train_mask_detector_1_MobileNetV2.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": 3,
   "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_1/\"))\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",
    "\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": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0., 1.], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "data[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(150528,)"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "data[0].flatten().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[INFO] training head...\n",
      "Epoch 1/20\n",
      "34/34 [==============================] - 12s 286ms/step - loss: 0.6755 - accuracy: 0.6476 - val_loss: 0.2289 - val_accuracy: 0.9891\n",
      "Epoch 2/20\n",
      "34/34 [==============================] - 9s 249ms/step - loss: 0.2476 - accuracy: 0.9616 - val_loss: 0.1048 - val_accuracy: 0.9855\n",
      "Epoch 3/20\n",
      "34/34 [==============================] - 9s 252ms/step - loss: 0.1218 - accuracy: 0.9886 - val_loss: 0.0671 - val_accuracy: 0.9819\n",
      "Epoch 4/20\n",
      "34/34 [==============================] - 9s 250ms/step - loss: 0.0936 - accuracy: 0.9923 - val_loss: 0.0469 - val_accuracy: 0.9964\n",
      "Epoch 5/20\n",
      "34/34 [==============================] - 9s 260ms/step - loss: 0.0632 - accuracy: 0.9947 - val_loss: 0.0372 - val_accuracy: 0.9964\n",
      "Epoch 6/20\n",
      "34/34 [==============================] - 9s 251ms/step - loss: 0.0584 - accuracy: 0.9906 - val_loss: 0.0311 - val_accuracy: 0.9964\n",
      "Epoch 7/20\n",
      "34/34 [==============================] - 8s 247ms/step - loss: 0.0427 - accuracy: 0.9909 - val_loss: 0.0274 - val_accuracy: 0.9964\n",
      "Epoch 8/20\n",
      "34/34 [==============================] - 9s 249ms/step - loss: 0.0409 - accuracy: 0.9915 - val_loss: 0.0232 - val_accuracy: 0.9964\n",
      "Epoch 9/20\n",
      "34/34 [==============================] - 9s 250ms/step - loss: 0.0345 - accuracy: 0.9921 - val_loss: 0.0205 - val_accuracy: 0.9964\n",
      "Epoch 10/20\n",
      "34/34 [==============================] - 8s 248ms/step - loss: 0.0398 - accuracy: 0.9911 - val_loss: 0.0191 - val_accuracy: 0.9964\n",
