我用Keras设计了一个NN，定义模型后的代码如下：

import { Timeline,DataSet } from 'vis-timeline';
import 'vis-timeline/lib/timeline/component/css/animation.css';
import 'vis-timeline/lib/timeline/component/css/currenttime.css';
import 'vis-timeline/lib/timeline/component/css/customtime.css';
import 'vis-timeline/lib/timeline/component/css/dataaxis.css';
import 'vis-timeline/lib/timeline/component/css/item.css';
import 'vis-timeline/lib/timeline/component/css/itemset.css';
import 'vis-timeline/lib/timeline/component/css/labelset.css';
import 'vis-timeline/lib/timeline/component/css/panel.css';
import 'vis-timeline/lib/timeline/component/css/pathStyles.css';
import 'vis-timeline/lib/timeline/component/css/timeaxis.css';
import 'vis-timeline/lib/timeline/component/css/timeline.css';

Template.kk.onRendered(() => {
  const container = $('#visualization');
  const items = new DataSet([
    { id: 1,content: 'item 1',start: '2014-04-20' },{ id: 2,content: 'item 2',start: '2014-04-14' },{ id: 3,content: 'item 3',start: '2014-04-18' },{
      id: 4,content: 'item 4',start: '2014-04-16',end: '2014-04-19',},{ id: 5,content: 'item 5',start: '2014-04-25' },{
      id: 6,content: 'item 6',start: '2014-04-27',type: 'point',]);
  const options = {};
  const tl = new Timeline(container[0],items,options);
  console.log('tl');
  console.log(tl);
});

当我开始进行拟合过程时，我会在第一个时期得到这种输出：

model.compile(optimizer= 'Adam',loss='mean_squared_error')
##callbacks
cb_checkpoint = ModelCheckpoint("model.h5",monitor='val_loss',save_weights_only=True,save_best_only=True,save_freq=1)
cb_Early_Stop=EarlyStopping( monitor='val_loss',patience=5)
cb_Reduce_LR = ReduceLROnPlateau(monitor='val_loss',factor=0.3,patience=5,verbose=0,mode='auto',min_delta=0.0001,cooldown=0,min_lr=0)

callbacks = [cb_checkpoint,cb_Early_Stop,cb_Reduce_LR]
history = model.fit(
          x = {'inputsA': inputsA,'inputsB': inputsB,'inputsC': inputsC,'inputsD': inputsD,'input_site_id': site_id,'input_building_id': building_id,'input_meter': meter,'input_primary_use': primary_use,'input_week': week,'input_floor_count': floor_count,'input_month': month,'input_hour': hour},y = {'predictions' : target},batch_size = 16,epochs = 1000,validation_split = 0.1,callbacks=callbacks)

我想抑制此输出，并仅在每个纪元结束时获得有关火车和val损失的更新。

我该如何实现？