我们可以定义一个模型,这个模型继承自nn.Module类。如果需要定义一个比Sequential模型更加复杂的模型,就需要定义nn.Module模型。
定义了__init__和 forward 两个方法,就实现了自定义的网络模型。
init(),定义模型架构,实现每个层的定义。
forward(),实现前向传播,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()
原文:https://www.cnblogs.com/leimu/p/13230723.html