PLearn 0.1
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RBM to be used when doing joint supervised learning by CD. More...
#include <InferenceRBM.h>
Public Member Functions | |
InferenceRBM () | |
Default constructor. | |
void | hiddenExpGivenVisible (const Mat &visible) |
void | hiddenExpGivenInputTarget (const Mat &input, const TVec< int > &target) |
void | hiddenExpGivenInput (const Mat &input) |
void | targetExpGivenInput (const Mat &input) |
Mat | getHiddenExpGivenVisible (const Mat &visible) |
Mat | getHiddenExpGivenInputTarget (const Mat &input, const TVec< int > &target) |
Mat | getHiddenExpGivenInput (const Mat &input) |
Mat | getTargetExpGivenInput (const Mat &input) |
void | supCDStep (const Mat &visible) |
void | unsupCDStep (const Mat &input) |
virtual void | setLearningRate (real the_learning_rate) |
virtual string | classname () const |
virtual OptionList & | getOptionList () const |
virtual OptionMap & | getOptionMap () const |
virtual RemoteMethodMap & | getRemoteMethodMap () const |
virtual InferenceRBM * | deepCopy (CopiesMap &copies) const |
virtual void | build () |
Post-constructor. | |
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 PPath & | declaringFile () |
Public Attributes | |
PP< RBMLayer > | input_layer |
### declare public option fields (such as build options) here Input layer (part of visible) | |
PP< RBMMultinomialLayer > | target_layer |
Target layer (other part of visible) | |
PP< RBMBinomialLayer > | hidden_layer |
Hidden. | |
PP< RBMMatrixConnection > | input_to_hidden |
Connection between input and hidden. | |
PP< RBMMatrixConnection > | target_to_hidden |
Connection between target and hidden. | |
string | exp_method |
How to compute hidden and target expectation given input Possible values are: | |
int | n_gibbs_steps |
Number of Gibbs steps to use if exp_method=="gibbs". | |
PP< PRandom > | random_gen |
Random numbers generator. | |
bool | use_fast_approximations |
Whether to use fast approximations in softplus computation. | |
Static Public Attributes | |
static StaticInitializer | _static_initializer_ |
Static Protected Member Functions | |
static void | declareOptions (OptionList &ol) |
Declares the class options. | |
static void | declareMethods (RemoteMethodMap &rmm) |
Declares the class methods. | |
Protected Attributes | |
PP< RBMMixedLayer > | visible_layer |
Visible layer (concatenation of input and target) | |
PP< RBMMixedConnection > | visible_to_hidden |
int | input_size |
Size of input layer. | |
int | target_size |
Size of target layer. | |
int | visible_size |
Size of visible layer. | |
int | hidden_size |
Size of hidden layer. | |
Private Types | |
typedef Object | inherited |
Private Member Functions | |
void | build_ () |
This does the actual building. | |
void | build_rbms () |
Private Attributes | |
Mat | v0 |
Mat | h0 |
RBM to be used when doing joint supervised learning by CD.
We have input, target and hidden layer. We can compute hidden given (input, target), target given input, or hidden given input.
Definition at line 60 of file InferenceRBM.h.
typedef Object PLearn::InferenceRBM::inherited [private] |
Reimplemented from PLearn::Object.
Definition at line 62 of file InferenceRBM.h.
PLearn::InferenceRBM::InferenceRBM | ( | ) |
Default constructor.
Definition at line 56 of file InferenceRBM.cc.
: n_gibbs_steps(0), input_size(0), target_size(0), visible_size(0), hidden_size(0) { }
string PLearn::InferenceRBM::_classname_ | ( | ) | [static] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
OptionList & PLearn::InferenceRBM::_getOptionList_ | ( | ) | [static] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
RemoteMethodMap & PLearn::InferenceRBM::_getRemoteMethodMap_ | ( | ) | [static] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
Object * PLearn::InferenceRBM::_new_instance_for_typemap_ | ( | ) | [static] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
StaticInitializer InferenceRBM::_static_initializer_ & PLearn::InferenceRBM::_static_initialize_ | ( | ) | [static] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
void PLearn::InferenceRBM::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::Object.
Definition at line 66 of file InferenceRBM.cc.
References PLearn::Object::build(), and build_().
{ inherited::build(); build_(); }
void PLearn::InferenceRBM::build_ | ( | ) | [private] |
This does the actual building.
Check (and set) sizes
Build visible layer
Build visible_to_hidden connection
If we have a random_gen, share it with the ones who do not
Reimplemented from PLearn::Object.
