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[RBM]

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- moved sigma and mean from WEIGHTS to RBM
- use Gibbs slider also for reconstruction draw


git-svn-id: http://moon:8086/svn/software/trunk/projects/RBM@287 b431acfa-c32f-4a4a-93f1-934dc6c82436
This commit is contained in:
jens
2016-06-13 18:05:17 +00:00
parent 82d93ca7b5
commit 80135d0498
4 changed files with 59 additions and 95 deletions
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Source/MainComponent.cpp
+33 -38
View File
@@ -74,7 +74,7 @@ MainComponent::MainComponent ()
WeightsSlider->addListener (this);
addAndMakeVisible (numEpochslabel = new Label ("Num Epochs label",
TRANS("100")));
TRANS("1000")));
numEpochslabel->setFont (Font (15.00f, Font::plain));
numEpochslabel->setJustificationType (Justification::centred);
numEpochslabel->setEditable (true, true, false);
@@ -118,7 +118,7 @@ MainComponent::MainComponent ()
createButton->addListener (this);
addAndMakeVisible (projectNameLabel = new Label ("Project Name label",
TRANS("TestPrj")));
TRANS("test")));
projectNameLabel->setFont (Font (15.00f, Font::plain));
projectNameLabel->setJustificationType (Justification::centred);
projectNameLabel->setEditable (true, true, false);
@@ -482,14 +482,14 @@ void MainComponent::buttonClicked (Button* buttonThatWasClicked)
else if (buttonThatWasClicked == reconstructButton)
{
//[UserButtonCode_reconstructButton] -- add your button handler code here..
redrawReconstruction();
redrawReconstruction(numGibbsSlider->getValue());
//[/UserButtonCode_reconstructButton]
}
else if (buttonThatWasClicked == ShakeButton)
{
//[UserButtonCode_ShakeButton] -- add your button handler code here..
m_weights->shuffle(weightInitLabel->getText().getFloatValue());
redrawReconstruction();
redrawReconstruction(numGibbsSlider->getValue());
redrawWeights((int)WeightsSlider->getValue());
//[/UserButtonCode_ShakeButton]
}
@@ -497,7 +497,7 @@ void MainComponent::buttonClicked (Button* buttonThatWasClicked)
{
//[UserButtonCode_testButton] -- add your button handler code here..
DrawTraining->setData(DrawReconstruction->getData());
redrawReconstruction();
redrawReconstruction(numGibbsSlider->getValue());
//[/UserButtonCode_testButton]
}
else if (buttonThatWasClicked == createButton)
@@ -546,17 +546,7 @@ void MainComponent::buttonClicked (Button* buttonThatWasClicked)
else if (buttonThatWasClicked == reconstructEquButton)
{
//[UserButtonCode_reconstructEquButton] -- add your button handler code here..
uint32_t i;
VectorXd V, H;
V = DrawTraining->getData();
for (i=0; i < 100; i++)
{
H = m_pRbm->toHidden(V);
DrawHidden->setData(H);
V = m_pRbm->toVisible(H);
DrawReconstruction->setData(V);
}
redrawReconstruction(100);
//[/UserButtonCode_reconstructEquButton]
}
else if (buttonThatWasClicked == rbmDoRaoBlackwellToggleButton)
@@ -575,7 +565,7 @@ void MainComponent::buttonClicked (Button* buttonThatWasClicked)
{
//[UserButtonCode_rbmUseVisibleGaussianToggleButton] -- add your button handler code here..
m_pRbm->setUseVisibleGaussian(buttonThatWasClicked->getToggleState());
redrawReconstruction();
redrawReconstruction(numGibbsSlider->getValue());
//[/UserButtonCode_rbmUseVisibleGaussianToggleButton]
}
else if (buttonThatWasClicked == rbmDoSparseToggleButton)
@@ -588,6 +578,10 @@ void MainComponent::buttonClicked (Button* buttonThatWasClicked)
{
//[UserButtonCode_rbmLearnVarianceButton] -- add your button handler code here..
m_pRbm->setDoLearnVariance(buttonThatWasClicked->getToggleState());
if (!buttonThatWasClicked->getToggleState())
{
m_pRbm->setSigma(sigmaLabel->getText().getFloatValue());
}
//[/UserButtonCode_rbmLearnVarianceButton]
}
else if (buttonThatWasClicked == rbmNormalizeDataToggleButton)
@@ -614,7 +608,7 @@ void MainComponent::sliderValueChanged (Slider* sliderThatWasMoved)
RowVectorXd t = m_layers.getAt((int)sliderThatWasMoved->getValue());
DrawTraining->setData(t);
redrawReconstruction();
redrawReconstruction(numGibbsSlider->getValue());
}
//[/UserSliderCode_patterSlider]
}
@@ -628,6 +622,7 @@ void MainComponent::sliderValueChanged (Slider* sliderThatWasMoved)
{
//[UserSliderCode_numGibbsSlider] -- add your slider handling code here..
