Textile Defect Detection Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UWgutvWVVUvl"
   },
   "source": [
    "# Textile Defect Detection Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ygVp11W7Mae1"
   },
   "source": [
    "*In the context of textile fabric, rare anomaly can occurs, hence compromizing the quality of the tissus. In order to avoid that in some scenario, it is crucial to detect the defect.*\n",
    "\n",
    "**Textile Defect Detection** is a *binary classification project* which is able to predict whether the given patch of the fabric is damaged or not *(good)*.\n",
    "\n",
    "![image.png](data:image/png;base64,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)\n",
    "\n",
    "**What will be covered?**\n",
    "\n",
    "\n",
    "*   Importing the Dataset *(HDF5 format)*.\n",
    "*   Pre-Exploration (Distribution of Classes)\n",
    "*   Preprocessig the dataset for binary classification.\n",
    "*   Exploring the dataset.\n",
    "*   Setting-up the model callbacks.\n",
    "*   Setting-up baseline metrics for the evaluation of actual models.\n",
    "*   Model building, training and evaluation *(Transfer Learning)*.\n",
    "*   FineTuning the Model.\n",
    "*   *Model's Deployment?*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W_TUlonvMiUb"
   },
   "source": [
    "## Importing Essential Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O0maVQU1Vsri"
   },
   "outputs": [],
   "source": [
    "# import the libraries\n",
    "import os\n",
    "import glob\n",
    "import pickle\n",
    "import tensorflow as tf    # used for creating tensors out of the dataset and training the model\n",
    "import tensorflow_hub as hub    # downloading and using pre-trained models\n",
    "import numpy as np\n",
    "import h5py   # to manipulate the dataset as it's in `.h5` format\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report   # essential for model's evaluation\n",
    "from sklearn.metrics import f1_score\n",
    "from tqdm import tqdm   # getting a progress bar for an iterable object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KNEN1indMov9"
   },
   "source": [
    "## Importing Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GxmLOtMsR3LN"
   },
   "source": [
    "**Dataset:** The project is based on the [public dataset](https://www.kaggle.com/datasets/belkhirnacim/textiledefectdetection?select=matchingtDATASET_test_32.h5) by the MVTec company. The main goal of this dataset is to explore self-supervised learning on texture images in order to solve anomaly detection problems and learn a robust representation of texture in lieu of traditional image processing features.\n",
    "\n",
    "The dataset is in **`.h5`** format containing organized by groups of defects, and dataset of patches of **`64x64 pixels`** where patches are sampled at randomized positions, and patches are extract with different angles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dP0IXrIaVsri"
   },
   "outputs": [],
   "source": [
    "# install kaggle library\n",
    "!pip install -q kaggle\n",
    "\n",
    "# create a hidden kaggle directory\n",
    "!mkdir ~/.kaggle\n",
    "\n",
    "# copy the `.json` file containing user's API token \n",
    "!cp kaggle.json ~/.kaggle/\n",
    "\n",
    "# making the `.json` file executable\n",
    "!chmod 600 ~/.kaggle/kaggle.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 107438,
     "status": "ok",
     "timestamp": 1666357622364,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "KEzP3jjPVsrk",
    "outputId": "7da128d5-a0c9-492f-be9e-adde401f0ce7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading textiledefectdetection.zip to /content\n",
      "100% 3.40G/3.40G [01:45<00:00, 46.0MB/s]\n",
      "100% 3.40G/3.40G [01:45<00:00, 34.7MB/s]\n"
     ]
    }
   ],
   "source": [
    "# download the `textile defect detection` dataset \n",
    "!kaggle datasets download -d belkhirnacim/textiledefectdetection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41344,
     "status": "ok",
     "timestamp": 1666357663687,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "L0IaAQZHVsrk",
    "outputId": "ad4872f4-fc78-42a0-fee9-3a219d4f4658"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  textiledefectdetection.zip\n",
      "  inflating: matchingtDATASET_test_32.h5  \n",
      "  inflating: matchingtDATASET_test_64.h5  \n",
      "  inflating: matchingtDATASET_train_32.h5  \n",
      "  inflating: matchingtDATASET_train_64.h5  \n",
      "  inflating: test32.csv              \n",
      "  inflating: test32.h5               \n",
      "  inflating: test64.csv              \n",
      "  inflating: test64.h5               \n",
      "  inflating: train32.csv             \n",
      "  inflating: train32.h5              \n",
      "  inflating: train64.csv             \n",
      "  inflating: train64.h5              \n"
     ]
    }
   ],
   "source": [
    "# unzip the dataset\n",
    "!unzip textiledefectdetection.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wBUGrlEGM2Sw"
   },
   "source": [
    "## Preprocessing the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rygyzlSo3KIz"
   },
   "source": [
    "### Pre-Exploration of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7ZtexRm83KI1"
   },
   "source": [
    "Exploration of the dataset is required to get understanding about our classes and to further improve the dataset if required.\n",
    "\n",
    "It will contain two parts:\n",
    "\n",
    "\n",
    "*   Exploring the **`train64.csv`** file that contains the information about the dataset that will be used for training the model.\n",
    "*   Exploring the manipulated dataset to see if the classes are properly labeled.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 633,
     "status": "ok",
     "timestamp": 1666263100054,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "vJlTD13u3KI2",
    "outputId": "32f0b53e-b0e3-441d-e915-31ac9c5f379b"
   },
   "outputs": [
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      "text/plain": [
       "   index  angle indication_type  indication_value  split\n",
       "0  48000      0            good                 0  train\n",
       "1  48001     80            good                 0  train\n",
       "2  48002    100            good                 0  train\n",
       "3  48003    100            good                 0  train\n",
       "4  48004      0            good                 0  train"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the `train64.csv` file\n",
    "train_df64 = pd.read_csv(\"/content/train64.csv\")\n",
    "train_df64[\"indication_type\"] = train_df64.indication_type.apply(lambda row: \"damaged\" if row!=\"good\" else \"good\")\n",
    "train_df64[\"indication_value\"] = train_df64.indication_value.apply(lambda row: 1 if row!=0 else 0)\n",
    "train_df64.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 718,
     "status": "ok",
     "timestamp": 1666263367326,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "damaged    0.83\n",
       "good       0.17\n",
       "Name: indication_type, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the classes distribution\n",
    "train_df64.indication_type.value_counts(normalize=True).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "executionInfo": {
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     "status": "ok",
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   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the distribution of classes\n",
    "(train_df64.indication_type.value_counts(normalize=True).round(2)*100).to_frame().plot.bar(legend=False, figsize=(8, 6))\n",
    "plt.xlabel('Classes', fontsize=18)\n",
    "plt.ylabel(\"Count (%)\", fontsize=18)\n",
    "plt.title(\"Distribution of Classes\", fontsize=20)\n",
    "plt.xticks(rotation=0, fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zk2VTvDQ3KI7"
   },
   "source": [
    "**⚠ Imbalanced Training Dataset!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UCVctEdG3XjZ"
   },
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aRkYyDn1XRtZ"
   },
   "source": [
    "The textile defect dataset actually has 6 different classes: `['good', 'color', 'cut', 'hole', 'thread', 'metal_contamination']` along with 8 different rotations `[0, 20, 40, 60, 80, 100, 120, 140]`. Given an image size, a train and test dataset are available with randomly generated patches. Source images from the train and test are non-overlapping.\n",
    "\n",
    "***But this (overly complicated) dataset is manipulated by `H5ToStorage` object for a binary classification task which contains the following preprocessing steps.***\n",
    "\n",
