PLearn 0.1
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Layer in an RBM formed with binomial units. More...
#include <RBMMixedLayer.h>
Public Member Functions | |
RBMMixedLayer () | |
Default constructor. | |
RBMMixedLayer (TVec< PP< RBMLayer > > the_sub_layers) | |
Constructor from the sub_layers. | |
virtual void | getUnitActivations (int i, PP< RBMParameters > rbmp, int offset=0) |
Uses "rbmp" to obtain the activations of unit "i" of this layer. | |
virtual void | getAllActivations (PP< RBMParameters > rbmp, int offset=0) |
Uses "rbmp" to obtain the activations of all units in this layer. | |
virtual void | generateSample () |
compute a sample, and update the sample field | |
virtual void | computeExpectation () |
compute the expectation | |
virtual void | bpropUpdate (const Vec &input, const Vec &output, Vec &input_gradient, const Vec &output_gradient) |
back-propagates the output gradient to the input | |
virtual string | classname () const |
virtual OptionList & | getOptionList () const |
virtual OptionMap & | getOptionMap () const |
virtual RemoteMethodMap & | getRemoteMethodMap () const |
virtual RBMMixedLayer * | deepCopy (CopiesMap &copies) const |
virtual void | build () |
Post-constructor. | |
virtual void | makeDeepCopyFromShallowCopy (CopiesMap &copies) |
Transforms a shallow copy into a deep copy. | |
RBMMixedLayer () | |
Default constructor. | |
RBMMixedLayer (TVec< PP< RBMLayer > > the_sub_layers) | |
Constructor from the sub_layers. | |
virtual void | setLearningRate (real the_learning_rate) |
Sets the learning rate, also in the sub_layers. | |
virtual void | setMomentum (real the_momentum) |
Sets the momentum, also in the sub_layers. | |
virtual void | setBatchSize (int the_batch_size) |
Sets batch_size and resize activations, expectations, and samples. | |
virtual void | setExpectation (const Vec &the_expectation) |
Copy the given expectation in the 'expectation' vector. | |
virtual void | setExpectationByRef (const Vec &the_expectation) |
Make the 'expectation' vector point to the given data vector (so no copy is performed). | |
virtual void | setExpectations (const Mat &the_expectations) |
Copy the given expectations in the 'expectations' matrix. | |
virtual void | setExpectationsByRef (const Mat &the_expectations) |
Make the 'expectations' matrix point to the given data matrix (so no copy is performed). | |
virtual void | getUnitActivation (int i, PP< RBMConnection > rbmc, int offset=0) |
Uses "rbmc" to compute the activation of unit "i" of this layer. | |
virtual void | getAllActivations (PP< RBMConnection > rbmc, int offset=0, bool minibatch=false) |
Uses "rbmc" to obtain the activations of all units in this layer. | |
virtual void | expectation_is_not_up_to_date () |
virtual void | generateSample () |
compute a sample, and update the sample field | |
virtual void | generateSamples () |
generate activations.length() samples | |
virtual void | computeExpectation () |
compute the expectation | |
virtual void | computeExpectations () |
compute the expectations according to activations | |
virtual void | fprop (const Vec &input, Vec &output) const |
forward propagation | |
virtual void | fprop (const Mat &inputs, Mat &outputs) |
Batch forward propagation. | |
virtual void | fprop (const Vec &input, const Vec &rbm_bias, Vec &output) const |
forward propagation with provided bias | |
virtual void | bpropUpdate (const Vec &input, const Vec &output, Vec &input_gradient, const Vec &output_gradient, bool accumulate=false) |
back-propagates the output gradient to the input | |
virtual void | bpropUpdate (const Mat &inputs, const Mat &outputs, Mat &input_gradients, const Mat &output_gradients, bool accumulate=false) |
Back-propagate the output gradient to the input, and update parameters. | |
virtual void | bpropUpdate (const Vec &input, const Vec &rbm_bias, const Vec &output, Vec &input_gradient, Vec &rbm_bias_gradient, const Vec &output_gradient) |
back-propagates the output gradient to the input and the bias | |
virtual real | fpropNLL (const Vec &target) |
Computes the negative log-likelihood of target given the internal activations of the layer. | |
virtual void | fpropNLL (const Mat &targets, const Mat &costs_column) |
Batch fpropNLL. | |
virtual void | bpropNLL (const Vec &target, real nll, Vec &bias_gradient) |
Computes the gradient of the negative log-likelihood of target with respect to the layer's bias, given the internal activations. | |
virtual void | bpropNLL (const Mat &targets, const Mat &costs_column, Mat &bias_gradients) |
virtual void | accumulatePosStats (const Vec &pos_values) |
Accumulates positive phase statistics. | |
virtual void | accumulateNegStats (const Vec &neg_values) |
Accumulates negative phase statistics. | |
virtual void | update () |
Update parameters according to accumulated statistics. | |
virtual void | update (const Vec &pos_values, const Vec &neg_values) |
Update parameters according to one pair of vectors. | |
virtual void | update (const Mat &pos_values, const Mat &neg_values) |
Update parameters according to several pairs of vectors. | |
virtual void | reset () |
resets activations, sample and expectation fields | |
virtual void | clearStats () |
resets the statistics and counts | |
virtual void | forget () |
forgets everything | |
virtual real | energy (const Vec &unit_values) const |
Compute -bias' unit_values. | |
virtual real | freeEnergyContribution (const Vec &unit_activations) const |
Computes ![]() | |
virtual void | freeEnergyContributionGradient (const Vec &unit_activations, Vec &unit_activations_gradient, real output_gradient=1, bool accumulate=false) const |
Computes gradient of the result of freeEnergyContribution ![]() | |
virtual int | getConfigurationCount () |
Returns a number of different configurations the layer can be in. | |
virtual void | getConfiguration (int conf_index, Vec &output) |
Computes the conf_index configuration of the layer. | |
virtual string | classname () const |
virtual OptionList & | getOptionList () const |
virtual OptionMap & | getOptionMap () const |
virtual RemoteMethodMap & | getRemoteMethodMap () const |
virtual RBMMixedLayer * | 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 () |
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 | |
TVec< PP< RBMLayer > > | sub_layers |
The concatenated RBMLayers composing this layer. | |
Static Public Attributes | |
static StaticInitializer | _static_initializer_ |
Static Protected Member Functions | |
static void | declareOptions (OptionList &ol) |
Declares the class options. | |
static void | declareOptions (OptionList &ol) |
Declares the class options. | |
Protected Attributes | |
TVec< int > | init_positions |
Initial index of the sub_layers. | |
TVec< int > | layer_of_unit |
layer_of_unit[i] is the index of sub_layer containing unit i | |
int | n_layers |
Number of sub-layers. | |
Private Types | |
typedef RBMLayer | inherited |
typedef RBMLayer | inherited |
Private Member Functions | |
void | build_ () |
This does the actual building. | |
void | build_ () |
This does the actual building. | |
Private Attributes | |
Vec | nlls |
Mat | mat_nlls |
Layer in an RBM formed with binomial units.
