Category:PyTorch
Article From:https://www.cnblogs.com/logicalsky/p/9122437.html
torch.randn
 torch.randn(*sizes, out=None) → Tensor(Tensor)

A tensor is returned, which contains a set of random numbers extracted from the standard normal distribution (mean 0, variance 1), whose shape is defined by variable parameter sizes. Parameters:
 sizes (int…) – An integer sequence that defines the shape of the output
 out (Tensor, optinal) – Result tensor
2D”
>>> import torch
>>> torch.randn(2,3)
tensor([[1.0413, 0.8792, 2.1381],
[ 0.9541, 2.3019, 0.5490]])
>>>
three dimensional”
>>> torch.randn(2,2,3)
tensor([[[ 0.4200, 0.4624, 0.3099],
[0.1227, 0.2452, 0.9840]],
[[0.8800, 0.5937, 1.4465],
[ 1.6523, 0.0170, 0.6393]]])
>>>
 Three dimensions based on twodimensional increase of one dimension, that is, (2, 2, 3) is a matrix containing 2 2 rows and three columns; the first number refers to the inclusion of several twodimensional matrices.
>>> torch.randn(2,2,2,3)
tensor([[[[1.1649, 1.1810, 0.3619],
[0.8433, 0.4411, 1.8187]],
[[ 0.4896, 0.4773, 0.0032],
[ 1.1269, 1.3638, 1.4495]]],
[[[0.1959, 0.5646, 0.7001],
[ 0.6796, 0.1164, 1.6833]],
[[0.2674, 0.2411, 1.5875],
[0.2804, 1.4775, 0.2448]]]])
>>>
 The four dimension adds one dimension, (2, 2, 2, 3) based on three dimensions, that is, 2 threedimensional matrices.
>>> a = torch.randn(2,2,2,3)
>>> torch.numel(a) # Calculate the number of elements in tensor, that is, the number of elements in the matrix.Twentyfour
 If n dimensional, and so on, based on the upper one dimension increase one dimension calculation.
>>> torch.randn(2,2,2,3)
tensor([[[[1.1649, 1.1810, 0.3619],
[0.8433, 0.4411, 1.8187]],
[[ 0.4896, 0.4773, 0.0032],
[ 1.1269, 1.3638, 1.4495]]],
[[[0.1959, 0.5646, 0.7001],
[ 0.6796, 0.1164, 1.6833]],
[[0.2674, 0.2411, 1.5875],
[0.2804, 1.4775, 0.2448]]]])
>>>
>>> a = torch.randn(2,2,2,3)
>>> torch.numel(a) # Calculate the number of elements in tensor, that is, the number of elements in the matrix.Twentyfour
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The pytorch tensor dimension understands.Md