--- /dev/null
+/*
+ Fast Artificial Neural Network Library (fann)
+ Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk)
+
+ This library is free software; you can redistribute it and/or
+ modify it under the terms of the GNU Lesser General Public
+ License as published by the Free Software Foundation; either
+ version 2.1 of the License, or (at your option) any later version.
+
+ This library is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+ Lesser General Public License for more details.
+
+ You should have received a copy of the GNU Lesser General Public
+ License along with this library; if not, write to the Free Software
+ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+*/
+
+#include "config.h"
+#include "fann.h"
+#include "string.h"
+
+#ifndef FIXEDFANN
+
+/* #define CASCADE_DEBUG */
+/* #define CASCADE_DEBUG_FULL */
+
+void fann_print_connections_raw(struct fann *ann)
+{
+ unsigned int i;
+
+ for(i = 0; i < ann->total_connections_allocated; i++)
+ {
+ if(i == ann->total_connections)
+ {
+ printf("* ");
+ }
+ printf("%f ", ann->weights[i]);
+ }
+ printf("\n\n");
+}
+
+/* Cascade training directly on the training data.
+ The connected_neurons pointers are not valid during training,
+ but they will be again after training.
+ */
+FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data(struct fann *ann, struct fann_train_data *data,
+ unsigned int max_neurons,
+ unsigned int neurons_between_reports,
+ float desired_error)
+{
+ float error;
+ unsigned int i;
+ unsigned int total_epochs = 0;
+ int desired_error_reached;
+
+ if(neurons_between_reports && ann->callback == NULL)
+ {
+ printf("Max neurons %3d. Desired error: %.6f\n", max_neurons, desired_error);
+ }
+
+ for(i = 1; i <= max_neurons; i++)
+ {
+ /* train output neurons */
+ total_epochs += fann_train_outputs(ann, data, desired_error);
+ error = fann_get_MSE(ann);
+ desired_error_reached = fann_desired_error_reached(ann, desired_error);
+
+ /* print current error */
+ if(neurons_between_reports &&
+ (i % neurons_between_reports == 0
+ || i == max_neurons || i == 1 || desired_error_reached == 0))
+ {
+ if(ann->callback == NULL)
+ {
+ printf
+ ("Neurons %3d. Current error: %.6f. Total error:%8.4f. Epochs %5d. Bit fail %3d",
+ i, error, ann->MSE_value, total_epochs, ann->num_bit_fail);
+ if((ann->last_layer-2) != ann->first_layer)
+ {
+ printf(". candidate steepness %.2f. function %s",
+ (ann->last_layer-2)->first_neuron->activation_steepness,
+ FANN_ACTIVATIONFUNC_NAMES[(ann->last_layer-2)->first_neuron->activation_function]);
+ }
+ printf("\n");
+ }
+ else if((*ann->callback) (ann, data, max_neurons,
+ neurons_between_reports, desired_error, total_epochs) == -1)
+ {
+ /* you can break the training by returning -1 */
+ break;
+ }
+ }
+
+ if(desired_error_reached == 0)
+ break;
+
+ if(fann_initialize_candidates(ann) == -1)
+ {
+ /* Unable to initialize room for candidates */
+ break;
+ }
+
+ /* train new candidates */
+ total_epochs += fann_train_candidates(ann, data);
+
+ /* this installs the best candidate */
+ fann_install_candidate(ann);
+ }
+
+ /* Train outputs one last time but without any desired error */
+ total_epochs += fann_train_outputs(ann, data, 0.0);
+
+ if(neurons_between_reports && ann->callback == NULL)
+ {
+ printf("Train outputs Current error: %.6f. Epochs %6d\n", fann_get_MSE(ann),
+ total_epochs);
+ }
+
+ /* Set pointers in connected_neurons
+ * This is ONLY done in the end of cascade training,
+ * since there is no need for them during training.
+ */
+ fann_set_shortcut_connections(ann);
+}
+
+FANN_EXTERNAL void FANN_API fann_cascadetrain_on_file(struct fann *ann, const char *filename,
+ unsigned int max_neurons,
+ unsigned int neurons_between_reports,
+ float desired_error)
+{
+ struct fann_train_data *data = fann_read_train_from_file(filename);
+
+ if(data == NULL)
+ {
+ return;
+ }
+ fann_cascadetrain_on_data(ann, data, max_neurons, neurons_between_reports, desired_error);
+ fann_destroy_train(data);
+}
+
+int fann_train_outputs(struct fann *ann, struct fann_train_data *data, float desired_error)
+{
+ float error, initial_error, error_improvement;
+ float target_improvement = 0.0;
+ float backslide_improvement = -1.0e20f;
+ unsigned int i;
+ unsigned int max_epochs = ann->cascade_max_out_epochs;
+ unsigned int stagnation = max_epochs;
+
+ /* TODO should perhaps not clear all arrays */
+ fann_clear_train_arrays(ann);
+
+ /* run an initial epoch to set the initital error */
+ initial_error = fann_train_outputs_epoch(ann, data);
+
+ if(fann_desired_error_reached(ann, desired_error) == 0)
+ return 1;
+
+ for(i = 1; i < max_epochs; i++)
+ {
+ error = fann_train_outputs_epoch(ann, data);
+
+ /*printf("Epoch %6d. Current error: %.6f. Bit fail %d.\n", i, error, ann->num_bit_fail); */
+
+ if(fann_desired_error_reached(ann, desired_error) == 0)
+ {
+#ifdef CASCADE_DEBUG
+ printf("Error %f < %f\n", error, desired_error);
+#endif
+ return i + 1;
+ }
+
+ /* Improvement since start of train */
+ error_improvement = initial_error - error;
+
+ /* After any significant change, set a new goal and
+ * allow a new quota of epochs to reach it */
+ if((error_improvement > target_improvement) || (error_improvement < backslide_improvement))
+ {
+ /*printf("error_improvement=%f, target_improvement=%f, backslide_improvement=%f, stagnation=%d\n", error_improvement, target_improvement, backslide_improvement, stagnation); */
+
+ target_improvement = error_improvement * (1.0f + ann->cascade_output_change_fraction);
+ backslide_improvement = error_improvement * (1.0f - ann->cascade_output_change_fraction);
+ stagnation = i + ann->cascade_output_stagnation_epochs;
+ }
+
+ /* No improvement in allotted period, so quit */
+ if(i >= stagnation)
+ {
+ return i + 1;
+ }
+ }
+
+ return max_epochs;
+}
+
+float fann_train_outputs_epoch(struct fann *ann, struct fann_train_data *data)
+{
+ unsigned int i;
+
+ fann_reset_MSE(ann);
+
+ for(i = 0; i < data->num_data; i++)
+ {
+ fann_run(ann, data->input[i]);
+ fann_compute_MSE(ann, data->output[i]);
+ fann_update_slopes_batch(ann, ann->last_layer - 1, ann->last_layer - 1);
+ }
+
+ switch (ann->training_algorithm)
+ {
+ case FANN_TRAIN_RPROP:
+ fann_update_weights_irpropm(ann, (ann->last_layer - 1)->first_neuron->first_con,
+ ann->total_connections);
+ break;
+ case FANN_TRAIN_QUICKPROP:
+ fann_update_weights_quickprop(ann, data->num_data,
+ (ann->last_layer - 1)->first_neuron->first_con,
+ ann->total_connections);
+ break;
+ case FANN_TRAIN_BATCH:
+ case FANN_TRAIN_INCREMENTAL:
+ fann_error((struct fann_error *) ann, FANN_E_CANT_USE_TRAIN_ALG);
+ }
+
+ return fann_get_MSE(ann);
+}
+
+int fann_reallocate_connections(struct fann *ann, unsigned int total_connections)
+{
+ /* The connections are allocated, but the pointers inside are
+ * first moved in the end of the cascade training session.
+ */
+
+#ifdef CASCADE_DEBUG
+ printf("realloc from %d to %d\n", ann->total_connections_allocated, total_connections);
+#endif
+ ann->connections =
+ (struct fann_neuron **) realloc(ann->connections,
+ total_connections * sizeof(struct fann_neuron *));
+ if(ann->connections == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return -1;
+ }
+
+ ann->weights = (fann_type *) realloc(ann->weights, total_connections * sizeof(fann_type));
+ if(ann->weights == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return -1;
+ }
+
+ ann->train_slopes =
+ (fann_type *) realloc(ann->train_slopes, total_connections * sizeof(fann_type));
+ if(ann->train_slopes == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return -1;
+ }
+
+ ann->prev_steps = (fann_type *) realloc(ann->prev_steps, total_connections * sizeof(fann_type));
+ if(ann->prev_steps == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return -1;
+ }
+
+ ann->prev_train_slopes =
+ (fann_type *) realloc(ann->prev_train_slopes, total_connections * sizeof(fann_type));
+ if(ann->prev_train_slopes == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return -1;
+ }
+
+ ann->total_connections_allocated = total_connections;
+
+ return 0;
+}
+
+int fann_reallocate_neurons(struct fann *ann, unsigned int total_neurons)
+{
+ struct fann_layer *layer_it;
+ struct fann_neuron *neurons;
+ unsigned int num_neurons = 0;
+ unsigned int num_neurons_so_far = 0;
+
+ neurons =
+ (struct fann_neuron *) realloc(ann->first_layer->first_neuron,
+ total_neurons * sizeof(struct fann_neuron));
+ ann->total_neurons_allocated = total_neurons;
+
+ if(neurons == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return -1;
+ }
+
+ /* Also allocate room for more train_errors */
+ ann->train_errors = (fann_type *) realloc(ann->train_errors, total_neurons * sizeof(fann_type));
+ if(ann->train_errors == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return -1;
+ }
+
+ if(neurons != ann->first_layer->first_neuron)
+ {
+ /* Then the memory has moved, also move the pointers */
+
+#ifdef CASCADE_DEBUG_FULL
+ printf("Moving neuron pointers\n");
+#endif
+
+ /* Move pointers from layers to neurons */
+ for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++)
+ {
+ num_neurons = layer_it->last_neuron - layer_it->first_neuron;
+ layer_it->first_neuron = neurons + num_neurons_so_far;
+ layer_it->last_neuron = layer_it->first_neuron + num_neurons;
+ num_neurons_so_far += num_neurons;
+ }
+ }
+
+ return 0;
+}
+
+int fann_initialize_candidates(struct fann *ann)
+{
+ /* The candidates are allocated after the normal neurons and connections,
+ * but there is an empty place between the real neurons and the candidate neurons,
+ * so that it will be possible to make room when the chosen candidate are copied in
+ * on the desired place.
