pytorch Variable与Tensor合并后 requires_grad()默认与修改方式_Python

pytorch更新完后合并了Variable与Tensor

torch.Tensor()能像Variable一样进行反向传播的更新，返回值为Tensor

Variable自动创建tensor，且返回值为Tensor，（所以以后不需要再用Variable）

Tensor创建后,默认requires_grad=Flase

可以通过xxx.requires_grad_()将默认的Flase修改为True

下面附代码及官方文档代码：

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									import torch

									from torch.autograd import Variable #使用Variabl必须调用库

									lis=torch.range(1,6).reshape((-1,3))#创建1~6 形状 

									#行不指定（-1意为由计算机自己计算）列为3的floattensor矩阵

									print(lis)

									print(lis.requires_grad) #查看默认的requires_grad是否是Flase

									lis.requires_grad_() #使用.requires_grad_()修改默认requires_grad为true

									print(lis.requires_grad)

结果如下：

tensor([[1., 2., 3.],
[4., 5., 6.]])
False
True

创建一个Variable，Variable必须接收Tensor数据不能直接写为 a=Variable(range(6)).reshape((-1,3))

否则报错 Variable data has to be a tensor, but got range

正确如下：

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									import torch

									from torch.autograd import Variable

									tensor=torch.FloatTensor(range(8)).reshape((-1,4))

									my_ten=Variable(tensor)

									print(my_ten)

									print(my_ten.requires_grad)

									my_ten.requires_grad_()

									print(my_ten.requires_grad)

结果：

tensor([[0., 1., 2., 3.],
[4., 5., 6., 7.]])
False
True

由上面可以看出，Tensor完全可以取代Variable。

下面给出官方文档：

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									# 默认创建requires_grad = False的Tensor  

									x = torch . ones ( 1 ) # create a tensor with requires_grad=False (default)

									x . requires_grad

									# out: False

									# 创建另一个Tensor，同样requires_grad = False

									y = torch . ones ( 1 ) # another tensor with requires_grad=False

									# both inputs have requires_grad=False. so does the output

									z = x + y

									# 因为两个Tensor x,y，requires_grad=False.都无法实现自动微分，

									# 所以操作（operation）z=x+y后的z也是无法自动微分，requires_grad=False

									z . requires_grad

									# out: False

									# then autograd won't track this computation. let's verify!

									# 因而无法autograd，程序报错

									z . backward ( ) 

									# out：程序报错：RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

									# now create a tensor with requires_grad=True

									w = torch . ones ( 1 , requires_grad = True ) 

									w . requires_grad

									# out: True

									# add to the previous result that has require_grad=False

									# 因为total的操作中输入Tensor w的requires_grad=True，因而操作可以进行反向传播和自动求导。

									total = w + z

									# the total sum now requires grad!

									total . requires_grad

									# out: True

									# autograd can compute the gradients as well

									total . backward ( ) 

									w . grad

									#out: tensor([ 1.])

									# and no computation is wasted to compute gradients for x, y and z, which don't require grad

									# 由于z，x，y的requires_grad=False,所以并没有计算三者的梯度

									z . grad == x . grad == y . grad == None

									# True

									existing_tensor . requires_grad_ ( ) 

									existing_tensor . requires_grad

									# out：True

或者直接用Tensor创建时给定requires_grad=True

				?

									my_tensor = torch.zeros(3,4,requires_grad = True) 

									my_tensor.requires_grad

									# out: True

				?

									lis=torch.range(1,6,requires_grad=True).reshape((-1,3))

									print(lis)

									print(lis.requires_grad)

									lis.requires_grad_()

									print(lis.requires_grad)