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Commit 6d896ab3 authored by Martin Beseda's avatar Martin Beseda
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WIP: Fixed memory leaks...

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src/Network/NeuralNetwork.cpp
+ 38
− 39
View file @ 6d896ab3
......@@ -17,7 +17,7 @@
namespace lib4neuro {
NeuralNetwork::NeuralNetwork() {
this->neurons = new ::std::vector<Neuron *>(0);
// this->neurons = new ::std::vector<Neuron *>(0);
// this->neuron_biases = new ::std::vector<double>(0);
// this->neuron_bias_indices = new ::std::vector<int>(0);
......@@ -56,14 +56,14 @@ namespace lib4neuro {
NeuralNetwork::~NeuralNetwork() {
if (this->neurons) {
for (auto n: *(this->neurons)) {
delete n;
n = nullptr;
}
delete this->neurons;
this->neurons = nullptr;
}
// if (this->neurons) {
// for (auto n: *(this->neurons)) {
// delete n;
// n = nullptr;
// }
// delete this->neurons;
// this->neurons = nullptr;
// }
// if (this->neuron_potentials) {
// delete this->neuron_potentials;
......@@ -171,7 +171,7 @@ namespace lib4neuro {
// TODO rework due to the changed structure of the class
// Neuron * active_neuron, * target_neuron;
//
// size_t n = this->neurons->size();
// size_t n = this->neurons.size();
// bool *part_of_subnetwork = new bool[n];
// ::std::fill(part_of_subnetwork, part_of_subnetwork + n, false);
//
......@@ -218,7 +218,7 @@ namespace lib4neuro {
//
// is_reachable_from_source[ idx ] = true;
//
// active_neuron = this->neurons->at( idx );
// active_neuron = this->neurons.at( idx );
//
// for(Connection* connection: *(active_neuron->get_connections_out())){
//
......@@ -267,7 +267,7 @@ namespace lib4neuro {
//
// is_reachable_from_destination[ idx ] = true;
//
// active_neuron = this->neurons->at( idx );
// active_neuron = this->neurons.at( idx );
//
// if(is_reachable_from_source[ idx ]){
// n_new_neurons++;
......@@ -332,11 +332,11 @@ namespace lib4neuro {
// neuron_local_mapping[i] = (int)idx;
// idx++;
//
// Neuron *new_neuron = this->neurons->at(i)->get_copy( );
// Neuron *new_neuron = this->neurons.at(i)->get_copy( );
//
// output_net->add_neuron( new_neuron );
// local_local_n_arr.push_back( new_neuron );
// local_n_arr.push_back( this->neurons->at(i) );
// local_n_arr.push_back( this->neurons.at(i) );
// }
// }
// for(size_t in: input_neuron_indices){
......@@ -401,13 +401,13 @@ namespace lib4neuro {
this->neuron_bias_indices.push_back((int) bias_idx);
}
this->outward_adjacency.push_back(new ::std::vector<std::pair<size_t, size_t>>(0));
this->inward_adjacency.push_back(new ::std::vector<std::pair<size_t, size_t>>(0));
this->outward_adjacency.push_back(std::make_shared<std::vector<std::pair<size_t, size_t>>>(::std::vector<std::pair<size_t, size_t>>(0)));
this->inward_adjacency.push_back(std::make_shared<std::vector<std::pair<size_t, size_t>>>(::std::vector<std::pair<size_t, size_t>>(0)));
this->neurons->push_back(n);
this->neurons.push_back(n);
this->layers_analyzed = false;
return this->neurons->size() - 1;
return this->neurons.size() - 1;
}
void NeuralNetwork::eval_single_debug(::std::vector<double> &input, ::std::vector<double> &output,
......@@ -454,7 +454,7 @@ namespace lib4neuro {
if (bias_idx >= 0) {
bias = this->neuron_biases.at(bias_idx);
}
potential = this->neurons->at(si)->activate(this->neuron_potentials.at(si), bias);
potential = this->neurons.at(si)->activate(this->neuron_potentials.at(si), bias);
std::cout << " applying bias: " << bias << " to neuron potential: " << this->neuron_potentials.at(si) << " -> " << potential << std::endl;
for (auto c: *this->outward_adjacency.at(si)) {
......@@ -476,7 +476,7 @@ namespace lib4neuro {
if (bias_idx >= 0) {
bias = this->neuron_biases.at(bias_idx);
}
output[i] = this->neurons->at(oi)->activate(this->neuron_potentials.at(oi), bias);
output[i] = this->neurons.at(oi)->activate(this->neuron_potentials.at(oi), bias);
std::cout << "setting the output[" << i << "] = " << output[i] << "(bias = " << bias << ")" << std::endl;
++i;
}
......@@ -615,7 +615,7 @@ namespace lib4neuro {
if (bias_idx >= 0) {
bias = this->neuron_biases.at(bias_idx);
}
potential = this->neurons->at(si)->activate(this->neuron_potentials.at(si), bias);
