B站刘二大人的Pytorch神度学习第四讲反向传播课后作业
import torch
x_data = [1.0,2.0,3.0]
y_data = [2.0,4.0,6.0]
w_1 = torch.Tensor([1.0])
w_2 = torch.Tensor([2.0])
b = torch.Tensor([1.0])
w_1.requires_grad = True # 计算梯度
w_2.requires_grad = True
b.requires_grad = True
def forward(x):
return w_1 * x * x + w_2 * x + b
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) # 计算Loss
l.backward() # 求梯度
lr = 0.01
print('\tgrad:',x, y, w_1.grad.item())
print('\tgrad:', w_2.grad.item())
print('\tgrad:', b.grad.item())
w_1.data = w_1.data - lr * w_1.grad.data
w_2.data = w_2.data - lr * w_2.grad.data
b.data = b.data -lr *b.grad.data
w_1.grad.data.zero_() # 请0
w_2.grad.data.zero_()
b.grad.data.zero_()
print("progress:",epoch,l.item) # 每个epoch的Loss
print("predict (after training)" , 4 ,forward(4).item())