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- refactored

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- switch between cd_hinton hid/linear and cd_jens using doGaussionVisible (temporary solution)
This commit is contained in:
jens
2024-01-24 19:22:57 +01:00
parent a5ed0be991
commit 34b20f4fb3
2 changed files with 60 additions and 108 deletions
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source/Rbm.cpp
+56 -107
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@@ -128,13 +128,51 @@ void Rbm::gibbs_hv(arma::mat &h_probs, arma::mat &v_probs) const
}
}
#if 1
void Rbm::contrastiveDivergence(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
void Rbm::cd(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
{
arma::mat v_probs(v_data);
arma::mat h_states = v_to_h(v_data);
arma::mat h_probs;
if (m_params.doGaussianVisible)
{
if (m_params.doGaussianHidden)
{
cd_hinton_hid_linear(v_data, dw, dbh, dbv);
}
else
{
cd_hinton_hid_binary(v_data, dw, dbh, dbv);
}
}
else
{
cd_jens(v_data, dw, dbh, dbv);
}
}
void Rbm::cd_hinton_hid_linear(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
{
// Start positive phase
arma::mat poshidprobs = v_to_h(v_data);
arma::mat posprods = v_data.t() * poshidprobs;
arma::mat poshidact = arma::sum(poshidprobs);
arma::mat posvisact = arma::sum(v_data);
// End of positive phase
arma::mat poshidstates = poshidprobs + arma::randn(arma::size(poshidprobs));
// Start negative phase
arma::mat negdata = prob(h_to_v(poshidstates));
arma::mat neghidprobs = v_to_h(negdata);
arma::mat negprods = negdata.t() * neghidprobs;
arma::mat neghidact = arma::sum(neghidprobs);
arma::mat negvisact = arma::sum(negdata);
// Update weight deltas
dw = posprods - negprods;
dbv = posvisact - negvisact;
dbh = poshidact - neghidact;
}
void Rbm::cd_hinton_hid_binary(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
{
// Start positive phase
arma::mat poshidprobs = prob(v_to_h(v_data));
arma::mat posprods = v_data.t() * poshidprobs;
@@ -157,89 +195,7 @@ void Rbm::contrastiveDivergence(arma::mat const &v_data, arma::mat &dw, arma::ma
dbh = poshidact - neghidact;
}
void Rbm::train(arma::mat const &batch, IListener* pListener)
{
Status status;
double dProgress = 100.0/(batch.n_rows*m_params.numEpochs);
double progress = 0;
int lastProgress = -100;
int batchRowIndex = 0;
arma::mat dbv(arma::zeros(1, m_bv.n_cols));
arma::mat dbh(arma::zeros(1, m_bh.n_cols));
arma::mat dwhv(arma::zeros(m_whv.n_rows, m_whv.n_cols));
arma::mat inc_whv = arma::zeros(m_whv.n_rows, m_whv.n_cols);
arma::mat inc_bv(arma::zeros(1, m_bv.n_cols));
arma::mat inc_bh(arma::zeros(1, m_bh.n_cols));
int trainingSizeRemain = batch.n_rows;
bool shouldAbort = false;
while (trainingSizeRemain && !shouldAbort)
{
int miniBatchSizeActual = std::min(m_params.miniBatchSize, trainingSizeRemain);
arma::mat miniBatch = batch.rows(batchRowIndex, batchRowIndex+miniBatchSizeActual-1);
trainingSizeRemain -= miniBatchSizeActual;
batchRowIndex += miniBatchSizeActual;
int numcases = std::min(m_params.miniBatchSize, (int)batch.n_rows);
arma::mat v_states(miniBatch);
// Create hidden layer base on training data
if (m_params.doSampleBatch)
{
// When the hidden units are being driven by data, always use stochastic binary states
v_states = sample(miniBatch);
}
for (int epoch=0; epoch < m_params.numEpochs; epoch++)
{
// Contrastive divergence learning: calculate gradients
contrastiveDivergence(v_states, dwhv, dbh, dbv);
// Adjust weight and biases
inc_bv = m_params.momentum*inc_bv + m_params.learningRate/numcases*dbv;
inc_bh = m_params.momentum*inc_bh + m_params.learningRate/numcases*dbh;
