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

摘自https://github.com/DevSinghSachan/ssl_text_classification