目录
- 概述
- 命名实例
- hmm
- 随机场
- 马尔科夫随机场
- crf
- 命名实例实战
- 数据集
- crf
- 预处理
- 主程序
概述
从今天开始我们将开启一段自然语言处理 (nlp) 的旅程. 自然语言处理可以让来处理, 理解, 以及运用人类的语言, 实现机器语言和人类语言之间的沟通桥梁.
命名实例
命名实例 (named entity) 指的是 nlp 任务中具有特定意义的实体, 包括人名, 地名, 机构名, 专有名词等. 举个例子:
- luke rawlence 代表人物
- aiimi 和 university of lincoln 代表组织
- milton keynes 代表地方
hmm
隐马可夫模型 (hidden markov model) 可以描述一个含有隐含未知参数的马尔可夫过程. 如图:
随机场
随机场 (random field) 包含两个要素: 位置 (site) 和相空间 (phase space). 当给每一个位置中按照某种分布随机赋予空间的一个值后, 其全体就叫做随机场. 举个例子, 位置好比是一亩亩农田, 相空间好比是各种庄稼. 我们可以给不同的地种上不同的庄稼. 这就好比给随机场的每个 “位置”, 赋予空间里不同的值. 随机场就是在哪块地里中什么庄稼.
马尔科夫随机场
马尔科夫随机场 (markov random field) 是一种特殊的随机场. 任何一块地里的庄稼的种类仅与它邻近的地里中的庄稼的种类有关. 那么这种集合就是一个马尔科夫随机场.
crf
条件随机场 (conditional random field) 是给定随机变量 x 条件下, 随机变量 y 的马尔科夫随机场. crf 是在给定一组变量的情况下, 求解另一组变量的条件概率的模型, 常用于序列标注问题.
公式如下:
命名实例实战
数据集
我们将会用到的是一个医疗命名的数据集, 内容如下:
crf
import tensorflow as tf
import tensorflow.keras.backend as k
import tensorflow.keras.layers as l
from tensorflow_addons.text import crf_log_likelihood, crf_decode
class crf(l.layer):
def __init__(self,
output_dim,
sparse_target=true,
**kwargs):
"""
args:
output_dim (int): the number of labels to tag each temporal input.
sparse_target (bool): whether the the ground-truth label represented in one-hot.
input shape:
(batch_size, sentence length, output_dim)
output shape:
(batch_size, sentence length, output_dim)
"""
super(crf, self).__init__(**kwargs)
self.output_dim = int(output_dim)
self.sparse_target = sparse_target
self.input_spec = l.inputspec(min_ndim=3)
self.supports_masking = false
self.sequence_lengths = none
self.transitions = none
def build(self, input_shape):
assert len(input_shape) == 3
f_shape = tf.tensorshape(input_shape)
input_spec = l.inputspec(min_ndim=3, axes={-1: f_shape[-1]})
if f_shape[-1] is none:
raise valueerror('the last dimension of the inputs to `crf` '
'should be defined. found `none`.')
if f_shape[-1] != self.output_dim:
raise valueerror('the last dimension of the input shape must be equal to output'
' shape. use a linear layer if needed.')
