class_docstr: Class to run multiple iterative algorithms and compare the results.
docstr: output the loss per iteration for different methods
docstr_tok: ["output","the","loss","per","iteration","for","different","methods"]
code: def loss_vals(self):
      """output the loss per iteration for different methods"""
      baseline = self.lrp.loss_wor(self.w_opt)
      loss_dict = {}
      for params, name in zip(self.params, self.names):
          losses = [self.lrp.loss_wor(w) - baseline for w in params]
          loss_dict[name] = [losses]
      return loss_dict
  
imports: import time
  from my_logistic_regression import MyLogisticRegression
  import numpy
label_desc: Use image node_id 4 for calling the CompareAlgs obj's underlying member method code with example usage: obj.loss_vals() and returns: loss_dict