The overall loss function for neural style transfer is calculated as a weighted sum of the content loss, style loss, and total variation loss. Specific hyperparameters—the content weight, style weight, and total variation weight—are applied to each respective loss component. Adjusting these weights allows for fine-tuning the balance between preserving the original content, applying the target style, and reducing high-frequency noise in the synthesized image.

# PyTorch
content_weight, style_weight, tv_weight = 1, 1e4, 10

def compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram):
    # Calculate the content, style, and total variance losses respectively
    contents_l = [content_loss(Y_hat, Y) * content_weight for Y_hat, Y in zip(
        contents_Y_hat, contents_Y)]
    styles_l = [style_loss(Y_hat, Y) * style_weight for Y_hat, Y in zip(
        styles_Y_hat, styles_Y_gram)]
    tv_l = tv_loss(X) * tv_weight
    # Add up all the losses
    l = sum(styles_l + contents_l + [tv_l])
    return contents_l, styles_l, tv_l, l