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Merge pull request #10 from rlunaro/master

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Add the posibility of change manually the weights of internal layers
This commit is contained in:
David Nogueira
2019-01-06 15:38:03 +00:00
committed by GitHub
parent 75116e157e 72f2688c7d
commit f3bb556188
13 changed files with 204 additions and 64 deletions
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8
.gitignore vendored
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@@ -214,3 +214,11 @@ _Pvt_Extensions/
ModelManifest.xml
/build
/.cproject
/.project
/IrisDatasetTest
/LayerTest
/MLPTest
/NodeTest
/mlp.a
/mlp.so

1
.settings/.gitignore vendored Normal file
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@@ -0,0 +1 @@
/language.settings.xml

7
Makefile
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@@ -1,7 +1,7 @@
#!/bin/bash
# Makefile for MLP
CC = g++
DEBUG = -g
DEBUG = -g3
PROJNAME = mlp
HEADERPATH = ./src
@@ -14,7 +14,8 @@ AUXLIBS =
INCLUDES = -I$(LOCALDEPSINCLUDES) -I$(AUXINCLUDES)
LIBS = -L$(AUXLIBS)
#LIBS += -L/usr/local/lib/
CFLAGS = -std=gnu++11 -std=c++11 -O3 -Wall -fmessage-length=0 -fPIC $(INCLUDES)
#rlunaro: removed optimization for tests: -O3
CFLAGS = -std=gnu++11 -std=c++11 -Wall -fmessage-length=0 -fPIC $(INCLUDES)
CFLAGS += $(DEBUG)
LFLAGS = $(LIBS)
#For verbosity
@@ -59,7 +60,7 @@ NodeTest: $(SOURCEPATH)/NodeTest.o $(SOURCEPATH)/MLP.o
$(CC) $^ $(CFLAGS) $(LFLAGS) -o $@
clean:
@echo Clean
rm -f *~ *.o *~
rm -f *~ $(SOURCEPATH)/*.o *~
@echo Success
cleanall:

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data/iris.mlp
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5
src/.gitignore vendored Normal file
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@@ -0,0 +1,5 @@
/IrisDatasetTest.o
/LayerTest.o
/MLP.o
/MLPTest.o
/NodeTest.o

41
src/Layer.h
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@@ -5,9 +5,6 @@
#ifndef LAYER_H
#define LAYER_H
#include "Utils.h"
#include "Node.h"
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
@@ -16,6 +13,8 @@
#include <vector>
#include <algorithm>
#include <cassert> // for assert()
#include "Node.h"
#include "Utils.h"
class Layer {
public:
@@ -68,13 +67,21 @@ public:
return m_nodes;
}
/**
* Return the internal list of nodes, but modifiable.
*/
std::vector<Node> & GetNodesChangeable() {
return m_nodes;
}
void GetOutputAfterActivationFunction(const std::vector<double> &input,
std::vector<double> * output) const {
assert(input.size() == m_num_inputs_per_node);
output->resize(m_num_nodes);
for (int i = 0; i < m_num_nodes; ++i) {
for (size_t i = 0; i < m_num_nodes; ++i) {
m_nodes[i].GetOutputAfterActivationFunction(input,
m_activation_function,
&((*output)[i]));
@@ -103,7 +110,7 @@ public:
dE_doj = deriv_error[i];
doj_dnetj = m_deriv_activation_function(net_sum);
for (int j = 0; j < m_num_inputs_per_node; j++) {
for (size_t j = 0; j < m_num_inputs_per_node; j++) {
(*deltas)[j] += dE_doj * doj_dnetj * m_nodes[i].GetWeights()[j];
dnetj_dwij = input_layer_activation[j];
@@ -116,6 +123,22 @@ public:
};
void SetWeights( std::vector<std::vector<double>> & weights )
{
if( 0 <= weights.size() && weights.size() <= m_num_nodes )
{
// traverse the list of nodes
size_t node_i = 0;
for( Node & node : m_nodes )
{
node.SetWeights( weights[node_i] );
node_i++;
}
}
else
throw new std::logic_error("Incorrect layer number in SetWeights call");
};
void SaveLayer(FILE * file) const {
fwrite(&m_num_nodes, sizeof(m_num_nodes), 1, file);
fwrite(&m_num_inputs_per_node, sizeof(m_num_inputs_per_node), 1, file);
@@ -124,7 +147,7 @@ public:
fwrite(&str_size, sizeof(size_t), 1, file);
fwrite(m_activation_function_str.c_str(), sizeof(char), str_size, file);
for (int i = 0; i < m_nodes.size(); i++) {
for (size_t i = 0; i < m_nodes.size(); i++) {
m_nodes[i].SaveNode(file);
}
};
@@ -149,15 +172,15 @@ public:
m_deriv_activation_function = (*pair).second;
m_nodes.resize(m_num_nodes);
for (int i = 0; i < m_nodes.size(); i++) {
for (size_t i = 0; i < m_nodes.size(); i++) {
m_nodes[i].LoadNode(file);
}
};
protected:
int m_num_inputs_per_node{ 0 };
int m_num_nodes{ 0 };
size_t m_num_inputs_per_node{ 0 };
size_t m_num_nodes{ 0 };
std::vector<Node> m_nodes;
std::string m_activation_function_str;

