X-Git-Url: https://ruin.nu/git/?p=germs.git;a=blobdiff_plain;f=fann%2Fsrc%2Ffann_io.c;fp=fann%2Fsrc%2Ffann_io.c;h=ce88733ab53e850e8d5fb28bca4a500fc23237c0;hp=0000000000000000000000000000000000000000;hb=40d817fd1c0ec184927450858ca95b722ae8acba;hpb=0ced9c229cf05fa677686df27eca9f167f11a87f

diff --git a/fann/src/fann_io.c b/fann/src/fann_io.c
new file mode 100644
index 0000000..ce88733
--- /dev/null
+++ b/fann/src/fann_io.c
@@ -0,0 +1,693 @@
+/*
+  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"
+
+/* Create a network from a configuration file.
+ */
+FANN_EXTERNAL struct fann *FANN_API fann_create_from_file(const char *configuration_file)
+{
+	struct fann *ann;
+	FILE *conf = fopen(configuration_file, "r");
+
+	if(!conf)
+	{
+		fann_error(NULL, FANN_E_CANT_OPEN_CONFIG_R, configuration_file);
+		return NULL;
+	}
+	ann = fann_create_from_fd(conf, configuration_file);
+	fclose(conf);
+	return ann;
+}
+
+/* Save the network.
+ */
+FANN_EXTERNAL int FANN_API fann_save(struct fann *ann, const char *configuration_file)
+{
+	return fann_save_internal(ann, configuration_file, 0);
+}
+
+/* Save the network as fixed point data.
+ */
+FANN_EXTERNAL int FANN_API fann_save_to_fixed(struct fann *ann, const char *configuration_file)
+{
+	return fann_save_internal(ann, configuration_file, 1);
+}
+
+/* INTERNAL FUNCTION
+   Used to save the network to a file.
+ */
+int fann_save_internal(struct fann *ann, const char *configuration_file, unsigned int save_as_fixed)
+{
+	int retval;
+	FILE *conf = fopen(configuration_file, "w+");
+
+	if(!conf)
+	{
+		fann_error((struct fann_error *) ann, FANN_E_CANT_OPEN_CONFIG_W, configuration_file);
+		return -1;
+	}
+	retval = fann_save_internal_fd(ann, conf, configuration_file, save_as_fixed);
+	fclose(conf);
+	return retval;
+}
+
+/* INTERNAL FUNCTION
+   Used to save the network to a file descriptor.
+ */
+int fann_save_internal_fd(struct fann *ann, FILE * conf, const char *configuration_file,
+						  unsigned int save_as_fixed)
+{
+	struct fann_layer *layer_it;
+	int calculated_decimal_point = 0;
+	struct fann_neuron *neuron_it, *first_neuron;
+	fann_type *weights;
+	struct fann_neuron **connected_neurons;
+	unsigned int i = 0;
+
+#ifndef FIXEDFANN
+	/* variabels for use when saving floats as fixed point variabels */
+	unsigned int decimal_point = 0;
+	unsigned int fixed_multiplier = 0;
+	fann_type max_possible_value = 0;
+	unsigned int bits_used_for_max = 0;
+	fann_type current_max_value = 0;
+#endif
+
+#ifndef FIXEDFANN
+	if(save_as_fixed)
+	{
+		/* save the version information */
+		fprintf(conf, FANN_FIX_VERSION "\n");
+	}
+	else
+	{
+		/* save the version information */
+		fprintf(conf, FANN_FLO_VERSION "\n");
+	}
+#else
+	/* save the version information */
+	fprintf(conf, FANN_FIX_VERSION "\n");
+#endif
+
+#ifndef FIXEDFANN
+	if(save_as_fixed)
+	{
+		/* calculate the maximal possible shift value */
+
+		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++)
+			{
+				/* look at all connections to each neurons, and see how high a value we can get */
+				current_max_value = 0;
+				for(i = neuron_it->first_con; i != neuron_it->last_con; i++)
+				{
+					current_max_value += fann_abs(ann->weights[i]);
+				}
+
+				if(current_max_value > max_possible_value)
+				{
+					max_possible_value = current_max_value;
+				}
+			}
+		}
+
+		for(bits_used_for_max = 0; max_possible_value >= 1; bits_used_for_max++)
+		{
+			max_possible_value /= 2.0;
+		}
+
+		/* The maximum number of bits we shift the fix point, is the number
+		 * of bits in a integer, minus one for the sign, one for the minus
+		 * in stepwise, and minus the bits used for the maximum.
