Patience' 'Perfume' 'Photo' 'Realize' 'Red' 'Red_2' 'Rice' 'Run' 'Ship'\n",
            " 'Shut down' 'Skimmer' 'Son' 'Spaghetti' 'Sweet milk' 'Thanks' 'To land'\n",
            " 'Trap' 'Uruguay' 'Water' 'Where' 'Women' 'Yellow' 'Yogurt']\n",
            "Model loaded..!\n",
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 64, 64, 3)]       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Model)                (None, 2, 2, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "global_max_pooling2d (Global (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 512)               0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "64 ['Accept' 'Appear' 'Argentina' 'Away' 'Barbecue' 'Bathe' 'Birthday'\n",
            " 'Bitter' 'Born' 'Breakfast' 'Bright' 'Buy' 'Call' 'Candy' 'Catch'\n",
            " 'Chewing-gum' 'Coin' 'Colors' 'Copy' 'Country' 'Dance' 'Deaf' 'Drawer'\n",
            " 'Enemy' 'Find' 'Food' 'Give' 'Green' 'Help' 'Hungry' 'Last name' 'Learn'\n",
            " 'Light-blue' 'Man' 'Map' 'Milk' 'Mock' 'Music' 'Name' 'None' 'Opaque'\n",
            " 'Patience' 'Perfume' 'Photo' 'Realize' 'Red' 'Red_2' 'Rice' 'Run' 'Ship'\n",
            " 'Shut down' 'Skimmer' 'Son' 'Spaghetti' 'Sweet milk' 'Thanks' 'To land'\n",
            " 'Trap' 'Uruguay' 'Water' 'Where' 'Women' 'Yellow' 'Yogurt']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhCe1THYcfG1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd3e0b9c-b2e4-48b0-aa7e-35aa630b5c0d"
      },
      "source": [
        "start = time.time()\n",
        "\n",
        "res = predictVideo(\n",
        "    path='Datasets/lsa64_split/test', \n",
        "    csv_file='Datasets/lsa64_videoperfolder/test.csv', \n",
        "    classes=classes,\n",
        "    verbose=1,\n",
        "    isOneTime = False)\n",
        "\n",
        "end = time.time()\n",
        "print('time:', end - start)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "640\n",
            "1 Video: 049_001_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "2 Video: 049_002_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "3 Video: 049_003_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "4 Video: 049_004_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "5 Video: 049_005_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "6 Video: 049_006_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "7 Video: 049_007_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "8 Video: 049_008_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "9 Video: 049_009_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "10 Video: 049_010_005.mp4 Expected: Yogurt Predicted: Yogurt\n",
            "11 \u001b[31mVideo: 009_001_005.mp4 Expected: Women Predicted: Yellow\u001b[0m\n",
            "12 \u001b[31mVideo: 009_002_005.mp4 Expected: Women Predicted: Opaque\u001b[0m\n",
            "13 \u001b[31mVideo: 009_003_005.mp4 Expected: Women Predicted: Son\u001b[0m\n",
            "14 Video: 009_004_005.mp4 Expected: Women Predicted: Women\n",
            "15 Video: 009_005_005.mp4 Expected: Women Predicted: Women\n",
            "16 \u001b[31mVideo: 009_006_005.mp4 Expected: Women Predicted: Son\u001b[0m\n",
            "17 Video: 009_007_005.mp4 Expected: Women Predicted: Women\n",
            "18 Video: 009_008_005.mp4 Expected: Women Predicted: Women\n",
            "19 \u001b[31mVideo: 009_009_005.mp4 Expected: Women Predicted: Drawer\u001b[0m\n",
            "20 Video: 009_010_005.mp4 Expected: Women Predicted: Women\n",
            "21 Video: 028_001_005.mp4 Expected: Where Predicted: Where\n",
            "22 \u001b[31mVideo: 028_002_005.mp4 Expected: Where Predicted: Perfume\u001b[0m\n",
            "23 Video: 028_003_005.mp4 Expected: Where Predicted: Where\n",
            "24 Video: 028_004_005.mp4 Expected: Where Predicted: Where\n",
            "25 \u001b[31mVideo: 028_005_005.mp4 Expected: Where Predicted: Perfume\u001b[0m\n",
            "26 Video: 028_006_005.mp4 Expected: Where Predicted: Where\n",
            "27 Video: 028_007_005.mp4 Expected: Where Predicted: Where\n",
            "28 \u001b[31mVideo: 028_008_005.mp4 Expected: Where Predicted: Perfume\u001b[0m\n",
            "29 Video: 028_009_005.mp4 Expected: Where Predicted: Where\n",
            "30 Video: 028_010_005.mp4 Expected: Where Predicted: Where\n",
            "31 Video: 043_001_005.mp4 Expected: Trap Predicted: Trap\n",
            "32 \u001b[31mVideo: 043_002_005.mp4 Expected: Trap Predicted: To land\u001b[0m\n",
            "33 Video: 043_003_005.mp4 Expected: Trap Predicted: Trap\n",
            "34 Video: 043_004_005.mp4 Expected: Trap Predicted: Trap\n",
            "35 Video: 043_005_005.mp4 Expected: Trap Predicted: Trap\n",
            "36 Video: 043_006_005.mp4 Expected: Trap Predicted: Trap\n",
            "37 Video: 043_007_005.mp4 Expected: Trap Predicted: Trap\n",
            "38 Video: 043_008_005.mp4 Expected: Trap Predicted: Trap\n",
            "39 Video: 043_009_005.mp4 Expected: Trap Predicted: Trap\n",
            "40 Video: 043_010_005.mp4 Expected: Trap Predicted: Trap\n",
            "41 Video: 025_001_005.mp4 Expected: Uruguay Predicted: Uruguay\n",
            "42 Video: 025_002_005.mp4 Expected: Uruguay Predicted: Uruguay\n",
            "43 Video: 025_003_005.mp4 Expected: Uruguay Predicted: Uruguay\n",
            "44 Video: 025_004_005.mp4 Expected: Uruguay Predicted: Uruguay\n",
            "45 Video: 025_005_005.mp4 Expected: Uruguay Predicted: Uruguay\n",
            "46 Video: 025_006_005.mp4 Expected: Uruguay Predicted: Uruguay\n",
            "47 \u001b[31mVideo: 025_007_005.mp4 Expected: Uruguay Predicted: Chewing-gum\u001b[0m\n",
            "48 Video: 025_008_005.mp4 Expected: Uruguay Predicted: Uruguay\n",
            "49 Video: 025_009_005.mp4 Expected: Uruguay Predicted: Uruguay\n",
            "50 Video: 025_010_005.mp4 Expected: Uruguay Predicted: Uruguay\n",
            "51 Video: 004_001_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "52 Video: 004_002_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "53 Video: 004_003_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "54 Video: 004_004_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "55 Video: 004_005_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "56 Video: 004_006_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "57 Video: 004_007_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "58 Video: 004_008_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "59 Video: 004_009_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "60 Video: 004_010_005.mp4 Expected: Yellow Predicted: Yellow\n",
            "61 Video: 022_001_005.mp4 Expected: Water Predicted: Water\n",
            "62 Video: 022_002_005.mp4 Expected: Water Predicted: Water\n",
            "63 Video: 022_003_005.mp4 Expected: Water Predicted: Water\n",
            "64 Video: 022_004_005.mp4 Expected: Water Predicted: Water\n",
            "65 Video: 022_005_005.mp4 Expected: Water Predicted: Water\n",
            "66 Video: 022_006_005.mp4 Expected: Water Predicted: Water\n",
            "67 Video: 022_007_005.mp4 Expected: Water Predicted: Water\n",
            "68 Video: 022_008_005.mp4 Expected: Water Predicted: Water\n",
            "69 Video: 022_009_005.mp4 Expected: Water Predicted: Water\n",
            "70 Video: 022_010_005.mp4 Expected: Water Predicted: Water\n",
