mirror of
https://github.com/davidalbertonogueira/MLP.git
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Add IRIS dataset usage example.
Add LoadModel and SaveModel methods.
This commit is contained in:
davidjacnogueira
157
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157
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150
data/iris.data
Normal file
150
data/iris.data
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@@ -0,0 +1,150 @@
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5.1,3.5,1.4,0.2,Iris-setosa
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4.9,3.0,1.4,0.2,Iris-setosa
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||||
4.7,3.2,1.3,0.2,Iris-setosa
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||||
4.6,3.1,1.5,0.2,Iris-setosa
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5.0,3.6,1.4,0.2,Iris-setosa
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||||
5.4,3.9,1.7,0.4,Iris-setosa
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||||
4.6,3.4,1.4,0.3,Iris-setosa
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||||
5.0,3.4,1.5,0.2,Iris-setosa
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||||
4.4,2.9,1.4,0.2,Iris-setosa
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||||
4.9,3.1,1.5,0.1,Iris-setosa
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||||
5.4,3.7,1.5,0.2,Iris-setosa
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||||
4.8,3.4,1.6,0.2,Iris-setosa
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4.8,3.0,1.4,0.1,Iris-setosa
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||||
4.3,3.0,1.1,0.1,Iris-setosa
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5.8,4.0,1.2,0.2,Iris-setosa
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5.7,4.4,1.5,0.4,Iris-setosa
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5.4,3.9,1.3,0.4,Iris-setosa
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5.1,3.5,1.4,0.3,Iris-setosa
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5.7,3.8,1.7,0.3,Iris-setosa
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5.1,3.8,1.5,0.3,Iris-setosa
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5.4,3.4,1.7,0.2,Iris-setosa
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5.1,3.7,1.5,0.4,Iris-setosa
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4.6,3.6,1.0,0.2,Iris-setosa
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5.1,3.3,1.7,0.5,Iris-setosa
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4.8,3.4,1.9,0.2,Iris-setosa
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5.0,3.0,1.6,0.2,Iris-setosa
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5.0,3.4,1.6,0.4,Iris-setosa
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5.2,3.5,1.5,0.2,Iris-setosa
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5.2,3.4,1.4,0.2,Iris-setosa
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4.7,3.2,1.6,0.2,Iris-setosa
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4.8,3.1,1.6,0.2,Iris-setosa
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5.4,3.4,1.5,0.4,Iris-setosa
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5.2,4.1,1.5,0.1,Iris-setosa
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5.5,4.2,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.0,3.2,1.2,0.2,Iris-setosa