+		 * This is devided by two, to allow multiplication of two fixed
+		 * point numbers.
+		 */
+		calculated_decimal_point = (sizeof(int) * 8 - 2 - bits_used_for_max) / 2;
+
+		if(calculated_decimal_point < 0)
+		{
+			decimal_point = 0;
+		}
+		else
+		{
+			decimal_point = calculated_decimal_point;
+		}
+
+		fixed_multiplier = 1 << decimal_point;
+
+#ifdef DEBUG
+		printf("calculated_decimal_point=%d, decimal_point=%u, bits_used_for_max=%u\n",
+			   calculated_decimal_point, decimal_point, bits_used_for_max);
+#endif
+
+		/* save the decimal_point on a seperate line */
+		fprintf(conf, "decimal_point=%u\n", decimal_point);
+	}
+#else
+	/* save the decimal_point on a seperate line */
+	fprintf(conf, "decimal_point=%u\n", ann->decimal_point);
+
+#endif
+
+	/* Save network parameters */
+	fprintf(conf, "num_layers=%u\n", ann->last_layer - ann->first_layer);
+	fprintf(conf, "learning_rate=%f\n", ann->learning_rate);
+	fprintf(conf, "connection_rate=%f\n", ann->connection_rate);
+	fprintf(conf, "shortcut_connections=%u\n", ann->shortcut_connections);
+	
+	fprintf(conf, "learning_momentum=%f\n", ann->learning_momentum);
+	fprintf(conf, "training_algorithm=%u\n", ann->training_algorithm);
+	fprintf(conf, "train_error_function=%u\n", ann->train_error_function);
+	fprintf(conf, "train_stop_function=%u\n", ann->train_stop_function);
+	fprintf(conf, "cascade_output_change_fraction=%f\n", ann->cascade_output_change_fraction);
+	fprintf(conf, "quickprop_decay=%f\n", ann->quickprop_decay);
+	fprintf(conf, "quickprop_mu=%f\n", ann->quickprop_mu);
+	fprintf(conf, "rprop_increase_factor=%f\n", ann->rprop_increase_factor);
+	fprintf(conf, "rprop_decrease_factor=%f\n", ann->rprop_decrease_factor);
+	fprintf(conf, "rprop_delta_min=%f\n", ann->rprop_delta_min);
+	fprintf(conf, "rprop_delta_max=%f\n", ann->rprop_delta_max);
+	fprintf(conf, "rprop_delta_zero=%f\n", ann->rprop_delta_zero);
+	fprintf(conf, "cascade_output_stagnation_epochs=%u\n", ann->cascade_output_stagnation_epochs);
+	fprintf(conf, "cascade_candidate_change_fraction=%f\n", ann->cascade_candidate_change_fraction);
+	fprintf(conf, "cascade_candidate_stagnation_epochs=%u\n", ann->cascade_candidate_stagnation_epochs);
+	fprintf(conf, "cascade_max_out_epochs=%u\n", ann->cascade_max_out_epochs);
+	fprintf(conf, "cascade_max_cand_epochs=%u\n", ann->cascade_max_cand_epochs);	
+	fprintf(conf, "cascade_num_candidate_groups=%u\n", ann->cascade_num_candidate_groups);
+
+#ifndef FIXEDFANN
+	if(save_as_fixed)
+	{
+		fprintf(conf, "bit_fail_limit=%u\n", (int) floor((ann->bit_fail_limit * fixed_multiplier) + 0.5));
+		fprintf(conf, "cascade_candidate_limit=%u\n", (int) floor((ann->cascade_candidate_limit * fixed_multiplier) + 0.5));
+		fprintf(conf, "cascade_weight_multiplier=%u\n", (int) floor((ann->cascade_weight_multiplier * fixed_multiplier) + 0.5));
+	}
+	else
+#endif	
+	{
+		fprintf(conf, "bit_fail_limit="FANNPRINTF"\n", ann->bit_fail_limit);
+		fprintf(conf, "cascade_candidate_limit="FANNPRINTF"\n", ann->cascade_candidate_limit);
+		fprintf(conf, "cascade_weight_multiplier="FANNPRINTF"\n", ann->cascade_weight_multiplier);
+	}
+
+	fprintf(conf, "cascade_activation_functions_count=%u\n", ann->cascade_activation_functions_count);
+	fprintf(conf, "cascade_activation_functions=");
+	for(i = 0; i < ann->cascade_activation_functions_count; i++)