            "71 Video: 051_001_005.mp4 Expected: Thanks Predicted: Thanks\n",
            "72 Video: 051_002_005.mp4 Expected: Thanks Predicted: Thanks\n",
            "73 Video: 051_003_005.mp4 Expected: Thanks Predicted: Thanks\n",
            "74 Video: 051_004_005.mp4 Expected: Thanks Predicted: Thanks\n",
            "75 \u001b[31mVideo: 051_005_005.mp4 Expected: Thanks Predicted: Give\u001b[0m\n",
            "76 Video: 051_006_005.mp4 Expected: Thanks Predicted: Thanks\n",
            "77 \u001b[31mVideo: 051_007_005.mp4 Expected: Thanks Predicted: Give\u001b[0m\n",
            "78 \u001b[31mVideo: 051_008_005.mp4 Expected: Thanks Predicted: Give\u001b[0m\n",
            "79 \u001b[31mVideo: 051_009_005.mp4 Expected: Thanks Predicted: Breakfast\u001b[0m\n",
            "80 Video: 051_010_005.mp4 Expected: Thanks Predicted: Thanks\n",
            "81 \u001b[31mVideo: 018_001_005.mp4 Expected: Skimmer Predicted: Opaque\u001b[0m\n",
            "82 \u001b[31mVideo: 018_002_005.mp4 Expected: Skimmer Predicted: Milk\u001b[0m\n",
            "83 \u001b[31mVideo: 018_003_005.mp4 Expected: Skimmer Predicted: Milk\u001b[0m\n",
            "84 Video: 018_004_005.mp4 Expected: Skimmer Predicted: Skimmer\n",
            "85 \u001b[31mVideo: 018_005_005.mp4 Expected: Skimmer Predicted: Milk\u001b[0m\n",
            "86 \u001b[31mVideo: 018_006_005.mp4 Expected: Skimmer Predicted: Bright\u001b[0m\n",
            "87 Video: 018_007_005.mp4 Expected: Skimmer Predicted: Skimmer\n",
            "88 Video: 018_008_005.mp4 Expected: Skimmer Predicted: Skimmer\n",
            "89 Video: 018_009_005.mp4 Expected: Skimmer Predicted: Skimmer\n",
            "90 Video: 018_010_005.mp4 Expected: Skimmer Predicted: Skimmer\n",
            "91 Video: 037_001_005.mp4 Expected: Ship Predicted: Ship\n",
            "92 Video: 037_002_005.mp4 Expected: Ship Predicted: Ship\n",
            "93 Video: 037_003_005.mp4 Expected: Ship Predicted: Ship\n",
            "94 Video: 037_004_005.mp4 Expected: Ship Predicted: Ship\n",
            "95 Video: 037_005_005.mp4 Expected: Ship Predicted: Ship\n",
            "96 Video: 037_006_005.mp4 Expected: Ship Predicted: Ship\n",
            "97 Video: 037_007_005.mp4 Expected: Ship Predicted: Ship\n",
            "98 Video: 037_008_005.mp4 Expected: Ship Predicted: Ship\n",
            "99 Video: 037_009_005.mp4 Expected: Ship Predicted: Ship\n",
            "100 \u001b[31mVideo: 037_010_005.mp4 Expected: Ship Predicted: Name\u001b[0m\n",
            "101 Video: 054_001_005.mp4 Expected: To land Predicted: To land\n",
            "102 Video: 054_002_005.mp4 Expected: To land Predicted: To land\n",
            "103 Video: 054_003_005.mp4 Expected: To land Predicted: To land\n",
            "104 Video: 054_004_005.mp4 Expected: To land Predicted: To land\n",
            "105 Video: 054_005_005.mp4 Expected: To land Predicted: To land\n",
            "106 Video: 054_006_005.mp4 Expected: To land Predicted: To land\n",
            "107 Video: 054_007_005.mp4 Expected: To land Predicted: To land\n",
            "108 Video: 054_008_005.mp4 Expected: To land Predicted: To land\n",
            "109 Video: 054_009_005.mp4 Expected: To land Predicted: To land\n",
            "110 Video: 054_010_005.mp4 Expected: To land Predicted: To land\n",
            "111 Video: 048_001_005.mp4 Expected: Spaghetti Predicted: Spaghetti\n",
            "112 Video: 048_002_005.mp4 Expected: Spaghetti Predicted: Spaghetti\n",
            "113 Video: 048_003_005.mp4 Expected: Spaghetti Predicted: Spaghetti\n",
            "114 Video: 048_004_005.mp4 Expected: Spaghetti Predicted: Spaghetti\n",
            "115 Video: 048_005_005.mp4 Expected: Spaghetti Predicted: Spaghetti\n",
            "116 \u001b[31mVideo: 048_006_005.mp4 Expected: Spaghetti Predicted: Dance\u001b[0m\n",
            "117 Video: 048_007_005.mp4 Expected: Spaghetti Predicted: Spaghetti\n",
            "118 Video: 048_008_005.mp4 Expected: Spaghetti Predicted: Spaghetti\n",
            "119 Video: 048_009_005.mp4 Expected: Spaghetti Predicted: Spaghetti\n",
            "120 Video: 048_010_005.mp4 Expected: Spaghetti Predicted: Spaghetti\n",
            "121 Video: 011_001_005.mp4 Expected: Son Predicted: Son\n",
            "122 \u001b[31mVideo: 011_002_005.mp4 Expected: Son Predicted: Colors\u001b[0m\n",
            "123 Video: 011_003_005.mp4 Expected: Son Predicted: Son\n",
            "124 Video: 011_004_005.mp4 Expected: Son Predicted: Son\n",
            "125 Video: 011_005_005.mp4 Expected: Son Predicted: Son\n",
            "126 \u001b[31mVideo: 011_006_005.mp4 Expected: Son Predicted: Women\u001b[0m\n",
            "127 \u001b[31mVideo: 011_007_005.mp4 Expected: Son Predicted: Yellow\u001b[0m\n",
            "128 \u001b[31mVideo: 011_008_005.mp4 Expected: Son Predicted: Bitter\u001b[0m\n",
            "129 Video: 011_009_005.mp4 Expected: Son Predicted: Son\n",
            "130 \u001b[31mVideo: 011_010_005.mp4 Expected: Son Predicted: Man\u001b[0m\n",
            "131 Video: 044_001_005.mp4 Expected: Rice Predicted: Rice\n",
            "132 Video: 044_002_005.mp4 Expected: Rice Predicted: Rice\n",
            "133 Video: 044_003_005.mp4 Expected: Rice Predicted: Rice\n",
            "134 Video: 044_004_005.mp4 Expected: Rice Predicted: Rice\n",
            "135 Video: 044_005_005.mp4 Expected: Rice Predicted: Rice\n",
            "136 Video: 044_006_005.mp4 Expected: Rice Predicted: Rice\n",
            "137 Video: 044_007_005.mp4 Expected: Rice Predicted: Rice\n",
            "138 Video: 044_008_005.mp4 Expected: Rice Predicted: Rice\n",
            "139 Video: 044_009_005.mp4 Expected: Rice Predicted: Rice\n",
            "140 Video: 044_010_005.mp4 Expected: Rice Predicted: Rice\n",
            "141 Video: 061_001_005.mp4 Expected: Run Predicted: Run\n",
            "142 Video: 061_002_005.mp4 Expected: Run Predicted: Run\n",
            "143 Video: 061_003_005.mp4 Expected: Run Predicted: Run\n",
            "144 Video: 061_004_005.mp4 Expected: Run Predicted: Run\n",
            "145 \u001b[31mVideo: 061_005_005.mp4 Expected: Run Predicted: Spaghetti\u001b[0m\n",
            "146 Video: 061_006_005.mp4 Expected: Run Predicted: Run\n",
            "147 Video: 061_007_005.mp4 Expected: Run Predicted: Run\n",
            "148 Video: 061_008_005.mp4 Expected: Run Predicted: Run\n",
            "149 Video: 061_009_005.mp4 Expected: Run Predicted: Run\n",
            "150 Video: 061_010_005.mp4 Expected: Run Predicted: Run\n",
            "151 \u001b[31mVideo: 020_001_005.mp4 Expected: Sweet milk Predicted: Bright\u001b[0m\n",
            "152 \u001b[31mVideo: 020_002_005.mp4 Expected: Sweet milk Predicted: Red\u001b[0m\n",
            "153 \u001b[31mVideo: 020_003_005.mp4 Expected: Sweet milk Predicted: Bright\u001b[0m\n",
            "154 Video: 020_004_005.mp4 Expected: Sweet milk Predicted: Sweet milk\n",
            "155 Video: 020_005_005.mp4 Expected: Sweet milk Predicted: Sweet milk\n",
            "156 Video: 020_006_005.mp4 Expected: Sweet milk Predicted: Sweet milk\n",
            "157 \u001b[31mVideo: 020_007_005.mp4 Expected: Sweet milk Predicted: Red\u001b[0m\n",
            "158 \u001b[31mVideo: 020_008_005.mp4 Expected: Sweet milk Predicted: Enemy\u001b[0m\n",
            "159 \u001b[31mVideo: 020_009_005.mp4 Expected: Sweet milk Predicted: Colors\u001b[0m\n",