Layer in an RBM formed with the concatenation of other layers.
Definition at line 54 of file DEPRECATED/RBMMixedLayer.h.
typedef RBMLayer PLearn::RBMMixedLayer::inherited [private] |
Reimplemented from PLearn::RBMLayer.
Definition at line 56 of file DEPRECATED/RBMMixedLayer.h.
typedef RBMLayer PLearn::RBMMixedLayer::inherited [private] |
Reimplemented from PLearn::RBMLayer.
Definition at line 55 of file RBMMixedLayer.h.
PLearn::RBMMixedLayer::RBMMixedLayer | ( | ) |
Constructor from the sub_layers.
Definition at line 55 of file DEPRECATED/RBMMixedLayer.cc.
References build().
: sub_layers( the_sub_layers ) { build(); }
PLearn::RBMMixedLayer::RBMMixedLayer | ( | ) |
Default constructor.
Constructor from the sub_layers.
string PLearn::RBMMixedLayer::_classname_ | ( | ) | [static] |
Reimplemented from PLearn::RBMLayer.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
static string PLearn::RBMMixedLayer::_classname_ | ( | ) | [static] |
Reimplemented from PLearn::RBMLayer.
OptionList & PLearn::RBMMixedLayer::_getOptionList_ | ( | ) | [static] |
Reimplemented from PLearn::RBMLayer.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
static OptionList& PLearn::RBMMixedLayer::_getOptionList_ | ( | ) | [static] |
Reimplemented from PLearn::RBMLayer.
static RemoteMethodMap& PLearn::RBMMixedLayer::_getRemoteMethodMap_ | ( | ) | [static] |
Reimplemented from PLearn::RBMLayer.
RemoteMethodMap & PLearn::RBMMixedLayer::_getRemoteMethodMap_ | ( | ) | [static] |
Reimplemented from PLearn::RBMLayer.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
Reimplemented from PLearn::RBMLayer.
Reimplemented from PLearn::RBMLayer.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
static Object* PLearn::RBMMixedLayer::_new_instance_for_typemap_ | ( | ) | [static] |
Reimplemented from PLearn::Object.
Object * PLearn::RBMMixedLayer::_new_instance_for_typemap_ | ( | ) | [static] |
Reimplemented from PLearn::Object.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
StaticInitializer RBMMixedLayer::_static_initializer_ & PLearn::RBMMixedLayer::_static_initialize_ | ( | ) | [static] |
Reimplemented from PLearn::RBMLayer.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
static void PLearn::RBMMixedLayer::_static_initialize_ | ( | ) | [static] |
Reimplemented from PLearn::RBMLayer.
void PLearn::RBMMixedLayer::accumulateNegStats | ( | const Vec & | neg_values | ) | [virtual] |
Accumulates negative phase statistics.
Reimplemented from PLearn::RBMLayer.
Definition at line 545 of file RBMMixedLayer.cc.
References i, init_positions, n_layers, PLearn::RBMLayer::neg_count, PLearn::RBMLayer::size, sub_layers, and PLearn::TVec< T >::subVec().
{ for( int i=0 ; i<n_layers ; i++ ) { Vec sub_neg_values = neg_values.subVec( init_positions[i], sub_layers[i]->size ); sub_layers[i]->accumulateNegStats( sub_neg_values ); } neg_count++; }
void PLearn::RBMMixedLayer::accumulatePosStats | ( | const Vec & | pos_values | ) | [virtual] |
Accumulates positive phase statistics.
Reimplemented from PLearn::RBMLayer.
Definition at line 534 of file RBMMixedLayer.cc.
References i, init_positions, n_layers, PLearn::RBMLayer::pos_count, PLearn::RBMLayer::size, sub_layers, and PLearn::TVec< T >::subVec().
{ for( int i=0 ; i<n_layers ; i++ ) { Vec sub_pos_values = pos_values.subVec( init_positions[i], sub_layers[i]->size ); sub_layers[i]->accumulatePosStats( sub_pos_values ); } pos_count++; }
void PLearn::RBMMixedLayer::bpropNLL | ( | const Mat & | targets, |
const Mat & | costs_column, | ||
Mat & | bias_gradients | ||
) | [virtual] |
Reimplemented from PLearn::RBMLayer.
Definition at line 475 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::batch_size, PLearn::TMat< T >::column(), computeExpectations(), i, init_positions, PLearn::OnlineLearningModule::input_size, PLearn::TMat< T >::length(), mat_nlls, n_layers, PLASSERT, PLearn::TMat< T >::resize(), PLearn::TVec< T >::size(), PLearn::RBMLayer::size, sub_layers, PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().
