PLearn 0.1
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#include <TorchLearner.h>
Public Member Functions | |
TorchLearner () | |
Default constructor. | |
virtual void | build () |
Simply calls inherited::build() then build_(). | |
virtual void | makeDeepCopyFromShallowCopy (CopiesMap &copies) |
Transforms a shallow copy into a deep copy. | |
virtual string | classname () const |
virtual OptionList & | getOptionList () const |
virtual OptionMap & | getOptionMap () const |
virtual RemoteMethodMap & | getRemoteMethodMap () const |
virtual TorchLearner * | deepCopy (CopiesMap &copies) const |
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 void | setTrainingSet (VMat training_set, bool call_forget=true) |
Overridden to convert to a Torch dataset. | |
virtual | ~TorchLearner () |
Free memory. | |
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 PPath & | declaringFile () |
Public Attributes | |
PP< TMachine > | machine |
PP< TTrainer > | trainer |
Static Public Attributes | |
static StaticInitializer | _static_initializer_ |
Static Protected Member Functions | |
static void | declareOptions (OptionList &ol) |
Declares this class' options. | |
Protected Attributes | |
int | outputsize_ |
Torch::Allocator * | allocator |
Allocator for Torch memory management. | |
Torch::Sequence * | inputs |
A sequence to provide inputs to the Torch machine. | |
PP< TTorchDataSetFromVMat > | torch_train_set |
The Torch version of the training set. | |
Private Types | |
typedef PLearner | inherited |
Private Member Functions | |
void | build_ () |
This does the actual building. |
Definition at line 59 of file TorchLearner.h.
typedef PLearner PLearn::TorchLearner::inherited [private] |
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 63 of file TorchLearner.h.
PLearn::TorchLearner::TorchLearner | ( | ) |
Default constructor.
Definition at line 55 of file TorchLearner.cc.
References allocator, and inputs.
: outputsize_(-1) { allocator = new Torch::Allocator; inputs = 0; }
PLearn::TorchLearner::~TorchLearner | ( | ) | [virtual] |
Free memory.
Definition at line 243 of file TorchLearner.cc.
References allocator.
{ delete allocator; }
string PLearn::TorchLearner::_classname_ | ( | ) | [static] |
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
OptionList & PLearn::TorchLearner::_getOptionList_ | ( | ) | [static] |
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
RemoteMethodMap & PLearn::TorchLearner::_getRemoteMethodMap_ | ( | ) | [static] |
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
Object * PLearn::TorchLearner::_new_instance_for_typemap_ | ( | ) | [static] |
Reimplemented from PLearn::Object.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
StaticInitializer TorchLearner::_static_initializer_ & PLearn::TorchLearner::_static_initialize_ | ( | ) | [static] |
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
void PLearn::TorchLearner::build | ( | ) | [virtual] |
Simply calls inherited::build() then build_().
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 106 of file TorchLearner.cc.
References PLearn::PLearner::build(), and build_().
Referenced by PLearn::SVMClassificationTorch::build(), and PLearn::SVMClassificationTorch::build_().
{ inherited::build(); build_(); }
void PLearn::TorchLearner::build_ | ( | ) | [private] |
This does the actual building.
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 115 of file TorchLearner.cc.
References allocator, inputs, PLearn::PLearner::inputsize_, machine, and outputsize_.
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. if (machine && machine->machine->outputs) outputsize_ = machine->machine->outputs->frame_size; // Initialize the inputs sequence. if (inputsize_ >= 0 && (!inputs || inputs->frame_size != inputsize_)) { allocator->free(inputs); // Free old input sequence. inputs = new(allocator) Torch::Sequence(1, inputsize_); } }
string PLearn::TorchLearner::classname | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
void PLearn::TorchLearner::computeCostsFromOutputs | ( | const Vec & | input, |
const Vec & | output, | ||
const Vec & | target, | ||
Vec & | costs | ||
) | const [virtual] |
Computes the costs from already computed output.
Implements PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 136 of file TorchLearner.cc.
{
// No cost computed for now.
}
Computes the output from the input.
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 145 of file TorchLearner.cc.
References PLearn::TVec< T >::data(), inputs, machine, outputsize_, PLASSERT, and PLearn::TVec< T >::resize().
Referenced by PLearn::SVMClassificationTorch::computeOutput().
{ PLASSERT( outputsize_ >= 0); output.resize(outputsize_); inputs->copyFrom(input.data()); machine->forward(inputs); machine->machine->outputs->copyTo(output.data()); }
void PLearn::TorchLearner::declareOptions | ( | OptionList & | ol | ) | [static, protected] |
Declares this class' options.
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 70 of file TorchLearner.cc.
References PLearn::OptionBase::buildoption, PLearn::declareOption(), PLearn::PLearner::declareOptions(), PLearn::OptionBase::learntoption, machine, PLearn::OptionBase::nosave, PLearn::PLearner::nstages, outputsize_, PLearn::redeclareOption(), PLearn::PLearner::seed_, and trainer.
Referenced by PLearn::SVMClassificationTorch::declareOptions().
{ // ### For the "flags" of each option, you should typically specify // ### one of OptionBase::buildoption, OptionBase::learntoption or // ### OptionBase::tuningoption. Another possible flag to be combined with // ### is OptionBase::nosave // Build options. declareOption(ol, "machine", &TorchLearner::machine, OptionBase::buildoption, "The Torch learning machine."); declareOption(ol, "trainer", &TorchLearner::trainer, OptionBase::buildoption, "The Torch trainer, responsible for training the machine."); // Learnt options. declareOption(ol, "outputsize", &TorchLearner::outputsize_, OptionBase::learntoption, "Saves the output size of this learner for faster access."); // Now call the parent class' declareOptions. inherited::declareOptions(ol); // Hide unused parent's options. redeclareOption(ol, "seed", &TorchLearner::seed_, OptionBase::nosave, "Torch learners in general will not use the PLearn seed."); redeclareOption(ol, "nstages", &TorchLearner::nstages, OptionBase::nosave, "A Torch learner is usually only trained on one stage."); }
static const PPath& PLearn::TorchLearner::declaringFile | ( | ) | [inline, static] |
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 127 of file TorchLearner.h.
