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antd-lua-plugin/lib/ann/fann/src/fann_train.c
Xuan Sang LE 38bd13b46b mimgrating from another repo
2018-09-19 15:08:49 +02:00

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/*
Fast Artificial Neural Network Library (fann)
Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
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 <math.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_SIN_SYMMETRIC:
return (fann_type) fann_sin_symmetric_derive(steepness, sum);
case FANN_COS_SYMMETRIC:
return (fann_type) fann_cos_symmetric_derive(steepness, sum);
case FANN_SIN:
return (fann_type) fann_sin_derive(steepness, sum);
case FANN_COS:
return (fann_type) fann_cos_derive(steepness, 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(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:
case FANN_SIN_SYMMETRIC:
case FANN_COS_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:
case FANN_SIN:
case FANN_COS:
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_API 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)
{
/*printf("resetMSE %d %f\n", ann->num_MSE, ann->MSE_value);*/
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->network_type == FANN_NETTYPE_LAYER)
{
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->network_type == FANN_NETTYPE_LAYER)
{
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->network_type == FANN_NETTYPE_LAYER)
{
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_allocated) * sizeof(fann_type));
}
/* if no room allocated for the variabels, allocate it now */
if(ann->prev_steps == NULL)
{
ann->prev_steps = (fann_type *) malloc(ann->total_connections_allocated * sizeof(fann_type));
if(ann->prev_steps == 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_allocated; i++)
ann->prev_steps[i] = delta_zero;
}
else
{
memset(ann->prev_steps, 0, (ann->total_connections_allocated) * 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 *) calloc(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;
}
}
else
{
memset(ann->prev_train_slopes, 0, (ann->total_connections_allocated) * 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;
/* 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;
/*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;
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) 0.0001); /* 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
{
next_step = fann_max(prev_step * decrease_factor, delta_min);
slope = 0;
}
if(slope < 0)
{
weights[i] -= next_step;
if(weights[i] < -1500)
weights[i] = -1500;
}
else
{
weights[i] += next_step;
if(weights[i] > 1500)
weights[i] = 1500;
}
/*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;
}
}
/* INTERNAL FUNCTION
The SARprop- algorithm
*/
void fann_update_weights_sarprop(struct fann *ann, unsigned int epoch, 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 = 0, 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; */
/* TODO: why is delta_min 0.0 in iRprop? SARPROP uses 1x10^-6 (Braun and Riedmiller, 1993) */
float delta_min = 0.000001f;
float delta_max = ann->rprop_delta_max; /*50.0; */
float weight_decay_shift = ann->sarprop_weight_decay_shift; /* ld 0.01 = -6.644 */
float step_error_threshold_factor = ann->sarprop_step_error_threshold_factor; /* 0.1 */
float step_error_shift = ann->sarprop_step_error_shift; /* ld 3 = 1.585 */
float T = ann->sarprop_temperature;
float MSE = fann_get_MSE(ann);
float RMSE = sqrtf(MSE);
unsigned int i = first_weight;
/* for all weights; TODO: are biases included? */
for(; i != past_end; i++)
{
/* TODO: confirm whether 1x10^-6 == delta_min is really better */
prev_step = fann_max(prev_steps[i], (fann_type) 0.000001); /* prev_step may not be zero because then the training will stop */
/* calculate SARPROP slope; TODO: better as new error function? (see SARPROP paper)*/
slope = -train_slopes[i] - weights[i] * (fann_type)fann_exp2(-T * epoch + weight_decay_shift);
/* TODO: is prev_train_slopes[i] 0.0 in the beginning? */
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);
/* TODO: are the signs inverted? see differences between SARPROP paper and iRprop */
if (slope < 0.0)
weights[i] += next_step;
else
weights[i] -= next_step;
}
else if(same_sign < 0.0)
{
if(prev_step < step_error_threshold_factor * MSE)
next_step = prev_step * decrease_factor + (float)rand() / RAND_MAX * RMSE * (fann_type)fann_exp2(-T * epoch + step_error_shift);
else
next_step = fann_max(prev_step * decrease_factor, delta_min);
slope = 0.0;
}
else
{
if(slope < 0.0)
weights[i] += prev_step;
else
weights[i] -= prev_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->last_neuron - layer->first_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 enum fann_activationfunc_enum FANN_API
fann_get_activation_function(struct fann *ann, int layer, int neuron)
{
struct fann_neuron* neuron_it = fann_get_neuron(ann, layer, neuron);
if (neuron_it == NULL)
{
return (enum fann_activationfunc_enum)-1; /* layer or neuron out of bounds */
}
else
{
return neuron_it->activation_function;
}
}
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 fann_type FANN_API
fann_get_activation_steepness(struct fann *ann, int layer, int neuron)
{
struct fann_neuron* neuron_it = fann_get_neuron(ann, layer, neuron);
if(neuron_it == NULL)
{
return -1; /* layer or neuron out of bounds */
}
else
{
return neuron_it->activation_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(float, rprop_delta_zero)
FANN_GET_SET(float, sarprop_weight_decay_shift)
FANN_GET_SET(float, sarprop_step_error_threshold_factor)
FANN_GET_SET(float, sarprop_step_error_shift)
FANN_GET_SET(float, sarprop_temperature)
FANN_GET_SET(enum fann_stopfunc_enum, train_stop_function)
FANN_GET_SET(fann_type, bit_fail_limit)
FANN_GET_SET(float, learning_momentum)
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