How to normalize a 4D numpy array?

Question

I have a three dimensional numpy array of images (CIFAR-10 dataset). The image array shape is like below:

a = np.random.rand(32, 32, 3)

Before I do any deep learning, I want to normalize the data to get better result. With a 1D array, I know we can do min max normalization like this:

v = np.random.rand(6)
(v - v.min())/(v.max() - v.min())

Out[68]:
array([ 0.89502294,  0.        ,  1.        ,  0.65069468,  0.63657915,
        0.08932196])

However, when it comes to a 3D array, I am totally lost. Specifically, I have the following questions:

1. Along which axis do we take the min and max?
2. How do we implement this with the 3D array?

I appreciate your help!

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EDIT: It turns out I need to work with a 4D Numpy array with shape (202, 32, 32, 3), so the first dimension would be the index for the image, and the last 3 dimensions are the actual image. It'll be great if someone can provide me with the code to normalize such a 4D array. Thanks!

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EDIT 2: Thanks to @Eric's code below, I've figured it out:

x_min = x.min(axis=(1, 2), keepdims=True)
x_max = x.max(axis=(1, 2), keepdims=True)

x = (x - x_min)/(x_max-x_min)

Answer 1

Assuming you're working with image data of shape (W, H, 3), you should probably normalize over each channel (axis=2) separately, as mentioned in the other answer.

You can do this with:

# keepdims makes the result shape (1, 1, 3) instead of (3,). This doesn't matter here, but
# would matter if you wanted to normalize over a different axis.
v_min = v.min(axis=(0, 1), keepdims=True)
v_max = v.max(axis=(0, 1), keepdims=True)
(v - v_min)/(v_max - v_min)