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未验证 提交 1558a1d3 编写于 作者: L lujun 提交者： GitHub
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Merge pull request #676 from kuke/srl_doc 

Refine the cn doc of srl
上级 1fbfe305 ec2fbf4c
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07.label_semantic_roles/README.cn.md
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......@@ -151,7 +151,7 @@ conll05st-release/
4. 构造以BIO法表示的标记；
5. 依据词典获取词对应的整数索引。
预处理完成之后一条训练样本包含9个特征，分别是：句子序列、谓词、谓词上下文（占 5 列）、谓词上下区域标志、标注序列。下表是一条训练样本的示例。
预处理完成之后一条训练样本数据包含9个域，分别是：句子序列、谓词、谓词上下文（占 5 列）、谓词上下区域标志、标注序列。下表是一条训练样本的示例。
| 句子序列 | 谓词 | 谓词上下文（窗口 = 5） | 谓词上下文区域标记 | 标注序列 |
|---|---|---|---|---|
......@@ -206,33 +206,34 @@ print('pred_dict_len: ', pred_dict_len)
- 定义输入数据维度及模型超参数。
```python
mark_dict_len = 2 # 谓上下文区域标志的维度，是一个0-1 2值特征，因此维度为2
word_dim = 32 # 词向量维度
mark_dim = 5 # 谓词上下文区域通过词表被映射为一个实向量，这个是相邻的维度
hidden_dim = 512 # LSTM隐层向量的维度 ： 512 / 4
depth = 8 # 栈式LSTM的深度
mix_hidden_lr = 1e-3
mark_dict_len = 2 # 谓上下文区域标志的维度，是一个0-1 2值特征，因此维度为2
word_dim = 32 # 词向量维度
mark_dim = 5 # 谓词上下文区域通过词表被映射为一个实向量，这个是相邻的维度
hidden_dim = 512 # LSTM隐层向量的维度 ： 512 / 4
depth = 8 # 栈式LSTM的深度
mix_hidden_lr = 1e-3 # linear_chain_crf层的基础学习率
IS_SPARSE = True
PASS_NUM = 10
BATCH_SIZE = 10
IS_SPARSE = True # 是否以稀疏方式更新embedding
PASS_NUM = 10 # 训练轮数
BATCH_SIZE = 10 # batch size 大小
embedding_name = 'emb'
```
这里需要特别说明的是hidden_dim = 512指定了LSTM隐层向量的维度为128维，关于这一点请参考PaddlePaddle官方文档中[lstmemory](http://www.paddlepaddle.org/doc/ui/api/trainer_config_helpers/layers.html#lstmemory)的说明。
这里需要特别说明的是，参数 `hidden_dim = 512` 实际指定了LSTM隐层向量的维度为128，关于这一点请参考PaddlePaddle官方文档中[dynamic_lstm](http://www.paddlepaddle.org/documentation/docs/zh/1.2/api_cn/layers_cn.html#dynamic-lstm)的说明。
- 如上文提到，我们用基于英文维基百科训练好的词向量来初始化序列输入、谓词上下文总共6个特征的embedding层参数，在训练中不更新。
```python
# 这里加载PaddlePaddle上版保存的二进制模型
# 这里加载PaddlePaddle保存的二进制参数
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)
```
- 8个LSTM单元以“正向/反向”的顺序对所有输入序列进行学习。
- 8个LSTM单元以“正向/反向”的顺序对所有输入序列进行学习，主要的执行逻辑如下：
1）为不同的输入特征分别定义embedding层
```python
def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
......@@ -252,8 +253,8 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
is_sparse=IS_SPARSE)
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
# Since word vector lookup table is pre-trained, we won't update it this time.
# trainable being False prevents updating the lookup table during training.
# 因词向量是预训练好的，这里不再训练embedding表，
# 参数属性trainable设置成False阻止了embedding表在训练过程中被更新
emb_layers = [
fluid.layers.embedding(
size=[word_dict_len, word_dim],
......@@ -263,9 +264,12 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
]