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5.5,3.5,1.3,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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4.4,3.0,1.3,0.2,Iris-setosa
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5.1,3.4,1.5,0.2,Iris-setosa
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5.0,3.5,1.3,0.3,Iris-setosa
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4.5,2.3,1.3,0.3,Iris-setosa
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4.4,3.2,1.3,0.2,Iris-setosa
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5.0,3.5,1.6,0.6,Iris-setosa
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5.1,3.8,1.9,0.4,Iris-setosa
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4.8,3.0,1.4,0.3,Iris-setosa
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5.1,3.8,1.6,0.2,Iris-setosa
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4.6,3.2,1.4,0.2,Iris-setosa
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5.3,3.7,1.5,0.2,Iris-setosa
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5.0,3.3,1.4,0.2,Iris-setosa
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7.0,3.2,4.7,1.4,Iris-versicolor
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6.4,3.2,4.5,1.5,Iris-versicolor
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6.9,3.1,4.9,1.5,Iris-versicolor
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5.5,2.3,4.0,1.3,Iris-versicolor
|
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6.5,2.8,4.6,1.5,Iris-versicolor
|
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5.7,2.8,4.5,1.3,Iris-versicolor
|
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6.3,3.3,4.7,1.6,Iris-versicolor
|
||||
4.9,2.4,3.3,1.0,Iris-versicolor
|
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6.6,2.9,4.6,1.3,Iris-versicolor
|
||||
5.2,2.7,3.9,1.4,Iris-versicolor
|
||||
5.0,2.0,3.5,1.0,Iris-versicolor
|
||||
5.9,3.0,4.2,1.5,Iris-versicolor
|
||||
6.0,2.2,4.0,1.0,Iris-versicolor
|
||||
6.1,2.9,4.7,1.4,Iris-versicolor
|
||||
5.6,2.9,3.6,1.3,Iris-versicolor
|
||||
6.7,3.1,4.4,1.4,Iris-versicolor
|
||||
5.6,3.0,4.5,1.5,Iris-versicolor
|
||||
5.8,2.7,4.1,1.0,Iris-versicolor
|
||||
6.2,2.2,4.5,1.5,Iris-versicolor
|
||||
5.6,2.5,3.9,1.1,Iris-versicolor
|
||||
5.9,3.2,4.8,1.8,Iris-versicolor
|
||||
6.1,2.8,4.0,1.3,Iris-versicolor
|
||||
6.3,2.5,4.9,1.5,Iris-versicolor
|
||||
6.1,2.8,4.7,1.2,Iris-versicolor
|
||||
6.4,2.9,4.3,1.3,Iris-versicolor
|
||||
6.6,3.0,4.4,1.4,Iris-versicolor
|
||||
6.8,2.8,4.8,1.4,Iris-versicolor
|
||||
6.7,3.0,5.0,1.7,Iris-versicolor
|
||||
6.0,2.9,4.5,1.5,Iris-versicolor
|
||||
5.7,2.6,3.5,1.0,Iris-versicolor
|
||||
5.5,2.4,3.8,1.1,Iris-versicolor
|
||||
5.5,2.4,3.7,1.0,Iris-versicolor
|
||||
5.8,2.7,3.9,1.2,Iris-versicolor
|
||||
6.0,2.7,5.1,1.6,Iris-versicolor
|
||||
5.4,3.0,4.5,1.5,Iris-versicolor
|
||||
6.0,3.4,4.5,1.6,Iris-versicolor
|
||||
6.7,3.1,4.7,1.5,Iris-versicolor
|
||||
6.3,2.3,4.4,1.3,Iris-versicolor
|
||||
5.6,3.0,4.1,1.3,Iris-versicolor
|
||||
5.5,2.5,4.0,1.3,Iris-versicolor
|
||||
5.5,2.6,4.4,1.2,Iris-versicolor
|
||||
6.1,3.0,4.6,1.4,Iris-versicolor
|
||||
5.8,2.6,4.0,1.2,Iris-versicolor
|
||||
5.0,2.3,3.3,1.0,Iris-versicolor
|
||||
5.6,2.7,4.2,1.3,Iris-versicolor
|
||||
5.7,3.0,4.2,1.2,Iris-versicolor
|
||||
5.7,2.9,4.2,1.3,Iris-versicolor
|
||||
6.2,2.9,4.3,1.3,Iris-versicolor
|
||||
5.1,2.5,3.0,1.1,Iris-versicolor
|
||||
5.7,2.8,4.1,1.3,Iris-versicolor
|
||||
