PLearn 0.1
PLearn::DenoisingRecurrentNet Member List
This is the complete list of members for PLearn::DenoisingRecurrentNet, including all inherited members.
_classname_()PLearn::DenoisingRecurrentNet [static]
_getOptionList_()PLearn::DenoisingRecurrentNet [static]
_getRemoteMethodMap_()PLearn::DenoisingRecurrentNet [static]
_isa_(const Object *o)PLearn::DenoisingRecurrentNet [static]
_new_instance_for_typemap_()PLearn::DenoisingRecurrentNet [static]
_static_initialize_()PLearn::DenoisingRecurrentNet [static]
_static_initializer_PLearn::DenoisingRecurrentNet [static]
acc_dynamic_connections_grPLearn::DenoisingRecurrentNet [mutable, protected]
acc_hidden_bias_grPLearn::DenoisingRecurrentNet [mutable, protected]
acc_input_connections_grPLearn::DenoisingRecurrentNet [mutable, protected]
acc_recons_bias_grPLearn::DenoisingRecurrentNet [mutable, protected]
acc_reconstruction_dynamic_connections_grPLearn::DenoisingRecurrentNet [mutable, protected]
acc_target_bias_grPLearn::DenoisingRecurrentNet [mutable, protected]
acc_target_connections_grPLearn::DenoisingRecurrentNet [mutable, protected]
applyMultipleSoftmaxToInputWindow(Vec input_reconstruction_activation, Vec input_reconstruction_prob)PLearn::DenoisingRecurrentNet [private]
applyWeightPenalty(Mat &weights, Mat &acc_weights_gr, int &down_size, int &up_size, real &lr)PLearn::DenoisingRecurrentNet [private]
asString() const PLearn::Object [virtual]
asStringRemoteTransmit() const PLearn::Object [virtual]
b_costsPLearn::PLearner [mutable, protected]
b_inputsPLearn::PLearner [mutable, protected]
b_outputsPLearn::PLearner [mutable, protected]
b_targetsPLearn::PLearner [mutable, protected]
b_weightsPLearn::PLearner [mutable, protected]
batchComputeOutputAndConfidence(VMat inputs, real probability, VMat outputs_and_confidence) const PLearn::PLearner [virtual]
bias_gradientPLearn::DenoisingRecurrentNet [mutable, protected]
bpropUpdateConnection(const Vec &input, const Vec &output, Vec &input_gradient, const Vec &output_gradient, Mat &weights, Mat &acc_weights_gr, int &down_size, int &up_size, real &lr, bool accumulate, bool using_penalty_factor)PLearn::DenoisingRecurrentNet [private]
bpropUpdateHiddenLayer(const Vec &input, const Vec &output, Vec &input_gradient, const Vec &output_gradient, Vec &bias, real &lr)PLearn::DenoisingRecurrentNet [private]
build()PLearn::DenoisingRecurrentNet [virtual]
build_()PLearn::DenoisingRecurrentNet [private]
build_from_train_set()PLearn::PLearner [inline, protected, virtual]
call(const string &methodname, int nargs, PStream &io)PLearn::Object [virtual]
changeOption(const string &optionname, const string &value)PLearn::Object
changeOptions(const map< string, string > &name_value)PLearn::Object [virtual]
clamp_units(const Vec layer_vector, PP< RBMLayer > layer, TVec< int > symbol_sizes) const PLearn::DenoisingRecurrentNet
clamp_units(const Vec layer_vector, PP< RBMLayer > layer, TVec< int > symbol_sizes, const Vec original_mask, Vec &formated_mask) const PLearn::DenoisingRecurrentNet
classname() const PLearn::DenoisingRecurrentNet [virtual]
clean_encoded_seqPLearn::DenoisingRecurrentNet [mutable, protected]
computeConfidenceFromOutput(const Vec &input, const Vec &output, real probability, TVec< pair< real, real > > &intervals) const PLearn::PLearner [virtual]
computeCostsFromOutputs(const Vec &input, const Vec &output, const Vec &target, Vec &costs) const PLearn::DenoisingRecurrentNet [virtual]
computeCostsOnly(const Vec &input, const Vec &target, Vec &costs) const PLearn::PLearner [virtual]
