Caffe学习记录：Cifar-10 自定义网络训练记录

迷南。 2022-08-09 13:50 135阅读 0赞

本部分的实验主要记录调整网络的过程，并记录实验结果。——Jeremy

## 模型1 ##

## 实验结果： ##

*  Iteration 60000, loss = 0.801631
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.5287**  
    Test net output \#1: loss = 1.47174 (\* 1 = 1.47174 loss)

从实验结果可以知道，当前模型的识别效果较差，只有 **52.87%** 的识别率，同时比较Train过程中的loss和测试过程中的loss，可以知道，二者相差较大，出现过拟合现象。。

下一个模型中，我们增加一个FC层，加大网络可学习的参数个数。

--------------------

## 模型2：(增加一个FC层) ##

## 实验结果： ##

*  Iteration 60000, loss = 0.609769
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.5467**  
    Test net output \#1: loss = 1.40912 (\* 1 = 1.40912 loss)

此结果与模型1相比，其识别效果有所提升，从**52.87%** 提高到 **54.67%** 。训练阶段的loss和测试阶段的loss都有所降低。因此，下一步我们再增加2个FC层的输出个数，来看看识别效果是否会进一步提升。

--------------------

## 模型3：(增加2个FC层的的输出个数) ##

## 实验结果： ##

*  Iteration 60000, loss = 0.603354
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy =0.5423**   
    Test net output \#1: loss = 1.45162

从模型3的实验结果中，我们可以发现，其识别效果并没有得到提升，可见，单独地增加FC层的输出个数并不是一个很好的方法。

因此，我在下一步中，考虑卷积层对识别结果的影响。在模型3的基础上增加一个卷积层。

--------------------

## 模型4：(增加一个卷积层) ##

<table> 
 <thead> 
  <tr> 
   <th>层类别</th> 
   <th align="center">具体信息</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>conv1</td> 
   <td align="center">output: 32, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool1</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">Sigmoid</td> 
  </tr> 
  <tr> 
   <td>norm1</td> 
   <td align="center">LRN</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td align="center">output: 16, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>ip1</td> 
   <td align="center">400</td> 
  </tr> 
  <tr> 
   <td>ip2</td> 
   <td align="center">200</td> 
  </tr> 
  <tr> 
   <td>ip3</td> 
   <td align="center">10</td> 
  </tr> 
 </tbody> 
</table>

## 实验结果： ##

*  Iteration 60000, loss = 0.690654
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.5815**   
    Test net output \#1: loss = 1.24061

从模型4的实验结果中，我们可以发现，增加了一个卷积层后，训练阶段的loss相比于模型3，提高了0.0873.而测试阶段的loss降低了0.21101.总的来说，这是一个好的现象，它在一定程度上降低了过拟合。  
在下一个模型中，我们在第2个卷积层后面添加一个MaxPooling，同时降低FC层的输出。

--------------------

## 模型5：(增加一个max poooling) ##

<table> 
 <thead> 
  <tr> 
   <th>层类别</th> 
   <th align="center">具体信息</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>conv1</td> 
   <td align="center">output: 32, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool1</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">Sigmoid</td> 
  </tr> 
  <tr> 
   <td>norm1</td> 
   <td align="center">LRN</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td align="center">output: 16, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool2</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>ip1</td> 
   <td align="center">200</td> 
  </tr> 
  <tr> 
   <td>ip2</td> 
   <td align="center">100</td> 
  </tr> 
  <tr> 
   <td>ip3</td> 
   <td align="center">10</td> 
  </tr> 
 </tbody> 
</table>

## 实验结果： ##

*  Iteration 60000, loss = 0.73147
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.6335**  
    Test net output \#1: loss = 1.06225

增加pool层后，训练阶段和测试阶段的loss的差别，进一步变小，过拟合有所降低，不过识别效果还是不佳，因此，在下一步中我考虑在pool层后面增加一个Sigmoid层。

--------------------

## 模型6：(增加一个Sigmoid层) ##

<table> 
 <thead> 
  <tr> 
   <th>层类别</th> 
   <th align="center">具体信息</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>conv1</td> 
   <td align="center">output: 32, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool1</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">Sigmoid</td> 
  </tr> 
  <tr> 
   <td>norm1</td> 
   <td align="center">LRN</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td align="center">output: 16, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool2</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">Sigmoid</td> 
  </tr> 
  <tr> 
   <td>ip1</td> 
   <td align="center">200</td> 
  </tr> 
  <tr> 
   <td>ip2</td> 
   <td align="center">100</td> 
  </tr> 
  <tr> 
   <td>ip3</td> 
   <td align="center">10</td> 
  </tr> 
 </tbody> 
</table>

## 实验结果： ##

*  Iteration 60000, loss = 2.29934
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.1**   
    Test net output \#1: loss = 2.30292  
    可见加了sigmoid层的方法在此模型中无效。

--------------------

## 模型7：(换成ReLU层) ##

<table> 
 <thead> 
  <tr> 
   <th>层类别</th> 
   <th align="center">具体信息</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>conv1</td> 
   <td align="center">output: 32, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool1</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">Sigmoid</td> 
  </tr> 
  <tr> 
   <td>norm1</td> 
   <td align="center">LRN</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td align="center">output: 16, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool2</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>ip1</td> 
   <td align="center">200</td> 
  </tr> 
  <tr> 
   <td>ip2</td> 
   <td align="center">100</td> 
  </tr> 
  <tr> 
   <td>ip3</td> 
   <td align="center">10</td> 
  </tr> 
 </tbody> 
</table>

