bool ViFann::setStructure(const Type &type, const QList<int> &neurons, const qreal &connectionRate)
{
#ifdef GPU
if(type != Standard)
{
LOG("The GPU version of FANN currently doesn't support shortcut, sparse or cascade networks.", QtFatalMsg);
exit(-1);
}
#endif
clear();
mType = type;
mInputCount = neurons.first();
mOutputCount = neurons.last();
mNeurons.clear();
for(mI = 0; mI < neurons.size(); ++mI)
{
if(neurons[mI] != 0) mNeurons.append(neurons[mI]);
}
if(mInput == NULL) delete [] mInput;
if(mOutput == NULL) delete [] mOutput;
mInput = new float[mInputCount];
mOutput = new float[mOutputCount];
unsigned int layers = mNeurons.size();
unsigned int layerNeurons[layers];
for(mI = 0; mI < layers; ++mI) layerNeurons[mI] = mNeurons[mI];
if(type == Standard) mNetwork = fann_create_standard_array(layers, layerNeurons);
#ifndef GPU
else if(type == Sparse)
{
mNetwork = fann_create_sparse_array(connectionRate, layers, layerNeurons);
mConnectionRate = connectionRate;
}
else if(type == Shortcut) mNetwork = fann_create_shortcut_array(layers, layerNeurons);
#endif
else return false;
fann_set_train_stop_function(mNetwork, FANN_STOPFUNC_MSE);
if(ENABLE_CALLBACK)
{
fann_set_callback(mNetwork, &ViFann::trainCallback);
mMseTotal.clear();
mMseCount = 0;
}
return true;
}
int main(int argc,char **argv)
{
unlink(histfile);
srand ( time ( NULL ) );
// printf ( "Reading data.\n" );
train_data = fann_read_train_from_file ( "train.dat" );
test_data = fann_read_train_from_file ( "test.dat" );
// signal ( 2, sig_term );
// fann_scale_train_data ( train_data, 0, 1.54 );
// fann_scale_train_data ( test_data, 0, 1.54 );
//cln_test_data=fann_duplicate_train_data(test_data);
cln_train_data=fann_duplicate_train_data(train_data);
printf ( "Creating cascaded network.\n" );
ann =
fann_create_shortcut ( 2, fann_num_input_train_data ( train_data ),
fann_num_output_train_data ( train_data ) );
fann_set_training_algorithm ( ann, FANN_TRAIN_RPROP );
fann_set_activation_function_hidden ( ann, FANN_SIGMOID );
fann_set_activation_function_output ( ann, FANN_SIGMOID);
fann_set_train_error_function ( ann, FANN_ERRORFUNC_LINEAR );
// if (fann_set_scaling_params(ann, train_data,-1.0f,1.0f,0.0f, 1.0f)==-1)
// printf("set scaling error: %s\n",fann_get_errno((struct fann_error*)ann));
// fann_scale_train_input(ann,train_data);
// fann_scale_output_train_data(train_data,0.0f,1.0f);
// fann_scale_input_train_data(train_data, -1.0,1.0f);
// fann_scale_output_train_data(test_data,-1.0f,1.0f);
// fann_scale_input_train_data(test_data, -1.0,1.0f);
//fann_scale_train(ann,train_data);
// fann_scale_train(ann,weight_data);
// fann_scale_train(ann,test_data);
/*
* fann_set_cascade_output_change_fraction(ann, 0.1f);
* ;
* fann_set_cascade_candidate_change_fraction(ann, 0.1f);
*
*/
// fann_set_cascade_output_stagnation_epochs ( ann, 180 );
//fann_set_cascade_weight_multiplier ( ann, ( fann_type ) 0.1f );
fann_set_callback ( ann, cascade_callback );
if ( !multi )
{
/* */
// steepness[0] = 0.22;
steepness[0] = 0.9;
steepness[1] = 1.0;
/*
* steepness[1] = 0.55;
* ;
* steepness[1] = 0.33;
* ;
* steepness[3] = 0.11;
* ;
* steepness[1] = 0.01;
*
*/
/*
* steepness = 0.5;
*
*/
// fann_set_cascade_activation_steepnesses ( ann, steepness, 2);
/*
* activation = FANN_SIN_SYMMETRIC;
*/
/*
* activation[0] = FANN_SIGMOID;
*
*/
activation[0] = FANN_SIGMOID;
/*
* activation[2] = FANN_ELLIOT_SYMMETRIC;
*
*/
activation[1] = FANN_LINEAR_PIECE;
/*
* activation[4] = FANN_GAUSSIAN_SYMMETRIC;
* ;
* activation[5] = FANN_SIGMOID;
*
*/
activation[2] = FANN_ELLIOT;
activation[3] = FANN_COS;
/*
*
*
*/
activation[4] = FANN_SIN;
fann_set_cascade_activation_functions ( ann, activation, 5);
/* fann_set_cascade_num_candidate_groups ( ann,
fann_num_input_train_data
( train_data ) ); */
}
else
{
/*
* fann_set_cascade_activation_steepnesses(ann, &steepness, 0.75);
*
*/
// fann_set_cascade_num_candidate_groups ( ann, 1 );
}
/* TODO: weight mult > 0.01 */
/* if ( training_algorithm == FANN_TRAIN_QUICKPROP )
{
fann_set_learning_rate ( ann, 0.35f );
}
else
{
fann_set_learning_rate ( ann, 0.7f );
}
fann_set_bit_fail_limit ( ann, ( fann_type ) 0.9f );*/
/*
* fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
*
*/
//fann_scale_output_train_data(train_data,0.0f,1.0f);
//fann_scale_input_train_data(train_data, -1.0f,1.0f);
// fann_scale_output_train_data(test_data, 0.0f,1.0f);
//fann_scale_input_train_data(test_data, -1.0f,1.0f);
// fann_randomize_weights ( ann, -0.2f, 0.2f );
fann_init_weights ( ann, train_data );
printf ( "Training network.\n" );
fann_cascadetrain_on_data ( ann, train_data, max_neurons,
1, desired_error );
fann_print_connections ( ann );
mse_train = fann_test_data ( ann, train_data );
bit_fail_train = fann_get_bit_fail ( ann );
mse_test = fann_test_data ( ann, test_data );
bit_fail_test = fann_get_bit_fail ( ann );
printf
( "\nTrain error: %.08f, Train bit-fail: %d, Test error: %.08f, Test bit-fail: %d\n\n",
mse_train, bit_fail_train, mse_test, bit_fail_test );
printf ( "Saving cascaded network.\n" );
fann_save ( ann, "cascaded.net" );
// printf ( "Cleaning up.\n" );
fann_destroy_train ( train_data );
fann_destroy_train ( test_data );
fann_destroy ( ann );
return 0;
}
