NnetComputer Class Reference

Collaboration diagram for NnetComputer:

Public Member Functions
	NnetComputer (const Nnet &nnet, const CuMatrixBase< BaseFloat > &input_feats, bool pad, Nnet *nnet_to_update=NULL)
void	Propagate ()
	The forward-through-the-layers part of the computation. More...
void	Backprop (CuMatrix< BaseFloat > *tmp_deriv)
BaseFloat	ComputeLastLayerDeriv (const Posterior &pdf_post, CuMatrix< BaseFloat > *deriv) const
	Computes objf derivative at last layer, and returns objective function summed over labels and multiplied by utterance_weight. More...
CuMatrixBase< BaseFloat > &	GetOutput ()

Private Attributes
const Nnet &	nnet_
std::vector< CuMatrix< BaseFloat > >	forward_data_
Nnet *	nnet_to_update_
std::vector< ChunkInfo >	chunk_info_

Detailed Description

Definition at line 32 of file nnet-compute.cc.

Constructor & Destructor Documentation

NnetComputer()

NnetComputer	(	const Nnet &	nnet,
		const CuMatrixBase< BaseFloat > &	input_feats,
		bool	pad,
		Nnet *	nnet_to_update = NULL
	)

Definition at line 64 of file nnet-compute.cc.

References NnetComputer::chunk_info_, Nnet::ComputeChunkInfo(), NnetComputer::forward_data_, rnnlm::i, Nnet::InputDim(), KALDI_ERR, Nnet::LeftContext(), NnetComputer::nnet_, CuMatrixBase< Real >::NumCols(), Nnet::NumComponents(), CuMatrixBase< Real >::NumRows(), CuMatrixBase< Real >::Range(), CuMatrix< Real >::Resize(), Nnet::RightContext(), and CuMatrixBase< Real >::Row().

                                                :
    nnet_(nnet), nnet_to_update_(nnet_to_update) {
  int32 dim = input_feats.NumCols();
  if (dim != nnet.InputDim()) {
    KALDI_ERR << "Feature dimension is " << dim << " but network expects "
              << nnet.InputDim();
  }
  forward_data_.resize(nnet.NumComponents() + 1);

  int32 left_context = (pad ? nnet_.LeftContext() : 0),
       right_context = (pad ? nnet_.RightContext() : 0);

  int32 num_rows = left_context + input_feats.NumRows() + right_context;
  nnet.ComputeChunkInfo(num_rows, 1, &chunk_info_);

  CuMatrix<BaseFloat> &input(forward_data_[0]);
  input.Resize(num_rows, dim);
  input.Range(left_context, input_feats.NumRows(),
              0, dim).CopyFromMat(input_feats);
  for (int32 i = 0; i < left_context; i++)
    input.Row(i).CopyFromVec(input_feats.Row(0));
  int32 last_row = input_feats.NumRows() - 1;
  for (int32 i = 0; i < right_context; i++)
    input.Row(num_rows - i - 1).CopyFromVec(input_feats.Row(last_row));
}

Member Function Documentation

Backprop()

void Backprop

(

CuMatrix< BaseFloat > *

tmp_deriv

)

Definition at line 141 of file nnet-compute.cc.

References Component::Backprop(), NnetComputer::chunk_info_, NnetComputer::forward_data_, Nnet::GetComponent(), KALDI_ASSERT, NnetComputer::nnet_, NnetComputer::nnet_to_update_, and Nnet::NumComponents().

Referenced by kaldi::nnet2::NnetGradientComputation().

                                                          {
  KALDI_ASSERT(nnet_to_update_ != NULL); // Or why do backprop?
  // If later this reasoning changes, we can change this
  // statement and add logic to make component_to_update, below,
  // NULL if necessary.
  
  for (int32 c = nnet_.NumComponents() - 1; c >= 0; c--) {
    const Component &component = nnet_.GetComponent(c);
    Component *component_to_update = &(nnet_to_update_->GetComponent(c));
    const CuMatrix<BaseFloat>  &input = forward_data_[c],
                            &output = forward_data_[c+1],
                      &output_deriv = *tmp_deriv;
    CuMatrix<BaseFloat> input_deriv;
    component.Backprop(chunk_info_[c], chunk_info_[c+1], input, output, output_deriv, 
                       component_to_update, &input_deriv);
    *tmp_deriv = input_deriv;
  }
}

ComputeLastLayerDeriv()

BaseFloat ComputeLastLayerDeriv	(	const Posterior &	pdf_post,
		CuMatrix< BaseFloat > *	deriv
	)		const

Computes objf derivative at last layer, and returns objective function summed over labels and multiplied by utterance_weight.

[Note: utterance_weight will normally be 1.0].

Definition at line 111 of file nnet-compute.cc.

