我們可以定義一個模型，這個模型繼承自nn.Module類。如果需要定義一個比Sequential模型更加複雜的模型，就需要定義nn.Module模型。
定義了__init__和 forward 兩個方法，就實現了自定義的網路模型。
init(),定義模型架構，實現每個層的定義。
forward(),實現前向傳播，返回y_pred

import torch


class TwoLayerNet(torch.nn.Module):
    def __init__(self, D_in, H, D_out):
        """
        In the constructor we instantiate two nn.Linear modules and assign them as
        member variables.
        """
        super(TwoLayerNet, self).__init__()
        self.linear1 = torch.nn.Linear(D_in, H)
        self.linear2 = torch.nn.Linear(H, D_out)

    def forward(self, x):
        """
        In the forward function we accept a Tensor of input data and we must return
        a Tensor of output data. We can use Modules defined in the constructor as
        well as arbitrary operators on Tensors.
        """
        h_relu = self.linear1(x).clamp(min=0)
        y_pred = self.linear2(h_relu)
        return y_pred



N, D_in, H, D_out = 64, 1000, 100, 10
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)

model = TwoLayerNet(D_in, H, D_out)

criterion = torch.nn.MSELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
for t in range(500):
   
    y_pred = model(x)
    loss = criterion(y_pred, y)
    print(t, loss.item())
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()