computeInputOutputConfMat(VMat inputs, real probability) const PLearn::PLearner
computeInputOutputMat(VMat inputs) const PLearn::PLearner
computeOutput(const Vec &input, Vec &output) const PLearn::DenoisingRecurrentNet [virtual]
computeOutputAndCosts(const Vec &input, const Vec &target, Vec &output, Vec &costs) const PLearn::PLearner [virtual]
computeOutputConfMat(VMat inputs, real probability) const PLearn::PLearner
computeOutputCovMat(const Mat &inputs, Mat &outputs, TVec< Mat > &covariance_matrices) const PLearn::PLearner [virtual]
computeOutputs(const Mat &input, Mat &output) const PLearn::PLearner [virtual]
computeOutputsAndCosts(const Mat &input, const Mat &target, Mat &output, Mat &costs) const PLearn::PLearner [virtual]
current_learning_ratePLearn::DenoisingRecurrentNet [mutable, protected]
dataPLearn::DenoisingRecurrentNet [protected]
declareMethods(RemoteMethodMap &rmm)PLearn::PLearner [protected, static]
declareOptions(OptionList &ol)PLearn::DenoisingRecurrentNet [protected, static]
declaringFile()PLearn::DenoisingRecurrentNet [inline, static]
deepCopy(CopiesMap &copies) const PLearn::DenoisingRecurrentNet [virtual]
deepCopyNoMap()PLearn::Object
DenoisingRecurrentNet()PLearn::DenoisingRecurrentNet
duration_to_number_of_timeframes(int duration)PLearn::DenoisingRecurrentNet [static]
dynamic_act_no_bias_contributionPLearn::DenoisingRecurrentNet [mutable, protected]
dynamic_connectionsPLearn::DenoisingRecurrentNet
dynamic_gradient_scale_factorPLearn::DenoisingRecurrentNet
dynamic_reconstruction_connectionsPLearn::DenoisingRecurrentNet
encode_artificialData(Mat seq) const PLearn::DenoisingRecurrentNet [private]
encode_onehot_diffNote_duration(Mat sequence, Mat &encoded_sequence, bool use_silence, int duration_nbits=20)PLearn::DenoisingRecurrentNet [static]
encode_onehot_note_octav_duration(Mat sequence, Mat &encoded_sequence, int prepend_zero_rows, bool use_silence, int octav_nbits, int duration_nbits=20)PLearn::DenoisingRecurrentNet [static]
encode_onehot_timeframe(Mat sequence, Mat &encoded_sequence, int prepend_zero_rows, bool use_silence=false)PLearn::DenoisingRecurrentNet [static]
encodeAndCreateSupervisedSequence(Mat seq) const PLearn::DenoisingRecurrentNet [private]
encodeAndCreateSupervisedSequence2(Mat seq) const PLearn::DenoisingRecurrentNet [private]
encoded_seqPLearn::DenoisingRecurrentNet [mutable, protected]
encodeSequence(Mat sequence, Mat &encoded_seq) const PLearn::DenoisingRecurrentNet
encodeSequenceAndPopulateLists(Mat seq, bool doNoise) const PLearn::DenoisingRecurrentNet [private]
encodingPLearn::DenoisingRecurrentNet
end_of_sequence_symbolPLearn::DenoisingRecurrentNet
expdirPLearn::PLearner
finalize()PLearn::PLearner [virtual]
finalizedPLearn::PLearner
forget()PLearn::DenoisingRecurrentNet [virtual]
forget_when_training_set_changesPLearn::PLearner [protected]
fpropHiddenReconstructionFromLastHidden(Vec theInput, Vec hidden, Mat reconstruction_weights, Mat &acc_weights_gr, Vec &reconstruction_bias, Vec &reconstruction_bias2, Vec hidden_reconstruction_activation_grad, Vec &reconstruction_prob, Vec clean_input, Vec hidden_gradient, double hidden_reconstruction_cost_weight, double lr)PLearn::DenoisingRecurrentNet [private]
fpropHiddenReconstructionFromLastHidden2(Vec theInput, Vec hidden, Mat reconstruction_weights, Mat &acc_weights_gr, Vec &reconstruction_bias, Vec &reconstruction_bias2, Vec hidden_reconstruction_activation_grad, Vec &reconstruction_prob, Vec clean_input, Vec hidden_gradient, double hidden_reconstruction_cost_weight, double lr)PLearn::DenoisingRecurrentNet [private]
