From e362ef7a8eb4d065d4e42150c92ffdf454647785 Mon Sep 17 00:00:00 2001
From: Martin Beseda <martin.beseda@vsb.cz>
Date: Mon, 4 Mar 2019 12:51:35 +0100
Subject: [PATCH] FIX: Merge
---
src/DataSet/DataSet.cpp | 18 ++++-------
src/DataSet/DataSet.h | 7 +---
src/Network/NeuralNetwork.cpp | 61 +++++++++++++++++------------------
src/Network/NeuralNetwork.h | 31 ++++++++----------
4 files changed, 50 insertions(+), 67 deletions(-)
diff --git a/src/DataSet/DataSet.cpp b/src/DataSet/DataSet.cpp
index 820c1e61..822f1f40 100644
--- a/src/DataSet/DataSet.cpp
+++ b/src/DataSet/DataSet.cpp
@@ -17,13 +17,10 @@ namespace lib4neuro {
this->n_elements = 0;
this->input_dim = 0;
this->output_dim = 0;
- this->gen = boost::random::mt19937(std::time(0));
}
DataSet::DataSet(std::string file_path) {
std::ifstream ifs(file_path);
- this->gen = boost::random::mt19937(std::time(0));
-
if(ifs.is_open()) {
try {
boost::archive::text_iarchive ia(ifs);
@@ -45,7 +42,6 @@ namespace lib4neuro {
this->data = *data_ptr;
this->input_dim = this->data[0].first.size();
this->output_dim = this->data[0].second.size();
- this->gen = boost::random::mt19937(std::time(0));
if(ns) {
this->normalization_strategy = ns;
@@ -66,7 +62,6 @@ namespace lib4neuro {
this->n_elements = 0;
this->input_dim = 1;
this->output_dim = 1;
- this->gen = boost::random::mt19937(std::time(0));
if(ns) {
this->normalization_strategy = ns;
@@ -87,7 +82,6 @@ namespace lib4neuro {
this->input_dim = bounds.size() / 2;
this->output_dim = output_dim;
this->n_elements = 0;
- this->gen = boost::random::mt19937(std::time(0));
if(ns) {
this->normalization_strategy = ns;
@@ -152,7 +146,7 @@ namespace lib4neuro {
}
for (unsigned int i = 0; i < bounds.size(); i += 2) {
- if (no_elems_in_one_dim == 1) {
+ if (no_elems_in_one_dim == 1) {
frac = 1;
} else {
frac = (bounds[i] - bounds[i+1]) / (no_elems_in_one_dim - 1);
@@ -406,17 +400,19 @@ namespace lib4neuro {
* Method returning random amount of data pairs between 1-max
*/
std::vector<std::pair<std::vector<double>, std::vector<double>>> DataSet::get_random_data_batch(size_t max) {
- if (max <= 0 || max >= this->data.size()) {
+ if (max <= 0) {
return this->data;
} else {
std::vector<std::pair<std::vector<double>, std::vector<double>>> newData;
- boost::random::uniform_int_distribution<> dist(0, this->data.size() - 1);
+ srand(time(NULL)); //TODO use Mersen twister from Boost
+ size_t n_chosen = rand() % std::min(max, this->data.size())+1;
+ n_chosen = max;
std::vector<size_t> chosens;
size_t chosen;
- for (int i = 0; i < max; i++) {
- chosen = dist(gen);
+ for (int i = 0; i < n_chosen; i++) {
+ chosen = rand() % this->data.size();
auto it = std::find(chosens.begin(), chosens.end(), chosen);
if (it != chosens.end()) {
diff --git a/src/DataSet/DataSet.h b/src/DataSet/DataSet.h
index fd20913a..87023134 100644
--- a/src/DataSet/DataSet.h
+++ b/src/DataSet/DataSet.h
@@ -12,9 +12,6 @@
#include <string>
#include <functional>
#include <limits>
-#include <boost/random/mersenne_twister.hpp>
-#include <boost/random/uniform_int_distribution.hpp>
-#include <ctime>
#include "../settings.h"
