import torch import numpy as np a = torch.tensor([[[1]]]) #只有一个数据的时候,获取其数值 print(a.item()) #tensor转化为nparray b = a.numpy() print(b,type(b),type(a)) #获取张量的形状 a = torch.tensor(np.arange(30).reshape(3,2,5)) print(a) print(a.shape) print(a.size()) print(a.size(0)) #形状变换 print(a.view([2,3,5])) #转置 b = torch.tensor(np.arange(15).reshape(3,5)) print(b) print(b.transpose(0,1)) print(b.T) #最大值 print(b.max(dim=-1)) D:\anaconda\python.exe C:/Users/liuxinyu/Desktop/pytorch_test/day1/张量的属性和方法.py 1 [[[1]]] <class 'numpy.ndarray'> <class 'torch.Tensor'> 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]]], dtype=torch.int32) torch.Size([3, 2, 5]) torch.Size([3, 2, 5]) 3 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]]], dtype=torch.int32) tensor([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]], dtype=torch.int32) tensor([[ 0, 5, 10], [ 1, 6, 11], [ 2, 7, 12], [ 3, 8, 13], [ 4, 9, 14]], dtype=torch.int32) tensor([[ 0, 5, 10], [ 1, 6, 11], [ 2, 7, 12], [ 3, 8, 13], [ 4, 9, 14]], dtype=torch.int32) torch.return_types.max( values=tensor([ 4, 9, 14], dtype=torch.int32), indices=tensor([4, 4, 4])) Process finished with exit code 0
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