diff --git a/Final Task/Final_Task.ipynb.ipynb b/Final Task/Final_Task.ipynb.ipynb
new file mode 100644
index 0000000..53ecc8a
--- /dev/null
+++ b/Final Task/Final_Task.ipynb.ipynb	
@@ -0,0 +1,1024 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rtI19Rt-H7Uc"
+      },
+      "source": [
+        "## Final Task:\n",
+        "This is your final evaluation for the project. As decided, we will be predicting images of people into three classes: `without_mask`, `mask_weared_incorrect` and `with_mask`. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "c2CiXcHQTbX8"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QKDPyiZTIm1c"
+      },
+      "source": [
+        "### Loading the dataset\n",
+        "Make a copy of the dataset given to you in your Google Drive (keep it outside, don't put it in any folder to avoid inconvenience). Ensure it is named as `Mask_Dataset` or change the path (the variable `data_dir`) accordingly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hNEMe7XsIjrK",
+        "outputId": "5cba9628-8fe4-44b4-9fdb-e6607a2b133d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Mount Google Drive\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "8CXzo4MOJOl8"
+      },
+      "outputs": [],
+      "source": [
+        "import pathlib\n",
+        "\n",
+        "path='/content/drive/MyDrive/Mask_Dataset/'\n",
+        "data_dir = pathlib.Path(path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YHPHkGyDKscK"
+      },
+      "source": [
+        "### Know the Dataset\n",
+        "Most of the code is written for you as you aren't used to these libraries. You are to go through the documentation for your benefit."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PzbSy-vXKjD-",
+        "outputId": "9b0ca462-7d7c-4c6b-e770-0ebca6052f03"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "8992\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print image count\n",
+        "image_count = len(list(data_dir.glob('*/*.png')))\n",
+        "print(image_count)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rFHWFYj5NCVm",
+        "outputId": "339c3792-1af8-439b-bf6d-63a9d4e61a56"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "['mask_weared_incorrect', 'with_mask', 'without_mask']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print Output Labels\n",
+        "import os\n",
+        "output_classes = os.listdir(data_dir)\n",
+        "print(output_classes)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 299
+        },
+        "id": "fESyMw90KaxN",
+        "outputId": "77dab5df-1f0a-4408-f841-8cbf4ae03382"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2994, 3004, 2994]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "# Plot count of each ouput label\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "count=[]\n",
+        "for label in output_classes:\n",
+        "  this_path=path+label\n",
+        "  dir=pathlib.Path(this_path)\n",
+        "  im_count=os.listdir(dir)\n",
+        "  count.append(len(im_count))\n",
+        "\n",
+        "print(count)\n",
+        "\n",
+        "plt.bar(output_classes,count)\n",
+        "plt.title(\"Statistics\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 657
+        },
+        "id": "HDSJ2Zk5a14s",
+        "outputId": "b931aa20-5ad3-4981-cb2a-e8cc696ba35d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7FBFA447A8D0>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7FBFA447A950>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7FBF9A8E5A10>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7FBFA447A8D0>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7FBFA4C59410>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Check some sample images (Use of cv2)\n",
+        "import random\n",
+        "import cv2\n",
+        "import glob\n",
+        "from google.colab.patches import cv2_imshow\n",
+        "\n",
+        "TEST_IMAGE_PATHS = list(glob.glob(path+'*/*.png'))\n",
+        "\n",
+        "images = random.sample(TEST_IMAGE_PATHS, k=5)\n",
+        "\n",
+        "for image_path in images:\n",
+        "    # print(image_path)\n",
+        "    img = cv2.imread(image_path,1)\n",
+        "    cv2_imshow(img)\n",
+        "\n",
+        "# Your code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jWBEMC1FUfXS",
+        "outputId": "81aadca0-a4e1-4af9-a6e9-ed0a5565ad0b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(128, 128, 3)\n",
+            "(128, 128, 3)\n",
+            "(128, 128, 3)\n",
+            "(128, 128, 3)\n",
+            "(128, 128, 3)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check shape of the images in your dataset. This will be helpful while specifying input_shape in your Transfer Learning Model\n",
