Lonkar_Prathmesh_552_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80944577-e6b3-427a-a7c5-091e4e585154",
   "metadata": {},
   "source": [
    "<H1><center><b>DSCI-552 Final Project (Deep Learning)</b></center></H1>"
   ]
  },
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "1e6e993e-06c8-487c-b15c-6ec873892668",
   "metadata": {},
   "source": [
    "![image.png](attachment:8dcbbe65-52d9-49e5-97c2-a79d5a6de01d.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf21a192-f4cd-493c-afbc-146b16f709cf",
   "metadata": {},
   "source": [
    "<b>Martian HiRISE Dataset</b>: https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=doi:10.48577/jpl.QJ9PYA  \n",
    "<br>\n",
    "<b>About</b>: This dataset was created to study Mars' seasonal frost cycle and its role in the planet's climate and surface evolution over the past ~2 billion years. Scientists are interested in identifying low-latitude frosted microclimates and their impact on climate, and future exploration of Mars will partially depend on finding potential habitable environments and resources for human explorers. Previous studies of Mars' seasonal frost cycle were limited by a trade-off between coverage and spatial resolution, as scientists were forced to take either a global perspective using coarse observations or a local perspective focused on higher-resolution observations. To overcome this limitation, data science techniques could be useful to identify frost at a global scale with both high-resolution visible and coarse resolution thermal datasets. Toward that goal, models are needed to help identify frost in large-scale, high-resolution imagery. The dataset provided here is meant to address that need. It contains high resolution visible data (from HiRISE on MRO) with frost labels, which can be used to train machine learning models for frost identification in large datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12bbddd5-6028-499b-8426-eac948baaa98",
   "metadata": {},
   "source": [
    "<b>Objective</b>: In this project, we are trying to build a classifier that distinguishes images of\n",
    "Martian terrain with frost using CNN image classification and transfer learning algorithms (EfficientNetB0,\n",
    "ResNet50, and VGG16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1435ff-b3c4-4146-beb3-f570ddfc0422",
   "metadata": {},
   "source": [
    "<b>Name</b>: Prathmesh Lonkar\n",
    "<br>\n",
    "<b>USC ID</b>: 3661203370\n",
    "<br>\n",
    "<b>Github Username</b>: plonkar10\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39bcd7d8-1205-4410-9b4a-7f1b7fb39696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'device_name': 'NVIDIA GeForce GTX 1650', 'compute_capability': (7, 5)}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking if Google Colab detects the GPU.\n",
    "import os\n",
    "import shutil\n",
    "import tensorflow as tf\n",
    "\n",
    "gpu_devices = tf.config.list_physical_devices('GPU')\n",
    "if gpu_devices:    \n",
    "    print(tf.config.experimental.get_device_details(gpu_devices[0]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a6b5ece5-7801-486c-b0c3-fca897af00d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4a31a39-fe22-463f-81a6-64e72abb9a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_SIZE = (299, 299)  # All images contained in this dataset are 299x299 (originally, to match Inception v3 input size).\n",
    "SEED = 17\n",
    "\n",
    "# Head directory containing all image subframes. Update with the relative path of your data directory.\n",
    "colab_folder = \"../data\"\n",
    "data_head_dir = Path(colab_folder + '/data')\n",
    "\n",
    "# Find all subframe directories\n",
    "subdirs = [Path(subdir.stem) for subdir in data_head_dir.iterdir() if subdir.is_dir()]\n",
    "src_image_ids = ['_'.join(a_path.name.split('_')[:3]) for a_path in subdirs]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108f0a5b-50a3-4672-9654-1dffdb629895",
   "metadata": {},
   "source": [
    "<b><h2>Data Loading and Pre-processing</h2></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "540cae11-a4b5-4e23-af6e-3761a30cd862",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load train/val/test subframe IDs\n",
    "def load_text_ids(file_path):\n",
    "    \"\"\"Simple helper to load all lines from a text file\"\"\"\n",
    "    with open(file_path, 'r') as f:\n",
    "        lines = [line.strip() for line in f.readlines()]\n",
    "    return lines\n",
    "\n",
    "# Load the subframe names for the three data subsets.\n",
    "train_ids = load_text_ids('./train_source_images.txt')\n",
    "validate_ids = load_text_ids('./val_source_images.txt')\n",
    "test_ids = load_text_ids('./test_source_images.txt')\n",
    "\n",
    "# Generate a list containing the dataset split for the matching subdirectory names.\n",
    "subdir_splits = []\n",
    "for src_id in src_image_ids:\n",
    "    if src_id in train_ids:\n",
    "        subdir_splits.append('train')\n",
    "    elif src_id in validate_ids:\n",
    "        subdir_splits.append('validate')\n",
    "    elif(src_id in test_ids):\n",
    "        subdir_splits.append('test')\n",
    "    else:\n",
    "        print(f'{src_id}: Did not find designated split in train/validate/test list.')\n",
