Face_Detection_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yFvEK1mra4R9"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.applications.vgg16 import VGG16,preprocess_input\n",
        "from keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from keras.optimizers import Adam,SGD\n",
        "import os\n",
        "import cv2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install opendatasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3EafVgONa7De",
        "outputId": "58e38de2-9525-4e5d-b696-2e064d9b88bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting opendatasets\n",
            "  Downloading opendatasets-0.1.22-py3-none-any.whl.metadata (9.2 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.66.5)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.6.17)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.7)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2024.8.30)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.32.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.2.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle->opendatasets) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.10)\n",
            "Downloading opendatasets-0.1.22-py3-none-any.whl (15 kB)\n",
            "Installing collected packages: opendatasets\n",
            "Successfully installed opendatasets-0.1.22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import opendatasets\n",
        "opendatasets.download(\"https://www.kaggle.com/datasets/omkargurav/face-mask-dataset\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwgWMicIbFs7",
        "outputId": "18bef484-40e6-4513-afea-c8e91885d4a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: waris hayat\n",
            "Your Kaggle Key: ··········\n",
            "Dataset URL: https://www.kaggle.com/datasets/omkargurav/face-mask-dataset\n",
            "Downloading face-mask-dataset.zip to ./face-mask-dataset\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 163M/163M [00:01<00:00, 143MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data = []\n",
        "categories = [\"with_mask\",\"without_mask\"]\n",
        "for cateogory in categories:\n",
        "    path = os.path.join(\"data\",cateogory)\n",
        "    labels = categories.index(cateogory)\n",
        "\n",
        "    for img in os.listdir(path):\n",
        "        image = os.path.join(path,img)\n",
        "        image = cv2.imread(image)\n",
        "        image = cv2.resize(image, (224, 224))\n",
        "\n",
        "        Data.append([image,labels])"
      ],
      "metadata": {
        "id": "ybg8rebGbKyC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "random.shuffle(Data)"
      ],
      "metadata": {
        "id": "NZtpS-89bcqw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Data = Data[:2000]\n",
        "\n",
        "len(Data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9M3veNbNbgcL",
        "outputId": "ceb8d994-118a-4eff-af81-b542f552f315"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2000"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X = []\n",
        "y = []\n",
        "\n",
        "for features,labels in Data:\n",
        "    X.append(features)\n",
        "    y.append(labels)\n"
      ],
      "metadata": {
        "id": "ejpz_ewFbidy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "df=pd.DataFrame()\n",
        "\n",
        "df[\"image_path\"] = X\n",
        "df[\"label\"] = y\n",
        "\n",
        "df[\"label\"].value_counts().plot(kind=\"bar\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "W58UaoE0blpK",
        "outputId": "bf035203-e3a4-473a-d330-461d8c30609c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='label'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "\n",
        "#rescale the image\n",
        "X = X/255"
      ],
      "metadata": {
        "id": "oo8FfBNrbn9I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "CSHr9H53brn5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vgg = VGG16()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpQ5zzYkbt1P",
        "outputId": "0fa2c30e-41aa-4a04-f03e-4b84b73aea0f"
      },
      "execution_count": null,
      "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.h5\n",
            "\u001b[1m553467096/553467096\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Model = Sequential()\n",
        "for layer in vgg.layers[:-1]:\n",
        "    Model.add(layer)"
      ],
      "metadata": {
        "id": "LHj_N0JabwI3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vgg.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "Z_orXEs1b0uG",
        "outputId": "2ef32ca4-b656-49d2-cdc1-a6e20560a237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"vgg16\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"vgg16\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │           \u001b[38;5;34m1,792\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m73,856\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m1,180,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc1 (\u001b[38;5;33mDense\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │     \u001b[38;5;34m102,764,544\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc2 (\u001b[38;5;33mDense\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │      \u001b[38;5;34m16,781,312\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ predictions (\u001b[38;5;33mDense\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1000\u001b[0m)                │       \u001b[38;5;34m4,097,000\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │     <span style=\"color: #00af00; text-decoration-color: #00af00\">102,764,544</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │      <span style=\"color: #00af00; text-decoration-color: #00af00\">16,781,312</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ predictions (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1000</span>)                │       <span style=\"color: #00af00; text-decoration-color: #00af00\">4,097,000</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m138,357,544\u001b[0m (527.79 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">138,357,544</span> (527.79 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m138,357,544\u001b[0m (527.79 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">138,357,544</span> (527.79 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in Model.layers:\n",
        "    layer.trainable = False"
      ],
      "metadata": {
