--- /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 <stdio.h>
+#include <stdlib.h>
+#include <stdarg.h>
+#include <string.h>
+
+#include "config.h"
+#include "fann.h"
+
+/*#define DEBUGTRAIN*/
+
+#ifndef FIXEDFANN
+/* INTERNAL FUNCTION
+ Calculates the derived of a value, given an activation function
+ and a steepness
+*/
+fann_type fann_activation_derived(unsigned int activation_function,
+ fann_type steepness, fann_type value, fann_type sum)
+{
+ switch (activation_function)
+ {
+ case FANN_LINEAR:
+ case FANN_LINEAR_PIECE:
+ case FANN_LINEAR_PIECE_SYMMETRIC:
+ return (fann_type) fann_linear_derive(steepness, value);
+ case FANN_SIGMOID:
+ case FANN_SIGMOID_STEPWISE:
+ value = fann_clip(value, 0.01f, 0.99f);
+ return (fann_type) fann_sigmoid_derive(steepness, value);
+ case FANN_SIGMOID_SYMMETRIC:
+ case FANN_SIGMOID_SYMMETRIC_STEPWISE:
+ value = fann_clip(value, -0.98f, 0.98f);
+ return (fann_type) fann_sigmoid_symmetric_derive(steepness, value);
+ case FANN_GAUSSIAN:
+ value = fann_clip(value, 0.01f, 0.99f);
+ return (fann_type) fann_gaussian_derive(steepness, value, sum);
+ case FANN_GAUSSIAN_SYMMETRIC:
+ value = fann_clip(value, -0.98f, 0.98f);
+ return (fann_type) fann_gaussian_symmetric_derive(steepness, value, sum);
+ case FANN_ELLIOT:
+ value = fann_clip(value, 0.01f, 0.99f);
+ return (fann_type) fann_elliot_derive(steepness, value, sum);
+ case FANN_ELLIOT_SYMMETRIC:
+ value = fann_clip(value, -0.98f, 0.98f);
+ return (fann_type) fann_elliot_symmetric_derive(steepness, value, sum);
+ case FANN_THRESHOLD:
+ fann_error(NULL, FANN_E_CANT_TRAIN_ACTIVATION);
+ }
+ return 0;
+}
+
+/* INTERNAL FUNCTION
+ Calculates the activation of a value, given an activation function
+ and a steepness
+*/
+fann_type fann_activation(struct fann * ann, unsigned int activation_function, fann_type steepness,
+ fann_type value)
+{
+ value = fann_mult(steepness, value);
+ fann_activation_switch(ann, activation_function, value, value);
+ return value;
+}
+
+/* Trains the network with the backpropagation algorithm.
+ */
+FANN_EXTERNAL void FANN_API fann_train(struct fann *ann, fann_type * input,
+ fann_type * desired_output)
+{
+ fann_run(ann, input);
+
+ fann_compute_MSE(ann, desired_output);
+
+ fann_backpropagate_MSE(ann);
+
+ fann_update_weights(ann);
+}
+#endif
+
+
+/* INTERNAL FUNCTION
+ Helper function to update the MSE value and return a diff which takes symmetric functions into account
+*/
+fann_type fann_update_MSE(struct fann *ann, struct fann_neuron* neuron, fann_type neuron_diff)
+{
+ float neuron_diff2;
+
+ switch (neuron->activation_function)
+ {
+ case FANN_LINEAR_PIECE_SYMMETRIC:
+ case FANN_THRESHOLD_SYMMETRIC:
+ case FANN_SIGMOID_SYMMETRIC:
+ case FANN_SIGMOID_SYMMETRIC_STEPWISE:
+ case FANN_ELLIOT_SYMMETRIC:
+ case FANN_GAUSSIAN_SYMMETRIC:
+ neuron_diff /= (fann_type)2.0;
+ break;
+ case FANN_THRESHOLD:
+ case FANN_LINEAR:
+ case FANN_SIGMOID:
+ case FANN_SIGMOID_STEPWISE:
+ case FANN_GAUSSIAN:
+ case FANN_GAUSSIAN_STEPWISE:
+ case FANN_ELLIOT:
+ case FANN_LINEAR_PIECE:
+ break;
+ }
+
+#ifdef FIXEDFANN
+ neuron_diff2 =
+ (neuron_diff / (float) ann->multiplier) * (neuron_diff / (float) ann->multiplier);
+#else
+ neuron_diff2 = (float) (neuron_diff * neuron_diff);
+#endif
+
+ ann->MSE_value += neuron_diff2;
+
+ /*printf("neuron_diff %f = (%f - %f)[/2], neuron_diff2=%f, sum=%f, MSE_value=%f, num_MSE=%d\n", neuron_diff, *desired_output, neuron_value, neuron_diff2, last_layer_begin->sum, ann->MSE_value, ann->num_MSE); */
+ if(fann_abs(neuron_diff) >= ann->bit_fail_limit)
+ {
+ ann->num_bit_fail++;
+ }
+
+ return neuron_diff;
+}
+
+/* Tests the network.
+ */
+FANN_EXTERNAL fann_type *FANN_API fann_test(struct fann *ann, fann_type * input,
+ fann_type * desired_output)
+{
+ fann_type neuron_value;
+ fann_type *output_begin = fann_run(ann, input);
+ fann_type *output_it;
+ const fann_type *output_end = output_begin + ann->num_output;
+ fann_type neuron_diff;
+ struct fann_neuron *output_neuron = (ann->last_layer - 1)->first_neuron;
+
+ /* calculate the error */
+ for(output_it = output_begin; output_it != output_end; output_it++)
+ {
+ neuron_value = *output_it;
+
+ neuron_diff = (*desired_output - neuron_value);
+
+ neuron_diff = fann_update_MSE(ann, output_neuron, neuron_diff);
+
+ desired_output++;
+ output_neuron++;
+ }
+ ann->num_MSE++;
+
+ return output_begin;
+}
+
+/* get the mean square error.
+ */
+FANN_EXTERNAL float FANN_API fann_get_MSE(struct fann *ann)
+{
+ if(ann->num_MSE)
+ {
+ return ann->MSE_value / (float) ann->num_MSE;
+ }
+ else
+ {
+ return 0;
+ }
+}
+
+FANN_EXTERNAL unsigned int fann_get_bit_fail(struct fann *ann)
+{
+ return ann->num_bit_fail;
+}
+
+/* reset the mean square error.
+ */
+FANN_EXTERNAL void FANN_API fann_reset_MSE(struct fann *ann)
+{
+ ann->num_MSE = 0;
+ ann->MSE_value = 0;
+ ann->num_bit_fail = 0;
+}
+
+#ifndef FIXEDFANN
+
+/* INTERNAL FUNCTION
+ compute the error at the network output
+ (usually, after forward propagation of a certain input vector, fann_run)
+ the error is a sum of squares for all the output units
+ also increments a counter because MSE is an average of such errors
+
+ After this train_errors in the output layer will be set to:
+ neuron_value_derived * (desired_output - neuron_value)
+ */
+void fann_compute_MSE(struct fann *ann, fann_type * desired_output)
+{
+ fann_type neuron_value, neuron_diff, *error_it = 0, *error_begin = 0;
+ struct fann_neuron *last_layer_begin = (ann->last_layer - 1)->first_neuron;
+ const struct fann_neuron *last_layer_end = last_layer_begin + ann->num_output;
+ const struct fann_neuron *first_neuron = ann->first_layer->first_neuron;
+
+ /* if no room allocated for the error variabels, allocate it now */
+ if(ann->train_errors == NULL)
+ {
+ ann->train_errors = (fann_type *) calloc(ann->total_neurons, sizeof(fann_type));
+ if(ann->train_errors == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return;
+ }
+ }
+ else
+ {
+ /* clear the error variabels */
+ memset(ann->train_errors, 0, (ann->total_neurons) * sizeof(fann_type));
+ }
+ error_begin = ann->train_errors;
+
+#ifdef DEBUGTRAIN
+ printf("\ncalculate errors\n");
+#endif
+ /* calculate the error and place it in the output layer */
+ error_it = error_begin + (last_layer_begin - first_neuron);
+
+ for(; last_layer_begin != last_layer_end; last_layer_begin++)
+ {
+ neuron_value = last_layer_begin->value;
+ neuron_diff = *desired_output - neuron_value;
+
+ neuron_diff = fann_update_MSE(ann, last_layer_begin, neuron_diff);
