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 datadirectory 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_b
from scipy.misc import imsave
from imageio import imwrite
import time

result_prefix = 'st_res_'+TARGET_IMG.split('.')[0]
iterations = 20

# Run scipy-based optimization (L-BFGS) over the pixels of the 
# generated image
# so as to minimize the neural style loss.
# This is our initial state: the target image.
# Note that `scipy.optimize.fmin_l_bfgs_b` can only process flat 
# vectors.
x = preprocess_image(TARGET_IMG, height=img_height...