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Merge pull request thunlp#2 from AdityaAS/master
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Bug fix. Model trains without exceptions now
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@THUCSTHanxu13
THUCSTHanxu13 authored Apr 13, 2018
2 parents df90d75 + 18de11c commit dfd7c95
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Showing 2 changed files with 24 additions and 22 deletions.
22 changes: 11 additions & 11 deletions jointD/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,21 +27,21 @@
tf.app.flags.DEFINE_float('nbatch_kg',100,'entity numbers used each training time')
tf.app.flags.DEFINE_float('margin',1.0,'entity numbers used each training time')
tf.app.flags.DEFINE_float('learning_rate_kg',0.001,'learning rate for kg')
tf.app.flags.DEFINE_float('ent_total',lib.getEntityTotal(),'total of entities')
tf.app.flags.DEFINE_float('rel_total',lib.getRelationTotal(),'total of relations')
tf.app.flags.DEFINE_float('tri_total',lib.getTripleTotal(),'total of triples')
tf.app.flags.DEFINE_float('katt_flag', 1, '1 for katt, 0 for att')
tf.app.flags.DEFINE_integer('ent_total',lib.getEntityTotal(),'total of entities')
tf.app.flags.DEFINE_integer('rel_total',lib.getRelationTotal(),'total of relations')
tf.app.flags.DEFINE_integer('tri_total',lib.getTripleTotal(),'total of triples')
tf.app.flags.DEFINE_integer('katt_flag', 1, '1 for katt, 0 for att')

tf.app.flags.DEFINE_string('model', 'cnn', 'neural models to encode sentences')
tf.app.flags.DEFINE_float('max_length',config['fixlen'],'maximum of number of words in one sentence')
tf.app.flags.DEFINE_float('pos_num', config['maxlen'] * 2 + 1,'number of position embedding vectors')
tf.app.flags.DEFINE_float('num_classes', config['textual_rel_total'],'maximum of relations')
tf.app.flags.DEFINE_integer('max_length',config['fixlen'],'maximum of number of words in one sentence')
tf.app.flags.DEFINE_integer('pos_num', config['maxlen'] * 2 + 1,'number of position embedding vectors')
tf.app.flags.DEFINE_integer('num_classes', config['textual_rel_total'],'maximum of relations')

tf.app.flags.DEFINE_float('hidden_size',230,'hidden feature size')
tf.app.flags.DEFINE_float('pos_size',5,'position embedding size')
tf.app.flags.DEFINE_integer('hidden_size',230,'hidden feature size')
tf.app.flags.DEFINE_integer('pos_size',5,'position embedding size')

tf.app.flags.DEFINE_float('max_epoch',20,'maximum of training epochs')
tf.app.flags.DEFINE_float('batch_size',160,'entity numbers used each training time')
tf.app.flags.DEFINE_integer('max_epoch',20,'maximum of training epochs')
tf.app.flags.DEFINE_integer('batch_size',160,'entity numbers used each training time')
tf.app.flags.DEFINE_float('learning_rate',0.5,'learning rate for nn')
tf.app.flags.DEFINE_float('weight_decay',0.00001,'weight_decay')
tf.app.flags.DEFINE_float('keep_prob',0.5,'dropout rate')
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24 changes: 13 additions & 11 deletions jointE/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,21 +27,21 @@
tf.app.flags.DEFINE_float('nbatch_kg',100,'entity numbers used each training time')
tf.app.flags.DEFINE_float('margin',1.0,'entity numbers used each training time')
tf.app.flags.DEFINE_float('learning_rate_kg',0.001,'learning rate for kg')
tf.app.flags.DEFINE_float('ent_total',lib.getEntityTotal(),'total of entities')
tf.app.flags.DEFINE_float('rel_total',lib.getRelationTotal(),'total of relations')
tf.app.flags.DEFINE_float('tri_total',lib.getTripleTotal(),'total of triples')
tf.app.flags.DEFINE_float('katt_flag', 1, '1 for katt, 0 for att')
tf.app.flags.DEFINE_integer('ent_total',lib.getEntityTotal(),'total of entities')
tf.app.flags.DEFINE_integer('rel_total',lib.getRelationTotal(),'total of relations')
tf.app.flags.DEFINE_integer('tri_total',lib.getTripleTotal(),'total of triples')
tf.app.flags.DEFINE_integer('katt_flag', 1, '1 for katt, 0 for att')

tf.app.flags.DEFINE_string('model', 'cnn', 'neural models to encode sentences')
tf.app.flags.DEFINE_float('max_length',config['fixlen'],'maximum of number of words in one sentence')
tf.app.flags.DEFINE_float('pos_num', config['maxlen'] * 2 + 1,'number of position embedding vectors')
tf.app.flags.DEFINE_float('num_classes', config['textual_rel_total'],'maximum of relations')
tf.app.flags.DEFINE_integer('max_length',config['fixlen'],'maximum of number of words in one sentence')
tf.app.flags.DEFINE_integer('pos_num', config['maxlen'] * 2 + 1,'number of position embedding vectors')
tf.app.flags.DEFINE_integer('num_classes', config['textual_rel_total'],'maximum of relations')

tf.app.flags.DEFINE_float('hidden_size',230,'hidden feature size')
tf.app.flags.DEFINE_float('pos_size',5,'position embedding size')
tf.app.flags.DEFINE_integer('hidden_size',230,'hidden feature size')
tf.app.flags.DEFINE_integer('pos_size',5,'position embedding size')

tf.app.flags.DEFINE_float('max_epoch',20,'maximum of training epochs')
tf.app.flags.DEFINE_float('batch_size',160,'entity numbers used each training time')
tf.app.flags.DEFINE_integer('max_epoch',20,'maximum of training epochs')
tf.app.flags.DEFINE_integer('batch_size',160,'entity numbers used each training time')
tf.app.flags.DEFINE_float('learning_rate',0.5,'learning rate for nn')
tf.app.flags.DEFINE_float('weight_decay',0.00001,'weight_decay')
tf.app.flags.DEFINE_float('keep_prob',0.5,'dropout rate')
Expand Down Expand Up @@ -74,6 +74,7 @@ def main(_):
print 'reading word embedding'
word_vec = np.load(export_path + 'vec.npy')
print 'reading training data'

instance_triple = np.load(export_path + 'train_instance_triple.npy')
instance_scope = np.load(export_path + 'train_instance_scope.npy')
train_len = np.load(export_path + 'train_len.npy')
Expand All @@ -84,6 +85,7 @@ def main(_):
train_mask = np.load(export_path + 'train_mask.npy')
train_head = np.load(export_path + 'train_head.npy')
train_tail = np.load(export_path + 'train_tail.npy')

print 'reading finished'
print 'mentions : %d' % (len(instance_triple))
print 'sentences : %d' % (len(train_len))
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