+ */
+ unsigned int neurons_to_allocate, connections_to_allocate;
+ unsigned int num_candidates = fann_get_cascade_num_candidates(ann);
+ unsigned int num_neurons = ann->total_neurons + num_candidates + 1;
+ unsigned int candidate_connections_in = ann->total_neurons - ann->num_output;
+ unsigned int candidate_connections_out = ann->num_output;
+
+ /* the number of connections going into a and out of a candidate is
+ * ann->total_neurons */
+ unsigned int num_connections =
+ ann->total_connections + (ann->total_neurons * (num_candidates + 1));
+ unsigned int first_candidate_connection = ann->total_connections + ann->total_neurons;
+ unsigned int first_candidate_neuron = ann->total_neurons + 1;
+ unsigned int connection_it, i, j, k, candidate_index;
+ struct fann_neuron *neurons;
+ fann_type initial_slope;
+
+ /* First make sure that there is enough room, and if not then allocate a
+ * bit more so that we do not need to allocate more room each time.
+ */
+ if(num_neurons > ann->total_neurons_allocated)
+ {
+ /* Then we need to allocate more neurons
+ * Allocate half as many neurons as already exist (at least ten)
+ */
+ neurons_to_allocate = num_neurons + num_neurons / 2;
+ if(neurons_to_allocate < num_neurons + 10)
+ {
+ neurons_to_allocate = num_neurons + 10;
+ }
+
+ if(fann_reallocate_neurons(ann, neurons_to_allocate) == -1)
+ {
+ return -1;
+ }
+ }
+
+ if(num_connections > ann->total_connections_allocated)
+ {
+ /* Then we need to allocate more connections
+ * Allocate half as many connections as already exist
+ * (at least enough for ten neurons)
+ */
+ connections_to_allocate = num_connections + num_connections / 2;
+ if(connections_to_allocate < num_connections + ann->total_neurons * 10)
+ {
+ connections_to_allocate = num_connections + ann->total_neurons * 10;
+ }
+
+ if(fann_reallocate_connections(ann, connections_to_allocate) == -1)
+ {
+ return -1;
+ }
+ }
+
+ /* Set the neurons.
+ */
+ connection_it = first_candidate_connection;
+ neurons = ann->first_layer->first_neuron;
+ candidate_index = first_candidate_neuron;
+
+ for(i = 0; i < ann->cascade_activation_functions_count; i++)
+ {
+ for(j = 0; j < ann->cascade_activation_steepnesses_count; j++)
+ {
+ for(k = 0; k < ann->cascade_num_candidate_groups; k++)
+ {
+ /* TODO candidates should actually be created both in
+ * the last layer before the output layer, and in a new layer.
+ */
+ neurons[candidate_index].value = 0;
+ neurons[candidate_index].sum = 0;
+
+ neurons[candidate_index].activation_function =
+ ann->cascade_activation_functions[i];
+ neurons[candidate_index].activation_steepness =
+ ann->cascade_activation_steepnesses[j];
+
+ neurons[candidate_index].first_con = connection_it;
+ connection_it += candidate_connections_in;
+ neurons[candidate_index].last_con = connection_it;
+ /* We have no specific pointers to the output weights, but they are
+ * available after last_con */
+ connection_it += candidate_connections_out;
+ ann->train_errors[candidate_index] = 0;
+ candidate_index++;
+ }
+ }
+ }
+
+ /* Now randomize the weights and zero out the arrays that needs zeroing out.
