model_building.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "model_building",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNSSiJNqj/XMAxUmDJBqN0Q",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/muhammadghazyy/mask_detector/blob/main/model_building.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cItzEx9gdcuS"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
        "from tensorflow.keras.applications import EfficientNetB7\n",
        "from tensorflow.keras.applications import MobileNetV2\n",
        "\n",
        "from google.colab import files"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNB38HqCrCuZ",
        "outputId": "29dca02f-a519-4c65-ddf9-9f62531fe243"
      },
      "source": [
        "print('GPU available' if tf.config.list_physical_devices('GPU') else 'GPU is not available')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU available\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-XJ9PiWsXSe"
      },
      "source": [
        "##Kaggle Errands\n",
        "https://www.kaggle.com/ashishjangra27/face-mask-12k-images-dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "id": "E2oy0n34sbVz",
        "outputId": "4e10b187-210e-4dbc-ccd3-84e4f76d0d24"
      },
      "source": [
        "#upload kaggle.json\n",
        "files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e5fba177-dcb1-4fa5-ab0c-3490ce7c0d63\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e5fba177-dcb1-4fa5-ab0c-3490ce7c0d63\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"muhammadghazy\",\"key\":\"95167f954e059f51836df3fffe7c492a\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjxhDjCxspge"
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpm9dGzOsr_s",
        "outputId": "92cc9f78-34ed-4a76-b4ef-c28ebe4927c8"
      },
      "source": [
        "!kaggle datasets download -d ashishjangra27/face-mask-12k-images-dataset"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading face-mask-12k-images-dataset.zip to /content\n",
            " 96% 318M/330M [00:03<00:00, 95.8MB/s]\n",
            "100% 330M/330M [00:03<00:00, 106MB/s] \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IN0gHajbtLSA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64e7b83a-fff4-45d4-c729-945ee9dc9d8a"
      },
      "source": [
        "!unzip face-mask-12k-images-dataset.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1855.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1856.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1857.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1858.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1859.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1860.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1861.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1862.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1863.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1864.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1865.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1866.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1867.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1868.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1869.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1870.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1871.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1872.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1873.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1874.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1875.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1876.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1877.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1878.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1879.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/188.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1880.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1881.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1882.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1883.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1884.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1885.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1886.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1887.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1888.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1889.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/189.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1890.png  \n",
            "  inflating: Face Mask Dataset/Train/WithoutMask/1891.png  \n",
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            "  inflating: Face Mask Dataset/Validation/WithoutMask/970.png  \n",
            "  inflating: Face Mask Dataset/Validation/WithoutMask/971.png  \n",
            "  inflating: Face Mask Dataset/Validation/WithoutMask/974.png  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1Jw8abQwPZh"
      },
      "source": [
        "##Initial Parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OnKij2ywOjE"
      },
      "source": [
        "input_shape = (120,120,3)\n",
        "batch_size = 32\n",
        "epochs = 30\n",
        "learn_rate = 0.001\n",
        "adam = tf.keras.optimizers.Adam(learning_rate=learn_rate, epsilon=False , amsgrad=False)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xEWXDW0ttYOV"
      },
      "source": [
        "## Initiating Training and Validation Sets\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qk-i3KSqtXOA"
      },
      "source": [
        "train_dir = '/content/Face Mask Dataset/Train'\n",
        "test_dir = '/content/Face Mask Dataset/Test'\n",
        "val_dir = '/content/Face Mask Dataset/Validation'"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "IiuEe2j-t0mp",
        "outputId": "b4d61543-e4be-454a-fb1e-2a6932bb181d"