+
+ if(ann->train_error_function)
+ { /* TODO make switch when more functions */
+ if(neuron_diff < -.9999999)
+ neuron_diff = -17.0;
+ else if(neuron_diff > .9999999)
+ neuron_diff = 17.0;
+ else
+ neuron_diff = (fann_type) log((1.0 + neuron_diff) / (1.0 - neuron_diff));
+ }
+
+ *error_it = fann_activation_derived(last_layer_begin->activation_function,
+ last_layer_begin->activation_steepness, neuron_value,
+ last_layer_begin->sum) * neuron_diff;
+
+ desired_output++;
+ error_it++;
+ }
+ ann->num_MSE++;
+}
+
+/* INTERNAL FUNCTION
+ Propagate the error backwards from the output layer.
+
+ After this the train_errors in the hidden layers will be:
+ neuron_value_derived * sum(outgoing_weights * connected_neuron)
+*/
+void fann_backpropagate_MSE(struct fann *ann)
+{
+ fann_type tmp_error;
+ unsigned int i;
+ struct fann_layer *layer_it;
+ struct fann_neuron *neuron_it, *last_neuron;
+ struct fann_neuron **connections;
+
+ fann_type *error_begin = ann->train_errors;
+ fann_type *error_prev_layer;
+ fann_type *weights;
+ const struct fann_neuron *first_neuron = ann->first_layer->first_neuron;
+ const struct fann_layer *second_layer = ann->first_layer + 1;
+ struct fann_layer *last_layer = ann->last_layer;
+
+ /* go through all the layers, from last to first.
+ * And propagate the error backwards */
+ for(layer_it = last_layer - 1; layer_it > second_layer; --layer_it)
+ {
+ last_neuron = layer_it->last_neuron;
+
+ /* for each connection in this layer, propagate the error backwards */
+ if(ann->connection_rate >= 1)
+ {
+ if(!ann->shortcut_connections)
+ {
+ error_prev_layer = error_begin + ((layer_it - 1)->first_neuron - first_neuron);
+ }
+ else
+ {
+ error_prev_layer = error_begin;
+ }
+
+ for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+
+ tmp_error = error_begin[neuron_it - first_neuron];
+ weights = ann->weights + neuron_it->first_con;
+ for(i = neuron_it->last_con - neuron_it->first_con; i--;)
+ {
+ /*printf("i = %d\n", i);
+ * printf("error_prev_layer[%d] = %f\n", i, error_prev_layer[i]);
+ * printf("weights[%d] = %f\n", i, weights[i]); */
+ error_prev_layer[i] += tmp_error * weights[i];
+ }
+ }
+ }
+ else
+ {
+ for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+
+ tmp_error = error_begin[neuron_it - first_neuron];
+ weights = ann->weights + neuron_it->first_con;
+ connections = ann->connections + neuron_it->first_con;
+ for(i = neuron_it->last_con - neuron_it->first_con; i--;)
+ {
+ error_begin[connections[i] - first_neuron] += tmp_error * weights[i];
+ }
+ }
+ }
+
+ /* then calculate the actual errors in the previous layer */
+ error_prev_layer = error_begin + ((layer_it - 1)->first_neuron - first_neuron);
+ last_neuron = (layer_it - 1)->last_neuron;
+
+ for(neuron_it = (layer_it - 1)->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ *error_prev_layer *= fann_activation_derived(neuron_it->activation_function,
