提交 891867ec 编写于 作者: Z zhangjinchao01

update srl demo

上级 0c7ac3d9
......@@ -17,24 +17,15 @@ import os
from optparse import OptionParser
def extract_dict_features(pair_file, feature_file, src_dict_file,
tgt_dict_file):
src_dict = set()
tgt_dict = set()
with open(pair_file) as fin, open(feature_file, 'w') as feature_out, open(
src_dict_file, 'w') as src_dict_out, open(tgt_dict_file,
'w') as tgt_dict_out:
def extract_dict_features(pair_file, feature_file):
with open(pair_file) as fin, open(feature_file, 'w') as feature_out:
for line in fin:
sentence, labels = line.strip().split('\t')
sentence, predicate, labels = line.strip().split('\t')
sentence_list = sentence.split()
labels_list = labels.split()
src_dict.update(sentence_list)
tgt_dict.update(labels_list)
verb_index = labels_list.index('B-V')
verb_feature = sentence_list[verb_index]
mark = [0] * len(labels_list)
if verb_index > 0:
......@@ -42,47 +33,50 @@ def extract_dict_features(pair_file, feature_file, src_dict_file,
ctx_n1 = sentence_list[verb_index - 1]
else:
ctx_n1 = 'bos'
ctx_n1_feature = ctx_n1
if verb_index > 1:
mark[verb_index - 2] = 1
ctx_n2 = sentence_list[verb_index - 2]
else:
ctx_n2 = 'bos'
mark[verb_index] = 1
ctx_0_feature = sentence_list[verb_index]
ctx_0 = sentence_list[verb_index]
if verb_index < len(labels_list) - 2:
mark[verb_index + 1] = 1
ctx_p1 = sentence_list[verb_index + 1]
else:
ctx_p1 = 'eos'
ctx_p1_feature = ctx_p1
if verb_index < len(labels_list) - 3:
mark[verb_index + 2] = 1
ctx_p2 = sentence_list[verb_index + 2]
else:
ctx_p2 = 'eos'
feature_str = sentence + '\t' \
+ verb_feature + '\t' \
+ ctx_n1_feature + '\t' \
+ ctx_0_feature + '\t' \
+ ctx_p1_feature + '\t' \
+ predicate + '\t' \
+ ctx_n2 + '\t' \
+ ctx_n1 + '\t' \
+ ctx_0 + '\t' \
+ ctx_p1 + '\t' \
+ ctx_p2 + '\t' \
+ ' '.join([str(i) for i in mark]) + '\t' \
+ labels
feature_out.write(feature_str + '\n')
src_dict_out.write('<unk>\n')
src_dict_out.write('\n'.join(list(src_dict)))
tgt_dict_out.write('\n'.join(list(tgt_dict)))
if __name__ == '__main__':
usage = '-p pair_file -f feature_file -s source dictionary -t target dictionary '
usage = '-p pair_file -f feature_file'
parser = OptionParser(usage)
parser.add_option('-p', dest='pair_file', help='the pair file')
parser.add_option(
'-f', dest='feature_file', help='the file to store feature')
parser.add_option(
'-s', dest='src_dict', help='the file to store source dictionary')
parser.add_option(
'-t', dest='tgt_dict', help='the file to store target dictionary')
parser.add_option('-f', dest='feature_file', help='the feature file')
(options, args) = parser.parse_args()
extract_dict_features(options.pair_file, options.feature_file,
options.src_dict, options.tgt_dict)
extract_dict_features(options.pair_file, options.feature_file)
......@@ -51,7 +51,7 @@ def read_sentences(words_file):
for line in fin:
line = line.strip()
if line == '':
sentences.append(s.lower())
sentences.append(s)
s = ''
else:
s += line + ' '
......@@ -64,6 +64,11 @@ def transform_labels(sentences, labels):
if len(labels[i]) == 1:
continue
else:
verb_list = []
for x in labels[i][0]:
if x !='-':
verb_list.append(x)
for j in xrange(1, len(labels[i])):
label_list = labels[i][j]
current_tag = 'O'
......@@ -88,8 +93,7 @@ def transform_labels(sentences, labels):
is_in_bracket = True
else:
print 'error:', ll
sen_lab_pair.append((sentences[i], label_seq))
sen_lab_pair.append((sentences[i], verb_list[j-1], label_seq))
return sen_lab_pair
......@@ -97,9 +101,9 @@ def write_file(sen_lab_pair, output_file):
with open(output_file, 'w') as fout:
for x in sen_lab_pair:
sentence = x[0]
label_seq = ' '.join(x[1])
assert len(sentence.split()) == len(x[1])
fout.write(sentence + '\t' + label_seq + '\n')
label_seq = ' '.join(x[2])
assert len(sentence.split()) == len(x[2])
fout.write(sentence + '\t' + x[1]+'\t' +label_seq + '\n')
if __name__ == '__main__':
......
