【Pytorch深度学习开发实践学习】B站刘二大人课程笔记整理lecture05 构建线性模型

lecture05 构建线性模型
课程网址
Pytorch深度学习实践

部分课件内容

import torch

x_data =torch.tensor([[1.0],[2.0],[3.0]])
y_data =torch.tensor([[2.0],[4.0],[6.0]])

class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel, self).__init__()
        self.linear = torch.nn.Linear(1,1)  # nn类构造Linear对象 包括权重和偏执

    def forward(self, x):
        y_pred = self.linear(x)  #可调用的对象
        return y_pred

model = LinearModel()

criterion = torch.nn.MSELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(100):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    optimizer.zero_grad()  #所有梯度的权重归零
    loss.backward()
    optimizer.step()  #自动更新
    print(epoch,loss.data)

print('w=',model.linear.weight.data)
print('b=',model.linear.bias.data)

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