static size_t train_sequence(
const uint8_t **field_buf,
const size_t *field_len,
size_t n_fields,
size_t n_items,
real *weights,
size_t weights_len,
const label *gold,
double t,
double *average_weights,
int use_dropout,
feat_hash_t dropout_seed)
{
label crap[n_items];
beam_search(field_buf, field_len, n_fields, n_items,
weights, weights_len, 0, use_dropout, dropout_seed, crap);
if (!memcmp(crap, gold, n_items*sizeof(label))) return 0;
size_t n_errs = 0;
size_t i;
for (i=0; i<n_items; i++)
n_errs += (gold[i] != crap[i]);
adjust_weights(field_buf, field_len, n_fields, n_items,
weights, weights_len, gold, (real)1.0,
t, average_weights,
use_dropout, dropout_seed);
adjust_weights(field_buf, field_len, n_fields, n_items,
weights, weights_len, crap, (real)(-1.0),
t, average_weights,
use_dropout, dropout_seed);
return n_errs;
}
void epoch(map *m, Inputs *in, int iteration, double timeCst, double *epsilon, int numEpoch)
{
register int i;
double radius;
int dst;
double theta;
radius = max_dbl(m->mapRadius * exp(-iteration / timeCst), 1);
radius *= radius;
neuron *n = find_bmu(m, in);
for(i = 0; i < m->latice_size; ++i)
{
dst = neuron_distance_to(&(m->lattice[i]), n);
if(dst < radius)
{
theta = exp(-dst / (2 * radius));
adjust_weights(&(m->lattice[i]), in, *epsilon, theta);
}
*epsilon = EPSILON * exp((double)-iteration / (numEpoch- iteration));
}
}
/*!\brief Train a network.
* \param net Pointer to a neural network.
*
* Before calling this routine, net_compute() and
* net_compute_output_error() should have been called to compute outputs
* for given inputs and to prepare the neural network for training by
* computing the output error. This routine performs the actual training
* by backpropagating the output error through the layers.
*/
void
net_train (network_t *net)
{
assert (net != NULL);
backward_pass (net);
adjust_weights (net);
}
int MPIDI_CH3_Rendezvous_rget_send_finish(MPIDI_VC_t * vc,
MPIDI_CH3_Pkt_rget_finish_t *rget_pkt)
{
int mpi_errno = MPI_SUCCESS;
int complete;
MPID_Request *sreq;
MPIDI_STATE_DECL(MPID_STATE_MPIDI_CH3I_RNDV_RGET_SEND_FINISH);
MPIDI_FUNC_ENTER(MPID_STATE_MPIDI_CH3I_RNDV_RGET_SEND_FINISH);
MPID_Request_get_ptr(rget_pkt->sender_req_id, sreq);
if (!MPIDI_CH3I_MRAIL_Finish_request(sreq)) {
return MPI_SUCCESS;
}
MPIDI_CH3I_MRAILI_RREQ_RNDV_FINISH(sreq);
#if 0
if(MPIDI_CH3I_RDMA_Process.has_hsam &&
((req->mrail.rndv_buf_sz > rdma_large_msg_rail_sharing_threshold))) {
/* Adjust the weights of different paths according to the
* timings obtained for the stripes */
adjust_weights(v->vc, req->mrail.stripe_start_time,
req->mrail.stripe_finish_time, req->mrail.initial_weight);
}
#endif
MPIDI_CH3U_Handle_send_req(vc, sreq, &complete);
if (complete != TRUE)
{
mpi_errno = MPIR_Err_create_code(
mpi_errno,
MPIR_ERR_FATAL,
FCNAME,
__LINE__,
MPI_ERR_OTHER,
"**fail",
0);
goto fn_exit;
}
#if defined(CKPT)
MPIDI_CH3I_CR_req_dequeue(sreq);
#endif /* defined(CKPT) */
fn_exit:
MPIDI_FUNC_EXIT(MPID_STATE_MPIDI_CH3I_RNDV_RGET_SEND_FINISH);
return mpi_errno;
}
