我使用HDF5编写了有关多标签分类的Caffe网络，这是一个名为'auto_train.prototxt'的原型文件。
name: "multilabel_net"
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
hdf5_data_param {
source: "examples/corel5k/train.h5list"
batch_size: 50
shuffle: 1
}
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TEST
}
hdf5_data_param {
source: "examples/corel5k/test.h5list"
batch_size: 50
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 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: 3
stride: 2
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "score"
type: "InnerProduct"
bottom: "fc7"
top: "score"
inner_product_param {
num_output: 260
}
}
layer {
name: "loss"
type: "SigmoidCrossEntropyLoss"
bottom: "score"
bottom: "label"
top: "loss"
}
layer {
name: "score"
type: "InnerProduct"
bottom: "fc7"
top: "score"
inner_product_param {
num_output: 260
}
include {
phase: TEST}
}
这是train.sh
./build/tools/caffe train \
-solver examples/corel5k/auto_train.prototxt \
-weights examples/corel5k/bvlc_reference_caffenet.caffemodel
但是当我运行此脚本时，出现了问题
[libprotobuf ERROR google/protobuf/text_format.cc:245] Error parsing text-format caffe.SolverParameter: 1:5: Message type "caffe.SolverParameter" has no field named "name".
F0316 15:57:16.892113 3464 upgrade_proto.cpp:1063] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse SolverParameter file: examples/corel5k/auto_train.prototxt
*** Check failure stack trace: ***
@ 0x7f79b3a4011d google::LogMessage::Fail()
@ 0x7f79b3a41fbd google::LogMessage::SendToLog()
@ 0x7f79b3a3fd38 google::LogMessage::Flush()
@ 0x7f79b3a4281e google::LogMessageFatal::~LogMessageFatal()
@ 0x7f79b4065ee7 caffe::ReadSolverParamsFromTextFileOrDie()
@ 0x40a8c5 train()
@ 0x407544 main
@ 0x7f79b25a0ec5 (unknown)
@ 0x407615 (unknown)
Aborted (core dumped)
我不知道发生了什么，寻求帮助
最佳答案
您将网络结构定义原型（也称为train_val.prototxt）与求解器定义原型（也称为solver.prototxt）混淆了。
有关这两个不同的prototxt文件，请参见例如AlexNet example。
网络结构定义train_val.prototxt定义网络结构，看起来像您编写的auto_train.prototxt文件。
但是，您缺少为优化过程定义元参数的solver definition prototxt，solver.prototxt。
在您的情况下，solver.prototxt类似于：
net: "examples/corel5k/auto_train.prototxt" # here is where you put the net structure file
test_iter: 1000
test_interval: 1000
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 100000
display: 20
max_iter: 450000
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "examples/corel5k/my_auto_snapshots"
solver_mode: GPU
您可以在
solver.protoxt here和here中找到有关如何设置元参数的信息。一旦有了适当的
solver.prototxt，就可以运行caffe：./build/tools/caffe train \
-solver examples/corel5k/solver.prototxt \
-weights examples/corel5k/bvlc_reference_caffenet.caffemodel