nn.py 文件源码

def loss(self, x, samples):
        _, proposal_output = self.forward(x, samples)
        batch_size = len(samples)
        means = proposal_output[:, 0]
        stds = proposal_output[:, 1]
        two_std_squares = 2 * stds * stds + util.epsilon
        two_pi_std_squares = math.pi * two_std_squares
        half_log_two_pi_std_squares = 0.5 * torch.log(two_pi_std_squares + util.epsilon)
        l = 0
        for b in range(batch_size):
            value = samples[b].value[0]
            mean = means[b]
            two_std_square = two_std_squares[b]
            half_log_two_pi_std_square = half_log_two_pi_std_squares[b]
            l += half_log_two_pi_std_square + ((value - mean)**2) / two_std_square
        return l