      },
      "source": [
        "tf.keras.preprocessing.image.load_img(test_dir+'/WithMask/1163.png')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=36x36 at 0x7F2E2AD80290>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZXAnBLavSWv"
      },
      "source": [
        "datagen = ImageDataGenerator(\n",
        "    rotation_range = 20,\n",
        "    width_shift_range = 0.15,\n",
        "    height_shift_range = 0.15,\n",
        "    shear_range = 0.15,\n",
        "    zoom_range = 0.2,\n",
        "    brightness_range = [0.4,1.4],\n",
        "    horizontal_flip= True,\n",
        "    vertical_flip = True,\n",
        "    fill_mode='nearest'\n",
        ")\n",
        "\n",
        "datagen_val , datagen_test = ImageDataGenerator(),ImageDataGenerator()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tt9HriFQwGyg",
        "outputId": "621b12bd-ff00-4fb2-eefd-cddeb390a50d"
      },
      "source": [
        "train_generator = datagen.flow_from_directory(\n",
        "    directory = train_dir,\n",
        "    class_mode = 'sparse',\n",
        "    target_size=(120,120),\n",
        "    batch_size = 32,\n",
        "    shuffle = True,\n",
        "    interpolation = 'nearest'\n",
        ")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 10000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkzjfWW2w-fS",
        "outputId": "999b87dc-2ad6-45a4-c02b-1ef50fe90515"
      },
      "source": [
        "val_generator = datagen_val.flow_from_directory(\n",
        "    directory = val_dir,\n",
        "    class_mode = 'sparse',\n",
        "    target_size = (120,120),\n",
        "    batch_size = 32,\n",
        "    shuffle = True,\n",
        "    interpolation = 'nearest'\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 800 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tO3bbqMjxPKH",
        "outputId": "719c33eb-638e-4ec4-c433-99fc64498e43"
      },
      "source": [
        "test_generator = datagen_test.flow_from_directory(\n",
        "    directory = test_dir,\n",
        "    class_mode = 'sparse',\n",
        "    target_size = (120,120),\n",
        "    batch_size = 32,\n",
        "    shuffle = True,\n",
        "    interpolation = 'nearest'\n",
        ")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 992 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BXp82ZCxz9E",
        "outputId": "b49b6682-7166-44ea-c5c4-71d1e0ab0ffc"
      },
      "source": [
        "labels = train_generator.class_indices\n",
        "labels = dict((i,j) for i,j in labels.items())\n",
        "n_classes = len(labels)\n",
        "print(labels)\n",
        "print(n_classes)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'WithMask': 0, 'WithoutMask': 1}\n",
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPZiyymuxsP0"
      },
      "source": [
        "##Model Building using EfficientNetB7"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y89rrWGixvYk",
        "outputId": "009cfabe-194e-4774-c05f-29ea607e8a9a"
      },
      "source": [
        "enb7 = EfficientNetB7(input_shape=input_shape , include_top=False, weights='imagenet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb7_notop.h5\n",
            "258080768/258076736 [==============================] - 6s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STezgBUNye88"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "                             enb7,\n",
        "                             tf.keras.layers.GlobalAveragePooling2D(),\n",
        "                             tf.keras.layers.Dense(256,activation=tf.nn.relu),\n",
        "                             tf.keras.layers.Dense(n_classes,activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3m2OEzTy88e",
        "outputId": "1e40bb8a-fac9-4c4c-d437-609313c771df"
      },
      "source": [
        "model.compile(loss = 'sparse_categorical_crossentropy', optimizer=adam,metrics=['acc'])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "efficientnetb7 (Functional)  (None, 4, 4, 2560)        64097687  \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 2560)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               655616    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 514       \n",
            "=================================================================\n",
            "Total params: 64,753,817\n",
            "Trainable params: 64,443,090\n",
            "Non-trainable params: 310,727\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xrCDKFxzMh4"
      },
      "source": [
        "step_size_train = train_generator.n // train_generator.batch_size\n",
        "step_size_val = val_generator.n // val_generator.batch_size"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKlOo7IhzVRp",
        "outputId": "b9093cbc-f04c-43ec-91a2-227453346085"
      },
      "source": [
        "history = model.fit(\n",
        "    train_generator,\n",
        "    validation_data = val_generator,\n",
        "    epochs = 2,\n",
        "    steps_per_epoch = step_size_train,\n",
        "    validation_steps = step_size_val\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "312/312 [==============================] - 353s 1s/step - loss: 0.0306 - acc: 0.9928 - val_loss: 0.0066 - val_acc: 0.9975\n",
            "Epoch 2/2\n",
            "312/312 [==============================] - 353s 1s/step - loss: 0.0599 - acc: 0.9848 - val_loss: 0.0074 - val_acc: 0.9987\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "jOF5ir5i4-jb",
        "outputId": "93c4a1cc-e9e1-47d3-c0a4-5ea097ee047d"
      },
      "source": [
        " plt.figure(figsize=(12,5))\n",
        "plt.title('Train and Val Accuracy ')\n",
        "plt.plot(history.history['acc'], label='train')\n",
        "plt.plot(history.history['val_acc'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATGXXYLb7JHS"
