4.caffe：train_val.prototxt、 solver.prototxt 、 deploy.prototxt（創建模型與編寫配置文件）

本文轉載自查看原文 2016-08-17 11:30 9358 caffe

一，train_val.prototxt

name: "CIFAR10_quick" layer { name: "cifar" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { # mirror: true # mean_file: "examples/cifar10/mean.binaryproto"uu mean_file: "myself/00b/00bmean.binaryproto" } data_param { # source: "examples/cifar10/cifar10_train_lmdb" source: "myself/00b/00b_train_lmdb" batch_size: 50 backend: LMDB } } layer { name: "cifar" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { # mean_file: "examples/cifar10/mean.binaryproto" mean_file: "myself/00b/00bmean.binaryproto" } data_param { # source: "examples/cifar10/cifar10_test_lmdb" source: "myself/00b/00b_val_lmdb" batch_size: 50 backend: LMDB } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 32 # pad: 1 kernel_size: 4 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: 2 stride: 2 } } layer { name: "relu1" type: "ReLU" bottom: "pool1" top: "pool1" } layer { 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: 4 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: 2 stride: 2 } } layer { name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 32 # pad: 2 kernel_size: 4 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: 2 stride: 2 } } layer { name: "conv4" type: "Convolution" bottom: "pool3" top: "conv4" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 32 # pad: 2 kernel_size: 4 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4" } layer { name: "pool4" type: "Pooling" bottom: "conv4" top: "pool4" pooling_param { pool: AVE kernel_size: 2 stride: 2 } } layer { name: "ip1" type: "InnerProduct" bottom: "pool4" top: "ip1" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 200 weight_filler { type: "gaussian" std: 0.1 } bias_filler { type: "constant" } } } layer { name: "ip2" type: "InnerProduct" bottom: "ip1" top: "ip2" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 3 weight_filler { type: "gaussian" std: 0.1 } bias_filler { type: "constant" } } } layer { name: "accuracy" type: "Accuracy" bottom: "ip2" bottom: "label" top: "accuracy" include { phase: TEST } } layer { name: "loss" type: "SoftmaxWithLoss" bottom: "ip2" bottom: "label" top: "loss" }

二，solver.prototxt

# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10 # The train/test net protocol buffer definition net: "myself/00b/train_val.prototxt" # test_iter specifies how many forward passes the test should carry out. # In the case of MNIST, we have test batch size 100 and 100 test iterations, # covering the full 10,000 testing images. test_iter: 10 # Carry out testing every 500 training iterations. test_interval: 70 # The base learning rate, momentum and the weight decay of the network. base_lr: 0.001 momentum: 0.9 weight_decay: 0.004 # The learning rate policy lr_policy: "fixed" # lr_policy: "step" gamma: 0.1 stepsize: 100 # Display every 100 iterations display: 10 # The maximum number of iterations max_iter: 2000 # snapshot intermediate results # snapshot: 3000 # snapshot_format: HDF5 snapshot_prefix: "myself/00b/00b" # solver mode: CPU or GPU solver_mode: CPU

三，deploy.prototxt

name: "CIFAR10_quick" layer { name: "data" type: "Input" top: "data" input_param { shape: { dim: 1 dim: 3 dim: 101 dim: 101 } } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" convolution_param { num_output: 32 kernel_size: 4 stride: 1 } } layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" } layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" convolution_param { num_output: 32 kernel_size: 4 stride: 1 } } layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2" } layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" convolution_param { num_output: 32 kernel_size: 4 stride: 1 } } layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3" } layer { name: "pool3" type: "Pooling" bottom: "conv3" top: "pool3" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv4" type: "Convolution" bottom: "pool3" top: "conv4" convolution_param { num_output: 32 kernel_size: 4 stride: 1 } } layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4" } layer { name: "pool4" type: "Pooling" bottom: "conv4" top: "pool4" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "ip1" type: "InnerProduct" bottom: "pool4" top: "ip1" inner_product_param { num_output: 200 } } layer { name: "ip2" type: "InnerProduct" bottom: "ip1" top: "ip2" inner_product_param { num_output: 3 } } layer { #name: "loss" name: "prob" type: "Softmax" bottom: "ip2" top: "prob" #top: "loss" }

