ImageDataGenerator具有一个preprocessing_function参数，该参数允许您传递在推理过程中使用的相同的preprocess_input函数.此功能将为您进行重新缩放，因此可以省略缩放:

ImageDataGenerator has a preprocessing_function argument which allows you to pass the same preprocess_input function that you are using during inference. This function will do the rescaling for you, so can omit the scaling:

from keras.applications.vgg16 import preprocess_input
train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

keras_applications中的大多数预训练模型使用相同的

Most of the pretrained models in keras_applications use the same preprocessing function. You can inspect the docstring to see what it does:

def preprocess_input(x, data_format=None, mode='caffe', **kwargs):
    """Preprocesses a tensor or Numpy array encoding a batch of images.
    # Arguments
        x: Input Numpy or symbolic tensor, 3D or 4D.
            The preprocessed data is written over the input data
            if the data types are compatible. To avoid this
            behaviour, `numpy.copy(x)` can be used.
        data_format: Data format of the image tensor/array.
        mode: One of "caffe", "tf" or "torch".
            - caffe: will convert the images from RGB to BGR,
                then will zero-center each color channel with
                respect to the ImageNet dataset,
                without scaling.
            - tf: will scale pixels between -1 and 1,
                sample-wise.
            - torch: will scale pixels between 0 and 1 and then
                will normalize each channel with respect to the
                ImageNet dataset.
    # Returns
        Preprocessed tensor or Numpy array.