def pred_tet(path_hyper_parameter=path_hyper_parameters,
path_test=None,
rate=1.0):
"""
测试集测试与模型评估
:param hyper_parameters: json, 超参数
:param path_test:str, path of test data, 测试集
:param rate: 比率, 抽出rate比率语料取训练
:return: None
"""
hyper_parameters = load_json(path_hyper_parameter)
if path_test: # 从外部引入测试数据地址
hyper_parameters['data']['val_data'] = path_test
time_start = time.time()
# graph初始化
graph = Graph(hyper_parameters)
print("graph init ok!")
graph.load_model()
print("graph load ok!")
ra_ed = graph.word_embedding
# 数据预处理
pt = PreprocessText()
y, x = read_and_process(hyper_parameters['data']['val_data'])
# 取该数据集的百分之几的语料测试
len_rate = int(len(y) * rate)
x = x[1:len_rate]
y = y[1:len_rate]
y_pred = []
count = 0
for x_one in x:
count += 1
ques_embed = ra_ed.sentence2idx(x_one)
if hyper_parameters['embedding_type'] == 'bert': # bert数据处理, token
x_val_1 = np.array([ques_embed[0]])
x_val_2 = np.array([ques_embed[1]])
x_val = [x_val_1, x_val_2]
else:
x_val = ques_embed
# 预测
pred = graph.predict(x_val)
pre = pt.prereocess_idx(pred[0])
label_pred = pre[0][0][0]
if count % 1000 == 0:
print(label_pred)
y_pred.append(label_pred)
print("data pred ok!")
# 预测结果转为int类型
index_y = [pt.l2i_i2l['l2i'][i] for i in y]
index_pred = [pt.l2i_i2l['l2i'][i] for i in y_pred]
target_names = [
pt.l2i_i2l['i2l'][str(i)] for i in list(set((index_pred + index_y)))
]
# 评估
report_predict = classification_report(index_y,
index_pred,
target_names=target_names,
digits=9)
print(report_predict)
print("耗时:" + str(time.time() - time_start))
def train(hyper_parameters=None, rate=1.0):
if not hyper_parameters:
hyper_parameters = {
'len_max': 2, # 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, 小心OOM
'embed_size': 768, # 字/词向量维度, bert取768, word取300, char可以更小些
'vocab_size': 20000, # 这里随便填的，会根据代码里修改
'trainable': False, # embedding是静态的还是动态的, 即控制可不可以微调
'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
'embedding_type': 'bert', # 级别, 嵌入类型, 还可以填'xlnet'、'random'、 'bert'、 'albert' or 'word2vec"
'gpu_memory_fraction': 0.86, #gpu使用率
'model': {'label': 17, # 类别数
'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
'dropout': 0.5, # 随机失活, 概率
'decay_step': 1000, # 学习率衰减step, 每N个step衰减一次
'decay_rate': 0.9, # 学习率衰减系数, 乘法
'epochs': 20, # 训练最大轮次
'patience': 3, # 早停,2-3就好
'lr': 2e-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
'l2': 1e-9, # l2正则化
'activate_classify': 'softmax', # 最后一个layer, 即分类激活函数
'loss': 'categorical_crossentropy', # 损失函数
'metrics': 'accuracy', # 保存更好模型的评价标准
'is_training': True, # 训练后者是测试模型
'model_path': path_model,
# 模型地址, loss降低则保存的依据, save_best_only=True, save_weights_only=True
'path_hyper_parameters': path_hyper_parameters, # 模型(包括embedding)，超参数地址,
'path_fineture': path_fineture, # 保存embedding trainable地址, 例如字向量、词向量、bert向量等
},
'embedding': {'layer_indexes': [24], # bert取的层数
# '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)
},
'data':{'train_data': path_baidu_qa_2019_train, # 训练数据
'val_data': path_baidu_qa_2019_valid # 验证数据
},
}
# 删除先前存在的模型\embedding微调模型等
delete_file(path_model_dir)
time_start = time.time()
# graph初始化
graph = Graph(hyper_parameters)
print("graph init ok!")
ra_ed = graph.word_embedding
# 数据预处理
pt = PreprocessText()
x_train, y_train = pt.preprocess_label_ques_to_idx(hyper_parameters['embedding_type'],
hyper_parameters['data']['train_data'],
ra_ed, rate=rate, shuffle=True)
x_val, y_val = pt.preprocess_label_ques_to_idx(hyper_parameters['embedding_type'],
hyper_parameters['data']['val_data'],
ra_ed, rate=rate, shuffle=True)
print("data propress ok!")