            "160 Video: 020_010_005.mp4 Expected: Sweet milk Predicted: Sweet milk\n",
            "161 Video: 052_001_005.mp4 Expected: Shut down Predicted: Shut down\n",
            "162 Video: 052_002_005.mp4 Expected: Shut down Predicted: Shut down\n",
            "163 Video: 052_003_005.mp4 Expected: Shut down Predicted: Shut down\n",
            "164 Video: 052_004_005.mp4 Expected: Shut down Predicted: Shut down\n",
            "165 Video: 052_005_005.mp4 Expected: Shut down Predicted: Shut down\n",
            "166 \u001b[31mVideo: 052_006_005.mp4 Expected: Shut down Predicted: Candy\u001b[0m\n",
            "167 Video: 052_007_005.mp4 Expected: Shut down Predicted: Shut down\n",
            "168 \u001b[31mVideo: 052_008_005.mp4 Expected: Shut down Predicted: Name\u001b[0m\n",
            "169 Video: 052_009_005.mp4 Expected: Shut down Predicted: Shut down\n",
            "170 Video: 052_010_005.mp4 Expected: Shut down Predicted: Shut down\n",
            "171 Video: 036_001_005.mp4 Expected: Music Predicted: Music\n",
            "172 Video: 036_002_005.mp4 Expected: Music Predicted: Music\n",
            "173 Video: 036_003_005.mp4 Expected: Music Predicted: Music\n",
            "174 Video: 036_004_005.mp4 Expected: Music Predicted: Music\n",
            "175 Video: 036_005_005.mp4 Expected: Music Predicted: Music\n",
            "176 Video: 036_006_005.mp4 Expected: Music Predicted: Music\n",
            "177 Video: 036_007_005.mp4 Expected: Music Predicted: Music\n",
            "178 \u001b[31mVideo: 036_008_005.mp4 Expected: Music Predicted: Give\u001b[0m\n",
            "179 Video: 036_009_005.mp4 Expected: Music Predicted: Music\n",
            "180 \u001b[31mVideo: 036_010_005.mp4 Expected: Music Predicted: Photo\u001b[0m\n",
            "181 Video: 041_001_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "182 Video: 041_002_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "183 Video: 041_003_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "184 Video: 041_004_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "185 Video: 041_005_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "186 Video: 041_006_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "187 Video: 041_007_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "188 Video: 041_008_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "189 Video: 041_009_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "190 Video: 041_010_005.mp4 Expected: Perfume Predicted: Perfume\n",
            "191 Video: 029_001_005.mp4 Expected: Mock Predicted: Mock\n",
            "192 Video: 029_002_005.mp4 Expected: Mock Predicted: Mock\n",
            "193 Video: 029_003_005.mp4 Expected: Mock Predicted: Mock\n",
            "194 Video: 029_004_005.mp4 Expected: Mock Predicted: Mock\n",
            "195 Video: 029_005_005.mp4 Expected: Mock Predicted: Mock\n",
            "196 Video: 029_006_005.mp4 Expected: Mock Predicted: Mock\n",
            "197 Video: 029_007_005.mp4 Expected: Mock Predicted: Mock\n",
            "198 Video: 029_008_005.mp4 Expected: Mock Predicted: Mock\n",
            "199 Video: 029_009_005.mp4 Expected: Mock Predicted: Mock\n",
            "200 Video: 029_010_005.mp4 Expected: Mock Predicted: Mock\n",
            "201 Video: 039_001_005.mp4 Expected: Name Predicted: Name\n",
            "202 \u001b[31mVideo: 039_002_005.mp4 Expected: Name Predicted: Deaf\u001b[0m\n",
            "203 Video: 039_003_005.mp4 Expected: Name Predicted: Name\n",
            "204 Video: 039_004_005.mp4 Expected: Name Predicted: Name\n",
            "205 Video: 039_005_005.mp4 Expected: Name Predicted: Name\n",
            "206 Video: 039_006_005.mp4 Expected: Name Predicted: Name\n",
            "207 Video: 039_007_005.mp4 Expected: Name Predicted: Name\n",
            "208 Video: 039_008_005.mp4 Expected: Name Predicted: Name\n",
            "209 Video: 039_009_005.mp4 Expected: Name Predicted: Name\n",
            "210 Video: 039_010_005.mp4 Expected: Name Predicted: Name\n",
            "211 Video: 032_001_005.mp4 Expected: Photo Predicted: Photo\n",
            "212 Video: 032_002_005.mp4 Expected: Photo Predicted: Photo\n",
            "213 Video: 032_003_005.mp4 Expected: Photo Predicted: Photo\n",
            "214 Video: 032_004_005.mp4 Expected: Photo Predicted: Photo\n",
            "215 Video: 032_005_005.mp4 Expected: Photo Predicted: Photo\n",
            "216 Video: 032_006_005.mp4 Expected: Photo Predicted: Photo\n",
            "217 Video: 032_007_005.mp4 Expected: Photo Predicted: Photo\n",
            "218 Video: 032_008_005.mp4 Expected: Photo Predicted: Photo\n",
            "219 Video: 032_009_005.mp4 Expected: Photo Predicted: Photo\n",
            "220 Video: 032_010_005.mp4 Expected: Photo Predicted: Photo\n",
            "221 Video: 040_001_005.mp4 Expected: Patience Predicted: Patience\n",
            "222 Video: 040_002_005.mp4 Expected: Patience Predicted: Patience\n",
            "223 Video: 040_003_005.mp4 Expected: Patience Predicted: Patience\n",
            "224 \u001b[31mVideo: 040_004_005.mp4 Expected: Patience Predicted: Ship\u001b[0m\n",
            "225 Video: 040_005_005.mp4 Expected: Patience Predicted: Patience\n",
            "226 Video: 040_006_005.mp4 Expected: Patience Predicted: Patience\n",
            "227 \u001b[31mVideo: 040_007_005.mp4 Expected: Patience Predicted: Where\u001b[0m\n",
            "228 Video: 040_008_005.mp4 Expected: Patience Predicted: Patience\n",
            "229 \u001b[31mVideo: 040_009_005.mp4 Expected: Patience Predicted: Where\u001b[0m\n",
            "230 Video: 040_010_005.mp4 Expected: Patience Predicted: Patience\n",
            "231 Video: 001_001_005.mp4 Expected: Opaque Predicted: Opaque\n",
            "232 Video: 001_002_005.mp4 Expected: Opaque Predicted: Opaque\n",
            "233 Video: 001_003_005.mp4 Expected: Opaque Predicted: Opaque\n",
            "234 \u001b[31mVideo: 001_004_005.mp4 Expected: Opaque Predicted: Away\u001b[0m\n",
            "235 \u001b[31mVideo: 001_005_005.mp4 Expected: Opaque Predicted: Bright\u001b[0m\n",
            "236 Video: 001_006_005.mp4 Expected: Opaque Predicted: Opaque\n",
            "237 Video: 001_007_005.mp4 Expected: Opaque Predicted: Opaque\n",
            "238 Video: 001_008_005.mp4 Expected: Opaque Predicted: Opaque\n",
            "239 Video: 001_009_005.mp4 Expected: Opaque Predicted: Opaque\n",
            "240 Video: 001_010_005.mp4 Expected: Opaque Predicted: Opaque\n",
            "241 Video: 038_001_005.mp4 Expected: None Predicted: None\n",
            "242 Video: 038_002_005.mp4 Expected: None Predicted: None\n",
            "243 Video: 038_003_005.mp4 Expected: None Predicted: None\n",
            "244 \u001b[31mVideo: 038_004_005.mp4 Expected: None Predicted: Deaf\u001b[0m\n",
            "245 Video: 038_005_005.mp4 Expected: None Predicted: None\n",
            "246 \u001b[31mVideo: 038_006_005.mp4 Expected: None Predicted: Chewing-gum\u001b[0m\n",
            "247 Video: 038_007_005.mp4 Expected: None Predicted: None\n",
            "248 \u001b[31mVideo: 038_008_005.mp4 Expected: None Predicted: Name\u001b[0m\n",
            "249 Video: 038_009_005.mp4 Expected: None Predicted: None\n",
            "250 Video: 038_010_005.mp4 Expected: None Predicted: None\n",
            "251 Video: 002_001_005.mp4 Expected: Red Predicted: Red\n",
            "252 \u001b[31mVideo: 002_002_005.mp4 Expected: Red Predicted: Green\u001b[0m\n",
