chainer中的损失函数保持为零

Question

我正在使用chainer并尝试进行主题建模。培训阶段的代码包含以下内容：

optimizer = O.Adam()
    optimizer.setup(self.train_model)
    clip = chainer.optimizer.GradientClipping(5.0)
    optimizer.add_hook(clip)

    j = 0
    msgs = defaultdict(list)

    for epoch in range(epochs):
        print "epoch : ",epoch
        data = prepare_topics(cuda.to_cpu(self.train_model.mixture.weights.W.data).copy(),
                              cuda.to_cpu(self.train_model.mixture.factors.W.data).copy(),
                              cuda.to_cpu(self.train_model.sampler.W.data).copy(),
                              self.words)

        top_words = print_top_words_per_topic(data)


        if j % 100 == 0 and j > 100:
            coherence = topic_coherence(top_words)
            for j in range(self.n_topics):
                print j, coherence[(j, 'cv')]
            kw = dict(top_words=top_words, coherence=coherence, epoch=epoch)

        data['doc_lengths'] = self.doc_lengths

        data['term_frequency'] = self.term_frequency

        for d, f in utils.chunks(self.batchsize, self.doc_ids, self.flattened):
            t0 = time.time()

            self.train_model.cleargrads()

            l = self.train_model.fit_partial(d.copy(), f.copy(), update_words = update_words, update_topics = update_topics)
            prior = self.train_model.prior()
            loss = prior * self.fraction
            loss.backward()
            optimizer.update()
            msg = ("J:{j:05d} E:{epoch:05d} L:{loss:1.3e} " 
                   "P:{prior:1.3e} R:{rate:1.3e}")
            prior.to_cpu()
            loss.to_cpu()
            t1 = time.time()
            dt = t1 - t0
            rate = self.batchsize / dt

            msgs["E"].append(epoch)
            msgs["L"].append(float(l))

            j += 1
        logs = dict(loss=float(l), epoch=epoch, j=j, prior=float(prior.data), rate=rate)
        print msg.format(**logs)
        print "\n ================================= \n"
        #serializers.save_hdf5("lda2vec.hdf5", self.model)
        msgs["loss_per_epoch"].append(float(l))

我执行的代码我得到了例如：

J:00200 E:00380 L:0.000e+00 P:-2.997e+04 R:2.421e+04

只有L（损失）不变，有人可以帮助知道为什么这个值保持为零？