如何将 nn.Sequential 代码转换为 nn.Linear

Question

我是深度学习的新手，我在学习时遇到了这个。

有没有办法将这里的 nn.Sequential() 函数转换为 nn.Linear()，因为使用 nn.Linear() 函数非常灵活。

class FashionMNISTModelV2(nn.Module):
def __init__(self, input_shape: int, hidden_units: int, output_shape: int):
    super().__init__()
    self.block_1 = nn.Sequential(
        nn.Conv2d(in_channels=input_shape, 
                  out_channels=hidden_units, 
                  kernel_size=3,
                  stride=1,
                  padding=1),
        nn.ReLU(),
        nn.Conv2d(in_channels=hidden_units, 
                  out_channels=hidden_units,
                  kernel_size=3,
                  stride=1,
                  padding=1),
        nn.ReLU(),
        nn.MaxPool2d(kernel_size=2,
                     stride=2)
    )
    self.block_2 = nn.Sequential(
        nn.Conv2d(hidden_units, hidden_units, 3, padding=1),
        nn.ReLU(),
        nn.Conv2d(hidden_units, hidden_units, 3, padding=1),
        nn.ReLU(),
        nn.MaxPool2d(2)
    )
    self.classifier = nn.Sequential(
        nn.Flatten(),
        nn.Linear(in_features=hidden_units*7*7, 
                  out_features=output_shape)
    )

def forward(self, x: torch.Tensor):
    x = self.block_1(x)
    x = self.block_2(x)
    x = self.classifier(x)
    return x

Answer 1

您可以将线性层与分类器分开定义为独立层：

self.linear = nn.Linear(in_features=hidden_units*7*7, 
                        out_features=output_shape))

那么在forward函数中，等效的实现是：

def forward(self, x: torch.Tensor):
    x = self.block_1(x)
    x = self.block_2(x)
    x = x.flatten(1)
    x = self.linear(x)
    return x