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 initialize_net(self):
      # Start all SVM parameters at zero
      self.net.params["cls_score"][0].data[...] = 0
      self.net.params["cls_score"][1].data[...] = 0
  
imports: from timer import Timer
  from sklearn import svm
  import numpy
label_desc: Use image node_id 5 for calling the SVMTrainer obj's underlying member method code with example usage: obj.initialize_net() without return types