1. torch.Tensor和numpy.ndarray相互转换
import torch import numpy as np # <class 'numpy.ndarray'> np_data = np.arange(6).reshape((2,3)) # <class 'torch.Tensor'> torch_data = torch.from_numpy(np_data) # <class 'numpy.ndarray'> tensor2array = torch_data.numpy() print('numpy array:\n',np_data,type(np_data), '\ntorch tensor:\n',torch_data,type(torch_data), '\ntensor to array:\n',tensor2array,type(tensor2array)) # numpy array:# [[0 1 2]# [3 4 5]] <class 'numpy.ndarray'># torch tensor:# tensor([[0, 1, 2],# [3, 4, 5]]) <class 'torch.Tensor'># tensor to array:# [[0 1 2]# [3 4 5]] <class 'numpy.ndarray'>torch.Tensor:是一个包含了一种数据类型元素的多维矩阵，缺省为torch.FloatTensor2. torch.Tensor和numpy.ndarray一些简单操作，如均值，绝对值，sin，log等 data = [-1,-2,1,2] tensor_default = torch.Tensor(data) tensor = torch.FloatTensor(data) print('tensor default type:\n',tensor_default, '\ntensor FloatTensor type:\n',tensor, '\nabs:', '\nnumpy:',np.abs(data), '\ntorch:',torch.abs(tensor), '\nsin:', '\nnumpy:',np.sin(data), '\ntorch:',torch.sin(tensor), '\nmean:', '\nnumpy:',np.mean(data), '\ntorch:',torch.mean(tensor),) # tensor default type:# tensor([-1., -2., 1., 2.])# tensor FloatTensor type:# tensor([-1., -2., 1., 2.])# abs:# numpy: [1 2 1 2]# torch: tensor([1., 2., 1., 2.])# sin:# numpy: [-0.84147098 -0.90929743 0.84147098 0.90929743]# torch: tensor([-0.8415, -0.9093, 0.8415, 0.9093])# mean:# numpy: 0.0# torch: tensor(0.)3. 矩阵乘法(正确的做法) data = [[1,2], [3,4]] tensor = torch.FloatTensor(data) print( '\nmatrix multiplication (matmul):', '\nnumpy:\n', np.matmul(data, data), # [[7, 10], [15, 22]] '\ntorch:\n', torch.mm(tensor, tensor)) # [[7, 10], [15, 22]] # matrix multiplication (matmul):# numpy:# [[ 7 10]# [15 22]]# torch:# tensor([[ 7., 10.],# [15., 22.]])转载于:https://www.cnblogs.com/jeshy/p/11183632.html
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