m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

update the SRL demo to add infering.

Browse Source
refactor_docs
caoying03 8 years ago
parent 2e527ad312
commit 0a21649f79
1 changed files with 116 additions and 53 deletions
Show Stats Download Patch File Download Diff File

169
demo/semantic_role_labeling/api_train_v2.py
Unescape View File

@ -1,26 +1,34 @@
import sys
import math
import numpy as np
import paddle.v2 as paddle
import gzip
import logging
import paddle.v2.dataset.conll05 as conll05
import paddle.v2.evaluator as evaluator
import paddle.v2 as paddle
logger = logging.getLogger('paddle')
logger.setLevel(logging.WARN)
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)
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
mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 512
depth = 8
default_std = 1 / math.sqrt(hidden_dim) / 3.0
mix_hidden_lr = 1e-3
#8 features
def d_type(size):
return paddle.data_type.integer_value_sequence(size)
def d_type(size):
return paddle.data_type.integer_value_sequence(size)
def db_lstm():
#8 features
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))
@ -31,11 +39,7 @@ def db_lstm():
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.)
emb_para = paddle.attr.Param(name='emb', initial_std=0., is_static=True)
std_0 = paddle.attr.Param(initial_std=0.)
std_default = paddle.attr.Param(initial_std=default_std)
@ -63,7 +67,6 @@ def db_lstm():
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)
@ -111,6 +114,33 @@ def db_lstm():
input=input_tmp[1], param_attr=lstm_para_attr)
], )
return feature_out
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 test_a_batch(inferer, test_data, tag_dict):
probs = inferer.infer(input=test_data, field='id')
assert len(probs) == sum(len(x[0]) for x in test_data)
for test_sample in test_data:
start_id = 0
pre_lab = [
tag_dict[probs[start_id + i]] for i in xrange(len(test_sample[0]))
]
print pre_lab
start_id += len(test_sample[0])
def main(is_predict=False):
paddle.init(use_gpu=False, trainer_count=1)
# define network topology
feature_out = db_lstm()
target = paddle.layer.data(name='target', type=d_type(label_dict_len))
crf_cost = paddle.layer.crf(size=label_dict_len,
input=feature_out,
label=target,
@ -120,29 +150,20 @@ def db_lstm():
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()
evaluator.sum(input=crf_dec)
evaluator.chunk(
input=crf_dec,
label=target,
chunk_scheme="IOB",
num_chunk_types=label_dict_len / 2)
# create parameters
parameters = paddle.parameters.create([crf_cost, crf_dec])
parameters = paddle.parameters.create(crf_cost)
parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
# create optimizer
optimizer = paddle.optimizer.Momentum(
@ -152,18 +173,12 @@ def main():
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))
update_equation=optimizer,
extra_layers=crf_dec)
trn_reader = paddle.batch(
reader = paddle.batch(
paddle.reader.shuffle(
conll05.test(), buf_size=8192), batch_size=10)
@ -179,12 +194,60 @@ def main():
'target': 8
}
trainer.train(
reader=trn_reader,
event_handler=event_handler,
num_passes=10000,
feeding=feeding)
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)
if event.batch_id % 1000 == 0:
result = trainer.test(reader=reader, feeding=feeding)
print "\nTest with Pass %d, Batch %d, %s" % (
event.pass_id, event.batch_id, result.metrics)
if isinstance(event, paddle.event.EndPass):
# save parameters
with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=reader, feeding=feeding)
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
if not is_predict:
trainer.train(
reader=reader,
event_handler=event_handler,
num_passes=10,
feeding=feeding)
else:
labels_reverse = {}
for (k, v) in label_dict.items():
labels_reverse[v] = k
test_creator = paddle.dataset.conll05.test()
predict = paddle.layer.crf_decoding(
size=label_dict_len,
input=feature_out,
param_attr=paddle.attr.Param(name='crfw'))
test_pass = 0
with gzip.open('params_pass_%d.tar.gz' % (test_pass)) as f:
parameters = paddle.parameters.Parameters.from_tar(f)
inferer = paddle.inference.Inference(
output_layer=predict, parameters=parameters)
# prepare test data
test_data = []
test_batch_size = 50
for idx, item in enumerate(test_creator()):
test_data.append(item[0:8])
if idx and (not idx % test_batch_size):
test_a_batch(inferer, test_data, labels_reverse)
test_data = []
test_a_batch(inferer, test_data, labels_reverse)
test_data = []
if __name__ == '__main__':
main()
main(is_predict=True)

Write Preview
Loading…
Cancel
Save