提交 9827a5c6 编写于 作者: Q qingqing01 提交者: GitHub

Merge pull request #1504 from qingqing01/srl_api_v2

semantic_role_labeling v2 api
import sys
import math
import numpy as np
import paddle.v2 as paddle
import paddle.v2.dataset.conll05 as conll05
def db_lstm():
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_len = len(verb_dict)
mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 512
depth = 8
#8 features
def d_type(size):
return paddle.data_type.integer_value_sequence(size)
word = paddle.layer.data(name='word_data', type=d_type(word_dict_len))
predicate = paddle.layer.data(name='verb_data', type=d_type(pred_len))
ctx_n2 = paddle.layer.data(name='ctx_n2_data', type=d_type(word_dict_len))
ctx_n1 = paddle.layer.data(name='ctx_n1_data', type=d_type(word_dict_len))
ctx_0 = paddle.layer.data(name='ctx_0_data', type=d_type(word_dict_len))
ctx_p1 = paddle.layer.data(name='ctx_p1_data', type=d_type(word_dict_len))
ctx_p2 = paddle.layer.data(name='ctx_p2_data', type=d_type(word_dict_len))
mark = paddle.layer.data(name='mark_data', type=d_type(mark_dict_len))
target = paddle.layer.data(name='target', type=d_type(label_dict_len))
default_std = 1 / math.sqrt(hidden_dim) / 3.0
emb_para = paddle.attr.Param(name='emb', initial_std=0., learning_rate=0.)
std_0 = paddle.attr.Param(initial_std=0.)
std_default = paddle.attr.Param(initial_std=default_std)
predicate_embedding = paddle.layer.embedding(
size=word_dim,
input=predicate,
param_attr=paddle.attr.Param(
name='vemb', initial_std=default_std))
mark_embedding = paddle.layer.embedding(
size=mark_dim, input=mark, param_attr=std_0)
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [
paddle.layer.embedding(
size=word_dim, input=x, param_attr=emb_para) for x in word_input
]
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
hidden_0 = paddle.layer.mixed(
size=hidden_dim,
bias_attr=std_default,
input=[
paddle.layer.full_matrix_projection(
input=emb, param_attr=std_default) for emb in emb_layers
])
mix_hidden_lr = 1e-3
lstm_para_attr = paddle.attr.Param(initial_std=0.0, learning_rate=1.0)
hidden_para_attr = paddle.attr.Param(
initial_std=default_std, learning_rate=mix_hidden_lr)
lstm_0 = paddle.layer.lstmemory(
input=hidden_0,
act=paddle.activation.Relu(),
gate_act=paddle.activation.Sigmoid(),
state_act=paddle.activation.Sigmoid(),
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):
mix_hidden = paddle.layer.mixed(
size=hidden_dim,
bias_attr=std_default,
input=[
paddle.layer.full_matrix_projection(
input=input_tmp[0], param_attr=hidden_para_attr),
paddle.layer.full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
])
lstm = paddle.layer.lstmemory(
input=mix_hidden,
act=paddle.activation.Relu(),
gate_act=paddle.activation.Sigmoid(),
state_act=paddle.activation.Sigmoid(),
reverse=((i % 2) == 1),
bias_attr=std_0,
param_attr=lstm_para_attr)
input_tmp = [mix_hidden, lstm]
feature_out = paddle.layer.mixed(
size=label_dict_len,
bias_attr=std_default,
input=[
paddle.layer.full_matrix_projection(
input=input_tmp[0], param_attr=hidden_para_attr),
paddle.layer.full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
], )
crf_cost = paddle.layer.crf(size=label_dict_len,
input=feature_out,
label=target,
param_attr=paddle.attr.Param(
name='crfw',
initial_std=default_std,
learning_rate=mix_hidden_lr))
crf_dec = paddle.layer.crf_decoding(
name='crf_dec_l',
size=label_dict_len,
input=feature_out,
label=target,
param_attr=paddle.attr.Param(name='crfw'))
return crf_cost, crf_dec
def load_parameter(file_name, h, w):
with open(file_name, 'rb') as f:
f.read(16) # skip header.
