diff --git a/src/Network/NeuralNetwork.cpp b/src/Network/NeuralNetwork.cpp
index b2e00d76142ffbd121331a99b21532f6f4569fde..e0e2cb760f812908b4936e4d58c719b534dbdd89 100644
--- a/src/Network/NeuralNetwork.cpp
+++ b/src/Network/NeuralNetwork.cpp
@@ -18,8 +18,8 @@
namespace lib4neuro {
NeuralNetwork::NeuralNetwork() {
this->neurons = new ::std::vector<Neuron *>(0);
- this->neuron_biases = new ::std::vector<double>(0);
- this->neuron_bias_indices = new ::std::vector<int>(0);
+// this->neuron_biases = 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);
@@ -29,8 +29,8 @@ namespace lib4neuro {
this->neuron_layers_feedforward = new ::std::vector<std::vector<size_t> *>(0);
this->neuron_layers_feedbackward = new ::std::vector<std::vector<size_t> *>(0);
- this->input_neuron_indices = new ::std::vector<size_t>(0);
- this->output_neuron_indices = new ::std::vector<size_t>(0);
+// this->input_neuron_indices = new ::std::vector<size_t>(0);
+// this->output_neuron_indices = new ::std::vector<size_t>(0);
this->delete_weights = true;
this->delete_biases = true;
@@ -65,35 +65,35 @@ namespace lib4neuro {
this->neurons = nullptr;
}
- if (this->neuron_potentials) {
- delete this->neuron_potentials;
- this->neuron_potentials = nullptr;
- }
-
- if (this->neuron_bias_indices) {
- delete this->neuron_bias_indices;
- this->neuron_bias_indices = nullptr;
- }
-
- if (this->output_neuron_indices) {
- delete this->output_neuron_indices;
- this->output_neuron_indices = nullptr;
- }
-
- if (this->input_neuron_indices) {
- delete this->input_neuron_indices;
- this->input_neuron_indices = nullptr;
- }
+// if (this->neuron_potentials) {
+// delete this->neuron_potentials;
+// this->neuron_potentials = nullptr;
+// }
+//
+// if (this->neuron_bias_indices) {
+// delete this->neuron_bias_indices;
+// this->neuron_bias_indices = nullptr;
+// }
+//
+// if (this->output_neuron_indices) {
+// delete this->output_neuron_indices;
+// this->output_neuron_indices = nullptr;
+// }
+//
+// if (this->input_neuron_indices) {
+// delete this->input_neuron_indices;
+// this->input_neuron_indices = nullptr;
+// }
// if (this->connection_weights && this->delete_weights) {
// delete this->connection_weights;
// this->connection_weights = nullptr;
// }
- if (this->neuron_biases && this->delete_biases) {
- delete this->neuron_biases;
- this->neuron_biases = nullptr;
- }
+// if (this->neuron_biases && this->delete_biases) {
+// delete this->neuron_biases;
+// this->neuron_biases = nullptr;
+// }
// if (this->connection_list) {
//
@@ -390,15 +390,15 @@ namespace lib4neuro {
size_t NeuralNetwork::add_neuron(Neuron *n, BIAS_TYPE bt, size_t bias_idx) {
if (bt == BIAS_TYPE::NO_BIAS) {
- this->neuron_bias_indices->push_back(-1);
+ this->neuron_bias_indices.push_back(-1);
} else if (bt == BIAS_TYPE::NEXT_BIAS) {
- this->neuron_bias_indices->push_back((int) this->neuron_biases->size());
- this->neuron_biases->resize(this->neuron_biases->size() + 1);
+ this->neuron_bias_indices.push_back((int) this->neuron_biases.size());
+ this->neuron_biases.resize(this->neuron_biases.size() + 1);
} else if (bt == BIAS_TYPE::EXISTING_BIAS) {
- if (bias_idx >= this->neuron_biases->size()) {
+ if (bias_idx >= this->neuron_biases.size()) {
::std::cerr << "The supplied bias index is too large!\n" << ::std::endl;
}
- this->neuron_bias_indices->push_back((int) bias_idx);
+ this->neuron_bias_indices.push_back((int) bias_idx);
}
this->outward_adjacency->push_back(new ::std::vector<std::pair<size_t, size_t>>(0));
@@ -412,15 +412,15 @@ namespace lib4neuro {
void NeuralNetwork::eval_single_debug(::std::vector<double> &input, ::std::vector<double> &output,
::std::vector<double> *custom_weights_and_biases) {
- if ((this->input_neuron_indices->size() * this->output_neuron_indices->size()) <= 0) {
+ if ((this->input_neuron_indices.size() * this->output_neuron_indices.size()) <= 0) {
