MobileNetV2+LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nirob\\AppData\\Local\\Temp\\ipykernel_4660\\1048294035.py:14: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use(\"seaborn\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "from IPython.display import HTML\n",
    "import cv2\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.applications.mobilenet_v2 import MobileNetV2\n",
    "from keras.models import Sequential\n",
    "from keras.layers import *\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.utils import plot_model\n",
    "plt.style.use(\"seaborn\")\n",
    "ax= plt.subplot()\n",
    "from sklearn.metrics import accuracy_score\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature extracting...\n",
      "Extracing data of class: Non Violence\n",
      "Extracing data of class: Violence\n",
      "Saved feature files successfully\n"
     ]
    }
   ],
   "source": [
    "IMAGE_HEIGHT, IMAGE_WIDTH = 64, 64\n",
    "SEQUENCE_LENGTH = 16\n",
    "CLASSES_LIST = [\"Non Violence\", \"Violence\"]\n",
    "video_dir=\"Our_Dataset/\"\n",
    "\n",
    "def frame_extraction(video_path):\n",
    "    video_reader = cv2.VideoCapture(video_path)\n",
    "    video_frame_count = video_reader.get(cv2.CAP_PROP_FRAME_COUNT)\n",
    "    skip_frames_window = max(int(video_frame_count / SEQUENCE_LENGTH), 1)\n",
    "    frame_list = []\n",
    "    for frame_counter in range(SEQUENCE_LENGTH):\n",
    "        video_reader.set(cv2.CAP_PROP_POS_FRAMES, skip_frames_window * frame_counter)\n",
    "        success, frame = video_reader.read()\n",
    "        if not success:\n",
    "            break\n",
    "        resized_frame = cv2.resize(frame, (IMAGE_HEIGHT, IMAGE_WIDTH))\n",
    "        normalized_frame = resized_frame / 255\n",
    "        frame_list.append(normalized_frame)\n",
    "\n",
    "    video_reader.release()\n",
    "    return frame_list\n",
    "\n",
    "def create_dataset():\n",
    "    features = []\n",
    "    labels = []\n",
    "    video_file_paths = []\n",
    "\n",
    "    for class_index, class_name in enumerate(CLASSES_LIST):\n",
    "        print(\"Extracing data of class:\", class_name)\n",
    "        files_list = os.listdir(os.path.join(video_dir, class_name))\n",
    "        for file_name in files_list:\n",
    "            video_file_path = os.path.join(video_dir, class_name, file_name)\n",
    "            frames = frame_extraction(video_file_path)\n",
    "            if len(frames) == SEQUENCE_LENGTH:\n",
    "                features.append(frames)\n",
    "                labels.append(class_index)\n",
    "                video_file_paths.append(video_file_path)\n",
    "\n",
    "    features = np.asarray(features)\n",
    "    labels = np.asarray(labels)\n",
    "    return features, labels, video_file_paths\n",
    "\n",
    "if __name__==\"__main__\":\n",
    "    print(\"Feature extracting...\")\n",
    "    features,labels,video_file_paths=create_dataset()\n",
    "    np.save(\"Features/features_Own.npy\", features)\n",
    "    np.save(\"Features/labels_Own.npy\", labels)\n",
    "    np.save(\"Features/video_file_paths_Own.npy\", video_file_paths)\n",
    "    print(\"Saved feature files successfully\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "features,labels=np.load(\"Features/features_Own.npy\"),np.load(\"Features/labels_Own.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoded_labels = to_categorical(labels)\n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features, one_hot_encoded_labels, test_size=0.2,\n",
    "                                                                            shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(899, 16, 64, 64, 3) (899, 2)\n",
      "(100, 16, 64, 64, 3) (100, 2)\n"
     ]
    }
   ],
   "source": [
    "print(features_train.shape,labels_train.shape)\n",
    "print(features_test.shape,labels_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.mobilenet_v2 import MobileNetV2\n",
    "\n",
    "mobilenet = MobileNetV2( include_top=False , weights=\"imagenet\")\n",
    "\n",
    "#Fine-Tuning to make the last 40 layer trainable\n",
    "mobilenet.trainable=True\n",
    "\n",
    "for layer in mobilenet.layers[:-40]:\n",
    "  layer.trainable=False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model():\n",
    " \n",
    "    model = Sequential()\n",
    "\n",
    "    ########################################################################################################################\n",