Definition at line 247 of file InferenceRBM.cc.
References PLearn::endl(), hidden_layer, hidden_size, input_layer, input_size, input_to_hidden, PLearn::PP< T >::isNull(), PLASSERT, random_gen, target_layer, target_size, target_to_hidden, visible_layer, visible_size, and visible_to_hidden.
Referenced by build().
{ MODULE_LOG << "build_() called" << endl; if( !input_layer || !target_layer || !hidden_layer || !input_to_hidden || !target_to_hidden ) { MODULE_LOG << "build_() aborted because layers and connections were" " not set" << endl; return; } input_size = input_layer->size; target_size = target_layer->size; visible_size = input_size + target_size; hidden_size = hidden_layer->size; PLASSERT(input_to_hidden->down_size == input_size); PLASSERT(input_to_hidden->up_size == hidden_size); PLASSERT(target_to_hidden->down_size == target_size); PLASSERT(target_to_hidden->up_size == hidden_size); visible_layer = new RBMMixedLayer(); visible_layer->sub_layers.resize(2); visible_layer->sub_layers[0] = input_layer; visible_layer->sub_layers[1] = target_layer; visible_layer->build(); PLASSERT(visible_layer->size == visible_size); visible_to_hidden = new RBMMixedConnection(); visible_to_hidden->sub_connections.resize(1,2); visible_to_hidden->sub_connections(0,0) = input_to_hidden; visible_to_hidden->sub_connections(0,1) = target_to_hidden; visible_to_hidden->build(); PLASSERT(visible_to_hidden->down_size == visible_size); PLASSERT(visible_to_hidden->up_size == hidden_size); if (random_gen) { if (input_layer->random_gen.isNull()) { input_layer->random_gen = random_gen; input_layer->forget(); } if (target_layer->random_gen.isNull()) { target_layer->random_gen = random_gen; target_layer->forget(); } if (visible_layer->random_gen.isNull()) { visible_layer->random_gen = random_gen; visible_layer->forget(); } if (hidden_layer->random_gen.isNull()) { hidden_layer->random_gen = random_gen; hidden_layer->forget(); } if (input_to_hidden->random_gen.isNull()) { input_to_hidden->random_gen = random_gen; input_to_hidden->forget(); } if (target_to_hidden->random_gen.isNull()) { target_to_hidden->random_gen = random_gen; target_to_hidden->forget(); } if (visible_to_hidden->random_gen.isNull()) { visible_to_hidden->random_gen = random_gen; visible_to_hidden->forget(); } } }
void PLearn::InferenceRBM::build_rbms | ( | ) | [private] |
string PLearn::InferenceRBM::classname | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
void PLearn::InferenceRBM::declareMethods | ( | RemoteMethodMap & | rmm | ) | [static, protected] |
Declares the class methods.
Reimplemented from PLearn::Object.
Definition at line 173 of file InferenceRBM.cc.
References PLearn::Object::_getRemoteMethodMap_(), PLearn::declareMethod(), getHiddenExpGivenInput(), getHiddenExpGivenInputTarget(), getHiddenExpGivenVisible(), getTargetExpGivenInput(), hiddenExpGivenInput(), hiddenExpGivenInputTarget(), hiddenExpGivenVisible(), PLearn::RemoteMethodMap::inherited(), setLearningRate(), supCDStep(), and targetExpGivenInput().