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
```
2) 定义深度双向LSTM结构
# 8 LSTM units are trained through alternating left-to-right / right-to-left order
# denoted by the variable `reverse`.
```python
# 共有8个LSTM单元被训练，每个单元的方向为从左到右或从右到左，
# 由参数`is_reverse`确定
hidden_0_layers = [
fluid.layers.fc(input=emb, size=hidden_dim, act='tanh')
for emb in emb_layers
......@@ -280,19 +284,9 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
gate_activation='sigmoid',
cell_activation='sigmoid')
# stack L-LSTM and R-LSTM with direct edges
# 用直连的边来堆叠L-LSTM、R-LSTM
input_tmp = [hidden_0, lstm_0]
# In PaddlePaddle, state features and transition features of a CRF are implemented
# by a fully connected layer and a CRF layer seperately. The fully connected layer
# with linear activation learns the state features, here we use fluid.layers.sums
# (fluid.layers.fc can be uesed as well), and the CRF layer in PaddlePaddle:
# fluid.layers.linear_chain_crf only
# learns the transition features, which is a cost layer and is the last layer of the network.
# fluid.layers.linear_chain_crf outputs the log probability of true tag sequence
# as the cost by given the input sequence and it requires the true tag sequence
# as target in the learning process.
for i in range(1, depth):
mix_hidden = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=hidden_dim, act='tanh'),
......@@ -321,128 +315,207 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
## 训练模型
- 我们根据网络拓扑结构和模型参数来构造出trainer用来训练，在构造时还需指定优化方法，这里使用最基本的SGD方法(momentum设置为0)，同时设定了学习率、正则等。
- 数据介绍部分提到CoNLL 2005训练集付费，这里我们使用测试集训练供大家学习。conll05.test()每次产生一条样本，包含9个特征，shuffle和组完batch后作为训练的输入。
- 我们根据网络拓扑结构和模型参数来进行训练，在构造时还需指定优化方法，这里使用最基本的SGD方法(momentum设置为0)，同时设定了学习率、正则等。
- 通过feeding来指定每一个数据和data_layer的对应关系。 例如 下面feeding表示: conll05.test()产生数据的第0列对应word_data层的特征。
定义训练过程的超参数
```python
use_cuda = False #在cpu上执行训练
save_dirname = "label_semantic_roles.inference.model" #训练得到的模型参数保存在文件中
is_local = True
```
- 可以使用event_handler回调函数来观察训练过程，或进行测试等。这里我们打印了训练过程的cost，该回调函数是trainer.train函数里设定。
### 数据输入层定义
定义了模型输入特征的格式，包括句子序列、谓词、谓词上下文的5个特征、和谓词上下区域标志
- 通过trainer.train函数训练
```python
# 句子序列
word = fluid.layers.data(
name='word_data', shape=[1], dtype='int64', lod_level=1)
# 谓词
predicate = fluid.layers.data(
name='verb_data', shape=[1], dtype='int64', lod_level=1)
# 谓词上下文5个特征
ctx_n2 = fluid.layers.data(
name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1)
ctx_n1 = fluid.layers.data(
name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1)
ctx_0 = fluid.layers.data(
name='ctx_0_data', shape=[1], dtype='int64', lod_level=1)
ctx_p1 = fluid.layers.data(
name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1)
ctx_p2 = fluid.layers.data(
name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
# 谓词上下区域标志
mark = fluid.layers.data(
name='mark_data', shape=[1], dtype='int64', lod_level=1)
```
### 定义网络结构
首先预训练并定义模型输入层
```python
def train(use_cuda, save_dirname=None, is_local=True):
# define network topology
# 句子序列
word = fluid.layers.data(
name='word_data', shape=[1], dtype='int64', lod_level=1)
# 谓词
predicate = fluid.layers.data(
name='verb_data', shape=[1], dtype='int64', lod_level=1)
# 谓词上下文5个特征
ctx_n2 = fluid.layers.data(
name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1)
ctx_n1 = fluid.layers.data(
name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1)
ctx_0 = fluid.layers.data(
name='ctx_0_data', shape=[1], dtype='int64', lod_level=1)
ctx_p1 = fluid.layers.data(
name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1)
ctx_p2 = fluid.layers.data(
name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
# 谓词上下区域标志
mark = fluid.layers.data(
name='mark_data', shape=[1], dtype='int64', lod_level=1)
# define network topology
feature_out = db_lstm(**locals())
# 标注序列
target = fluid.layers.data(
name='target', shape=[1], dtype='int64', lod_level=1)
# 学习 CRF 的转移特征
crf_cost = fluid.layers.linear_chain_crf(
input=feature_out,
label=target,
#预训练谓词和谓词上下区域标志
predicate_embedding = fluid.layers.embedding(
input=predicate,
size=[pred_dict_len, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='vemb')
mark_embedding = fluid.layers.embedding(
input=mark,
size=[mark_dict_len, mark_dim],
dtype='float32',
is_sparse=IS_SPARSE)
#句子序列和谓词上下文5个特征并预训练
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
# 因词向量是预训练好的，这里不再训练embedding表，
# 参数属性trainable设置成False阻止了embedding表在训练过程中被更新
emb_layers = [
fluid.layers.embedding(
size=[word_dict_len, word_dim],
input=x,
param_attr=fluid.ParamAttr(
name='crfw', learning_rate=mix_hidden_lr))
avg_cost = fluid.layers.mean(crf_cost)
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
learning_rate=0.01,
decay_steps=100000,
decay_rate=0.5,
staircase=True))
sgd_optimizer.minimize(avg_cost)
