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- cleaned up

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git-svn-id: http://moon:8086/svn/software/trunk/projects/Rbm@828 b431acfa-c32f-4a4a-93f1-934dc6c82436
This commit is contained in:
jens
2022-01-18 15:22:28 +00:00
parent 9a8ad70a0b
commit d6ea343d4a
6 changed files with 44 additions and 172 deletions
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source/AStack.cpp
-16
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@@ -141,22 +141,6 @@ bool AStack::saveWeights(const std::string &dir)
return true;
}
arma::mat AStack::trainingBatchFrom(size_t layerId, const arma::mat& batch)
{
arma::mat thisBatch = batch;
Layer *pLayer = getLayer(0);
while (pLayer)
{
if (pLayer->id() == layerId)
{
break;
}
thisBatch = pLayer->toHiddenProbs(thisBatch);
pLayer = pLayer->next;
}
return thisBatch;
}
arma::mat& AStack::trainingBatch()
{
return m_trainingBatch;
source/AStack.hpp
-2
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@@ -69,9 +69,7 @@ public:
bool loadWeights(const std::string &dir);
bool saveWeights(const std::string &dir);
virtual void train(size_t layerId, const arma::mat& batch, Rbm::IListener* pListener) = 0;
virtual void train(const arma::mat& batch, Rbm::IListener* pListener) = 0;
virtual arma::mat trainingBatchFrom(size_t layerId, const arma::mat& batch) = 0;
size_t numTraining();
void addTraining(const arma::mat &toAdd);
source/DeepStack.hpp
+2 -2
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@@ -28,9 +28,9 @@ public:
DeepStack(const DeepStack& orig) = delete;
virtual ~DeepStack();
arma::mat trainingBatchFrom(size_t layerId, const arma::mat& batch);
void train(const arma::mat& batch, Rbm::IListener* pListener) override;
void train(size_t layerId, const arma::mat& batch, Rbm::IListener* pListener) override;
arma::mat trainingBatchFrom(size_t layerId, const arma::mat& batch);
void train(size_t layerId, const arma::mat& batch, Rbm::IListener* pListener);
arma::mat upPass(size_t layerId, arma::mat const &v);
arma::mat downPass(size_t layerId, arma::mat const &h);
source/RnnStack.cpp
+4 -28
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@@ -61,26 +61,6 @@ arma::mat RnnStack::vc_to_v(const arma::mat& vc) const
return vc.submat(0, 0, vc.n_rows - 1, numVisible - m_numContext - 1);
}
arma::mat RnnStack::trainingBatchFrom(size_t layerId, const arma::mat& batch)
{
Layer *pLayer = getLayer(0);
arma::mat c = arma::zeros(batch.n_rows, m_numContext);
arma::mat v = vc_to_v(batch);
arma::mat vc = v_to_vc(v, c);
while (pLayer)
{
if (layerId == pLayer->id())
{
break;
}
c = pLayer->to_h_gibbs(vc);
v = arma::shift(vc_to_v(vc), layerId, 1);
vc = v_to_vc(v, c);
pLayer = pLayer->next;
}
return vc;
}
void RnnStack::train(const arma::mat& batch, Rbm::IListener* pListener)
{
// thisBatch = {padding | batch}
@@ -101,10 +81,6 @@ void RnnStack::train(const arma::mat& batch, Rbm::IListener* pListener)
}
}
void RnnStack::train(size_t layerId, const arma::mat& batch, Rbm::IListener* pListener)
{
}
arma::mat RnnStack::step_forward(arma::mat &state, const arma::mat& v_curr)
{
arma::mat c = arma::zeros(1, m_numContext);
@@ -121,11 +97,11 @@ arma::mat RnnStack::step_forward(arma::mat &state, const arma::mat& v_curr)
for (int j=0; j < getSeqLen(); j++)
{
Layer *pLayer = getLayer(j);
arma::mat v = arma::join_rows(state.row(j), z);
arma::mat v = arma::join_rows(z, state.row(j));
arma::mat vc = v_to_vc(v, c);
c = pLayer->to_h_gibbs(vc);
vc = pLayer->to_v_gibbs(c);
r = to_next(vc_to_v(vc));
pLayer->gibbs_vh(vc, c);
r = vc_to_v(vc);
}
return r;
}
source/RnnStack.hpp
+11 -2
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@@ -25,9 +25,7 @@ public:
RnnStack(const RnnStack& orig) = delete;
virtual ~RnnStack();
arma::mat trainingBatchFrom(size_t layerId, const arma::mat& batch) override;
void train(const arma::mat& batch, Rbm::IListener* pListener) override;
void train(size_t layerId, const arma::mat& batch, Rbm::IListener* pListener) override;
size_t getSeqLen();