+		fprintf(conf, "%u ", ann->cascade_activation_functions[i]);
+	fprintf(conf, "\n");
+	
+	fprintf(conf, "cascade_activation_steepnesses_count=%u\n", ann->cascade_activation_steepnesses_count);
+	fprintf(conf, "cascade_activation_steepnesses=");
+	for(i = 0; i < ann->cascade_activation_steepnesses_count; i++)
+	{
+#ifndef FIXEDFANN
+		if(save_as_fixed)
+			fprintf(conf, "%u ", (int) floor((ann->cascade_activation_steepnesses[i] * fixed_multiplier) + 0.5));
+		else
+#endif	
+			fprintf(conf, FANNPRINTF" ", ann->cascade_activation_steepnesses[i]);
+	}
+	fprintf(conf, "\n");
+
+	fprintf(conf, "layer_sizes=");
+	for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++)
+	{
+		/* the number of neurons in the layers (in the last layer, there is always one too many neurons, because of an unused bias) */
+		fprintf(conf, "%u ", layer_it->last_neuron - layer_it->first_neuron);
+	}
+	fprintf(conf, "\n");
+
+
+	fprintf(conf, "neurons (num_inputs, activation_function, activation_steepness)=");
+	for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++)
+	{
+		/* the neurons */
+		for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++)
+		{
+#ifndef FIXEDFANN
+			if(save_as_fixed)
+			{
+				fprintf(conf, "(%u, %u, %u) ", neuron_it->last_con - neuron_it->first_con,
+						neuron_it->activation_function,
+						(int) floor((neuron_it->activation_steepness * fixed_multiplier) + 0.5));
+			}
+			else
+			{
+				fprintf(conf, "(%u, %u, " FANNPRINTF ") ", neuron_it->last_con - neuron_it->first_con,
+						neuron_it->activation_function, neuron_it->activation_steepness);
+			}
+#else
+			fprintf(conf, "(%u, %u, " FANNPRINTF ") ", neuron_it->last_con - neuron_it->first_con,
+					neuron_it->activation_function, neuron_it->activation_steepness);
+#endif
+		}
+	}
+	fprintf(conf, "\n");
+
+	connected_neurons = ann->connections;
+	weights = ann->weights;
+	first_neuron = ann->first_layer->first_neuron;
+
+	/* Now save all the connections.
+	 * We only need to save the source and the weight,
+	 * since the destination is given by the order.
+	 * 
+	 * The weight is not saved binary due to differences
+	 * in binary definition of floating point numbers.
+	 * Especially an iPAQ does not use the same binary
+	 * representation as an i386 machine.
+	 */
+	fprintf(conf, "connections (connected_to_neuron, weight)=");
+	for(i = 0; i < ann->total_connections; i++)
+	{
+#ifndef FIXEDFANN
+		if(save_as_fixed)
+		{
+			/* save the connection "(source weight) " */
+			fprintf(conf, "(%u, %d) ",
+					connected_neurons[i] - first_neuron,
+					(int) floor((weights[i] * fixed_multiplier) + 0.5));
+		}
+		else
+		{
+			/* save the connection "(source weight) " */
+			fprintf(conf, "(%u, " FANNPRINTF ") ", connected_neurons[i] - first_neuron, weights[i]);
+		}
+#else
+		/* save the connection "(source weight) " */
+		fprintf(conf, "(%u, " FANNPRINTF ") ", connected_neurons[i] - first_neuron, weights[i]);
+#endif
+
+	}
+	fprintf(conf, "\n");
+
+	return calculated_decimal_point;
+}
+
+struct fann *fann_create_from_fd_1_1(FILE * conf, const char *configuration_file);
+
+#define fann_scanf(type, name, val) \
+{ \
+	if(fscanf(conf, name"="type"\n", val) != 1) \
+	{ \
+		fann_error(NULL, FANN_E_CANT_READ_CONFIG, name, configuration_file); \
+		fann_destroy(ann); \
+		return NULL; \
+	} \
+}
+
+/* INTERNAL FUNCTION
+   Create a network from a configuration file descriptor.
