pyTorch深度学习多层感知机的实现

#画图使用
def xyplot(x,y,name,size):
	plt.figure(figsize=size)
	plt.plot(x.detach().numpy(),y.detach().numpy())
	plt.xlabel('x')
	plt.ylabel(name+'(x)')
	plt.show()

#relu
x = torch.arange(-8,8,0.01,requires_grad=True)
y = x.relu()
xyplot(x,y,'relu')

y.sum().backward()
xyplot(x,x.grad,'grad of relu')

def relu(x):
	return torch.max(input=x,others,other=torch.tensor(0.0))

class MulPeceptron(nn.Module):
    def __init__(self,in_features,out_features):
        super().__init__()
        self.fc = nn.Linear(in_features=in_features,out_features=256)
        self.out = nn.Linear(in_features=256,out_features=out_features)
    def forward(self,t):
        t = t.flatten(start_dim=1)
        t = self.fc(t)
        t = F.relu(t)
        t = self.out(t)
        return t