PLearn 0.1
Public Member Functions | Static Public Member Functions | Static Public Attributes | Protected Member Functions | Private Types
PLearn::SoftmaxLossVariable Class Reference

#include <SoftmaxLossVariable.h>

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List of all members.

Public Member Functions

 SoftmaxLossVariable ()
 Default constructor for persistence.
 SoftmaxLossVariable (Variable *input1, Variable *input2)
virtual string classname () const
virtual OptionListgetOptionList () const
virtual OptionMapgetOptionMap () const
virtual RemoteMethodMapgetRemoteMethodMap () const
virtual SoftmaxLossVariabledeepCopy (CopiesMap &copies) const
virtual void build ()
 Post-constructor.
virtual void recomputeSize (int &l, int &w) const
 Recomputes the length l and width w that this variable should have, according to its parent variables.
virtual void fprop ()
 compute output given input
virtual void bprop ()
virtual void bbprop ()
 compute an approximation to diag(d^2/dinput^2) given diag(d^2/doutput^2), with diag(d^2/dinput^2) ~=~ (doutput/dinput)' diag(d^2/doutput^2) (doutput/dinput) In particular: if 'C' depends on 'y' and 'y' depends on x ...
virtual void symbolicBprop ()
 compute a piece of new Var graph that represents the symbolic derivative of this Var
virtual void rfprop ()

Static Public Member Functions

static string _classname_ ()
 SoftmaxLossVariable.
static OptionList_getOptionList_ ()
static RemoteMethodMap_getRemoteMethodMap_ ()
static Object_new_instance_for_typemap_ ()
static bool _isa_ (const Object *o)
static void _static_initialize_ ()
static const PPathdeclaringFile ()

Static Public Attributes

static StaticInitializer _static_initializer_

Protected Member Functions

void build_ ()
 This does the actual building.

Private Types

typedef BinaryVariable inherited

Detailed Description

Definition at line 53 of file SoftmaxLossVariable.h.


Member Typedef Documentation

Reimplemented from PLearn::BinaryVariable.

Definition at line 55 of file SoftmaxLossVariable.h.


Constructor & Destructor Documentation

PLearn::SoftmaxLossVariable::SoftmaxLossVariable ( ) [inline]

Default constructor for persistence.

Definition at line 59 of file SoftmaxLossVariable.h.

{}
PLearn::SoftmaxLossVariable::SoftmaxLossVariable ( Variable input1,
Variable input2 
)

Definition at line 58 of file SoftmaxLossVariable.cc.

References build_().

    : inherited(input1, input2, 1, 1)
{
    build_();
}

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Member Function Documentation

string PLearn::SoftmaxLossVariable::_classname_ ( ) [static]

SoftmaxLossVariable.

Reimplemented from PLearn::BinaryVariable.

Definition at line 56 of file SoftmaxLossVariable.cc.

OptionList & PLearn::SoftmaxLossVariable::_getOptionList_ ( ) [static]

Reimplemented from PLearn::BinaryVariable.

Definition at line 56 of file SoftmaxLossVariable.cc.

RemoteMethodMap & PLearn::SoftmaxLossVariable::_getRemoteMethodMap_ ( ) [static]

Reimplemented from PLearn::BinaryVariable.

Definition at line 56 of file SoftmaxLossVariable.cc.

bool PLearn::SoftmaxLossVariable::_isa_ ( const Object o) [static]

Reimplemented from PLearn::BinaryVariable.

Definition at line 56 of file SoftmaxLossVariable.cc.

Object * PLearn::SoftmaxLossVariable::_new_instance_for_typemap_ ( ) [static]

Reimplemented from PLearn::Object.

Definition at line 56 of file SoftmaxLossVariable.cc.

StaticInitializer SoftmaxLossVariable::_static_initializer_ & PLearn::SoftmaxLossVariable::_static_initialize_ ( ) [static]

Reimplemented from PLearn::BinaryVariable.

Definition at line 56 of file SoftmaxLossVariable.cc.

void PLearn::SoftmaxLossVariable::bbprop ( ) [virtual]

compute an approximation to diag(d^2/dinput^2) given diag(d^2/doutput^2), with diag(d^2/dinput^2) ~=~ (doutput/dinput)' diag(d^2/doutput^2) (doutput/dinput) In particular: if 'C' depends on 'y' and 'y' depends on x ...

d^2C/dx^2 = d^2C/dy^2 * (dy/dx)^2 + dC/dy * d^2y/dx^2 (diaghessian) (gradient)

Reimplemented from PLearn::Variable.

Definition at line 108 of file SoftmaxLossVariable.cc.

References PLERROR.

{
    PLERROR("SofmaxVariable::bbprop() not implemented");
}
void PLearn::SoftmaxLossVariable::bprop ( ) [virtual]

Implements PLearn::Variable.

Definition at line 92 of file SoftmaxLossVariable.cc.

References PLearn::Variable::gradientdata, i, PLearn::BinaryVariable::input1, PLearn::BinaryVariable::input2, PLearn::Variable::nelems(), PLearn::safeexp(), and PLearn::Variable::valuedata.

{
    int classnum = (int)input2->valuedata[0];
    real input_index = input1->valuedata[classnum];
    real vali = valuedata[0];
    for(int i=0; i<input1->nelems(); i++)
    {
        if (i!=classnum)
            //input1->gradientdata[i] = -gradientdata[i]/*?*/*vali*vali*safeexp(input1->valuedata[i]-input_index);
            input1->gradientdata[i] = -gradientdata[i]*vali*vali*safeexp(input1->valuedata[i]-input_index);
        else
            input1->gradientdata[i] = gradientdata[i]*vali*(1.-vali);
    }
}

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void PLearn::SoftmaxLossVariable::build ( ) [virtual]

Post-constructor.