inc_whv = m_params.momentum*inc_whv + m_params.learningRate*(dwhv/numcases - m_params.weightDecay*m_whv);
m_bv += inc_bv;
m_bh += inc_bh;
m_whv += inc_whv;
progress += dProgress*miniBatchSizeActual;
status.progress = (int)(progress + 0.5);
// Update status
if (status.progress != lastProgress)
{
lastProgress = status.progress;
// Calculate error
status.err = rms_error_accu(miniBatch - prob(h_to_v(prob(v_to_h(v_states)))));
if (pListener)
{
if(!pListener->onProgress(this, status))
{
shouldAbort = true;
break;
}
}
}
} // Number of epochs
} // number of mini batches
// Update final status
status.err_total = rms_error_accu(batch - prob(h_to_v(prob(v_to_h(batch)))));
if (pListener)
{
pListener->onProgress(this, status);
}
}
#else
void Rbm::contrastiveDivergence(arma::mat const &v_states, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
void Rbm::cd_jens(arma::mat const &v_states, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
{
arma::mat v_probs(v_states);
arma::mat h_states = v_to_h(v_states);
@@ -300,6 +256,7 @@ void Rbm::contrastiveDivergence(arma::mat const &v_states, arma::mat &dw, arma::
dbv -= sum(v_probs, 0);
dbh -= sum(h_probs, 0);
}
void Rbm::train(arma::mat const &batch, IListener* pListener)
{
Status status;
@@ -311,10 +268,9 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
arma::mat dbv(arma::zeros(1, m_bv.n_cols));
arma::mat dbh(arma::zeros(1, m_bh.n_cols));
arma::mat dwhv(arma::zeros(m_whv.n_rows, m_whv.n_cols));
arma::mat momentum_whv = arma::zeros(m_whv.n_rows, m_whv.n_cols);
arma::mat momentum_bv(arma::zeros(1, m_bv.n_cols));
arma::mat momentum_bh(arma::zeros(1, m_bh.n_cols));
arma::mat penalty_whv = arma::zeros(m_whv.n_rows, m_whv.n_cols);
arma::mat inc_whv = arma::zeros(m_whv.n_rows, m_whv.n_cols);
arma::mat inc_bv(arma::zeros(1, m_bv.n_cols));
arma::mat inc_bh(arma::zeros(1, m_bh.n_cols));
int trainingSizeRemain = batch.n_rows;
@@ -325,9 +281,7 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
arma::mat miniBatch = batch.rows(batchRowIndex, batchRowIndex+miniBatchSizeActual-1);
trainingSizeRemain -= miniBatchSizeActual;
batchRowIndex += miniBatchSizeActual;
int scaler = std::min(m_params.miniBatchSize, (int)batch.n_rows);
double learning_rate = m_params.learningRate/scaler;
double weight_decay = m_params.weightDecay/scaler;
int numcases = std::min(m_params.miniBatchSize, (int)batch.n_rows);
arma::mat v_states(miniBatch);
@@ -341,20 +295,16 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
for (int epoch=0; epoch < m_params.numEpochs; epoch++)
{
// Contrastive divergence learning: calculate gradients
contrastiveDivergence(v_states, dwhv, dbh, dbv);
cd(v_states, dwhv, dbh, dbv);
// Adjust weight and biases
penalty_whv = weight_decay*arma::sign(m_whv);
inc_bv = m_params.momentum*inc_bv + m_params.learningRate/numcases*dbv;
inc_bh = m_params.momentum*inc_bh + m_params.learningRate/numcases*dbh;
inc_whv = m_params.momentum*inc_whv + m_params.learningRate*(dwhv/numcases - m_params.weightDecay*m_whv);
status.L1 = accu(abs(m_whv));
status.L2 = accu(m_whv % m_whv);
momentum_bv = m_params.momentum*momentum_bv + dbv;
momentum_bh = m_params.momentum*momentum_bh + dbh;
momentum_whv = m_params.momentum*momentum_whv + dwhv - status.L2*penalty_whv;
m_bv += learning_rate*momentum_bv;
m_bh += learning_rate*momentum_bh;
m_whv += learning_rate*momentum_whv;
m_bv += inc_bv;
m_bh += inc_bh;
m_whv += inc_whv;
progress += dProgress*miniBatchSizeActual;
status.progress = (int)(progress + 0.5);
@@ -388,7 +338,6 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
pListener->onProgress(this, status);
}
}
#endif
arma::mat Rbm::prob(const arma::mat &src)
{