model_cvae.py 文件源码

def calcLoss(self, t, categ_vec_h, categ_vec_c, mu, ln_var,wei_arr=None):
        k = self.sample_size;
        loss = None
        t_pred = [t_e[1:] + [2] for t_e in t]
        t_pred = [xp.asarray(tp_e, dtype=xp.int32) for tp_e in t_pred]
        t = self.denoiseInput(t)
        t_vec = self.makeEmbedBatch(t)

        for l in range(k):
            z = F.gaussian(mu, ln_var)
            if loss is None:
                loss = self.decode(z, categ_vec_h, categ_vec_c, t_vec, t_pred,wei_arr) / (k * self.batch_size)
            elif loss is not None:
                loss += self.decode(z, categ_vec_h, categ_vec_c, t_vec, t_pred,wei_arr) / (k * self.batch_size)
        C = 0.005 * (self.epoch_now - self.kl_zero_epoch) / self.epoch  # 0.02
        if self.epoch_now > self.kl_zero_epoch: loss+= C * F.gaussian_kl_divergence(mu, ln_var) / self.batch_size
        return loss