在Keras中用Bert进行情感分析

之前在BERT实战——基于Keras一文中介绍了两个库 keras_bert 和 bert4keras，但是由于 bert4keras 处于开发阶段，有些函数名称和位置等等发生了变化，那篇文章只用了 bert4keras 进行情感分析，新开了一篇文章将 2 个库都用一遍， bert4keras 也使用最新版本。害怕 bert4keras 后续继续变化，需要稳定的可以先采用 keras_bert 。

数据集：https://github.com/bojone/bert4keras/tree/master/examples/datasets

1.使用keras_bert

配置一些超参数，导入需要的包和设置文件路径

import json
import numpy as np
import pandas as pdfrom keras_bert import load_trained_model_from_checkpoint, Tokenizer# 超参数
maxlen = 100
batch_size = 16
droup_out_rate = 0.5
learning_rate = 1e-5
epochs = 15

path_prefix = "./test"
# 预训练模型目录
config_path = path_prefix + "/chinese_L-12_H-768_A-12/bert_config.json"
checkpoint_path = path_prefix + "/chinese_L-12_H-768_A-12/bert_model.ckpt"
dict_path = path_prefix + "/chinese_L-12_H-768_A-12/vocab.txt"

读取数据和构造训练样本

# 读取数据
neg = pd.read_excel(path_prefix + "/data/neg.xls", header=None)
pos = pd.read_excel(path_prefix + "/data/pos.xls", header=None)

# 构建训练数据
data = []

for d in neg[0]:
    data.append((d, 0))

for d in pos[0]:
    data.append((d, 1))

读取字典

# 读取字典
token_dict = load_vocabulary(dict_path)
# 建立分词器
tokenizer = Tokenizer(token_dict)

拆分为训练集和测试集

# 按照9:1的比例划分训练集和验证集
random_order = list(range(len(data)))
np.random.shuffle(random_order)
train_data = [data[j] for i, j in enumerate(random_order) if i % 10 != 0]
valid_data = [data[j] for i, j in enumerate(random_order) if i % 10 == 0]

序列padding 和 训练用的生成器

def seq_padding(X, padding=0):
    L = [len(x) for x in X]
    ML = max(L)
    return np.array([
        np.concatenate([x, [padding] * (ML - len(x))]) if len(x) < ML else x for x in X
    ])


class data_generator:
    def __init__(self, data, batch_size=batch_size):
        self.data = data
        self.batch_size = batch_size
        self.steps = len(self.data) // self.batch_size
        if len(self.data) % self.batch_size != 0:
            self.steps += 1
    def __len__(self):
        return self.steps
    def __iter__(self):
        while True:
            idxs = list(range(len(self.data)))
            np.random.shuffle(idxs)
            X1, X2, Y = [], [], []
            for i in idxs:
                d = self.data[i]
                text = d[0][:maxlen]
                x1, x2 = tokenizer.encode(first=text)
                y = d[1]
                X1.append(x1)
                X2.append(x2)
                Y.append([y])
                if len(X1) == self.batch_size or i == idxs[-1]:
                    X1 = seq_padding(X1)
                    X2 = seq_padding(X2)
                    Y = seq_padding(Y)
                    yield [X1, X2], Y
                    [X1, X2, Y] = [], [], []

读取 bert 模型并增加一个全连接层用于预测

from keras.layers import *
from keras.models import Model
import keras.backend as K
from keras.optimizers import Adam

# trainable设置True对Bert进行微调
# 默认不对Bert模型进行调参
bert_model = load_trained_model_from_checkpoint(config_path, checkpoint_path, , trainable=True)

x1_in = Input(shape=(None,))
x2_in = Input(shape=(None,))

x = bert_model([x1_in, x2_in])
x = Lambda(lambda x: x[:, 0])(x)
x = Dropout(droup_out_rate)(x)
p = Dense(1, activation='sigmoid')(x)

model = Model([x1_in, x2_in], p)
model.compile(
    loss='binary_crossentropy',
    optimizer=Adam(learning_rate),
    metrics=['accuracy']
)
model.summary()

开始训练

train_D = data_generator(train_data)
valid_D = data_generator(valid_data)

model.fit_generator(
    train_D.__iter__(),
    steps_per_epoch=len(train_D),
    epochs=epochs,
    validation_data=valid_D.__iter__(),
    validation_steps=len(valid_D)
)

2.使用bert4keras

为防止 bert4keras 又调整，导致代码和最新版本不适配，这里记录更新时间

更新时间：2019-11-09

配置超参数，导入需要的包和设置预训练模型的路径

import json
import numpy as np
import pandas as pd
import os
from bert4keras.bert import build_bert_model
from bert4keras.backend import set_gelu
from bert4keras.utils import Tokenizer, load_vocab
set_gelu('tanh') # 切换gelu版本