+ */
+#ifdef CASCADE_DEBUG_FULL
+ printf("random cand weight [%d ... %d]\n", first_candidate_connection, num_connections - 1);
+#endif
+ if(ann->training_algorithm == FANN_TRAIN_RPROP)
+ {
+ initial_slope = ann->rprop_delta_zero;
+ }
+ else
+ {
+ initial_slope = 0.0;
+ }
+ for(i = first_candidate_connection; i < num_connections; i++)
+ {
+ ann->weights[i] = fann_random_weight();
+ /*ann->weights[i] = fann_rand(-0.25,0.25);*/
+ ann->train_slopes[i] = 0;
+ ann->prev_steps[i] = 0;
+ ann->prev_train_slopes[i] = initial_slope;
+ }
+
+ return 0;
+}
+
+int fann_train_candidates(struct fann *ann, struct fann_train_data *data)
+{
+ fann_type best_cand_score = 0.0;
+ fann_type target_cand_score = 0.0;
+ fann_type backslide_cand_score = -1.0e20f;
+ unsigned int i;
+ unsigned int max_epochs = ann->cascade_max_cand_epochs;
+ unsigned int stagnation = max_epochs;
+
+ if(ann->cascade_candidate_scores == NULL)
+ {
+ ann->cascade_candidate_scores =
+ (fann_type *) malloc(fann_get_cascade_num_candidates(ann) * sizeof(fann_type));
+ if(ann->cascade_candidate_scores == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return 0;
+ }
+ }
+
+ for(i = 0; i < max_epochs; i++)
+ {
+ best_cand_score = fann_train_candidates_epoch(ann, data);
+
+ if(best_cand_score / ann->MSE_value > ann->cascade_candidate_limit)
+ {
+#ifdef CASCADE_DEBUG
+ printf("above candidate limit %f/%f > %f", best_cand_score, ann->MSE_value,
+ ann->cascade_candidate_limit);
+#endif
+ return i + 1;
+ }
+
+ if((best_cand_score > target_cand_score) || (best_cand_score < backslide_cand_score))
+ {
+#ifdef CASCADE_DEBUG_FULL
+ printf("Best candidate score %f, real score: %f\n", ann->MSE_value - best_cand_score,
+ best_cand_score);
+ /* printf("best_cand_score=%f, target_cand_score=%f, backslide_cand_score=%f, stagnation=%d\n", best_cand_score, target_cand_score, backslide_cand_score, stagnation); */
+#endif
+
+ target_cand_score = best_cand_score * (1.0f + ann->cascade_candidate_change_fraction);
+ backslide_cand_score = best_cand_score * (1.0f - ann->cascade_candidate_change_fraction);
+ stagnation = i + ann->cascade_candidate_stagnation_epochs;
+ }
+
+ /* No improvement in allotted period, so quit */
+ if(i >= stagnation)
+ {
+#ifdef CASCADE_DEBUG
+ printf("Stagnation with %d epochs, best candidate score %f, real score: %f\n", i + 1,
+ ann->MSE_value - best_cand_score, best_cand_score);
+#endif
+ return i + 1;
+ }
+ }
+
+#ifdef CASCADE_DEBUG
+ printf("Max epochs %d reached, best candidate score %f, real score: %f\n", max_epochs,
+ ann->MSE_value - best_cand_score, best_cand_score);
+#endif
+ return max_epochs;
+}
+
+void fann_update_candidate_slopes(struct fann *ann)
+{
+ struct fann_neuron *neurons = ann->first_layer->first_neuron;
+ struct fann_neuron *first_cand = neurons + ann->total_neurons + 1;
+ struct fann_neuron *last_cand = first_cand + fann_get_cascade_num_candidates(ann);
+ struct fann_neuron *cand_it;
+ unsigned int i, j, num_connections;
+ unsigned int num_output = ann->num_output;
+ fann_type max_sum, cand_sum, activation, derived, error_value, diff, cand_score;
+ fann_type *weights, *cand_out_weights, *cand_slopes, *cand_out_slopes;
+ fann_type *output_train_errors = ann->train_errors + (ann->total_neurons - ann->num_output);
+
+ for(cand_it = first_cand; cand_it < last_cand; cand_it++)
+ {
+ cand_score = ann->cascade_candidate_scores[cand_it - first_cand];
+ error_value = 0.0;
+
+ /* code more or less stolen from fann_run to fast forward pass
+ */
+ cand_sum = 0.0;
+ num_connections = cand_it->last_con - cand_it->first_con;
+ weights = ann->weights + cand_it->first_con;
+
+ /* unrolled loop start */
+ i = num_connections & 3; /* same as modulo 4 */
+ switch (i)
+ {
+ case 3:
+ cand_sum += weights[2] * neurons[2].value;
+ case 2:
+ cand_sum += weights[1] * neurons[1].value;
+ case 1:
+ cand_sum += weights[0] * neurons[0].value;
+ case 0:
+ break;
+ }
+
+ for(; i != num_connections; i += 4)
+ {
+ cand_sum +=
+ weights[i] * neurons[i].value +