6.3,3.3,6.0,2.5,Iris-virginica
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
7.1,3.0,5.9,2.1,Iris-virginica
|
||||
6.3,2.9,5.6,1.8,Iris-virginica
|
||||
6.5,3.0,5.8,2.2,Iris-virginica
|
||||
7.6,3.0,6.6,2.1,Iris-virginica
|
||||
4.9,2.5,4.5,1.7,Iris-virginica
|
||||
7.3,2.9,6.3,1.8,Iris-virginica
|
||||
6.7,2.5,5.8,1.8,Iris-virginica
|
||||
7.2,3.6,6.1,2.5,Iris-virginica
|
||||
6.5,3.2,5.1,2.0,Iris-virginica
|
||||
6.4,2.7,5.3,1.9,Iris-virginica
|
||||
6.8,3.0,5.5,2.1,Iris-virginica
|
||||
5.7,2.5,5.0,2.0,Iris-virginica
|
||||
5.8,2.8,5.1,2.4,Iris-virginica
|
||||
6.4,3.2,5.3,2.3,Iris-virginica
|
||||
6.5,3.0,5.5,1.8,Iris-virginica
|
||||
7.7,3.8,6.7,2.2,Iris-virginica
|
||||
7.7,2.6,6.9,2.3,Iris-virginica
|
||||
6.0,2.2,5.0,1.5,Iris-virginica
|
||||
6.9,3.2,5.7,2.3,Iris-virginica
|
||||
5.6,2.8,4.9,2.0,Iris-virginica
|
||||
7.7,2.8,6.7,2.0,Iris-virginica
|
||||
6.3,2.7,4.9,1.8,Iris-virginica
|
||||
6.7,3.3,5.7,2.1,Iris-virginica
|
||||
7.2,3.2,6.0,1.8,Iris-virginica
|
||||
6.2,2.8,4.8,1.8,Iris-virginica
|
||||
6.1,3.0,4.9,1.8,Iris-virginica
|
||||
6.4,2.8,5.6,2.1,Iris-virginica
|
||||
7.2,3.0,5.8,1.6,Iris-virginica
|
||||
7.4,2.8,6.1,1.9,Iris-virginica
|
||||
7.9,3.8,6.4,2.0,Iris-virginica
|
||||
6.4,2.8,5.6,2.2,Iris-virginica
|
||||
6.3,2.8,5.1,1.5,Iris-virginica
|
||||
6.1,2.6,5.6,1.4,Iris-virginica
|
||||
7.7,3.0,6.1,2.3,Iris-virginica
|
||||
6.3,3.4,5.6,2.4,Iris-virginica
|
||||
6.4,3.1,5.5,1.8,Iris-virginica
|
||||
6.0,3.0,4.8,1.8,Iris-virginica
|
||||
6.9,3.1,5.4,2.1,Iris-virginica
|
||||
6.7,3.1,5.6,2.4,Iris-virginica
|
||||
6.9,3.1,5.1,2.3,Iris-virginica
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
6.8,3.2,5.9,2.3,Iris-virginica
|
||||
6.7,3.3,5.7,2.5,Iris-virginica
|
||||
6.7,3.0,5.2,2.3,Iris-virginica
|
||||
6.3,2.5,5.0,1.9,Iris-virginica
|
||||
6.5,3.0,5.2,2.0,Iris-virginica
|
||||
6.2,3.4,5.4,2.3,Iris-virginica
|
||||
5.9,3.0,5.1,1.8,Iris-virginica
|
||||
BIN
data/iris.mlp
Normal file
BIN
data/iris.mlp
Normal file
Binary file not shown.
157
src/IrisDatasetTest.cpp
Normal file
157
src/IrisDatasetTest.cpp
Normal file
@@ -0,0 +1,157 @@
|
||||
//============================================================================
|
||||
// Name : Main.cpp
|
||||
// Author : David Nogueira
|
||||
//============================================================================
|
||||
#include "MLP.h"
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
#include <fstream>
|
||||
#include <vector>
|
||||
#include <array>
|
||||
#include <algorithm>
|
||||
#include "microunit.h"
|
||||
#include "easylogging++.h"
|
||||
|
||||
INITIALIZE_EASYLOGGINGPP
|
||||
|
||||
|
||||
// Example illustrating practical use of this MLP lib.
|
||||
// Disclaimer: This is NOT an example of good machine learning practices
|
||||
// regarding training/testing dataset partitioning.
|
||||
|
||||
const char *iris_dataset = "../../data/iris.data";
|
||||
const std::array<std::string, 3> class_names =
|
||||
{ "Iris-setosa", "Iris-versicolor", "Iris-virginica" };
|
||||
|
||||
|
||||
bool load_data(int *samples,
|
||||
std::vector<double> *input,
|
||||
std::vector<double> *iris_class) {
|
||||
// Load the iris data-set.
|
||||
FILE *in = fopen(iris_dataset, "r");
|
||||
if (!in) {
|
||||
LOG(ERROR) << "Could not open file: " << iris_dataset << ".";
|
||||
return false;
|
||||
}
|
||||
|
||||
// Loop through the data to get a count.