{ // Insert a backpointer to remote methods; note that this // different than for declareOptions() rmm.inherited(inherited::_getRemoteMethodMap_()); declareMethod( rmm, "hiddenExpGivenVisible", &InferenceRBM::hiddenExpGivenVisible, (BodyDoc("Computes the hidden layer's expectation given the visible"), ArgDoc ("visible", "Visible layer's values"))); declareMethod( rmm, "hiddenExpGivenInput", &InferenceRBM::hiddenExpGivenInput, (BodyDoc("Computes the hidden layer's expectation given the input"), ArgDoc ("input", "Input layer's values"))); declareMethod( rmm, "hiddenExpGivenInputTarget", &InferenceRBM::hiddenExpGivenInputTarget, (BodyDoc("Computes the hidden layer's expectation given the input\n" "and the target"), ArgDoc ("input", "Input layer's values"), ArgDoc ("target", "Target (as an index)"))); declareMethod( rmm, "targetExpGivenInput", &InferenceRBM::targetExpGivenInput, (BodyDoc("Computes the target layer's expectation given the input"), ArgDoc ("input", "Input layer's values"))); declareMethod( rmm, "getHiddenExpGivenVisible", &InferenceRBM::getHiddenExpGivenVisible, (BodyDoc("Computes the hidden layer's expectation given the visible"), ArgDoc ("visible", "Visible layer's values"), RetDoc ("Hidden layer's expectation"))); declareMethod( rmm, "getHiddenExpGivenInput", &InferenceRBM::getHiddenExpGivenInput, (BodyDoc("Computes the hidden layer's expectation given the input"), ArgDoc ("input", "Input layer's values"), RetDoc ("Hidden layer's expectation"))); declareMethod( rmm, "getHiddenExpGivenInputTarget", &InferenceRBM::getHiddenExpGivenInputTarget, (BodyDoc("Computes the hidden layer's expectation given the input\n" "and the target"), ArgDoc ("input", "Input layer's values"), ArgDoc ("target", "Target (as an index)"), RetDoc ("Hidden layer's expectation"))); declareMethod( rmm, "getTargetExpGivenInput", &InferenceRBM::getTargetExpGivenInput, (BodyDoc("Computes the target layer's expectation given the input"), ArgDoc ("input", "Input layer's values"), RetDoc ("Target layer's expectation"))); declareMethod( rmm, "supCDStep", &InferenceRBM::supCDStep, (BodyDoc("Performs one step of CD and updates the parameters"), ArgDoc ("visible", "Visible layer's values"))); declareMethod( rmm, "setLearningRate", &InferenceRBM::setLearningRate, (BodyDoc("Sets the learning rate of underlying modules"), ArgDoc ("the_learning_rate", "The learning rate"))); }
void PLearn::InferenceRBM::declareOptions | ( | OptionList & | ol | ) | [static, protected] |
Declares the class options.
Reimplemented from PLearn::Object.
Definition at line 91 of file InferenceRBM.cc.
References PLearn::OptionBase::buildoption, PLearn::declareOption(), PLearn::Object::declareOptions(), exp_method, hidden_layer, hidden_size, input_layer, input_size, input_to_hidden, PLearn::OptionBase::learntoption, n_gibbs_steps, random_gen, target_layer, target_size, target_to_hidden, use_fast_approximations, visible_layer, visible_size, and visible_to_hidden.
{ // declareOption(ol, "myoption", &InferenceRBM::myoption, // OptionBase::buildoption, // "Help text describing this option"); declareOption(ol, "input_layer", &InferenceRBM::input_layer, OptionBase::buildoption, "Input layer (part of visible)"); declareOption(ol, "target_layer", &InferenceRBM::target_layer, OptionBase::buildoption, "Target layer (part of visible)"); declareOption(ol, "hidden_layer", &InferenceRBM::hidden_layer, OptionBase::buildoption, "Hidden layer"); declareOption(ol, "input_to_hidden", &InferenceRBM::input_to_hidden, OptionBase::buildoption, "Connection between input and hidden layers"); declareOption(ol, "target_to_hidden", &InferenceRBM::target_to_hidden, OptionBase::buildoption, "Connection between target and hidden layers"); declareOption(ol, "exp_method", &InferenceRBM::exp_method, OptionBase::buildoption, "How to compute hidden and target expectation given input.\n" "Possible values are:\n" " - \"exact\": exact inference, O(target_size), default\n" " - \"gibbs\": estimation by Gibbs sampling\n" ); declareOption(ol, "n_gibbs_steps", &InferenceRBM::n_gibbs_steps, OptionBase::buildoption, "Number of Gibbs steps to use if exp_method==\"gibbs\""); declareOption(ol, "random_gen", &InferenceRBM::random_gen, OptionBase::buildoption, "Random numbers generator"); declareOption(ol, "use_fast_approximations", &InferenceRBM::use_fast_approximations, OptionBase::buildoption, "Whether to use fast approximations in softplus computation"); declareOption(ol, "visible_layer", &InferenceRBM::visible_layer, OptionBase::learntoption, "Visible layer (input+target)"); declareOption(ol, "visible_to_hidden", &InferenceRBM::visible_to_hidden, OptionBase::learntoption, "Connection between visible and hidden layers"); declareOption(ol, "input_size", &InferenceRBM::input_size, OptionBase::learntoption, "Size of input_layer"); declareOption(ol, "target_size", &InferenceRBM::target_size, OptionBase::learntoption, "Size of target_layer"); declareOption(ol, "visible_size", &InferenceRBM::visible_size, OptionBase::learntoption, "Size of visible_layer"); declareOption(ol, "hidden_size", &InferenceRBM::hidden_size, OptionBase::learntoption, "Size of hidden_layer"); // Now call the parent class' declareOptions inherited::declareOptions(ol); }
static const PPath& PLearn::InferenceRBM::declaringFile | ( | ) | [inline, static] |
Reimplemented from PLearn::Object.
