如何在Keras中預處理VGG16微調訓練集？

Question

我對Keras VGG16型號進行了很好的調整，但我不確定在訓練階段的預處理。

我創建了一個火車發電機如下：

train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
        train_folder,
        target_size=(IMAGE_SIZE, IMAGE_SIZE),
        batch_size=train_batchsize,
        class_mode="categorical"
    )

重新縮放是否足夠或我必須應用其他預處理功能？

當我使用網絡對圖像進行分類時，我使用以下代碼：

from keras.models import load_model
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np

img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)

我認為這是正確的預處理，我應該在訓練之前應用它。

謝謝你的幫助。

Answer 1

ImageDataGenerator有一個preprocessing_function參數，允許您傳遞在推理期間使用的相同preprocess_input函數。 此功能將為您進行重新縮放，因此可以省略縮放：

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

keras_applications中的大多數預訓練模型使用相同的預處理函數 。 您可以檢查docstring以查看它的作用：

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.