3D_CNN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alexsalman/CSE247/blob/main/3D_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kk7I8NauauE3"
      },
      "source": [
        "####**3D Convolutional Neural Network**\n",
        "######*I am using 3D Convolutional Neural Network to extract the temporal and spatial information which are merged slowly throughout the whole network.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8ibtd5HKtZk",
        "outputId": "3f20d494-3fc5-4773-d099-34c71cd7a15a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.0\n"
          ]
        }
      ],
      "source": [
        "# required libraries\n",
        "import os\n",
        "import cv2\n",
        "import random\n",
        "import numpy as np\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import drive\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Flatten, Conv3D, MaxPooling3D, Dropout\n",
        "from keras.layers import BatchNormalization, GlobalAveragePooling3D\n",
        "from keras import regularizers\n",
        "%matplotlib inline\n",
        "print(tf.version.VERSION)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "iffdFOf1CEAN"
      },
      "outputs": [],
      "source": [
        "# set Numpy, Python, and Tensorflow seeds to get consistent results on every execution\n",
        "seed_constant = 27\n",
        "np.random.seed(seed_constant)\n",
        "random.seed(seed_constant)\n",
        "tf.random.set_seed(seed_constant)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "mcLh22LiOHyn",
        "outputId": "d1798c42-957e-47fb-fd78-e0861da82b18"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/gdrive/My Drive/247'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# mount dataset from google drive\n",
        "drive.mount('/content/gdrive/', force_remount=True)\n",
        "gdrive_path = '/content/gdrive' + '/My Drive/247/'\n",
        "os.chdir(gdrive_path)\n",
        "os.getcwd()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "oeDK8SzumZ1Q"
      },
      "outputs": [],
      "source": [
        "# frame dimention\n",
        "IMAGE_HEIGHT, IMAGE_WIDTH = 128, 128\n",
        "# frame number for each video (depth)\n",
        "SEQUENCE_LENGTH = 16\n",
        "# video dir path\n",
        "DATASET_DIR = gdrive_path + 'Circle_Cropped_videos_labeled'\n",
        "# DATASET_DIR = gdrive_path + 'Cropped_videos'\n",
        "# labels of classes\n",
        "CLASSES_LIST = ['hemostasis', 'inflammatory', 'proliferative', 'maturation']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "HUTeIqzpZc9J"
      },
      "outputs": [],
      "source": [
        "# image cropping\n",
        "def crop_center_square(frame):\n",
        "    y, x = frame.shape[0:2]\n",
        "    min_dim = min(y, x)\n",
        "    start_x = (x // 2) - (min_dim // 2)\n",
        "    start_y = (y // 2) - (min_dim // 2)\n",
        "    return frame[start_y:start_y+min_dim,start_x:start_x+min_dim]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "QRDbHG0TZkYJ"
      },
      "outputs": [],
      "source": [
        "def load_video(path, resize=(128, 128)):\n",
        "    video_reader = cv2.VideoCapture(path)\n",
        "    frames = []\n",
        "    try:\n",
        "        while True:\n",
        "            ret, frame = video_reader.read()\n",
        "            if not ret:\n",
        "                  break\n",
        "            # frame = crop_center_square(frame)\n",
        "            frame = cv2.resize(frame, resize)\n",
        "            frame = frame[:, :, [2, 1, 0]]\n",
        "            black_frame = frame\n",
        "            frames.append(frame)\n",
        "    finally:\n",
        "        video_reader.release()\n",
        "    return np.array(frames) / 255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ljUWHW6Jqzu-"
      },
      "outputs": [],
      "source": [
        "def create_dataset(state):\n",
        "    # Declared Empty Lists to store the features, labels and video file path values.\n",
        "    features = []\n",
        "    labels = []\n",
        "    video_files_paths = []\n",
        "    # Iterating through all the classes mentioned in the classes list\n",
        "    for class_index, class_name in enumerate(CLASSES_LIST):\n",
        "        # Display the name of the class whose data is being extracted.\n",
        "        print(f'Extracting Data of Class: {class_name} {state}')\n",
        "        # Get the list of video files present in the specific class name directory.\n",
        "        files_list = os.listdir(os.path.join(DATASET_DIR, class_name))\n",
        "        # Iterate through all the files present in the files list.\n",
        "        for file_name in files_list:\n",
        "            # Get the complete video path.\n",
