如何理解Cifar10预测输出？ [英] How to understand the Cifar10 prediction output?

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问题描述

我已经训练了 Cifar10 （ caffe ）模型为两类分类。行人和非行人。培训看起来不错，我已经在 caffemodel 文件中更新权重。我使用两个标签1为行人和2为非行人，以及行人（64×160）和背景图像（64×160）的图像。
训练后，我用正图像（行人图像）和负像（背景图像）测试。我的测试 prototxt 文件如下所示

  name：CIFAR10_quick_test 
 layers 
 {
 name：data
 type：MEMORY_DATA 
 top：data
 top：label
 memory_data_param 
 {
 batch_size：1 
 channels：3 
 height：160 
 width：64 
} 
 transform_param 
 {
 crop_size：64 
 mirror：false 
 mean_file：../../examples/cifar10/mean.binaryproto
} 
} 
 layers {
 name：conv1
 type：CONVOLUTION 
 bottom：data
 top：conv1
 blobs_lr：1 
 blobs_lr：2 
 convolution_param {
 num_output：32 
 pad：2 
 kernel_size：5 
 stride：1 
} 
} 
 layers {
 name：pool1
 type：POOLING 
 bottom：conv1
 top：pool1
 pooling_param {
 pool：MAX 
 kernel_size：3 
 stride：2 
} 
} 
 layers {
 name：relu1
 type：RELU 
 bottom：pool1
 top：pool1
} 
 layers {
 name：conv2
 type：CONVOLUTION 
 bottom：pool1
 top： conv2
 blobs_lr：1 
 blobs_lr：2 
 convolution_param {
 num_output：32 
 pad：2 
 kernel_size：5 
 stride：1 
} 
} 
 layer {
 name：relu2
 type：RELU 
 bottom：conv2
 top：conv2 
} 
 layer {
 name：pool2
 type：POOLING 
 bottom：conv2
 top：pool2
 pooling_param {
 pool：AVE 
 kernel_size：3 
 stride：2 
} 
} 
 layers {
 name：conv3
 type：CONVOLUTION 
 bottom：pool2
 top：conv3
 blobs_lr：1 
 blobs_lr：2 
 convolution_param {
 num_output：64 
 pad：2 
 kernel_size：5 
 stride：1 
} 
} 
 layers {
 name：relu3
 type ：RELU 
 bottom：conv3
 top：conv3
} 
 layers {
 name：pool3
 type：POOLING 
 bottom：conv3
 top：pool3
 pooling_param {
 pool：AVE 
 kernel_size：3 
 stride：2 
} $ b b} 
 layers {
 name：ip1
 type：INNER_PRODUCT 
 bottom：pool3
 top：ip1
 blobs_lr：1 
 blobs_lr：2 
 inner_product_param {
 num_output：64 
} 
} 
 layers {
 name：ip2
 type： INNER_PRODUCT 
 bottom：ip1
 top：ip2
 blobs_lr：1 
 blobs_lr：2 
 inner_product_param {
 num_output：10 
 } 
} 
 layers {
 name：prob
 type：SOFTMAX 
 bottom：ip2
 top：prob
 }

对于测试，我使用 test_predict_imagenet.cpp 并对路径和图片大小做了一些修改。



我无法弄清楚测试输出。当我用正图像测试时，我得到输出为

  I0813 01：55：30.378114 7668 test_predict_cifarnet.cpp：72] 1 
 I0813 01：55：30.379082 7668 test_predict_cifarnet.cpp：72] 3.90971e-007 
 I0813 01：55：30.381088 7668 test_predict_cifarnet.cpp：72] 0.00406029 
 I0813 01：55：30.383090 7668 test_predict_cifarnet.cpp：72] 0.995887 
 I0813 01：55：30.384119 7668 test_predict_cifarnet.cpp：72] 1.96203e-006 
 I0813 01：55：30.385095 7668 test_predict_cifarnet.cpp：72] 3.50333e-005 
 I0813 01：55：30.386119 7668 test_predict_cifarnet.cpp：72] 1.2796e-008 
 I0813 01：55：30.387097 7668 test_predict_cifarnet.cpp：72] 1.48836e-005 
 I0813 01:55： 30.389093 7668 test_predict_cifarnet.cpp：72] 1.12237e-007 
 I0813 01：55：30.390100 7668 test_predict_cifarnet.cpp：72] 4.71238e-008 
 I0813 01：55：30.391101 7668 test_predict_cifarnet.cpp：72] 9.04134e-008