fpropHiddenSymmetricDynamicMatrix(Vec hidden, Mat reconstruction_weights, Vec &reconstruction_prob, Vec clean_input, Vec hidden_gradient, double hidden_reconstruction_cost_weight, double lr)PLearn::DenoisingRecurrentNet [private]
fpropInputReconstructionFromHidden(Vec hidden, Mat reconstruction_weights, Vec &input_reconstruction_bias, Vec &input_reconstruction_prob, Vec clean_input)PLearn::DenoisingRecurrentNet [private]
fpropUpdateInputReconstructionFromHidden(Vec hidden, Mat &reconstruction_weights, Mat &acc_weights_gr, Vec &input_reconstruction_bias, Vec &input_reconstruction_prob, Vec clean_input, Vec hidden_gradient, double input_reconstruction_cost_weight, double lr)PLearn::DenoisingRecurrentNet [private]
generate(int t, int n)PLearn::DenoisingRecurrentNet
generateArtificial()PLearn::DenoisingRecurrentNet
getDurationBit(int duration)PLearn::DenoisingRecurrentNet [static]
getDynamicConnectionsWeightMatrix()PLearn::DenoisingRecurrentNet [private]
getDynamicReconstructionConnectionsWeightMatrix()PLearn::DenoisingRecurrentNet [private]
getExperimentDirectory() const PLearn::PLearner [inline]
getInputConnectionsWeightMatrix()PLearn::DenoisingRecurrentNet [private]
getInputWindow(Mat sequence, int startpos, int winsize)PLearn::DenoisingRecurrentNet [inline, static]
getNoteAndOctave(int midi_number, int &note, int &octave)PLearn::DenoisingRecurrentNet [inline, static]
getOption(const string &optionname) const PLearn::Object
getOptionList() const PLearn::DenoisingRecurrentNet [virtual]
getOptionMap() const PLearn::DenoisingRecurrentNet [virtual]
getOptionsToRemoteTransmit() const PLearn::Object [virtual]
getOptionsToSave() const PLearn::Object [virtual]
getOutputNames() const PLearn::PLearner [virtual]
getRemoteMethodMap() const PLearn::DenoisingRecurrentNet [virtual]
getSequence(int i, Mat &seq) const PLearn::DenoisingRecurrentNet
getTargetConnectionsWeightMatrix(int tar)PLearn::DenoisingRecurrentNet [private]
getTestCostIndex(const string &costname) const PLearn::PLearner
getTestCostNames() const PLearn::DenoisingRecurrentNet [virtual]
getTrainCostIndex(const string &costname) const PLearn::PLearner
getTrainCostNames() const PLearn::DenoisingRecurrentNet [virtual]
getTrainingSet() const PLearn::PLearner [inline]
getTrainStatsCollector()PLearn::PLearner [inline]
getValidationSet() const PLearn::PLearner [inline]
hasOption(const string &optionname) const PLearn::Object
hidden2_act_no_bias_listPLearn::DenoisingRecurrentNet [mutable, protected]
hidden2_listPLearn::DenoisingRecurrentNet [mutable, protected]
hidden_act_no_bias_listPLearn::DenoisingRecurrentNet [mutable, protected]
hidden_connectionsPLearn::DenoisingRecurrentNet
hidden_gradientPLearn::DenoisingRecurrentNet [mutable, protected]
hidden_layerPLearn::DenoisingRecurrentNet
hidden_layer2PLearn::DenoisingRecurrentNet
hidden_listPLearn::DenoisingRecurrentNet [mutable, protected]
hidden_noise_probPLearn::DenoisingRecurrentNet
hidden_reconstruction_biasPLearn::DenoisingRecurrentNet
hidden_reconstruction_bias2PLearn::DenoisingRecurrentNet
hidden_reconstruction_cost_weightPLearn::DenoisingRecurrentNet
hidden_reconstruction_lrPLearn::DenoisingRecurrentNet
hidden_reconstruction_probPLearn::DenoisingRecurrentNet [mutable, protected]
hidden_temporal_gradientPLearn::DenoisingRecurrentNet [mutable, protected]
info() const PLearn::Object [virtual]
inherited typedefPLearn::DenoisingRecurrentNet [private]
initTrain()PLearn::PLearner [protected]
inject_zero_forcing_noise(Mat sequence, double noise_prob) const PLearn::DenoisingRecurrentNet