+ neuron_it->activation_steepness, neuron_it->value, neuron_it->sum);
+ error_prev_layer++;
+ }
+
+ }
+}
+
+/* INTERNAL FUNCTION
+ Update weights for incremental training
+*/
+void fann_update_weights(struct fann *ann)
+{
+ struct fann_neuron *neuron_it, *last_neuron, *prev_neurons;
+ fann_type tmp_error, delta_w, *weights;
+ struct fann_layer *layer_it;
+ unsigned int i;
+ unsigned int num_connections;
+
+ /* store some variabels local for fast access */
+ const float learning_rate = ann->learning_rate;
+ const float learning_momentum = ann->learning_momentum;
+ struct fann_neuron *first_neuron = ann->first_layer->first_neuron;
+ struct fann_layer *first_layer = ann->first_layer;
+ const struct fann_layer *last_layer = ann->last_layer;
+ fann_type *error_begin = ann->train_errors;
+ fann_type *deltas_begin, *weights_deltas;
+
+ /* if no room allocated for the deltas, allocate it now */
+ if(ann->prev_weights_deltas == NULL)
+ {
+ ann->prev_weights_deltas =
+ (fann_type *) calloc(ann->total_connections_allocated, sizeof(fann_type));
+ if(ann->prev_weights_deltas == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return;
+ }
+ }
+
+#ifdef DEBUGTRAIN
+ printf("\nupdate weights\n");
+#endif
+ deltas_begin = ann->prev_weights_deltas;
+ prev_neurons = first_neuron;
+ for(layer_it = (first_layer + 1); layer_it != last_layer; layer_it++)
+ {
+#ifdef DEBUGTRAIN
+ printf("layer[%d]\n", layer_it - first_layer);
+#endif
+ last_neuron = layer_it->last_neuron;
+ if(ann->connection_rate >= 1)
+ {
+ if(!ann->shortcut_connections)
+ {
+ prev_neurons = (layer_it - 1)->first_neuron;
+ }
+ for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ tmp_error = error_begin[neuron_it - first_neuron] * learning_rate;
+ num_connections = neuron_it->last_con - neuron_it->first_con;
+ weights = ann->weights + neuron_it->first_con;
+ weights_deltas = deltas_begin + neuron_it->first_con;
+ for(i = 0; i != num_connections; i++)
+ {
+ delta_w = tmp_error * prev_neurons[i].value + learning_momentum * weights_deltas[i];
+ weights[i] += delta_w ;
+ weights_deltas[i] = delta_w;
+ }
+ }
+ }
+ else
+ {
+ for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ tmp_error = error_begin[neuron_it - first_neuron] * learning_rate;
+ num_connections = neuron_it->last_con - neuron_it->first_con;
+ weights = ann->weights + neuron_it->first_con;
+ weights_deltas = deltas_begin + neuron_it->first_con;
+ for(i = 0; i != num_connections; i++)
+ {
+ delta_w = tmp_error * prev_neurons[i].value + learning_momentum * weights_deltas[i];
+ weights[i] += delta_w;
+ weights_deltas[i] = delta_w;
+ }
+ }
+ }
+ }
+}
+
+/* INTERNAL FUNCTION
+ Update slopes for batch training
+ layer_begin = ann->first_layer+1 and layer_end = ann->last_layer-1
+ will update all slopes.
