Demo_notebooks/train_FineNet.ipynb

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    "# Training FineNet\n",
    "Code for FineNet in paper \"Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge\" at ICB 2018: https://arxiv.org/pdf/1712.09401.pdf\n",
    "\n",
    "If you use whole or partial function in this code, please cite paper:\n",
    "\n",
    "    @inproceedings{Nguyen_MinutiaeNet,\n",
    "\tauthor    = {Dinh-Luan Nguyen and Kai Cao and Anil K. Jain},\n",
    "\ttitle     = {Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge},\n",
    "\tbooktitle = {The 11th International Conference on Biometrics, 2018},\n",
    "\tyear      = {2018},\n",
    "\t}\n",
    "\n",
    "Prepare your data as follows:\n",
    "- Prepare minutiae and non-minutiae image patches with any sizes. I suggest to use `44x44` size\n",
    "- Put all images in corresponding folers (`minu`, `non_minu`) in \n",
    "    - `Dataset/train`,\n",
    "    - `Dataset/test`,\n",
    "    - `Dataset/validate`.\n",
    "- Run following code\n",
    "\n",
    "Beside running in this notebook, you can run via command line with file [FineNet_train.py](../FineNet/FineNet_train.py)"
   ]
  },
  {
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   "source": [
    "import sys,os\n",
    "sys.path.append(os.path.realpath('../FineNet'))\n",
    "\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from FineNet_model import FineNetmodel, plot_confusion_matrix\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '2'\n",
    "os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
    "\n",
    "\n",
    "output_dir = '../output_FineNet/'+datetime.now().strftime('%Y%m%d-%H%M%S')\n",
    "\n",
    "# Prepare model model saving directory.\n",
    "save_dir = os.path.join(os.getcwd(), output_dir)\n",
    "log_dir = os.path.join(os.getcwd(), output_dir + '/logs')\n",
    "\n",
    "# Training parameters\n",
    "batch_size = 32\n",
    "epochs = 200\n",
    "num_classes = 2\n",
    "\n",
    "# Subtracting pixel mean improves accuracy\n",
    "subtract_pixel_mean = True\n",
    "\n",
    "# Model size, patch\n",
    "model_type = 'patch224batch32'\n",
    "\n",
    "\n",
    "# =============== DATA loading ========================\n",
    "\n",
    "train_path = '../Dataset/train/'\n",
    "test_path = '../Dataset/validate/'\n",
    "\n",
    "input_shape = (224, 224, 3)\n",
    "\n",
    "# Using data augmentation technique for training\n",
    "datagen = ImageDataGenerator(\n",
    "        # set input mean to 0 over the dataset\n",
    "        featurewise_center=False,\n",
    "        # set each sample mean to 0\n",
    "        samplewise_center=False,\n",
    "        # divide inputs by std of dataset\n",
    "        featurewise_std_normalization=False,\n",
    "        # divide each input by its std\n",
    "        samplewise_std_normalization=False,\n",
    "        # apply ZCA whitening\n",
    "        zca_whitening=False,\n",
    "        # randomly rotate images in the range (deg 0 to 180)\n",
    "        rotation_range=180,\n",
    "        # randomly shift images horizontally\n",
    "        width_shift_range=0.5,\n",
    "        # randomly shift images vertically\n",
    "        height_shift_range=0.5,\n",
    "        # randomly flip images\n",
    "        horizontal_flip=True,\n",
    "        # randomly flip images\n",
    "        vertical_flip=True)\n",
    "\n",
    "train_batches = datagen.flow_from_directory(train_path, target_size=(input_shape[0], input_shape[1]), classes=['minu', 'non_minu'], batch_size=batch_size)\n",
    "# Feed data from directory into batches\n",
    "test_gen = ImageDataGenerator()\n",
    "test_batches = test_gen.flow_from_directory(test_path, target_size=(input_shape[0], input_shape[1]), classes=['minu', 'non_minu'], batch_size=batch_size)\n",
    "\n",
    "\n",
    "# =============== end DATA loading ========================\n",
    "\n",
    "\n",
    "\n",
    "def lr_schedule(epoch):\n",
    "    \"\"\"Learning Rate Schedule\n",
    "    \"\"\"\n",
    "    lr = 0.5e-2\n",
    "    if epoch > 180:\n",
    "        lr *= 0.5e-3\n",
    "    elif epoch > 150:\n",
    "        lr *= 1e-3\n",
    "    elif epoch > 60:\n",
    "        lr *= 5e-2\n",
    "    elif epoch > 30:\n",
    "        lr *= 5e-1\n",
    "    print('Learning rate: ', lr)\n",
    "    return lr\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#============== Define model ==================\n",
    "\n",
    "model = FineNetmodel(num_classes = num_classes,\n",
    "                     pretrained_path = '../Models/FineNet.h5',\n",
    "                     input_shape=input_shape)\n",
    "\n",
    "# Save model architecture\n",
    "#plot_model(model, to_file='./modelFineNet.pdf',show_shapes=True)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=Adam(lr=lr_schedule(0)),\n",
    "              metrics=['accuracy'])\n",
    "#model.summary()\n",
    "\n",
    "#============== End define model ==============\n",
    "\n",
    "\n",
    "#============== Other stuffs for loging and parameters ==================\n",
    "model_name = 'FineNet_%s_model.{epoch:03d}.h5' % model_type\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "if not os.path.isdir(log_dir):\n",
    "    os.makedirs(log_dir)\n",
    "\n",
    "filepath = os.path.join(save_dir, model_name)\n",
    "\n",
    "\n",
    "# Show in tensorboard\n",
    "tensorboard = TensorBoard(log_dir=log_dir, histogram_freq=0, write_graph=True, write_images=False)\n",
    "\n",
    "# Prepare callbacks for model saving and for learning rate adjustment.\n",
    "checkpoint = ModelCheckpoint(filepath=filepath,\n",
    "                             monitor='val_acc',\n",
    "                             verbose=1,\n",
    "                             save_best_only=True)\n",
    "\n",
    "lr_scheduler = LearningRateScheduler(lr_schedule)\n",
    "\n",
    "lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),\n",
    "                               cooldown=0,\n",
    "                               patience=5,\n",
    "                               min_lr=0.5e-6)\n",
    "\n",
    "callbacks = [checkpoint, lr_reducer, lr_scheduler, tensorboard]\n",
    "\n",
    "#============== End other stuffs  ==================\n",
    "\n",
    "# Begin training\n",
    "model.fit_generator(train_batches,\n",
    "                    validation_data=test_batches,\n",
    "                    epochs=epochs, verbose=1,\n",
    "                    callbacks=callbacks)\n",
    "\n",
    "\n",
    "\n",
    "# Plot confusion matrix\n",
    "score = model.evaluate_generator(test_batches)\n",
    "print 'Test accuracy:', score[1]\n",
    "predictions = model.predict_generator(test_batches)\n",
    "test_labels = test_batches.classes[test_batches.index_array]\n",
    "\n",
    "cm = confusion_matrix(test_labels, np.argmax(predictions,axis=1))\n",
    "cm_plot_labels = ['minu','non_minu']\n",
    "plot_confusion_matrix(cm, cm_plot_labels, title='Confusion Matrix')"
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