// CvANN_MLPDemo.cpp : Defines the entry point for the console application.
//
// Sample code for CvANN_MLP Artificial Neural Network module of OpenCV.
// We stick pretty much to the basics here. See full reference at
// http://www.seas.upenn.edu/~bensapp/opencvdocs/ref/opencvref_ml.htm
//
// Author : Feroz M Basheer
//
// DISCLAIMER:THIS CODE IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND, WHETHER
// EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE WARRANTIES OF
// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. THE
// ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THIS CODE IS WITH YOU.
#include "stdafx.h"
#include "cv.h"
#include "ml.h"
// The neural network
CvANN_MLP machineBrain;
// Read the training data and train the network.
void trainMachine()
{
int i;
//The number of training samples.
int train_sample_count;
//The training data matrix.
//Note that we are limiting the number of training data samples to 1000 here.
//The data sample consists of two inputs and an output. That's why 3.
float td[1000][3];
//Read the training file
/*
A sample file contents(say we are training the network for generating
the mean given two numbers) would be:
5
12 16 14
10 5 7.5
8 10 9
5 4 4.5
12 6 9
*/
FILE *fin;
fin = fopen("train.txt", "r");
//Get the number of samples.
fscanf(fin, "%d", &train_sample_count);
printf("Found training file with %d samples...\n", train_sample_count);
//Create the matrices
//Input data samples. Matrix of order (train_sample_count x 2)
CvMat* trainData = cvCreateMat(train_sample_count, 2, CV_32FC1);
//Output data samples. Matrix of order (train_sample_count x 1)
CvMat* trainClasses = cvCreateMat(train_sample_count, 1, CV_32FC1);
//The weight of each training data sample. We'll later set all to equal weights.
CvMat* sampleWts = cvCreateMat(train_sample_count, 1, CV_32FC1);
//The matrix representation of our ANN. We'll have four layers.
CvMat* neuralLayers = cvCreateMat(4, 1, CV_32SC1);
CvMat trainData1, trainClasses1, neuralLayers1, sampleWts1;
cvGetRows(trainData, &trainData1, 0, train_sample_count);
cvGetRows(trainClasses, &trainClasses1, 0, train_sample_count);
cvGetRows(trainClasses, &trainClasses1, 0, train_sample_count);
cvGetRows(sampleWts, &sampleWts1, 0, train_sample_count);
cvGetRows(neuralLayers, &neuralLayers1, 0, 4);
//Setting the number of neurons on each layer of the ANN
/*
We have in Layer 1: 2 neurons (2 inputs)
Layer 2: 3 neurons (hidden layer)
Layer 3: 3 neurons (hidden layer)
Layer 4: 1 neurons (1 output)
*/
cvSet1D(&neuralLayers1, 0, cvScalar(2));
cvSet1D(&neuralLayers1, 1, cvScalar(3));
cvSet1D(&neuralLayers1, 2, cvScalar(3));
cvSet1D(&neuralLayers1, 3, cvScalar(1));
//Read and populate the samples.
for (i=0;i<train_sample_count;i++)
fscanf(fin,"%f %f %f",&td[i][0],&td[i][1],&td[i][2]);
fclose(fin);
//Assemble the ML training data.
for (i=0; i<train_sample_count; i++)
{
//Input 1
cvSetReal2D(&trainData1, i, 0, td[i][0]);
//Input 2
cvSetReal2D(&trainData1, i, 1, td[i][1]);
//Output
cvSet1D(&trainClasses1, i, cvScalar(td[i][2]));
//Weight (setting everything to 1)
cvSet1D(&sampleWts1, i, cvScalar(1));
}
//Create our ANN.
machineBrain.create(neuralLayers);
//Train it with our data.
//See the Machine learning reference at http://www.seas.upenn.edu/~bensapp/opencvdocs/ref/opencvref_ml.htm#ch_ann
machineBrain.train(
trainData,
trainClasses,
sampleWts,
0,
CvANN_MLP_TrainParams(
cvTermCriteria(
CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
100000,
1.0
),
CvANN_MLP_TrainParams::BACKPROP,
0.01,
0.05
)
);
}
// Predict the output with the trained ANN given the two inputs.
void Predict(float data1, float data2)
{
float _sample[2];
CvMat sample = cvMat(1, 2, CV_32FC1, _sample);
float _predout[1];
CvMat predout = cvMat(1, 1, CV_32FC1, _predout);
sample.data.fl[0] = data1;
sample.data.fl[1] = data2;
machineBrain.predict(&sample, &predout);
printf("%f \n",predout.data.fl[0]);
}
int _tmain(int argc, _TCHAR* argv[])
{
int wait;
// Train the neural network with the samples
trainMachine();
// Now try predicting some values with the trained network
Predict(15.0,20.0);
Predict(1.0,5.0);
Predict(12.0,3.0);
//I'll wait for an integer. :)
scanf("%d",&wait);
return 0;
}
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