Caffe部署中的幾個train-test-solver-prototxt-deploy等說明

轉載地址： http://blog.csdn.net/lg1259156776/article/details/52550865

1:神經網絡中，我們通過最小化神經網絡來訓練網絡，所以在訓練時最後一層是損失函數層（LOSS），

在測試時我們通過準確率來評價該網絡的優劣，因此最後一層是準確率層（ACCURACY）。

但是當我們真正要使用訓練好的數據時，我們需要的是網絡給我們輸入結果，對於分類問題，我們需要獲得分類結果，如下右圖最後一層我們得到

的是概率，我們不需要訓練及測試階段的LOSS，ACCURACY層了。

下圖是能過$CAFFE_ROOT/Python/draw_net.py繪製$CAFFE_ROOT/models/caffe_reference_caffnet/train_val.prototxt   , $CAFFE_ROOT/models/caffe_reference_caffnet/deploy.prototxt，分別代表訓練時與最後使用時的網絡結構。

 

我們一般將train與test放在同一個.prototxt中，需要在data層輸入數據的source，

而在使用時.prototxt只需要定義輸入圖片的大小通道數據參數即可，如下圖所示，分別是

$CAFFE_ROOT/models/caffe_reference_caffnet/train_val.prototxt   , $CAFFE_ROOT/models/caffe_reference_caffnet/deploy.prototxt的data層

訓練時, solver.prototxt中使用的是rain_val.prototxt

./build/tools/caffe/train -solver ./models/bvlc_reference_caffenet/solver.prototxt

使用上面訓練的網絡提取特徵，使用的網絡模型是deploy.prototxt

./build/tools/extract_features.bin models/bvlc_refrence_caffenet.caffemodel models/bvlc_refrence_caffenet/deploy.prototxt

2：

*_train_test.prototxt文件：這是訓練與測試網絡配置文件

*_deploy.prototxt文件：這是模型構造文件
deploy.prototxt文件書寫：
注意在輸出層的類型發生了變化一個是SoftmaxWithLoss，另一個是Softmax。另外爲了方便區分訓練與應用輸出，訓練是輸出時是loss，應用時是prob。

deploy.prototxt文件代碼
 
name: "CIFAR10_quick"
 layer {               #該層去掉
  name: "cifar"
   type: "Data"
   top: "data"
   top: "label"
   include {
     phase: TRAIN
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"
   }
   data_param {
     source: "examples/cifar10/cifar10_train_lmdb"
     batch_size: 100
     backend: LMDB
   }
 }
 layer {             #該層去掉
  name: "cifar"
   type: "Data"
   top: "data"
   top: "label"
   include {
     phase: TEST
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"
   }
   data_param {
     source: "examples/cifar10/cifar10_test_lmdb"
     batch_size: 100
     backend: LMDB
   }
 }
 layer {                        #將下方的weight_filler、bias_filler全部刪除
  name: "conv1"
   type: "Convolution"
   bottom: "data"
   top: "conv1"
   param {
     lr_mult: 1
   }
   param {
     lr_mult: 2
   }
   convolution_param {
     num_output: 32
     pad: 2
     kernel_size: 5
     stride: 1
     weight_filler {
       type: "gaussian"
       std: 0.0001
     }
     bias_filler {
       type: "constant"
     }
   }
 }
 layer {
   name: "pool1"
   type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
 layer {
   name: "relu1"
   type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
 layer {                         #weight_filler、bias_filler刪除
  name: "conv2"
   type: "Convolution"
   bottom: "pool1"
   top: "conv2"
   param {
     lr_mult: 1
   }
   param {
     lr_mult: 2
   }
   convolution_param {
     num_output: 32
     pad: 2
     kernel_size: 5
     stride: 1
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
     }
   }
 }
 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
   }
 }
 layer {                         #weight_filler、bias_filler刪除
  name: "conv3"
   type: "Convolution"
   bottom: "pool2"
   top: "conv3"
   param {
     lr_mult: 1
   }
   param {
     lr_mult: 2
   }
   convolution_param {
     num_output: 64
     pad: 2
     kernel_size: 5
     stride: 1
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
     }
   }
 }
 layer {
   name: "relu3"
   type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
 layer {
   name: "pool3"
   type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
     pool: AVE
     kernel_size: 3
     stride: 2
   }
 }
 layer {                       #weight_filler、bias_filler刪除
  name: "ip1"
   type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
   param {
     lr_mult: 1
   }
   param {
     lr_mult: 2
   }
   inner_product_param {
     num_output: 64
     weight_filler {
       type: "gaussian"
       std: 0.1
     }
     bias_filler {
       type: "constant"
     }
   }
 }
 layer {                              # weight_filler、bias_filler刪除
  name: "ip2"
   type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
   param {
     lr_mult: 1
   }
   param {
     lr_mult: 2
   }
   inner_product_param {
     num_output: 10
     weight_filler {
       type: "gaussian"
       std: 0.1
     }
     bias_filler {
       type: "constant"
     }
   }
 }
 layer {                                  #將該層刪除
  name: "accuracy"
   type: "Accuracy"
   bottom: "ip2"
   bottom: "label"
   top: "accuracy"
   include {
     phase: TEST
   }
 }
 layer {                                 #修改
  name: "loss"       #---loss  修改爲  prob
   type: "SoftmaxWithLoss"             # SoftmaxWithLoss 修改爲 softmax
   bottom: "ip2"
   bottom: "label"          #去掉
  top: "loss"
 }