(相关资料图)
这些方法只是开辟了空间，所附的初始值（非常大，非常小，0），后面还需要我们进行数据的存入。
torch.empty()：返回一个没有初始化的Tensor，默认是FloatTensor类型。#torch.empty(d1,d2,d3)函数输入的是shape torch.empty(2,3,5)#tensor([[[-1.9036e-22, 6.8944e-43, 0.0000e+00, 0.0000e+00, -1.0922e-20],# [ 6.8944e-43, -2.8812e-24, 6.8944e-43, -5.9272e-21, 6.8944e-43],# [ 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00]],## [[ 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00],# [ 0.0000e+00, 0.0000e+00, 1.4013e-45, 0.0000e+00, 0.0000e+00],# [ 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00]]])#torch.FloatTensor(d1,d2,d3)torch.FloatTensor(2,2)#tensor([[-0.0000e+00, 4.5907e-41],# [-7.3327e-21, 6.8944e-43]])#torch.IntTensor(d1,d2,d3)torch.IntTensor(2,2)#tensor([[ 0, 1002524760],# [-1687359808, 492]], dtype=torch.int32)随机均匀分布：rand/rand_like,randint
rand:[0,1)均匀分布；randint(min,max,[d1,d2,d3]) 返回[min,max)的整数均匀分布
#torch.rand(d1,d2,d3)torch.rand(2,2)#tensor([[0.8670, 0.6158],# [0.0895, 0.2391]])#rand_like()a=torch.rand(3,2)torch.rand_like(a)#tensor([[0.2846, 0.3605],# [0.3359, 0.2789],# [0.5637, 0.6276]])#randint(min,max,[d1,d2,d3])torch.randint(1,10,[3,3,3])#tensor([[[3, 3, 8],# [2, 7, 7],# [6, 5, 9]],## [[7, 9, 9],# [6, 3, 9],# [1, 5, 6]],## [[5, 4, 8],# [7, 1, 2],# [3, 4, 4]]])随机正态分布 randn
randn返回一组符合N(0,1)正态分布的随机数据
#randn(d1,d2,d3)torch.randn(2,2)#tensor([[ 0.3729, 0.0548],# [-1.9443, 1.2485]])#normal(mean,std) 需要给出均值和方差torch.normal(mean=torch.full([10],0.),std=torch.arange(1,0,-0.1))#tensor([-0.8547, 0.1985, 0.1879, 0.7315, -0.3785, -0.3445, 0.7092, 0.0525, 0.2669, 0.0744])#后面需要用reshape修正成自己想要的形状#full([d1,d2,d3],num)torch.full([2,2],6)#tensor([[6, 6],# [6, 6]])torch.full([],6)#tensor(6) 标量torch.full([1],6)#tensor([6]) 向量#torch.arange(min,max,step):返回一个[min,max),步长为step的集体数组，默认为1torch.arange(0,10)#tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])torch.arange(0,10,2)#tensor([0, 2, 4, 6, 8])#torch.linspace(min,max,steps):返回一个[min,max],数量为steps的数组torch.linspace(1,10,11)#tensor([ 1.0000, 1.9000, 2.8000, 3.7000, 4.6000, 5.5000, 6.4000, 7.3000,# 8.2000, 9.1000, 10.0000])#torch.logspace(a,b,steps):返回一个[10^a,10^b]，数量为steps的数组torch.logspace(0,1,10)#tensor([ 1.0000, 1.2915, 1.6681, 2.1544, 2.7826, 3.5938, 4.6416, 5.9948,# 7.7426, 10.0000])#torch.ones(d1,d2)torch.ones(2,2)#tensor([[1., 1.],# [1., 1.]])#torch.zeros(d1,d2)torch.zeros(2,2)#tensor([[0., 0.],# [0., 0.]])#torch.eye() 只能接收一个或两个参数torch.eye(3)#tensor([[1., 0., 0.],# [0., 1., 0.],# [0., 0., 1.]])torch.eye(2,3)#tensor([[1., 0., 0.],# [0., 1., 0.]])torch.randperm(8)#tensor([2, 6, 7, 5, 3, 4, 1, 0])a=torch.rand(4,3,28,28)a[0].shape#torch.Size([3, 28, 28])a[0,0,0,0]#tensor(0.9373)a[:2].shape#torch.Size([2, 3, 28, 28])a[1:].shape#torch.Size([3, 3, 28, 28])a[:,:,0:28:2,:].shape#torch.Size([4, 3, 14, 28])a.index_select(0,torch.tensor([0,2])).shape#torch.Size([2, 3, 28, 28])a.index_select(1,torch.tensor([0,2])).shape#torch.Size([4, 2, 28, 28])a[...].shape#torch.Size([4, 3, 28, 28])a[0,...].shape#torch.Size([3, 28, 28])a[:,2,...].shape#torch.Size([4, 28, 28])#torch.masked_select 取出掩码对应位置的值x=torch.randn(3,4)mask=x.ge(0.5)torch.masked_select(x,mask)#tensor([1.6950, 1.2207, 0.6035])x=torch.randn(3,4)torch.take(x,torch.tensor([0,1,5]))#tensor([-2.2092, -0.2652, 0.4848])
