caffe中的lr_policy

// 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.