NSTOptimizerAndLossConfigs.py

# Create an optimizer. The paper recommends LBFGS, but Adam works okay, too:
opt = tf.optimizers.Adam(learning_rate=0.005, beta_1=0.99, epsilon=1e-1)

# To optimize this, use a weighted combination of the two losses to get the total loss:
style_weight=1e-2
content_weight=1e4

def style_content_loss(outputs):
    style_outputs = outputs['style']
    content_outputs = outputs['content']
    style_loss = tf.add_n([tf.reduce_mean((style_outputs[name]-style_targets[name])**2) 
                           for name in style_outputs.keys()])
    style_loss *= style_weight / len(style_layers)

    content_loss = tf.add_n([tf.reduce_mean((content_outputs[name]-content_targets[name])**2) 
                             for name in content_outputs.keys()])
    content_loss *= content_weight / len(content_layers)
    loss = style_loss + content_loss
    return loss