from gcn_ner import GCNNer
if __name__ == '__main__':
GCNNer.train_and_save(dataset='./data/train.conll',
saving_dir='./data/',
epochs=31)
# GCNNer.train_and_save(dataset='./data/labeled.conll', saving_dir='./data/unlabeled_50_random', epochs=20, al_args=al_list, load_ckpt="./data/unlabeled_50/ner-gcn-9.tf")
# my_data = genfromtxt('unlabeled_50_scores_sorted.csv', delimiter=',')
# al_length = 3750
# al_list = list(my_data[:3750,0].astype(np.int))
# print("Total finetuning samples: {}".format(len(al_list)))
# GCNNer.train_and_save(dataset='./data/labeled.conll', saving_dir='./data/unlabeled_50_uncertain_2', epochs=20, al_args=al_list, load_ckpt="./data/unlabeled_50/ner-gcn-9.tf")
my_data = genfromtxt('unlabeled_50_scores_sorted.csv', delimiter=',')
al_length = 3750
al_list = list(my_data[:3750, 0].astype(np.int))
al_list.extend(range(45112, 45112 + 15177))
print("Total finetuning samples: {}".format(len(al_list)))
GCNNer.train_and_save(dataset='./data/labeled_and_unlabeled_50.conll',
saving_dir='./data/unlabeled_50_uncertain_combined',
epochs=20,
al_args=al_list,
load_ckpt="./data/unlabeled_50/ner-gcn-9.tf")
# al_length = 3750
# al_list = list(np.random.randint(0,45112,al_length))
# al_list.extend(range(45112, 45112+15177))
# print("Total finetuning samples: {}".format(len(al_list)))
# GCNNer.train_and_save(dataset='./data/labeled_and_unlabeled_50.conll', saving_dir='./data/unlabeled_50_random_combined', epochs=20, al_args=al_list, load_ckpt="./data/unlabeled_50/ner-gcn-9.tf")
# my_data = genfromtxt('unlabeled_50_scores_sorted.csv', delimiter=',')
# al_length = 3750
# al_list = list(my_data[:3750,0].astype(np.int))
# al_list.extend(range(45112, 45112+15177))
# print("Total finetuning samples: [UC] {}".format(len(al_list)))
# GCNNer.train_and_save(dataset='./data/labeled_and_unlabeled_50.conll', saving_dir='./data/unlabeled_50_uncertain_combined_scratch', epochs=30, al_args=al_list)
def train(self, data, saving_dir = './data/', epochs=2, bucket_size=10): tf.reset_default_graph() (file, gcn_model) = GCNNer.train_and_save(dataset = data, saving_dir = saving_dir, epochs = epochs, bucket_size = bucket_size) self.save_model(file, gcn_model)