#include <nnet-parametric-relu.h>

Inheritance diagram for ParametricRelu:

Collaboration diagram for ParametricRelu:

[legend]

Public Member Functions
	ParametricRelu (int32 dim_in, int32 dim_out)

	~ParametricRelu ()

Component *	Copy () const
	Copy component (deep copy),. More...

ComponentType	GetType () const
	Get Type Identification of the component,. More...

void	InitData (std::istream &is)
	Initialize the content of the component by the 'line' from the prototype,. More...

void	ReadData (std::istream &is, bool binary)
	Reads the component content. More...

void	WriteData (std::ostream &os, bool binary) const
	Writes the component content. More...

int32	NumParams () const
	Number of trainable parameters,. More...

void	GetGradient (VectorBase< BaseFloat > *gradient) const
	Get gradient reshaped as a vector,. More...

void	GetParams (VectorBase< BaseFloat > *params) const
	Get the trainable parameters reshaped as a vector,. More...

void	SetParams (const VectorBase< BaseFloat > &params)
	Set the trainable parameters from, reshaped as a vector,. More...

std::string	Info () const
	Print some additional info (after <ComponentName> and the dims),. More...

std::string	InfoGradient () const
	Print some additional info about gradient (after <...> and dims),. More...

void	PropagateFnc (const CuMatrixBase< BaseFloat > &in, CuMatrixBase< BaseFloat > *out)
	Abstract interface for propagation/backpropagation. More...

void	BackpropagateFnc (const CuMatrixBase< BaseFloat > &in, const CuMatrixBase< BaseFloat > &out, const CuMatrixBase< BaseFloat > &out_diff, CuMatrixBase< BaseFloat > *in_diff)
	Backward pass transformation (to be implemented by descending class...) More...

void	Update (const CuMatrixBase< BaseFloat > &input, const CuMatrixBase< BaseFloat > &diff)
	Compute gradient and update parameters,. More...

Public Member Functions inherited from UpdatableComponent
	UpdatableComponent (int32 input_dim, int32 output_dim)

virtual	~UpdatableComponent ()

bool	IsUpdatable () const
	Check if contains trainable parameters,. More...

virtual void	SetTrainOptions (const NnetTrainOptions &opts)
	Set the training options to the component,. More...

const NnetTrainOptions &	GetTrainOptions () const
	Get the training options from the component,. More...

virtual void	SetLearnRateCoef (BaseFloat val)
	Set the learn-rate coefficient,. More...

virtual void	SetBiasLearnRateCoef (BaseFloat val)
	Set the learn-rate coefficient for bias,. More...

Public Member Functions inherited from Component
	Component (int32 input_dim, int32 output_dim)
	Generic interface of a component,. More...

virtual	~Component ()

virtual bool	IsMultistream () const
	Check if component has 'Recurrent' interface (trainable and recurrent),. More...

int32	InputDim () const
	Get the dimension of the input,. More...

int32	OutputDim () const
	Get the dimension of the output,. More...

void	Propagate (const CuMatrixBase< BaseFloat > &in, CuMatrix< BaseFloat > *out)
	Perform forward-pass propagation 'in' -> 'out',. More...

void	Backpropagate (const CuMatrixBase< BaseFloat > &in, const CuMatrixBase< BaseFloat > &out, const CuMatrixBase< BaseFloat > &out_diff, CuMatrix< BaseFloat > *in_diff)
	Perform backward-pass propagation 'out_diff' -> 'in_diff'. More...

void	Write (std::ostream &os, bool binary) const
	Write the component to a stream,. More...

Private Attributes
CuVector< BaseFloat >	alpha_
	Vector of 'alphas', one value per neuron. More...

CuVector< BaseFloat >	beta_
	Vector of 'betas', one value per neuron. More...

CuVector< BaseFloat >	alpha_corr_
	Vector of 'alpha' updates. More...

CuVector< BaseFloat >	beta_corr_
	Vector of 'beta' updates. More...

CuMatrix< BaseFloat >	alpha_aux_
	Auxiliary matrix for getting 'alpha' updates,. More...

CuMatrix< BaseFloat >	beta_aux_
	Auxiliary matrix for getting 'beta' updates,. More...

BaseFloat	alpha_learn_rate_coef_
	Controls learning rate for alpha (0.0 disables learning),. More...

BaseFloat	beta_learn_rate_coef_
	Controls learning rate for beta (0.0 disables learning),. More...

