cookbooks/ConvLSTM.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hBPO04GFQKlw"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "import json\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.image as img"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7fZzohUQRJl",
        "outputId": "f33a0f5c-b2d2-4f87-e80f-a67ac46b7681"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SEQUENCE_LENGTH=10\n",
        "IMAGE_HEIGHT=128\n",
        "IMAGE_WIDTH=128"
      ],
      "metadata": {
        "id": "TNLXzMCDQS1-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_features():\n",
        "    '''\n",
        "    This function will extract the data of the selected classes and create the required dataset.\n",
        "    Returns:\n",
        "        features:          A list containing the extracted frames of the videos.\n",
        "        labels:            A list containing the indexes of the classes associated with the videos.\n",
        "        video_files_paths: A list containing the paths of the videos in the disk.\n",
        "    '''\n",
        "\n",
        "    # Declared Empty Lists to store the features, labels and video file path values.\n",
        "    features = []\n",
        "    labels = []\n",
        "    video_files_paths = []\n",
        "    file_namelist=[]\n",
        "    DATASET_DIR_List=[\"/content/drive/My Drive/DeepFakeDataset/dataset10/real\",\"/content/drive/My Drive/DeepFakeDataset/dataset10/fake\"]\n",
        "    frames=[]\n",
        "    with open('/content/drive/My Drive/DeepFakeDataset/deepfake-detection-challenge/metadata.json', 'r') as file:\n",
        "      datajson = json.load(file)\n",
        "    datajsonkeys=list(datajson.keys())\n",
        "    filenamelist=[]\n",
        "    for DATASET_DIR in DATASET_DIR_List:\n",
        "      # Get the list of video files present in the specific class name directory.\n",
        "      files_list = os.listdir(DATASET_DIR)\n",
        "\n",
        "      # Iterate through all the files present in the files list.\n",
        "      for val in datajsonkeys:\n",
        "        cleaned_val=val.split(\".\")[0]\n",
        "        filenamelist.append([os.path.join(DATASET_DIR,filename) for filename in files_list if filename.startswith(cleaned_val)])\n",
        "\n",
        "    cleaned_filenamelist=list(filter(None, filenamelist))\n",
        "    for valsize in cleaned_filenamelist:\n",
        "      if len(valsize)==10:\n",
        "        frames_list=[]\n",
        "        for newimg in valsize:\n",
        "          label_string=newimg.split(\"/\")[-2]\n",
        "          normalized_frame = img_to_array(load_img(newimg)) / 255\n",
        "          # Append the normalized frame into the frames list\n",
        "          frames_list.append(normalized_frame)\n",
        "        if label_string==\"real\":\n",
        "          labels.append(1)\n",
        "        else:\n",
        "          labels.append(0)\n",
        "        frames.append(frames_list)\n",
        "\n",
        "    features = np.asarray(frames)\n",
        "    labels = np.array(labels)\n",
        "    return features,labels"
      ],
      "metadata": {
        "id": "oB7yXVIYQUYT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "#Taking the EDA data\n",
        "with open('/content/drive/My Drive/DeepFakeDataset/Models/ConvLSTM/data30/features30.pickle', 'rb') as data:\n",
        "    features = pickle.load(data)\n",
        "with open('/content/drive/My Drive/DeepFakeDataset/Models/ConvLSTM/data30/labels30.pickle', 'rb') as data:\n",
        "    labels = pickle.load(data)"
      ],
      "metadata": {
        "id": "7Ecai7ozTenw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Using Keras's to_categorical method to convert labels into one-hot-encoded vectors\n",
        "one_hot_encoded_labels = to_categorical(labels)"
      ],
      "metadata": {
        "id": "Crt_LvkVTkT1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(one_hot_encoded_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWnlIWROTn1q",
        "outputId": "d1f6d002-bf6f-4ef9-c254-fa8699229877"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "245\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the Data into Train ( 75% ) and Test Set ( 25% ).\n",
        "features_train, features_test, labels_train, labels_test = train_test_split(features, one_hot_encoded_labels,\n",
        "                                                                            test_size = 0.25, shuffle = True,\n",
        "                                                                            random_state =75 )"
      ],
      "metadata": {
        "id": "Jb_Ot1i-Tpu1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.utils import plot_model"
      ],
      "metadata": {
        "id": "4wXVePCdTsnH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_convlstm_model():\n",
        "    '''\n",
