net.py 文件源码

def __call__(self, x, sigmoid=True):
        """AutoEncoder"""
        mu, ln_var = self.encode(x)
        batchsize = len(mu.data)
        # reconstruction loss
        rec_loss = 0
        for l in six.moves.range(self.k):
            z = F.gaussian(mu, ln_var)
            rec_loss += F.bernoulli_nll(x, self.decode(z, sigmoid=False)) \
                / (self.k * batchsize)
        loss = rec_loss + \
            self.C * gaussian_kl_divergence(mu, ln_var) / batchsize
        chainer.report({'loss': loss}, self)
        return loss