*Memos:
- My post explainsadd().
- My post explainsmul().
- My post explainsdiv().
- My post explainsremainder().
- My post explainsfmod().
sub() can do subtraction with two of the 0D or more D tensors of zero or more elements or scalars or the 0D or more D tensor of zero or more elements and a scalar, getting the 0D or more D tensor of zero or more elements as shown below:
*Memos:
sub()can be used withtorch or a tensor.- The 1st argument(
input) withtorch(Type:tensororscalarofint,floatorcomplex) or using a tensor(Type:tensorofint,floatorcomplex)(Required). - The 2nd argument with
torchor the 1st argument with a tensor isother(Required-Type:tensororscalarofint,floatorcomplex). - The 3rd argument with
torchor the 2nd argument with a tensor isalpha(Optional-Default:1-Type:tensororscalarofint,floatorcomplex). *otheris multiplied byalpha(inputor a tensor-(otherxalpha)). - There is
outargument withtorch(Optional-Default:None-Type:tensor):*Memos:out=must be used.- My post explains
outargument.
- subtract() is the alias of
sub().
importtorchtensor1=torch.tensor([9,7,6])tensor2=torch.tensor([[4,-4,3],[-2,5,-5]])torch.sub(input=tensor1,other=tensor2)tensor1.sub(other=tensor2)torch.sub(input=tensor1,other=tensor2,alpha=1)torch.sub(input=tensor1,other=tensor2,alpha=torch.tensor(1))# tensor([[5, 11, 3], [11, 2, 11]])torch.sub(input=tensor1,other=tensor2,alpha=0)torch.sub(input=tensor1,other=tensor2,alpha=torch.tensor(0))# tensor([[9, 7, 6], [9, 7, 6]])torch.sub(input=tensor1,other=tensor2,alpha=2)torch.sub(input=tensor1,other=tensor2,alpha=torch.tensor(2))# tensor([[1, 15, 0], [13, -3, 16]])torch.sub(input=tensor1,other=tensor2,alpha=-1)torch.sub(input=tensor1,other=tensor2,alpha=torch.tensor(-1))# tensor([[13, 3, 9], [7, 12, 1]])torch.sub(input=tensor1,other=tensor2,alpha=-2)torch.sub(input=tensor1,other=tensor2,alpha=torch.tensor(-2))# tensor([[17, -1, 12], [5, 17, -4]])torch.sub(input=9,other=tensor2)torch.sub(input=9,other=tensor2,alpha=1)torch.sub(input=9,other=tensor2,alpha=torch.tensor(1))# tensor([[5, 13, 6], [11, 4, 14]])torch.sub(input=tensor1,other=4)torch.sub(input=tensor1,other=4,alpha=1)torch.sub(input=tensor1,other=4,alpha=torch.tensor(1))# tensor([5, 3, 2])torch.sub(input=9,other=4)torch.sub(input=9,other=4,alpha=1)torch.sub(input=9,other=4,alpha=torch.tensor(1))# tensor(5)tensor1=torch.tensor([9.,7.,6.])tensor2=torch.tensor([[4.,-4.,3.],[-2.,5.,-5.]])torch.sub(input=tensor1,other=tensor2)torch.sub(input=tensor1,other=tensor2,alpha=1.)torch.sub(input=tensor1,other=tensor2,alpha=torch.tensor(1.))# tensor([[5., 11., 3.], [11., 2., 11.]])torch.sub(input=9.,other=tensor2)torch.sub(input=9.,other=tensor2,alpha=1.)torch.sub(input=9.,other=tensor2,alpha=torch.tensor(1.))# tensor([[5., 13., 6.], [11., 4., 14.]])torch.sub(input=tensor1,other=4)torch.sub(input=tensor1,other=4,alpha=1.)torch.sub(input=tensor1,other=4,alpha=torch.tensor(1.))# tensor([5., 3., 2.])torch.sub(input=9.,other=4)torch.sub(input=9.,other=4,alpha=1.)torch.sub(input=9.,other=4,alpha=torch.tensor(1.))# tensor(5.)tensor1=torch.tensor([9.+0.j,7.+0.j,6.+0.j])tensor2=torch.tensor([[4.+0.j,-4.+0.j,3.+0.j],[-2.+0.j,5.+0.j,-5.+0.j]])torch.sub(input=tensor1,other=tensor2)torch.sub(input=tensor1,other=tensor2,alpha=1.+0.j)torch.sub(input=tensor1,other=tensor2,alpha=torch.tensor(1.+0.j))# tensor([[5.+0.j, 11.+0.j, 3.+0.j],# [11.+0.j, 2.+0.j, 11.+0.j]])torch.sub(input=9.+0.j,other=tensor2)torch.sub(input=9.+0.j,other=tensor2,alpha=1.+0.j)torch.sub(input=9.+0.j,other=tensor2,alpha=torch.tensor(1.+0.j))# tensor([[5.+0.j, 13.+0.j, 6.+0.j],# [11.+0.j, 4.+0.j, 14.+0.j]])torch.sub(input=tensor1,other=4.+0.j)torch.sub(input=tensor1,other=4.+0.j,alpha=1.+0.j)torch.sub(input=tensor1,other=4.+0.j,alpha=torch.tensor(1.+0.j))# tensor([5.+0.j, 3.+0.j, 2.+0.j])torch.sub(input=9.+0.j,other=4.+0.j)torch.sub(input=9.+0.j,other=4.+0.j,alpha=1.+0.j)torch.sub(input=9.+0.j,other=4.+0.j,alpha=torch.tensor(1.+0.j))# tensor(5.+0.j)Top comments(0)
Subscribe
For further actions, you may consider blocking this person and/orreporting abuse