+
+*/
+void fann_update_slopes_batch(struct fann *ann, struct fann_layer *layer_begin,
+ struct fann_layer *layer_end)
+{
+ struct fann_neuron *neuron_it, *last_neuron, *prev_neurons, **connections;
+ fann_type tmp_error;
+ unsigned int i, num_connections;
+
+ /* store some variabels local for fast access */
+ struct fann_neuron *first_neuron = ann->first_layer->first_neuron;
+ fann_type *error_begin = ann->train_errors;
+ fann_type *slope_begin, *neuron_slope;
+
+ /* if no room allocated for the slope variabels, allocate it now */
+ if(ann->train_slopes == NULL)
+ {
+ ann->train_slopes =
+ (fann_type *) calloc(ann->total_connections_allocated, sizeof(fann_type));
+ if(ann->train_slopes == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return;
+ }
+ }
+
+ if(layer_begin == NULL)
+ {
+ layer_begin = ann->first_layer + 1;
+ }
+
+ if(layer_end == NULL)
+ {
+ layer_end = ann->last_layer - 1;
+ }
+
+ slope_begin = ann->train_slopes;
+
+#ifdef DEBUGTRAIN
+ printf("\nupdate slopes\n");
+#endif
+
+ prev_neurons = first_neuron;
+
+ for(; layer_begin <= layer_end; layer_begin++)
+ {
+#ifdef DEBUGTRAIN
+ printf("layer[%d]\n", layer_begin - ann->first_layer);
+#endif
+ last_neuron = layer_begin->last_neuron;
+ if(ann->connection_rate >= 1)
+ {
+ if(!ann->shortcut_connections)
+ {
+ prev_neurons = (layer_begin - 1)->first_neuron;
+ }
+
+ for(neuron_it = layer_begin->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ tmp_error = error_begin[neuron_it - first_neuron];
+ neuron_slope = slope_begin + neuron_it->first_con;
+ num_connections = neuron_it->last_con - neuron_it->first_con;
+ for(i = 0; i != num_connections; i++)
+ {
+ neuron_slope[i] += tmp_error * prev_neurons[i].value;
+ }
+ }
+ }
+ else
+ {
+ for(neuron_it = layer_begin->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ tmp_error = error_begin[neuron_it - first_neuron];
+ neuron_slope = slope_begin + neuron_it->first_con;
+ num_connections = neuron_it->last_con - neuron_it->first_con;
+ connections = ann->connections + neuron_it->first_con;
+ for(i = 0; i != num_connections; i++)
+ {
+ neuron_slope[i] += tmp_error * connections[i]->value;
+ }
+ }
+ }
+ }
+}
+
+/* INTERNAL FUNCTION
+ Clears arrays used for training before a new training session.
+ Also creates the arrays that do not exist yet.
+ */
+void fann_clear_train_arrays(struct fann *ann)
+{
+ unsigned int i;
+ fann_type delta_zero;
+
+ /* if no room allocated for the slope variabels, allocate it now
+ * (calloc clears mem) */
+ if(ann->train_slopes == NULL)
+ {
+ ann->train_slopes =
+ (fann_type *) calloc(ann->total_connections_allocated, sizeof(fann_type));
+ if(ann->train_slopes == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return;
+ }
+ }
+ else
+ {
+ memset(ann->train_slopes, 0, (ann->total_connections) * sizeof(fann_type));
+ }
+
+ /* if no room allocated for the variabels, allocate it now */
+ if(ann->prev_steps == NULL)
+ {
+ ann->prev_steps = (fann_type *) calloc(ann->total_connections_allocated, sizeof(fann_type));
+ if(ann->prev_steps == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return;
+ }
+ }
+ else
+ {
+ memset(ann->prev_steps, 0, (ann->total_connections) * sizeof(fann_type));
+ }
+
+ /* if no room allocated for the variabels, allocate it now */
+ if(ann->prev_train_slopes == NULL)
+ {
+ ann->prev_train_slopes =
