Adversarial Training的pytorch的实现

      
      

       
       def at_loss(embedder, encoder, clf, batch, perturb_norm_length=5.0):
       
       
    embedded = embedder(batch) # [seq_len,batch,hidden_dim]
       
       
    embedded.retain_grad()
       
       
    ce = F.cross_entropy((clf(encoder(embedded, batch)[0])), batch.labels)
       
       
    ce.backward()
       
       

       
       
    d = embedded.grad.data.transpose(0, 1).contiguous() # [batch,seq_len,hidden]
       
       
    d = get_normalized_vector(d)
       
       
    d = d.transpose(0, 1).contiguous() # [seq_len,batch,hidden]
       
       

       
       
    d = embedder(batch) + (perturb_norm_length * Variable(d))
       
       
    loss = F.cross_entropy(clf(encoder(d, batch)[0]), batch.labels)
       
       
    return loss
      
      
      
      

       
       
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