train_mask_detector_3.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 MaxPooling2D\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.layers import Conv2D\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_2/\"))\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",
    "\t# image = load_img(imagePath, target_size=(100, 100))\n",
    "\timage = load_img(imagePath, target_size=(100, 100), color_mode='grayscale')\n",
    "\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": [
       "(100, 100, 1)"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "data[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "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": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"model\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_1 (InputLayer)         [(None, 100, 100, 1)]     0         \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 98, 98, 100)       1000      \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 32, 32, 100)       0         \n_________________________________________________________________\nflatten (Flatten)            (None, 102400)            0         \n_________________________________________________________________\ndropout (Dropout)            (None, 102400)            0         \n_________________________________________________________________\ndense (Dense)                (None, 64)                6553664   \n_________________________________________________________________\ndense_1 (Dense)              (None, 2)                 130       \n=================================================================\nTotal params: 6,554,794\nTrainable params: 6,554,794\nNon-trainable params: 0\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",
    "model_input = Input(shape=(100, 100, 1))\n",
    "headModel = Conv2D(200, (3,3), activation=\"relu\")(model_input)\n",
    "headModel = Conv2D(100, (3,3), activation=\"relu\",)(model_input)\n",
    "headModel = MaxPooling2D(pool_size=(3, 3))(headModel)\n",
    "headModel = Flatten(name=\"flatten\")(headModel)\n",
    "headModel = Dropout(0.5)(headModel)\n",
    "headModel = Dense(64, activation=\"relu\")(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=model_input, outputs=headModel)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[INFO] training head...\n",
      "Epoch 1/20\n",
      "25/25 [==============================] - 2s 41ms/step - loss: 0.6631 - accuracy: 0.5709 - val_loss: 0.5394 - val_accuracy: 0.7723\n",
      "Epoch 2/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.5831 - accuracy: 0.6973 - val_loss: 0.4694 - val_accuracy: 0.7723\n",
      "Epoch 3/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.5749 - accuracy: 0.6869 - val_loss: 0.3978 - val_accuracy: 0.8416\n",
      "Epoch 4/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.4943 - accuracy: 0.7580 - val_loss: 0.3912 - val_accuracy: 0.8515\n",
      "Epoch 5/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.5094 - accuracy: 0.7691 - val_loss: 0.3836 - val_accuracy: 0.8465\n",
      "Epoch 6/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.5065 - accuracy: 0.7566 - val_loss: 0.3675 - val_accuracy: 0.8465\n",
      "Epoch 7/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.5003 - accuracy: 0.7538 - val_loss: 0.3824 - val_accuracy: 0.8317\n",
      "Epoch 8/20\n",
      "25/25 [==============================] - 1s 27ms/step - loss: 0.5306 - accuracy: 0.7292 - val_loss: 0.3936 - val_accuracy: 0.8267\n",
      "Epoch 9/20\n",
      "25/25 [==============================] - 1s 27ms/step - loss: 0.5016 - accuracy: 0.7544 - val_loss: 0.5051 - val_accuracy: 0.7376\n",
      "Epoch 10/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.5218 - accuracy: 0.7359 - val_loss: 0.4248 - val_accuracy: 0.8069\n",
      "Epoch 11/20\n",
      "25/25 [==============================] - 1s 27ms/step - loss: 0.4674 - accuracy: 0.7812 - val_loss: 0.3924 - val_accuracy: 0.8366\n",
      "Epoch 12/20\n",
      "25/25 [==============================] - 1s 27ms/step - loss: 0.4811 - accuracy: 0.7620 - val_loss: 0.3593 - val_accuracy: 0.8317\n",
      "Epoch 13/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.4806 - accuracy: 0.7669 - val_loss: 0.3746 - val_accuracy: 0.8218\n",
      "Epoch 14/20\n",
      "25/25 [==============================] - 1s 27ms/step - loss: 0.4435 - accuracy: 0.8056 - val_loss: 0.3780 - val_accuracy: 0.8267\n",
      "Epoch 15/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.4947 - accuracy: 0.7665 - val_loss: 0.3677 - val_accuracy: 0.8218\n",
      "Epoch 16/20\n",
      "25/25 [==============================] - 1s 27ms/step - loss: 0.4214 - accuracy: 0.8234 - val_loss: 0.3591 - val_accuracy: 0.8366\n",
      "Epoch 17/20\n",
      "25/25 [==============================] - 1s 28ms/step - loss: 0.4579 - accuracy: 0.7699 - val_loss: 0.3638 - val_accuracy: 0.8317\n",
      "Epoch 18/20\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.4721 - accuracy: 0.7763 - val_loss: 0.3773 - val_accuracy: 0.8317\n",
      "Epoch 19/20\n",
      "25/25 [==============================] - 1s 27ms/step - loss: 0.4436 - accuracy: 0.8004 - val_loss: 0.3720 - val_accuracy: 0.8366\n",
      "Epoch 20/20\n",
      "25/25 [==============================] - 1s 27ms/step - loss: 0.4638 - accuracy: 0.7692 - val_loss: 0.3479 - val_accuracy: 0.8465\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": 12,
   "metadata": {},
   "outputs": [
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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": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[INFO] evaluating network...\n                          precision    recall  f1-score   support\n\n   ./dataset_2/with_mask       0.85      0.84      0.85       101\n./dataset_2/without_mask       0.84      0.85      0.85       101\n\n                accuracy                           0.85       202\n               macro avg       0.85      0.85      0.85       202\n            weighted avg       0.85      0.85      0.85       202\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": 14,
   "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_3.h5\", save_format=\"h5\")"
   ]
  }
 ]
}