自定义参数初始化方法

def weight_init(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_normal_(m.weight)
        nn.init.constant_(m.bias, 0)
    # 也可以判断是否为conv2d，使用相应的初始化方式 
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
     # 是否为批归一化层
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)
# 2. 初始化网络结构        
model = Net(in_dim, n_hidden_1, n_hidden_2, out_dim)
# 3. 将weight_init应用在子模块上
model.apply(weight_init)

　　自定义参数初始化方法

原博客：https://blog.csdn.net/dss_dssssd/article/details/83990511

def weight_init(m):    if isinstance(m, nn.Linear):        nn.init.xavier_normal_(m.weight)        nn.init.constant_(m.bias, 0)    # 也可以判断是否为conv2d，使用相应的初始化方式     elif isinstance(m, nn.Conv2d):        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')     # 是否为批归一化层    elif isinstance(m, nn.BatchNorm2d):        nn.init.constant_(m.weight, 1)        nn.init.constant_(m.bias, 0)# 2. 初始化网络结构        model = Net(in_dim, n_hidden_1, n_hidden_2, out_dim)# 3. 将weight_init应用在子模块上model.apply(weight_init)————————————————版权声明：本文为CSDN博主「墨氲」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。原文链接：https://blog.csdn.net/dss_dssssd/article/details/83990511