The normal implementation should call simply inherited::build(), then this class's build_(). This method should be callable again at later times, after modifying some option fields to change the "architecture" of the object.

Reimplemented from PLearn::BinaryVariable.

Definition at line 65 of file SoftmaxLossVariable.cc.

References PLearn::BinaryVariable::build(), and build_().

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void PLearn::SoftmaxLossVariable::build_ ( ) [protected]

This does the actual building.

Reimplemented from PLearn::BinaryVariable.

Definition at line 72 of file SoftmaxLossVariable.cc.

References PLearn::BinaryVariable::input2, and PLERROR.

Referenced by build(), and SoftmaxLossVariable().

{    
    if(input2 && !input2->isScalar())
        PLERROR("In RowAtPositionVariable: position must be a scalar");
}

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string PLearn::SoftmaxLossVariable::classname ( ) const [virtual]

Reimplemented from PLearn::Object.

Definition at line 56 of file SoftmaxLossVariable.cc.

static const PPath& PLearn::SoftmaxLossVariable::declaringFile ( ) [inline, static]

Reimplemented from PLearn::BinaryVariable.

Definition at line 62 of file SoftmaxLossVariable.h.

:
    void build_();
SoftmaxLossVariable * PLearn::SoftmaxLossVariable::deepCopy ( CopiesMap copies) const [virtual]

Reimplemented from PLearn::BinaryVariable.

Definition at line 56 of file SoftmaxLossVariable.cc.

void PLearn::SoftmaxLossVariable::fprop ( ) [virtual]

compute output given input

Implements PLearn::Variable.

Definition at line 81 of file SoftmaxLossVariable.cc.

References i, PLearn::BinaryVariable::input1, PLearn::BinaryVariable::input2, PLearn::Variable::nelems(), PLearn::safeexp(), PLearn::sum(), and PLearn::Variable::valuedata.

{
    int classnum = (int)input2->valuedata[0];
    real input_index = input1->valuedata[classnum];
    real sum=0;
    for(int i=0; i<input1->nelems(); i++)
        sum += safeexp(input1->valuedata[i]-input_index);
    valuedata[0] = 1.0/sum;
}

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OptionList & PLearn::SoftmaxLossVariable::getOptionList ( ) const [virtual]

Reimplemented from PLearn::Object.

Definition at line 56 of file SoftmaxLossVariable.cc.

OptionMap & PLearn::SoftmaxLossVariable::getOptionMap ( ) const [virtual]

Reimplemented from PLearn::Object.

Definition at line 56 of file SoftmaxLossVariable.cc.

RemoteMethodMap & PLearn::SoftmaxLossVariable::getRemoteMethodMap ( ) const [virtual]

Reimplemented from PLearn::Object.

Definition at line 56 of file SoftmaxLossVariable.cc.

void PLearn::SoftmaxLossVariable::recomputeSize ( int l,
int w 
) const [virtual]

Recomputes the length l and width w that this variable should have, according to its parent variables.

This is used for ex. by sizeprop() The default version stupidly returns the current dimensions, so make sure to overload it in subclasses if this is not appropriate.

Reimplemented from PLearn::Variable.

Definition at line 78 of file SoftmaxLossVariable.cc.

{ l=1; w=1; }
void PLearn::SoftmaxLossVariable::rfprop ( ) [virtual]

Reimplemented from PLearn::Variable.

Definition at line 123 of file SoftmaxLossVariable.cc.

References i, PLearn::BinaryVariable::input1, PLearn::BinaryVariable::input2, PLearn::TVec< T >::length(), PLearn::Variable::nelems(), PLearn::BinaryVariable::resizeRValue(), PLearn::Variable::rValue, PLearn::Variable::rvaluedata, PLearn::safeexp(), PLearn::sum(), and PLearn::Variable::valuedata.

{
    if (rValue.length()==0) resizeRValue();

    int classnum = (int)input2->valuedata[0];
    real input_index = input1->valuedata[classnum];
    real vali = valuedata[0];
    real sum = 0;
    for(int i=0; i<input1->nelems(); i++)
    {
        real res =vali * input1->rvaluedata[i];
        if (i != classnum)
            sum -= res * vali* safeexp(input1->valuedata[i]-input_index);
        else sum += res * (1 - vali);
    }
    rvaluedata[0] = sum;
}

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void PLearn::SoftmaxLossVariable::symbolicBprop ( ) [virtual]

compute a piece of new Var graph that represents the symbolic derivative of this Var

Reimplemented from PLearn::Variable.

Definition at line 114 of file SoftmaxLossVariable.cc.

References PLearn::exp(), PLearn::Variable::g, PLearn::BinaryVariable::input1, PLearn::BinaryVariable::input2, PLearn::Var::length(), and PLearn::Variable::Var.

{
    Var gi = -g * Var(this) * Var(this) * exp(input1-input1(input2));
    Var gindex = new RowAtPositionVariable(g * Var(this), input2, input1->length());
    input1->accg(gi+gindex);
}

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Member Data Documentation

Reimplemented from PLearn::BinaryVariable.

Definition at line 62 of file SoftmaxLossVariable.h.


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