size_t numContext();
@@ -48,6 +46,17 @@ public:
return v.cols(NUM_CODES, 2*NUM_CODES-1);
}
void setParams(Rbm::Params const &param)
{
Layer *pLayer = getLayer(0);
while (pLayer)
{
pLayer->params() = param;
pLayer = pLayer->next;
}
}
private:
size_t m_numContext;
};
source/poet.cpp
+27 -122
View File
@@ -137,60 +137,6 @@ arma::mat createTraining(const string &filename, size_t seq_len)
return batch;
}
struct Rnn
{
Rnn(Layer *layer)
: nV(layer->numVisible() - layer->numHidden())
, nH(layer->numHidden())
, nVx(layer->numVisibleX())
, nVy(layer->numVisibleY())
, nC(layer->numHidden())
{
}
size_t nV;
size_t nH;
size_t nVx;
size_t nVy;
size_t nC;
arma::mat vcVec_to_vMat(arma::mat const &vcVec)
{
arma::mat vVec = vcVec.submat(0, 0, 0, nV-1);
arma::mat vMat = arma::reshape(vVec, nVx, nVy);
return vMat;
}
arma::mat curr_vec(arma::mat const &vcVec)
{
arma::mat vMat = vcVec_to_vMat(vcVec);
return vMat.col(0);
}
arma::mat vcVec_next_step(arma::mat const &vCurr, arma::mat const &vcVec_last, arma::mat const &h)
{
arma::mat vMat_last = arma::shift(vcVec_to_vMat(vcVec_last), 1, 1);
vMat_last.col(0) = arma::zeros(nVx, 1);
vMat_last.col(1) = vCurr;
arma::mat vVec = vMat_last.as_row();
arma::mat vcVec_next = arma::join_rows(vVec, h);
return vcVec_next;
}
};
arma::mat to_curr(arma::mat v)
{
return v.cols(0, NUM_CODES-1);
}
arma::mat to_next(arma::mat v)
{
return v.cols(NUM_CODES, 2*NUM_CODES-1);
}
#define CREATE_TRAINING 0
#define DO_TRAINING 0
#define DO_FORWARD 1
@@ -211,17 +157,36 @@ int main()
// Load training
stack->loadTrainingBatch(".");
arma::mat t_vc = stack->trainingBatch();
Rbm::Params params = stack->getLayer(0)->params();
#if DO_TRAINING
int numGibbs = params.numGibbs;
params.numGibbs = 1;
stack->setParams(params);
RbmListener listener;
for (int i=0; i < stack->getSeqLen(); i++)
{
// stack->getLayer(i)->weightsInit(0.1,0);
stack->getLayer(i)->weightsInit(0.01,0);
}
// Phase 10
params.numEpochs = 2000;
stack->setParams(params);
stack->train(t_vc, &listener);
// Phase 11
params.numEpochs = 10000;
stack->setParams(params);
stack->train(t_vc, &listener);
// Phase 20
params.numGibbs = numGibbs;
stack->setParams(params);
stack->train(t_vc, &listener);
stack->saveWeights(".");
stack->save(".");
@@ -244,81 +209,21 @@ int main()
#endif
#if DO_FORWARD
params.gibbsDoSampleHidden = false;
params.gibbsDoSampleVisible = false;
stack->setParams(params);
arma::mat state;
for (int i=0; i < t_vc.n_rows; i++)
{
arma::mat curr = stack->to_curr(t_vc.row(i));
arma::mat next = stack->to_next(t_vc.row(i));
arma::mat r = stack->step_forward(state, curr);
char c = idx2ch(r.index_max());
arma::mat next = stack->to_next(r);
char c = idx2ch(next.index_max());
putchar(c);
}
printf("\n");
#else
return 0;
Layer *layer = stack->getLayer(0);
int numTraining = stack->trainingBatch().n_rows;
arma::mat t = stack->trainingBatch();
arma::mat h = layer->toHiddenProbs(t);
arma::mat r = layer->toVisibleProbs(h);
layer->params().gibbsDoSampleHidden = false;
layer->params().gibbsDoSampleVisible = false;
Rnn rnn(layer);
printf("\nStimulus: Training\n");
h = layer->toHiddenProbs(t);
r = t;
for (int i=0; i < numTraining; i++)
{
arma::mat r = t.row(i);
layer->gibbs_vh(r, h);
layer->gibbs_hv(h, r);
arma::mat curr = rnn.curr_vec(r);
char c = idx2ch(curr.index_max());
putchar(c);
}
printf("\n");
arma::mat v = t.row(0);
printf("Stimulus: Next char and current h\n");
for (int i=1; i < numTraining+1; i++)
{
arma::mat r = v;
layer->gibbs_vh(r, h);
arma::mat curr = rnn.curr_vec(r);
v = rnn.vcVec_next_step(curr, r, h);
layer->gibbs_vh(v, h);
char c = idx2ch(curr.index_max());
putchar(c);
}
printf("\n");
v = t.row(0);
h = layer->toHiddenProbs(v);
printf("Stimulus: Current h\n");
for (int i=1; i < numTraining+1; i++)
{
arma::mat r = v;
layer->gibbs_hv(h, r);
arma::mat curr = rnn.curr_vec(r);
v = rnn.vcVec_next_step(curr, arma::zeros(1, r.n_cols), h);
layer->gibbs_vh(v, h);
char c = idx2ch(curr.index_max());
putchar(c);
}
#endif
printf("\n\nEnd of program\n");
printf("\nEnd of program\n");
return 0;
}