net: "models/individually/viper_trainval.prototxt" test_iter: 7 test_interval: 50 test_initialization: truedisplay: 20 #每迭代20次顯示一次訓練結果 average_loss: 20 iter_size: 2lr_policy: "stepdecr" base_lr: 0.1 gamma: 0.04 stepsize: 100 max_iter: 2700 min_lr: 0.0005momentum: 0.9 weight_decay: 0.0005snapshot_prefix: "external/exp/snapshots/individually/viper" solver_mode: GPU

下面詳細解釋各參數：

message SolverParameter {//////////////////////////////////////////////////////////////////////////////// Specifying the train and test networks//// Exactly one train net must be specified using one of the following fields:// train_net_param, train_net, net_param, net// One or more test nets may be specified using any of the following fields:// test_net_param, test_net, net_param, net// If more than one test net field is specified (e.g., both net and// test_net are specified), they will be evaluated in the field order given// above: (1) test_net_param, (2) test_net, (3) net_param/net.// A test_iter must be specified for each test_net.// A test_level and/or a test_stage may also be specified for each test_net.//////////////////////////////////////////////////////////////////////////////// Proto filename for the train net, possibly combined with one or more// test nets.optional string net = 24;// Inline train net param, possibly combined with one or more test nets.optional NetParameter net_param = 25;optional string train_net = 1; // Proto filename for the train net.repeated string test_net = 2; // Proto filenames for the test nets.optional NetParameter train_net_param = 21; // Inline train net params.repeated NetParameter test_net_param = 22; // Inline test net params.// The states for the train/test nets. Must be unspecified or// specified once per net.//// By default, all states will have solver = true;// train_state will have phase = TRAIN,// and all test_state's will have phase = TEST.// Other defaults are set according to the NetState defaults.optional NetState train_state = 26;repeated NetState test_state = 27;// The number of iterations for each test net.repeated int32 test_iter = 3;// The number of iterations between two testing phases.optional int32 test_interval = 4 [default = 0];optional bool test_compute_loss = 19 [default = false];// If true, run an initial test pass before the first iteration,// ensuring memory availability and printing the starting value of the loss.optional bool test_initialization = 32 [default = true];optional float base_lr = 5; // The base learning rate// the number of iterations between displaying info. If display = 0, no info// will be displayed.optional int32 display = 6;// Display the loss averaged over the last average_loss iterationsoptional int32 average_loss = 33 [default = 1];optional int32 max_iter = 7; // the maximum number of iterations// accumulate gradients over `iter_size` x `batch_size` instancesoptional int32 iter_size = 36 [default = 1];// 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))))// - stepdecr: return base_lr * (1 - gamma * (floor(iter / step)))// - smoothstep: return base_lr * gamma ^ (iter / step)//// where base_lr, max_iter, gamma, step, stepvalue and power are defined// in the solver parameter protocol buffer, and iter is the current iteration.optional string lr_policy = 8;optional float gamma = 9; // The parameter to compute the learning rate.optional float power = 10; // The parameter to compute the learning rate.optional float momentum = 11; // The momentum value.optional float weight_decay = 12; // The weight decay.optional float min_lr = 39; // The minimum learning rate.// regularization types supported: L1 and L2// controlled by weight_decayoptional string regularization_type = 29 [default = "L2"];// the stepsize for learning rate policy "step"optional int32 stepsize = 13;// the stepsize for learning rate policy "multistep"repeated int32 stepvalue = 34;// Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,// whenever their actual L2 norm is larger.optional float clip_gradients = 35 [default = -1];optional int32 snapshot = 14 [default = 0]; // The snapshot intervaloptional string snapshot_prefix = 15; // The prefix for the snapshot.// whether to snapshot diff in the results or not. Snapshotting diff will help// debugging but the final protocol buffer size will be much larger.optional bool snapshot_diff = 16 [default = false];enum SnapshotFormat {HDF5 = 0;BINARYPROTO = 1;}optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];// the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.enum SolverMode {CPU = 0;GPU = 1;}optional SolverMode solver_mode = 17 [default = GPU];// the device_id will that be used in GPU mode. Use device_id = 0 in default.repeated int32 device_id = 18;// If non-negative, the seed with which the Solver will initialize the Caffe// random number generator -- useful for reproducible results. Otherwise,// (and by default) initialize using a seed derived from the system clock.optional int64 random_seed = 20 [default = -1];// Solver typeenum SolverType {SGD = 0;NESTEROV = 1;ADAGRAD = 2;RMSPROP = 3;ADADELTA = 4;}optional SolverType solver_type = 30 [default = SGD];// numerical stability for AdaGradoptional float delta = 31 [default = 1e-8];// RMSProp decay value// MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)optional float rms_decay = 38;// If true, print information about the state of the net that may help with// debugging learning problems.optional bool debug_info = 23 [default = false];// If false, don't save a snapshot after training finishes.optional bool snapshot_after_train = 28 [default = true]; }// A message that stores the solver snapshots message SolverState {optional int32 iter = 1; // The current iterationoptional string learned_net = 2; // The file that stores the learned net.repeated BlobProto history = 3; // The history for sgd solversoptional int32 current_step = 4 [default = 0]; // The current step for learning rate }enum Phase {TRAIN = 0;TEST = 1; }message NetState {optional Phase phase = 1 [default = TEST];optional int32 level = 2 [default = 0];repeated string stage = 3; }

總結

以上是生活随笔為你收集整理的SolverParameter的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。