Hands-On Transfer Learning with Python

Postprocessing

The tricks in solving the problem aren't over yet. As mentioned in the Preprocessing subsection, we standardize the pixel values between -1 to +1 for the purpose of ensuring that our network trains properly. Also, the LAB colorspace has values between -128 and +128 for the two color channels. Thus, the following two postprocessing steps are performed:

• We multiply each pixel value by 128 to bring values into the required range of color channels
• We concatenate the grayscale input image with the output two-channel image to achieve the hallucinated color image

The following snippet performs the postprocessing steps to achieve hallucinated color images:

sample_img = []for filename in test_files: sample_img.append(sp.misc.imresize(load_img(IMG_DIR+filename), ...