Simple sequential model example

6

@hodophile

do this:

package org.example;

import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration;
import org.deeplearning4j.nn.transferlearning.TransferLearning;
import org.nd4j.linalg.activations.impl.ActivationSoftmax;
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.Adam;

public class TestForumMlp {

    public static void main(String...args) throws Exception {

        String SIMPLE_MLP = "simple_mlp.h5";

        System.out.println(SIMPLE_MLP);
        MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights(SIMPLE_MLP, false);
        MultiLayerNetwork transferLearning = new TransferLearning.Builder(model)
                .fineTuneConfiguration(new FineTuneConfiguration.Builder()
                        .updater(new Adam()).build())
                .addLayer(new OutputLayer.Builder()
                        .nIn(10)
                        .nOut(10)

                        .activation(new ActivationSoftmax())
                        .build())
                .build();


        INDArray input = Nd4j.create(DataType.FLOAT, 256, 100);
        INDArray output = model.output(input);

        transferLearning.fit(input, output);


    }



}

Remove the extra output layer and you should be good to go. 99% of the time model import is used for inference then you add your updaters and other training information on top of that.

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