THROW_INVALID_ARGUMENT_ERROR("Input and output neurons have not been specified!");
}
- if (this->input_neuron_indices->size() != input.size()) {
+ if (this->input_neuron_indices.size() != input.size()) {
THROW_INVALID_ARGUMENT_ERROR("Data input size != Network input size");
}
- if (this->output_neuron_indices->size() != output.size()) {
+ if (this->output_neuron_indices.size() != output.size()) {
THROW_INVALID_ARGUMENT_ERROR("Data output size != Network output size");
}
@@ -433,12 +433,12 @@ namespace lib4neuro {
/* reset of the output and the neuron potentials */
::std::fill(output.begin(), output.end(), 0.0);
- ::std::fill(this->neuron_potentials->begin(), this->neuron_potentials->end(), 0.0);
+ ::std::fill(this->neuron_potentials.begin(), this->neuron_potentials.end(), 0.0);
/* set the potentials of the input neurons */
- for (size_t i = 0; i < this->input_neuron_indices->size(); ++i) {
- this->neuron_potentials->at(this->input_neuron_indices->at(i)) = input[i];
- std::cout << this->neuron_potentials->at(this->input_neuron_indices->at(i)) << ", ";
+ for (size_t i = 0; i < this->input_neuron_indices.size(); ++i) {
+ this->neuron_potentials.at(this->input_neuron_indices.at(i)) = input[i];
+ std::cout << this->neuron_potentials.at(this->input_neuron_indices.at(i)) << ", ";
}
std::cout << std::endl;
@@ -450,18 +450,18 @@ namespace lib4neuro {
for (auto si: *layer) {
bias = 0.0;
- bias_idx = this->neuron_bias_indices->at(si);
+ bias_idx = this->neuron_bias_indices.at(si);
if (bias_idx >= 0) {
- bias = this->neuron_biases->at(bias_idx);
+ bias = this->neuron_biases.at(bias_idx);
}
- 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;
+ 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)) {
size_t ti = c.first;
size_t ci = c.second;
- this->neuron_potentials->at(ti) +=
+ this->neuron_potentials.at(ti) +=
this->connection_list.at(ci)->eval(this->connection_weights) * potential;
std::cout << " adding input to neuron " << ti << " += " << this->connection_list.at(ci)->eval(this->connection_weights) << "*" << potential << std::endl;
@@ -470,13 +470,13 @@ namespace lib4neuro {
}
unsigned int i = 0;
- for (auto oi: *this->output_neuron_indices) {
+ for (auto oi: this->output_neuron_indices) {
bias = 0.0;
- bias_idx = this->neuron_bias_indices->at(oi);
+ bias_idx = this->neuron_bias_indices.at(oi);
if (bias_idx >= 0) {
- bias = this->neuron_biases->at(bias_idx);
+ 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;
}
@@ -548,8 +548,8 @@ namespace lib4neuro {
this->connection_weights.at(i) = (*parameters).at(i);
}
- for (unsigned int i = 0; i < this->neuron_biases->size(); ++i) {
- (*this->neuron_biases).at(i) = (*parameters).at(i + this->connection_weights.size());
+ for (unsigned int i = 0; i < this->neuron_biases.size(); ++i) {
+ (this->neuron_biases).at(i) = (*parameters).at(i + this->connection_weights.size());
}
}
}
@@ -561,9 +561,10 @@ namespace lib4neuro {
this->connection_weights.clear();
}
- if (this->neuron_biases) {
- delete this->neuron_biases;
- }
+ this->neuron_biases.clear();
+// if (this->neuron_biases) {
+// delete this->neuron_biases;
+// }
this->connection_weights = parent_network.connection_weights;
this->neuron_biases = parent_network.neuron_biases;
@@ -576,15 +577,15 @@ namespace lib4neuro {
::std::vector<double>& output,
::std::vector<double>* custom_weights_and_biases) {
- if ((this->input_neuron_indices->size() * this->output_neuron_indices->size()) <= 0) {
+ if ((this->input_neuron_indices.size() * this->output_neuron_indices.size()) <= 0) {
THROW_INVALID_ARGUMENT_ERROR("Input and output neurons have not been specified!");
}
- if (this->input_neuron_indices->size() != input.size()) {
+ if (this->input_neuron_indices.size() != input.size()) {
THROW_INVALID_ARGUMENT_ERROR("Data input size != Network input size");
}
- if (this->output_neuron_indices->size() != output.size()) {