|
||||
char line[1024];
|
||||
while (!feof(in) && fgets(line, 1024, in)) {
|
||||
++(*samples);
|
||||
}
|
||||
fseek(in, 0, SEEK_SET);
|
||||
|
||||
LOG(INFO) << "Loading " << (*samples)
|
||||
<< " data points from " << iris_dataset << ".";
|
||||
// Allocate memory for input and output data.
|
||||
input->resize((*samples) * 4);
|
||||
iris_class->resize((*samples) * 3);
|
||||
|
||||
// Read the file into our arrays.
|
||||
int i, j;
|
||||
for (i = 0; i < (*samples); ++i) {
|
||||
double *p = &((*input)[0]) + i * 4;
|
||||
double *c = &((*iris_class)[0]) + i * 3;
|
||||
c[0] = c[1] = c[2] = 0.0;
|
||||
|
||||
fgets(line, 1024, in);
|
||||
|
||||
char *split = strtok(line, ",");
|
||||
for (j = 0; j < 4; ++j) {
|
||||
p[j] = atof(split);
|
||||
split = strtok(0, ",");
|
||||
}
|
||||
|
||||
split[strlen(split) - 1] = 0;
|
||||
if (strcmp(split, class_names[0].c_str()) == 0) {
|
||||
c[0] = 1.0;
|
||||
} else if (strcmp(split, class_names[1].c_str()) == 0) {
|
||||
c[1] = 1.0;
|
||||
} else if (strcmp(split, class_names[2].c_str()) == 0) {
|
||||
c[2] = 1.0;
|
||||
} else {
|
||||
LOG(ERROR) << "Unknown iris_class " << split
|
||||
<< ".";
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
fclose(in);
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, char *argv[]) {
|
||||
LOG(INFO) << "Train MLP with IRIS dataset using backpropagation.";
|
||||
int samples = 0;
|
||||
std::vector<double> input;
|
||||
std::vector<double> iris_class;
|
||||
|
||||
// Load the data from file.
|
||||
if (!load_data(&samples, &input, &iris_class)) {
|
||||
LOG(ERROR) << "Error processing input file.";
|
||||
return -1;
|
||||
}
|
||||
|
||||
std::vector<TrainingSample> training_set;
|
||||
for (int j = 0; j < samples; ++j) {
|
||||
std::vector<double> training_set_input;
|
||||
std::vector<double> training_set_output;
|
||||
training_set_input.reserve(4);
|
||||
for (int i = 0; i < 4; i++)
|
||||
training_set_input.push_back(*(&(input[0]) + j * 4 + i));
|
||||
training_set_output.reserve(3);
|
||||
for (int i = 0; i < 3; i++)
|
||||
training_set_output.push_back(*(&(iris_class[0]) + j * 3 + i));
|
||||
training_set.emplace_back(std::move(training_set_input),
|
||||
std::move(training_set_output));
|
||||
}
|
||||
std::vector<TrainingSample> training_sample_set_with_bias(std::move(training_set));
|
||||
//set up bias
|
||||
for (auto & training_sample_with_bias : training_sample_set_with_bias) {
|
||||
training_sample_with_bias.AddBiasValue(1);
|
||||
}
|
||||
|
||||
{
|
||||
/* 4 inputs + 1 bias.
|
||||
* 1 hidden layer(s) of 4 neurons.
|
||||
* 3 outputs (1 per iris_class)
|
||||
*/
|
||||
MLP my_mlp({ 4 + 1, 4 ,3 }, { "sigmoid", "linear" }, false);
|
||||
|
||||
|
||||
int loops = 5000;
|
||||
|
||||
|
||||
// Train the network with backpropagation.
