caffe 利用VGG训练自己的数据

写这个是因为有童鞋在跑VGG的时候遇到各种问题，供参考一下。

网络结构

以VGG16为例，自己跑的细胞数据

solver.prototxt：

net: "/media/dl/source/Experiment/cell/test/vgg/vgg16.prototxt"
test_iter:
test_interval:
base_lr: 0.0001
lr_policy: "step"
gamma: 0.1
stepsize:
display:
max_iter:
momentum: 0.9
weight_decay: 0.0005
snapshot:
snapshot_prefix: "/media/dl/source/Experiment/cell/test/vgg/vgg"
solver_mode: GPU

vgg16.prototxt：

注意，这里的数据层我是用的“ImageData”格式，也就是没有转为LMDB，直接导入图片进去的，因为我用的服务器，为了方便。如果为了更高效，还是使用LMDB数据库的形式。使用LMDB数据库形式的数据层我也写了下，放在这个prototxt后面作为补充。

另外，注意修改最后一个全连接层的num_output为自己的类别数。并修改该层的名字，如我改为了“cellfc8”，是为了finetune vgg时重新训练该层，不使用该层的预训练参数。

 name: "VGG16"
 layer {
   name: "data"
   type: "ImageData"
   top: "data"
   top: "label"
   include {
     phase: TRAIN
   }
   # transform_param {
   #   mirror: true
   #   crop_size:
   #   mean_file: "data/ilsvrc12_shrt_256/imagenet_mean.binaryproto"
   # }

   image_data_param {
     source: "/media/dl/source/Experiment/cell/data/trainnew2_resize/trainnew.txt"
     batch_size:
     shuffle:true
     #is_color: false
     new_height:
     new_width:
   }
 }
 layer {
   name: "data"
   type: "ImageData"
   top: "data"
   top: "label"
   include {
     phase: TEST
   }
   # transform_param {
   #   mirror: false
   #   crop_size:
   #   mean_file: "data/ilsvrc12_shrt_256/imagenet_mean.binaryproto"
   # }

