face-mask-detection-v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import MobileNetV2\n",
    "from tensorflow.keras.layers import AveragePooling2D\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "from imutils import paths\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "INIT_LR = 1e-4\n",
    "EPOCHS = 20\n",
    "BS = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "labels = []\n",
    "imagePaths = list(paths.list_images('./dataset/'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\nlp_course\\lib\\site-packages\\PIL\\Image.py:960: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
      "  \"Palette images with Transparency expressed in bytes should be \"\n"
     ]
    }
   ],
   "source": [
    "for imagePath in imagePaths:\n",
    "    label = imagePath.split(os.path.sep)[-2]\n",
    "    image = load_img(imagePath, target_size=(224, 224))\n",
    "    image = img_to_array(image)\n",
    "    image = preprocess_input(image)\n",
    "    \n",
    "    data.append(image)\n",
    "    if 'with_mask' in label:\n",
    "        labels.append('with_mask')\n",
    "    else:\n",
    "        labels.append('without_mask')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array(data, dtype=\"float32\")\n",
    "labels = np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = LabelBinarizer()\n",
    "labels = lb.fit_transform(labels)\n",
    "labels = to_categorical(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "(trainX, testX, trainY, testY) = train_test_split(data, labels,test_size=0.20, stratify=labels, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "aug = ImageDataGenerator(\n",
    "\trotation_range=20,\n",
    "\tzoom_range=0.15,\n",
    "\twidth_shift_range=0.2,\n",
    "\theight_shift_range=0.2,\n",
    "\tshear_range=0.15,\n",
    "\thorizontal_flip=True,\n",
    "\tfill_mode=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\nlp_course\\lib\\site-packages\\keras_applications\\mobilenet_v2.py:294: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "  warnings.warn('`input_shape` is undefined or non-square, '\n"
     ]
    }
   ],
   "source": [
    "baseModel = MobileNetV2(weights=\"imagenet\", include_top=False,\n",
    "\tinput_tensor=Input(shape=(224, 224, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "headModel = baseModel.output\n",
    "headModel = AveragePooling2D(pool_size=(7, 7))(headModel)\n",
    "headModel = Flatten(name=\"flatten\")(headModel)\n",
    "headModel = Dense(128, activation=\"relu\")(headModel)\n",
    "headModel = Dropout(0.5)(headModel)\n",
    "headModel = Dense(2, activation=\"softmax\")(headModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=baseModel.input, outputs=headModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in baseModel.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
    "    metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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, 225, 225, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 112, 112, 32) 864         Conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         \n",
      "                                                                 block_2_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         \n",
      "                                                                 block_4_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                \n",
      "                                                                 block_5_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         \n",
      "                                                                 block_7_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                \n",
      "                                                                 block_8_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                \n",
      "                                                                 block_9_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        \n",
      "                                                                 block_11_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               \n",
      "                                                                 block_12_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        \n",
      "                                                                 block_14_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               \n",
