95 lines
5.0 KiB
Python
95 lines
5.0 KiB
Python
# -*- coding: UTF-8 -*-
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# !/usr/bin/python
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# @time :2019/8/28 15:08
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# @author :Mo
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# @function :train of textcnn-char-bert with baidu-qa-2019 in question title
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# # 使用cpu
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# import os
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# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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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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os.environ["TF_KERAS"] = "1"
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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_baidu_qa_2019_train, path_baidu_qa_2019_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 PreprocessText, delete_file
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# 模型图
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from keras_textclassification.m02_TextCNN.graph import TextCNNGraph 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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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, bert越长会越慢, 占用空间也会变大, 本地win10-4G设为20就好, 过大小心OOM
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'embed_size': 768, # 字/词向量维度, bert取768, word取300, char可以更小些
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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', 注意:word2vec模式下训练语料要首先切好
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'embedding_type': 'xlnet', # 级别, 嵌入类型, 还可以填'xlnet'、'random'、 'bert'、 'albert' or 'word2vec"
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'gpu_memory_fraction': 0.66, #gpu使用率
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'model': {'label': 17, # 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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'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,bert取5e-5,其他取1e-3, 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': 'softmax', # 最后一个layer, 即分类激活函数
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'loss': 'categorical_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': [i for i in range(24)], # bert取的层数
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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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'xlnet_embed':{'attention_type': 'bi', # or 'uni'
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'memory_len': 0,
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'target_len': 32, },
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},
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'data':{'train_data': path_baidu_qa_2019_train, # 训练数据
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'val_data': path_baidu_qa_2019_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和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 = PreprocessText(path_model_dir)
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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=1)
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