Пример #1
0
Teuchos::RCP<Anasazi::SolverManager<Scalar,MV,OP> >
build_eigsolver(const Teuchos::RCP<const Teuchos::Comm<int> > &comm,
             Teuchos::ParameterList& test_params,
             Teuchos::RCP<Anasazi::Eigenproblem<Scalar,MV,OP> > problem)
{
  typedef Anasazi::Eigenproblem<Scalar,MV,OP> AEigProb;
  Teuchos::RCP<Anasazi::SolverManager<Scalar,MV,OP> > solver;

  Teuchos::ParameterList aparams;
  if (test_params.isSublist("Anasazi")) {
    aparams = test_params.sublist("Anasazi");
  }

  std::string solver_type("not specified");
  Ifpack2::getParameter(test_params, "eigen_solver_type", solver_type);
  if (solver_type == "BlockKrylovSchur") {
    // if (comm->getRank() == 0) std::cout << aparams << std::endl;
    solver = Teuchos::rcp(new Anasazi::BlockKrylovSchurSolMgr<Scalar,MV,OP>(problem,aparams));
  }
  else if (solver_type == "not specified") {
    throw std::runtime_error("Error in build_eigsolver: solver_type not specified.");
  }
  else {
    std::ostringstream os;
    os << "Error in build_eigsolver: solver_type ("<<solver_type<<") not recognized.";
    os << "\nIfpack2's test-driver recognizes these solvers: PseudoBlockCG, PesudoBlockGmres, BlockGmres, TFQMR.";
    std::string str = os.str();
    throw std::runtime_error(str);
  }
  return solver;
}
Пример #2
0
 virtual void InitSolverFromProtoString(const string& proto) {
   SolverParameter param;
   CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
   // Set the solver_mode according to current Caffe::mode.
   switch (Caffe::mode()) {
     case Caffe::CPU:
       param.set_solver_mode(SolverParameter_SolverMode_CPU);
       break;
     case Caffe::GPU:
       param.set_solver_mode(SolverParameter_SolverMode_GPU);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
   }
   InitSolver(param);
   delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD ||
       solver_type() == SolverParameter_SolverType_RMSPROP) ?
       param.delta() : 0;
 }
Пример #3
0
 virtual void InitSolverFromProtoString(const string& proto) {
   SolverParameter param;
   CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
   // Disable saving a final snapshot so the tests don't pollute the user's
   // working directory with useless snapshots.
   param.set_snapshot_after_train(false);
   // Set the solver_mode according to current Caffe::mode.
   switch (Caffe::mode()) {
     case Caffe::CPU:
       param.set_solver_mode(SolverParameter_SolverMode_CPU);
       break;
     case Caffe::GPU:
       param.set_solver_mode(SolverParameter_SolverMode_GPU);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
   }
   InitSolver(param);
   delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD ||
   					solver_type() == SolverParameter_SolverType_RMSPROP) ?
        param.delta() : 0;
 }
Пример #4
0
  void CheckLeastSquaresUpdate(
      const vector<shared_ptr<Blob<Dtype> > >& updated_params) {
    const int D = channels_ * height_ * width_;

    const Blob<Dtype>& updated_weights = *updated_params[0];
    const Blob<Dtype>& updated_bias = *updated_params[1];

    Net<Dtype>& net = *this->solver_->net();
    ASSERT_TRUE(net.has_layer("innerprod"));
    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
        net.layer_by_name("innerprod")->blobs();
    ASSERT_EQ(2, param_blobs.size());
    const Blob<Dtype>& solver_updated_weights = *param_blobs[0];
    ASSERT_EQ(D, solver_updated_weights.count());
    const double kPrecision = 1e-2;
    const double kMinPrecision = 1e-7;
    for (int i = 0; i < D; ++i) {
      const Dtype expected_updated_weight = updated_weights.cpu_data()[i];
      const Dtype solver_updated_weight = solver_updated_weights.cpu_data()[i];
      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
          std::min(fabs(expected_updated_weight), fabs(solver_updated_weight)));
      EXPECT_NEAR(expected_updated_weight, solver_updated_weight, error_margin);
    }
    const Blob<Dtype>& solver_updated_bias_blob = *param_blobs[1];
    ASSERT_EQ(1, solver_updated_bias_blob.count());
    const Dtype expected_updated_bias = updated_bias.cpu_data()[0];
    const Dtype solver_updated_bias = solver_updated_bias_blob.cpu_data()[0];
    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
          std::min(fabs(expected_updated_bias), fabs(solver_updated_bias)));
    EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);

