How do I train an RBM on color images?

I am having a hard time understanding the strategy for inputting the color. Most tutorials on RBMs only train grayscale images.

If the image is grayscale, the input units can be binary, and I can normalize the gray scale value to [0,1], and then treat them like probabilities in the input layer. Or whiten the dataset and use Gaussian units in the input layer.

How do I treat color images? Obviously, the input units cannot be binary - unless I replicate the units for each of the three color channels? Or what is the popular strategy?

Topic rbm

Category Data Science

1
answered at

I am working on a similar project. The idea is still under experiment so I do not guarantee results. You may convert your images to grey scaled images having 8 levels, then you can use these 8 levels as a visible layer connected to hidden layer having (width * height) of your images, then use this hidden layer as a visible layer to a hidden layer with the number of your classes.

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