Method Solve

src/caffe/solver.cpp:270–316

template <typename Dtype>
void Solver<Dtype>::Solve(const char* resume_file) {
  CHECK(Caffe::root_solver());
  LOG(INFO) << "Solving " << net_->name();
  LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();

  // Initialize to false every time we start solving.
  requested_early_exit_ = false;

  if (resume_file) {
    LOG(INFO) << "Restoring previous solver status from " << resume_file;
    Restore(resume_file);
  }

  // For a network that is trained by the solver, no bottom or top vecs
  // should be given, and we will just provide dummy vecs.
  int start_iter = iter_;
  Step(param_.max_iter() - iter_);
  // If we haven't already, save a snapshot after optimization, unless
  // overridden by setting snapshot_after_train := false
  if (param_.snapshot_after_train()
      && (!param_.snapshot() || iter_ % param_.snapshot() != 0)) {
    Snapshot();
  }
  if (requested_early_exit_) {
    LOG(INFO) << "Optimization stopped early.";
    return;
  }
  // After the optimization is done, run an additional train and test pass to
  // display the train and test loss/outputs if appropriate (based on the
  // display and test_interval settings, respectively).  Unlike in the rest of
  // training, for the train net we only run a forward pass as we've already
  // updated the parameters "max_iter" times -- this final pass is only done to
  // display the loss, which is computed in the forward pass.
  if (param_.display() && iter_ % param_.display() == 0) {
    int average_loss = this->param_.average_loss();
    Dtype loss;
    net_->Forward(&loss);

    UpdateSmoothedLoss(loss, start_iter, average_loss);

    LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss_;
  }
  if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
    TestAll();
  }
  LOG(INFO) << "Optimization Done.";
}

template <typename Dtype>
void Solver<Dtype>::TestAll() {