    "        subdir_splits.append(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1aff13cc-caa7-4385-8487-baf46de5a477",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from PIL import Image\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0cb3ccf-ef8c-429f-a0f4-1b1d0c04b360",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update the batch and buffer size as per your model requirements\n",
    "buffer_size = 64\n",
    "batch_size = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5f8320-91b1-4368-9819-eaa9c82d7557",
   "metadata": {},
   "source": [
    "\n",
    "<b>Data Augmentation</b>:\n",
    "For Data Augmentation, 70% of the shuffled training set is selected, and one of the augmentations from 90 degree rotation, 180 degree rotation, 270 degree rotation, and horizontal image flip are performed randomly on each selected image.    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "62649dcf-62b2-415e-82bb-16bbbd3bd842",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_preprocess(img_loc, label):\n",
    "\n",
    "    def _inner_function(img_loc, label):\n",
    "        # Convert tensor to native type.\n",
    "        img_loc_str = img_loc.numpy().decode('utf-8')\n",
    "        label_str = label.numpy().decode('utf-8')\n",
    "        img = Image.open(img_loc_str).convert('RGB')\n",
    "        # Convert PIL image to numpy array\n",
    "        img = np.array(img)\n",
    "        img = tf.image.resize(img, [299, 299])\n",
    "        # Normalize the image to the [0, 1] range\n",
    "        img = img / 255.0\n",
    "        return img, 1 if label_str=='frost' else 0\n",
    "\n",
    "    # Wrap the Python function\n",
    "    X, y = tf.py_function(_inner_function, [img_loc, label], [tf.float32, tf.int64])\n",
    "    # Set the shape of the tensors\n",
    "    X.set_shape([299, 299, 3])\n",
    "    y.set_shape([]) # Scalar label\n",
    "    return X, y\n",
    "\n",
    "def load_subdir_data(dir_path, image_size, seed=None):\n",
    "    \"\"\"Helper to create a TF dataset from each image subdirectory\"\"\"\n",
    "\n",
    "    # Grab only the classes that (1) we want to keep and (2) exist in this directory.\n",
    "    tile_dir = dir_path / Path('tiles')\n",
    "    label_dir = dir_path /Path('labels')\n",
    "\n",
    "    loc_list = []\n",
    "    for folder in os.listdir(tile_dir):\n",
    "        if os.path.isdir(os.path.join(tile_dir, folder)):\n",
    "            for file in os.listdir(os.path.join(tile_dir, folder)):\n",
    "                if file.endswith(\".png\"):\n",
    "                    loc_list.append((os.path.join(os.path.join(tile_dir, folder), file), folder))\n",
    "    return loc_list\n",
    "\n",
    "def train_data_augmentation(augment_train_data):\n",
    "  new_aug_list = []\n",
    "  for i, img_data in enumerate(augment_train_data):\n",
    "    imgpath_to_mod = augment_train_data[i][0]\n",
    "    original_image = cv2.imread(imgpath_to_mod)\n",
    "    random_number = np.random.randint(1, 5)\n",
    "    # Apply different transformations based on the random number.\n",
    "    if random_number == 1:\n",
    "        # Rotate 90 degrees.\n",
    "        modified_image = cv2.rotate(original_image, cv2.ROTATE_90_CLOCKWISE)\n",
    "    elif random_number == 2:\n",
    "        # Rotate 180 degrees.\n",
    "        modified_image = cv2.rotate(original_image, cv2.ROTATE_180)\n",
    "    elif random_number == 3:\n",
    "        # Rotate 270 degrees.\n",
    "        modified_image = cv2.rotate(original_image, cv2.ROTATE_90_COUNTERCLOCKWISE)\n",
    "    else:\n",
    "        # Flip the image (horizontal flip).\n",
    "        modified_image = cv2.flip(original_image, 1)\n",
    "\n",
    "    # Save the modified image to the augmented data location with the same name.\n",
    "    temp = os.path.normpath(imgpath_to_mod).split(os.sep)\n",
    "    temp[2] = \"augmented_data\"\n",
    "    temp[-1] = temp[-1].split(\".\")[0] + \"_modified.\" + temp[-1].split(\".\")[1]\n",
    "    modified_image_path = os.path.join(*temp)\n",
    "\n",
    "    new_aug_list.append((modified_image_path, augment_train_data[i][1]))\n",
    "    aug_pth = os.path.normpath(modified_image_path).split(os.sep)[:-1]\n",
    "    aug_pth = os.path.join(*aug_pth)\n",
    "    if not os.path.exists(aug_pth):\n",
    "        os.makedirs(aug_pth)\n",
    "    cv2.imwrite(modified_image_path, modified_image)\n",
    "  return new_aug_list\n",
    "\n",
    "# Loop over all subframes, loading each into a list\n",
    "tf_data_train, tf_data_test, tf_data_val = [], [], []\n",
    "tf_dataset_train, tf_dataset_test, tf_dataset_val = [], [], []\n",
    "\n",
    "for subdir, split in zip(subdirs, subdir_splits):\n",
    "    full_path = data_head_dir / subdir\n",
    "    if split=='validate':\n",
    "        tf_data_val.extend(load_subdir_data(full_path, IMAGE_SIZE, SEED))\n",
    "    elif split=='train':\n",
    "        tf_data_train.extend(load_subdir_data(full_path, IMAGE_SIZE, SEED))\n",
    "    elif split=='test':\n",
    "        tf_data_test.extend(load_subdir_data(full_path, IMAGE_SIZE, SEED))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3c97bc1f-eba9-47c3-9a0f-4da1bce41609",
   "metadata": {},
   "outputs": [],
   "source": [
