import torch
X=torch.arange(12,dtype=torch.float32).reshape((3,4))
Y=torch.tensor([[2.0,1,4,3],[1,2,3,4],[4,3,2,1]])
print(X,Y,torch.cat((X,Y),dim=0),torch.cat((X,Y),dim=1),sep='\n')
print(torch.arange(6),torch.arange(6).reshape(2,3),sep='\n')
print(X,X[0],X[2],X[0:2],sep='\n')
Z=torch.arange(24).reshape((2,3,4))
X=X.reshape((4,3))
Y=torch.tensor([[2.0,1,4,3],[1,2,3,4],[4,3,2,1]]).reshape((4,3))
print(Z,Y,sep='\n')
# print(X,Z,X+Z,sep='\n') #RuntimeError: The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 2
x=torch.rand(2,3,4)
print(x)
x_with_2n3_dimention=x[1,:,:]
scalar_x=x[1,1,1]#first value from each dimension
#numpy like slicing
print(x_with_2n3_dimention)
x=torch.rand(2,3)
print(x)
print(x[:,1:])#skipping first column
print(x[:-1,:])#skipping last row
#transpose
print(x,x.t(),sep='\n')
y=torch.arange(12).reshape(3,4)
x=torch.arange(18).reshape(3,6)
print(x,y,torch.cat((x,y),dim=1),sep='\n')
print(torch.cat((x,x)),torch.cat((x,x),dim=1),sep='\n')
print(torch.cat((y,y)),torch.cat((y,y),dim=1),sep='\n')#默认拼接dim=0
t1=torch.cat((x,x))
t2=torch.stack((x,x))
print(t1,t1.size(),t2,t2.size(),sep='\n')
t3=t2.view(-1)
print(t3.storage())
x1=torch.rand(2,3,3)#a tensor of size 3,2,1
splitted=x1.split(split_size=2,dim=0)
print('x1: ',x1,'splitted: ',splitted,sep='\n')#2 tensors of 2x2 and 1x2 size
#squeeze and unsqueeze
csqueeze=x2.squeeze()#remove the 1 sized dimention,如果没有1 sized，则不移除。
print(x2,squeeze,sep='\n')
x3=torch.rand(3)
with_fake_dimention=x3.unsqueeze(0)
print(x3,with_fake_dimention,sep='\n')#added a fake zeroth dimension
输出
tensor([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]])
tensor([[2., 1., 4., 3.],
[1., 2., 3., 4.],
[4., 3., 2., 1.]])
tensor([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 2., 1., 4., 3.],
[ 1., 2., 3., 4.],
[ 4., 3., 2., 1.]])
tensor([[ 0., 1., 2., 3., 2., 1., 4., 3.],
[ 4., 5., 6., 7., 1., 2., 3., 4.],
[ 8., 9., 10., 11., 4., 3., 2., 1.]])
tensor([0, 1, 2, 3, 4, 5])
tensor([[0, 1, 2],
[3, 4, 5]])
tensor([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]])
tensor([0., 1., 2., 3.])
tensor([ 8., 9., 10., 11.])
tensor([[0., 1., 2., 3.],
[4., 5., 6., 7.]])
tensor([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
tensor([[2., 1., 4.],
[3., 1., 2.],
[3., 4., 4.],
[3., 2., 1.]])
tensor([[[0.6340, 0.3403, 0.3935, 0.6987],
[0.0420, 0.0932, 0.6484, 0.7204],
[0.7527, 0.8322, 0.4876, 0.8779]],
[[0.8270, 0.5663, 0.0538, 0.8164],
[0.1572, 0.2372, 0.6838, 0.9293],
[0.1142, 0.0425, 0.9784, 0.4684]]])
tensor([[0.8270, 0.5663, 0.0538, 0.8164],
[0.1572, 0.2372, 0.6838, 0.9293],
[0.1142, 0.0425, 0.9784, 0.4684]])
tensor([[0.4269, 0.0942, 0.2748],
[0.9709, 0.2098, 0.8590]])
Traceback (most recent call last):
File "F:/WorkPlacePy/PycharmProjects1/Pytorch深度学习实战书籍练习/chapter1.py", line 39, in <module>
csqueeze=x2.squeeze()#remove the 1 sized dimention,如果没有1 sized，则不移除。
NameError: name 'x2' is not defined
tensor([[0.0942, 0.2748],
[0.2098, 0.8590]])
tensor([[0.4269, 0.0942, 0.2748]])
tensor([[0.4269, 0.0942, 0.2748],
[0.9709, 0.2098, 0.8590]])
tensor([[0.4269, 0.9709],
[0.0942, 0.2098],
[0.2748, 0.8590]])
tensor([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17]])
tensor([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
tensor([[ 0, 1, 2, 3, 4, 5, 0, 1, 2, 3],
[ 6, 7, 8, 9, 10, 11, 4, 5, 6, 7],
[12, 13, 14, 15, 16, 17, 8, 9, 10, 11]])
tensor([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17],
[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17]])
tensor([[ 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11, 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17, 12, 13, 14, 15, 16, 17]])
tensor([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
tensor([[ 0, 1, 2, 3, 0, 1, 2, 3],
[ 4, 5, 6, 7, 4, 5, 6, 7],
[ 8, 9, 10, 11, 8, 9, 10, 11]])
tensor([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17],
[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17]])
torch.Size([6, 6])
tensor([[[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17]],
[[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17]]])
torch.Size([2, 3, 6])
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[torch.LongStorage of size 36]
x1:
tensor([[[0.3491, 0.7096, 0.6774],
[0.8273, 0.2891, 0.0725],
[0.7264, 0.0226, 0.3569]],
[[0.4286, 0.9802, 0.5050],
[0.3303, 0.7117, 0.2414],
[0.9537, 0.7252, 0.8141]]])
splitted:
(tensor([[[0.3491, 0.7096, 0.6774],
[0.8273, 0.2891, 0.0725],
[0.7264, 0.0226, 0.3569]],
[[0.4286, 0.9802, 0.5050],
[0.3303, 0.7117, 0.2414],
[0.9537, 0.7252, 0.8141]]]),)
Process finished with exit code 1