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- vectorized logSigmoid() and gaussProb()

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- added switch RBM_SPARSE
- added some experimental expect functions


git-svn-id: http://moon:8086/svn/software/trunk/projects/RBM@24 b431acfa-c32f-4a4a-93f1-934dc6c82436
This commit is contained in:
jens
2014-10-12 18:18:09 +00:00
parent 19c69ac02a
commit 0166b986cb
4 changed files with 95 additions and 51 deletions
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Source/DrawComponent.cpp
+3 -1
View File
@@ -231,11 +231,13 @@ void DrawComponent::setData (const VectorXd& data)
{
double a;
m_data = data;
for (int i=0; i < m_height; i++)
{
for (int j=0; j < m_width; j++)
{
a = std::min<double>(std::max<double>((double)data[i*m_width + j], 0), 1);
a = std::min<double>(std::max<double>((double)m_data[i*m_width + j], 0), 1);
m_pG->setColour(Colour(Colours::white).greyLevel(a));
m_pG->fillRect(m_scaleX*j, m_scaleY*i, m_scaleX, m_scaleY);
}
Source/Layer.hpp
+20 -18
View File
@@ -65,29 +65,24 @@ public:
m_states.fill(value);
}
void probsUpdate(Layer &layer, Weights &weights, double lambda = 1.0, double variance = 1.0)
{
probsUpdateLogistic(layer, weights, lambda, variance);
}
void probsUpdateLogistic(Layer &layer, Weights &weights, double lambda, double variance)
void probsUpdateLogistic(Layer &layer, Weights &weights, double lambda = 1.0, double variance = 1.0)
{
uint32_t i;
for (i=0; i < m_numUnits; i++)
{
m_probs(i) = logSigmoid(lambda/variance*accum(layer, weights, i));
m_probs(i) = lambda/variance*accum(layer, weights, i);
}
logSigmoid(m_probs);
}
void probsUpdateGaussian(Layer &layer, Weights &weights, double lambda, double variance)
{
uint32_t i;
for (i=0; i < m_numUnits; i++)
{
m_probs(i) = gaussProb(lambda*accum(layer, weights, i), variance);
m_probs(i) = lambda*accum(layer, weights, i);
}
gaussProb(m_probs, variance);
}
void statesUpdateStochastic()
@@ -128,22 +123,29 @@ protected:
VectorXd m_states;
virtual double accum(Layer &layer, Weights &weights, uint32_t index) = 0;
inline double logSigmoid(double x) const
void logSigmoid(const VectorXd &x)
{
return 1./(1 + exp(-x));
uint32_t i;
for (i=0; i < m_numUnits; i++)
{
m_probs[i] = 1./(1 + exp(-(double)x[i]));
}
}
inline double gaussProb(double x, double var) const
void gaussProb(const VectorXd &x, double var)
{
double k = 1.0/sqrt(var*2*3.14159265359);
double mu = 0;
double x2 = (x-mu)*(x-mu);
return k*exp(-x2/(2*var));
uint32_t i;
for (i=0; i < m_numUnits; i++)
{
double x2 = ((double)x[i]-mu) * ((double)x[i]-mu);
m_probs[i] = k*exp(-x2/(2*var));
}
}
};
#endif // LAYER_HPP
Source/MainComponent.cpp
+9 -7
View File
@@ -414,16 +414,18 @@ void MainComponent::buttonClicked (Button* buttonThatWasClicked)
else if (buttonThatWasClicked == reconstructEquButton)
{
//[UserButtonCode_reconstructEquButton] -- add your button handler code here..
uint32_t i;
const VectorXd *pV, *pH;
// Draw2->setData(m_pRbm->expectVisible(Draw->getData(), 100));
pV = (const VectorXd*)&Draw->getData();
uint32_t i;
VectorXd V, H;
V = Draw->getData();
for (i=0; i < 100; i++)
{
pH = (const VectorXd*)&m_pRbm->toHidden(*pV);
DrawHidden->setData(*pH);
pV = (const VectorXd*)&m_pRbm->toVisible(*pH);
Draw2->setData(*pV);
H = m_pRbm->toHidden(V);
DrawHidden->setData(H);
V = m_pRbm->toVisible(H);
Draw2->setData(V);
}
//[/UserButtonCode_reconstructEquButton]
}
Source/Rbm.hpp
+63 -25
View File
@@ -36,8 +36,6 @@ public:
Rbm(Weights &weights, RbmListener *pListener = nullptr)
: m_w(weights)
, m_pListener(pListener)
, m_tv(weights.getNumVisible())
, m_th(weights.getNumHidden())
, m_progress(0)
{
Noise_Init(&m_noise, 0x32727155);
@@ -65,7 +63,7 @@ public:
{
m_w.hiddenBias().array() += mu*h.states().array();
}
//#define RBM_SPARSE
void train(LayerArray<VisibleLayer> &vt, uint32_t numEpochs, double mu, uint32_t numGibbs = 1, bool useExpectations = false, bool doRaoBlackwell = false, bool useProbsForHiddenReconstruction = false, bool doRobbinsMonro = false)
{
uint32_t t, i;
@@ -81,9 +79,15 @@ public:
double dProgress = 1.0/numEpochs;
m_progress = 0;
#ifdef RBM_SPARSE
const double lambda = 0.05;
const double variance = 0.4;
const double penalty = 0.05;
#else
const double lambda = 1.0;
const double variance = 1.0;
const double penalty = 0.0;
#endif
if (useExpectations)
{
@@ -95,7 +99,7 @@ public:
for (i=0; i < vt.getSize(); i++)
{
// Create hidden layer base on training data
ht[i].probsUpdate(vt[i], w, lambda, variance);
ht[i].probsUpdateLogistic(vt[i], w, lambda, variance);
}
}
@@ -104,7 +108,7 @@ public:
for (i=0; i < vt.getSize(); i++)
{
t = (uint32_t)(0.5 + (vt.getSize()-1)*Noise_Uniform(&m_noise, 0.5));
h.probsUpdate(vt[t], w, lambda, variance);
h.probsUpdateLogistic(vt[t], w, lambda, variance);
// Create hidden layer base on training data
if (doRobbinsMonro)
@@ -134,7 +138,12 @@ public:
pH->statesUpdateStochastic();
// Create visible reconstruction (a fantasy...)
