Pytorch builds a simple linear model

import torch
import matplotlib.pyplot as plt
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)
    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)

epochs = 100
lossOP = []
for epoch in range(epochs):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    lossOP.append(loss)
    print(epoch,loss)
    print(epoch,loss.item())

    optimizer.zero_grad()#梯度归0
    loss.backward()
    optimizer.step()#梯度更新
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.item())

x = [i for i in range(epochs)]
plt.plot(x,lossOP)
plt.title("LOSS-OP")
plt.show()