TorchLearner * PLearn::TorchLearner::deepCopy | ( | CopiesMap & | copies | ) | const [virtual] |
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
void PLearn::TorchLearner::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!).
Reimplemented from PLearn::PLearner.
Definition at line 157 of file TorchLearner.cc.
References machine, outputsize_, and PLearn::PLearner::stage.
{ stage = 0; outputsize_ = -1; if (machine) machine->reset(); }
OptionList & PLearn::TorchLearner::getOptionList | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
OptionMap & PLearn::TorchLearner::getOptionMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
RemoteMethodMap & PLearn::TorchLearner::getRemoteMethodMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 65 of file TorchLearner.cc.
TVec< string > PLearn::TorchLearner::getTestCostNames | ( | ) | const [virtual] |
Returns the names of the costs computed by computeCostsFromOutpus (and thus the test method).
Implements PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 168 of file TorchLearner.cc.
{ // No cost computed for now. return TVec<string>(); }
TVec< string > PLearn::TorchLearner::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.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 177 of file TorchLearner.cc.
Referenced by PLearn::SVMClassificationTorch::getTrainCostNames().
{ // No cost computed for now. return TVec<string>(); }
void PLearn::TorchLearner::makeDeepCopyFromShallowCopy | ( | CopiesMap & | copies | ) | [virtual] |
Transforms a shallow copy into a deep copy.
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 186 of file TorchLearner.cc.
References PLearn::PLearner::makeDeepCopyFromShallowCopy(), and PLERROR.
Referenced by PLearn::SVMClassificationTorch::makeDeepCopyFromShallowCopy().
{ inherited::makeDeepCopyFromShallowCopy(copies); // ### ex: // deepCopyField(trainvec, copies); // ### Remove this line when you have fully implemented this method. PLERROR("TorchLearner::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!"); }
int PLearn::TorchLearner::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 200 of file TorchLearner.cc.
References machine, outputsize_, and PLASSERT.
{ // Compute and return the size of this learner's output, (which typically // may depend on its inputsize(), targetsize() and set options). PLASSERT( machine ); PLASSERT( outputsize_ >= 0 || machine->machine->outputs ); if (outputsize_ >=0) return outputsize_; return machine->machine->outputs->frame_size; }
Overridden to convert to a Torch dataset.
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 214 of file TorchLearner.cc.
References allocator, inputs, PLearn::PLearner::setTrainingSet(), and torch_train_set.
Referenced by PLearn::SVMClassificationTorch::setTrainingSet().
{ inherited::setTrainingSet(training_set, call_forget); torch_train_set = new TTorchDataSetFromVMat(training_set); allocator->free(inputs); // Free old input sequence. inputs = new(allocator) Torch::Sequence(1, training_set->inputsize()); }
void PLearn::TorchLearner::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 224 of file TorchLearner.cc.
References machine, PLearn::PLearner::nstages, outputsize_, PLERROR, PLWARNING, PLearn::PLearner::stage, torch_train_set, and trainer.
{ if (stage >= nstages) { PLWARNING("In TorchLearner::train - Learner has already been trained, skipping training"); return; } if (!trainer || !machine) PLERROR("In TorchLearner::train - You must set both the 'trainer' and 'machine' options " "before calling train()"); trainer->train((TTorchDataSetFromVMat*) torch_train_set); if (machine->machine->outputs) // Update outputsize_ outputsize_ = machine->machine->outputs->frame_size; stage = 1; }
Reimplemented from PLearn::PLearner.
Reimplemented in PLearn::SVMClassificationTorch.
Definition at line 127 of file TorchLearner.h.
Torch::Allocator* PLearn::TorchLearner::allocator [protected] |
Allocator for Torch memory management.
Definition at line 76 of file TorchLearner.h.
Referenced by build_(), setTrainingSet(), TorchLearner(), and ~TorchLearner().
Torch::Sequence* PLearn::TorchLearner::inputs [protected] |
A sequence to provide inputs to the Torch machine.
Definition at line 79 of file TorchLearner.h.
Referenced by build_(), computeOutput(), setTrainingSet(), and TorchLearner().
PP<TMachine> PLearn::TorchLearner::machine |
Definition at line 90 of file TorchLearner.h.
Referenced by build_(), PLearn::SVMClassificationTorch::build_(), computeOutput(), declareOptions(), PLearn::SVMClassificationTorch::declareOptions(), forget(), outputsize(), and train().
int PLearn::TorchLearner::outputsize_ [protected] |
Definition at line 71 of file TorchLearner.h.
Referenced by build_(), computeOutput(), declareOptions(), forget(), outputsize(), and train().
PP<TTorchDataSetFromVMat> PLearn::TorchLearner::torch_train_set [protected] |
The Torch version of the training set.
Definition at line 82 of file TorchLearner.h.
Referenced by setTrainingSet(), and train().
PP<TTrainer> PLearn::TorchLearner::trainer |
Definition at line 91 of file TorchLearner.h.
Referenced by PLearn::SVMClassificationTorch::build_(), declareOptions(), PLearn::SVMClassificationTorch::declareOptions(), and train().