|
||||
LOG(INFO) << "Training for " << loops << " loops over data.";
|
||||
my_mlp.UpdateMiniBatch(training_sample_set_with_bias, .01, loops, 0.10, false);
|
||||
|
||||
my_mlp.SaveMLPNetwork(std::string("../../data/iris.mlp"));
|
||||
}
|
||||
//Destruction/Construction of a MLP object to show off saving and loading a trained model
|
||||
{
|
||||
MLP my_mlp(std::string("../../data/iris.mlp"));
|
||||
|
||||
int correct = 0;
|
||||
for (int j = 0; j < samples; ++j) {
|
||||
std::vector<double> guess;
|
||||
my_mlp.GetOutput(training_sample_set_with_bias[j].input_vector(), &guess);
|
||||
size_t class_id;
|
||||
my_mlp.GetOutputClass(guess, &class_id);
|
||||
|
||||
if (iris_class[j * 3 + 0] == 1.0 && class_id == 0) {
|
||||
++correct;
|
||||
} else if (iris_class[j * 3 + 1] == 1.0 && class_id == 1) {
|
||||
++correct;
|
||||
} else if (iris_class[j * 3 + 2] == 1.0 && class_id == 2) {
|
||||
++correct;
|
||||
}
|
||||
}
|
||||
LOG(INFO) << correct << "/" << samples
|
||||
<< " (" << ((double)correct / samples * 100.0) << "%).";
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
48
src/Layer.h
48
src/Layer.h
@@ -42,11 +42,12 @@ public:
|
||||
std::pair<std::function<double(double)>,
|
||||
std::function<double(double)> > *pair;
|
||||
bool ret_val = utils::ActivationFunctionsManager::Singleton().
|
||||
GetActivationFunctionPair(activation_function,
|
||||
&pair);
|
||||
GetActivationFunctionPair(activation_function,
|
||||
&pair);
|
||||
assert(ret_val);
|
||||
m_activation_function = (*pair).first;
|
||||
m_deriv_activation_function = (*pair).second;
|
||||
m_activation_function_str = activation_function;
|
||||
};
|
||||
|
||||
~Layer() {
|
||||
@@ -58,7 +59,7 @@ public:
|
||||
int GetInputSize() const {
|
||||
return m_num_inputs_per_node;
|
||||
};
|
||||
|
||||
|
||||
int GetOutputSize() const {
|
||||
return m_num_nodes;
|
||||
};
|
||||
@@ -114,11 +115,52 @@ public:
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
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);
|
||||
|
||||
size_t str_size = m_activation_function_str.size();
|
||||
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++) {
|
||||
m_nodes[i].SaveNode(file);
|
||||
}
|
||||
};
|
||||
void LoadLayer(FILE * file) {
|
||||
m_nodes.clear();
|
||||
|
||||
fread(&m_num_nodes, sizeof(m_num_nodes), 1, file);
|
||||
fread(&m_num_inputs_per_node, sizeof(m_num_inputs_per_node), 1, file);
|
||||
|
||||
size_t str_size = 0;
|
||||
fread(&str_size, sizeof(size_t), 1, file);
|
||||
m_activation_function_str.resize(str_size);
|
||||
fread(&(m_activation_function_str[0]), sizeof(char), str_size, file);
|
||||
|
||||
std::pair<std::function<double(double)>,
|
||||
std::function<double(double)> > *pair;
|
||||
bool ret_val = utils::ActivationFunctionsManager::Singleton().