    "1.   `64x64 pixels greyscale-image` train and test dataset *(user's choice)* are selected.\n",
    "2.   The above mentioned *6 classes* are collapsed into only *2 classes* `[\"damaged\", \"good\"]` with TensorFlow for binary classification. *During this step, the imbalance of the classes is reduced by using data augmentation on `['good']` class training data to produce its three other versions per training sample.*\n",
    "3.   The image and labels tensors are then loaded and saved back into the storage without burdening the memory, and resulting into a new dataset that is compatible with [tf.data.datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets). *During this stage, the `\"good\"` category will produce 4 extra records by data augmentation to eliminate the class imbalance.*\n",
    "4.   `metadata` is created which contains *file-location as key* and the *rotation as it's value* for exploration purpose.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZdzfRZapVsrl"
   },
   "outputs": [],
   "source": [
    "# create an object that will take the dataset and produce the dataset in a format required for tensorflow dataset's API\n",
    "class H5ToStorage:\n",
    "  def __init__(self, hdf_path, ds_name=\"train\"):\n",
    "    self.path = hdf_path\n",
    "\n",
    "    self.classes = []\n",
    "    with h5py.File(self.path, 'r') as hf:\n",
    "      for class_ in hf:\n",
    "        self.classes.append(class_)\n",
    "\n",
    "    self.name = ds_name\n",
    "\n",
    "  # a generator to load the (img, class, angle)\n",
    "  def generate_img_arr(self):\n",
    "    for class_ in self.classes:\n",
    "      with h5py.File(self.path, 'r') as hf:\n",
    "        for angle in hf[class_]:\n",
    "            for img in hf[class_][f\"{angle}\"]:\n",
    "                yield img, class_, angle\n",
    "  \n",
    "  # utilize the generator to create new images and load it back to Storage\n",
    "  def generate_train_dirs(self):\n",
    "    # create the dataset's directories\n",
    "    path = f\"./dataset/{self.name}\"\n",
    "    os.makedirs(f\"{path}/good/\")\n",
    "    os.makedirs(f\"{path}/damaged/\")\n",
    "\n",
    "    # random_bright = tf.keras.layers.RandomBrightness(factor=0.05)\n",
    "    random_flip = tf.keras.layers.RandomFlip(\"horizontal_and_vertical\")\n",
    "\n",
    "    gen = self.generate_img_arr()\n",
    "    metadata = {}\n",
    "\n",
    "    for i, data in enumerate(gen):\n",
    "        img, label, angle = data\n",
    "        if label == \"good\":\n",
    "          for j in range(4):\n",
    "            img_path = f\"{path}/{label}/{i}_aug{j}.jpeg\"\n",
    "            img = random_flip(tf.expand_dims(np.squeeze(img), axis=2)*255., training=True)\n",
    "            plt.imsave(img_path, np.squeeze(img), cmap=\"gray\")\n",
    "        else:\n",
    "          img_path = f\"{path}/damaged/{i}.jpeg\"\n",
    "          plt.imsave(img_path, np.squeeze(img)*255., cmap=\"gray\")\n",
    "\n",
    "        metadata[img_path] = angle\n",
    "    return metadata\n",
    "\n",
    "  def generate_test_dirs(self):\n",
    "    # create the dataset's directories\n",
    "    path = f\"./dataset/{self.name}\"\n",
    "    os.makedirs(f\"{path}/good/\")\n",
    "    os.makedirs(f\"{path}/damaged/\")\n",
    "\n",
    "    gen = self.generate_img_arr()\n",
    "    metadata = {}\n",
    "\n",
    "    for i, data in enumerate(gen):\n",
    "        img, label, angle = data\n",
    "        if label == \"good\":\n",
    "          img_path = f\"{path}/{label}/{i}.jpeg\"\n",
    "          plt.imsave(img_path, np.squeeze(img)*255., cmap=\"gray\")\n",
    "        else:\n",
    "          img_path = f\"{path}/damaged/{i}.jpeg\"\n",
    "          plt.imsave(img_path, np.squeeze(img)*255., cmap=\"gray\")\n",
    "\n",
    "        metadata[img_path] = angle\n",
    "\n",
    "    return metadata\n",
    "\n",
    "  def to_storage(self):\n",
    "    if self.name == \"train\":\n",
    "      self.generate_train_dirs()\n",
    "\n",
    "    elif self.name == \"test\":\n",
    "      self.generate_test_dirs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "taptAf0sVsrm"
   },
   "outputs": [],
   "source": [
    "# generate train data\n",
    "train_gen = H5ToStorage(\"/content/matchingtDATASET_train_64.h5\", \"train\")\n",
    "train_dict = train_gen.to_storage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vsZHYiIbVsrm"
   },
   "outputs": [],
   "source": [
    "# generate test data\n",
    "test_gen = H5ToStorage(\"/content/matchingtDATASET_test_64.h5\", \"test\")\n",
    "test_dict = test_gen.to_storage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lLMZPErBVsrm"
   },
   "outputs": [],
   "source": [
    "# train data & test data paths\n",
    "test_dir = \"/content/dataset/test\"\n",
    "train_dir = \"/content/dataset/train\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5685,
     "status": "ok",
     "timestamp": 1666270851987,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "PviTYmPMVsrn",
    "outputId": "50897e60-f93e-4d2a-a9c6-9695e1608ee5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 108000 files belonging to 2 classes.\n",
      "The class_names are as follows: ['damaged', 'good']\n",
      "Found 36000 files belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# create train data pipeline\n",
    "IMG_SIZE = (64, 64) # define image size\n",
    "train_data = tf.keras.utils.image_dataset_from_directory(directory=train_dir,\n",
    "                                                         image_size=IMG_SIZE,\n",
    "                                                         color_mode=\"grayscale\",\n",
    "                                                         label_mode=\"int\", #  label type is encoding of the form `[1, 2, 3, ...]`\n",
    "                                                         batch_size=64) # batch_size is 32 by default, but 128 will be used for this use-case\n",
    "# classes in train_data\n",
    "class_names = train_data.class_names\n",
    "print(f\"The class_names are as follows: {class_names}\")\n",
    "\n",
    "train_data = train_data.prefetch(buffer_size=tf.data.AUTOTUNE).\\\n",
    "                        cache()\n",
    "\n",
    "\n",
    "# create test data pipeline\n",
    "test_data = tf.keras.utils.image_dataset_from_directory(directory=test_dir,\n",
    "                                                        image_size=IMG_SIZE,\n",
    "                                                        color_mode=\"grayscale\",\n",
    "                                                        shuffle=False,\n",
    "                                                        batch_size=64,\n",
    "                                                        label_mode=\"int\")\n",
    "\n",
    "test_data = test_data.prefetch(buffer_size=tf.data.AUTOTUNE).\\\n",
    "                      cache() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5iB0-vbiMw86"
   },
   "source": [
    "## Exploration of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YMtihR5ocXem"
   },
   "source": [
    "Exploration of the dataset is required to get understanding about our classes and to further improve the dataset if required.\n",
    "\n",
    "It will contain two parts:\n",
    "\n",
    "\n",
    "*   Exploring the generated dataset and check if the class imbalance is reduced as expected.\n",
    "*   Exploring the manipulated dataset to see if the classes are properly labeled.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 544,
     "status": "ok",
     "timestamp": 1666264187587,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "Rs3BzAoCVsrk",
    "outputId": "3b2f2558-866a-4abe-cbb5-758f65974e09"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "damaged    0.56\n",
       "good       0.44\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the proportions of classes\n",
    "damaged_counts = 0\n",
    "for _ in os.listdir(f\"{train_dir}/damaged\"):\n",
    "  damaged_counts += 1\n",
    "\n",
    "good_counts = 0\n",
    "for _ in os.listdir(f\"{train_dir}/good\"):\n",
    "  good_counts += 1\n",
    "\n",
    "total = damaged_counts + good_counts\n",
    "\n",
    "new_train_df64 = pd.Series({\"damaged\": damaged_counts/total,\n",
    "                            \"good\": good_counts/total}).round(2)\n",
    "new_train_df64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jMXrYqYjtXZN"
   },
   "source": [
    "**Class Imbalance is significantly reduced!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "executionInfo": {
     "elapsed": 931,
     "status": "ok",
     "timestamp": 1666264238093,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "WkXil_7GwN1Z",
