def train_network5():
"""
Training the third network on folds 1 and 3, evaluated on fold 2 (150.000 iterations, 75k on each set)
"""
for x in range(57,60,2):
resNetClassifier.train('train_set1', 2500*x, log_dir_network5)
resNetClassifier.train('train_set2', 2500*x+2500, log_dir_network5)
def train_network6():
"""
Training the second network on folds 2 and 3, evaluated on fold 1 (150.000 iterations, 75k on each set)
"""
for x in range(1,60,2):
resNetClassifier.train('train_set2', 2500*x, log_dir_network6)
resNetClassifier.train('train_set3', 2500*x+2500, log_dir_network6)
def train_network1():
"""
Training the first network on folds 1 and 2, evaluated on fold 3 (150.000 iterations, 75k on each set)
"""
resNetClassifier.train('train_set2', 125000, log_dir_network1)
for x in range(51,60,2):
resNetClassifier.train('train_set1', 2500*x, log_dir_network1)
resNetClassifier.train('train_set2', 2500*x+2500, log_dir_network1)
resNetClassifier.eval('train_set3', log_dir_network1)
def train_network3():
for x in range(1, 60, 2):
resNetClassifier.train('train_set3', 2500 * x, log_dir_network3)
resNetClassifier.train('train_set1', 2500 * x + 2500, log_dir_network3)
resNetClassifier.eval('train_set2', log_dir_network3)