+ (fann_type *) malloc(ann->total_connections_allocated * sizeof(fann_type));
+ if(ann->prev_train_slopes == NULL)
+ {
+ fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM);
+ return;
+ }
+ }
+
+ if(ann->training_algorithm == FANN_TRAIN_RPROP)
+ {
+ delta_zero = ann->rprop_delta_zero;
+ for(i = 0; i < ann->total_connections; i++)
+ {
+ ann->prev_train_slopes[i] = delta_zero;
+ }
+ }
+ else
+ {
+ memset(ann->prev_train_slopes, 0, (ann->total_connections) * sizeof(fann_type));
+ }
+}
+
+/* INTERNAL FUNCTION
+ Update weights for batch training
+ */
+void fann_update_weights_batch(struct fann *ann, unsigned int num_data, unsigned int first_weight,
+ unsigned int past_end)
+{
+ fann_type *train_slopes = ann->train_slopes;
+ fann_type *weights = ann->weights;
+ const float epsilon = ann->learning_rate / num_data;
+ unsigned int i = first_weight;
+
+ for(; i != past_end; i++)
+ {
+ weights[i] += train_slopes[i] * epsilon;
+ train_slopes[i] = 0.0;
+ }
+}
+
+/* INTERNAL FUNCTION
+ The quickprop training algorithm
+ */
+void fann_update_weights_quickprop(struct fann *ann, unsigned int num_data,
+ unsigned int first_weight, unsigned int past_end)
+{
+ fann_type *train_slopes = ann->train_slopes;
+ fann_type *weights = ann->weights;
+ fann_type *prev_steps = ann->prev_steps;
+ fann_type *prev_train_slopes = ann->prev_train_slopes;
+
+ fann_type w, prev_step, slope, prev_slope, next_step;
+
+ float epsilon = ann->learning_rate / num_data;
+ float decay = ann->quickprop_decay; /*-0.0001;*/
+ float mu = ann->quickprop_mu; /*1.75; */
+ float shrink_factor = (float) (mu / (1.0 + mu));
+
+ unsigned int i = first_weight;
+
+ for(; i != past_end; i++)
+ {
+ w = weights[i];
+ prev_step = prev_steps[i];
+ slope = train_slopes[i] + decay * w;
+ prev_slope = prev_train_slopes[i];
+ next_step = 0.0;
+
+ if(prev_step > 999 || prev_step < -999)
+ {
+ /* Used for BP */
+ prev_step = prev_steps[i];
+ }
+
+ /* The step must always be in direction opposite to the slope. */
+ if(prev_step > 0.001)
+ {
+ /* If last step was positive... */
+ if(slope > 0.0)
+ {
+ /* Add in linear term if current slope is still positive. */
+ next_step += epsilon * slope;
+ }
+
+ /*If current slope is close to or larger than prev slope... */
+ if(slope > (shrink_factor * prev_slope))
+ {
+ next_step += mu * prev_step; /* Take maximum size negative step. */
+ }
+ else
+ {
+ next_step += prev_step * slope / (prev_slope - slope); /* Else, use quadratic estimate. */
+ }
+ }
+ else if(prev_step < -0.001)
+ {
+ /* If last step was negative... */
+ if(slope < 0.0)
+ {
+ /* Add in linear term if current slope is still negative. */
+ next_step += epsilon * slope;
+ }
+
+ /* If current slope is close to or more neg than prev slope... */
+ if(slope < (shrink_factor * prev_slope))
+ {
+ next_step += mu * prev_step; /* Take maximum size negative step. */
+ }
+ else
+ {
+ next_step += prev_step * slope / (prev_slope - slope); /* Else, use quadratic estimate. */
+ }
+ }
+ else
+ {
+ /* Last step was zero, so use only linear term. */
+ next_step += epsilon * slope;
+ }
+
+ if(next_step > 1000 || next_step < -1000)
+ {
+ /*
+ printf("quickprop[%d] weight=%f, slope=%f, prev_slope=%f, next_step=%f, prev_step=%f\n",
+ i, weights[i], slope, prev_slope, next_step, prev_step);
+
+ if(next_step > 1000)
+ next_step = 1000;
+ else
+ next_step = -1000;
+ */
+ }
+
+ /* update global data arrays */