{ computeExpectations(); PLASSERT( targets.width() == input_size ); PLASSERT( targets.length() == batch_size ); PLASSERT( costs_column.width() == 1 ); PLASSERT( costs_column.length() == batch_size ); bias_gradients.resize( batch_size, size ); for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Mat sub_targets = targets.subMatColumns(begin, size_i); Mat sub_bias_gradients = bias_gradients.subMatColumns(begin, size_i); // TODO: something else than store mat_nlls... sub_layers[i]->bpropNLL( sub_targets, mat_nlls.column(i), sub_bias_gradients ); } }
void PLearn::RBMMixedLayer::bpropNLL | ( | const Vec & | target, |
real | nll, | ||
Vec & | bias_gradient | ||
) | [virtual] |
Computes the gradient of the negative log-likelihood of target with respect to the layer's bias, given the internal activations.
Reimplemented from PLearn::RBMLayer.
Definition at line 457 of file RBMMixedLayer.cc.
References computeExpectation(), i, init_positions, PLearn::OnlineLearningModule::input_size, n_layers, nlls, PLASSERT, PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), PLearn::RBMLayer::size, sub_layers, and PLearn::TVec< T >::subVec().
{ computeExpectation(); PLASSERT( target.size() == input_size ); bias_gradient.resize( size ); for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Vec sub_target = target.subVec(begin, size_i); Vec sub_bias_gradient = bias_gradient.subVec(begin, size_i); sub_layers[i]->bpropNLL( sub_target, nlls[i], sub_bias_gradient ); } }
void PLearn::RBMMixedLayer::bpropUpdate | ( | const Vec & | input, |
const Vec & | output, | ||
Vec & | input_gradient, | ||
const Vec & | output_gradient, | ||
bool | accumulate = false |
||
) | [virtual] |
back-propagates the output gradient to the input
Implements PLearn::RBMLayer.
Definition at line 310 of file RBMMixedLayer.cc.
References i, init_positions, n_layers, PLASSERT, PLASSERT_MSG, PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), PLearn::RBMLayer::size, sub_layers, and PLearn::TVec< T >::subVec().
{ PLASSERT( input.size() == size ); PLASSERT( output.size() == size ); PLASSERT( output_gradient.size() == size ); if( accumulate ) { PLASSERT_MSG( input_gradient.size() == size, "Cannot resize input_gradient AND accumulate into it" ); } else // Note that, by construction of 'size', the whole gradient vector // should be cleared in the calls to sub_layers->bpropUpdate(..) below. input_gradient.resize( size ); for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Vec sub_input = input.subVec( begin, size_i ); Vec sub_output = output.subVec( begin, size_i ); Vec sub_input_gradient = input_gradient.subVec( begin, size_i ); Vec sub_output_gradient = output_gradient.subVec( begin, size_i ); sub_layers[i]->bpropUpdate( sub_input, sub_output, sub_input_gradient, sub_output_gradient, accumulate ); } }
void PLearn::RBMMixedLayer::bpropUpdate | ( | const Mat & | inputs, |
const Mat & | outputs, | ||
Mat & | input_gradients, | ||
const Mat & | output_gradients, | ||
bool | accumulate = false |
||
) | [virtual] |
Back-propagate the output gradient to the input, and update parameters.
Implements PLearn::RBMLayer.
Definition at line 344 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::batch_size, i, init_positions, PLearn::TMat< T >::length(), n_layers, PLASSERT, PLASSERT_MSG, PLearn::TMat< T >::resize(), PLearn::TVec< T >::size(), PLearn::RBMLayer::size, sub_layers, PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().
{ PLASSERT( inputs.width() == size ); PLASSERT( outputs.width() == size ); PLASSERT( output_gradients.width() == size ); int batch_size = inputs.length(); PLASSERT( outputs.length() == batch_size ); PLASSERT( output_gradients.length() == batch_size ); if( accumulate ) { PLASSERT_MSG( input_gradients.width() == size && input_gradients.length() == batch_size, "Cannot resize input_gradients and accumulate into it" ); } else // Note that, by construction of 'size', the whole gradient vector // should be cleared in the calls to sub_layers->bpropUpdate(..) below. input_gradients.resize(batch_size, size); for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Mat sub_inputs = inputs.subMatColumns( begin, size_i ); Mat sub_outputs = outputs.subMatColumns( begin, size_i ); Mat sub_input_gradients = input_gradients.subMatColumns( begin, size_i ); Mat sub_output_gradients = output_gradients.subMatColumns( begin, size_i ); sub_layers[i]->bpropUpdate( sub_inputs, sub_outputs, sub_input_gradients, sub_output_gradients, accumulate ); } }
void PLearn::RBMMixedLayer::bpropUpdate | ( | const Vec & | input, |
const Vec & | rbm_bias, | ||
const Vec & | output, | ||
Vec & | input_gradient, | ||
Vec & | rbm_bias_gradient, | ||
const Vec & | output_gradient | ||
) | [virtual] |
back-propagates the output gradient to the input and the bias
Reimplemented from PLearn::RBMLayer.
Definition at line 385 of file RBMMixedLayer.cc.
References i, init_positions, n_layers, PLASSERT, PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), PLearn::RBMLayer::size, sub_layers, and PLearn::TVec< T >::subVec().
{ PLASSERT( input.size() == size ); PLASSERT( rbm_bias.size() == size ); PLASSERT( output.size() == size ); PLASSERT( output_gradient.size() == size ); input_gradient.resize( size ); rbm_bias_gradient.resize( size ); for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Vec sub_input = input.subVec( begin, size_i ); Vec sub_rbm_bias = rbm_bias.subVec( begin, size_i ); Vec sub_output = output.subVec( begin, size_i ); Vec sub_input_gradient = input_gradient.subVec( begin, size_i ); Vec sub_rbm_bias_gradient = rbm_bias_gradient.subVec( begin, size_i); Vec sub_output_gradient = output_gradient.subVec( begin, size_i ); sub_layers[i]->bpropUpdate( sub_input, sub_rbm_bias, sub_output, sub_input_gradient, sub_rbm_bias_gradient, sub_output_gradient ); } }
void PLearn::RBMMixedLayer::bpropUpdate | ( | const Vec & | input, |
const Vec & | output, | ||
Vec & | input_gradient, | ||
const Vec & | output_gradient | ||
) | [virtual] |
back-propagates the output gradient to the input
Implements PLearn::RBMLayer.
