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第二章：Tensor

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dhadiudhj
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wyy4github
2019-05-20 10:50:51 +08:00
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Tensor.md
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@ -22,7 +22,6 @@ x = torch.empty(5, 3)
print(x)
```
输出结果：
![](Image/输出01.PNG)
<h4>2.构建随机初始化矩阵
@ -31,7 +30,6 @@ x = torch.rand(5, 3)
print(x)
```
输出结果：
![](Image/输出02.PNG)
<h4>3.构造一个long类型的矩阵，且全为0
@ -40,7 +38,6 @@ x = torch.zeros(5, 3, dtype=torch.long)
print(x)
```
输出结果：
![](Image/输出03.PNG)
<h4>4.创建张量，并自定义数据
@ -49,7 +46,6 @@ x = torch.tensor([5.5, 3])
print(x)
```
输出结果：
![](Image/输出04.PNG)
<h4>5.基于已有的tensor创建新的tensor
@ -63,7 +59,6 @@ print(x)
# 矩阵的大小相同
```
输出结果：
![](Image/输出05.PNG)
<h4>6.获取张量的维度
@ -71,7 +66,6 @@ print(x)
print(x.size())
```
输出结果：
![](Image/输出06.PNG)
<h4>7.张量的加法运算
@ -97,7 +91,6 @@ y.add_(x)
print(y)
```
输出结果：
![](Image/输出07.PNG)
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
@ -110,3 +103,22 @@ print(x[:,1])
输出结果：
![](./Image/输出08.PNG)
<h4>8.view()函数改变tensor的大小或形状
```buildoutcfg
x = torch.randn(4, 4)
y = x.view(16)
z = x.view(-1, 8) # the size -1 is inferred from other dimensions
print(x.size(), y.size(), z.size())
```
输出结果：
![](Image/输出09.PNG)
<h4>9.item()函数获取tensor的value值
```buildoutcfg
x = torch.randn(1)
print(x)
print(x.item())
```
输出结果：
![](Image/输出10.PNG)

59
tensorTest.py
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@ -1,30 +1,39 @@
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
# x = torch.empty(5, 3)
# print(x)
#
print(x.size())
# x = torch.rand(5, 3)
# print(x)
#
y = torch.rand(5, 3)
# #加法方式1
print(x + y)
# #加法方式2
# #print(torch.add(x, y))
# x = torch.zeros(5, 3, dtype=torch.long)
# print(x)
#
print(x[:,1])
# 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])
# x = torch.randn(4, 4)
# y = x.view(16)
# z = x.view(-1, 8) # the size -1 is inferred from other dimensions
# print(x.size(), y.size(), z.size())
x = torch.randn(1)
print(x)
print(x.item())