......@@ -14,6 +14,10 @@
# limitations under the License.
set -e
wget http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz
wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/verbDict.txt --no-check-certificate
wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/targetDict.txt --no-check-certificate
wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/wordDict.txt --no-check-certificate
wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/emb --no-check-certificate
tar -xzvf conll05st-tests.tar.gz
rm conll05st-tests.tar.gz
cp ./conll05st-release/test.wsj/words/test.wsj.words.gz .
......@@ -22,4 +26,4 @@ gunzip test.wsj.words.gz
gunzip test.wsj.props.gz
python extract_pairs.py -w test.wsj.words -p test.wsj.props -o test.wsj.seq_pair
python extract_dict_feature.py -p test.wsj.seq_pair -f feature -s src.dict -t tgt.dict
python extract_dict_feature.py -p test.wsj.seq_pair -f feature
......@@ -17,11 +17,15 @@ from paddle.trainer.PyDataProvider2 import *
UNK_IDX = 0
def hook(settings, word_dict, label_dict, **kwargs):
def hook(settings, word_dict, label_dict, predicate_dict, **kwargs):
settings.word_dict = word_dict
settings.label_dict = label_dict
settings.predicate_dict = predicate_dict
#all inputs are integral and sequential type
settings.slots = [
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(predicate_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
......@@ -31,27 +35,33 @@ def hook(settings, word_dict, label_dict, **kwargs):
integer_value_sequence(len(label_dict))]
@provider(init_hook=hook)
def process(obj, file_name):
def get_batch_size(yeild_data):
return len(yeild_data[0])
@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size,
can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_name):
with open(file_name, 'r') as fdata:
for line in fdata:
sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = \
sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = \
line.strip().split('\t')
words = sentence.split()
sen_len = len(words)
word_slot = [obj.word_dict.get(w, UNK_IDX) for w in words]
word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
predicate_slot = [obj.word_dict.get(predicate, UNK_IDX)] * sen_len
ctx_n1_slot = [obj.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_slot = [obj.word_dict.get(ctx_0, UNK_IDX)] * sen_len
ctx_p1_slot = [obj.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
marks = mark.split()
mark_slot = [int(w) for w in marks]
label_list = label.split()
label_slot = [obj.label_dict.get(w) for w in label_list]
yield word_slot, predicate_slot, ctx_n1_slot, \
ctx_0_slot, ctx_p1_slot, mark_slot, label_slot
label_slot = [settings.label_dict.get(w) for w in label_list]
yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot
......@@ -19,8 +19,9 @@ import sys
from paddle.trainer_config_helpers import *
#file paths
word_dict_file = './data/src.dict'
label_dict_file = './data/tgt.dict'
word_dict_file = './data/wordDict.txt'
label_dict_file = './data/targetDict.txt'
predicate_file= './data/verbDict.txt'
train_list_file = './data/train.list'
test_list_file = './data/test.list'
......@@ -31,8 +32,10 @@ if not is_predict:
#load dictionaries
word_dict = dict()
label_dict = dict()
predicate_dict = dict()
with open(word_dict_file, 'r') as f_word, \
open(label_dict_file, 'r') as f_label:
open(label_dict_file, 'r') as f_label, \
open(predicate_file, 'r') as f_pre:
for i, line in enumerate(f_word):
w = line.strip()
word_dict[w] = i
......@@ -41,6 +44,11 @@ if not is_predict:
w = line.strip()
label_dict[w] = i
for i, line in enumerate(f_pre):
w = line.strip()
predicate_dict[w] = i
if is_test:
train_list_file = None
......@@ -51,91 +59,169 @@ if not is_predict:
module='dataprovider',
obj='process',
args={'word_dict': word_dict,
'label_dict': label_dict})
'label_dict': label_dict,
'predicate_dict': predicate_dict })
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_len = len(predicate_dict)