        "            video_file_path = os.path.join(DATASET_DIR, class_name, file_name)\n",
        "            # create testing data\n",
        "            if state == 'test':\n",
        "                # get the mouse number\n",
        "                mouse_number = int(video_file_path.split(' ')[2].split('_')[1].split('-')[1])\n",
        "                # get the mouse side (L or R)\n",
        "                mouse_side = video_file_path.split(' ')[2].split('_')[1].split('-')[2]\n",
        "                if mouse_number == 4 and mouse_side == 'L':\n",
        "                    frames = load_video(video_file_path)\n",
        "                    features.append(frames)\n",
        "                    labels.append(class_index)\n",
        "                    video_files_paths.append(video_file_path)\n",
        "            # create validation data\n",
        "            elif state == 'valid':\n",
        "                # get the mouse number\n",
        "                mouse_number = int(video_file_path.split(' ')[2].split('_')[1].split('-')[1])\n",
        "                # get the mouse side (L or R)\n",
        "                mouse_side = video_file_path.split(' ')[2].split('_')[1].split('-')[2]\n",
        "                if mouse_number == 4 and mouse_side == 'R':\n",
        "                    frames = load_video(video_file_path)\n",
        "                    features.append(frames)\n",
        "                    labels.append(class_index)\n",
        "                    video_files_paths.append(video_file_path)\n",
        "            # create training data\n",
        "            else:\n",
        "                # get the mouse number\n",
        "                mouse_number = int(video_file_path.split(' ')[2].split('_')[1].split('-')[1])\n",
        "                if mouse_number != 4:\n",
        "                    frames = load_video(video_file_path)\n",
        "                    features.append(frames)\n",
        "                    labels.append(class_index)\n",
        "                    video_files_paths.append(video_file_path)\n",
        "    # Converting the list to numpy arrays\n",
        "    features = np.asarray(features)\n",
        "    # print(features)\n",
        "    labels = np.array(labels)\n",
        "    # Return the frames, class index, and video file path.\n",
        "    return features, labels, video_files_paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8rpanz9rASe",
        "outputId": "010affed-afdc-4beb-b04b-d58e45842a09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting Data of Class: hemostasis train\n",
            "Extracting Data of Class: inflammatory train\n",
            "Extracting Data of Class: proliferative train\n",
            "Extracting Data of Class: maturation train\n",
            "Extracting Data of Class: hemostasis test\n",
            "Extracting Data of Class: inflammatory test\n",
            "Extracting Data of Class: proliferative test\n",
            "Extracting Data of Class: maturation test\n",
            "Extracting Data of Class: hemostasis valid\n",
            "Extracting Data of Class: inflammatory valid\n",
            "Extracting Data of Class: proliferative valid\n",
            "Extracting Data of Class: maturation valid\n"
          ]
        }
      ],
      "source": [
        "# 6 mice for training, 2 mice for test and validation (one wound on each mice for test one for validation)\n",
        "features_train, labels_train, video_files_paths_train = create_dataset('train')\n",
        "features_test, labels_test, video_files_paths_test = create_dataset('test')\n",
        "features_valid, labels_valid, video_files_paths_valid = create_dataset('valid')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "dtJkK4qTAulC"
      },
      "outputs": [],
      "source": [
        "# labels to catogorical\n",
        "labels_train = keras.utils.to_categorical(labels_train)\n",
        "labels_test = keras.utils.to_categorical(labels_test)\n",
        "labels_valid = keras.utils.to_categorical(labels_valid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "N-9ykP4ig7IW"
      },
      "outputs": [],
      "source": [
        "def create_3D_CNN_model():\n",
        "    sample_shape = (16, 128, 128, 3)\n",
        "    model = Sequential()\n",
        "\n",
        "    model.add(Conv3D(16, (3,3,3), activation='relu', kernel_initializer='he_uniform',\n",
        "                     kernel_regularizer=regularizers.L2(l2=1e-4),\n",
        "                     input_shape=sample_shape))\n",
        "    model.add(MaxPooling3D(2))\n",
        "    model.add(Dropout(0.3))\n",
        "\n",
        "    model.add(Conv3D(32, (3,3,3), activation='relu', kernel_initializer='he_uniform',\n",
        "                     kernel_regularizer=regularizers.L2(l2=1e-4)))\n",