# The CRF decoding layer is used for evaluation and inference.
# It shares weights with CRF layer. The sharing of parameters among multiple layers
# is specified by using the same parameter name in these layers. If true tag sequence
# is provided in training process, `fluid.layers.crf_decoding` calculates labelling error
# for each input token and sums the error over the entire sequence.
# Otherwise, `fluid.layers.crf_decoding` generates the labelling tags.
crf_decode = fluid.layers.crf_decoding(
input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.conll05.test(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
feeder = fluid.DataFeeder(
feed_list=[
word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target
],
place=place)
exe = fluid.Executor(place)
def train_loop(main_program):
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
start_time = time.time()
batch_id = 0
for pass_id in six.moves.xrange(PASS_NUM):
for data in train_data():
cost = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
cost = cost[0]
if batch_id % 10 == 0:
print("avg_cost: " + str(cost))
if batch_id != 0:
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(cost) < 60.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'mark_data'
], [feature_out], exe)
return
batch_id = batch_id + 1
train_loop(fluid.default_main_program())
name=embedding_name, trainable=False)) for x in word_input
]
#加入谓词和谓词上下区域标志的预训练结果
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
```
定义8个LSTM单元以“正向/反向”的顺序对所有输入序列进行学习。
```python
# 共有8个LSTM单元被训练，每个单元的方向为从左到右或从右到左，
# 由参数`is_reverse`确定
# 第一层栈结构
hidden_0_layers = [
fluid.layers.fc(input=emb, size=hidden_dim, act='tanh')
for emb in emb_layers
]
hidden_0 = fluid.layers.sums(input=hidden_0_layers)
lstm_0 = fluid.layers.dynamic_lstm(
input=hidden_0,
size=hidden_dim,
candidate_activation='relu',
gate_activation='sigmoid',
cell_activation='sigmoid')
# 用直连的边来堆叠L-LSTM、R-LSTM
input_tmp = [hidden_0, lstm_0]
# 其余的栈结构
for i in range(1, depth):
mix_hidden = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=hidden_dim, act='tanh'),
fluid.layers.fc(input=input_tmp[1], size=hidden_dim, act='tanh')
])
lstm = fluid.layers.dynamic_lstm(
input=mix_hidden,
size=hidden_dim,
candidate_activation='relu',
gate_activation='sigmoid',
cell_activation='sigmoid',
is_reverse=((i % 2) == 1))
input_tmp = [mix_hidden, lstm]
# 取最后一个栈式LSTM的输出和这个LSTM单元的输入到隐层映射，
# 经过一个全连接层映射到标记字典的维度，来学习 CRF 的状态特征
feature_out = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=label_dict_len, act='tanh'),
fluid.layers.fc(input=input_tmp[1], size=label_dict_len, act='tanh')
])
# 学习 CRF 的转移特征
crf_cost = fluid.layers.linear_chain_crf(
input=feature_out,
label=target,
param_attr=fluid.ParamAttr(
name='crfw', learning_rate=mix_hidden_lr))
avg_cost = fluid.layers.mean(crf_cost)
# 使用最基本的SGD优化方法(momentum设置为0)
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
learning_rate=0.01,
decay_steps=100000,
decay_rate=0.5,
staircase=True))
sgd_optimizer.minimize(avg_cost)
```
数据介绍部分提到CoNLL 2005训练集付费，这里我们使用测试集训练供大家学习。conll05.test()每次产生一条样本，包含9个特征，shuffle和组完batch后作为训练的输入。
```python
crf_decode = fluid.layers.crf_decoding(
input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.conll05.test(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
```
通过feeder来指定每一个数据和data_layer的对应关系, 下面的feeder表示 conll05.test()产生数据的第0列对应的data_layer是 `word`。
```python
feeder = fluid.DataFeeder(
feed_list=[
word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target
],
place=place)
exe = fluid.Executor(place)
```
开始训练
```python
main_program = fluid.default_main_program()
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
start_time = time.time()
batch_id = 0
for pass_id in six.moves.xrange(PASS_NUM):
for data in train_data():
cost = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
cost = cost[0]
if batch_id % 10 == 0:
print("avg_cost: " + str(cost))
if batch_id != 0:
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(cost) < 60.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'mark_data'
], [feature_out], exe)
break
batch_id = batch_id + 1
```
......@@ -450,99 +523,88 @@ def train(use_cuda, save_dirname=None, is_local=True):
训练完成之后，需要依据某个我们关心的性能指标选择最优的模型进行预测，可以简单的选择测试集上标记错误最少的那个模型。以下我们给出一个使用训练后的模型进行预测的示例。
首先设置预测过程的参数
```python
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be fed
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# Setup inputs by creating LoDTensors to represent sequences of words.