+ */
+struct fann *fann_create_from_fd(FILE * conf, const char *configuration_file)
+{
+	unsigned int num_layers, layer_size, input_neuron, i, num_connections;
+
+#ifdef FIXEDFANN
+	unsigned int decimal_point, multiplier;
+#endif
+	struct fann_neuron *first_neuron, *neuron_it, *last_neuron, **connected_neurons;
+	fann_type *weights;
+	struct fann_layer *layer_it;
+	struct fann *ann = NULL;
+
+	char *read_version;
+
+	read_version = (char *) calloc(strlen(FANN_CONF_VERSION "\n"), 1);
+	if(read_version == NULL)
+	{
+		fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
+		return NULL;
+	}
+
+	fread(read_version, 1, strlen(FANN_CONF_VERSION "\n"), conf);	/* reads version */
+
+	/* compares the version information */
+	if(strncmp(read_version, FANN_CONF_VERSION "\n", strlen(FANN_CONF_VERSION "\n")) != 0)
+	{
+#ifdef FIXEDFANN
+		if(strncmp(read_version, "FANN_FIX_1.1\n", strlen("FANN_FIX_1.1\n")) == 0)
+		{
+#else
+		if(strncmp(read_version, "FANN_FLO_1.1\n", strlen("FANN_FLO_1.1\n")) == 0)
+		{
+#endif
+			free(read_version);
+			return fann_create_from_fd_1_1(conf, configuration_file);
+		}
+
+		free(read_version);
+		fann_error(NULL, FANN_E_WRONG_CONFIG_VERSION, configuration_file);
+
+		return NULL;
+	}
+
+	free(read_version);
+
+#ifdef FIXEDFANN
+    fann_scanf("%u", "decimal_point", &decimal_point);
+	multiplier = 1 << decimal_point;
+#endif
+
+    fann_scanf("%u", "num_layers", &num_layers);
+
+	ann = fann_allocate_structure(num_layers);
+	if(ann == NULL)
+	{
+		return NULL;
+	}
+
+    fann_scanf("%f", "learning_rate", &ann->learning_rate);
+    fann_scanf("%f", "connection_rate", &ann->connection_rate);
+    fann_scanf("%u", "shortcut_connections", &ann->shortcut_connections);
+	fann_scanf("%f", "learning_momentum", &ann->learning_momentum);
+	fann_scanf("%u", "training_algorithm", (unsigned int *)&ann->training_algorithm);
+	fann_scanf("%u", "train_error_function", (unsigned int *)&ann->train_error_function);
+	fann_scanf("%u", "train_stop_function", (unsigned int *)&ann->train_stop_function);
+	fann_scanf("%f", "cascade_output_change_fraction", &ann->cascade_output_change_fraction);
+	fann_scanf("%f", "quickprop_decay", &ann->quickprop_decay);
+	fann_scanf("%f", "quickprop_mu", &ann->quickprop_mu);
+	fann_scanf("%f", "rprop_increase_factor", &ann->rprop_increase_factor);
+	fann_scanf("%f", "rprop_decrease_factor", &ann->rprop_decrease_factor);
+	fann_scanf("%f", "rprop_delta_min", &ann->rprop_delta_min);
+	fann_scanf("%f", "rprop_delta_max", &ann->rprop_delta_max);
+	fann_scanf("%f", "rprop_delta_zero", &ann->rprop_delta_zero);
+	fann_scanf("%u", "cascade_output_stagnation_epochs", &ann->cascade_output_stagnation_epochs);
+	fann_scanf("%f", "cascade_candidate_change_fraction", &ann->cascade_candidate_change_fraction);
+	fann_scanf("%u", "cascade_candidate_stagnation_epochs", &ann->cascade_candidate_stagnation_epochs);
+	fann_scanf("%u", "cascade_max_out_epochs", &ann->cascade_max_out_epochs);
+	fann_scanf("%u", "cascade_max_cand_epochs", &ann->cascade_max_cand_epochs);	
+	fann_scanf("%u", "cascade_num_candidate_groups", &ann->cascade_num_candidate_groups);
+
+	fann_scanf(FANNSCANF, "bit_fail_limit", &ann->bit_fail_limit);
+	fann_scanf(FANNSCANF, "cascade_candidate_limit", &ann->cascade_candidate_limit);
+	fann_scanf(FANNSCANF, "cascade_weight_multiplier", &ann->cascade_weight_multiplier);
+
+
+	fann_scanf("%u", "cascade_activation_functions_count", &ann->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);
+		fann_destroy(ann);
+		return NULL;
+	}
+
+	fscanf(conf, "cascade_activation_functions=");
+	for(i = 0; i < ann->cascade_activation_functions_count; i++)
+		fscanf(conf, "%u ", (unsigned int *)&ann->cascade_activation_functions[i]);
+	
+	fann_scanf("%u", "cascade_activation_steepnesses_count", &ann->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);
+		fann_destroy(ann);
