大连金广建设集团网站,网站建设全部流程图,合肥公司网站开发,做网站怎么弄文章目录1.类型判断isinstance2.Dimension实例3.Tensor常用操作4.索引和切片5.Tensor维度变换6.Broadcast自动扩展7.合并与分割8.基本运算9.统计属性10.高阶OP大伙都这么聪明#xff0c;注释就只写最关键的咯1.类型判断isinstance
常见类型如下#xff1a; a torch.randn(…
文章目录1.类型判断isinstance2.Dimension实例3.Tensor常用操作4.索引和切片5.Tensor维度变换6.Broadcast自动扩展7.合并与分割8.基本运算9.统计属性10.高阶OP大伙都这么聪明注释就只写最关键的咯1.类型判断isinstance
常见类型如下 a torch.randn(2,3)a.type()data torch.FloatTensor()
isinstance(data,torch.cuda.FloatTensor)
data data.cuda()
isinstance(data,torch.cuda.FloatTensor)2.Dimension实例
Dim0Loss
torch.tensor(1.0)
torch.tensor(1.3)
atorch.tensor(2.2)
a.shape
a.size()Dim1biaslinear input
a torch.tensor([1.1])
a.size()
a
a.shape
b torch.FloatTensor(1)
b.size()Dim2linear input batch a torch.randn(2,3)aa.shapea.size(0)a.size(1)a.shape[1]Dim3RNN input batch
a toch.rand(1,2,3)
a
a.shape
a[0]Dim4:图像输入[b, c, h, w]
a torch.rand(2,3,28,28)
a
a.shape
list(a.shape)Mixed:
a torch.rand(2,3,28,28)
a.numel()
a.dim()
a torch.tensor(1)
a.dim()3.Tensor常用操作
Import from numpy
a np.array([2,3.3])
torch.from_numpy(a)
a np.ones([2,3])
torch.from_numpy(a)Import from List
torch.tensor([2.,3.2])
torch.FloatTensor([2.,3.2])#不推荐
torch.tensor([[2.,3.2],[1.,22.3]])uninitialized
torch.empty(1)
torch.Tensor(2,3)
torch.IntTensor(2,3)
torch.FloatTensor(2,3)set default type
torch.tensor([1.2,3]).type()
torch.set_default_tensor_type(torch.DoubleTensor)
torch.tensor([1.2,3]).type()rand/rand_like, randint,randn
torch.rand(3,3)#[0, 1]
a torch.rand(3,3)
torch.rand_like(a)
torch.randint(1,10,[3,3])#[min, max)
torch.randn(3,3)#N(0,1)fullarange/range
torch.full([2,3],7)
torch.full([],7)
torch.full([1],7)
torch.arange(0,10)
torch.arange(0,10,2)
torch.range(0,10)#不建议
torch.normal(meantorch.full([10],0), stdtorch.arange(1,0,-0.1))#N(mean,std)linspace/logspace
torch.linspace(0,10,steps4)
torch.linspace(0,10,steps11)
torch.logspace(0,10,steps4)Ones/zeros/eye
torch.ones(3,3)
torch.zeros(3,3)
torch.eys(3,4)
torch.eys(3,3)
#常用快捷操作
a torch.zeros(3,3)
torch.ones_like(a)randperm
a torch.rand(2,3)
a
b torch.rand(2,2)
b
a,b
torch.randperm(10)4.索引和切片
Indexingdim0 优先
a torch.rand(4,3,28,28)
a[0].shape
a[0,0,2,4]#取值select first/last N
a[:2].shape#第一维[0,2)
a[:2,:1,:,:].shape#第一维[0,2),第二维[0,1)
a[:2,1:,:,:].shape#第一维[0,2),第二维[1,_]
a[:2,-1:,:,:].shape#第一维[0,2),第二维(1,_]select by steps
a[:,:,0:28:2,0:28:2].shape
a[:,:,::2,::2].shapeselect by specific index
a.index_select(0,torch.tensor([0,2])).shape
a.index_select(1,torch.tensor([1,2]).shape
a.index_select(2,torch.arange(8)).shape …:表全部
a[...].shape
a[:,1,...].shape
a[...,:2].shapeselect by mask
x torch.randn(3,4)
mask x.ge(0.5)
mask
x
torch.masked_select(x,mask)
torch.masked_select(x,mask).shapeselect by flatten index b torch.tensor([[4,3,5],[6,7,8]])
torch.take(b,torch.tensor([0,2,5]))#返回展开后的索引5.Tensor维度变换
View reshape
a torch.rand(4,1,28,28)
a.shape
a.view(4,28*28)
a.view(4*28,28).shape
b a.view(4,784)#不推荐合并尽量写成连乘帮助记忆原数据各维度作用Squeeze v.s. unsqueeze
a.shpae
a.unsqueeze(0).shape#第一个空位
a.unsqueeze(-1).shape#最后一个空位
a.unsqueeze(4).shape#第五个空位
a.unsqueeze(-4).shape#倒数第4个空位
a.unsqueeze(-5).shape#导数第5个空位
a.unsqueeze(5).shape#第6个空位没有a torch.tensor([1.2,2.3])
a.unsqueeze(-1)
a.unsqueeze(0)b torch.rand(32)
f torch.rand(4,32,14,14)
b b.unsqueeze(1).unsqueeze(2).unsqueeze(0)#每次处理后重新排序
b.shapeb.squeeze().shape#压缩所有为1
b.squeeze(0).shape
b.squeeze(-1).shape