   image_data_param {
     source: "/media/dl/source/Experiment/cell/data/val2_resize/valnew.txt"
     batch_size:
     #is_color: false
     new_height:
     new_width:
   }
 }
 layer {
   bottom: "data"
   top: "conv1_1"
   name: "conv1_1"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv1_1"
   top: "conv1_1"
   name: "relu1_1"
   type: "ReLU"
 }
 layer {
   bottom: "conv1_1"
   top: "conv1_2"
   name: "conv1_2"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv1_2"
   top: "conv1_2"
   name: "relu1_2"
   type: "ReLU"
 }
 layer {
   bottom: "conv1_2"
   top: "pool1"
   name: "pool1"
   type: "Pooling"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }
 layer {
   bottom: "pool1"
   top: "conv2_1"
   name: "conv2_1"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv2_1"
   top: "conv2_1"
   name: "relu2_1"
   type: "ReLU"
 }
 layer {
   bottom: "conv2_1"
   top: "conv2_2"
   name: "conv2_2"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv2_2"
   top: "conv2_2"
   name: "relu2_2"
   type: "ReLU"
 }
 layer {
   bottom: "conv2_2"
   top: "pool2"
   name: "pool2"
   type: "Pooling"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }
 layer {
   bottom: "pool2"
   top: "conv3_1"
   name: "conv3_1"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv3_1"
   top: "conv3_1"
   name: "relu3_1"
   type: "ReLU"
 }
 layer {
   bottom: "conv3_1"
   top: "conv3_2"
   name: "conv3_2"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv3_2"
   top: "conv3_2"
   name: "relu3_2"
   type: "ReLU"
 }
 layer {
   bottom: "conv3_2"
   top: "conv3_3"
   name: "conv3_3"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv3_3"
   top: "conv3_3"
   name: "relu3_3"
   type: "ReLU"
 }
 layer {
   bottom: "conv3_3"
   top: "pool3"
   name: "pool3"
   type: "Pooling"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }
 layer {
   bottom: "pool3"
   top: "conv4_1"
   name: "conv4_1"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv4_1"
   top: "conv4_1"
   name: "relu4_1"
   type: "ReLU"
 }
 layer {
   bottom: "conv4_1"
   top: "conv4_2"
   name: "conv4_2"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv4_2"
   top: "conv4_2"
   name: "relu4_2"
   type: "ReLU"
 }
 layer {
   bottom: "conv4_2"
   top: "conv4_3"
   name: "conv4_3"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv4_3"
   top: "conv4_3"
   name: "relu4_3"
   type: "ReLU"
 }
 layer {
   bottom: "conv4_3"
   top: "pool4"
   name: "pool4"
   type: "Pooling"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }
 layer {
   bottom: "pool4"
   top: "conv5_1"
   name: "conv5_1"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv5_1"
   top: "conv5_1"
   name: "relu5_1"
   type: "ReLU"
 }
 layer {
   bottom: "conv5_1"
   top: "conv5_2"
   name: "conv5_2"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv5_2"
   top: "conv5_2"
   name: "relu5_2"
   type: "ReLU"
 }
 layer {
   bottom: "conv5_2"
   top: "conv5_3"
   name: "conv5_3"
   type: "Convolution"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   bottom: "conv5_3"
   top: "conv5_3"
   name: "relu5_3"
   type: "ReLU"
 }
 layer {
   bottom: "conv5_3"
   top: "pool5"
   name: "pool5"
   type: "Pooling"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }
 layer {
   bottom: "pool5"
   top: "fc6"
   name: "fc6"
   type: "InnerProduct"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   inner_product_param {
     num_output:
     weight_filler {
       type: "gaussian"
       std: 0.005
     }
     bias_filler {
       type: "constant"
       value: 0.1
     }
   }
 }
 layer {
   bottom: "fc6"
   top: "fc6"
   name: "relu6"
   type: "ReLU"
 }
 layer {
   bottom: "fc6"
   top: "fc6"
   name: "drop6"
   type: "Dropout"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
 layer {
   bottom: "fc6"
   top: "fc7"
   name: "fc7"
   type: "InnerProduct"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   inner_product_param {
     num_output:
     weight_filler {
       type: "gaussian"
       std: 0.005
     }
     bias_filler {
       type: "constant"
       value: 0.1
     }
   }
 }
 layer {
   bottom: "fc7"
   top: "fc7"
   name: "relu7"
   type: "ReLU"
 }
 layer {
   bottom: "fc7"
   top: "fc7"
   name: "drop7"
   type: "Dropout"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
 layer {
   bottom: "fc7"
   top: "fc8"
   name: "cellfc8"
   type: "InnerProduct"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   inner_product_param {
     num_output: 7 #改为自己的类别数
     weight_filler {
       type: "gaussian"
       std: 0.005
     }
     bias_filler {
       type: "constant"
       value: 0.1
     }
   }
 }
 layer {
   name: "accuracy_at_1"
   type: "Accuracy"
   bottom: "fc8"
   bottom: "label"
   top: "accuracy_at_1"
   accuracy_param {
     top_k:
   }
   include {
     phase: TEST
   }
 }
 layer {
   name: "accuracy_at_5"
   type: "Accuracy"
   bottom: "fc8"
   bottom: "label"
   top: "accuracy_at_5"
   accuracy_param {
     top_k:
   }
   include {
     phase: TEST
   }
 }
 layer {
   bottom: "fc8"
   bottom: "label"
   top: "loss"
   name: "loss"
   type: "SoftmaxWithLoss"
 }

如果使用LMDB数据库形式，将前面的数据层改为：

 name: "vgg"
 layer {
   name: "data"
   type: "Data"
   top: "data"
   top: "label"
   include {
     phase: TRAIN
   }
   transform_param {
     mirror: true
     crop_size:
 #如果图片大于224，则使用crop的方式，小于则使用下面的new_height和new_width
    # new_height:
     #new_width:
     mean_file: "vggface/face_mean.binaryproto"
   }
   data_param {
     source: "vggface/face_train_lmdb"
     batch_size:
     backend: LMDB
   }
 }
 layer {
   name: "data"
   type: "Data"
   top: "data"
   top: "label"
   include {
     phase: TEST
   }
   transform_param {
     mirror: false
     crop_size:
 #如果图片大于224，则使用crop的方式，小于则使用下面的new_height和new_width
    # new_height:
     #new_width:
     mean_file: "vggface/face_mean.binaryproto"
   }
   data_param {
     source: "vggface/face_val_lmdb"
     batch_size:
     backend: LMDB
   }
 }

训练

放一个shell命令：

#!/usr/bin/env sh

TOOLS=/home/dl/caffe-jonlong/build/tools

$TOOLS/caffe train \
  -solver=/media/dl/source/Experiment/cell/test/vgg/solver.prototxt \
  -weights=/media/dl/source/Experiment/cell/test/vgg/VGG_ILSVRC_16_layers.caffemodel \
  -gpu=all \

预训练模型VGG_ILSVRC_16_layers.caffemodel的下载地址为

 http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel