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

Trains a stack of OnlineLearningModule, which are layers. More...

#include <StackedModulesLearner.h>

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List of all members.

Public Member Functions

 StackedModulesLearner ()
 Default constructor.
virtual int outputsize () const
 Returns the size of this learner's output, (which typically may depend on its inputsize(), targetsize() and set options).
virtual void forget ()
 (Re-)initializes the PLearner in its fresh state (that state may depend on the 'seed' option) and sets 'stage' back to 0 (this is the stage of a fresh learner!).
virtual void train ()
 The role of the train method is to bring the learner up to stage==nstages, updating the train_stats collector with training costs measured on-line in the process.
virtual void computeOutput (const Vec &input, Vec &output) const
 Computes the output from the input.
virtual void computeCostsFromOutputs (const Vec &input, const Vec &output, const Vec &target, Vec &costs) const
 Computes the costs from already computed output.
virtual TVec< std::string > getTestCostNames () const
 Returns the names of the costs computed by computeCostsFromOutpus (and thus the test method).
virtual TVec< std::string > getTrainCostNames () const
 Returns the names of the objective costs that the train method computes and for which it updates the VecStatsCollector train_stats.
virtual string classname () const
virtual OptionListgetOptionList () const
virtual OptionMapgetOptionMap () const
virtual RemoteMethodMapgetRemoteMethodMap () const
virtual StackedModulesLearnerdeepCopy (CopiesMap &copies) const
virtual void build ()
 Finish building the object; just call inherited::build followed by build_()
virtual void makeDeepCopyFromShallowCopy (CopiesMap &copies)
 Transforms a shallow copy into a deep copy.

Static Public Member Functions

static string _classname_ ()
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 ()

Public Attributes

TVec< PP< OnlineLearningModule > > modules
 Layers of the learner.
TVec< string > cost_funcs
 Names of the cost functions to apply on output.
string hessian_estimation
 Estimation of the second-order terms.

Static Public Attributes

static StaticInitializer _static_initializer_

Static Protected Member Functions

static void declareOptions (OptionList &ol)
 Declares the class options.

Protected Attributes

int nmodules
 Number of module layers.
TVec< PP< OnlineLearningModule > > cost_modules
 Modules that will compute the costs.
int ncosts
 Number of cost modules.
TVec< Vecvalues
 stores the input and output values of the functions
TVec< Vecgradients
 stores the gradients
TVec< Vecdiag_hessians
 stores the diagonal of Hessians

Private Types

typedef PLearner inherited

Private Member Functions

void build_ ()
 This does the actual building.
void buildOptions ()
void buildCostFunctions ()
void buildLayers ()

Detailed Description

Trains a stack of OnlineLearningModule, which are layers.

The OnlineLearningModule's are disposed like superposed layers: outputs of module i are the inputs of module (i+1), the last layer is the output layer. Another TVec of modules contains the cost modules. The first one is used during the training phase as the cost to minimize, the other ones are only measured.

Todo:
Finish this class...
Deprecated:
Use ../ModulesLearner instead

Definition at line 63 of file StackedModulesLearner.h.


Member Typedef Documentation

Reimplemented from PLearn::PLearner.

Definition at line 65 of file StackedModulesLearner.h.


Constructor & Destructor Documentation

PLearn::StackedModulesLearner::StackedModulesLearner ( )

Default constructor.

Definition at line 61 of file StackedModulesLearner.cc.

References PLearn::PLearner::random_gen.

    : cost_funcs( 1, "mse" ),
      hessian_estimation( "none" ),
      nmodules( 0 ),
      ncosts( 1 )
{
    random_gen = new PRandom();
}

Member Function Documentation

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

Reimplemented from PLearn::PLearner.

Definition at line 59 of file StackedModulesLearner.cc.

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

Reimplemented from PLearn::PLearner.

Definition at line 59 of file StackedModulesLearner.cc.

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

Reimplemented from PLearn::PLearner.

Definition at line 59 of file StackedModulesLearner.cc.

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

Reimplemented from PLearn::PLearner.

Definition at line 59 of file StackedModulesLearner.cc.

