pytorch基础2

下面是常见函数的代码例子

import torch
import numpy as np
print("分割线-----------------------------------------")
#加减乘除操作
a = torch.rand(3,4)
b = torch.rand(4)
print(a)
print(b)
print(torch.add(a, b))
print(torch.sub(a, b))
print(torch.mul(a, b))
print(torch.div(a, b))
print(torch.all(torch.eq(a - b,torch.sub(a,b))))#判断torch的减法和python的减法结果是否一致
print("分割线-----------------------------------------")
#矩阵乘法（点乘和叉乘）matmul  mm  @   *
a = torch.ones(2,2)*3
b = torch.ones(2,2)
print(a*b)#点乘积
print(a.matmul(b))#叉乘积
print(a@b)#叉乘积
print(a.mm(b))#叉乘积，相比于前两种，这一种只能适合二维数组的乘积
a = torch.rand(4,3,28,64)
b = torch.rand(4,3,64,32)
#torch.mm(a,b).shape#此时会报错，mm只适合二维
print(torch.matmul(a,b).shape)#torch.Size([4, 3, 28, 32])
b = torch.rand(4,1,64,32)
torch.matmul(a, b).shape #torch.Size([4, 3, 28, 32])
b = torch.rand(4,64,32)
#torch.matmul(a, b).shape ,报错，因为b的4对应a的3无法进行广播，所以报错
print("分割线-----------------------------------------")
#power的使用
a = torch.full([2,2],3)
print(a.pow(2))
print(a**2)
aa = a**2
print(aa.sqrt())
print(aa**(0.5))
print(aa.rsqrt())#开根号后的倒数
print("分割线-----------------------------------------")
#floor(),ceil(),round(),trunc(),frac()的使用
a = torch.tensor(3.14)
print(a.floor(),a.ceil(),a.trunc(),a.frac())#后两个是取整，和取小数
print(a.round()) #四舍五入
print("分割线-----------------------------------------")
#clamp 和 dim,keepdim
grad = torch.rand(2,3)*15
print(grad.max(),grad.median(), grad.min())
print(grad)
print(grad.clamp(10))#小于10的都变成10
print(grad.clamp(3,10))#不在3到10之间的变为3或者10
a = torch.randn(4,10)
print(a.max(dim=1))#返回每一列最大值组成的数组和对应的下标
print(a.argmax(dim = 1))#这个只返回最大值对应的下标
print(a.max(dim=1,keepdim=True))#keepdim的作用是使返回值维度是否仍然为原来的维度不变
print(a.argmax(dim=1,keepdim=True))
print("分割线-----------------------------------------")
#topk和kthvalue
print(a.topk(3,dim=1))#返回每行最大的三个数和下标
print(a.topk(3,dim=1,largest=False))#返回每行最小的三个数和下标
print(a.kthvalue(5,dim=1))#返回每行第五大的数字和对应数字的下标
print("分割线-----------------------------------------")
#矩阵的比较，cat的使用
m = torch.rand(2,2)
n = torch.rand(2,2)
print(m,n)
print(m == n)
print(m.eq(n))
print(m > n)
a =  torch.rand(4,32,8)
b = torch.rand(5,32,8)
print(torch.cat([a,b],dim = 0).shape)#按行进行拼接,torch.Size([9, 32, 8])
#更详细的拼接可以看下面的图
a1 = torch.rand(4,3,32,32)
a2 = torch.rand(4,1,32,32)
#torch.cat([a1,a2],dim = 0).shape#报错原因是如果进行维度0上进行拼接，则要保证其他维度必须一致
a1 = torch.rand(4,3,14,32)
a2 = torch.rand(4,3,14,32)
print(torch.cat([a1,a2],dim=2).shape)#torch.Size([4, 3, 28, 32])
print("分割线-----------------------------------------")
#stack和split的使用
#用来进行维度的扩充，这个就是在dim =2进行扩充
print(torch.stack([a1,a2],dim=2).shape)#torch.Size([4, 3, 2, 14, 32])
aa , bb =a1.split([2,2],dim=0)#拆分成两份每份数目是2，2
print(aa.shape,bb.shape)
aaa,bbb = a1.split(2,dim=0)#每份长度为2
print(aaa.shape,bbb.shape)
aa,bb = a1.chunk(2,dim = 0)#拆成两块，每块一个
print("分割线-----------------------------------------")
#where，gather的使用
cond = torch.rand(2,2)
print(cond)
a = torch.zeros(2,2)
b = torch.ones(2,2)
s = torch.where(cond>0.5,a,b)
print(s)#如果大于0.5对应位置为a的对应位置的值，否则为b的对应位置的值
prob = torch.randn(4,10)
idx = prob.topk(dim =1,k=3)
id = idx[1]
print(id)#索引下标
label= torch.arange(10)+100
d = torch.gather(label.expand(4,10),dim =1,index = id)#获取对应索引下标的值
print(d)

