garbage_classification_semaphore.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6819c984",
   "metadata": {},
   "source": [
    "# About Dataset\n",
    "\n",
    "Recycling trash is a crucial part of protecting our environment. Garbage must be divided into categories with similar recycling processes in order to enable the recycling process. The percentage of recycled waste can rise considerably if it is possible to separate domestic trash into several categories. Using the classes given, we will train the model in this notebook to categorize the input images and output the trash classification.\n",
    "\n",
    "The six classification included in the **Garbage Classification Dataset** are: \n",
    "1.   cardboard (393) \n",
    "2.   glass (491)\n",
    "3.   metal (400)\n",
    "4.   paper(584)\n",
    "5.   plastic (472)\n",
    "6.   trash(127)\n",
    "<br> \n",
    "\n",
    "The objective is to train the model to be able to predict the input images based on the provided classifications."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ivUXM07C2lfE",
   "metadata": {
    "id": "ivUXM07C2lfE"
   },
   "source": [
    "**Step 1** : load the libraries using the import libraries\n",
    "\n",
    "Data Pre-Processing <br><br>\n",
    "This section is the part wherein the data will be loaded & preprocessed into its needed variables and analyzed with its attributes to better understand the data and how it can be utilized by the different libraries that we will be importing in this section of the notebook. The first thing that we could notice would be the inclusion of the imports of the needed libraries from the ones that will be used to handle data and from the ones that would be used to handle the models that would then handle the data. The rest of the libraries that I would be importing for this notebook would be libaries concerning score and model evaluation that would be needed for the further calibration of the models that I will be using."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hr6tEp_X-IKJ",
   "metadata": {
    "id": "hr6tEp_X-IKJ"
   },
   "source": [
    "1. ``import tensorflow as tf`` - - Tensorflow is imported as *tf*. It automates data collection, tracks models, monitors performance, and retains models using best practices. It is a multiple-machine learning library that is open-sourced and can be used end-to-end.\n",
    "\n",
    "2. ``from tensorflow import keras`` - It is a high-level neural library that runs on TensorFlow. Similar to TensorFlow, Keras provides APIs to build and train models. It has a Python built-in that makes it more user-friendly.\n",
    "\n",
    "3. ``from tensorflow.keras.models import Sequential`` - The input_shape argument is used to help define the layers that will be in the network as the function executes and creates each layer.  \n",
    "\n",
    "4. ``from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D`` - A ``dense`` layer is used when any features can be associated with any other featuree in a data point. If nearby associations among features are important, such as when detecting objects, these ``conv`` layer are important. for classfying or detecting,neighboorhoods matter. Pixels at the opposite corners can be helpful. A ``flatten`` layer is used when you need to linearize a multidimensional output to pass it into a dense layer. ``Dropouts`` reduce too mmuch association between features by dropping weights.\n",
    "\n",
    "\n",
    "5. ``from tensorflow.keras.preprocessing.image import ImageDataGenerator`` - In image augmentation, different transformmmation are applied to original images to create mmultiple tranformed copies. Using the Keras ``ImageDataGenerator``class, yit can easily enhance the immages using a variety of techniques such as rotation, standardization, shifts, brightness, flips, and more. \n",
    "\n",
    "6. ``from PIL import Image`` - with PIL. you can open, manipulate, and save a wide range of image file formmmats using the Python programmming language. It is a free, open-source library that supports many different image formats.\n",
    "\n",
    "7. ``from pathlib import Path`` - Path is one of the most important classes provided by the pathlib module. It represents a path to a directory or file on our computer's file system. \n",
    "\n",
    "8. ``import os`` - It allows to create and remove directories, fetch contenst, change the current directory, and more. Importing OS module is the first step in interacting with the underlying operating system.\n",
    "\n",
    "9. ``import numpy as np`` - NumPy is imported as *np*. It is a general-purpose package for handling arrays. It is the core package for using Python in scientific computing. `` -\n",
    "\n",
    "10. ``import matplotlib.pyplot as plt`` - Pyplot is imported as *plt*. The Matplotlib library provides graphical plotting capabilities and data visualization in Pythion and NumPy, Their numerical extension. \n",
    "\n",
    "10. ``from torchvision.datasets import ImageFolder`` - Deep learning model uses ImageFolder class to load images from our train and val folders into a PyThorch dataset. \n",
    "\n",
    "10. ``import torchvision.transforms as T`` - it allows to change brightness, contrast, saturation, and hu of an image randomly. Also, cropping the image with four corners and acentral crop. lastly, converting the image to grayscale.\n",
    "\n",
    "5. ``print (\"Done with declaration. Current version of Tenserflow :\", tf.__version__)`` - It allows to print <b>*Done with the library declaration. Current version of Tenserflow is 2.8.2* </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1258038f",
   "metadata": {
    "id": "1258038f",
    "outputId": "9adbfe1e-bf28-47e5-d511-2a8d9b4394a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with library declaration, Current version of Tensorflow is:  2.9.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "#import tensorflow.contrib.keras as keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from tensorflow import keras\n",
    "from PIL import Image\n",
    "from pathlib import Path\n",
    "import scipy\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from torchvision.datasets import ImageFolder\n",
    "import torchvision.transforms as T\n",
    "\n",
    "print(\"Done with library declaration, Current version of Tensorflow is: \", tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "AyqUNyGo2xqU",
   "metadata": {
    "id": "AyqUNyGo2xqU"
   },
   "source": [
    "**Step 2** : Load and transform data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "QtLA4urmH2DL",
   "metadata": {
    "id": "QtLA4urmH2DL"
   },
   "source": [
