FANN_EXTERNAL void FANN_API fann_print_parameters(struct fann *ann)
{
struct fann_layer *layer_it;
printf("Input layer :%4d neurons, 1 bias\n", ann->num_input);
for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer - 1; layer_it++)
{
printf(" Hidden layer :%4d neurons, 1 bias\n",
layer_it->last_neuron - layer_it->first_neuron - 1);
}
printf("Output layer :%4d neurons\n", ann->num_output);
printf("Total neurons and biases :%4d\n", fann_get_total_neurons(ann));
printf("Total connections :%4d\n", ann->total_connections);
printf("Training algorithm : %s\n", FANN_TRAIN_NAMES[ann->training_algorithm]);
printf("Training error function : %s\n", FANN_ERRORFUNC_NAMES[ann->train_error_function]);
printf("Training stop function : %s\n", FANN_STOPFUNC_NAMES[ann->train_stop_function]);
printf("Bit fail limit :%8.3f\n", ann->bit_fail_limit);
printf("Learning rate :%8.3f\n", ann->learning_rate);
printf("Learning momentum :%8.3f\n", ann->learning_momentum);
printf("Quickprop decay :%11.6f\n", ann->quickprop_decay);
printf("Quickprop mu :%8.3f\n", ann->quickprop_mu);
printf("RPROP increase factor :%8.3f\n", ann->rprop_increase_factor);
printf("RPROP decrease factor :%8.3f\n", ann->rprop_decrease_factor);
printf("RPROP delta min :%8.3f\n", ann->rprop_delta_min);
printf("RPROP delta max :%8.3f\n", ann->rprop_delta_max);
/* TODO: dump scale parameters */
}
/*! ann:__tostring()
*# Converts a neural net to a string for Lua's virtual machine
*x print(ann)
*-
*/
static int ann_tostring(lua_State *L)
{
struct fann **ann;
ann = luaL_checkudata(L, 1, FANN_METATABLE);
luaL_argcheck(L, ann != NULL, 1, "'neural net' expected");
lua_pushfstring(L, "[[FANN neural network: %d %d %d]]", fann_get_num_input(*ann),
fann_get_num_output(*ann), fann_get_total_neurons(*ann));
return 1;
}
FANN_EXTERNAL void FANN_API fann_print_connections(struct fann *ann)
{
struct fann_layer *layer_it;
struct fann_neuron *neuron_it;
unsigned int i;
int value;
char *neurons;
unsigned int num_neurons = fann_get_total_neurons(ann) - fann_get_num_output(ann);
neurons = (char *) fann_malloc(num_neurons + 1);
if(neurons == NULL)
{
fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
return;
}
neurons[num_neurons] = 0;
printf("Layer / Neuron ");
for(i = 0; i < num_neurons; i++)
{
printf("%d", i % 10);
}
printf("\n");
for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer; layer_it++)
{
for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++)
{
memset(neurons, (int) '.', num_neurons);
for(i = neuron_it->first_con; i < neuron_it->last_con; i++)
{
if(ann->weights[i] < 0)
{
#ifdef FIXEDFANN
value = (int) ((ann->weights[i] / (double) ann->multiplier) - 0.5);
#else
value = (int) ((ann->weights[i]) - 0.5);
#endif
if(value < -25)
value = -25;
neurons[ann->connections[i] - ann->first_layer->first_neuron] = (char)('a' - value);
}
else
{
#ifdef FIXEDFANN
value = (int) ((ann->weights[i] / (double) ann->multiplier) + 0.5);
#else
value = (int) ((ann->weights[i]) + 0.5);
#endif
if(value > 25)
value = 25;
neurons[ann->connections[i] - ann->first_layer->first_neuron] = (char)('A' + value);
}
}
printf("L %3d / N %4d %s\n", layer_it - ann->first_layer,
neuron_it - ann->first_layer->first_neuron, neurons);
}
}
fann_free(neurons);
}
FANN_EXTERNAL void FANN_API fann_print_parameters(struct fann *ann)
{
struct fann_layer *layer_it;
#ifndef FIXEDFANN
unsigned int i;
#endif
printf("Input layer :%4d neurons, 1 bias\n", ann->num_input);
for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer - 1; layer_it++)
{
if(ann->network_type == FANN_NETTYPE_SHORTCUT)
{
printf(" Hidden layer :%4d neurons, 0 bias\n",
layer_it->last_neuron - layer_it->first_neuron);
}
else
{
printf(" Hidden layer :%4d neurons, 1 bias\n",
layer_it->last_neuron - layer_it->first_neuron - 1);
}
}
printf("Output layer :%4d neurons\n", ann->num_output);
printf("Total neurons and biases :%4d\n", fann_get_total_neurons(ann));
printf("Total connections :%4d\n", ann->total_connections);
printf("Connection rate :%8.3f\n", ann->connection_rate);
printf("Network type : %s\n", FANN_NETTYPE_NAMES[ann->network_type]);
#ifdef FIXEDFANN
printf("Decimal point :%4d\n", ann->decimal_point);
printf("Multiplier :%4d\n", ann->multiplier);
#else
printf("Training algorithm : %s\n", FANN_TRAIN_NAMES[ann->training_algorithm]);
printf("Training error function : %s\n", FANN_ERRORFUNC_NAMES[ann->train_error_function]);
printf("Training stop function : %s\n", FANN_STOPFUNC_NAMES[ann->train_stop_function]);
#endif
#ifdef FIXEDFANN
printf("Bit fail limit :%4d\n", ann->bit_fail_limit);
#else
printf("Bit fail limit :%8.3f\n", ann->bit_fail_limit);
printf("Learning rate :%8.3f\n", ann->learning_rate);
printf("Learning momentum :%8.3f\n", ann->learning_momentum);
printf("Quickprop decay :%11.6f\n", ann->quickprop_decay);
printf("Quickprop mu :%8.3f\n", ann->quickprop_mu);
printf("RPROP increase factor :%8.3f\n", ann->rprop_increase_factor);
printf("RPROP decrease factor :%8.3f\n", ann->rprop_decrease_factor);
printf("RPROP delta min :%8.3f\n", ann->rprop_delta_min);
printf("RPROP delta max :%8.3f\n", ann->rprop_delta_max);
printf("Cascade output change fraction :%11.6f\n", ann->cascade_output_change_fraction);
printf("Cascade candidate change fraction :%11.6f\n", ann->cascade_candidate_change_fraction);
printf("Cascade output stagnation epochs :%4d\n", ann->cascade_output_stagnation_epochs);
printf("Cascade candidate stagnation epochs :%4d\n", ann->cascade_candidate_stagnation_epochs);
printf("Cascade max output epochs :%4d\n", ann->cascade_max_out_epochs);
printf("Cascade max candidate epochs :%4d\n", ann->cascade_max_cand_epochs);
printf("Cascade weight multiplier :%8.3f\n", ann->cascade_weight_multiplier);
printf("Cascade candidate limit :%8.3f\n", ann->cascade_candidate_limit);
for(i = 0; i < ann->cascade_activation_functions_count; i++)
printf("Cascade activation functions[%d] : %s\n", i,
FANN_ACTIVATIONFUNC_NAMES[ann->cascade_activation_functions[i]]);
for(i = 0; i < ann->cascade_activation_steepnesses_count; i++)
printf("Cascade activation steepnesses[%d] :%8.3f\n", i,
ann->cascade_activation_steepnesses[i]);
printf("Cascade candidate groups :%4d\n", ann->cascade_num_candidate_groups);
printf("Cascade no. of candidates :%4d\n", fann_get_cascade_num_candidates(ann));
/* TODO: dump scale parameters */
#endif
}
