// this does two sampling steps for a rbm. TODO: Do one sampling step less please.
rbm get_next(rbm* r) {
// first sample the hidden layer from the visible one
std::vector<int> hidden = sample_hidden(r, r->visible);
// make a new rbm
rbm r2 = *r;
std::vector<int> new_visible = sample_visible(r, r->hidden);
r2.visible = new_visible;
// you don't have to sample the hidden one
//std::vector<int> new_hidden = sample_hidden(&r2, r2.visible);
//r2.hidden = new_hidden;
return r2;
}
void train_crbm(dataset_blas *train_set, int nvisible, int nhidden, int nlabel,
int epoch, double lr,
int minibatch, double momentum, char *model_file){
crbm m;
int i, j, k;
int nepoch;
double *v1, *y1;
uint8_t *l;
int batch_size, niter;
int wc;
int err;
double lik;
double delta;
int *idx;
time_t start_t, end_t;
init_crbm(&m, nvisible, nhidden, nlabel);
//wc = (int)cblas_dasum(train_set->N * train_set->n_feature, train_set->input, 1);
niter = (train_set->N-1)/minibatch + 1;
delta = lr / (1.0*minibatch);
/*
* shuffle training data
v1 = (double*)malloc(minibatch * m.nvisible * sizeof(double));
y1 = (double*)malloc(minibatch * m.ncat * sizeof(double));
l = (uint8_t*)malloc(minibatch * sizeof(uint8_t));
idx = (int*)malloc(train_set->N * sizeof(int));
for(i = 0; i < train_set->N; i++)
idx[i] = i;*/
bzero(w_u, sizeof(w_u));
bzero(u_u, sizeof(u_u));
bzero(bh_u, sizeof(bh_u));
bzero(bv_u, sizeof(bv_u));
bzero(by_u, sizeof(by_u));
//shuffle(idx, train_set->N);
for(nepoch = 0; nepoch < epoch; nepoch++){
lik = 0;
err = 0;
start_t = time(NULL);
for(k = 0; k < niter; k++){
#ifdef DEBUG
if((k+1) % 200 == 0){
printf("batch %d\n", k+1);
}
#endif
if(k == niter - 1){
batch_size = train_set->N - minibatch * (niter-1);
}else{
batch_size = minibatch;
}
v1 = train_set->input + train_set->n_feature * minibatch * k;
y1 = train_set->label + train_set->nlabel * minibatch * k;
l = train_set->output + minibatch * k;
/*
* shuffle training data
for(i = 0; i < batch_size; i++){
cblas_dcopy(m.nvisible, train_set->input + m.nvisible * idx[k*minibatch+i],
1, v1 + m.nvisible * i, 1);
cblas_dcopy(m.ncat, train_set->label + m.ncat * idx[k*minibatch+i],
1, y1 + m.ncat * i, 1);
l[i] = train_set->output[idx[k*minibatch+i]];
}*/
get_hidden(&m, v1, y1, ph1, batch_size);
sample_hidden(&m, ph1, h1, batch_size);
get_visible(&m, h1, pv, batch_size);
sample_visible(&m, pv, v2, batch_size);
get_class(&m, h1, py, batch_size);
sample_class(&m, py, y2, batch_size);
get_hidden(&m, v2, y2, ph2, batch_size);
sample_hidden(&m, ph2, h2, batch_size);
//lik += get_likelihood(&m, v1, pv, batch_size);
err += get_error(&m, y2, l, batch_size);
//update w_u
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.nhidden, m.nvisible, batch_size,
1.0, ph2, m.nhidden, v2, m.nvisible,
0, a, m.nvisible);
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.nhidden, m.nvisible, batch_size,
1.0, ph1, m.nhidden, v1, m.nvisible,
-1, a, m.nvisible);
cblas_daxpy(m.nvisible * m.nhidden, momentum, w_u, 1,
a, 1);
cblas_dcopy(m.nvisible * m.nhidden, a, 1, w_u, 1);
//update u_u
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.nhidden, m.ncat, batch_size,
1.0, ph2, m.nhidden, y2, m.ncat,
0, a, m.ncat);
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.nhidden, m.ncat, batch_size,
1.0, ph1, m.nhidden, y1, m.ncat,
-1, a, m.ncat);
cblas_daxpy(m.ncat * m.nhidden, momentum, u_u, 1,
a, 1);
cblas_dcopy(m.ncat * m.nhidden, a, 1, u_u, 1);
//update bv_u
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.nvisible, 1, batch_size,
1.0, v2, m.nvisible, I, 1,
0, a, 1);
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.nvisible, 1, batch_size,
1.0, v1, m.nvisible, I, 1,
-1, a, 1);
cblas_daxpy(m.nvisible, momentum, bv_u, 1,
a, 1);
cblas_dcopy(m.nvisible, a, 1, bv_u, 1);
//update by_u
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.ncat, 1, batch_size,
1.0, y2, m.ncat, I, 1,
0, a, 1);
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.ncat, 1, batch_size,
1.0, y1, m.ncat, I, 1,
-1, a, 1);
cblas_daxpy(m.ncat, momentum, by_u, 1,
a, 1);
cblas_dcopy(m.ncat, a, 1, by_u, 1);
//update bh_u
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.nhidden, 1, batch_size,
1.0, ph2, m.nhidden, I, 1,
0, a, 1);
cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
m.nhidden, 1, batch_size,
1.0, ph1, m.nhidden, I, 1,
-1, a, 1);
cblas_daxpy(m.nhidden, momentum, bh_u, 1,
a, 1);
cblas_dcopy(m.nhidden, a, 1, bh_u, 1);
//change parameter
cblas_daxpy(m.nvisible * m.nhidden, delta, w_u, 1,
m.w, 1);
cblas_daxpy(m.ncat * m.nhidden, delta, u_u, 1,
m.u, 1);
cblas_daxpy(m.nvisible, delta, bv_u, 1,
m.bv, 1);
cblas_daxpy(m.ncat, delta, by_u, 1,
m.by, 1);
cblas_daxpy(m.nhidden, delta, bh_u, 1,
m.bh, 1);
}
end_t = time(NULL);
printf("[epoch %d] error:%.5lf%%\ttime:%.2fmin\n", nepoch + 1,
err * 100.0 / train_set->N, (end_t - start_t) / 60.0);
}
dump_model(&m, model_file);
//print_prob(&m, train_set, "../data/rsm/test.prob");
/*
* shuffle training data
free(v1);
free(y1);
free(l);*/
free_crbm(&m);
}