Definition at line 104 of file DEPRECATED/RBMMixedLayer.cc.
References PLERROR.
{ PLERROR( "RBMMixedLayer::bpropUpdate not implemented yet." ); }
virtual void PLearn::RBMMixedLayer::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::RBMLayer.
void PLearn::RBMMixedLayer::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::RBMLayer.
Definition at line 162 of file DEPRECATED/RBMMixedLayer.cc.
References PLearn::RBMLayer::build(), and build_().
Referenced by RBMMixedLayer().
{ inherited::build(); build_(); }
void PLearn::RBMMixedLayer::build_ | ( | ) | [private] |
This does the actual building.
Reimplemented from PLearn::RBMLayer.
void PLearn::RBMMixedLayer::build_ | ( | ) | [private] |
This does the actual building.
Reimplemented from PLearn::RBMLayer.
Definition at line 134 of file DEPRECATED/RBMMixedLayer.cc.
References PLearn::RBMLayer::activations, PLearn::TVec< T >::append(), PLearn::RBMLayer::expectation, PLearn::RBMLayer::expectation_is_up_to_date, i, init_positions, layer_of_unit, PLearn::merge(), n_layers, PLearn::TVec< T >::resize(), PLearn::RBMLayer::sample, PLearn::TVec< T >::size(), PLearn::RBMLayer::size, sub_layers, and PLearn::RBMLayer::units_types.
Referenced by build().
{ units_types = ""; size = 0; activations.resize( 0 ); sample.resize( 0 ); expectation.resize( 0 ); expectation_is_up_to_date = false; layer_of_unit.resize( 0 ); n_layers = sub_layers.size(); init_positions.resize( n_layers ); for( int i=0 ; i<n_layers ; i++ ) { init_positions[i] = size; PP<RBMLayer> cur_layer = sub_layers[i]; units_types += cur_layer->units_types; size += cur_layer->size; activations = merge( activations, cur_layer->activations ); sample = merge( sample, cur_layer->sample ); expectation = merge( expectation, cur_layer->expectation ); layer_of_unit.append( TVec<int>( cur_layer->size, i ) ); } }
string PLearn::RBMMixedLayer::classname | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
virtual string PLearn::RBMMixedLayer::classname | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
void PLearn::RBMMixedLayer::clearStats | ( | ) | [virtual] |
resets the statistics and counts
Reimplemented from PLearn::RBMLayer.
Definition at line 598 of file RBMMixedLayer.cc.
References i, n_layers, PLearn::RBMLayer::neg_count, PLearn::RBMLayer::pos_count, and sub_layers.
Referenced by update().
{ for( int i=0 ; i<n_layers ; i++ ) sub_layers[i]->clearStats(); pos_count = 0; neg_count = 0; }
void PLearn::RBMMixedLayer::computeExpectation | ( | ) | [virtual] |
compute the expectation
Implements PLearn::RBMLayer.
Definition at line 93 of file DEPRECATED/RBMMixedLayer.cc.
References PLearn::RBMLayer::expectation_is_up_to_date, i, n_layers, and sub_layers.
Referenced by bpropNLL(), and fpropNLL().
{ if( expectation_is_up_to_date ) return; for( int i=0 ; i<n_layers ; i++ ) sub_layers[i]->computeExpectation(); expectation_is_up_to_date = true; }
virtual void PLearn::RBMMixedLayer::computeExpectation | ( | ) | [virtual] |
compute the expectation
Implements PLearn::RBMLayer.
void PLearn::RBMMixedLayer::computeExpectations | ( | ) | [virtual] |
compute the expectations according to activations
Implements PLearn::RBMLayer.
Definition at line 235 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::expectations_are_up_to_date, i, n_layers, and sub_layers.
Referenced by bpropNLL().
{ if( expectations_are_up_to_date ) return; for( int i=0 ; i < n_layers ; i++ ) sub_layers[i]->computeExpectations(); expectations_are_up_to_date = true; }
static void PLearn::RBMMixedLayer::declareOptions | ( | OptionList & | ol | ) | [static, protected] |
Declares the class options.
Reimplemented from PLearn::RBMLayer.
void PLearn::RBMMixedLayer::declareOptions | ( | OptionList & | ol | ) | [static, protected] |
Declares the class options.
Reimplemented from PLearn::RBMLayer.
Definition at line 111 of file DEPRECATED/RBMMixedLayer.cc.
References PLearn::OptionBase::buildoption, PLearn::declareOption(), PLearn::RBMLayer::declareOptions(), init_positions, layer_of_unit, PLearn::OptionBase::learntoption, n_layers, and sub_layers.
{ declareOption(ol, "sub_layers", &RBMMixedLayer::sub_layers, OptionBase::buildoption, "The concatenated RBMLayers composing this layer."); declareOption(ol, "init_positions", &RBMMixedLayer::init_positions, OptionBase::learntoption, " Initial index of the sub_layers."); declareOption(ol, "layer_of_unit", &RBMMixedLayer::layer_of_unit, OptionBase::learntoption, "layer_of_unit[i] is the index of sub_layer containing unit" " i."); declareOption(ol, "n_layers", &RBMMixedLayer::n_layers, OptionBase::learntoption, "Number of sub-layers."); // Now call the parent class' declareOptions inherited::declareOptions(ol); }
static const PPath& PLearn::RBMMixedLayer::declaringFile | ( | ) | [inline, static] |
static const PPath& PLearn::RBMMixedLayer::declaringFile | ( | ) | [inline, static] |
virtual RBMMixedLayer* PLearn::RBMMixedLayer::deepCopy | ( | CopiesMap & | copies | ) | const [virtual] |
Reimplemented from PLearn::RBMLayer.
