diff --git a/src/Network/NeuralNetwork.cpp b/src/Network/NeuralNetwork.cpp
index ee9fb4739242b171b09f4b1edc826d3c56eae023..445c736ac54db4a99f772b0c04e878bcd370cbf6 100644
--- a/src/Network/NeuralNetwork.cpp
+++ b/src/Network/NeuralNetwork.cpp
@@ -8,10 +8,10 @@
#include <iostream>
#include <4neuro.h>
-#include "../message.h"
+#include "message.h"
#include "NeuralNetwork.h"
#include "NeuralNetworkSerialization.h"
-#include "../exceptions.h"
+#include "exceptions.h"
namespace lib4neuro {
@@ -144,8 +144,11 @@ namespace lib4neuro {
}
- NeuralNetwork *NeuralNetwork::get_subnet(std::vector<size_t> &input_neuron_indices,
+ NeuralNetwork *NeuralNetwork::get_subnet(::std::vector<size_t> &input_neuron_indices,
::std::vector<size_t> &output_neuron_indices) {
+
+ THROW_NOT_IMPLEMENTED_ERROR();
+
NeuralNetwork *output_net = nullptr;
// TODO rework due to the changed structure of the class
// Neuron * active_neuron, * target_neuron;
@@ -433,11 +436,11 @@ namespace lib4neuro {
void NeuralNetwork::copy_parameter_space(std::vector<double> *parameters) {
if (parameters != nullptr) {
for (unsigned int i = 0; i < this->connection_weights->size(); ++i) {
- (*this->connection_weights)[i] = (*parameters)[i];
+ (*this->connection_weights).at(i) = (*parameters).at(i);
}
for (unsigned int i = 0; i < this->neuron_biases->size(); ++i) {
- (*this->neuron_biases)[i] = (*parameters)[i + this->connection_weights->size()];
+ (*this->neuron_biases).at(i) = (*parameters).at(i + this->connection_weights->size());
}
}
}
@@ -459,7 +462,7 @@ namespace lib4neuro {
this->delete_weights = false;
}
- void NeuralNetwork::eval_single(std::vector<double> &input, ::std::vector<double> &output,
+ void NeuralNetwork::eval_single(::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) {
THROW_INVALID_ARGUMENT_ERROR("Input and output neurons have not been specified!");
@@ -681,26 +684,91 @@ namespace lib4neuro {
}
}
- void NeuralNetwork::print_weights() {
- printf("Connection weights: ");
+ void NeuralNetwork::write_weights() {
+ std::cout << "Connection weights: ";
+ if (this->connection_weights) {
+ for (size_t i = 0; i < this->connection_weights->size() - 1; ++i) {
+ std::cout << this->connection_weights->at(i) << ", ";
+ }
+ std::cout << this->connection_weights->at(this->connection_weights->size() - 1) << std::endl;
+ }
+ }
+
+ void NeuralNetwork::write_weights(std::string file_path) {
+ std::ofstream ofs(file_path);
+
+ if(!ofs.is_open()) {
+ THROW_RUNTIME_ERROR("File " + file_path + " can not be opened!");
+ }
+
+ ofs << "Connection weights: ";
+
+ if (this->connection_weights) {
+ for (size_t i = 0; i < this->connection_weights->size() - 1; ++i) {
+ ofs << this->connection_weights->at(i) << ", ";
+ }
+ ofs << this->connection_weights->at(this->connection_weights->size() - 1) << std::endl;
+ }
+ }
+
+ void NeuralNetwork::write_weights(std::ofstream* file_path) {
+ *file_path << "Connection weights: ";
+
if (this->connection_weights) {
for (size_t i = 0; i < this->connection_weights->size() - 1; ++i) {
- printf("%f, ", this->connection_weights->at(i));
+ *file_path << this->connection_weights->at(i) << ", ";
}
- printf("%f", this->connection_weights->at(this->connection_weights->size() - 1));
+ *file_path << this->connection_weights->at(this->connection_weights->size() - 1) << std::endl;
}
+ }
- printf("\n");
+ 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) << ", ";
+ }
+ std::cout << this->neuron_biases->at(this->neuron_biases->size() - 1) << std::endl;
+ }
}
- void NeuralNetwork::print_stats() {
+ void NeuralNetwork::write_biases(std::string file_path) {
+ std::ofstream ofs(file_path);
+
+ if(!ofs.is_open()) {
+ THROW_RUNTIME_ERROR("File " + file_path + " can not be opened!");
+ }
+
+ 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) << ", ";
+ }
+ 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) << ", ";
+ }
+ *file_path << this->neuron_biases->at(this->neuron_biases->size() - 1) << std::endl;
+ }
+ }
+
+ void NeuralNetwork::write_stats() {
