VGG-16 Loss calculation functions

loss calculation.py

def content_loss(base,final):
    return K.sum(K.square(final-base))		# square error

def gram_matrix(x):
    features = K.batch_flatten(x)		#flatten -> converts multidimension matrix to single dimensional array
    gram = K.dot(features,K.transpose(features))
    return gram

def style_loss(style,final):
    s = gram_matrix(style)
    f = gram_matrix(final)
    channels = 3				# RGB image
    size = img_nrows * img_ncols
    return K.sum(K.square(s-f))/(4.0*(channels**2)*(size**2)) # ** is 'raise to' operator in python.

def total_var_loss(x):
    a = K.square(x[:,:,:img_nrows-1,:img_ncols-1] - x[:,:,1:,:img_ncols-1])
    b = K.square(x[:,:,:img_nrows-1,:img_ncols-1] - x[:,:,:img_nrows-1,1:])
    return K.sum(K.pow(a+b,1.25))