FaceMaskDetection_(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FaceMaskDetection .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgc-wsIYu9jM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "1f808e5a-7781-4699-8ee8-6ade78492f68"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8c2Haaxu9hf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
        "from tensorflow.keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHIlZo_vu9bZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# re-size all the images to this\n",
        "IMAGE_SIZE = [224, 224]\n",
        "\n",
        "train_path = 'gdrive/My Drive/MaskData1/Train'\n",
        "valid_path = 'gdrive/My Drive/MaskData1/Test'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EY1YFCRB0ju2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6bdb201a-1fb1-41a0-b1b4-fa5dfa562e84"
      },
      "source": [
        "# useful for getting number of output classes\n",
        "folders = glob('gdrive/My Drive/MaskData1/Train/*')\n",
        "print(len(folders))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PISL1W1N1NZT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "4c3a5793-3f16-4cc5-81e4-04776a2d6579"
      },
      "source": [
        "resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMbsGhaL1lcq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# don't train existing weights\n",
        "for layer in resnet.layers:\n",
        "    layer.trainable = False"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CEyES2j1puY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# our layers - you can add more if you want\n",
        "x = Flatten()(resnet.output)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ltg8snEK1w3-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = Dense(len(folders), activation='softmax')(x)\n",
        "\n",
        "# create a model object\n",
        "model = Model(inputs=resnet.input, outputs=prediction)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mPF3feR13IS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "489f48e2-0a2c-4cd8-a25c-797dc51b5d5c"
      },
      "source": [
        "# view the structure of the model\n",
        "model.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 100352)       0           conv5_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 2)            200706      flatten[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 23,788,418\n",
            "Trainable params: 200,706\n",
            "Non-trainable params: 23,587,712\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdYROM4w16ic",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# tell the model what cost and optimization method to use\n",
        "model.compile(\n",
        "  loss='categorical_crossentropy',\n",
        "  optimizer='adam',\n",
        "  metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8enFuzrD2Dts",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvcs40x12LI7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6aa752de-e256-4d14-bfff-d7b700134fef"
      },
      "source": [
        "# Make sure you provide the same target size as initialied for the image size\n",
        "training_set = train_datagen.flow_from_directory('gdrive/My Drive/MaskData1/Train',\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'categorical')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1194 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COSpF8Pm2Tdb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1357538f-d69b-4ed8-dd06-8412dd1773ed"
      },
      "source": [
        "test_set = test_datagen.flow_from_directory('gdrive/My Drive/MaskData1/Test',\n",
        "                                            target_size = (224, 224),\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'categorical')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 244 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGWpSNcP2atS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "94a064f5-86d0-41c6-ff92-f7e4334d27f9"
      },
      "source": [
        "# fit the model\n",
        "r = model.fit_generator(\n",
        "  training_set,\n",
        "  validation_data=test_set,\n",
        "  epochs=50,\n",
        "  steps_per_epoch=len(training_set),\n",
        "  validation_steps=len(test_set)\n",
        ")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-14-869679ccb0bc>:7: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/50\n",
            "38/38 [==============================] - 726s 19s/step - loss: 2.3197 - accuracy: 0.6064 - val_loss: 0.5341 - val_accuracy: 0.7623\n",
            "Epoch 2/50\n",
            "38/38 [==============================] - 19s 494ms/step - loss: 0.6135 - accuracy: 0.6868 - val_loss: 0.4609 - val_accuracy: 0.7992\n",
            "Epoch 3/50\n",
            "38/38 [==============================] - 19s 490ms/step - loss: 0.5203 - accuracy: 0.7546 - val_loss: 0.3351 - val_accuracy: 0.8770\n",
            "Epoch 4/50\n",