当我用负片测试时，得到的输出为

  I0813 01：53：40.896139 10856 test_predict_cifarnet.cpp：72] 1 
 I0813 01：53：40.897117 10856 test_predict_cifarnet.cpp：72 ] 6.20882e-006 
 I0813 01：53：40.898115 10856 test_predict_cifarnet.cpp：72] 7.10468e-005 
 I0813 01：53：40.900184 10856 test_predict_cifarnet.cpp：72] 0.999911 
 I0813 01 ：53：40.901185 10856 test_predict_cifarnet.cpp：72] 3.4275e-006 
 I0813 01：53：40.902189 10856 test_predict_cifarnet.cpp：72] 2.38526e-007 
 I0813 01：53：40.903192 10856 test_predict_cifarnet.cpp ：72] 2.29073e-007 
 I0813 01：53：40.905187 10856 test_predict_cifarnet.cpp：72] 1.7243e-006 
 I0813 01：53：40.906188 10856 test_predict_cifarnet.cpp：72] 5.40765e-007 
 I0813 01：53：40.908195 10856 test_predict_cifarnet.cpp：72] 1.57534e-006 
 I0813 01：53：40.909195 10856 test_predict_cifarnet.cpp：72] 3.72312e-006 
  
 
 
 
如何理解测试输出？
 
 
 
有没有更有效的测试算法 
 
解决方案
为什么你有 num_output：10 最后一层 ip2 ？你只需要双向分类器？您为什么要使用标签1和2而不是0和1？
 
 
 
 您获得：您有11个输出： c $ c>label从数据层输出，其他10个输出是softmax层的10向量输出。不清楚10向量的值是什么，因为你只使用两个标签进行训练，因此10个条目中有8个没有被监督。此外，从第一个输出判断，似乎两个测试都是标签 1 而不是 2 的样本。 
 
 
 您应该做什么： 
 
 1.将最上层完全连接的图层更改为只有两个输出（我也将格式更改为匹配新版本protobuff）
  layer {
 name：ip2 / pedestrains
 type： InnerProduct
 bottom：ip1
 top：ip2
 param {
 lr_mult：1 
 decay_mult：1 
} 
 {
 lr_mult：2 
 decay_mult：0 
} 
 inner_product_param {
 num_output：2＃这是你需要更改的
} 
} 
  
 2。将训练数据中的二进制标签更改为0/1而不是1/2。 
 
 
 
现在你可以再次训练，看看你得到了什么。
 
I have trained Cifar10(caffe) model for two classes classification. Pedestrian and non-pedestrian. Training looks fine, I have updated weights in a caffemodel file. I used two labels 1 for pedestrians and 2 for non-pedestrians, together with images for pedestrians (64 x 160) and background images (64 x 160).
After training, I do testing with positive image(pedestrian image) and negative image (background image). My testing prototxt file is as shown below
name: "CIFAR10_quick_test"
layers 
{
  name: "data"
  type: MEMORY_DATA
  top: "data"
  top: "label"
  memory_data_param 
  {
    batch_size: 1
    channels: 3
    height: 160
    width: 64
  }
  transform_param 
  {
    crop_size: 64
    mirror: false
    mean_file: "../../examples/cifar10/mean.binaryproto"
  }
}
layers {
  name: "conv1"
  type: CONVOLUTION
  bottom: "data"
  top: "conv1"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layers {
  name: "pool1"
  type: POOLING
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "relu1"
  type: RELU
  bottom: "pool1"
  top: "pool1"
}
layers {
  name: "conv2"
  type: CONVOLUTION
  bottom: "pool1"
  top: "conv2"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layers {
  name: "relu2"
  type: RELU
  bottom: "conv2"
  top: "conv2"
}
layers {
  name: "pool2"
  type: POOLING
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "conv3"
  type: CONVOLUTION
  bottom: "pool2"
  top: "conv3"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layers {
  name: "relu3"
  type: RELU
  bottom: "conv3"
  top: "conv3"
}
layers {
  name: "pool3"
  type: POOLING
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "ip1"
  type: INNER_PRODUCT
  bottom: "pool3"
  top: "ip1"
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 64
  }
}
layers {
  name: "ip2"
  type: INNER_PRODUCT
  bottom: "ip1"
  top: "ip2"
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 10
  }
}
layers {
  name: "prob"
  type: SOFTMAX
  bottom: "ip2"
  top: "prob"
}

