/**
* @brief Update the neural net during training
* @param net Backprop neural net object
*/
void bp_update(bp * net, int current_hidden_layer)
{
bp_dropouts(net);
bp_feed_forward(net);
bp_backprop(net, current_hidden_layer);
bp_learn(net, current_hidden_layer);
bp_clear_dropouts(net);
}
static void test_backprop()
{
bp net;
int no_of_inputs=10;
int no_of_hiddens=4;
int hidden_layers=2;
int no_of_outputs=5;
int i,l;
unsigned int random_seed = 123;
printf("test_backprop...");
bp_init(&net,
no_of_inputs, no_of_hiddens,
hidden_layers,
no_of_outputs, &random_seed);
assert((&net)->inputs!=0);
assert((&net)->hiddens!=0);
assert((&net)->outputs!=0);
/* set some inputs */
for (i = 0; i < no_of_inputs; i++) {
bp_set_input(&net, i, i/(float)no_of_inputs);
(&net)->inputs[i]->BPerror = 999;
}
for (l = 0; l < hidden_layers; l++) {
for (i = 0; i < bp_hiddens_in_layer(&net,l); i++) {
(&net)->hiddens[l][i]->BPerror = 999;
}
}
/* set some target outputs */
for (i = 0; i < no_of_outputs; i++) {
(&net)->outputs[i]->BPerror = 999;
bp_set_output(&net, i, i/(float)no_of_inputs);
}
/* feed forward */
bp_feed_forward(&net);
bp_backprop(&net,0);
/* check for non-zero backprop error values */
for (i = 0; i < no_of_inputs; i++) {
assert((&net)->inputs[i]->BPerror != 999);
}
for (l = 0; l < hidden_layers; l++) {
for (i = 0; i < bp_hiddens_in_layer(&net,l); i++) {
assert((&net)->hiddens[l][i]->BPerror != 999);
}
}
for (i = 0; i < no_of_outputs; i++) {
assert((&net)->outputs[i]->BPerror != 999);
}
bp_free(&net);
printf("Ok\n");
}