+ if (this->output_neuron_indices.size() != output.size()) {
THROW_INVALID_ARGUMENT_ERROR("Data output size != Network output size");
}
@@ -597,11 +598,11 @@ namespace lib4neuro {
/* reset of the output and the neuron potentials */
::std::fill(output.begin(), output.end(), 0.0);
- ::std::fill(this->neuron_potentials->begin(), this->neuron_potentials->end(), 0.0);
+ ::std::fill(this->neuron_potentials.begin(), this->neuron_potentials.end(), 0.0);
/* set the potentials of the input neurons */
- for (size_t i = 0; i < this->input_neuron_indices->size(); ++i) {
- this->neuron_potentials->at(this->input_neuron_indices->at(i)) = input[i];
+ for (size_t i = 0; i < this->input_neuron_indices.size(); ++i) {
+ this->neuron_potentials.at(this->input_neuron_indices.at(i)) = input[i];
}
/* we iterate through all the feed-forward layers and transfer the signals */
@@ -610,30 +611,30 @@ namespace lib4neuro {
for (auto si: *layer) {
bias = 0.0;
- bias_idx = this->neuron_bias_indices->at(si);
+ bias_idx = this->neuron_bias_indices.at(si);
if (bias_idx >= 0) {
- bias = this->neuron_biases->at(bias_idx);
+ 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;
size_t ci = c.second;
- this->neuron_potentials->at(ti) +=
+ this->neuron_potentials.at(ti) +=
this->connection_list.at(ci)->eval(this->connection_weights) * potential;
}
}
}
unsigned int i = 0;
- for (auto oi: *this->output_neuron_indices) {
+ for (auto oi: this->output_neuron_indices) {
bias = 0.0;
- bias_idx = this->neuron_bias_indices->at(oi);
+ bias_idx = this->neuron_bias_indices.at(oi);
if (bias_idx >= 0) {
- bias = this->neuron_biases->at(bias_idx);
+ 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;
}
}
@@ -671,11 +672,11 @@ namespace lib4neuro {
active_neuron = dynamic_cast<NeuronDifferentiable *> (this->neurons->at(neuron_idx));
if (active_neuron) {
- bias_idx = this->neuron_bias_indices->at(neuron_idx);
- neuron_potential = this->neuron_potentials->at(neuron_idx);
+ bias_idx = this->neuron_bias_indices.at(neuron_idx);
+ neuron_potential = this->neuron_potentials.at(neuron_idx);
if (bias_idx >= 0) {
- neuron_bias = this->neuron_biases->at(bias_idx);
+ neuron_bias = this->neuron_biases.at(bias_idx);
gradient[bias_shift + bias_idx] += scaling_backprog[neuron_idx] *
active_neuron->activation_function_eval_derivative_bias(
neuron_potential, neuron_bias);
@@ -746,11 +747,11 @@ namespace lib4neuro {
if (active_neuron) {
std::cout << " [backpropagation] active neuron: " << neuron_idx << std::endl;
- bias_idx = this->neuron_bias_indices->at(neuron_idx);
- neuron_potential = this->neuron_potentials->at(neuron_idx);
+ bias_idx = this->neuron_bias_indices.at(neuron_idx);
+ neuron_potential = this->neuron_potentials.at(neuron_idx);
if (bias_idx >= 0) {
- neuron_bias = this->neuron_biases->at(bias_idx);
+ neuron_bias = this->neuron_biases.at(bias_idx);
gradient[bias_shift + bias_idx] += scaling_backprog[neuron_idx] *
active_neuron->activation_function_eval_derivative_bias(
neuron_potential, neuron_bias);
@@ -808,8 +809,8 @@ namespace lib4neuro {
// Init weight guess ("optimal" for logistic activation functions)
boost::random::uniform_real_distribution<> dist(-1, 1);
- for (size_t i = 0; i < this->neuron_biases->size(); i++) {
- this->neuron_biases->at(i) = dist(gen);
+ for (size_t i = 0; i < this->neuron_biases.size(); i++) {
+ this->neuron_biases.at(i) = dist(gen);
}
}
@@ -820,7 +821,7 @@ namespace lib4neuro {
void NeuralNetwork::scale_biases(double alpha) {
for(size_t i = 0; i < this->get_n_biases(); ++i){
- this->neuron_biases->at( i ) *= alpha;
+ this->neuron_biases.at( i ) *= alpha;
}
}
@@ -836,11 +837,11 @@ namespace lib4neuro {
}
size_t NeuralNetwork::get_n_inputs() {
- return this->input_neuron_indices->size();
+ return this->input_neuron_indices.size();
}
size_t NeuralNetwork::get_n_outputs() {