    // Check the solver's history -- should contain the previous update value.
    if (solver_type() == SolverParameter_SolverType_SGD) {
      const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
      ASSERT_EQ(2, history.size());
      for (int i = 0; i < D; ++i) {
        const Dtype expected_history = updated_weights.cpu_diff()[i];
        const Dtype solver_history = history[0]->cpu_data()[i];
        const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
            std::min(fabs(expected_history), fabs(solver_history)));
        EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
      }
      const Dtype expected_history = updated_bias.cpu_diff()[0];
      const Dtype solver_history = history[1]->cpu_data()[0];
      const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
          std::min(fabs(expected_history), fabs(solver_history)));
      EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
    }
  }
Пример #5
0
Teuchos::RCP<Belos::SolverManager<Scalar,MV,OP> >
build_solver(Teuchos::ParameterList& test_params,
             Teuchos::RCP<Belos::LinearProblem<Scalar,MV,OP> > problem)
{
  Teuchos::RCP<Belos::SolverManager<Scalar,MV,OP> > solver;

  Teuchos::ParameterList bparams;
  if (test_params.isSublist("Belos")) {
    bparams = test_params.sublist("Belos");
  }
  Teuchos::RCP<Teuchos::ParameterList> rcpparams = Teuchos::rcp(&bparams,false);

  std::string solver_type("not specified");
  Ifpack2::getParameter(test_params, "solver_type", solver_type);
  if (solver_type == "PseudoBlockCG") {
    solver = Teuchos::rcp(new Belos::PseudoBlockCGSolMgr<Scalar,MV,OP>(problem,rcpparams));
  }
  else if (solver_type == "BlockCG") {
    solver = Teuchos::rcp(new Belos::BlockCGSolMgr<Scalar,MV,OP>(problem,rcpparams));
  }
// PseudoBlockGmres does not work right now with QD
#ifndef USING_QD
  else if (solver_type == "PseudoBlockGmres") {
    solver = Teuchos::rcp(new Belos::PseudoBlockGmresSolMgr<Scalar,MV,OP>(problem,rcpparams));
  }
#endif
  else if (solver_type == "BlockGmres") {
    solver = Teuchos::rcp(new Belos::BlockGmresSolMgr<Scalar,MV,OP>(problem,rcpparams));
  }
  else if (solver_type == "TFQMR") {
    solver = Teuchos::rcp(new Belos::TFQMRSolMgr<Scalar,MV,OP>(problem,rcpparams));
  }
  else if (solver_type == "not specified") {
    throw std::runtime_error("Error in build_solver: solver_type not specified.");
  }
  else {
    std::ostringstream os;
    os << "Error in build_solver: solver_type ("<<solver_type<<") not recognized.";
    os << "\nIfpack2's test-driver recognizes these solvers: PseudoBlockCG, BlockCG, PesudoBlockGmres, BlockGmres, TFQMR.";
    std::string str = os.str();
    throw std::runtime_error(str);
  }

  return solver;
}
Пример #6
0
Teuchos::RCP<Belos::SolverManager<Scalar,MV,OP> >
build_solver(const Teuchos::RCP<const Teuchos::Comm<int> > &comm,
             Teuchos::ParameterList& test_params,
             Teuchos::RCP<Belos::LinearProblem<Scalar,MV,OP> > problem)
{
  typedef Belos::LinearProblem<Scalar,MV,OP> BLinProb;
  Teuchos::RCP<Belos::SolverManager<Scalar,MV,OP> > solver;

  Teuchos::ParameterList bparams;
  if (test_params.isSublist("Belos")) {
    bparams = test_params.sublist("Belos");
  }
  Teuchos::RCP<Teuchos::ParameterList> rcpparams = Teuchos::rcpFromRef(bparams);

  std::string solver_type("not specified");
  Ifpack2::getParameter(test_params, "solver_type", solver_type);
  if (solver_type == "BlockGmres") {
    // if (comm->getRank() == 0) std::cout << *rcpparams << std::endl;
    solver = Teuchos::rcp(new Belos::BlockGmresSolMgr<Scalar,MV,OP>(problem,rcpparams));
  }
  // else if (solver_type == "PseudoBlockGmres") {
  //   solver = Teuchos::rcp(new Belos::PseudoBlockGmresSolMgr<Scalar,MV,OP>(problem,rcpparams));
  // }
  // else if (solver_type == "PseudoBlockCG") {
  //   solver = Teuchos::rcp(new Belos::PseudoBlockCGSolMgr<Scalar,MV,OP>(problem,rcpparams));
  // }
  // else if (solver_type == "TFQMR") {
  //   solver = Teuchos::rcp(new Belos::TFQMRSolMgr<Scalar,MV,OP>(problem,rcpparams));
  // }
  else if (solver_type == "not specified") {
    throw std::runtime_error("Error in build_solver: solver_type not specified.");
  }
  else {
    std::ostringstream os;
    os << "Error in build_solver: solver_type ("<<solver_type<<") not recognized.";
    os << "\nIfpack2's test-driver recognizes these solvers: PseudoBlockCG, PesudoBlockGmres, BlockGmres, TFQMR.";
    std::string str = os.str();
    throw std::runtime_error(str);
  }
  return solver;
}
Пример #7
0
  // Compute an update value given the current state of the train net,
  // using the analytical formula for the least squares gradient.
  // updated_params will store the updated weight and bias results,
  // using the blobs' diffs to hold the update values themselves.
  void ComputeLeastSquaresUpdate(const Dtype learning_rate,
      const Dtype weight_decay, const Dtype momentum,
      vector<shared_ptr<Blob<Dtype> > >* updated_params) {
    const int N = num_;
    const int D = channels_ * height_ * width_;