      "                                                                 block_15_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 1, 1, 1280)   0           out_relu[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 1280)         0           average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 128)          163968      flatten[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 128)          0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 2)            258         dropout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 2,422,210\n",
      "Trainable params: 164,226\n",
      "Non-trainable params: 2,257,984\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = ModelCheckpoint('model1.h5',\n",
    "                             monitor='val_loss',\n",
    "                             mode='min',\n",
    "                             save_best_only=True,\n",
    "                             verbose=1)\n",
    "\n",
    "earlystop = EarlyStopping(monitor='val_loss',\n",
    "                          min_delta=0,\n",
    "                          patience=9,\n",
    "                          verbose=1,\n",
    "                          restore_best_weights=True\n",
    "                          )\n",
    "callbacks=[checkpoint, earlystop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow_core._api.v2.config' has no attribute 'list_physical_devices'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-817240faa0fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlist_physical_devices\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'GPU'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow_core._api.v2.config' has no attribute 'list_physical_devices'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 95 steps, validate on 767 samples\n",
      "Epoch 1/20\n",
      "94/95 [============================>.] - ETA: 8s - loss: 0.5008 - accuracy: 0.7592 \n",
      "Epoch 00001: val_loss improved from inf to 0.42845, saving model to model1.h5\n",
      "95/95 [==============================] - 802s 8s/step - loss: 0.4987 - accuracy: 0.7597 - val_loss: 0.4284 - val_accuracy: 0.8043\n",
      "Epoch 2/20\n",
      "94/95 [============================>.] - ETA: 6s - loss: 0.2937 - accuracy: 0.8814 \n",
      "Epoch 00002: val_loss improved from 0.42845 to 0.35269, saving model to model1.h5\n",
      "95/95 [==============================] - 675s 7s/step - loss: 0.2933 - accuracy: 0.8817 - val_loss: 0.3527 - val_accuracy: 0.8451\n",
      "Epoch 3/20\n",
      "94/95 [============================>.] - ETA: 6s - loss: 0.2256 - accuracy: 0.9177 \n",
      "Epoch 00003: val_loss improved from 0.35269 to 0.28765, saving model to model1.h5\n",
      "95/95 [==============================] - 665s 7s/step - loss: 0.2257 - accuracy: 0.9166 - val_loss: 0.2877 - val_accuracy: 0.8709\n",
      "Epoch 4/20\n",
      "94/95 [============================>.] - ETA: 7s - loss: 0.2026 - accuracy: 0.9267 \n",
      "Epoch 00004: val_loss did not improve from 0.28765\n",
      "95/95 [==============================] - 695s 7s/step - loss: 0.2024 - accuracy: 0.9272 - val_loss: 0.2912 - val_accuracy: 0.8791\n",
      "Epoch 5/20\n",
      "94/95 [============================>.] - ETA: 7s - loss: 0.1693 - accuracy: 0.9404 \n",
      "Epoch 00005: val_loss did not improve from 0.28765\n",
      "95/95 [==============================] - 699s 7s/step - loss: 0.1703 - accuracy: 0.9397 - val_loss: 0.3299 - val_accuracy: 0.8736\n",
      "Epoch 6/20\n",
      "94/95 [============================>.] - ETA: 7s - loss: 0.1484 - accuracy: 0.9447 \n",
      "Epoch 00006: val_loss did not improve from 0.28765\n",
      "95/95 [==============================] - 710s 7s/step - loss: 0.1475 - accuracy: 0.9453 - val_loss: 0.2900 - val_accuracy: 0.8845\n",
      "Epoch 7/20\n",
      "94/95 [============================>.] - ETA: 7s - loss: 0.1431 - accuracy: 0.9487 \n",
      "Epoch 00007: val_loss improved from 0.28765 to 0.19516, saving model to model1.h5\n",
      "95/95 [==============================] - 693s 7s/step - loss: 0.1441 - accuracy: 0.9483 - val_loss: 0.1952 - val_accuracy: 0.9076\n",
      "Epoch 8/20\n",
      "94/95 [============================>.] - ETA: 7s - loss: 0.1360 - accuracy: 0.9517 \n",
      "Epoch 00008: val_loss did not improve from 0.19516\n",
      "95/95 [==============================] - 692s 7s/step - loss: 0.1370 - accuracy: 0.9512 - val_loss: 0.2372 - val_accuracy: 0.8995\n",
      "Epoch 9/20\n",
      "94/95 [============================>.] - ETA: 7s - loss: 0.1200 - accuracy: 0.9560 \n",
      "Epoch 00009: val_loss did not improve from 0.19516\n",
      "95/95 [==============================] - 693s 7s/step - loss: 0.1194 - accuracy: 0.9562 - val_loss: 0.3772 - val_accuracy: 0.8723\n",
      "Epoch 10/20\n",
