Difference between class_weight and loss_weights in Keras
From Keras Team at GitHub:


  loss_weights parameter on compile is used to define how much each of
  your model output loss contributes to the final loss value ie. it
  weighs the model output losses. You could have a model with 2 outputs
  where one is the primary output and the other auxiliary. eg. 1. *
  primary + 0.3 * auxiliary. The default values for loss weights is 1.
  
  class_weight parameter on fit is used to weigh the importance of each
  sample based on the class they belong to, during training. This is
  typically used when you have an uneven distribution of samples per
  class.