cavis/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/conf/JsonTest.java

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 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://www.apache.org/licenses/LICENSE-2.0.
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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 * under the License.
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 * SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.nn.conf;

import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.LossLayer;
import org.junit.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.impl.*;

import static org.junit.Assert.assertEquals;

/**
 * Created by Alex on 26/06/2017.
 */
public class JsonTest extends BaseDL4JTest {

    @Test
    public void testJsonLossFunctions() {

        ILossFunction[] lossFunctions = new ILossFunction[] {new LossBinaryXENT(), new LossBinaryXENT(),
                        new LossCosineProximity(), new LossHinge(), new LossKLD(), new LossKLD(), new LossL1(),
                        new LossL1(), new LossL2(), new LossL2(), new LossMAE(), new LossMAE(), new LossMAPE(),
                        new LossMAPE(), new LossMCXENT(), new LossMSE(), new LossMSE(), new LossMSLE(), new LossMSLE(),
                        new LossNegativeLogLikelihood(), new LossNegativeLogLikelihood(), new LossPoisson(),
                        new LossSquaredHinge(), new LossFMeasure(), new LossFMeasure(2.0)};

        Activation[] outputActivationFn = new Activation[] {Activation.SIGMOID, //xent
                        Activation.SIGMOID, //xent
                        Activation.TANH, //cosine
                        Activation.TANH, //hinge -> trying to predict 1 or -1
                        Activation.SIGMOID, //kld -> probab so should be between 0 and 1
                        Activation.SOFTMAX, //kld + softmax
                        Activation.TANH, //l1
                        Activation.SOFTMAX, //l1 + softmax
                        Activation.TANH, //l2
                        Activation.SOFTMAX, //l2 + softmax
                        Activation.IDENTITY, //mae
                        Activation.SOFTMAX, //mae + softmax
                        Activation.IDENTITY, //mape
                        Activation.SOFTMAX, //mape + softmax
                        Activation.SOFTMAX, //mcxent
                        Activation.IDENTITY, //mse
                        Activation.SOFTMAX, //mse + softmax
                        Activation.SIGMOID, //msle  -   requires positive labels/activations due to log
                        Activation.SOFTMAX, //msle + softmax
                        Activation.SIGMOID, //nll
                        Activation.SOFTMAX, //nll + softmax
                        Activation.SIGMOID, //poisson - requires positive predictions due to log... not sure if this is the best option
                        Activation.TANH, //squared hinge
                        Activation.SIGMOID, //f-measure (binary, single sigmoid output)
                        Activation.SOFTMAX //f-measure (binary, 2-label softmax output)
        };

        int[] nOut = new int[] {1, //xent
                        3, //xent
                        5, //cosine
                        3, //hinge
                        3, //kld
                        3, //kld + softmax
                        3, //l1
                        3, //l1 + softmax
                        3, //l2
                        3, //l2 + softmax
                        3, //mae
                        3, //mae + softmax
                        3, //mape
                        3, //mape + softmax
                        3, //mcxent
                        3, //mse
                        3, //mse + softmax
                        3, //msle
                        3, //msle + softmax
                        3, //nll
                        3, //nll + softmax
                        3, //poisson
                        3, //squared hinge
                        1, //f-measure (binary, single sigmoid output)
                        2, //f-measure (binary, 2-label softmax output)
        };

        for (int i = 0; i < lossFunctions.length; i++) {

            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).updater(Updater.ADAM).list()
                            .layer(0, new DenseLayer.Builder().nIn(4).nOut(nOut[i]).activation(Activation.TANH).build())
                            .layer(1, new LossLayer.Builder().lossFunction(lossFunctions[i])
                                            .activation(outputActivationFn[i]).build())
                            .validateOutputLayerConfig(false).build();

            String json = conf.toJson();
            String yaml = conf.toYaml();

            MultiLayerConfiguration fromJson = MultiLayerConfiguration.fromJson(json);
            MultiLayerConfiguration fromYaml = MultiLayerConfiguration.fromYaml(yaml);

            assertEquals(conf, fromJson);
            assertEquals(conf, fromYaml);
        }
    }

}