            "253 Video: 002_003_005.mp4 Expected: Red Predicted: Red\n",
            "254 \u001b[31mVideo: 002_004_005.mp4 Expected: Red Predicted: Green\u001b[0m\n",
            "255 \u001b[31mVideo: 002_005_005.mp4 Expected: Red Predicted: Green\u001b[0m\n",
            "256 Video: 002_006_005.mp4 Expected: Red Predicted: Red\n",
            "257 \u001b[31mVideo: 002_007_005.mp4 Expected: Red Predicted: Green\u001b[0m\n",
            "258 Video: 002_008_005.mp4 Expected: Red Predicted: Red\n",
            "259 Video: 002_009_005.mp4 Expected: Red Predicted: Red\n",
            "260 \u001b[31mVideo: 002_010_005.mp4 Expected: Red Predicted: Green\u001b[0m\n",
            "261 Video: 062_001_005.mp4 Expected: Realize Predicted: Realize\n",
            "262 Video: 062_002_005.mp4 Expected: Realize Predicted: Realize\n",
            "263 Video: 062_003_005.mp4 Expected: Realize Predicted: Realize\n",
            "264 \u001b[31mVideo: 062_004_005.mp4 Expected: Realize Predicted: Country\u001b[0m\n",
            "265 Video: 062_005_005.mp4 Expected: Realize Predicted: Realize\n",
            "266 Video: 062_006_005.mp4 Expected: Realize Predicted: Realize\n",
            "267 Video: 062_007_005.mp4 Expected: Realize Predicted: Realize\n",
            "268 Video: 062_008_005.mp4 Expected: Realize Predicted: Realize\n",
            "269 Video: 062_009_005.mp4 Expected: Realize Predicted: Realize\n",
            "270 Video: 062_010_005.mp4 Expected: Realize Predicted: Realize\n",
            "271 \u001b[31mVideo: 003_001_005.mp4 Expected: Green Predicted: Enemy\u001b[0m\n",
            "272 Video: 003_002_005.mp4 Expected: Green Predicted: Green\n",
            "273 \u001b[31mVideo: 003_003_005.mp4 Expected: Green Predicted: Red\u001b[0m\n",
            "274 \u001b[31mVideo: 003_004_005.mp4 Expected: Green Predicted: Red\u001b[0m\n",
            "275 Video: 003_005_005.mp4 Expected: Green Predicted: Green\n",
            "276 Video: 003_006_005.mp4 Expected: Green Predicted: Green\n",
            "277 Video: 003_007_005.mp4 Expected: Green Predicted: Green\n",
            "278 Video: 003_008_005.mp4 Expected: Green Predicted: Green\n",
            "279 Video: 003_009_005.mp4 Expected: Green Predicted: Green\n",
            "280 Video: 003_010_005.mp4 Expected: Green Predicted: Green\n",
            "281 Video: 012_001_005.mp4 Expected: Man Predicted: Man\n",
            "282 Video: 012_002_005.mp4 Expected: Man Predicted: Man\n",
            "283 Video: 012_003_005.mp4 Expected: Man Predicted: Man\n",
            "284 Video: 012_004_005.mp4 Expected: Man Predicted: Man\n",
            "285 Video: 012_005_005.mp4 Expected: Man Predicted: Man\n",
            "286 Video: 012_006_005.mp4 Expected: Man Predicted: Man\n",
            "287 Video: 012_007_005.mp4 Expected: Man Predicted: Man\n",
            "288 Video: 012_008_005.mp4 Expected: Man Predicted: Man\n",
            "289 Video: 012_009_005.mp4 Expected: Man Predicted: Man\n",
            "290 Video: 012_010_005.mp4 Expected: Man Predicted: Man\n",
            "291 \u001b[31mVideo: 021_001_005.mp4 Expected: Milk Predicted: Bright\u001b[0m\n",
            "292 Video: 021_002_005.mp4 Expected: Milk Predicted: Milk\n",
            "293 Video: 021_003_005.mp4 Expected: Milk Predicted: Milk\n",
            "294 Video: 021_004_005.mp4 Expected: Milk Predicted: Milk\n",
            "295 \u001b[31mVideo: 021_005_005.mp4 Expected: Milk Predicted: Bright\u001b[0m\n",
            "296 \u001b[31mVideo: 021_006_005.mp4 Expected: Milk Predicted: Sweet milk\u001b[0m\n",
            "297 \u001b[31mVideo: 021_007_005.mp4 Expected: Milk Predicted: Away\u001b[0m\n",
            "298 \u001b[31mVideo: 021_008_005.mp4 Expected: Milk Predicted: Bright\u001b[0m\n",
            "299 Video: 021_009_005.mp4 Expected: Milk Predicted: Milk\n",
            "300 Video: 021_010_005.mp4 Expected: Milk Predicted: Milk\n",
            "301 Video: 063_001_005.mp4 Expected: Give Predicted: Give\n",
            "302 Video: 063_002_005.mp4 Expected: Give Predicted: Give\n",
            "303 Video: 063_003_005.mp4 Expected: Give Predicted: Give\n",
            "304 Video: 063_004_005.mp4 Expected: Give Predicted: Give\n",
            "305 Video: 063_005_005.mp4 Expected: Give Predicted: Give\n",
            "306 Video: 063_006_005.mp4 Expected: Give Predicted: Give\n",
            "307 Video: 063_007_005.mp4 Expected: Give Predicted: Give\n",
            "308 Video: 063_008_005.mp4 Expected: Give Predicted: Give\n",
            "309 Video: 063_009_005.mp4 Expected: Give Predicted: Give\n",
            "310 Video: 063_010_005.mp4 Expected: Give Predicted: Give\n",
            "311 Video: 056_001_005.mp4 Expected: Help Predicted: Help\n",
            "312 Video: 056_002_005.mp4 Expected: Help Predicted: Help\n",
            "313 Video: 056_003_005.mp4 Expected: Help Predicted: Help\n",
            "314 Video: 056_004_005.mp4 Expected: Help Predicted: Help\n",
            "315 Video: 056_005_005.mp4 Expected: Help Predicted: Help\n",
            "316 Video: 056_006_005.mp4 Expected: Help Predicted: Help\n",
            "317 Video: 056_007_005.mp4 Expected: Help Predicted: Help\n",
            "318 Video: 056_008_005.mp4 Expected: Help Predicted: Help\n",
            "319 Video: 056_009_005.mp4 Expected: Help Predicted: Help\n",
            "320 Video: 056_010_005.mp4 Expected: Help Predicted: Help\n",
            "321 Video: 006_001_005.mp4 Expected: Light-blue Predicted: Light-blue\n",
            "322 Video: 006_002_005.mp4 Expected: Light-blue Predicted: Light-blue\n",
            "323 Video: 006_003_005.mp4 Expected: Light-blue Predicted: Light-blue\n",
            "324 Video: 006_004_005.mp4 Expected: Light-blue Predicted: Light-blue\n",
            "325 Video: 006_005_005.mp4 Expected: Light-blue Predicted: Light-blue\n",
            "326 Video: 006_006_005.mp4 Expected: Light-blue Predicted: Light-blue\n",
            "327 \u001b[31mVideo: 006_007_005.mp4 Expected: Light-blue Predicted: Man\u001b[0m\n",
            "328 Video: 006_008_005.mp4 Expected: Light-blue Predicted: Light-blue\n",
            "329 Video: 006_009_005.mp4 Expected: Light-blue Predicted: Light-blue\n",
            "330 \u001b[31mVideo: 006_010_005.mp4 Expected: Light-blue Predicted: Bitter\u001b[0m\n",
            "331 Video: 033_001_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "332 Video: 033_002_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "333 Video: 033_003_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "334 Video: 033_004_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "335 Video: 033_005_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "336 Video: 033_006_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "337 Video: 033_007_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "338 Video: 033_008_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "339 Video: 033_009_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "340 Video: 033_010_005.mp4 Expected: Hungry Predicted: Hungry\n",
            "341 Video: 016_001_005.mp4 Expected: Learn Predicted: Learn\n",
            "342 Video: 016_002_005.mp4 Expected: Learn Predicted: Learn\n",
            "343 Video: 016_003_005.mp4 Expected: Learn Predicted: Learn\n",
            "344 Video: 016_004_005.mp4 Expected: Learn Predicted: Learn\n",
