self.model.summary()
self.compile()
return self.model
def compile(self):
self.model.compile(optimizer=Adam(1e-5),
loss=self.crf.loss_function,
metrics=[self.crf.accuracy])
if __name__ == '__main__':
from DataProcess.process_data import DataProcess
from keras.utils.vis_utils import plot_model
dp = DataProcess(data_type='msra', model='bert')
train_data, train_label, test_data, test_label = dp.get_data(one_hot=True)
md = BERTBILSTMCRF(vocab_size=dp.vocab_size, n_class=dp.tag_size)
md.creat_model()
model = md.model
plot_model(model, to_file='picture/BERT_BILSTM_CRF.png', show_shapes=True)
exit()
model.fit(train_data, train_label, batch_size=64, epochs=2,
validation_data=[test_data, test_label])
def train_sample(train_model='BERTBILSTMCRF',
# ['BERTBILSTMCRF', 'BILSTMAttentionCRF', 'BILSTMCRF',
# 'IDCNNCRF', 'IDCNNCRF2']
epochs=15,
log = None,
):
# bert需要不同的数据参数 获取训练和测试数据
if train_model == 'BERTBILSTMCRF':
dp = DataProcess(data_type='msra', max_len=max_len, model='bert')
else:
dp = DataProcess(data_type='msra', max_len=max_len)
train_data, train_label, test_data, test_label = dp.get_data(one_hot=True)
log.info("----------------------------数据信息 START--------------------------")
log.info(f"当前使用数据集 MSRA")
# log.info(f"train_data:{train_data.shape}")
log.info(f"train_label:{train_label.shape}")
# log.info(f"test_data:{test_data.shape}")
log.info(f"test_label:{test_label.shape}")
log.info("----------------------------数据信息 END--------------------------")
if train_model == 'BERTBILSTMCRF':
model_class = BERTBILSTMCRF(dp.vocab_size, dp.tag_size, max_len=max_len)
elif train_model == 'BILSTMAttentionCRF':
model_class = BILSTMAttentionCRF(dp.vocab_size, dp.tag_size)
elif train_model == 'BILSTMCRF':
model_class = BILSTMCRF(dp.vocab_size, dp.tag_size)
elif train_model == 'IDCNNCRF':
model_class = IDCNNCRF(dp.vocab_size, dp.tag_size, max_len=max_len)
else:
model_class = IDCNNCRF2(dp.vocab_size, dp.tag_size, max_len=max_len)
model = model_class.creat_model()
callback = TrainHistory(log=log, model_name=train_model) # 自定义回调 记录训练数据
early_stopping = EarlyStopping(monitor='val_crf_viterbi_accuracy', patience=2, mode='max') # 提前结束
model.fit(train_data, train_label, batch_size=32, epochs=epochs,
validation_data=[test_data, test_label],
callbacks=[callback, early_stopping])
# 计算 f1 和 recall值
pre = model.predict(test_data)
pre = np.array(pre)
test_label = np.array(test_label)
pre = np.argmax(pre, axis=2)
test_label = np.argmax(test_label, axis=2)
pre = pre.reshape(pre.shape[0] * pre.shape[1], )
test_label = test_label.reshape(test_label.shape[0] * test_label.shape[1], )
f1score = f1_score(pre, test_label, average='macro')
recall = recall_score(pre, test_label, average='macro')
log.info("================================================")
log.info(f"--------------:f1: {f1score} --------------")
log.info(f"--------------:recall: {recall} --------------")
log.info("================================================")
# 把 f1 和 recall 添加到最后一个记录数据里面
info_list = callback.info
if info_list and len(info_list)>0:
last_info = info_list[-1]
last_info['f1'] = f1score
last_info['recall'] = recall
return info_list
return self.model
def compile(self):
self.model.compile('adam',
loss=self.crf.loss_function,
metrics=[self.crf.accuracy])
if __name__ == '__main__':
from DataProcess.process_data import DataProcess
from sklearn.metrics import f1_score
import numpy as np
from keras.utils.vis_utils import plot_model
dp = DataProcess(data_type='msra')
train_data, train_label, test_data, test_label = dp.get_data(one_hot=True)
lstm_crf = BILSTMCRF(vocab_size=dp.vocab_size, n_class=7)
lstm_crf.creat_model()
model = lstm_crf.model
plot_model(model, to_file='picture/BILSTM_CRF.png', show_shapes=True)
exit()
model.fit(train_data,
train_label,
batch_size=64,
epochs=2,
validation_data=[test_data, test_label])