# Here each word is the basic element of these LoDTensors and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
# which has only one lod level. Then the created LoDTensors will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that lod info should be a list of lists.
lod = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
pred = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=pred_dict_len - 1)
ctx_n2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_n1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_0 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
mark = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=mark_dict_len - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'word_data'
assert feed_target_names[1] == 'verb_data'
assert feed_target_names[2] == 'ctx_n2_data'
assert feed_target_names[3] == 'ctx_n1_data'
assert feed_target_names[4] == 'ctx_0_data'
assert feed_target_names[5] == 'ctx_p1_data'
assert feed_target_names[6] == 'ctx_p2_data'
assert feed_target_names[7] == 'mark_data'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word,
feed_target_names[1]: pred,
feed_target_names[2]: ctx_n2,
feed_target_names[3]: ctx_n1,
feed_target_names[4]: ctx_0,
feed_target_names[5]: ctx_p1,
feed_target_names[6]: ctx_p2,
feed_target_names[7]: mark
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
use_cuda = False #在cpu上进行预测
save_dirname = "label_semantic_roles.inference.model" #调用训练好的模型进行预测
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
```
设置输入，用LoDTensor来表示输入的词序列，这里每个词的形状 base_shape都是[1]，是因为每个词都是用一个id来表示的。假如基于长度的LoD是[[3, 4, 2]]，这是一个单层的LoD，那么构造出的LoDTensor就包含3个序列，其长度分别为3、4和2。
注意LoD是个列表的列表
整个程序的入口如下：
```python
def main(use_cuda, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
lod = [[3, 4, 2]]
base_shape = [1]
# 构造假数据作为输入，整数随机数的范围是[low, high]
word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
pred = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=pred_dict_len - 1)
ctx_n2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_n1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_0 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
mark = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=mark_dict_len - 1)
```
# Directory for saving the trained model
save_dirname = "label_semantic_roles.inference.model"
使用fluid.io.load_inference_model加载inference_program，feed_target_names是模型的输入变量的名称，fetch_targets是预测对象。
train(use_cuda, save_dirname, is_local)
infer(use_cuda, save_dirname)
```python
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
```
构造feed字典 {feed_target_name: feed_target_data}，results是由预测目标构成的列表
```python
assert feed_target_names[0] == 'word_data'
assert feed_target_names[1] == 'verb_data'
assert feed_target_names[2] == 'ctx_n2_data'
assert feed_target_names[3] == 'ctx_n1_data'
assert feed_target_names[4] == 'ctx_0_data'
assert feed_target_names[5] == 'ctx_p1_data'
assert feed_target_names[6] == 'ctx_p2_data'
assert feed_target_names[7] == 'mark_data'
```
执行预测
main(use_cuda=False)
```python
results = exe.run(inference_program,
feed={
feed_target_names[0]: word,
feed_target_names[1]: pred,
feed_target_names[2]: ctx_n2,
feed_target_names[3]: ctx_n1,
feed_target_names[4]: ctx_0,
feed_target_names[5]: ctx_p1,
feed_target_names[6]: ctx_p2,
feed_target_names[7]: mark
},
fetch_list=fetch_targets,
return_numpy=False)
```
输出结果
```python
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
```
## 总结
语义角色标注是许多自然语言理解任务的重要中间步骤。这篇教程中我们以语义角色标注任务为例，介绍如何利用PaddlePaddle进行序列标注任务。教程中所介绍的模型来自我们发表的论文\[[10](#参考文献)\]。由于 CoNLL 2005 SRL任务的训练数据目前并非完全开放，教程中只使用测试数据作为示例。在这个过程中，我们希望减少对其它自然语言处理工具的依赖，利用神经网络数据驱动、端到端学习的能力，得到一个和传统方法可比、甚至更好的模型。在论文中我们证实了这种可能性。关于模型更多的信息和讨论可以在论文中找到。
......