return np.fromfile(f, dtype=np.float32).reshape(h, w)
def main():
paddle.init(use_gpu=False, trainer_count=1)
# define network topology
crf_cost, crf_dec = db_lstm()
# create parameters
parameters = paddle.parameters.create([crf_cost, crf_dec])
# create optimizer
optimizer = paddle.optimizer.Momentum(
momentum=0,
learning_rate=2e-2,
regularization=paddle.optimizer.L2Regularization(rate=8e-4),
model_average=paddle.optimizer.ModelAverage(
average_window=0.5, max_average_window=10000), )
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
trainer = paddle.trainer.SGD(cost=crf_cost,
parameters=parameters,
update_equation=optimizer)
parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
trn_reader = paddle.reader.batched(
paddle.reader.shuffle(
conll05.test(), buf_size=8192), batch_size=10)
trainer.train(
reader=trn_reader, event_handler=event_handler, num_passes=10000)
if __name__ == '__main__':
main()
...@@ -17,5 +17,6 @@ import imikolov ...@@ -17,5 +17,6 @@ import imikolov
import imdb import imdb
import cifar import cifar
import movielens import movielens
import conll05
__all__ = ['mnist', 'imikolov', 'imdb', 'cifar', 'movielens'] __all__ = ['mnist', 'imikolov', 'imdb', 'cifar', 'movielens', 'conll05']
...@@ -12,10 +12,10 @@ ...@@ -12,10 +12,10 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import paddle.v2.dataset.common
import tarfile import tarfile
import gzip import gzip
import itertools import itertools
from common import download
__all__ = ['test, get_dict', 'get_embedding'] __all__ = ['test, get_dict', 'get_embedding']
""" """
...@@ -160,7 +160,6 @@ def reader_creator(corpus_reader, ...@@ -160,7 +160,6 @@ def reader_creator(corpus_reader,
ctx_p2 = 'eos' ctx_p2 = 'eos'
word_idx = [word_dict.get(w, UNK_IDX) for w in sentence] word_idx = [word_dict.get(w, UNK_IDX) for w in sentence]
pred_idx = [predicate_dict.get(predicate)] * sen_len
ctx_n2_idx = [word_dict.get(ctx_n2, UNK_IDX)] * sen_len ctx_n2_idx = [word_dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_idx = [word_dict.get(ctx_n1, UNK_IDX)] * sen_len ctx_n1_idx = [word_dict.get(ctx_n1, UNK_IDX)] * sen_len
...@@ -168,38 +167,30 @@ def reader_creator(corpus_reader, ...@@ -168,38 +167,30 @@ def reader_creator(corpus_reader,
ctx_p1_idx = [word_dict.get(ctx_p1, UNK_IDX)] * sen_len ctx_p1_idx = [word_dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_idx = [word_dict.get(ctx_p2, UNK_IDX)] * sen_len ctx_p2_idx = [word_dict.get(ctx_p2, UNK_IDX)] * sen_len
pred_idx = [predicate_dict.get(predicate)] * sen_len
label_idx = [label_dict.get(w) for w in labels] label_idx = [label_dict.get(w) for w in labels]
yield word_idx, pred_idx, ctx_n2_idx, ctx_n1_idx, \ yield word_idx, ctx_n2_idx, ctx_n1_idx, \
ctx_0_idx, ctx_p1_idx, ctx_p2_idx, mark, label_idx ctx_0_idx, ctx_p1_idx, ctx_p2_idx, pred_idx, mark, label_idx
return reader() return reader
def get_dict(): def get_dict():
word_dict = load_dict( word_dict = load_dict(download(WORDDICT_URL, 'conll05st', WORDDICT_MD5))
common.download(WORDDICT_URL, 'conll05st', WORDDICT_MD5)) verb_dict = load_dict(download(VERBDICT_URL, 'conll05st', VERBDICT_MD5))
verb_dict = load_dict( label_dict = load_dict(download(TRGDICT_URL, 'conll05st', TRGDICT_MD5))
common.download(VERBDICT_URL, 'conll05st', VERBDICT_MD5))
label_dict = load_dict(
common.download(TRGDICT_URL, 'conll05st', TRGDICT_MD5))
return word_dict, verb_dict, label_dict return word_dict, verb_dict, label_dict
def get_embedding(): def get_embedding():
return common.download(EMB_URL, 'conll05st', EMB_MD5) return download(EMB_URL, 'conll05st', EMB_MD5)
def test(): def test():
word_dict, verb_dict, label_dict = get_dict() word_dict, verb_dict, label_dict = get_dict()
reader = corpus_reader( reader = corpus_reader(
common.download(DATA_URL, 'conll05st', DATA_MD5), download(DATA_URL, 'conll05st', DATA_MD5),
words_name='conll05st-release/test.wsj/words/test.wsj.words.gz', words_name='conll05st-release/test.wsj/words/test.wsj.words.gz',
props_name='conll05st-release/test.wsj/props/test.wsj.props.gz') props_name='conll05st-release/test.wsj/props/test.wsj.props.gz')
return reader_creator(reader, word_dict, verb_dict, label_dict) return reader_creator(reader, word_dict, verb_dict, label_dict)
if __name__ == '__main__':
print get_embedding()
for f in test():
print f
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