+ weights[i + 1] * neurons[i + 1].value +
+ weights[i + 2] * neurons[i + 2].value + weights[i + 3] * neurons[i + 3].value;
+ }
+ /*
+ * for(i = 0; i < num_connections; i++){
+ * cand_sum += weights[i] * neurons[i].value;
+ * }
+ */
+ /* unrolled loop end */
+
+ max_sum = 150/cand_it->activation_steepness;
+ if(cand_sum > max_sum)
+ cand_sum = max_sum;
+ else if(cand_sum < -max_sum)
+ cand_sum = -max_sum;
+
+ activation =
+ fann_activation(ann, cand_it->activation_function, cand_it->activation_steepness,
+ cand_sum);
+ /* printf("%f = sigmoid(%f);\n", activation, cand_sum); */
+
+ cand_it->sum = cand_sum;
+ cand_it->value = activation;
+
+ derived = fann_activation_derived(cand_it->activation_function,
+ cand_it->activation_steepness, activation, cand_sum);
+
+ /* The output weights is located right after the input weights in
+ * the weight array.
+ */
+ cand_out_weights = weights + num_connections;
+
+ cand_out_slopes = ann->train_slopes + cand_it->first_con + num_connections;
+ for(j = 0; j < num_output; j++)
+ {
+ diff = (activation * cand_out_weights[j]) - output_train_errors[j];
+#ifdef CASCADE_DEBUG_FULL
+ /* printf("diff = %f = (%f * %f) - %f;\n", diff, activation, cand_out_weights[j], output_train_errors[j]); */
+#endif
+ cand_out_slopes[j] -= 2.0f * diff * activation;
+#ifdef CASCADE_DEBUG_FULL
+ /* printf("cand_out_slopes[%d] <= %f += %f * %f;\n", j, cand_out_slopes[j], diff, activation); */
+#endif
+ error_value += diff * cand_out_weights[j];
+ cand_score -= (diff * diff);
+#ifdef CASCADE_DEBUG_FULL
+ /* printf("cand_score[%d][%d] = %f -= (%f * %f)\n", cand_it - first_cand, j, cand_score, diff, diff); */
+
+ printf("cand[%d]: error=%f, activation=%f, diff=%f, slope=%f\n", cand_it - first_cand,
+ output_train_errors[j], (activation * cand_out_weights[j]), diff,
+ -2.0 * diff * activation);
+#endif
+ }
+
+ ann->cascade_candidate_scores[cand_it - first_cand] = cand_score;
+ error_value *= derived;
+
+ cand_slopes = ann->train_slopes + cand_it->first_con;
+ for(i = 0; i < num_connections; i++)
+ {
+ cand_slopes[i] -= error_value * neurons[i].value;
+ }
+ }
+}
+
+void fann_update_candidate_weights(struct fann *ann, unsigned int num_data)
+{
+ struct fann_neuron *first_cand = (ann->last_layer - 1)->last_neuron + 1; /* there is an empty neuron between the actual neurons and the candidate neuron */
+ struct fann_neuron *last_cand = first_cand + fann_get_cascade_num_candidates(ann) - 1;
+
+ switch (ann->training_algorithm)
+ {
+ case FANN_TRAIN_RPROP:
+ fann_update_weights_irpropm(ann, first_cand->first_con,
+ last_cand->last_con + ann->num_output);
+ break;
+ case FANN_TRAIN_QUICKPROP:
+ fann_update_weights_quickprop(ann, num_data, first_cand->first_con,
+ last_cand->last_con + ann->num_output);
+ break;
+ case FANN_TRAIN_BATCH:
+ case FANN_TRAIN_INCREMENTAL:
+ fann_error((struct fann_error *) ann, FANN_E_CANT_USE_TRAIN_ALG);
+ break;
+ }
+}
+
+fann_type fann_train_candidates_epoch(struct fann *ann, struct fann_train_data *data)
+{
+ unsigned int i, j;
+ unsigned int best_candidate;
+ fann_type best_score;
+ unsigned int num_cand = fann_get_cascade_num_candidates(ann);
+ fann_type *output_train_errors = ann->train_errors + (ann->total_neurons - ann->num_output);
+ struct fann_neuron *output_neurons = (ann->last_layer - 1)->first_neuron;
+
+ for(i = 0; i < num_cand; i++)
+ {
+ /* The ann->MSE_value is actually the sum squared error */
+ ann->cascade_candidate_scores[i] = ann->MSE_value;
+ }
+ /*printf("start score: %f\n", ann->MSE_value); */
+
+ for(i = 0; i < data->num_data; i++)
+ {
+ fann_run(ann, data->input[i]);
+
+ for(j = 0; j < ann->num_output; j++)
+ {
+ /* TODO only debug, but the error is in opposite direction, this might be usefull info */
+ /* if(output_train_errors[j] != (ann->output[j] - data->output[i][j])){
+ * printf("difference in calculated error at %f != %f; %f = %f - %f;\n", output_train_errors[j], (ann->output[j] - data->output[i][j]), output_train_errors[j], ann->output[j], data->output[i][j]);