      },
      "source": [
        "##Model Building using MobileNetV2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rShDlg697RLx",
        "outputId": "5d6ed806-5ca7-4090-a83e-ddec90cbb243"
      },
      "source": [
        "mnv2 = MobileNetV2(input_shape=(128,128,3),include_top=False,weights='imagenet')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_128_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEJ20c-G7n2u"
      },
      "source": [
        "model2 = tf.keras.Sequential([\n",
        "                             mnv2,\n",
        "                             tf.keras.layers.GlobalAveragePooling2D(),\n",
        "                             tf.keras.layers.Dense(128,activation=tf.nn.relu),\n",
        "                             tf.keras.layers.Dense(n_classes,activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZTWrCvjL76cI",
        "outputId": "3ac34464-6313-4d5c-bdbe-14466d9d0d58"
      },
      "source": [
        "model2.compile(loss = 'sparse_categorical_crossentropy', optimizer=adam,metrics=['acc'])\n",
        "model2.summary()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_128 (Functi (None, 4, 4, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_2 ( (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               163968    \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 2)                 258       \n",
            "=================================================================\n",
            "Total params: 2,422,210\n",
            "Trainable params: 2,388,098\n",
            "Non-trainable params: 34,112\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zj52DHjP74qs"
      },
      "source": [
        "step_size_train = train_generator.n // train_generator.batch_size\n",
        "step_size_val = val_generator.n // val_generator.batch_size"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eP09fFhv7-z8",
        "outputId": "22bd9ca1-a288-41b7-f49c-9a8ef41650b0"
      },
      "source": [
        "history2 = model2.fit(\n",
        "    train_generator,\n",
        "    validation_data = val_generator,\n",
        "    epochs = epochs,\n",
        "    steps_per_epoch = step_size_train,\n",
        "    validation_steps = step_size_val\n",
        ")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "312/312 [==============================] - 90s 177ms/step - loss: 0.1080 - acc: 0.9641 - val_loss: 0.3230 - val_acc: 0.9613\n",
            "Epoch 2/30\n",
            "312/312 [==============================] - 55s 176ms/step - loss: 0.0527 - acc: 0.9840 - val_loss: 0.1506 - val_acc: 0.9900\n",
            "Epoch 3/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0331 - acc: 0.9903 - val_loss: 0.8798 - val_acc: 0.9237\n",
            "Epoch 4/30\n",
            "312/312 [==============================] - 56s 178ms/step - loss: 0.0311 - acc: 0.9903 - val_loss: 0.7671 - val_acc: 0.9175\n",
            "Epoch 5/30\n",
            "312/312 [==============================] - 54s 174ms/step - loss: 0.0248 - acc: 0.9919 - val_loss: 0.1110 - val_acc: 0.9900\n",
            "Epoch 6/30\n",
            "312/312 [==============================] - 55s 174ms/step - loss: 0.0174 - acc: 0.9943 - val_loss: 0.0704 - val_acc: 0.9937\n",
            "Epoch 7/30\n",
            "312/312 [==============================] - 54s 172ms/step - loss: 0.0230 - acc: 0.9936 - val_loss: 0.3829 - val_acc: 0.9712\n",
            "Epoch 8/30\n",
            "312/312 [==============================] - 54s 172ms/step - loss: 0.0202 - acc: 0.9935 - val_loss: 0.0657 - val_acc: 0.9962\n",
            "Epoch 9/30\n",
            "312/312 [==============================] - 53s 171ms/step - loss: 0.0255 - acc: 0.9906 - val_loss: 0.0545 - val_acc: 0.9912\n",
            "Epoch 10/30\n",
            "312/312 [==============================] - 53s 171ms/step - loss: 0.0169 - acc: 0.9947 - val_loss: 0.0725 - val_acc: 0.9850\n",
            "Epoch 11/30\n",
            "312/312 [==============================] - 53s 171ms/step - loss: 0.0195 - acc: 0.9943 - val_loss: 0.0995 - val_acc: 0.9875\n",
            "Epoch 12/30\n",
            "312/312 [==============================] - 53s 171ms/step - loss: 0.0235 - acc: 0.9927 - val_loss: 0.1528 - val_acc: 0.9875\n",
            "Epoch 13/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0178 - acc: 0.9943 - val_loss: 0.0522 - val_acc: 0.9912\n",
            "Epoch 14/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0183 - acc: 0.9947 - val_loss: 0.0632 - val_acc: 0.9912\n",
            "Epoch 15/30\n",
            "312/312 [==============================] - 53s 171ms/step - loss: 0.0197 - acc: 0.9936 - val_loss: 0.0195 - val_acc: 0.9987\n",
            "Epoch 16/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0145 - acc: 0.9952 - val_loss: 0.0013 - val_acc: 0.9987\n",
            "Epoch 17/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0162 - acc: 0.9944 - val_loss: 0.1053 - val_acc: 0.9912\n",
            "Epoch 18/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0201 - acc: 0.9927 - val_loss: 0.3987 - val_acc: 0.9712\n",
            "Epoch 19/30\n",
            "312/312 [==============================] - 53s 171ms/step - loss: 0.0145 - acc: 0.9950 - val_loss: 1.1631 - val_acc: 0.8925\n",
            "Epoch 20/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0169 - acc: 0.9947 - val_loss: 2.1618e-06 - val_acc: 1.0000\n",
            "Epoch 21/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0140 - acc: 0.9946 - val_loss: 0.0519 - val_acc: 0.9900\n",
            "Epoch 22/30\n",
            "312/312 [==============================] - 53s 169ms/step - loss: 0.0126 - acc: 0.9960 - val_loss: 3.1406e-04 - val_acc: 1.0000\n",
            "Epoch 23/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0099 - acc: 0.9969 - val_loss: 0.0081 - val_acc: 0.9987\n",