Definition at line 128 of file InferenceRBM.h.
:
//##### Protected Options ###############################################
InferenceRBM * PLearn::InferenceRBM::deepCopy | ( | CopiesMap & | copies | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
Definition at line 450 of file InferenceRBM.cc.
References hidden_layer, and hiddenExpGivenInput().
Referenced by declareMethods().
{ hiddenExpGivenInput(input); return hidden_layer->getExpectations(); }
Mat PLearn::InferenceRBM::getHiddenExpGivenInputTarget | ( | const Mat & | input, |
const TVec< int > & | target | ||
) |
Definition at line 437 of file InferenceRBM.cc.
References hidden_layer, and hiddenExpGivenInputTarget().
Referenced by declareMethods().
{ hiddenExpGivenInputTarget(input, target); return hidden_layer->getExpectations(); }
Definition at line 431 of file InferenceRBM.cc.
References hidden_layer, and hiddenExpGivenVisible().
Referenced by declareMethods().
{ hiddenExpGivenVisible(visible); return hidden_layer->getExpectations(); }
OptionList & PLearn::InferenceRBM::getOptionList | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
OptionMap & PLearn::InferenceRBM::getOptionMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
RemoteMethodMap & PLearn::InferenceRBM::getRemoteMethodMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 54 of file InferenceRBM.cc.
Definition at line 444 of file InferenceRBM.cc.
References target_layer, and targetExpGivenInput().
Referenced by declareMethods().
{ targetExpGivenInput(input); return target_layer->getExpectations(); }
void PLearn::InferenceRBM::hiddenExpGivenInput | ( | const Mat & | input | ) |
Definition at line 404 of file InferenceRBM.cc.
References PLearn::TMat< T >::clear(), PLearn::TMat< T >::column(), PLearn::TMat< T >::fill(), hidden_layer, hidden_size, hiddenExpGivenVisible(), i, input_size, PLearn::TMat< T >::length(), PLASSERT, PLearn::TMat< T >::subMatColumns(), target_layer, target_size, targetExpGivenInput(), visible_size, and PLearn::TMat< T >::width().
Referenced by declareMethods(), and getHiddenExpGivenInput().
{ PLASSERT(input.width() == input_size); int batch_size = input.length(); targetExpGivenInput(input); Mat target_exp = target_layer->getExpectations(); Mat visible(batch_size, visible_size); visible.subMatColumns(0, input_size) << input; Mat hidden_exp(batch_size, hidden_size); for (int i=0; i<target_size; i++) { visible.subMatColumns(input_size, target_size).clear(); visible.column(input_size+i).fill(1.); hiddenExpGivenVisible(visible); for (int k=0; k<batch_size; k++) hidden_exp(k) += target_exp(k,i) * hidden_layer->getExpectations()(k); } hidden_layer->setExpectations(hidden_exp); }
void PLearn::InferenceRBM::hiddenExpGivenInputTarget | ( | const Mat & | input, |
const TVec< int > & | target | ||
) |
Definition at line 338 of file InferenceRBM.cc.
References PLearn::get_pointer(), hidden_layer, input_size, input_to_hidden, PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLASSERT, target_to_hidden, and PLearn::TMat< T >::width().
Referenced by declareMethods(), and getHiddenExpGivenInputTarget().
{ int batch_size = input.length(); PLASSERT(input.width() == input_size); PLASSERT(target.length() == batch_size); input_to_hidden->setAsDownInputs(input); hidden_layer->getAllActivations(get_pointer(input_to_hidden), 0, true); for (int k=0; k<batch_size; k++) hidden_layer->activations(k) += target_to_hidden->weights(target[k]); hidden_layer->expectations_are_up_to_date = false; hidden_layer->computeExpectations(); }
void PLearn::InferenceRBM::hiddenExpGivenVisible | ( | const Mat & | visible | ) |
Definition at line 329 of file InferenceRBM.cc.
References PLearn::get_pointer(), hidden_layer, PLASSERT, visible_size, visible_to_hidden, and PLearn::TMat< T >::width().