        "    This function will construct the required convlstm model.\n",
        "    Returns:\n",
        "        model: It is the required constructed convlstm model.\n",
        "    '''\n",
        "\n",
        "    # We will use a Sequential model for model construction\n",
        "    model = Sequential()\n",
        "\n",
        "    # Define the Model Architecture.\n",
        "    ########################################################################################################################\n",
        "\n",
        "    model.add(ConvLSTM2D(filters = 4, kernel_size = (3, 3), activation = 'tanh',data_format = \"channels_last\",\n",
        "                         recurrent_dropout=0.2, return_sequences=True, input_shape = (SEQUENCE_LENGTH,\n",
        "                                                                                      IMAGE_HEIGHT, IMAGE_WIDTH, 3)))\n",
        "\n",
        "    model.add(MaxPooling3D(pool_size=(1, 2, 2), padding='same', data_format='channels_last'))\n",
        "    model.add(TimeDistributed(Dropout(0.2)))\n",
        "\n",
        "    model.add(ConvLSTM2D(filters = 8, kernel_size = (3, 3), activation = 'tanh', data_format = \"channels_last\",\n",
        "                         recurrent_dropout=0.2, return_sequences=True))\n",
        "\n",
        "    model.add(MaxPooling3D(pool_size=(1, 2, 2), padding='same', data_format='channels_last'))\n",
        "    model.add(TimeDistributed(Dropout(0.2)))\n",
        "\n",
        "    model.add(ConvLSTM2D(filters = 14, kernel_size = (3, 3), activation = 'tanh', data_format = \"channels_last\",\n",
        "                         recurrent_dropout=0.2, return_sequences=True))\n",
        "\n",
        "    model.add(MaxPooling3D(pool_size=(1, 2, 2), padding='same', data_format='channels_last'))\n",
        "    model.add(TimeDistributed(Dropout(0.2)))\n",
        "\n",
        "    model.add(ConvLSTM2D(filters = 16, kernel_size = (3, 3), activation = 'tanh', data_format = \"channels_last\",\n",
        "                         recurrent_dropout=0.2, return_sequences=True))\n",
        "\n",
        "    model.add(MaxPooling3D(pool_size=(1, 2, 2), padding='same', data_format='channels_last'))\n",
        "    #model.add(TimeDistributed(Dropout(0.2)))\n",
        "\n",
        "    model.add(Flatten())\n",
        "\n",
        "    model.add(Dense(2, activation = \"softmax\"))\n",
        "\n",
        "    ########################################################################################################################\n",
        "\n",
        "    # Display the models summary.\n",
        "    model.summary()\n",
        "\n",
        "    # Return the constructed convlstm model.\n",
        "    return model"
      ],
      "metadata": {
        "id": "xXrSXEPbTw-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Construct the required convlstm model.\n",
        "convlstm_model = create_convlstm_model()\n",
        "\n",
        "# Display the success message.\n",
        "print(\"Model Created Successfully!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fVIh21pTypk",
        "outputId": "c5d44873-aa93-406e-b017-64354424b152"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv_lstm2d (ConvLSTM2D)    (None, 30, 126, 126, 4)   1024      \n",
            "                                                                 \n",
            " max_pooling3d (MaxPooling3D  (None, 30, 63, 63, 4)    0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " time_distributed (TimeDistr  (None, 30, 63, 63, 4)    0         \n",
            " ibuted)                                                         \n",
            "                                                                 \n",
            " conv_lstm2d_1 (ConvLSTM2D)  (None, 30, 61, 61, 8)     3488      \n",
            "                                                                 \n",
            " max_pooling3d_1 (MaxPooling  (None, 30, 31, 31, 8)    0         \n",
            " 3D)                                                             \n",
            "                                                                 \n",
            " time_distributed_1 (TimeDis  (None, 30, 31, 31, 8)    0         \n",
            " tributed)                                                       \n",
            "                                                                 \n",
            " conv_lstm2d_2 (ConvLSTM2D)  (None, 30, 29, 29, 14)    11144     \n",
            "                                                                 \n",
            " max_pooling3d_2 (MaxPooling  (None, 30, 15, 15, 14)   0         \n",
            " 3D)                                                             \n",
            "                                                                 \n",
            " time_distributed_2 (TimeDis  (None, 30, 15, 15, 14)   0         \n",