+        "for image_path in images:\n",
+        "    img = cv2.imread(image_path,1)\n",
+        "    print(img.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "52BhBWRab5yc"
+      },
+      "outputs": [],
+      "source": [
+        "# Check if all the images have same shape, else you need to resize them to some common size\n",
+        "image_count = len(list(data_dir.glob('*/*.png')))\n",
+        "TEST_IMAGE_PATHS = list(glob.glob(path+'*/*.png'))\n",
+        "count=0\n",
+        "for image_path in TEST_IMAGE_PATHS:\n",
+        "  img = cv2.imread(image_path,1)\n",
+        "  if(img.shape!=(128,128,3)):\n",
+        "    count = count + 1\n",
+        "    print(\"All pictures are not of same shape\")\n",
+        "    break"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# If the shape is variable, reshape to a common size \n",
+        "if(count>0):\n",
+        "  print(\"shape not same\")\n",
+        "else:\n",
+        "  print(\"shape is same\")\n",
+        "  SHAPE = (128,128,3)\n",
+        "# If it is same, prove it"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CqPABIJi_MsF",
+        "outputId": "e5251b04-a9a3-43b5-9773-4c7bdd881b2b"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "shape is same\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zSoUXS1cRbnu"
+      },
+      "source": [
+        "### Model Definition\n",
+        "Choose a model for Transfer Learning (You may also experment with multiple models and keep all of them in this notebook)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QKZmIgXMTHfy"
+      },
+      "outputs": [],
+      "source": [
+        "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten, Dropout\n",
+        "from tensorflow.keras.models import Model\n",
+        "from tensorflow.keras.preprocessing import image\n",
+        "from tensorflow.keras.models import Sequential"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9xWLUibHRNGj",
+        "outputId": "50424f75-164c-4279-e057-7790279f0baf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+            "58892288/58889256 [==============================] - 0s 0us/step\n",
+            "58900480/58889256 [==============================] - 0s 0us/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Choose and define base model\n",
+        "from tensorflow.keras.applications.vgg16 import VGG16\n",
+        "\n",
+        "base_model = VGG16(weights = 'imagenet',include_top=False,input_shape=SHAPE)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "J3TwB_GLd7BU",
+        "outputId": "cac93d47-d910-4819-eba0-057c91d31cab"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"vgg16\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " input_1 (InputLayer)        [(None, 128, 128, 3)]     0         \n",
+            "                                                                 \n",
+            " block1_conv1 (Conv2D)       (None, 128, 128, 64)      1792      \n",
+            "                                                                 \n",
+            " block1_conv2 (Conv2D)       (None, 128, 128, 64)      36928     \n",
+            "                                                                 \n",
+            " block1_pool (MaxPooling2D)  (None, 64, 64, 64)        0         \n",
+            "                                                                 \n",
+            " block2_conv1 (Conv2D)       (None, 64, 64, 128)       73856     \n",
+            "                                                                 \n",
+            " block2_conv2 (Conv2D)       (None, 64, 64, 128)       147584    \n",
+            "                                                                 \n",
+            " block2_pool (MaxPooling2D)  (None, 32, 32, 128)       0         \n",
+            "                                                                 \n",
+            " block3_conv1 (Conv2D)       (None, 32, 32, 256)       295168    \n",
+            "                                                                 \n",
+            " block3_conv2 (Conv2D)       (None, 32, 32, 256)       590080    \n",
+            "                                                                 \n",
+            " block3_conv3 (Conv2D)       (None, 32, 32, 256)       590080    \n",
+            "                                                                 \n",
+            " block3_pool (MaxPooling2D)  (None, 16, 16, 256)       0         \n",
+            "                                                                 \n",
+            " block4_conv1 (Conv2D)       (None, 16, 16, 512)       1180160   \n",
+            "                                                                 \n",
