pytorch-基本数据类型

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import torch
a = torch.randn(2,3)
print(a)
print(a.type())
print(type(a))

tensor([[ 0.2685, -0.2450, -0.1803],
        [-0.5779,  1.8447, -2.2098]])
torch.FloatTensor
<class 'torch.Tensor'>

isinstance(a, torch.FloatTensor)

True

isinstance(a, torch.cuda.FloatTensor)

False

data = a.cuda()  # 数据放到cuda上
isinstance(data, torch.cuda.FloatTensor)

True

Dimension / rank0

torch.tensor(1.)

tensor(1.)

torch.tensor(1.3)

tensor(1.3000)

a = torch.tensor(2.2) # dimemsion = 1,标量
a.shape

torch.Size([])

len(a.shape)

a.size()

torch.Size([])

b = torch.randn(3, 3)
b

tensor([[ 0.4503,  1.4839, -0.2727],
        [ 0.7436, -0.7034, -1.6083],
        [ 0.4326, -0.2785,  0.4935]])

b = torch.randn([3, 3])
b

tensor([[ 0.2073,  0.0413,  0.0724],
        [-1.5728, -0.5479, -0.8047],
        [ 1.7011,  0.8673, -0.3816]])

b.size() # 返回形状

torch.Size([3, 3])

b.size(0)

c = torch.randn([2, 3])
print(c.size())
print(c.size(0))
print(c.size(1))
print(c.size(2)) # 此行报错

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



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

IndexError                                Traceback (most recent call last)

<ipython-input-43-d437c71a5a89> in <module>
      3 print(c.size(0))
      4 print(c.size(1))
----> 5 print(c.size(2))


IndexError: Dimension out of range (expected to be in range of [-2, 1], but got 2)

Dimension 1 / rank 1

torch.tensor([1.1])  # 传入list数据

tensor([1.1000])

torch.tensor([1.1, 1.2])

tensor([1.1000, 1.2000])

torch.FloatTensor(1)  # 传入数据维度

tensor([2.])

torch.FloatTensor(2)

tensor([0., 0.])

import numpy as np
import torch
data = np.ones(2)
print(data)
torch.from_numpy(data)

[1. 1.]





tensor([1., 1.], dtype=torch.float64)

e = torch.tensor([1, 2])
e

tensor([1, 2])

e.size(0)

e.size(-1)

a=torch.ones(2)
a.shape

torch.Size([2])

a.size(0)

a.size(-1)

a=torch.ones(1)
a.shape

torch.Size([1])

a.size(0)

a.size(-1)

Dimension 2

a = torch.randn(2,3)
a

tensor([[ 0.1747,  1.2848, -0.4329],
        [-2.2200,  2.2615, -0.6228]])

a.shape

torch.Size([2, 3])

a.size(0)

a.size(1)

a.shape[0]

a.shape[1]

Dimension 3

a = torch.rand(1,2,3)
a

tensor([[[0.4654, 0.6985, 0.7499],
         [0.0807, 0.6140, 0.5457]]])

a = torch.rand(2,2,3)
a

tensor([[[0.4916, 0.7213, 0.5852],
         [0.0629, 0.4214, 0.5142]],

        [[0.4386, 0.4954, 0.8755],
         [0.3940, 0.2207, 0.4918]]])

a.shape

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

a.shape[0]

a.shape[1]

a.shape[2]

print(a[0])
print(a[1])

tensor([[0.4916, 0.7213, 0.5852],
        [0.0629, 0.4214, 0.5142]])
tensor([[0.4386, 0.4954, 0.8755],
        [0.3940, 0.2207, 0.4918]])

list(a.shape)

[2, 2, 3]

Dimension 4

a = torch.randn(2,3,28,28)
a

tensor([[[[ 0.9770, -1.5422, -1.4004,  ...,  1.4447,  0.3687, -1.1456],
          [-0.0616,  0.7368,  0.2303,  ...,  0.2191, -0.9384,  0.1178],
          [-0.6595, -0.6673,  0.0521,  ...,  0.2304, -1.2156, -0.5742],
          ...,
          [-2.0420, -0.2665,  0.5631,  ..., -1.1558,  0.1600,  0.2163],
          [-0.6479,  0.3045, -0.6552,  ..., -0.4643,  0.5231, -0.7562],
          [ 0.1142,  0.4732, -1.1978,  ..., -1.6959, -0.6284,  0.5963]],