+ * } */
+
+ /*
+ * output_train_errors[j] = (data->output[i][j] - ann->output[j])/2;
+ * output_train_errors[j] = ann->output[j] - data->output[i][j];
+ */
+
+ output_train_errors[j] = (data->output[i][j] - ann->output[j]);
+
+ switch (output_neurons[j].activation_function)
+ {
+ case FANN_LINEAR_PIECE_SYMMETRIC:
+ case FANN_SIGMOID_SYMMETRIC:
+ case FANN_SIGMOID_SYMMETRIC_STEPWISE:
+ case FANN_THRESHOLD_SYMMETRIC:
+ case FANN_ELLIOT_SYMMETRIC:
+ case FANN_GAUSSIAN_SYMMETRIC:
+ output_train_errors[j] /= 2.0;
+ break;
+ case FANN_LINEAR:
+ case FANN_THRESHOLD:
+ case FANN_SIGMOID:
+ case FANN_SIGMOID_STEPWISE:
+ case FANN_GAUSSIAN:
+ case FANN_GAUSSIAN_STEPWISE:
+ case FANN_ELLIOT:
+ case FANN_LINEAR_PIECE:
+ break;
+ }
+ }
+
+ fann_update_candidate_slopes(ann);
+ }
+
+ fann_update_candidate_weights(ann, data->num_data);
+
+ /* find the best candidate score */
+ best_candidate = 0;
+ best_score = ann->cascade_candidate_scores[best_candidate];
+ for(i = 1; i < num_cand; i++)
+ {
+ /*struct fann_neuron *cand = ann->first_layer->first_neuron + ann->total_neurons + 1 + i;
+ * printf("candidate[%d] = activation: %s, steepness: %f, score: %f\n",
+ * i, FANN_ACTIVATIONFUNC_NAMES[cand->activation_function],
+ * cand->activation_steepness, ann->cascade_candidate_scores[i]); */
+
+ if(ann->cascade_candidate_scores[i] > best_score)
+ {
+ best_candidate = i;
+ best_score = ann->cascade_candidate_scores[best_candidate];
+ }
+ }
+
+ ann->cascade_best_candidate = ann->total_neurons + best_candidate + 1;
+#ifdef CASCADE_DEBUG_FULL
+ printf("Best candidate[%d]: with score %f, real score: %f\n", best_candidate,
+ ann->MSE_value - best_score, best_score);
+#endif
+
+ return best_score;
+}
+
+/* add a layer ad the position pointed to by *layer */
+struct fann_layer *fann_add_layer(struct fann *ann, struct fann_layer *layer)
+{
+ int layer_pos = layer - ann->first_layer;
+ int num_layers = ann->last_layer - ann->first_layer + 1;
+ int i;
+
+ /* allocate the layer */
+ struct fann_layer *layers =
+ (struct fann_layer *) realloc(ann->first_layer, num_layers * sizeof(struct fann_layer));
+ if(layers == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return NULL;
+ }
+
+ /* copy layers so that the free space is at the right location */
+ for(i = num_layers - 1; i >= layer_pos; i--)
+ {
+ layers[i] = layers[i - 1];
+ }
+
+ /* the newly allocated layer is empty */
+ layers[layer_pos].first_neuron = layers[layer_pos + 1].first_neuron;
+ layers[layer_pos].last_neuron = layers[layer_pos + 1].first_neuron;
+
+ /* Set the ann pointers correctly */
+ ann->first_layer = layers;
+ ann->last_layer = layers + num_layers;
+
+#ifdef CASCADE_DEBUG_FULL
+ printf("add layer at pos %d\n", layer_pos);
+#endif
+
+ return layers + layer_pos;
+}
+
+void fann_set_shortcut_connections(struct fann *ann)
+{
+ struct fann_layer *layer_it;
+ struct fann_neuron *neuron_it, **neuron_pointers, *neurons;
+ unsigned int num_connections = 0, i;
+
+ neuron_pointers = ann->connections;
+ neurons = ann->first_layer->first_neuron;
+
+ 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++)
+ {
+
+ neuron_pointers += num_connections;
+ num_connections = neuron_it->last_con - neuron_it->first_con;
+
+ for(i = 0; i != num_connections; i++)
+ {
+ neuron_pointers[i] = neurons + i;
+ }
+ }
+ }
+}
+
+void fann_add_candidate_neuron(struct fann *ann, struct fann_layer *layer)
+{
+ unsigned int num_connections_in = layer->first_neuron - ann->first_layer->first_neuron;
+ unsigned int num_connections_out =
+ (ann->last_layer - 1)->last_neuron - (layer + 1)->first_neuron;
+ unsigned int num_connections_move = num_connections_out + num_connections_in;
+
+ unsigned int candidate_con, candidate_output_weight;
+ int i;
+
+ struct fann_layer *layer_it;
+ struct fann_neuron *neuron_it, *neuron_place, *candidate;
+
+ /* We know that there is enough room for the new neuron
+ * (the candidates are in the same arrays), so move
+ * the last neurons to make room for this neuron.