        "    model.add(MaxPooling3D(2))\n",
        "    model.add(Dropout(0.3))\n",
        "\n",
        "    model.add(GlobalAveragePooling3D())\n",
        "    model.add(Dropout(0.3))\n",
        "\n",
        "    model.add(Dense(16, activation='relu', kernel_initializer='he_uniform',\n",
        "                    kernel_regularizer=regularizers.L2(l2=1e-4)))\n",
        "    model.add(Dropout(0.4))\n",
        "\n",
        "    model.add(Dense(8, activation='relu', kernel_initializer='he_uniform',\n",
        "                    kernel_regularizer=regularizers.L2(l2=1e-4)))\n",
        "\n",
        "    model.add(Dense(len(CLASSES_LIST), activation='softmax'))\n",
        "\n",
        "    model.summary(line_length = 125)\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4_GxXZBcHlB",
        "outputId": "507eeeaf-9cb3-4e00-c20f-0e47c4e93f5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_____________________________________________________________________________________________________________________________\n",
            " Layer (type)                                           Output Shape                                      Param #            \n",
            "=============================================================================================================================\n",
            " conv3d (Conv3D)                                        (None, 14, 126, 126, 16)                          1312               \n",
            "                                                                                                                             \n",
            " max_pooling3d (MaxPooling3D)                           (None, 7, 63, 63, 16)                             0                  \n",
            "                                                                                                                             \n",
            " dropout (Dropout)                                      (None, 7, 63, 63, 16)                             0                  \n",
            "                                                                                                                             \n",
            " conv3d_1 (Conv3D)                                      (None, 5, 61, 61, 32)                             13856              \n",
            "                                                                                                                             \n",
            " max_pooling3d_1 (MaxPooling3D)                         (None, 2, 30, 30, 32)                             0                  \n",
            "                                                                                                                             \n",
            " dropout_1 (Dropout)                                    (None, 2, 30, 30, 32)                             0                  \n",
            "                                                                                                                             \n",
            " global_average_pooling3d (GlobalAveragePooling3D)      (None, 32)                                        0                  \n",
            "                                                                                                                             \n",
            " dropout_2 (Dropout)                                    (None, 32)                                        0                  \n",
            "                                                                                                                             \n",
            " dense (Dense)                                          (None, 16)                                        528                \n",
            "                                                                                                                             \n",
            " dropout_3 (Dropout)                                    (None, 16)                                        0                  \n",
            "                                                                                                                             \n",
            " dense_1 (Dense)                                        (None, 8)                                         136                \n",
            "                                                                                                                             \n",
            " dense_2 (Dense)                                        (None, 4)                                         36                 \n",
            "                                                                                                                             \n",
            "=============================================================================================================================\n",
