regarding https://plus.google.com/113952791760990667476/posts/Q5fwQY6…

…JeEq - stopped the adagrad attempt.

include/caffe/solver.hpp

@@ -50,12 +50,14 @@ class SGDSolver : public Solver<Dtype> {
 
  protected:
   virtual void PreSolve();
-  virtual Dtype GetLearningRate();
+  Dtype GetLearningRate();
   virtual void ComputeUpdateValue();
   virtual void SnapshotSolverState(SolverState * state);
   virtual void RestoreSolverState(const SolverState& state);
   // history maintains the historical momentum data.
   vector<shared_ptr<Blob<Dtype> > > history_;
+
+  DISABLE_COPY_AND_ASSIGN(SGDSolver);
 };
 
 

src/caffe/proto/caffe.proto

@@ -112,16 +112,6 @@ message SolverParameter {
   // 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 = 14 [ default = false];
-  // Adagrad solver parameters
-  // For Adagrad, we will first run normal sgd using the sgd parameters above
-  // for adagrad_skip iterations, and then kick in the adagrad algorithm, with
-  // the learning rate being adagrad_gamma * adagrad_skip. Note that the adagrad
-  // algorithm will NOT use the learning rate multiplier that is specified in
-  // the layer parameter specifications, as it will adjust the learning rate
-  // of individual parameters in a data-dependent way.
-  //    WORK IN PROGRESS: not actually implemented yet.
-  optional float adagrad_gamma = 15; // adagrad learning rate multiplier
-  optional float adagrad_skip = 16; // the steps to skip before adagrad kicks in
 }
 
 // A message that stores the solver snapshots

src/caffe/solver.cpp

@@ -116,7 +116,7 @@ Dtype SGDSolver<Dtype>::GetLearningRate() {
 
 template <typename Dtype>
 void SGDSolver<Dtype>::PreSolve() {
-  // First of all, see if we need to initialize the history
+  // Initialize the history
   vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   history_.clear();
   for (int i = 0; i < net_params.size(); ++i) {