07.label_semantic_roles/index.cn.html
浏览文件 @ 1558a1d3
......@@ -193,7 +193,7 @@ conll05st-release/
4. 构造以BIO法表示的标记；
5. 依据词典获取词对应的整数索引。
预处理完成之后一条训练样本包含9个特征，分别是：句子序列、谓词、谓词上下文（占 5 列）、谓词上下区域标志、标注序列。下表是一条训练样本的示例。
预处理完成之后一条训练样本数据包含9个域，分别是：句子序列、谓词、谓词上下文（占 5 列）、谓词上下区域标志、标注序列。下表是一条训练样本的示例。
| 句子序列 | 谓词 | 谓词上下文（窗口 = 5） | 谓词上下文区域标记 | 标注序列 |
|---|---|---|---|---|
......@@ -248,33 +248,34 @@ print('pred_dict_len: ', pred_dict_len)
- 定义输入数据维度及模型超参数。
```python
mark_dict_len = 2 # 谓上下文区域标志的维度，是一个0-1 2值特征，因此维度为2
word_dim = 32 # 词向量维度
mark_dim = 5 # 谓词上下文区域通过词表被映射为一个实向量，这个是相邻的维度
hidden_dim = 512 # LSTM隐层向量的维度 ： 512 / 4
depth = 8 # 栈式LSTM的深度
mix_hidden_lr = 1e-3
mark_dict_len = 2 # 谓上下文区域标志的维度，是一个0-1 2值特征，因此维度为2
word_dim = 32 # 词向量维度
mark_dim = 5 # 谓词上下文区域通过词表被映射为一个实向量，这个是相邻的维度
hidden_dim = 512 # LSTM隐层向量的维度 ： 512 / 4
depth = 8 # 栈式LSTM的深度
mix_hidden_lr = 1e-3 # linear_chain_crf层的基础学习率
IS_SPARSE = True
PASS_NUM = 10
BATCH_SIZE = 10
IS_SPARSE = True # 是否以稀疏方式更新embedding
PASS_NUM = 10 # 训练轮数
BATCH_SIZE = 10 # batch size 大小
embedding_name = 'emb'
```
这里需要特别说明的是hidden_dim = 512指定了LSTM隐层向量的维度为128维，关于这一点请参考PaddlePaddle官方文档中[lstmemory](http://www.paddlepaddle.org/doc/ui/api/trainer_config_helpers/layers.html#lstmemory)的说明。
这里需要特别说明的是，参数 `hidden_dim = 512` 实际指定了LSTM隐层向量的维度为128，关于这一点请参考PaddlePaddle官方文档中[dynamic_lstm](http://www.paddlepaddle.org/documentation/docs/zh/1.2/api_cn/layers_cn.html#dynamic-lstm)的说明。
- 如上文提到，我们用基于英文维基百科训练好的词向量来初始化序列输入、谓词上下文总共6个特征的embedding层参数，在训练中不更新。
```python
# 这里加载PaddlePaddle上版保存的二进制模型
# 这里加载PaddlePaddle保存的二进制参数
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)
```
- 8个LSTM单元以“正向/反向”的顺序对所有输入序列进行学习。
- 8个LSTM单元以“正向/反向”的顺序对所有输入序列进行学习，主要的执行逻辑如下：
1）为不同的输入特征分别定义embedding层
```python
def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
......@@ -294,8 +295,8 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
is_sparse=IS_SPARSE)
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
# Since word vector lookup table is pre-trained, we won't update it this time.
# trainable being False prevents updating the lookup table during training.
# 因词向量是预训练好的，这里不再训练embedding表，
# 参数属性trainable设置成False阻止了embedding表在训练过程中被更新
emb_layers = [
fluid.layers.embedding(
size=[word_dict_len, word_dim],
......@@ -305,9 +306,12 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
]
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
```
2) 定义深度双向LSTM结构
# 8 LSTM units are trained through alternating left-to-right / right-to-left order
# denoted by the variable `reverse`.
```python
# 共有8个LSTM单元被训练，每个单元的方向为从左到右或从右到左，
# 由参数`is_reverse`确定
hidden_0_layers = [
fluid.layers.fc(input=emb, size=hidden_dim, act='tanh')
for emb in emb_layers
......@@ -322,19 +326,9 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
gate_activation='sigmoid',
cell_activation='sigmoid')
# stack L-LSTM and R-LSTM with direct edges
# 用直连的边来堆叠L-LSTM、R-LSTM
input_tmp = [hidden_0, lstm_0]