            "Total params: 15,868\n",
            "Trainable params: 15,868\n",
            "Non-trainable params: 0\n",
            "_____________________________________________________________________________________________________________________________\n",
            "Model Created Successfully!\n"
          ]
        }
      ],
      "source": [
        "# Construct the required convlstm model.\n",
        "model = create_3D_CNN_model()\n",
        " \n",
        "# Display the success message. \n",
        "print(\"Model Created Successfully!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwYEkaYLoyb_",
        "outputId": "611cb4ac-936f-481f-d879-3963640f3ca9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "31/31 [==============================] - 8s 172ms/step - loss: 1.5209 - accuracy: 0.2811 - val_loss: 1.3318 - val_accuracy: 0.3897\n",
            "Epoch 2/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 1.2885 - accuracy: 0.4102 - val_loss: 1.2833 - val_accuracy: 0.3971\n",
            "Epoch 3/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 1.2217 - accuracy: 0.4477 - val_loss: 1.2153 - val_accuracy: 0.3971\n",
            "Epoch 4/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 1.1914 - accuracy: 0.4514 - val_loss: 1.1585 - val_accuracy: 0.3971\n",
            "Epoch 5/100\n",
            "31/31 [==============================] - 4s 143ms/step - loss: 1.1414 - accuracy: 0.4643 - val_loss: 1.0918 - val_accuracy: 0.3971\n",
            "Epoch 6/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 1.1005 - accuracy: 0.5043 - val_loss: 1.0224 - val_accuracy: 0.5331\n",
            "Epoch 7/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 1.0413 - accuracy: 0.5172 - val_loss: 0.9112 - val_accuracy: 0.5846\n",
            "Epoch 8/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.9862 - accuracy: 0.5590 - val_loss: 0.8933 - val_accuracy: 0.6103\n",
            "Epoch 9/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.9351 - accuracy: 0.5560 - val_loss: 0.8377 - val_accuracy: 0.6103\n",
            "Epoch 10/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.9260 - accuracy: 0.5627 - val_loss: 0.8248 - val_accuracy: 0.6287\n",
            "Epoch 11/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.8734 - accuracy: 0.5923 - val_loss: 0.7828 - val_accuracy: 0.6176\n",
            "Epoch 12/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.8720 - accuracy: 0.5812 - val_loss: 0.7738 - val_accuracy: 0.6360\n",
            "Epoch 13/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.8681 - accuracy: 0.5683 - val_loss: 0.7760 - val_accuracy: 0.6140\n",
            "Epoch 14/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.8674 - accuracy: 0.5664 - val_loss: 0.7864 - val_accuracy: 0.6029\n",
            "Epoch 15/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.8455 - accuracy: 0.5959 - val_loss: 0.7639 - val_accuracy: 0.6434\n",
            "Epoch 16/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.8508 - accuracy: 0.5849 - val_loss: 0.7544 - val_accuracy: 0.6287\n",
            "Epoch 17/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.8261 - accuracy: 0.5892 - val_loss: 0.7461 - val_accuracy: 0.6287\n",
            "Epoch 18/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.8221 - accuracy: 0.6015 - val_loss: 0.7289 - val_accuracy: 0.6140\n",
            "Epoch 19/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.8149 - accuracy: 0.6224 - val_loss: 0.7411 - val_accuracy: 0.6324\n",
            "Epoch 20/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.8066 - accuracy: 0.6039 - val_loss: 0.7319 - val_accuracy: 0.6213\n",
            "Epoch 21/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.8025 - accuracy: 0.6150 - val_loss: 0.7297 - val_accuracy: 0.6360\n",
            "Epoch 22/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.7838 - accuracy: 0.6341 - val_loss: 0.7126 - val_accuracy: 0.6324\n",
            "Epoch 23/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.7756 - accuracy: 0.6371 - val_loss: 0.7005 - val_accuracy: 0.6324\n",
            "Epoch 24/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.7972 - accuracy: 0.6328 - val_loss: 0.7040 - val_accuracy: 0.6176\n",
            "Epoch 25/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.7589 - accuracy: 0.6574 - val_loss: 0.6924 - val_accuracy: 0.6213\n",
            "Epoch 26/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.7576 - accuracy: 0.6617 - val_loss: 0.6751 - val_accuracy: 0.6471\n",
            "Epoch 27/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.7374 - accuracy: 0.6482 - val_loss: 0.6636 - val_accuracy: 0.6618\n",
            "Epoch 28/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.7501 - accuracy: 0.6371 - val_loss: 0.6736 - val_accuracy: 0.6434\n",
