指路☞ 《PyTorch深度学习实践》完结合集_哔哩哔哩_bilibili
知识补充：
1、从左到右是前向，从右到左是反向传播
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import torch
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
#tensor张量
w = torch.Tensor([1.0])
#计算梯度，默认不计算
w.requires_grad = True
def forward(x):
return x*w
def loss(x, y):
y_p = forward(x)
return (y_p - y)**2
print("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.backward()
"""
backward把这个链路上之前所有的梯度都求出来，
把梯度存到w里，计算图就没有了，下一次会出现新的计算图
"""
print('tgrad:', x, y, w.grad.item())
#item把值取出来，变成一个标量
w.data = w.data - 0.01*w.grad.data
"""
grad也是Tensor,如果直接用grad，是重新建立计算图
"""
w.grad.data.zero_()
#清零
print('progress:', epoch, l.item())
print("after training", 4, forward(4).item())
部分运行结果：
progress: 89 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 90 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 91 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 92 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 93 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 94 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 95 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 96 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 97 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 98 9.094947017729282e-13 grad: 1.0 2.0 -7.152557373046875e-07 grad: 2.0 4.0 -2.86102294921875e-06 grad: 3.0 6.0 -5.7220458984375e-06 progress: 99 9.094947017729282e-13 after training 4 7.999998569488525 Process finished with exit code 0
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设函数为y=x^2+x+1
import torch
x_data = [1.0, 2.0, 3.0]
y_data = [4.0, 9.0, 16.0]
w1 = torch.tensor([1.0])
w1.requires_grad = True
w2 = torch.tensor([1.0])
w2.requires_grad = True
b = torch.tensor([1.0])
b.requires_grad = True
def forward(x):
return x * x * w1 + x * w2 + b
def loss(x, y):
y_p = forward(x)
return (y_p - y)**2
print("before training", 4, forward(4).item())
for epoch in range(6000):
for x, y in zip(x_data, y_data):
l = loss(x, y)
l.backward()
print("tgrad", x, y, '{:.6f}'.format(w1.grad.item()), '{:.6f}'.format(w2.grad.item()), '{:.6f}'.format(b.grad.item()))
w1.data = w1.data - 0.01 * w1.grad.data
w2.data = w2.data - 0.01 * w2.grad.data
b.data = b.data - 0.01 * b.grad.data
w1.grad.data.zero_()
w2.grad.data.zero_()
b.grad.data.zero_()
print("progress:", epoch, '{:.6f}'.format(l.item()))
print("after training", 4, forward(4).item())
部分运行结果：