            " tributed)                                                       \n",
            "                                                                 \n",
            " conv_lstm2d_3 (ConvLSTM2D)  (None, 30, 13, 13, 16)    17344     \n",
            "                                                                 \n",
            " max_pooling3d_3 (MaxPooling  (None, 30, 7, 7, 16)     0         \n",
            " 3D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 23520)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 2)                 47042     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 80,042\n",
            "Trainable params: 80,042\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model Created Successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the structure of the contructed model.\n",
        "plot_model(convlstm_model, to_file = '/content/drive/My Drive/DeepFakeDataset/Models/ConvLSTM/data30/convlstm_model_structure_plot_30.png', show_shapes = True, show_layer_names = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_hP32YiHT0lu",
        "outputId": "0106be54-c804-43ac-9139-88f645197eb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an Instance of Early Stopping Callback\n",
        "early_stopping_callback = EarlyStopping(monitor = 'val_loss', patience = 10, mode = 'min', restore_best_weights = True)\n",
        "\n",
        "# Compile the model and specify loss function, optimizer and metrics values to the model\n",
        "convlstm_model.compile(loss = 'categorical_crossentropy', optimizer = 'Adam', metrics = [\"accuracy\"])\n",
        "\n",
        "# Start training the model.\n",
        "convlstm_model_training_history = convlstm_model.fit(x = features_train, y = labels_train, epochs = 35, batch_size = 4,\n",
        "                                                     shuffle = True, validation_split = 0.2,\n",
        "                                                     callbacks = [early_stopping_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3yOrlcfBT2xS",
        "outputId": "8fcae931-793c-4f7c-90d9-ced93c3a00d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/35\n",
            "37/37 [==============================] - 79s 2s/step - loss: 0.5610 - accuracy: 0.8014 - val_loss: 0.5509 - val_accuracy: 0.7838\n",
            "Epoch 2/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.5155 - accuracy: 0.8082 - val_loss: 0.5352 - val_accuracy: 0.7838\n",
            "Epoch 3/35\n",
            "37/37 [==============================] - 55s 2s/step - loss: 0.5240 - accuracy: 0.8082 - val_loss: 0.5723 - val_accuracy: 0.7838\n",
            "Epoch 4/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.5464 - accuracy: 0.8082 - val_loss: 0.5390 - val_accuracy: 0.7838\n",
            "Epoch 5/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.5022 - accuracy: 0.8082 - val_loss: 0.5220 - val_accuracy: 0.7838\n",
            "Epoch 6/35\n",
            "37/37 [==============================] - 56s 2s/step - loss: 0.5055 - accuracy: 0.8082 - val_loss: 0.5215 - val_accuracy: 0.7838\n",
            "Epoch 7/35\n",
            "37/37 [==============================] - 56s 2s/step - loss: 0.5020 - accuracy: 0.8082 - val_loss: 0.5214 - val_accuracy: 0.7838\n",
            "Epoch 8/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4889 - accuracy: 0.8082 - val_loss: 0.5317 - val_accuracy: 0.7838\n",
            "Epoch 9/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.5018 - accuracy: 0.8082 - val_loss: 0.5306 - val_accuracy: 0.7838\n",
            "Epoch 10/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.5219 - accuracy: 0.8082 - val_loss: 0.5320 - val_accuracy: 0.7838\n",
            "Epoch 11/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.5304 - accuracy: 0.8082 - val_loss: 0.5216 - val_accuracy: 0.7838\n",
            "Epoch 12/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.5007 - accuracy: 0.8082 - val_loss: 0.5225 - val_accuracy: 0.7838\n",
            "Epoch 13/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4959 - accuracy: 0.8082 - val_loss: 0.5253 - val_accuracy: 0.7838\n",
            "Epoch 14/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.5033 - accuracy: 0.8082 - val_loss: 0.5260 - val_accuracy: 0.7838\n",
            "Epoch 15/35\n",
            "37/37 [==============================] - 54s 1s/step - loss: 0.4882 - accuracy: 0.8082 - val_loss: 0.5186 - val_accuracy: 0.7838\n",
            "Epoch 16/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4930 - accuracy: 0.8082 - val_loss: 0.5174 - val_accuracy: 0.7838\n",
            "Epoch 17/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4913 - accuracy: 0.8082 - val_loss: 0.5141 - val_accuracy: 0.7838\n",
            "Epoch 18/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4965 - accuracy: 0.8082 - val_loss: 0.5020 - val_accuracy: 0.7838\n",