RBMMixedLayer * PLearn::RBMMixedLayer::deepCopy | ( | CopiesMap & | copies | ) | const [virtual] |
Reimplemented from PLearn::RBMLayer.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
Compute -bias' unit_values.
Reimplemented from PLearn::RBMLayer.
Definition at line 713 of file RBMMixedLayer.cc.
References i, init_positions, n_layers, PLearn::TVec< T >::size(), sub_layers, and PLearn::TVec< T >::subVec().
{ real energy = 0; for ( int i = 0; i < n_layers; ++i ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Vec values = unit_values.subVec( begin, size_i ); energy += sub_layers[i]->energy(values); } return energy; }
void PLearn::RBMMixedLayer::expectation_is_not_up_to_date | ( | ) | [virtual] |
Reimplemented from PLearn::RBMLayer.
Definition at line 192 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::expectation_is_up_to_date, i, n_layers, and sub_layers.
{ for( int i=0 ; i<n_layers ; i++ ) sub_layers[i]->expectation_is_not_up_to_date(); expectation_is_up_to_date = false; }
void PLearn::RBMMixedLayer::forget | ( | ) | [virtual] |
forgets everything
Reimplemented from PLearn::RBMLayer.
Definition at line 607 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::forget(), i, n_layers, PLWARNING, PLearn::RBMLayer::random_gen, and sub_layers.
{ inherited::forget(); if( !random_gen ) { PLWARNING("RBMMixedLayer: cannot forget() without random_gen"); return; } for( int i=0; i<n_layers; i++ ) { if( !(sub_layers[i]->random_gen) ) sub_layers[i]->random_gen = random_gen; sub_layers[i]->forget(); } }
forward propagation
Reimplemented from PLearn::RBMLayer.
Definition at line 249 of file RBMMixedLayer.cc.
References i, init_positions, PLearn::OnlineLearningModule::input_size, n_layers, PLearn::OnlineLearningModule::output_size, PLASSERT, PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), sub_layers, and PLearn::TVec< T >::subVec().
{ PLASSERT( input.size() == input_size ); output.resize( output_size ); for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Vec sub_input = input.subVec(begin, size_i); Vec sub_output = output.subVec(begin, size_i); sub_layers[i]->fprop( sub_input, sub_output ); } }
void PLearn::RBMMixedLayer::fprop | ( | const Vec & | input, |
const Vec & | rbm_bias, | ||
Vec & | output | ||
) | const [virtual] |
forward propagation with provided bias
Reimplemented from PLearn::RBMLayer.
Definition at line 287 of file RBMMixedLayer.cc.
References i, init_positions, PLearn::OnlineLearningModule::input_size, n_layers, PLearn::OnlineLearningModule::output_size, PLASSERT, PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), sub_layers, and PLearn::TVec< T >::subVec().
{ PLASSERT( input.size() == input_size ); PLASSERT( rbm_bias.size() == input_size ); output.resize( output_size ); for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Vec sub_input = input.subVec(begin, size_i); Vec sub_rbm_bias = rbm_bias.subVec(begin, size_i); Vec sub_output = output.subVec(begin, size_i); sub_layers[i]->fprop( sub_input, sub_rbm_bias, sub_output ); } }
Batch forward propagation.
Reimplemented from PLearn::RBMLayer.
Definition at line 268 of file RBMMixedLayer.cc.
References i, init_positions, PLearn::TMat< T >::length(), n_layers, PLASSERT, PLearn::TMat< T >::resize(), PLearn::TVec< T >::size(), PLearn::RBMLayer::size, sub_layers, PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().
{ int mbatch_size = inputs.length(); PLASSERT( inputs.width() == size ); outputs.resize( mbatch_size, size ); for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Mat sub_inputs = inputs.subMatColumns(begin, size_i); Mat sub_outputs = outputs.subMatColumns(begin, size_i); // GCC bug? This doesn't work: // sub_layers[i]->fprop( sub_inputs, sub_outputs ); sub_layers[i]->OnlineLearningModule::fprop( sub_inputs, sub_outputs ); } }
Computes the negative log-likelihood of target given the internal activations of the layer.
Reimplemented from PLearn::RBMLayer.
Definition at line 415 of file RBMMixedLayer.cc.
References computeExpectation(), i, init_positions, PLearn::OnlineLearningModule::input_size, n_layers, nlls, PLASSERT, PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), sub_layers, and PLearn::TVec< T >::subVec().
{ computeExpectation(); PLASSERT( target.size() == input_size ); nlls.resize(n_layers); real ret = 0; real nlli = 0; for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; nlli = sub_layers[i]->fpropNLL( target.subVec(begin, size_i)); nlls[i] = nlli; ret += nlli; } return ret; }
Batch fpropNLL.
Reimplemented from PLearn::RBMLayer.
Definition at line 435 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::batch_size, PLearn::TMat< T >::clear(), PLearn::TMat< T >::column(), computeExpectation(), i, init_positions, PLearn::OnlineLearningModule::input_size, j, PLearn::TMat< T >::length(), mat_nlls, n_layers, PLASSERT, PLearn::TMat< T >::resize(), PLearn::TVec< T >::size(), sub_layers, PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().
{ computeExpectation(); PLASSERT( targets.width() == input_size ); PLASSERT( targets.length() == batch_size ); PLASSERT( costs_column.width() == 1 ); PLASSERT( costs_column.length() == batch_size ); costs_column.clear(); mat_nlls.resize(batch_size, n_layers); for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; sub_layers[i]->fpropNLL( targets.subMatColumns(begin, size_i), mat_nlls.column(i) ); for( int j=0; j < batch_size; ++j ) costs_column(j,0) += mat_nlls(j, i); } }
Computes This quantity is used for computing the free energy of a sample x in the OTHER layer of an RBM, from which unit_activations was computed.
Reimplemented from PLearn::RBMLayer.
Definition at line 727 of file RBMMixedLayer.cc.
References i, init_positions, n_layers, PLearn::TVec< T >::size(), sub_layers, and PLearn::TVec< T >::subVec().