self.input_spec = input_spec
self.transitions = self.add_weight(name='transitions',
shape=[self.output_dim, self.output_dim],
initializer='glorot_uniform',
trainable=true)
self.built = true
def compute_mask(self, inputs, mask=none):
# just pass the received mask from previous layer, to the next layer or
# manipulate it if this layer changes the shape of the input
return mask
def call(self, inputs, sequence_lengths=none, training=none, **kwargs):
sequences = tf.convert_to_tensor(inputs, dtype=self.dtype)
if sequence_lengths is not none:
assert len(sequence_lengths.shape) == 2
assert tf.convert_to_tensor(sequence_lengths).dtype == 'int32'
seq_len_shape = tf.convert_to_tensor(sequence_lengths).get_shape().as_list()
assert seq_len_shape[1] == 1
self.sequence_lengths = k.flatten(sequence_lengths)
else:
self.sequence_lengths = tf.ones(tf.shape(inputs)[0], dtype=tf.int32) * (
tf.shape(inputs)[1]
)
viterbi_sequence, _ = crf_decode(sequences,
self.transitions,
self.sequence_lengths)
output = k.one_hot(viterbi_sequence, self.output_dim)
return k.in_train_phase(sequences, output)
@property
def loss(self):
def crf_loss(y_true, y_pred):
y_pred = tf.convert_to_tensor(y_pred, dtype=self.dtype)
log_likelihood, self.transitions = crf_log_likelihood(
y_pred,
tf.cast(k.argmax(y_true), dtype=tf.int32) if self.sparse_target else y_true,
self.sequence_lengths,
transition_params=self.transitions,
)
return tf.reduce_mean(-log_likelihood)
return crf_loss
@property
def accuracy(self):
def viterbi_accuracy(y_true, y_pred):
# -1e10 to avoid zero at sum(mask)
mask = k.cast(
k.all(k.greater(y_pred, -1e10), axis=2), k.floatx())
shape = tf.shape(y_pred)
sequence_lengths = tf.ones(shape[0], dtype=tf.int32) * (shape[1])
y_pred, _ = crf_decode(y_pred, self.transitions, sequence_lengths)
if self.sparse_target:
y_true = k.argmax(y_true, 2)
y_pred = k.cast(y_pred, 'int32')
y_true = k.cast(y_true, 'int32')
corrects = k.cast(k.equal(y_true, y_pred), k.floatx())
return k.sum(corrects * mask) / k.sum(mask)
return viterbi_accuracy
def compute_output_shape(self, input_shape):
tf.tensorshape(input_shape).assert_has_rank(3)
return input_shape[:2] + (self.output_dim,)
def get_config(self):
config = {
'output_dim': self.output_dim,
'sparse_target': self.sparse_target,
'supports_masking': self.supports_masking,
'transitions': k.eval(self.transitions)
}
base_config = super(crf, self).get_config()
return dict(base_config, **config)
预处理
import numpy as np
import tensorflow as tf
def build_data():
"""
获取数据
:return: 返回数据(词, 标签) / 所有词汇总的字典
"""
# 存放数据
datas = []
# 存放x
sample_x = []
# 存放y
sample_y = []
# 存放词
vocabs = {'unk'}
# 遍历
for line in open("data/train.txt", encoding="utf-8"):
# 拆分
line = line.rstrip().split('\t')
# 取出字符
char = line[0]
# 如果字符为空, 跳过
if not char:
continue
# 取出字符对应标签
cate = line[-1]
# append
sample_x.append(char)
sample_y.append(cate)
vocabs.add(char)
# 遇到标点代表句子结束
if char in ['。', '?', '!', '！', '？']:
datas.append([sample_x, sample_y])