    "1. ``data_dir = Path('Garbage')`` - Data directories identify master sources of data as as replicas of the data in a distrbuted database. In this case, the path will be ***Garbage***.\n",
    "\n",
    "2. ``transformer = T.Compose([T.Resize((32, 32)), T.ToTensor()])`` - It allows to resize the imagee to 32 x 32 pixels and the ToTensor converts preprocessed images into Neural Networks. \n",
    "\n",
    "3. ``dataset = ImageFolder(data_dir, transform = transformer)`` - Assigned the transform images to the dataset. The ImageFolder class to allows to load the images from our train and val folders into a PyThorch dataset.\n",
    "\n",
    "4. ``print(dataset.classes)`` - It allows to print the classes in the dataset. Classes: ***cardboard***, ***glass***, ***metal***, ***paper***, ***plastic***, and ***trash***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "03b16a0a",
   "metadata": {
    "id": "03b16a0a",
    "outputId": "24909e0f-0042-4ff5-a1a7-cdeec7c77558"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']\n"
     ]
    }
   ],
   "source": [
    "# collect directory\n",
    "data_dir = Path('Garbage\\original_images')\n",
    "\n",
    "transformer = T.Compose([T.Resize((32, 32)), T.ToTensor()])\n",
    "dataset = ImageFolder(data_dir, transform = transformer)\n",
    "\n",
    "# display class names\n",
    "print(dataset.classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zRjIuMJZ3eV9",
   "metadata": {
    "id": "zRjIuMJZ3eV9"
   },
   "source": [
    "**Step 3** : Plot and inspect the classes in the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "NGYD42WOXF42",
   "metadata": {
    "id": "NGYD42WOXF42"
   },
   "source": [
    "\n",
    "1. ``fig = plt.figure()`` - function is to create a figure object. When the figure's size needs to be tweaked and more than one axes object needs to be added to a figure, we must explicity use plt.figure(). \n",
    "\n",
    "2. ``ax = fig.add_axes([0,0,1,1])`` - It allows to plot the data for the classes. The dimension is set to ***0,0,1,1*** the first two array **0,0** are the horizontal and vertical coordinate of the lower left corner. The ***1,1*** are the width and height of the subplot.\n",
    "\n",
    "3. ``counts = [393,491,400]`` - It is used to set the total counts of each class in the database. in this case we have six classes.\n",
    "\n",
    "4. ``ax.bar(dataset.classes,counts)`` - It is used for bar plot. the range of classes can be shown here in a barplot\n",
    "\n",
    "5. ``plt.title('Class Distribution')`` - It allows to set a title ***Class Distribution*** for the figure. It can be found on the upper side. \n",
    "\n",
    "6. ``plt.show()`` - is the pyplot module of the matplotlob library, this function allows all figures to be displayed. In this case, the total counts of all classes in the dataset will be displayed. It also helps to visualize more easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "598c6649",
   "metadata": {
    "id": "598c6649",
    "outputId": "837c87a9-905f-4810-9492-1a5262d755f5"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display class distribution\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "counts = [393,491,400,584,472,127]\n",
    "ax.bar(dataset.classes,counts)\n",
    "plt.title('Class Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Rv8FE-4Z3XhV",
   "metadata": {
    "id": "Rv8FE-4Z3XhV"
   },
   "source": [
    "**Step 4** : Split and store the data into train and test data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "-7dGgUWzdb5k",
   "metadata": {
    "id": "-7dGgUWzdb5k"
   },
   "source": [
    "1. ``PATH_TRAIN = r\"Garbage\\original_images\"`` - The train data used will come from garbage file images.\n",
    "2. ``PATH_TEST = r\"Garbage\\processed_images\"`` - The test data used will come from processed images. \n",
    "3. ``class_names = ['cardboard', 'glass', 'metal','paper','plastic','trash']`` - Will set all the classes in the class_names so that every time it is called it will return specific classes in the dataset\n",
    "\n",
    "4. ``imagepath_cardboard = r\"Garbage\\original_images\\cardboard\"`` - The cardboard class is assigned to imagepath_cardboard.\n",
    "5. ``graypath_cardboard = r\"Garbage\\processed_images\\cardboard\"`` - The processed image of cardboard class is assigned to graypath_cardboard.\n",
    "\n",
    "6. ``File_listing = os.listdir(imagepath_cardboard)`` <br>\n",
    "  ``for file in File_listing:`` - The os module provides a function which lists all the files in the specified path using the names provided by the fuction. In this case the specified is ***imagepath_cardboard***. With the use ``im.resize and img.convert`` functio, the cardboard images is resize by 32 x 32 pixels and in grayscale. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "082da59f",
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH_TEST = r\"Garbage\\original_images\"\n",
    "PATH_TRAIN = r\"Garbage\\processed_images\"\n",
    "class_names = ['cardboard', 'glass', 'metal','paper','plastic','trash']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90282619",
   "metadata": {
    "id": "90282619"
   },
   "outputs": [],
   "source": [
    "imagepath_cardboard = r\"Garbage\\original_images\\cardboard\"\n",
    "graypath_cardboard = r\"Garbage\\processed_images\\cardboard\"\n",
    "File_listing = os.listdir(imagepath_cardboard)\n",
    "for file in File_listing:\n",
    "    im = Image.open(imagepath_cardboard + '\\\\' + file) \n",
    "    img = im.resize((32,32))\n",
    "    gray = img.convert('L')\n",
    "    gray.save(graypath_cardboard + '\\\\' + file, \"JPEG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ea011fa",
   "metadata": {
    "id": "0ea011fa"
   },
   "outputs": [],
   "source": [
    "imagepath_glass = r\"Garbage\\original_images\\glass\"\n",
    "graypath_glass = r\"Garbage\\processed_images\\glass\"\n",
    "File_listing = os.listdir(imagepath_glass)\n",
    "for file in File_listing:\n",
    "    im = Image.open(imagepath_glass + '\\\\' + file) \n",
    "    img = im.resize((32,32))\n",
    "    gray = img.convert('L')\n",
    "    gray.save(graypath_glass + '\\\\' + file, \"JPEG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aaa6303a",
   "metadata": {
    "id": "aaa6303a"
   },
   "outputs": [],
   "source": [
    "imagepath_metal = r\"Garbage\\original_images\\metal\"\n",
    "graypath_metal = r\"Garbage\\processed_images\\metal\"\n",
    "File_listing = os.listdir(imagepath_metal)\n",
    "for file in File_listing:\n",
    "    im = Image.open(imagepath_metal + '\\\\' + file) \n",
    "    img = im.resize((32,32))\n",
    "    gray = img.convert('L')\n",
    "    gray.save(graypath_metal + '\\\\' + file, \"JPEG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd627322",
   "metadata": {},