#include "../NormalizationStrategy/NormalizationStrategy.h"
@@ -29,8 +26,6 @@ namespace lib4neuro {
private:
- boost::random::mt19937 gen;
-
/**
* Number of elements in the data set
*/
@@ -293,7 +288,7 @@ namespace lib4neuro {
* @param max
* @return
*/
- LIB4NEURO_API std::vector<std::pair<std::vector<double>, std::vector<double>>> get_random_data_batch(size_t max);
+ LIB4NEURO_API std::vector<std::pair<std::vector<double>, std::vector<double>>> get_random_data_batch(size_t max);
};
}
#endif //INC_4NEURO_DATASET_H
diff --git a/src/Network/NeuralNetwork.cpp b/src/Network/NeuralNetwork.cpp
index 263c930e..262acd3a 100644
--- a/src/Network/NeuralNetwork.cpp
+++ b/src/Network/NeuralNetwork.cpp
@@ -35,8 +35,6 @@ namespace lib4neuro {
this->delete_weights = true;
this->delete_biases = true;
this->layers_analyzed = false;
-
- this->gen = boost::random::mt19937(std::time(0));
}
NeuralNetwork::NeuralNetwork(std::string filepath) {
@@ -547,30 +545,6 @@ namespace lib4neuro {
this->delete_weights = false;
}
- void NeuralNetwork::get_jacobian(std::vector<std::vector<double>> &jacobian, std::pair<std::vector<double>, std::vector<double>> &data, std::vector<double> &error) {
-
- std::vector<double> fv(this->get_n_outputs());
-
- jacobian.resize(this->get_n_outputs());
- error.resize(this->get_n_outputs());
- for(size_t i = 0; i < this->get_n_outputs(); ++i){
- jacobian[i].resize(this->get_n_weights() + this->get_n_biases());
- std::fill(jacobian[i].begin(), jacobian[i].end(), 0);
- }
-
- this->eval_single( data.first, fv );
-
- std::vector<double> error_partial(this->get_n_outputs());
- std::fill(error_partial.begin(), error_partial.end(), 0.0);
-
- for( size_t i = 0; i < this->get_n_outputs(); ++i){
- error_partial[i] = 1;
- this->add_to_gradient_single(data.first, error_partial, 1.0, jacobian[i]);
- error[i] = data.second[i] - fv[i];
- error_partial[i] = 0;
- }
- }
-
void NeuralNetwork::eval_single(::std::vector<double>& input,
::std::vector<double>& output,
::std::vector<double>* custom_weights_and_biases) {
@@ -789,7 +763,7 @@ namespace lib4neuro {
void NeuralNetwork::randomize_weights() {
-
+ boost::random::mt19937 gen(std::time(0));
// Init weight guess ("optimal" for logistic activation functions)
double r = 4 * sqrt(6. / (this->connection_weights->size()));
@@ -803,7 +777,7 @@ namespace lib4neuro {
void NeuralNetwork::randomize_biases() {
-
+ boost::random::mt19937 gen(std::time(0));
// Init weight guess ("optimal" for logistic activation functions)
boost::random::uniform_real_distribution<> dist(-1, 1);
@@ -1226,8 +1200,6 @@ namespace lib4neuro {
"doesn't specify input and output layers, which are compulsory!");
}
- this->gen = boost::random::mt19937(std::time(0));
-
this->neurons = new ::std::vector<Neuron *>(0);
this->neuron_biases = new ::std::vector<double>(0);
this->neuron_potentials = new ::std::vector<double>(0);
@@ -1256,8 +1228,8 @@ namespace lib4neuro {
COUT_DEBUG("# of outputs: " << out_dim << std::endl);
WRITE_TO_OFS_DEBUG(ofs, "Fully connected feed-forward network being constructed:" << std::endl
- << "# of inputs: " << inp_dim << std::endl
- << "# of outputs: " << out_dim << std::endl);