else:
word_dict_len = get_config_arg('dict_len', int)
label_dict_len = get_config_arg('label_len', int)
pred_len = get_config_arg('pred_len', int)
############################## Hyper-parameters ##################################
mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 128
hidden_dim = 512
depth = 8
emb_lr = 1e-2
fc_lr = 1e-2
lstm_lr = 2e-2
########################### Optimizer #######################################
settings(
batch_size=150,
learning_method=AdamOptimizer(),
learning_rate=1e-3,
learning_method=MomentumOptimizer(momentum=0),
learning_rate=2e-2,
regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25)
is_async=False,
model_average=ModelAverage(average_window=0.5,
max_average_window=10000),
)
#6 features
####################################### network ##############################
#8 features and 1 target
word = data_layer(name='word_data', size=word_dict_len)
predicate = data_layer(name='verb_data', size=word_dict_len)
predicate = data_layer(name='verb_data', size=pred_len)
ctx_n2 = data_layer(name='ctx_n2_data', size=word_dict_len)
ctx_n1 = data_layer(name='ctx_n1_data', size=word_dict_len)
ctx_0 = data_layer(name='ctx_0_data', size=word_dict_len)
ctx_p1 = data_layer(name='ctx_p1_data', size=word_dict_len)
ctx_p2 = data_layer(name='ctx_p2_data', size=word_dict_len)
mark = data_layer(name='mark_data', size=mark_dict_len)
if not is_predict:
target = data_layer(name='target', size=label_dict_len)
ptt = ParameterAttribute(name='src_emb', learning_rate=emb_lr)
layer_attr = ExtraLayerAttribute(drop_rate=0.5)
fc_para_attr = ParameterAttribute(learning_rate=fc_lr)
lstm_para_attr = ParameterAttribute(initial_std=0., learning_rate=lstm_lr)
para_attr = [fc_para_attr, lstm_para_attr]
word_embedding = embedding_layer(size=word_dim, input=word, param_attr=ptt)
predicate_embedding = embedding_layer(
size=word_dim, input=predicate, param_attr=ptt)
ctx_n1_embedding = embedding_layer(size=word_dim, input=ctx_n1, param_attr=ptt)
ctx_0_embedding = embedding_layer(size=word_dim, input=ctx_0, param_attr=ptt)
ctx_p1_embedding = embedding_layer(size=word_dim, input=ctx_p1, param_attr=ptt)
mark_embedding = embedding_layer(size=mark_dim, input=mark)
default_std=1/math.sqrt(hidden_dim)/3.0
emb_para = ParameterAttribute(name='emb', initial_std=0., learning_rate=0.)
std_0 = ParameterAttribute(initial_std=0.)
std_default = ParameterAttribute(initial_std=default_std)
word_embedding = embedding_layer(size=word_dim, input=word, param_attr=emb_para)
predicate_embedding = embedding_layer(size=word_dim, input=predicate, param_attr=ParameterAttribute(name='vemb',initial_std=default_std))
ctx_n2_embedding = embedding_layer(size=word_dim, input=ctx_n2, param_attr=emb_para)
ctx_n2_embedding = embedding_layer(size=word_dim, input=ctx_n2, param_attr=emb_para)
ctx_n1_embedding = embedding_layer(size=word_dim, input=ctx_n1, param_attr=emb_para)
ctx_0_embedding = embedding_layer(size=word_dim, input=ctx_0, param_attr=emb_para)
ctx_p1_embedding = embedding_layer(size=word_dim, input=ctx_p1, param_attr=emb_para)
ctx_p2_embedding = embedding_layer(size=word_dim, input=ctx_p2, param_attr=emb_para)
mark_embedding = embedding_layer(name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
hidden_0 = mixed_layer(
name='hidden0',
size=hidden_dim,
bias_attr=std_default,
input=[
full_matrix_projection(input=word_embedding),
full_matrix_projection(input=predicate_embedding),
full_matrix_projection(input=ctx_n1_embedding),
full_matrix_projection(input=ctx_0_embedding),
full_matrix_projection(input=ctx_p1_embedding),
full_matrix_projection(input=mark_embedding),
full_matrix_projection(input=word_embedding, param_attr=std_default),
full_matrix_projection(input=predicate_embedding, param_attr=std_default),
full_matrix_projection(input=ctx_n2_embedding, param_attr=std_default),
full_matrix_projection(input=ctx_n1_embedding, param_attr=std_default),