    "    \n",
    "    #Specifying Input to match features shape\n",
    "    model.add(Input(shape = (SEQUENCE_LENGTH, IMAGE_HEIGHT, IMAGE_WIDTH, 3)))\n",
    "    \n",
    "    # Passing mobilenet in the TimeDistributed layer to handle the sequence\n",
    "    model.add(TimeDistributed(mobilenet))\n",
    "    \n",
    "    model.add(Dropout(0.25))\n",
    "                                    \n",
    "    model.add(TimeDistributed(Flatten()))\n",
    "\n",
    "    \n",
    "    lstm_fw = LSTM(units=32)\n",
    "    lstm_bw = LSTM(units=32, go_backwards = True)  \n",
    "\n",
    "    model.add(Bidirectional(lstm_fw, backward_layer = lstm_bw))\n",
    "    \n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Dense(256,activation='relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Dense(128,activation='relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Dense(64,activation='relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Dense(32,activation='relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "    \n",
    "    \n",
    "    model.add(Dense(len(CLASSES_LIST), activation = 'softmax'))\n",
    " \n",
    "    ########################################################################################################################\n",
    " \n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " time_distributed (TimeDist  (None, 16, 2, 2, 1280)    2257984   \n",
      " ributed)                                                        \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 16, 2, 2, 1280)    0         \n",
      "                                                                 \n",
      " time_distributed_1 (TimeDi  (None, 16, 5120)          0         \n",
      " stributed)                                                      \n",
      "                                                                 \n",
      " bidirectional (Bidirection  (None, 64)                1319168   \n",
      " al)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               16640     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 2)                 66        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3637090 (13.87 MB)\n",
      "Trainable params: 3060642 (11.68 MB)\n",
      "Non-trainable params: 576448 (2.20 MB)\n",
      "_________________________________________________________________\n",
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n"
     ]
    }
   ],
   "source": [
    "Own_dataset_mobi_Lstm=create_model()\n",
    "plot_model(Own_dataset_mobi_Lstm, to_file=\"lstm_model.png\",show_shapes=True,show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "90/90 [==============================] - 73s 697ms/step - loss: 0.6994 - accuracy: 0.5132 - val_loss: 0.6944 - val_accuracy: 0.5000 - lr: 0.0100\n",
      "Epoch 2/50\n",
      "90/90 [==============================] - 59s 653ms/step - loss: 0.6902 - accuracy: 0.5063 - val_loss: 0.6923 - val_accuracy: 0.5278 - lr: 0.0100\n",
      "Epoch 3/50\n",
      "90/90 [==============================] - 58s 650ms/step - loss: 0.6911 - accuracy: 0.5466 - val_loss: 0.6884 - val_accuracy: 0.5222 - lr: 0.0100\n",
      "Epoch 4/50\n",
      "90/90 [==============================] - 59s 652ms/step - loss: 0.6818 - accuracy: 0.5591 - val_loss: 0.6783 - val_accuracy: 0.6111 - lr: 0.0100\n",
      "Epoch 5/50\n",
      "90/90 [==============================] - 57s 639ms/step - loss: 0.6610 - accuracy: 0.6300 - val_loss: 0.6398 - val_accuracy: 0.7111 - lr: 0.0100\n",
      "Epoch 6/50\n",
      "90/90 [==============================] - 57s 629ms/step - loss: 0.6139 - accuracy: 0.6968 - val_loss: 0.5530 - val_accuracy: 0.7778 - lr: 0.0100\n",
      "Epoch 7/50\n",
      "90/90 [==============================] - 56s 628ms/step - loss: 0.5210 - accuracy: 0.7914 - val_loss: 0.6363 - val_accuracy: 0.6722 - lr: 0.0100\n",
      "Epoch 8/50\n",
      "90/90 [==============================] - 55s 610ms/step - loss: 0.4075 - accuracy: 0.8776 - val_loss: 0.5742 - val_accuracy: 0.7500 - lr: 0.0100\n",
      "Epoch 9/50\n",
      "90/90 [==============================] - 50s 562ms/step - loss: 0.3725 - accuracy: 0.8734 - val_loss: 1.1499 - val_accuracy: 0.6056 - lr: 0.0100\n",
      "Epoch 10/50\n",
      "90/90 [==============================] - 51s 571ms/step - loss: 0.3613 - accuracy: 0.8720 - val_loss: 0.4980 - val_accuracy: 0.8056 - lr: 0.0100\n",
      "Epoch 11/50\n",