    "augment_folder_path = os.path.join(colab_folder, \"augmented_data\")\n",
    "if os.path.exists(augment_folder_path):\n",
    "    shutil.rmtree(augment_folder_path)\n",
    "#os.makedirs(augment_folder_path)\n",
    "\n",
    "# Shuffling Train set and segregatting into X & y.\n",
    "random.shuffle(tf_data_train)\n",
    "augment_train_data = tf_data_train[:int(0.7 * len(tf_data_train))]\n",
    "new_aug_list = train_data_augmentation(augment_train_data)\n",
    "tf_data_train.extend(new_aug_list)\n",
    "random.shuffle(tf_data_train)\n",
    "img_list, label_list = zip(*tf_data_train)\n",
    "img_list_t = tf.convert_to_tensor(img_list)\n",
    "lb_list_t = tf.convert_to_tensor(label_list)\n",
    "\n",
    "tf_dataset_train = tf.data.Dataset.from_tensor_slices((img_list_t, lb_list_t))\n",
    "tf_dataset_train = tf_dataset_train.map(load_and_preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "tf_dataset_train = tf_dataset_train.shuffle(buffer_size=buffer_size).batch(batch_size)\n",
    "\n",
    "\n",
    "# Shuffling Validation set and segregatting into X & y.\n",
    "random.shuffle(tf_data_val)\n",
    "img_list, label_list = zip(*tf_data_val)\n",
    "img_list_t = tf.convert_to_tensor(img_list)\n",
    "lb_list_t = tf.convert_to_tensor(label_list)\n",
    "\n",
    "tf_dataset_val = tf.data.Dataset.from_tensor_slices((img_list_t, lb_list_t))\n",
    "tf_dataset_val = tf_dataset_val.map(load_and_preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "tf_dataset_val = tf_dataset_val.shuffle(buffer_size=buffer_size).batch(batch_size)\n",
    "\n",
    "\n",
    "# Shuffling Test set and segregatting into X & y.\n",
    "random.shuffle(tf_data_test)\n",
    "img_list, label_list = zip(*tf_data_test)\n",
    "img_list_t = tf.convert_to_tensor(img_list)\n",
    "lb_list_t = tf.convert_to_tensor(label_list)\n",
    "\n",
    "tf_dataset_test = tf.data.Dataset.from_tensor_slices((img_list_t, lb_list_t))\n",
    "tf_dataset_test = tf_dataset_test.map(load_and_preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "tf_dataset_test = tf_dataset_test.shuffle(buffer_size=buffer_size).batch(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ed02df-2c8b-4b50-8a2a-d70b936f64ed",
   "metadata": {},
   "source": [
    "<b><h2>Building the CNN + MLP model Architecture</h2></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2df02acf-192f-4dc9-b2aa-dc4f36f74366",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization, Dropout\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1a5de5-2e03-4f62-9c8d-61c1a75dca16",
   "metadata": {},
   "source": [
    "<b>Model Architecture</b>:  \n",
    "The CNN + MLP architecture consists of 3 CNN layers and 1 Dense layer with L2 regularized penaly and a 30% dropout layer thereafter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eaa4f536-2572-4124-898b-11e82b0725b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "6307/6307 [==============================] - 777s 123ms/step - loss: 12.3439 - accuracy: 0.7657 - val_loss: 0.8041 - val_accuracy: 0.8575\n",
      "Epoch 2/20\n",
      "6307/6307 [==============================] - 758s 120ms/step - loss: 0.5368 - accuracy: 0.8385 - val_loss: 0.5147 - val_accuracy: 0.8083\n",
      "Epoch 3/20\n",
      "6307/6307 [==============================] - 738s 117ms/step - loss: 0.3956 - accuracy: 0.8741 - val_loss: 0.8372 - val_accuracy: 0.7414\n",
      "Epoch 4/20\n",
      "6307/6307 [==============================] - 741s 117ms/step - loss: 0.3469 - accuracy: 0.8954 - val_loss: 0.7596 - val_accuracy: 0.7712\n",
      "Epoch 5/20\n",
      "6307/6307 [==============================] - 738s 117ms/step - loss: 0.3097 - accuracy: 0.9130 - val_loss: 0.8612 - val_accuracy: 0.7214\n",
      "Epoch 6/20\n",
      "6307/6307 [==============================] - 826s 131ms/step - loss: 0.2895 - accuracy: 0.9219 - val_loss: 0.4569 - val_accuracy: 0.8463\n",
      "Epoch 7/20\n",
      "6307/6307 [==============================] - 748s 119ms/step - loss: 0.2709 - accuracy: 0.9313 - val_loss: 1.3118 - val_accuracy: 0.6767\n",
      "Epoch 8/20\n",
      "6307/6307 [==============================] - 717s 114ms/step - loss: 0.2555 - accuracy: 0.9373 - val_loss: 0.9394 - val_accuracy: 0.7851\n",
      "Epoch 9/20\n",
      "6307/6307 [==============================] - 720s 114ms/step - loss: 0.2435 - accuracy: 0.9430 - val_loss: 0.6725 - val_accuracy: 0.8599\n",
      "Epoch 10/20\n",
      "6307/6307 [==============================] - 728s 115ms/step - loss: 0.2307 - accuracy: 0.9468 - val_loss: 0.4084 - val_accuracy: 0.9083\n",
      "Epoch 11/20\n",
      "6307/6307 [==============================] - 734s 116ms/step - loss: 0.2247 - accuracy: 0.9509 - val_loss: 0.8098 - val_accuracy: 0.7889\n",
      "Epoch 12/20\n",
      "6307/6307 [==============================] - 730s 116ms/step - loss: 0.2170 - accuracy: 0.9549 - val_loss: 0.8535 - val_accuracy: 0.8275\n",
      "Epoch 13/20\n",
      "6307/6307 [==============================] - 726s 115ms/step - loss: 0.2049 - accuracy: 0.9583 - val_loss: 0.5835 - val_accuracy: 0.8369\n",
      "Epoch 14/20\n",
      "6307/6307 [==============================] - 727s 115ms/step - loss: 0.1998 - accuracy: 0.9607 - val_loss: 0.9100 - val_accuracy: 0.7644\n",
      "Epoch 15/20\n",