            "345 Video: 016_005_005.mp4 Expected: Learn Predicted: Learn\n",
            "346 Video: 016_006_005.mp4 Expected: Learn Predicted: Learn\n",
            "347 \u001b[31mVideo: 016_007_005.mp4 Expected: Learn Predicted: Away\u001b[0m\n",
            "348 Video: 016_008_005.mp4 Expected: Learn Predicted: Learn\n",
            "349 Video: 016_009_005.mp4 Expected: Learn Predicted: Learn\n",
            "350 Video: 016_010_005.mp4 Expected: Learn Predicted: Learn\n",
            "351 Video: 034_001_005.mp4 Expected: Map Predicted: Map\n",
            "352 Video: 034_002_005.mp4 Expected: Map Predicted: Map\n",
            "353 Video: 034_003_005.mp4 Expected: Map Predicted: Map\n",
            "354 Video: 034_004_005.mp4 Expected: Map Predicted: Map\n",
            "355 Video: 034_005_005.mp4 Expected: Map Predicted: Map\n",
            "356 \u001b[31mVideo: 034_006_005.mp4 Expected: Map Predicted: Mock\u001b[0m\n",
            "357 Video: 034_007_005.mp4 Expected: Map Predicted: Map\n",
            "358 Video: 034_008_005.mp4 Expected: Map Predicted: Map\n",
            "359 Video: 034_009_005.mp4 Expected: Map Predicted: Map\n",
            "360 Video: 034_010_005.mp4 Expected: Map Predicted: Map\n",
            "361 Video: 007_001_005.mp4 Expected: Colors Predicted: Colors\n",
            "362 Video: 007_002_005.mp4 Expected: Colors Predicted: Colors\n",
            "363 Video: 007_003_005.mp4 Expected: Colors Predicted: Colors\n",
            "364 Video: 007_004_005.mp4 Expected: Colors Predicted: Colors\n",
            "365 Video: 007_005_005.mp4 Expected: Colors Predicted: Colors\n",
            "366 Video: 007_006_005.mp4 Expected: Colors Predicted: Colors\n",
            "367 \u001b[31mVideo: 007_007_005.mp4 Expected: Colors Predicted: Man\u001b[0m\n",
            "368 Video: 007_008_005.mp4 Expected: Colors Predicted: Colors\n",
            "369 \u001b[31mVideo: 007_009_005.mp4 Expected: Colors Predicted: Food\u001b[0m\n",
            "370 \u001b[31mVideo: 007_010_005.mp4 Expected: Colors Predicted: Man\u001b[0m\n",
            "371 Video: 023_001_005.mp4 Expected: Food Predicted: Food\n",
            "372 Video: 023_002_005.mp4 Expected: Food Predicted: Food\n",
            "373 Video: 023_003_005.mp4 Expected: Food Predicted: Food\n",
            "374 Video: 023_004_005.mp4 Expected: Food Predicted: Food\n",
            "375 Video: 023_005_005.mp4 Expected: Food Predicted: Food\n",
            "376 \u001b[31mVideo: 023_006_005.mp4 Expected: Food Predicted: Bitter\u001b[0m\n",
            "377 Video: 023_007_005.mp4 Expected: Food Predicted: Food\n",
            "378 Video: 023_008_005.mp4 Expected: Food Predicted: Food\n",
            "379 Video: 023_009_005.mp4 Expected: Food Predicted: Food\n",
            "380 Video: 023_010_005.mp4 Expected: Food Predicted: Food\n",
            "381 Video: 014_001_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "382 Video: 014_002_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "383 Video: 014_003_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "384 Video: 014_004_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "385 Video: 014_005_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "386 Video: 014_006_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "387 Video: 014_007_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "388 Video: 014_008_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "389 Video: 014_009_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "390 Video: 014_010_005.mp4 Expected: Drawer Predicted: Drawer\n",
            "391 Video: 057_001_005.mp4 Expected: Dance Predicted: Dance\n",
            "392 Video: 057_002_005.mp4 Expected: Dance Predicted: Dance\n",
            "393 \u001b[31mVideo: 057_003_005.mp4 Expected: Dance Predicted: Trap\u001b[0m\n",
            "394 Video: 057_004_005.mp4 Expected: Dance Predicted: Dance\n",
            "395 Video: 057_005_005.mp4 Expected: Dance Predicted: Dance\n",
            "396 \u001b[31mVideo: 057_006_005.mp4 Expected: Dance Predicted: Trap\u001b[0m\n",
            "397 Video: 057_007_005.mp4 Expected: Dance Predicted: Dance\n",
            "398 Video: 057_008_005.mp4 Expected: Dance Predicted: Dance\n",
            "399 Video: 057_009_005.mp4 Expected: Dance Predicted: Dance\n",
            "400 Video: 057_010_005.mp4 Expected: Dance Predicted: Dance\n",
            "401 \u001b[31mVideo: 010_001_005.mp4 Expected: Enemy Predicted: Colors\u001b[0m\n",
            "402 Video: 010_002_005.mp4 Expected: Enemy Predicted: Enemy\n",
            "403 Video: 010_003_005.mp4 Expected: Enemy Predicted: Enemy\n",
            "404 \u001b[31mVideo: 010_004_005.mp4 Expected: Enemy Predicted: Bitter\u001b[0m\n",
            "405 Video: 010_005_005.mp4 Expected: Enemy Predicted: Enemy\n",
            "406 \u001b[31mVideo: 010_006_005.mp4 Expected: Enemy Predicted: Green\u001b[0m\n",
            "407 \u001b[31mVideo: 010_007_005.mp4 Expected: Enemy Predicted: Away\u001b[0m\n",
            "408 \u001b[31mVideo: 010_008_005.mp4 Expected: Enemy Predicted: Water\u001b[0m\n",
            "409 \u001b[31mVideo: 010_009_005.mp4 Expected: Enemy Predicted: Bright\u001b[0m\n",
            "410 \u001b[31mVideo: 010_010_005.mp4 Expected: Enemy Predicted: Red_2\u001b[0m\n",
            "411 Video: 064_001_005.mp4 Expected: Find Predicted: Find\n",
            "412 Video: 064_002_005.mp4 Expected: Find Predicted: Find\n",
            "413 Video: 064_003_005.mp4 Expected: Find Predicted: Find\n",
            "414 Video: 064_004_005.mp4 Expected: Find Predicted: Find\n",
            "415 Video: 064_005_005.mp4 Expected: Find Predicted: Find\n",
            "416 Video: 064_006_005.mp4 Expected: Find Predicted: Find\n",
            "417 Video: 064_007_005.mp4 Expected: Find Predicted: Find\n",
            "418 Video: 064_008_005.mp4 Expected: Find Predicted: Find\n",
            "419 Video: 064_009_005.mp4 Expected: Find Predicted: Find\n",
            "420 Video: 064_010_005.mp4 Expected: Find Predicted: Find\n",
            "421 Video: 035_001_005.mp4 Expected: Coin Predicted: Coin\n",
            "422 Video: 035_002_005.mp4 Expected: Coin Predicted: Coin\n",
            "423 Video: 035_003_005.mp4 Expected: Coin Predicted: Coin\n",
            "424 Video: 035_004_005.mp4 Expected: Coin Predicted: Coin\n",
            "425 Video: 035_005_005.mp4 Expected: Coin Predicted: Coin\n",
            "426 Video: 035_006_005.mp4 Expected: Coin Predicted: Coin\n",
            "427 Video: 035_007_005.mp4 Expected: Coin Predicted: Coin\n",
            "428 Video: 035_008_005.mp4 Expected: Coin Predicted: Coin\n",
            "429 Video: 035_009_005.mp4 Expected: Coin Predicted: Coin\n",
            "430 Video: 035_010_005.mp4 Expected: Coin Predicted: Coin\n",
            "431 Video: 042_001_005.mp4 Expected: Deaf Predicted: Deaf\n",
            "432 Video: 042_002_005.mp4 Expected: Deaf Predicted: Deaf\n",
            "433 Video: 042_003_005.mp4 Expected: Deaf Predicted: Deaf\n",
            "434 Video: 042_004_005.mp4 Expected: Deaf Predicted: Deaf\n",
            "435 Video: 042_005_005.mp4 Expected: Deaf Predicted: Deaf\n",
            "436 Video: 042_006_005.mp4 Expected: Deaf Predicted: Deaf\n",
            "437 Video: 042_007_005.mp4 Expected: Deaf Predicted: Deaf\n",
            "438 Video: 042_008_005.mp4 Expected: Deaf Predicted: Deaf\n",
            "439 Video: 042_009_005.mp4 Expected: Deaf Predicted: Deaf\n",