+ prev_steps[i] = next_step;
+
+ w += next_step;
+ if(w > 1500)
+ weights[i] = 1500;
+ else if(w < -1500)
+ weights[i] = -1500;
+ else
+ weights[i] = w;
+
+ prev_train_slopes[i] = slope;
+ train_slopes[i] = 0.0;
+ }
+}
+
+/* INTERNAL FUNCTION
+ The iRprop- algorithm
+*/
+void fann_update_weights_irpropm(struct fann *ann, unsigned int first_weight, unsigned int past_end)
+{
+ fann_type *train_slopes = ann->train_slopes;
+ fann_type *weights = ann->weights;
+ fann_type *prev_steps = ann->prev_steps;
+ fann_type *prev_train_slopes = ann->prev_train_slopes;
+
+ fann_type prev_step, slope, prev_slope, next_step, same_sign;
+
+ /* These should be set from variables */
+ float increase_factor = ann->rprop_increase_factor; /*1.2; */
+ float decrease_factor = ann->rprop_decrease_factor; /*0.5; */
+ float delta_min = ann->rprop_delta_min; /*0.0; */
+ float delta_max = ann->rprop_delta_max; /*50.0; */
+
+ unsigned int i = first_weight;
+
+ for(; i != past_end; i++)
+ {
+ prev_step = fann_max(prev_steps[i], (fann_type) 0.001); /* prev_step may not be zero because then the training will stop */
+ slope = train_slopes[i];
+ prev_slope = prev_train_slopes[i];
+
+ same_sign = prev_slope * slope;
+
+ if(same_sign > 0.0)
+ {
+ next_step = fann_min(prev_step * increase_factor, delta_max);
+ }
+ else if(same_sign < 0.0)
+ {
+ next_step = fann_max(prev_step * decrease_factor, delta_min);
+ slope = 0;
+ }
+ else
+ {
+ next_step = 0.0;
+ }
+
+ if(slope < 0)
+ {
+ weights[i] -= next_step;
+ }
+ else
+ {
+ weights[i] += next_step;
+ }
+
+ /*if(i == 2){
+ * printf("weight=%f, slope=%f, next_step=%f, prev_step=%f\n", weights[i], slope, next_step, prev_step);
+ * } */
+
+ /* update global data arrays */
+ prev_steps[i] = next_step;
+ prev_train_slopes[i] = slope;
+ train_slopes[i] = 0.0;
+ }
+}
+
+#endif
+
+FANN_GET_SET(enum fann_train_enum, training_algorithm)
+FANN_GET_SET(float, learning_rate)
+
+FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden(struct fann *ann,
+ enum fann_activationfunc_enum activation_function)
+{
+ struct fann_neuron *last_neuron, *neuron_it;
+ struct fann_layer *layer_it;
+ struct fann_layer *last_layer = ann->last_layer - 1; /* -1 to not update the output layer */
+
+ for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++)
+ {
+ last_neuron = layer_it->last_neuron;
+ for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ neuron_it->activation_function = activation_function;
+ }
+ }
+}
+
+FANN_EXTERNAL struct fann_layer* FANN_API fann_get_layer(struct fann *ann, int layer)
+{
+ if(layer <= 0 || layer >= (ann->last_layer - ann->first_layer))
+ {
+ fann_error((struct fann_error *) ann, FANN_E_INDEX_OUT_OF_BOUND, layer);
+ return NULL;
+ }
+
+ return ann->first_layer + layer;
+}
+
+FANN_EXTERNAL struct fann_neuron* FANN_API fann_get_neuron_layer(struct fann *ann, struct fann_layer* layer, int neuron)
+{
+ if(neuron >= (layer->first_neuron - layer->last_neuron))
+ {
+ fann_error((struct fann_error *) ann, FANN_E_INDEX_OUT_OF_BOUND, neuron);
+ return NULL;
+ }
+
+ return layer->first_neuron + neuron;
+}
+
+FANN_EXTERNAL struct fann_neuron* FANN_API fann_get_neuron(struct fann *ann, unsigned int layer, int neuron)
+{
+ struct fann_layer *layer_it = fann_get_layer(ann, layer);
+ if(layer_it == NULL)
+ return NULL;
+ return fann_get_neuron_layer(ann, layer_it, neuron);
