课上代码

import torch
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
 
w = torch.tensor([1.0]) # w的初值为1.0
w.requires_grad = True # 需要计算梯度
 
def forward(x):
    return x*w  # w是一个Tensor
 
 
def loss(x, y):
    y_pred = forward(x)
    return (y_pred - y)**2
 
print("predict (before training)", 4, forward(4).item())
 
for epoch in range(100):
    for x, y in zip(x_data, y_data):
        l =loss(x,y) # l是一个张量，tensor主要是在建立计算图 forward, compute the loss
        l.backward() #  backward,compute grad for Tensor whose requires_grad set to True
        print('\tgrad:', x, y, w.grad.item())
        w.data = w.data - 0.01 * w.grad.data   # 权重更新时，注意grad也是一个tensor
 
        w.grad.data.zero_() # after update, remember set the grad to zero
 
    print('progress:', epoch, l.item()) # 取出loss使用l.item，不要直接使用l（l是tensor会构建计算图）
 
print("predict (after training)", 4, forward(4).item())

作业代码

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

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

w1 = torch.Tensor([1.0])#初始权值
w1.requires_grad = True#计算梯度，默认是不计算的
w2 = torch.Tensor([1.0])
w2.requires_grad = True
b = torch.Tensor([1.0])
b.requires_grad = True

def forward(x):
    return w1 * x**2 + w2 * x + b

def loss(x,y):#构建计算图
    y_pred = forward(x)
    return (y_pred-y) **2

print('Predict (befortraining)',4,forward(4))

for epoch in range(100):
    l = loss(1, 2)#为了在for循环之前定义l,以便之后的输出，无实际意义
    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 - 0.01*w1.grad.data #注意这里的grad是一个tensor，所以要取他的data
        w2.data = w2.data - 0.01 * w2.grad.data
        b.data = b.data - 0.01 * b.grad.data
        w1.grad.data.zero_() #释放之前计算的梯度
        w2.grad.data.zero_()
        b.grad.data.zero_()
    print('Epoch:',epoch,l.item())

print('Predict(after training)',4,forward(4).item())