# -*- coding: utf-8 -*-
# @Time  : 2018/1/17 16:37
# @Author : Zhiwei Zhong
# @Site  : 
# @File  : Numpy_Pytorch.py
# @Software: PyCharm

import torch
import numpy as np

np_data = np.arange(6).reshape((2, 3))

# numpy 转为 pytorch格式

torch_data = torch.from_numpy(np_data)
print(
  '\n numpy', np_data,
  '\n torch', torch_data,
)
'''
 numpy [[0 1 2]
 [3 4 5]] 
 torch 
 0 1 2
 3 4 5
[torch.LongTensor of size 2x3]
'''
# torch 转为numpy
tensor2array = torch_data.numpy()
print(tensor2array)
"""
[[0 1 2]
 [3 4 5]]
"""
# 运算符
# abs 、 add 、和numpy类似
data = [[1, 2], [3, 4]]
tensor = torch.FloatTensor(data)    # 转为32位浮点数，torch接受的都是Tensor的形式，所以运算前先转化为Tensor
print(
  '\n numpy', np.matmul(data, data),
  '\n torch', torch.mm(tensor, tensor)    # torch.dot()是点乘
)
'''
 numpy [[ 7 10]
 [15 22]] 
 torch 
 7 10
 15 22
[torch.FloatTensor of size 2x2]
'''