*My post explains diagonal() and diag_embed().
eye() can create a 2D tensor with zero or more 1., 1, 1.+0.j or True on the diagonal and zero or more 0., 0, 0.+0.j or False elsewhere as shown below:
*Memos:
-
eye()can be used with torch but not with a tensor. - The returned tensor has zero or more floating-point numbers(Default), integers, complex numbers or boolean values.
- The 2nd argument(
int) withtorchisn(Required) which is the number of rows. - The 3rd argument(
int) withtorchism(Optional-Default:n) which is the number of columns.
import torch
torch.eye(0)
# tensor([], size=(0, 0))
torch.eye(1)
# tensor([[1.]])
torch.eye(2)
# tensor([[1., 0.],
# [0., 1.]])
torch.eye(3)
# tensor([[1., 0., 0.],
# [0., 1., 0.],
# [0., 0., 1.]])
torch.eye(4)
# tensor([[1., 0., 0., 0.],
# [0., 1., 0., 0.],
# [0., 0., 1., 0.],
# [0., 0., 0., 1.]])
torch.eye(4, 0)
# tensor([], size=(4, 0))
torch.eye(4, 1)
# tensor([[1.],
# [0.],
# [0.],
# [0.]])
torch.eye(4, 2)
# tensor([[1., 0.],
# [0., 1.],
# [0., 0.],
# [0., 0.]])
torch.eye(4, 3)
# tensor([[1., 0., 0.],
# [0., 1., 0.],
# [0., 0., 1.],
# [0., 0., 0.]])
torch.eye(4, 4)
# tensor([[1., 0., 0., 0.],
# [0., 1., 0., 0.],
# [0., 0., 1., 0.],
# [0., 0., 0., 1.]])
torch.eye(4, 5)
# tensor([[1., 0., 0., 0., 0.],
# [0., 1., 0., 0., 0.],
# [0., 0., 1., 0., 0.],
# [0., 0., 0., 1., 0.]])
torch.eye(4, 6)
# tensor([[1., 0., 0., 0., 0., 0.],
# [0., 1., 0., 0., 0., 0.],
# [0., 0., 1., 0., 0., 0.],
# [0., 0., 0., 1., 0., 0.]])
torch.eye(4, 6, dtype=torch.int64)
# tensor([[1, 0, 0, 0, 0, 0],
# [0, 1, 0, 0, 0, 0],
# [0, 0, 1, 0, 0, 0],
# [0, 0, 0, 1, 0, 0]])
torch.eye(4, 6, dtype=torch.complex64)
# tensor([[1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
# [0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
# [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
# [0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j]])
torch.eye(4, 6, dtype=torch.bool)
# tensor([[True, False, False, False, False, False],
# [False, True, False, False, False, False],
# [False, False, True, False, False, False],
# [False, False, False, True, False, False]])
diag() can create a 2D tensor with a 1D tensor on the diagonal and zero or more 0, 0., 0.+0.j or False elsewhere or extract a 1D tensor from a 2D tensor on the diagonal as shown below:
*Memos:
-
diag()can be used withtorchor a tensor. - Only a 2D or 1D tensor can be used.
- A 2D tensor creates a 1D tensor.
- A 1D tensor creates a 2D tensor.
- The tensor of zero or more integers, floating-point numbers, complex numbers or boolean values can be used.
- The 2nd argument(
int) withtorchor the 1st argument(int) with a tensor isdiagonal(Optional-Default:0).
import torch
my_tensor = torch.tensor([7, -4, 5])
torch.diag(my_tensor)
torch.diag(my_tensor, diagonal=0)
# tensor([[7, 0, 0],
# [0, -4, 0],
# [0, 0, 5]])
torch.diag(my_tensor, diagonal=1)
# tensor([[0, 7, 0, 0],
# [0, 0, -4, 0],
# [0, 0, 0, 5],
# [0, 0, 0, 0]])
torch.diag(my_tensor, diagonal=-1)
# tensor([[0, 0, 0, 0],
# [7, 0, 0, 0],
# [0, -4, 0, 0],
# [0, 0, 5, 0]])
torch.diag(my_tensor, diagonal=2)
# tensor([[0, 0, 7, 0, 0],
# [0, 0, 0, -4, 0],
# [0, 0, 0, 0, 5],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]])
torch.diag(my_tensor, diagonal=-2)
# tensor([[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [7, 0, 0, 0, 0],
# [0, -4, 0, 0, 0],
# [0, 0, 5, 0, 0]])
my_tensor = torch.tensor([7., -4., 5.])