{ real freeEnergy = 0; Vec act; for ( int i = 0; i < n_layers; ++i ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; act = unit_activations.subVec( begin, size_i ); freeEnergy += sub_layers[i]->freeEnergyContribution(act); } return freeEnergy; }
void PLearn::RBMMixedLayer::freeEnergyContributionGradient | ( | const Vec & | unit_activations, |
Vec & | unit_activations_gradient, | ||
real | output_gradient = 1 , |
||
bool | accumulate = false |
||
) | const [virtual] |
Computes gradient of the result of freeEnergyContribution with respect to unit_activations.
Optionally, a gradient with respect to freeEnergyContribution can be given
Reimplemented from PLearn::RBMLayer.
Definition at line 743 of file RBMMixedLayer.cc.
References i, init_positions, n_layers, PLearn::TVec< T >::size(), sub_layers, and PLearn::TVec< T >::subVec().
{ Vec act; Vec gact; for ( int i = 0; i < n_layers; ++i ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; act = unit_activations.subVec( begin, size_i ); gact = unit_activations_gradient.subVec( begin, size_i ); sub_layers[i]->freeEnergyContributionGradient( act, gact, output_gradient, accumulate); } }
virtual void PLearn::RBMMixedLayer::generateSample | ( | ) | [virtual] |
compute a sample, and update the sample field
Implements PLearn::RBMLayer.
void PLearn::RBMMixedLayer::generateSample | ( | ) | [virtual] |
compute a sample, and update the sample field
Implements PLearn::RBMLayer.
Definition at line 87 of file DEPRECATED/RBMMixedLayer.cc.
References i, n_layers, and sub_layers.
{ for( int i=0 ; i<n_layers ; i++ ) sub_layers[i]->generateSample(); }
void PLearn::RBMMixedLayer::generateSamples | ( | ) | [virtual] |
generate activations.length() samples
Implements PLearn::RBMLayer.
Definition at line 212 of file RBMMixedLayer.cc.
References i, n_layers, and sub_layers.
{ for( int i=0 ; i<n_layers ; i++ ) sub_layers[i]->generateSamples(); }
void PLearn::RBMMixedLayer::getAllActivations | ( | PP< RBMConnection > | rbmc, |
int | offset = 0 , |
||
bool | minibatch = false |
||
) | [virtual] |
Uses "rbmc" to obtain the activations of all units in this layer.
Unit 0 of this layer corresponds to unit "offset" of "rbmc".
Reimplemented from PLearn::RBMLayer.
Definition at line 179 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::getAllActivations(), i, n_layers, and sub_layers.
{ inherited::getAllActivations( rbmc, offset, minibatch ); for( int i=0 ; i<n_layers ; i++ ) { if( minibatch ) sub_layers[i]->expectations_are_up_to_date = false; else sub_layers[i]->expectation_is_up_to_date = false; } }
void PLearn::RBMMixedLayer::getAllActivations | ( | PP< RBMParameters > | rbmp, |
int | offset = 0 |
||
) | [virtual] |
Uses "rbmp" to obtain the activations of all units in this layer.
Unit 0 of this layer corresponds to unit "offset" of "rbmp".
Implements PLearn::RBMLayer.
Definition at line 77 of file DEPRECATED/RBMMixedLayer.cc.
References PLearn::RBMLayer::expectation_is_up_to_date, i, init_positions, n_layers, and sub_layers.
{ for( int i=0 ; i<n_layers ; i++ ) { int total_offset = offset + init_positions[i]; sub_layers[i]->getAllActivations( rbmp, total_offset ); } expectation_is_up_to_date = false; }
Computes the conf_index configuration of the layer.
Reimplemented from PLearn::RBMLayer.
Definition at line 775 of file RBMMixedLayer.cc.
References getConfigurationCount(), i, init_positions, PLearn::TVec< T >::length(), n_layers, PLASSERT, PLearn::TVec< T >::size(), PLearn::RBMLayer::size, sub_layers, and PLearn::TVec< T >::subVec().
{ PLASSERT( output.length() == size ); PLASSERT( conf_index >= 0 && conf_index < getConfigurationCount() ); int conf_i = conf_index; for ( int i = 0; i < n_layers; ++i ) { int conf_layer_i = sub_layers[i]->getConfigurationCount(); int begin = init_positions[i]; int size_i = sub_layers[i]->size; Vec output_i = output.subVec( begin, size_i ); sub_layers[i]->getConfiguration(conf_i % conf_layer_i, output_i); conf_i /= conf_layer_i; } }
int PLearn::RBMMixedLayer::getConfigurationCount | ( | ) | [virtual] |
Returns a number of different configurations the layer can be in.
Reimplemented from PLearn::RBMLayer.
Definition at line 760 of file RBMMixedLayer.cc.
References i, PLearn::RBMLayer::INFINITE_CONFIGURATIONS, n_layers, and sub_layers.
Referenced by getConfiguration().
{ int count = 1; for ( int i = 0; i < n_layers; ++i ) { int cc_layer_i = sub_layers[i]->getConfigurationCount(); // Avoiding overflow if ( INFINITE_CONFIGURATIONS/cc_layer_i <= count ) return INFINITE_CONFIGURATIONS; count *= cc_layer_i; } return count; }
OptionList & PLearn::RBMMixedLayer::getOptionList | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
virtual OptionList& PLearn::RBMMixedLayer::getOptionList | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
virtual OptionMap& PLearn::RBMMixedLayer::getOptionMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
OptionMap & PLearn::RBMMixedLayer::getOptionMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
virtual RemoteMethodMap& PLearn::RBMMixedLayer::getRemoteMethodMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
RemoteMethodMap & PLearn::RBMMixedLayer::getRemoteMethodMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 49 of file DEPRECATED/RBMMixedLayer.cc.
void PLearn::RBMMixedLayer::getUnitActivation | ( | int | i, |
PP< RBMConnection > | rbmc, | ||
int | offset = 0 |
||
) | [virtual] |
Uses "rbmc" to compute the activation of unit "i" of this layer.