    "outputId": "2933a075-60ce-4545-a21b-2e1da5691906"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the classes distribution\n",
    "(new_train_df64*100).to_frame().plot.bar(legend=False, figsize=(8, 6))\n",
    "plt.xlabel('Classes', fontsize=18)\n",
    "plt.ylabel(\"Count (%)\", fontsize=18)\n",
    "plt.title(\"Distribution of Classes\", fontsize=20)\n",
    "plt.xticks(rotation=0, fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RXU5aJgoe4eG"
   },
   "source": [
    "**Due to the data augmentation during the preprocessing step, the previously Imbalance of Classes has now been reduced significantly!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8VB8FFjydSfK"
   },
   "source": [
    "**⚠ Warning**\n",
    "\n",
    "The classes were heavily imbalanced due to the fact that the 5 categories **`['color', 'cut', 'hole', 'thread', 'metal_contamination']`** containing 8000 images each were collapsed into one class **`[\"damaged\"]`** which had 40000 images.\n",
    "\n",
    "As neural networks were planned to be used for training, this **`1:5`** class imbalance would have made a *20-30%* difference. It is also to be noted that the **`accuracy`** as an evaluation metric is not a robust. **`F1-Score, Precision & Recall`** might provide with more information to evaluate the model's results whether there is class imbalance or not.\n",
    "\n",
    "*The remaning class imbalance couldn't be reduced as the training data is prone to small changes and will be left as is due to the fact that a more robust model architecture will be used as a feature extractor* 😈."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 373
    },
    "executionInfo": {
     "elapsed": 23856,
     "status": "ok",
     "timestamp": 1666264489615,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "XA6yq1XvVsrn",
    "outputId": "c100993e-20d3-47ab-da9b-73ae3c53f2f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explore the train data\n",
    "def explore_data(n_rows, n_cols, figsize):\n",
    "    count = 0\n",
    "    fig, axs = plt.subplots(n_rows, n_cols, figsize=figsize) # plot 20 figures\n",
    "    fig.suptitle(\"Training Data Exploration\", fontsize=18, y=0.95)\n",
    "    for data, ax in zip(train_data.shuffle(10000).take(1).unbatch(), axs.ravel()):\n",
    "        img, label = data\n",
    "\n",
    "        ax.axis(\"off\")\n",
    "        ax.set_title(f\"{class_names[label]}\")\n",
    "        ax.imshow(np.squeeze(img), cmap=\"gray\")\n",
    "\n",
    "        count+=1\n",
    "        if count == n_rows*n_cols:\n",
    "          break;\n",
    "\n",
    "explore_data(n_rows=2, n_cols=6, figsize=(15, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q-lpVCJ_M9M0"
   },
   "source": [
    "## Baseline Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r_h566RGh-Tb"
   },
   "source": [
    "Baseline Model is neccessary to obtain a baseline metric on which the actual model will be evaluated on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 879,
     "status": "ok",
     "timestamp": 1666264503511,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "gbuoql9o0hpW",
    "outputId": "e76379ec-e410-4e5f-aed1-fbdb5ec272bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 64, 64, 3)         6         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 63, 63, 16)        208       \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 62, 62, 32)        2080      \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 60, 60, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 30, 30, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 27, 27, 128)       131200    \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 13, 13, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 10, 10, 512)       1049088   \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 512)              0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 1)                 513       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,201,591\n",
      "Trainable params: 1,201,591\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# create a baseline model to get the baseline metric\n",
    "baseline_model = tf.keras.Sequential(\n",
    "    [\n",
    "        tf.keras.layers.Input(shape=(64, 64, 1), name=\"input_layer\"),\n",
    "        tf.keras.layers.Conv2D(3, 1, padding=\"valid\", activation=\"relu\"),\n",
    "        tf.keras.layers.Conv2D(16, 2, activation=\"relu\"),\n",
    "        tf.keras.layers.Conv2D(32, 2, activation=\"relu\"),\n",
    "        tf.keras.layers.Conv2D(64, 3, activation=\"relu\", kernel_regularizer=\"l2\"),\n",
    "        tf.keras.layers.MaxPool2D(),\n",
    "        tf.keras.layers.Conv2D(128, 4, activation=\"relu\", kernel_regularizer=\"l2\"),\n",
    "        tf.keras.layers.MaxPool2D(),\n",
    "        tf.keras.layers.Conv2D(512, 4, activation=\"relu\", kernel_regularizer=\"l2\"),\n",
    "        tf.keras.layers.GlobalAveragePooling2D(),\n",
    "        tf.keras.layers.Dense(1, activation=\"sigmoid\", name=\"output_layer\")\n",
    "    ]\n",
    ")\n",
    "\n",
    "# get model summary\n",
    "baseline_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JJOSr6lZNB4c"
   },
   "source": [
    "### Baseline Model's Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "24oMVcKBX9s0"
   },
   "outputs": [],
   "source": [
    "# define callbacks\n",
    "# Setup ReduceLROnPlateau callback to reduce learning rate if model's val_loss doesn't improve for 1 epochs\n",
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\",  \n",
    "                                                 factor=0.2, # multiply the learning rate by 0.2 (reduce by 5x)\n",
    "                                                 patience=1,\n",
    "                                                 verbose=1, # print out when learning rate goes down \n",
    "                                                 min_lr=1e-7)\n",
    "\n",
    "# Setup EarlyStopping callback to stop training if model's val_loss doesn't improve for 3 epochs\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\", # watch the val loss metric\n",
    "                                                  patience=3, # if val_accuracy decreases for 3 epochs in a row, stop training\n",
    "                                                  restore_best_weights=True,\n",
    "                                                  verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8zjNkhn70h7z"
   },
   "outputs": [],
   "source": [
    "# compile the `baseline_model`\n",
    "baseline_model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
    "                      optimizer=tf.keras.optimizers.Adam(),\n",
    "                      metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 491648,
     "status": "ok",
     "timestamp": 1666265001018,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "lf4khWlT3nai",
    "outputId": "d95df387-5b7c-4768-b894-c53d81bd02fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1688/1688 [==============================] - 79s 41ms/step - loss: 0.5955 - accuracy: 0.8427 - val_loss: 0.4121 - val_accuracy: 0.8399 - lr: 0.0010\n",
      "Epoch 2/20\n",
      "1688/1688 [==============================] - 61s 36ms/step - loss: 0.3205 - accuracy: 0.8932 - val_loss: 0.3609 - val_accuracy: 0.8553 - lr: 0.0010\n",
      "Epoch 3/20\n",
      "1687/1688 [============================>.] - ETA: 0s - loss: 0.2906 - accuracy: 0.9019\n",
      "Epoch 3: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "1688/1688 [==============================] - 61s 36ms/step - loss: 0.2906 - accuracy: 0.9019 - val_loss: 0.3802 - val_accuracy: 0.8487 - lr: 0.0010\n",
      "Epoch 4/20\n",
      "1688/1688 [==============================] - 62s 37ms/step - loss: 0.2436 - accuracy: 0.9195 - val_loss: 0.2896 - val_accuracy: 0.8876 - lr: 2.0000e-04\n",
      "Epoch 5/20\n",
      "1688/1688 [==============================] - ETA: 0s - loss: 0.2340 - accuracy: 0.9224\n",
      "Epoch 5: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "1688/1688 [==============================] - 60s 36ms/step - loss: 0.2340 - accuracy: 0.9224 - val_loss: 0.3027 - val_accuracy: 0.8848 - lr: 2.0000e-04\n",
      "Epoch 6/20\n",
      "1687/1688 [============================>.] - ETA: 0s - loss: 0.2187 - accuracy: 0.9293\n",
      "Epoch 6: ReduceLROnPlateau reducing learning rate to 8.000000525498762e-06.\n",
      "1688/1688 [==============================] - 60s 36ms/step - loss: 0.2187 - accuracy: 0.9293 - val_loss: 0.3561 - val_accuracy: 0.8570 - lr: 4.0000e-05\n",
      "Epoch 7/20\n",
      "1688/1688 [==============================] - ETA: 0s - loss: 0.2134 - accuracy: 0.9314\n",