 运行结果如下

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D:\anaconda\anaconda\pythonw.exe D:/Code/Python/龙良曲pytorch学习/高级操作.py 分割线----------------------------------------- tensor([[0.5581, 0.2369, 0.1379, 0.3702],         [0.1565, 0.1022, 0.5839, 0.1778],         [0.0204, 0.1498, 0.5276, 0.4219]]) tensor([0.7969, 0.9313, 0.0608, 0.0245]) tensor([[1.3551, 1.1682, 0.1988, 0.3947],         [0.9535, 1.0335, 0.6448, 0.2023],         [0.8173, 1.0811, 0.5884, 0.4464]]) tensor([[-0.2388, -0.6944,  0.0771,  0.3457],         [-0.6404, -0.8291,  0.5231,  0.1533],         [-0.7766, -0.7815,  0.4667,  0.3974]]) tensor([[0.4448, 0.2206, 0.0084, 0.0091],         [0.1247, 0.0952, 0.0355, 0.0044],         [0.0162, 0.1395, 0.0321, 0.0103]]) tensor([[ 0.7003,  0.2544,  2.2669, 15.1075],         [ 0.1964,  0.1097,  9.5973,  7.2539],         [ 0.0255,  0.1609,  8.6706, 17.2148]]) tensor(True) 分割线----------------------------------------- tensor([[3., 3.],         [3., 3.]]) tensor([[6., 6.],         [6., 6.]]) tensor([[6., 6.],         [6., 6.]]) tensor([[6., 6.],         [6., 6.]]) torch.Size([4, 3, 28, 32]) 分割线----------------------------------------- tensor([[9., 9.],         [9., 9.]]) tensor([[9., 9.],         [9., 9.]]) tensor([[3., 3.],         [3., 3.]]) tensor([[3., 3.],         [3., 3.]]) tensor([[0.3333, 0.3333],         [0.3333, 0.3333]]) 分割线----------------------------------------- tensor(3.) tensor(4.) tensor(3.) tensor(0.1400) tensor(3.) 分割线----------------------------------------- tensor(14.8811) tensor(8.5843) tensor(5.4463) tensor([[10.3914, 14.8811,  8.5843],         [10.6012,  5.4463,  5.7588]]) tensor([[10.3914, 14.8811, 10.0000],         [10.6012, 10.0000, 10.0000]]) tensor([[10.0000, 10.0000,  8.5843],         [10.0000,  5.4463,  5.7588]]) torch.return_types.max( values=tensor([1.1859, 0.7394, 1.2261, 0.5407]), indices=tensor([5, 1, 2, 4])) tensor([5, 1, 2, 4]) torch.return_types.max( values=tensor([[1.1859],         [0.7394],         [1.2261],         [0.5407]]), indices=tensor([[5],         [1],         [2],         [4]])) tensor([[5],         [1],         [2],         [4]]) 分割线----------------------------------------- torch.return_types.topk( values=tensor([[ 1.1859,  0.8406,  0.7883],         [ 0.7394,  0.4172,  0.2871],         [ 1.2261,  0.9851,  0.9759],         [ 0.5407,  0.1773, -0.2789]]), indices=tensor([[5, 4, 7],         [1, 2, 4],         [2, 8, 4],         [4, 1, 8]])) torch.return_types.topk( values=tensor([[-1.7351, -0.3469, -0.3116],         [-1.8399, -1.1521, -0.3790],         [-1.3753, -0.6663, -0.2762],         [-1.6875, -1.5461, -0.9697]]), indices=tensor([[0, 8, 6],         [3, 5, 0],         [7, 1, 5],         [0, 2, 3]])) torch.return_types.kthvalue( values=tensor([-0.1758,  0.0470, -0.2039, -0.6223]), indices=tensor([2, 9, 3, 6])) 分割线----------------------------------------- tensor([[0.9107, 0.4905],         [0.6499, 0.3425]]) tensor([[0.6911, 0.9619],         [0.1428, 0.5437]]) tensor([[False, False],         [False, False]]) tensor([[False, False],         [False, False]]) tensor([[ True, False],         [ True, False]]) torch.Size([9, 32, 8]) torch.Size([4, 3, 28, 32]) 分割线----------------------------------------- torch.Size([4, 3, 2, 14, 32]) torch.Size([2, 3, 14, 32]) torch.Size([2, 3, 14, 32]) torch.Size([2, 3, 14, 32]) torch.Size([2, 3, 14, 32]) 分割线----------------------------------------- tensor([[0.7541, 0.3861],         [0.9605, 0.7175]]) tensor([[0., 1.],         [0., 0.]]) tensor([[5, 4, 6],         [8, 2, 3],         [8, 6, 4],         [6, 2, 1]]) tensor([[105, 104, 106],         [108, 102, 103],         [108, 106, 104],         [106, 102, 101]])   Process finished with exit code 0