nn.modulelist和nn.sequential

 知乎文章链接：https://zhuanlan.zhihu.com/p/64990232

class net6(nn.Module):
    def __init__(self):
        super(net6, self).__init__()
        self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(3)])

    def forward(self, x):
        for layer in self.linears:
            x = layer(x)
        return x

net = net6()
print(net)
# net6(
#   (linears): ModuleList(
#     (0): Linear(in_features=10, out_features=10, bias=True)
#     (1): Linear(in_features=10, out_features=10, bias=True)
#     (2): Linear(in_features=10, out_features=10, bias=True)
#   )
# )

 这个是比较一般的方法，但如果不想这么麻烦，我们也可以用 Sequential 来实现，如 net7 所示！注意 * 这个操作符，它可以把一个 list 拆开成一个个独立的元素。但是，请注意这个 list 里面的模块必须是按照想要的顺序来进行排列的。在 场景一 中，我个人觉得使用 net7 这种方法比较方便和整洁。

class net7(nn.Module):
    def __init__(self):
        super(net7, self).__init__()
        self.linear_list = [nn.Linear(10, 10) for i in range(3)]
        self.linears = nn.Sequential(*self.linears_list)

    def forward(self, x):
        self.x = self.linears(x)
        return x

net = net7()
print(net)
# net7(
#   (linears): Sequential(
#     (0): Linear(in_features=10, out_features=10, bias=True)
#     (1): Linear(in_features=10, out_features=10, bias=True)
#     (2): Linear(in_features=10, out_features=10, bias=True)
#   )
# )

class net5(nn.Module):
    def __init__(self):
        super(net5, self).__init__()
        self.block = nn.Sequential(nn.Conv2d(1,20,5),
                                    nn.ReLU(),
                                    nn.Conv2d(20,64,5),
                                    nn.ReLU())
    def forward(self, x):
        x = self.block(x)
        return x

net = net5()
print(net)
# net5(
#   (block): Sequential(
#     (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
#     (1): ReLU()
#     (2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
#     (3): ReLU()
#   )
# )