+            " block4_conv2 (Conv2D)       (None, 16, 16, 512)       2359808   \n",
+            "                                                                 \n",
+            " block4_conv3 (Conv2D)       (None, 16, 16, 512)       2359808   \n",
+            "                                                                 \n",
+            " block4_pool (MaxPooling2D)  (None, 8, 8, 512)         0         \n",
+            "                                                                 \n",
+            " block5_conv1 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
+            "                                                                 \n",
+            " block5_conv2 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
+            "                                                                 \n",
+            " block5_conv3 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
+            "                                                                 \n",
+            " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 14,714,688\n",
+            "Trainable params: 14,714,688\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print base model summary and have a look at the layers\n",
+        "base_model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "F_Heq3C1eKd-"
+      },
+      "outputs": [],
+      "source": [
+        "# As we're using Transfer Learning, you do not need to train all the layers. Freeze all of the layers or train some layers (experiment)\n",
+        "base_model.trainable = False"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MKx1EtUJea6D"
+      },
+      "outputs": [],
+      "source": [
+        "# Append Fully connected/custom Conv2D/Dropout/MaxPooling layers to the base model\n",
+        "inputs = Input(shape = SHAPE)\n",
+        "x = base_model(inputs, training = False)\n",
+        "x = Flatten()(x)\n",
+        "x = Dense(2048,activation = 'relu')(x)\n",
+        "x = Dropout(0.25,seed=1)(x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "q6I3oTTNgP8L"
+      },
+      "outputs": [],
+      "source": [
+        "# Add the final output layer\n",
+        "output_ = Dense(3,activation = 'softmax')(x)\n",
+        "model = Model(inputs,output_)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6aVQocJwgN5r",
+        "outputId": "dfd31a18-0811-44f3-d0a6-d2806b20e9cc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"model\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " input_2 (InputLayer)        [(None, 128, 128, 3)]     0         \n",
+            "                                                                 \n",
+            " vgg16 (Functional)          (None, 4, 4, 512)         14714688  \n",
+            "                                                                 \n",
+            " flatten (Flatten)           (None, 8192)              0         \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 2048)              16779264  \n",
+            "                                                                 \n",
+            " dropout (Dropout)           (None, 2048)              0         \n",
+            "                                                                 \n",
+            " dense_1 (Dense)             (None, 3)                 6147      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 31,500,099\n",
+            "Trainable params: 16,785,411\n",
+            "Non-trainable params: 14,714,688\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print your model's summary\n",
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qdC71fUBgXAg"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile you model (set the parameters like loss/optimizers/metrics)\n",
+        "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
+        "              loss=tf.keras.losses.CategoricalCrossentropy(), \n",
+        "              metrics='accuracy')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RdUSMLggifex"
+      },
+      "source": [
+        "### Data Augmentation and Pre-processing\n",
+        "Augment the data. You may also try dyanamic augmentation using [`tf.keras.preprocessing.image.ImageDataGenerator `](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator).   \n",
+        "You may use [`tf.keras.applications.vgg16.preprocess_input`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/vgg16/preprocess_input)(or some other base model's utility) for pre-processing (can also be passed as a parameter to `ImageDataGenerator`)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "DBscSsvkgn39"
+      },
+      "outputs": [],
+      "source": [
+        "from keras.applications.vgg16 import preprocess_input # Change according to your base model\n",
