RBMTests.java

package org.deeplearning4j.models.featuredetectors.rbm;

import static org.junit.Assert.*;

import org.deeplearning4j.linalg.api.ndarray.INDArray;
import org.deeplearning4j.linalg.factory.NDArrays;
import org.deeplearning4j.linalg.lossfunctions.LossFunctions;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.junit.Test;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;


/**
 * Created by agibsonccc on 8/27/14.
 */
public class RBMTests {
    private static Logger log = LoggerFactory.getLogger(RBMTests.class);


    @Test
    public void testBasic() {
        float[][] data = new float[][]
                {
                        {1,1,1,0,0,0},
                        {1,0,1,0,0,0},
                        {1,1,1,0,0,0},
                        {0,0,1,1,1,0},
                        {0,0,1,1,0,0},
                        {0,0,1,1,1,0},
                        {0,0,1,1,1,0}
                };


        INDArray input = NDArrays.create(data);

        NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
                .lossFunction(LossFunctions.LossFunction.RMSE_XENT)
                .learningRate(1e-1f).nIn(6).nOut(4).build();
        Model rbm = new RBM.Builder().configure(conf).withInput(input).build();
        rbm.fit(input,null);



    }


}