::std::cout << "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;
if(this->normalization_strategy) {
- ::std::cout << "Normalization stratey maximum value: "
+ ::std::cout << "Normalization strategy maximum value: "
<< this->normalization_strategy->get_max_value() << std::endl
<< "Normalization strategy minimum value: "
<< this->normalization_strategy->get_min_value()
@@ -708,6 +776,44 @@ namespace lib4neuro {
}
}
+ void NeuralNetwork::write_stats(std::string file_path) {
+ std::ofstream ofs(file_path);
+
+ if(!ofs.is_open()) {
+ THROW_RUNTIME_ERROR("File " + file_path + " can not be opened!");
+ }
+
+ 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;
+
+ if(this->normalization_strategy) {
+ ofs << "Normalization strategy maximum value: "
+ << this->normalization_strategy->get_max_value() << std::endl
+ << "Normalization strategy minimum value: "
+ << this->normalization_strategy->get_min_value()
+ << std::endl;
+ }
+
+ ofs.close();
+ }
+
+ void NeuralNetwork::write_stats(std::ofstream* file_path) {
+ *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;
+
+ if(this->normalization_strategy) {
+ *file_path << "Normalization strategy maximum value: "
+ << this->normalization_strategy->get_max_value() << std::endl
+ << "Normalization strategy minimum value: "
+ << this->normalization_strategy->get_min_value()
+ << std::endl;
+ }
+ }
+
std::vector<double> *NeuralNetwork::get_parameter_ptr_biases() {
return this->neuron_biases;
}
@@ -929,9 +1035,9 @@ namespace lib4neuro {
unsigned int inp_dim = neuron_numbers->at(0); //!< Network input dimension
unsigned int out_dim = neuron_numbers->back(); //!< Network output dimension
- COUT_DEBUG(<< "Fully connected feed-forward network:" << std::endl)
- COUT_DEBUG(<< "# of inputs: " << inp_dim << std::endl);
- COUT_DEBUG(<< "# of outputs: " << out_dim << std::endl);
+ COUT_DEBUG("Fully connected feed-forward network being constructed:" << std::endl);
+ COUT_DEBUG("# of inputs: " << inp_dim << std::endl);
+ COUT_DEBUG("# of outputs: " << out_dim << std::endl);
std::vector<size_t> input_layer_neuron_indices;
std::vector<size_t> previous_layer_neuron_indices;
@@ -947,9 +1053,8 @@ namespace lib4neuro {
input_layer_neuron_indices = current_layer_neuron_indices;
/* Creation of HIDDEN layers */
-
for(unsigned int i = 1; i <= neuron_numbers->size()-2; i++) {
- COUT_DEBUG(<< "Hidden layer #d: " << neuron_numbers->at(i) << " neurons" << std::endl);
+ COUT_DEBUG("Hidden layer #" << i << ": " << neuron_numbers->at(i) << " neurons" << std::endl);
previous_layer_neuron_indices.reserve(neuron_numbers->at(i-1));
previous_layer_neuron_indices = current_layer_neuron_indices;
current_layer_neuron_indices.clear();
@@ -967,7 +1072,7 @@ namespace lib4neuro {
}
case NEURON_TYPE::CONSTANT: {
- neuron_id = this->add_neuron(new NeuronConstant, BIAS_TYPE::NEXT_BIAS);
+ THROW_INVALID_ARGUMENT_ERROR("Constant neurons can't be used in fully connected feed-forward networks!");
break;
}
diff --git a/src/Network/NeuralNetwork.h b/src/Network/NeuralNetwork.h
index 598833aa1c890d934d20cb27701d3cc34483f1aa..ba0e7aea6559861171827b54dd21c2813b9afe4d 100644
--- a/src/Network/NeuralNetwork.h
+++ b/src/Network/NeuralNetwork.h
@@ -332,12 +332,52 @@ namespace lib4neuro {
/**
*
*/
- LIB4NEURO_API void print_weights();
+ LIB4NEURO_API void write_weights();
/**
*
*/
- LIB4NEURO_API void print_stats();
+ LIB4NEURO_API void write_weights(std::string file_path);
+
+ /**
+ *
+ * @param file_path
+ */
+ LIB4NEURO_API void write_weights(std::ofstream* file_path);
+
+ /**
+ *
+ */
+ LIB4NEURO_API void write_biases();
+
+ /**
+ *
+ * @param file_name
+ */
+ LIB4NEURO_API void write_biases(std::string file_path);
+
+ /**
+ *
+ * @param file_path
+ */
+ LIB4NEURO_API void write_biases(std::ofstream* file_path);
+
+ /**
+ *
+ */
+ LIB4NEURO_API void write_stats();
+
+ /**
+ *
+ * @param file_path
+ */
+ LIB4NEURO_API void write_stats(std::string file_path);
+
+ /**
+ *
+ * @param file_path
+ */
+ LIB4NEURO_API void write_stats(std::ofstream* file_path);
/**
*