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=' ') print(torch.arange(6),torch.arange(6).reshape(2,3),sep=' ') print(X,X[0],X[2],X[0:2],sep=' ') 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=' ') # print(X,Z,X+Z,sep=' ') #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=' ') y=torch.arange(12).reshape(3,4) x=torch.arange(18).reshape(3,6) print(x,y,torch.cat((x,y),dim=1),sep=' ') print(torch.cat((x,x)),torch.cat((x,x),dim=1),sep=' ') print(torch.cat((y,y)),torch.cat((y,y),dim=1),sep=' ')#默认拼接dim=0 t1=torch.cat((x,x)) t2=torch.stack((x,x)) print(t1,t1.size(),t2,t2.size(),sep=' ') 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=' ')#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=' ') x3=torch.rand(3) with_fake_dimention=x3.unsqueeze(0) print(x3,with_fake_dimention,sep=' ')#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]) 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.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