/
hierarchical_rnn.py
152 lines (140 loc) · 7.16 KB
/
hierarchical_rnn.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
import evaluations as evals
import data_utilities as du
import utilities as util
import minibatch as mb
import time
import sys
import numpy as np
import lasagne
import theano.tensor as T
import theano
import logging
import arg_parser as ap
import multi_channle_fn as fn
def main(args):
logging.info("loading data...")
fake_train, fake_dev, fake_test = du.load_fake(doc_ling=False, sent_ling=False)
true_train, true_dev, true_test = du.load_true(doc_ling=False, sent_ling=False)
if args.debug:
true_train = true_train[0][:100]
fake_train = fake_train[:10]
true_dev = true_dev[:100]
fake_dev = fake_dev[:10]
true_test = true_test[:100]
fake_test = fake_test[:10]
if args.rnn_type == 'gru':
args.rnn = lasagne.layers.GRULayer
elif args.rnn_type == 'lstm':
args.rnn = lasagne.layers.LSTMLayer
else:
args.rnn = lasagne.layers.RecurrentLayer
logging.info("building dictionary...")
word_dict, char_dict = util.build_dict(None, max_words=0, dict_file=["word_dict", "char_dict"])
logging.info("creating embedding matrix...")
word_embed = util.words2embedding(word_dict, 100, args.embedding_file)
char_embed = util.char2embedding(char_dict, 30)
(args.word_vocab_size, args.word_embed_size) = word_embed.shape
(args.char_vocab_size, args.char_embed_size) = char_embed.shape
logging.info("compiling Theano function...")
att_fn, eval_fn, train_fn, params = create_theano_function(word_embed, char_embed, values=None)
logging.info("batching examples...")
dev_examples = mb.vec_minibatch(fake_dev + true_dev, word_dict, char_dict, args, False,
char=False, sent_ling=False, doc_ling=False)
test_examples = mb.vec_minibatch(fake_test + true_test, word_dict, char_dict, args, False,
char=False, sent_ling=False, doc_ling=False)
temp = []
for true_batch in true_train:
temp += true_batch
true_train = temp
del temp
train_examples = mb.doc_minibatch(fake_train + true_train, args.batch_size)
# train_examples = mb.train_doc_minibatch(fake_train, true_train, args)
logging.info("checking network...")
dev_acc = evals.eval_vec_batch(eval_fn, dev_examples, char=False, sent_ling=False, doc_ling=False)
print('Dev A: %.2f P:%.2f R:%.2f F:%.2f' % dev_acc)
test_acc = evals.eval_vec_batch(eval_fn, test_examples, char=False, sent_ling=False, doc_ling=False)
print('Performance on Test set: A: %.2f P:%.2f R:%.2f F:%.2f' % test_acc)
prev_fsc = 0
stop_count = 0
best_fsc = 0
best_acc = 0
logging.info("training %d examples" % len(train_examples))
start_time = time.time()
n_updates = 0
for epoch in range(args.epoches):
np.random.shuffle(train_examples)
# if epoch > 3:
# logging.info("compiling Theano function again...")
# args.learning_rate *= 0.9
# att_fn, eval_fn, train_fn, params = create_theano_function(
# word_embed, char_embed, values=[x.get_value() for x in params])
for batch_x, _ in train_examples:
batch_x, batch_y = zip(*batch_x)
batch_x = util.vectorization(list(batch_x), word_dict, char_dict, max_char_length=args.max_char)
batch_rnn, batch_sent_mask, batch_word_mask, _ = \
util.mask_padding(batch_x, args.max_sent, args.max_word, args.max_char)
batch_y = np.array(list(batch_y))
train_loss = train_fn(batch_rnn, batch_word_mask, batch_sent_mask, batch_y)
n_updates += 1
if n_updates % 100 == 0 and epoch > 7:
logging.info('Epoch = %d, loss = %.2f, elapsed time = %.2f (s)' %
(epoch, train_loss, time.time() - start_time))
# dev_acc = evals.eval_batch(eval_fn, dev_examples, word_dict, char_dict, args)
dev_acc = evals.eval_vec_batch(eval_fn, dev_examples, char=False, sent_ling=False, doc_ling=False)
logging.info('Dev A: %.2f P:%.2f R:%.2f F:%.2f' % dev_acc)
if dev_acc[3] > best_fsc and dev_acc[0] > best_acc:
best_fsc = dev_acc[3]
best_acc = dev_acc[0]
logging.info('Best dev f1: epoch = %d, n_udpates = %d, f1 = %.2f %%'
% (epoch, n_updates, dev_acc[3]))
record = 'Best dev accuracy: epoch = %d, n_udpates = %d ' % \
(epoch, n_updates) + ' Dev A: %.2f P:%.2f R:%.2f F:%.2f' % dev_acc
test_acc = evals.eval_vec_batch(eval_fn, test_examples, char=False, sent_ling=False, doc_ling=False)
print('Performance on Test set: A: %.2f P:%.2f R:%.2f F:%.2f' % test_acc)
# util.save_params('char_not_params_%.2f' % test_acc[3], params,
# epoch=epoch, n_updates=n_updates)
if prev_fsc > dev_acc[3]:
stop_count += 1
else:
stop_count = 0
if stop_count == 6:
print("stopped")
prev_fsc = dev_acc[3]
print(record)
print('Performance on Test set: A: %.2f P:%.2f R:%.2f F:%.2f' % test_acc)
return
def create_theano_function(word_embed, char_embed, values=None):
word_x = T.itensor3('word_x')
word_mask = T.tensor3('word_mask')
sent_mask = T.matrix('sent_mask')
label_y = T.ivector('label_y')
att_out, network_output, loss = fn.build_fn(word_x=word_x, word_mask=word_mask, sent_mask=sent_mask,
label_y=label_y, word_embed=word_embed, char_embed=None,
args=args)
if values is not None:
lasagne.layers.set_all_param_values(network_output, values, trainable=True)
params = lasagne.layers.get_all_params(network_output, trainable=True)
if args.optimizer == 'sgd':
updates = lasagne.updates.sgd(loss, params, args.learning_rate)
elif args.optimizer == 'momentum':
updates = lasagne.updates.momentum(loss, params, args.learning_rate)
train_fn = theano.function([word_x, word_mask, sent_mask, label_y],
loss, updates=updates)
prediction = lasagne.layers.get_output(network_output, deterministic=True)
eval_fn = theano.function([word_x, word_mask, sent_mask], prediction)
fn_check_attention = theano.function([word_x, word_mask, sent_mask], att_out)
return fn_check_attention, eval_fn, train_fn, params
if __name__ == '__main__':
args = ap.get_args()
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)s %(message)s", datefmt="%m-%d %H:%M")
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)s %(message)s", datefmt="%m-%d %H:%M")
logging.info(' '.join(sys.argv))
# args.debug=True
args.word_att = "dot"
args.batch_size = 14
# args.optimizer = "momentum"
args.learning_rate = 0.2
args.dropout_rate = 0.5
main(args)