def content_loss(base, combination):
    return K.sum(K.square(combination - base))



outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])
content_layer = 'block5_conv2'
style_layers = ['block1_conv1',
            'block2_conv1',
            'block3_conv1',
            'block4_conv1',
            'block5_conv1']
total_variation_weight = 1e-4
style_weight = 1.
content_weight = 0.025
#K here refers to the keras backend
loss = K.variable(0.)

layer_features = outputs_dict[content_layer]
target_image_features = layer_features[0, :, :, :]
combination_features = layer_features[2, :, :, :]
loss += content_weight * content_loss(target_image_features,
                         combination_features)

return K.sum(K.square(combination - base)) # that's exactly the definition of L2-norm