References NnetComputer::forward_data_, rnnlm::i, rnnlm::j, KALDI_ASSERT, KALDI_VLOG, kaldi::Log(), NnetComputer::nnet_, CuMatrixBase< Real >::NumCols(), Nnet::NumComponents(), CuMatrixBase< Real >::NumRows(), and CuMatrix< Real >::Resize().

Referenced by kaldi::nnet2::NnetGradientComputation().

                                                                                {
  // TODO: convert this to proper CUDA code, c.f. ComputeObjfAndDeriv
  // in nnet-update.cc (I'm not sure, though, that this code is ever reached.)
  int32 num_components = nnet_.NumComponents();
  double tot_objf = 0.0, tot_weight = 0.0;
  const CuMatrix<BaseFloat> &last_layer_output = forward_data_[num_components];
  int32 num_frames = last_layer_output.NumRows(),
          num_pdfs = last_layer_output.NumCols();
  KALDI_ASSERT(pdf_post.size() == static_cast<size_t>(num_frames));
  deriv->Resize(num_frames, num_pdfs); // will zero it.
  for (int32 i = 0; i < deriv->NumRows(); i++) {
    for (size_t j = 0; j < pdf_post[i].size(); j++) {
      int32 label = pdf_post[i][j].first;
      BaseFloat weight = pdf_post[i][j].second;
      KALDI_ASSERT(label >= 0 && label < num_pdfs);
      BaseFloat this_prob = last_layer_output(i, label);
      KALDI_ASSERT(this_prob > 0.99e-20); // We floored to 1.0e-20 in SoftmaxLayer.
      tot_objf += weight * Log(this_prob);
      tot_weight += weight;
      (*deriv)(i, label) += weight / this_prob; // could be "=", assuming the
      // labels are all distinct.
    }
  }
  KALDI_VLOG(4) << "Objective function is " << (tot_objf/tot_weight) <<
      " per frame over " << tot_weight << " samples.";
  return tot_objf;  
}

GetOutput()

CuMatrixBase<BaseFloat>& GetOutput ( )

inline

Definition at line 54 of file nnet-compute.cc.

References NnetComputer::forward_data_.

Referenced by kaldi::nnet2::NnetComputation(), and kaldi::nnet2::NnetComputationChunked().

{ return forward_data_.back(); }

Propagate()

void Propagate

(

)

The forward-through-the-layers part of the computation.

This is the forward part of the computation.

Definition at line 95 of file nnet-compute.cc.

References Component::BackpropNeedsInput(), Component::BackpropNeedsOutput(), NnetComputer::chunk_info_, NnetComputer::forward_data_, Nnet::GetComponent(), NnetComputer::nnet_, NnetComputer::nnet_to_update_, Nnet::NumComponents(), and Component::Propagate().

Referenced by kaldi::nnet2::NnetComputation(), kaldi::nnet2::NnetComputationChunked(), and kaldi::nnet2::NnetGradientComputation().

                             {
  for (int32 c = 0; c < nnet_.NumComponents(); c++) {
    const Component &component = nnet_.GetComponent(c);
    CuMatrix<BaseFloat> &input = forward_data_[c],
                     &output = forward_data_[c+1];
    component.Propagate(chunk_info_[c], chunk_info_[c+1], input, &output);
    const Component *prev_component = (c == 0 ? NULL : &(nnet_.GetComponent(c-1)));
    bool will_do_backprop = (nnet_to_update_ != NULL),
         keep_last_output = will_do_backprop &&
                             ((c>0 && prev_component->BackpropNeedsOutput()) ||
                              component.BackpropNeedsInput());
    if (!keep_last_output)
      forward_data_[c].Resize(0, 0); // We won't need this data; save memory.
  }
}

Member Data Documentation

chunk_info_

std::vector<ChunkInfo> chunk_info_

private

Definition at line 61 of file nnet-compute.cc.

Referenced by NnetComputer::Backprop(), NnetComputer::NnetComputer(), and NnetComputer::Propagate().

forward_data_

std::vector<CuMatrix<BaseFloat> > forward_data_

private

Definition at line 58 of file nnet-compute.cc.

Referenced by NnetComputer::Backprop(), NnetComputer::ComputeLastLayerDeriv(), NnetComputer::GetOutput(), NnetComputer::NnetComputer(), and NnetComputer::Propagate().

nnet_

const Nnet& nnet_

private

Definition at line 57 of file nnet-compute.cc.

Referenced by NnetComputer::Backprop(), NnetComputer::ComputeLastLayerDeriv(), NnetComputer::NnetComputer(), and NnetComputer::Propagate().

nnet_to_update_

Nnet* nnet_to_update_

private

Definition at line 59 of file nnet-compute.cc.

Referenced by NnetComputer::Backprop(), and NnetComputer::Propagate().

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

• nnet2/nnet-compute.cc