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

Reimplemented from PLearn::Object.

Definition at line 59 of file StackedModulesLearner.cc.

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

Reimplemented from PLearn::PLearner.

Definition at line 59 of file StackedModulesLearner.cc.

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

Finish building the object; just call inherited::build followed by build_()

Reimplemented from PLearn::PLearner.

Definition at line 266 of file StackedModulesLearner.cc.

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

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void PLearn::StackedModulesLearner::build_ ( ) [private]

This does the actual building.

Reimplemented from PLearn::PLearner.

Definition at line 115 of file StackedModulesLearner.cc.

References buildCostFunctions(), buildLayers(), buildOptions(), PLearn::PLearner::inputsize_, PLearn::PLearner::random_gen, and PLearn::PLearner::seed_.

Referenced by build().

{
    // ### This method should do the real building of the object,
    // ### according to set 'options', in *any* situation.
    // ### Typical situations include:
    // ###  - Initial building of an object from a few user-specified options
    // ###  - Building of a "reloaded" object: i.e. from the complete set of
    // ###    all serialised options.
    // ###  - Updating or "re-building" of an object after a few "tuning"
    // ###    options have been modified.
    // ### You should assume that the parent class' build_() has already been
    // ### called.

    // build the layers
    buildOptions();

    // initialize random generator from seed
    random_gen->manual_seed( seed_ );

    // if train_set is not set, we don't know inputsize nor targetsize
    if( inputsize_ >= 0 ) // we don't use inputsize() because it crashes if <0
        buildLayers();

    // build cost functions
    buildCostFunctions();
}

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void PLearn::StackedModulesLearner::buildCostFunctions ( ) [private]

Definition at line 183 of file StackedModulesLearner.cc.

References cost_funcs, cost_modules, hessian_estimation, i, ncosts, and outputsize().

Referenced by build_().

{
    // build cost functions
    for( int i=0 ; i<ncosts ; i++ )
    {
        string cf = cost_funcs[i];
        if( cf == "mse" )
        {
            PP<SquaredErrModule> p_mse;
            // if the first module is not already a SquaredErrModule,
            // allocate a new one
            if( !(p_mse = dynamic_cast<SquaredErrModule*>(
                    (OnlineLearningModule*) cost_modules[i] )) )
            {
                p_mse = new SquaredErrModule();
                cost_modules[i] = p_mse;
            }
        }
        else if( cf == "NLL" )
        {
            PP<NLLErrModule> p_nll;
            // if the first module is not already a NLLErrModule,
            // allocate a new one
            if( !(p_nll = dynamic_cast<NLLErrModule*>(
                    (OnlineLearningModule*) cost_modules[i] )) )
            {
                p_nll = new NLLErrModule();
                cost_modules[i] = p_nll;
            }
        }

        cost_modules[i]->input_size = outputsize();
        if( hessian_estimation == "diag" )
            cost_modules[i]->estimate_simpler_diag_hessian = false;
        else
            cost_modules[i]->estimate_simpler_diag_hessian = true;
        cost_modules[i]->build();
    }
}

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void PLearn::StackedModulesLearner::buildLayers ( ) [private]

Definition at line 223 of file StackedModulesLearner.cc.

References diag_hessians, gradients, hessian_estimation, i, PLearn::PLearner::inputsize(), modules, nmodules, PLWARNING, PLearn::TVec< T >::resize(), and values.

Referenced by build_().

{
    // first values will be "input" values
    int size = inputsize();
    values.resize( nmodules+1 );
    values[0].resize( size );
    gradients.resize( nmodules+1 );
    gradients[0].resize( size );
    if( hessian_estimation != "none" )
    {
        diag_hessians.resize( nmodules+1 );
        diag_hessians[0].resize( size );
    }

    for( int i=0 ; i<nmodules ; i++ )
    {
        PP<OnlineLearningModule> p_module = modules[i];
        if( p_module->input_size != size )
        {
            PLWARNING( "StackedModulesLearner::buildLayers(): module '%d'\n"
                       "has an input size of '%d', but previous layer's output"
                       " size\n"
                       "is '%d'. Resizing module '%d'.\n",
                       i, p_module->input_size, size, i);
            p_module->input_size = size;
        }

        if( hessian_estimation == "diag" )
            p_module->estimate_simpler_diag_hessian = false;
        else
            p_module->estimate_simpler_diag_hessian = true;

        p_module->build();

        size = p_module->output_size;
        values[i+1].resize( size );
        gradients[i+1].resize( size );
        if( hessian_estimation != "none" )
            diag_hessians[i+1].resize( size );
    }
}

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void PLearn::StackedModulesLearner::buildOptions ( ) [private]

Definition at line 142 of file StackedModulesLearner.cc.

References cost_funcs, cost_modules, hessian_estimation, i, PLearn::TVec< T >::length(), PLearn::lowerstring(), modules, ncosts, nmodules, PLERROR, PLWARNING, and PLearn::TVec< T >::resize().

Referenced by build_().

{
    nmodules = modules.length();
    ncosts = cost_funcs.length();

    // check length of cost_modules
    int ncm = cost_modules.length();
    if( ncm != 0 && ncm != ncosts )
        PLWARNING( "StackedModulesLearner::buildOptions(): 'cost_modules' is"
                   " set,\n"
                   "but its length differ from 'cost_funcs.length()'"
                   " (%d != %d).\n",
                   ncm, ncosts);
    cost_modules.resize( ncosts );

    // check string's values
    for( int i=0 ; i<ncosts ; i++ )
    {
        string cf = lowerstring( cost_funcs[i] );
        if( cf == "nll" )
            cost_funcs[i] = "NLL";
        else if( cf == "mse" || cf == "" )
            cost_funcs[i] = "mse";
        else
            PLERROR( "StackedModulesLearner::buildOptions(): cost function\n"
                     "'%s' is unknown.\n", cost_funcs[i].c_str() );
    }

    // hessian estimation
    string h_est = lowerstring( hessian_estimation );
    if( h_est == "none" || h_est == "" )
        hessian_estimation = "none";
    else if( h_est == "diag" )
        hessian_estimation = h_est;
    else if( h_est == "simpler_diag" )
        hessian_estimation = h_est;
    else
        PLERROR( "StackedModulesLearner::buildOptions(): hessian_estimation\n"
                 "value '%s' is unknown.\n", hessian_estimation.c_str() );
}

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

Reimplemented from PLearn::Object.

Definition at line 59 of file StackedModulesLearner.cc.

void PLearn::StackedModulesLearner::computeCostsFromOutputs ( const Vec input,
const Vec output,
const Vec target,
Vec costs 
) const [virtual]

Computes the costs from already computed output.

Implements PLearn::PLearner.

Definition at line 442 of file StackedModulesLearner.cc.

References PLearn::TVec< T >::append(), PLearn::TVec< T >::copy(), cost_modules, i, and ncosts.

{
    Vec out_tgt = output.copy();
    out_tgt.append( target );
    for( int i=0 ; i<ncosts ; i++ )
    {
        Vec cost(1);
        cost_modules[i]->fprop( out_tgt, cost );
        costs[i] = cost[0];
    }
}

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void PLearn::StackedModulesLearner::computeOutput ( const Vec input,
Vec output 
) const [virtual]

Computes the output from the input.

Reimplemented from PLearn::PLearner.

Definition at line 430 of file StackedModulesLearner.cc.

References i, modules, nmodules, outputsize(), PLearn::TVec< T >::resize(), and values.

{
    values[0] << input;

    // fprop
    for( int i=0 ; i<nmodules ; i++ )
        modules[i]->fprop( values[i], values[i+1] );

    output.resize( outputsize() );
    output << values[ nmodules ];
}

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void PLearn::StackedModulesLearner::declareOptions ( OptionList ol) [static, protected]

Declares the class options.

Reimplemented from PLearn::PLearner.

Definition at line 70 of file StackedModulesLearner.cc.

References PLearn::OptionBase::buildoption, cost_funcs, cost_modules, PLearn::declareOption(), PLearn::PLearner::declareOptions(), hessian_estimation, PLearn::OptionBase::learntoption, modules, ncosts, nmodules, and PLearn::PLearner::random_gen.

