def TrainModel(self, datapath, epoch = 2, save_step = 1000, batch_size = 32, filename = 'model_speech/speech_model2'):
'''
训练模型
参数：
datapath: 数据保存的路径
epoch: 迭代轮数
save_step: 每多少步保存一次模型
filename: 默认保存文件名，不含文件后缀名
'''
data=DataSpeech(datapath)
data.LoadDataList('train')
num_data = data.GetDataNum() # 获取数据的数量
for epoch in range(epoch): # 迭代轮数
print('[running] train epoch %d .' % epoch)
n_step = 0 # 迭代数据数
while True:
try:
print('[message] epoch %d . Have train datas %d+'%(epoch, n_step*save_step))
# data_genetator是一个生成器函数
yielddatas = data.data_genetator(batch_size, self.AUDIO_LENGTH)
#self._model.fit_generator(yielddatas, save_step, nb_worker=2)
self._model.fit_generator(yielddatas, save_step)
n_step += 1
except StopIteration:
print('[error] generator error. please check data format.')
break
self.SaveModel(comment='_e_'+str(epoch)+'_step_'+str(n_step * save_step))
def TestModel(self, datapath, str_dataset='dev'):
'''
测试检验模型效果
'''
data = DataSpeech(datapath)
data.LoadDataList(str_dataset)
num_data = DataSpeech.GetDataNum() # 获取数据的数量
try:
gen = data.data_genetator(num_data)
for i in range(1):
X, y = gen
r = self._model.test_on_batch(X, y)
print(r)
except StopIteration:
print('[Error] Model Test Error. please check data format.')