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.FloatTensor
2. 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.]])
原文：https://www.cnblogs.com/jeshy/p/11183632.html