#超参数
maxlen = 100
batch_size = 16
droup_out_rate = 0.5
learning_rate = 1e-5
epochs = 15
path_prefix = "./test"
# 预训练模型路径
config_path = path_prefix + "/chinese_L-12_H-768_A-12/bert_config.json"
checkpoint_path = path_prefix + "/chinese_L-12_H-768_A-12/bert_model.ckpt"
dict_path = path_prefix + "/chinese_L-12_H-768_A-12/vocab.txt"

读取数据和构造训练样本

# 读取数据
neg = pd.read_excel(path_prefix + "/data/neg.xls", header=None)
pos = pd.read_excel(path_prefix + "/data/pos.xls", header=None)

data, tokens = [], {}
# 读取词典
_token_dict = load_vocab(dict_path)
# 建立临时分词器
_tokenizer = Tokenizer(_token_dict)

for d in neg[0]:
    data.append((d, 0))
    for t in _tokenizer.tokenize(d):
        tokens[t] = tokens.get(t, 0) + 1

for d in pos[0]:
    data.append((d, 1))
    for t in _tokenizer.tokenize(d):
        tokens[t] = tokens.get(t, 0) + 1

精简字典，只留下本任务用到的字

tokens = {i: j for i, j in tokens.items() if j >= 4}
# token_dict是本任务需要用到的字
# keep_words是在bert中保留的字表
token_dict, keep_words = {}, []

for t in ['[PAD]', '[UNK]', '[CLS]', '[SEP]']:
    token_dict[t] = len(token_dict)
    keep_words.append(_token_dict[t])

for t in tokens:
    if t in _token_dict and t not in token_dict:
        token_dict[t] = len(token_dict)
        keep_words.append(_token_dict[t])

# 建立分词器
tokenizer = Tokenizer(token_dict)

拆分训练集和测试集

if not os.path.exists('./random_order.json'):
    random_order = list(range(len(data)))
    np.random.shuffle(random_order)
    json.dump(
        random_order,
        open('./random_order.json', 'w'),
        indent=4
    )
else:
    random_order = json.load(open('./random_order.json'))


# 按照9:1的比例划分训练集和验证集
train_data = [data[j] for i, j in enumerate(random_order) if i % 10 != 0]
valid_data = [data[j] for i, j in enumerate(random_order) if i % 10 == 0]

padding和生成器

def seq_padding(X, padding=0):
    L = [len(x) for x in X]
    ML = max(L)
    return np.array([
        np.concatenate([x, [padding] * (ML - len(x))]) if len(x) < ML else x for x in X
    ])


class data_generator:
    def __init__(self, data, batch_size=batch_size):
        self.data = data
        self.batch_size = batch_size
        self.steps = len(self.data) // self.batch_size
        if len(self.data) % self.batch_size != 0:
            self.steps += 1
    def __len__(self):
        return self.steps
    def __iter__(self):
        while True:
            idxs = list(range(len(self.data)))
            np.random.shuffle(idxs)
            X1, X2, Y = [], [], []
            for i in idxs:
                d = self.data[i]
                text = d[0][:maxlen]
                x1, x2 = tokenizer.encode(text)
                y = d[1]
                X1.append(x1)
                X2.append(x2)
                Y.append([y])
                if len(X1) == self.batch_size or i == idxs[-1]:
                    X1 = seq_padding(X1)
                    X2 = seq_padding(X2)
                    Y = seq_padding(Y)
                    yield [X1, X2], Y
                    [X1, X2, Y] = [], [], []

读取 bert 模型并增加一个全连接层用于预测

from keras.layers import *
from keras.models import Model
import keras.backend as K
from keras.optimizers import Adam

model = build_bert_model(
    config_path,
    checkpoint_path,
    # 只保留keep_words中的字，精简原字表
    keep_words=keep_words,
)

output = Lambda(lambda x: x[:, 0])(model.output)
output = Dropout(droup_out_rate)(output)
output = Dense(1, activation='sigmoid')(output)
model = Model(model.input, output)

model.compile(
    loss='binary_crossentropy',
    optimizer=Adam(learning_rate),
    metrics=['accuracy']
)
model.summary()

开始训练

train_D = data_generator(train_data)
valid_D = data_generator(valid_data)

model.fit_generator(
    train_D.__iter__(),
    steps_per_epoch=len(train_D),
    epochs=epochs,
    validation_data=valid_D.__iter__(),
    validation_steps=len(valid_D)
)

 

 

 

 

 

 

来源：https://www.cnblogs.com/dogecheng/p/11824494.html