一行命令即可 官网

pip install torch===1.6.0 torchvision===0.7.0 - https://download.pytorch.org/whl/torch_stable.html

时间较久,耐心等待

测试自己是否安装成功

运行命令测试

import torch
x = torch.rand(5,3)
print(x)

输出

tensor([[0.5096, 0.1209, 0.7721],
        [0.9486, 0.8676, 0.2157],
        [0.0586, 0.3467, 0.5015],
        [0.9470, 0.5654, 0.9317],
        [0.2127, 0.2386, 0.0629]])

开始学习PyTorch

不初始化的创建张量

import torch
x = torch.empty([5,5])
print(x)

输出

tensor([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]])

随机创建一个0-1的张量

import torch
x = torch.rand(5,5)
print(x)

输出

tensor([[0.3369, 0.5339, 0.8419, 0.6857, 0.6241],
        [0.4991, 0.1691, 0.8356, 0.4574, 0.0395],
        [0.9714, 0.2975, 0.9322, 0.5213, 0.8509],
        [0.3037, 0.8690, 0.3481, 0.2538, 0.9513],
        [0.0156, 0.9516, 0.3674, 0.1831, 0.6466]])

创建全为0的张量

import torch
x = torch.zeros(5,5, dtype=torch.float32)
print(x)

创建的时候可以通过dtype指定数据类型

输出

tensor([[0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.]])

使用数据来直接创建张量

import torch
x = torch.zeros([5,5], dtype=torch.float32)
print(x)

输出

tensor([5., 5.])

使用原有tensor创建新的tensor

import torch
x = torch.tensor([5,5], dtype=torch.float32)
x = x.new_zeros(5, 3)
y = torch.rand_like(x)
print(x)
print(y)

输出

tensor([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]])
tensor([[0.5552, 0.3333, 0.0426],
        [0.3861, 0.3945, 0.6658],
        [0.6978, 0.3508, 0.4813],
        [0.8193, 0.2274, 0.8384],
        [0.9360, 0.9226, 0.1453]])

观察tensor的维度信息

x = torch.rand(3,3)
x.size()

输出

torch.Size([3, 3])

一些简单的运算

x = torch.tensor([1])
y = torch.tensor([3])
'''
方式1
'''
z = x + y
'''
方式2
''' 
z = torch.add(x, y)
'''
方式3
'''
result = torch.empty(1)
# 不初始化数据
torch.add(x, y, out=result)
# 将结果返回到result中
'''
方式4
'''
x.add_(y)

输出

tensor([4])

索引操作

x = torch.rand(5,5)
x[:,:]
x[1,:]
x[:,1]
x[1,1]

分别输出

tensor([[0.4012, 0.2604, 0.1720, 0.0996, 0.7806],
        [0.8734, 0.9087, 0.4828, 0.3543, 0.2375],
        [0.0924, 0.9040, 0.4408, 0.9758, 0.2250],
        [0.7179, 0.7244, 0.6165, 0.1142, 0.7363],
        [0.8504, 0.0391, 0.0753, 0.4530, 0.7372]])
tensor([0.8734, 0.9087, 0.4828, 0.3543, 0.2375])
tensor([0.2604, 0.9087, 0.9040, 0.7244, 0.0391])
tensor(0.9087)

维度变换

x = torch.rand(4,4)
x.view(16)
x.view(8,2)
x.view(-1,8)

分别输出

tensor([0.9277, 0.9547, 0.9487, 0.9841, 0.4114, 0.1693, 0.8691, 0.3954, 0.4679,
        0.7914, 0.7456, 0.0522, 0.0043, 0.2097, 0.5932, 0.9797])
tensor([[0.9277, 0.9547],
        [0.9487, 0.9841],
        [0.4114, 0.1693],
        [0.8691, 0.3954],
        [0.4679, 0.7914],
        [0.7456, 0.0522],
        [0.0043, 0.2097],
        [0.5932, 0.9797]])
tensor([[0.9277, 0.9547, 0.9487, 0.9841, 0.4114, 0.1693, 0.8691, 0.3954],
        [0.4679, 0.7914, 0.7456, 0.0522, 0.0043, 0.2097, 0.5932, 0.9797]])

注意:必须维度变换数据的数量必须保持一致