   "outputs": [],
   "source": [
    "imagepath_paper = r\"Garbage\\original_images\\paper\"\n",
    "graypath_paper = r\"Garbage\\processed_images\\paper\"\n",
    "File_listing = os.listdir(imagepath_paper)\n",
    "for file in File_listing:\n",
    "    im = Image.open(imagepath_paper + '\\\\' + file) \n",
    "    img = im.resize((32,32))\n",
    "    gray = img.convert('L')\n",
    "    gray.save(graypath_paper + '\\\\' + file, \"JPEG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d2bad80",
   "metadata": {},
   "outputs": [],
   "source": [
    "imagepath_plastic = r\"Garbage\\original_images\\plastic\"\n",
    "graypath_plastic = r\"Garbage\\processed_images\\plastic\"\n",
    "File_listing = os.listdir(imagepath_plastic)\n",
    "for file in File_listing:\n",
    "    im = Image.open(imagepath_plastic + '\\\\' + file) \n",
    "    img = im.resize((32,32))\n",
    "    gray = img.convert('L')\n",
    "    gray.save(graypath_plastic + '\\\\' + file, \"JPEG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c74f589",
   "metadata": {},
   "outputs": [],
   "source": [
    "imagepath_trash = r\"Garbage\\original_images\\trash\"\n",
    "graypath_trash = r\"Garbage\\processed_images\\trash\"\n",
    "File_listing = os.listdir(imagepath_trash)\n",
    "for file in File_listing:\n",
    "    im = Image.open(imagepath_trash + '\\\\' + file) \n",
    "    img = im.resize((32,32))\n",
    "    gray = img.convert('L')\n",
    "    gray.save(graypath_trash + '\\\\' + file, \"JPEG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_NWniB5Vhw03",
   "metadata": {
    "id": "_NWniB5Vhw03"
   },
   "source": [
    "1. ``train_dir = os.path.join(PATH_TRAIN)`` <br>\n",
    "``test_dir = os.path.join(PATH_TEST)`` <br> \n",
    "- The dataset is divided into two groups: the train data and the test data. The train data is where we teach the machine learning algorithm to predict specific images, while the test data is where we validate the algorithm's progress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ba8e1a87",
   "metadata": {
    "id": "ba8e1a87"
   },
   "outputs": [],
   "source": [
    "train_dir = os.path.join(PATH_TRAIN)\n",
    "test_dir = os.path.join(PATH_TEST)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Whjad73ljwBF",
   "metadata": {
    "id": "Whjad73ljwBF"
   },
   "source": [
    "1. ``os.path.join`` - is a Python submodule of OS used for common pathname manipulation and provides a portable way o levergae operating system dependent functionality. In this case, we will use it in every class that our dataset has. for example, the ***imagepath_cardboard*** will intelligently join the path components which is the ***imagepath_cardboard_dir***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70e53a9d",
   "metadata": {
    "id": "70e53a9d"
   },
   "outputs": [],
   "source": [
    "imagepath_cardboard_dir = os.path.join(imagepath_cardboard)\n",
    "imagepath_glass_dir = os.path.join(imagepath_glass)\n",
    "imagepath_metal_dir = os.path.join(imagepath_metal)\n",
    "imagepath_paper_dir = os.path.join(imagepath_paper)\n",
    "imagepath_plastic_dir = os.path.join(imagepath_plastic)\n",
    "imagepath_trash_dir = os.path.join(imagepath_trash)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "goo76aRSlzW5",
   "metadata": {
    "id": "goo76aRSlzW5"
   },
   "source": [
    "1. ``len(os.listdir(PATH_TRAIN))`` - it allows to display the list of file in the  ***PATH_TRAIN***. It returns the total count of the list in the specified path. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f02e1341",
   "metadata": {
    "id": "f02e1341",
    "outputId": "6621f9e9-4049-4953-aa0b-18a8e42ba7e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(PATH_TRAIN))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mbQ6iOsjmZCw",
   "metadata": {
    "id": "mbQ6iOsjmZCw"
   },
   "source": [
    "1. ``IMG_HEIGHT = 32`` <br>\n",
    "``IMG_WIDTH = 32`` <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca5cd7ca",
   "metadata": {
    "id": "ca5cd7ca"
   },
   "outputs": [],
   "source": [
    "IMG_HEIGHT = 32\n",
    "IMG_WIDTH = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oPzPsBHPnNVm",
   "metadata": {
    "id": "oPzPsBHPnNVm"
   },
   "source": [
    "1. ``image_gen = ImageDataGenerator(rescale=1./255)`` - Using the Keras ImageDataGeneratorclass, it can easily transform the image to range from range ***0,255*** to ***0,1***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e670d39",
   "metadata": {
    "id": "8e670d39"
   },
   "outputs": [],
   "source": [
    "image_gen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faTngSr3oEPT",
   "metadata": {
    "id": "faTngSr3oEPT"
   },
   "source": [
    "1. the train folder contain a single folder inside which all the train images are present. Here are the most commonly used attributes along with the ``flow_from_directory()`` method. The ``directory`` where the classes of folders are present. We set the shuffle to ***true*** to shuffle the order of the images. The ``target_size``which we set the input image to ***32 x 32*** pixels. lastly, the ``class_mode`` where we set to ***categorical*** because we have more than two classes in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aaf491b1",
   "metadata": {
    "id": "aaf491b1",
    "outputId": "b3cd7ce9-d7a8-45e5-dd87-b3fda86223ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2527 images belonging to 6 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data_gen = image_gen.flow_from_directory(\n",
    "    directory = train_dir, \n",
    "    shuffle=True, \n",
    "    target_size = (IMG_HEIGHT, IMG_WIDTH),\n",
    "    class_mode='categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pFCWmAVcvO3O",
   "metadata": {
    "id": "pFCWmAVcvO3O"
   },
   "source": [
    "1. Same as above, the test folder contain a single folder inside which all the test images are present. Here are the most commonly used attributes along with the ``flow_from_directory()`` method. The ``directory`` where the classes of folders are present. We set the shuffle to ***true*** to shuffle the order of the images. The ``target_size``which we set the input image to ***32 x 32*** pixels. lastly, the ``class_mode`` where we set to ***categorical*** because we have more than two classes in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c76648f",
   "metadata": {
    "id": "8c76648f",
    "outputId": "7e7cbf53-aa36-4656-cecd-68d34e0e9a61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2527 images belonging to 6 classes.\n"
     ]
    }
   ],
   "source": [
    "test_data_gen = image_gen.flow_from_directory(\n",
    "    directory = test_dir, \n",
    "    shuffle=True, \n",
    "    target_size = (IMG_HEIGHT, IMG_WIDTH),\n",
