《Pytorch深度学习实践》作业2

import torch
import numpy as np
import matplotlib.pyplot as plt

x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]

w1 = torch.Tensor([0.0])
w1.requires_grad = True    #是否需要计算梯度 = true

w2 = torch.Tensor([0.0])
w2.requires_grad = True

b = torch.Tensor([0.0])
b.requires_grad = True

epoch_list = []
loss_list = []


def forward(x):
    return w1 * x * x + w2 * x + b

def loss(x, y):       #创建一个计算图
    y_pred = forward(x)
    return (y_pred - y) ** 2

learning_rate = 0.01

print('predict (before training)', 4, forward(4).item())

for epoch in range(10000):
    for x, y in zip(x_data, y_data):
        l = loss(x, y)
        l.backward()
        print('tgrad:', x, y, w1.grad.item(), w2.grad.item(), b.grad.item())
        w1.data = w1.data - learning_rate * w1.grad.data
        w2.data = w2.data - learning_rate * w2.grad.data
        b.data = b.data - learning_rate * b.grad.data

        w1.grad.data.zero_()
        w2.grad.data.zero_()
        b.grad.data.zero_()

    print('progress:', epoch, l.item())  # l.item()：取结果最后一次梯度下降得到的loss
    epoch_list.append(epoch)
    loss_list.append(l.item())

print('predict (after training)', 4, forward(4).item(), w1, w2, b)

plt.plot(epoch_list,loss_list)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.show()