简介

在深度学习中，我们通常会频繁地对数据进行操作；要操作一般就需要先创建。

官方介绍

The torch package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient serialization of Tensors and arbitrary types, and other useful utilities

It has a CUDA counterpart, that enables you to run your tensor computations on an NVIDIA GPU with compute capability >= 3.0

我的介绍

在 PyTorch 中，torch.Tensor是进行存储和进行变换数据的主要工具

• tensor 是什么意思？上翻译：
  
  一般可译作张量，张量可以看作是一个多维数组

创建

• 这里直接上代码

# 导入PyTorch
import torch"""官方文档地址：https://pytorch.org/docs/2.1/torch.html#creation-ops
"""#
def create_empty_torch(a,b):"""Args:a:b:创建一个 [a] x [b] 的未初始化的 Tensor:return: Returns a tensor filled with uninitialized data."""empty = torch.empty(a,b)print(empty)def create_zero_torch():"""创建一个 7x5 的 long 类型全是 0 的 TensorReturns:Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument size"""zero = torch.zeros(7,5,dtype=torch.long)print(zero)def create_data_torch():"""Constructs a tensor with no autograd history (also known as a "leaf tensor", see Autograd mechanics) by copying data:return:"""data = torch.tensor([12.5,7])print(data)def create_data_2_torch():data = torch.tensor([12.5, 7])# 返回的 tensor 默认具有相同的 torch.dtype 和 torch.devicedata = data.new_ones(2, 1, dtype=torch.float64)print(data)# 指定新的数据类型data = torch.randn_like(data, dtype=torch.float)print(data)if __name__ == '__main__':create_data_2_torch()

• 测试结果我就不贴图了，太费事，直接运行自己看

结束语

本系列教程仅针对入门初学者或者非此行业人员，敬请期待！