This activation is computed by the "i+offset"-th unit of "rbmc"
Reimplemented from PLearn::RBMLayer.
Definition at line 167 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::getUnitActivation(), i, j, layer_of_unit, and sub_layers.
{ inherited::getUnitActivation( i, rbmc, offset ); int j = layer_of_unit[i]; sub_layers[j]->expectation_is_up_to_date = false; }
void PLearn::RBMMixedLayer::getUnitActivations | ( | int | i, |
PP< RBMParameters > | rbmp, | ||
int | offset = 0 |
||
) | [virtual] |
Uses "rbmp" to obtain the activations of unit "i" of this layer.
This activation vector is computed by the "i+offset"-th unit of "rbmp"
Implements PLearn::RBMLayer.
Definition at line 63 of file DEPRECATED/RBMMixedLayer.cc.
References PLearn::RBMLayer::expectation_is_up_to_date, i, init_positions, j, layer_of_unit, and sub_layers.
{ int j = layer_of_unit[i]; PP<RBMLayer> layer = sub_layers[i]; int sub_index = i - init_positions[j]; int total_offset = offset + init_positions[j]; layer->getUnitActivations( sub_index, rbmp, total_offset ); expectation_is_up_to_date = false; }
void PLearn::RBMMixedLayer::makeDeepCopyFromShallowCopy | ( | CopiesMap & | copies | ) | [virtual] |
Transforms a shallow copy into a deep copy.
Reimplemented from PLearn::RBMLayer.
Definition at line 169 of file DEPRECATED/RBMMixedLayer.cc.
References PLearn::deepCopyField(), init_positions, layer_of_unit, PLearn::RBMLayer::makeDeepCopyFromShallowCopy(), and sub_layers.
{ inherited::makeDeepCopyFromShallowCopy(copies); deepCopyField(sub_layers, copies); deepCopyField(init_positions, copies); deepCopyField(layer_of_unit, copies); }
virtual void PLearn::RBMMixedLayer::makeDeepCopyFromShallowCopy | ( | CopiesMap & | copies | ) | [virtual] |
Transforms a shallow copy into a deep copy.
Reimplemented from PLearn::RBMLayer.
void PLearn::RBMMixedLayer::reset | ( | ) | [virtual] |
resets activations, sample and expectation fields
Reimplemented from PLearn::RBMLayer.
Definition at line 590 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::expectation_is_up_to_date, i, n_layers, and sub_layers.
{ for( int i=0 ; i<n_layers ; i++ ) sub_layers[i]->reset(); expectation_is_up_to_date = false; }
void PLearn::RBMMixedLayer::setBatchSize | ( | int | the_batch_size | ) | [virtual] |
Sets batch_size and resize activations, expectations, and samples.
Reimplemented from PLearn::RBMLayer.
Definition at line 89 of file RBMMixedLayer.cc.
References i, n_layers, PLearn::RBMLayer::setBatchSize(), and sub_layers.
Referenced by setExpectations(), and setExpectationsByRef().
{ inherited::setBatchSize( the_batch_size ); for( int i = 0; i < n_layers; i++ ) sub_layers[i]->setBatchSize( the_batch_size ); }
void PLearn::RBMMixedLayer::setExpectation | ( | const Vec & | the_expectation | ) | [virtual] |
Copy the given expectation in the 'expectation' vector.
Reimplemented from PLearn::RBMLayer.
Definition at line 99 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::expectation, PLearn::RBMLayer::expectation_is_up_to_date, i, n_layers, and sub_layers.
{ expectation << the_expectation; expectation_is_up_to_date=true; for( int i = 0; i < n_layers; i++ ) sub_layers[i]->expectation_is_up_to_date=true; }
void PLearn::RBMMixedLayer::setExpectationByRef | ( | const Vec & | the_expectation | ) | [virtual] |
Make the 'expectation' vector point to the given data vector (so no copy is performed).
Reimplemented from PLearn::RBMLayer.
Definition at line 110 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::expectation, PLearn::RBMLayer::expectation_is_up_to_date, i, init_positions, n_layers, sub_layers, and PLearn::TVec< T >::subVec().
{ expectation = the_expectation; expectation_is_up_to_date=true; // Rearrange pointers for( int i = 0; i < n_layers; i++ ) { int init_pos = init_positions[i]; PP<RBMLayer> layer = sub_layers[i]; int layer_size = layer->size; layer->setExpectationByRef( expectation.subVec(init_pos, layer_size) ); } }
void PLearn::RBMMixedLayer::setExpectations | ( | const Mat & | the_expectations | ) | [virtual] |
Copy the given expectations in the 'expectations' matrix.
Reimplemented from PLearn::RBMLayer.
Definition at line 130 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::batch_size, PLearn::RBMLayer::expectations, PLearn::RBMLayer::expectations_are_up_to_date, i, PLearn::TMat< T >::length(), n_layers, setBatchSize(), and sub_layers.
{ batch_size = the_expectations.length(); setBatchSize( batch_size ); expectations << the_expectations; expectations_are_up_to_date=true; for( int i = 0; i < n_layers; i++ ) sub_layers[i]->expectations_are_up_to_date=true; }
void PLearn::RBMMixedLayer::setExpectationsByRef | ( | const Mat & | the_expectations | ) | [virtual] |
Make the 'expectations' matrix point to the given data matrix (so no copy is performed).
Reimplemented from PLearn::RBMLayer.
Definition at line 143 of file RBMMixedLayer.cc.
References PLearn::RBMLayer::batch_size, PLearn::RBMLayer::expectations, PLearn::RBMLayer::expectations_are_up_to_date, i, init_positions, PLearn::TMat< T >::length(), n_layers, setBatchSize(), sub_layers, and PLearn::TMat< T >::subMatColumns().