Additional Inherited Members
Public Types inherited from Component
enum	ComponentType { kUnknown = 0x0, kUpdatableComponent = 0x0100, kAffineTransform, kLinearTransform, kConvolutionalComponent, kLstmProjected, kBlstmProjected, kRecurrentComponent, kActivationFunction = 0x0200, kSoftmax, kHiddenSoftmax, kBlockSoftmax, kSigmoid, kTanh, kParametricRelu, kDropout, kLengthNormComponent, kTranform = 0x0400, kRbm, kSplice, kCopy, kTranspose, kBlockLinearity, kAddShift, kRescale, kKlHmm = 0x0800, kSentenceAveragingComponent, kSimpleSentenceAveragingComponent, kAveragePoolingComponent, kMaxPoolingComponent, kFramePoolingComponent, kParallelComponent, kMultiBasisComponent }
	Component type identification mechanism,. More...

Static Public Member Functions inherited from Component
static const char *	TypeToMarker (ComponentType t)
	Converts component type to marker,. More...

static ComponentType	MarkerToType (const std::string &s)
	Converts marker to component type (case insensitive),. More...

static Component *	Init (const std::string &conf_line)
	Initialize component from a line in config file,. More...

static Component *	Read (std::istream &is, bool binary)
	Read the component from a stream (static method),. More...

Static Public Attributes inherited from Component
static const struct key_value	kMarkerMap []
	The table with pairs of Component types and markers (defined in nnet-component.cc),. More...

Protected Attributes inherited from UpdatableComponent
NnetTrainOptions	opts_
	Option-class with training hyper-parameters,. More...

BaseFloat	learn_rate_coef_
	Scalar applied to learning rate for weight matrices (to be used in ::Update method),. More...

BaseFloat	bias_learn_rate_coef_
	Scalar applied to learning rate for bias (to be used in ::Update method),. More...

Protected Attributes inherited from Component
int32	input_dim_
	Data members,. More...

int32	output_dim_
	Dimension of the output of the Component,. More...

Detailed Description

Definition at line 34 of file nnet-parametric-relu.h.

Constructor & Destructor Documentation

◆ ParametricRelu()

ParametricRelu	(	int32	dim_in,
		int32	dim_out
	)

inline

Definition at line 36 of file nnet-parametric-relu.h.

Referenced by ParametricRelu::Copy().

                                              :
     UpdatableComponent(dim_in, dim_out),
     alpha_(dim_out),
     beta_(dim_out),
     alpha_corr_(dim_out),
     beta_corr_(dim_out),
     alpha_learn_rate_coef_(0.0),
     beta_learn_rate_coef_(0.0)
   { }

◆ ~ParametricRelu()

~ParametricRelu ( )

inline

Definition at line 46 of file nnet-parametric-relu.h.

47 { }

Member Function Documentation

◆ BackpropagateFnc()

void BackpropagateFnc	(	const CuMatrixBase< BaseFloat > &	in,
		const CuMatrixBase< BaseFloat > &	out,
		const CuMatrixBase< BaseFloat > &	out_diff,
		CuMatrixBase< BaseFloat > *	in_diff
	)

inlinevirtual

Backward pass transformation (to be implemented by descending class...)

Implements Component.

Definition at line 158 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::beta_, and CuMatrixBase< Real >::DiffParametricRelu().

                                                           {
     // in_diff = (in > 0 ? alpha * out_diff : beta * out_diff)
     in_diff->DiffParametricRelu(in, out_diff, alpha_, beta_);
   }

◆ Copy()

Component* Copy ( ) const

inlinevirtual

Copy component (deep copy),.

Implements Component.

Definition at line 49 of file nnet-parametric-relu.h.

References ParametricRelu::ParametricRelu().

49 { return new ParametricRelu(*this); }

kaldi::nnet1::ParametricRelu::ParametricRelu

ParametricRelu(int32 dim_in, int32 dim_out)

Definition: nnet-parametric-relu.h:36

◆ GetGradient()

void GetGradient ( VectorBase< BaseFloat > * gradient ) const

inlinevirtual

Get gradient reshaped as a vector,.

Implements UpdatableComponent.

Definition at line 113 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::alpha_corr_, ParametricRelu::beta_, ParametricRelu::beta_corr_, VectorBase< Real >::Dim(), KALDI_ASSERT, ParametricRelu::NumParams(), and VectorBase< Real >::Range().

                                                           {
     KALDI_ASSERT(gradient->Dim() == NumParams());
     int32 alpha_num_elem = alpha_.Dim();
     int32 beta_num_elem = beta_.Dim();
     gradient->Range(0, alpha_num_elem).CopyFromVec(Vector<BaseFloat>(alpha_corr_));
     gradient->Range(alpha_num_elem, beta_num_elem).CopyFromVec(Vector<BaseFloat>(beta_corr_));
   }

◆ GetParams()

void GetParams ( VectorBase< BaseFloat > * params ) const

inlinevirtual

Get the trainable parameters reshaped as a vector,.

Implements UpdatableComponent.