      "6307/6307 [==============================] - 728s 115ms/step - loss: 0.1913 - accuracy: 0.9637 - val_loss: 0.3987 - val_accuracy: 0.9001\n",
      "Epoch 16/20\n",
      "6307/6307 [==============================] - 743s 118ms/step - loss: 0.1855 - accuracy: 0.9660 - val_loss: 0.6596 - val_accuracy: 0.8261\n",
      "Epoch 17/20\n",
      "6307/6307 [==============================] - 727s 115ms/step - loss: 0.1850 - accuracy: 0.9659 - val_loss: 0.9386 - val_accuracy: 0.7426\n",
      "Epoch 18/20\n",
      "6307/6307 [==============================] - 735s 117ms/step - loss: 0.1801 - accuracy: 0.9677 - val_loss: 0.2233 - val_accuracy: 0.9425\n",
      "Epoch 19/20\n",
      "6307/6307 [==============================] - 748s 118ms/step - loss: 0.1785 - accuracy: 0.9692 - val_loss: 1.0527 - val_accuracy: 0.7471\n",
      "Epoch 20/20\n",
      "6307/6307 [==============================] - 737s 117ms/step - loss: 0.1711 - accuracy: 0.9718 - val_loss: 0.4036 - val_accuracy: 0.8867\n"
     ]
    }
   ],
   "source": [
    "# Define the model\n",
    "model = Sequential()\n",
    "\n",
    "# Convolutional layers\n",
    "model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(299, 299, 3)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "\n",
    "# Flatten layer\n",
    "model.add(Flatten())\n",
    "\n",
    "# Dense layer with dropout and L2 regularization\n",
    "model.add(Dense(256, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.1)))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "# Output layer\n",
    "model.add(Dense(2, activation='softmax'))\n",
    "\n",
    "# Compile the model\n",
    "adam_opt = Adam(learning_rate=0.00001)\n",
    "model.compile(optimizer=adam_opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Early stopping callback\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\n",
    "\n",
    "\n",
    "# Train the model using tf.data.Dataset\n",
    "history = model.fit(tf_dataset_train, epochs=20, validation_data=tf_dataset_val, callbacks=[early_stopping])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "188716a0-0d57-4515-8955-588697308b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xticks([i for i in range(len(history.history['loss']))], [i for i in range(1, len(history.history['loss'])+1)])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bdf4a4c9-6263-4c26-b2a9-02fa1b14a360",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4053e086-a4c7-4f6f-8b90-544a11f15658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1603/1603 [==============================] - 117s 73ms/step - loss: 0.2149 - accuracy: 0.9481\n",
      "\n",
      "Test Loss: 0.21487747132778168\n",
      "Test Accuracy: 0.9481400847434998\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set using tf.data.Dataset\n",
    "test_loss, test_accuracy = model.evaluate(tf_dataset_test)\n",
    "print(f'\\nTest Loss: {test_loss}')\n",
    "print(f'Test Accuracy: {test_accuracy}')\n",
    "\n",
    "model.save(\"./model_CNN_MLP.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "edf5ed30-29fd-4c4e-a1bd-d06d5e5872f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on the test batch dataset.\n",
    "def report_metrics(dataset_type, tf_dataset, i_model, model_name=\"CNN + MLP\"):\n",
    "  y_true = []\n",
    "  y_pred_probs = []\n",
    "\n",
    "  for batch in tf_dataset:\n",
    "    X_batch, y_batch = batch\n",
    "    y_true.extend(y_batch.numpy())\n",
    "    \n",
    "    # Make predictions\n",
    "    batch_pred_probs = i_model.predict(X_batch, verbose=0)\n",
    "    y_pred_probs.extend(batch_pred_probs)\n",
    "\n",
    "  # Convert predicted probabilities to class labels\n",
    "  y_pred_classes = np.argmax(y_pred_probs, axis=1)\n",
    "\n",
    "  # Generate a classification report\n",
    "  print(f'\\nModel {model_name} Classification Report for {dataset_type} dataset:')\n",
    "  print(classification_report(y_true, y_pred_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "194d331b-f7f5-423c-a007-c44a12560650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model CNN + MLP Classification Report for Training dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99     20776\n",
      "           1       1.00      0.98      0.99     29678\n",
      "\n",
      "    accuracy                           0.99     50454\n",
      "   macro avg       0.99      0.99      0.99     50454\n",
      "weighted avg       0.99      0.99      0.99     50454\n",
      "\n",
      "\n",
      "Model CNN + MLP Classification Report for Validation dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.84      0.91      7654\n",
      "           1       0.74      0.99      0.85      3632\n",
      "\n",
      "    accuracy                           0.89     11286\n",
      "   macro avg       0.87      0.91      0.88     11286\n",
      "weighted avg       0.91      0.89      0.89     11286\n",
      "\n",
      "\n",
      "Model CNN + MLP Classification Report for Testing dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.91      0.92      4418\n",
      "           1       0.95      0.97      0.96      8405\n",
      "\n",
      "    accuracy                           0.95     12823\n",
      "   macro avg       0.95      0.94      0.94     12823\n",
