『PyTorch』第五弹_深入理解Tensor对象_上：初始化以及尺寸调整

2024年4月28日 191次阅读 来源: pytorch

一、创建Tensor

特殊方法：

t.arange(1,6,2)
t.linspace(1,10,3)
t.randn(2,3)  # 标准分布，*size
t.randperm(5)  # 随机排序，从0到n
t.normal(means=t.arange(0, 11), std=t.arange(1, 0, -0.1))

概览：

"""创建空Tensor"""
a = t.Tensor(2, 3)
# 创建和b大小一致的Tensor
c = t.Tensor(a.size())
print(a,c)  # 数值取决于内存空间状态

-9.6609e+30  7.9594e-43 -4.1334e+27
 7.9594e-43 -4.1170e+27  7.9594e-43
[torch.FloatTensor of size 2x3]
 
-9.6412e+30  7.9594e-43 -9.6150e+30
 7.9594e-43 -4.1170e+27  7.9594e-43
[torch.FloatTensor of size 2x3]
"""由list/tuple创建Tensor"""
b = t.Tensor([[1,2,3],[4,5,6]])
print(b)  # 根据list初始化Tensor

print(b.tolist())
print(b)  # 并非inplace转换

 1  2  3
 4  5  6
[torch.FloatTensor of size 2x3]

[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]

 1  2  3
 4  5  6
[torch.FloatTensor of size 2x3]
# 等价写法，查看元素个数(2*3=6)
print(b.numel())
print(b.nelement())

6
6
# 传入tuple等价于传入list
d = t.Tensor((2,3))
print(d)

 2
 3
[torch.FloatTensor of size 2]
"""创建特定Tensor"""
print(t.eye(2,3))
print(t.ones(2,3))
print(t.zeros(2,3))
print(t.arange(1,6,2))
print(t.linspace(1,10,3))
# 几个特殊初始化方法
print(t.randn(2,3))  # 标准分布，*size
print(t.randperm(5))  # 随机排序，从0到n
print(t.normal(means=t.arange(0, 11), std=t.arange(1, 0, -0.1)))

 1  0  0
 0  1  0
[torch.FloatTensor of size 2x3]


 1  1  1
 1  1  1
[torch.FloatTensor of size 2x3]


 0  0  0
 0  0  0
[torch.FloatTensor of size 2x3]


 1
 3
 5
[torch.FloatTensor of size 3]


  1.0000
  5.5000
 10.0000
[torch.FloatTensor of size 3]


-0.9959 -0.8446  0.7241
 3.0315 -0.5367  1.0722
[torch.FloatTensor of size 2x3]


 4
 3
 2
 1
 0
[torch.LongTensor of size 5]


 -0.5880
  1.2708
  1.5530
  3.2490
  4.7693
  4.9497
  6.0663
  6.1482
  7.9109
  8.9492
 10.0000
[torch.FloatTensor of size 11]

二、尺度调整

特殊方法：

a.view(-1,3）
b.unsqueeze_(0)
b.resize_(3,3)

概览：

a = t.arange(0,6)
print(a.view(2,3))  # 非inplace
print(a.view(-1,3))  # -1为自动计算大小

 0  1  2
 3  4  5
[torch.FloatTensor of size 2x3]


 0  1  2
 3  4  5
[torch.FloatTensor of size 2x3]
b = a.view(-1,3)
b.unsqueeze_(0)
print(b)
print(b.size())

(0 ,.,.) = 
  0  1  2
  3  4  5
[torch.FloatTensor of size 1x2x3]

torch.Size([1, 2, 3])

c = b.view(1,1,1,2,3)
print(c.squeeze_(0))  # 压缩第0个1
print(c.squeeze_())  # 压缩全部的1

(0 ,0 ,.,.) = 
  0  1  2
  3  4  5
[torch.FloatTensor of size 1x1x2x3]


 0  1  2
 3  4  5
[torch.FloatTensor of size 2x3]


# view要求前后元素数相同，resize_没有这个要求
# resize_没有对应的非inplace操作版本
print(b.resize_(1,3))
print(b.resize_(3,3))
print(b)

 0  1  2
[torch.FloatTensor of size 1x3]


 0.0000e+00  1.0000e+00  2.0000e+00
 3.0000e+00  4.0000e+00  5.0000e+00
 3.3845e+15  0.0000e+00  0.0000e+00
[torch.FloatTensor of size 3x3]


 0.0000e+00  1.0000e+00  2.0000e+00
 3.0000e+00  4.0000e+00  5.0000e+00
 3.3845e+15  0.0000e+00  0.0000e+00
[torch.FloatTensor of size 3x3]

 

    原文作者：pytorch
    原文地址: https://www.cnblogs.com/hellcat/p/8445186.html
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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