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