56
src/MLP.cpp
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@@ -3,6 +3,7 @@
// Author : David Nogueira
//============================================================================
#include "MLP.h"
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
@@ -10,6 +11,7 @@
#include <fstream>
#include <vector>
#include <algorithm>
#include "easylogging++.h"
@@ -48,7 +50,7 @@ void MLP::CreateMLP(const std::vector<uint64_t> & layers_nodes,
m_num_outputs = m_layers_nodes[m_layers_nodes.size() - 1];
m_num_hidden_layers = m_layers_nodes.size() - 2;
for (int i = 0; i < m_layers_nodes.size() - 1; i++) {
for (size_t i = 0; i < m_layers_nodes.size() - 1; i++) {
m_layers.emplace_back(Layer(m_layers_nodes[i],
m_layers_nodes[i + 1],
layers_activfuncs[i],
@@ -65,7 +67,7 @@ void MLP::SaveMLPNetwork(const std::string & filename)const {
fwrite(&m_num_hidden_layers, sizeof(m_num_hidden_layers), 1, file);
if (!m_layers_nodes.empty())
fwrite(&m_layers_nodes[0], sizeof(m_layers_nodes[0]), m_layers_nodes.size(), file);
for (int i = 0; i < m_layers.size(); i++) {
for (size_t i = 0; i < m_layers.size(); i++) {
m_layers[i].SaveLayer(file);
}
fclose(file);
@@ -83,7 +85,7 @@ void MLP::LoadMLPNetwork(const std::string & filename) {
if (!m_layers_nodes.empty())
fread(&m_layers_nodes[0], sizeof(m_layers_nodes[0]), m_layers_nodes.size(), file);
m_layers.resize(m_layers_nodes.size() - 1);
for (int i = 0; i < m_layers.size(); i++) {
for (size_t i = 0; i < m_layers.size(); i++) {
m_layers[i].LoadLayer(file);
}
fclose(file);
@@ -103,7 +105,7 @@ void MLP::GetOutput(const std::vector<double> &input,
std::vector<double> temp_out(temp_size, 0.0);
temp_in = input;
for (int i = 0; i < m_layers.size(); ++i) {
for (size_t i = 0; i < m_layers.size(); ++i) {
if (i > 0) {
//Store this layer activation
if (all_layers_activations != nullptr)
@@ -152,8 +154,9 @@ void MLP::Train(const std::vector<TrainingSample> &training_sample_set_with_bias
int max_iterations,
double min_error_cost,
bool output_log) {
int num_examples = training_sample_set_with_bias.size();
int num_features = training_sample_set_with_bias[0].GetInputVectorSize();
//rlunaro.03/01/2019. the compiler says that these variables are unused
//int num_examples = training_sample_set_with_bias.size();
//int num_features = training_sample_set_with_bias[0].GetInputVectorSize();
//{
// int layer_i = -1;
@@ -174,7 +177,7 @@ void MLP::Train(const std::vector<TrainingSample> &training_sample_set_with_bias
// }
//}
size_t i = 0;
int i = 0;
double current_iteration_cost_function = 0.0;
for (i = 0; i < max_iterations; i++) {
@@ -199,7 +202,7 @@ void MLP::Train(const std::vector<TrainingSample> &training_sample_set_with_bias
temp_training << training_sample_with_bias << "\t\t";
temp_training << "Predicted output: [";
for (int i = 0; i < predicted_output.size(); i++) {
for (size_t i = 0; i < predicted_output.size(); i++) {
if (i != 0)
temp_training << ", ";
temp_training << predicted_output[i];
@@ -210,7 +213,7 @@ void MLP::Train(const std::vector<TrainingSample> &training_sample_set_with_bias
}
for (int j = 0; j < predicted_output.size(); j++) {
for (size_t j = 0; j < predicted_output.size(); j++) {
current_iteration_cost_function +=
(std::pow)((correct_output[j] - predicted_output[j]), 2);
deriv_error_output[j] =
@@ -259,3 +262,38 @@ void MLP::Train(const std::vector<TrainingSample> &training_sample_set_with_bias
};
size_t MLP::GetNumLayers()
{
return m_layers.size();
}
std::vector<std::vector<double>> MLP::GetLayerWeights( size_t layer_i )
{
std::vector<std::vector<double>> ret_val;
// check parameters
if( 0 <= layer_i && layer_i < m_layers.size() )
{
Layer current_layer = m_layers[layer_i];
for( Node & node : current_layer.GetNodesChangeable() )
{
ret_val.push_back( node.GetWeights() );
}
return ret_val;
}
else
throw new std::logic_error("Incorrect layer number in GetLayerWeights call");
}
void MLP::SetLayerWeights( size_t layer_i, std::vector<std::vector<double>> & weights )
{
// check parameters
if( 0 <= layer_i && layer_i < m_layers.size() )
{
m_layers[layer_i].SetWeights( weights );
}
else
throw new std::logic_error("Incorrect layer number in SetLayerWeights call");
}

