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Shell 0.02% Python 1.87% HTML 88.89% Jupyter Notebook 9.22%

esun_sar_baseline's Introduction

esun_sar_baseline

簡述:

  • 把每種 data (ccba, cdtx, dp, remit, custinfo) 都先經過各別的 encoder 變成一個 embedding
    • encoder原始碼請見: link
  • 把顧客上述的 embedding 依據資料時間排序
  • 丟到一個 Transformer 來進行預測 (Maxlen=1024, DebertaTransformerEncoder)
  • loss_func:
    def cost_sensetive_bce_loss(output, target, epsilon=1e-7, w_tp=99, w_tn=0, w_fp=1, w_fn=99):
    
      fn = w_fn * torch.mean(target * torch.log(output+epsilon))
      tp = w_tp * torch.mean(target * torch.log((1-output)+epsilon))
      fp = w_fp * torch.mean((1-target) * torch.log((1-output)+epsilon))
      tn = w_tn * torch.mean((1-target) * torch.log(output+epsilon))
      return -(fn+tp+fp+tn)
    

esun_sar_baseline's People

Contributors

axotzero avatar nana2929 avatar

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