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

Images and Channels
In case you’re not familiar with the meaning of channels, pixel values, and
how images are represented as tensors, this is a brief overview of these
topics.
To illustrate how images are represented, let’s create three separate images
first:
image_r = np.zeros((5, 5), dtype=np.uint8)
image_r[:, 0] = 255
image_r[:, 1] = 128
image_g = np.zeros((5, 5), dtype=np.uint8)
image_g[:, 1] = 128
image_g[:, 2] = 255
image_g[:, 3] = 128
image_b = np.zeros((5, 5), dtype=np.uint8)
image_b[:, 3] = 128
image_b[:, 4] = 255
Each of these images is five-by-five pixels and is represented by a five-byfive
matrix. It is a two-dimensional representation, which means it is a
single-channel image. Moreover, its dtype is np.uint8, which only accepts
values from zero to 255.
If an image has only one channel, it is a grayscale image.
The range of pixel values goes from zero (black) to 255
(white), and everything in between is a shade of gray.
Classifying Images | 269