text: import paddlex as pdx
  from paddlex import transforms as T
  
  # 下载和解压蔬菜分类数据集
  veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
  pdx.utils.download_and_decompress(veg_dataset, path='./')
  
  # 定义训练和验证时的transforms
  # API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
  train_transforms = T.Compose(
      [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
  
  eval_transforms = T.Compose([
      T.ResizeByShort(short_size=256), T.CenterCrop(crop_size=224), T.Normalize()
  ])
  
  # 定义训练和验证所用的数据集
  # API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
  train_dataset = pdx.datasets.ImageNet(
      data_dir='vegetables_cls',
      file_list='vegetables_cls/train_list.txt',
      label_list='vegetables_cls/labels.txt',
      transforms=train_transforms,
      shuffle=True)
  
  eval_dataset = pdx.datasets.ImageNet(
      data_dir='vegetables_cls',
      file_list='vegetables_cls/val_list.txt',
      label_list='vegetables_cls/labels.txt',
      transforms=eval_transforms)
  
  # 初始化模型，并进行训练
  # 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
  num_classes = len(train_dataset.labels)
  model = pdx.cls.ShuffleNetV2(num_classes=num_classes)
  
  # API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
  # 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
  model.train(
      num_epochs=10,
      train_dataset=train_dataset,
      train_batch_size=32,
      eval_dataset=eval_dataset,
      lr_decay_epochs=[4, 6, 8],
      learning_rate=0.01,
      save_dir='output/shufflenetv2',
      use_vdl=True)
  