m_pRbm->setNumGibbs((uint32_t)sliderThatWasMoved->getValue());
redrawReconstruction(sliderThatWasMoved->getValue());
//[/UserSliderCode_numGibbsSlider]
}
else if (sliderThatWasMoved == m_progressBarSlider)
@@ -680,14 +675,16 @@ void MainComponent::labelTextChanged (Label* labelThatHasChanged)
{
//[UserLabelCode_lambdaLabel] -- add your label text handling code here..
m_pRbm->setLambda(labelThatHasChanged->getText().getFloatValue());
redrawReconstruction();
redrawReconstruction(numGibbsSlider->getValue());
//[/UserLabelCode_lambdaLabel]
}
else if (labelThatHasChanged == sigmaLabel)
{
//[UserLabelCode_sigmaLabel] -- add your label text handling code here..
m_pRbm->setSigma(labelThatHasChanged->getText().getFloatValue());
redrawReconstruction();
DrawVars->setData(m_pRbm->getSigma());
redrawReconstruction(numGibbsSlider->getValue());
//[/UserLabelCode_sigmaLabel]
}
else if (labelThatHasChanged == sparsityLabel)
@@ -844,7 +841,7 @@ void MainComponent::create(const char *pFilename)
resized();
redrawReconstruction();
redrawReconstruction(numGibbsSlider->getValue());
redrawWeights((int)WeightsSlider->getValue());
}
@@ -868,7 +865,7 @@ void MainComponent::onChanged(const LayerArray &obj)
void MainComponent::onEpochTrained(const Rbm &obj)
{
// mylog("Training %f %%\n", obj.getProgress()*100);
redrawReconstruction();
redrawReconstruction(numGibbsSlider->getValue());
redrawWeights((int)WeightsSlider->getValue());
m_progressBarSlider->setValue((int)(100*obj.getProgress()), dontSendNotification);
}
@@ -881,25 +878,23 @@ void MainComponent::onDraw(DrawComponent &obj)
}
if (&obj == DrawTraining)
{
redrawReconstruction();
redrawReconstruction(numGibbsSlider->getValue());
}
}
void MainComponent::redrawReconstruction()
void MainComponent::redrawReconstruction(uint32_t numIter)
{
RowVectorXd h = m_pRbm->toHidden(DrawTraining->getData());
DrawHidden->setData(h);
RowVectorXd v = m_pRbm->toVisible(DrawHidden->getData());
DrawReconstruction->setData(v);
// RowVectorXd dataV(m_weights->getNumVisible());
// DrawHidden->setData(m_pRbm->toHidden(dataV));
// RowVectorXd dataH(m_weights->getNumHidden());
// VectorXd v = m_pRbm->toVisible(dataH);
// DrawReconstruction->setData(v);
uint32_t i;
VectorXd V, H;
V = DrawTraining->getData();
for (i=0; i < numIter; i++)
{
H = m_pRbm->toHidden(V);
DrawHidden->setData(H);
V = m_pRbm->toVisible(H);
DrawReconstruction->setData(V);
}
if ((m_vNumX == 27) && (m_vNumY == 4))
{
@@ -933,14 +928,14 @@ void MainComponent::redrawWeights(int index)
{
VectorXd w = m_weights->weights().col(index);
DrawWeights->setData(w);
DrawVars->setData(m_weights->sigma());
DrawVars->setData(m_pRbm->getSigma());
}
void MainComponent::run()
{
// trainButton->setEnabled(false);
m_pRbm->train(m_layers, numEpochslabel->getText().getIntValue(), 100);
m_pRbm->train(m_layers, numEpochslabel->getText().getIntValue());
// trainButton->setEnabled(true);
}
Source/MainComponent.h
+1 -1
View File
@@ -93,7 +93,7 @@ private:
void onChanged(const LayerArray &obj);
void onEpochTrained(const Rbm &obj);
void onDraw(DrawComponent &obj);
void redrawReconstruction();
void redrawReconstruction(uint32_t numIter);
void redrawWeights(int index);
void run();
String m_baseDir;
Source/Rbm.hpp
+23 -21
View File
@@ -41,9 +41,9 @@ public:
: m_w(weights)
, m_v(weights.getNumVisible())
, m_h(weights.getNumHidden())
, m_sigmas(weights.getNumVisible())
, m_pListener(pListener)
, m_progress(0)
, m_sigma(1.0)
, m_sigmaDecay(1.0)
, m_weightDecay(0.0)
, m_lambda(1.0)
@@ -222,7 +222,7 @@ public:
return t1;
}
void train(const LayerArray &vt, uint32_t numEpochs, uint32_t batchSize, double sigmaMin = 0.05)
void train(const LayerArray &vt, uint32_t numEpochs, double sigmaMin = 0.05)