+        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+        "\n",
+        "# Your code \n",
+        "datagen = ImageDataGenerator(\n",
+        "    rescale = 1./255,\n",
+        "    rotation_range=20,\n",
+        "    width_shift_range=0.2,\n",
+        "    height_shift_range=0.2,\n",
+        "    horizontal_flip=True,\n",
+        "    shear_range = 0.2,\n",
+        "    zoom_range = 0.2,\n",
+        "    validation_split = 0.1\n",
+        ")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IcKPxCpOkcuG"
+      },
+      "source": [
+        "### Training and Validation Dataset  \n",
+        "Split the dataset into training and validation (We'll be looking for your validation accuracy, assume we are using complete dataset for now). \n",
+        "\n",
+        "Hint: `flow_from_directory` used with `ImageDataGenerator` will simplify things for you."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sB7hb3ybkJRq",
+        "outputId": "0acbc10a-e1ed-43e0-f7b5-a6ea77fe643e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 8094 images belonging to 3 classes.\n",
+            "Found 898 images belonging to 3 classes.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your code\n",
+        "train_generator = datagen.flow_from_directory(\n",
+        "    path,\n",
+        "    target_size=SHAPE[:-1],\n",
+        "    subset='training'\n",
+        ")\n",
+        "\n",
+        "val_generator = datagen.flow_from_directory(\n",
+        "    path,\n",
+        "    target_size=SHAPE[:-1],\n",
+        "    subset='validation'\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZZPsjpT1mp3z"
+      },
+      "source": [
+        "### Training  \n",
+        "Train your model for some epochs and plot the graph. Try and save your best model. Experiment with the parameters of `model.fit`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Gs2X14MBmu7W",
+        "outputId": "bd9b3bf6-8c5b-446a-a661-be11952113a1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/20\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.5189 - accuracy: 0.8614INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 103s 354ms/step - loss: 0.5189 - accuracy: 0.8614 - val_loss: 0.2201 - val_accuracy: 0.9143\n",
+            "Epoch 2/20\n",
+            "253/253 [==============================] - 71s 282ms/step - loss: 0.2115 - accuracy: 0.9192 - val_loss: 0.2449 - val_accuracy: 0.8964\n",
+            "Epoch 3/20\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.1767 - accuracy: 0.9369INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 78s 305ms/step - loss: 0.1767 - accuracy: 0.9369 - val_loss: 0.1778 - val_accuracy: 0.9376\n",
+            "Epoch 4/20\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.1713 - accuracy: 0.9371INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 80s 317ms/step - loss: 0.1713 - accuracy: 0.9371 - val_loss: 0.1518 - val_accuracy: 0.9388\n",
+            "Epoch 5/20\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.1538 - accuracy: 0.9453INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 83s 328ms/step - loss: 0.1538 - accuracy: 0.9453 - val_loss: 0.1332 - val_accuracy: 0.9510\n",
+            "Epoch 6/20\n",
+            "253/253 [==============================] - 77s 304ms/step - loss: 0.1534 - accuracy: 0.9433 - val_loss: 0.1494 - val_accuracy: 0.9499\n",
+            "Epoch 7/20\n",
+            "253/253 [==============================] - 75s 296ms/step - loss: 0.1276 - accuracy: 0.9524 - val_loss: 0.1370 - val_accuracy: 0.9510\n",
+            "Epoch 8/20\n",
+            "253/253 [==============================] - 76s 301ms/step - loss: 0.1390 - accuracy: 0.9486 - val_loss: 0.1402 - val_accuracy: 0.9499\n",
+            "Epoch 9/20\n",
+            "253/253 [==============================] - 73s 290ms/step - loss: 0.1329 - accuracy: 0.9522 - val_loss: 0.2010 - val_accuracy: 0.9287\n",
+            "Epoch 10/20\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.1214 - accuracy: 0.9558INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 77s 303ms/step - loss: 0.1214 - accuracy: 0.9558 - val_loss: 0.1175 - val_accuracy: 0.9610\n",
+            "Epoch 11/20\n",
+            "253/253 [==============================] - 71s 282ms/step - loss: 0.1130 - accuracy: 0.9569 - val_loss: 0.1114 - val_accuracy: 0.9577\n",
+            "Epoch 12/20\n",
+            "253/253 [==============================] - 72s 286ms/step - loss: 0.1179 - accuracy: 0.9576 - val_loss: 0.1202 - val_accuracy: 0.9555\n",
+            "Epoch 13/20\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.1025 - accuracy: 0.9628INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 76s 302ms/step - loss: 0.1025 - accuracy: 0.9628 - val_loss: 0.1077 - val_accuracy: 0.9644\n",