      "Epoch 7: ReduceLROnPlateau reducing learning rate to 1.6000001778593287e-06.\n",
      "Restoring model weights from the end of the best epoch: 4.\n",
      "1688/1688 [==============================] - 61s 36ms/step - loss: 0.2134 - accuracy: 0.9314 - val_loss: 0.3044 - val_accuracy: 0.8827 - lr: 8.0000e-06\n",
      "Epoch 7: early stopping\n"
     ]
    }
   ],
   "source": [
    "# train the `baseline_model`\n",
    "EPOCHS = 20\n",
    "STEPS_PER_EPOCH = len(train_data)\n",
    "\n",
    "baseline_training_history = baseline_model.fit(train_data,\n",
    "                                               epochs=EPOCHS,\n",
    "                                               steps_per_epoch=STEPS_PER_EPOCH,\n",
    "                                               validation_data=test_data.shuffle(10000),\n",
    "                                               validation_steps=int(0.5 * len(test_data)),\n",
    "                                               callbacks=[reduce_lr, early_stopping]\n",
    "                                               )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z8olof2_NFj3"
   },
   "source": [
    "### Getting the Baseline Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9300,
     "status": "ok",
     "timestamp": 1666265148648,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "C_DMP3WW4FX5",
    "outputId": "7a5d4409-a432-4a5a-e96a-d994d056ede8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 9s 16ms/step - loss: 0.2932 - accuracy: 0.8871\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.293188214302063, 0.8871111273765564]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# baseline_model's loss and accuracy\n",
    "baseline_model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "04_fagqvkkHi"
   },
   "source": [
    "Well, the baseline_model's accuracy doesn't look bad actually. Getting around 89% accuracy is pretty good though it mostly depend on the data quality and the use-case.\n",
    "\n",
    "***But is accuracy actually a robust evaluation metric?***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vrGsv61wiI41"
   },
   "outputs": [],
   "source": [
    "# create a function to plot evaluation curves\n",
    "def plot_evaluation_curves(history, restored_weight_epoch=None, annotate_loc=None):\n",
    "\n",
    "    epochs = range(len(history.history['loss']))\n",
    "    val_loss, val_accuracy = history.history[\"val_loss\"], history.history[\"val_accuracy\"]\n",
    "    train_loss, train_accuracy = history.history[\"loss\"], history.history[\"accuracy\"]\n",
    "\n",
    "    # plot loss curves\n",
    "    plt.figure(figsize=(5, 4))\n",
    "    plt.plot(epochs, train_loss, label='training_loss')\n",
    "    plt.plot(epochs, val_loss, label='val_loss')\n",
    "    plt.title('Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend();\n",
    "    if restored_weight_epoch:\n",
    "      plt.axvline(restored_weight_epoch-1, color=\"red\", linestyle=\"--\")\n",
    "    if annotate_loc:  \n",
    "      plt.annotate(\"Restored Weights\", xy=(restored_weight_epoch-1, 0.05), xytext=annoatate_loc,\n",
    "             arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "             )\n",
    "\n",
    "    # plot accuracy curves\n",
    "    plt.figure(figsize=(5, 4))\n",
    "    plt.plot(epochs, train_accuracy, label='training_accuracy')\n",
    "    plt.plot(epochs, val_accuracy, label='val_accuracy')\n",
    "    plt.title('Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend();\n",
    "    if restored_weight_epoch:\n",
    "      plt.axvline(restored_weight_epoch-1, color=\"red\", linestyle=\"--\", label=\"Restored Accuracy\")\n",
    "    if annotate_loc:\n",
    "      plt.annotate(\"Restored Weights\", xy=(restored_weight_epoch-1, 0.94), xytext=(0.5, 0.93),\n",
    "             arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 623
    },
    "executionInfo": {
     "elapsed": 1786,
     "status": "ok",
     "timestamp": 1666265166798,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "zfQ-bZ9QCj9O",
    "outputId": "0858d383-9350-4ad8-ddc8-60b1645f3188"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the accuracy and loss curves\n",
    "plot_evaluation_curves(baseline_training_history, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_yxk-pTRlRBc"
   },
   "source": [
    "The training and validation loss does explain the model's improvement.\n",
    "\n",
    "**⚠ *But then again, considering the class imbalance in the training dataset, is accuracy really a robust evaluation metric?* Let's find out!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Kc22GrGtAsTu"
   },
   "outputs": [],
   "source": [
    "# function to get the model's ground truth and predicted labels\n",
    "def get_labels(dataset, model):\n",
    "    y_labels = []\n",
    "    for _, label in tqdm(dataset.unbatch()):\n",
    "        y_labels.append(label)\n",
    "\n",
    "    y_preds = model.predict(dataset, verbose=1)\n",
    "    return y_labels, y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11258,
     "status": "ok",
     "timestamp": 1666265227165,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "_dXAftrIDfLZ",
    "outputId": "674af5fd-7eb6-4ca4-b7c2-34160615e4e8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "36000it [00:05, 7041.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 6s 11ms/step\n"
     ]
    }
   ],
   "source": [
    "# get the labels for baseline model\n",
    "y_labels, y_preds = get_labels(test_data, baseline_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 28,
     "status": "ok",
     "timestamp": 1666265227170,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "VP6CBMrMFU9J",
    "outputId": "8d4e22d5-8876-475e-9a57-e30ea21aeb06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<tf.Tensor: shape=(), dtype=int32, numpy=0>,\n",
       "  <tf.Tensor: shape=(), dtype=int32, numpy=0>],\n",
       " array([[0.],\n",
       "        [0.]], dtype=float32))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the labels\n",
    "y_labels[:2], np.round(y_preds[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1119,
     "status": "ok",
     "timestamp": 1666265243649,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "zlSoAQgOETzT",
    "outputId": "1d3743a9-244e-4038-b7e0-92d3dd5a8b26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############### Classification Report ###############\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.91      0.93     30000\n",
      "           1       0.63      0.79      0.70      6000\n",
      "\n",
      "    accuracy                           0.89     36000\n",
      "   macro avg       0.79      0.85      0.82     36000\n",
      "weighted avg       0.90      0.89      0.89     36000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# get the classification report\n",
    "print(\"############### Classification Report ###############\\n\\n\")\n",
    "print(classification_report(y_labels, np.round(y_preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uG3yeFmwmHqv"
   },
   "source": [
    "The precision, recall and f1-score of each classes tell a different story. It is a bit complicated to evaluate the results due to class imbalance in the test dataset. Anyways, the `f1-score` *(which considers both the precision and recall)* of `'good'` class is faring a lot worse than the the `'damaged'` class due to the lack of variety of class's training data quality in the dataset. *Although, the augmented training data had been generated but it wasn't too overly complicated due to the uniqueness of the dataset which also resulted in the lack of the variety of the training data for `['good']` class.*\n",
    "\n",
    "**But would this trend continue after using a pre-trained state-of-the-art architecture for feature extraction. Let's find out!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LPg16q27NNAk"
   },
   "source": [
    "## Transfer Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3P-Oa-TOwxev"
   },
   "source": [
    "**Transfer learning** is an optimization that allows rapid progress or improved performance when modeling the second task. Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned reducing the time to re-train a model from scrath."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jolNILN1OYkb"
   },
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jl_1zej6wZca"
   },
   "source": [