            "Epoch 19/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4937 - accuracy: 0.8082 - val_loss: 0.5325 - val_accuracy: 0.7838\n",
            "Epoch 20/35\n",
            "37/37 [==============================] - 54s 1s/step - loss: 0.5318 - accuracy: 0.8082 - val_loss: 0.5151 - val_accuracy: 0.7838\n",
            "Epoch 21/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4941 - accuracy: 0.8082 - val_loss: 0.5307 - val_accuracy: 0.7838\n",
            "Epoch 22/35\n",
            "37/37 [==============================] - 56s 2s/step - loss: 0.4946 - accuracy: 0.8082 - val_loss: 0.5164 - val_accuracy: 0.7838\n",
            "Epoch 23/35\n",
            "37/37 [==============================] - 56s 2s/step - loss: 0.4885 - accuracy: 0.8082 - val_loss: 0.4806 - val_accuracy: 0.7838\n",
            "Epoch 24/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4698 - accuracy: 0.7877 - val_loss: 0.4864 - val_accuracy: 0.7838\n",
            "Epoch 25/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4535 - accuracy: 0.8082 - val_loss: 0.4364 - val_accuracy: 0.8378\n",
            "Epoch 26/35\n",
            "37/37 [==============================] - 54s 1s/step - loss: 0.4040 - accuracy: 0.8356 - val_loss: 0.4202 - val_accuracy: 0.8108\n",
            "Epoch 27/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4304 - accuracy: 0.8219 - val_loss: 0.4616 - val_accuracy: 0.7838\n",
            "Epoch 28/35\n",
            "37/37 [==============================] - 54s 1s/step - loss: 0.4347 - accuracy: 0.7945 - val_loss: 0.4647 - val_accuracy: 0.7838\n",
            "Epoch 29/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.4105 - accuracy: 0.8151 - val_loss: 0.4271 - val_accuracy: 0.8378\n",
            "Epoch 30/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.3315 - accuracy: 0.8493 - val_loss: 0.5020 - val_accuracy: 0.7027\n",
            "Epoch 31/35\n",
            "37/37 [==============================] - 54s 1s/step - loss: 0.3305 - accuracy: 0.8288 - val_loss: 0.4804 - val_accuracy: 0.8378\n",
            "Epoch 32/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.2769 - accuracy: 0.8904 - val_loss: 0.4179 - val_accuracy: 0.8378\n",
            "Epoch 33/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.2560 - accuracy: 0.8904 - val_loss: 0.5082 - val_accuracy: 0.8378\n",
            "Epoch 34/35\n",
            "37/37 [==============================] - 54s 1s/step - loss: 0.3778 - accuracy: 0.8082 - val_loss: 0.5076 - val_accuracy: 0.7838\n",
            "Epoch 35/35\n",
            "37/37 [==============================] - 55s 1s/step - loss: 0.2366 - accuracy: 0.9247 - val_loss: 0.4916 - val_accuracy: 0.8378\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the trained model.\n",
        "model_evaluation_history = convlstm_model.evaluate(features_test, labels_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NPnAacMdT5cX",
        "outputId": "714dd2a0-55b7-470a-8e3a-3f4045ef4915"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 2s 936ms/step - loss: 0.7873 - accuracy: 0.6935\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime as dt"
      ],
      "metadata": {
        "id": "2R6lAA9gYzhg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the loss and accuracy from model_evaluation_history.\n",
        "model_evaluation_loss, model_evaluation_accuracy = model_evaluation_history\n",
        "\n",
        "# Define the string date format.\n",
        "# Get the current Date and Time in a DateTime Object.\n",
        "# Convert the DateTime object to string according to the style mentioned in date_time_format string.\n",
        "date_time_format = '%Y_%m_%d__%H_%M_%S'\n",
        "current_date_time_dt = dt.datetime.now()\n",
        "current_date_time_string = dt.datetime.strftime(current_date_time_dt, date_time_format)\n",
        "\n",
        "# Define a useful name for our model to make it easy for us while navigating through multiple saved models.\n",
        "model_file_name = f'convlstm_model_30___Date_Time_{current_date_time_string}___Loss_{model_evaluation_loss}___Accuracy_{model_evaluation_accuracy}.h5'\n",
        "\n",
        "# Save your Model.\n",
        "convlstm_model.save(\"/content/drive/My Drive/DeepFakeDataset/Models/ConvLSTM/data30/\"+model_file_name)"
      ],
      "metadata": {
        "id": "16QD0ogqT8pU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_metric(model_training_history, metric_name_1, metric_name_2, plot_name):\n",
        "    '''\n",
        "    This function will plot the metrics passed to it in a graph.\n",
        "    Args:\n",
        "        model_training_history: A history object containing a record of training and validation\n",
        "                                loss values and metrics values at successive epochs\n",
        "        metric_name_1:          The name of the first metric that needs to be plotted in the graph.\n",