# 清空
sample_x = []
sample_y = []
# set转换为字典存储出现过的字
word_dict = {wd: index for index, wd in enumerate(list(vocabs))}
print("vocab_size:", len(word_dict))
return datas, word_dict
def modify_data():
# 获取数据
datas, word_dict = build_data()
x, y = zip(*datas)
print(x[:5])
print(y[:5])
# tokenizer
tokenizer = tf.keras.preprocessing.text.tokenizer()
tokenizer.fit_on_texts(word_dict)
x_train = tokenizer.texts_to_sequences(x)
# 填充
x_train = tf.keras.preprocessing.sequence.pad_sequences(x_train, 150)
print(x_train[:5])
class_dict = {
'o': 0,
'treatment-i': 1,
'treatment-b': 2,
'body-b': 3,
'body-i': 4,
'signs-i': 5,
'signs-b': 6,
'check-b': 7,
'check-i': 8,
'disease-i': 9,
'disease-b': 10
}
# tokenize
x_train = [[word_dict[char] for char in data[0]] for data in datas]
y_train = [[class_dict[label] for label in data[1]] for data in datas]
print(x_train[:5])
print(y_train[:5])
# padding
x_train = tf.keras.preprocessing.sequence.pad_sequences(x_train, 150)
y_train = tf.keras.preprocessing.sequence.pad_sequences(y_train, 150)
y_train = np.expand_dims(y_train, 2)
# ndarray
x_train = np.asarray(x_train)
y_train = np.asarray(y_train)
print(x_train.shape)
print(y_train.shape)
return x_train, y_train
if __name__ == '__main__':
modify_data()
主程序
import tensorflow as tf
from pre_processing import modify_data
from crf import crf
# 超参数
epochs = 10 # 迭代次数
batch_size = 64 # 单词训练样本数目
learning_rate = 0.00003 # 学习率
vocab_size = 1759 + 1
optimizer = tf.keras.optimizers.adam(learning_rate=learning_rate) # 优化器
loss = tf.keras.losses.categoricalcrossentropy() # 损失
def main():
# 获取数据
x_train, y_train = modify_data()
model = tf.keras.sequential([
tf.keras.layers.embedding(vocab_size, 300),
tf.keras.layers.bidirectional(tf.keras.layers.lstm(128, dropout=0.5, recurrent_dropout=0.5, return_sequences=true)),
tf.keras.layers.bidirectional(tf.keras.layers.lstm(64, dropout=0.5, recurrent_dropout=0.5, return_sequences=true)),
tf.keras.layers.timedistributed(tf.keras.layers.dense(1)),
crf(1, sparse_target=true)
])
# 组合
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
# summery
model.build([none, 150])
print(model.summary())
# 保存
checkpoint = tf.keras.callbacks.modelcheckpoint(
"../model/model.h5", monitor='val_loss',
verbose=1, save_best_only=true, mode='min',
save_weights_only=true
)
# 训练
model.fit(x_train, y_train, validation_split=0.2, epochs=epochs, batch_size=batch_size, callbacks=[checkpoint])
if __name__ == '__main__':
main()
输出结果:
vocab_size: 1759
([‘≠≠，’, ‘男’, ‘，’, ‘双’, ‘塔’, ‘山’, ‘人’, ‘，’, ‘主’, ‘因’, ‘咳’, ‘嗽’, ‘、’, ‘少’, ‘痰’, ‘1’, ‘个’, ‘月’, ‘，’, ‘加’, ‘重’, ‘3’, ‘天’, ‘，’, ‘抽’, ‘搐’, ‘1’, ‘次’, ‘于’, ‘2’, ‘0’, ‘1’, ‘6’, ‘年’, ‘1’, ‘2’, ‘月’, ‘0’, ‘8’, ‘日’, ‘0’, ‘7’, ‘:’, ‘0’, ‘0’, ‘以’, ‘1’, ‘、’, ‘肺’, ‘炎’, ‘2’, ‘、’, ‘抽’, ‘搐’, ‘待’, ‘查’, ‘收’, ‘入’, ‘院’, ‘。’], [‘性’, ‘疼’, ‘痛’, ‘1’, ‘年’, ‘收’, ‘入’, ‘院’, ‘。’], [‘，’, ‘男’, ‘，’, ‘4’, ‘岁’, ‘，’, ‘河’, ‘北’, ‘省’, ‘承’, ‘德’, ‘市’, ‘双’, ‘滦’, ‘区’, ‘陈’, ‘栅’, ‘子’, ‘乡’, ‘陈’, ‘栅’, ‘子’, ‘村’, ‘人’, ‘，’, ‘主’, ‘因’, ‘”‘, ‘咳’, ‘嗽’, ‘、’, ‘咳’, ‘痰’, ‘，’, ‘伴’, ‘发’, ‘热’, ‘6’, ‘天’, ‘”‘, ‘于’, ‘2’, ‘0’, ‘1’, ‘6’, ‘年’, ‘1’, ‘2’, ‘月’, ‘1’, ‘3’, ‘日’, ‘1’, ‘1’, ‘:’, ‘4’, ‘7’, ‘以’, ‘支’, ‘气’, ‘管’, ‘肺’, ‘炎’, ‘收’, ‘入’, ‘院’, ‘。’], [‘2’, ‘年’, ‘膀’, ‘胱’, ‘造’, ‘瘘’, ‘口’, ‘出’, ‘尿’, ‘1’, ‘年’, ‘于’, ‘2’, ‘0’, ‘1’, ‘7’, ‘-‘, ‘-‘, ‘0’, ‘2’, ‘-‘, ‘-‘, ‘0’, ‘6’, ‘收’, ‘入’, ‘院’, ‘。’], [‘;’, ‘n’, ‘b’, ‘s’, ‘p’, ‘;’, ‘郎’, ‘鸿’, ‘雁’, ‘女’, ‘5’, ‘9’, ‘岁’, ‘已’, ‘婚’, ‘ ‘, ‘汉’, ‘族’, ‘ ‘, ‘河’, ‘北’, ‘承’, ‘德’, ‘双’, ‘滦’, ‘区’, ‘人’, ‘，’, ‘现’, ‘住’, ‘电’, ‘厂’, ‘家’, ‘属’, ‘院’, ‘，’, ‘主’, ‘因’, ‘肩’, ‘颈’, ‘部’, ‘疼’, ‘痛’, ‘1’, ‘0’, ‘余’, ‘年’, ‘，’, ‘加’, ‘重’, ‘2’, ‘个’, ‘月’, ‘于’, ‘2’, ‘0’, ‘1’, ‘6’, ‘-‘, ‘0’, ‘1’, ‘-‘, ‘1’, ‘8’, ‘ ‘, ‘9’, ‘:’, ‘1’, ‘9’, ‘收’, ‘入’, ‘院’, ‘。’])
([‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘signs-b’, ‘signs-i’, ‘o’, ‘signs-b’, ‘signs-i’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘signs-b’, ‘signs-i’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘disease-b’, ‘disease-i’, ‘o’, ‘o’, ‘signs-b’, ‘signs-i’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’], [‘o’, ‘signs-b’, ‘signs-i’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’], [‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘signs-b’, ‘signs-i’, ‘o’, ‘signs-b’, ‘signs-i’, ‘o’, ‘o’, ‘signs-b’, ‘signs-i’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘disease-b’, ‘disease-i’, ‘disease-i’, ‘disease-i’, ‘disease-i’, ‘o’, ‘o’, ‘o’, ‘o’], [‘o’, ‘o’, ‘body-b’, ‘body-i’, ‘body-i’, ‘body-i’, ‘body-i’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’], [‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘body-b’, ‘body-i’, ‘body-i’, ‘signs-b’, ‘signs-i’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’, ‘o’])
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(7836, 150)
(7836, 150, 1)
model: “sequential”
_________________________________________________________________
layer (type) output shape param #
=================================================================
embedding (embedding) (none, none, 300) 528000
_________________________________________________________________
bidirectional (bidirectional (none, none, 256) 439296
_________________________________________________________________
bidirectional_1 (bidirection (none, none, 128) 164352
_________________________________________________________________
time_distributed (timedistri (none, none, 1) 129
_________________________________________________________________
crf (crf) (none, none, 1) 1
=================================================================
total params: 1,131,778
trainable params: 1,131,778
non-trainable params: 0
_________________________________________________________________
none
2021-11-23 00:31:29.846318: i tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] none of the mlir optimization passes are enabled (registered 2)
epoch 1/10
10/98 [==>………………………] – eta: 7:52 – loss: 5.2686e-08 – accuracy: 0.9232
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