    // Run a forward pass, and manually compute the update values from the
    // result.
    Net<Dtype>& net = *this->solver_->net();
    vector<Blob<Dtype>*> empty_bottom_vec;
    net.Forward(empty_bottom_vec);
    ASSERT_TRUE(net.has_blob("data"));
    const Blob<Dtype>& data = *net.blob_by_name("data");
    ASSERT_TRUE(net.has_blob("targets"));
    const Blob<Dtype>& targets = *net.blob_by_name("targets");
    ASSERT_TRUE(net.has_layer("innerprod"));
    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
        net.layer_by_name("innerprod")->blobs();
    const int num_param_blobs = 2;
    ASSERT_EQ(num_param_blobs, param_blobs.size());
    const Blob<Dtype>& weights = *param_blobs[0];
    const Blob<Dtype>& bias = *param_blobs[1];
    ASSERT_EQ(D * N, data.count());
    ASSERT_EQ(N, targets.count());
    ASSERT_EQ(D, weights.count());
    ASSERT_EQ(1, bias.count());

    updated_params->clear();
    updated_params->resize(num_param_blobs);
    for (int i = 0; i < num_param_blobs; ++i) {
      (*updated_params)[i].reset(new Blob<Dtype>());
    }
    Blob<Dtype>& updated_weights = *(*updated_params)[0];
    updated_weights.ReshapeLike(weights);
    Blob<Dtype>& updated_bias = *(*updated_params)[1];
    updated_bias.ReshapeLike(bias);

    for (int i = 0; i <= D; ++i) {
      // Compute the derivative with respect to the ith weight (i.e., the ith
      // element of the gradient).
      Dtype grad = 0;
      for (int j = 0; j <= D; ++j) {
        // Compute element (i, j) of X^T * X.
        Dtype element = 0;
        for (int k = 0; k < N; ++k) {
          // (i, k) in X^T (== (k, i) in X) times (k, j) in X.
          const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
          const Dtype element_j = (j == D) ? 1 : data.cpu_data()[k * D + j];
          element += element_i * element_j;
        }
        if (j == D) {
          grad += element * bias.cpu_data()[0];
        } else {
          grad += element * weights.cpu_data()[j];
        }
      }
      for (int k = 0; k < N; ++k) {
        const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
        grad -= element_i * targets.cpu_data()[k];
      }
      // Scale the gradient over the N samples.
      grad /= N;
      // Add the weight decay to the gradient.
      grad += weight_decay *
          ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
      // Finally, compute update.
      const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
      ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
      Dtype update_value = learning_rate * grad;
      const Dtype history_value = (i == D) ?
            history[1]->cpu_data()[0] : history[0]->cpu_data()[i];
      const Dtype temp = momentum * history_value;
      switch (solver_type()) {
      case SolverParameter_SolverType_SGD:
        update_value += temp;
        break;
      case SolverParameter_SolverType_NESTEROV:
        update_value += temp;
        // step back then over-step
        update_value = (1 + momentum) * update_value - temp;
        break;
      case SolverParameter_SolverType_ADAGRAD:
        update_value /= std::sqrt(history_value + grad * grad) + delta_;
        break;
      case SolverParameter_SolverType_RMSPROP: {
        const Dtype rms_decay = 0.95;
        update_value /= std::sqrt(rms_decay*history_value
            + grad * grad * (1 - rms_decay)) + delta_;
        }
        break;
      default:
        LOG(FATAL) << "Unknown solver type: " << solver_type();
      }
      if (i == D) {
        updated_bias.mutable_cpu_diff()[0] = update_value;
        updated_bias.mutable_cpu_data()[0] = bias.cpu_data()[0] - update_value;
      } else {
        updated_weights.mutable_cpu_diff()[i] = update_value;
        updated_weights.mutable_cpu_data()[i] =
            weights.cpu_data()[i] - update_value;
      }
    }
  }