        "id": "cWUwJfYAb292"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "id": "vPnlUjOhb9MP",
        "outputId": "860f8d14-ac49-4a62-c42e-1dcc3f08b39e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │           \u001b[38;5;34m1,792\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m73,856\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m1,180,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc1 (\u001b[38;5;33mDense\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │     \u001b[38;5;34m102,764,544\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc2 (\u001b[38;5;33mDense\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │      \u001b[38;5;34m16,781,312\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │     <span style=\"color: #00af00; text-decoration-color: #00af00\">102,764,544</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │      <span style=\"color: #00af00; text-decoration-color: #00af00\">16,781,312</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m134,260,544\u001b[0m (512.16 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">134,260,544</span> (512.16 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m134,260,544\u001b[0m (512.16 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">134,260,544</span> (512.16 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Model.add(Dense(1,activation=\"sigmoid\")) #output layers\n",
        "\n",
        "Model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "TrjbP4picBoo",
        "outputId": "dcb6c900-a5b0-4365-cd1b-7525e257d5c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │           \u001b[38;5;34m1,792\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m73,856\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m1,180,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc1 (\u001b[38;5;33mDense\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │     \u001b[38;5;34m102,764,544\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc2 (\u001b[38;5;33mDense\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │      \u001b[38;5;34m16,781,312\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │           \u001b[38;5;34m4,097\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │     <span style=\"color: #00af00; text-decoration-color: #00af00\">102,764,544</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ fc2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │      <span style=\"color: #00af00; text-decoration-color: #00af00\">16,781,312</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,097</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m134,264,641\u001b[0m (512.18 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">134,264,641</span> (512.18 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m4,097\u001b[0m (16.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,097</span> (16.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m134,260,544\u001b[0m (512.16 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">134,260,544</span> (512.16 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Model.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "ehjNRBgHcPFY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=Model.fit(X_train,y_train,epochs=5,validation_data=(X_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJ6d-w3dcEzS",
        "outputId": "1d7a8dcb-5c34-4147-de9d-3b7486c63792"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1274s\u001b[0m 29s/step - accuracy: 0.5527 - loss: 0.7463 - val_accuracy: 0.7733 - val_loss: 0.5001\n",
            "Epoch 2/5\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1275s\u001b[0m 29s/step - accuracy: 0.8080 - loss: 0.4703 - val_accuracy: 0.8817 - val_loss: 0.3877\n",
            "Epoch 3/5\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1278s\u001b[0m 29s/step - accuracy: 0.8774 - loss: 0.3727 - val_accuracy: 0.8783 - val_loss: 0.3463\n",
            "Epoch 4/5\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1282s\u001b[0m 29s/step - accuracy: 0.8919 - loss: 0.3308 - val_accuracy: 0.8967 - val_loss: 0.3039\n",
            "Epoch 5/5\n",
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1281s\u001b[0m 29s/step - accuracy: 0.9045 - loss: 0.3028 - val_accuracy: 0.8867 - val_loss: 0.3061\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " import pickle\n",
        " pickle.dump(Model,open(\"model.pkl\",\"wb\"))\n",
        " pickle.dump(history,open(\"history.pkl\",\"wb\"))"
      ],
      "metadata": {
        "id": "y_ffipm2cIXa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "from google.colab.patches import cv2_imshow"
      ],
      "metadata": {
        "id": "BsXBt66n2nFu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cap = cv2.VideoCapture(0)"
      ],
      "metadata": {
        "id": "LCNX2ecp2-ww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "while True:\n",
        "  ret,frame = cap.read()\n",
        "\n",
        "  #take photo and predict the person wearing mask or not\n",
        "\n",
        "  cv2_imshow(frame)\n",
        "\n",
        "  if cv2.waitKey(1) & 0xFF == ord(\"x\"):\n",
        "    break\n",
        "cap.release()\n",
        "cv2.destroyAllWindows()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "Kw7hOd6f3moF",
        "outputId": "36f61646-a060-46fd-b798-875db7e65e95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'NoneType' object has no attribute 'clip'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-90-e01110a189ba>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0;31m#take photo and predict the person wearing mask or not\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m   \u001b[0mcv2_imshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;36m0xFF\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/patches/__init__.py\u001b[0m in \u001b[0;36mcv2_imshow\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m     16\u001b[0m       \u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0man\u001b[0m \u001b[0mNxM\u001b[0m \u001b[0mBGRA\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m   \"\"\"\n\u001b[0;32m---> 18\u001b[0;31m   \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m255\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'uint8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m   \u001b[0;31m# cv2 stores colors as BGR; convert to RGB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'clip'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AfFnhJUuEYNJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}