{
uint32_t t, i;
uint32_t epoch;
@@ -230,7 +230,7 @@ public:
double dProgress = 1.0/numEpochs;
double kTrain = 1.0/vt.getSize();
batchSize = vt.getSize();
size_t batchSize = vt.getSize();
MatrixXd v(batchSize, m_w.getNumVisible());
MatrixXd h(batchSize, m_w.getNumHidden());
@@ -252,19 +252,18 @@ public:
batch = vt.data();
m_w.mean() = calcMean(batch);
if (m_doLearnVariance)
{
m_w.sigma() = calcSigma(batch);
m_sigmas = calcSigma(batch);
}
if (m_doNormalizeData)
{
RowVectorXd mean = calcMean(batch);
for (i=0; i < batchSize; i++)
{
RowVectorXd x = batch.row(i);
batch.row(i) = normalizeData(x, m_w.mean(), m_w.sigma());
batch.row(i) = normalizeData(x, mean, m_sigmas);
}
}
@@ -310,12 +309,12 @@ public:
{
if (!m_useProbsForHiddenReconstruction)
{
sampleGaussian(v, m_w.sigma().replicate(batchSize, 1));
sampleGaussian(v, m_sigmas.replicate(batchSize, 1));
}
}
else
{
probsLogistic(v, m_w.sigma().replicate(batchSize, 1));
probsLogistic(v, m_sigmas.replicate(batchSize, 1));
if (!m_useProbsForHiddenReconstruction)
{
sample(v);
@@ -367,9 +366,9 @@ public:
}
m_w.hiddenBias() += deltaBiasH;
if (m_w.sigma()[0] > sigmaMin)
if (m_sigmas[0] > sigmaMin)
{
m_w.sigma().array() *= m_sigmaDecay;
m_sigmas.array() *= m_sigmaDecay;
}
m_progress += dProgress;
@@ -395,12 +394,13 @@ public:
double getEnergy(const VectorXd& visible, const VectorXd& hidden)
{
double energy;
double sigma = m_sigmas.array().mean();
energy = m_w.visibleBias() * visible;
energy += m_w.hiddenBias() * hidden;
energy += visible.transpose() * m_w.weights() * hidden;
return -energy/(m_sigma*m_sigma);
return -energy/(sigma*sigma);
}
RowVectorXd const & toHidden(const RowVectorXd& v)
@@ -419,18 +419,23 @@ public:
if (m_useVisibleGaussian)
{
// probsGaussian(v, m_w.sigma());
// probsGaussian(v, m_sigmas);
}
else
{
probsLogistic(m_v, m_w.sigma());
probsLogistic(m_v, m_sigmas);
}
return m_v;
}
void setSigma(double value)
{
m_sigma = value;
m_sigmas.fill(value);
}
RowVectorXd& getSigma()
{
return m_sigmas;
}
void setSigmaDecay(double value)
@@ -481,10 +486,6 @@ public:
void setDoLearnVariance(bool flag)
{
m_doLearnVariance = flag;
if (!flag)
{
m_w.sigma().fill(m_sigma);
}
}
void setNumGibbs(uint32_t value)
@@ -517,10 +518,11 @@ private:
Weights &m_w;
RowVectorXd m_h;
RowVectorXd m_v;
RowVectorXd m_sigmas;
RbmListener *m_pListener;
noise_gen_t m_noise;
double m_progress;
double m_sigma;
double m_sigmaDecay;
double m_weightDecay;
double m_lambda;
Source/Weights.hpp
+2 -35
View File
@@ -80,8 +80,6 @@ public:
m_numHidden = numHidden;
m_w.resize(m_numVisible, m_numHidden);
m_sigma.resize(m_numVisible);
m_mean.resize(m_numVisible);
m_bv.resize(m_numVisible);
m_bh.resize(m_numHidden);
shuffle(1.0);
@@ -92,25 +90,8 @@ public:
uint32_t i, j;
double kdev = stdDev*sqrt(12.0);
for (i=0; i < m_numVisible; i++)
{
m_sigma(i) = 1; //kdev*Noise_Uniform(&m_noise);
}
for (i=0; i < m_numVisible; i++)
{
m_mean(i) = 0; //kdev*Noise_Uniform(&m_noise);
}
for (i=0; i < m_numVisible; i++)
{
m_bv(i) = 0; //kdev*Noise_Uniform(&m_noise);
}
for (j=0; j < m_numHidden; j++)
{
m_bh(j) = 0; //kdev*Noise_Uniform(&m_noise);
}
m_bv.array().fill(Noise_Uniform(&m_noise));
m_bh.array().fill(Noise_Uniform(&m_noise));
for (i=0; i < m_numVisible; i++)
{
@@ -126,8 +107,6 @@ public:
m_bv = rhs.m_bv;
m_bh = rhs.m_bh;
m_w = rhs.m_w;
m_sigma = rhs.m_sigma;
m_mean = rhs.m_mean;
return *this;
}
@@ -142,16 +121,6 @@ public:
return m_bv;
}
RowVectorXd& sigma()
{
return m_sigma;
}
RowVectorXd& mean()
{
return m_mean;
}
RowVectorXd& hiddenBias()
{
return m_bh;
@@ -294,8 +263,6 @@ private:
MatrixXd m_w;
RowVectorXd m_bv;
RowVectorXd m_bh;
RowVectorXd m_sigma;
RowVectorXd m_mean;
void free()
{