1 import torch
 2 import numpy as np
 3 
 4 # details about math operation in torch can be found in: http://pytorch.org/docs/torch.html#math-operations
 5 
 6 # convert numpy to tensor or vise versa
 7 np_data = np.arange(6).reshape((2, 3))
 8 torch_data = torch.from_numpy(np_data)
 9 tensor2array = torch_data.numpy()
10 print(
11     '\nnumpy array:', np_data,          # [[0 1 2], [3 4 5]]
12     '\ntorch tensor:', torch_data,      #  0  1  2 \n 3  4  5    [torch.LongTensor of size 2x3]
13     '\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]
14 )
15 
16 
17 # abs
18 data = [-1, -2, 1, 2]
19 tensor = torch.FloatTensor(data)  # 32-bit floating point
20 print(
21     '\nabs',
22     '\nnumpy: ', np.abs(data),          # [1 2 1 2]
23     '\ntorch: ', torch.abs(tensor)      # [1 2 1 2]
24 )
25 
26 # sin
27 print(
28     '\nsin',
29     '\nnumpy: ', np.sin(data),      # [-0.84147098 -0.90929743  0.84147098  0.90929743]
30     '\ntorch: ', torch.sin(tensor)  # [-0.8415 -0.9093  0.8415  0.9093]
31 )
32 
33 # mean
34 print(
35     '\nmean',
36     '\nnumpy: ', np.mean(data),         # 0.0
37     '\ntorch: ', torch.mean(tensor)     # 0.0
38 )
39 
40 # matrix multiplication
41 data = [[1,2], [3,4]]
42 tensor = torch.FloatTensor(data)  # 32-bit floating point
43 # correct method
44 print(
45     '\nmatrix multiplication (matmul)',
46     '\nnumpy: ', np.matmul(data, data),     # [[7, 10], [15, 22]]
47     '\ntorch: ', torch.mm(tensor, tensor)   # [[7, 10], [15, 22]]
48 )
49 # incorrect method
50 data = np.array(data)
51 print(
52     '\nmatrix multiplication (dot)',
53     '\nnumpy: ', data.dot(data),        # [[7, 10], [15, 22]]
54     '\ntorch: ', tensor.dot(tensor)     # this will convert tensor to [1,2,3,4], you'll get 30.0
55 )