- return this->output_neuron_indices->size();
+ return this->output_neuron_indices.size();
}
size_t NeuralNetwork::get_n_weights() {
@@ -848,11 +849,11 @@ namespace lib4neuro {
}
size_t NeuralNetwork::get_n_biases() {
- return this->neuron_biases->size();
+ return this->neuron_biases.size();
}
int NeuralNetwork::get_neuron_bias_index(size_t neuron_idx) {
- return this->neuron_bias_indices->at(neuron_idx);
+ return this->neuron_bias_indices.at(neuron_idx);
}
size_t NeuralNetwork::get_n_neurons() {
@@ -860,21 +861,24 @@ namespace lib4neuro {
}
void NeuralNetwork::specify_input_neurons(std::vector<size_t> &input_neurons_indices) {
- if (!this->input_neuron_indices) {
- this->input_neuron_indices = new ::std::vector<size_t>(input_neurons_indices);
- } else {
- delete this->input_neuron_indices;
- this->input_neuron_indices = new ::std::vector<size_t>(input_neurons_indices);
- }
+ this->input_neuron_indices = input_neurons_indices;
+
+// if (!this->input_neuron_indices) {
+// this->input_neuron_indices = new ::std::vector<size_t>(input_neurons_indices);
+// } else {
+// delete this->input_neuron_indices;
+// this->input_neuron_indices = new ::std::vector<size_t>(input_neurons_indices);
+// }
}
void NeuralNetwork::specify_output_neurons(std::vector<size_t> &output_neurons_indices) {
- if (!this->output_neuron_indices) {
- this->output_neuron_indices = new ::std::vector<size_t>(output_neurons_indices);
- } else {
- delete this->output_neuron_indices;
- this->output_neuron_indices = new ::std::vector<size_t>(output_neurons_indices);
- }
+ this->output_neuron_indices = output_neurons_indices;
+// if (!this->output_neuron_indices) {
+// this->output_neuron_indices = new ::std::vector<size_t>(output_neurons_indices);
+// } else {
+// delete this->output_neuron_indices;
+// this->output_neuron_indices = new ::std::vector<size_t>(output_neurons_indices);
+// }
}
void NeuralNetwork::write_weights() {
@@ -918,11 +922,11 @@ namespace lib4neuro {
void NeuralNetwork::write_biases() {
std::cout << "Network biases: ";
- if(this->neuron_biases) {
- for(unsigned int i = 0; i < this->neuron_biases->size() - 1; i++) {
- std::cout << this->neuron_biases->at(i) << ", ";
+ if(!this->neuron_biases.empty()) {
+ for(unsigned int i = 0; i < this->neuron_biases.size() - 1; i++) {
+ std::cout << this->neuron_biases.at(i) << ", ";
}
- std::cout << this->neuron_biases->at(this->neuron_biases->size() - 1) << std::endl;
+ std::cout << this->neuron_biases.at(this->neuron_biases.size() - 1) << std::endl;
}
}
@@ -935,22 +939,22 @@ namespace lib4neuro {
ofs << "Network biases: ";
- if(this->neuron_biases) {
- for(unsigned int i = 0; i < this->neuron_biases->size() - 1; i++) {
- ofs << this->neuron_biases->at(i) << ", ";
+ if(!this->neuron_biases.empty()) {
+ for(unsigned int i = 0; i < this->neuron_biases.size() - 1; i++) {
+ ofs << this->neuron_biases.at(i) << ", ";
}
- ofs << this->neuron_biases->at(this->neuron_biases->size() - 1) << std::endl;
+ ofs << this->neuron_biases.at(this->neuron_biases.size() - 1) << std::endl;
}
}
void NeuralNetwork::write_biases(std::ofstream* file_path) {
*file_path << "Network biases: ";
- if(this->neuron_biases) {
- for(unsigned int i = 0; i < this->neuron_biases->size() - 1; i++) {
- *file_path << this->neuron_biases->at(i) << ", ";
+ if(!this->neuron_biases.empty()) {
+ for(unsigned int i = 0; i < this->neuron_biases.size() - 1; i++) {
+ *file_path << this->neuron_biases.at(i) << ", ";
}
- *file_path << this->neuron_biases->at(this->neuron_biases->size() - 1) << std::endl;
+ *file_path << this->neuron_biases.at(this->neuron_biases.size() - 1) << std::endl;
}
}
@@ -959,7 +963,7 @@ namespace lib4neuro {
<< "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;
+ << "Number of active biases: " << this->neuron_biases.size() << ::std::endl;
if(this->normalization_strategy) {
::std::cout << std::flush
