def TrainModel(self, datapath, epoch = 2, save_step = 1000, batch_size = 32, filename = 'model_speech/speech_model24'): ''' 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, 'train') num_data = data.GetDataNum() # 获取数据的数量 yielddatas = data.data_genetator(batch_size, self.AUDIO_LENGTH) 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 #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)) self.TestModel(self.datapath, str_dataset='train', data_count = 4) self.TestModel(self.datapath, str_dataset='dev', data_count = 4)
def TrainModel(self, datapath, epoch = 2, save_step = 1000, batch_size = 32, filename = abspath + 'model_speech/m' + ModelName + '/speech_model'+ModelName): ''' 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, 'train') #首先获取的是 train数据集 num_data = data.GetDataNum() # 获取数据的数量 yielddatas = data.data_genetator(batch_size, self.AUDIO_LENGTH) #将所有的数据使用生成器进行batch_size的封装,封装成一个个的对象 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 #self._model.fit_generator(yielddatas, save_step, nb_worker=2) # 利用Python的生成器,逐个生成数据的batch并进行训练。生成器与模型将并行执行以提高效率。例如,该函数允许我们在CPU上进行实时的数据提升,同时在GPU上进行模型训练 self._model.fit_generator(yielddatas, save_step) # self._model这个是初始化调用creatmodel返回的模型 # samples_per_epoch:整数,当模型处理的样本达到此数目时计一个epoch结束,执行下一个epoch 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)) #进行模型的保存 self.TestModel(self.datapath, str_dataset='train', data_count = 4) #进行训练集模型的测试 self.TestModel(self.datapath, str_dataset='dev', data_count = 4) # 进行 验证集模型的测试
def TrainModel(self, datapath, epoch=2, save_step=1000, batch_size=32): ''' 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, 'train') # num_data = data.GetDataNum() # 获取数据的数量 txt_loss = open( os.path.join(os.getcwd(), 'speech_log_file', 'Test_Report_loss.txt'), mode='a', encoding='UTF-8') txt_obj = open( os.path.join(os.getcwd(), 'speech_log_file', 'Test_Report_accuracy.txt'), mode='a', encoding='UTF-8') saver = tf.train.Saver() with tf.Session() as sess: # sess.run(tf.global_variables_initializer()) saver.restore(sess,os.path.join(os.getcwd(), 'speech_model_file','speech.module-50')) summary_merge = tf.summary.merge_all() train_writter = tf.summary.FileWriter('summary_file',sess.graph) for i in range(51,epoch): yielddatas = data.data_genetator(batch_size, self.MAX_TIME) pbar = tqdm(yielddatas) train_epoch = 0 train_epoch_size = save_step for input,_ in pbar: feed = {self.input_data: input[0],self.label_data: input[1],self.input_length:input[2],self.label_length:input[3], self.is_train:True} _,loss,train_summary = sess.run([self.optimize,self.loss,summary_merge],feed_dict=feed) train_writter.add_summary(train_summary,train_epoch+i*train_epoch_size) pr = 'epoch:%d/%d,train_epoch: %d/%d ,loss: %s'% (epoch,i,train_epoch_size,train_epoch,loss) pbar.set_description(pr) txt = pr + '\n' txt_loss.write(txt) if train_epoch == train_epoch_size: break train_epoch +=1 if train_epoch%3000==0: self.TestMode(data, sess, i,txt_obj) saver.save(sess, os.path.join(os.getcwd(), 'speech_model_file', 'speech.module'), global_step=i) txt_loss.close()
def TrainModel(self, epoch=2, save_step=1000, batch_size=32, start_nstep=0): ''' 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(self.datapath_thchs30, self.datapath_stcmds, 'train') num_data = data.GetDataNum() # 获取数据的数量 yielddatas = data.data_genetator(batch_size, self.AUDIO_LENGTH) for epoch in range(epoch): # 迭代轮数 self.logger.debug("train epoch %s." % epoch) # print('[running] train epoch %d .' % epoch) n_step = start_nstep # 迭代数据数 while True: try: self.logger.debug('epoch %d . Have train datas %d+' % (epoch, n_step * save_step)) # print('[message] epoch %d . Have train datas %d+'%(epoch, n_step*save_step)) # data_genetator是一个生成器函数 #self._model.fit_generator(yielddatas, save_step, nb_worker=2) self._model.fit_generator(yielddatas, save_step) n_step += 1 except StopIteration: self.logger.error( "generator error. please check data format.") # print('[error] generator error. please check data format.') break self.SaveModel(filename='speech_model_%s_e_%s_step_%s' % (ModelName, epoch, n_step * save_step)) self.TestModel(str_dataset='train', data_count=4) self.TestModel(str_dataset='dev', data_count=4)
def TrainModel(self, datapath, epoch=2, save_step=1000, batch_size=32, filename=abspath + 'model_speech/m' + ModelName + '/speech_model' + ModelName): ''' 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, 'train') num_data = data.GetDataNum() # 获取数据的数量 yielddatas = data.data_genetator(batch_size, self.AUDIO_LENGTH) # 冻结层 for layer in self._model.layers: layerName = str(layer.name) print("layerNAME:" + layerName) if layerName.startswith("conv2d_3") or layerName.startswith( "conv2d_4" ) or layerName.startswith("conv2d_5") or layerName.startswith( "conv2d_6") or layerName.startswith( "conv2d_7") or layerName.startswith( "conv2d_8") or layerName.startswith("conv2d_9"): layer.trainable = False self._model.compile(optimizer='rmsprop', loss='mse') # 可训练层 for x in self._model.trainable_weights: print("可训练层:" + x.name) print('\n') # 不可训练层 for x in self._model.non_trainable_weights: print("冻结层:" + x.name) print('\n') 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 # 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)) self.TestModel(self.datapath, str_dataset='train', data_count=4) self.TestModel(self.datapath, str_dataset='dev', data_count=4)