    "    class_mode='categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "xY41ehWXwGi3",
   "metadata": {
    "id": "xY41ehWXwGi3"
   },
   "source": [
    "1.  The ``plotImages`` function is to create a figure object. When the figure's size needs to be tweaked and more than one axes object needs to be added to a figure, we must explicity use plt.figure(). This ``(figsize=(20,20))`` function allows to set the size of the figure, In this case the figure is set by ***20:20*** ratio. Also, The ``ax.imshow`` display the image in 2D regualr raster. The ``axes.flatten()`` it returns the flattened version of the array. The ``plt.show()`` is the pyplot allows all sets figures to be displayed. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5rWM6CJc4EoW",
   "metadata": {
    "id": "5rWM6CJc4EoW"
   },
   "source": [
    "**Step 5** : Display and examine the images from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8f99b380",
   "metadata": {
    "id": "8f99b380"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2527 images belonging to 6 classes.\n"
     ]
    }
   ],
   "source": [
    "#plt.figure()\n",
    "#plt.imshow(sample_training_images[0])\n",
    "#plt.show()\n",
    "sample_data_gen = image_gen.flow_from_directory(\n",
    "    directory = test_dir, \n",
    "    shuffle=True, \n",
    "    target_size = (200, 200),\n",
    "    class_mode='categorical')\n",
    "\n",
    "sample_training_images, _= next(sample_data_gen)\n",
    "def plotImages(images_arr):\n",
    "    fig, axes = plt.subplots(1,4, figsize=(30,30))\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip(images_arr, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8da68b75",
   "metadata": {
    "id": "8da68b75",
    "outputId": "62fda88e-0fd6-4650-a657-c615e55038fd"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3000x3000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotImages(sample_training_images[:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "x88IvOJs4RRJ",
   "metadata": {
    "id": "x88IvOJs4RRJ"
   },
   "source": [
    "**Step 6** : Builing theee the Neural Network by using the Sequentil API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "E7hv4buAyAlu",
   "metadata": {
    "id": "E7hv4buAyAlu"
   },
   "source": [
    "1. ``model = Sequential()`` -  It is a model to use when there is only one input and one output on the data you need to show. It is not appropriate to use this model if you have multiple inputs and outputs. It helps you train and infer conclusions from your model. the ``input_shape=(28,28)`` represent the resolution of the image: ***32 x 32*** pixels. ``Dense`` It creates a new dense layer and returns the necessary output data for your model to use. Activation is the element-wise activation function used to pass through the activation arguments,  relu is the rectified linear unit activation function, and Sigmoid is equivalent to a 2-element Softmax, where the second element is taken to be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "27b8a493",
   "metadata": {
    "id": "27b8a493"
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=(IMG_HEIGHT,IMG_WIDTH, 3)),\n",
    "    MaxPooling2D(pool_size=2),\n",
    "\n",
    "    Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'),\n",
    "    MaxPooling2D(pool_size=2),\n",
    "    \n",
    "    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'),\n",
    "    MaxPooling2D(pool_size=2),\n",
    "    \n",
    "    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'),\n",
    "    MaxPooling2D(pool_size=2),\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    Dense(6, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "TYnMQMEl4Z7e",
   "metadata": {
    "id": "TYnMQMEl4Z7e"
   },
   "source": [
    "**Step 7** : Defining an optimmizer, a loss function, and other useful training parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c06294e8",
   "metadata": {
    "id": "c06294e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 32, 32, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 16, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 16, 16, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 8, 8, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 8, 8, 32)          18464     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 4, 4, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 4, 4, 32)          9248      \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 2, 2, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 6)                 774       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 47,878\n",
      "Trainable params: 47,878\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "batch_size = 45\n",
    "epochs = 60\n",
    "model.compile(optimizer='adam',\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7ed1a142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=425x207>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import visualkeras\n",
    "visualkeras.layered_view(model, legend=True, spacing=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbnfIjiI4oZ-",
   "metadata": {
    "id": "fbnfIjiI4oZ-"
   },
   "source": [
    "**Step 8** : Training the model by using the train dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "S_YEeKMdaB_J",
   "metadata": {
    "id": "S_YEeKMdaB_J"
   },
   "source": [
    "1. The os module provides a function which lists all the files in the specified path using the names provided by the fuction. for example, ***imagepath_cardboard_dir*** with the num_cardboard_train.\n",
    "\n",
    "2. ``total_train = num_cardboard_train + num_glass_train + num_metal_train`` - total number of train set for ***cardboard***, ***glass***, ***metal***, ***paper***, ***plastic***, and ***trash***.\n",
    "\n",
    "3. ``total_test = num_cardboard_test + num_glass_test + num_metal_test`` - total number of test set for ***cardboard***, ***glass***, ***metal***, ***paper***, ***plastic***, and ***trash***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "31666089",
   "metadata": {
    "id": "31666089"
   },
   "outputs": [],
   "source": [
    "num_cardboard_train = len(os.listdir(imagepath_cardboard_dir))\n",
    "num_glass_train = len(os.listdir(imagepath_glass_dir))\n",
    "num_metal_train = len(os.listdir(imagepath_metal_dir))\n",
    "num_paper_train = len(os.listdir(imagepath_cardboard_dir))\n",
    "num_plastic_train = len(os.listdir(imagepath_glass_dir))\n",
    "num_trash_train = len(os.listdir(imagepath_trash_dir))\n",
    "\n",
    "num_cardboard_test = len(os.listdir(graypath_cardboard))\n",
    "num_glass_test = len(os.listdir(graypath_glass))\n",
    "num_metal_test = len(os.listdir(graypath_metal))\n",
