keras遇到bert实战一（bert实现分类）
说明：最近一直在做关系抽取的任务，此次仅仅是记录一个实用的简单示例
参考https://www.cnblogs.com/jclian91/p/12301056.html
参考https://blog.csdn.net/asialee_bird/article/details/102747435
import pandas as pd import codecs, gc import numpy as np from sklearn.model_selection import KFold from keras_bert import load_trained_model_from_checkpoint, Tokenizer from keras.metrics import top_k_categorical_accuracy from keras.layers import * from keras.callbacks import * from keras.models import Model import keras.backend as K from keras.optimizers import Adam from keras.utils import to_categorical # 读取训练集和测试集 train_df = pd.read_csv(r'D:\Program Files\FileRecv\情感分析数据集/data_train.csv', sep='\t', names=['id', 'type', 'contents', 'labels']).astype(str) test_df = pd.read_csv(r'D:\Program Files\FileRecv\情感分析数据集/data_test.csv', sep='\t', names=['id', 'type', 'contents']).astype(str) train_df = train_df[:200] test_df = test_df[:20] maxlen = 100 # 设置序列长度为120，要保证序列长度不超过512 # 预训练好的模型 config_path = r'C:\Users\Downloads\chinese_L-12_H-768_A-12/bert_config.json' checkpoint_path = r'C:\Users\Downloads\chinese_L-12_H-768_A-12/bert_model.ckpt' dict_path = r'C:\Users\Downloads\chinese_L-12_H-768_A-12/vocab.txt' # 将词表中的词编号转换为字典 token_dict = {} with codecs.open(dict_path, 'r', 'utf8') as reader: for line in reader: token = line.strip() token_dict[token] = len(token_dict) # 重写tokenizer class OurTokenizer(Tokenizer): def _tokenize(self, text): R = [] for c in text: if c in self._token_dict: R.append(c) elif self._is_space(c): R.append('[unused1]') # 用[unused1]来表示空格类字符 else: R.append('[UNK]') # 不在列表的字符用[UNK]表示 return R tokenizer = OurTokenizer(token_dict) # 让每条文本的长度相同，用0填充 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 ]) # data_generator只是一种为了节约内存的数据方式 class data_generator: def __init__(self, data, batch_size=32, shuffle=True): self.data = data self.batch_size = batch_size self.shuffle = shuffle 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))) if self.shuffle: 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[:, 0, :] [X1, X2, Y] = [], [], [] # 计算top-k正确率,当预测值的前k个值中存在目标类别即认为预测正确 def acc_top2(y_true, y_pred): return top_k_categorical_accuracy(y_true, y_pred, k=2) # bert模型设置 def build_bert(nclass): bert_model = load_trained_model_from_checkpoint(config_path, checkpoint_path, seq_len=None) # 加载预训练模型 for l in bert_model.layers: l.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) # 取出[CLS]对应的向量用来做分类 p = Dense(nclass, activation='softmax')(x) model = Model([x1_in, x2_in], p) model.compile(loss='categorical_crossentropy', optimizer=Adam(1e-5), # 用足够小的学习率 metrics=['accuracy', acc_top2]) print(model.summary()) return model # 训练数据、测试数据和标签转化为模型输入格式 DATA_LIST = [] for data_row in train_df.iloc[:].itertuples(): DATA_LIST.append((data_row.contents, to_categorical(data_row.labels, 3))) DATA_LIST = np.array(DATA_LIST) DATA_LIST_TEST = [] for data_row in test_df.iloc[:].itertuples(): DATA_LIST_TEST.append((data_row.contents, to_categorical(0, 3))) DATA_LIST_TEST = np.array(DATA_LIST_TEST) # 交叉验证训练和测试模型 def run_cv(nfold, data, data_labels, data_test): kf = KFold(n_splits=nfold, shuffle=True, random_state=520).split(data) train_model_pred = np.zeros((len(data), 3)) test_model_pred = np.zeros((len(data_test), 3)) for i, (train_fold, test_fold) in enumerate(kf): X_train, X_valid, = data[train_fold, :], data[test_fold, :] model = build_bert(3) early_stopping = EarlyStopping(monitor='val_acc', patience=3) # 早停法，防止过拟合 plateau = ReduceLROnPlateau(monitor="val_acc", verbose=1, mode='max', factor=0.5, patience=2) # 当评价指标不在提升时，减少学习率 checkpoint = ModelCheckpoint('./bert_dump/' + str(i) + '.hdf5', monitor='val_acc', verbose=2, save_best_only=True, mode='max', save_weights_only=True) # 保存最好的模型 train_D = data_generator(X_train, shuffle=True) valid_D = data_generator(X_valid, shuffle=True) test_D = data_generator(data_test, shuffle=False) # 模型训练 model.fit_generator( train_D.__iter__(), steps_per_epoch=len(train_D), epochs=5, validation_data=valid_D.__iter__(), validation_steps=len(valid_D), callbacks=[early_stopping, plateau, checkpoint], ) # model.load_weights('./bert_dump/' + str(i) + '.hdf5') # return model train_model_pred[test_fold, :] = model.predict_generator(valid_D.__iter__(), steps=len(valid_D), verbose=1) test_model_pred += model.predict_generator(test_D.__iter__(), steps=len(test_D), verbose=1) del model gc.collect() # 清理内存 K.clear_session() # clear_session就是清除一个session # break return train_model_pred, test_model_pred # n折交叉验证 train_model_pred, test_model_pred = run_cv(2, DATA_LIST, None, DATA_LIST_TEST) test_pred = [np.argmax(x) for x in test_model_pred] # 将测试集预测结果写入文件 output = pd.DataFrame({'id': test_df.id, 'sentiment': test_pred}) output.to_csv('results.csv', index=None)