+ */
+
+ /* first move the pointers to neurons in the layer structs */
+ for(layer_it = ann->last_layer - 1; layer_it != layer; layer_it--)
+ {
+#ifdef CASCADE_DEBUG_FULL
+ printf("move neuron pointers in layer %d, first(%d -> %d), last(%d -> %d)\n",
+ layer_it - ann->first_layer,
+ layer_it->first_neuron - ann->first_layer->first_neuron,
+ layer_it->first_neuron - ann->first_layer->first_neuron + 1,
+ layer_it->last_neuron - ann->first_layer->first_neuron,
+ layer_it->last_neuron - ann->first_layer->first_neuron + 1);
+#endif
+ layer_it->first_neuron++;
+ layer_it->last_neuron++;
+ }
+
+ /* also move the last neuron in the layer that needs the neuron added */
+ layer->last_neuron++;
+
+ /* this is the place that should hold the new neuron */
+ neuron_place = layer->last_neuron - 1;
+
+#ifdef CASCADE_DEBUG_FULL
+ printf("num_connections_in=%d, num_connections_out=%d\n", num_connections_in,
+ num_connections_out);
+#endif
+
+ candidate = ann->first_layer->first_neuron + ann->cascade_best_candidate;
+
+ /* the output weights for the candidates are located after the input weights */
+ candidate_output_weight = candidate->last_con;
+
+ /* move the actual output neurons and the indexes to the connection arrays */
+ for(neuron_it = (ann->last_layer - 1)->last_neuron - 1; neuron_it != neuron_place; neuron_it--)
+ {
+#ifdef CASCADE_DEBUG_FULL
+ printf("move neuron %d -> %d\n", neuron_it - ann->first_layer->first_neuron - 1,
+ neuron_it - ann->first_layer->first_neuron);
+#endif
+ *neuron_it = *(neuron_it - 1);
+
+ /* move the weights */
+#ifdef CASCADE_DEBUG_FULL
+ printf("move weight[%d ... %d] -> weight[%d ... %d]\n", neuron_it->first_con,
+ neuron_it->last_con - 1, neuron_it->first_con + num_connections_move - 1,
+ neuron_it->last_con + num_connections_move - 2);
+#endif
+ for(i = neuron_it->last_con - 1; i >= (int)neuron_it->first_con; i--)
+ {
+#ifdef CASCADE_DEBUG_FULL
+ printf("move weight[%d] = weight[%d]\n", i + num_connections_move - 1, i);
+#endif
+ ann->weights[i + num_connections_move - 1] = ann->weights[i];
+ }
+
+ /* move the indexes to weights */
+ neuron_it->last_con += num_connections_move;
+ num_connections_move--;
+ neuron_it->first_con += num_connections_move;
+
+ /* set the new weight to the newly allocated neuron */
+ ann->weights[neuron_it->last_con - 1] =
+ (ann->weights[candidate_output_weight]) * ann->cascade_weight_multiplier;
+ candidate_output_weight++;
+ }
+
+ /* Now inititalize the actual neuron */
+ neuron_place->value = 0;
+ neuron_place->sum = 0;
+ neuron_place->activation_function = candidate->activation_function;
+ neuron_place->activation_steepness = candidate->activation_steepness;
+ neuron_place->last_con = (neuron_place + 1)->first_con;
+ neuron_place->first_con = neuron_place->last_con - num_connections_in;
+#ifdef CASCADE_DEBUG_FULL
+ printf("neuron[%d] = weights[%d ... %d] activation: %s, steepness: %f\n",
+ neuron_place - ann->first_layer->first_neuron, neuron_place->first_con,
+ neuron_place->last_con - 1, FANN_ACTIVATIONFUNC_NAMES[neuron_place->activation_function],
+ neuron_place->activation_steepness);/* TODO remove */
+#endif
+
+ candidate_con = candidate->first_con;
+ /* initialize the input weights at random */
+#ifdef CASCADE_DEBUG_FULL
+ printf("move cand weights[%d ... %d] -> [%d ... %d]\n", candidate_con,
+ candidate_con + num_connections_in - 1, neuron_place->first_con,
+ neuron_place->last_con - 1);
+#endif
