pytorch fundation

import torch
import numpy as np

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

# abs 绝对值计算
data = [-1, -2, 1, 2]
tensor = torch.FloatTensor(data)  # 转换成32位浮点 tensor
print(
    'nabs',
    'nnumpy: ', np.abs(data),          # [1 2 1 2]
    'ntorch: ', torch.abs(tensor)      # [1 2 1 2]
)

# sin   三角函数 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  均值
print(
    'nmean',
    'nnumpy: ', np.mean(data),         # 0.0
    'ntorch: ', torch.mean(tensor)     # 0.0
)

# matrix multiplication 矩阵点乘
data = [[1,2], [3,4]]
tensor = torch.FloatTensor(data)  # 转换成32位浮点 tensor
# correct method
print(
    'nmatrix multiplication (matmul)',
    'nnumpy: ', np.matmul(data, data),     # [[7, 10], [15, 22]]
    'ntorch: ', torch.mm(tensor, tensor)   # [[7, 10], [15, 22]]
)

# !!!!  下面是错误的方法 !!!!
data = np.array(data)
print(
    'nmatrix multiplication (dot)',
    'nnumpy: ', data.dot(data),        # [[7, 10], [15, 22]] 在numpy 中可行
    'ntorch: ', tensor.dot(tensor)     # torch 会转换[1,2,3,4].dot([1,2,3,4) = 30.0
)

# tensor.dot()在(>= 0.3.0)版本之后只能支持一维数组的内积
tensor.dot(tensor)     # torch 会转换成 [1,2,3,4].dot([1,2,3,4]) = 30.0 并且 tensor 只能是一维数组tensor