            "Epoch 29/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.7234 - accuracy: 0.6648 - val_loss: 0.6632 - val_accuracy: 0.6544\n",
            "Epoch 30/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.6880 - accuracy: 0.6765 - val_loss: 0.6416 - val_accuracy: 0.6838\n",
            "Epoch 31/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.7204 - accuracy: 0.6654 - val_loss: 0.6451 - val_accuracy: 0.6691\n",
            "Epoch 32/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6956 - accuracy: 0.6851 - val_loss: 0.6432 - val_accuracy: 0.6838\n",
            "Epoch 33/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.6982 - accuracy: 0.6753 - val_loss: 0.6532 - val_accuracy: 0.6434\n",
            "Epoch 34/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.7145 - accuracy: 0.6661 - val_loss: 0.6442 - val_accuracy: 0.6875\n",
            "Epoch 35/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.7027 - accuracy: 0.6833 - val_loss: 0.6288 - val_accuracy: 0.6949\n",
            "Epoch 36/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6903 - accuracy: 0.6863 - val_loss: 0.6272 - val_accuracy: 0.6912\n",
            "Epoch 37/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6586 - accuracy: 0.7017 - val_loss: 0.6195 - val_accuracy: 0.6838\n",
            "Epoch 38/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6640 - accuracy: 0.6950 - val_loss: 0.6140 - val_accuracy: 0.6838\n",
            "Epoch 39/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6850 - accuracy: 0.6931 - val_loss: 0.6207 - val_accuracy: 0.6985\n",
            "Epoch 40/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.6835 - accuracy: 0.6937 - val_loss: 0.6344 - val_accuracy: 0.6875\n",
            "Epoch 41/100\n",
            "31/31 [==============================] - 4s 143ms/step - loss: 0.6666 - accuracy: 0.7036 - val_loss: 0.6090 - val_accuracy: 0.6875\n",
            "Epoch 42/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.6545 - accuracy: 0.7048 - val_loss: 0.6152 - val_accuracy: 0.6654\n",
            "Epoch 43/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.6662 - accuracy: 0.6956 - val_loss: 0.6318 - val_accuracy: 0.6838\n",
            "Epoch 44/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.6515 - accuracy: 0.7073 - val_loss: 0.6029 - val_accuracy: 0.6949\n",
            "Epoch 45/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.6477 - accuracy: 0.7116 - val_loss: 0.6030 - val_accuracy: 0.6728\n",
            "Epoch 46/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6438 - accuracy: 0.7134 - val_loss: 0.6819 - val_accuracy: 0.6875\n",
            "Epoch 47/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6362 - accuracy: 0.7232 - val_loss: 0.6033 - val_accuracy: 0.6801\n",
            "Epoch 48/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.6385 - accuracy: 0.7171 - val_loss: 0.5864 - val_accuracy: 0.6875\n",
            "Epoch 49/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.6196 - accuracy: 0.7294 - val_loss: 0.5860 - val_accuracy: 0.7022\n",
            "Epoch 50/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6375 - accuracy: 0.7066 - val_loss: 0.5948 - val_accuracy: 0.6801\n",
            "Epoch 51/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6097 - accuracy: 0.7208 - val_loss: 0.5858 - val_accuracy: 0.7059\n",
            "Epoch 52/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6284 - accuracy: 0.7220 - val_loss: 0.5859 - val_accuracy: 0.6949\n",
            "Epoch 53/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.6312 - accuracy: 0.7177 - val_loss: 0.6115 - val_accuracy: 0.6949\n",
            "Epoch 54/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.6178 - accuracy: 0.7282 - val_loss: 0.5884 - val_accuracy: 0.6985\n",
            "Epoch 55/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5933 - accuracy: 0.7386 - val_loss: 0.5985 - val_accuracy: 0.6801\n",
            "Epoch 56/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.6056 - accuracy: 0.7368 - val_loss: 0.5920 - val_accuracy: 0.6765\n",
            "Epoch 57/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.6035 - accuracy: 0.7325 - val_loss: 0.5727 - val_accuracy: 0.7096\n",
            "Epoch 58/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6109 - accuracy: 0.7294 - val_loss: 0.5840 - val_accuracy: 0.7096\n",
            "Epoch 59/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5895 - accuracy: 0.7497 - val_loss: 0.5908 - val_accuracy: 0.6912\n",
            "Epoch 60/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.5994 - accuracy: 0.7374 - val_loss: 0.6224 - val_accuracy: 0.6912\n",
            "Epoch 61/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5822 - accuracy: 0.7442 - val_loss: 0.5791 - val_accuracy: 0.6912\n",
            "Epoch 62/100\n",
