Caffe部署中的幾個train-test-solver-prototxt-deploy等說明<三>

2016-10-13 14:16?147人閱讀?評論(0)?收藏?舉報 ?分類： caffe（18）?

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，所以在訓練時最后一層是損失函數層（LOSS），

在測試時我們通過準確率來評價該網絡的優劣，因此最后一層是準確率層（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，對于分類問題，我們需要獲得分類結果，如下右圖最后一層我們得到

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$CAFFE_ROOT/Python/draw_net.py繪制$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后使用時的網絡結構。

?

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

[plain]?view plaincopy

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

使用上面訓練的網絡提取特征，使用的網絡模型是deploy.prototxt

[plain]?view plaincopy

./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。另外為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。
[plain]?view plaincopy

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??修改為??prob??

???type:?"SoftmaxWithLoss"?????????????#?SoftmaxWithLoss?修改為?softmax??

???bottom:?"ip2"??

???bottom:?"label"??????????#去掉??

??top:?"loss"??

?}??

總結

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