            "Epoch 24/30\n",
            "312/312 [==============================] - 53s 169ms/step - loss: 0.0133 - acc: 0.9956 - val_loss: 0.2176 - val_acc: 0.9775\n",
            "Epoch 25/30\n",
            "312/312 [==============================] - 53s 169ms/step - loss: 0.0121 - acc: 0.9960 - val_loss: 0.1712 - val_acc: 0.9825\n",
            "Epoch 26/30\n",
            "312/312 [==============================] - 53s 169ms/step - loss: 0.0127 - acc: 0.9957 - val_loss: 0.0027 - val_acc: 0.9987\n",
            "Epoch 27/30\n",
            "312/312 [==============================] - 53s 169ms/step - loss: 0.0197 - acc: 0.9940 - val_loss: 2.9131 - val_acc: 0.8988\n",
            "Epoch 28/30\n",
            "312/312 [==============================] - 53s 170ms/step - loss: 0.0176 - acc: 0.9944 - val_loss: 2.0881 - val_acc: 0.5962\n",
            "Epoch 29/30\n",
            "312/312 [==============================] - 52s 168ms/step - loss: 0.0125 - acc: 0.9958 - val_loss: 0.0833 - val_acc: 0.9950\n",
            "Epoch 30/30\n",
            "312/312 [==============================] - 53s 169ms/step - loss: 0.0099 - acc: 0.9967 - val_loss: 0.0421 - val_acc: 0.9975\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "crJAM8zq8qDw",
        "outputId": "751cd6e6-ff21-4d6c-b366-0d5587685ace"
      },
      "source": [
        "plt.figure(figsize=(12,5))\n",
        "plt.title('Train and Val Accuracy ')\n",
        "plt.plot( history2.history['acc'],'r', label='Training Acc')\n",
        "plt.plot( history2.history['val_acc'],'b',  label='Validation Acc')\n",
        "plt.legend(loc=0)\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrAnwaH1flCi"
      },
      "source": [
        "## Another MobileNetV2 Building but With Non-Trainable parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xq-nUpdIfkXM",
        "outputId": "4f41658f-1274-4d39-cb47-626002b860b9"
      },
      "source": [
        "mnv2nt = MobileNetV2(input_shape=(128,128,3),include_top=False,weights='imagenet')\n",
        "for layer in mnv2nt.layers:\n",
        "  layer.trainable = False\n",
        "mnv2nt.summary()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenetv2_1.00_128\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 64, 64, 32)   864         input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 64, 64, 32)   128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 64, 64, 32)   0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 64, 64, 32)   288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 64, 64, 32)   128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 64, 64, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 64, 64, 16)   512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 64, 64, 16)   64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 64, 64, 96)   1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 64, 64, 96)   384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 64, 64, 96)   0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 65, 65, 96)   0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 32, 32, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 32, 32, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 32, 32, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 32, 32, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 32, 32, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 32, 32, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 32, 32, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 32, 32, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 32, 32, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 32, 32, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 32, 32, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 32, 32, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 32, 32, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 33, 33, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 16, 16, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 16, 16, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 16, 16, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 16, 16, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 16, 16, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 16, 16, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 16, 16, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 16, 16, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 16, 16, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 16, 16, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 16, 16, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 16, 16, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 16, 16, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 16, 16, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 17, 17, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 8, 8, 192)    1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 8, 8, 192)    768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 8, 8, 192)    0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 8, 8, 64)     12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 8, 8, 64)     256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 8, 8, 384)    24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 8, 8, 64)     24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 8, 8, 64)     256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 8, 8, 64)     0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 8, 8, 384)    24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 8, 8, 64)     24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 8, 8, 64)     256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 8, 8, 64)     0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 8, 8, 384)    24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 