@@ -981,7 +985,7 @@ namespace lib4neuro {
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;
+ << "Number of active biases: " << this->neuron_biases.size() << ::std::endl;
if(this->normalization_strategy) {
ofs << "Normalization strategy maximum value: "
@@ -998,7 +1002,7 @@ namespace lib4neuro {
*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;
+ << "Number of active biases: " << this->neuron_biases.size() << ::std::endl;
if(this->normalization_strategy) {
*file_path << "Normalization strategy maximum value: "
@@ -1010,7 +1014,7 @@ namespace lib4neuro {
}
std::vector<double>* NeuralNetwork::get_parameter_ptr_biases() {
- return this->neuron_biases;
+ return &this->neuron_biases;
}
std::vector<double>* NeuralNetwork::get_parameter_ptr_weights() {
@@ -1049,7 +1053,7 @@ namespace lib4neuro {
}
/* buffer preparation */
- this->neuron_potentials->resize(this->get_n_neurons());
+ this->neuron_potentials.resize(this->get_n_neurons());
/* space allocation */
if (this->neuron_layers_feedforward) {
@@ -1104,7 +1108,7 @@ namespace lib4neuro {
active_set_size[0] = this->get_n_inputs();
size_t i = 0;
for (i = 0; i < this->get_n_inputs(); ++i) {
- active_eval_set[i] = this->input_neuron_indices->at(i);
+ active_eval_set[i] = this->input_neuron_indices.at(i);
}
size_t active_ni;
@@ -1149,7 +1153,7 @@ namespace lib4neuro {
//
// active_set_size[0] = this->get_n_outputs();
// for(i = 0; i < this->get_n_outputs(); ++i){
-// active_eval_set[i] = this->output_neuron_indices->at(i);
+// active_eval_set[i] = this->output_neuron_indices.at(i);
// }
//
// while(active_set_size[idx1] > 0){
@@ -1233,9 +1237,9 @@ namespace lib4neuro {
}
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->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);
@@ -1245,8 +1249,8 @@ namespace lib4neuro {
this->neuron_layers_feedforward = new ::std::vector<std::vector<size_t> *>(0);
this->neuron_layers_feedbackward = new ::std::vector<std::vector<size_t> *>(0);
- this->input_neuron_indices = new ::std::vector<size_t>(0);
- this->output_neuron_indices = new ::std::vector<size_t>(0);
+// this->input_neuron_indices = new ::std::vector<size_t>(0);
+// this->output_neuron_indices = new ::std::vector<size_t>(0);
this->delete_weights = true;
this->delete_biases = true;
@@ -1344,11 +1348,11 @@ namespace lib4neuro {
}
/* Init variables containing indices of INPUT nad OUTPUT neurons */
- this->input_neuron_indices = new ::std::vector<size_t>(inp_dim);
- this->output_neuron_indices = new ::std::vector<size_t>(out_dim);
+// this->input_neuron_indices = new ::std::vector<size_t>(inp_dim);
+// this->output_neuron_indices = new ::std::vector<size_t>(out_dim);
- *this->input_neuron_indices = input_layer_neuron_indices;
- *this->output_neuron_indices = current_layer_neuron_indices;
+ this->input_neuron_indices = input_layer_neuron_indices;
+ this->output_neuron_indices = current_layer_neuron_indices;
this->analyze_layer_structure();
}
diff --git a/src/Network/NeuralNetwork.h b/src/Network/NeuralNetwork.h
index 9f69eb331cf787f38d0d1801c7eadb305632463a..ad2d36f351501d1f657ca0fc31595e3889335251 100644
--- a/src/Network/NeuralNetwork.h
+++ b/src/Network/NeuralNetwork.h
@@ -60,12 +60,12 @@ namespace lib4neuro {
/**
*
*/
- std::vector<size_t> *input_neuron_indices = nullptr;
+ std::vector<size_t> input_neuron_indices; // = nullptr;
/**
*
*/
- std::vector<size_t> *output_neuron_indices = nullptr;
+ std::vector<size_t> output_neuron_indices; // = nullptr;
/**
*
@@ -75,17 +75,17 @@ namespace lib4neuro {
/**
*
*/
- std::vector<double> *neuron_biases = nullptr;
+ std::vector<double> neuron_biases; // = nullptr;
/**
*
*/
- std::vector<int> *neuron_bias_indices = nullptr;
+ std::vector<int> neuron_bias_indices; // = nullptr;
/**
*
*/
- std::vector<double> *neuron_potentials = nullptr;
+ std::vector<double> neuron_potentials; // = nullptr;
/**
*