參考一：

模型就用程序自帶的caffenet模型，位置在 models/bvlc_reference_caffenet/文件夾下, 將需要的兩個配置文件，復制到myfile文件夾內

# sudo cp models/bvlc_reference_caffenet/solver.prototxt examples/myfile/ # sudo cp models/bvlc_reference_caffenet/train_val.prototxt examples/myfile/

修改train_val.protxt，只需要修改兩個階段的data層就可以了，其它可以不用管。

name: "CaffeNet" layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: true crop_size: 227 mean_file: "examples/myfile/mean.binaryproto" } data_param { source: "examples/myfile/img_train_lmdb" batch_size: 256 backend: LMDB } } layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { mirror: false crop_size: 227 mean_file: "examples/myfile/mean.binaryproto" } data_param { source: "examples/myfile/img_test_lmdb" batch_size: 50 backend: LMDB } }

實際上就是修改兩個data layer的mean_file和source這兩個地方，其它都沒有變化。

修改其中的solver.prototxt

# sudo vi examples/myfile/solver.prototxt

net: "examples/myfile/train_val.prototxt" test_iter: 2 test_interval: 50 base_lr: 0.001 lr_policy: "step" gamma: 0.1 stepsize: 100 display: 20 max_iter: 500 momentum: 0.9 weight_decay: 0.005 solver_mode: GPU

100個測試數據，batch_size為50，因此test_iter設置為2，就能全cover了。在訓練過程中，調整學習率，逐步變小。

參考二：

前面做好了lmdb和均值文件，下面以Googlenet為例修改網絡並訓練模型。

我們將caffe-master\models下的bvlc_googlenet文件夾復制到caffe-master\examples\imagenet下。（因為我們的lmdb和均值都在這里，放一起方便些）

打開train_val.txt，修改：

1.修改data層：

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layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 224
mean_file: "examples/imagenet/mydata_mean.binaryproto" #均值文件
#mean_value: 104 #這些注釋掉
#mean_value: 117
#mean_value: 123
}
data_param {
source: "examples/imagenet/mydata_train_lmdb" #訓練集的lmdb
batch_size: 32 #根據GPU修改
backend: LMDB
}
}

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layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size: 224
mean_file: "examples/imagenet/mydata_mean.binaryproto" #均值文件
#mean_value: 104
#mean_value: 117
#mean_value: 123
}
data_param {
source: "examples/imagenet/mydata_val_lmdb" #驗證集lmdb
batch_size: 50 #和solver中的test_iter相乘約等於驗證集大小
backend: LMDB
}
}

2.修改輸出：

由於Googlenet有三個輸出，所以改三個地方，其他網絡一般只有一個輸出，則改一個地方即可。

如果是微調，那么輸出層的層名也要修改。（參數根據層名來初始化，由於輸出改了，該層參數就不對應了，因此要改名）

[plain]

layer {
  name: "loss1/classifier"
  type: "InnerProduct"
  bottom: "loss1/fc"
  top: "loss1/classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000 #改成你的數據集類別數
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

[plain]

layer {
  name: "loss2/classifier"
  type: "InnerProduct"
  bottom: "loss2/fc"
  top: "loss2/classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000 #改成你的數據集類別數
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

[plain]

layer {
  name: "loss3/classifier"
  type: "InnerProduct"
  bottom: "pool5/7x7_s1"
  top: "loss3/classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000 #改成你的數據集類別數
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

3.打開deploy.prototxt，修改：

[plain]

layer {
  name: "loss3/classifier"
  type: "InnerProduct"
  bottom: "pool5/7x7_s1"
  top: "loss3/classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000 #改成你的數據集類別數
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