potential = this->neurons.at(si)->activate(this->neuron_potentials.at(si), bias);
for (auto c: *this->outward_adjacency.at(si)) {
size_t ti = c.first;
......@@ -634,7 +634,7 @@ namespace lib4neuro {
if (bias_idx >= 0) {
bias = this->neuron_biases.at(bias_idx);
}
output[i] = this->neurons->at(oi)->activate(this->neuron_potentials.at(oi), bias);
output[i] = this->neurons.at(oi)->activate(this->neuron_potentials.at(oi), bias);
++i;
}
}
......@@ -653,8 +653,7 @@ namespace lib4neuro {
NeuronDifferentiable *active_neuron;
/* initial error propagation */
::std::vector<size_t> *current_layer = this->neuron_layers_feedforward.at(
this->neuron_layers_feedforward.size() - 1);
std::shared_ptr<::std::vector<size_t>> current_layer = this->neuron_layers_feedforward.at(this->neuron_layers_feedforward.size() - 1);
//TODO might not work in the future as the output neurons could be permuted
for (size_t i = 0; i < current_layer->size(); ++i) {
neuron_idx = current_layer->at(i);
......@@ -669,7 +668,7 @@ namespace lib4neuro {
for (size_t i = 0; i < current_layer->size(); ++i) {
neuron_idx = current_layer->at(i);
active_neuron = dynamic_cast<NeuronDifferentiable *> (this->neurons->at(neuron_idx));
active_neuron = dynamic_cast<NeuronDifferentiable *> (this->neurons.at(neuron_idx));
if (active_neuron) {
bias_idx = this->neuron_bias_indices.at(neuron_idx);
......@@ -690,7 +689,7 @@ namespace lib4neuro {
size_t ti = c.first;
size_t ci = c.second;
neuron_potential_t = this->neurons->at(ti)->get_last_activation_value( );
neuron_potential_t = this->neurons.at(ti)->get_last_activation_value( );
connection_weight = this->connection_list.at(ci)->eval(this->connection_weights);
this->connection_list.at(ci)->eval_partial_derivative(*this->get_parameter_ptr_weights(),
......@@ -722,7 +721,7 @@ namespace lib4neuro {
NeuronDifferentiable *active_neuron;
/* initial error propagation */
::std::vector<size_t> *current_layer = this->neuron_layers_feedforward.at(
std::shared_ptr<::std::vector<size_t>> current_layer = this->neuron_layers_feedforward.at(
this->neuron_layers_feedforward.size() - 1);
//TODO might not work in the future as the output neurons could be permuted
std::cout << "Error scaling on the output layer: ";
......@@ -742,7 +741,7 @@ namespace lib4neuro {
for (size_t i = 0; i < current_layer->size(); ++i) {
neuron_idx = current_layer->at(i);
active_neuron = dynamic_cast<NeuronDifferentiable *> (this->neurons->at(neuron_idx));
active_neuron = dynamic_cast<NeuronDifferentiable *> (this->neurons.at(neuron_idx));
if (active_neuron) {
std::cout << " [backpropagation] active neuron: " << neuron_idx << std::endl;
......@@ -767,7 +766,7 @@ namespace lib4neuro {
size_t ti = c.first;
size_t ci = c.second;
neuron_activation_t = this->neurons->at(ti)->get_last_activation_value( );
neuron_activation_t = this->neurons.at(ti)->get_last_activation_value( );
connection_weight = this->connection_list.at(ci)->eval(this->connection_weights);
std::cout << " [backpropagation] value ("<<ti<< "): " << neuron_activation_t << ", scaling: " << scaling_backprog[neuron_idx] << std::endl;
......@@ -857,7 +856,7 @@ namespace lib4neuro {
}
size_t NeuralNetwork::get_n_neurons() {
return this->neurons->size();
return this->neurons.size();
}
void NeuralNetwork::specify_input_neurons(std::vector<size_t> &input_neurons_indices) {
......@@ -960,7 +959,7 @@ namespace lib4neuro {
void NeuralNetwork::write_stats() {
::std::cout << std::flush
<< "Number of neurons: " << this->neurons->size() << ::std::endl
<< "Number of neurons: " << this->neurons.size() << ::std::endl
<< "Number of connections: " << this->connection_list.size() << ::std::endl
<< "Number of active weights: " << this->connection_weights.size() << ::std::endl
<< "Number of active biases: " << this->neuron_biases.size() << ::std::endl;
......@@ -982,7 +981,7 @@ namespace lib4neuro {
THROW_RUNTIME_ERROR("File " + file_path + " can not be opened!");
}
ofs << "Number of neurons: " << this->neurons->size() << ::std::endl
ofs << "Number of neurons: " << this->neurons.size() << ::std::endl
<< "Number of connections: " << this->connection_list.size() << ::std::endl