v.probsUpdate(*pH, w, lambda, variance);
#ifdef RBM_SPARSE
v.probsUpdateGaussian(*pH, w, lambda, variance);
#else
v.probsUpdateLogistic(*pH, w);
#endif
if (useProbsForHiddenReconstruction)
{
v.states() = v.probs();
@@ -145,7 +154,7 @@ public:
}
// Create hidden reconstruction
pH->probsUpdate(v, w, lambda, variance);
pH->probsUpdateLogistic(v, w, lambda, variance);
}
// Update weights (negative phase)
if (doRaoBlackwell)
@@ -166,17 +175,18 @@ public:
}
}
#if 0
#ifdef RBM_SPARSE
{
HiddenLayer th(m_w.getNumHidden());
VectorXd m(th.states());
m.fill(0);
for (i=0; i < vt.getSize(); i++)
{
ht[i].probsUpdate(vt[t], w, lambda, variance);
ht[i].statesUpdateStochastic();
th += ht[i];
m += expectHidden(vt[i].states(), lambda, variance, 10);
}
th *= 1.0/vt.getSize();
th += -0.02;
m.array() = penalty - m.array();
m *= 1.0/vt.getSize();
th.states() = m;
hiddenBiasUpdate(th, -mu);
}
#endif
@@ -186,8 +196,6 @@ public:
w = m_w;
}
getEnergy(v, *pH);
m_progress += dProgress;
if (m_pListener)
{
@@ -233,7 +241,7 @@ public:
for (j=0; j < vts.getSize(); j++)
{
h[j].setNumUnits(m_w.getNumHidden());
h[j].probsUpdate(vts.getAt(j), m_w);
h[j].probsUpdateLogistic(vts.getAt(j), m_w);
// h[j].statesAssignfromProbs();
h[j].statesUpdateStochastic();
}
@@ -272,7 +280,7 @@ public:
// Reconstruct
for (i=0; i < vts.getSize(); i++)
{
vts.getAt(i).probsUpdate(h[i], m_w);
vts.getAt(i).probsUpdateLogistic(h[i], m_w);
}
printf("A fantasy... (v^, t>)\n");
@@ -284,29 +292,59 @@ public:
delete [] h;
}
const VectorXd& toHidden(const VectorXd& visible)
VectorXd toHidden(const VectorXd& visible, double lambda = 1.0, double variance = 1.0)
{
HiddenLayer th(m_w.getNumHidden());
VisibleLayer tv(m_w.getNumVisible(), (const VectorXd*)&visible);
m_th.probsUpdate(tv, m_w);
th.probsUpdateLogistic(tv, m_w, lambda, variance);
return m_th.probs();
return th.probs();
}
const VectorXd& toVisible(const VectorXd& hidden)
VectorXd toVisible(const VectorXd& hidden, double lambda = 1.0, double variance = 1.0)
{
HiddenLayer th(m_w.getNumHidden(), (const VectorXd*)&hidden);
VisibleLayer tv(m_w.getNumVisible());
m_tv.probsUpdate(th, m_w);
tv.probsUpdateLogistic(th, m_w, lambda, variance);
return m_tv.probs();
return tv.probs();
}
VectorXd expectHidden(VectorXd visible, uint32_t numIter, double lambda = 1.0, double variance = 1.0)
{
uint32_t i;
VisibleLayer v(m_w.getNumVisible(), (const VectorXd*)&visible);
HiddenLayer h(m_w.getNumHidden());
for (i=0; i < numIter; i++)
{
h.probsUpdateLogistic(v, (Weights&)m_w, lambda, variance);
v.probsUpdateGaussian(h, (Weights&)m_w, lambda, variance);
}
return h.probs();
}
VectorXd expectVisible(VectorXd visible, uint32_t numIter, double lambda = 1.0, double variance = 1.0)
{
uint32_t i;
VisibleLayer v(m_w.getNumVisible(), (const VectorXd*)&visible);
HiddenLayer h(m_w.getNumHidden());
for (i=0; i < numIter; i++)
{
h.probsUpdateLogistic(v, (Weights&)m_w, lambda, variance);
v.probsUpdateLogistic(h, (Weights&)m_w, lambda, variance);
}
return v.probs();
}
private:
Weights &m_w;
RbmListener *m_pListener;
VisibleLayer m_tv;
HiddenLayer m_th;
noise_gen_t m_noise;
double m_progress;