<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[SpringQAQ的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/SpringQAQ</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; SpringQAQ]]></copyright><item><title><![CDATA[通过SingleThreadExecutor源码窥探面向对象设计模式]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/95383293</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/95383293</guid><author>SpringQAQ</author><pubDate>Wed, 10 Jul 2019 21:33:34 +0800</pubDate><description><![CDATA[本文4300+字，实际文字大概300，建议阅读时间10分钟

我们知道Java预留了四种多线程池
其中一种为SingleThreadExecutor，如同其字面意思就是单线程池,看一下源码:
源码来自Executors类中静态方法
public static ExecutorService newSingleThreadExecutor() {
        return new Finali...]]></description><category></category></item><item><title><![CDATA[Mybatis框架下Sql语句在java代码中查询不到结果]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/89889344</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/89889344</guid><author>SpringQAQ</author><pubDate>Mon, 06 May 2019 19:17:46 +0800</pubDate><description><![CDATA[Mybatis框架下Sql语句在java代码中查询不到结果

如题所示

贴出log信息


Creating a new SqlSession
SqlSession [org.apache.ibatis.session.defaults.DefaultSqlSession@7804b036] was not registered for synchronization because synch...]]></description><category></category></item><item><title><![CDATA[SpringBoot乱码]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/89889160</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/89889160</guid><author>SpringQAQ</author><pubDate>Mon, 06 May 2019 19:04:28 +0800</pubDate><description><![CDATA[1.接受表单乱码

添加配置类


import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.http.converter.HttpMessageConverter;
impor...]]></description><category></category></item><item><title><![CDATA[Bp网络(回归)Numpy]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81590258</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81590258</guid><author>SpringQAQ</author><pubDate>Sat, 11 Aug 2018 19:54:43 +0800</pubDate><description><![CDATA[
import numpy as np
import matplotlib.pyplot as plt

INPUT_SIZE = 2
HIDDEN_SIZE = 4
OUTPUT_SIZE = 2
LEARNING_RATE = 0.000001

def get_weights(last, next):
    return np.random.rand(last, next)


def g...]]></description><category></category></item><item><title><![CDATA[反向传播算法笔记：BackpropgationAlgorithm]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81568374</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81568374</guid><author>SpringQAQ</author><pubDate>Fri, 10 Aug 2018 18:49:47 +0800</pubDate><description><![CDATA[

]]></description><category></category></item><item><title><![CDATA[CNN 卷积神经网络实践（基于Mnist数据集）]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81461703</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81461703</guid><author>SpringQAQ</author><pubDate>Mon, 06 Aug 2018 21:30:22 +0800</pubDate><description><![CDATA[
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
batch_size = 50

# MNIST dataset...]]></description><category></category></item><item><title><![CDATA[LSTM 循环神经网络实践（基于Mnist数据集）]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81461576</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81461576</guid><author>SpringQAQ</author><pubDate>Mon, 06 Aug 2018 21:24:36 +0800</pubDate><description><![CDATA[
import torch
from torch import nn
import torchvision.datasets as dsets
import torch.utils.data as Data
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

...]]></description><category></category></item><item><title><![CDATA[numpy 形状]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269446</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269446</guid><author>SpringQAQ</author><pubDate>Sun, 29 Jul 2018 11:32:18 +0800</pubDate><description><![CDATA[
import numpy as np
from numpy import *

a = arange(6)
# shape属性：改变自身形状。-1自适应
print(a)
a.shape = 2, -1
print(a)
# reshape方法： 返回一个指定形状的数组，不改变自身
display = a.reshape(3, -1)
print(a)
print(display)

# 使用n...]]></description><category></category></item><item><title><![CDATA[numpy数组排序]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269442</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269442</guid><author>SpringQAQ</author><pubDate>Sun, 29 Jul 2018 11:31:51 +0800</pubDate><description><![CDATA[
from numpy import *
import numpy as np

