/*!\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);
}
/*!\brief Train a network in batch mode.
* \param net Pointer to a neural network.
*
* Before calling this routine, net_begin_batch() should have been
* called (at the start of the batch) to begin batch training.
* Furthermore, for the current input/target pair, net_compute() and
* net_compute_output_error() should have been called to compute outputs
* for given the inputs and to prepare the neural network for training
* by computing the output error using the given targets. This routine
* performs the actual training by backpropagating the output error
* through the layers, but does not change the weights. The weights
* will be changed when (at the end of the batch) net_end_batch() is
* called.
*/
void
net_train_batch (network_t *net)
{
assert (net != NULL);
net->no_of_patterns++;
backward_pass (net);
adjust_deltas_batch (net);
}
void main()
{
int i, j, k,
trialnr;
/* input pars */
getpars();
/* input training set and test set */
getsets();
if (maxtrials>20)
maxtrials=20;
if (bias1==1)
in_mod++;
if (bias2==1)
hid_mod++;
hi_in_mod = in_mod+hid_mod;
cell_mod=hi_in_mod;
for (i=0;i<num_blocks;i++)
cell_mod+=(2+block_size[i]);
ges_mod = cell_mod+out_mod;
if (ges_mod>max_units)
{
printf("Program terminated!\n");
printf("You have to set the constant max_units at begin\n");
printf("of the program file greater or equal %d and then\n",ges_mod);
printf("compile the program again.\n");
exit(0);
}
srand(ran_sta);
for (trialnr=0;trialnr<maxtrials;trialnr++)
{
outfile = outf[trialnr];
weightfile = weig[trialnr];
fp1 = fopen(outfile, "w");
fprintf(fp1,"Trial Nr.:%.1d\n",trialnr);
fclose(fp1);
fp2 = fopen(weightfile, "w");
fprintf(fp2,"Trial Nr.:%.1d\n",trialnr);
fclose(fp2);
initia();
examples=0;
epoch=0;
maxepoch=maxepoch_init;
stop_learn=0;
learn = 1;
while (learn == 1)
{
/* executing the environment
and setting the input
*/
execute_act();
/* forward pass */
forward_pass();
if (targ==1) /* only if target for this input */
{
/* compute error */
for (k=cell_mod,j=0;k<ges_mod;k++,j++)
{
error[j]= target_a[j] - Yk_mod_new[k];
};
/* Training error */
comp_err();
}
/* backward pass */
if (targ==1) /* only if target for this input */
{
backward_pass();
}
else
{
derivatives();
}
/* set old activations */
for (i=0;i<ges_mod;i++)
{
Yk_mod_old[i] = Yk_mod_new[i];
}
/* update weights */
if (weight_up==1)
{
weight_up=0;
weight_update();
}
/* stop if maxepoch reached */
if (epoch>maxepoch)
learn=0;
}
weight_out();
test();
}
exit(0);
}