|
||||
GetActivationFunctionPair(m_activation_function_str,
|
||||
&pair);
|
||||
assert(ret_val);
|
||||
m_activation_function = (*pair).first;
|
||||
m_deriv_activation_function = (*pair).second;
|
||||
|
||||
m_nodes.resize(m_num_nodes);
|
||||
for (int 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 };
|
||||
std::vector<Node> m_nodes;
|
||||
|
||||
std::string m_activation_function_str;
|
||||
std::function<double(double)> m_activation_function;
|
||||
std::function<double(double)> m_deriv_activation_function;
|
||||
};
|
||||
|
||||
91
src/MLP.cpp
91
src/MLP.cpp
@@ -12,11 +12,82 @@
|
||||
#include <algorithm>
|
||||
#include "easylogging++.h"
|
||||
|
||||
bool MLP::ExportNNWeights(std::vector<double> *weights) const {
|
||||
return true;
|
||||
|
||||
|
||||
//desired call sintax : MLP({64*64,20,4}, {"sigmoid", "linear"},
|
||||
MLP::MLP(const std::vector<uint64_t> & layers_nodes,
|
||||
const std::vector<std::string> & layers_activfuncs,
|
||||
bool use_constant_weight_init,
|
||||
double constant_weight_init) {
|
||||
assert(layers_nodes.size() >= 2);
|
||||
assert(layers_activfuncs.size() + 1 == layers_nodes.size());
|
||||
|
||||
CreateMLP(layers_nodes,
|
||||
layers_activfuncs,
|
||||
use_constant_weight_init,
|
||||
constant_weight_init);
|
||||
};
|
||||
bool MLP::ImportNNWeights(const std::vector<double> & weights) {
|
||||
return true;
|
||||
|
||||
MLP::MLP(std::string & filename) {
|
||||
LoadMLPNetwork(filename);
|
||||
}
|
||||
|
||||
MLP::~MLP() {
|
||||
m_num_inputs = 0;
|
||||
m_num_outputs = 0;
|
||||
m_num_hidden_layers = 0;
|
||||
m_layers_nodes.clear();
|
||||
m_layers.clear();
|
||||
};
|
||||
|
||||
void MLP::CreateMLP(const std::vector<uint64_t> & layers_nodes,
|
||||
const std::vector<std::string> & layers_activfuncs,
|
||||
bool use_constant_weight_init,
|
||||
double constant_weight_init) {
|
||||
m_layers_nodes = layers_nodes;
|
||||
m_num_inputs = m_layers_nodes[0];
|
||||
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++) {
|
||||
m_layers.emplace_back(Layer(m_layers_nodes[i],
|
||||
m_layers_nodes[i + 1],
|
||||
layers_activfuncs[i],
|
||||
use_constant_weight_init,
|
||||
constant_weight_init));
|
||||
}
|
||||
};
|
||||
|
||||
void MLP::SaveMLPNetwork(std::string & filename)const {
|
||||
FILE * file;
|
||||
file = fopen(filename.c_str(), "wb");
|
||||
fwrite(&m_num_inputs, sizeof(m_num_inputs), 1, file);
|
||||
fwrite(&m_num_outputs, sizeof(m_num_outputs), 1, file);
|
||||
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++) {
|
||||
m_layers[i].SaveLayer(file);
|
||||
}
|
||||
fclose(file);
|
||||
};
|
||||
void MLP::LoadMLPNetwork(std::string & filename) {
|
||||
m_layers_nodes.clear();
|
||||
m_layers.clear();
|
||||
|
||||
FILE * file;
|
||||
file = fopen(filename.c_str(), "rb");
|
||||
fread(&m_num_inputs, sizeof(m_num_inputs), 1, file);
|
||||
fread(&m_num_outputs, sizeof(m_num_outputs), 1, file);
|
||||
fread(&m_num_hidden_layers, sizeof(m_num_hidden_layers), 1, file);
|
||||
m_layers_nodes.resize(m_num_hidden_layers + 2);
|
||||
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++) {
|
||||
m_layers[i].LoadLayer(file);
|
||||
}
|
||||
fclose(file);
|
||||
};
|
||||
|
||||
void MLP::GetOutput(const std::vector<double> &input,
|
||||
@@ -80,7 +151,8 @@ void MLP::UpdateWeights(const std::vector<std::vector<double>> & all_layers_acti
|
||||
void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set_with_bias,
|
||||
double learning_rate,
|
||||
int max_iterations,
|
||||
double min_error_cost) {
|
||||
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();
|
||||
|
||||
@@ -102,23 +174,28 @@ void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set
|
||||
// }
|
||||
// }
|
||||
//}
|
||||
|
||||
size_t i = 0;
|
