gistfile1.txt

package org.deeplearning4j;

import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.graph.ElementWiseVertex;
import org.deeplearning4j.nn.conf.graph.MergeVertex;
import org.deeplearning4j.nn.conf.graph.ReshapeVertex;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.EmbeddingLayer;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.junit.Test;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Sgd;

public class Debug8382 {

    @Test
    public void test() {
        ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
                .updater(new Sgd(0.01))
                .graphBuilder()
                .addInputs("input1", "input2", "input3") //can use any label for this
                .addLayer("e_1", new EmbeddingLayer.Builder().nIn(10).nOut(5).build(), "input1")
                .addLayer("e_2", new EmbeddingLayer.Builder().nIn(10).nOut(5).build(), "input2")
                .addLayer("d_1", new DenseLayer.Builder().nIn(1).nOut(5).build(), "input3")
                .addVertex("e_1_reshape", new ReshapeVertex(-1, 1, 5), "e_1")
                .addVertex("e_2_reshape", new ReshapeVertex(-1, 1, 5), "e_2")
                .addVertex("d_1_reshape", new ReshapeVertex(-1, 1, 5), "d_1")
                .addVertex("stacking_1", new MergeVertex(), "e_1_reshape", "e_2_reshape", "d_1_reshape")
                .addLayer("a_plus_b", new tensors_sum(), "stacking_1")
                .addLayer("a_plus_b_square", new tensors_square(), "a_plus_b")
                .addLayer("a_square_b_square", new tensors_square(), "stacking_1")
                .addLayer("a_sq_plus_b_sq", new tensors_sum(), "a_square_b_square")
                .addVertex("2ab", new ElementWiseVertex(ElementWiseVertex.Op.Subtract), "a_plus_b_square", "a_sq_plus_b_sq")
                .addLayer("ab", new tensors_by_2(), "2ab")
                .addLayer("ab_sum", new tensors_sum(), "ab")
                .addVertex("2d_out", new ReshapeVertex(-1, 1), "ab_sum")
                .setOutputs("2d_out")
                .build();

        ComputationGraph cg = new ComputationGraph(conf);
        cg.init();

        INDArray x = Nd4j.ones(DataType.FLOAT, 1, 1);
        INDArray out = cg.outputSingle(x, x, x);

        System.out.println(out);
        System.out.println(out.shapeInfoToString());
    }


    public static class tensors_sum extends SameDiffLambdaLayer {
        @Override
        public SDVariable defineLayer(SameDiff sd, SDVariable x) {
            return sd.sum(x, false, 1);
        }
    }

    public static class tensors_square extends SameDiffLambdaLayer {
        @Override
        public SDVariable defineLayer(SameDiff sd, SDVariable x) {
            return sd.math.square(x);
        }
    }

    public static class tensors_by_2 extends SameDiffLambdaLayer {
        @Override
        public SDVariable defineLayer(SameDiff sd, SDVariable x) {
            return x.mul(0.5);
        }
    }
}