Mse Class Reference

#include <nnet-loss.h>

Inheritance diagram for Mse:

Collaboration diagram for Mse:

Public Member Functions
	Mse (LossOptions &opts)
	~Mse ()
void	Eval (const VectorBase< BaseFloat > &frame_weights, const CuMatrixBase< BaseFloat > &net_out, const CuMatrixBase< BaseFloat > &target, CuMatrix< BaseFloat > *diff)
	Evaluate mean square error using target-matrix,. More...
void	Eval (const VectorBase< BaseFloat > &frame_weights, const CuMatrixBase< BaseFloat > &net_out, const Posterior &target, CuMatrix< BaseFloat > *diff)
	Evaluate mean square error using target-posteior,. More...
std::string	Report ()
	Generate string with error report. More...
BaseFloat	AvgLoss ()
	Get loss value (frame average),. More...
Public Member Functions inherited from LossItf
	LossItf (LossOptions &opts)
virtual	~LossItf ()

Private Attributes
double	frames_
double	loss_
double	frames_progress_
double	loss_progress_
std::vector< float >	loss_vec_
CuVector< BaseFloat >	frame_weights_
CuMatrix< BaseFloat >	tgt_mat_
CuMatrix< BaseFloat >	diff_pow_2_

Additional Inherited Members
Protected Attributes inherited from LossItf
LossOptions	opts_
Timer	timer_

Detailed Description

Definition at line 149 of file nnet-loss.h.

Constructor & Destructor Documentation

Mse()

Mse ( LossOptions &  opts )

inline

Definition at line 151 of file nnet-loss.h.

                        :
    LossItf(opts),
    frames_(0.0),
    loss_(0.0),
    frames_progress_(0.0),
    loss_progress_(0.0)
  { }

~Mse()

~Mse ( )

inline

Definition at line 159 of file nnet-loss.h.

  { }

Member Function Documentation

AvgLoss()

BaseFloat AvgLoss ( )

inlinevirtual

Get loss value (frame average),.

Implements LossItf.

Definition at line 178 of file nnet-loss.h.

                      {
    if (frames_ == 0) return 0.0;
    return loss_ / frames_;
  }

Eval() [1/2]

void Eval ( const VectorBase< BaseFloat > &  frame_weights, const CuMatrixBase< BaseFloat > &  net_out, const CuMatrixBase< BaseFloat > &  target, CuMatrix< BaseFloat > *  diff  )

virtual

Evaluate mean square error using target-matrix,.

Implements LossItf.

Definition at line 228 of file nnet-loss.cc.

References CuMatrixBase< Real >::AddMat(), VectorBase< Real >::Dim(), Xent::frame_weights_, Xent::frames_, Xent::frames_progress_, KALDI_ASSERT, KALDI_ISFINITE, KALDI_LOG, LossOptions::loss_report_frames, Xent::loss_vec_, CuMatrixBase< Real >::MulElements(), CuMatrixBase< Real >::MulRowsVec(), CuMatrixBase< Real >::NumCols(), CuMatrixBase< Real >::NumRows(), LossItf::opts_, VectorBase< Real >::Sum(), and CuMatrixBase< Real >::Sum().

Referenced by main().

                                          {
  // check inputs,
  KALDI_ASSERT(net_out.NumCols() == target.NumCols());
  KALDI_ASSERT(net_out.NumRows() == target.NumRows());
  KALDI_ASSERT(net_out.NumRows() == frame_weights.Dim());

  KALDI_ASSERT(KALDI_ISFINITE(frame_weights.Sum()));
  KALDI_ASSERT(KALDI_ISFINITE(net_out.Sum()));
  KALDI_ASSERT(KALDI_ISFINITE(target.Sum()));

  int32 num_frames = frame_weights.Sum();
  KALDI_ASSERT(num_frames >= 0.0);

  // get frame_weights to GPU,
  frame_weights_ = frame_weights;

  // compute derivative w.r.t. neural nerwork outputs
  *diff = net_out;  // y
  diff->AddMat(-1.0, target);  // (y - t)
  diff->MulRowsVec(frame_weights_);  // weighting,

