Super Kai (Kazuya Ito)
Posted on • Edited on
trace, reciprocal and rsqrt in PyTorch
*Memos:
- My post explainssquare().
- My post explainspow().
- My post explainsfloat_power().
- My post explainsabs() andsqrt().
- My post explainsgcd() andlcm().
trace() can get the 0D tensor of the sum of the zero or more elements of diagonal from the 2D tensor of zero or more elements as shown below:
*Memos:
trace()can be used withtorch or a tensor.- The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,floatorcomplex).
importtorchmy_tensor=torch.tensor([[0,1,2],[3,4,5],[6,7,8]])torch.trace(input=my_tensor)my_tensor.trace()# tensor(12)my_tensor=torch.tensor([[0.,1.,2.],[3.,4.,5.],[6.,7.,8.]])torch.trace(input=my_tensor)# tensor(12.)my_tensor=torch.tensor([[0.+0.j,1.+0.j,2.+0.j],[3.+0.j,4.+0.j,5.+0.j],[6.+0.j,7.+0.j,8.+0.j]])torch.trace(input=my_tensor)# tensor(12.+0.j)my_tensor=torch.tensor([[0,1,2,3],[4,5,6,7],[8,9,10,11]])torch.trace(input=my_tensor)# tensor(15)my_tensor=torch.tensor([[0,1,2],[3,4,5]])torch.trace(input=my_tensor)# tensor(4)my_tensor=torch.tensor([[0,1,2],[3,4,5],[6,7,8],[9,10,11]])torch.trace(input=my_tensor)# tensor(12)my_tensor=torch.tensor([[]])torch.trace(input=my_tensor)# tensor(0.)reciprocal() can get the 0D or more D tensor of zero or more reciprocals from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
reciprocal()can be used withtorchor a tensor.- The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - There is
outargument withtorch(Optional-Default:None-Type:tensor):*Memos:out=must be used.- My post explains
outargument.
reciprocal()returns afloattype tensor except wheninputor a tensor is acomplextype tensor.
importtorchmy_tensor=torch.tensor(-4.)torch.reciprocal(input=my_tensor)my_tensor.reciprocal()# tensor(-0.2500)my_tensor=torch.tensor([-4.,-3.,-2.,-1.,0.,1.,2.,3.])torch.reciprocal(input=my_tensor)# tensor([-0.2500, -0.3333, -0.5000, -1.0000,inf,1.0000,0.5000,0.3333])my_tensor=torch.tensor([[-4.,-3.,-2.,-1.],[0.,1.,2.,3.]])torch.reciprocal(input=my_tensor)# tensor([[-0.2500, -0.3333, -0.5000, -1.0000],# [inf, 1.0000, 0.5000, 0.3333]])my_tensor=torch.tensor([[[-4.,-3.],[-2.,-1.]],[[0.,1.],[2.,3.]]])torch.reciprocal(input=my_tensor)# tensor([[[-0.2500, -0.3333], [-0.5000, -1.0000]],# [[inf, 1.0000], [0.5000, 0.3333]]])my_tensor=torch.tensor([[[-4,-3],[-2,-1]],[[0,1],[2,3]]])torch.reciprocal(input=my_tensor)# tensor([[[-0.2500, -0.3333], [-0.5000, -1.0000]],# [[inf, 1.0000], [0.5000, 0.3333]]])my_tensor=torch.tensor([[[-4.+0.j,-3.+0.j],[-2.+0.j,-1.+0.j]],[[0.+0.j,1.+0.j],[2.+0.j,3.+0.j]]])torch.reciprocal(input=my_tensor)# tensor([[[-0.2500-0.j, -0.3333-0.j], [-0.5000-0.j, -1.0000-0.j]],# [[nan+nanj, 1.0000-0.j], [ 0.5000-0.j, 0.3333-0.j]]])my_tensor=torch.tensor([[[True,False],[True,False]],[[False,True],[False,True]]])torch.reciprocal(input=my_tensor)# tensor([[[1., inf], [1., inf]],# [[inf, 1.], [inf, 1.]]])rsqrt() can get the 0D or more D tensor of the zero or more reciprocals of square root from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
rsqrt()can be used withtorchor a tensor.- The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - There is
outargument withtorch(Optional-Default:None-Type:tensor):*Memos:out=must be used.- My post explains
outargument.
rsqrt()returns afloattype tensor except wheninputor a tensor is acomplextype tensor.
importtorchmy_tensor=torch.tensor(-3.)torch.rsqrt(input=my_tensor)my_tensor.rsqrt()# tensor(nan)my_tensor=torch.tensor([-3.,-2.,-1.,0.,1.,2.,3.,4.])torch.rsqrt(input=my_tensor)# tensor([nan, nan, nan, inf, 1.0000, 0.7071, 0.5774, 0.5000])my_tensor=torch.tensor([[-3.,-2.,-1.,0.],[1.,2.,3.,4.]])torch.rsqrt(input=my_tensor)# tensor([[nan, nan, nan, inf],# [1.0000, 0.7071, 0.5774, 0.5000]])my_tensor=torch.tensor([[[-3.,-2.],[-1.,0.]],[[1.,2.],[3.,4.]]])torch.rsqrt(input=my_tensor)# tensor([[[nan, nan],# [nan, inf]],# [[1.0000, 0.7071],# [0.5774, 0.5000]]])my_tensor=torch.tensor([[[-3,-2],[-1,0]],[[1,2],[3,4]]])torch.rsqrt(input=my_tensor)# tensor([[[nan, nan],# [nan, inf]],# [[1.0000, 0.7071],# [0.5774, 0.5000]]])my_tensor=torch.tensor([[[-3.+0.j,-2.+0.j],[-1.+0.j,0.+0.j]],[[1.+0.j,2.+0.j],[3.+0.j,4.+0.j]]])torch.rsqrt(input=my_tensor)# tensor([[[0.0000-0.5774j, 0.0000-0.7071j],# [0.0000-1.0000j, nan+nanj]],# [[1.0000-0.0000j, 0.7071-0.0000j],# [0.5774-0.0000j, 0.5000-0.0000j]]])my_tensor=torch.tensor([[[True,False],[True,False]],[[False,True],[False,True]]])torch.rsqrt(input=my_tensor)# tensor([[[1., inf],# [1., inf]],# [[inf, 1.],# [inf, 1.]]])Top comments(0)
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