# In PaddlePaddle, state features and transition features of a CRF are implemented
# by a fully connected layer and a CRF layer seperately. The fully connected layer
# with linear activation learns the state features, here we use fluid.layers.sums
# (fluid.layers.fc can be uesed as well), and the CRF layer in PaddlePaddle:
# fluid.layers.linear_chain_crf only
# learns the transition features, which is a cost layer and is the last layer of the network.
# fluid.layers.linear_chain_crf outputs the log probability of true tag sequence
# as the cost by given the input sequence and it requires the true tag sequence
# as target in the learning process.
for i in range(1, depth):
mix_hidden = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=hidden_dim, act='tanh'),
......@@ -363,128 +357,207 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
## 训练模型
- 我们根据网络拓扑结构和模型参数来构造出trainer用来训练，在构造时还需指定优化方法，这里使用最基本的SGD方法(momentum设置为0)，同时设定了学习率、正则等。
- 数据介绍部分提到CoNLL 2005训练集付费，这里我们使用测试集训练供大家学习。conll05.test()每次产生一条样本，包含9个特征，shuffle和组完batch后作为训练的输入。
- 我们根据网络拓扑结构和模型参数来进行训练，在构造时还需指定优化方法，这里使用最基本的SGD方法(momentum设置为0)，同时设定了学习率、正则等。
- 通过feeding来指定每一个数据和data_layer的对应关系。 例如 下面feeding表示: conll05.test()产生数据的第0列对应word_data层的特征。
定义训练过程的超参数
```python
use_cuda = False #在cpu上执行训练
save_dirname = "label_semantic_roles.inference.model" #训练得到的模型参数保存在文件中
is_local = True
```
- 可以使用event_handler回调函数来观察训练过程，或进行测试等。这里我们打印了训练过程的cost，该回调函数是trainer.train函数里设定。
### 数据输入层定义
定义了模型输入特征的格式，包括句子序列、谓词、谓词上下文的5个特征、和谓词上下区域标志
- 通过trainer.train函数训练
```python
# 句子序列
word = fluid.layers.data(
name='word_data', shape=[1], dtype='int64', lod_level=1)
# 谓词
predicate = fluid.layers.data(
name='verb_data', shape=[1], dtype='int64', lod_level=1)
# 谓词上下文5个特征
ctx_n2 = fluid.layers.data(
name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1)
ctx_n1 = fluid.layers.data(
name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1)
ctx_0 = fluid.layers.data(
name='ctx_0_data', shape=[1], dtype='int64', lod_level=1)
ctx_p1 = fluid.layers.data(
name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1)
ctx_p2 = fluid.layers.data(
name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
# 谓词上下区域标志
mark = fluid.layers.data(
name='mark_data', shape=[1], dtype='int64', lod_level=1)
```
### 定义网络结构
首先预训练并定义模型输入层
```python
def train(use_cuda, save_dirname=None, is_local=True):
# define network topology
# 句子序列
word = fluid.layers.data(
name='word_data', shape=[1], dtype='int64', lod_level=1)
# 谓词
predicate = fluid.layers.data(
name='verb_data', shape=[1], dtype='int64', lod_level=1)
# 谓词上下文5个特征
ctx_n2 = fluid.layers.data(
name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1)
ctx_n1 = fluid.layers.data(
name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1)
ctx_0 = fluid.layers.data(
name='ctx_0_data', shape=[1], dtype='int64', lod_level=1)
ctx_p1 = fluid.layers.data(
name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1)
ctx_p2 = fluid.layers.data(
name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
# 谓词上下区域标志
mark = fluid.layers.data(
name='mark_data', shape=[1], dtype='int64', lod_level=1)
# define network topology
feature_out = db_lstm(**locals())
# 标注序列
target = fluid.layers.data(
name='target', shape=[1], dtype='int64', lod_level=1)
# 学习 CRF 的转移特征
crf_cost = fluid.layers.linear_chain_crf(
input=feature_out,
label=target,
#预训练谓词和谓词上下区域标志
predicate_embedding = fluid.layers.embedding(
input=predicate,
size=[pred_dict_len, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='vemb')
mark_embedding = fluid.layers.embedding(
input=mark,
size=[mark_dict_len, mark_dim],
dtype='float32',
is_sparse=IS_SPARSE)
#句子序列和谓词上下文5个特征并预训练
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
# 因词向量是预训练好的，这里不再训练embedding表，
# 参数属性trainable设置成False阻止了embedding表在训练过程中被更新
emb_layers = [
fluid.layers.embedding(
size=[word_dict_len, word_dim],
input=x,
param_attr=fluid.ParamAttr(
name='crfw', learning_rate=mix_hidden_lr))
avg_cost = fluid.layers.mean(crf_cost)
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
learning_rate=0.01,
decay_steps=100000,
decay_rate=0.5,
staircase=True))
sgd_optimizer.minimize(avg_cost)