inject_zero_forcing_noise(Vec sequence, double noise_prob) const PLearn::DenoisingRecurrentNet
input_connectionsPLearn::DenoisingRecurrentNet
input_layerPLearn::DenoisingRecurrentNet
input_listPLearn::DenoisingRecurrentNet [mutable, protected]
input_noise_probPLearn::DenoisingRecurrentNet
input_reconstruction_biasPLearn::DenoisingRecurrentNet
input_reconstruction_cost_weightPLearn::DenoisingRecurrentNet
input_reconstruction_lrPLearn::DenoisingRecurrentNet
input_reconstruction_probPLearn::DenoisingRecurrentNet [mutable, protected]
input_symbol_sizesPLearn::DenoisingRecurrentNet
input_window_sizePLearn::DenoisingRecurrentNet
inputsize() const PLearn::PLearner [virtual]
inputsize_PLearn::PLearner [protected]
isStatefulLearner() const PLearn::PLearner [virtual]
L1_penalty_factorPLearn::DenoisingRecurrentNet
L2_penalty_factorPLearn::DenoisingRecurrentNet
load(const PPath &filename)PLearn::Object [virtual]
locateSequenceBoundaries(VMat dataset, TVec< int > &boundaries, real end_of_sequence_symbol)PLearn::DenoisingRecurrentNet [static]
makeDeepCopyFromShallowCopy(CopiesMap &copies)PLearn::DenoisingRecurrentNet [virtual]
masks_listPLearn::DenoisingRecurrentNet [mutable, protected]
master_sends_testset_rowsPLearn::PLearner
mean_encoded_vecPLearn::DenoisingRecurrentNet
n_examplesPLearn::PLearner [protected]
nb_stage_reconstructionPLearn::DenoisingRecurrentNet
nb_stage_targetPLearn::DenoisingRecurrentNet
newread(PStream &in, unsigned int id=UINT_MAX)PLearn::Object
newwrite(PStream &out) const PLearn::Object [virtual]
nll_listPLearn::DenoisingRecurrentNet [mutable, protected]
noisePLearn::DenoisingRecurrentNet
noisy_recurrent_lrPLearn::DenoisingRecurrentNet
nSequences() const PLearn::DenoisingRecurrentNet [inline]
nserversPLearn::PLearner
nstagesPLearn::PLearner
nTestCosts() const PLearn::PLearner [virtual]
nTrainCosts() const PLearn::PLearner [virtual]
Object(bool call_build_=false)PLearn::Object
oldread(istream &in)PLearn::Object [virtual]
outputsize() const PLearn::DenoisingRecurrentNet [virtual]
parallelize_herePLearn::PLearner
parseOptionName(const string &optionname, Object *&final_object, OptionList::iterator &option_iter, string &option_index)PLearn::Object
parseOptionName(const string &optionname, const Object *&final_object, OptionList::iterator &option_iter, string &option_index) const PLearn::Object
partition(TVec< double > part, TVec< double > periode, TVec< double > vel) const PLearn::DenoisingRecurrentNet
PLearner()PLearn::PLearner
PPointable()PLearn::PPointable [inline]
PPointable(const PPointable &other)PLearn::PPointable [inline]
prediction_cost_weightPLearn::DenoisingRecurrentNet
prepareToSendResults(PStream &out, int nres)PLearn::Object [static]
processDataSet(VMat dataset) const PLearn::PLearner [virtual]
random_genPLearn::PLearner [mutable, protected]
read(istream &in)PLearn::Object [virtual]
readOptionVal(PStream &in, const string &optionname, unsigned int id=UINT_MAX)PLearn::Object
recurrent_lrPLearn::DenoisingRecurrentNet
recurrentFprop(Vec train_costs, Vec train_n_items, bool useDynamicConnections=true) const PLearn::DenoisingRecurrentNet [private]
recurrentUpdate(real input_reconstruction_weight, real hidden_reconstruction_cost_weight, real temporal_gradient_contribution, real prediction_cost_weight, real inputAndDynamicPart, Vec train_costs, Vec train_n_items)PLearn::DenoisingRecurrentNet
ref() const PLearn::PPointable [inline]
remote_test(VMat testset, PP< VecStatsCollector > test_stats, bool rtestoutputs, bool rtestcosts) const PLearn::PLearner [virtual]
remote_useOnTrain() const PLearn::PLearner [virtual]
