Pytorch Autograd 基础（三）

import torch

a = torch.ones(2, 3, requires_grad=True)
print(a)

b1 = 2 * a  # b1 由 a 计算得来，继承了 a 当前额 requires_grad属性
print(b1)

a.requires_grad = False # 关闭 Autograd，不再追踪计算历史
b2 = 2 * a  # b2 由 a 计算得来，继承了 a 当前额 requires_grad属性
print(b2) # b2 也 关闭了 Autograd
print(b2.requires_grad) 

tensor([[1., 1., 1.],
        [1., 1., 1.]], requires_grad=True)
tensor([[2., 2., 2.],
        [2., 2., 2.]], grad_fn=<MulBackward0>)
tensor([[2., 2., 2.],
        [2., 2., 2.]])
False

a.requires_grad = True
print(b2)

tensor([[2., 2., 2.],
        [2., 2., 2.]])

b2.requires_grad= True
print(b2)

tensor([[2., 2., 2.],
        [2., 2., 2.]], requires_grad=True)

a = torch.ones(2, 3, requires_grad=True) * 2
b = torch.ones(2, 3, requires_grad=True) * 3
c1 = a + b  # Autograd 自动打开
print(c1)

with torch.no_grad(): # 在这个上下文中临时关闭 Autograd
    c2 = a + b
    
print(c2)
c3 = a * b  # Autograd 任然自动打开
print(c3)

tensor([[5., 5., 5.],
        [5., 5., 5.]], grad_fn=<AddBackward0>)
tensor([[5., 5., 5.],
        [5., 5., 5.]])
tensor([[6., 6., 6.],
        [6., 6., 6.]], grad_fn=<MulBackward0>)

def add_tensors1(x, y):
    return x + y


@torch.no_grad() # 关闭 Augograd
def add_tensors2(x, y):
    return x + y


a = torch.ones(2, 3, requires_grad=True) * 2
b = torch.ones(2, 3, requires_grad=True) * 3
c1 = add_tensors1(a, b) # c1由a和b计算而来，跟随a和b，打开Autograd
print(c1)

c2 = add_tensors2(a, b) # 由于有@torch.no_grad()，c2关闭了Autograd
print(c2)

tensor([[5., 5., 5.],
        [5., 5., 5.]], grad_fn=<AddBackward0>)
tensor([[5., 5., 5.],
        [5., 5., 5.]])