Commited minutiae

minutiae/MinutiaeNet/CoarseNet/CoarseNet_train.py

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