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BIN
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112
Tensor.md
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112
Tensor.md
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@ -0,0 +1,112 @@
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||||
# Tensor
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## 什么是Tensor
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||||
Tensor（张量）是PyTorch最基本的操作对象，表示的是一个多维的矩阵，比如零维就是一个点，一维就向量，二维就是一般的矩阵，多维就相当于一个多维的数组，与python的numpy是对应的，而且PyTorch的Tensor可以和numpy的ndarray相互转换，唯一不同的是PyTorch可以再GPU上运行，而numpy的ndarray只能在CPU上运行。
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Tensor的基本数据类型包括
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* 32位浮点型 torch.FloatTensor（默认）
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||||
* 64位浮点型 torch.DoubleTensor
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||||
* 16位整型 torch.ShortTensor
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* 32位整型 torch.IntTensor
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* 64位整型 torch.LongTensor
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||||
## Tensor的基本操作
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||||
### 引入torch包
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||||
```buildoutcfg
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||||
from __future__ import print_function
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import torch
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||||
```
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||||
### 矩阵构建
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||||
<h4>1.构建一个5x3矩阵，不做初始化
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||||
```buildoutcfg
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||||
x = torch.empty(5, 3)
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||||
print(x)
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||||
```
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||||
输出结果：
|
||||
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||||
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||||
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||||
<h4>2.构建随机初始化矩阵
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||||
```buildoutcfg
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||||
x = torch.rand(5, 3)
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||||
print(x)
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||||
```
|
||||
输出结果：
|
||||
|
||||
|
||||
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||||
<h4>3.构造一个long类型的矩阵，且全为0
|
||||
```buildoutcfg
|
||||
x = torch.zeros(5, 3, dtype=torch.long)
|
||||
print(x)
|
||||
```
|
||||
输出结果：
|
||||
|
||||
|
||||
|
||||
<h4>4.创建张量，并自定义数据
|
||||
```buildoutcfg
|
||||
x = torch.tensor([5.5, 3])
|
||||
print(x)
|
||||
```
|
||||
输出结果：
|
||||
|
||||
|
||||
|
||||
<h4>5.基于已有的tensor创建新的tensor
|
||||
```buildoutcfg
|
||||
x = x.new_ones(5, 3, dtype=torch.double)
|
||||
print(x)
|
||||
|
||||
x = torch.randn_like(x, dtype=torch.float)
|
||||
# 重写了数据类型
|
||||
print(x)
|
||||
# 矩阵的大小相同
|
||||
```
|
||||
输出结果：
|
||||
|
||||
|
||||
|
||||
<h4>6.获取张量的维度
|
||||
```buildoutcfg
|
||||
print(x.size())
|
||||
```
|
||||
输出结果：
|
||||
|
||||
|
||||
|
||||
<h4>7.张量的加法运算
|
||||
<h5>(1)直接使用“+”运算符
|
||||
```buildoutcfg
|
||||
y = torch.rand(5, 3)
|
||||
print(x + y)
|
||||
```
|
||||
<h5>(2)使用add()函数
|
||||
```
|
||||
print(torch.add(x, y))
|
||||
```
|
||||
<h5>(3)提供一个输出tensor作为参数
|
||||
```buildoutcfg
|
||||
result = torch.empty(5, 3)
|
||||
torch.add(x, y, out=result)
|
||||
print(result)
|
||||
```
|
||||
<h5>(4)使用add_()函数做加法，并取代原张量
|
||||
```buildoutcfg
|
||||
# adds x to y
|
||||
y.add_(x)
|
||||
print(y)
|
||||
```
|
||||
输出结果：
|
||||
|
||||
|
||||
|
||||
注意：任何使张量发生变化的操作都有一个前缀'_'。例如：x_copy_(y),x_t()，将会改变x
|
||||
|
||||
<h4>8.张量的索引操作
|
||||
```buildoutcfg
|
||||
#切片：取tensor矩阵每行的第二个元素作为输出
|
||||
print(x[:,1])
|
||||
```
|
||||
输出结果：
|
||||
|
||||
|
||||
@ -1,5 +1,30 @@
|
||||
from __future__ import print_function
|
||||
import torch
|
||||
|
||||
x = torch.empty(5, 3)
|
||||
print(x)
|
||||
|
||||
x = torch.rand(5, 3)
|
||||
print(x)
|
||||
|
||||
x = torch.zeros(5, 3, dtype=torch.long)
|
||||
print(x)
|
||||
|
||||
x = torch.tensor([5.5, 3])
|
||||
print(x)
|
||||
|
||||
x = x.new_ones(5, 3, dtype=torch.double)
|
||||
print(x)
|
||||
x = torch.randn_like(x, dtype=torch.float)
|
||||
# 重写了数据类型
|
||||
print(x)
|
||||
# 结果大小相同
|
||||
#
|
||||
print(x.size())
|
||||
#
|
||||
y = torch.rand(5, 3)
|
||||
# #加法方式1
|
||||
print(x + y)
|
||||
# #加法方式2
|
||||
# #print(torch.add(x, y))
|
||||
#
|
||||
print(x[:,1])
|
||||
Reference in New Issue
Block a user