caffe使用

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

巴特西

caffe使用

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