+
+ for(i = 0; i < (int)num_connections_in; i++)
+ {
+ ann->weights[i + neuron_place->first_con] = ann->weights[i + candidate_con];
+#ifdef CASCADE_DEBUG_FULL
+ printf("move weights[%d] -> weights[%d] (%f)\n", i + candidate_con,
+ i + neuron_place->first_con, ann->weights[i + neuron_place->first_con]);
+#endif
+ }
+
+ /* Change some of main variables */
+ ann->total_neurons++;
+ ann->total_connections += num_connections_in + num_connections_out;
+
+ return;
+}
+
+void fann_install_candidate(struct fann *ann)
+{
+ struct fann_layer *layer;
+
+ layer = fann_add_layer(ann, ann->last_layer - 1);
+ fann_add_candidate_neuron(ann, layer);
+ return;
+}
+
+#endif /* FIXEDFANN */
+
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidates(struct fann *ann)
+{
+ return ann->cascade_activation_functions_count *
+ ann->cascade_activation_steepnesses_count *
+ ann->cascade_num_candidate_groups;
+}
+
+FANN_GET_SET(float, cascade_output_change_fraction)
+FANN_GET_SET(unsigned int, cascade_output_stagnation_epochs)
+FANN_GET_SET(float, cascade_candidate_change_fraction)
+FANN_GET_SET(unsigned int, cascade_candidate_stagnation_epochs)
+FANN_GET_SET(unsigned int, cascade_num_candidate_groups)
+FANN_GET_SET(fann_type, cascade_weight_multiplier)
+FANN_GET_SET(fann_type, cascade_candidate_limit)
+FANN_GET_SET(unsigned int, cascade_max_out_epochs)
+FANN_GET_SET(unsigned int, cascade_max_cand_epochs)
+
+FANN_GET(unsigned int, cascade_activation_functions_count)
+FANN_GET(enum fann_activationfunc_enum *, cascade_activation_functions)
+
+FANN_EXTERNAL void fann_set_cascade_activation_functions(struct fann *ann,
+ enum fann_activationfunc_enum *
+ cascade_activation_functions,
+ unsigned int
+ cascade_activation_functions_count)
+{
+ if(ann->cascade_activation_functions_count != cascade_activation_functions_count)
+ {
+ ann->cascade_activation_functions_count = cascade_activation_functions_count;
+
+ /* reallocate mem */
+ ann->cascade_activation_functions =
+ (enum fann_activationfunc_enum *)realloc(ann->cascade_activation_functions,
+ ann->cascade_activation_functions_count * sizeof(enum fann_activationfunc_enum));
+ if(ann->cascade_activation_functions == NULL)
+ {
+ fann_error((struct fann_error*)ann, FANN_E_CANT_ALLOCATE_MEM);
+ return;
+ }
+ }
+
+ memmove(ann->cascade_activation_functions, cascade_activation_functions,
+ ann->cascade_activation_functions_count * sizeof(enum fann_activationfunc_enum));
+}
+
+FANN_GET(unsigned int, cascade_activation_steepnesses_count)
+FANN_GET(fann_type *, cascade_activation_steepnesses)
+
+FANN_EXTERNAL void fann_set_cascade_activation_steepnesses(struct fann *ann,
+ fann_type *
+ cascade_activation_steepnesses,
+ unsigned int
+ cascade_activation_steepnesses_count)
+{
+ if(ann->cascade_activation_steepnesses_count != cascade_activation_steepnesses_count)
+ {
+ ann->cascade_activation_steepnesses_count = cascade_activation_steepnesses_count;
+
+ /* reallocate mem */
+ ann->cascade_activation_steepnesses =
+ (fann_type *)realloc(ann->cascade_activation_steepnesses,
+ ann->cascade_activation_steepnesses_count * sizeof(fann_type));
+ if(ann->cascade_activation_steepnesses == NULL)
+ {
+ fann_error((struct fann_error*)ann, FANN_E_CANT_ALLOCATE_MEM);
+ return;
+ }
+ }
+
+ memmove(ann->cascade_activation_steepnesses, cascade_activation_steepnesses,
+ ann->cascade_activation_steepnesses_count * sizeof(fann_type));
+}
projects / germs.git / blobdiff