            "38/38 [==============================] - 18s 486ms/step - loss: 0.4008 - accuracy: 0.8224 - val_loss: 0.3462 - val_accuracy: 0.8402\n",
            "Epoch 5/50\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.3398 - accuracy: 0.8576 - val_loss: 0.3301 - val_accuracy: 0.8484\n",
            "Epoch 6/50\n",
            "38/38 [==============================] - 18s 481ms/step - loss: 0.3559 - accuracy: 0.8492 - val_loss: 0.3484 - val_accuracy: 0.8484\n",
            "Epoch 7/50\n",
            "38/38 [==============================] - 18s 486ms/step - loss: 0.4157 - accuracy: 0.7982 - val_loss: 0.3144 - val_accuracy: 0.8934\n",
            "Epoch 8/50\n",
            "38/38 [==============================] - 19s 489ms/step - loss: 0.2632 - accuracy: 0.8836 - val_loss: 0.2629 - val_accuracy: 0.9016\n",
            "Epoch 9/50\n",
            "38/38 [==============================] - 18s 484ms/step - loss: 0.2802 - accuracy: 0.8702 - val_loss: 0.3139 - val_accuracy: 0.8811\n",
            "Epoch 10/50\n",
            "38/38 [==============================] - 18s 482ms/step - loss: 0.3567 - accuracy: 0.8417 - val_loss: 0.5995 - val_accuracy: 0.7295\n",
            "Epoch 11/50\n",
            "38/38 [==============================] - 18s 485ms/step - loss: 0.2930 - accuracy: 0.8794 - val_loss: 0.2543 - val_accuracy: 0.9016\n",
            "Epoch 12/50\n",
            "38/38 [==============================] - 18s 486ms/step - loss: 0.2952 - accuracy: 0.8819 - val_loss: 0.4971 - val_accuracy: 0.8238\n",
            "Epoch 13/50\n",
            "38/38 [==============================] - 18s 479ms/step - loss: 0.3147 - accuracy: 0.8710 - val_loss: 0.4451 - val_accuracy: 0.8115\n",
            "Epoch 14/50\n",
            "38/38 [==============================] - 19s 490ms/step - loss: 0.2165 - accuracy: 0.9162 - val_loss: 0.2636 - val_accuracy: 0.9139\n",
            "Epoch 15/50\n",
            "38/38 [==============================] - 18s 485ms/step - loss: 0.2483 - accuracy: 0.8953 - val_loss: 0.2237 - val_accuracy: 0.9303\n",
            "Epoch 16/50\n",
            "38/38 [==============================] - 18s 485ms/step - loss: 0.2081 - accuracy: 0.9204 - val_loss: 0.2155 - val_accuracy: 0.9385\n",
            "Epoch 17/50\n",
            "38/38 [==============================] - 19s 499ms/step - loss: 0.2846 - accuracy: 0.8886 - val_loss: 0.3049 - val_accuracy: 0.8934\n",
            "Epoch 18/50\n",
            "38/38 [==============================] - 18s 485ms/step - loss: 0.3496 - accuracy: 0.8526 - val_loss: 0.2520 - val_accuracy: 0.9016\n",
            "Epoch 19/50\n",
            "38/38 [==============================] - 18s 484ms/step - loss: 0.1880 - accuracy: 0.9229 - val_loss: 0.2428 - val_accuracy: 0.9180\n",
            "Epoch 20/50\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.1884 - accuracy: 0.9288 - val_loss: 0.6390 - val_accuracy: 0.7582\n",
            "Epoch 21/50\n",
            "38/38 [==============================] - 18s 478ms/step - loss: 0.2085 - accuracy: 0.9129 - val_loss: 0.2444 - val_accuracy: 0.9180\n",
            "Epoch 22/50\n",
            "38/38 [==============================] - 18s 481ms/step - loss: 0.1821 - accuracy: 0.9313 - val_loss: 0.2252 - val_accuracy: 0.9262\n",
            "Epoch 23/50\n",
            "38/38 [==============================] - 18s 480ms/step - loss: 0.1859 - accuracy: 0.9179 - val_loss: 0.2153 - val_accuracy: 0.9221\n",
            "Epoch 24/50\n",
            "38/38 [==============================] - 18s 482ms/step - loss: 0.1473 - accuracy: 0.9447 - val_loss: 0.2268 - val_accuracy: 0.9016\n",
            "Epoch 25/50\n",
            "38/38 [==============================] - 18s 484ms/step - loss: 0.1480 - accuracy: 0.9372 - val_loss: 0.2068 - val_accuracy: 0.9180\n",
            "Epoch 26/50\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.2465 - accuracy: 0.8970 - val_loss: 0.2450 - val_accuracy: 0.9139\n",
            "Epoch 27/50\n",
            "38/38 [==============================] - 18s 484ms/step - loss: 0.1549 - accuracy: 0.9347 - val_loss: 0.2234 - val_accuracy: 0.9180\n",
            "Epoch 28/50\n",
            "38/38 [==============================] - 19s 492ms/step - loss: 0.2046 - accuracy: 0.9179 - val_loss: 0.2345 - val_accuracy: 0.9262\n",
            "Epoch 29/50\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.1365 - accuracy: 0.9439 - val_loss: 0.2246 - val_accuracy: 0.9221\n",
            "Epoch 30/50\n",
            "38/38 [==============================] - 18s 482ms/step - loss: 0.2083 - accuracy: 0.9154 - val_loss: 0.3580 - val_accuracy: 0.8811\n",
            "Epoch 31/50\n",
            "38/38 [==============================] - 19s 495ms/step - loss: 0.2059 - accuracy: 0.9121 - val_loss: 0.5826 - val_accuracy: 0.8402\n",
            "Epoch 32/50\n",
            "38/38 [==============================] - 18s 484ms/step - loss: 0.2788 - accuracy: 0.8844 - val_loss: 0.2032 - val_accuracy: 0.9016\n",
            "Epoch 33/50\n",
            "38/38 [==============================] - 19s 498ms/step - loss: 0.3456 - accuracy: 0.8760 - val_loss: 0.2200 - val_accuracy: 0.9303\n",