{
    declareOption(ol, "modules", &StackedModulesLearner::modules,
                  OptionBase::buildoption,
                  "Layers of the learner");

    declareOption(ol, "cost_funcs", &StackedModulesLearner::cost_funcs,
                  OptionBase::buildoption,
                  "Names of the cost functions to apply on output. First one\n"
                  "will be used ass the cost function to optimize. For the"
                  " moment,\n"
                  "supported value are:\n"
                  "   - \"mse\" (default)\n"
                  "   - \"NLL\"\n");

    declareOption(ol, "hessian_estimation",
                  &StackedModulesLearner::hessian_estimation,
                  OptionBase::buildoption,
                  "Estimation of the second-order terms. One of:\n"
                  "  - \"none\": using only first-order derivative for"
                  " update,\n"
                  "  - \"diag\": estimating the diagonal of the hessian,\n"
                  "  - \"simpler_diag\": positive estimation of the diagonal\n"
                 );

    declareOption(ol, "random_gen", &StackedModulesLearner::random_gen,
                  OptionBase::buildoption,
                  "Random numbers generator.");

    declareOption(ol, "nmodules", &StackedModulesLearner::nmodules,
                  OptionBase::learntoption,
                  "Number of module layers");

    declareOption(ol, "cost_modules", &StackedModulesLearner::cost_modules,
                  OptionBase::learntoption,
                  "Modules that will compute the costs");

    declareOption(ol, "ncosts", &StackedModulesLearner::ncosts,
                  OptionBase::learntoption,
                  "Number of cost modules");

    // Now call the parent class' declareOptions
    inherited::declareOptions(ol);
}

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

Reimplemented from PLearn::PLearner.

Definition at line 155 of file StackedModulesLearner.h.

:
    //#####  Protected Options  ###############################################
StackedModulesLearner * PLearn::StackedModulesLearner::deepCopy ( CopiesMap copies) const [virtual]

Reimplemented from PLearn::PLearner.

Definition at line 59 of file StackedModulesLearner.cc.

void PLearn::StackedModulesLearner::forget ( ) [virtual]

(Re-)initializes the PLearner in its fresh state (that state may depend on the 'seed' option) and sets 'stage' back to 0 (this is the stage of a fresh learner!).

(Re-)initialize the PLearner in its fresh state (that state may depend on the 'seed' option) and sets 'stage' back to 0 (this is the stage of a fresh learner!)

A typical forget() method should do the following:

  • initialize a random number generator with the seed option
  • initialize the learner's parameters, using this random generator
  • stage = 0

Reimplemented from PLearn::PLearner.

Definition at line 303 of file StackedModulesLearner.cc.

References PLearn::TVec< T >::clear(), diag_hessians, gradients, hessian_estimation, i, modules, nmodules, PLearn::PLearner::random_gen, PLearn::PLearner::seed_, PLearn::PLearner::stage, and values.

{

    random_gen->manual_seed( seed_ );

    // reset inputs
    values[0].clear();
    gradients[0].clear();
    if( hessian_estimation != "none" )
        diag_hessians[0].clear();

    // reset modules and outputs
    for( int i=0 ; i<nmodules ; i++ )
    {
        modules[i]->forget();
        values[i+1].clear();
        gradients[i+1].clear();
        if( hessian_estimation != "none" )
            diag_hessians[i+1].clear();
    }

    stage = 0;
}

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

Reimplemented from PLearn::Object.

Definition at line 59 of file StackedModulesLearner.cc.

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

Reimplemented from PLearn::Object.

Definition at line 59 of file StackedModulesLearner.cc.

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

Reimplemented from PLearn::Object.

Definition at line 59 of file StackedModulesLearner.cc.

TVec< string > PLearn::StackedModulesLearner::getTestCostNames ( ) const [virtual]

Returns the names of the costs computed by computeCostsFromOutpus (and thus the test method).

Implements PLearn::PLearner.

Definition at line 457 of file StackedModulesLearner.cc.

References cost_funcs.

