Explicación de la dimensión Torch.mean ()

a = torch.arange(6) * 1.
# print(a)
a = a.reshape(2, 1, 3)
print(a)
print(a.shape)
b = a.mean(dim=0)
print(b)
print(b.shape)

--------------------------------------------
res:
tensor([[[0., 1., 2.]],

        [[3., 4., 5.]]])
torch.Size([2, 1, 3])
tensor([[1.5000, 2.5000, 3.5000]])
torch.Size([1, 3])

Tridimensional (m, n, q):

ls = [

[[1,2], [3,4]],

[[5,6], [7,8]]

]

l. forma = 2 * 2 * 2

dim = 0,

Agregue filas y columnas fijas:

(1 + 5) / 2 = 3 ，

(2 + 6) / 2 = 4,

(3 + 7) / 2 = 5,

(4 + 8) / 2 = 6,

ls.mean =

[

[3,4], [5,6]

]  

ls.forma = (2 * 2)

---------------------------------------------

tenue = 1

Columna fija, adición de filas

(1+ 3) / 2 = 2,

(2 + 4) / 2 = 3,

(5 + 7) / 2 = 6,

(6 + 8) / 2 = 7,

ls.mean (tenue = 1) =

[

[2,3],

[6,7]

]

ls.forma = (2 * 2)  

a = torch.arange(8) * 1.
# print(a)
a = a.reshape(2, 2, 2)
print(a)
print(a.shape)
b = a.mean(dim=1)
print(b)
print(b.shape)
-------------------------------

tensor([[[0., 1.],
         [2., 3.]],

        [[4., 5.],
         [6., 7.]]])
torch.Size([2, 2, 2])
tensor([[1., 2.],
        [5., 6.]])
torch.Size([2, 2])