def style_loss(style_end_points, stylized_end_points, style_weights):
"""Style loss.
Args:
style_end_points: dict mapping VGG16 layer names to their corresponding
Tensor value for the style input.
stylized_end_points: dict mapping VGG16 layer names to their corresponding
Tensor value for the stylized input.
style_weights: dict mapping layer names to their associated style loss
weight. Keys that are missing from the dict won't have their style
loss computed.
Returns:
Tensor for the total style loss, dict mapping loss names to losses.
"""
total_style_loss = np.float32(0.0)
style_loss_dict = {}
for name, weight in style_weights.iteritems():
loss = tf.reduce_mean(
(learning_utils.gram_matrix(stylized_end_points[name]) -
learning_utils.gram_matrix(style_end_points[name]))**2)
weighted_loss = weight * loss
style_loss_dict['style_loss/' + name] = loss
style_loss_dict['weighted_style_loss/' + name] = weighted_loss
total_style_loss += weighted_loss
style_loss_dict['total_style_loss'] = total_style_loss
return total_style_loss, style_loss_dict
def style_loss(style_end_points, stylized_end_points, style_weights):
"""Style loss.
Args:
style_end_points: dict mapping VGG16 layer names to their corresponding
Tensor value for the style input.
stylized_end_points: dict mapping VGG16 layer names to their corresponding
Tensor value for the stylized input.
style_weights: dict mapping layer names to their associated style loss
weight. Keys that are missing from the dict won't have their style
loss computed.
Returns:
Tensor for the total style loss, dict mapping loss names to losses.
"""
total_style_loss = np.float32(0.0)
style_loss_dict = {}
for name, weight in style_weights.items():
loss = tf.reduce_mean(
(learning_utils.gram_matrix(stylized_end_points[name]) -
learning_utils.gram_matrix(style_end_points[name])) ** 2)
weighted_loss = weight * loss
style_loss_dict['style_loss/' + name] = loss
style_loss_dict['weighted_style_loss/' + name] = weighted_loss
total_style_loss += weighted_loss
style_loss_dict['total_style_loss'] = total_style_loss
return total_style_loss, style_loss_dict