    "num_paper_test = len(os.listdir(graypath_paper))\n",
    "num_plastic_test = len(os.listdir(graypath_plastic))\n",
    "num_trash_test = len(os.listdir(graypath_trash))\n",
    "\n",
    "total_train = num_cardboard_train + num_glass_train + num_metal_train + num_paper_train + num_plastic_train + num_trash_train\n",
    "total_test = num_cardboard_test + num_glass_test + num_metal_test + num_paper_test + num_plastic_test + num_trash_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ZdHQcWCgzkA0",
   "metadata": {
    "id": "ZdHQcWCgzkA0"
   },
   "source": [
    "1. In this model the number of epochs is set to 60 this number represent the number of times the training set to run. A model that is well-fit produces more accurate results, as it generalizes to similar data to that on which it was trained. ``validation_data`` Data on which to evaluate the loss and any model metrics at the end of each epoch. ``validation_steps`` Only relevant if validation_data is provided and is a tf. data dataset. Total number of steps to draw before stopping when performing validation at the end of every epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57f863e5",
   "metadata": {
    "id": "57f863e5",
    "outputId": "d6aa2030-7926-4794-c658-856f3f82e743"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "52/52 [==============================] - 17s 318ms/step - loss: 1.7310 - accuracy: 0.2291 - val_loss: 1.7209 - val_accuracy: 0.2467\n",
      "Epoch 2/60\n",
      "52/52 [==============================] - 6s 107ms/step - loss: 1.7051 - accuracy: 0.2598 - val_loss: 1.6693 - val_accuracy: 0.2349\n",
      "Epoch 3/60\n",
      "52/52 [==============================] - 4s 85ms/step - loss: 1.6228 - accuracy: 0.2892 - val_loss: 1.5717 - val_accuracy: 0.3482\n",
      "Epoch 4/60\n",
      "52/52 [==============================] - 5s 96ms/step - loss: 1.5400 - accuracy: 0.3636 - val_loss: 1.5166 - val_accuracy: 0.3449\n",
      "Epoch 5/60\n",
      "52/52 [==============================] - 5s 93ms/step - loss: 1.4760 - accuracy: 0.3879 - val_loss: 1.4331 - val_accuracy: 0.4297\n",
      "Epoch 6/60\n",
      "52/52 [==============================] - 5s 91ms/step - loss: 1.4269 - accuracy: 0.4342 - val_loss: 1.3399 - val_accuracy: 0.4749\n",
      "Epoch 7/60\n",
      "52/52 [==============================] - 5s 92ms/step - loss: 1.3674 - accuracy: 0.4600 - val_loss: 1.3171 - val_accuracy: 0.4760\n",
      "Epoch 8/60\n",
      "52/52 [==============================] - 5s 91ms/step - loss: 1.3747 - accuracy: 0.4561 - val_loss: 1.2554 - val_accuracy: 0.5385\n",
      "Epoch 9/60\n",
      "52/52 [==============================] - 5s 92ms/step - loss: 1.2953 - accuracy: 0.4943 - val_loss: 1.2450 - val_accuracy: 0.5167\n",
      "Epoch 10/60\n",
      "52/52 [==============================] - 5s 90ms/step - loss: 1.2335 - accuracy: 0.5361 - val_loss: 1.1797 - val_accuracy: 0.5469\n",
      "Epoch 11/60\n",
      "52/52 [==============================] - 5s 92ms/step - loss: 1.1972 - accuracy: 0.5508 - val_loss: 1.1494 - val_accuracy: 0.5731\n",
      "Epoch 12/60\n",
      "52/52 [==============================] - 4s 86ms/step - loss: 1.1498 - accuracy: 0.5658 - val_loss: 1.1558 - val_accuracy: 0.5714\n",
      "Epoch 13/60\n",
      "52/52 [==============================] - 5s 98ms/step - loss: 1.1438 - accuracy: 0.5673 - val_loss: 1.0934 - val_accuracy: 0.6060\n",
      "Epoch 14/60\n",
      "52/52 [==============================] - 5s 96ms/step - loss: 1.1515 - accuracy: 0.5737 - val_loss: 1.1074 - val_accuracy: 0.5765\n",
      "Epoch 15/60\n",
      "52/52 [==============================] - 5s 100ms/step - loss: 1.0675 - accuracy: 0.6040 - val_loss: 1.0164 - val_accuracy: 0.6239\n",
      "Epoch 16/60\n",
      "52/52 [==============================] - 5s 91ms/step - loss: 1.0809 - accuracy: 0.5911 - val_loss: 1.0298 - val_accuracy: 0.6228\n",
      "Epoch 17/60\n",
      "52/52 [==============================] - 5s 98ms/step - loss: 1.0266 - accuracy: 0.6194 - val_loss: 0.9734 - val_accuracy: 0.6496\n",
      "Epoch 18/60\n",
      "52/52 [==============================] - 5s 96ms/step - loss: 1.0503 - accuracy: 0.6055 - val_loss: 1.0537 - val_accuracy: 0.6177\n",
      "Epoch 19/60\n",
      "52/52 [==============================] - 5s 98ms/step - loss: 0.9397 - accuracy: 0.6605 - val_loss: 0.8647 - val_accuracy: 0.6836\n",
      "Epoch 20/60\n",
      "52/52 [==============================] - 5s 92ms/step - loss: 0.9336 - accuracy: 0.6482 - val_loss: 0.8531 - val_accuracy: 0.6802\n",
      "Epoch 21/60\n",
      "52/52 [==============================] - 5s 94ms/step - loss: 0.9063 - accuracy: 0.6629 - val_loss: 0.8334 - val_accuracy: 0.7042\n",
      "Epoch 22/60\n",
      "52/52 [==============================] - 5s 91ms/step - loss: 0.8795 - accuracy: 0.6833 - val_loss: 0.7771 - val_accuracy: 0.7249\n",
      "Epoch 23/60\n",
      "52/52 [==============================] - 5s 88ms/step - loss: 0.8450 - accuracy: 0.6867 - val_loss: 0.7719 - val_accuracy: 0.7204\n",
      "Epoch 24/60\n",
      "52/52 [==============================] - 5s 90ms/step - loss: 0.8298 - accuracy: 0.6899 - val_loss: 0.7850 - val_accuracy: 0.7165\n",
      "Epoch 25/60\n",
      "52/52 [==============================] - 5s 90ms/step - loss: 0.8030 - accuracy: 0.7037 - val_loss: 0.7862 - val_accuracy: 0.7081\n",
      "Epoch 26/60\n",
      "52/52 [==============================] - 5s 90ms/step - loss: 0.7824 - accuracy: 0.7139 - val_loss: 0.7001 - val_accuracy: 0.7550\n",
      "Epoch 27/60\n",
      "52/52 [==============================] - 5s 91ms/step - loss: 0.7399 - accuracy: 0.7378 - val_loss: 0.7092 - val_accuracy: 0.7500\n",
      "Epoch 28/60\n",
      "52/52 [==============================] - 5s 93ms/step - loss: 0.7163 - accuracy: 0.7354 - val_loss: 0.6559 - val_accuracy: 0.7656\n",
      "Epoch 29/60\n",
      "52/52 [==============================] - 5s 89ms/step - loss: 0.7106 - accuracy: 0.7452 - val_loss: 0.6232 - val_accuracy: 0.7729\n",
      "Epoch 30/60\n",
      "52/52 [==============================] - 5s 93ms/step - loss: 0.6693 - accuracy: 0.7560 - val_loss: 0.6262 - val_accuracy: 0.7746\n",
      "Epoch 31/60\n",
      "52/52 [==============================] - 5s 91ms/step - loss: 0.6525 - accuracy: 0.7661 - val_loss: 0.5973 - val_accuracy: 0.8019\n",
      "Epoch 32/60\n",
      "52/52 [==============================] - 5s 91ms/step - loss: 0.6255 - accuracy: 0.7901 - val_loss: 0.6439 - val_accuracy: 0.7634\n",
      "Epoch 33/60\n",
      "52/52 [==============================] - 5s 91ms/step - loss: 0.6398 - accuracy: 0.7697 - val_loss: 0.5646 - val_accuracy: 0.8052\n",
      "Epoch 34/60\n",