         [[-0.3389,  0.2129,  1.2783,  ...,  1.0402,  0.4531,  1.9179],
          [ 0.4201,  0.6000, -0.6048,  ..., -0.2739,  0.6021, -0.4467],
          [-0.2578, -0.5420, -0.7303,  ...,  0.9527,  1.8283,  0.5765],
          ...,
          [-0.3595, -0.5524,  0.8008,  ...,  0.5622,  0.3685, -0.5666],
          [ 1.4712, -0.8457,  0.3197,  ...,  0.3807,  0.0580,  0.1099],
          [-0.5920, -3.1865, -1.2504,  ...,  1.4477,  1.4455,  0.4195]],

         [[-1.3461,  0.9136, -0.1402,  ..., -0.0558, -0.9873,  1.2209],
          [-0.9963,  0.1460,  0.5685,  ...,  1.1063,  0.4424,  1.2003],
          [-1.2245, -0.2666,  1.2451,  ..., -0.5278,  1.5485, -0.2553],
          ...,
          [ 0.2660, -0.3592, -1.9605,  ..., -1.2485, -0.8174, -0.2745],
          [-0.9404,  1.3743,  0.4145,  ..., -1.7326,  0.6057, -0.7291],
          [ 0.3479,  0.3329,  0.4403,  ..., -1.5186,  0.2652,  3.2966]]],


        [[[ 0.7794,  0.1165, -0.0097,  ...,  0.0154, -0.1022,  1.0874],
          [ 0.8231,  0.2086,  0.8247,  ...,  1.3846,  0.4115, -0.7875],
          [-0.5599, -0.8777,  0.0860,  ..., -0.4499,  0.8062, -0.8251],
          ...,
          [ 1.0082, -0.0869,  0.1664,  ...,  0.7052, -0.5506, -1.0844],
          [ 1.1836,  0.7602,  2.2287,  ...,  0.3032, -0.7607, -0.8534],
          [ 0.4466,  1.3760, -1.9631,  ..., -0.7653, -0.3874,  0.1126]],

         [[ 1.4206,  0.9637,  0.6838,  ...,  0.6099, -0.7173, -0.9166],
          [-0.5624, -0.6536, -1.2994,  ...,  1.3861, -2.2174,  1.6432],
          [-0.5166, -0.9876,  0.3641,  ...,  0.0136,  0.3891, -0.7152],
          ...,
          [-1.9587, -0.0290,  0.7256,  ..., -0.8681,  1.2109,  1.4550],
          [-0.7089,  0.0853,  0.9076,  ..., -1.9827,  0.4440,  0.2140],
          [ 0.9694, -0.3007,  0.5784,  ...,  0.1984, -0.4576,  0.2178]],

         [[ 0.0707, -0.7766,  0.3134,  ..., -1.1565,  0.8534, -0.1165],
          [-0.7874, -0.7807,  1.5859,  ...,  0.3828,  0.9948,  0.1778],
          [-0.2240,  0.8667,  1.3384,  ...,  1.1469,  1.5945, -1.0732],
          ...,
          [-0.6839, -0.9666, -0.2751,  ..., -0.6123, -2.7403,  0.2976],
          [ 0.7267, -0.4425, -0.5677,  ..., -1.3017,  0.4829, -0.3434],
          [-0.1188,  0.3201, -0.8754,  ..., -0.2463,  1.2385,  0.4045]]]])

print(a.shape)
print(a.size())

torch.Size([2, 3, 28, 28])
torch.Size([2, 3, 28, 28])

print(a.shape[0])
print(a.size(0))
print(a.shape[1])
print(a.size(1))
print(a.shape[2])
print(a.size(2))
print(a.shape[3])
print(a.size(3))

Mixed

a = torch.randn(2,3,28,28)
a.shape

torch.Size([2, 3, 28, 28])

a.numel() # 计算参数量2x3x28x28

print(a.dim())  #  dim() takes no arguments (1 given)

a = torch.tensor(1)
a.dim() # 标量

a = torch.tensor([1,2])
a.dim()

a = torch.tensor([2,3])
a.dim()

a = torch.tensor(2,3)  # 报错，只接受一个位置参数

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

TypeError                                 Traceback (most recent call last)

<ipython-input-125-31ef262ceaf8> in <module>
----> 1 a = torch.tensor(2,3)


TypeError: tensor() takes 1 positional argument but 2 were given

a = torch.randn(1,2,3)
a

tensor([[[-0.4289,  0.4551,  0.4237],
         [-0.6139,  1.2619,  0.8672]]])

a.dim()

print(a.shape[0])
print(a.size(0))

1
1