8, 8, 64)     24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 8, 8, 64)     256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 8, 8, 64)     0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 8, 8, 384)    24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 8, 8, 384)    1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 8, 8, 384)    0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 8, 8, 384)    3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 8, 8, 384)    1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 8, 8, 384)    0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 8, 8, 96)     36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 8, 8, 96)     384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 8, 8, 576)    55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 8, 8, 96)     55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 8, 8, 96)     384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 8, 8, 96)     0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 8, 8, 576)    55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 8, 8, 96)     55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 8, 8, 96)     384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 8, 8, 96)     0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 8, 8, 576)    55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 9, 9, 576)    0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 4, 4, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 4, 4, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 4, 4, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 4, 4, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 4, 4, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 4, 4, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 4, 4, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 4, 4, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 4, 4, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 4, 4, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 4, 4, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 4, 4, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 4, 4, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 4, 4, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 4, 4, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 4, 4, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 4, 4, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 4, 4, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 4, 4, 1280)   0           Conv_1_bn[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 2,257,984\n",
            "Trainable params: 0\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56SZUwtphxta",
        "outputId": "b8963d4a-fe1a-46d7-bbaa-2d7abeb6739a"
      },
      "source": [
        "last_layer = mnv2nt.get_layer('out_relu')\n",
        "print('last layer output shape: ', last_layer.output_shape)\n",
        "last_output = last_layer.output"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "last layer output shape:  (None, 4, 4, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AOHObiv7h7wJ",
        "outputId": "5e6b6031-eafb-4f0f-caec-09f4f7e93fd2"
      },
      "source": [
        "x = tf.keras.layers.Flatten()(last_output)\n",
        "x = tf.keras.layers.Dense(256, activation=tf.nn.relu)(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Dense(1,activation='softmax')(x)\n",
        "\n",
        "model3 = tf.keras.Model(mnv2nt.input,x)\n",
        "model3.compile(\n",
        "    optimizer = adam,\n",
        "    loss= \"sparse_categorical_crossentropy\",\n",
        "    metrics=['acc']\n",
        ")\n",
        "\n",
        "model3.summary()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 64, 64, 32)   864         input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 64, 64, 32)   128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 64, 64, 32)   0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 64, 64, 32)   288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 64, 64, 32)   128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 64, 64, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 64, 64, 16)   512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 64, 64, 16)   64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 64, 64, 96)   1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 64, 64, 96)   384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 64, 64, 96)   0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 65, 65, 96)   0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 32, 32, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 32, 32, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 32, 32, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 32, 32, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 32, 32, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 32, 32, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 32, 32, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 32, 32, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 32, 32, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 32, 32, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 32, 32, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 32, 32, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 32, 32, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 33, 33, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 16, 16, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 16, 16, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 16, 16, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 16, 16, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 16, 16, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 16, 16, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 16, 16, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 16, 16, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 16, 16, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 16, 16, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 16, 16, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 16, 16, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 16, 16, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 16, 16, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 17, 17, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 8, 8, 192)    1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 8, 8, 192)    768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 8, 8, 192)    0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 8, 8, 64)     12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 8, 8, 64)     256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 8, 8, 384)    24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 8, 8, 64)     24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 8, 8, 64)     256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 8, 8, 64)     0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 8, 8, 384)    24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 8, 8, 64)     24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 8, 8, 64)     256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 8, 8, 64)     0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 8, 8, 384)    24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 8, 8, 64)     24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 8, 8, 64)     256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 8, 8, 64)     0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 8, 8, 384)    24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 8, 8, 384)    1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 8, 8, 384)    0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 8, 8, 384)    3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 8, 8, 384)    1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 8, 8, 384)    0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 8, 8, 96)     36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 8, 8, 96)     384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 8, 8, 576)    55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 8, 8, 96)     55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 8, 8, 96)     384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 8, 8, 96)     0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 8, 8, 576)    55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 8, 8, 96)     55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 8, 8, 96)     384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 8, 8, 96)     0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 8, 8, 576)    55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 9, 9, 576)    0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 4, 4, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 4, 4, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 4, 4, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 4, 4, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 4, 4, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 4, 4, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 4, 4, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 4, 4, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 4, 4, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 4, 4, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 4, 4, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 4, 4, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 4, 4, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 4, 4, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 4, 4, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 4, 4, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 4, 4, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 4, 4, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 4, 4, 1280)   0           Conv_1_bn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten_4 (Flatten)             (None, 20480)        0           out_relu[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_12 (Dense)                (None, 256)          5243136     flatten_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 256)          0           dense_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_13 (Dense)                (None, 1)            257         dropout_3[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 7,501,377\n",
            "Trainable params: 5,243,393\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5IItjJfjUGk"
      },
      "source": [
        "history3 = model3.fit(\n",
        "    train_generator,\n",
        "    validation_data = val_generator,\n",
        "    epochs = epochs,\n",
        "    steps_per_epoch = step_size_train,\n",
        "    validation_steps = step_size_val\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GBneQHKd5Wr9"
      },
      "source": [
        "## Saving model into HDF5 File Format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cuCvYn-85YIq"
      },
      "source": [
        "model.save('mask_enb7.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JeagmE5t8w0A",
        "outputId": "47063f88-e592-4eb0-b790-3b9a3807dddb"
      },
      "source": [
        "model2.save('mask_mnv2.h5')"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvko9DMq9Eig"
      },
      "source": [
        "The model file is way too big. The model with mobilenet is around 500MB and the efficientnet one is around 700MB. The file size is too big to be uploaded to GitHub."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSked2SRkBlc"
      },
      "source": [
        "model3.save('mask_mnv2nt.h5')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}