# The CRF decoding layer is used for evaluation and inference.
# It shares weights with CRF layer. The sharing of parameters among multiple layers
# is specified by using the same parameter name in these layers. If true tag sequence
# is provided in training process, `fluid.layers.crf_decoding` calculates labelling error
# for each input token and sums the error over the entire sequence.
# Otherwise, `fluid.layers.crf_decoding` generates the labelling tags.
crf_decode = fluid.layers.crf_decoding(
input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.conll05.test(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
feeder = fluid.DataFeeder(
feed_list=[
word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target
],
place=place)
exe = fluid.Executor(place)
def train_loop(main_program):
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
start_time = time.time()
batch_id = 0
for pass_id in six.moves.xrange(PASS_NUM):
for data in train_data():
cost = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
cost = cost[0]
if batch_id % 10 == 0:
print("avg_cost: " + str(cost))
if batch_id != 0:
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(cost) < 60.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'mark_data'
], [feature_out], exe)
return
batch_id = batch_id + 1
train_loop(fluid.default_main_program())
name=embedding_name, trainable=False)) for x in word_input
]
#加入谓词和谓词上下区域标志的预训练结果
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
```
定义8个LSTM单元以“正向/反向”的顺序对所有输入序列进行学习。
```python
# 共有8个LSTM单元被训练，每个单元的方向为从左到右或从右到左，
# 由参数`is_reverse`确定
# 第一层栈结构
hidden_0_layers = [
fluid.layers.fc(input=emb, size=hidden_dim, act='tanh')
for emb in emb_layers
]
hidden_0 = fluid.layers.sums(input=hidden_0_layers)
lstm_0 = fluid.layers.dynamic_lstm(
input=hidden_0,
size=hidden_dim,
candidate_activation='relu',
gate_activation='sigmoid',
cell_activation='sigmoid')
# 用直连的边来堆叠L-LSTM、R-LSTM
input_tmp = [hidden_0, lstm_0]
# 其余的栈结构
for i in range(1, depth):
mix_hidden = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=hidden_dim, act='tanh'),
fluid.layers.fc(input=input_tmp[1], size=hidden_dim, act='tanh')
])
lstm = fluid.layers.dynamic_lstm(
input=mix_hidden,
size=hidden_dim,
candidate_activation='relu',
gate_activation='sigmoid',
cell_activation='sigmoid',
is_reverse=((i % 2) == 1))
input_tmp = [mix_hidden, lstm]
# 取最后一个栈式LSTM的输出和这个LSTM单元的输入到隐层映射，
# 经过一个全连接层映射到标记字典的维度，来学习 CRF 的状态特征
feature_out = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=label_dict_len, act='tanh'),
fluid.layers.fc(input=input_tmp[1], size=label_dict_len, act='tanh')
])
# 学习 CRF 的转移特征
crf_cost = fluid.layers.linear_chain_crf(
input=feature_out,
label=target,
param_attr=fluid.ParamAttr(
name='crfw', learning_rate=mix_hidden_lr))
avg_cost = fluid.layers.mean(crf_cost)
# 使用最基本的SGD优化方法(momentum设置为0)
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
learning_rate=0.01,
decay_steps=100000,
decay_rate=0.5,
staircase=True))
sgd_optimizer.minimize(avg_cost)
```
数据介绍部分提到CoNLL 2005训练集付费，这里我们使用测试集训练供大家学习。conll05.test()每次产生一条样本，包含9个特征，shuffle和组完batch后作为训练的输入。
```python
crf_decode = fluid.layers.crf_decoding(
input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.conll05.test(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
```
通过feeder来指定每一个数据和data_layer的对应关系, 下面的feeder表示 conll05.test()产生数据的第0列对应的data_layer是 `word`。
```python
feeder = fluid.DataFeeder(
feed_list=[
word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target
],
place=place)
exe = fluid.Executor(place)
```
开始训练
```python
main_program = fluid.default_main_program()
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
start_time = time.time()
batch_id = 0
for pass_id in six.moves.xrange(PASS_NUM):
for data in train_data():
cost = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
cost = cost[0]
if batch_id % 10 == 0:
print("avg_cost: " + str(cost))
if batch_id != 0:
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(cost) < 60.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'mark_data'
], [feature_out], exe)
break
batch_id = batch_id + 1
```
......@@ -492,99 +565,88 @@ def train(use_cuda, save_dirname=None, is_local=True):
训练完成之后，需要依据某个我们关心的性能指标选择最优的模型进行预测，可以简单的选择测试集上标记错误最少的那个模型。以下我们给出一个使用训练后的模型进行预测的示例。
首先设置预测过程的参数
```python
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be fed
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# Setup inputs by creating LoDTensors to represent sequences of words.