      "weighted avg       0.95      0.95      0.95     12823\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report_metrics('Training', tf_dataset_train, model)\n",
    "report_metrics('Validation', tf_dataset_val, model)\n",
    "report_metrics('Testing', tf_dataset_test, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f175bf-edc6-4db4-a90b-72063189b4ae",
   "metadata": {},
   "source": [
    "<b><h2>Building on the Transfer-Learning algorithms (EfficientNetB0, ResNet50, VGG16)</h2></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d75735cc-5cc3-448a-ba70-50412e60de3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers, models\n",
    "from keras.applications import EfficientNetB0, ResNet50, VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3ee9fb81-e3ab-4ad0-accc-7c0f2152cf61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
      "16705208/16705208 [==============================] - 1s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94765736/94765736 [==============================] - 5s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58889256/58889256 [==============================] - 3s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# Load pre-trained models.\n",
    "base_model_efn = EfficientNetB0(include_top=False, weights='imagenet', input_shape=(299, 299, 3))\n",
    "base_model_rsn50 = ResNet50(include_top=False, weights='imagenet', input_shape=(299, 299, 3))\n",
    "base_model_vgg16 = VGG16(include_top=False, weights='imagenet', input_shape=(299, 299, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7fcd3490-0d41-454c-b23b-42a4d11f73ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Freeze all layers in the base models.\n",
    "for layer in base_model_efn.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "for layer in base_model_rsn50.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "for layer in base_model_vgg16.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "108a5500-100f-4cff-a83d-cbe632578679",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a model for EfficientNetB0.\n",
    "model_efn = models.Sequential([\n",
    "    base_model_efn,\n",
    "    layers.GlobalAveragePooling2D(),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(256),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.ReLU(),\n",
    "    layers.Dropout(0.3),\n",
    "    layers.Dense(2, activation='softmax')\n",
    "])\n",
    "\n",
    "# Compile the model.\n",
    "adam_opt = Adam(learning_rate=0.00001)\n",
    "model_efn.compile(optimizer=adam_opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f403135-f6d5-41da-b83d-5553038617fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a model for ResNet50.\n",
    "model_rsn50 = models.Sequential([\n",
    "    base_model_rsn50,\n",
    "    layers.GlobalAveragePooling2D(),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(256),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.ReLU(),\n",
    "    layers.Dropout(0.3),\n",
    "    layers.Dense(2, activation='softmax')\n",
    "])\n",
    "\n",
    "# Compile the model.\n",
    "adam_opt = Adam(learning_rate=0.00001)\n",
    "model_rsn50.compile(optimizer=adam_opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d0be1403-108b-409d-9179-d723963dff0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a model for VGG16.\n",
    "model_vgg16 = models.Sequential([\n",
    "    base_model_vgg16,\n",
    "    layers.GlobalAveragePooling2D(),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(256),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.ReLU(),\n",
    "    layers.Dropout(0.3),\n",
    "    layers.Dense(2, activation='softmax')\n",
    "])\n",
    "\n",
    "# Compile the model.\n",
    "adam_opt = Adam(learning_rate=0.00001)\n",
    "model_vgg16.compile(optimizer=adam_opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ded35c5b-6f9d-442d-a22d-3e3f67c13c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "========== Model Training for EfficientNetB0 ==========\n",
      "\n",
      "\n",
      "Epoch 1/15\n",
      "6307/6307 [==============================] - 736s 115ms/step - loss: 0.7307 - accuracy: 0.5351 - val_loss: 0.6429 - val_accuracy: 0.6934\n",
      "Epoch 2/15\n",
      "6307/6307 [==============================] - 704s 111ms/step - loss: 0.7082 - accuracy: 0.5560 - val_loss: 0.6183 - val_accuracy: 0.6782\n",
      "Epoch 3/15\n",
      "6307/6307 [==============================] - 724s 115ms/step - loss: 0.7010 - accuracy: 0.5589 - val_loss: 0.9433 - val_accuracy: 0.3218\n",
      "Epoch 4/15\n",
      "6307/6307 [==============================] - 711s 113ms/step - loss: 0.6969 - accuracy: 0.5642 - val_loss: 0.6649 - val_accuracy: 0.7730\n",
      "Epoch 5/15\n",
      "6307/6307 [==============================] - 712s 113ms/step - loss: 0.6919 - accuracy: 0.5647 - val_loss: 0.9226 - val_accuracy: 0.3218\n",
      "Epoch 6/15\n",
      "6307/6307 [==============================] - 708s 112ms/step - loss: 0.6882 - accuracy: 0.5689 - val_loss: 0.7814 - val_accuracy: 0.3218\n",
      "Epoch 7/15\n",
      "6307/6307 [==============================] - 710s 112ms/step - loss: 0.6866 - accuracy: 0.5708 - val_loss: 0.9274 - val_accuracy: 0.3218\n",
      "Epoch 8/15\n",
