pytorch学习(1)
pytorch的基本数据类型
- 在torch中默认的数据类型是32位浮点型(torch.FloatTensor)
- 可以通过torch.set_default_tensor_type()函数设置默认的数据类型,但该函数只支持设置浮点型数据类型
Data type dtype CPU tensor GPU tensor 32-bit floating torch.float torch.FloatTensor torch.cuda.FloatTensor 6-bit floating torch.double torch.DoubleTensor torch.cuda.DoubleTensor 16-bit floating torch.half torch.HalfTensor torch.cuda.HalfTensor 8-bit integer(unsigned) torch.uint8 torch.ByteTensor torch.cuda.ByteTensor 8-bit integer(signed) torch.int8 torch.CharTensor torch.cuda.CharTensor 16-bit integer(signed) torch.short torch.ShortTensor torch.cuda.ShortTensor 32-bit integer(signed) torch.int torch.IntTensor torch.cuda.IntTensor 64-bit integer(signed) torch.long torch.LongTensor torch.cuda.LongTensor
字符串的表达
- one-hot编码(独热编码)
- word embedding(词嵌入)
- word2vec
- GloVe
标量
- loss 一般是一个标量,标量维度等于0
import torch
a = torch.tensor(1)
print(a)
print(type(a))
print(a.dim()) #检验维度的方法
print(len(a.shape)) #检验维度的方法,与a.dim()返回值相同
cpu
True
torch.Size([4]) #表示该张量为1维,且该tensor中含有四个元素
torch.Size([4])
维度dim=1(bias,linear layer input 线性层的输入)
dim=1 ---linear layer input 线性层的输入
import torch
b = torch.tensor([1,2,3,4])
print(b)
print(b.dim())
print(b.type())
print(b.device)
print(b.shape)
print(b.item) #得到tensor中的元素值
tensor([[1, 2, 3],
[5, 6, 7]])
2
torch.LongTensor
cpu
torch.Size([2, 3])
维度dim=3(RNN循环神经网络)
import torch
d = torch.tensor([[[1,2,3,4],[5,6,7,8]]])
print(d)
print(d.device)
print(d.type())
print(d.dim())
print(d.shape)
print(torch.numel(d)) #---输出元素的个数
tensor([[[1, 2, 3, 4],
[5, 6, 7, 8]]])
cpu
torch.LongTensor
3
torch.Size([1, 2, 4])
8
维度dim=4(CNN卷积神经网络)
`python
import torch
dim_4 = torch.rand(2,3,32,32)
创建一个矩阵,数值随机[照片数量,rgb色彩通道,高度,宽度]
print(dim_4.shape)
print(dim_4)
print(dim_4.type)
print(dim_4.dim())
Original: https://www.cnblogs.com/311dih/p/16583846.html
Author: 叁_311
Title: Four—pytorch学习—基本数据类型/标量/张量/dim值
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