            "440 \u001b[31mVideo: 042_010_005.mp4 Expected: Deaf Predicted: Candy\u001b[0m\n",
            "441 Video: 026_001_005.mp4 Expected: Country Predicted: Country\n",
            "442 \u001b[31mVideo: 026_002_005.mp4 Expected: Country Predicted: Buy\u001b[0m\n",
            "443 Video: 026_003_005.mp4 Expected: Country Predicted: Country\n",
            "444 Video: 026_004_005.mp4 Expected: Country Predicted: Country\n",
            "445 Video: 026_005_005.mp4 Expected: Country Predicted: Country\n",
            "446 Video: 026_006_005.mp4 Expected: Country Predicted: Country\n",
            "447 Video: 026_007_005.mp4 Expected: Country Predicted: Country\n",
            "448 Video: 026_008_005.mp4 Expected: Country Predicted: Country\n",
            "449 Video: 026_009_005.mp4 Expected: Country Predicted: Country\n",
            "450 Video: 026_010_005.mp4 Expected: Country Predicted: Country\n",
            "451 Video: 060_001_005.mp4 Expected: Copy Predicted: Copy\n",
            "452 Video: 060_002_005.mp4 Expected: Copy Predicted: Copy\n",
            "453 \u001b[31mVideo: 060_003_005.mp4 Expected: Copy Predicted: Rice\u001b[0m\n",
            "454 \u001b[31mVideo: 060_004_005.mp4 Expected: Copy Predicted: Music\u001b[0m\n",
            "455 Video: 060_005_005.mp4 Expected: Copy Predicted: Copy\n",
            "456 Video: 060_006_005.mp4 Expected: Copy Predicted: Copy\n",
            "457 Video: 060_007_005.mp4 Expected: Copy Predicted: Copy\n",
            "458 Video: 060_008_005.mp4 Expected: Copy Predicted: Copy\n",
            "459 Video: 060_009_005.mp4 Expected: Copy Predicted: Copy\n",
            "460 Video: 060_010_005.mp4 Expected: Copy Predicted: Copy\n",
            "461 Video: 019_001_005.mp4 Expected: Bitter Predicted: Bitter\n",
            "462 \u001b[31mVideo: 019_002_005.mp4 Expected: Bitter Predicted: Opaque\u001b[0m\n",
            "463 \u001b[31mVideo: 019_003_005.mp4 Expected: Bitter Predicted: Opaque\u001b[0m\n",
            "464 Video: 019_004_005.mp4 Expected: Bitter Predicted: Bitter\n",
            "465 Video: 019_005_005.mp4 Expected: Bitter Predicted: Bitter\n",
            "466 \u001b[31mVideo: 019_006_005.mp4 Expected: Bitter Predicted: Bright\u001b[0m\n",
            "467 \u001b[31mVideo: 019_007_005.mp4 Expected: Bitter Predicted: Man\u001b[0m\n",
            "468 Video: 019_008_005.mp4 Expected: Bitter Predicted: Bitter\n",
            "469 \u001b[31mVideo: 019_009_005.mp4 Expected: Bitter Predicted: Bright\u001b[0m\n",
            "470 \u001b[31mVideo: 019_010_005.mp4 Expected: Bitter Predicted: Bright\u001b[0m\n",
            "471 Video: 015_001_005.mp4 Expected: Born Predicted: Born\n",
            "472 Video: 015_002_005.mp4 Expected: Born Predicted: Born\n",
            "473 Video: 015_003_005.mp4 Expected: Born Predicted: Born\n",
            "474 Video: 015_004_005.mp4 Expected: Born Predicted: Born\n",
            "475 Video: 015_005_005.mp4 Expected: Born Predicted: Born\n",
            "476 Video: 015_006_005.mp4 Expected: Born Predicted: Born\n",
            "477 Video: 015_007_005.mp4 Expected: Born Predicted: Born\n",
            "478 Video: 015_008_005.mp4 Expected: Born Predicted: Born\n",
            "479 Video: 015_009_005.mp4 Expected: Born Predicted: Born\n",
            "480 Video: 015_010_005.mp4 Expected: Born Predicted: Born\n",
            "481 \u001b[31mVideo: 005_001_005.mp4 Expected: Bright Predicted: Bitter\u001b[0m\n",
            "482 \u001b[31mVideo: 005_002_005.mp4 Expected: Bright Predicted: Opaque\u001b[0m\n",
            "483 Video: 005_003_005.mp4 Expected: Bright Predicted: Bright\n",
            "484 Video: 005_004_005.mp4 Expected: Bright Predicted: Bright\n",
            "485 Video: 005_005_005.mp4 Expected: Bright Predicted: Bright\n",
            "486 Video: 005_006_005.mp4 Expected: Bright Predicted: Bright\n",
            "487 \u001b[31mVideo: 005_007_005.mp4 Expected: Bright Predicted: Colors\u001b[0m\n",
            "488 \u001b[31mVideo: 005_008_005.mp4 Expected: Bright Predicted: Skimmer\u001b[0m\n",
            "489 \u001b[31mVideo: 005_009_005.mp4 Expected: Bright Predicted: Skimmer\u001b[0m\n",
            "490 \u001b[31mVideo: 005_010_005.mp4 Expected: Bright Predicted: Skimmer\u001b[0m\n",
            "491 Video: 031_001_005.mp4 Expected: Breakfast Predicted: Breakfast\n",
            "492 Video: 031_002_005.mp4 Expected: Breakfast Predicted: Breakfast\n",
            "493 Video: 031_003_005.mp4 Expected: Breakfast Predicted: Breakfast\n",
            "494 Video: 031_004_005.mp4 Expected: Breakfast Predicted: Breakfast\n",
            "495 Video: 031_005_005.mp4 Expected: Breakfast Predicted: Breakfast\n",
            "496 \u001b[31mVideo: 031_006_005.mp4 Expected: Breakfast Predicted: Catch\u001b[0m\n",
            "497 Video: 031_007_005.mp4 Expected: Breakfast Predicted: Breakfast\n",
            "498 Video: 031_008_005.mp4 Expected: Breakfast Predicted: Breakfast\n",
            "499 Video: 031_009_005.mp4 Expected: Breakfast Predicted: Breakfast\n",
            "500 Video: 031_010_005.mp4 Expected: Breakfast Predicted: Breakfast\n",
            "501 Video: 046_001_005.mp4 Expected: Candy Predicted: Candy\n",
            "502 Video: 046_002_005.mp4 Expected: Candy Predicted: Candy\n",
            "503 Video: 046_003_005.mp4 Expected: Candy Predicted: Candy\n",
            "504 Video: 046_004_005.mp4 Expected: Candy Predicted: Candy\n",
            "505 Video: 046_005_005.mp4 Expected: Candy Predicted: Candy\n",
            "506 Video: 046_006_005.mp4 Expected: Candy Predicted: Candy\n",
            "507 Video: 046_007_005.mp4 Expected: Candy Predicted: Candy\n",
            "508 Video: 046_008_005.mp4 Expected: Candy Predicted: Candy\n",
            "509 Video: 046_009_005.mp4 Expected: Candy Predicted: Candy\n",
            "510 Video: 046_010_005.mp4 Expected: Candy Predicted: Candy\n",
            "511 Video: 030_001_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "512 Video: 030_002_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "513 Video: 030_003_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "514 Video: 030_004_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "515 Video: 030_005_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "516 Video: 030_006_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "517 Video: 030_007_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "518 Video: 030_008_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "519 Video: 030_009_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "520 Video: 030_010_005.mp4 Expected: Birthday Predicted: Birthday\n",
            "521 Video: 055_001_005.mp4 Expected: Catch Predicted: Catch\n",
            "522 \u001b[31mVideo: 055_002_005.mp4 Expected: Catch Predicted: Give\u001b[0m\n",
            "523 Video: 055_003_005.mp4 Expected: Catch Predicted: Catch\n",
            "524 Video: 055_004_005.mp4 Expected: Catch Predicted: Catch\n",
            "525 Video: 055_005_005.mp4 Expected: Catch Predicted: Catch\n",
            "526 Video: 055_006_005.mp4 Expected: Catch Predicted: Catch\n",
            "527 Video: 055_007_005.mp4 Expected: Catch Predicted: Catch\n",
            "528 Video: 055_008_005.mp4 Expected: Catch Predicted: Catch\n",
            "529 Video: 055_009_005.mp4 Expected: Catch Predicted: Catch\n",
