在我正在做的 coursera 指導項目中，講師使用from skimage.transform import rescaleimage_rescaled = rescale(rescale(image,0.5),2.0)扭曲圖像。在我自己的設備上發生的錯誤（并且在項目的 jupyter notebook 上沒有出現，可能是由于模塊和 python 的版本不同）是通道的數量增加image_rescaled了1.例如=>images_normal.shape = (256,256,256,3)和images_with_twice_reshape.shape=(256,256,256,4)如果我使用，則不會出現此問題rescaled(rescale(image,2.0),0.5)。這是在較新版本的 python/skimage 中使用還是我做錯了什么？對于其他參考（沒有從源代碼中刪除任何內容，但用#s 突出顯示了重要部分）：import osimport refrom scipy import ndimage, miscfrom skimage.transform import resize, rescalefrom matplotlib import pyplotimport numpy as npdef train_batches(just_load_dataset=False):    batches = 256 # Number of images to have at the same time in a batch    batch = 0 # Number if images in the current batch (grows over time and then resets for each batch)    batch_nb = 0 # Batch current index        ep = 4 # Number of epochs    images = []    x_train_n = []    x_train_down = []        x_train_n2 = [] # Resulting high res dataset    x_train_down2 = [] # Resulting low res dataset        for root, dirnames, filenames in os.walk("data/cars_train.nosync"):        for filename in filenames:            if re.search("\.(jpg|jpeg|JPEG|png|bmp|tiff)$", filename):                filepath = os.path.join(root, filename)                image = pyplot.imread(filepath)                if len(image.shape) > 2:                                            image_resized = resize(image, (256, 256)) # Resize the image so that every image is the same size#########################通過上面的代碼，我得到了x_train_n2.shape = (256,256,256,3)和x_train_down2.shape=(256,256,256,4)。