+ << "# of inputs: " << inp_dim << std::endl
+ << "# of outputs: " << out_dim << std::endl);
std::vector<size_t> input_layer_neuron_indices;
std::vector<size_t> previous_layer_neuron_indices;
@@ -1348,5 +1320,30 @@ namespace lib4neuro {
this->analyze_layer_structure();
}
+
+ void NeuralNetwork::get_jacobian(std::vector<std::vector<double>> &jacobian, std::pair<std::vector<double>, std::vector<double>> &data, std::vector<double> &error) {
+
+ std::vector<double> fv(this->get_n_outputs());
+
+ jacobian.resize(this->get_n_outputs());
+ error.resize(this->get_n_outputs());
+ for(size_t i = 0; i < this->get_n_outputs(); ++i){
+ jacobian[i].resize(this->get_n_weights() + this->get_n_biases());
+ std::fill(jacobian[i].begin(), jacobian[i].end(), 0);
+ }
+
+ this->eval_single( data.first, fv );
+
+ std::vector<double> error_partial(this->get_n_outputs());
+ std::fill(error_partial.begin(), error_partial.end(), 0.0);
+
+ for( size_t i = 0; i < this->get_n_outputs(); ++i){
+ error_partial[i] = 1;
+ this->add_to_gradient_single(data.first, error_partial, 1.0, jacobian[i]);
+ error[i] = data.second[i] - fv[i];
+ error_partial[i] = 0;
+ }
+ }
+
}
diff --git a/src/Network/NeuralNetwork.h b/src/Network/NeuralNetwork.h
index 0b06cd2b..8788a23c 100644
--- a/src/Network/NeuralNetwork.h
+++ b/src/Network/NeuralNetwork.h
@@ -17,10 +17,6 @@
#include <algorithm>
#include <utility>
#include <fstream>
-#include <boost/random/mersenne_twister.hpp>
-#include <boost/random/uniform_real_distribution.hpp>
-#include <ctime>
-
#include "../settings.h"
#include "../Neuron/Neuron.h"
@@ -32,8 +28,6 @@
#include "../NetConnection/ConnectionFunctionIdentity.h"
#include "../NormalizationStrategy/NormalizationStrategy.h"
-
-
namespace lib4neuro {
/**
@@ -55,9 +49,8 @@ namespace lib4neuro {
*
*/
class NeuralNetwork {
-
protected:
- boost::random::mt19937 gen;
+
/**
*
*/
@@ -169,6 +162,18 @@ namespace lib4neuro {
public:
+ /**
+ * Runs @data through the network and then computes partial derivatives with respect to each output function and adds them
+ * to seperate vectors in @jacobian. Also computes the out error and stores in the vector @error
+ * @param[out] jacobian
+ * @param[in] data
+ * @param[out] error
+ */
+ LIB4NEURO_API virtual void
+ get_jacobian(std::vector<std::vector<double>> &jacobian, std::pair<std::vector<double>, std::vector<double>> &data, std::vector<double> &error);
+
+
+
/**
*
* @param input
@@ -279,16 +284,6 @@ namespace lib4neuro {
LIB4NEURO_API void
add_existing_connection(size_t n1_idx, size_t n2_idx, size_t connection_idx, NeuralNetwork &parent_network);
- /**
- * Runs @data through the network and then computes partial derivatives with respect to each output function and adds them
- * to seperate vectors in @jacobian. Also computes the out error and stores in the vector @error
- * @param[out] jacobian
- * @param[in] data
- * @param[out] error
- */
- LIB4NEURO_API virtual void
- get_jacobian(std::vector<std::vector<double>> &jacobian, std::pair<std::vector<double>, std::vector<double>> &data, std::vector<double> &error);
-
/**
*
*/
--
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