珠海网站建设网络有限公司,济南建设集团,营销型网站与普通网站的区别,郑州%公司 网站建设二维可以想象成一张纸#xff0c; 三维可以想象成多张纸叠在一块 四维可以想成多沓纸 求和时#xff0c;如果没设定keepdimTrue,则会消去相加的那一维度#xff0c;否则则将维度变为1
A torch.arange(20).reshape(5, 4)
A,A.shape, A.sum()(tensor([[ 0, 1, 2, 3],[ 4,…二维可以想象成一张纸 三维可以想象成多张纸叠在一块 四维可以想成多沓纸 求和时如果没设定keepdimTrue,则会消去相加的那一维度否则则将维度变为1
A torch.arange(20).reshape(5, 4)
A,A.shape, A.sum()(tensor([[ 0, 1, 2, 3],[ 4, 5, 6, 7],[ 8, 9, 10, 11],[12, 13, 14, 15],[16, 17, 18, 19]]),torch.Size([5, 4]),tensor(190))指定求和汇总张量的轴
A_sum_axis0 A.sum(axis0)
A_sum_axis0, A_sum_axis0.shape(tensor([40, 45, 50, 55]), torch.Size([4]))A_sum_axis1 A.sum(axis1)
A_sum_axis1, A_sum_axis1.shape(tensor([ 6, 22, 38, 54, 70]), torch.Size([5]))# 等价于A.SUM()
A.sum(axis[0, 1]), A.sum(axis[0, 1]).shape(tensor(190), torch.Size([]))# 三维 测试SA torch.arange(20 * 2).reshape(2, 5, 4)
SA, SA.shape(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],[24, 25, 26, 27],[28, 29, 30, 31],[32, 33, 34, 35],[36, 37, 38, 39]]]),torch.Size([2, 5, 4]))SA_sum_axis0 SA.sum(axis0)
SA_sum_axis0, SA_sum_axis0.shape(tensor([[20, 22, 24, 26],[28, 30, 32, 34],[36, 38, 40, 42],[44, 46, 48, 50],[52, 54, 56, 58]]),torch.Size([5, 4]))SA_sum_axis1 SA.sum(axis1)
SA_sum_axis1, SA_sum_axis1.shape(tensor([[ 40, 45, 50, 55],[140, 145, 150, 155]]),torch.Size([2, 4]))SA_sum_axis2 SA.sum(axis2)
SA_sum_axis2, SA_sum_axis2.shape(tensor([[ 6, 22, 38, 54, 70],[ 86, 102, 118, 134, 150]]),torch.Size([2, 5]))四维矩阵求和
A torch.arange(20*2*2).reshape((2,2,5,4))
A, A.shape(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],[24, 25, 26, 27],[28, 29, 30, 31],[32, 33, 34, 35],[36, 37, 38, 39]]], [[[40, 41, 42, 43], [44, 45, 46, 47], [48, 49, 50, 51], [52, 53, 54, 55], [56, 57, 58, 59]], [[60, 61, 62, 63],[64, 65, 66, 67],[68, 69, 70, 71],[72, 73, 74, 75],[76, 77, 78, 79]]]]),torch.Size([2, 2, 5, 4]))A_sum_axis0 A.sum(axis0)
A_sum_axis0, A_sum_axis0.shape(tensor([[[ 40, 42, 44, 46],[ 48, 50, 52, 54],[ 56, 58, 60, 62],[ 64, 66, 68, 70],[ 72, 74, 76, 78]],[[ 80, 82, 84, 86],[ 88, 90, 92, 94],[ 96, 98, 100, 102],[104, 106, 108, 110],[112, 114, 116, 118]]]),torch.Size([2, 5, 4]))A_sum_axis1 A.sum(axis1)
A_sum_axis1, A_sum_axis1.shape(tensor([[[ 20, 22, 24, 26],[ 28, 30, 32, 34],[ 36, 38, 40, 42],[ 44, 46, 48, 50],[ 52, 54, 56, 58]],[[100, 102, 104, 106],[108, 110, 112, 114],[116, 118, 120, 122],[124, 126, 128, 130],[132, 134, 136, 138]]]),torch.Size([2, 5, 4]))
