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- load training, save weights

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- provide params at construction time

git-svn-id: http://moon:8086/svn/software/trunk/projects/Rbm@566 b431acfa-c32f-4a4a-93f1-934dc6c82436
This commit is contained in:
jens
2019-10-22 23:03:02 +00:00
parent de33b3ecbd
commit 0c71b2e6b8
3 changed files with 116 additions and 53 deletions
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source/Rbm.cpp
+19 -17
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@@ -14,19 +14,21 @@
#include "Rbm.hpp"
#include "noise.h"
Rbm::Rbm(arma::mat &w, arma::mat &bv, arma::mat &bh)
: m_w(w)
Rbm::Rbm(const Params& params, arma::mat &w, arma::mat &bv, arma::mat &bh)
: m_params(params)
, m_w(w)
, m_bv(bv)
, m_bh(bh)
{
Noise_Init(&m_noise, 0x32727155);
uniform(m_w);
uniform(m_bh);
uniform(m_bv);
uniform(m_w, 0.0, 0.1);
uniform(m_bh, 0.0, 0.1);
uniform(m_bv, 0.0, 0.1);
}
Rbm::Rbm(const Rbm& orig)
: m_w(orig.m_w)
: m_params(orig.m_params)
, m_w(orig.m_w)
, m_bv(orig.m_bv)
, m_bh(orig.m_bh)
{
@@ -36,7 +38,7 @@ Rbm::~Rbm()
{
}
void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize, const Params& params, IListener* pListener)
void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize, IListener* pListener)
{
size_t epoch;
size_t gibbs;
@@ -66,8 +68,8 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
trainingSizeRemain -= toSlice;
batchRowIndex += toSlice;
size_t miniBatchSizeActual = miniBatch.n_rows;
double learning_rate = params.m_learningRate/std::min(miniBatchSizeActual, trainingSize);
double weight_decay = params.m_weightDecay/std::min(miniBatchSizeActual, trainingSize);
double learning_rate = m_params.m_learningRate/std::min(miniBatchSizeActual, trainingSize);
double weight_decay = m_params.m_weightDecay/std::min(miniBatchSizeActual, trainingSize);
arma::mat vis_state(miniBatchSizeActual, m_w.n_rows);
arma::mat vis_probs(miniBatchSizeActual, m_w.n_rows);
@@ -78,7 +80,7 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
{
// Create hidden layer base on training data
if (params.m_doSampleBatch)
if (m_params.m_doSampleBatch)
{
// When the hidden units are being driven by data, always use stochastic binary states
vis_state = sample(miniBatch);
@@ -92,7 +94,7 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
hid_probs = probsLogistic(hid_state);
// Sample hidden
if (params.m_doRaoBlackwell)
if (m_params.m_doRaoBlackwell)
{
hid_state = hid_probs;
}
@@ -106,7 +108,7 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
grad_bias_v = sum(vis_state, 0);
grad_bias_h = sum(hid_state, 0);
for (gibbs=0; gibbs < params.m_numGibbs; gibbs++)
for (gibbs=0; gibbs < m_params.m_numGibbs; gibbs++)
{
// Create hidden representation given v
@@ -115,7 +117,7 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
// Create visible reconstruction (a fantasy...) given hid
vis_state = hid_state * m_w.t() + arma::repmat(m_bv, miniBatchSizeActual, 1);
vis_probs = probsLogistic(vis_state);
if (params.m_doSampleVisible)
if (m_params.m_doSampleVisible)
{
vis_state = sample(vis_probs);
}
@@ -139,9 +141,9 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
status.L1 = accu(abs(m_w));
status.L2 = accu(m_w % m_w);
momentum_bias_v = params.m_momentum*momentum_bias_v + grad_bias_v;
momentum_bias_h = params.m_momentum*momentum_bias_h + grad_bias_h;
momentum_weights = params.m_momentum*momentum_weights + grad_weight - status.L2*penalty_weights;
momentum_bias_v = m_params.m_momentum*momentum_bias_v + grad_bias_v;
momentum_bias_h = m_params.m_momentum*momentum_bias_h + grad_bias_h;
momentum_weights = m_params.m_momentum*momentum_weights + grad_weight - status.L2*penalty_weights;
m_bv += learning_rate*momentum_bias_v;
m_bh += learning_rate*momentum_bias_h;
@@ -154,7 +156,7 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
status.epoch = epoch;
arma::mat diffErr = miniBatch - vis_probs;
arma::mat diffErr_squared = diffErr % diffErr;
status.err = accu(diffErr_squared);
status.err = accu(diffErr_squared)/diffErr_squared.n_elem;
if (pListener)
{