# Here each word is the basic element of these LoDTensors and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
# which has only one lod level. Then the created LoDTensors will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that lod info should be a list of lists.
lod = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
pred = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=pred_dict_len - 1)
ctx_n2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_n1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_0 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
mark = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=mark_dict_len - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'word_data'
assert feed_target_names[1] == 'verb_data'
assert feed_target_names[2] == 'ctx_n2_data'
assert feed_target_names[3] == 'ctx_n1_data'
assert feed_target_names[4] == 'ctx_0_data'
assert feed_target_names[5] == 'ctx_p1_data'
assert feed_target_names[6] == 'ctx_p2_data'
assert feed_target_names[7] == 'mark_data'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word,
feed_target_names[1]: pred,
feed_target_names[2]: ctx_n2,
feed_target_names[3]: ctx_n1,
feed_target_names[4]: ctx_0,
feed_target_names[5]: ctx_p1,
feed_target_names[6]: ctx_p2,
feed_target_names[7]: mark
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
use_cuda = False #在cpu上进行预测
save_dirname = "label_semantic_roles.inference.model" #调用训练好的模型进行预测
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
```
设置输入，用LoDTensor来表示输入的词序列，这里每个词的形状 base_shape都是[1]，是因为每个词都是用一个id来表示的。假如基于长度的LoD是[[3, 4, 2]]，这是一个单层的LoD，那么构造出的LoDTensor就包含3个序列，其长度分别为3、4和2。
注意LoD是个列表的列表
整个程序的入口如下：
```python
def main(use_cuda, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
lod = [[3, 4, 2]]
base_shape = [1]
# 构造假数据作为输入，整数随机数的范围是[low, high]
word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
pred = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=pred_dict_len - 1)
ctx_n2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_n1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_0 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
mark = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=mark_dict_len - 1)
```
# Directory for saving the trained model
save_dirname = "label_semantic_roles.inference.model"
使用fluid.io.load_inference_model加载inference_program，feed_target_names是模型的输入变量的名称，fetch_targets是预测对象。
train(use_cuda, save_dirname, is_local)
infer(use_cuda, save_dirname)
```python
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
```
构造feed字典 {feed_target_name: feed_target_data}，results是由预测目标构成的列表
```python
assert feed_target_names[0] == 'word_data'
assert feed_target_names[1] == 'verb_data'
assert feed_target_names[2] == 'ctx_n2_data'
assert feed_target_names[3] == 'ctx_n1_data'
assert feed_target_names[4] == 'ctx_0_data'
assert feed_target_names[5] == 'ctx_p1_data'
assert feed_target_names[6] == 'ctx_p2_data'
assert feed_target_names[7] == 'mark_data'
```
执行预测
main(use_cuda=False)
```python
results = exe.run(inference_program,
feed={
feed_target_names[0]: word,
feed_target_names[1]: pred,
feed_target_names[2]: ctx_n2,
feed_target_names[3]: ctx_n1,
feed_target_names[4]: ctx_0,
feed_target_names[5]: ctx_p1,
feed_target_names[6]: ctx_p2,
feed_target_names[7]: mark
},
fetch_list=fetch_targets,
return_numpy=False)
```
输出结果
```python
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
```
## 总结
语义角色标注是许多自然语言理解任务的重要中间步骤。这篇教程中我们以语义角色标注任务为例，介绍如何利用PaddlePaddle进行序列标注任务。教程中所介绍的模型来自我们发表的论文\[[10](#参考文献)\]。由于 CoNLL 2005 SRL任务的训练数据目前并非完全开放，教程中只使用测试数据作为示例。在这个过程中，我们希望减少对其它自然语言处理工具的依赖，利用神经网络数据驱动、端到端学习的能力，得到一个和传统方法可比、甚至更好的模型。在论文中我们证实了这种可能性。关于模型更多的信息和讨论可以在论文中找到。
......
07.label_semantic_roles/train.py
浏览文件 @ 1558a1d3
......@@ -104,7 +104,7 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
def train(use_cuda, save_dirname=None, is_local=True):
# define network topology
# define data layers
word = fluid.layers.data(
name='word_data', shape=[1], dtype='int64', lod_level=1)
predicate = fluid.layers.data(
......
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