caffe 学习笔记之solver层_综合

caffe的protobuf中去掉注释和删除的部分的solver的定义为：

message SolverParameter {optional string net = 24;//net路径optional NetParameter net_param = 25;optional string train_net = 1; repeated string test_net = 2;optional NetParameter train_net_param = 21; repeated NetParameter test_net_param = 22; optional NetState train_state = 26;repeated NetState test_state = 27;repeated int32 test_iter = 3;//test时取batch_size的图片进行测试的次数，取的总图片数num=test_iter*batch_size,通常要与测试的图片数量有关optional int32 test_interval = 4 [default = 0];//每test_interval进行一次testoptional bool test_compute_loss = 19 [default = false];optional bool test_initialization = 32 [default = true];//网络初始时进行一次testoptional float base_lr = 5;//初始学习率optional int32 display = 6;//日志输出间隔的迭代次数optional int32 average_loss = 33 [default = 1];optional int32 max_iter = 7; //最大迭代次数optional int32 iter_size = 36 [default = 1];//`iter_size`x`batch_size`个实例进行一次梯度计算optional string lr_policy = 8;//学习率策略optional float gamma = 9; optional float power = 10; optional float momentum = 11; //动量值，通常取0.9optional float weight_decay = 12; //权重衰减通常取0.0005optional string regularization_type = 29 [default = "L2"];//正则化类型，{"L1","L2"}optional int32 stepsize = 13;//step policy时的参数repeated int32 stepvalue = 34;//multi_step policy时的参数optional float clip_gradients = 35 [default = -1];optional int32 snapshot = 14 [default = 0]; //snapshot时的间隔次数，为0则不保存中间态optional string snapshot_prefix = 15;//snapshot时保存的文件前缀，optional bool snapshot_diff = 16 [default = false];//是否保存梯度，用于辅助debug,会增大保存文件的尺寸enum SnapshotFormat {HDF5 = 0; BINARYPROTO = 1;}optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];//保存格式类型enum SolverMode { CPU = 0; GPU = 1;}optional SolverMode solver_mode = 17 [default = GPU];optional int32 device_id = 18 [default = 0];optional int64 random_seed = 20 [default = -1];optional string type = 40 [default = "SGD"];//优化器类型，{"SGD","Nesterov","AdaGrad","RMSProp","AdaDelta","ADAM"}optional float delta = 31 [default = 1e-8];optional float momentum2 = 39 [default = 0.999];optional float rms_decay = 38 [default = 0.99];optional bool debug_info = 23 [default = false];//若为真，打印有关网络的信息，可用于debugoptional bool snapshot_after_train = 28 [default = true];//若为假，则训练完毕后不执行snapshot操作optional bool layer_wise_reduce = 41 [default = true];//用于数据并行训练的重叠计算和通讯操作
}

相关的其它meaaage有：NetState

message NetState {optional Phase phase = 1 [default = TEST];//{"TRAIN","TEST"}optional int32 level = 2 [default = 0];repeated string stage = 3;
}

NetParameter

message NetParameter {optional string name = 1; //net的名字optional bool force_backward = 5 [default = false];//层是否进行反向传播自动地取决于网络架构和学习状态，为真则强制进行反向传播计算optional NetState state = 6;optional bool debug_info = 7 [default = false];//在网络进行forward,backword,update时打印debugging信息// The layers that make up the net. Each of their configurations, including connectivity and behavior, is specified as a LayerParameter.repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.
}

Phase

enum Phase {TRAIN = 0;TEST = 1;
}

学习率的更新方式为：

  //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.

caffe的六种优化器介绍有优化方法概述

一个solver文件例子有AlexNet in caffe

net: "models/bvlc_alexnet/train_val.prototxt"
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: "models/bvlc_alexnet/caffe_alexnet_train"
solver_mode: GPU