TestDistributionDeserializer.java

package org.deeplearning4j.regressiontest;

import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.distribution.*;
import org.junit.Test;
import org.nd4j.shade.jackson.databind.ObjectMapper;

import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;

/**
 * Created by Alex on 08/05/2017.
 */
public class TestDistributionDeserializer {

    @Test
    public void testDistributionDeserializer() throws Exception {
        //Test current format:
        Distribution[] distributions = new Distribution[]{
                new NormalDistribution(3,0.5),
                new UniformDistribution(-2,1),
                new GaussianDistribution(2,1.0),
                new BinomialDistribution(10,0.3)
        };

        ObjectMapper om = NeuralNetConfiguration.mapper();

        for( Distribution d : distributions){
            String json = om.writeValueAsString(d);
            Distribution fromJson = om.readValue(json, Distribution.class);

            assertEquals(d, fromJson);
        }
    }

    @Test
    public void testDistributionDeserializerLegacyFormat() throws Exception {
        ObjectMapper om = NeuralNetConfiguration.mapper();

        String normalJson = "{\n" +
                "          \"normal\" : {\n" +
                "            \"mean\" : 0.1,\n" +
                "            \"std\" : 1.2\n" +
                "          }\n" +
                "        }";

        Distribution nd = om.readValue(normalJson, Distribution.class);
        assertTrue(nd instanceof NormalDistribution);
        NormalDistribution normDist = (NormalDistribution)nd;
        assertEquals(0.1, normDist.getMean(), 1e-6);
        assertEquals(1.2, normDist.getStd(), 1e-6);


        String uniformJson = "{\n" +
                "                \"uniform\" : {\n" +
                "                  \"lower\" : -1.1,\n" +
                "                  \"upper\" : 2.2\n" +
                "                }\n" +
                "              }";

        Distribution ud = om.readValue(uniformJson, Distribution.class);
        assertTrue(ud instanceof UniformDistribution);
        UniformDistribution uniDist = (UniformDistribution) ud;
        assertEquals(-1.1, uniDist.getLower(), 1e-6);
        assertEquals(2.2, uniDist.getUpper(), 1e-6);


        String gaussianJson = "{\n" +
                "                \"gaussian\" : {\n" +
                "                  \"mean\" : 0.1,\n" +
                "                  \"std\" : 1.2\n" +
                "                }\n" +
                "              }";

        Distribution gd = om.readValue(gaussianJson, Distribution.class);
        assertTrue(gd instanceof GaussianDistribution);
        GaussianDistribution gDist = (GaussianDistribution)gd;
        assertEquals(0.1, gDist.getMean(), 1e-6);
        assertEquals(1.2, gDist.getStd(), 1e-6);

        String bernoulliJson = "{\n" +
                "                \"binomial\" : {\n" +
                "                  \"numberOfTrials\" : 10,\n" +
                "                  \"probabilityOfSuccess\" : 0.3\n" +
                "                }\n" +
                "              }";

        Distribution bd = om.readValue(bernoulliJson, Distribution.class);
        assertTrue(bd instanceof BinomialDistribution);
        BinomialDistribution binDist = (BinomialDistribution)bd;
        assertEquals(10, binDist.getNumberOfTrials());
        assertEquals(0.3, binDist.getProbabilityOfSuccess(), 1e-6);
    }

}