||||
double current_iteration_cost_function = 0.0;
|
||||
|
||||
for (i = 0; i < max_iterations; i++) {
|
||||
current_iteration_cost_function = 0.0;
|
||||
for (auto & training_sample_with_bias : training_sample_set_with_bias) {
|
||||
|
||||
std::vector<double> predicted_output;
|
||||
std::vector< std::vector<double> > all_layers_activations;
|
||||
|
||||
GetOutput(training_sample_with_bias.input_vector(),
|
||||
&predicted_output,
|
||||
&all_layers_activations);
|
||||
|
||||
const std::vector<double> & correct_output =
|
||||
training_sample_with_bias.output_vector();
|
||||
|
||||
assert(correct_output.size() == predicted_output.size());
|
||||
std::vector<double> deriv_error_output(predicted_output.size());
|
||||
|
||||
if ((i % (max_iterations / 100)) == 0) {
|
||||
if (output_log && ((i % (max_iterations / 10)) == 0)) {
|
||||
std::stringstream temp_training;
|
||||
temp_training << training_sample_with_bias << "\t\t";
|
||||
|
||||
@@ -144,7 +221,7 @@ void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set
|
||||
learning_rate);
|
||||
}
|
||||
|
||||
if ((i % (max_iterations / 100)) == 0)
|
||||
if (output_log && ((i % (max_iterations / 10)) == 0))
|
||||
LOG(INFO) << "Iteration " << i << " cost function f(error): "
|
||||
<< current_iteration_cost_function;
|
||||
if (current_iteration_cost_function < min_error_cost)
|
||||
|
||||
45
src/MLP.h
45
src/MLP.h
@@ -22,28 +22,13 @@ public:
|
||||
//desired call sintax : MLP({64*64,20,4}, {"sigmoid", "linear"},
|
||||
MLP(const std::vector<uint64_t> & layers_nodes,
|
||||
const std::vector<std::string> & layers_activfuncs,
|
||||
bool use_constant_weight_init = true,
|
||||
double constant_weight_init = 0.5) {
|
||||
assert(layers_nodes.size() >= 2);
|
||||
assert(layers_activfuncs.size() + 1 == layers_nodes.size());
|
||||
bool use_constant_weight_init = false,
|
||||
double constant_weight_init = 0.5);
|
||||
MLP(std::string & filename);
|
||||
~MLP();
|
||||
|
||||
CreateMLP(layers_nodes,
|
||||
layers_activfuncs,
|
||||
use_constant_weight_init,
|
||||
constant_weight_init);
|
||||
}
|
||||
|
||||
|
||||
~MLP() {
|
||||
m_num_inputs = 0;
|
||||
m_num_outputs = 0;
|
||||
m_num_hidden_layers = 0;
|
||||
m_layers_nodes.clear();
|
||||
m_layers.clear();
|
||||
};
|
||||
|
||||
bool ExportNNWeights(std::vector<double> *weights)const;
|
||||
bool ImportNNWeights(const std::vector<double> & weights);
|
||||
void SaveMLPNetwork(std::string & filename)const;
|
||||
void LoadMLPNetwork(std::string & filename);
|
||||
|
||||
void GetOutput(const std::vector<double> &input,
|
||||
std::vector<double> * output,
|
||||
@@ -53,7 +38,8 @@ public:
|
||||
void UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set_with_bias,
|
||||
double learning_rate,
|
||||
int max_iterations = 5000,
|
||||
double min_error_cost = 0.001);
|
||||
double min_error_cost = 0.001,
|
||||
bool output_log = true);
|
||||
protected:
|
||||
void UpdateWeights(const std::vector<std::vector<double>> & all_layers_activations,
|
||||
const std::vector<double> &error,
|
||||
@@ -62,20 +48,7 @@ private:
|
||||
void CreateMLP(const std::vector<uint64_t> & layers_nodes,
|
||||
const std::vector<std::string> & layers_activfuncs,
|
||||
bool use_constant_weight_init,
|
||||
double constant_weight_init = 0.5) {
|
||||
m_layers_nodes = layers_nodes;
|
||||
m_num_inputs = m_layers_nodes[0];
|
||||
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++) {
|
||||
m_layers.emplace_back(Layer(m_layers_nodes[i],
|
||||
m_layers_nodes[i + 1],
|
||||
layers_activfuncs[i],
|
||||
use_constant_weight_init,
|
||||
constant_weight_init));
|
||||
}
|
||||
}
|
||||
double constant_weight_init = 0.5);
|
||||
int m_num_inputs{ 0 };
|
||||
int m_num_outputs{ 0 };
|
||||