            "31/31 [==============================] - 4s 144ms/step - loss: 0.5822 - accuracy: 0.7509 - val_loss: 0.6028 - val_accuracy: 0.6691\n",
            "Epoch 63/100\n",
            "31/31 [==============================] - 4s 143ms/step - loss: 0.5829 - accuracy: 0.7374 - val_loss: 0.5939 - val_accuracy: 0.6654\n",
            "Epoch 64/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5900 - accuracy: 0.7478 - val_loss: 0.6055 - val_accuracy: 0.6618\n",
            "Epoch 65/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5797 - accuracy: 0.7399 - val_loss: 0.6589 - val_accuracy: 0.6728\n",
            "Epoch 66/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5771 - accuracy: 0.7485 - val_loss: 0.5736 - val_accuracy: 0.7022\n",
            "Epoch 67/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5733 - accuracy: 0.7460 - val_loss: 0.6733 - val_accuracy: 0.6875\n",
            "Epoch 68/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.5928 - accuracy: 0.7386 - val_loss: 0.6470 - val_accuracy: 0.6581\n",
            "Epoch 69/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5792 - accuracy: 0.7276 - val_loss: 0.6174 - val_accuracy: 0.6728\n",
            "Epoch 70/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.5762 - accuracy: 0.7540 - val_loss: 0.5780 - val_accuracy: 0.6985\n",
            "Epoch 71/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5812 - accuracy: 0.7417 - val_loss: 0.6157 - val_accuracy: 0.6618\n",
            "Epoch 72/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.5644 - accuracy: 0.7478 - val_loss: 0.6039 - val_accuracy: 0.6838\n",
            "Epoch 73/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5731 - accuracy: 0.7423 - val_loss: 0.6085 - val_accuracy: 0.6654\n",
            "Epoch 74/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.5640 - accuracy: 0.7429 - val_loss: 0.5766 - val_accuracy: 0.7059\n",
            "Epoch 75/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5503 - accuracy: 0.7601 - val_loss: 0.6212 - val_accuracy: 0.6765\n",
            "Epoch 76/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5557 - accuracy: 0.7688 - val_loss: 0.6418 - val_accuracy: 0.6912\n",
            "Epoch 77/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.5666 - accuracy: 0.7565 - val_loss: 0.5851 - val_accuracy: 0.6875\n",
            "Epoch 78/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.5651 - accuracy: 0.7460 - val_loss: 0.5673 - val_accuracy: 0.7096\n",
            "Epoch 79/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.6102 - accuracy: 0.7269 - val_loss: 0.5878 - val_accuracy: 0.6801\n",
            "Epoch 80/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.5614 - accuracy: 0.7411 - val_loss: 0.5856 - val_accuracy: 0.6912\n",
            "Epoch 81/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.5472 - accuracy: 0.7601 - val_loss: 0.5757 - val_accuracy: 0.6912\n",
            "Epoch 82/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5608 - accuracy: 0.7540 - val_loss: 0.7126 - val_accuracy: 0.7059\n",
            "Epoch 83/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5739 - accuracy: 0.7534 - val_loss: 0.6260 - val_accuracy: 0.6618\n",
            "Epoch 84/100\n",
            "31/31 [==============================] - 4s 144ms/step - loss: 0.5642 - accuracy: 0.7565 - val_loss: 0.6668 - val_accuracy: 0.6912\n",
            "Epoch 85/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.5419 - accuracy: 0.7657 - val_loss: 0.5810 - val_accuracy: 0.6985\n",
            "Epoch 86/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.6236 - accuracy: 0.7288 - val_loss: 0.6342 - val_accuracy: 0.6654\n",
            "Epoch 87/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5436 - accuracy: 0.7565 - val_loss: 0.6290 - val_accuracy: 0.6838\n",
            "Epoch 88/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.5710 - accuracy: 0.7466 - val_loss: 0.5955 - val_accuracy: 0.6838\n",
            "Epoch 89/100\n",
            "31/31 [==============================] - 4s 143ms/step - loss: 0.6068 - accuracy: 0.7306 - val_loss: 0.5790 - val_accuracy: 0.7059\n",
            "Epoch 90/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5481 - accuracy: 0.7706 - val_loss: 0.6294 - val_accuracy: 0.6765\n",
            "Epoch 91/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.5302 - accuracy: 0.7577 - val_loss: 0.5952 - val_accuracy: 0.6912\n",
            "Epoch 92/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5474 - accuracy: 0.7614 - val_loss: 0.5908 - val_accuracy: 0.6912\n",
            "Epoch 93/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5381 - accuracy: 0.7681 - val_loss: 0.5822 - val_accuracy: 0.7169\n",
            "Epoch 94/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5448 - accuracy: 0.7712 - val_loss: 0.6240 - val_accuracy: 0.6801\n",