      "94/95 [============================>.] - ETA: 7s - loss: 0.1221 - accuracy: 0.9537 \n",
      "Epoch 00010: val_loss did not improve from 0.19516\n",
      "95/95 [==============================] - 695s 7s/step - loss: 0.1241 - accuracy: 0.9525 - val_loss: 0.2556 - val_accuracy: 0.8954\n",
      "Epoch 11/20\n",
      "94/95 [============================>.] - ETA: 7s - loss: 0.1238 - accuracy: 0.9537 \n",
      "Epoch 00011: val_loss did not improve from 0.19516\n",
      "95/95 [==============================] - 713s 8s/step - loss: 0.1233 - accuracy: 0.9542 - val_loss: 0.3324 - val_accuracy: 0.8859\n",
      "Epoch 12/20\n",
      "94/95 [============================>.] - ETA: 6s - loss: 0.1010 - accuracy: 0.9664 \n",
      "Epoch 00012: val_loss did not improve from 0.19516\n",
      "95/95 [==============================] - 687s 7s/step - loss: 0.1011 - accuracy: 0.9657 - val_loss: 0.4240 - val_accuracy: 0.8587\n",
      "Epoch 13/20\n",
      "94/95 [============================>.] - ETA: 6s - loss: 0.0994 - accuracy: 0.9660 \n",
      "Epoch 00013: val_loss did not improve from 0.19516\n",
      "95/95 [==============================] - 664s 7s/step - loss: 0.0986 - accuracy: 0.9664 - val_loss: 0.2532 - val_accuracy: 0.9022\n",
      "Epoch 14/20\n",
      "94/95 [============================>.] - ETA: 6s - loss: 0.1020 - accuracy: 0.9620 \n",
      "Epoch 00014: val_loss did not improve from 0.19516\n",
      "95/95 [==============================] - 689s 7s/step - loss: 0.1021 - accuracy: 0.9618 - val_loss: 0.2469 - val_accuracy: 0.9062\n",
      "Epoch 15/20\n",
      "94/95 [============================>.] - ETA: 6s - loss: 0.1025 - accuracy: 0.9610 \n",
      "Epoch 00015: val_loss did not improve from 0.19516\n",
      "95/95 [==============================] - 666s 7s/step - loss: 0.1024 - accuracy: 0.9611 - val_loss: 0.2593 - val_accuracy: 0.8981\n",
      "Epoch 16/20\n",
      "94/95 [============================>.] - ETA: 6s - loss: 0.0942 - accuracy: 0.9644 \n",
      "Epoch 00016: val_loss did not improve from 0.19516\n",
      "Restoring model weights from the end of the best epoch\n",
      "95/95 [==============================] - 684s 7s/step - loss: 0.0954 - accuracy: 0.9641 - val_loss: 0.3704 - val_accuracy: 0.8764\n",
      "Epoch 00016: early stopping\n"
     ]
    }
   ],
   "source": [
    "H = model.fit(\n",
    "    aug.flow(trainX, trainY, batch_size=BS),\n",
    "    steps_per_epoch=len(trainX) // BS,\n",
    "    validation_data=(testX, testY),\n",
    "    validation_steps=len(testX) // BS,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks=callbacks,\n",
    "    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "767/1 [==================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 47s 61ms/sample\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(testX, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = np.argmax(preds, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   with_mask       0.96      0.85      0.90       383\n",
      "without_mask       0.87      0.97      0.91       384\n",
      "\n",
      "    accuracy                           0.91       767\n",
      "   macro avg       0.91      0.91      0.91       767\n",
      "weighted avg       0.91      0.91      0.91       767\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(testY.argmax(axis=1), preds,target_names=lb.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14cc3eb7d08>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = EPOCHS-4\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n",
    "plt.plot(np.arange(0, N), H.history[\"accuracy\"], label=\"train_acc\")\n",
    "plt.plot(np.arange(0, N), H.history[\"val_accuracy\"], label=\"val_acc\")\n",
    "plt.title(\"Training Loss and Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy\")\n",
    "plt.legend(loc=\"lower left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9087353324641461"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "metrics.accuracy_score(testY.argmax(axis=1), preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['with_mask', 'without_mask'], dtype='<U12')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.95761544, 0.04238459]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "img = cv2.imread('./dataset/with_mask/0_0_0 copy 10.jpg')\n",
    "image = load_img('./dataset/with_mask/0_0_0 copy 10.jpg', target_size=(224, 224))\n",
    "image = img_to_array(image)\n",
    "image = preprocess_input(image)\n",
    "image=np.expand_dims(image, axis=0)\n",
    "model.predict(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}