{
    // Return the names of the costs computed by computeCostsFromOutputs
    return cost_funcs;
}
TVec< string > PLearn::StackedModulesLearner::getTrainCostNames ( ) const [virtual]

Returns the names of the objective costs that the train method computes and for which it updates the VecStatsCollector train_stats.

Implements PLearn::PLearner.

Definition at line 463 of file StackedModulesLearner.cc.

References cost_funcs.

{
    // Return the names of the objective costs that the train method computes
    // and for which it updates the VecStatsCollector train_stats
    return cost_funcs;
}
void PLearn::StackedModulesLearner::makeDeepCopyFromShallowCopy ( CopiesMap copies) [virtual]

Transforms a shallow copy into a deep copy.

Reimplemented from PLearn::PLearner.

Definition at line 273 of file StackedModulesLearner.cc.

References cost_funcs, cost_modules, PLearn::deepCopyField(), diag_hessians, gradients, PLearn::PLearner::makeDeepCopyFromShallowCopy(), modules, PLearn::PLearner::random_gen, and values.

{
    inherited::makeDeepCopyFromShallowCopy(copies);

    // ### Call deepCopyField on all "pointer-like" fields
    // ### that you wish to be deepCopied rather than
    // ### shallow-copied.
    // ### ex:
    // deepCopyField(trainvec, copies);

    deepCopyField(modules, copies);
    deepCopyField(cost_funcs, copies);
    deepCopyField(random_gen, copies);
    deepCopyField(cost_modules, copies);
    deepCopyField(values, copies);
    deepCopyField(gradients, copies);
    deepCopyField(diag_hessians, copies);
}

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int PLearn::StackedModulesLearner::outputsize ( ) const [virtual]

Returns the size of this learner's output, (which typically may depend on its inputsize(), targetsize() and set options).

Implements PLearn::PLearner.

Definition at line 293 of file StackedModulesLearner.cc.

References PLearn::TVec< T >::length(), nmodules, and values.

Referenced by buildCostFunctions(), computeOutput(), and train().

{
    // Compute and return the size of this learner's output (which typically
    // may depend on its inputsize(), targetsize() and set options).
    if( nmodules < 0 || values.length() <= nmodules )
        return -1;
    else
        return values[ nmodules ].length();
}

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void PLearn::StackedModulesLearner::train ( ) [virtual]

The role of the train method is to bring the learner up to stage==nstages, updating the train_stats collector with training costs measured on-line in the process.

Implements PLearn::PLearner.

Definition at line 336 of file StackedModulesLearner.cc.

References PLearn::TVec< T >::append(), PLearn::PLearner::computeOutputAndCosts(), cost_modules, diag_hessians, PLearn::VMat::getExample(), gradients, hessian_estimation, i, PLearn::PLearner::initTrain(), PLearn::PLearner::inputsize(), PLearn::VMat::length(), modules, ncosts, nmodules, PLearn::PLearner::nstages, outputsize(), PLearn::sample(), PLearn::PLearner::stage, PLearn::TVec< T >::subVec(), PLearn::PLearner::targetsize(), PLearn::PLearner::train_set, PLearn::PLearner::train_stats, and values.

{
    // The role of the train method is to bring the learner up to
    // stage==nstages, updating train_stats with training costs measured
    // on-line in the process.

    /* TYPICAL CODE:

    static Vec input;  // static so we don't reallocate memory each time...
    static Vec target; // (but be careful that static means shared!)
    input.resize(inputsize());    // the train_set's inputsize()
    target.resize(targetsize());  // the train_set's targetsize()
    real weight;

    // This generic PLearner method does a number of standard stuff useful for
    // (almost) any learner, and return 'false' if no training should take
    // place. See PLearner.h for more details.
    if (!initTrain())
        return;

    while(stage<nstages)
    {
        // clear statistics of previous epoch
        train_stats->forget();

        //... train for 1 stage, and update train_stats,
        // using train_set->getExample(input, target, weight)
        // and train_stats->update(train_costs)

        ++stage;
        train_stats->finalize(); // finalize statistics for this epoch
    }
    */