full_matrix_projection(input=ctx_0_embedding, param_attr=std_default),
full_matrix_projection(input=ctx_p1_embedding, param_attr=std_default),
full_matrix_projection(input=ctx_p2_embedding, param_attr=std_default),
full_matrix_projection(input=mark_embedding, param_attr=std_default)
])
lstm_0 = lstmemory(input=hidden_0, layer_attr=layer_attr)
mix_hidden_lr = 1e-3
lstm_para_attr = ParameterAttribute(initial_std=0.0, learning_rate=1.0)
hidden_para_attr = ParameterAttribute(initial_std=default_std, learning_rate=mix_hidden_lr)
lstm_0 = lstmemory(name='lstm0',
input=hidden_0,
act=ReluActivation(),
gate_act=SigmoidActivation(),
state_act=SigmoidActivation(),
bias_attr=std_0,
param_attr=lstm_para_attr)
#stack L-LSTM and R-LSTM with direct edges
input_tmp = [hidden_0, lstm_0]
for i in range(1, depth):
fc = fc_layer(input=input_tmp, size=hidden_dim, param_attr=para_attr)
mix_hidden = mixed_layer(name='hidden'+str(i),
size=hidden_dim,
bias_attr=std_default,
input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
]
)
lstm = lstmemory(name='lstm'+str(i),
input=mix_hidden,
act=ReluActivation(),
gate_act=SigmoidActivation(),
state_act=SigmoidActivation(),
reverse=((i % 2)==1),
bias_attr=std_0,
param_attr=lstm_para_attr)
input_tmp = [mix_hidden, lstm]
feature_out = mixed_layer(name='output',
size=label_dict_len,
bias_attr=std_default,
input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
],
)
lstm = lstmemory(
input=fc,
act=ReluActivation(),
reverse=(i % 2) == 1,
layer_attr=layer_attr)
input_tmp = [fc, lstm]
prob = fc_layer(
input=input_tmp,
size=label_dict_len,
act=SoftmaxActivation(),
param_attr=para_attr)
if not is_predict:
cls = classification_cost(input=prob, label=target)
outputs(cls)
crf_l = crf_layer( name = 'crf',
size = label_dict_len,
input = feature_out,
label = target,
param_attr=ParameterAttribute(name='crfw',initial_std=default_std, learning_rate=mix_hidden_lr)
)
crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
size = label_dict_len,
input = feature_out,
label = target,
param_attr=ParameterAttribute(name='crfw')
)
eval = sum_evaluator(input=crf_dec_l)
outputs(crf_l)
else:
outputs(prob)
crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
size = label_dict_len,
input = feature_out,
param_attr=ParameterAttribute(name='crfw')
)
outputs(crf_dec_l)
......@@ -26,7 +26,7 @@ UNK_IDX = 0
class Prediction():
def __init__(self, train_conf, dict_file, model_dir, label_file):
def __init__(self, train_conf, dict_file, model_dir, label_file, predicate_dict_file):
"""
train_conf: trainer configure.
dict_file: word dictionary file name.
......@@ -35,16 +35,19 @@ class Prediction():
self.dict = {}
self.labels = {}
self.predicate_dict={}
self.labels_reverse = {}
self.load_dict_label(dict_file, label_file)
self.load_dict_label(dict_file, label_file, predicate_dict_file)
len_dict = len(self.dict)
len_label = len(self.labels)
len_pred = len(self.predicate_dict)
conf = parse_config(
train_conf,
'dict_len=' + str(len_dict) +
'dict_len=' + str(len_dict) +
',label_len=' + str(len_label) +
',pred_len=' + str(len_pred) +
',is_predict=True')
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
......@@ -52,15 +55,17 @@ class Prediction():
slots = [
integer_value_sequence(len_dict),
integer_value_sequence(len_pred),
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(2)
]
]
self.converter = DataProviderConverter(slots)
def load_dict_label(self, dict_file, label_file):
def load_dict_label(self, dict_file, label_file, predicate_dict_file):
"""
Load dictionary from self.dict_file.
"""
......@@ -71,39 +76,42 @@ class Prediction():
self.labels[line.strip()] = line_count
self.labels_reverse[line_count] = line.strip()
for line_count, line in enumerate(open(predicate_dict_file, 'r')):
self.predicate_dict[line.strip()] = line_count
def get_data(self, data_file):
"""
Get input data of paddle format.