  // Compute MeanSquareError loss of mini-batch
  diff_pow_2_ = *diff;
  diff_pow_2_.MulElements(diff_pow_2_);  // (y - t)^2
  diff_pow_2_.MulRowsVec(frame_weights_);  // w*(y - t)^2
  double mean_square_error = 0.5 * diff_pow_2_.Sum();  // sum the matrix,

  KALDI_ASSERT(KALDI_ISFINITE(mean_square_error));

  // accumulate
  loss_ += mean_square_error;
  frames_ += num_frames;

  // progressive loss reporting
  if (opts_.loss_report_frames > 0) {
    frames_progress_ += num_frames;
    loss_progress_ += mean_square_error;
    if (frames_progress_ > opts_.loss_report_frames) {
      KALDI_LOG << "ProgressLoss[last "
                << static_cast<int>(frames_progress_/100/3600) << "h of "
                << static_cast<int>(frames_/100/3600) << "h]: "
                << loss_progress_/frames_progress_ << " (Mse)";
      // store
      loss_vec_.push_back(loss_progress_/frames_progress_);
      // reset
      frames_progress_ = 0;
      loss_progress_ = 0.0;
    }
  }
}

Eval() [2/2]

void Eval ( const VectorBase< BaseFloat > &  frame_weights, const CuMatrixBase< BaseFloat > &  net_out, const Posterior &  target, CuMatrix< BaseFloat > *  diff  )

virtual

Evaluate mean square error using target-posteior,.

Implements LossItf.

Definition at line 283 of file nnet-loss.cc.

References Xent::Eval(), KALDI_ASSERT, CuMatrixBase< Real >::NumCols(), CuMatrixBase< Real >::NumRows(), kaldi::nnet1::PosteriorToMatrix(), and Xent::tgt_mat_.

                                          {
  int32 num_frames = net_out.NumRows(),
    num_nn_outputs = net_out.NumCols();
  KALDI_ASSERT(num_frames == post.size());

  // convert posterior to matrix,
  PosteriorToMatrix(post, num_nn_outputs, &tgt_mat_);

  // call the other eval function,
  Eval(frame_weights, net_out, tgt_mat_, diff);
}

Report()

std::string Report ( )

virtual

Generate string with error report.

Implements LossItf.

Definition at line 299 of file nnet-loss.cc.

References Xent::frames_, and Xent::loss_vec_.

Referenced by main().

                      {
  // compute root mean square,
  int32 num_tgt = diff_pow_2_.NumCols();
  BaseFloat root_mean_square = sqrt(loss_/frames_/num_tgt);
  // build the message,
  std::ostringstream oss;
  oss << "AvgLoss: " << loss_/frames_ << " (Mse), "
      << "[RMS " << root_mean_square << ", frames "
      << frames_ << "]" << std::endl;
  oss << "progress: [";
  std::copy(loss_vec_.begin(), loss_vec_.end(),
            std::ostream_iterator<float>(oss, " "));
  oss << "]" << std::endl;
  return oss.str();
}

Member Data Documentation

diff_pow_2_

CuMatrix<BaseFloat> diff_pow_2_

private

Definition at line 193 of file nnet-loss.h.

frame_weights_

CuVector<BaseFloat> frame_weights_

private

Definition at line 191 of file nnet-loss.h.

frames_

double frames_

private

Definition at line 184 of file nnet-loss.h.

frames_progress_

double frames_progress_

private

Definition at line 187 of file nnet-loss.h.

loss_

double loss_

private

Definition at line 185 of file nnet-loss.h.

loss_progress_

double loss_progress_

private

Definition at line 188 of file nnet-loss.h.

loss_vec_

std::vector<float> loss_vec_

private

Definition at line 189 of file nnet-loss.h.

tgt_mat_

CuMatrix<BaseFloat> tgt_mat_

private

Definition at line 192 of file nnet-loss.h.

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The documentation for this class was generated from the following files:

• nnet/nnet-loss.h
• nnet/nnet-loss.cc