      "6307/6307 [==============================] - 727s 115ms/step - loss: 0.6849 - accuracy: 0.5730 - val_loss: 0.7048 - val_accuracy: 0.4016\n",
      "Epoch 9/15\n",
      "6307/6307 [==============================] - 729s 115ms/step - loss: 0.6817 - accuracy: 0.5734 - val_loss: 0.9123 - val_accuracy: 0.3218\n",
      "Epoch 10/15\n",
      "6307/6307 [==============================] - 721s 114ms/step - loss: 0.6798 - accuracy: 0.5784 - val_loss: 1.0334 - val_accuracy: 0.3218\n",
      "Epoch 11/15\n",
      "6307/6307 [==============================] - 721s 114ms/step - loss: 0.6801 - accuracy: 0.5766 - val_loss: 1.2345 - val_accuracy: 0.3218\n",
      "Epoch 12/15\n",
      "6307/6307 [==============================] - 715s 113ms/step - loss: 0.6776 - accuracy: 0.5777 - val_loss: 0.8840 - val_accuracy: 0.3218\n",
      "\n",
      "========== Model Training for ResNet50 ==========\n",
      "\n",
      "\n",
      "Epoch 1/15\n",
      "6307/6307 [==============================] - 1070s 168ms/step - loss: 0.5021 - accuracy: 0.7535 - val_loss: 0.6250 - val_accuracy: 0.6393\n",
      "Epoch 2/15\n",
      "6307/6307 [==============================] - 1035s 164ms/step - loss: 0.4439 - accuracy: 0.7931 - val_loss: 0.8856 - val_accuracy: 0.4722\n",
      "Epoch 3/15\n",
      "6307/6307 [==============================] - 1024s 162ms/step - loss: 0.4221 - accuracy: 0.8054 - val_loss: 1.0192 - val_accuracy: 0.4457\n",
      "Epoch 4/15\n",
      "6307/6307 [==============================] - 1036s 164ms/step - loss: 0.4060 - accuracy: 0.8146 - val_loss: 1.5514 - val_accuracy: 0.3433\n",
      "Epoch 5/15\n",
      "6307/6307 [==============================] - 1038s 164ms/step - loss: 0.4003 - accuracy: 0.8146 - val_loss: 0.7019 - val_accuracy: 0.6250\n",
      "Epoch 6/15\n",
      "6307/6307 [==============================] - 1049s 166ms/step - loss: 0.3897 - accuracy: 0.8222 - val_loss: 0.7757 - val_accuracy: 0.5713\n",
      "Epoch 7/15\n",
      "6307/6307 [==============================] - 1040s 165ms/step - loss: 0.3815 - accuracy: 0.8261 - val_loss: 0.9849 - val_accuracy: 0.4983\n",
      "Epoch 8/15\n",
      "6307/6307 [==============================] - 1050s 166ms/step - loss: 0.3766 - accuracy: 0.8276 - val_loss: 0.7821 - val_accuracy: 0.5941\n",
      "Epoch 9/15\n",
      "6307/6307 [==============================] - 988s 157ms/step - loss: 0.3695 - accuracy: 0.8341 - val_loss: 0.9195 - val_accuracy: 0.5409\n",
      "Epoch 10/15\n",
      "6307/6307 [==============================] - 1012s 160ms/step - loss: 0.3683 - accuracy: 0.8341 - val_loss: 1.6160 - val_accuracy: 0.3497\n",
      "Epoch 11/15\n",
      "6307/6307 [==============================] - 1013s 160ms/step - loss: 0.3625 - accuracy: 0.8368 - val_loss: 1.0080 - val_accuracy: 0.4978\n",
      "\n",
      "========== Model Training for VGG16 ==========\n",
      "\n",
      "\n",
      "Epoch 1/15\n",
      "6307/6307 [==============================] - 1676s 265ms/step - loss: 0.4974 - accuracy: 0.7528 - val_loss: 0.5733 - val_accuracy: 0.7209\n",
      "Epoch 2/15\n",
      "6307/6307 [==============================] - 1558s 247ms/step - loss: 0.3953 - accuracy: 0.8163 - val_loss: 0.5807 - val_accuracy: 0.7325\n",
      "Epoch 3/15\n",
      "6307/6307 [==============================] - 1655s 262ms/step - loss: 0.3554 - accuracy: 0.8403 - val_loss: 0.5474 - val_accuracy: 0.7540\n",
      "Epoch 4/15\n",
      "6307/6307 [==============================] - 1667s 264ms/step - loss: 0.3325 - accuracy: 0.8522 - val_loss: 0.5263 - val_accuracy: 0.7625\n",
      "Epoch 5/15\n",
      "6307/6307 [==============================] - 1603s 254ms/step - loss: 0.3132 - accuracy: 0.8622 - val_loss: 0.5270 - val_accuracy: 0.7642\n",
      "Epoch 6/15\n",
      "6307/6307 [==============================] - 1596s 253ms/step - loss: 0.2955 - accuracy: 0.8721 - val_loss: 0.4933 - val_accuracy: 0.7838\n",
      "Epoch 7/15\n",
      "2098/6307 [========>.....................] - ETA: 14:36 - loss: 0.2908 - accuracy: 0.8746"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6307/6307 [==============================] - 1594s 253ms/step - loss: 0.2863 - accuracy: 0.8764 - val_loss: 0.5029 - val_accuracy: 0.7760\n",
      "Epoch 8/15\n",
      "6307/6307 [==============================] - 1658s 263ms/step - loss: 0.2763 - accuracy: 0.8816 - val_loss: 0.5083 - val_accuracy: 0.7754\n",
      "Epoch 9/15\n",
      "6307/6307 [==============================] - 1615s 256ms/step - loss: 0.2653 - accuracy: 0.8877 - val_loss: 0.5023 - val_accuracy: 0.7838\n",
      "Epoch 10/15\n",
      "2998/6307 [=============>................] - ETA: 11:41 - loss: 0.2592 - accuracy: 0.8899"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6307/6307 [==============================] - 1620s 257ms/step - loss: 0.2558 - accuracy: 0.8913 - val_loss: 0.4982 - val_accuracy: 0.7847\n",
      "Epoch 11/15\n",
      "6307/6307 [==============================] - 1606s 254ms/step - loss: 0.2488 - accuracy: 0.8949 - val_loss: 0.5241 - val_accuracy: 0.7788\n",
      "Epoch 12/15\n",
      "6307/6307 [==============================] - 1600s 254ms/step - loss: 0.2423 - accuracy: 0.8995 - val_loss: 0.4649 - val_accuracy: 0.8062\n",
      "Epoch 13/15\n",
      "6307/6307 [==============================] - 1612s 256ms/step - loss: 0.2386 - accuracy: 0.9004 - val_loss: 0.4672 - val_accuracy: 0.8068\n",
      "Epoch 14/15\n",
      "6307/6307 [==============================] - 1733s 275ms/step - loss: 0.2295 - accuracy: 0.9044 - val_loss: 0.4682 - val_accuracy: 0.8095\n",
      "Epoch 15/15\n",
      "6307/6307 [==============================] - 1619s 257ms/step - loss: 0.2308 - accuracy: 0.9053 - val_loss: 0.4627 - val_accuracy: 0.8116\n"
     ]
    }
   ],
   "source": [
    "# Train each model using the batch datasets.\n",
    "models_list = [model_efn, model_rsn50, model_vgg16]\n",
    "history_list = []\n",
    "\n",
    "for i, model in enumerate(models_list):\n",
    "    if i == 0:\n",
    "      model_name = \"EfficientNetB0\"\n",
    "    elif i == 1:\n",
    "      model_name = \"ResNet50\"\n",
    "    else:\n",
    "      model_name = \"VGG16\"\n",
    "    early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "    print(\"\\n========== Model Training for {} ==========\\n\\n\".format(model_name))\n",
    "    history = model.fit(tf_dataset_train, epochs=15, validation_data=tf_dataset_val, callbacks=[early_stopping])\n",
    "    history_list.append(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a834da62-01ca-4bc4-a7ae-e84d98f7f13a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training history\n",
    "max_epoch_len = 0\n",
    "for i, history in enumerate(history_list):\n",
    "    if i == 0:\n",
    "      model_name = \"EfficientNetB0\"\n",
    "    elif i == 1:\n",
    "      model_name = \"ResNet50\"\n",
    "    else:\n",
    "      model_name = \"VGG16\"\n",
    "    plt.plot(history.history['loss'], label=f'Training Loss - {model_name}')\n",
    "    plt.plot(history.history['val_loss'], label=f'Validation Loss - {model_name}')\n",
    "    if len(history.history['loss']) > max_epoch_len:\n",
    "      max_epoch_len = len(history.history['loss'])\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xticks([i for i in range(max_epoch_len)], [i for i in range(1, max_epoch_len+1)])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "17c64cfa-49bd-4dc0-8b57-e18c153f46d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1a73039d-4c86-4680-9481-22d7a42f35af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1603/1603 [==============================] - 247s 153ms/step - loss: 0.8055 - accuracy: 0.3445\n",
      "Model EfficientNetB0 - Test Loss: 0.8054841160774231\n",
      "Model EfficientNetB0 - Test Accuracy: 0.3445371687412262\n",
      "\n",
      "Note: Unable to save the EfficientNetB0 model at the moment due to tensorflow version compatibility issue.\n",
      "Skipping the model saving process for now.\n",
      "\n",
      "Model EfficientNetB0 Classification Report for Training dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      1.00      0.58     20776\n",
      "           1       0.00      0.00      0.00     29678\n",
      "\n",
      "    accuracy                           0.41     50454\n",
      "   macro avg       0.21      0.50      0.29     50454\n",
      "weighted avg       0.17      0.41      0.24     50454\n",
      "\n",
      "\n",
      "Model EfficientNetB0 Classification Report for Validation dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      1.00      0.81      7654\n",
      "           1       0.00      0.00      0.00      3632\n",
      "\n",
      "    accuracy                           0.68     11286\n",
      "   macro avg       0.34      0.50      0.40     11286\n",
      "weighted avg       0.46      0.68      0.55     11286\n",
      "\n",
      "\n",
      "Model EfficientNetB0 Classification Report for Testing dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.34      1.00      0.51      4418\n",
      "           1       0.00      0.00      0.00      8405\n",
      "\n",
      "    accuracy                           0.34     12823\n",
      "   macro avg       0.17      0.50      0.26     12823\n",
      "weighted avg       0.12      0.34      0.18     12823\n",
      "\n",
      " 721/1603 [============>.................] - ETA: 5:03 - loss: 0.3774 - accuracy: 0.8235\n",
      "Model ResNet50 Classification Report for Training dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.59      0.71     20776\n",
      "           1       0.77      0.95      0.85     29678\n",
      "\n",
      "    accuracy                           0.80     50454\n",
      "   macro avg       0.83      0.77      0.78     50454\n",
      "weighted avg       0.82      0.80      0.79     50454\n",
      "\n",
      "\n",
      "Model ResNet50 Classification Report for Validation dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.47      0.64      7654\n",
      "           1       0.47      0.99      0.64      3632\n",
      "\n",
      "    accuracy                           0.64     11286\n",
      "   macro avg       0.73      0.73      0.64     11286\n",
      "weighted avg       0.82      0.64      0.64     11286\n",
      "\n",
      "\n",
      "Model ResNet50 Classification Report for Testing dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.62      0.72      4418\n",
      "           1       0.83      0.94      0.88      8405\n",
      "\n",
      "    accuracy                           0.83     12823\n",