            "Epoch 95/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.5309 - accuracy: 0.7620 - val_loss: 0.5637 - val_accuracy: 0.7132\n",
            "Epoch 96/100\n",
            "31/31 [==============================] - 4s 140ms/step - loss: 0.5366 - accuracy: 0.7638 - val_loss: 0.5755 - val_accuracy: 0.6912\n",
            "Epoch 97/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.5202 - accuracy: 0.7829 - val_loss: 0.6281 - val_accuracy: 0.6838\n",
            "Epoch 98/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.5347 - accuracy: 0.7712 - val_loss: 0.5725 - val_accuracy: 0.7022\n",
            "Epoch 99/100\n",
            "31/31 [==============================] - 4s 142ms/step - loss: 0.5457 - accuracy: 0.7540 - val_loss: 0.6747 - val_accuracy: 0.6765\n",
            "Epoch 100/100\n",
            "31/31 [==============================] - 4s 141ms/step - loss: 0.5337 - accuracy: 0.7657 - val_loss: 0.6430 - val_accuracy: 0.6801\n"
          ]
        }
      ],
      "source": [
        "# Compile the model and specify loss function, optimizer and metrics values to the model\n",
        "model.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer= keras.optimizers.Adam(0.0008, decay=1e-4),\n",
        "              metrics=['accuracy'])\n",
        "# Start training the model.\n",
        "cnn_3d_model_training_history = model.fit(x = features_train,\n",
        "                                          y = labels_train,\n",
        "                                          epochs=100,\n",
        "                                          batch_size=54,\n",
        "                                          shuffle = True,\n",
        "                                          validation_data = (features_valid, labels_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "3vimsgjjbXvL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88884428-9fa0-4b41-90c8-50827247eb87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 1s 44ms/step - loss: 0.8168 - accuracy: 0.6581\n",
            "\n",
            "\n",
            "Train accuracy: 79.951 % || Test accuracy: 65.809 % || Val accuracy: 68.015 %\n",
            "Train loss: 0.478 || Test loss: 0.817 || Val loss: 0.643\n"
          ]
        }
      ],
      "source": [
        "model_evaluation_history = model.evaluate(features_test, labels_test)\n",
        "print('\\n')\n",
        "train_loss, train_acc = model.evaluate(features_train, labels_train, verbose=0)\n",
        "test_loss, test_acc = model.evaluate(features_test, labels_test, verbose=0)\n",
        "val_loss, val_acc = model.evaluate(features_valid, labels_valid, verbose=0)\n",
        "print(f'Train accuracy: {train_acc*100:.3f} % || Test accuracy: {test_acc*100:.3f} % || Val accuracy: {val_acc*100:.3f} %')\n",
        "print(f'Train loss: {train_loss:.3f} || Test loss: {test_loss:.3f} || Val loss: {val_loss:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ivmaK9BlbnRQ"
      },
      "outputs": [],
      "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'3D_CNN_model___Date_Time_{current_date_time_string}___Loss_{model_evaluation_loss}___Accuracy_{model_evaluation_accuracy}.h5'\n",
        "# Change dir\n",
        "gdrive_path = '/content/gdrive' + '/My Drive/247/Saved_models/'\n",
        "os.chdir(gdrive_path)\n",
        "# Create a floder for the model files\n",
        "!mkdir -p cnn_3d_{current_date_time_string}\n",
        "# Save your Model.\n",
        "model.save('convlstm_' + str(current_date_time_string) + '/' + model_file_name)\n",
        "# Save model weights\n",
        "model.save_weights('convlstm_' + str(current_date_time_string) + '/' + 'weights')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "OwU8TwPrbsKB"
      },
      "outputs": [],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "cvKY05ncbwof",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "27e4a055-8f70-480c-f25e-8c6f9381cf14"
      },
      "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"
          }
        }
      ],
      "source": [
        "plot_metric(cnn_3d_model_training_history, 'loss', 'val_loss', 'Total Loss vs Total Validation Loss')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "o1iaaDHBb5i0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "3d4dc07e-351a-451b-a00c-c2cc200cfed4"
      },
      "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"
          }
        }
      ],
      "source": [
        "plot_metric(cnn_3d_model_training_history, 'accuracy', 'val_accuracy', 'Total Accuracy vs Total Validation Accuracy') "
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "background_execution": "on",
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "3D_CNN.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMqx6JCNKQL60Hp4gFYTugu",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}