"""
with open(data_file, 'r') as fdata:
for line in fdata:
sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = line.strip(
sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = line.strip(
).split('\t')
words = sentence.split()
sen_len = len(words)
word_slot = [self.dict.get(w, UNK_IDX) for w in words]
predicate_slot = [self.dict.get(predicate, UNK_IDX)] * sen_len
predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)] * sen_len
ctx_n2_slot = [self.dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_slot = [self.dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_slot = [self.dict.get(ctx_0, UNK_IDX)] * sen_len
ctx_p1_slot = [self.dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_slot = [self.dict.get(ctx_p2, UNK_IDX)] * sen_len
marks = mark.split()
mark_slot = [int(w) for w in marks]
yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot
yield word_slot, predicate_slot, ctx_n1_slot, \
ctx_0_slot, ctx_p1_slot, mark_slot
def predict(self, data_file):
def predict(self, data_file, output_file):
"""
data_file: file name of input data.
"""
input = self.converter(self.get_data(data_file))
output = self.network.forwardTest(input)
prob = output[0]["value"]
lab = list(np.argsort(-prob)[:, 0])
lab = output[0]["id"].tolist()
with open(data_file, 'r') as fin, open('predict.res', 'w') as fout:
with open(data_file, 'r') as fin, open(output_file, 'w') as fout:
index = 0
for line in fin:
sen = line.split('\t')[0]
......@@ -115,8 +123,8 @@ class Prediction():
def option_parser():
usage = ("python predict.py -c config -w model_dir "
"-d word dictionary -l label_file -i input_file")
usage = ("python predict.py -c config -w model_dir "
"-d word dictionary -l label_file -i input_file -p pred_dict_file")
parser = OptionParser(usage="usage: %s [options]" % usage)
parser.add_option(
"-c",
......@@ -137,6 +145,13 @@ def option_parser():
dest="label_file",
default=None,
help="label file")
parser.add_option(
"-p",
"--predict_dict_file",
action="store",
dest="predict_dict_file",
default=None,
help="predict_dict_file")
parser.add_option(
"-i",
"--data",
......@@ -150,6 +165,14 @@ def option_parser():
dest="model_path",
default=None,
help="model path")
parser.add_option(
"-o",
"--output_file",
action="store",
dest="output_file",
default=None,
help="output file")
return parser.parse_args()
......@@ -160,10 +183,12 @@ def main():
dict_file = options.dict_file
model_path = options.model_path
label_file = options.label_file
predict_dict_file = options.predict_dict_file
output_file = options.output_file
swig_paddle.initPaddle("--use_gpu=0")
predict = Prediction(train_conf, dict_file, model_path, label_file)
predict.predict(data_file)
predict = Prediction(train_conf, dict_file, model_path, label_file, predict_dict_file)
predict.predict(data_file,output_file)
if __name__ == '__main__':
......
......@@ -26,15 +26,18 @@ LOG=`get_best_pass $log`
LOG=(${LOG})
best_model_path="output/pass-${LOG[1]}"
config_file=db_lstm.py
dict_file=./data/src.dict
label_file=./data/tgt.dict
dict_file=./data/wordDict.txt
label_file=./data/targetDict.txt
predicate_dict_file=./data/verbDict.txt
input_file=./data/feature
output_file=predict.res
python predict.py \
-c $config_file \
-w $best_model_path \
-l $label_file \
-p $predicate_dict_file \
-d $dict_file \
-i $input_file
-i $input_file \
-o $output_file
......@@ -16,12 +16,18 @@
set -e
paddle train \
--config=./db_lstm.py \
--use_gpu=0 \
--log_period=5000 \
--trainer_count=1 \
--show_parameter_stats_period=5000 \
--saving_period=1 \
--save_dir=./output \
--trainer_count=4 \
--log_period=10 \
--num_passes=500 \
--use_gpu=false \
--show_parameter_stats_period=10 \
--test_all_data_in_one_period=1 \
--local=1 \
--num_passes=10000 \
--test_period=0 \
--average_test_period=10000000 \
--init_model_path=./data \
--load_missing_parameter_strategy=rand \
--dot_period=100 \
2>&1 | tee 'train.log'
......@@ -30,8 +30,6 @@ Several new files appear in the `data `directory as follows.