print(len(y_train))
# 训练
graph.fit(x_train, y_train, x_val, y_val)
print("耗时:" + str(time.time()-time_start))
def pred_input(path_hyper_parameter=path_hyper_parameters):
"""
输入预测
:param path_hyper_parameter: str, 超参存放地址
:return: None
"""
# 加载超参数
hyper_parameters = load_json(path_hyper_parameter)
pt = PreprocessText(path_model_dir)
# 模式初始化和加载
graph = Graph(hyper_parameters)
graph.load_model()
ra_ed = graph.word_embedding
ques = '我要打王者荣耀'
# str to token
ques_embed = ra_ed.sentence2idx(ques)
if hyper_parameters['embedding_type'] in ['bert', 'albert']:
x_val_1 = np.array([ques_embed[0]])
x_val_2 = np.array([ques_embed[1]])
x_val = [x_val_1, x_val_2]
else:
x_val = ques_embed
# 预测
pred = graph.predict(x_val)
# 取id to label and pred
pre = pt.prereocess_idx(pred[0])
print(pre)
while True:
print("请输入: ")
ques = input()
ques_embed = ra_ed.sentence2idx(ques)
print(ques_embed)
if hyper_parameters['embedding_type'] in ['bert', 'albert']:
x_val_1 = np.array([ques_embed[0]])
x_val_2 = np.array([ques_embed[1]])
x_val = [x_val_1, x_val_2]
else:
x_val = ques_embed
pred = graph.predict(x_val)
pre = pt.prereocess_idx(pred[0])
print(pre)
'embedding_type': 'random',
'is_training': False,
'model_path': path_model_fast_text_baiduqa_2019,
},
'embedding': {
'embedding_type': 'random',
'corpus_path': path_embedding_random_char,
'level_type': 'char',
'embed_size': 300,
'len_max': 50,
},
}
# ns = np.array([1,2,3,4])
# print(type(ns))
pt = PreprocessText
graph = FastTextGraph(hyper_parameters)
graph.load_model()
ra_ed = graph.word_embedding
ques = '你好呀'
ques_embed = ra_ed.sentence2idx(ques)
pred = graph.predict(np.array([ques_embed]))
pre = pt.prereocess_idx(pred[0])
print(pre)
while True:
print("请输入: ")
ques = input()
ques_embed = ra_ed.sentence2idx(ques)
pred = graph.predict(np.array([ques_embed]))
pre = pt.prereocess_idx(pred[0])
print(pre)
'lr': 1e-3,
'l2': 1e-6,
'activate_classify': 'softmax',
'embedding_type': 'random',
'is_training': True,
'model_path': path_model_fast_text_baiduqa_2019,
},
'embedding': {
'embedding_type': 'random',
'corpus_path': path_embedding_random_char,
'level_type': 'char',
'embed_size': 300,
'len_max': 50,
},
}
graph = Graph(hyper_parameters)
ra_ed = graph.word_embedding
pt = PreprocessText()
x_train, y_train = pt.preprocess_baidu_qa_2019_idx(
path_baidu_qa_2019_train, ra_ed)
x_val, y_val = pt.preprocess_baidu_qa_2019_idx(path_baidu_qa_2019_valid,
ra_ed)
print(len(y_train))
graph.fit(x_train, y_train, x_val, y_val)
# 1425170/1425170 [==============================] - 83s 58us/step - loss: 0.9383 - acc: 0.7106 - val_loss: 2.4205 - val_acc: 0.5029
# Epoch 00001: val_loss improved from inf to 2.42050, saving model to D:\workspace\pythonMyCode\django_project\ClassificationTextChinese/data/model/fast_text/model_fast_text.f5
# Epoch 2/20
# 验证集准确率50%左右
# time时间大约在2*4轮=8分钟左右
def train(hyper_parameters=None, rate=1.0):
if not hyper_parameters:
hyper_parameters = {
'len_max': 56, # 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, 小心OOM
'embed_size': 300, # 字/词向量维度, bert取768, word取300, char可以更小些
'vocab_size': 20000, # 这里随便填的，会根据代码里修改
'trainable': True, # embedding是静态的还是动态的, 即控制可不可以微调
'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
'embedding_type': 'random', # 级别, 嵌入类型, 还可以填'xlnet'、'random'、 'bert'、 'albert' or 'word2vec"
# 'gpu_memory_fraction': 0.76, #gpu使用率
'model': {'label': 17, # 类别数
'batch_size': 256, # 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
'dropout': 0.5, # 随机失活, 概率
'decay_step': 1000, # 学习率衰减step, 每N个step衰减一次
'decay_rate': 0.999, # 学习率衰减系数, 乘法
'filters': [3, 7, 7],
'filters_num': 300, # 卷积个数 论文中 filters_num=150,300
'epochs': 20, # 训练最大轮次
'patience': 3, # 早停,2-3就好
'lr': 1e-3, # 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
'l2': 1e-9, # l2正则化
'activate_classify': 'softmax', # 最后一个layer, 即分类激活函数
'loss': 'categorical_crossentropy', # 损失函数
'metrics': 'accuracy', # 保存更好模型的评价标准
'optimizer_name': 'Adam', # 优化器, 可选['Adam', 'Radam', 'RAdam,Lookahead'], win10下必须使用GPU, 原因未知
'is_training': True, # 训练后者是测试模型
'path_model_dir': path_model_dir, # 模型目录
'model_path': path_model,
# 模型地址, loss降低则保存的依据, save_best_only=True, save_weights_only=True
'path_hyper_parameters': path_hyper_parameters, # 模型(包括embedding)，超参数地址,
'path_fineture': path_fineture, # 保存embedding trainable地址, 例如字向量、词向量、bert向量等
},
'embedding': {'layer_indexes': [24], # bert取的层数
# 'ngram_ns': [3],
# 'corpus_path': path_baidu_qa_2019_train,
},
'data':{'train_data': path_ccks_2020_el_cls_train, # 训练数据
'val_data': path_ccks_2020_el_cls_dev # 验证数据
},
}
# 删除先前存在的模型\embedding微调模型等
delete_file(path_model_dir)
time_start = time.time()
# graph初始化
graph = Graph(hyper_parameters)
print("graph init ok!")
ra_ed = graph.word_embedding
# 数据预处理
pt = PreprocessSim(path_model_dir)
x_train, y_train = pt.preprocess_label_ques_to_idx(hyper_parameters['embedding_type'],
hyper_parameters['data']['train_data'],
ra_ed, rate=rate, shuffle=True)
x_val, y_val = pt.preprocess_label_ques_to_idx(hyper_parameters['embedding_type'],
hyper_parameters['data']['val_data'],
ra_ed, rate=rate, shuffle=True)
print("data propress ok!")
print(len(y_train))
# 训练
graph.fit(x_train, y_train, x_val, y_val)
# 训练
# graph.fit_generator(embed=ra_ed, rate=rate)
print("耗时:" + str(time.time()-time_start))