        "        metric_name_2:          The name of the second metric that needs to be plotted in the graph.\n",
        "        plot_name:              The title of the graph.\n",
        "    '''\n",
        "\n",
        "    # Get metric values using metric names as identifiers.\n",
        "    metric_value_1 = model_training_history.history[metric_name_1]\n",
        "    metric_value_2 = model_training_history.history[metric_name_2]\n",
        "\n",
        "    # Construct a range object which will be used as x-axis (horizontal plane) of the graph.\n",
        "    epochs = range(len(metric_value_1))\n",
        "\n",
        "    # Plot the Graph.\n",
        "    plt.plot(epochs, metric_value_1, 'blue', label = metric_name_1)\n",
        "    plt.plot(epochs, metric_value_2, 'red', label = metric_name_2)\n",
        "\n",
        "    # Add title to the plot.\n",
        "    plt.title(str(plot_name))\n",
        "\n",
        "    # Add legend to the plot.\n",
        "    plt.legend()"
      ],
      "metadata": {
        "id": "zOwRrU7PT_Jk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the training and validation loss metrices.\n",
        "plot_metric(convlstm_model_training_history, 'loss', 'val_loss', 'Total Loss vs Total Validation Loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "Hk-mUqLyUA4r",
        "outputId": "a5f26fd9-fdae-47de-b612-77fc1bfe25f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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qgBNwnOpMAuYpvv7662nTpg2jR48G4Pbbb6dmzZrMmDGDDRs2sHPnTu644w4GD44t6HDbtm1ccsklZGdnU7NmTe677z6OPvpoFixYwHnnnceOHTsoKCjg5ZdfpnXr1pxxxhmsWLGC/Px8brnlFoYMGVKu04YUGxkbTq9eUKtlU67q/B7ss4+J+iefwDffwHvvwciRMc0zv3gxXH899D2pDnWvu4LGs9/htOYfMmFCBZ6E4ziVxpAhQ5gUFq43adIkhg8fzquvvsrcuXOZMWMGV199NbFOG/Pwww8jInzxxRe8+OKLDB8+nG3btjF27FiuvPJKcnJyyM7OJjMzk2nTptG6dWvmzZvHl19+Sb9+/eJzcqqaVK/u3btrvBg9WrVOHdWfv16p+rvfqe6xh+pJJ6nWrKm6alXU5eTlqR56qGrjxsFhGzeqtm+vv9RprAfXXKDr1sXNZMeptixcuDDRJuiBBx6oK1eu1JycHD388MN1x44dOnr0aO3cubN26dJF69Spo6tXr1ZV1fr16xdbznfffacHHXSQqqqefPLJ+p///Oe3fb169dJ58+bp+PHjtWPHjnrXXXfp119/raqqixcv1n322Uevu+46nTlzZrHlR7pWQLYWo6sp26IHm/tm2zZ4c25reP99aNzYhs2efDK0alV6AQH33msPAw8/HBy2xx4wfTrp9WrxRl4/pj+1stQyHMdJfk4//XQmT57MxIkTGTJkCOPHjyc3N5c5c+aQk5NDixYt2LZtW1zqOvPMM5kyZQp169ZlwIABvP/+++y///7MnTuXzp07c/PNNzNmzJi41JXSQn/EESbMkyYBbdqY2A8cCDffHHUZX34Jt95qY6qGDg3b0a4dNd99myaykUNv7wdJuHyYkwDy8xNtgVMOhgwZwoQJE5g8eTKnn346mzZtYq+99iI9PZ0ZM2bw/fffx1xm7969GR+E6H399df88MMPHHDAASxdupR9992XK664gsGDBzN//nxWrVpFvXr1OPvss7n22mvjNrd9ysxHH4m0NDj1VBvM+vPP0KBdOxskFSU7d9qU9HvsAY88svsiU9Iti5eGvcJZLwxgx4CTqfX+tOgGX8Wb7GxbIatBA2jSJPKrcWPrk9iwwdbDXbNm13v4dqNGcNdd0LZt5Z9HVeeXX6zDr08f+9FFWJVM1Wa+Pukk6N49QhnhGX1Vs0rnoIMOYsuWLWRkZNCqVSvOOussBg4cSOfOnenRowcHHnhgzGVeeumlXHLJJXTu3JmaNWvyzDPPULt2bSZNmsRzzz1Heno6LVu25MYbb2T27Nlce+211KhRg/T0dB599NH4nFhxPp1EveLpo1dVnTlTFVRffDH2Y2+/3Y599dXi83zxheowxlvG009Xzc8vu7Gxsn276s03q6alqdaqpSpidhT3SkuLnF6zpmpGhmr37qoNGqg2bKj61FOqBQWFqtuwQfW661TPP3+3XY6q6r337rqmN90UMcv//me7jzuuhHLmzrXv49prq9WFTgYffVUhVh99woW96CveQp+fr9qqleof/hDbcXPmmP6dfXbpeQ86SPXBtsGf/IorKufPmZOj2qWL1XnuuabC+fmq69erLlmi+tlnqtOmqb7wgupDD6mOGaN6ww2q999vae+/r7pggeq6dYXt/e471SOPtHIHDlRdvVp37FD95z9VmzTZpWOvvFLxp1il+OUX1b32Uj32WNULL7SL9Pjju2U777xd1/CbbyKUM3++atOmqvXrW6YLLrBogGqAC330uNBH4PLLVWvXVt28Obr827aZeLdubbpZGn/9q13JzRf+yTb+7//KZ3BJ7Nhhol2zpmqLFqqvvx7/OvLzVe+7Twtq19ZtDZvqZS1fUlA95hjV2bNV999ftVOnOD+8FBSobtmiunq16tdfW6t25kzVZcviWEkF8o9/2Hc/a5Z9RyecYE9Qb7/9W5aNG1Xr1rX7Z1qaPR0VYtEiu1m0bm0361tv3fWkuH175Z5PAqiKQj9//nzt0qVLodchhxxS4fW60Edg1iw70xdeiC7/9ddb/qlTo8v/9deW/x/35KsOHWofxo0ru8HF8eWX5l4Bq2ft2vjXETB7tuo5PRbqZ/RQBV1+1FlasM7uei++qLG7w7ZvV/38c9VnnlH905/srtG+vWrLluYuKs7tVKOGPVYlswiEt+ZDbN6s2rWrndvcuapqD1agmp1tT5jNm1ujQlXtR9Sqld28v/pqVzmhG0i/flZPCrNw4UItqEauqrJSUFDgQh+J/HxrJJ18csn5cnOt9V+jhj19x0L37qo9e6r9c485xlrc06aV2eZC5OWp3n23+eGbNlWdNCk+5Ubghx9MV8GEaOyDOzTvltutCZqRoTp9uubnW4v+gANUd+4MO3jbNtU1a0y03nvPfNbnnKN68MGq6em7xLtuXbtYQ4eqjhypetVV1nq9+27VRx9Vfe456xh55x3Va65RrVfPbgSnn646b16FnXuZue8+O6+icc8rV6pmZqq2aqUFy77Xzp1Vu3WzXdOmhd0sly61fM2a2c28KE88YT/KXr3ssSBFWbp0qebm5rrYl0BBQYHm5ubq0qVLd9tXktCn1OLgJXHllfDYY7ZweKNGhfdt2wb//Cf87W8WOHHRRRY7X79+9OXfe69NbrlkCezXfLNFXnz1lY3KrV3bonHCX6G02rWhRo3ILxF7/+9/LZD/5JNtRawWLeJ7cQJefNFmbVa1RbWuv94ijgCL7Dn3XFi0CE44gTU/Kt/N20SHlhvZg00WXhopvrh1a+jSxaJRunSxV/v2FhIVLWvXwv332xwUW7bAoEEWItuzZ1zOu1xs3Qr77gsHHbT7nEoAX3wBvXqxtVkbWi/9iP8buycXX2yLm/3ud9CzxQ9MXN0HNm+GGTPs+kTipZdsro1OnWw6j732qtjzSgA7d+5kxYoVcYtTT1Xq1KlDZmYm6enphdJLWhw84S34oq+KaNGrqn70kbWgnn9+V1p+vn3ee2/bd9JJ1j9ZFr7/3sq4884gYdUqG5o7dKg9SvTvr3r00aqHHaaalaXaoYPqvvvao0bLlvbo37y5tdgbN7ZRvA0bWqdc69bWyq3Als68eTaK+IgjSnCLb91qkSAdOmjB73+v/214vL5Z/wzNu3Ckpd95p/knnn/eWvS5ufE1cv161b/8xa4PmB/8o4/iW0es3H+/2fLhh8Xnee893Sk1dUaNY3Tz2l2+9n/9eYV+w36a13AP8+eUxtSp9jR0wAH26OU4YVDdXTeqJuoZGaqDB9vnDz5Q7WHuZ83KUg0boVxmDj9ctXPn8pdT2WzebB2sLVuq/vhj9Me9+aYWF1xSsWzerHrXXXZjBLtT9+ypOmCA6vDh5u656y7VJ59UnTLFYhrjfdNRtRtfy5Z2Ay+BjRtVL6r1jP4WIVVQoLp6te783QG6mQb6wND/RV/nzJmqjRrZOQfD5h1H1YX+N/74R3NzDxpkZ56ZaX2m8Yoe+de/rNxIbtZkpaDAHjpq1LCbX6zH/v73qm3ahHUqVia//GIX/eyzrbOye3cTwLp1NWLH7n77qZ51luqDD1r4aXkjWR54wMot5cI98ohlWzHyL7bxpz9ZWFe9enrz0bO0SRPVX3+Nod45c8yf36KFi73zGy70Af/9r51xw4aqf/ubNcjiyerVJpi33BLfciuSRx/Vwi6nGHn3XTv+wQfja1e5+fln80HNnq361lvW0XvKKeYGCwl/7dr2GHbVVaoTJ8bW0bl1q0XJHHVUidkKCmy4Q1aWakF+geqIEVZ3nTqq77+v771nH597LsbzW7TIInrOPTfGA51UxYU+jKlTLTCkoghFDVaFwIE5c+wJp1+/sj/VFBSo9uljHowqE/23fLnqSy+pXn21dUrUqWN/hTZtovOVq9oIMlCdMaPEbJ9+atkefTRI2LHD4neD4/LzbWLVXr3KcB6jR9sX+NNPZTjYSTVc6CuRxx+3qzpnTqItKZmNG60vODOz/O7rDz+0c7733vjYVuls326dx23aWCv/mWdKzv/rr9aaP/LIUou+4AKLDt20qfg899yjZXP5LVxYvscxJ6Vwoa9E1q61EPprr020JcVTUGBejJo1VT/+OD5l9u1rbuMtW+JTXkL46SdzxYDqZZdZ6zsSoc6YUlrzmzaZyF9wQcnV5uZaw/zyy8tg83HH2d260IAGpzpSktBXmzj6yuTEE2164+++szD4ZOOf/7SV2u69F66+Oj5lfvopHHoo3Hkn3HhjfMpMCHl5cN11Frffu7fFr4ePW9i2zRaU/93v4MMPSyxq7FhblvjTT+GQQ0qu9swzbR37VaugXr0Y7H39dRtfMXmyTdXqVD6TJ8NDD0Hdujb4pl49e4W2Q++HHVahYz88jr6SGTfOGnyltZZ37rRw6Mqc8PKTT6wlP2hQ/PsRTjrJQtxTYvDm+PEWvZORYRctxIMP2pf7/vslHl5QYDMgdOkS3XUOub+efjpGO/PyVPfZp9ROYacC6dvXQl579lTt2FG1bVsL/a1fv/DUHunp0c+rUgZw103lsmmTuXqLPoovX646ebK5dXr3tsd6MHEcMED1jjtMPyrK/bFunUUftm0b3WRtsTJ3rp3PbbfFv+yE8PnndrFq1bJpCH791YS/d+9S1Xv2bLsWjzwSXVUFBTaG7ve/L4Od//d/Vtn8+WU42Ck3rVvbVB+RKCiwCK3lyy30qk6dUl1+ZaXcQg/0AxYDS4DrI+wfAeQCOcHrwrB9w4Fvgtfw0upKBaFXNR94ixa7ovoyMnbd2GvVsj/0lVdaA/HCC60hED6PV1aWBVWMH2+/kfKSn28t7vR0CyGvKE45xcJXyzvfWna2TW0zYkSCJ25cu9ZabLBrQrkoRtdddJHdyGN5ugkNss3JKYONdeqoXnxxjAemMPn50U9XWx42bLAv7a67Ss+bm2t/9AYNCj8lxolyCT2QBnwL7AvUAuYBHYvkGQE8FOHYJsDS4L1xsN24pPpSRehffnmXcO+7r+qwYRaR98knxQ8uWr/eZrW95RabCDE0JXlaWvQtw0jk5dnTBVg/YkXyxRf2tHrZZWUbRDVzps1sEBrvEJoWPyEDskLk5an++c9mTK9epbbmN22y7+7882OrZt06exK85JIy2Hj++XZnqYhHtarIv/5lgrpoUcXWE5pb5Y03osu/cqUN3NtzzzLc0UumvEJ/GDA97PMNwA1F8hQn9MOAx8I+PwYMK6m+VBH6ggIboFWeEOedO817cOKJ9k1dc03s/vzNm60lD/YEURnx/cOHW30NGqiecYbN0FhSy7agwGZz7N1bf5s18+9/N8F8+GFLO/HEBIu9qnW6rFpVaraxY83msjTazj3XbnAxu+9CfrP77ou90lTklFPsenTrVrGPhI89ZvVEmE2yWL77ziKlmjeP642ovEJ/GvBE2Odziop6IPSrgfnAZKBNkH4NcHNYvluAayLUMRLIBrL33nvvuJ14qrBzp7lxQPXUU6Mf0fvDDzZDcFqaCWZlsWOHDUa96CKbqy3UD3XCCSaCIa3Mz7eVqkIekcxMe+opOvAqNHp3wIAYpwpIEN262XUvy03144+17PMHHXGEtRYrs3c/Wdl7b+ukBtUbb6y4eq64wp6kYr3mixfbnyMjI7abRAlUhtA3BWoH2xcD72sMQh/+SpUWfbwpKLDGmoj590sb3fvppzZatVGj+E2LXxby8uzp9uqrzYUFdg6HHWbTvYSmoPn3v0tueIUaTv36Vb7Y//CDrf9xxBH21HHZZWbvZ5/tftPNzjY7H3qobHUVFNhc/2X6G0yYYJW/9VbZKk8V1qzR30bwnXeedXrNmlUxdR17bLAQRRmYN88iMdq1U12xotymlCT0pcbRi8hhwO2qekLw+QYAVf17MfnTgPWquoeIDAOOUtWLg32PAR+o6ovF1ZcKcfQVyauv2rTkLVta3HWkReknT4ZzzrE8b75pU6UnA6o2vuC11+wFFsd/xhlQs2bpxz/5pK0V0LevHV+3bsXZunq1hdBPnGjLAQBkZdkSAvPn27oFYOMk2rffNdX+J5/Ae+9ZPPyee5at7ocegssvt2tTq5aF7m/fvvt7Xh4ccwwMHx5MT79zp61/0KULvP12XK5DleTtt2HAAIQorBMAACAASURBVPjgA+jWzdZCKCiAefN2X4yivLRqBf36wdNPl+342bPh2GMhI8PGZZRjnYFyxdEDNbFO1Hbs6ow9qEieVmHbfwA+CbabAN9hHbGNg+0mJdXnLfrS+fRTe+rbc8/CkVoFBTYaHqzFXJFz+iSKJ5+0J4Ljjit9bp0ff7SW90kn2Xw8Z55p67Q++KAtYDV7tuUJPXWvWWOd3kceuSv8uXNnC3sNnyQyP9+WdH35ZVsYa/Bgi8IMdb7H2glblI0bLWIr5PJq0MBGHWdk2NNPx47mHurUaVee006zBbnybw9myKzOs1qOGWNfYGjeiY8/tlb98OHxrWftWrvW99xTvnI+/NDGbHTpUq7OdOIQXjkA+BqLvrkpSBsDDAq2/w4sCG4CM4ADw449HwvLXAKcV1pdLvTRsXSpxV2np9sArW3bdnWCnnlm1fBll5Wnn7b/8bHH7i72335rbpZevXaJddu29rldu8IrGoaPY9l7b+vLANUDD7SxALEuU7txo3XAxmMcRF5edG7fBQts1uOmTc32nm1Wa15aum654MryG1FVGTzYvsRwbrnFLlA8l+EMjXKLxyCoadPsh3j44fbll4FyC31lvlzoo2f9elvzAmzGTLAFmKrCzJnl5dlnTciPPtqecG67zTpAQ+J98MGWlpNT+Hrk51srPjtb9bXXzJd+/fU2pf1NN5nbtCpev23bLLrp6KNVn+dM3UgjHXLiFn3rrap5PuUiI8O+0HB27DBfeuPGcfGHq+quhQa+/z4+5b3ySuEl8GLEhT6F2b7dBhXVqaP6wguJtqZyee45eyIPdfD26mWt+W+/TbRliWX5JFt44bqGjyio3nBDoi2qRFatsh/EAw/svm/xYouQ6ds3PpFJo0dbLGyS3Eld6KsBqeyqKYlp02x2glTsjygzBQWq3btrQYeOetGFBQoWkLNbnk8+sRjYtm0tDj8VeOMNk7Xi1hIODXKIdCOIlaOOKuOcFRVDSUKfhHMrOmWhTp1EW5AYTjgBLrigXMEKqYcIXHYZsmghD582g1694LzzICcHyM2F++6Dzp1tutHx42HFCnjuuURbHR+ysy0UqmvXyPtHjoSTToI//9lCwMrDggXJE9JWCi70jpOKDB0KTZuS/thDTJ6Yz2n1prLqiNPQjAyL22zQAB5/3OJIjz8epkyx7o2qTnY2dOxoUwNHQsTidPfYw+KUt28vWz25ufbq1KnstlYiLvSOk4rUqWODDl5/nRaH7MO4dSfSc+uHTGpxOTs//9IC/i+6yOLKBw2Cb7+FRYsSbXX5UDWh7xE5lPw39trLxH7+fLj55rLVtWCBvXuL3nGchHLppTZq7uCDYfJk3ntmJUNX/IOrniwiTiedZO9TplS+jfFk5UpYs6Z0oQc75xEj4IEHYMuW2OtyoXccJylo08bEb+pUOPVUhg2vxdVX28jbJ58My5eRYeJY1YU+NKI+GqEHGDbMhhf/73+x17Vggbl/WreO/dgE4ELvONWIu+6yKSQuuaSIvg0caO6cn35KmG3lJjvb5tI4+ODo8h9+OKSlwcyZsdcV6ogVif3YBOBC7zjViJo1YcIE2HtvOOUUa/AD5qdXhbfeSqh95SI72zpHo50EqUED6N691LV/d0ODSZuqiNsGXOgdp9rRpImtKf7zz/CHP9gkaXTpYq6equq+CXXEdu8e23F9+sBnn8Gvv0Z/zJo1sH69C73jOMnNQQdZ6Pzs2TBqFChirfp33olN9JKF77+Hdeui98+HOPJI2LHDxD5aqlhHLLjQO0615eST4fbb4dln4c47MaHfuhXefz/RpsVOrB2xIY44wvzssbhvQkJfRWLowYXecao1t9xiaxfccgs8suBIaNiwarpvsrMhPd1G/MZC48bWeRtLh+yCBeb/atEitroSiAu941RjatSwUMtBg2D0VbX5vkM/eOMNW6gjEWzfXrbIn+xsE+zatWM/9sgjbXWZHTuiy1/FIm7Ahd5xqj3p6baS1tFHw62zB9q0CHPmVHzFeXk2OvXJJy3es0cPe6LIzIRvvom+nGhHxBZHnz7WLxHNOatWqTluQrjQO45DnToWibOq6wDyqcGyB9+IfyWqMGkSXHml+cYbNbJonwsvhBdesAFIV1xh+R5/PPpyv/0WNm0qu9D37m3v0bhvVq+GjRtd6B3HqZo0bAgT3m3K3Hq92Dx+CrNnx7mCf/8bhgyxdxG4+GKbPXPxYtiwAf7zH7j3Xhg82NZg3bYtunLL2hEbYq+9oEOH6IS+CkbcgAu94zhhNG0K+189iIML5nFh3+9ZuDCOhT/9tEWqbN4MH30E998PZ54J++9vnQUhRo2yUMlXXomu3DlzzDdfHvHt08dsys8vOV9oamMXesdxqjJ7nDMIgBML3qBvX/juuzgU+vXXNsXC8OE2PLckjjkG9tsPxo6NruzsbJt/Pj297Pb16WM3oHnzSs63YAE0a1blFkBwoXccpzDt28OBB3JDpyn8+qvNjbN6dTnLfO45a7WfdVbpeWvUMLfOrFmU+khRUGAt+rK6bUL06WPvpblvFiyoUvHzIaISehHpJyKLRWSJiFxfQr5TRURFpEfwua2I/CoiOcErylu04zgJZeBAGmZ/wPRJm/jxR1ubpCyz+QImxs89Z4W0ahXdMSNGQK1a8NhjJef75hszrLxCn5kJ++5b8sApVbvxVDG3DUQh9CKSBjwM9Ac6AsNEpGOEfA2BK4FPi+z6VlW7Bq9RcbDZcZyKZtAg2LmTnuunM2mSuaYnTixjWTNn2hQF554b/THNm8Opp8K4cTZatzjK2xEbTp8+9hRR3BiCFSvMvZOKQg8cAixR1aWqugOYAAyOkO+vwP8BUXaVO46TtBx2mPXMvvEG/fvDPvuUY8DsuHEW0jM4kmyUwMUXWyjjpEnF58nOhnr14MADy2hcGEceaZ3Axa20VUUjbiA6oc8Alod9XhGk/YaIdAPaqGqkOU7bicjnIvKhiPSOVIGIjBSRbBHJzs3NjdZ2x3EqirQ0W4XprbeQ/DwGD4Z334VffomxnK1b4aWX4PTTTZBjoU8fE/CSOmWzsyErq/QO3mjrg+LdNyku9CUiIjWA+4CrI+xeDeytqlnAVcALItKoaCZVfVxVe6hqj+bNm5fXJMdx4sGgQRbf/vHHDBpkYe3vvRdjGa+9ZvMhx+K2CRGKtf/0U8jJ2X1/fj7MnRsftw1Au3a22lZxHbILFtj8Nk2bxqe+SiQaoV8JtAn7nBmkhWgIdAI+EJFlwKHAFBHpoarbVXUdgKrOAb4F9o+H4Y7jVDDHH28dolOm0KePDVx9/fUYyxg3zvw+vSM+zJfOuefasN1InbJffWVPDPESehFz38ycaR2vRamCUx+EiEboZwPtRaSdiNQChgK/eetUdZOqNlPVtqraFvgEGKSq2SLSPOjMRUT2BdoDS+N+Fo7jxJ8GDeDYY+H110mvqQwYAG++WfqYot9Ytcr8PeecU3hAVCw0aWKjaZ9/fvewn3h2xIbo08diSb/9tnB6QUFqC72q5gGXAdOBRcAkVV0gImNEZFAph/cB5otIDjAZGKWq68trtOM4lcTAgSZ6X33F4MGQm2vjnqJi/HgTyLK4bcK5+GJz/7z4YuH07Gy7Ge0fRydBcX76H36wDooqGEMPUfroVXWqqu6vqvup6p1B2q2quls/vKoeparZwfbLqnpQEFrZTVUrYKYkx3EqjIED7f2NN+jXzwafRuW+UbUVTQ47zAZglYdDD7UpiMeOLexSyc6Gbt3K/rQQiQMPtNDOon76KtwRCz4y1nGcksjMNDGdMoU99oCjjooyzDInx8SxvK152NUp+/nnu9w1O3daHfF024Tq6tPHhd5xnGrGoEG2MMeaNQwebJNNLl5cyjHjxllH7hlnxMeGs8+G+vV3hVouXGhhQPEWejChX7bM3DUhFiyA1q1hzz3jX18l4ELvOE7JnH66xdWfcQaD+trC4SW6b3butPnlBw60ztQo+flne1p46KEIHb6NGsGwYTBhgg2iqoiO2BBHHmnv4a36KtwRCy70juOURseONlfNrFm0+dNp9Oyyo2T3zTvv2HKApbhtQos13XsvHHechacPHgyXXw6PPhrhgFGjLJzy+edN6PfYw2a5jDedOlnLPST0BQU2WtaF3nGclGboUHObTJ3KU3nn8snH+cUv7TpunE3l26/fbrs2b7Zp5keOtPD6Tp3g2mthzRpbeOr99y18/8YbYeXKIgd3726vxx6D2bNtO54dsSHS0qBXr12RN8uW2Q3Ghd5xnJRn5Ei4+246LZjII1zCm29EGFS0YYP5dc4803z0Ybz7rk3jfuqpNkFaz5622NTy5fDFF3D33bZu7SOPmPfnyisj2DBqlM2wFo+piUviyCNtDv0ff6zyHbHgQu84Tixcey16402M5N80vuu63UeQvvQSbN++m9tmxw4YPRr23tsaymvXwssv23KxmZmFi9hvP7j1Vtv/RtGA7KFDzV8PFSv0oXj6WbNc6B3HqX7IHX/lw86j+cOSe9nxl78V3jlunPn0u3UrlPzQQzZ1/AMPmIaWthjU1Vebrl52mXXS/kaDBhaBAxUr9FlZFuXz4Yf2BNGmza4bTBXEhd5xnNgQYcc9/2Ic51DrLzebioONoP34Y2vNi/yWPTcXxowxl/2AAdFVEVpz5Icf4Lbbiuz8619t6uJ27eJzPpFIT4cjjrAO2SoecQMQh7k9Hcepbhx5dA1aNnyKTk220O3yy621u3SpCXyR5QJvucVa5ffdF1sdRxxh3QIPPGCN+KysYEeTJhbyWdH06QM332x3nWOPrfj6KhBv0TuOEzO1asHxA2py8tYX0WOOhfPPhwcfNEEMc7rPn28drqNHQ4cOsddz1102I8HIkTFMphYvQn76HTuqfIvehd5xnDIxaBAsz63DZze+ZiE069cX6oRVhT/+0ULSd3O/REnjxtaiz862aJxK5ZBDoHZt23ahdxynOtK/vy3s9Oq7DWDqVHjySYuKCXjtNZgxw/zzMQyQ3Y0hQ+CEE+Cmm2zZ1kqjdm2bUA2sg7kKIxppgv0E0qNHD80ODW92HCepOfZYm7594cLC6du3mzbWrWtzj5V3pb+lS61RPWCAhV1WGuPHw/TpFk2U5IjIHFWNGIrkLXrHccrM4ME2O8A33xROf+ABE+f774/Pcq777mvun1deKcci5WXhrLOqhMiXhgu94zhlZlCw9FD4JGc//gh33GFzmvXtG7+6rr7apkwYPXr3xaacknGhdxynzLRta2uChLeyb7rJXDf/+Ed860pPh8cfNz99WTt3qysu9I7jlIvBg22c1Nq1MHcuPP00XHFF+ReWisRhh1mo5T//CevWxb/8VMWF3nGccjFokM3k++abNhFZs2Y2SKqiOOMMq2/u3IqrI9VwoXccp1x07w4ZGTa18EcfmX9+jz0qrr7QCFkX+uhxoXccp1yIWKt+9Wro0gUuuKBi62vSxGbB/Pzziq0nlYhK6EWkn4gsFpElInJ9CflOFREVkR5haTcExy0WkRPiYbTjOMnFsGHWWfrPf9q6HRVNt24u9LFQaoSriKQBDwN9gRXAbBGZoqoLi+RrCFwJfBqW1hEYChwEtAbeE5H9VbWyZ61wHKcC6d3bVo+qU6dy6svKspG3W7ZAw4aVU2dVJpoW/SHAElVdqqo7gAnA4Aj5/gr8H7AtLG0wMEFVt6vqd8CSoDzHcVKMyhJ52OWnnzev8uqsykQj9BnA8rDPK4K03xCRbkAbVX0r1mOD40eKSLaIZOfm5kZluOM41ZfQuibuvomOcnfGikgN4D7g6rKWoaqPq2oPVe3RvHnz8prkOE6K07q1TV/sQh8d0cxCsRJoE/Y5M0gL0RDoBHwgtqpMS2CKiAyK4ljHcZyYETH3jYdYRkc0LfrZQHsRaScitbDO1d8GPKvqJlVtpqptVbUt8AkwSFWzg3xDRaS2iLQD2gOfxf0sHMepdnTrZqv8bd+eaEuSn1KFXlXzgMuA6cAiYJKqLhCRMUGrvaRjFwCTgIXANGC0R9w4jhMPsrIgL8/E3imZqCYQVdWpwNQiabcWk/eoIp/vBO4so32O4zgRCR8hG+qcdSLjI2Mdx6mS7LefxdB7h2zpuNA7jlMlqVEDunZ1oY8GF3rHcaosWVk2aCrfe/5KxIXecZwqS1YWbN0KX3+daEuSGxd6x3GqLD5CNjpc6B3HqbJ06AC1a7vQl4YLveM4VZb0dFswPNlHyP7tbxU/T39JRBVH7ziOk6x06waTJ4OqTY2QjEydCt98k7j6vUXvOE6VJisLNmyAH35ItCXFs2wZ/PQT7NiRmPpd6B3HqdKERsgmq59++3ZYtcq2Q++VjQu94zhVmoMPtsFTyeqnX77c3EoAKxM0d68LveM4VZp69eDAA5O3Rb9s2a7tFSsSY4MLveM4VZ6srKoh9N6idxzHKSNZWSaiP/2UaEt2Z9kySEuDunW9Re84jlNmknmE7LJl0KaNvVzoHcdxykjXrvaerELfti1kZLjrxnEcp8w0bmximoyRNyGhz8xMXIveR8Y6jpMSdOuWfC36UAx927a7tgsKLBy0MvEWveM4KUFWFixZAps3J9qSXYRi6EOum7y8xHQYu9A7jpMShEbIzpuXWDvCCYVWhlw3kBj3TVRCLyL9RGSxiCwRkesj7B8lIl+ISI6IfCQiHYP0tiLya5CeIyJj430CjuM4sCvyJpn89OFCn5Fh24nokC3VRy8iacDDQF9gBTBbRKao6sKwbC+o6tgg/yDgPqBfsO9bVe0aX7Mdx3EK06oVtGiRXH76UAx9RobNmw/J26I/BFiiqktVdQcwARgcnkFVw71i9QGNn4mO4zjRkWwjZEMx9DVrwl572XsiWvTRCH0GsDzs84ogrRAiMlpEvgXuBq4I29VORD4XkQ9FpHekCkRkpIhki0h2bm5uDOY7juPsols3WLAAtm1LtCVGKLQSLNKmdevkbdFHhao+rKr7AX8Gbg6SVwN7q2oWcBXwgog0inDs46raQ1V7NG/ePF4mOY5TzcjKgvx8+PLLRFtihAs9WIdssrboVwJtwj5nBmnFMQE4GUBVt6vqumB7DvAtsH/ZTHUcxymZZJqbfseOXTH0ITIykrdFPxtoLyLtRKQWMBSYEp5BRNqHfTwR+CZIbx505iIi+wLtgaXxMNxxHKco7dpBo0bJIfThMfQhQqNjtZJ7