For testing, I used test_predict_imagenet.cpp and did some modifications especially for paths and image size.



I can't figure out the test output. When I test with positive image, I got the output as
I0813 01:55:30.378114  7668 test_predict_cifarnet.cpp:72] 1
I0813 01:55:30.379082  7668 test_predict_cifarnet.cpp:72] 3.90971e-007
I0813 01:55:30.381088  7668 test_predict_cifarnet.cpp:72] 0.00406029
I0813 01:55:30.383090  7668 test_predict_cifarnet.cpp:72] 0.995887
I0813 01:55:30.384119  7668 test_predict_cifarnet.cpp:72] 1.96203e-006
I0813 01:55:30.385095  7668 test_predict_cifarnet.cpp:72] 3.50333e-005
I0813 01:55:30.386119  7668 test_predict_cifarnet.cpp:72] 1.2796e-008
I0813 01:55:30.387097  7668 test_predict_cifarnet.cpp:72] 1.48836e-005
I0813 01:55:30.389093  7668 test_predict_cifarnet.cpp:72] 1.12237e-007
I0813 01:55:30.390100  7668 test_predict_cifarnet.cpp:72] 4.71238e-008
I0813 01:55:30.391101  7668 test_predict_cifarnet.cpp:72] 9.04134e-008

When I test with a negative image, I got the output as
I0813 01:53:40.896139 10856 test_predict_cifarnet.cpp:72] 1
I0813 01:53:40.897117 10856 test_predict_cifarnet.cpp:72] 6.20882e-006
I0813 01:53:40.898115 10856 test_predict_cifarnet.cpp:72] 7.10468e-005
I0813 01:53:40.900184 10856 test_predict_cifarnet.cpp:72] 0.999911
I0813 01:53:40.901185 10856 test_predict_cifarnet.cpp:72] 3.4275e-006
I0813 01:53:40.902189 10856 test_predict_cifarnet.cpp:72] 2.38526e-007
I0813 01:53:40.903192 10856 test_predict_cifarnet.cpp:72] 2.29073e-007
I0813 01:53:40.905187 10856 test_predict_cifarnet.cpp:72] 1.7243e-006
I0813 01:53:40.906188 10856 test_predict_cifarnet.cpp:72] 5.40765e-007
I0813 01:53:40.908195 10856 test_predict_cifarnet.cpp:72] 1.57534e-006
I0813 01:53:40.909195 10856 test_predict_cifarnet.cpp:72] 3.72312e-006

How to understand the testing output?



Is there any more effecient testing algorithm for testing the model from video feed (frame by frame from video clip)?
 解决方案 
Why do you have num_output: 10 for the last layer ip2? you only need 2-way classifier? Why are you using labels 1 and 2 instead of 0 and 1?



What you got: You have 11 outputs: one is the "label" output from the data layer, and the other 10 outputs are the 10-vector output of the softmax layer. It is unclear what the values of the 10-vector are since you only trained using two labels, thus 8 out of 10 entries were not supervised at all. Moreover, judging by the first output it seems both tests were samples with label 1 and not 2.



What you should do:

1. Change the topmost fully connected layer to have only two outputs (I also changed the format to match the new version protobuff)
layer {
  name: "ip2/pedestrains"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2 # This is what you need changing
  }
}

2. Change the binary labels in your training data to 0/1 rather that 1/2.  



Now you can train again and see what you get.


                        
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