Open SeekPoint opened 7 years ago
rzai@rzai00:~/prj/chineseNER/KG/workDir$ python ../main.py train --train data/traindata --dev data/devdata --test data/testdata --score_dir trainResult Using gpu device 0: GeForce GTX 1080 (CNMeM is disabled, cuDNN 5105) choose model type .................. LSTMbaseline embedding size : 150 embeddings num : 316001 {'I-PRO': 2, 'B-PRO': 1, 'B-ORG': 9, 'I-COM': 8, 'I-PER': 4, 'B-COM': 7, 'O': 0, 'I-LOC': 6, 'I-ORG': 10, 'B-LOC': 5, 'B-PER': 3} model training args : Namespace(CRF=False, action='train', batch_size=64, corpus='data/rawdata', dev='data/devdata', drop_pro=0.35, emb_lrate=0.01, embeddings='vector/baidu_charvectors_150', epoch=10, eva='data/testdata', fix_emb=True, gradbound=-0.1, highreg_weight=0.1, istoken=False, lowreg_weight=0.1, lrate=0.02, model='', model_type='LSTMbaseline', mweight=0.9, nc=3, net_size=[150, 450, 300, 11], opt='adaGrad', output='result', score_dir='trainResult', second_layer='forward', test='data/testdata', train='data/traindata', variance=0.01, wbound=-0.1, wdecay=0.0) whether fixing word embedding : True model size : [150, 450, 300, 11] ; model type : LSTMbaseline ; model second layer type : forward adopt sequence based training .............. adopt learning method ............. adaGrad current learning rate ..............0.02 new epoch start.....................0 batch loss : 143.739120483 batch loss : 32.5928192139 ............................................................................................. batch loss : 4.02528572083 batch loss : 2.54068398476 epoch done ............... sum loss : 728.014112115 trainscore ........ epoch : 3 P: 0.712865819988 R : 0.399690402476 F : 0.512200068248
devscore ........ epoch : 3 P: 0.597014925364 R : 0.30769230769 F : 0.406091480781
Segmentation fault (core dumped) rzai@rzai00:~/prj/chineseNER/KG/workDir$
rzai@rzai00:~/prj/chineseNER/KG/workDir$ python ../main.py train --train data/traindata --dev data/devdata --test data/testdata --score_dir trainResult Using gpu device 0: GeForce GTX 1080 (CNMeM is disabled, cuDNN 5105) choose model type .................. LSTMbaseline embedding size : 150 embeddings num : 316001 {'I-PRO': 2, 'B-PRO': 1, 'B-ORG': 9, 'I-COM': 8, 'I-PER': 4, 'B-COM': 7, 'O': 0, 'I-LOC': 6, 'I-ORG': 10, 'B-LOC': 5, 'B-PER': 3} model training args : Namespace(CRF=False, action='train', batch_size=64, corpus='data/rawdata', dev='data/devdata', drop_pro=0.35, emb_lrate=0.01, embeddings='vector/baidu_charvectors_150', epoch=10, eva='data/testdata', fix_emb=True, gradbound=-0.1, highreg_weight=0.1, istoken=False, lowreg_weight=0.1, lrate=0.02, model='', model_type='LSTMbaseline', mweight=0.9, nc=3, net_size=[150, 450, 300, 11], opt='adaGrad', output='result', score_dir='trainResult', second_layer='forward', test='data/testdata', train='data/traindata', variance=0.01, wbound=-0.1, wdecay=0.0) whether fixing word embedding : True model size : [150, 450, 300, 11] ; model type : LSTMbaseline ; model second layer type : forward adopt sequence based training .............. adopt learning method ............. adaGrad current learning rate ..............0.02 new epoch start.....................0 batch loss : 143.739120483 batch loss : 32.5928192139 ............................................................................................. batch loss : 4.02528572083 batch loss : 2.54068398476 epoch done ............... sum loss : 728.014112115 trainscore ........ epoch : 3 P: 0.712865819988 R : 0.399690402476 F : 0.512200068248
devscore ........ epoch : 3 P: 0.597014925364 R : 0.30769230769 F : 0.406091480781
Segmentation fault (core dumped) rzai@rzai00:~/prj/chineseNER/KG/workDir$