MUuNulHVPBG5DJgOpAFPqeoCERkDZKvqFOAyETkO2AlsAIYHh/cBxojITqAAGKWq6yviRBzHcWrUsFZ9MoRYhodWhsjIgK1bYdMm2HPPyrMlqikQVHUqMLVI2q1h21cWc9zLwMvlMdBxHCcWsrLgscfMV5+Wljg7Igl9+KCpyhR6HxnrOE5KkZUFv/4Kixcn1o7wGPoQIaGv7A5ZF3rHcVKKZBkhGx5DHyIk+pXdIetC7zhOSnHggVCnTuI7ZIuGVoLF0YO36B3HccpFzZrQuXNyCn2tWjZCtrJb9D4fveM4KUe3bjBuHAwZYouS7LmnvYput2sHFTFGc8cOa7UXFXpIzAIkLvSO46QcZ58N8+dDTg5s3AgbNsDOnbvna9LEBjWFJhyLF5Fi6ENkZMD338e3vtJwoXccJ+Xo1Qv++99dn1UtEmfjxl3CP2sW3HADfPQRHHtsfOuPFFoZIjMTPv44vvWVhvvoHcdJeUSgXj3rDO3YEY44Ai6/3Hzmb78d//pKE/r16+3GU1m40DuOUy2pXx/69IFp0+JfdqQY+hCJWIDEhd5xnGpLv342rfHy5aXnjYVIMfQhEjFoyoXecZxqS//+9h7vVn2k0MoQiRg05ULvOE61pUMHa3nH20+/bBnss0/kfS70juM4lYiIterfe89i3+NBSTH0AA0b2lTK7rpxHMepJPr1gy1b4H//i095JcXQh6jsQVMu9I7jVGuOPdY6TePlvikptDJEZS8p6ELvOE61plEjG2BVmUJf2UsKutA7jlPt6dfPpkxYtar8ZYVi6ENhlJHIzIQff4S8vPLXFw0u9I7jVHviGWa5bJkJeaQY+hAZGVBQYGJfGbjQO45T7enc2aZHiJfQl+S2gcJLClYGLvSO41R7RMx98+675XenRCP0lT0NQlRCLyL9RGSxiCwRkesj7B8lIl+ISI6IfCQiHcP23RAct1hEToin8Y7jOPGif3+b2fKTT8peRmkx9CGSrkUvImnAw0B/oCMwLFzIA15Q1c6q2hW4G7gvOLYjMBQ4COgHPBKU5ziOk1Qcd5x1opbHfbNiRekx9ABNm9oc+MnUoj8EWKKqS1V1BzABGByeQVU3h32sD2iwPRiYoKrbVfU7YElQnuM4TlKx555w2GHlC7OMJrQSzFVUmSGW0Qh9BhA+t9uKIK0QIjJaRL7FWvRXxHKs4zhOMtCvH8ydC2vWlO34aIUeKnd0bNw6Y1X1YVXdD/gzcHMsx4rISBHJFpHs3NzceJnkOI4TE6Ewy+nTy3Z8NDH0ITIykst1sxJoE/Y5M0grjgnAybEcq6qPq2oPVe3RvCJW6nUcx4mCrl1hr73K7r6JJoY+RGgaBNXS85aXaIR+NtBeRNqJSC2sc3VKeAYRaR/28UTgm2B7CjBURGqLSDugPfBZ+c12HMeJPzVqmPvmnXcgPz/246MJrQyRkQHbt8O6dbHXEyulCr2q5gGXAdOBRcAkVV0gImNEZFCQ7TIRWSAiOcBVwPDg2AXAJGAhMA0YrapluHyO4ziVQ//+tqbr7NmxHxuL0FdmiGUUDxigqlOBqUXSbg3bvrKEY+8E7iyrgY7jOJVJ377Wsn/7bTj00OiPizaGPkT4koJdu8ZsZkz4yFjHcZwwmjaFQw6JPZ5+xQqbvyYW103ouIrGhd5xHKcI/fqZ6yaWIMBYQisBWra0JwcXesdxnATQv79Fw7z7bvTHxCr0NWua2FdGiKULveM4ThF69IBmzWILs4wlhj5EZQ2acqF3HMcpQo0acPzxNnCqoCC6Y2KJoQ9RWUsKutA7juNEoH9/89HPnRtd/lhCK0NU1nw3LvSO4zgROP54e4/WfVMWoc/MhM2bYcuW2I6LFRd6x3GcCOy1l/nq33qr9LyxxtCHqKwFSFzoHcdximHYMPj0U3j++ZLzxRpDH6KyRse60DuO4xTDFVdAr15wySXw7bfF54s1tDKEt+gdx3ESTM2aMH68vQ8bZi6aSISEfp99Yiu/skbHutA7juOUwN57wxNP2EjZW26JnGfZMgvJjCWGHqBuXZtywVv0juM4CebUU2HkSLj77sijZUMx9OnpsZddGSGWLvSO4zhRcP/90KEDnHsu/PRT4X1lCa0MURmDplzoHcdxoqBePZgwATZsgBEjCo+YLY/Qe4vecRwniTj4YPjHP2wQ1b/+ZWk7d5Ythj5EZqY9IWzfHjczd8OF3nEcJwYuvRQGDYLrrrPpEcoaQx8iFHmzenXcTNwNF3rHcZwYEIGnnrKRs8OGwZdfWnp5WvRQse4bF3rHcZwYadoUnnsOvvkGRo2ytPIKfUV2yLrQO47jlIGjj4Ybb4RVq8oWQx+iMgZNxTBzsuM4jhPObbfBjBmwfn3ZYugB9tgD6tdPAteNiPQTkcUiskREro+w/yoRWSgi80XkPyKyT9i+fBHJCV5T4mm84zhOIklPtwFUM2aUvQwRa9VXpOum1Ba9iKQBDwN9gRXAbBGZoqoLw7J9DvRQ1a0icglwNzAk2PerqnaNs92O4zhJQb169ioPFb2kYDQt+kOAJaq6VFV3ABOAweEZVHWGqm4NPn4ClNFb5TiOU/2o6BZ9NEKfASwP+7wiSCuOC4DwNVnqiEi2iHwiIidHOkBERgZ5snNzc6MwyXEcJ3XIzLRO3WjXp42VuEbdiMjZQA/gnrDkfVS1B3Am8ICI7Ff0OFV9XFV7qGqP5s2bx9Mkx3GcpCczE/Lydp9DJ15EI/QrgTbhNgVphRCR44CbgEGq+ttgXlVdGbwvBT4Assphr+M4TspR0SGW0Qj9bKC9iLQTkVrAUKBQ9IyIZAGPYSL/U1h6YxGpHWw3A44AwjtxHcdxqj0VPTq21KgbVc0TkcuA6UAa8JSqLhCRMUC2qk7BXDUNgJdEBOAHVR0EdAAeE5EC7KZyV5FoHcdxnGpPRS8pGNWAKVWdCkwtknZr2PZxxRz3X6BzeQx0HMdJdfbay5YrTKTrxnEcx6lAatSA1q0rrkXvQu84jpMEVOSgKZ/rxnEcJwkYMAC2bi09X1lwoXccx0kCbrqp4sp2143jOE6K40LvOI6T4rjQO47jpDgu9I7jOCmOC73jOE6K40LvOI6T4rjQO47jpDgu9I7jOCmOqGqibSiEiOQC35ejiGbA2jiZUxlUNXvBba4sqprNVc1eSC2b91HViCs3JZ3QlxcRyQ5WtKoSVDV7wW2uLKqazVXNXqg+NrvrxnEcJ8VxoXccx0lxUlHoH0+0ATFS1ewFt7myqGo2VzV7oZrYnHI+esdxHKcwqdiidxzHccJwoXccx0lxUkboRaSfiCwWkSUicn2i7YkGEVkmIl+ISI6IZCfankiIyFMi8pOIfBmW1kRE3hWRb4L3xom0sSjF2Hy7iKwMrnWOiAxIpI3hiEgbEZkhIgtFZIGIXBmkJ+11LsHmZL7OdUTkMxGZF9j8lyC9nYh8GmjHRBGplWhboUR7nxGR78KucddSC1PVKv8C0oBvgX2BWsA8oGOi7YrC7mVAs0TbUYqNfYBuwJdhaXcD1wfb1wP/l2g7o7D5duCaRNtWjL2tgG7BdkPga6BjMl/nEmxO5ussQINgOx34FDgUmAQMDdLHApck2tZS7H0GOC2WslKlRX8IsERVl6rqDmACMDjBNqUEqjoTWF8keTDwbLD9LHBypRpVCsXYnLSo6mpVnRtsbwEWARkk8XUuweakRY2fg4/pwUuBY4DJQXrSXOcS7I2ZVBH6DGB52OcV5YSg7AAAAk1JREFUJPmPLkCBd0RkjoiMTLQxMdBCVVcH2z8CLRJpTAxcJiLzA9dO0rhBwhGRtkAW1nqrEte5iM2QxNdZRNJEJAf4CXgX8wRsVNW8IEtSaUdRe1U1dI3vDK7x/SJSu7RyUkXoqyq9VLUb0B8YLSJ9Em1QrKg9V1aFGN1Hgf2ArsBq4B+JNWd3RKQB8DLwR1XdHL4vWa9zBJuT+jqrar6qdgUyMU/AgQk2qUSK2isinYAbMLt7Ak2AP5dWTqoI/UqgTdjnzCAtqVHVlcH7T8Cr2A+vKrBGRFoBBO8/JdieUlHVNcGfpgD4N0l2rUUkHRPM8ar6SpCc1Nc5ks3Jfp1DqOpGYAZwGLCniNQMdiWldoTZ2y9wm6mqbgeeJoprnCpCPxtoH/Se1wKGAlMSbFOJiEh9EWkY2gaOB74s+aikYQowPNgeDryeQFuiIiSYAX8gia61iAjwJLBIVe8L25W017k4m5P8OjcXkT2D7bpAX6xvYQZwWpAtaa5zMfZ+FXbzF6w/odRrnDIjY4MwrgewCJynVPXOBJtUIiKyL9aKB6gJvJCMNovIi8BR2NSoa4DbgNewSIW9sSmlz1DVpOn8LMbmozB3gmLRTheH+b8Tioj0AmYBXwAFQfKNmM87Ka9zCTYPI3mv88FYZ2sa1sidpKpjgv/iBMwN8jlwdtBaTigl2Ps+0ByLyskBRoV12kYuK1WE3nEcx4lMqrhuHMdxnGJwoXccx0lxXOgdx3FSHBd6x3GcFMeF3nEcJ8VxoXccx0lxXOgdx3FSnP8HefS00UdXp/AAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the training and validation accuracy metrices.\n",
        "plot_metric(convlstm_model_training_history, 'accuracy', 'val_accuracy', 'Total Accuracy vs Total Validation Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "5hDf50kvY92w",
        "outputId": "b84010ea-d68e-42c1-b8cf-30c9ba71d190"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}