pytorch之 compare with numpy

 import torch
 import numpy as np

 # details about math operation in torch can be found in: http://pytorch.org/docs/torch.html#math-operations

 # convert numpy to tensor or vise versa
 np_data = np.arange(6).reshape((2, 3))
 torch_data = torch.from_numpy(np_data)
 tensor2array = torch_data.numpy()
 print(
     '\nnumpy array:', np_data,          # [[0 1 2], [3 4 5]]
     '\ntorch tensor:', torch_data,      #  0  1  2 \n 3  4  5    [torch.LongTensor of size 2x3]
     '\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]
 )

 # abs
 data = [-1, -2, 1, 2]
 tensor = torch.FloatTensor(data)  # 32-bit floating point
 print(
     '\nabs',
     '\nnumpy: ', np.abs(data),          # [1 2 1 2]
     '\ntorch: ', torch.abs(tensor)      # [1 2 1 2]
 )

 # sin
 print(
     '\nsin',
     '\nnumpy: ', np.sin(data),      # [-0.84147098 -0.90929743  0.84147098  0.90929743]
     '\ntorch: ', torch.sin(tensor)  # [-0.8415 -0.9093  0.8415  0.9093]
 )

 # mean
 print(
     '\nmean',
     '\nnumpy: ', np.mean(data),         # 0.0
     '\ntorch: ', torch.mean(tensor)     # 0.0
 )

 # matrix multiplication
 data = [[1,2], [3,4]]
 tensor = torch.FloatTensor(data)  # 32-bit floating point
 # correct method
 print(
     '\nmatrix multiplication (matmul)',
     '\nnumpy: ', np.matmul(data, data),     # [[7, 10], [15, 22]]
     '\ntorch: ', torch.mm(tensor, tensor)   # [[7, 10], [15, 22]]
 )
 # incorrect method
 data = np.array(data)
 print(
     '\nmatrix multiplication (dot)',
     '\nnumpy: ', data.dot(data),        # [[7, 10], [15, 22]]
     '\ntorch: ', tensor.dot(tensor)     # this will convert tensor to [1,2,3,4], you'll get 30.0
 )

仅为自己练习，没有其他用途