    "The pre-trained model used for this use case was **BiT-m R50x1** trained on the **angiosperm** tree of the **ImageNet21k dataset**. The intuition behind choosing such a model was because the resnet architecture was robust due to residual connections and also the angiosperms which the model was trained on might exhibit the same low level features as that of the fabric's threads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "121WOGasX9sx"
   },
   "outputs": [],
   "source": [
    "# feature extractor trained on angiosperms\n",
    "module = hub.KerasLayer(\"https://tfhub.dev/google/experts/bit/r50x1/in21k/angiosperm/1\",\n",
    "                        trainable=False,\n",
    "                        name='feature_extraction_layer',\n",
    "                        input_shape=IMG_SIZE+(3,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OgJw-RxNX9sy"
   },
   "outputs": [],
   "source": [
    "# build the sequential model\n",
    "model = tf.keras.Sequential(\n",
    "    [\n",
    "        tf.keras.layers.Conv2D(3, 1, padding=\"same\", activation=\"relu\"),  # the following feature extraction layer won't accept the data of shape `(None, 64, 64, 1)`\n",
    "                                                                          # to solve this issue, it is required that the channels are expanded form `1` to `3` by using 1x1 convolution\n",
    "        module,  # feature extractor layer\n",
    "        tf.keras.layers.Dense(256, activation=\"relu\", name=\"dense_layer\"),\n",
    "        tf.keras.layers.Dense(1, activation='sigmoid', name='output_layer')  # making predictions\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4374,
     "status": "ok",
     "timestamp": 1666271929702,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "Wwxu27flX9sy",
    "outputId": "17cc5193-2f81-4f8e-ab87-6f7801fa1811"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 64, 64, 3)         6         \n",
      "                                                                 \n",
      " feature_extraction_layer (K  (None, 2048)             23500352  \n",
      " erasLayer)                                                      \n",
      "                                                                 \n",
      " dense_layer (Dense)         (None, 256)               524544    \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 1)                 257       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 24,025,159\n",
      "Trainable params: 524,807\n",
      "Non-trainable params: 23,500,352\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# build the model and check summary\n",
    "model.build((None, 64, 64, 1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 67,
     "status": "ok",
     "timestamp": 1666271929708,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "qXIesz3MX9sz",
    "outputId": "9b53e43a-737d-41ff-b801-9440aba8476e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: The conv2d's training is set to True\n",
      "2: The feature_extraction_layer's training is set to False\n",
      "3: The dense_layer's training is set to True\n",
      "4: The output_layer's training is set to True\n"
     ]
    }
   ],
   "source": [
    "# check if the training of the feature extractor is set to inference\n",
    "for i, layer in enumerate(model.layers):\n",
    "  print(f\"{i+1}: The {layer.name}'s training is set to {layer.trainable}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wRspGtIDyauw"
   },
   "source": [
    "Every layer's training is set except the feature extractor's (which is solely used for inference)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 533
    },
    "executionInfo": {
     "elapsed": 59,
     "status": "ok",
     "timestamp": 1666271929711,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "wjALZgyxX9sz",
    "outputId": "d89012cd-5fc9-4249-dfe0-4ac52690912d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot the model\n",
    "tf.keras.utils.plot_model(\n",
    "    model,\n",
    "    # to_file='model.png',\n",
    "    show_shapes=True,\n",
    "    show_dtype=False,\n",
    "    show_layer_names=True,\n",
    "    rankdir='TB',\n",
    "    expand_nested=False,\n",
    "    dpi=96,\n",
    "    layer_range=None,\n",
    "    show_layer_activations=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9c5pUmg_X9s0"
   },
   "outputs": [],
   "source": [
    "# Define the loss function\n",
    "binary_categorical_loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LxL_1oemX9s1"
   },
   "outputs": [],
   "source": [
    "# Define the optimizer\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NWKT6bt5X9s1"
   },
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(\n",
    "      loss=binary_categorical_loss,\n",
    "      optimizer=optimizer,\n",
    "      metrics=[\"accuracy\"]\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5888,
     "status": "ok",
     "timestamp": 1666272734794,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "KeyWHr-qX9s1",
    "outputId": "30551cd5-b83e-49d9-88a5-7916ed9d039e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1688/1688 [==============================] - 215s 117ms/step - loss: 0.2182 - accuracy: 0.9490 - val_loss: 0.2456 - val_accuracy: 0.9161 - lr: 0.0100\n",
      "Epoch 2/10\n",
      "1688/1688 [==============================] - 182s 108ms/step - loss: 0.1009 - accuracy: 0.9611 - val_loss: 0.1834 - val_accuracy: 0.9339 - lr: 0.0100\n",
      "Epoch 3/10\n",
      "1688/1688 [==============================] - ETA: 0s - loss: 0.0895 - accuracy: 0.9654\n",
      "Epoch 3: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
      "1688/1688 [==============================] - 183s 109ms/step - loss: 0.0895 - accuracy: 0.9654 - val_loss: 0.3847 - val_accuracy: 0.8886 - lr: 0.0100\n",
      "Epoch 4/10\n",
      "1688/1688 [==============================] - ETA: 0s - loss: 0.0519 - accuracy: 0.9797\n",
      "Epoch 4: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.\n",
      "Restoring model weights from the end of the best epoch: 2.\n",
      "1688/1688 [==============================] - 183s 109ms/step - loss: 0.0519 - accuracy: 0.9797 - val_loss: 0.5110 - val_accuracy: 0.8975 - lr: 0.0020\n",
      "Epoch 4: early stopping\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "EPOCHS = 10\n",
    "STEPS_PER_EPOCH = len(train_data)\n",
    "\n",
    "training_history_1 = model.fit(train_data,\n",
    "                             epochs=EPOCHS,\n",
    "                             steps_per_epoch=STEPS_PER_EPOCH,\n",
    "                             validation_data=test_data.shuffle(10000),\n",
    "                             validation_steps=int(0.5 * len(test_data)),\n",
    "                             callbacks=[reduce_lr, early_stopping]\n",
    "                             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yu2-eQo8NRyF"
   },
   "source": [
    "### Model's Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41724,
     "status": "ok",
     "timestamp": 1666272857952,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "exi8nQc_h2sq",
    "outputId": "e8799d93-cce4-43f3-9400-3c34eb2f6723"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 25s 43ms/step - loss: 0.1871 - accuracy: 0.9316\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.18707706034183502, 0.9315555691719055]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1666273345474,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "1TYQpgvJiJ61",
    "outputId": "5fbd6e54-0da7-4939-d790-62c3850ca0b3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YY9aX2OwjY8xk1/ZDgReA/lVQr/KUl16yuwLP2LoQpo6EOokw6nOIqOHVj39iZhrrd2d79D3bNYjhsSFJ5T4/YcIEtm7dSseOHQkNDSUiIoJatWqRnp7Opk2buOqqq8jIyCA3N5e7776bca4e+aZNm5KSkkJOTg4DBgzgkksu4YcffiAhIYGvvvqKatWqMXr0aAYPHszw4cNp2rQpN998MzNnziQ/P59PPvmExMRE9u/fz8iRI9m9ezc9evTg22+/ZeXKlcTFxZ3x5zLG8OCDDzJ37lxEhIkTJ5KcnMyePXtITk4mOzubgoICXnvtNS666CJuvfVWUlJSEBFuueUW7r333kp/t+608LoBW4wx24wxecBUYFipH6Tkv3h1wFS6MqXOZvtS63KKsS3hD19BtVp2V+QVzzzzDC1atCA1NZXnnnuOVatW8a9//YtNrl73t99+m5UrV5KSksLLL7/MgQMHfvcemzdv5s9//jNpaWnUrFmTz8q5MHtcXByrVq1i/PjxPP/88wA88cQT9O3bl7S0NIYPH87OnTvdqvvzzz8nNTWV1atXM3/+fB544AH27NnDRx99xJVXXln8XMeOHUlNTWXXrl2sW7eOtWvXMmbMmAp+W6dzZxxeApBR4nEmcGHpjUTkz8B9QBjQt6w3EpFxwDiAxo0bn2utypNuusla+uvMxzt/hA+vh5qNrbCLrG1LGWdqiXlLt27dThuL9vLLL/PFF18AkJGRwebNm4mNjT3tNc2aNaOj65BG586d2b59e5nvfc011xRv8/nnnwOwdOnS4vfv378/tWq594dm6dKljBgxguDgYOrVq0fv3r1ZsWIFXbt25ZZbbiE/P5+rrrqKjh070rx5c7Zt28add97JoEGDuOKKK9z/Qs7AY50WxphXjDEtgIeAieVs84YxposxpkudOnU89dGqIjIzrZs/ylwJHwyHmHi4eQZEOfv/UvXqpzpoFi1axPz581m2bBmrV6+mU6dOZY5VCw8PL74fHBxc7vG/k9udaZvK6tWrF4sXLyYhIYHRo0fz/vvvU6tWLVavXk2fPn2YPHkyY8eO9chnuRN4u4BGJR43dK0rz1TgqsoUpVS5dqfClKuheizcPBOi69tdkddFR0dz5MiRMp87fPgwtWrVIjIykvT0dH788UePf/7FF1/M9OnTAfjmm284ePCgW6/r2bMn06ZNo7CwkP3797N48WK6devGjh07qFevHrfddhtjx45l1apVZGVlUVRUxLXXXsuTTz7JqlWrPFK7O7u0K4BWItIMK+huAEaW3EBEWhljNrseDgI2o5Sn/boWplxldUzcPBNiGthdkS1iY2O5+OKLad++PdWqVaNevXrFz/Xv35/JkyfTtm1b2rRpQ/fu3T3++Y899hgjRoxgypQp9OjRg/r16xMdffae8auvvpply5Zx/vnnIyI8++yz1K9fn/fee4/nnnuO0NBQoqKieP/999m1axdjxoyhqKgIgKefftojtYsxZ+9fEJGBwEtAMPC2MeYpEZkEpBhjZojIv4B+QD5wELjDGJN2pvfs0qWLSUlJqfQPoCqoTx9ruWiRnVW4b98GeHcQhETA6NlQ2/3zJz1tw4YNtG3b1rbPt9uJEycIDg4mJCSEZcuWMX78eFJtumRAWf8WIrLSGNOlrO3dmjzAGDMHmFNq3aMl7t997qUqW/XoYXcF7svaDO8NhaBQq2VnY9gp2LlzJ9dffz1FRUWEhYXx5ptv2l2S23S2FKfy0C5ClTuwFd4bAhirgyK2hd0VOV6rVq34+eefT1t34MABLrvsst9tu2DBgt/1ENtJA0/5roM7rJZdwQlrN7ZOG7srUuWIjY21bbf2XGjgOdW111rLcgac2u5wJrw3GPJyrN3Yenq2oqo8DTynKmP0vc/I3g3vDobjh6xBxfHn2V2RChAaeMq3HNlr7cYe3Q+jvoSEC+yuSAUQDTzlO45mwfvDIHsX3PQ5NOpqd0UqwOh8eMo3HPvNCruDv8DIadDEj4bN+LgzzUe3fft22rdv78Vq7KUtPKcqYwiBbY4fss6gyNoMIz6GZr3srkgFKA08p3rkEbsrsORmwwfXwt71cMNH0NKHgtgdcydYp7x5Uv0OMOCZcp+eMGECjRo14s9//jMAjz/+OCEhISxcuJCDBw+Sn5/Pk08+ybBhw8p9j7Lk5uYyfvx4UlJSCAkJ4YUXXuDSSy8lLS2NMWPGkJeXR1FREZ999hkNGjTg+uuvJzMzk8LCQh555BGSk5Mr9WN7gwaess+JHPjwOtiTCte/D609MwVQoEtOTuaee+4pDrzp06czb9487rrrLmJiYsjKyqJ79+4MHTr0nC5088orryAirF27lvT0dK644go2bdrE5MmTufvuu7nxxhvJy8ujsLCQOXPm0KBBA2bPng1Ykxb4Aw08pxowwFrOnWvP5+cdg49vgMwVMPxtSBxkTx2VdYaWWFXp1KkT+/btY/fu3ezfv59atWpRv3597r33XhYvXkxQUBC7du1i79691K/v/mwyS5cu5c477wQgMTGRJk2asGnTJnr06MFTTz1FZmYm11xzDa1ataJDhw7cf//9PPTQQwwePJiePXtW1Y/rUdpp4VTHj1s3O+TnwtQRsON/cPXrkKSziZ2r6667jk8//ZRp06aRnJzMhx9+yP79+1m5ciWpqanUq1fvnK/ZWp6RI0cyY8YMqlWrxsCBA/nuu+9o3bo1q1atokOHDkycOJFJkyZ55LOqmrbwlHcVnIBpN8G27+GqV+G86+yuyC8lJydz2223kZWVxffff8/06dOpW7cuoaGhLFy4kB07dpzze/bs2ZMPP/yQvn37smnTJnbu3EmbNm3Ytm0bzZs356677mLnzp2sWbOGxMREateuzU033UTNmjV56623quCn9DwNPOU9BXkw/WbY8i0MeRk6jjz7a1SZkpKSOHLkCAkJCcTHx3PjjTcyZMgQOnToQJcuXUhMTDzn97z99tsZP348HTp0ICQkhHfffZfw8HCmT5/OlClTCA0NpX79+jz88MOsWLGCBx54gKCgIEJDQ3nttdeq4Kf0PLfmw6sKOh+ezbw9H15hgXXd2A0zYODz0O0273xuFXD6fHi+pErmw1MBaPBg731WUSF8Mc4Kuyuf9uuwU/7NfwJv+1KIPz/gL7LsNX/5i3c+p6gQvrwd1n0Gl0+CHrd753PVadauXcuoUaNOWxceHs7y5cttqsge/hF4udnWhVsAmveBxMHQZqDjr1bl84qKYObdsGYqXDoRLg6cibGNMec0xs1uHTp08Iv56s5FRQ7H+cewlLDq8IcZ0PU22J8OM++Cf7aGtwfAslesiSLVuenT59RxvKpgDMz5C/w8BXo9CL0fqLrP8rKIiAgOHDhQoV845RnGGA4cOEBERMQ5vc7/Oi2MsU7lSZ8FG2bBPte1gup3gMQh0HYw1G0HfvTX1xZV2WlhDHz9V1j+Glx8D/R7PKD+PfLz88nMzPTYODdVMRERETRs2JDQ0NDT1p+p08L/Aq+037ZZwZc+CzJ+AgzUamYFX+IQaNgVgvyjIetVVRV4xsC3j8AP/4but8OVfw+osFO+L7B7aWs3h4vvsm5H9sLG2VYA/jjZ+qWLqmcd72s7GJr2gpAwuysObN89aX3vXcdq2Cmf4/+BV1J0Pehyi3XLPQybvoH0mbBmOqx8B8JrWCeoJw6Glv0gvPx5wlQFfP8sLHkeLrgZBjynYad8TmAFXkkRNazTls67DvKPw7ZFVstv4xxY+4l1Qefml1otv9YDoLrvXErOK66/3rPvt+QFWPgUnD8SBr+khxGUT/L/Y3jnqrAAdi471emRnQkSDE0uslp+bQdDjYber8ufLXsF5j0MHa6zJgMICra7IuVggd1pURnGWHOxnez02J9urW/QyRV+QwL3WqjHjlnLyMjKvc9Pb1rDT9oNg2vfhuDA3WlQ/kEDz11Zm2HDTCv8dq201sW2OtXj26BT4OyqeaKXNuUdmHUPtBkE178HwaFnf41SVSywe2k9Ka4V9LzPumXvhvTZVgD+72VY+iJEN7Amqmw7GJpc7Oxf8J8/sMKu1RVw3TvO/i6U3/CbwHt76S/0TaxL07jq3vnAmAbWSe7dbrOuqLVpntXy+/kDWPEmRNSENgOsXd8WfSGskruG/mTNdPjqDqvT5/opEBJud0VKucUvAi8r5wQvfruJZ+am88fezbm9T0uqhXnxwHhkbeg4wrrlHYOtC071+K7+GEKqWRefaTsEWl8J1Wp5rzZvS/sCvvgjNL3EuuhO6Lmd2qOUnfzmGN6+7Fz+PmcDX6buJqFmNR4Z3I4rk+rZewJ3Yb41i0v6LGv398geCAqxwiBxsHWLibevvjOpyDG8DTOtCTwbdYObPrPOcVbKxwRUp8XybQd4bEYa6b8eoWerOJ4YmkTzOj4wgLioCHavOtXpcWCLtT6hy6lOj7iW9tZY0rvvWsvRo93bfuPX1tTsDTrCqC90mi7lswIq8AAKCot4f9kOXvx2E7kFhYzt2Zw7+7YkMsxH9tCNgf0brbM8Nsyyhr4A1Ek8NdYvvqP/nImwZT58PALqJcGoL6FaTbsrUqpcARd4J+07ksszc9P5fNUu4mtEMHFQOwZ2qO9785QdyrB2edNnWVfqMkVQo5HV45s4GBr38P74tawsaxkXd+bttn0PH11v9WD/YYZ1PFMpH1bpwBOR/sC/gGDgLWPMM6Wevw8YCxQA+4FbjDFnnKTOk+PwUrb/xiNfpbFhTzaXtIzj8aHtaFnXR3e5jh6ATXOtlt/W76DwBETGunp8h1gTnHqjI8CdY3jb/wcfDodaTeHmWc47/U75pUoFnogEA5uAy4FMYAUwwhizvsQ2lwLLjTHHRGQ80McYk3ym9/X0wOOCwiI+XL6T57/ZyPG8Qm69pBl3XtaKqHAf2c0ty4kca3cxfZY17OVENoRFWRMbtB0CrS63zgmuCmcLvIyfrFmmYxrA6NkQVbdq6lDKwyo78LgbsMUYs831ZlOBYUBx4BljFpbY/kfgpoqXWzEhwUHcfFFTBp0Xz7Nfp/P64m18mbqL/xvUjiHnxfvebi5Ys7UkXWXdCvLgl8XWcb/0ObD+SwgKhea9XT2+g7wXOrtWwgfXWlNr3TxTw04FDHdaeMOB/saYsa7Ho4ALjTF3lLP9f4BfjTFPlvHcOGAcQOPGjTtX5GLB7lq18yCPfrWOdbuy6dE8lieGJdG6no/u5pZWVAiZK071+B7cDgg0utDV4zsYajer3GeU18LbsxreG2INrB4zF2okVO5zlPKyyu7Suh14InITcAfQ2xhz4kzv641zaQuLDB//tJPn5m0k50QBYy5qyt39WhEd4UenQRkDe9NOze6yd621vl77Uz2+9dqfe49vWYG3Nw3eHWTtVo+eDbWaeOInUMqrKht4PYDHjTFXuh7/FcAY83Sp7foB/8YKu31nK8qbkwf8djSP5+alM3VFBnFR4Tw8MJGrOib45m7u2fz2y6ke350/AgZqNrGO+SUOtgYFuzM907Rp1jLZdah1X7oVdsFhMGa2NZO0Un6osoEXgtVpcRmwC6vTYqQxJq3ENp2AT7FagpvdKcqO2VJSMw7x2FfrWJ15mG5Na/PEsCTaxsd4tQaPytlnnd62YZY1wWlRPlSv45rSfgg06+Xeea5ZW+Ddgdb90XN8a4C0UufIE8NSBgIvYQ1LedsY85SITAJSjDEzRGQ+0AHY43rJTmPM0DO9p13TQxUVGaalZPDs1+lk5xYwqnsT7r28NTWq+dFubllys2HzN1bLb/O3kJcD4TFWT2/iYGtZ8uyIjAxrWT0f3hkEhXnWbmzdRHvqV8pDAnbgcWUcOpbHc/M28tFPO4mtHsaEAW25plMCQUF+uJtbWn4u/PK91emxcQ4cOwDB4dYYv7aui5gPGg4FJ+DaI5B3FEbPss6kUMrPaeCdwdrMwzzy1TpSMw7RuUktJg1LIqlBFY19s0NRoXWsL32WFYCHM0CC4OMgKMiFsbHW0JP48+2uVCmP0MA7i6Iiw6erMnlmbjqHjuVxU/cm3H95G2pE+vlubmnGWMNO0mfBH5+1dmOX/A8SOttdmVIeo4HnpsPH8nnh241M+XEHNSPDmNA/kcmUS9AAABCVSURBVOGdGwbGbm5pVXUhbqVsdqbAC5ALNHhGjchQnhjWnpl3XkLzuOo8+NkarnntB9ZmHra7NKWUB2gLrxzGGD5ftYun56Zz4OgJRnRrzANXtKFW9TC7S/OMmTOt5ZAh9tahlIfpLm0lZOfm8+K3m3h/2Q5iIkJ44MpEkrs2IjgQd3OVCgC6S1sJMRGhPDYkidl3XUKrutE8/MVarn71f6RmHLK7tMrZuNG6KeUg2sI7B8YYvkrdzVNzNpCVc4LkLo14sH8itf1xN1c7LVSA0haeh4gIV3VK4Lv7e3Prxc34ZGUmlz6/iCk/7qCwyJ4/HEop92ngVUB0RCgTB7dj7t09aRcfwyNfrmPYK0tZueOg3aUppc5AA68SWteL5qPbLuTfIzqRdSSPa1/7gQc+WU1WzhlnxlJK2UQDr5JEhCHnN2DB/b35Y+/mfPHzLi59fhHv/u8XCgqL7C5PKVWCdlp42JZ9OTw+I42lW7JoGx/DpGFJdG3qg1f6mj/fWvbrZ28dSnmYjsPzMmMMc9f9ypOz1rP7cC7XdEpgwsBE6kZ74WpkSjmc9tJ6mYgwsEM88+/vze19WjBzzW4ue/57/rvUh3ZzU1Otm1IOoi08L9i2P4fHZ65n8ab9tKkXzaRhSVzY3OZrvOo4PBWgtIVns+Z1onhvTFdeH9WZnBMFJL/xI3dP/Zm92bl2l6aUo2jgeYmIcGVSfebf15u7+rZk7rpf6fv8It5cvI18X9nNVSrAaeB5WbWwYO67og3f3tuLC5vH8tScDQz41xJ+2JJld2lKBTwNPJs0ia3O26O78tYfunCioJCRby3nzx+tYs/h43aXplTACrG7AKfr164el7SKY/L3W3lt0VYWpu/jzr6tuPWSZoSFVOHfo7//vereWykfpb20PiTjt2M8MXM98zfspXmd6jwxNImererYXZZSfkV7af1Eo9qRvHVzF94Z3ZXCIsOo//7E+A9WsutQFezm/vCDdVPKQbSF56Ny8wt5a8k2/rNwC4JwR9+WjO3ZjPCQYM98gI7DUwFKW3h+KCI0mDv6tmL+fb3p3boOz83bSP+XlrBo4z67S1PKb2ng+biGtSKZPKoz79/SDQFGv7OCce+nkPHbMbtLU8rvaOD5iV6t6zD3np482L8NSzZn0e+F7/nX/M3k5hfaXZpSfkMDz4+EhwRze5+WLLi/N/3a1uPF+Zu44sXFLNiw1+7SlPIL2mnhx5ZuzuKxGevYuv8olyXW5bEhSTSOjXTvxSdnSunYseoKVMoGOh9eAMsrKOLdH37hX/M3k19k+FPvFtzepwURoR7qzVXKz2gvbQALCwliXK8WLLi/D/2T6vPygs30e+F7vkn7lTP+MZs//9Ssx0o5hLbwAsyyrQd4bMY6Nu3NoU+bOjw+JImmcdV/v6GOw1MBSlt4DtKjRSyz7+rJxEFtSdl+kCteXMzz8zZyPE97c5XSwAtAocFBjO3ZnO/u782g8+L5z8It9Hvhe+au3XPm3VylApxbgSci/UVko4hsEZEJZTzfS0RWiUiBiAz3fJmqIurGRPBickem/7EH0REhjP9wFX94+ye27s+xuzSlbHHWwBORYOAVYADQDhghIu1KbbYTGA185OkCVeV1a1abWXdewmND2pG68xD9X1rMzt+OUVikrT3lLO7Mh9cN2GKM2QYgIlOBYcD6kxsYY7a7ntO5yn1USHAQYy5uxuDzGvCPr9MZve9WqoUGk/PcQqLCQ4iOCCE6IpToiBBiXMuS66xtQokpsS4yLBgRsftHU8pt7gReApBR4nEmcGFFPkxExgHjABo3blyRt1CVVCc6nOevO5+V3Rrx5c+7yc7N50huAUdy88n47RhHcgvIzs0n50QBZzvcFxwkp4dl8f1ToRhdIjxjIkKJKh2kYSEEBWloKu/w6ozHxpg3gDfAGpbizc9Wp+u85n90DgZuGFLm80VFhqN5BRzJLSDnhBWI2bkFxeF4+vLU+j2Hc9m079T6s+02i0BUWKkQLCMoi4M0/NQ2MSVanyHB2v+mzs6dwNsFNCrxuKFrnfJn//yntRxSduAFBYkrdEIr/BHGGI7nF5YbjieXJYM050QBB3Ly2J51tHjbPDeu6hYZFvz7lqU7Lc4SLdQqnVJf+QR3Am8F0EpEmmEF3Q3AyCqtSgUEESEyLITIsBDqxURU+H1yS4WmOy3Ow8fzyTx4rHh9bv7ZQzM8JKjEcUorBEsf3zy9xVmyRWqt11P6fNtZA88YUyAidwDzgGDgbWNMmohMAlKMMTNEpCvwBVALGCIiTxhjkqq0cuUYEaHBRIQGUyc6vMLvkVdQVByUxccpSwdmGUG6Nzu3+P5RNwZv14kOJ6lBjOtWg6QGMTSuHamdOz7CrWN4xpg5wJxS6x4tcX8F1q6uUj4pLCSI2iFh1K4eVuH3KCwy5LjCsrwW59Z9OaTtzmbJ5qzi45fR4SG0LRWCLetGEarHHb1OL9OolJuCg4QakaHUiDz7cc3c/EI2/nqE9XuySdt9mLTd2Xz8087iXeuwkCDa1Isubg22a1CDtvHRRIbpr2RV0skDnCrDNdKoUaMzb6c8prDI8EuW1QK0blYQHjqWD0CQQLO46sWtwJPLWpVolTqRzoenlI8yxrD7cC5puw4XB+H63YfZfTi3eJsGNSJoVxyCMSQl1KBBjQg9LliOMwWetp+dato0a5mcbG8dDiciJNSsRkLNalyRVL94/W9H81hfohWYtvswC9L3Fg8GrxkZelorsF18DM3rRBGsg7jPSFt4TqXz4fmdY3kFbNhzhPW7T7UGN/56pHicYkRoEIn1T+8caVM/2nFDZbSFp1QAiAwLoXOTWnRuUqt4XX5hEVv25Zx2THBG6m4+XL4TsDpaWtaJcnWMWEHYrkEMNapVfEC5P9PAU8qPhQYH0TY+hrbxMQzvbI0MKyoyZBw8dloILtmSxec/nzpBqlHtaiTFu44LJlhBWDc6POCPC2rgKRVggoKEJrHVaRJbnYEd4ovX7zuS6+oUyS4+Pvh12q/Fz8dFhZ3eOdKgBk1qRwbU5A4aeEo5RN3oCOq2ieDSNnWL1x3JzWfDniMlOkeyeXPxNgpcg6ajwkNoGx9dvCuc1CCGVnWj/fa8Yw08p/r0U7srUD4gOiKUbs1q061Z7eJ1JwoK2bw357QQnJ6SwTHXqXVhwUG0qhd1WudI2/gYqof7fpz4foWqasTF2V2B8lHhIcG0T6hB+4QaxesKiwzbDxwtPi64fnc28zfsY3pKJmBN89Ustnpxx8jJ3eLYqIqf/1wVNPCc6t13reXo0XZWofxEcJDQok4ULepEMfT8BoA1aPrX7FzSdp06c+TnnYeYtWZP8evqx0ScdvpcUoMYGtaqZlvniAaeU2ngqUoSEeJrVCO+RjX6tatXvP7QsZODpk/1Ei/cuI+Tc8HWqBZKu/iY03qIm8dV98okrhp4SimPqhkZxkUt47io5anDJsfzCkn/1XXq3B5rOeXHHZwosAZNh4cEkVg/+rRe4rbxMR4fNK2Bp5SqctXCgunUuBadGp8aNF1QWMS2rKNWK9C1Wzx7zW4+/skaNB0k0KJOFK+P6kzzOlEeqUMDTylli5DgIFrXi6Z1vWiu7mStM8aQefB48SQKabuzqVuJ2bJ/95keeyellKokEaFR7Uga1Y6kf/v6Z3/BOdLAc6o5c86+jVIBRgPPqSIj7a5AKa/zz/NDVOW9+qp1U8pBNPCcavp066aUg2jgKaUcQwNPKeUYGnhKKcfQwFNKOYZtF/ERkf3AjnN8WRyQVQXleJrW6Xn+UqvW6VkVqbOJMaZOWU/YFngVISIp5V2NyJdonZ7nL7VqnZ7l6Tp1l1Yp5RgaeEopx/C3wHvD7gLcpHV6nr/UqnV6lkfr9KtjeEopVRn+1sJTSqkK08BTSjmGTwaeiPQXkY0iskVEJpTxfLiITHM9v1xEmnq/SrfqHC0i+0Uk1XUba0ONb4vIPhFZV87zIiIvu36GNSJygbdrLFHL2WrtIyKHS3yfj3q7RlcdjURkoYisF5E0Ebm7jG1s/17drNP271REIkTkJxFZ7arziTK28czvvDHGp25AMLAVaA6EAauBdqW2uR2Y7Lp/AzDNR+scDfzH5u+zF3ABsK6c5wcCcwEBugPLfbjWPsAsO79PVx3xwAWu+9HApjL+7W3/Xt2s0/bv1PUdRbnuhwLLge6ltvHI77wvtvC6AVuMMduMMXnAVGBYqW2GAe+57n8KXCbev9ClO3XazhizGPjtDJsMA943lh+BmiIS753qTudGrT7BGLPHGLPKdf8IsAFIKLWZ7d+rm3XazvUd5bgehrpupXtTPfI774uBlwBklHicye//kYq3McYUAIeBWK9UV0YNLmXVCXCta5fmUxFp5J3Szom7P4ev6OHa9ZkrIkl2F+PateqE1Sopyae+1zPUCT7wnYpIsIikAvuAb40x5X6flfmd98XACyQzgabGmPOAbzn1F0pVzCqs8yTPB/4NfGlnMSISBXwG3GOMybazljM5S50+8Z0aYwqNMR2BhkA3EWlfFZ/ji4G3CyjZEmroWlfmNiISAtQADnilujJqcPldncaYA8aYE66HbwGdvVTbuXDn+/YJxpjsk7s+xpg5QKiIxJ3lZVVCREKxQuRDY8znZWziE9/r2er0pe/UVcMhYCHQv9RTHvmd98XAWwG0EpFmIhKGdYByRqltZgA3u+4PB74zrqOZXnTWOksdsxmKdQzF18wA/uDqVewOHDbG7LG7qLKISP2Tx21EpBvW/19v/6HDVcN/gQ3GmBfK2cz279WdOn3hOxWROiJS03W/GnA5kF5qM4/8zvvcVcuMMQUicgcwD6sn9G1jTJqITAJSjDEzsP4Rp4jIFqyD3Df4aJ13ichQoMBV52hv1ykiH2P1xMWJSCbwGNZBYYwxk4E5WD2KW4BjwBhv13iSG7UOB8aLSAFwHLjBhj90ABcDo4C1ruNOAA8DjUvU6gvfqzt1+sJ3Gg+8JyLBWIE73Rgzqyp+5/XUMqWUY/jiLq1SSlUJDTyllGNo4CmlHEMDTynlGBp4SinH0MBTVU5ECkvMxpEqZcwsU4n3blre7CpKleZz4/BUQDruOm1IKVtpC0/ZRkS2i8izIrLWNR9aS9f6piLynWvShQUi0ti1vp6IfOE60X21iFzkeqtgEXnTNZfaN67R+ojIXa654NaIyFSbfkzlQzTwlDdUK7VLm1ziucPGmA7Af4CXXOv+DbznmnThQ+Bl1/qXge9dJ7pfAKS51rcCXjHGJAGHgGtd6ycAnVzv86eq+uGU/9AzLVSVE5EcY0xUGeu3A32NMdtcJ7n/aoyJFZEsIN4Yk+9av8cYEyci+4GGJSZkODnt0bfGmFauxw8BocaYJ0XkayAHawaQL0vMuaYcSlt4ym6mnPvn4kSJ+4WcOjY9CHgFqzW4wjXLhnIwDTxlt+QSy2Wu+z9w6uTwG4ElrvsLgPFQPGFkjfLeVESCgEbGmIXAQ1jTCf2ulamcRf/iKW+oVmK2DoCvjTEnh6bUEpE1WK20Ea51dwLviMgDwH5OzTRyN/CGiNyK1ZIbD5Q35VIw8IErFAV42TXXmnIwPYanbOM6htfFGJNldy3KGXSXVinlGNrCU0o5hrbwlFKOoYGnlHIMDTyllGNo4CmlHEMDTynlGP8PGkYx2DPCmaAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_evaluation_curves(training_history_1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SgCr_2w9y3te"
   },
   "source": [
    "The accuracy has increased by 5% which is a big improvement which exhibits the tradeoff between accuracy and number of parameters.\n",
    "\n",
    "**Has the `F1-Score` improved too?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 30745,
     "status": "ok",
     "timestamp": 1666272800231,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "WXi_HSn4JYQ8",
    "outputId": "ad0365c3-eb6e-4744-ee69-5d7f040fd46a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "36000it [00:05, 7043.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 24s 40ms/step\n",
      "\n",
      "\n",
      "############### Classification Report ###############\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.95      0.96     30000\n",
      "           1       0.76      0.86      0.81      6000\n",
      "\n",
      "    accuracy                           0.93     36000\n",
      "   macro avg       0.87      0.90      0.88     36000\n",
      "weighted avg       0.94      0.93      0.93     36000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create classification_report\n",
    "y_labels_1, y_preds_1 = get_labels(test_data, model)\n",
    "\n",
    "print(\"\\n\\n############### Classification Report ###############\\n\\n\")\n",
    "print(classification_report(y_labels_1, np.round(y_preds_1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qhcwVG7lzsSF"
   },
   "source": [
    "There is a big improvement in the **F1-Score** of the `'good'` class. Precision and Recall have improved and the model has become better but it's still not enough.\n",
    "\n",
    "**Let's perform fine-tuning which might hopefully improve the model's performance!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14188,
     "status": "ok",
     "timestamp": 1666272895364,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "CLqrbNOrYdtI",
    "outputId": "16e6821b-9acc-4ae6-85a6-9b48c466be15"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 41). These functions will not be directly callable after loading.\n"
     ]
    }
   ],
   "source": [
    "# model.save(\"/content/drive/MyDrive/Portfolio/Anomaly Detection/V6/save_model_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16128,
     "status": "ok",
     "timestamp": 1666273386403,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "GvncCf6pji5g",
    "outputId": "b714ff9a-0d2a-48b2-9c9d-4b53c1af17d2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 41). These functions will not be directly callable after loading.\n"
     ]
    }
   ],
   "source": [
    "# !touch '/content/drive/MyDrive/Portfolio/Anomaly Detection/V6/pickle_1.pickle'\n",
    "# dbfile = open('/content/drive/MyDrive/Portfolio/Anomaly Detection/V6/pickle_1.pickle', 'ab')\n",
    "# pickle.dump(training_history_1, dbfile)\n",
    "# dbfile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "51TJnl27N4om"
   },
   "source": [
    "## FineTuning the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pgzguS-83kDh"
   },
   "source": [
    "Fine-Tuning a model usually involves an additional step which is to set the pre-trained model for training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XmbqIzrmOdsp"
   },
   "source": [
    "### Setting up Feature Extractor for Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3xvlpL1TjuQh"
   },
   "outputs": [],
   "source": [
    "# # load the model\n",
    "# model = tf.keras.models.load_model(\"/content/drive/MyDrive/Portfolio/Anomaly Detection/V6/save_model_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1666275964877,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "-DXjlUlExaPm",
    "outputId": "ac6a62c9-5c21-481f-baf6-4d6235721f8a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: The conv2d's training is set to True\n",
      "2: The feature_extraction_layer's training is set to True\n",
      "3: The dense_layer's training is set to True\n",
      "4: The output_layer's training is set to True\n"
     ]
    }
   ],
   "source": [
    "# set the feature extractor's layer to training\n",
    "model.layers[1].trainable = True\n",
    "\n",
    "# check if the training of the feature extractor is set to training\n",
    "for i, layer in enumerate(model.layers):\n",
    "  print(f\"{i+1}: The {layer.name}'s training is set to {layer.trainable}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ew3653eHxu6H"
   },
   "outputs": [],
   "source": [
    "# Define the optimizer\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RYD3i_Jmx5h9"
   },
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(\n",
    "      loss=binary_categorical_loss,\n",
    "      optimizer=optimizer,\n",
    "      metrics=[\"accuracy\"]\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1927378,
     "status": "ok",
     "timestamp": 1666177130165,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "cGjDrwb7q-Ta",
    "outputId": "249ef1b1-0519-4c51-b63d-4fb381df52c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1125/1125 [==============================] - 214s 162ms/step - loss: 0.0570 - accuracy: 0.9850 - val_loss: 0.2293 - val_accuracy: 0.9541 - lr: 1.0000e-04\n",
      "Epoch 2/10\n",
      "1125/1125 [==============================] - ETA: 0s - loss: 0.0085 - accuracy: 0.9971\n",
      "Epoch 2: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n",
      "1125/1125 [==============================] - 176s 156ms/step - loss: 0.0085 - accuracy: 0.9971 - val_loss: 0.4795 - val_accuracy: 0.9332 - lr: 1.0000e-04\n",
      "Epoch 3/10\n",
      "1125/1125 [==============================] - ETA: 0s - loss: 1.9380e-05 - accuracy: 1.0000\n",
      "Epoch 3: ReduceLROnPlateau reducing learning rate to 3.999999898951501e-06.\n",
      "1125/1125 [==============================] - 176s 156ms/step - loss: 1.9380e-05 - accuracy: 1.0000 - val_loss: 0.2626 - val_accuracy: 0.9607 - lr: 2.0000e-05\n",
      "Epoch 4/10\n",
      "1125/1125 [==============================] - ETA: 0s - loss: 2.3093e-06 - accuracy: 1.0000\n",
      "Epoch 4: ReduceLROnPlateau reducing learning rate to 7.999999979801942e-07.\n",
      "1125/1125 [==============================] - 176s 156ms/step - loss: 2.3093e-06 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9613 - lr: 4.0000e-06\n",
      "Epoch 5/10\n",
      "1125/1125 [==============================] - ETA: 0s - loss: 2.0529e-06 - accuracy: 1.0000\n",
      "Epoch 5: ReduceLROnPlateau reducing learning rate to 1.600000018697756e-07.\n",
      "1125/1125 [==============================] - 176s 156ms/step - loss: 2.0529e-06 - accuracy: 1.0000 - val_loss: 0.2530 - val_accuracy: 0.9626 - lr: 8.0000e-07\n",
      "Epoch 6/10\n",
      "1125/1125 [==============================] - ETA: 0s - loss: 1.9924e-06 - accuracy: 1.0000\n",
