Commited minutiae
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Training FineNet\n",
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"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",
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"\n",
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"If you use whole or partial function in this code, please cite paper:\n",
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"\n",
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" @inproceedings{Nguyen_MinutiaeNet,\n",
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"\tauthor = {Dinh-Luan Nguyen and Kai Cao and Anil K. Jain},\n",
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"\ttitle = {Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge},\n",
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"\tbooktitle = {The 11th International Conference on Biometrics, 2018},\n",
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"\tyear = {2018},\n",
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"\t}\n",
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"\n",
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"Prepare your data as follows:\n",
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"- Prepare minutiae and non-minutiae image patches with any sizes. I suggest to use `44x44` size\n",
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"- Put all images in corresponding folers (`minu`, `non_minu`) in \n",
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" - `Dataset/train`,\n",
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" - `Dataset/test`,\n",
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" - `Dataset/validate`.\n",
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"- Run following code\n",
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"\n",
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"Beside running in this notebook, you can run via command line with file [FineNet_train.py](../FineNet/FineNet_train.py)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import sys,os\n",
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"sys.path.append(os.path.realpath('../FineNet'))\n",
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"\n",
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"from keras.optimizers import Adam\n",
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"from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard\n",
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"from keras.callbacks import ReduceLROnPlateau\n",
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"from keras.preprocessing.image import ImageDataGenerator\n",
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"from FineNet_model import FineNetmodel, plot_confusion_matrix\n",
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"\n",
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"import numpy as np\n",
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"import os\n",
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"from sklearn.metrics import confusion_matrix\n",
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"from datetime import datetime\n",
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"\n",
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"\n",
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"os.environ[\"CUDA_VISIBLE_DEVICES\"] = '2'\n",
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"os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
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"\n",
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"\n",
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"output_dir = '../output_FineNet/'+datetime.now().strftime('%Y%m%d-%H%M%S')\n",
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"\n",
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"# Prepare model model saving directory.\n",
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"save_dir = os.path.join(os.getcwd(), output_dir)\n",
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"log_dir = os.path.join(os.getcwd(), output_dir + '/logs')\n",
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"\n",
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"# Training parameters\n",
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"batch_size = 32\n",
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"epochs = 200\n",
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"num_classes = 2\n",
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"\n",
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"# Subtracting pixel mean improves accuracy\n",
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"subtract_pixel_mean = True\n",
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"\n",
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"# Model size, patch\n",
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"model_type = 'patch224batch32'\n",
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"\n",
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"\n",
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"# =============== DATA loading ========================\n",
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"\n",
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"train_path = '../Dataset/train/'\n",
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"test_path = '../Dataset/validate/'\n",
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"\n",
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"input_shape = (224, 224, 3)\n",
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"\n",
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"# Using data augmentation technique for training\n",
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"datagen = ImageDataGenerator(\n",
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" # set input mean to 0 over the dataset\n",
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" featurewise_center=False,\n",
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" # set each sample mean to 0\n",
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" samplewise_center=False,\n",
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" # divide inputs by std of dataset\n",
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" featurewise_std_normalization=False,\n",
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" # divide each input by its std\n",
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" samplewise_std_normalization=False,\n",
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" # apply ZCA whitening\n",
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" zca_whitening=False,\n",
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" # randomly rotate images in the range (deg 0 to 180)\n",
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" rotation_range=180,\n",
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" # randomly shift images horizontally\n",
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" width_shift_range=0.5,\n",
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" # randomly shift images vertically\n",
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" height_shift_range=0.5,\n",
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" # randomly flip images\n",
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" horizontal_flip=True,\n",
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" # randomly flip images\n",
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" vertical_flip=True)\n",
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"\n",
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"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",
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"# Feed data from directory into batches\n",
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"test_gen = ImageDataGenerator()\n",
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"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",
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"\n",
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"\n",
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"# =============== end DATA loading ========================\n",
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"\n",
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"\n",
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"\n",
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"def lr_schedule(epoch):\n",
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" \"\"\"Learning Rate Schedule\n",
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" \"\"\"\n",
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" lr = 0.5e-2\n",
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" if epoch > 180:\n",
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" lr *= 0.5e-3\n",
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" elif epoch > 150:\n",
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" lr *= 1e-3\n",
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" elif epoch > 60:\n",
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" lr *= 5e-2\n",
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" elif epoch > 30:\n",
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" lr *= 5e-1\n",
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" print('Learning rate: ', lr)\n",
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" return lr\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"#============== Define model ==================\n",
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"\n",
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"model = FineNetmodel(num_classes = num_classes,\n",
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" pretrained_path = '../Models/FineNet.h5',\n",
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" input_shape=input_shape)\n",
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"\n",
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"# Save model architecture\n",
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"#plot_model(model, to_file='./modelFineNet.pdf',show_shapes=True)\n",
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"\n",
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"model.compile(loss='categorical_crossentropy',\n",
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" optimizer=Adam(lr=lr_schedule(0)),\n",
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" metrics=['accuracy'])\n",
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"#model.summary()\n",
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"\n",
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"#============== End define model ==============\n",
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"\n",
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"\n",
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"#============== Other stuffs for loging and parameters ==================\n",
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"model_name = 'FineNet_%s_model.{epoch:03d}.h5' % model_type\n",
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"if not os.path.isdir(save_dir):\n",
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" os.makedirs(save_dir)\n",
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"if not os.path.isdir(log_dir):\n",
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" os.makedirs(log_dir)\n",
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"\n",
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"filepath = os.path.join(save_dir, model_name)\n",
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"\n",
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"\n",
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"# Show in tensorboard\n",
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"tensorboard = TensorBoard(log_dir=log_dir, histogram_freq=0, write_graph=True, write_images=False)\n",
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"\n",
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"# Prepare callbacks for model saving and for learning rate adjustment.\n",
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"checkpoint = ModelCheckpoint(filepath=filepath,\n",
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" monitor='val_acc',\n",
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" verbose=1,\n",
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" save_best_only=True)\n",
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"\n",
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"lr_scheduler = LearningRateScheduler(lr_schedule)\n",
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"\n",
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"lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),\n",
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" cooldown=0,\n",
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" patience=5,\n",
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" min_lr=0.5e-6)\n",
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"\n",
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"callbacks = [checkpoint, lr_reducer, lr_scheduler, tensorboard]\n",
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"\n",
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"#============== End other stuffs ==================\n",
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"\n",
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"# Begin training\n",
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"model.fit_generator(train_batches,\n",
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" validation_data=test_batches,\n",
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" epochs=epochs, verbose=1,\n",
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" callbacks=callbacks)\n",
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"\n",
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"\n",
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"\n",
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"# Plot confusion matrix\n",
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"score = model.evaluate_generator(test_batches)\n",
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"print 'Test accuracy:', score[1]\n",
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"predictions = model.predict_generator(test_batches)\n",
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"test_labels = test_batches.classes[test_batches.index_array]\n",
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"\n",
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"cm = confusion_matrix(test_labels, np.argmax(predictions,axis=1))\n",
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"cm_plot_labels = ['minu','non_minu']\n",
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"plot_confusion_matrix(cm, cm_plot_labels, title='Confusion Matrix')"
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]
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}
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],
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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