DL0321EN-4-1-Comparing-Models-py-v1.0.ipynb

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   "source": [
    "<a href=\"https://cognitiveclass.ai\"><img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width = 400> </a>\n",
    "\n",
    "<h1 align=center><font size = 5>Peer Review Final Assignment</font></h1>"
   ]
  },
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   "source": [
    "## Introduction\n"
   ]
  },
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   "source": [
    "In this lab, you will build an image classifier using the VGG16 pre-trained model, and you will evaluate it and compare its performance to the model we built in the last module using the ResNet50 pre-trained model. Good luck!"
   ]
  },
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "<font size = 3>    \n",
    "\n",
    "1. <a href=\"#item41\">Download Data \n",
    "2. <a href=\"#item42\">Part 1</a>\n",
    "3. <a href=\"#item43\">Part 2</a>  \n",
    "4. <a href=\"#item44\">Part 3</a>  \n",
    "\n",
    "</font>\n",
    "    \n",
    "</div>"
   ]
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   "source": [
    "   "
   ]
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   "source": [
    "<a id=\"item41\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the <code>wget</code> command to download the data for this assignment from here: https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0321EN/data/concrete_data_week4.zip"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Use the following cells to download the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'Iterable' from 'collections' (c:\\Users\\Windows\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\collections\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ImageDataGenerator \n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential \n",
      "File \u001b[1;32mc:\\Users\\Windows\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\__init__.py:3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m absolute_import\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m utils\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m applications\n",
      "File \u001b[1;32mc:\\Users\\Windows\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\utils\\__init__.py:25\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnp_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m to_categorical\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnp_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m normalize\n\u001b[1;32m---> 25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmulti_gpu_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m multi_gpu_model\n",
      "File \u001b[1;32mc:\\Users\\Windows\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\utils\\multi_gpu_utils.py:7\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m division\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m print_function\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmerge\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m concatenate\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend \u001b[38;5;28;01mas\u001b[39;00m K\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Lambda\n",
      "File \u001b[1;32mc:\\Users\\Windows\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\layers\\__init__.py:4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m absolute_import\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneric_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deserialize_keras_object\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mengine\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Layer\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mengine\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mengine\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m InputLayer\n",
      "File \u001b[1;32mc:\\Users\\Windows\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\engine\\__init__.py:8\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtopology\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Layer\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtopology\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_source_inputs\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtraining\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Model\n",
      "File \u001b[1;32mc:\\Users\\Windows\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\engine\\training.py:23\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OrderedEnqueuer\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneric_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Progbar\n\u001b[1;32m---> 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m callbacks \u001b[38;5;28;01mas\u001b[39;00m cbks\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlegacy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m interfaces\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_standardize_input_data\u001b[39m(data, names, shapes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m     28\u001b[0m                             check_batch_axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m     29\u001b[0m                             exception_prefix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m):\n",
      "File \u001b[1;32mc:\\Users\\Windows\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\callbacks.py:18\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deque\n\u001b[0;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OrderedDict\n\u001b[1;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Iterable\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneric_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Progbar\n\u001b[0;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend \u001b[38;5;28;01mas\u001b[39;00m K\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'Iterable' from 'collections' (c:\\Users\\Windows\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\collections\\__init__.py)"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator \n",
    "import keras \n",
    "from keras.models import Sequential \n",
    "from keras.layers import Dense \n",
    "from keras.applications import ResNet50, VGG16 \n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import skillsnetwork \n",
    "## get the data\n",
