[人工智能]Pyroch入门笔记-张量数据类型

Pytorch基本数据类型

All is about Tensor

How to denote string

• One-hot
• Embedding
  • Word2Vec
  • glove

Data type

import torch

#创建一个(2,3)的tensor
a = torch.randn(2,3)
a.type()

'torch.FloatTensor'

type(a)

torch.Tensor

isinstance(a, torch.FloatTensor)

True

data = a
isinstance(data, torch.cuda.FloatTensor)

False

data = data.cuda()
isinstance(data, torch.cuda.FloatTensor)

True

torch.tensor(1.)

tensor(1.)

torch.tensor(1.3)

tensor(1.3000)

a = torch.tensor(2.2)
a.shape

torch.Size([])

len(a.shape)

0

a.size()

torch.Size([])

torch.tensor([1.1])

tensor([1.1000])

torch.tensor([1.1,2.2])

tensor([1.1000, 2.2000])

torch.FloatTensor(1)

tensor([0.])

torch.FloatTensor(2)

tensor([0., 0.])

import numpy as np
data = np.ones(2)
data

array([1., 1.])

torch.from_numpy(data)

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

a = torch.ones(2)
a.shape

torch.Size([2])

a = torch.randn(2,3)
a

tensor([[-2.3587,  0.5037,  0.8426],
        [-0.1395,  0.5736,  0.0038]])

a.shape

torch.Size([2, 3])

a.size(0)

2

a.size(0)

2

a.size(1)

3

a.shape[1]

3

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

tensor([[[0.5847, 0.4324, 0.2659],
         [0.2969, 0.0352, 0.1932]]])

a.shape

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

a[0]

tensor([[0.8617, 0.3860, 0.4491],
        [0.9014, 0.7236, 0.5982]])

list(a.shape)

[1, 2, 3]

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

tensor([[[[0.8828, 0.6617, 0.0595,  ..., 0.2063, 0.6698, 0.9692],
          [0.2762, 0.8724, 0.5322,  ..., 0.9264, 0.4652, 0.1117],
          [0.0797, 0.7335, 0.5584,  ..., 0.7487, 0.3993, 0.8792],
          ...,
          [0.4200, 0.7895, 0.0608,  ..., 0.2230, 0.4791, 0.9895],
          [0.6966, 0.6509, 0.7475,  ..., 0.9357, 0.2222, 0.5091],
          [0.9888, 0.3824, 0.3494,  ..., 0.0992, 0.1751, 0.9104]],

         [[0.9390, 0.1380, 0.6556,  ..., 0.8514, 0.8710, 0.1470],
          [0.0582, 0.1105, 0.2613,  ..., 0.5967, 0.6255, 0.5425],
          [0.3768, 0.3766, 0.6858,  ..., 0.8140, 0.6406, 0.3226],
          ...,
          [0.4511, 0.9047, 0.8087,  ..., 0.5146, 0.3643, 0.3774],
          [0.6760, 0.4325, 0.8090,  ..., 0.6828, 0.6155, 0.3927],
          [0.5951, 0.2969, 0.2370,  ..., 0.4704, 0.6218, 0.6115]],

         [[0.9448, 0.0337, 0.4944,  ..., 0.5077, 0.3605, 0.1576],
          [0.8310, 0.1867, 0.1950,  ..., 0.7729, 0.1453, 0.5513],
          [0.0218, 0.1491, 0.4334,  ..., 0.6247, 0.9791, 0.0589],
          ...,
          [0.0835, 0.9804, 0.0470,  ..., 0.5622, 0.3367, 0.5041],
          [0.8451, 0.3326, 0.0539,  ..., 0.6224, 0.7943, 0.8747],
          [0.5174, 0.0323, 0.3455,  ..., 0.7088, 0.1844, 0.6642]]],


        [[[0.1665, 0.6587, 0.3739,  ..., 0.7705, 0.2233, 0.5640],
          [0.6353, 0.2907, 0.2761,  ..., 0.0375, 0.9990, 0.1509],
          [0.8480, 0.1878, 0.5105,  ..., 0.9123, 0.3218, 0.2966],
          ...,
          [0.7924, 0.1838, 0.1870,  ..., 0.2371, 0.3911, 0.2764],
          [0.0753, 0.4554, 0.5595,  ..., 0.3365, 0.0462, 0.5924],
          [0.2841, 0.0991, 0.6688,  ..., 0.3673, 0.4465, 0.1054]],

         [[0.4828, 0.7561, 0.5818,  ..., 0.4361, 0.5383, 0.2329],
          [0.6777, 0.8608, 0.3140,  ..., 0.2832, 0.5151, 0.4656],
          [0.1136, 0.0579, 0.4239,  ..., 0.9170, 0.6756, 0.3849],
          ...,
          [0.0961, 0.5933, 0.5865,  ..., 0.5757, 0.6700, 0.8369],
          [0.0503, 0.9264, 0.4842,  ..., 0.2266, 0.0400, 0.3625],
          [0.7243, 0.7906, 0.8159,  ..., 0.0406, 0.3719, 0.6781]],

         [[0.6152, 0.3097, 0.1880,  ..., 0.8094, 0.8706, 0.4427],
          [0.1928, 0.2313, 0.4480,  ..., 0.8179, 0.0032, 0.4803],
          [0.4220, 0.3285, 0.3260,  ..., 0.1025, 0.1659, 0.6078],
          ...,
          [0.1934, 0.3361, 0.6843,  ..., 0.8997, 0.9822, 0.9209],
          [0.1084, 0.8956, 0.9310,  ..., 0.6991, 0.9082, 0.5535],
          [0.0576, 0.6069, 0.9415,  ..., 0.7191, 0.9698, 0.4304]]]])

a.shape

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

a.numel()

4704

a.dim()

4

a = torch.tensor(1)
a.dim()

0