+            "Epoch 14/20\n",
+            "253/253 [==============================] - 74s 292ms/step - loss: 0.1098 - accuracy: 0.9603 - val_loss: 0.1126 - val_accuracy: 0.9644\n",
+            "Epoch 15/20\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.0999 - accuracy: 0.9657INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 78s 308ms/step - loss: 0.0999 - accuracy: 0.9657 - val_loss: 0.0908 - val_accuracy: 0.9655\n",
+            "Epoch 16/20\n",
+            "253/253 [==============================] - 75s 298ms/step - loss: 0.1120 - accuracy: 0.9595 - val_loss: 0.1015 - val_accuracy: 0.9610\n",
+            "Epoch 17/20\n",
+            "253/253 [==============================] - 77s 302ms/step - loss: 0.1019 - accuracy: 0.9640 - val_loss: 0.1197 - val_accuracy: 0.9599\n",
+            "Epoch 18/20\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.1000 - accuracy: 0.9645INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 77s 306ms/step - loss: 0.1000 - accuracy: 0.9645 - val_loss: 0.0971 - val_accuracy: 0.9688\n",
+            "Epoch 19/20\n",
+            "253/253 [==============================] - 72s 284ms/step - loss: 0.1020 - accuracy: 0.9638 - val_loss: 0.0918 - val_accuracy: 0.9677\n",
+            "Epoch 20/20\n",
+            "253/253 [==============================] - 70s 277ms/step - loss: 0.1080 - accuracy: 0.9597 - val_loss: 0.0934 - val_accuracy: 0.9655\n"
+          ]
+        }
+      ],
+      "source": [
+        "from gc import callbacks\n",
+        "from keras.callbacks import ModelCheckpoint\n",
+        "# ModelCheckpoint is helpful to save the model giving best results (brownie points)\n",
+        "\n",
+        "ckptpath = '/content/drive/MyDrive/Mask_models/vgg16'\n",
+        "model_checkpoint_callback = ModelCheckpoint(\n",
+        "    filepath=ckptpath,\n",
+        "    monitor='val_accuracy',\n",
+        "    mode='auto',\n",
+        "    save_best_only=True)\n",
+        "\n",
+        "history = model.fit(train_generator,\n",
+        "                    epochs=20,\n",
+        "                    validation_data=val_generator,\n",
+        "                    verbose=1,\n",
+        "                    callbacks=[model_checkpoint_callback]\n",
+        "                  )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FTvRa1FXri4R"
+      },
+      "source": [
+        "### Evaluate the performance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Fine tuning the model\n",
+        "\n",
+        "base_model.trainable = True\n",
+        "\n",
+        "model.compile(optimizer=tf.keras.optimizers.Adam(1e-5),\n",
+        "              loss=tf.keras.losses.CategoricalCrossentropy(), \n",
+        "              metrics='accuracy')\n",
+        "\n",
+        "history2 = model.fit(train_generator,\n",
+        "                    epochs=5,\n",
+        "                    validation_data=val_generator,\n",
+        "                    verbose=1,\n",
+        "                    callbacks=[model_checkpoint_callback]\n",
+        "                  )"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "49_Xhnf6S9YF",
+        "outputId": "1a23bbbe-9365-41e0-db7f-ab36e886514a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.0844 - accuracy: 0.9701INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 97s 367ms/step - loss: 0.0844 - accuracy: 0.9701 - val_loss: 0.0631 - val_accuracy: 0.9822\n",
+            "Epoch 2/5\n",
+            "253/253 [==============================] - 85s 337ms/step - loss: 0.0470 - accuracy: 0.9838 - val_loss: 0.0765 - val_accuracy: 0.9744\n",
+            "Epoch 3/5\n",
+            "253/253 [==============================] - 86s 340ms/step - loss: 0.0337 - accuracy: 0.9896 - val_loss: 0.0727 - val_accuracy: 0.9755\n",
+            "Epoch 4/5\n",
+            "253/253 [==============================] - 85s 335ms/step - loss: 0.0377 - accuracy: 0.9868 - val_loss: 0.0696 - val_accuracy: 0.9788\n",
+            "Epoch 5/5\n",
+            "253/253 [==============================] - ETA: 0s - loss: 0.0307 - accuracy: 0.9899INFO:tensorflow:Assets written to: /content/drive/MyDrive/Mask_models/vgg16/assets\n",
+            "253/253 [==============================] - 90s 355ms/step - loss: 0.0307 - accuracy: 0.9899 - val_loss: 0.0414 - val_accuracy: 0.9855\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot training & validation loss/accuracy values\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "fig,ax = plt.subplots(ncols=2, figsize=(18,5))\n",
+        "\n",
+        "ax[0].plot(range(1,26),\n",
+        "           history.history['loss']+history2.history['loss'], \n",