      "52/52 [==============================] - 4s 82ms/step - loss: 0.5677 - accuracy: 0.7998 - val_loss: 0.5157 - val_accuracy: 0.8248\n",
      "Epoch 35/60\n",
      "52/52 [==============================] - 4s 82ms/step - loss: 0.5932 - accuracy: 0.7939 - val_loss: 0.5137 - val_accuracy: 0.8292\n",
      "Epoch 36/60\n",
      "52/52 [==============================] - 4s 81ms/step - loss: 0.5216 - accuracy: 0.8095 - val_loss: 0.5468 - val_accuracy: 0.8069\n",
      "Epoch 37/60\n",
      "52/52 [==============================] - 4s 82ms/step - loss: 0.5413 - accuracy: 0.8167 - val_loss: 0.5535 - val_accuracy: 0.8030\n",
      "Epoch 38/60\n",
      "52/52 [==============================] - 4s 82ms/step - loss: 0.5237 - accuracy: 0.8190 - val_loss: 0.4492 - val_accuracy: 0.8454\n",
      "Epoch 39/60\n",
      "52/52 [==============================] - 5s 91ms/step - loss: 0.5375 - accuracy: 0.8179 - val_loss: 0.4761 - val_accuracy: 0.8265\n",
      "Epoch 40/60\n",
      "52/52 [==============================] - 5s 90ms/step - loss: 0.4852 - accuracy: 0.8293 - val_loss: 0.4143 - val_accuracy: 0.8616\n",
      "Epoch 41/60\n",
      "52/52 [==============================] - 5s 88ms/step - loss: 0.4416 - accuracy: 0.8443 - val_loss: 0.3969 - val_accuracy: 0.8733\n",
      "Epoch 42/60\n",
      "52/52 [==============================] - 5s 88ms/step - loss: 0.4465 - accuracy: 0.8431 - val_loss: 0.4177 - val_accuracy: 0.8594\n",
      "Epoch 43/60\n",
      "52/52 [==============================] - 5s 86ms/step - loss: 0.4632 - accuracy: 0.8346 - val_loss: 0.4445 - val_accuracy: 0.8359\n",
      "Epoch 44/60\n",
      "52/52 [==============================] - 5s 96ms/step - loss: 0.3919 - accuracy: 0.8689 - val_loss: 0.3284 - val_accuracy: 0.9018\n",
      "Epoch 45/60\n",
      "52/52 [==============================] - 5s 99ms/step - loss: 0.3837 - accuracy: 0.8695 - val_loss: 0.4119 - val_accuracy: 0.8482\n",
      "Epoch 46/60\n",
      "52/52 [==============================] - 4s 84ms/step - loss: 0.3628 - accuracy: 0.8755 - val_loss: 0.3663 - val_accuracy: 0.8733\n",
      "Epoch 47/60\n",
      "52/52 [==============================] - 5s 89ms/step - loss: 0.3422 - accuracy: 0.8888 - val_loss: 0.3280 - val_accuracy: 0.8823\n",
      "Epoch 48/60\n",
      "52/52 [==============================] - 5s 89ms/step - loss: 0.3507 - accuracy: 0.8786 - val_loss: 0.2700 - val_accuracy: 0.9169\n",
      "Epoch 49/60\n",
      "52/52 [==============================] - 5s 90ms/step - loss: 0.3116 - accuracy: 0.8972 - val_loss: 0.3313 - val_accuracy: 0.8940\n",
      "Epoch 50/60\n",
      "52/52 [==============================] - 5s 95ms/step - loss: 0.3419 - accuracy: 0.8726 - val_loss: 0.3350 - val_accuracy: 0.8834\n",
      "Epoch 51/60\n",
      "52/52 [==============================] - 5s 102ms/step - loss: 0.3630 - accuracy: 0.8702 - val_loss: 0.3952 - val_accuracy: 0.8577\n",
      "Epoch 52/60\n",
      "52/52 [==============================] - 5s 93ms/step - loss: 0.2880 - accuracy: 0.9135 - val_loss: 0.2531 - val_accuracy: 0.9174\n",
      "Epoch 53/60\n",
      "52/52 [==============================] - 5s 90ms/step - loss: 0.2765 - accuracy: 0.9110 - val_loss: 0.2574 - val_accuracy: 0.9196\n",
      "Epoch 54/60\n",
      "52/52 [==============================] - 5s 87ms/step - loss: 0.2556 - accuracy: 0.9183 - val_loss: 0.2169 - val_accuracy: 0.9375\n",
      "Epoch 55/60\n",
      "52/52 [==============================] - 4s 84ms/step - loss: 0.2309 - accuracy: 0.9321 - val_loss: 0.2228 - val_accuracy: 0.9302\n",
      "Epoch 56/60\n",
      "52/52 [==============================] - 6s 110ms/step - loss: 0.3099 - accuracy: 0.8936 - val_loss: 0.4344 - val_accuracy: 0.8371\n",
      "Epoch 57/60\n",
      "52/52 [==============================] - 4s 85ms/step - loss: 0.3019 - accuracy: 0.8972 - val_loss: 0.2259 - val_accuracy: 0.9308\n",
      "Epoch 58/60\n",
      "52/52 [==============================] - 4s 80ms/step - loss: 0.2173 - accuracy: 0.9327 - val_loss: 0.1801 - val_accuracy: 0.9459\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/60\n",
      "52/52 [==============================] - 4s 75ms/step - loss: 0.2195 - accuracy: 0.9278 - val_loss: 0.1807 - val_accuracy: 0.9526\n",
      "Epoch 60/60\n",
      "52/52 [==============================] - 4s 76ms/step - loss: 0.2202 - accuracy: 0.9296 - val_loss: 0.2046 - val_accuracy: 0.9308\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_data_gen,\n",
    "    validation_data = train_data_gen,\n",
    "    steps_per_epoch= total_train // batch_size,\n",
    "    epochs = epochs,\n",
    "    validation_steps= total_test // batch_size,\n",
    "    callbacks = [tf.keras.callbacks.EarlyStopping(\n",
    "                monitor='val_loss',\n",
    "                min_delta=0.01,\n",
    "                patience=7)]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "BeP0d2ol4vA1",
   "metadata": {
    "id": "BeP0d2ol4vA1"
   },
   "source": [
    "**Step 9** : Evaluating the performance of the model using the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "45cfb948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 58ms/step - loss: 0.4392 - accuracy: 0.8504\n",
      "Test accuracy: 85.04155278205872 Test Loss: 0.43915390968322754 \n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_data_gen)\n",
    "print('Test accuracy: {} Test Loss: {} '.format(test_acc*100, test_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "X4fwRD0zaWEs",
   "metadata": {
    "id": "X4fwRD0zaWEs"
   },
   "source": [
    "1. ``import plotly.graph_objects as goimport plotly.graph_objects as go `` - IT contains a automatically-generated hierarchy of Python classes that represent non-leaf nodes in this figure schema. \n",
    "\n",
    "2. ``fig = go.Figure()`` - is a Python package for creating, manipulation, nad rendering graphocal figures. Graphs are represented by figures, which are data structure. \n",
    "\n",
    "\n",
    "5. ``fig.show()`` - is the pyplot module of the matplotlob library, this function allows all figures to be displayed. It also helps to visualize more easily.\n",
    "\n",
    "Below shows the evaluation of the model. It shows the accuracy and losses for training as the number of epoch increases. This model achieved the high overall 92% accuracy with the Adam optimizer and has 60 number of epoch. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7eec4966",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_acc = history.history['accuracy'] # store training accuracy in history\n",
    "val_acc = history.history['val_accuracy'] # store validation accuracy in history\n",
    "train_loss = history.history['loss'] # store training loss in history\n",
    "val_loss = history.history['val_loss'] # store validation loss in history\n",