14
src/MLP.h
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@@ -5,10 +5,6 @@
#ifndef MLP_H
#define MLP_H
#include "Layer.h"
#include "Sample.h"
#include "Utils.h"
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
@@ -16,6 +12,10 @@
#include <fstream>
#include <vector>
#include <algorithm>
#include <exception>
#include "Layer.h"
#include "Sample.h"
#include "Utils.h"
class MLP {
public:
@@ -40,6 +40,10 @@ public:
int max_iterations = 5000,
double min_error_cost = 0.001,
bool output_log = true);
size_t GetNumLayers();
std::vector<std::vector<double>> GetLayerWeights( size_t layer_i );
void SetLayerWeights( size_t layer_i, std::vector<std::vector<double>> & weights );
protected:
void UpdateWeights(const std::vector<std::vector<double>> & all_layers_activations,
const std::vector<double> &error,
@@ -49,7 +53,7 @@ private:
const std::vector<std::string> & layers_activfuncs,
bool use_constant_weight_init,
double constant_weight_init = 0.5);
int m_num_inputs{ 0 };
size_t m_num_inputs{ 0 };
int m_num_outputs{ 0 };
int m_num_hidden_layers{ 0 };
std::vector<uint64_t> m_layers_nodes;

89
src/MLPTest.cpp
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@@ -36,7 +36,6 @@ UNIT(LearnAND) {
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
@@ -46,7 +45,7 @@ UNIT(LearnAND) {
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (int i = 0; i < num_outputs; i++) {
for (size_t i = 0; i < num_outputs; i++) {
bool predicted_output = output[i] > 0.5 ? true : false;
bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
ASSERT_TRUE(predicted_output == correct_output);
@@ -76,7 +75,6 @@ UNIT(LearnNAND) {
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
@@ -86,7 +84,7 @@ UNIT(LearnNAND) {
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (int i = 0; i < num_outputs; i++) {
for (size_t i = 0; i < num_outputs; i++) {
bool predicted_output = output[i] > 0.5 ? true : false;
bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
ASSERT_TRUE(predicted_output == correct_output);
@@ -116,7 +114,6 @@ UNIT(LearnOR) {
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
@@ -126,7 +123,7 @@ UNIT(LearnOR) {
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (int i = 0; i < num_outputs; i++) {
for (size_t i = 0; i < num_outputs; i++) {
bool predicted_output = output[i] > 0.5 ? true : false;
bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
ASSERT_TRUE(predicted_output == correct_output);
@@ -156,7 +153,6 @@ UNIT(LearnNOR) {
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
@@ -166,7 +162,7 @@ UNIT(LearnNOR) {
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (int i = 0; i < num_outputs; i++) {
for (size_t i = 0; i < num_outputs; i++) {
bool predicted_output = output[i] > 0.5 ? true : false;
bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
ASSERT_TRUE(predicted_output == correct_output);
@@ -194,7 +190,6 @@ UNIT(LearnXOR) {
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
@@ -204,7 +199,7 @@ UNIT(LearnXOR) {
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (int i = 0; i < num_outputs; i++) {
for (size_t i = 0; i < num_outputs; i++) {
bool predicted_output = output[i] > 0.5 ? true : false;
bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
ASSERT_TRUE(predicted_output == correct_output);
