Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

image_rgb = np.stack([image_r, image_g, image_b], axis=2)
Let’s see what those same matrices represent, once we consider them
channels (unfortunately, the visual impact is completely lost in print).
Before moving on with our classification problem, we need to address the shape
issue: Different frameworks (and Python packages) use different conventions for
the shape of the images.
Shape (NCHW vs NHWC)
"What do these acronyms stand for?"
It’s quite simple, actually:
• N stands for the Number of images (in a mini-batch, for instance).
• C stands for the number of Channels (or filters) in each image.
• H stands for each image’s Height.
• W stands for each image’s Width.
Thus the acronyms indicate the expected shape of the mini-batch:
• NCHW: (number of images, channels, height, width)
• NHWC: (number of images, height, width, channels)
Basically, everyone agrees that the number of images comes first, and that height
and width are an inseparable duo. It all comes down to the channels (or filters):
Classifying Images | 271