            "Epoch 34/50\n",
            "38/38 [==============================] - 18s 483ms/step - loss: 0.1504 - accuracy: 0.9489 - val_loss: 0.2142 - val_accuracy: 0.9303\n",
            "Epoch 35/50\n",
            "38/38 [==============================] - 18s 478ms/step - loss: 0.1488 - accuracy: 0.9464 - val_loss: 0.2296 - val_accuracy: 0.9180\n",
            "Epoch 36/50\n",
            "38/38 [==============================] - 19s 494ms/step - loss: 0.1742 - accuracy: 0.9322 - val_loss: 0.1938 - val_accuracy: 0.9467\n",
            "Epoch 37/50\n",
            "38/38 [==============================] - 18s 486ms/step - loss: 0.1525 - accuracy: 0.9389 - val_loss: 0.2106 - val_accuracy: 0.9344\n",
            "Epoch 38/50\n",
            "38/38 [==============================] - 18s 477ms/step - loss: 0.1189 - accuracy: 0.9615 - val_loss: 0.2075 - val_accuracy: 0.9180\n",
            "Epoch 39/50\n",
            "38/38 [==============================] - 18s 480ms/step - loss: 0.2758 - accuracy: 0.8928 - val_loss: 0.1892 - val_accuracy: 0.9262\n",
            "Epoch 40/50\n",
            "38/38 [==============================] - 19s 491ms/step - loss: 0.2428 - accuracy: 0.9129 - val_loss: 1.2060 - val_accuracy: 0.6066\n",
            "Epoch 41/50\n",
            "38/38 [==============================] - 19s 510ms/step - loss: 0.2799 - accuracy: 0.8945 - val_loss: 0.2405 - val_accuracy: 0.9262\n",
            "Epoch 42/50\n",
            "38/38 [==============================] - 19s 511ms/step - loss: 0.2080 - accuracy: 0.9238 - val_loss: 0.2061 - val_accuracy: 0.9221\n",
            "Epoch 43/50\n",
            "38/38 [==============================] - 19s 503ms/step - loss: 0.1340 - accuracy: 0.9389 - val_loss: 0.2389 - val_accuracy: 0.9303\n",
            "Epoch 44/50\n",
            "38/38 [==============================] - 19s 499ms/step - loss: 0.1022 - accuracy: 0.9648 - val_loss: 0.2163 - val_accuracy: 0.9221\n",
            "Epoch 45/50\n",
            "38/38 [==============================] - 19s 497ms/step - loss: 0.1874 - accuracy: 0.9188 - val_loss: 0.2181 - val_accuracy: 0.9344\n",
            "Epoch 46/50\n",
            "38/38 [==============================] - 19s 497ms/step - loss: 0.1006 - accuracy: 0.9564 - val_loss: 0.2969 - val_accuracy: 0.9016\n",
            "Epoch 47/50\n",
            "38/38 [==============================] - 19s 505ms/step - loss: 0.2060 - accuracy: 0.9188 - val_loss: 0.3611 - val_accuracy: 0.8934\n",
            "Epoch 48/50\n",
            "38/38 [==============================] - 19s 498ms/step - loss: 0.2492 - accuracy: 0.9121 - val_loss: 0.2845 - val_accuracy: 0.9221\n",
            "Epoch 49/50\n",
            "38/38 [==============================] - 19s 511ms/step - loss: 0.1625 - accuracy: 0.9347 - val_loss: 0.5158 - val_accuracy: 0.8770\n",
            "Epoch 50/50\n",
            "38/38 [==============================] - 19s 491ms/step - loss: 0.2293 - accuracy: 0.9112 - val_loss: 0.2187 - val_accuracy: 0.9344\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-N-cZpS2jOj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "e14ae193-d5f3-4c4e-a905-6c285ff60340"
      },
      "source": [
        "# plot the loss\n",
        "plt.plot(r.history['loss'], label='train loss')\n",
        "plt.plot(r.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('LossVal_loss')\n",
        "\n",
        "# plot the accuracy\n",
        "plt.plot(r.history['accuracy'], label='train acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('AccVal_acc')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ikopXSE2oHa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict(test_set)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vtgpg_3g2snU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "bbce5d0f-6822-4711-f59c-1819960fd848"
      },
      "source": [
        "import numpy as np\n",
        "y_pred = np.argmax(y_pred, axis=1)\n",
        "print(y_pred)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 0 0 1 1 1 0 0 1 1 0 0 1 0 1 0 1 0 0\n",
            " 1 1 0 1 0 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0\n",
            " 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1\n",
            " 1 1 1 0 0 0 1 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 0 0 0\n",
            " 0 1 1 0 1 0 1 1 1 0 0 1 0 0 0 0 0 1 1 1 1 0 1 0 1 0 0 0 1 1 1 0 0 0 0 0 1\n",
            " 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 1 1\n",
            " 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 1 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXg9rmYc8rC7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save the Trained Model\n",
        "#from tensorflow.keras.models import load_model\n",
        "\n",
        "model.save('maskModel_resnet50.h5')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYVg0TU084z6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nu3RQScgd2wu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}