@@ -230,7 +225,6 @@ UNIT(LearnNOT) {
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
@@ -240,7 +234,7 @@ UNIT(LearnNOT) {
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (int i = 0; i < num_outputs; i++) {
for (size_t i = 0; i < num_outputs; i++) {
bool predicted_output = output[i] > 0.5 ? true : false;
bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
ASSERT_TRUE(predicted_output == correct_output);
@@ -268,7 +262,6 @@ UNIT(LearnX1) {
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
@@ -278,7 +271,7 @@ UNIT(LearnX1) {
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (int i = 0; i < num_outputs; i++) {
for (size_t i = 0; i < num_outputs; i++) {
bool predicted_output = output[i] > 0.5 ? true : false;
bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
ASSERT_TRUE(predicted_output == correct_output);
@@ -306,7 +299,6 @@ UNIT(LearnX2) {
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
@@ -316,7 +308,7 @@ UNIT(LearnX2) {
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (int i = 0; i < num_outputs; i++) {
for (size_t i = 0; i < num_outputs; i++) {
bool predicted_output = output[i] > 0.5 ? true : false;
bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
ASSERT_TRUE(predicted_output == correct_output);
@@ -325,6 +317,71 @@ UNIT(LearnX2) {
LOG(INFO) << "Trained with success." << std::endl;
}
UNIT(GetWeightsSetWeights) {
LOG(INFO) << "Train X2 function, read internal weights" << std::endl;
std::vector<TrainingSample> training_set =
{
{ { 0, 0 },{ 0.0 } },
{ { 0, 1 },{ 1.0 } },
{ { 1, 0 },{ 0.0 } },
{ { 1, 1 },{ 1.0 } }
};
bool bias_already_in = false;
std::vector<TrainingSample> training_sample_set_with_bias(training_set);
//set up bias
if (!bias_already_in) {
for (auto & training_sample_with_bias : training_sample_set_with_bias) {
training_sample_with_bias.AddBiasValue(1);
}
}
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
MLP my_mlp({ num_features, 2, num_outputs }, { "sigmoid", "linear" });
//Train MLP
my_mlp.Train(training_sample_set_with_bias, 0.5, 500, 0.25);
// get layer weights
std::vector<std::vector<double>> weights = my_mlp.GetLayerWeights( 1 );
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (size_t i = 0; i < num_outputs; i++) {
bool predicted_output = output[i] > 0.5 ? true : false;
std::cout << "PREDICTED OUTPUT IS NOW: " << output[i] << std::endl;
bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
ASSERT_TRUE(predicted_output == correct_output);
}
}
// the expected value of the internal weights
// after training are 1.65693 -0.538749
ASSERT_TRUE( 1.6 <= weights[0][0] && weights[0][0] <= 1.7 );
ASSERT_TRUE( -0.6 <= weights[0][1] && weights[0][1] <= -0.5 );
// now, we are going to inject a weight value of 0.0
// and check that the new output value is nonsense
std::vector<std::vector<double>> zeroWeights = { { 0.0, 0.0 } };
my_mlp.SetLayerWeights( 1, zeroWeights );
for (const auto & training_sample : training_sample_set_with_bias) {
std::vector<double> output;
my_mlp.GetOutput(training_sample.input_vector(), &output);
for (size_t i = 0; i < num_outputs; i++) {
ASSERT_TRUE( -0.0001L <= output[i] && output[i] <= 0.0001L );
}
}
LOG(INFO) << "Trained with success." << std::endl;
}
int main(int argc, char* argv[]) {
START_EASYLOGGINGPP(argc, argv);
microunit::UnitTester::Run();

