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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
View File
@@ -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)
{
source/Rbm.hpp
+4 -3
View File
@@ -24,7 +24,7 @@ public:
struct Params
{
Params()
: m_weightDecay(0.01)
: m_weightDecay(0.0)
, m_learningRate(0.1)
, m_momentum(0.5)
, m_doRaoBlackwell(true)
@@ -65,11 +65,11 @@ public:
}
};
Rbm(arma::mat &w, arma::mat &bv, arma::mat &bh);
Rbm(const Params& params, arma::mat &w, arma::mat &bv, arma::mat &bh);
Rbm(const Rbm& orig);
virtual ~Rbm();
void train(arma::mat const &batch, size_t numEpochs, size_t sizeMiniBatch, Params const &params, IListener *pListener);
void train(arma::mat const &batch, size_t numEpochs, size_t sizeMiniBatch, IListener *pListener);
arma::mat toHidden(const arma::mat &v);
arma::mat toVisible(const arma::mat &h);
arma::mat& weights();
@@ -78,6 +78,7 @@ public:
private:
noise_gen_t m_noise;
const Params &m_params;
arma::mat &m_w;
arma::mat &m_bh;
arma::mat &m_bv;
source/main.cpp
+93 -33
View File
@@ -23,45 +23,105 @@ class RbmListener : public Rbm::IListener
}
};
arma::mat loadTraining(const char *pFilename)
{
uint32_t numTraining = 0;
uint32_t numVisible = 0;
FILE *pFile;
pFile = fopen(pFilename,"r");
if (!pFile)
{
return 0;
}
int result = fscanf(pFile, "%d\n", &numTraining);
if (result < 0)
{
return 0;
}
result = fscanf(pFile, "%d\n", &numVisible);
if (result < 0)
{
return 0;
}
arma::mat data = arma::zeros(numTraining, numVisible);
uint32_t i, j;
for (i=0; i < numTraining; i++)
{
for (j=0; j < numVisible; j++)
{
float v;
int result = fscanf(pFile, "%f", &v);
if (result > 0)
{
data(i, j) = v;
}
}
}
fclose(pFile);
return data;
}
void saveWeight(const char *pFilename, size_t numVisibleX, size_t numVisibleY, const arma::mat &w, const arma::mat &bv, const arma::mat &bh)
{
FILE *pFile;
pFile = fopen(pFilename,"w");
if (!pFile)
return;
size_t numHidden = bh.n_elem;
size_t numVisible = bv.n_elem;
fprintf(pFile, "%d %d %d\n", (int)numVisibleX, (int)numVisibleY, (int)numHidden);
uint32_t i, j;
for (i=0; i < numVisible; i++)
{
fprintf(pFile, "%3.6f\n", bv(i));
}
for (i=0; i < numHidden; i++)
{
fprintf(pFile, "%3.6f\n", bh(i));
}
for (i=0; i < numVisible; i++)
{
for (j=0; j < numHidden; j++)
{
fprintf(pFile, "%3.6f ", w(i,j));
}
fprintf(pFile, "\n");
}
fclose(pFile);
}
int main()
{
printf("Hallo, Welt!\n");
// Position of a particle // |
arma::vec Pos = {{0}, // | (0,1)
{1}}; // +---x-->
// Rotation matrix
double phi = -3.1416/2;
arma::mat RotM = {{+cos(phi), -sin(phi)},
{+sin(phi), +cos(phi)}};
Pos.print("Current position of the particle:");
std::cout << "Rotating the point " << phi*180/3.1416 << " deg" << std::endl;
Pos = RotM*Pos;
Pos.print("New position of the particle:"); // ^
// x (1,0)
// |
arma::mat Z = arma::eye<arma::mat>(4000,4000);
// Z.print("Z:");
printf("Z.n_rows = %d\n", (int)Z.n_rows);
printf("Z.n_cols = %d\n", (int)Z.n_cols);
printf("Z.n_elem = %d\n", (int)Z.n_elem);
// +------>
RbmListener statusDisplay;
Rbm::Params params;
arma::mat w = arma::zeros(28*28, 64);
arma::mat bv = arma::zeros(1, 28*28);
arma::mat bh = arma::zeros(1, 64);
Rbm rbm(w, bv, bh);
arma::mat batch = arma::randu(100, 28*28);
rbm.train(batch, 10, 10, params, &statusDisplay);
arma::mat v = arma::randu(100, 28*28);
arma::mat batch = loadTraining("mnist_2.training.dat");
size_t numTraining = batch.n_rows;
size_t numVisible = batch.n_cols;
size_t numHidden = 64;
printf("Loaded %d training samples\n", (int)numTraining);
arma::mat w = arma::zeros(numVisible, numHidden);
arma::mat bv = arma::zeros(1, numVisible);
arma::mat bh = arma::zeros(1, numHidden);
Rbm rbm(params, w, bv, bh);
rbm.train(batch, 100, 100, &statusDisplay);
saveWeight("mnist_2.weights.dat", 28, 28, w, bv, bh);
arma::mat v = arma::randu(numTraining, numVisible);
arma::mat h = rbm.toHidden(v);
arma::mat r = rbm.toVisible(h);
return 0;