      "Epoch 6: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "1125/1125 [==============================] - 176s 156ms/step - loss: 1.9924e-06 - accuracy: 1.0000 - val_loss: 0.2713 - val_accuracy: 0.9604 - lr: 1.6000e-07\n",
      "Epoch 7/10\n",
      "1125/1125 [==============================] - 175s 156ms/step - loss: 1.9722e-06 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9618 - lr: 1.0000e-07\n",
      "Epoch 8/10\n",
      "1125/1125 [==============================] - 176s 156ms/step - loss: 1.9396e-06 - accuracy: 1.0000 - val_loss: 0.2533 - val_accuracy: 0.9644 - lr: 1.0000e-07\n",
      "Epoch 9/10\n",
      "1125/1125 [==============================] - 175s 156ms/step - loss: 1.8729e-06 - accuracy: 1.0000 - val_loss: 0.2540 - val_accuracy: 0.9633 - lr: 1.0000e-07\n",
      "Epoch 10/10\n",
      "1125/1125 [==============================] - 175s 156ms/step - loss: 1.7522e-06 - accuracy: 1.0000 - val_loss: 0.2520 - val_accuracy: 0.9623 - lr: 1.0000e-07\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "EPOCHS = 10\n",
    "STEPS_PER_EPOCH = len(train_data)\n",
    "\n",
    "training_history_2 = model.fit(train_data,\n",
    "                             epochs=EPOCHS,\n",
    "                             steps_per_epoch=STEPS_PER_EPOCH,\n",
    "                             validation_data=test_data.shuffle(10000),\n",
    "                             validation_steps=int(0.5 * len(test_data)),\n",
    "                             callbacks=[reduce_lr, early_stopping]\n",
    "                             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sir6aw1LN-fw"
   },
   "source": [
    "### Evaluating the FineTuned Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 32528,
     "status": "ok",
     "timestamp": 1666336755091,
     "user": {
      "displayName": "MUHAMMAD DANISH",
      "userId": "02895509678558640132"
     },
     "user_tz": -300
    },
    "id": "g9LPnJ6Cx-71",
    "outputId": "6e7677d2-f857-4d18-e4cf-56e53a794ef3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 34s 45ms/step - loss: 0.2647 - accuracy: 0.9613\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.2646585702896118, 0.9613333344459534]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate the fine-tuned model\n",
    "model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "executionInfo": {
     "elapsed": 1041,
     "status": "ok",
     "timestamp": 1666177499656,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "kl39yiJM3wVa",
    "outputId": "083ed9bb-5f5d-4b63-e8f2-8a97f03d4554"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the accuracy and loss curves\n",
    "plot_evaluation_curves(training_history_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 45241,
     "status": "ok",
     "timestamp": 1666358115060,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "VWI-5z8XJveM",
    "outputId": "9338b514-8c6a-4717-f5b0-df91eab760f9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "36000it [00:12, 2966.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 31s 41ms/step\n",
      "\n",
      "\n",
      "############### Classification Report ###############\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98     30000\n",
      "           1       0.85      0.94      0.89      6000\n",
      "\n",
      "    accuracy                           0.96     36000\n",
      "   macro avg       0.92      0.95      0.93     36000\n",
      "weighted avg       0.96      0.96      0.96     36000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create classification_report\n",
    "y_labels_2, y_preds_2 = get_labels(test_data, model)\n",
    "\n",
    "print(\"\\n\\n############### Classification Report ###############\\n\\n\")\n",
    "print(classification_report(y_labels_2, y_preds_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gK6fH6K-36Gf"
   },
   "source": [
    "**Model has improved significantly!**\n",
    "\n",
    "The F1-Score of `'good'` class has jumped to `0.89` and the `macro-average f1-score` is `0.93` which proves that the model is performing better than the baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15943,
     "status": "ok",
     "timestamp": 1666270510140,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "ASvculKCyWBY",
    "outputId": "00d58fe9-be49-4648-b2d9-026e5c8babbc"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 41). These functions will not be directly callable after loading.\n"
     ]
    }
   ],
   "source": [
    "# model.save(\"/content/drive/MyDrive/Portfolio/Anomaly Detection/V6/save_model_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11190,
     "status": "ok",
     "timestamp": 1666362898411,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "Ole3ffrkKyzW",
    "outputId": "ea95bdc3-7879-4b7f-aabf-103c41d61c0b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 41). These functions will not be directly callable after loading.\n"
     ]
    }
   ],
   "source": [
    "# model.save(\"/content/drive/MyDrive/Portfolio/Anomaly Detection/V6/save_model_3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GI5aKJi2OC8Q"
   },
   "source": [
    "## Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "executionInfo": {
     "elapsed": 932,
     "status": "ok",
     "timestamp": 1666362172675,
     "user": {
      "displayName": "B INFOs",
      "userId": "12264414988845169074"
     },
     "user_tz": -300
    },
    "id": "AxVhpKc7alX3",
    "outputId": "43e7a438-c5c4-453a-b670-d43ae477fa57"
   },
   "outputs": [
    {
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       "\n",
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       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baseline Model</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model (Transfer Learning)</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine Tuned Model</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
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       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
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       "          const element = document.querySelector('#df-9df134fc-fc9c-424e-a294-cb6c9bb6db28');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "                           Precision  Recall  F1-Score\n",
       "Baseline Model                  0.79    0.85      0.82\n",
       "Model (Transfer Learning)       0.87    0.90      0.88\n",
       "Fine Tuned Model                0.92    0.95      0.93"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create data frame to show the metrics of the models\n",
    "baseline_precision, baseline_recall, baseline_f1 = 0.79, 0.85, 0.82\n",
    "model_precision, model_recall, model_f1 = 0.87, 0.90, 0.88\n",
    "fineTuned_model_precision, fineTuned_model_recall, fineTuned_model_f1 = 0.92, 0.95, 0.93\n",
    "\n",
    "metrics_df = pd.DataFrame([[baseline_precision, baseline_recall, baseline_f1],\n",
    "                           [model_precision, model_recall, model_f1],\n",
    "                           [fineTuned_model_precision, fineTuned_model_recall, fineTuned_model_f1]],\n",
    "                          index=['Baseline Model', 'Model (Transfer Learning)', 'Fine Tuned Model'])\n",
    "\n",
    "metrics_df.columns = ['Precision', 'Recall', 'F1-Score']\n",
    "\n",
    "metrics_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
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    "executionInfo": {
     "elapsed": 1261,
     "status": "ok",
     "timestamp": 1666362755455,
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      "displayName": "B INFOs",
      "userId": "12264414988845169074"
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     "user_tz": -300
    },
    "id": "6jVn21XjalRT",
    "outputId": "3ca4bef3-e2cc-4ae9-d8e9-8c22ddf48722"
   },
   "outputs": [
    {
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = px.bar(\n",
    "    data_frame=metrics_df,\n",
    "    barmode=\"group\",\n",
    "    title=\"Models' Comparison\",\n",
    "    width=650,\n",
    "    height=500\n",
    ")\n",
    "\n",
    "fig.update_layout(xaxis_title=\"Models\", yaxis_title=\"Metrics\", legend_title_text=\"Metric\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F13k1f6X9gHh"
   },
   "source": [
    "**As seen from the Bar Plot above, Fine Tuned model is performing better than other models and is thus the final chosen model!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "27p5VFUA-CZe"
   },
   "source": [
    "## Model Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o2jBf-yJ-GQD"
   },
   "source": [
    "This section will be covered seperately!"
   ]
  }
 ],
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