# numpy数组排序
# 先看一个例子输入姓名和值 输出姓名根据值大小排序
names = array(['spring', 'oko', 'james', 'cisco'])
weights = array([20.8, 3.0, 40.2, 99])
# argsort 反回由小到大的索引array list
...]]></description><category></category></item><item><title><![CDATA[常用计算方法]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269438</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269438</guid><author>SpringQAQ</author><pubDate>Sun, 29 Jul 2018 11:31:12 +0800</pubDate><description><![CDATA[
from numpy import *
# 常用计算方法
a = arange(1, 10)
a.shape = 3, -1
&quot;&quot;&quot;
[[1 2 3]
 [4 5 6]
 [7 8 9]]
&quot;&quot;&quot;
print(a)
# 对所有元素求和
display = sum(a)  # 45
print(display)
# 对指定维度求和
display = sum(a, axis=0)  # 沿着第一维...]]></description><category></category></item><item><title><![CDATA[numpy数据类型]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269432</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269432</guid><author>SpringQAQ</author><pubDate>Sun, 29 Jul 2018 11:30:56 +0800</pubDate><description><![CDATA[
from numpy import *
# numpy数据类型
# 复数数组
a = array([1 + 1j, 2, 3, 4])
# 查看类型 complex128
print(a.dtype)
# 查看实部 [1. 2. 3. 4.]
print(a.real)
# 查看虚部 [1. 0. 0. 0.]
print(a.imag)
# 改变虚部的值
a.imag = [1, 2, 3, ...]]></description><category></category></item><item><title><![CDATA[numpy数组]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269409</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269409</guid><author>SpringQAQ</author><pubDate>Sun, 29 Jul 2018 11:29:16 +0800</pubDate><description><![CDATA[
from numpy import *
import numpy as np
# numpy数组
lst = [0, 1, 2, 3]
a = array(lst)
print(a)
# a 的数据类型
print(type(a))
# 元素类型
print(a.dtype)
# 每个元素所占空间大小
print(a.itemsize)
# 改变a的形状
a.shape = 2, -1
prin...]]></description><category></category></item><item><title><![CDATA[matplotlib 作图]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269405</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269405</guid><author>SpringQAQ</author><pubDate>Sun, 29 Jul 2018 11:28:53 +0800</pubDate><description><![CDATA[
import numpy as np
import matplotlib.pyplot as plt
# matplotlib 作图
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
plt.plot(x, y)

plt.show()
plt.plot(x, np.sin(x), 'r-', x, np.sin(2 * x), 'g+')
plt...]]></description><category></category></item><item><title><![CDATA[# numpy预览]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269395</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/81269395</guid><author>SpringQAQ</author><pubDate>Sun, 29 Jul 2018 11:28:28 +0800</pubDate><description><![CDATA[
import numpy as np
import matplotlib.pyplot as plt
# numpy预览
a = [1, 2, 3, 4]
print(a)
a = np.array(a)

print(a)
a = a + 1
print(a)
a = a + a
print(a)
a = np.array([1, 2, 1, 4])
b = np.array([2, 3, 4...]]></description><category></category></item><item><title><![CDATA[Pytorch 实验（一） Minist手写数据集测试 1]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/80959321</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/80959321</guid><author>SpringQAQ</author><pubDate>Sun, 08 Jul 2018 14:56:33 +0800</pubDate><description><![CDATA[Minist手写数据集测试（个人实践笔记，如有纰漏，烦请指出）        import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms

input_size = 28 * 28 # image size of MNIST d...]]></description><category></category></item><item><title><![CDATA[Pytorch (二) Regression 回归]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/80955196</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/80955196</guid><author>SpringQAQ</author><pubDate>Sat, 07 Jul 2018 22:41:46 +0800</pubDate><description><![CDATA[       Regression就我现在学到的知识而言，我所目前知道的问题分两类，一类是回归分析，另一类是分类。初学者水平较低，仅为个人学习笔记，如有纰漏，请大佬们指出～今天所学的就是回归 Regression。1.搭建神经网络 2.定义损失函数 3.训练网络 先贴出代码import torch 
import torch.nn.functional as F #import activatio...]]></description><category></category></item><item><title><![CDATA[Pytorch (一) 杂七杂八整理]]></title><link>https://blog.youkuaiyun.com/SpringQAQ/article/details/80911097</link><guid>https://blog.youkuaiyun.com/SpringQAQ/article/details/80911097</guid><author>SpringQAQ</author><pubDate>Sat, 07 Jul 2018 15:13:47 +0800</pubDate><description><![CDATA[        tensor 包含有 requires_grad属性，意为计算梯度值 设置为True时，会计算该张量的梯度，相反False不会计算梯度。通过调用反向传播方法：.backward(),计算梯度值，并且积累到.grad属性上，所以在每次反向传播时，一般需要把梯度置零，.zero_grad()方法。实例：（请注意各个方法应该是由谁调用）# optimizer 是优化器
optimizer...]]></description><category></category></item></channel></rss>