    "await skillsnetwork.prepare(\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0321EN/data/concrete_data_week4.zip\", overwrite=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you unzip the data, you fill find the data has already been divided into a train, validation, and test sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
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   "source": [
    "<a id=\"item42\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, you will design a classifier using the VGG16 pre-trained model. Just like the ResNet50 model, you can import the model <code>VGG16</code> from <code>keras.applications</code>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will essentially build your classifier as follows:\n",
    "1. Import libraries, modules, and packages you will need. Make sure to import the *preprocess_input* function from <code>keras.applications.vgg16</code>.\n",
    "2. Use a batch size of 100 images for both training and validation.\n",
    "3. Construct an ImageDataGenerator for the training set and another one for the validation set. VGG16 was originally trained on 224 × 224 images, so make sure to address that when defining the ImageDataGenerator instances.\n",
    "4. Create a sequential model using Keras. Add VGG16 model to it and dense layer.\n",
    "5. Compile the mode using the adam optimizer and the categorical_crossentropy loss function.\n",
    "6. Fit the model on the augmented data using the ImageDataGenerators."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following cells to create your classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 2\n",
    "image_resize = 224 \n",
    "batch_size_training = 100 \n",
    "batch_size_validation = 100 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.4.1'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import keras \n",
    "keras.__version__ \n",
    "# 3.4.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'keras.legacy.preprocessing'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlegacy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ImageDataGenerator\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'keras.legacy.preprocessing'"
     ]
    }
   ],
   "source": [
    "from keras.legacy.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras in c:\\users\\windows\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (2.1.6)\n",
      "Collecting keras\n",
      "  Using cached keras-3.4.1-py3-none-any.whl.metadata (5.8 kB)\n",
      "Requirement already satisfied: absl-py in c:\\users\\windows\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from keras) (2.1.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\windows\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from keras) (1.26.1)\n",
      "Requirement already satisfied: rich in c:\\users\\windows\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from keras) (13.7.1)\n",
      "Requirement already satisfied: namex in c:\\users\\windows\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from keras) (0.0.8)\n",
      "Requirement already satisfied: h5py in c:\\users\\windows\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from keras) (3.11.0)\n",
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      "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\windows\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from rich->keras) (3.0.0)\n",
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      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\windows\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras) (0.1.2)\n",
      "Using cached keras-3.4.1-py3-none-any.whl (1.1 MB)\n",
      "Installing collected packages: keras\n",
      "  Attempting uninstall: keras\n",
      "    Found existing installation: Keras 2.1.6\n",
      "    Uninstalling Keras-2.1.6:\n",
      "      Successfully uninstalled Keras-2.1.6\n",
      "Successfully installed keras-3.4.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.0 -> 24.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"item43\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, you will evaluate your deep learning models on a test data. For this part, you will need to do the following:\n",
    "\n",
    "1. Load your saved model that was built using the ResNet50 model. \n",
    "2. Construct an ImageDataGenerator for the test set. For this ImageDataGenerator instance, you only need to pass the directory of the test images, target size, and the **shuffle** parameter and set it to False.\n",
    "3. Use the **evaluate_generator** method to evaluate your models on the test data, by passing the above ImageDataGenerator as an argument. You can learn more about **evaluate_generator** [here](https://keras.io/models/sequential/).\n",
    "4. Print the performance of the classifier using the VGG16 pre-trained model.\n",
    "5. Print the performance of the classifier using the ResNet pre-trained model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following cells to evaluate your models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"item44\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this model, you will predict whether the images in the test data are images of cracked concrete or not. You will do the following:\n",
    "\n",
    "1. Use the **predict_generator** method to predict the class of the images in the test data, by passing the test data ImageDataGenerator instance defined in the previous part as an argument. You can learn more about the **predict_generator** method [here](https://keras.io/models/sequential/).\n",
    "2. Report the class predictions of the first five images in the test set. You should print something list this:\n",
    "\n",
    "<center>\n",
    "    <ul style=\"list-style-type:none\">\n",
    "        <li>Positive</li>  \n",
    "        <li>Negative</li> \n",
    "        <li>Positive</li>\n",
    "        <li>Positive</li>\n",
    "        <li>Negative</li>\n",
    "    </ul>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following cells to make your predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "This notebook was created by Alex Aklson."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "This notebook is part of a course on **Coursera** called *AI Capstone Project with Deep Learning*. If you accessed this notebook outside the course, you can take this course online by clicking [here](https://cocl.us/DL0321EN_Coursera_Week4_LAB1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "Copyright &copy; 2020 [IBM Developer Skills Network](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
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