14
src/Node.h
View File

@@ -5,8 +5,6 @@
#ifndef NODE_H
#define NODE_H
#include "Utils.h"
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
@@ -15,6 +13,8 @@
#include <vector>
#include <algorithm>
#include <cassert> // for assert()
#include <exception>
#include "Utils.h"
#define CONSTANT_WEIGHT_INITIALIZATION 0
@@ -81,6 +81,14 @@ public:
return m_weights;
}
void SetWeights( std::vector<double> & weights ){
// check size of the weights vector
if( weights.size() == m_num_inputs )
m_weights = weights;
else
throw new std::logic_error("Incorrect weight size in SetWeights call");
}
size_t GetWeightsVectorSize() const {
return m_weights.size();
}
@@ -141,7 +149,7 @@ public:
};
protected:
int m_num_inputs{ 0 };
size_t m_num_inputs{ 0 };
double m_bias{ 0.0 };
std::vector<double> m_weights;
};

6
src/NodeTest.cpp
View File

@@ -81,7 +81,6 @@ UNIT(LearnAND) {
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
Node my_node(num_features);
Train(my_node, training_sample_set_with_bias, 0.1, 100);
@@ -116,7 +115,6 @@ UNIT(LearnNAND) {
training_sample_with_bias.AddBiasValue(1);
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
Node my_node(num_features);
Train(my_node, training_sample_set_with_bias, 0.1, 100);
@@ -151,7 +149,6 @@ UNIT(LearnOR) {
training_sample_with_bias.AddBiasValue(1);
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
Node my_node(num_features);
Train(my_node, training_sample_set_with_bias, 0.1, 100);
@@ -185,7 +182,6 @@ UNIT(LearnNOR) {
training_sample_with_bias.AddBiasValue(1);
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
Node my_node(num_features);
Train(my_node, training_sample_set_with_bias, 0.1, 100);
@@ -218,7 +214,6 @@ UNIT(LearnNOT) {
training_sample_with_bias.AddBiasValue(1);
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
Node my_node(num_features);
Train(my_node, training_sample_set_with_bias, 0.1, 100);
@@ -253,7 +248,6 @@ UNIT(LearnXOR) {
training_sample_with_bias.AddBiasValue(1);
}
}
size_t num_examples = training_sample_set_with_bias.size();
size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
Node my_node(num_features);
Train(my_node, training_sample_set_with_bias, 0.1, 100);

6
src/Sample.h
View File

@@ -30,7 +30,7 @@ public:
protected:
virtual void PrintMyself(std::ostream& stream) const {
stream << "Input vector: [";
for (int i = 0; i < m_input_vector.size(); i++) {
for (size_t i = 0; i < m_input_vector.size(); i++) {
if (i != 0)
stream << ", ";
stream << m_input_vector[i];
@@ -59,7 +59,7 @@ public:
protected:
virtual void PrintMyself(std::ostream& stream) const {
stream << "Input vector: [";
for (int i = 0; i < m_input_vector.size(); i++) {
for (size_t i = 0; i < m_input_vector.size(); i++) {
if (i != 0)
stream << ", ";
stream << m_input_vector[i];
@@ -69,7 +69,7 @@ protected:
stream << "; ";
stream << "Output vector: [";
for (int i = 0; i < m_output_vector.size(); i++) {
for (size_t i = 0; i < m_output_vector.size(); i++) {
if (i != 0)
stream << ", ";
stream << m_output_vector[i];

7
src/Utils.h
View File

@@ -5,7 +5,6 @@
#ifndef UTILS_H
#define UTILS_H
#include "Chrono.h"
#include <stdlib.h>
#include <math.h>
#include <numeric>
@@ -22,6 +21,8 @@
#include <typeinfo>
#include <typeindex>
#include <cassert>
#include "Chrono.h"
#ifdef _WIN32
#include <time.h>
#else
@@ -110,11 +111,11 @@ inline void Softmax(std::vector<double> *output) {
size_t num_elements = output->size();
std::vector<double> exp_output(num_elements);
double exp_total = 0.0;
for (int i = 0; i < num_elements; i++) {
for (size_t i = 0; i < num_elements; i++) {
exp_output[i] = exp((*output)[i]);
exp_total += exp_output[i];
}
for (int i = 0; i < num_elements; i++) {
for (size_t i = 0; i < num_elements; i++) {
(*output)[i] = exp_output[i] / exp_total;
}
}