[人工智能]PyTorch深度学习实践——使用PyTorh实现线性模型

在本节的课程中，利用PyTorh实现了线性模型，总结如下：

给出代码：

import torch
x_data = torch.tensor([[1.0],[2.0],[3.0]])
y_data = torch.tensor([[2.0],[4.0],[6.0]])
print(x_data.shape)
class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel,self).__init__()
        self.linear = torch.nn.Linear(1,1)

    def forward(self,x):
        y_pred = self.linear(x)
        return y_pred
model = LinearModel()

criterion = torch.nn.MSELoss(size_average=False)
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)

epoch_list=[]
loss_list=[]

for epoch in range(100):
  for x,y in zip(x_data,y_data):

    y_pred = model(x)
    print(y_pred.shape)
    loss = criterion(y_pred,y)
    #
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
print('w=',model.linear.weight.item())
print("b=",model.linear.bias.item())

x_test = torch.tensor([[4.0]])
y_test = model(x_test)
print('y_pred',y_test.data)

未完待续。。。。