Referenced by declareMethods(), getHiddenExpGivenVisible(), hiddenExpGivenInput(), and supCDStep().
{ PLASSERT(visible.width() == visible_size); visible_to_hidden->setAsDownInputs(visible); hidden_layer->getAllActivations(get_pointer(visible_to_hidden), 0, true); hidden_layer->computeExpectations(); }
void PLearn::InferenceRBM::makeDeepCopyFromShallowCopy | ( | CopiesMap & | copies | ) | [virtual] |
Transforms a shallow copy into a deep copy.
Reimplemented from PLearn::Object.
Definition at line 72 of file InferenceRBM.cc.
References PLearn::deepCopyField(), h0, hidden_layer, input_layer, input_to_hidden, PLearn::Object::makeDeepCopyFromShallowCopy(), random_gen, target_layer, target_to_hidden, v0, visible_layer, and visible_to_hidden.
{ inherited::makeDeepCopyFromShallowCopy(copies); // deepCopyField(trainvec, copies); deepCopyField(input_layer, copies); deepCopyField(target_layer, copies); deepCopyField(hidden_layer, copies); deepCopyField(input_to_hidden, copies); deepCopyField(target_to_hidden, copies); deepCopyField(random_gen, copies); deepCopyField(visible_layer, copies); deepCopyField(visible_to_hidden, copies); deepCopyField(v0, copies); deepCopyField(h0, copies); }
void PLearn::InferenceRBM::setLearningRate | ( | real | the_learning_rate | ) | [virtual] |
Definition at line 490 of file InferenceRBM.cc.
References hidden_layer, visible_layer, and visible_to_hidden.
Referenced by declareMethods().
{ visible_layer->setLearningRate(the_learning_rate); visible_to_hidden->setLearningRate(the_learning_rate); hidden_layer->setLearningRate(the_learning_rate); }
void PLearn::InferenceRBM::supCDStep | ( | const Mat & | visible | ) |
Definition at line 456 of file InferenceRBM.cc.
References PLearn::get_pointer(), h0, hidden_layer, hidden_size, hiddenExpGivenVisible(), PLearn::TMat< T >::length(), PLASSERT, PLearn::TMat< T >::resize(), v0, visible_layer, visible_size, visible_to_hidden, and PLearn::TMat< T >::width().
Referenced by declareMethods().
{ PLASSERT(visible.width() == visible_size); int batch_size = visible.length(); v0.resize(batch_size,visible_size); v0 << visible; // positive phase hiddenExpGivenVisible(visible); h0.resize(batch_size, hidden_size); h0 << hidden_layer->getExpectations(); // Down propagation visible_to_hidden->setAsUpInputs(h0); visible_layer->getAllActivations(get_pointer(visible_to_hidden), 0, true); visible_layer->computeExpectations(); visible_layer->generateSamples(); // Negative phase hiddenExpGivenVisible(visible_layer->samples); // Update visible_layer->update(v0, visible_layer->samples); visible_to_hidden->update(v0, h0, visible_layer->samples, hidden_layer->getExpectations()); hidden_layer->update(h0, hidden_layer->getExpectations()); }
void PLearn::InferenceRBM::targetExpGivenInput | ( | const Mat & | input | ) |
Definition at line 355 of file InferenceRBM.cc.
References PLearn::get_pointer(), hidden_layer, hidden_size, i, input_size, input_to_hidden, j, PLearn::TMat< T >::length(), PLASSERT, PLearn::softplus(), PLearn::tabulated_softplus(), target_layer, target_size, target_to_hidden, use_fast_approximations, and PLearn::TMat< T >::width().
Referenced by declareMethods(), getTargetExpGivenInput(), and hiddenExpGivenInput().
{ PLASSERT(input.width() == input_size); int batch_size = input.length(); // input contains samples (or expectations) from input_layer input_to_hidden->setAsDownInputs(input); // hidden_layer->activations = bias + input_to_hidden.weights * input hidden_layer->getAllActivations(get_pointer(input_to_hidden), 0, true); target_layer->setBatchSize(batch_size); // target_layer->activations[k][i] = // bias[i] + sum_j softplus(W_ji + hidden_layer->activations[k][j]) Mat hidden_act = hidden_layer->activations; Mat target_act = target_layer->activations; Vec target_b = target_layer->bias; Mat t_to_h_w = target_to_hidden->weights; for (int k=0; k<batch_size; k++) { target_act(k) << target_b; real* target_act_k = target_act[k]; real* hidden_act_kj = hidden_act[k]; for (int j=0; j<hidden_size; j++, hidden_act_kj++) { real* target_act_ki = target_act_k; // copy real* t_to_h_w_ji = t_to_h_w[j]; for (int i=0; i<target_size; i++, target_act_ki++, t_to_h_w_ji++) { PLASSERT(*target_act_ki == target_act(k,i)); PLASSERT(*t_to_h_w_ji == t_to_h_w(j,i)); PLASSERT(*hidden_act_kj == hidden_act(k,j)); if (use_fast_approximations) *target_act_ki += tabulated_softplus(*t_to_h_w_ji + *hidden_act_kj); else *target_act_ki += softplus(*t_to_h_w_ji + *hidden_act_kj); } } } target_layer->expectations_are_up_to_date = false; target_layer->computeExpectations(); }
void PLearn::InferenceRBM::unsupCDStep | ( | const Mat & | input | ) |
Definition at line 485 of file InferenceRBM.cc.
References PLCHECK_MSG.
{ PLCHECK_MSG(false, "Not implemented yet"); }
Reimplemented from PLearn::Object.
Definition at line 128 of file InferenceRBM.h.
How to compute hidden and target expectation given input Possible values are:
Definition at line 87 of file InferenceRBM.h.
Referenced by declareOptions().
Mat PLearn::InferenceRBM::h0 [mutable, private] |
Definition at line 180 of file InferenceRBM.h.
Referenced by makeDeepCopyFromShallowCopy(), and supCDStep().
Hidden.
Definition at line 75 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), getHiddenExpGivenInput(), getHiddenExpGivenInputTarget(), getHiddenExpGivenVisible(), hiddenExpGivenInput(), hiddenExpGivenInputTarget(), hiddenExpGivenVisible(), makeDeepCopyFromShallowCopy(), setLearningRate(), supCDStep(), and targetExpGivenInput().
int PLearn::InferenceRBM::hidden_size [protected] |
Size of hidden layer.
Definition at line 156 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), hiddenExpGivenInput(), supCDStep(), and targetExpGivenInput().
### declare public option fields (such as build options) here Input layer (part of visible)
Definition at line 69 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), and makeDeepCopyFromShallowCopy().
int PLearn::InferenceRBM::input_size [protected] |
Size of input layer.
Definition at line 147 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), hiddenExpGivenInput(), hiddenExpGivenInputTarget(), and targetExpGivenInput().
Connection between input and hidden.
Definition at line 78 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), hiddenExpGivenInputTarget(), makeDeepCopyFromShallowCopy(), and targetExpGivenInput().
Number of Gibbs steps to use if exp_method=="gibbs".
Definition at line 90 of file InferenceRBM.h.
Referenced by declareOptions().
Random numbers generator.
Definition at line 93 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), and makeDeepCopyFromShallowCopy().
Target layer (other part of visible)
Definition at line 72 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), getTargetExpGivenInput(), hiddenExpGivenInput(), makeDeepCopyFromShallowCopy(), and targetExpGivenInput().
int PLearn::InferenceRBM::target_size [protected] |
Size of target layer.
Definition at line 150 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), hiddenExpGivenInput(), and targetExpGivenInput().
Connection between target and hidden.
Definition at line 81 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), hiddenExpGivenInputTarget(), makeDeepCopyFromShallowCopy(), and targetExpGivenInput().
Whether to use fast approximations in softplus computation.
Definition at line 96 of file InferenceRBM.h.
Referenced by declareOptions(), and targetExpGivenInput().
Mat PLearn::InferenceRBM::v0 [mutable, private] |
Definition at line 179 of file InferenceRBM.h.
Referenced by makeDeepCopyFromShallowCopy(), and supCDStep().
PP<RBMMixedLayer> PLearn::InferenceRBM::visible_layer [protected] |
Visible layer (concatenation of input and target)
Definition at line 142 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), makeDeepCopyFromShallowCopy(), setLearningRate(), and supCDStep().
int PLearn::InferenceRBM::visible_size [protected] |
Size of visible layer.
Definition at line 153 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), hiddenExpGivenInput(), hiddenExpGivenVisible(), and supCDStep().
PP<RBMMixedConnection> PLearn::InferenceRBM::visible_to_hidden [protected] |
Definition at line 144 of file InferenceRBM.h.
Referenced by build_(), declareOptions(), hiddenExpGivenVisible(), makeDeepCopyFromShallowCopy(), setLearningRate(), and supCDStep().