如果是微調，該層層名和train_val.prototxt修改一致。

接着，打開solver，修改：

[plain]

net: "examples/imagenet/bvlc_googlenet/train_val.prototxt" #路徑不要錯
test_iter: 1000 #前面已說明該值
test_interval: 4000 #迭代多少次測試一次
test_initialization: false
display: 40
average_loss: 40
base_lr: 0.01
lr_policy: "step"
stepsize: 320000 #迭代多少次改變一次學習率
gamma: 0.96
max_iter: 10000000 #迭代次數
momentum: 0.9
weight_decay: 0.0002
snapshot: 40000
snapshot_prefix: "examples/imagenet/bvlc_googlenet" #生成的caffemodel保存在imagenet下，形如bvlc_googlenet_iter_***.caffemodel
solver_mode: GPU

這時，我們回到caffe-master\examples\imagenet下，打開train_caffenet.sh，修改：

(如果是微調，在腳本里加入-weights **/**/**.caffemodel即可，即用來微調的caffemodel路徑)

[plain]

#!/usr/bin/env sh

./build/tools/caffe train \
    -solver examples/imagenet/bvlc_googlenet/solver.prototxt -gpu 0

（如果有多個GPU，可自行選擇）然后，在caffe-master下執行改腳本即可開始訓練:$caffe-master ./examples/imagenet/train_caffenet.sh

訓練得到的caffemodel就可以用來做圖像分類了，此時，需要（1）得到的labels.txt，（2）得到的mydata_mean.binaryproto，（3）得到的caffemodel以及已經修改過的deploy.prototxt，共四個文件，具體過程看：http://blog.csdn.net/sinat_30071459/article/details/50974695

參考三：

_train_test.prototxt,_deploy.prototxt,*_slover.prototxt文件編寫時注意

1、*_train_test.prototxt文件

這是訓練與測試網絡配置文件

（1）在數據層中參數include{

phase：TRAIN/TEST

}

TRAIN與TEST不能有“...”否則會報錯，還好提示信息里，會提示哪一行出現了問題，如下圖：

數字8就代表配置文件的第8行出現了錯誤

（2）卷積層和全連接層相似：卷積層（Convolution），全連接層（InnerProduct，容易翻譯成內積層）相似處有兩個【1】：都有兩個param{lr_mult：1

decay_mult：1

}

param{lr_mult： 2

decay_mult: 0

}

【2】：convolution_param{}與inner_product_param{}里面的參數相似，甚至相同

今天有事，明天再續！

續上！

（3）平均值文件*_mean.binaryproto要放在transform_param{}里，訓練與測試數據集放在data_param{}里

2.*_deploy.prototxt文件

【1】*_deploy.prototxt文件的構造和*_train_test.prototxt文件的構造稍有不同首先沒有test網絡中的test模塊，只有訓練模塊

【2】數據層的寫法和原來也有不同，更加簡潔：

input: "data" input_dim: 1 input_dim: 3 input_dim: 32 input_dim: 32

注意紅色部分，那是數據層的名字，沒有這個的話，第一卷積層無法找到數據，我一開始沒有加這句就報錯。下面的四個參數有點類似batch_size（1,3,32,32）里四個參數

【3】卷積層和全連接層中weight_filler{}與bias_filler{}兩個參數不用再填寫，應為這兩個參數的值，由已經訓練好的模型*.caffemodel文件提供

【4】輸出層的變化（1）沒有了test模塊測試精度（2）輸出層

*_train_test.prototxt文件：

layer{ name: "loss" type: "SoftmaxWithLoss"#注意此處與下面的不同 bottom: "ip2" bottom: "label"#注意標簽項在下面沒有了，因為下面的預測屬於哪個標簽，因此不能提供標簽 top: "loss" }

*_deploy.prototxt文件：

layer { name: "prob" type: "Softmax" bottom: "ip2" top: "prob" }

***注意在兩個文件中輸出層的類型都發生了變化一個是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。

3、*_slover.prototxt

net: "test.prototxt" #訓練網絡的配置文件 test_iter: 100 #test_iter 指明在測試階段有多上個前向過程（也就是有多少圖片）被執行。在MNIST例子里，在網絡配置文件里已經設置test網絡的batch size=100，這里test_iter 設置為100，那在測試階段共有100*100=10000 圖片被處理 test_interval: 500 #每500次訓練迭代后，執行一次test base_lr: 0.01 #學習率初始化為0.01 momentum：0.9 #u=0.9 weight_decay:0.0005 # lr_policy: "inv" gamma: 0.0001 power: 0.75 #以上三個參數都和降低學習率有關，詳細的學習策略和計算公式見下面 // The learning rate decay policy. The currently implemented learning rate

// policies are as follows:

// - fixed: always return base_lr.

