class_docstr: Weighted l1 loss and cross entropy loss for SiamRPN training
  
  Parameters
  ----------
  batch_size : int, default 128
      training batch size per device (CPU/GPU).
class_docstr_tok: ["Weighted","l1","loss","and","cross","entropy","loss","for","SiamRPN","training","Parameters","--","--","--","--","--","batch_size",":","int",",","default","128","training","batch","size","per","device","(","CPU\/GPU",")","."]
docstr: Compute cross_entropy_loss
docstr_tok: ["Compute","cross_entropy_loss"]
code: def cross_entropy_loss(self, F, pred, label, pos_index, neg_index):
      """Compute cross_entropy_loss"""
      pred = pred.reshape(self.b, 2, self.loc_c // 2, self.h, self.h)
      pred = F.transpose(pred, axes=((0, 2, 3, 4, 1)))
      pred = pred.reshape(-1, 2)
      label = label.reshape(-1)
      loss_pos = self.get_cls_loss(F, pred, label, pos_index)
      loss_neg = self.get_cls_loss(F, pred, label, neg_index)
      return loss_pos * 0.5 + loss_neg * 0.5
  
imports: from __future__ import absolute_import
  from mxnet import gluon
  from mxnet import nd
  from mxnet.gluon.loss import Loss, _apply_weighting, _reshape_like
label_desc: Use image node_id 4 for calling the SiamRPNLoss obj's underlying member method code with example usage: obj.cross_entropy_loss(F, pred, label, pos_index, neg_index) and returns: unknown