Definition at line 121 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::beta_, VectorBase< Real >::Dim(), KALDI_ASSERT, ParametricRelu::NumParams(), and VectorBase< Real >::Range().

                                                       {
     KALDI_ASSERT(params->Dim() == NumParams());
     int32 alpha_num_elem = alpha_.Dim();
     int32 beta_num_elem = beta_.Dim();
     params->Range(0, alpha_num_elem).CopyFromVec(Vector<BaseFloat>(alpha_));
     params->Range(alpha_num_elem, beta_num_elem).CopyFromVec(Vector<BaseFloat>(beta_));
   }

◆ GetType()

ComponentType GetType ( ) const

inlinevirtual

Get Type Identification of the component,.

Implements Component.

Definition at line 50 of file nnet-parametric-relu.h.

References Component::kParametricRelu.

50 { return kParametricRelu; }

kaldi::nnet1::Component::kParametricRelu

Definition: nnet-component.h:64

◆ Info()

std::string Info ( ) const

inlinevirtual

Print some additional info (after <ComponentName> and the dims),.

Reimplemented from Component.

Definition at line 137 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::alpha_learn_rate_coef_, ParametricRelu::beta_, ParametricRelu::beta_learn_rate_coef_, kaldi::nnet1::MomentStatistics(), and kaldi::nnet1::ToString().

                          {
     return std::string("\n  alpha") +
       MomentStatistics(alpha_) +
       ", alpha-lr-coef " + ToString(alpha_learn_rate_coef_) +
       "\n  beta" + MomentStatistics(beta_) +
       ", beta-lr-coef " + ToString(beta_learn_rate_coef_);
   }

◆ InfoGradient()

std::string InfoGradient ( ) const

inlinevirtual

Print some additional info about gradient (after <...> and dims),.

Reimplemented from Component.

Definition at line 144 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_corr_, ParametricRelu::alpha_learn_rate_coef_, ParametricRelu::beta_corr_, ParametricRelu::beta_learn_rate_coef_, kaldi::nnet1::MomentStatistics(), and kaldi::nnet1::ToString().

                                  {
     return std::string("\n  alpha_grad") +
       MomentStatistics(alpha_corr_) +
       ", alpha-lr-coef " + ToString(alpha_learn_rate_coef_) +
       "\n  beta_grad" + MomentStatistics(beta_corr_) +
       ", beta-lr-coef " + ToString(beta_learn_rate_coef_);
   }

◆ InitData()

void InitData ( std::istream & is )

inlinevirtual

Initialize the content of the component by the 'line' from the prototype,.

Implements UpdatableComponent.

Definition at line 52 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::alpha_learn_rate_coef_, ParametricRelu::beta_, ParametricRelu::beta_learn_rate_coef_, KALDI_ERR, kaldi::ReadBasicType(), and kaldi::ReadToken().

                                 {
     // define options
     BaseFloat alpha = 1.0, beta = 0.0;
 
     // parse config
     std::string token;
     while (is >> std::ws, !is.eof()) {
       ReadToken(is, false, &token);
        if (token == "<Alpha>") ReadBasicType(is, false, &alpha);
       else if (token == "<Beta>") ReadBasicType(is, false, &beta);
       else if (token == "<AlphaLearnRateCoef>") ReadBasicType(is, false, &alpha_learn_rate_coef_);
       else if (token == "<BetaLearnRateCoef>") ReadBasicType(is, false, &beta_learn_rate_coef_);
       else KALDI_ERR << "Unknown token " << token << ", a typo in config?"
                   << " (Alpha|Beta|AlphaLearnRateCoef|BetaLearnRateCoef)";
     }
 
     // Initialize trainable parameters,
     alpha_.Set(alpha);
     beta_.Set(beta);
   }

◆ NumParams()

int32 NumParams ( ) const

inlinevirtual

Number of trainable parameters,.

Implements UpdatableComponent.

Definition at line 109 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, and ParametricRelu::beta_.

Referenced by ParametricRelu::GetGradient(), ParametricRelu::GetParams(), and ParametricRelu::SetParams().

                           {
     return alpha_.Dim() + beta_.Dim();
   }

◆ PropagateFnc()

void PropagateFnc	(	const CuMatrixBase< BaseFloat > &	in,
		CuMatrixBase< BaseFloat > *	out
	)

inlinevirtual

Abstract interface for propagation/backpropagation.

Forward pass transformation (to be implemented by descending class...)

Implements Component.

Definition at line 152 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::beta_, and CuMatrixBase< Real >::ParametricRelu().

                                                   {
     // out = (in < 0.0 ? aplha*in : beta*in)
     out->ParametricRelu(in, alpha_, beta_);
   }

◆ ReadData()

void ReadData	(	std::istream &	is,
		bool	binary
	)

inlinevirtual

Reads the component content.

Reimplemented from Component.