// - step: return base_lr * gamma ^ (floor(iter / step))

// - exp: return base_lr * gamma ^ iter

// - inv: return base_lr * (1 + gamma * iter) ^ (- power)

// - multistep: similar to step but it allows non uniform steps defined by

// stepvalue

// - poly: the effective learning rate follows a polynomial decay, to be

// zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)

// - sigmoid: the effective learning rate follows a sigmod decay

// return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))

// where base_lr, max_iter, gamma, step, stepvalue and power are defined

// in the solver parameter protocol buffer, and iter is the current iteration. display:100 #每100次迭代，顯示結果 snapshot: 5000 #每5000次迭代，保存一次快照 snapshot_prefix: "path_prefix" #快照保存前綴：更准確的說是快照保存路徑+前綴，應為文件名后的名字是固定的 solver_mode:GPU #選擇解算器是用cpu還是gpu

批處理文件編寫：

F:/caffe/caffe-windows-master/bin/caffe.exe train --solver=C:/Users/Administrator/Desktop/caffe_test/cifar-10/cifar10_slover_prototxt --gpu=all pause

參考四：

原

將train_val.prototxt 轉換成deploy.prototxt

分類：caffe

（282）（0）

1.刪除輸入數據（如：type:data...inckude{phase: TRAIN}），然后添加一個數據維度描述。

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input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
force_backward: true

input: "data" 
input_dim: 1 
input_dim: 3 
input_dim: 224 
input_dim: 224
force_backward: true

2.移除最后的“loss” 和“accuracy” 層，加入“prob”層。

[plain] view plain copy print ?

layers {
name: "prob"
type: SOFTMAX
bottom: "fc8"
top: "prob"
}

layers {
  name: "prob"
  type: SOFTMAX
  bottom: "fc8"
  top: "prob"
}

如果train_val文件中還有其他的預處理層，就稍微復雜點。如下，在'data'層，在‘data’層和‘conv1’層(with bottom:”data” / top:”conv1″). 插入一個層來計算輸入數據的均值。

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layer {
name: “mean”
type: “Convolution”
<strong>bottom: “data”
top: “data”</strong>
param {
lr_mult: 0
decay_mult: 0
}
…}

在deploy.prototxt文件中，“mean” 層必須保留，只是容器改變，相應的‘conv1’也要改變 ( bottom:”mean”/ top:”conv1″ )。

[plain]

layer {
name: “mean”
type: “Convolution”
<strong>bottom: “data”
top: “mean“</strong>
param {
lr_mult: 0
decay_mult: 0
}
…}

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猜您在找 Caffe中deploy.prototxt 和 train_val.prototxt 區別 caffe生成deploy.prototxt文件 caffe之solver.prototxt文件參數設置 4'.deploy.prototxt Caffe--solver.prototxt配置文件參數設置及含義 Caffe之prototxt caffe mnist實例 --lenet_train_test.prototxt 網絡配置詳解 python 解析prototxt文件梯度優化算法總結以及solver及train.prototxt中相關參數解釋利用Caffe訓練模型(solver、deploy、train_val) + python如何使用已訓練模型

4.caffe：train_val.prototxt、 solver.prototxt 、 deploy.prototxt（ 創建模型與編寫配置文件）

*_train_test.prototxt,*_deploy.prototxt,*_slover.prototxt文件編寫時注意

將train_val.prototxt 轉換成deploy.prototxt

免責聲明！

4.caffe：train_val.prototxt、 solver.prototxt 、 deploy.prototxt（創建模型與編寫配置文件）

_train_test.prototxt,_deploy.prototxt,*_slover.prototxt文件編寫時注意