      "Epoch 11/20\n",
      "34/34 [==============================] - 8s 248ms/step - loss: 0.0198 - accuracy: 0.9980 - val_loss: 0.0180 - val_accuracy: 0.9964\n",
      "Epoch 12/20\n",
      "34/34 [==============================] - 8s 248ms/step - loss: 0.0240 - accuracy: 0.9964 - val_loss: 0.0163 - val_accuracy: 1.0000\n",
      "Epoch 13/20\n",
      "34/34 [==============================] - 9s 249ms/step - loss: 0.0268 - accuracy: 0.9914 - val_loss: 0.0158 - val_accuracy: 1.0000\n",
      "Epoch 14/20\n",
      "34/34 [==============================] - 9s 250ms/step - loss: 0.0183 - accuracy: 0.9962 - val_loss: 0.0146 - val_accuracy: 1.0000\n",
      "Epoch 15/20\n",
      "34/34 [==============================] - 9s 249ms/step - loss: 0.0187 - accuracy: 0.9973 - val_loss: 0.0138 - val_accuracy: 1.0000\n",
      "Epoch 16/20\n",
      "34/34 [==============================] - 9s 256ms/step - loss: 0.0249 - accuracy: 0.9928 - val_loss: 0.0131 - val_accuracy: 1.0000\n",
      "Epoch 17/20\n",
      "34/34 [==============================] - 9s 250ms/step - loss: 0.0117 - accuracy: 0.9983 - val_loss: 0.0123 - val_accuracy: 1.0000\n",
      "Epoch 18/20\n",
      "34/34 [==============================] - 9s 249ms/step - loss: 0.0203 - accuracy: 0.9943 - val_loss: 0.0123 - val_accuracy: 1.0000\n",
      "Epoch 19/20\n",
      "34/34 [==============================] - 8s 248ms/step - loss: 0.0136 - accuracy: 0.9972 - val_loss: 0.0111 - val_accuracy: 1.0000\n",
      "Epoch 20/20\n",
      "34/34 [==============================] - 9s 249ms/step - loss: 0.0127 - accuracy: 0.9980 - val_loss: 0.0108 - val_accuracy: 1.0000\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": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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kMzONQioiGaG/ncVXAycA651z7WZWClwVWFUjRGVpDuvq29JdhohIoPp7RHAa8J5zrsnMLsO7Arg5uLJGhp6LypwbEUMgiYgEor9BcBfQbmbHA18H1gEPBFbVCFFVmktnPEl9S2e6SxERCUx/gyDuvD+LFwH/4Zy7EygIrqyRYYpGIRWRDNDfIGgxs/+Fd9roU2YWAqLBlTUypF5UJiIyVvU3CBYDnXjXE2zDG0DujsCqGiEmF+cAsKlBp5CKyNjVryDwf/k/BBSZ2aeBDufcmO8jyI6GqSjMVtOQiIxp/R1i4rPA34GLgM8Cr5rZhUEWNlJUleaqaUhExrT+XkfwTWC+c64OwMzKgT8BjwdV2EgxpTSHl9c1pLsMEZHA9LePINQTAr6GAew7qlWV5rJtVwed8US6SxERCUR/jwieNbOlwH/5zxfTa3jpsaqqNBfnoLZxN9PL89NdjojIkOtXEDjnbjazC4AP+ovucc49GVxZI0dlyrUECgIRGYv6fWMa59wTwBMB1jIi7bmWQGcOicgYddAgMLMWoK+Bdgxwzrkxf+uu8vwYsUiIzRqOWkTGqIMGgXNuzA8jcSihkDGlJEd3KhORMSsjzvw5XFW6L4GIjGGZFQTtOwe1m4ajFpGxLHOC4KUfw49nQ9fAbzRTWZpLS2ec5t3dARQmIpJemRMEU+ZDdzuseXbAu1ZqOGoRGcMyJwiqToP8Cnj71wPfdc8ppDpzSETGnswJglAYZp8L7/8ROlsGtKuOCERkLMucIACYfT4kOuG9Zwa0W34sQmleloJARMakzAqCKfOhcPKgmocqS3Op0XDUIjIGZVYQhEIw+zxY92fY3TSgXStLcnREICJjUmYFAfjNQ13w3sAGT60qzaW2cTfxRDKgwkRE0iPzgmDySVBcNeDmoeMri4knHX98Z3tAhYmIpEegQWBmZ5rZe2a21sxu6WP918zsHTN7y8z+bGZHBFmP/6Ze89D65wd0pfEnjp3AEWW53P3COl1hLCJjSmBBYGZh4E7gLGAWcImZzeq12RvAPOfccXi3vbw9qHr2Mft8SMbh3d/3e5dwyPjigum8WdPMq/8Y3FAVIiIjUZBHBKcAa51z651zXcAjwKLUDZxzzzvnenpgXwGmBFjPXhOPh5JpA24euvDkKZTlZXHPsvUBFSYiMvyCDILJwOaU5zX+sgO5GujzBH8zu8bMqs2sur6+/vArM4M558M/lkHbjn7vlh0Nc8XpU3nu3Tre2zawi9JEREaqEdFZbGaXAfOAO/pa75y7xzk3zzk3r7y8fGjedPb54BKwesmAdvvcqUeQEw3rqEBExowgg6AWqEx5PsVftg8z+wTwTeAc51xngPXsa8JsKJs54OahkrwsFs+v5LcratnarLGHRGT0CzIIXgNmmtk0M8sCLgb2+fPbzE4EfoYXAnUB1rK/nuahjX+FloGdEnr1h6bhgHtf+kcwtYmIDKPAgsA5FweuB5YCq4HHnHOrzOxWMzvH3+wOIB/4bzNbYWYDa6c5XLPPA5eEd347oN0qS3P59HETefjVTbpHgYiMeoH2ETjnnnbOHeWcm+Gc+56/7DvOuSX+/CeccxOccyf4j3MO/opDbPyxUH4srHpywLtes3A6bV0JHnp1YwCFiYgMnxHRWZxWc86HTS/Dri0D2m32pCIWzBzHfX/dQGc8EVBxIiLBUxDMPh9wsOo3A9712g/PoL6lk9+8sV8fuIjIqKEgGHckVMwdVPPQ6TPKmD2pkJ8tW08yqWEnRGR0UhCA12lc83do2nzobVOYGf/y4Rmsr2/jT6s1GJ2IjE4KAvCbhxjUUcHZcyqYUpLDz3SBmYiMUgoCgNJpMOlEWDXwO5dFwiG+uGA6r29spHqDBqMTkdFHQdBj9nmw5Q3YOfCLxC6aN4WS3Ch3v6CjAhEZfRQEPWaf500H0TyUmxXh8tOm8qfV21lbp8HoRGR0URD0KK7ybm4/iOYhgMtPO4LsaIifL9OwEyIyuigIUs0+D7athB1rB7xrWX6Mi06u5Mk3atm+qyOA4kREgqEgSDXrXG86iOYhgC8smEY8meS+v24YspJERIKmIEhVNBmqTht089ARZXmcNXciD72ykZYODUYnIqODgqC32edB3TtQ9+6gdv+XhdNp6YzzX3/fNMSFiYgEQ0HQ26xFgA26eei4KcWcPqOMX770D7riyaGtTUQkAAqC3goqYOqHvOYhN7jxg/7lwzPYvquT367QYHQiMvIpCPoy+1zYsQa2rxrU7gtnjuOYigLu0WB0IjIKKAj6cuwisNCgm4fMjGs/PIP361r5y5rhvQOniMhAKQj6kl8O0xYeVvPQp46byKSibA07ISIjnoLgQGafDzvXw9Y3B7V7NBzi6gXT+fs/drJ8U+MQFyciMnQUBAdy7GcgFBn0NQUAF8+vpCgnyj06KhCREUxBcCC5pTD9I14/wSCbh/JiET536hEsfWcb6+tbh7Y+EZEhoiA4mNnnQ9MmqF0+6Je44vSpRMMhfv6iBqMTkZFJQXAwx5wNoehhNQ+VF8S48OQpPLG8hroWDUYnIiOPguBgckrgyI/Dqt9AcvBXCX9xwXS6E0nu/9uGIStNRGSoKAgOZfb5sKsGal4b9EtMG5fHJ2dV8MDLG3U7SxEZcRQEh3L0WRCOHVbzEMDNZx5NcW6Uz/7sZX747Lsah0hERgwFwaFkF8LMf/KbhxKDfpkZ5fk8c9NCLjq5krv+so5z7/wra7brtpYikn4Kgv6YfR60boNNrxzWy+THIvzwwuP4+eXz2L6rg0//+0v84kWNRyQi6aUg6I+jzoRIzmE3D/X4p1kTWPrVhSycWc5tT63m0l+8Sm3T7iF5bRGRgQo0CMzsTDN7z8zWmtktfaxfaGbLzSxuZhcGWcthieXDUWfAO7+F9qHp7B2XH+Pnl5/MDy+Yy1s1TZz542U8+UYNbpAXr4mIDFZgQWBmYeBO4CxgFnCJmc3qtdkm4Erg4aDqGDIfuBY6muG+s6C5Zkhe0sxYPL+KZ25ayNEVBXz10Te5/uE3aGzrGpLXFxHpjyCPCE4B1jrn1jvnuoBHgEWpGzjnNjjn3gJG/ik0R5wOlz0Bu7bAL/4J6lYP2UtXleXy6L+cxv8882j+8M42PvmTZfzlPQ1fLSLDI8ggmAxsTnle4y8bMDO7xsyqzay6vr5+SIoblGkL4aqnwSXh3k/CxpeH7KXDIeNLHzmSJ7/0QYpyolx532t8+zdvs7tr8GcqiYj0x6joLHbO3eOcm+ecm1deXp7eYirmwtV/gLzx8OC5sPr3Q/rycyYX8bsbPsQXPjSNB1/ZyKd++iIrNjcN6XuIiKQKMghqgcqU51P8ZaNfyRHw+aUwYQ489jmovndIXz47GuZbn57Fw1/8AB3dCS6462/85E9r6E6M/BY0ERl9ggyC14CZZjbNzLKAi4ElAb7f8MorgyuWwJGfgN9/FZ7//qCHqz6Q02eM45mvLGTR8ZP4yZ/e58K7/qbhrEVkyAUWBM65OHA9sBRYDTzmnFtlZrea2TkAZjbfzGqAi4Cfmdng7hafLll5cPHDcMJl8MIP4PdfgUR8SN+iKCfKjxafwJ3/fBIbd7Zz9k9f5MFXNuo0UxEZMjbafqHMmzfPVVdXp7uMfTkHz90GL/4bHP0puPCXEM0Z8rfZvquDmx9/i2Vr6vnI0eXcfsFxjC/MHvL3EZGxx8xed87N62vdqOgsHvHM4OPfhrP/Dd57Gh5YNGQXnqWaUJjN/VfN59ZFs3llfQOf/Mkynn1765C/j4hkFgXBUDrli3DRr2DLG3DvmUN24VkqM+Py06by+xsWUFmay7X/uZyvP/Ymuzq6h/y9RCQzKAiG2uxz4bJfQ8tW78Kz7e8E8jZHjs/nietO58aPHcmTb9Rw1k9e5NX1DYG8l4iMbQqCIExbAFc94114dt+ZsPFvgbxNNBzia2cczePXnU40bFz881f4/jOr6YzrIjQR6T8FQVAq5sAX/uhdePbAubD6d4G91UlVJTx14wIunl/Fz15Yz6L/+CvvbtsV2PuJyNiiIAhScZV3FfLE4+Cxy+HvPx/yaw165MUifP/8ufzyinnsaO3knH//q+51ICL9oiAIWm4pXL4EZp4BT/8PuPtD8PYTh3W3s4P5+LETWPqVhXzkaN3rQET6R0EwHLJyvQvPzr0bEl3w+OfhzlPgjf+ExNCf7VOWH+NnnzuZ2y88zrvXwU+W8Zs3anURmoj0SReUDbdkwusvePHfYNtKKKqED94EJ14WyEVomxra+dpjK6je2Mgnjp3AgpnjmFGez5Hj85lQGMPMhvw9RWTkOdgFZQqCdHEO3v+jFwibX/U6lU+/HuZ9HmIFQ/pWiaTj7hfW8bMX1rGrY+8QGHlZYWaMz2dGeT4zyvO86fh8jijLJRYJD2kNIpJeCoKRzDnY8JIXCOv/AtnFcOqX4APXQE7JEL+Vo76lk7X1rayrb2NdXSvr6ltZX9+2Tz9COGRUlebuDYfyfGaMz+PI8gKKcqNDWpOIDA8FwWhRUw0v/h9vmIqsAph/NZz2ZcgfH/hbt3fFWV/fxrr6Vj8gvPn1O9roinvDX5vBCZXFfPyY8XzsmAkcO7FATUsio4SCYLTZ9ja89CNY9SSEs+CkK+CDN0LRlGEvJZF01DbuZm19CytrdvHce3W86d8oZ2JRNh87ZjwfP3Y8p88YR3ZUzUkiI5WCYLRqWOcFwpuPAAZzL4QZH4PKD3jXKKTpr/G6lg7+8l49z62u48X362nrSpAdDfGhI8fxsWMm8LFjxlNRpFFRRUYSBcFo17QZ/vZTWPFf0NXiLSuY6AVC1anetOI4CEeGvbTOeIJX1+/kuXfr+NPq7dQ0en0NsycVek1Ix07guMlFhEJqQhJJJwXBWJFMwPZV3llGm17xps2bvXXRXJh8sh8Mp0LlfMguGtbynHOsrWvlz+/W8dzqOqo37iTpYFx+Fh89ejwLjypnalkeE4uzKcvLUv+CyDBSEIxlzbWw+RXY9Ko33bbSG+wOgwmz9z1qGObmpMa2Ll5YU8+f363jhffq9jl1NRYJMbEom4lFOUwszmZSr+nEohwKsyMKC5EhoiDIJJ0t3tlHPUcNNdV7m5PyxsO4o6BsOpQdCaUzoGwGlEyDaLBt+t2JJO9ubaG2aTdbm3eztbmDLU3edGvTbrbt6qD3sEh5WWEmFucwscgLiEnFORxRlktVWS5HlOZSqqMKkX5TEGSy1Oak2uWwc53XCd2+I2Uj865wLpvuh8ORXkCUzoCSIyAc/LUD8USSupZOtjbvZktTxz5TLzQ62NHauc8++bEIVaW5KeGQxxFl3vOJRTmE1S8hsoeCQPa3u8kPhfXQsHZvQDSsg87mvdtZ2AuD0hlQOs27piFvPOSV+/Pl3iMrN/CSO7oT1DS2s7Gh59HGxp3tbGpoZ3NjO92Jvd/laNioLNl79FBVlsfk4hzMvFNiE0lH0jniCUfCuf2WJf1l8aQj6U8jIePYiYWcUFXMuPxY4D+vyFA6WBAM/2kmMjLkFHudy5NP3ne5c9De4AXCnnDwg2Lzq9B5gPscZOVD3riUkCjvNe8/L5gAscJB9VVkR8McOb6AI8fvPwRHIunY2rybTQ3tbGhoZ+PONjb5gVG9oZHWzngfrzh4VaW5nFhVzImVxZxYVcKxEwvJimgMRxmddEQgA9O9G9p2QFudN22tg7b6vY/Wur3r2xv8juteIjleIBRMhPwJUFDhTyd6y/MrvGU5JUPSue2cY2dbF1ubOwBvCI1IyAiFjLAZ4VCvhxnhcK91ZnTGk6ysbeaNTY2s2NzE8k2NbN/lNVdlRULMmVTIiVUlXkBUlTCpKHtAfRjOOXZ1xP1msd3UNnn9J1uadrOluYMdLZ3EomEKsiMUxCIUZEfIz45QkB0lPxahsOd5LOovj1Dor8vPjhANK6gymZqGJD2SCWjf6YdEHbTWQ+s2aPEfrdv3Tvs60ghn+aEwwQuK/AleOGQXeUc02UX+o3jvslghhIbvCuetzbt5Y1MTb2xq5I1NTaysbabTH5JjfEGME6uKOaHSC4djKwpp2t3Fliavo7znF/wWvwN9S1PHfkcukZAxoTCbycU5lBfE6IwnaOmI09IRp7UzTktHNy0dceL9uAFRTjRMSW6UsvwYpXlZlOVlUZqXRWl+z3zK8vwsCmL9P2srkXS0dcVp7YjT1unV1trpzbf4y9q7E8QiYfKywuTGIt40K0JebN9pblZYoRUABYGMfF1t+4fDnsDYBi3bvWUdzeAOcVOfWOHecOgdGpFsb7jvSMw7MonE/OfZ/rrsXsv97aL++lDkoEcpPWdHvbG5cU9AbGhoP+D2ZXlZTOo5M6o4h0nFPdMcJhV5v/wP1entnKMznvQDotsPiPh+z3ft7mZnexc727xHQ6s33d3d9+eZFQ5RkhelNC9GWV4WRblROruTtHZ209aZ2OeXfXvX0N5oKSsSIjcrTJ4fDLkx7yjo6IoCTqgs5oTKYqaU5OissQFQEMjY4Rx0tXqBsLvJm3Y09Xre1zJ/Gu+A5OH0F5gXDuEYRLJ6Tf1HOGufbTpdlIYOx85OIxLLIze/kPz8AgoKColm53mBE809wNSfD2cFdg3I7q4EDW2dXji0dbHTD4iGti52pixvbu/2mqZi3l/v+dlR8mPeL+v87IjXBBWLkOdP87Mj5GV5TVR5sQg50TBd8SRtXXHau+K0dSZo70p4810J2jt7Tfds4z1vbu/i3W0te464xuVn7QmF4/1HYfbhn+GWSDo2NrSxZnsr729vYU2dN/3HjjayIiFKcrMozo1SlBOlODeL4pwoxbm956MU5XjbFedEiYyAIxx1FsvYYebdryFWMPhB+BJxLxDiHV6fR7wT4v60e3fKuo79t0t0+tv3zHftXZbo2jttb9vzPJboZFK8i0nxlNcb8M8d8o5MQhGv6SsU9s7o6j1vYX+bUMr6yN75cJZ/9LM3vHIiMaaEs5gSyd4bZlkxyM2Cidn7Bls44t3XMASEQ/58CEJJCDtvGnLeI4w/v3f7nKzwYQ1l3nPEtWJzI29sbuLNzU38aXXdnvUzyvM4obKEE/yO/KMrCg7YzJRMOjY3trNmeytrtrd4v/S3t7K2vnXPiLsAk4tzOGpCPh88chyJpKOpvYum3d00tXdT07ibxvYumnd3H/R25AWxCEW50ZQmuRhl+Vl7nnvzsT3zuVnD+6tZRwQiwy2Z9IKnezd0tx9g6s93te+7zCW8vpdk3J9P+tO4t3zP+pT51H0SXV54xTt6hZq/rK/O/aFkIQhFvWtTQmFvPhTp43nkIOtSp1G6nNGw21HXlmBba4KtLXF2dUPChSEcoawwn4qSfMYX59HSkaCupZO6lk52tHTQndj78xbnRCgvyGZ8QYzygpg3zc/a9yZN4ei+R4QRLyiToSzakxF2xUPs6grRFA/T1AmNnSF2dhgNnY7G3Un/6KqTna3eUVZnvO/POzsaoiwlLErzshiXH+OsORWcWDW4+5ToiEBkJAmFICvPe4w0ifi+ARHv2HukE+/0A6fbD5X4YTxPePfr3u/5AbaNd0GyLWVd9579s5JxJia6mZjs5vhEHBfqhmgc6wm1Vv+xudfP2nOk0iMONPqPAQoB+f5j0gE38o/MLAShMC4vBGYkCfkPI+mMhDMSGPGEkWgy4juNuIPuJGzv/ipUXTPwAg9BQSAie4X9v8ZHYkj1056elGRyT2B0d3execcuxuXHKMyJ9rW1//QQ/TCJ7v3DMdHrqGpPc2HHvk2H8U7v6MwlwTkvqFyScDJB2J8/6COZYNqsI4fwk9or0CAwszOB/4vXWvgL59wPeq2PAQ8AJwMNwGLn3IYgaxKRDBEKQcjr84jGYHp+aborOmxBdTkH1pVtZmHgTuAsYBZwiZnN6rXZ1UCjc+5I4MfAD4OqR0RE+hbkOU2nAGudc+udc13AI8CiXtssAu735x8HPm46MVhEZFgFGQST2bd7psZf1uc2zrk40AyU9X4hM7vGzKrNrLq+vj6gckVEMlP6r3LoB+fcPc65ec65eeXl5ekuR0RkTAkyCGqBypTnU/xlfW5jZhGgCK/TWEREhkmQQfAaMNPMpplZFnAxsKTXNkuAK/z5C4Hn3Gi7wk1EZJQL7PRR51zczK4HluKdPnqvc26Vmd0KVDvnlgC/BB40s7XATrywEBGRYRTodQTOuaeBp3st+07KfAdwUZA1iIjIwY26sYbMrB7YOMjdxwE7DrlV+qi+w6P6Dt9Ir1H1Dd4Rzrk+z7YZdUFwOMys+kCDLo0Equ/wqL7DN9JrVH3BGBWnj4qISHAUBCIiGS7TguCedBdwCKrv8Ki+wzfSa1R9AcioPgIREdlfph0RiIhILwoCEZEMNyaDwMzONLP3zGytmd3Sx/qYmT3qr3/VzKYOY22VZva8mb1jZqvM7KY+tvmImTWb2Qr/8Z2+XivAGjeY2Ur/vfe7QbR5fup/fm+Z2UnDWNvRKZ/LCjPbZWZf6bXNsH9+ZnavmdWZ2dspy0rN7I9m9r4/7fNms2Z2hb/N+2Z2RV/bBFDbHWb2rv/v96SZFR9g34N+FwKu8btmVpvy73j2AfY96P/3AOt7NKW2DWa24gD7DstneFicc2PqgTecxTpgOpAFvAnM6rXNl4C7/fmLgUeHsb6JwEn+fAGwpo/6PgL8Po2f4QZg3EHWnw08g3efv1OBV9P4b70N70KZtH5+wELgJODtlGW3A7f487cAP+xjv1JgvT8t8edLhqG2M4CIP//Dvmrrz3ch4Bq/C/yPfnwHDvr/Paj6eq3/P8B30vkZHs5jLB4RjOgb4jjntjrnlvvzLcBq9r9Pw0i3CHjAeV4Bis1sYhrq+Diwzjk32CvNh4xzbhneeFmpUr9n9wPn9rHrJ4E/Oud2OucagT8CZwZdm3PuD867BwjAK3ijA6fNAT6//ujP//fDdrD6/N8dnwX+a6jfd7iMxSAYshviBM1vkjoReLWP1aeZ2Ztm9oyZzR7eynDAH8zsdTO7po/1/fmMh8PFHPg/Xzo/vx4TnHNb/fltwIQ+thkJn+Xn8Y7w+nKo70LQrvebr+49QNPaSPj8FgDbnXPvH2B9uj/DQxqLQTAqmFk+8ATwFefcrl6rl+M1dxwP/Dvwm2Eu70POuZPw7jf9ZTNbOMzvf0jmDW1+DvDffaxO9+e3H+e1EYy4c7XN7JtAHHjoAJuk87twFzADOAHYitf8MhJdwsGPBkb8/6exGAQj/oY4ZhbFC4GHnHO/7r3eObfLOdfqzz8NRM1s3HDV55yr9ad1wJN4h9+p+vMZB+0sYLlzbnvvFen+/FJs72ky86d1fWyTts/SzK4EPg1c6gfVfvrxXQiMc267cy7hnEsCPz/Ae6f1u+j//jgfePRA26TzM+yvsRgEI/qGOH574i+B1c65Hx1gm4qePgszOwXv32lYgsrM8sysoGcer1Px7V6bLQEu988eOhVoTmkCGS4H/CssnZ9fL6nfsyuA3/axzVLgDDMr8Zs+zvCXBcrMzgT+J3COc679ANv057sQZI2p/U7nHeC9+/P/PUifAN51ztX0tTLdn2G/pbu3OogH3lkta/DOJvimv+xWvC89QDZek8Ja4O/A9GGs7UN4TQRvASv8x9nAtcC1/jbXA6vwzoB4BTh9GOub7r/vm34NPZ9fan0G3Ol/viuBecP875uH94u9KGVZWj8/vFDaCnTjtVNfjdfv9GfgfeBPQKm/7TzgFyn7ft7/Lq4Frhqm2tbita33fAd7zqKbBDx9sO/CMH5+D/rfr7fwfrlP7F2j/3y//+/DUZ+//Fc937uUbdPyGR7OQ0NMiIhkuLHYNCQiIgOgIBARyXAKAhGRDKcgEBHJcAoCEZEMpyAQ8ZlZwvYd2XTIRrI0s6mpI1eKjCSRdBcgMoLsds6dkO4iRIabjghEDsEfT/52f0z5v5vZkf7yqWb2nD8o2p/NrMpfPsEf4/9N/3G6/1JhM/u5efeh+IOZ5fjb32je/SneMrNH0vRjSgZTEIjsldOraWhxyrpm59xc4D+An/jL/h243zl3HN6gbT/1l/8UeMF5g96dhHdFKcBM4E7n3GygCbjAX34LcKL/OtcG86OJHJiuLBbxmVmrcy6/j+UbgI8559b7AwZuc86VmdkOvGEPuv3lW51z48ysHpjinOtMeY2pePcdmOk//wYQdc7dZmbPAq14o6T+xvkD5okMFx0RiPSPO8D8QHSmzCfY20f3Kbyxm04CXvNHtBQZNgoCkf5ZnDJ92Z//G95olwCXAi/6838GrgMws7CZFR3oRc0sBFQ6554HvoE3JPp+RyUiQdJfHiJ75fS6AfmzzrmeU0hLzOwtvL/qL/GX3QDcZ2Y3A/XAVf7ym4B7zOxqvL/8r8MbubIvYeA//bAw4KfOuaYh+nlE+kV9BCKH4PcRzHPO7Uh3LSJBUNOQiEiG0xGBiEiG0xGBiEiGUxCIiGQ4BYGISIZTEIiIZDgFgYhIhvt/1CiIy9kXrgAAAAAASUVORK5CYII=\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": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[INFO] evaluating network...\n",
      "                          precision    recall  f1-score   support\n",
      "\n",
      "   ./dataset_1/with_mask       1.00      1.00      1.00       138\n",
      "./dataset_1/without_mask       1.00      1.00      1.00       138\n",
      "\n",
      "                accuracy                           1.00       276\n",
      "               macro avg       1.00      1.00      1.00       276\n",
      "            weighted avg       1.00      1.00      1.00       276\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": 16,
   "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_1.h5\", save_format=\"h5\")"
   ]
  }
 ]
}