I just try to find out how I can use Caffe. To do so, I just took a look at the different .prototxt files in the examples folder. There is one option I don't understand:
# The learning rate policy
lr_policy: "inv"
Possible values seem to be:
"fixed""inv""step""multistep""stepearly""poly"
Could somebody please explain those options?
If you look inside the /caffe-master/src/caffe/proto/caffe.proto file (you can find it online here) you will see the following descriptions:
// 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.
It is a common practice to decrease the learning rate (lr) as the optimization/learning process progresses. However, it is not clear how exactly the learning rate should be decreased as a function of the iteration number.
If you use DIGITS as an interface to Caffe, you will be able to visually see how the different choices affect the learning rate.
fixed: the learning rate is kept fixed throughout the learning process.
inv: the learning rate is decaying as ~1/T
step: the learning rate is piecewise constant, dropping every X iterations
multistep: piecewise constant at arbitrary intervals
You can see exactly how the learning rate is computed in the function SGDSolver<Dtype>::GetLearningRate (solvers/sgd_solver.cpp line ~30).
Recently, I came across an interesting and unconventional approach to learning-rate tuning: Leslie N. Smith's work "No More Pesky Learning Rate Guessing Games". In his report, Leslie suggests to use lr_policy that alternates between decreasing and increasing the learning rate. His work also suggests how to implement this policy in Caffe.
来源：https://stackoverflow.com/questions/30033096/what-is-lr-policy-in-caffe