{ batch_size = the_expectations.length(); setBatchSize( batch_size ); expectations = the_expectations; expectations_are_up_to_date=true; // Rearrange pointers for( int i = 0; i < n_layers; i++ ) { int init_pos = init_positions[i]; PP<RBMLayer> layer = sub_layers[i]; int layer_size = layer->size; layer->setExpectationsByRef(expectations.subMatColumns(init_pos, layer_size)); } }
void PLearn::RBMMixedLayer::setLearningRate | ( | real | the_learning_rate | ) | [virtual] |
Sets the learning rate, also in the sub_layers.
Reimplemented from PLearn::RBMLayer.
Definition at line 67 of file RBMMixedLayer.cc.
References i, n_layers, PLearn::RBMLayer::setLearningRate(), and sub_layers.
{ inherited::setLearningRate( the_learning_rate ); for( int i=0 ; i<n_layers ; i++ ) sub_layers[i]->setLearningRate( the_learning_rate ); }
void PLearn::RBMMixedLayer::setMomentum | ( | real | the_momentum | ) | [virtual] |
Sets the momentum, also in the sub_layers.
Reimplemented from PLearn::RBMLayer.
Definition at line 78 of file RBMMixedLayer.cc.
References i, n_layers, PLearn::RBMLayer::setMomentum(), and sub_layers.
{ inherited::setMomentum( the_momentum ); for( int i=0 ; i<n_layers ; i++ ) sub_layers[i]->setMomentum( the_momentum ); }
Update parameters according to one pair of vectors.
Reimplemented from PLearn::RBMLayer.
Definition at line 564 of file RBMMixedLayer.cc.
References i, init_positions, n_layers, PLearn::TVec< T >::size(), sub_layers, and PLearn::TVec< T >::subVec().
{ for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Vec sub_pos_values = pos_values.subVec( begin, size_i ); Vec sub_neg_values = neg_values.subVec( begin, size_i ); sub_layers[i]->update( sub_pos_values, sub_neg_values ); } }
Update parameters according to several pairs of vectors.
Reimplemented from PLearn::RBMLayer.
Definition at line 577 of file RBMMixedLayer.cc.
References i, init_positions, n_layers, PLearn::TVec< T >::size(), sub_layers, and PLearn::TMat< T >::subMatColumns().
{ for( int i=0 ; i<n_layers ; i++ ) { int begin = init_positions[i]; int size_i = sub_layers[i]->size; Mat sub_pos_values = pos_values.subMatColumns( begin, size_i ); Mat sub_neg_values = neg_values.subMatColumns( begin, size_i ); sub_layers[i]->update( sub_pos_values, sub_neg_values ); } }
void PLearn::RBMMixedLayer::update | ( | ) | [virtual] |
Update parameters according to accumulated statistics.
Reimplemented from PLearn::RBMLayer.
Definition at line 556 of file RBMMixedLayer.cc.
References clearStats(), i, n_layers, and sub_layers.
{ for( int i=0 ; i<n_layers ; i++ ) sub_layers[i]->update(); clearStats(); }
static StaticInitializer PLearn::RBMMixedLayer::_static_initializer_ [static] |
Reimplemented from PLearn::RBMLayer.
Definition at line 99 of file DEPRECATED/RBMMixedLayer.h.
TVec< int > PLearn::RBMMixedLayer::init_positions [protected] |
Initial index of the sub_layers.
Definition at line 110 of file DEPRECATED/RBMMixedLayer.h.
Referenced by accumulateNegStats(), accumulatePosStats(), bpropNLL(), bpropUpdate(), build_(), declareOptions(), energy(), fprop(), fpropNLL(), freeEnergyContribution(), freeEnergyContributionGradient(), getAllActivations(), getConfiguration(), getUnitActivations(), makeDeepCopyFromShallowCopy(), setExpectationByRef(), setExpectationsByRef(), and update().
TVec< int > PLearn::RBMMixedLayer::layer_of_unit [protected] |
layer_of_unit[i] is the index of sub_layer containing unit i
Definition at line 113 of file DEPRECATED/RBMMixedLayer.h.
Referenced by build_(), declareOptions(), getUnitActivation(), getUnitActivations(), and makeDeepCopyFromShallowCopy().
Mat PLearn::RBMMixedLayer::mat_nlls [mutable, private] |
Definition at line 249 of file RBMMixedLayer.h.
Referenced by bpropNLL(), and fpropNLL().
int PLearn::RBMMixedLayer::n_layers [protected] |
Number of sub-layers.
Definition at line 116 of file DEPRECATED/RBMMixedLayer.h.
Referenced by accumulateNegStats(), accumulatePosStats(), bpropNLL(), bpropUpdate(), build_(), clearStats(), computeExpectation(), computeExpectations(), declareOptions(), energy(), expectation_is_not_up_to_date(), forget(), fprop(), fpropNLL(), freeEnergyContribution(), freeEnergyContributionGradient(), generateSample(), generateSamples(), getAllActivations(), getConfiguration(), getConfigurationCount(), reset(), setBatchSize(), setExpectation(), setExpectationByRef(), setExpectations(), setExpectationsByRef(), setLearningRate(), setMomentum(), and update().
Vec PLearn::RBMMixedLayer::nlls [mutable, private] |
Definition at line 248 of file RBMMixedLayer.h.
Referenced by bpropNLL(), and fpropNLL().
The concatenated RBMLayers composing this layer.
Definition at line 62 of file DEPRECATED/RBMMixedLayer.h.
Referenced by accumulateNegStats(), accumulatePosStats(), bpropNLL(), bpropUpdate(), build_(), clearStats(), computeExpectation(), computeExpectations(), declareOptions(), energy(), expectation_is_not_up_to_date(), forget(), fprop(), fpropNLL(), freeEnergyContribution(), freeEnergyContributionGradient(), generateSample(), generateSamples(), getAllActivations(), getConfiguration(), getConfigurationCount(), getUnitActivation(), getUnitActivations(), makeDeepCopyFromShallowCopy(), reset(), setBatchSize(), setExpectation(), setExpectationByRef(), setExpectations(), setExpectationsByRef(), setLearningRate(), setMomentum(), and update().