int m_num_hidden_layers{ 0 };
|
||||
|
||||
@@ -39,7 +39,7 @@ 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" }, false);
|
||||
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
|
||||
//Train MLP
|
||||
my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
|
||||
|
||||
@@ -79,7 +79,7 @@ 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" }, false);
|
||||
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
|
||||
//Train MLP
|
||||
my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
|
||||
|
||||
@@ -119,7 +119,7 @@ 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" }, false);
|
||||
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
|
||||
//Train MLP
|
||||
my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
|
||||
|
||||
@@ -159,7 +159,7 @@ 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" }, false);
|
||||
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
|
||||
//Train MLP
|
||||
my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
|
||||
|
||||
@@ -197,7 +197,7 @@ 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" }, false);
|
||||
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
|
||||
//Train MLP
|
||||
my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
|
||||
|
||||
@@ -233,7 +233,7 @@ 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" }, false);
|
||||
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
|
||||
//Train MLP
|
||||
my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
|
||||
|
||||
@@ -271,7 +271,7 @@ 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" }, false);
|
||||
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
|
||||
//Train MLP
|
||||
my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
|
||||
|
||||
@@ -309,7 +309,7 @@ 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" }, false);
|
||||
MLP my_mlp({ num_features, 2 ,num_outputs }, { "sigmoid", "linear" });
|
||||
//Train MLP
|
||||
my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
|
||||
|
||||
|
||||
20
src/Node.h
20
src/Node.h
@@ -53,7 +53,7 @@ public:
|
||||
} else {
|
||||
m_weights.resize(m_num_inputs);
|
||||
std::generate_n(m_weights.begin(),
|
||||
m_num_inputs,
|
||||
m_num_inputs,
|
||||
utils::gen_rand());
|
||||
}
|
||||
}
|
||||
@@ -97,7 +97,7 @@ public:
|
||||
|
||||
void GetOutputAfterActivationFunction(const std::vector<double> &input,
|
||||
std::function<double(double)> activation_function,
|
||||
double * output) const {
|
||||
double * output) const {
|
||||
double inner_prod = 0.0;
|
||||
GetInputInnerProdWithWeights(input, &inner_prod);
|
||||
*output = activation_function(inner_prod);
|
||||
@@ -109,7 +109,7 @@ public:
|
||||
double threshold = 0.5) const {
|
||||
double value;
|
||||
GetOutputAfterActivationFunction(input, activation_function, &value);
|
||||
*bool_output = (value >threshold) ? true : false;
|
||||
*bool_output = (value > threshold) ? true : false;
|
||||
};
|
||||
|
||||
void UpdateWeights(const std::vector<double> &x,
|
||||
@@ -126,6 +126,20 @@ public:
|
||||
m_weights[weight_id] += learning_rate*increment;
|
||||
}
|
||||
|
||||
void SaveNode(FILE * file) const {
|
||||
fwrite(&m_num_inputs, sizeof(m_num_inputs), 1, file);
|
||||
fwrite(&m_bias, sizeof(m_bias), 1, file);
|
||||
fwrite(&m_weights[0], sizeof(m_weights[0]), m_weights.size(), file);
|
||||
};
|
||||
void LoadNode(FILE * file) {
|
||||
m_weights.clear();
|
||||
|
||||
fread(&m_num_inputs, sizeof(m_num_inputs), 1, file);
|
||||
fread(&m_bias, sizeof(m_bias), 1, file);
|
||||
m_weights.resize(m_num_inputs);
|
||||
fread(&m_weights[0], sizeof(m_weights[0]), m_weights.size(), file);
|
||||
};
|
||||
|
||||
protected:
|
||||
int m_num_inputs{ 0 };
|
||||
double m_bias{ 0.0 };
|
||||
|
||||
Reference in New Issue
Block a user