nn.py 文件源码

def loss(self, x, samples):
        # FoldedNormal logpdf
        # https://en.wikipedia.org/wiki/Folded_normal_distribution
        _, proposal_output = self.forward(x, samples)
        batch_size = len(samples)
        locations = proposal_output[:, 0]
        scales = proposal_output[:, 1]
        two_scales = 2 * scales + util.epsilon
        half_log_two_pi_scales = 0.5 * torch.log(math.pi * two_scales + util.epsilon)
        l = 0
        for b in range(batch_size):
            value = samples[b].value[0]
            if value < 0:
                l -= 0
            else:
                location = locations[b]
                two_scale = two_scales[b]
                half_log_two_pi_scale = half_log_two_pi_scales[b]
                logpdf_1 = -half_log_two_pi_scale - ((value - location)**2) / two_scale
                logpdf_2 = -half_log_two_pi_scale - ((value + location)**2) / two_scale
                l -= util.logsumexp(torch.cat([logpdf_1, logpdf_2]))
        return l