95 lines
4.8 KiB
Python
95 lines
4.8 KiB
Python
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# -*- coding: UTF-8 -*-
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# !/usr/bin/python
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# @time :2019/6/8 14:37
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# @author :Mo
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# @function :train of bert-fune with baidu-qa-2019 in question title
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# 适配linux
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import pathlib
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import sys
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import os
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project_path = str(pathlib.Path(os.path.abspath(__file__)).parent.parent.parent)
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sys.path.append(project_path)
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# 地址
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from keras_textclassification.conf.path_config import path_model, path_fineture, path_model_dir, path_hyper_parameters
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# 训练验证数据地址
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from keras_textclassification.conf.path_config import path_sim_webank_train, path_sim_webank_valid
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# 数据预处理, 删除文件目录下文件
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from keras_textclassification.data_preprocess.text_preprocess import PreprocessSim, delete_file
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# 模型图
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from keras_textclassification.m00_Bert.graph import BertGraph as Graph
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# 计算时间
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import time
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def train(hyper_parameters=None, rate=1.0):
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"""
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训练函数
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:param hyper_parameters: json, 超参数
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:param rate: 比率, 抽出rate比率语料取训练
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:return: None
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"""
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if not hyper_parameters:
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hyper_parameters = {
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'len_max': 32, # 句子最大长度, 固定 推荐20-50
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'embed_size': 768, # 字/词向量维度
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'vocab_size': 20000, # 这里随便填的，会根据代码里修改
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'trainable': True, # embedding是静态的还是动态的, 即控制可不可以微调
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'level_type': 'char', # 级别, 最小单元, 字/词, 填 'char' or 'word'
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'embedding_type': 'bert', # 级别, 嵌入类型, 还可以填'xlnet'、'random'、 'bert'、 'albert' or 'word2vec"
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'gpu_memory_fraction': 0.76, #gpu使用率
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'model': {'label': 2, # 类别数
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'batch_size': 2, # 批处理尺寸, 感觉原则上越大越好,尤其是样本不均衡的时候, batch_size设置影响比较大
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'filters': [2, 3, 4, 5], # 卷积核尺寸
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'filters_num': 300, # 卷积个数 text-cnn:300-600
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'channel_size': 1, # CNN通道数
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'dropout': 0.5, # 随机失活, 概率
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'decay_step': 100, # 学习率衰减step, 每N个step衰减一次
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'decay_rate': 0.9, # 学习率衰减系数, 乘法
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'epochs': 20, # 训练最大轮次
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'patience': 3, # 早停,2-3就好
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'lr': 5e-5, # 学习率, 对训练会有比较大的影响, 如果准确率一直上不去,可以考虑调这个参数
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'l2': 1e-9, # l2正则化
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'activate_classify': 'sigmoid', # 最后一个layer, 即分类激活函数
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'loss': 'binary_crossentropy', # 损失函数
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'metrics': 'accuracy', # 保存更好模型的评价标准
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'is_training': True, # 训练后者是测试模型
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'model_path': path_model,
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# 模型地址, loss降低则保存的依据, save_best_only=True, save_weights_only=True
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'path_hyper_parameters': path_hyper_parameters, # 模型(包括embedding)，超参数地址,
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'path_fineture': path_fineture, # 保存embedding trainable地址, 例如字向量、词向量、bert向量等
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},
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'embedding': {'layer_indexes': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], # bert取的层数,包括embedding层
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# 'corpus_path': '', # embedding预训练数据地址,不配则会默认取conf里边默认的地址, keras-bert可以加载谷歌版bert,百度版ernie(需转换，https://github.com/ArthurRizar/tensorflow_ernie),哈工大版bert-wwm(tf框架，https://github.com/ymcui/Chinese-BERT-wwm)
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},
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'data':{'train_data': path_sim_webank_train, # 训练数据
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'val_data': path_sim_webank_valid # 验证数据
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},
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}
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# 删除先前存在的模型\embedding微调模型等
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delete_file(path_model_dir)
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time_start = time.time()
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# graph初始化
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graph = Graph(hyper_parameters)
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print("graph init ok!")
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ra_ed = graph.word_embedding
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# 数据预处理
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pt = PreprocessSim()
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x_train, y_train = pt.preprocess_label_ques_to_idx(hyper_parameters['embedding_type'],
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hyper_parameters['data']['train_data'],
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ra_ed, rate=rate, shuffle=True)
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x_val, y_val = pt.preprocess_label_ques_to_idx(hyper_parameters['embedding_type'],
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hyper_parameters['data']['val_data'],
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ra_ed, rate=rate, shuffle=True)
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print("data propress ok!")
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print(len(y_train))
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# 训练
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graph.fit(x_train, y_train, x_val, y_val)
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print("耗时:" + str(time.time()-time_start))
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if __name__=="__main__":
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train(rate=0.1)
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