            "530 \u001b[31mVideo: 055_010_005.mp4 Expected: Catch Predicted: Coin\u001b[0m\n",
            "531 Video: 059_001_005.mp4 Expected: Buy Predicted: Buy\n",
            "532 Video: 059_002_005.mp4 Expected: Buy Predicted: Buy\n",
            "533 \u001b[31mVideo: 059_003_005.mp4 Expected: Buy Predicted: Country\u001b[0m\n",
            "534 Video: 059_004_005.mp4 Expected: Buy Predicted: Buy\n",
            "535 Video: 059_005_005.mp4 Expected: Buy Predicted: Buy\n",
            "536 Video: 059_006_005.mp4 Expected: Buy Predicted: Buy\n",
            "537 \u001b[31mVideo: 059_007_005.mp4 Expected: Buy Predicted: Hungry\u001b[0m\n",
            "538 Video: 059_008_005.mp4 Expected: Buy Predicted: Buy\n",
            "539 \u001b[31mVideo: 059_009_005.mp4 Expected: Buy Predicted: Country\u001b[0m\n",
            "540 Video: 059_010_005.mp4 Expected: Buy Predicted: Buy\n",
            "541 Video: 017_001_005.mp4 Expected: Call Predicted: Call\n",
            "542 Video: 017_002_005.mp4 Expected: Call Predicted: Call\n",
            "543 Video: 017_003_005.mp4 Expected: Call Predicted: Call\n",
            "544 Video: 017_004_005.mp4 Expected: Call Predicted: Call\n",
            "545 \u001b[31mVideo: 017_005_005.mp4 Expected: Call Predicted: Water\u001b[0m\n",
            "546 Video: 017_006_005.mp4 Expected: Call Predicted: Call\n",
            "547 Video: 017_007_005.mp4 Expected: Call Predicted: Call\n",
            "548 \u001b[31mVideo: 017_008_005.mp4 Expected: Call Predicted: Away\u001b[0m\n",
            "549 \u001b[31mVideo: 017_009_005.mp4 Expected: Call Predicted: Water\u001b[0m\n",
            "550 Video: 017_010_005.mp4 Expected: Call Predicted: Call\n",
            "551 Video: 047_001_005.mp4 Expected: Chewing-gum Predicted: Chewing-gum\n",
            "552 Video: 047_002_005.mp4 Expected: Chewing-gum Predicted: Chewing-gum\n",
            "553 Video: 047_003_005.mp4 Expected: Chewing-gum Predicted: Chewing-gum\n",
            "554 Video: 047_004_005.mp4 Expected: Chewing-gum Predicted: Chewing-gum\n",
            "555 Video: 047_005_005.mp4 Expected: Chewing-gum Predicted: Chewing-gum\n",
            "556 \u001b[31mVideo: 047_006_005.mp4 Expected: Chewing-gum Predicted: None\u001b[0m\n",
            "557 Video: 047_007_005.mp4 Expected: Chewing-gum Predicted: Chewing-gum\n",
            "558 Video: 047_008_005.mp4 Expected: Chewing-gum Predicted: Chewing-gum\n",
            "559 \u001b[31mVideo: 047_009_005.mp4 Expected: Chewing-gum Predicted: Find\u001b[0m\n",
            "560 Video: 047_010_005.mp4 Expected: Chewing-gum Predicted: Chewing-gum\n",
            "561 Video: 053_001_005.mp4 Expected: Appear Predicted: Appear\n",
            "562 Video: 053_002_005.mp4 Expected: Appear Predicted: Appear\n",
            "563 Video: 053_003_005.mp4 Expected: Appear Predicted: Appear\n",
            "564 \u001b[31mVideo: 053_004_005.mp4 Expected: Appear Predicted: Trap\u001b[0m\n",
            "565 Video: 053_005_005.mp4 Expected: Appear Predicted: Appear\n",
            "566 Video: 053_006_005.mp4 Expected: Appear Predicted: Appear\n",
            "567 Video: 053_007_005.mp4 Expected: Appear Predicted: Appear\n",
            "568 Video: 053_008_005.mp4 Expected: Appear Predicted: Appear\n",
            "569 Video: 053_009_005.mp4 Expected: Appear Predicted: Appear\n",
            "570 Video: 053_010_005.mp4 Expected: Appear Predicted: Appear\n",
            "571 Video: 024_001_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "572 Video: 024_002_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "573 Video: 024_003_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "574 Video: 024_004_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "575 Video: 024_005_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "576 Video: 024_006_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "577 Video: 024_007_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "578 Video: 024_008_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "579 Video: 024_009_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "580 Video: 024_010_005.mp4 Expected: Argentina Predicted: Argentina\n",
            "581 Video: 058_001_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "582 Video: 058_002_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "583 Video: 058_003_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "584 Video: 058_004_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "585 Video: 058_005_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "586 Video: 058_006_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "587 Video: 058_007_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "588 Video: 058_008_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "589 Video: 058_009_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "590 Video: 058_010_005.mp4 Expected: Bathe Predicted: Bathe\n",
            "591 Video: 045_001_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "592 Video: 045_002_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "593 Video: 045_003_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "594 Video: 045_004_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "595 Video: 045_005_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "596 Video: 045_006_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "597 Video: 045_007_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "598 Video: 045_008_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "599 Video: 045_009_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "600 Video: 045_010_005.mp4 Expected: Barbecue Predicted: Barbecue\n",
            "601 Video: 050_001_005.mp4 Expected: Accept Predicted: Accept\n",
            "602 Video: 050_002_005.mp4 Expected: Accept Predicted: Accept\n",
            "603 Video: 050_003_005.mp4 Expected: Accept Predicted: Accept\n",
            "604 Video: 050_004_005.mp4 Expected: Accept Predicted: Accept\n",
            "605 Video: 050_005_005.mp4 Expected: Accept Predicted: Accept\n",
            "606 Video: 050_006_005.mp4 Expected: Accept Predicted: Accept\n",
            "607 Video: 050_007_005.mp4 Expected: Accept Predicted: Accept\n",
            "608 Video: 050_008_005.mp4 Expected: Accept Predicted: Accept\n",
            "609 \u001b[31mVideo: 050_009_005.mp4 Expected: Accept Predicted: Spaghetti\u001b[0m\n",
            "610 Video: 050_010_005.mp4 Expected: Accept Predicted: Accept\n",
            "611 Video: 013_001_005.mp4 Expected: Away Predicted: Away\n",
            "612 Video: 013_002_005.mp4 Expected: Away Predicted: Away\n",
            "613 Video: 013_003_005.mp4 Expected: Away Predicted: Away\n",
            "614 Video: 013_004_005.mp4 Expected: Away Predicted: Away\n",
            "615 Video: 013_005_005.mp4 Expected: Away Predicted: Away\n",
            "616 Video: 013_006_005.mp4 Expected: Away Predicted: Away\n",
            "617 Video: 013_007_005.mp4 Expected: Away Predicted: Away\n",
            "618 Video: 013_008_005.mp4 Expected: Away Predicted: Away\n",
            "619 \u001b[31mVideo: 013_009_005.mp4 Expected: Away Predicted: Bright\u001b[0m\n",
            "620 Video: 013_010_005.mp4 Expected: Away Predicted: Away\n",
