参考网址
1、Tensors
Tensors are similar to NumPy's ndaeeays,不同的是可以在GPU上使用和加速计算。
导入包
from __future__ import print_function
import torch建立5*3的矩阵，未初始化
x = torch.empty(5,3)
print(x)out
tensor([[ 1.4395e-36, 4.5848e-41, 1.4395e-36],
[ 4.5848e-41, 1.4395e-36, 4.5848e-41],
[ 1.4395e-36, 4.5848e-41, 2.8026e-45],
[-1.9501e+00, 8.5165e+23, 0.0000e+00],
[ 2.5223e-43, 0.0000e+00, 0.0000e+00]])建立随机初始化矩阵
x = torch.rand(5,3)
print(x)out
tensor([[ 0.8074, 0.9175, 0.8109],
[ 0.3313, 0.5902, 0.9179],
[ 0.6562, 0.3283, 0.9798],
[ 0.8218, 0.0817, 0.4454],
[ 0.5934, 0.0040, 0.3411]])建立零初始化矩阵，数据类型是Long
...
x = torch.zeros(5,3,dtype = torch.long)
print(x)
...out
tensor([[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0]])建立一个tensor数据来源于data
x = torch.tensor([5.5,3])
print(x)out
tensor([ 5.5000, 3.0000])在原有tnesor的基础上形成新的tensor，会继承原有tensor的shapee和dtype等属性，当然我么也可以修改这些属性
x = x.new_ones(5,3,dtype = torch.double)
print(x)
x = torch.randn_like(x,dype = torch.float)
print(x)out
tensor([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=torch.float64)
tensor([[-0.0730, -0.0716, -0.8259],
[-1.7004, 0.8790, -0.0659],
[-0.8969, 0.8736, -0.6035],
[-0.1539, -2.9178, -0.7456],
[-0.0245, 0.4075, 1.4904]])获取tensor的size
print(x.size())out
torch.Size([5, 3])torch.size是一个元组，支持所有元组(tuple)的操作
2、对Tensor的操作
实现加法的四种方式
方法一L
print(x+y)方法二
print(torch.add(x,y))方法三：输出给额外的tensor
result = torch.empty(5,3)
torch.add(x,y ,out= result)
print (result)方法四：原地替换-结果存放在y中
print(y)所有原地替换
所有原地替换tensor的操作都有后缀，比如x.copy(y)，会改变x
使用标准的numpy操作
print(x[：1]out
tensor([-0.0716, 0.8790, 0.8736, -2.9178, 0.4075])使用torch.view 改变tensor的形状
x = torch.randn(4,4)
y = x.view(16)
z = x.view(-1,8) # the size -1 is inferred from other dimensions
print (x.size(),y.xize(),z.size())out
torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])tensor转化为numpy的数字，使用item
x = torch.rnadn(1)
print(x)
print(x.item())Torch Tensor 和numpy的相互转换
a = torch.ones(5)
print (a) out
tensor([ 1., 1., 1., 1., 1.])并且改变tensor的值会同时改变numpy的值
a.add_(1)
print(a)
print(b)out
tensor([ 2., 2., 2., 2., 2.])
[ 2. 2. 2. 2. 2.]将numpy array转化为pytorch Tensor
import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a,1,out = a )
print(a)
print（b）out
[ 2. 2. 2. 2. 2.]
tensor([ 2., 2., 2., 2., 2.], dtype=torch.float64)所有在cpu上的tensor都支持numpy转化，除了char形的tensor
CUDA Tensors
Tensors 可以被移动到其他设备使用.to的方法
...
if torch.cuda.is_avaulable():
device = torch.device(“cuda”)
y = torch.ones_like(x,device = devcie)
x= x.to(device)
z = x+y
print(z)
print(z.to(“cpu”,torch.double))
...out
tensor([-1.0620], device='cuda:0')
tensor([-1.0620], dtype=torch.float64)以上就是pytorch教程之Tensor学习笔记的详细内容，更多关于pytorch教程的资料请关注编程网其它相关文章！