+        "           label='training loss',\n",
+        "           color=\"brown\", \n",
+        "           marker=\"o\")\n",
+        "ax[0].plot(range(1,26),\n",
+        "           history.history['val_loss']+history2.history['val_loss'], \n",
+        "           label='validation loss',\n",
+        "           color=\"green\", \n",
+        "           marker=\"o\")\n",
+        "\n",
+        "ax[0].set_xlabel(\"epochs\", fontsize = 14)\n",
+        "\n",
+        "ax[0].set_ylabel(\"loss\",\n",
+        "              color=\"red\",\n",
+        "              fontsize=14)\n",
+        "ax[0].legend(loc='best')\n",
+        "ax[0].grid(True)\n",
+        "\n",
+        "# ax[1]=ax.twinx()\n",
+        "ax[1].plot(range(1,26),\n",
+        "           history.history['accuracy']+history2.history['accuracy'],\n",
+        "         label='training accuracy',\n",
+        "        #  color=\"blue\",\n",
+        "         marker=\"o\")\n",
+        "ax[1].plot(range(1,26),\n",
+        "           history.history['val_accuracy']+history2.history['val_accuracy'],\n",
+        "         label='validation accuracy',\n",
+        "        #  color=\"blue\",\n",
+        "         marker=\"o\")\n",
+        "\n",
+        "ax[1].set_xlabel(\"epochs\", fontsize = 14)\n",
+        "\n",
+        "ax[1].set_ylabel(\"Accuracy\",color=\"blue\",fontsize=14)\n",
+        "\n",
+        "ax[1].legend(loc='best')\n",
+        "ax[1].grid(True)\n",
+        "\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "MOnz4qCBcJVU",
+        "outputId": "5321280f-a0b9-4bf6-c326-7b977de4a598"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1296x360 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "best_model = tf.keras.models.load_model('/content/drive/MyDrive/Mask_models/vgg16')"
+      ],
+      "metadata": {
+        "id": "9dHGk_1ZbQup"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "eval = best_model.evaluate(val_generator)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fMiC40RZbgUM",
+        "outputId": "0bcea3bb-b01f-47b4-b709-40f72fa1c9df"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "29/29 [==============================] - 10s 262ms/step - loss: 0.0458 - accuracy: 0.9866\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Evaluation Accuracy : {:.1f}%'.format(100*eval[1]),'\\nEvaluation Loss : {:.6f}'.format(eval[0]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cmeq_DwqGIBk",
+        "outputId": "e073ec11-f5f9-4ff5-86c7-4f042dae86ff"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Evaluation Accuracy : 98.7% \n",
+            "Evaluation Loss : 0.045850\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fJ-ZtU84r66Z"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import classification_report, confusion_matrix\n",
+        "import numpy as np\n",
+        "\n",
+        "y_test = val_generator.classes\n",
+        "x_test = val_generator.filepaths\n",
+        "y_pred = []\n",
+        "\n",
+        "def prepare(impath):\n",
+        "  img = cv2.imread(impath)\n",
+        "  img = cv2.resize(img,(128,128))\n",
+        "  return img.reshape(-1,128,128,3)\n",
+        "\n",
+        "for i in x_test:\n",
+        "  img = prepare(i)\n",
+        "  pred = best_model.predict(img)\n",
+        "  # print(pred)\n",
+        "  y_pred.append(np.argmax(pred))\n",
+        "\n",
+        "y_pr = np.array(y_pred)\n",
+        "# print classification report"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 0 -> mask_weared_incorrect\n",
+        "# 1 -> with_mask\n",
+        "# 2 -> without_mask\n",
+        "\n",
+        "print(classification_report(y_test, y_pr))\n",
+        "print('\\nConfusion matrix: \\n')\n",
+        "print(confusion_matrix(y_test, y_pr))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t5WYvjKkptPU",
+        "outputId": "c12a1b72-dd8b-4da6-c59e-c01a350b5bb7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       1.00      0.01      0.03       299\n",
+            "           1       0.46      1.00      0.63       300\n",
+            "           2       1.00      0.81      0.89       299\n",
+            "\n",
+            "    accuracy                           0.61       898\n",
+            "   macro avg       0.82      0.61      0.52       898\n",
+            "weighted avg       0.82      0.61      0.52       898\n",
+            "\n",
+            "\n",
+            "Confusion matrix: \n",
+            "\n",
+            "[[  4 295   0]\n",
+            " [  0 300   0]\n",
+            " [  0  58 241]]\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "Copy of Final Task.ipynb",
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file