            "621 Video: 008_001_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "622 Video: 008_002_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "623 Video: 008_003_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "624 Video: 008_004_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "625 Video: 008_005_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "626 Video: 008_006_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "627 Video: 008_007_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "628 Video: 008_008_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "629 Video: 008_009_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "630 Video: 008_010_005.mp4 Expected: Red_2 Predicted: Red_2\n",
            "631 Video: 027_001_005.mp4 Expected: Last name Predicted: Last name\n",
            "632 \u001b[31mVideo: 027_002_005.mp4 Expected: Last name Predicted: Ship\u001b[0m\n",
            "633 Video: 027_003_005.mp4 Expected: Last name Predicted: Last name\n",
            "634 Video: 027_004_005.mp4 Expected: Last name Predicted: Last name\n",
            "635 Video: 027_005_005.mp4 Expected: Last name Predicted: Last name\n",
            "636 Video: 027_006_005.mp4 Expected: Last name Predicted: Last name\n",
            "637 \u001b[31mVideo: 027_007_005.mp4 Expected: Last name Predicted: Deaf\u001b[0m\n",
            "638 \u001b[31mVideo: 027_008_005.mp4 Expected: Last name Predicted: Country\u001b[0m\n",
            "639 Video: 027_009_005.mp4 Expected: Last name Predicted: Last name\n",
            "640 Video: 027_010_005.mp4 Expected: Last name Predicted: Last name\n",
            "\n",
            "Accuray: 530/640 , 0.828125\n",
            "time: 966.1584324836731\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVMwsF3WchUj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "a0adc5a6-832e-46f5-c870-b1a1954afdf9"
      },
      "source": [
        "sorted(res)\n",
        "ticks=range(len(res))\n",
        "height=list(res.values())\n",
        "tick_label=list(res.keys())\n",
        "\n",
        "fig = plt.figure(figsize=(12, 12))\n",
        "plt.barh(ticks,height,tick_label=tick_label,color=['b','m'],height=0.5, align='center')\n",
        "plt.title(\"Accuray on Test\")\n",
        "plt.ylabel(\"Word\")\n",
        "plt.xlabel(\"Number right / total\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZ0TsPK9LW0M",
        "outputId": "7d2ae3fb-a0a4-421c-fda8-e59753e0bbc1"
      },
      "source": [
        "a = np.array(list(res.values()))\n",
        "print(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[10 10 10 10 10 10  9 10 10 10 10 10  9 10 10 10  9 10 10 10 10 10 10  9\n",
            " 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10  9 10\n",
            "  9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8L-fwATKUsS",
        "outputId": "d024e21c-ee45-419a-da95-8a55e03c7c4e"
      },
      "source": [
        "# exchange result dict to confusion array\n",
        "a = np.zeros((64,64), dtype=int)\n",
        "a_true = list(res.keys())\n",
        "a_pred = list(res.keys())\n",
        "print(a_true)\n",
        "\n",
        "matrix = list(res.values())\n",
        "for w_class in a_true:\n",
        "  for word in list(res[w_class]):\n",
        "    a[a_true.index(w_class)][a_true.index(word)] = res[w_class][word]\n",
        "\n",
        "print(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Yogurt', 'Women', 'Where', 'Trap', 'Uruguay', 'Yellow', 'Water', 'Thanks', 'Skimmer', 'Ship', 'To land', 'Spaghetti', 'Son', 'Rice', 'Run', 'Sweet milk', 'Shut down', 'Music', 'Perfume', 'Mock', 'Name', 'Photo', 'Patience', 'Opaque', 'None', 'Red', 'Realize', 'Green', 'Man', 'Milk', 'Give', 'Help', 'Light-blue', 'Hungry', 'Learn', 'Map', 'Colors', 'Food', 'Drawer', 'Dance', 'Enemy', 'Find', 'Coin', 'Deaf', 'Country', 'Copy', 'Bitter', 'Born', 'Bright', 'Breakfast', 'Candy', 'Birthday', 'Catch', 'Buy', 'Call', 'Chewing-gum', 'Appear', 'Argentina', 'Bathe', 'Barbecue', 'Accept', 'Away', 'Red_2', 'Last name']\n",
            "[[10  0  0 ...  0  0  0]\n",
            " [ 0  5  0 ...  0  0  0]\n",
            " [ 0  0  7 ...  0  0  0]\n",
            " ...\n",
            " [ 0  0  0 ...  9  0  0]\n",
            " [ 0  0  0 ...  0 10  0]\n",
            " [ 0  0  0 ...  0  0  7]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opYvAuOGmi2z",
        "outputId": "43f6b14b-62ea-4b27-aee8-db54543524dd"
      },
      "source": [
        "# remove class with 90% predicted from confusion array\n",
        "i=0\n",
        "while i < (np.shape(a)[0]):\n",
        "    c_true = 0\n",
        "    true_max = 0\n",
        "    for j in range(np.shape(a)[1]):\n",
        "        if a[i][j] > 0:\n",
        "            c_true += 1\n",
        "            if true_max < a[i][j]:\n",
        "              true_max = a[i][j]\n",
        "    if c_true <= 2 and true_max >= 9:\n",
        "        a = np.delete(a,i,0)\n",
        "        a_true = np.delete(a_true,i,0)\n",
        "        i-=1\n",
        "    i+=1\n",
        "\n",
        "i=0\n",
        "while i < (np.shape(a)[1]):\n",
        "    c_pred = 0\n",
        "    for j in range(np.shape(a)[0]):\n",
        "      if a[j][i] > 0:\n",
        "          c_pred += 1\n",
        "    if c_pred == 0:\n",
        "        a = np.delete(a,i,1)\n",
        "        a_pred = np.delete(a_pred,i,0)\n",
        "        i-=1\n",
        "    i+=1\n",
        "\n",
        "print('a_true: ',a_true)\n",
        "print('a_pred: ',a_pred)\n",
        "print(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a_true:  ['Women' 'Where' 'Thanks' 'Skimmer' 'Son' 'Sweet milk' 'Shut down' 'Music'\n",
            " 'Patience' 'Opaque' 'None' 'Red' 'Green' 'Milk' 'Light-blue' 'Colors'\n",
            " 'Dance' 'Enemy' 'Copy' 'Bitter' 'Bright' 'Catch' 'Buy' 'Call'\n",
            " 'Chewing-gum' 'Last name']\n",
            "a_pred:  ['Women' 'Where' 'Trap' 'Yellow' 'Water' 'Thanks' 'Skimmer' 'Ship' 'Son'\n",
            " 'Rice' 'Sweet milk' 'Shut down' 'Music' 'Perfume' 'Name' 'Photo'\n",
            " 'Patience' 'Opaque' 'None' 'Red' 'Green' 'Man' 'Milk' 'Give' 'Light-blue'\n",
            " 'Hungry' 'Colors' 'Food' 'Drawer' 'Dance' 'Enemy' 'Find' 'Coin' 'Deaf'\n",
            " 'Country' 'Copy' 'Bitter' 'Bright' 'Breakfast' 'Candy' 'Catch' 'Buy'\n",
            " 'Call' 'Chewing-gum' 'Away' 'Red_2' 'Last name']\n",
            "[[5 0 0 ... 0 0 0]\n",
            " [0 7 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 1 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 7]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "hBVb17DPmlPz",
        "outputId": "02fc726d-2507-45fa-9274-b77adc045e7b"
      },
      "source": [
        "# draw the confusion matrix\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df_cm = pd.DataFrame(a, index = [i for i in a_true], columns = [i for i in a_pred])\n",
        "plt.figure(figsize=(18,10))\n",
        "sn.set(font_scale=1.1) # for label size\n",
        "sn.heatmap(df_cm, annot=True, cmap = 'Greens') # font size\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "wReN3LOJ1HUI",
        "outputId": "70b7ea74-90a3-43db-ede1-2bb08863ed2d"
      },
      "source": [
        "# draw the confusion matrix\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df_cm = pd.DataFrame(a, index = [i for i in a_true], columns = [i for i in a_pred])\n",
        "plt.figure(figsize=(16,9))\n",
        "sn.set(font_scale=1.3) # for label size\n",
        "sn.heatmap(df_cm, annot=True, cmap = 'Greens') # font size\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}