    "\n",
    "epochs = range(1, len(train_acc) + 1)\n",
    "\n",
    "plt.plot(epochs, train_acc, 'g*-', label = 'Training accuracy')\n",
    "plt.plot(epochs, val_acc, 'r', label = 'Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, train_loss, 'g*-', label = 'Training loss')\n",
    "plt.plot(epochs, val_loss, 'r', label = 'Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce9f07ca",
   "metadata": {},
   "source": [
    "Below shows that the CNN successfully classifies majority of all different household garbage. However, there is a great probability to some classes that the model may mistake garbage with glass and metal, or paper and glass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8e31a4a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 2s 23ms/step\n",
      "79/79 [==============================] - 5s 59ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   cardboard       0.15      0.13      0.14       403\n",
      "       glass       0.20      0.21      0.20       501\n",
      "       metal       0.16      0.19      0.17       410\n",
      "       paper       0.26      0.26      0.26       594\n",
      "     plastic       0.15      0.15      0.15       482\n",
      "       trash       0.04      0.03      0.03       137\n",
      "\n",
      "    accuracy                           0.18      2527\n",
      "   macro avg       0.16      0.16      0.16      2527\n",
      "weighted avg       0.18      0.18      0.18      2527\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Confusion Matrix and Classification Report\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import np_utils\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "(X,y) = (train_data_gen[0], train_data_gen[1])\n",
    "y_test = train_test_split(X,y,test_size=0.2, random_state=4)\n",
    "\n",
    "nb_classes = 6\n",
    "Y_train = model.predict(train_data_gen)\n",
    "y_train = np.argmax(Y_train, axis=1)\n",
    "Y_test = model.predict(test_data_gen)\n",
    "y_test = np.argmax(Y_test, axis=1)\n",
    "cm = confusion_matrix(y_test, y_train)\n",
    "\n",
    "#visualize confusion matrix\n",
    "matrix = pd.DataFrame((metrics.confusion_matrix(y_test, y_train)), ('cardboard', 'glass', 'metal','paper','plastic','trash'),\n",
    "                      ('cardboard', 'glass', 'metal','paper','plastic','trash'))\n",
    "\n",
    "heatmap = sns.heatmap(matrix, annot = False, annot_kws = {'size': 14}, fmt = 'd', cmap = 'crest')\n",
    "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), fontsize = 12)\n",
    "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), fontsize = 12)\n",
    "plt.title('CNN Confusion Matrix', fontsize = 15, pad = 12, color = 'darkblue')\n",
    "plt.ylabel('True label', fontsize = 14)\n",
    "plt.xlabel('Predicted label', fontsize = 14)\n",
    "plt.show()\n",
    "\n",
    "print('Classification Report')\n",
    "target_names = ['cardboard', 'glass', 'metal','paper','plastic','trash']\n",
    "print(classification_report(train_data_gen.classes, y_test, target_names=target_names,zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fec6d33",
   "metadata": {},
   "source": [
    "Below shows the summary accuracy of each class which includes cardboard, glass, metal, paper, plastic, and trash. The accuracy was achieved with highest fine-tuning combinations of 60 epoch, 45 batch size, 0.01 learning rate, and an Adam optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "961b405e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cardboard Glass Metal Paper Plastic Trash\n",
      "[0.74950534 0.6747131  0.71428571 0.65453106 0.67431737 0.91808469]\n"
     ]
    }
   ],
   "source": [
    "# to identify the accuracy of each class \n",
    "FP = cm.sum(axis=0) - np.diag(cm)  \n",
    "FN = cm.sum(axis=1) - np.diag(cm)\n",
    "TP = np.diag(cm)\n",
    "TN = cm.sum() - (FP + FN + TP)\n",
    "\n",
    "\n",
    "# Overall accuracy\n",
    "print (\"Cardboard Glass Metal Paper Plastic Trash\")\n",
    "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
    "print (ACC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4974cde7",
   "metadata": {
    "id": "4974cde7"
   },
   "source": [
    "## Display Predictions (Random Images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "40e59cf8",
   "metadata": {
    "id": "40e59cf8"
   },
   "outputs": [],
   "source": [
    "from skimage.transform import resize "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "75e5f759",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from keras.preprocessing import image\n",
    "import keras.utils as ku\n",
    "import numpy as np\n",
    "number_to_class = ['cardboard',\\\n",
    "                   'glass',\\\n",
    "                   'metal',\n",
    "                   'paper',\\\n",
    "                   'plastic',\\\n",
    "                   'trash',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d2b55e3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 32ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x2000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_x, test_y = train_data_gen.__getitem__(1)\n",
    "# test_x is used to train the data\n",
    "# test_y refers to the actual answer of the testing set data\n",
    "# preds refers to the predicted class\n",
    "preds = model.predict(test_x)\n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "for i in range(12):\n",
    "    plt.subplot(4,4, i+1)\n",
    "    plt.title('pred:%s / truth:%s' % (number_to_class[np.argmax(preds[i])], number_to_class[np.argmax(test_y[i])]), size=18)\n",
    "    plt.imshow(test_x[i])\n",
    "#shows some testing results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40df84df",
   "metadata": {
    "id": "40df84df"
   },
   "source": [
    "---\n",
    "### Class: Cardboard\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "df2f05e6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "id": "df2f05e6",
    "outputId": "db02ba2a-cc15-4118-d75c-2594dc5b7dc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 30ms/step\n",
      "Probability: 0.9574093\n",
      "Classified: cardboard \n",
      " cardboard\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loaded Image')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img = 'Garbage/processed_images/cardboard/cardboard_321.jpg'\n",
    "img = ku.load_img(test_img, target_size = (32,32))\n",
    "img = ku.img_to_array(img, dtype=np.uint8)\n",
    "img = np.array(img)/255.0\n",
    "prediction = model.predict(img[np.newaxis, ...])\n",
    "\n",
    "#print(\"Predicted shape\",p.shape)\n",
    "print(\"Probability:\",np.max(prediction[0], axis=-1))\n",
    "predicted_class = number_to_class[np.argmax(prediction[0], axis=-1)]\n",