torch.diag(my_tensor)
# tensor([[7., 0., 0.],
# [0., -4., 0.],
# [0., 0., 5.]])
my_tensor = torch.tensor([7+0j, -4+0j, 5+0j])
torch.diag(my_tensor)
# tensor([[7.+0.j, 0.+0.j, 0.+0.j],
# [0.+0.j, -4.+0.j, 0.+0.j],
# [0.+0.j, 0.+0.j, 5.+0.j]])
my_tensor = torch.tensor([True, True, True])
torch.diag(my_tensor)
# tensor([[True, False, False],
# [False, True, False],
# [False, False, True]])
my_tensor = torch.tensor([[7, -4, 5],
[-6, -3, 8],
[9, 1, -2]])
torch.diag(my_tensor)
torch.diag(my_tensor, diagonal=0)
# tensor([7, -3, -2])
torch.diag(my_tensor, diagonal=1)
# tensor([-4, 8])
torch.diag(my_tensor, diagonal=-1)
# tensor([-6, -1])
torch.diag(my_tensor, diagonal=2)
# tensor([5])
torch.diag(my_tensor, diagonal=-2)
tensor([9])
diagflat() can create a 2D tensor with a 0D or more D tensor on the diagonal and zero or more 0, 0., 0.+0.j or False elsewhere as shown below:
*Memos:
-
diagflat()can be used withtorchor a tensor. - The tensor of zero or more integers, floating-point numbers, complex numbers or boolean values can be used.
- The 2nd argument(
int) withtorchor the 1st argument(int) with a tensor isoffset(Optional-Default:0).
import torch
my_tensor = torch.tensor([7, -4, 5])
torch.diagflat(my_tensor)
torch.diagflat(my_tensor, offset=0)
# tensor([[7, 0, 0],
# [0, -4, 0],
# [0, 0, 5]])
torch.diagflat(my_tensor, offset=1)
# tensor([[0, 7, 0, 0],
# [0, 0, -4, 0],
# [0, 0, 0, 5],
# [0, 0, 0, 0]])
torch.diagflat(my_tensor, offset=-1)
# tensor([[0, 0, 0, 0],
# [7, 0, 0, 0],
# [0, -4, 0, 0],
# [0, 0, 5, 0]])
torch.diagflat(my_tensor, offset=2)
# tensor([[0, 0, 7, 0, 0],
# [0, 0, 0, -4, 0],
# [0, 0, 0, 0, 5],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]])
torch.diagflat(my_tensor, offset=-2)
# tensor([[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [7, 0, 0, 0, 0],
# [0, -4, 0, 0, 0],
# [0, 0, 5, 0, 0]])
my_tensor = torch.tensor([7., -4., 5.])
torch.diagflat(my_tensor)
# tensor([[7., 0., 0.],
# [0., -4., 0.],
# [0., 0., 5.]])
my_tensor = torch.tensor([7+0j, -4+0j, 5+0j])
torch.diagflat(my_tensor)
# tensor([[7.+0.j, 0.+0.j, 0.+0.j],
# [0.+0.j, -4.+0.j, 0.+0.j],
# [0.+0.j, 0.+0.j, 5.+0.j]])
my_tensor = torch.tensor([True, True, True])
torch.diagflat(my_tensor)
# tensor([[True, False, False],
# [False, True, False],
# [False, False, True]])
my_tensor = torch.tensor([[7, -4, 5],
[-6, -3, 8],
[9, 1, -2]])
torch.diagflat(my_tensor)
torch.diagflat(my_tensor, offset=0)
# tensor([[7, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, -4, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 5, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, -6, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, -3, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 8, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 9, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 1, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, -2]])
torch.diagflat(my_tensor, offset=1)
# tensor([[0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, -4, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 5, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, -6, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, -3, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 8, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 9, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, -2],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
torch.diagflat(my_tensor, offset=-1)
# tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [7, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0,-4, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, -6, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, -3, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 8, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 9, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, -2, 0]])
torch.diagflat(my_tensor, offset=2)
# tensor([[0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, -4, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, -6, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, -3, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
torch.diagflat(my_tensor, offset=-2)
# tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, -4, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, -6, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, -3, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0]])
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