class_docstr: Trains post-hoc detection SVMs for all classes using the algorithm
  and hyper-parameters of traditional R-CNN.
class_docstr_tok: ["Trains","post-hoc","detection","SVMs","for","all","classes","using","the","algorithm","and","hyper-parameters","of","traditional","R-CNN","."]
code: def update_net(self, cls_ind, w, b):
      self.net.params["cls_score"][0].data[cls_ind, :] = w
      self.net.params["cls_score"][1].data[cls_ind] = b
  
imports: from timer import Timer
  from sklearn import svm
  import numpy
label_desc: Use image node_id 6 for calling the SVMTrainer obj's underlying member method code with example usage: obj.update_net(cls_ind, w, b) without return types