      "90/90 [==============================] - 53s 585ms/step - loss: 0.2664 - accuracy: 0.9152 - val_loss: 0.6392 - val_accuracy: 0.7833 - lr: 0.0100\n",
      "Epoch 12/50\n",
      "90/90 [==============================] - 55s 614ms/step - loss: 0.2815 - accuracy: 0.8957 - val_loss: 0.5428 - val_accuracy: 0.8611 - lr: 0.0100\n",
      "Epoch 13/50\n",
      "90/90 [==============================] - 57s 635ms/step - loss: 0.1906 - accuracy: 0.9388 - val_loss: 0.9214 - val_accuracy: 0.7389 - lr: 0.0100\n",
      "Epoch 14/50\n",
      "90/90 [==============================] - 57s 636ms/step - loss: 0.1615 - accuracy: 0.9541 - val_loss: 0.7103 - val_accuracy: 0.8167 - lr: 0.0100\n",
      "Epoch 15/50\n",
      "90/90 [==============================] - ETA: 0s - loss: 0.1502 - accuracy: 0.9513\n",
      "Epoch 15: ReduceLROnPlateau reducing learning rate to 0.005999999865889549.\n",
      "90/90 [==============================] - 56s 628ms/step - loss: 0.1502 - accuracy: 0.9513 - val_loss: 2.1122 - val_accuracy: 0.6056 - lr: 0.0100\n",
      "Epoch 16/50\n",
      "90/90 [==============================] - 57s 632ms/step - loss: 0.1202 - accuracy: 0.9597 - val_loss: 0.9097 - val_accuracy: 0.7722 - lr: 0.0060\n",
      "Epoch 17/50\n",
      "90/90 [==============================] - 57s 632ms/step - loss: 0.1204 - accuracy: 0.9666 - val_loss: 0.6139 - val_accuracy: 0.8444 - lr: 0.0060\n",
      "Epoch 18/50\n",
      "90/90 [==============================] - 54s 605ms/step - loss: 0.0980 - accuracy: 0.9680 - val_loss: 0.5330 - val_accuracy: 0.8722 - lr: 0.0060\n",
      "Epoch 19/50\n",
      "90/90 [==============================] - 54s 596ms/step - loss: 0.1276 - accuracy: 0.9555 - val_loss: 0.5285 - val_accuracy: 0.8611 - lr: 0.0060\n",
      "Epoch 20/50\n",
      "90/90 [==============================] - 54s 603ms/step - loss: 0.0865 - accuracy: 0.9750 - val_loss: 0.4040 - val_accuracy: 0.8833 - lr: 0.0060\n",
      "Epoch 21/50\n",
      "90/90 [==============================] - 54s 600ms/step - loss: 0.0628 - accuracy: 0.9791 - val_loss: 0.2841 - val_accuracy: 0.9167 - lr: 0.0060\n",
      "Epoch 22/50\n",
      "90/90 [==============================] - 54s 596ms/step - loss: 0.0540 - accuracy: 0.9805 - val_loss: 0.4021 - val_accuracy: 0.8889 - lr: 0.0060\n",
      "Epoch 23/50\n",
      "90/90 [==============================] - 54s 597ms/step - loss: 0.0574 - accuracy: 0.9791 - val_loss: 0.2490 - val_accuracy: 0.9444 - lr: 0.0060\n",
      "Epoch 24/50\n",
      "90/90 [==============================] - 54s 600ms/step - loss: 0.0535 - accuracy: 0.9889 - val_loss: 0.3643 - val_accuracy: 0.9278 - lr: 0.0060\n",
      "Epoch 25/50\n",
      "90/90 [==============================] - 54s 600ms/step - loss: 0.0451 - accuracy: 0.9875 - val_loss: 0.4055 - val_accuracy: 0.9111 - lr: 0.0060\n",
      "Epoch 26/50\n",
      "90/90 [==============================] - 54s 597ms/step - loss: 0.0689 - accuracy: 0.9861 - val_loss: 0.3613 - val_accuracy: 0.9111 - lr: 0.0060\n",
      "Epoch 27/50\n",
      "90/90 [==============================] - 54s 597ms/step - loss: 0.0880 - accuracy: 0.9694 - val_loss: 0.2901 - val_accuracy: 0.9167 - lr: 0.0060\n",
      "Epoch 28/50\n",
      "90/90 [==============================] - ETA: 0s - loss: 0.0908 - accuracy: 0.9708\n",
      "Epoch 28: ReduceLROnPlateau reducing learning rate to 0.003600000031292438.\n",
      "90/90 [==============================] - 54s 599ms/step - loss: 0.0908 - accuracy: 0.9708 - val_loss: 0.3192 - val_accuracy: 0.9167 - lr: 0.0060\n",
      "Epoch 29/50\n",
      "90/90 [==============================] - 54s 599ms/step - loss: 0.0607 - accuracy: 0.9791 - val_loss: 0.4001 - val_accuracy: 0.8889 - lr: 0.0036\n",
      "Epoch 30/50\n",
      "90/90 [==============================] - 54s 602ms/step - loss: 0.0402 - accuracy: 0.9930 - val_loss: 0.2873 - val_accuracy: 0.9111 - lr: 0.0036\n",
      "Epoch 31/50\n",
      "90/90 [==============================] - 54s 597ms/step - loss: 0.0270 - accuracy: 0.9944 - val_loss: 0.2975 - val_accuracy: 0.9111 - lr: 0.0036\n",
      "Epoch 32/50\n",
      "90/90 [==============================] - 53s 595ms/step - loss: 0.0614 - accuracy: 0.9805 - val_loss: 0.3257 - val_accuracy: 0.9000 - lr: 0.0036\n",
      "Epoch 33/50\n",
      "90/90 [==============================] - ETA: 0s - loss: 0.0320 - accuracy: 0.9944\n",
      "Epoch 33: ReduceLROnPlateau reducing learning rate to 0.0021599999628961085.\n",