conll05st-release:the test data set of CoNll-2005 shared task
test.wsj.words:the Wall Street Journal data sentences
test.wsj.props: the propositional arguments
src.dict:the dictionary of words in sentences
tgt.dict:the labels dictionary
feature: the extracted features from data set
```
......@@ -67,6 +65,8 @@ def hook(settings, word_dict, label_dict, **kwargs):
settings.label_dict = label_dict
#all inputs are integral and sequential type
settings.slots = [
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(predicate_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
......@@ -77,34 +77,39 @@ def hook(settings, word_dict, label_dict, **kwargs):
```
The corresponding data iterator is as following:
```
@provider(use_seq=True, init_hook=hook)
def process(obj, file_name):
@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size,
can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_name):
with open(file_name, 'r') as fdata:
for line in fdata:
sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = line.strip().split('\t')
sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = \
line.strip().split('\t')
words = sentence.split()
sen_len = len(words)
word_slot = [obj.word_dict.get(w, UNK_IDX) for w in words]
word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
predicate_slot = [obj.word_dict.get(predicate, UNK_IDX)] * sen_len
ctx_n1_slot = [obj.word_dict.get(ctx_n1, UNK_IDX) ] * sen_len
ctx_0_slot = [obj.word_dict.get(ctx_0, UNK_IDX) ] * sen_len
ctx_p1_slot = [obj.word_dict.get(ctx_p1, UNK_IDX) ] * sen_len
predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
marks = mark.split()
mark_slot = [int(w) for w in marks]
label_list = label.split()
label_slot = [obj.label_dict.get(w) for w in label_list]
yield word_slot, predicate_slot, ctx_n1_slot, ctx_0_slot, ctx_p1_slot, mark_slot, label_slot
label_slot = [settings.label_dict.get(w) for w in label_list]
yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot
```
The `process`function yield 7 lists which are six features and labels.
The `process`function yield 9 lists which are 8 features and label.
### Neural Network Config
`db_lstm.py` is the neural network config file to load the dictionaries and define the data provider module and network architecture during the training procedure.
Seven `data_layer` load instances from data provider. Six features are transformed into embedddings respectively, and mixed by `mixed_layer` . Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
Nine `data_layer` load instances from data provider. Eight features are transformed into embedddings respectively, and mixed by `mixed_layer` . Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
### Run Training
The script for training is `train.sh`, user just need to execute:
......@@ -115,24 +120,36 @@ The content in `train.sh`:
```
paddle train \
--config=./db_lstm.py \
--use_gpu=0 \
--log_period=5000 \
--trainer_count=1 \
--show_parameter_stats_period=5000 \
--saving_period=1 \
--save_dir=./output \
--trainer_count=4 \
--log_period=10 \
--num_passes=500 \
--use_gpu=false \
--show_parameter_stats_period=10 \
--test_all_data_in_one_period=1 \
--local=1 \
--num_passes=10000 \
--test_period=0 \
--average_test_period=10000000 \
--init_model_path=./data \
--load_missing_parameter_strategy=rand \
--dot_period=100 \
2>&1 | tee 'train.log'
```
- \--config=./db_lstm.py : network config file.
- \--save_di=./output: output path to save models.
- \--trainer_count=4 : set thread number (or GPU count).
- \--log_period=10 : print log every 20 batches.
- \--num_passes=500: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
- \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train.
- \--show_parameter_stats_period=10: show parameter statistic every 100 batches.
- \--test_all_data_in_one_period=1: test all data in every testing.
- \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train, until now crf_layer do not support GPU
- \--log_period=500: print log every 20 batches.
- \--trainer_count=1: set thread number (or GPU count).
- \--show_parameter_stats_period=5000: show parameter statistic every 100 batches.
- \--saving_period=1: save model per pass
- \--save_dir=./output: output path to save models.
- \--local=1: traing in local mode
- \--num_passes=10000: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
- \--test_period=0: run testing each pass
- \--average_test_period=10000000: do test on average parameter every average_test_period batches
- \--init_model_path=./data: parameter initialization path
- \--load_missing_parameter_strategy=rand: random initialization unexisted parameters
- \--dot_period=100: print a dot per 100 batches
After training, the models will be saved in directory `output`.
......@@ -166,11 +183,13 @@ The script for prediction is `predict.sh`, user just need to execute:
In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file
```
python predict.py
-c $config_file
-w $model_path
-l $label_file
-d $dict_file
-i $input_file
-c $config_file \
-w $best_model_path \
-l $label_file \
-p $predicate_dict_file \
-d $dict_file \
-i $input_file \
-o $output_file
```
`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix.
......
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