train_mask_detector_2_MobileNetV2.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary packages\n",
    "import tensorflow as tf\n",
    "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",
    "from imutils import paths\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import argparse\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the initial learning rate, number of epochs to train for,\n",
    "# and batch size\n",
    "INIT_LR = 1e-4\n",
    "EPOCHS = 20\n",
    "BS = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[INFO] loading images...\n"
     ]
    }
   ],
   "source": [
    "# grab the list of images in our dataset directory, then initialize\n",
    "# the list of data (i.e., images) and class images\n",
    "print(\"[INFO] loading images...\")\n",
    "imagePaths = list(paths.list_images(\"./dataset_2/train/\"))\n",
    "data = []\n",
    "labels = []\n",
    "\n",
    "# loop over the image paths\n",
    "for imagePath in imagePaths:\n",
    "\t# extract the class label from the filename\n",
    "\tlabel = imagePath.split(os.path.sep)[-2]\n",
    "\tif label == 'with_mask':\n",
    "\t\tif labels.count('with_mask') > labels.count('without_mask'):\n",
    "\t\t\tcontinue\n",
    "\n",
    "\t# load the input image (224x224) and preprocess it\n",
    "\timage = load_img(imagePath, target_size=(224, 224))\n",
    "\timage = img_to_array(image)\n",
    "\timage = preprocess_input(image)\n",
    "\n",
    "\t# update the data and labels lists, respectively\n",
    "\tdata.append(image)\n",
    "\tlabels.append(label)\n",
    "\n",
    "# convert the data and labels to NumPy arrays\n",
    "data = np.array(data, dtype=\"float32\")\n",
    "labels = np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0., 1.], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# perform one-hot encoding on the labels\n",
    "lb = LabelBinarizer()\n",
    "labels = lb.fit_transform(labels)\n",
    "labels = to_categorical(labels)\n",
    "labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "data[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(150528,)"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "data[0].flatten().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# partition the data into training and testing splits using 75% of\n",
    "# the data for training and the remaining 25% for testing\n",
    "(trainX, testX, trainY, testY) = train_test_split(data, labels,\n",
    "\ttest_size=0.20, stratify=labels, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct the training image generator for data augmentation\n",
    "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": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:`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"
     ]
    }
   ],
   "source": [
    "# load the MobileNetV2 network, ensuring the head FC layer sets are\n",
    "# left off\n",
    "baseModel = MobileNetV2(weights=\"imagenet\", include_top=False,\n",
    "\tinput_tensor=Input(shape=(224, 224, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "t[0][0]            \n__________________________________________________________________________________________________\nblock_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         \n__________________________________________________________________________________________________\nblock_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             \n__________________________________________________________________________________________________\nblock_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          \n__________________________________________________________________________________________________\nblock_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        \n__________________________________________________________________________________________________\nblock_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          \n__________________________________________________________________________________________________\nblock_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       \n__________________________________________________________________________________________________\nblock_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     \n__________________________________________________________________________________________________\nblock_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            \n__________________________________________________________________________________________________\nblock_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         \n                                                                 block_7_project_BN[0][0]         \n__________________________________________________________________________________________________\nblock_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                \n__________________________________________________________________________________________________\nblock_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             \n__________________________________________________________________________________________________\nblock_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          \n__________________________________________________________________________________________________\nblock_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        \n__________________________________________________________________________________________________\nblock_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          \n__________________________________________________________________________________________________\nblock_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       \n__________________________________________________________________________________________________\nblock_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     \n__________________________________________________________________________________________________\nblock_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            \n__________________________________________________________________________________________________\nblock_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                \n                                                                 block_8_project_BN[0][0]         \n__________________________________________________________________________________________________\nblock_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                \n__________________________________________________________________________________________________\nblock_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             \n__________________________________________________________________________________________________\nblock_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          \n__________________________________________________________________________________________________\nblock_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        \n__________________________________________________________________________________________________\nblock_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          \n__________________________________________________________________________________________________\nblock_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       \n__________________________________________________________________________________________________\nblock_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     \n__________________________________________________________________________________________________\nblock_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            \n__________________________________________________________________________________________________\nblock_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                \n                                                                 block_9_project_BN[0][0]         \n__________________________________________________________________________________________________\nblock_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                \n__________________________________________________________________________________________________\nblock_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            \n__________________________________________________________________________________________________\nblock_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         \n__________________________________________________________________________________________________\nblock_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       \n__________________________________________________________________________________________________\nblock_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         \n__________________________________________________________________________________________________\nblock_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      \n__________________________________________________________________________________________________\nblock_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    \n__________________________________________________________________________________________________\nblock_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           \n__________________________________________________________________________________________________\nblock_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        \n__________________________________________________________________________________________________\nblock_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            \n__________________________________________________________________________________________________\nblock_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         \n__________________________________________________________________________________________________\nblock_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       \n__________________________________________________________________________________________________\nblock_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         \n__________________________________________________________________________________________________\nblock_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      \n__________________________________________________________________________________________________\nblock_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    \n__________________________________________________________________________________________________\nblock_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           \n__________________________________________________________________________________________________\nblock_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        \n                                                                 block_11_project_BN[0][0]        \n__________________________________________________________________________________________________\nblock_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               \n__________________________________________________________________________________________________\nblock_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            \n__________________________________________________________________________________________________\nblock_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         \n__________________________________________________________________________________________________\nblock_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       \n__________________________________________________________________________________________________\nblock_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         \n__________________________________________________________________________________________________\nblock_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      \n__________________________________________________________________________________________________\nblock_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    \n__________________________________________________________________________________________________\nblock_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           \n__________________________________________________________________________________________________\nblock_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               \n                                                                 block_12_project_BN[0][0]        \n__________________________________________________________________________________________________\nblock_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               \n__________________________________________________________________________________________________\nblock_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            \n__________________________________________________________________________________________________\nblock_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         \n__________________________________________________________________________________________________\nblock_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       \n__________________________________________________________________________________________________\nblock_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               \n__________________________________________________________________________________________________\nblock_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         \n__________________________________________________________________________________________________\nblock_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      \n__________________________________________________________________________________________________\nblock_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    \n__________________________________________________________________________________________________\nblock_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           \n__________________________________________________________________________________________________\nblock_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        \n__________________________________________________________________________________________________\nblock_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            \n__________________________________________________________________________________________________\nblock_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         \n__________________________________________________________________________________________________\nblock_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       \n__________________________________________________________________________________________________\nblock_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         \n__________________________________________________________________________________________________\nblock_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      \n__________________________________________________________________________________________________\nblock_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    \n__________________________________________________________________________________________________\nblock_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           \n__________________________________________________________________________________________________\nblock_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        \n                                                                 block_14_project_BN[0][0]        \n__________________________________________________________________________________________________\nblock_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               \n__________________________________________________________________________________________________\nblock_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            \n__________________________________________________________________________________________________\nblock_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         \n__________________________________________________________________________________________________\nblock_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       \n__________________________________________________________________________________________________\nblock_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         \n__________________________________________________________________________________________________\nblock_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      \n__________________________________________________________________________________________________\nblock_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    \n__________________________________________________________________________________________________\nblock_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           \n__________________________________________________________________________________________________\nblock_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               \n                                                                 block_15_project_BN[0][0]        \n__________________________________________________________________________________________________\nblock_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               \n__________________________________________________________________________________________________\nblock_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            \n__________________________________________________________________________________________________\nblock_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         \n__________________________________________________________________________________________________\nblock_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       \n__________________________________________________________________________________________________\nblock_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         \n__________________________________________________________________________________________________\nblock_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      \n__________________________________________________________________________________________________\nblock_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    \n__________________________________________________________________________________________________\nblock_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           \n__________________________________________________________________________________________________\nConv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        \n__________________________________________________________________________________________________\nConv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     \n__________________________________________________________________________________________________\nout_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  \n__________________________________________________________________________________________________\naverage_pooling2d (AveragePooli (None, 1, 1, 1280)   0           out_relu[0][0]                   \n__________________________________________________________________________________________________\nflatten (Flatten)               (None, 1280)         0           average_pooling2d[0][0]          \n__________________________________________________________________________________________________\ndense (Dense)                   (None, 128)          163968      flatten[0][0]                    \n__________________________________________________________________________________________________\ndropout (Dropout)               (None, 128)          0           dense[0][0]                      \n__________________________________________________________________________________________________\ndense_1 (Dense)                 (None, 2)            258         dropout[0][0]                    \n==================================================================================================\nTotal params: 2,422,210\nTrainable params: 2,388,098\nNon-trainable params: 34,112\n__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# construct the head of the model that will be placed on top of the\n",
    "# the base model\n",
    "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)\n",
    "\n",
    "# place the head FC model on top of the base model (this will become\n",
    "# the actual model we will train)\n",
    "model = Model(inputs=baseModel.input, outputs=headModel)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop over all layers in the base model and freeze them so they will\n",
    "# *not* be updated during the first training process\n",
    "for layer in baseModel.layers:\n",
    "\tlayer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[INFO] compiling model...\n"
     ]
    }
   ],
   "source": [
    "# compile our model\n",
    "print(\"[INFO] compiling model...\")\n",
    "opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
    "\tmetrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[INFO] training head...\n",
      "Epoch 1/20\n",
      "101/101 [==============================] - 30s 269ms/step - loss: 0.5430 - accuracy: 0.7924 - val_loss: 0.3827 - val_accuracy: 0.8233\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.4206 - accuracy: 0.8249 - val_loss: 0.3446 - val_accuracy: 0.8822\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.3583 - accuracy: 0.8522 - val_loss: 0.3242 - val_accuracy: 0.8810\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 26s 253ms/step - loss: 0.3397 - accuracy: 0.8524 - val_loss: 0.3211 - val_accuracy: 0.8847\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 26s 255ms/step - loss: 0.3389 - accuracy: 0.8510 - val_loss: 0.3169 - val_accuracy: 0.8785\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.3341 - accuracy: 0.8575 - val_loss: 0.3066 - val_accuracy: 0.8859\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 26s 253ms/step - loss: 0.3076 - accuracy: 0.8653 - val_loss: 0.3026 - val_accuracy: 0.8847\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.3127 - accuracy: 0.8670 - val_loss: 0.2946 - val_accuracy: 0.8883\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 26s 253ms/step - loss: 0.2893 - accuracy: 0.8791 - val_loss: 0.2869 - val_accuracy: 0.8871\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.2908 - accuracy: 0.8779 - val_loss: 0.3023 - val_accuracy: 0.8736\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.2771 - accuracy: 0.8862 - val_loss: 0.3094 - val_accuracy: 0.8589\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.2852 - accuracy: 0.8823 - val_loss: 0.2741 - val_accuracy: 0.8896\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.2759 - accuracy: 0.8705 - val_loss: 0.2858 - val_accuracy: 0.8724\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.2736 - accuracy: 0.8782 - val_loss: 0.2652 - val_accuracy: 0.8933\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.2515 - accuracy: 0.8942 - val_loss: 0.3004 - val_accuracy: 0.8577\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 26s 254ms/step - loss: 0.2623 - accuracy: 0.8882 - val_loss: 0.2869 - val_accuracy: 0.8724\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 26s 257ms/step - loss: 0.2656 - accuracy: 0.9025 - val_loss: 0.2723 - val_accuracy: 0.8859\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 26s 255ms/step - loss: 0.2716 - accuracy: 0.8855 - val_loss: 0.2573 - val_accuracy: 0.8933\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 26s 255ms/step - loss: 0.2475 - accuracy: 0.9001 - val_loss: 0.2653 - val_accuracy: 0.8834\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 26s 257ms/step - loss: 0.2478 - accuracy: 0.8885 - val_loss: 0.2527 - val_accuracy: 0.8982\n"
     ]
    }
   ],
   "source": [
    "# train the head of the network\n",
    "print(\"[INFO] training head...\")\n",
    "H = model.fit(\n",
    "\taug.flow(trainX, trainY, batch_size=BS),\n",
    "\tsteps_per_epoch=len(trainX) // BS,\n",
    "\tvalidation_data=(testX, testY),\n",
    "\tvalidation_steps=len(testX) // BS,\n",
    "\tepochs=EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def plot_graphs(history, string):\n",
    "  plt.plot(history.history[string])\n",
    "  plt.plot(history.history['val_'+string])\n",
    "  plt.xlabel(\"Epochs\")\n",
    "  plt.ylabel(string)\n",
    "  plt.legend([string, 'val_'+string])\n",
    "  plt.show()\n",
    "  \n",
    "plot_graphs(H, \"accuracy\")\n",
    "plot_graphs(H, \"loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[INFO] evaluating network...\n",
      "                                precision    recall  f1-score   support\n",
      "\n",
      "   ./dataset_2/train/with_mask       0.94      0.94      0.94       671\n",
      "./dataset_2/train/without_mask       0.72      0.70      0.71       144\n",
      "\n",
      "                      accuracy                           0.90       815\n",
      "                     macro avg       0.83      0.82      0.82       815\n",
      "                  weighted avg       0.90      0.90      0.90       815\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# make predictions on the testing set\n",
    "print(\"[INFO] evaluating network...\")\n",
    "predIdxs = model.predict(testX, batch_size=BS)\n",
    "\n",
    "# for each image in the testing set we need to find the index of the\n",
    "# label with corresponding largest predicted probability\n",
    "predIdxs = np.argmax(predIdxs, axis=1)\n",
    "\n",
    "# show a nicely formatted classification report\n",
    "print(classification_report(testY.argmax(axis=1), predIdxs,\n",
    "\ttarget_names=lb.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[INFO] saving mask detector model...\n"
     ]
    }
   ],
   "source": [
    "# serialize the model to disk\n",
    "print(\"[INFO] saving mask detector model...\")\n",
    "model.save(\"trained_model_2_MobileNetV2.h5\", save_format=\"h5\")"
   ]
  }
 ]
}