<< "Number of active weights: " << this->connection_weights.size() << ::std::endl
<< "Number of active biases: " << this->neuron_biases.size() << ::std::endl;
......@@ -999,7 +998,7 @@ namespace lib4neuro {
}
void NeuralNetwork::write_stats(std::ofstream* file_path) {
*file_path << "Number of neurons: " << this->neurons->size() << ::std::endl
*file_path << "Number of neurons: " << this->neurons.size() << ::std::endl
<< "Number of connections: " << this->connection_list.size() << ::std::endl
<< "Number of active weights: " << this->connection_weights.size() << ::std::endl
<< "Number of active biases: " << this->neuron_biases.size() << ::std::endl;
......@@ -1033,14 +1032,14 @@ namespace lib4neuro {
void NeuralNetwork::add_inward_connection(size_t s, size_t t, size_t con_idx) {
if (!this->inward_adjacency.at(s)) {
this->inward_adjacency.at(s) = new ::std::vector<std::pair<size_t, size_t>>(0);
this->inward_adjacency.at(s) = std::make_shared<std::vector<std::pair<size_t, size_t>>>(::std::vector<std::pair<size_t, size_t>>(0));
}
this->inward_adjacency.at(s)->push_back(std::pair<size_t, size_t>(t, con_idx));
}
void NeuralNetwork::add_outward_connection(size_t s, size_t t, size_t con_idx) {
if (!this->outward_adjacency.at(s)) {
this->outward_adjacency.at(s) = new ::std::vector<std::pair<size_t, size_t>>(0);
this->outward_adjacency.at(s) = std::make_shared<std::vector<std::pair<size_t, size_t>>>(::std::vector<std::pair<size_t, size_t>>(0));
}
this->outward_adjacency.at(s)->push_back(std::pair<size_t, size_t>(t, con_idx));
}
......@@ -1079,7 +1078,7 @@ namespace lib4neuro {
// this->neuron_layers_feedbackward = new ::std::vector<std::vector<size_t>*>(0);
auto n = this->neurons->size();
auto n = this->neurons.size();
/* helpful counters */
::std::vector<size_t> inward_saturation(n);
......@@ -1116,7 +1115,7 @@ namespace lib4neuro {
while (active_set_size[idx1] > 0) {
/* we add the current active set as the new outward layer */
::std::vector<size_t> *new_feedforward_layer = new ::std::vector<size_t>(active_set_size[idx1]);
std::shared_ptr<::std::vector<size_t>> new_feedforward_layer = std::make_shared<::std::vector<size_t>>(::std::vector<size_t>(active_set_size[idx1]));
this->neuron_layers_feedforward.push_back(new_feedforward_layer);
//we iterate through the active neurons and propagate the signal
......@@ -1237,13 +1236,13 @@ namespace lib4neuro {
"doesn't specify input and output layers, which are compulsory!");
}
this->neurons = new ::std::vector<Neuron *>(0);
// this->neurons = new ::std::vector<Neuron *>(0);
// this->neuron_biases = new ::std::vector<double>(0);
// this->neuron_potentials = new ::std::vector<double>(0);
// this->neuron_bias_indices = new ::std::vector<int>(0);
// this->connection_weights = new ::std::vector<double>(0);
this->connection_list = ::std::vector<std::shared_ptr<ConnectionFunctionGeneral>>(0);
// this->connection_list = ::std::vector<std::shared_ptr<ConnectionFunctionGeneral>>(0);
// this->inward_adjacency = new ::std::vector<std::vector<std::pair<size_t, size_t>> *>(0);
// this->outward_adjacency = new ::std::vector<std::vector<std::pair<size_t, size_t>> *>(0);
//
......
src/Network/NeuralNetwork.h
+ 5
− 5
View file @ 6d896ab3
......@@ -55,7 +55,7 @@ namespace lib4neuro {
/**
*
*/
std::vector<Neuron *> *neurons = nullptr;
std::vector<Neuron *> neurons; // = nullptr;
/**
*
......@@ -95,22 +95,22 @@ namespace lib4neuro {
/**
*
*/
std::vector<std::vector<std::pair<size_t, size_t>> *> inward_adjacency; // = nullptr;
std::vector<std::shared_ptr<std::vector<std::pair<size_t, size_t>>>> inward_adjacency; // = nullptr;
/**
*
*/
std::vector<std::vector<std::pair<size_t, size_t>> *> outward_adjacency; // = nullptr;
std::vector<std::shared_ptr<std::vector<std::pair<size_t, size_t>>>> outward_adjacency; // = nullptr;
/**
*
*/
std::vector<std::vector<size_t> *> neuron_layers_feedforward; // = nullptr;
std::vector<std::shared_ptr<std::vector<size_t>>> neuron_layers_feedforward; // = nullptr;
/**
*
*/
std::vector<std::vector<size_t> *> neuron_layers_feedbackward; // = nullptr;
std::vector<std::shared_ptr<std::vector<size_t>>> neuron_layers_feedbackward; // = nullptr;
/**
*
......
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