report_progressPLearn::PLearner
resetInternalState()PLearn::PLearner [virtual]
resize_lists(int l) const PLearn::DenoisingRecurrentNet [private]
run()PLearn::Object [virtual]
save(const PPath &filename) const PLearn::Object [virtual]
save_trainingset_prefixPLearn::PLearner
seed_PLearn::PLearner
seqPLearn::DenoisingRecurrentNet [mutable, protected]
setExperimentDirectory(const PPath &the_expdir)PLearn::PLearner [virtual]
setLearningRate(real the_learning_rate)PLearn::DenoisingRecurrentNet
setOption(const string &optionname, const string &value)PLearn::Object
setTrainingSet(VMat training_set, bool call_forget=true)PLearn::DenoisingRecurrentNet [virtual]
setTrainStatsCollector(PP< VecStatsCollector > statscol)PLearn::PLearner [virtual]
setValidationSet(VMat validset)PLearn::PLearner [virtual]
splitRawMaskedSupervisedSequence(Mat seq, bool doNoise) const PLearn::DenoisingRecurrentNet [private]
stagePLearn::PLearner
sub_test(VMat testset, PP< VecStatsCollector > test_stats, bool rtestoutputs, bool rtestcosts) const PLearn::PLearner [virtual]
target_connectionsPLearn::DenoisingRecurrentNet
target_layersPLearn::DenoisingRecurrentNet
target_layers_n_of_target_elementsPLearn::DenoisingRecurrentNet
target_layers_weightsPLearn::DenoisingRecurrentNet
target_prediction_act_no_bias_listPLearn::DenoisingRecurrentNet [mutable, protected]
target_prediction_listPLearn::DenoisingRecurrentNet [mutable, protected]
target_symbol_sizesPLearn::DenoisingRecurrentNet
targets_listPLearn::DenoisingRecurrentNet [mutable, protected]
targetsize() const PLearn::PLearner [virtual]
targetsize_PLearn::PLearner [protected]
test(VMat testset, PP< VecStatsCollector > test_stats, VMat testoutputs=0, VMat testcosts=0) const PLearn::DenoisingRecurrentNet [virtual]
test_minibatch_sizePLearn::PLearner
testset_boundariesPLearn::DenoisingRecurrentNet [mutable, protected]
tied_hidden_reconstruction_weightsPLearn::DenoisingRecurrentNet
tied_input_reconstruction_weightsPLearn::DenoisingRecurrentNet
train()PLearn::DenoisingRecurrentNet [virtual]
train_setPLearn::PLearner [protected]
train_statsPLearn::PLearner [protected]
trainset_boundariesPLearn::DenoisingRecurrentNet [protected]
trainUnconditionalPredictor()PLearn::DenoisingRecurrentNet [private]
unconditionalFprop(Vec train_costs, Vec train_n_items) const PLearn::DenoisingRecurrentNet [private]
unref() const PLearn::PPointable [inline]
updateInputReconstructionFromHidden(Vec hidden, Mat &reconstruction_weights, Mat &acc_weights_gr, Vec &input_reconstruction_bias, Vec input_reconstruction_prob, Vec clean_input, Vec hidden_gradient, double input_reconstruction_cost_weight, double lr)PLearn::DenoisingRecurrentNet [private]
updateTargetLayer(Vec &grad, Vec &bias, real &lr)PLearn::DenoisingRecurrentNet [private]
usage() const PLearn::PPointable [inline]
use(VMat testset, VMat outputs) const PLearn::PLearner [virtual]
use_a_separate_random_generator_for_testingPLearn::PLearner
use_target_layers_masksPLearn::DenoisingRecurrentNet
useOnTrain(Mat &outputs) const PLearn::PLearner [virtual]
validation_setPLearn::PLearner [protected]
verbosityPLearn::PLearner
visi_bias_gradientPLearn::DenoisingRecurrentNet [mutable, protected]
weightsize() const PLearn::PLearner [virtual]
weightsize_PLearn::PLearner [protected]
write(ostream &out) const PLearn::Object [virtual]
writeOptionVal(PStream &out, const string &optionname) const PLearn::Object
~Object()PLearn::Object [virtual]
~PPointable()PLearn::PPointable [inline, virtual]
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