Definition at line 73 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::alpha_learn_rate_coef_, ParametricRelu::beta_, ParametricRelu::beta_learn_rate_coef_, kaldi::ExpectToken(), KALDI_ASSERT, KALDI_ERR, Component::output_dim_, kaldi::Peek(), kaldi::PeekToken(), kaldi::ReadBasicType(), and kaldi::ReadToken().

                                              {
     // Read all the '<Tokens>' in arbitrary order,
     while ('<' == Peek(is, binary)) {
       int first_char = PeekToken(is, binary);
       switch (first_char) {
         case 'A': ExpectToken(is, binary, "<AlphaLearnRateCoef>");
           ReadBasicType(is, binary, &alpha_learn_rate_coef_);
           break;
         case 'B': ExpectToken(is, binary, "<BetaLearnRateCoef>");
           ReadBasicType(is, binary, &beta_learn_rate_coef_);
           break;
         default:
           std::string token;
           ReadToken(is, false, &token);
           KALDI_ERR << "Unknown token: " << token;
       }
     }
     // ParametricRelu scaling parameters
     alpha_.Read(is, binary);
     beta_.Read(is, binary);
     KALDI_ASSERT(alpha_.Dim() == output_dim_);
     KALDI_ASSERT(beta_.Dim() == output_dim_);
   }

◆ SetParams()

void SetParams ( const VectorBase< BaseFloat > & params )

inlinevirtual

Set the trainable parameters from, reshaped as a vector,.

Implements UpdatableComponent.

Definition at line 129 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::beta_, VectorBase< Real >::Dim(), KALDI_ASSERT, ParametricRelu::NumParams(), and VectorBase< Real >::Range().

                                                       {
     KALDI_ASSERT(params.Dim() == NumParams());
     int32 alpha_num_elem = alpha_.Dim();
     int32 beta_num_elem = beta_.Dim();
     alpha_.CopyFromVec(params.Range(0, alpha_num_elem));
     beta_.CopyFromVec(params.Range(alpha_num_elem, beta_num_elem));
   }

◆ Update()

void Update	(	const CuMatrixBase< BaseFloat > &	input,
		const CuMatrixBase< BaseFloat > &	diff
	)

inlinevirtual

Compute gradient and update parameters,.

Implements UpdatableComponent.

Definition at line 166 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::alpha_aux_, ParametricRelu::alpha_corr_, ParametricRelu::alpha_learn_rate_coef_, ParametricRelu::beta_, ParametricRelu::beta_aux_, ParametricRelu::beta_corr_, ParametricRelu::beta_learn_rate_coef_, NnetTrainOptions::learn_rate, NnetTrainOptions::momentum, and UpdatableComponent::opts_.

                                                    {
     // we use these hyperparameters,
     const BaseFloat alpha_lr = opts_.learn_rate * alpha_learn_rate_coef_;
     const BaseFloat beta_lr = opts_.learn_rate * beta_learn_rate_coef_;
     const BaseFloat mmt = opts_.momentum;
 
     if (alpha_learn_rate_coef_ > 0.0) {
        // get gradient,
        alpha_aux_ = input;
        alpha_aux_.ApplyFloor(0.0); // masking positive Relu inputs,
        alpha_aux_.MulElements(diff);
        alpha_corr_.AddRowSumMat(1.0, alpha_aux_, mmt);
        // update,
        alpha_.AddVec(-alpha_lr, alpha_corr_);
     }
     if (beta_learn_rate_coef_ > 0.0) {
        // get gradient,
        beta_aux_ = input;
        beta_aux_.ApplyCeiling(0.0); // masking positive Relu inputs,
        beta_aux_.MulElements(diff);
        beta_corr_.AddRowSumMat(1.0, beta_aux_, mmt);
        beta_.AddVec(-beta_lr, beta_corr_);
     }
   }

◆ WriteData()

void WriteData	(	std::ostream &	os,
		bool	binary
	)		const

inlinevirtual

Writes the component content.

Reimplemented from Component.

Definition at line 97 of file nnet-parametric-relu.h.

References ParametricRelu::alpha_, ParametricRelu::alpha_learn_rate_coef_, ParametricRelu::beta_, ParametricRelu::beta_learn_rate_coef_, kaldi::WriteBasicType(), and kaldi::WriteToken().

                                                     {
     WriteToken(os, binary, "<AlphaLearnRateCoef>");
     WriteBasicType(os, binary, alpha_learn_rate_coef_);
     WriteToken(os, binary, "<BetaLearnRateCoef>");
     WriteBasicType(os, binary, beta_learn_rate_coef_);
 
     // ParametricRelu scales for each neuron,
     if (!binary) os << "\n";
     alpha_.Write(os, binary);
     beta_.Write(os, binary);
   }