    Vec input( inputsize() );
    Vec target( targetsize() );
    real weight;
    Vec train_costs( ncosts );
    Vec output( outputsize() );
    int nsamples = train_set->length();

    if( !initTrain() )
        return;

    for( ; stage < nstages ; stage++ )
    {
        // clear stats of previous epoch
        train_stats->forget();
        for( int sample=0 ; sample < nsamples ; sample++ )
        {
            train_set->getExample( sample, input, target, weight );

            // fprop
            computeOutputAndCosts(input, target, output, train_costs);
            output.append( target );

            // bprop
            Vec out_gradient(1,1); // the gradient wrt the cost is '1'
            Vec out_dh(1); // the hessian wrt the cost is '0'

            if( hessian_estimation != "none" ) // bbpropUpdate
            {
                cost_modules[0]->bbpropUpdate( output,
                                               train_costs.subVec(0,1),
                                               gradients[ nmodules ],
                                               out_gradient,
                                               diag_hessians[ nmodules ],
                                               out_dh );

                for( int i=nmodules-1 ; i>=0 ; i-- )
                    modules[i]->bbpropUpdate( values[i], values[i+1],
                                              gradients[i], gradients[i+1],
                                              diag_hessians[i],
                                              diag_hessians[i+1] );
            }
            else // bpropUpdate
            {
                cost_modules[0]->bpropUpdate( output,
                                              train_costs.subVec(0,1),
                                              gradients[ nmodules ],
                                              out_gradient );

                for( int i=nmodules-1 ; i>=0 ; i-- )
                    modules[i]->bpropUpdate( values[i], values[i+1],
                                             gradients[i], gradients[i+1] );
            }

            train_stats->update( train_costs );
        }
        train_stats->finalize(); // finalize statistics for this epoch
    }
}

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Member Data Documentation

Reimplemented from PLearn::PLearner.

Definition at line 155 of file StackedModulesLearner.h.

Names of the cost functions to apply on output.

First one will be used as the cost function to optimize. For the moment, supported value are:

  • "mse" (default)
  • "NLL"

Definition at line 77 of file StackedModulesLearner.h.

Referenced by buildCostFunctions(), buildOptions(), declareOptions(), getTestCostNames(), getTrainCostNames(), and makeDeepCopyFromShallowCopy().

Modules that will compute the costs.

Definition at line 173 of file StackedModulesLearner.h.

Referenced by buildCostFunctions(), buildOptions(), computeCostsFromOutputs(), declareOptions(), makeDeepCopyFromShallowCopy(), and train().

stores the diagonal of Hessians

Definition at line 187 of file StackedModulesLearner.h.

Referenced by buildLayers(), forget(), makeDeepCopyFromShallowCopy(), and train().

stores the gradients

Definition at line 184 of file StackedModulesLearner.h.

Referenced by buildLayers(), forget(), makeDeepCopyFromShallowCopy(), and train().

Estimation of the second-order terms.

One of:

  • "none": using only first-order derivative for update,
  • "diag": estimating the diagonal of the hessian,
  • "simpler_diag": positive estimation of the diagonal

Definition at line 83 of file StackedModulesLearner.h.

Referenced by buildCostFunctions(), buildLayers(), buildOptions(), declareOptions(), forget(), and train().

Layers of the learner.

Definition at line 71 of file StackedModulesLearner.h.

Referenced by buildLayers(), buildOptions(), computeOutput(), declareOptions(), forget(), makeDeepCopyFromShallowCopy(), and train().

Number of cost modules.

Definition at line 176 of file StackedModulesLearner.h.

Referenced by buildCostFunctions(), buildOptions(), computeCostsFromOutputs(), declareOptions(), and train().

Number of module layers.

Definition at line 170 of file StackedModulesLearner.h.

Referenced by buildLayers(), buildOptions(), computeOutput(), declareOptions(), forget(), outputsize(), and train().

stores the input and output values of the functions

Definition at line 181 of file StackedModulesLearner.h.

Referenced by buildLayers(), computeOutput(), forget(), makeDeepCopyFromShallowCopy(), outputsize(), and train().


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