+		return NULL;
+	}
+
+	fscanf(conf, "cascade_activation_steepnesses=");
+	for(i = 0; i < ann->cascade_activation_steepnesses_count; i++)
+		fscanf(conf, FANNSCANF" ", &ann->cascade_activation_steepnesses[i]);
+
+#ifdef FIXEDFANN
+	ann->decimal_point = decimal_point;
+	ann->multiplier = multiplier;
+#endif
+
+#ifdef FIXEDFANN
+	fann_update_stepwise(ann);
+#endif
+
+#ifdef DEBUG
+	printf("creating network with %d layers\n", num_layers);
+	printf("input\n");
+#endif
+
+	fscanf(conf, "layer_sizes=");
+	/* determine how many neurons there should be in each layer */
+	for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++)
+	{
+		if(fscanf(conf, "%u ", &layer_size) != 1)
+		{
+			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONFIG, "layer_sizes", configuration_file);
+			fann_destroy(ann);
+			return NULL;
+		}
+		/* we do not allocate room here, but we make sure that
+		 * last_neuron - first_neuron is the number of neurons */
+		layer_it->first_neuron = NULL;
+		layer_it->last_neuron = layer_it->first_neuron + layer_size;
+		ann->total_neurons += layer_size;
+#ifdef DEBUG
+		if(ann->shortcut_connections && layer_it != ann->first_layer)
+		{
+			printf("  layer       : %d neurons, 0 bias\n", layer_size);
+		}
+		else
+		{
+			printf("  layer       : %d neurons, 1 bias\n", layer_size - 1);
+		}
+#endif
+	}
+
+	ann->num_input = ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1;
+	ann->num_output = ((ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron);
+	if(!ann->shortcut_connections)
+	{
+		/* one too many (bias) in the output layer */
+		ann->num_output--;
+	}
+
+	/* allocate room for the actual neurons */
+	fann_allocate_neurons(ann);
+	if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
+	{
+		fann_destroy(ann);
+		return NULL;
+	}
+
+	last_neuron = (ann->last_layer - 1)->last_neuron;
+	fscanf(conf, "neurons (num_inputs, activation_function, activation_steepness)=");
+	for(neuron_it = ann->first_layer->first_neuron; neuron_it != last_neuron; neuron_it++)
+	{
+		if(fscanf
+		   (conf, "(%u, %u, " FANNSCANF ") ", &num_connections, (unsigned int *)&neuron_it->activation_function,
+			&neuron_it->activation_steepness) != 3)
+		{
+			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_NEURON, configuration_file);
+			fann_destroy(ann);
+			return NULL;
+		}
+		neuron_it->first_con = ann->total_connections;
+		ann->total_connections += num_connections;
+		neuron_it->last_con = ann->total_connections;
+	}
+
+	fann_allocate_connections(ann);
+	if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
+	{
+		fann_destroy(ann);
+		return NULL;
+	}
+
+	connected_neurons = ann->connections;
+	weights = ann->weights;
+	first_neuron = ann->first_layer->first_neuron;
+
+	fscanf(conf, "connections (connected_to_neuron, weight)=");
+	for(i = 0; i < ann->total_connections; i++)
+	{
+		if(fscanf(conf, "(%u, " FANNSCANF ") ", &input_neuron, &weights[i]) != 2)
+		{
+			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONNECTIONS, configuration_file);
+			fann_destroy(ann);
+			return NULL;
+		}
+		connected_neurons[i] = first_neuron + input_neuron;
+	}
+
+#ifdef DEBUG
+	printf("output\n");
+#endif
+	return ann;
+}
+
+
+/* INTERNAL FUNCTION
+   Create a network from a configuration file descriptor. (backward compatible read of version 1.1 files)
+ */
+struct fann *fann_create_from_fd_1_1(FILE * conf, const char *configuration_file)
+{
+	unsigned int num_layers, layer_size, input_neuron, i, shortcut_connections, num_connections;
+	unsigned int activation_function_hidden, activation_function_output;
+#ifdef FIXEDFANN
+	unsigned int decimal_point, multiplier;
+#endif
+	fann_type activation_steepness_hidden, activation_steepness_output;
+	float learning_rate, connection_rate;
+	struct fann_neuron *first_neuron, *neuron_it, *last_neuron, **connected_neurons;
+	fann_type *weights;
+	struct fann_layer *layer_it;