b.squeeze(1).shape#不是1所以不变
b.squeeze(-4).shape#导数第四个Expand broadcasting/ repeatmemory copied
a torch.rand(4,32,14,14)
b.shape
b.expand(4,32,14,14).shape
b.repeat(4,32,1,1).shape.t转置
a torch.randn(3,4)
a
a.t()#注意高维转置不了Transpose
a.shape
a1 a.transpose(1,3)#交换次序
a1.shapepermute
b torch.rand(4,3,28,32)
b.transpose(1,3).shape
b.transpose(1,3).transpose(1,2).shape
b.permute(0,2,3,1).shape#按索引排列6.Broadcast自动扩展
broadcast是不同size的tensor进行加减时自动进行的机制其主要思想以及特点如下
从最右边的维度开始匹配前面维度缺失的补1直到维度相同从最右边的维度开始匹配维度不等但有一个是1则扩展到相同的值实例如下 节约内存核心是利用expand只进行逻辑上的扩展而不会实际拷贝
7.合并与分割
Cat
a torch.rand(4,3,32,32)
b torch.rand(5,3,32,32)
a2 torch.rand(4,1,32,32)
torch.cat([a,a2],dim1).shape
torch.cat([a,a2],dim0).shape#Error多个维度不同stack
a1torch.rand(4,3,16,32)
a2torch.rand(4,3,16,32)
torch.cat([a1,a2],dim2).shape
torch.stack([a1,a2],dim2).shapeCat与stack的区别前者是合并dim后者是增加dim后者要求所有一摸一样 Split: by len
c torch.rand(2,32,8)
aa, bb c.split([1,1],dim0)
aa.shape, bb.shape
aa, bb c.split([2,0],dim0)
aa.shape, bb.shape
aa, bb c.split(1,dim0)
aa.shape, bb.shape
aa, bb c.split(2,dim0)Chunk: by num
c.shape
aa, bb c.chunk(2,dim0)8.基本运算
basic
a torch.rand(3,4)
a
b torch.rand(4)
b
ab
torch.add(a,b)
torch.all(torch.eq(a-b,torch.sub(a,b)))
torch.all(torch.eq(a*b,torch.mul(a,b)))
torch.all(torch.eq(a/b,torch.div(a,b)))matmul
a
b torch.ones(2,2)
b
torch.mm(a,b)#only for 2dim
torch.matmul(a,b)
aba torch.rand(4,784)
x torch.rand(4,784)
w torch.rand(512,784)
(xw.t()).shape2d tensor matmul?
a torch.rand(4,3,28,64)
b torch.rand(4,3,64,32)
torch.mm(a,b).shape#用不了2D以上
torch.matmul(a,b).shape#只计算最后两维
b torch.rand(4,1,64,32)
torch.matmul(a,b).shape#触发Broadcast机制Power
a torch.full([2,2],3)
a.pow(2)
a**2
aa a**2
aa.sqrt()
aa.rsqrt()#平方根的倒数
aa**(0.5)Exp log
a torch.exp(torch.ones(2,2))
a
torch.log(a)Approximation
a torch.tensor(3.14)
a.floor(),a.ceil(),a.trunc(),a.frac()#分别是向下取整向上取整整数部分小数部分
a torch.tensor(3.4999)
a.round()#4舍5入
a torch.tensor(3.5)
a.round()clamp
grad torch.rand(2,3)*15
grad
grad.max()
grad.median()
grad.clamp(10)
grad.clamp(0,10)简单解释一下 torch.clamp(input, minNone, maxNone, *, outNone) 限定一个范围input tensor中数值低于min的返回min高于max的返回max 9.统计属性
norm1norm2
a torch.full([8],1)
a
ba.view(2,4)
ca.view(2,2,2)
b
c
a.norm(1),b.norm(1),c.norm(1)#1范数绝对值之和
a.norm(2),b.norm(2),c.norm(2)#2范数欧几里得范数平方和再开方
b.norm(1,dim1)
b.norm(2,dim1)
c.norm(1,dim0)
c.norm(2,dim0)mean, sum, min, max, prodargmin, argmax
a torch.arange(8).view(2,4).float()
a
a.min(),a.max(),a.mean(),a.prod()
a.sum()
a.argmax(),a.argmin()#返回索引位置atorch.rand(2,3,4)
a
a.argmax()
atorch.randn(4,10)
a
a.argmax()
a.argmax(dim1)#相应维度上索引dim, keepdim
a
a.max(dim1)
a.max(dim1,keepdimTrue)#保持输出的维度Top-k or k-th
a
a.topk(3,dim1)
a.topk(3,dim1,largestFalse)#1维最小的前3个
a.kthvalue(8,dim1)
a.kthvalue(3)#默认选择最后一个维度compare
a
a0
torch.gt(a,0)
(a0).type()
torch.gt(a,0)
a!0
atorch.ones(2,3)
btorch.randn(2,3)
torch.eq(a,b)
torch.eq(a,a)#逐元素比较
torch.equal(a,a)# 张量比较10.高阶OP
Where condition成立则填充x否则填充y
cond torch.rand(2,2)
cond
a torch.zeros(2,2)
b torch.ones(2,2)
a,b
torch.where(cond0.5,a,b)
torch.where(cond0.5,a,b)gather
输入input和索引index不会Broadcastoutput形状与index相同索引与dim方向相同index.size(dim)input.size(dim)保证有元素可取
prob torch.randn(4,10)
prob
idx prob.topk(dim1,k3)
idx
idxidx[1]
idx
label torch.arange(10)100
label
torch.gather(label.expand(4,10),dim1,indexidx.long())#其中有
label.expand(4,10)
idx.long()
