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[RBM]

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- miniBatchSize is parameter of train()

git-svn-id: http://moon:8086/svn/software/trunk/projects/RBM@307 b431acfa-c32f-4a4a-93f1-934dc6c82436
This commit is contained in:
jens
2016-07-07 23:34:03 +00:00
parent 0259727a2c
commit 2eabaa42ec
3 changed files with 15 additions and 28 deletions
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Source/Rbm.cpp
+9 -15
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@@ -17,7 +17,6 @@ Rbm::Rbm(Weights &weights, const MatrixXd &batch)
, m_variableSigma(weights.getNumVisible())
, m_progress(0)
{
setMiniBatchSize(batch.rows());
Noise_Init(&m_noise, 0x32727155);
m_variableSigma.fill(m_params.m_constantSigma);
updateHiddenBatch();
@@ -198,17 +197,17 @@ MatrixXd Rbm::calcZ(MatrixXd &v, MatrixXd &h)
return t1;
}
void Rbm::train(uint32_t numEpochs, double sigmaMin)
void Rbm::train(size_t numEpochs, size_t miniBatchSize, double sigmaMin)
{
uint32_t i;
uint32_t epoch;
uint32_t gibbs;
size_t i;
size_t epoch;
size_t gibbs;
size_t trainingSize = m_batch.rows();
size_t trainingSizeRemain = trainingSize;
size_t batchRowIndex = 0;
double dProgress = 1.0/(numEpochs*(double)trainingSize/std::min(m_params.m_miniBatchSize, trainingSize));
double dProgress = 1.0/(numEpochs*(double)trainingSize/std::min(miniBatchSize, trainingSize));
MatrixXd dBiasV_curr(MatrixXd::Zero(1, m_w.getNumVisible()));
MatrixXd dBiasH_curr(MatrixXd::Zero(1, m_w.getNumHidden()));
@@ -221,14 +220,14 @@ void Rbm::train(uint32_t numEpochs, double sigmaMin)
while (trainingSizeRemain)
{
cout << "trainingSizeRemain: " << trainingSizeRemain << endl;
size_t toSlice = std::min(m_params.m_miniBatchSize, trainingSizeRemain);
size_t toSlice = std::min(miniBatchSize, trainingSizeRemain);
MatrixXd batch = m_batch.block(batchRowIndex, 0, toSlice, m_w.getNumVisible());
trainingSizeRemain -= toSlice;
batchRowIndex += toSlice;
size_t batchSize = batch.rows();
double mu_w = m_params.m_muWeights/std::min(m_params.m_miniBatchSize, trainingSize);
double mu_biasV = m_params.m_muWeights/std::min(m_params.m_miniBatchSize, trainingSize);
double mu_biasH = m_params.m_muWeights/std::min(m_params.m_miniBatchSize, trainingSize);
double mu_w = m_params.m_muWeights/std::min(miniBatchSize, trainingSize);
double mu_biasV = m_params.m_muWeights/std::min(miniBatchSize, trainingSize);
double mu_biasH = m_params.m_muWeights/std::min(miniBatchSize, trainingSize);
MatrixXd batch_sampled(batchSize, m_w.getNumVisible());
MatrixXd v_sampled(batchSize, m_w.getNumVisible());
@@ -514,11 +513,6 @@ void Rbm::setNumGibbs(size_t value)
onParamsChanged();
}
void Rbm::setMiniBatchSize(size_t size)
{
m_params.m_miniBatchSize = size;
}
void Rbm::setMuWeights(double value)
{
m_params.m_muWeights = value;