准备数据如何在python中进行深入学习

我已经完成了关于Kaggle learn的深入学习课程，并开始为MNIST数字数据集编写模型。我喜欢理解我学习的代码，我遇到了以下几点：

def data_prep(raw):
    out_y = keras.utils.to_categorical(raw.label, num_classes)

    num_images = raw.shape[0]
    x_as_array = raw.values[:,1:]
    x_shaped_array = x_as_array.reshape(num_images, img_rows, img_cols, 1)
    out_x = x_shaped_array / 255
    return out_x, out_y

这部分真让我困惑。大部分我都不懂。有人能一步一步地解释一下每一行代码的作用吗？如果我在一个有多种颜色的彩色图像上这样做，这将如何工作？ 我知道这有点宽泛。稍后，我将做一些涉及彩色图像的事情，但我不确定如何做，因为我可以看到黑白“参数”（数组形状中的1除以255）

---

旁注：

raw

是一个数据框架，在每行上方添加注释以解释其用途：

#input is a 2D dataframe of images
def data_prep(raw):
    #convert the classes in raw to a binary matrix
    #also known as one hot encoding and is typically done in ML
    out_y = keras.utils.to_categorical(raw.label, num_classes)

    #first dimension of raw is the number of images; each row in the df represents an image
    num_images = raw.shape[0]

    #remove the first column in each row which is likely a header and convert the rest into an array of values
    #ML algorithms usually do not take in a pandas dataframe 
    x_as_array = raw.values[:,1:]

    #reshape the images into 3 dimensional
    #1st dim: number of images
    #2nd dim: height of each image (i.e. rows when represented as an array)
    #3rd dim: width of each image (i.e. columns when represented as an array)
    #4th dim: the number of pixels which is 3 (RGB) for colored images and 1 for gray-scale images
    x_shaped_array = x_as_array.reshape(num_images, img_rows, img_cols, 1)

    #this normalizes (i.e. 0-1) the image pixels since they range from 1-255. 
    out_x = x_shaped_array / 255

    return out_x, out_y

---

要处理彩色图像，数组中的第四维大小应为3。有关CNN及其输入的更深入信息，请查看此页。

这个问题太宽泛了。这应该给你一个开始的地方。您是否尝试过在每个步骤后检查每个变量的输出以了解其意义？这将是迈出的伟大的第一步。谢谢，现在这更有意义了！