## 实验结果： ##

*  Iteration 60000, loss = 0.620338
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.6391**   
    Test net output \#1: loss = 1.05354

--------------------

## 模型8：(全部换成ReLU层) ##

<table> 
 <thead> 
  <tr> 
   <th>层类别</th> 
   <th align="center">具体信息</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>conv1</td> 
   <td align="center">output: 32, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool1</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>norm1</td> 
   <td align="center">LRN</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td align="center">output: 16, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool2</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>ip1</td> 
   <td align="center">200</td> 
  </tr> 
  <tr> 
   <td>ip2</td> 
   <td align="center">100</td> 
  </tr> 
  <tr> 
   <td>ip3</td> 
   <td align="center">10</td> 
  </tr> 
 </tbody> 
</table>

## 实验结果： ##

*  Iteration 60000, loss = 0.416507
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.6794**   
    Test net output \#1: loss = 1.15119

在两个卷积层后面加上ReLU层后，识别效果提升了较多，识别率为**67.94%**。

--------------------

## 模型9：(加一个Dropout层) ##

<table> 
 <thead> 
  <tr> 
   <th>层类别</th> 
   <th align="center">具体信息</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>conv1</td> 
   <td align="center">output: 32, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool1</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>norm1</td> 
   <td align="center">LRN</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td align="center">output: 16, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool2</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>ip1</td> 
   <td align="center">200</td> 
  </tr> 
  <tr> 
   <td>dropout</td> 
   <td align="center">dropout, 0.5</td> 
  </tr> 
  <tr> 
   <td>ip2</td> 
   <td align="center">100</td> 
  </tr> 
  <tr> 
   <td>ip3</td> 
   <td align="center">10</td> 
  </tr> 
 </tbody> 
</table>

## 实验结果： ##

*  Iteration 60000, loss = 0.563472
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.6728**   
    Test net output \#1: loss = 1.03333  
    从实验结果可以知道，加了Dropout层之后，虽然没有提高识别效果，但是降低了过拟合。因此，下一步增加FC层的输出看看。

--------------------

## 模型10：(增加FC层的输出个数) ##

<table> 
 <thead> 
  <tr> 
   <th>层类别</th> 
   <th align="center">具体信息</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>conv1</td> 
   <td align="center">output: 32, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool1</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>norm1</td> 
   <td align="center">LRN</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td align="center">output: 16, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool2</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>ip1</td> 
   <td align="center">400</td> 
  </tr> 
  <tr> 
   <td>dropout</td> 
   <td align="center">dropout, 0.5</td> 
  </tr> 
  <tr> 
   <td>ip2</td> 
   <td align="center">150</td> 
  </tr> 
  <tr> 
   <td>ip3</td> 
   <td align="center">10</td> 
  </tr> 
 </tbody> 
</table>

## 实验结果： ##

*  Iteration 60000, loss = 0.446714
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.6903**   
    Test net output \#1: loss = 0.990431

--------------------

## 模型11：(再增加一个Dropout) ##

<table> 
 <thead> 
  <tr> 
   <th>层类别</th> 
   <th align="center">具体信息</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>conv1</td> 
   <td align="center">output: 32, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool1</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>norm1</td> 
   <td align="center">LRN</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td align="center">output: 16, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool2</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>ip1</td> 
   <td align="center">400</td> 
  </tr> 
  <tr> 
   <td>dropout</td> 
   <td align="center">dropout, 0.5</td> 
  </tr> 
  <tr> 
   <td>ip2</td> 
   <td align="center">200</td> 
  </tr> 
  <tr> 
   <td>dropout</td> 
   <td align="center">dropout, 0.5</td> 
  </tr> 
  <tr> 
   <td>ip3</td> 
   <td align="center">10</td> 
  </tr> 
 </tbody> 
</table>

## 实验结果： ##

*  Iteration 60000, loss = 0.586936
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.7013**   
    Test net output \#1: loss = 0.92605

--------------------

## 模型12：(调整卷积层的输出) ##

<table> 
 <thead> 
  <tr> 
   <th>层类别</th> 
   <th align="center">具体信息</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>conv1</td> 
   <td align="center">output: 48, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool1</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>norm1</td> 
   <td align="center">LRN</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td align="center">output: 32, kernel: 5, stride: 1 pad: 2</td> 
  </tr> 
  <tr> 
   <td>pool2</td> 
   <td align="center">pool: MAX, kernel: 3, stride: 2</td> 
  </tr> 
  <tr> 
   <td>relu1</td> 
   <td align="center">ReLU</td> 
  </tr> 
  <tr> 
   <td>ip1</td> 
   <td align="center">400</td> 
  </tr> 
  <tr> 
   <td>dropout</td> 
   <td align="center">dropout, 0.5</td> 
  </tr> 
  <tr> 
   <td>ip2</td> 
   <td align="center">200</td> 
  </tr> 
  <tr> 
   <td>dropout</td> 
   <td align="center">dropout, 0.5</td> 
  </tr> 
  <tr> 
   <td>ip3</td> 
   <td align="center">10</td> 
  </tr> 
 </tbody> 
</table>

## 实验结果： ##

*  Iteration 60000, loss = 0.273988
 *  Iteration 60000, Testing net (\#0)  
    Test net output \#0: **accuracy = 0.7088**   
    Test net output \#1: loss = 1.1117

本文地址：[http://blog.csdn.net/linj\_m/article/details/49428601][http_blog.csdn.net_linj_m_article_details_49428601]

[http_blog.csdn.net_linj_m_article_details_49428601]: http://blog.csdn.net/linj_m/article/details/49428601