      "90/90 [==============================] - 54s 595ms/step - loss: 0.0320 - accuracy: 0.9944 - val_loss: 0.3332 - val_accuracy: 0.9056 - lr: 0.0036\n"
     ]
    }
   ],
   "source": [
    "early_stopping_callback = EarlyStopping(monitor = 'val_accuracy', patience = 10, restore_best_weights = True)\n",
    "\n",
    "# Create ReduceLROnPlateau Callback to reduce overfitting by decreasing learning\n",
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n",
    "                                                  factor=0.6,\n",
    "                                                  patience=5,\n",
    "                                                  min_lr=0.00005,\n",
    "                                                  verbose=1)\n",
    " \n",
    "# Compiling the model \n",
    "Own_dataset_mobi_Lstm.compile(loss = 'categorical_crossentropy', optimizer = 'sgd', metrics = [\"accuracy\"]) \n",
    " \n",
    "# Fitting the model \n",
    "MobBiLSTM_model_history = Own_dataset_mobi_Lstm.fit(x = features_train, y = labels_train, epochs = 50, batch_size = 8 ,\n",
    "                                             shuffle = True, validation_split = 0.2, callbacks = [early_stopping_callback,reduce_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 3s 581ms/step - loss: 0.1806 - accuracy: 0.9500\n"
     ]
    }
   ],
   "source": [
    "model_evaluation_history = Own_dataset_mobi_Lstm.evaluate(features_test, labels_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 5s 566ms/step\n"
     ]
    }
   ],
   "source": [
    "labels_predict = Own_dataset_mobi_Lstm.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decoding the data to use in Metrics\n",
    "labels_predict = np.argmax(labels_predict , axis=1)\n",
    "labels_test_normal = np.argmax(labels_test , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score is :  0.95\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "AccScore = accuracy_score(labels_predict, labels_test_normal)\n",
    "print('Accuracy Score is : ', AccScore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nirob\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "Own_dataset_mobi_Lstm.save('Own_dataset_mobi_Lstm.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 3s 663ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report of mobilenet_v2:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.93      0.94        42\n",
      "           1       0.95      0.97      0.96        58\n",
      "\n",
      "    accuracy                           0.95       100\n",
      "   macro avg       0.95      0.95      0.95       100\n",
      "weighted avg       0.95      0.95      0.95       100\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc\n",
    "from sklearn.model_selection import validation_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.utils import to_categorical\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming `predicted_labels` are the predictions of your model on the test set\n",
    "predicted_labels = Own_dataset_mobi_Lstm.predict(features_test)\n",
    "predicted_labels = np.argmax(predicted_labels, axis=1)\n",
    "true_labels = np.argmax(labels_test, axis=1)\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(true_labels, predicted_labels)\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.title(\"confusion_matrix of mobilenet_v2\")\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')\n",
    "plt.show()\n",
    "\n",
    "# Classification Report\n",
    "print(\"\\nClassification Report of mobilenet_v2:\")\n",
    "print(classification_report(true_labels, predicted_labels))\n",
    "\n",
    "# ROC Curve\n",
    "fpr, tpr, _ = roc_curve(true_labels, predicted_labels)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve of mobilenet_v2')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming `MobBiLSTM_model_history` is the returned object from your model's fit method\n",
    "history = MobBiLSTM_model_history\n",
    "\n",
    "# Plotting the loss curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=[6,4])\n",
    "plt.plot(history.history['loss'], 'black', linewidth=2.0)\n",
    "plt.plot(history.history['val_loss'], 'blue', linewidth=2.0)\n",
    "plt.legend(['Training Loss', 'Validation Loss'], fontsize=14)\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.ylabel('Loss', fontsize=10)\n",
    "plt.title('Loss Curves of Mobilenet_v2', fontsize=12)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}