caffe使用finetume

训练时, solver.prototxt中使用的是train_val.prototxt

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

 使用上面训练的网络提取特征，使用的网络模型是deploy.prototxt

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

 Caffe finetune

1、准备finetune的数据

image文件夹子里面放好来finetune的图片

train.txt中放上finetune的训练图片绝对路径，及其对应的类别

test.txt中放上finetune的测试图片绝对路径，及其对应的类别

 

 

2、更改train_val.prototxt

更改最后一层

a)输出个数改变

b)最后一层学习率变大，由2变成20

 

3、更改solver.prototxt

a)stepsize变小：由100000变成20000

b)max_iter变小：450000变成50000

c)base_lr变小：0.01变成0.001

d)test_iter变小：1000变成100

 

4、调用命令finetune

caffe % ./build/tools/caffe train -solver models/finetune_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0

 注意：学习率有两个是一个是weight,一个是bias的学习率，一般bias的学习率是weight的两倍

　　　　decay是权值衰减，是加了正则项目，防止overfitting

 the global weight_decay multiplies the parameter-specific decay_mult

solver.prototxt具体设置解释：

 

rmsprop:
net: "examples/mnist/lenet_train_test.prototxt"
test_iter: 100
test_interval: 500
#The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01
momentum: 0.0
weight_decay: 0.0005
#The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
display: 100
max_iter: 10000
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet_rmsprop"
solver_mode: GPU
type: "RMSProp"
rms_decay: 0.98
 
 
Adam:
net: "examples/mnist/lenet_train_test.prototxt"
test_iter: 100
test_interval: 500
#All parameters are from the cited paper above
base_lr: 0.001
momentum: 0.9
momentum2: 0.999
#since Adam dynamically changes the learning rate, we set the base learning
#rate to a fixed value
lr_policy: "fixed"
display: 100
#The maximum number of iterations
max_iter: 10000
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
type: "Adam"
solver_mode: GPU
 
multistep:
net: "examples/mnist/lenet_train_test.prototxt"
test_iter: 100
test_interval: 500
#The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
#The learning rate policy
lr_policy: "multistep"
gamma: 0.9
stepvalue: 5000
stepvalue: 7000
stepvalue: 8000
stepvalue: 9000
stepvalue: 9500
# Display every 100 iterations
display: 100
#The maximum number of iterations
max_iter: 10000
#snapshot intermediate results
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet_multistep"
#solver mode: CPU or GPU
solver_mode: GPU

　　

卷积层的group参数，可以实现channel-wise的卷积操作