    "print(\"Classified:\",predicted_class,'\\n' , number_to_class[np.argmax(test_img[0])])\n",
    "\n",
    "plt.axis('off')\n",
    "plt.imshow(img.squeeze())\n",
    "plt.imshow(img)\n",
    "plt.title(\"Loaded Image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd69bf10",
   "metadata": {},
   "source": [
    "---\n",
    "### Class: Glass\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "43c6213a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 27ms/step\n",
      "Probability: 0.8717817\n",
      "Classified: glass \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loaded Image')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img = 'Garbage/processed_images/glass/glass_341.jpg'\n",
    "img = ku.load_img(test_img, target_size = (32,32))\n",
    "img = ku.img_to_array(img, dtype=np.uint8)\n",
    "img = np.array(img)/255.0\n",
    "prediction = model.predict(img[np.newaxis, ...])\n",
    "\n",
    "#print(\"Predicted shape\",p.shape)\n",
    "print(\"Probability:\",np.max(prediction[0], axis=-1))\n",
    "predicted_class = number_to_class[np.argmax(prediction[0], axis=-1)]\n",
    "print(\"Classified:\",predicted_class,'\\n')\n",
    "\n",
    "plt.axis('off')\n",
    "plt.imshow(img.squeeze())\n",
    "plt.title(\"Loaded Image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29712b0d",
   "metadata": {},
   "source": [
    "---\n",
    "### Class: Metal\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2f35651c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 28ms/step\n",
      "Probability: 0.9952005\n",
      "Classified: metal \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loaded Image')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img = 'Garbage/processed_images/metal/metal_100.jpg'\n",
    "img = ku.load_img(test_img, target_size = (32,32))\n",
    "img = ku.img_to_array(img, dtype=np.uint8)\n",
    "img = np.array(img)/255.0\n",
    "prediction = model.predict(img[np.newaxis, ...])\n",
    "\n",
    "#print(\"Predicted shape\",p.shape)\n",
    "print(\"Probability:\",np.max(prediction[0], axis=-1))\n",
    "predicted_class = number_to_class[np.argmax(prediction[0], axis=-1)]\n",
    "print(\"Classified:\",predicted_class,'\\n')\n",
    "\n",
    "plt.axis('off')\n",
    "plt.imshow(img.squeeze())\n",
    "plt.title(\"Loaded Image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f6aebc",
   "metadata": {},
   "source": [
    "---\n",
    "### Class: Paper\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "c3131d57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 29ms/step\n",
      "Probability: 0.95934737\n",
      "Classified: paper \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loaded Image')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img = 'Garbage/processed_images/paper/paper_324.jpg'\n",
    "img = ku.load_img(test_img, target_size = (32,32))\n",
    "img = ku.img_to_array(img, dtype=np.uint8)\n",
    "img = np.array(img)/255.0\n",
    "prediction = model.predict(img[np.newaxis, ...])\n",
    "\n",
    "#print(\"Predicted shape\",p.shape)\n",
    "print(\"Probability:\",np.max(prediction[0], axis=-1))\n",
    "predicted_class = number_to_class[np.argmax(prediction[0], axis=-1)]\n",
    "print(\"Classified:\",predicted_class,'\\n')\n",
    "\n",
    "plt.axis('off')\n",
    "plt.imshow(img.squeeze())\n",
    "plt.title(\"Loaded Image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d4b709d",
   "metadata": {},
   "source": [
    "---\n",
    "### Class: Plastic\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0d807b17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 28ms/step\n",
      "Probability: 0.8370965\n",
      "Classified: plastic \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loaded Image')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img = 'Garbage/processed_images/plastic/plastic_155.jpg'\n",
    "img = ku.load_img(test_img, target_size = (32,32))\n",
    "img = ku.img_to_array(img, dtype=np.uint8)\n",
    "img = np.array(img)/255.0\n",
    "prediction = model.predict(img[np.newaxis, ...])\n",
    "\n",
    "#print(\"Predicted shape\",p.shape)\n",
    "print(\"Probability:\",np.max(prediction[0], axis=-1))\n",
    "predicted_class = number_to_class[np.argmax(prediction[0], axis=-1)]\n",
    "print(\"Classified:\",predicted_class,'\\n')\n",
    "\n",
    "plt.axis('off')\n",
    "plt.imshow(img.squeeze())\n",
    "plt.title(\"Loaded Image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0858f450",
   "metadata": {},
   "source": [
    "---\n",
    "### Class: Trash\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "608dc9d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 29ms/step\n",
      "Probability: 0.98495126\n",
      "Classified: trash \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loaded Image')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img = 'Garbage/processed_images/trash/trash_032.jpg'\n",
    "img = ku.load_img(test_img, target_size = (32,32))\n",
    "img = ku.img_to_array(img, dtype=np.uint8)\n",
    "img = np.array(img)/255.0\n",
    "prediction = model.predict(img[np.newaxis, ...])\n",
    "\n",
    "#print(\"Predicted shape\",p.shape)\n",
    "print(\"Probability:\",np.max(prediction[0], axis=-1))\n",
    "predicted_class = number_to_class[np.argmax(prediction[0], axis=-1)]\n",
    "print(\"Classified:\",predicted_class,'\\n')\n",
    "\n",
    "plt.axis('off')\n",
    "plt.imshow(img.squeeze())\n",
    "plt.title(\"Loaded Image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gwoKfcxLcvac",
   "metadata": {
    "id": "gwoKfcxLcvac"
   },
   "source": [
    "\n",
    "\n",
    "> ### Sempahore (Member's Contribution):\n",
    "\n",
    "In order to simulate a professional remote environment and to have real-time communication and participation in the project, our team came up with the idea to hold a conference call using Zoom. This, in our opinion, is the greatest plan of action for producing effective results when working remotely. Each team member makes a contribution to the project's success. From the planning stage to the creation stage of the notebook that trains a sequential multinomial logistic regression neural network using the processed images.\n",
    "\n",
    "*   Marquez, Jennifer C.   - *Coding, Documentation, Images Collection*\n",
    "*   Martinez, Rikki Mae M. - *Coding, Documentation, Images Collection*\n",
    "*   Rario, Marianne C.     - *Coding, Documentation, Images Collection*\n",
    "\n",
    "\n",
    "![title](Garbage/download.png)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "99171f66",
    "8e96cf52",
    "40df84df",
    "86a10061",
    "a3b4c06e"
   ],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}