Hands-On Transfer Learning with Python

Style transfer in action

The final piece of the puzzle is to use all the building blocks and perform style transfer in action! The art/style and content images are available from the data directory for reference. The following snippet outlines how loss and gradients are evaluated. We also write back outputs after regular intervals/iterations (5, 10, and so on) to understand how the process of neural style transfer transforms the images in consideration after a certain number of iterations as depicted in the following snippet:

from scipy.optimize import fmin_l_bfgs_bfrom scipy.misc import imsavefrom imageio import imwriteimport timeresult_prefix = 'st_res_'+TARGET_IMG.split('.')[0]iterations = 20# Run scipy-based optimization (L-BFGS) over ...