在实现贝叶斯卷积神经网络时，经常会遇到KL散度出现NaN的问题。这主要是由于在计算概率分布时，分母为0导致的。解决方法是在分母中加入一个小的常数，使其不等于0。具体而言，在使用PyTorch实现贝叶斯卷积层时，可以使用以下代码：

class BayesianConv2d(nn.Module): 
     
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(BayesianConv2d, self).__init__()
 
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups  
 
        self.sigma_weight = Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size))
        self.mean_weight = Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size))
        self.register_buffer('eps_weight', torch.zeros(out_channels, in_channels // groups, kernel_size, kernel_size))
 
        if bias:
            self.sigma_bias = Parameter(torch.Tensor(out_channels))
            self.mean_bias = Parameter(torch.Tensor(out_channels))
            self.register_buffer('eps_bias', torch.zeros(out_channels))
        else:
            self.register_parameter('bias', None)
 
        self.reset_parameters()
 
    def reset_parameters(self):
        k = self.kernel_size * self.kernel_size * self.in_channels // self.groups
        # 初始化均值和标准差，这里初始化遵循了论文中的建议
        self.mean_weight.data.normal_(0, math.sqrt(2. / k))
        self.sigma_weight.data.fill_(0.001)
        if self.bias is not None:
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
            self.mean_bias.data.normal_(0, math.sqrt(1. / fan_in))
            self.sigma_bias.data.fill_(0.001)
 
    def forward(self, input):
        # 计算输入数据的四维 shape
        if len(input.shape) == 2:
            input = input.unsqueeze(2).unsqueeze(3)
 
        batch_size, _, _, _ = input.shape
        # 开始计算权重的 eps
        weight_eps = torch.randn(self.out