+}
+
+FANN_EXTERNAL void FANN_API fann_set_activation_function(struct fann *ann,
+ enum fann_activationfunc_enum
+ activation_function,
+ int layer,
+ int neuron)
+{
+ struct fann_neuron* neuron_it = fann_get_neuron(ann, layer, neuron);
+ if(neuron_it == NULL)
+ return;
+
+ neuron_it->activation_function = activation_function;
+}
+
+FANN_EXTERNAL void FANN_API fann_set_activation_function_layer(struct fann *ann,
+ enum fann_activationfunc_enum
+ activation_function,
+ int layer)
+{
+ struct fann_neuron *last_neuron, *neuron_it;
+ struct fann_layer *layer_it = fann_get_layer(ann, layer);
+
+ if(layer_it == NULL)
+ return;
+
+ last_neuron = layer_it->last_neuron;
+ for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ neuron_it->activation_function = activation_function;
+ }
+}
+
+
+FANN_EXTERNAL void FANN_API fann_set_activation_function_output(struct fann *ann,
+ enum fann_activationfunc_enum activation_function)
+{
+ struct fann_neuron *last_neuron, *neuron_it;
+ struct fann_layer *last_layer = ann->last_layer - 1;
+
+ last_neuron = last_layer->last_neuron;
+ for(neuron_it = last_layer->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ neuron_it->activation_function = activation_function;
+ }
+}
+
+FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden(struct fann *ann,
+ fann_type steepness)
+{
+ struct fann_neuron *last_neuron, *neuron_it;
+ struct fann_layer *layer_it;
+ struct fann_layer *last_layer = ann->last_layer - 1; /* -1 to not update the output layer */
+
+ for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++)
+ {
+ last_neuron = layer_it->last_neuron;
+ for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ neuron_it->activation_steepness = steepness;
+ }
+ }
+}
+
+FANN_EXTERNAL void FANN_API fann_set_activation_steepness(struct fann *ann,
+ fann_type steepness,
+ int layer,
+ int neuron)
+{
+ struct fann_neuron* neuron_it = fann_get_neuron(ann, layer, neuron);
+ if(neuron_it == NULL)
+ return;
+
+ neuron_it->activation_steepness = steepness;
+}
+
+FANN_EXTERNAL void FANN_API fann_set_activation_steepness_layer(struct fann *ann,
+ fann_type steepness,
+ int layer)
+{
+ struct fann_neuron *last_neuron, *neuron_it;
+ struct fann_layer *layer_it = fann_get_layer(ann, layer);
+
+ if(layer_it == NULL)
+ return;
+
+ last_neuron = layer_it->last_neuron;
+ for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ neuron_it->activation_steepness = steepness;
+ }
+}
+
+FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output(struct fann *ann,
+ fann_type steepness)
+{
+ struct fann_neuron *last_neuron, *neuron_it;
+ struct fann_layer *last_layer = ann->last_layer - 1;
+
+ last_neuron = last_layer->last_neuron;
+ for(neuron_it = last_layer->first_neuron; neuron_it != last_neuron; neuron_it++)
+ {
+ neuron_it->activation_steepness = steepness;
+ }
+}
+
+FANN_GET_SET(enum fann_errorfunc_enum, train_error_function)
+FANN_GET_SET(fann_callback_type, callback)
+FANN_GET_SET(float, quickprop_decay)
+FANN_GET_SET(float, quickprop_mu)
+FANN_GET_SET(float, rprop_increase_factor)
+FANN_GET_SET(float, rprop_decrease_factor)
+FANN_GET_SET(float, rprop_delta_min)
+FANN_GET_SET(float, rprop_delta_max)
+FANN_GET_SET(enum fann_stopfunc_enum, train_stop_function)
+FANN_GET_SET(fann_type, bit_fail_limit)
+FANN_GET_SET(float, learning_momentum)
projects / germs.git / blobdiff