+	struct fann *ann;
+
+#ifdef FIXEDFANN
+	if(fscanf(conf, "%u\n", &decimal_point) != 1)
+	{
+		fann_error(NULL, FANN_E_CANT_READ_CONFIG, "decimal_point", configuration_file);
+		return NULL;
+	}
+	multiplier = 1 << decimal_point;
+#endif
+
+	if(fscanf(conf, "%u %f %f %u %u %u " FANNSCANF " " FANNSCANF "\n", &num_layers, &learning_rate,
+		&connection_rate, &shortcut_connections, &activation_function_hidden,
+		&activation_function_output, &activation_steepness_hidden,
+		&activation_steepness_output) != 8)
+	{
+		fann_error(NULL, FANN_E_CANT_READ_CONFIG, "parameters", configuration_file);
+		return NULL;
+	}
+
+	ann = fann_allocate_structure(num_layers);
+	if(ann == NULL)
+	{
+		return NULL;
+	}
+	ann->connection_rate = connection_rate;
+	ann->shortcut_connections = shortcut_connections;
+	ann->learning_rate = learning_rate;
+
+#ifdef FIXEDFANN
+	ann->decimal_point = decimal_point;
+	ann->multiplier = multiplier;
+#endif
+
+#ifdef FIXEDFANN
+	fann_update_stepwise(ann);
+#endif
+
+#ifdef DEBUG
+	printf("creating network with learning rate %f\n", learning_rate);
+	printf("input\n");
+#endif
+
+	/* determine how many neurons there should be in each layer */
+	for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++)
+	{
+		if(fscanf(conf, "%u ", &layer_size) != 1)
+		{
+			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_NEURON, configuration_file);
+			fann_destroy(ann);
+			return NULL;
+		}
+		/* we do not allocate room here, but we make sure that
+		 * last_neuron - first_neuron is the number of neurons */
+		layer_it->first_neuron = NULL;
+		layer_it->last_neuron = layer_it->first_neuron + layer_size;
+		ann->total_neurons += layer_size;
+#ifdef DEBUG
+		if(ann->shortcut_connections && layer_it != ann->first_layer)
+		{
+			printf("  layer       : %d neurons, 0 bias\n", layer_size);
+		}
+		else
+		{
+			printf("  layer       : %d neurons, 1 bias\n", layer_size - 1);
+		}
+#endif
+	}
+
+	ann->num_input = ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1;
+	ann->num_output = ((ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron);
+	if(!ann->shortcut_connections)
+	{
+		/* one too many (bias) in the output layer */
+		ann->num_output--;
+	}
+
+	/* allocate room for the actual neurons */
+	fann_allocate_neurons(ann);
+	if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
+	{
+		fann_destroy(ann);
+		return NULL;
+	}
+
+	last_neuron = (ann->last_layer - 1)->last_neuron;
+	for(neuron_it = ann->first_layer->first_neuron; neuron_it != last_neuron; neuron_it++)
+	{
+		if(fscanf(conf, "%u ", &num_connections) != 1)
+		{
+			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_NEURON, configuration_file);
+			fann_destroy(ann);
+			return NULL;
+		}
+		neuron_it->first_con = ann->total_connections;
+		ann->total_connections += num_connections;
+		neuron_it->last_con = ann->total_connections;
+	}
+
+	fann_allocate_connections(ann);
+	if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
+	{
+		fann_destroy(ann);
+		return NULL;
+	}
+
+	connected_neurons = ann->connections;
+	weights = ann->weights;
+	first_neuron = ann->first_layer->first_neuron;
+
+	for(i = 0; i < ann->total_connections; i++)
+	{
+		if(fscanf(conf, "(%u " FANNSCANF ") ", &input_neuron, &weights[i]) != 2)
+		{
+			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONNECTIONS, configuration_file);
+			fann_destroy(ann);
+			return NULL;
+		}
+		connected_neurons[i] = first_neuron + input_neuron;
+	}
+
+	fann_set_activation_steepness_hidden(ann, activation_steepness_hidden);
+	fann_set_activation_steepness_output(ann, activation_steepness_output);
+	fann_set_activation_function_hidden(ann, (enum fann_activationfunc_enum)activation_function_hidden);
+	fann_set_activation_function_output(ann, (enum fann_activationfunc_enum)activation_function_output);
+
+#ifdef DEBUG
+	printf("output\n");
+#endif
+	return ann;
+}