      "   macro avg       0.84      0.78      0.80     12823\n",
      "weighted avg       0.83      0.83      0.82     12823\n",
      "\n",
      "1603/1603 [==============================] - 759s 472ms/step - loss: 0.1383 - accuracy: 0.9530\n",
      "Model VGG16 - Test Loss: 0.1383139044046402\n",
      "Model VGG16 - Test Accuracy: 0.9529750943183899\n",
      "\n",
      "Model VGG16 Classification Report for Training dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.93      0.93     20776\n",
      "           1       0.95      0.95      0.95     29678\n",
      "\n",
      "    accuracy                           0.95     50454\n",
      "   macro avg       0.94      0.94      0.94     50454\n",
      "weighted avg       0.95      0.95      0.95     50454\n",
      "\n",
      "\n",
      "Model VGG16 Classification Report for Validation dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.72      0.84      7654\n",
      "           1       0.63      1.00      0.77      3632\n",
      "\n",
      "    accuracy                           0.81     11286\n",
      "   macro avg       0.81      0.86      0.81     11286\n",
      "weighted avg       0.88      0.81      0.82     11286\n",
      "\n",
      "\n",
      "Model VGG16 Classification Report for Testing dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.95      0.93      4418\n",
      "           1       0.97      0.95      0.96      8405\n",
      "\n",
      "    accuracy                           0.95     12823\n",
      "   macro avg       0.94      0.95      0.95     12823\n",
      "weighted avg       0.95      0.95      0.95     12823\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate each model on the test set\n",
    "for i, model in enumerate(models_list):\n",
    "    if i == 0:\n",
    "      model_name = \"EfficientNetB0\"\n",
    "    elif i == 1:\n",
    "      model_name = \"ResNet50\"\n",
    "    else:\n",
    "      model_name = \"VGG16\"\n",
    "\n",
    "    test_loss, test_accuracy = model.evaluate(tf_dataset_test)\n",
    "    print(f'Model {model_name} - Test Loss: {test_loss}')\n",
    "    print(f'Model {model_name} - Test Accuracy: {test_accuracy}')\n",
    "    try:\n",
    "        model.save(\"./model_{}.h5\".format(model_name))\n",
    "    except:\n",
    "        print(\"\\nNote: Unable to save the {} model at the moment due to tensorflow version compatibility issue.\\nSkipping the model saving process for now.\".format(model_name))\n",
    "\n",
    "    # Make predictions\n",
    "    report_metrics('Training', tf_dataset_train, model, model_name=model_name)\n",
    "    report_metrics('Validation', tf_dataset_val, model, model_name=model_name)\n",
    "    report_metrics('Testing', tf_dataset_test, model, model_name=model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb382a2a-55da-490a-abd7-0df7cc932a12",
   "metadata": {},
   "source": [
    "<b><h2>Result comparision of CNN+MLP, EfficientNetB0, ResNet50, and VGG16 algorithms</h2></b>  "
   ]
  },
  {
   "attachments": {
    "72509430-508c-4b29-b167-41a831b6fad1.png": {
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"
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"
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "de451f81-a5be-4e82-8b6c-6d1760e17fde",
   "metadata": {},
   "source": [
    "<b><h4>1) Test result obtained for the CNN + MLP architecture:</h4></b>   \n",
    "![image.png](attachment:95964bab-023f-465c-98fa-bf0ea5f86517.png)\n",
    "    \n",
    "\n",
    "<b><h4>2) Test result obtained for the EfficientNetB0 architecture:</h4></b>    \n",
    "![image.png](attachment:72509430-508c-4b29-b167-41a831b6fad1.png)\n",
    "    \n",
    "\n",
    "<b><h4>3) Test result obtained for the ResNet50 architecture:</h4></b>    \n",
    "![image.png](attachment:fe9f9c86-c55a-4bcc-83ae-2e815f49e40f.png)\n",
    "    \n",
    "\n",
    "<b><h4>4) Test result obtained for the VGG16 architecture:</h4></b>     \n",
    "![image.png](attachment:ce4d9a3c-8c17-4e3b-a016-7ea9ce5b34e4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb47bc6-7aa0-4a56-b602-49c4eeb8d592",
   "metadata": {},
   "source": [
    "<b>Understanding the results:</b>    \n",
    "1) The CNN+MLP and VGG16 architectures perform the best with test accuracy of <b>95%</b>. The weighted avg Precision value of <b>0.95</b> for both models tells us that, out of all positively predicted class, 95% of them are predicted correctly. Similarly for weighted avg Recall value of <b>0.95</b> for both models tells us that, out of all actual positive class, 95% were correctly predicted. For both of these models, we get an f1-score of <b>95%</b>, which is the harmonic mean of precision and recall values.   \n",
    "<br>   \n",
    "2) ResNet50 is our second best performing model architecture with accuracy of <b>83%</b>. And Precision, Recall, and F1-score values as 0.83, 0.83, and 0.82 respectively.    \n",
    "<br>    \n",
    "3) The EfficientNet50 architecture performs poorly on the dataset, as it is only able to classify the 0 class.\n",
    "<br>\n",
    "4) As a final verdict, we can rely more on the CNN+MLP and VGG16 models in this case as their scores and plots have shown us steady results as compared to other 2 models."
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