69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
"""Code for FineNet in paper "Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge" at ICB 2018
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https://arxiv.org/pdf/1712.09401.pdf
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If you use whole or partial function in this code, please cite paper:
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@inproceedings{Nguyen_MinutiaeNet,
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author = {Dinh-Luan Nguyen and Kai Cao and Anil K. Jain},
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title = {Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge},
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booktitle = {The 11th International Conference on Biometrics, 2018},
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year = {2018},
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}
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"""
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from __future__ import absolute_import
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from __future__ import division
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import os
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os.environ['KERAS_BACKEND'] = 'tensorflow'
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from datetime import datetime
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from MinutiaeNet_utils import *
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from keras import backend as K
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from keras.optimizers import SGD, Adam
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from CoarseNet_utils import *
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from CoarseNet_model import *
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import argparse
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parser = argparse.ArgumentParser(description='Minutiae Net')
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parser.add_argument('lr', type=str, default="0.005",
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help='Setting learning rate')
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parser.add_argument('GPU', type=str, default="0",
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help='Choosing GPU')
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args = parser.parse_args()
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os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
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config = K.tf.ConfigProto(gpu_options=K.tf.GPUOptions(allow_growth=True))
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sess = K.tf.Session(config=config)
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K.set_session(sess)
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batch_size = 2
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use_multiprocessing = False
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input_size = 400
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# Can use multiple folders for training
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train_set = ['../Dataset/CoarseNet_train/',]
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validate_set = ['../path/to/your/data/',]
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pretrain_dir = '../Models/CoarseNet.h5'
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output_dir = '../output_CoarseNet/'+datetime.now().strftime('%Y%m%d-%H%M%S')
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FineNet_dir = '../Models/FineNet.h5'
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if __name__ =='__main__':
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output_dir = '../output_CoarseNet/trainResults/' + datetime.now().strftime('%Y%m%d-%H%M%S')
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logging = init_log(output_dir)
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logging.info("Learning rate = %s", args.lr)
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logging.info("Pretrain dir = %s", pretrain_dir)
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train(input_shape=(input_size, input_size), train_set=train_set, output_dir=output_dir,
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pretrain_dir=pretrain_dir, batch_size=batch_size, test_set=validate_set,
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learning_config=Adam(lr=float(args.lr), beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=0.9),
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logging=logging) |