Java DenseLayer类代码示例

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
/** Returns the network configuration, 2 hidden DenseLayers of size 50.
 */
private static MultiLayerConfiguration getDeepDenseLayerNetworkConfiguration() {
    final int numHiddenNodes = 50;
    return new NeuralNetConfiguration.Builder()
            .seed(seed)
            .iterations(iterations)
            .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
            .learningRate(learningRate)
            .weightInit(WeightInit.XAVIER)
            .updater(Updater.NESTEROVS).momentum(0.9)
            .list()
            .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
                    .activation(Activation.TANH).build())
            .layer(1, new DenseLayer.Builder().nIn(numHiddenNodes).nOut(numHiddenNodes)
                    .activation(Activation.TANH).build())
            .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
                    .activation(Activation.IDENTITY)
                    .nIn(numHiddenNodes).nOut(numOutputs).build())
            .pretrain(false).backprop(true).build();
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
@Override
   protected MultiLayerConfiguration getConfiguration()
   {
return new NeuralNetConfiguration.Builder().seed(parameters.getSeed()).iterations(parameters.getIterations())
	.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(parameters.getLearningRate()).l2(0.001)
	.list(4)
	.layer(0,
		new DenseLayer.Builder().nIn(parameters.getInputSize()).nOut(250).weightInit(WeightInit.XAVIER)
			.updater(Updater.ADAGRAD).activation("relu").build())
	.layer(1,
		new DenseLayer.Builder().nIn(250).nOut(10).weightInit(WeightInit.XAVIER)
			.updater(Updater.ADAGRAD).activation("relu").build())
	.layer(2,
		new DenseLayer.Builder().nIn(10).nOut(250).weightInit(WeightInit.XAVIER)
			.updater(Updater.ADAGRAD).activation("relu").build())
	.layer(3,
		new OutputLayer.Builder().nIn(250).nOut(parameters.getInputSize()).weightInit(WeightInit.XAVIER)
			.updater(Updater.ADAGRAD).activation("relu")
			.lossFunction(LossFunctions.LossFunction.MSE).build())
	.pretrain(false).backprop(true).build();
   }
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
private static MultiLayerConfiguration net(int nIn, int nOut) {
    return new NeuralNetConfiguration.Builder()
            .seed(42)
            .iterations(1)
            .activation(Activation.RELU)
            .weightInit(WeightInit.XAVIER)
            .learningRate(0.1)
            .regularization(true).l2(1e-4)
            .list(
                    new DenseLayer.Builder().nIn(nIn).nOut(3).build(),
                    new DenseLayer.Builder().nIn(3).nOut(3).build(),
                    new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                            .activation(Activation.SOFTMAX)
                            .nIn(3)
                            .nOut(nOut)
                            .build()
            )
            .build();
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
public static MultiLayerNetwork getOriginalNet(int seed){
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(seed)
            .weightInit(WeightInit.XAVIER)
            .activation(Activation.TANH)
            .convolutionMode(ConvolutionMode.Same)
            .updater(new Sgd(0.3))
            .list()
            .layer(new ConvolutionLayer.Builder().nOut(3).kernelSize(2,2).stride(1,1).build())
            .layer(new SubsamplingLayer.Builder().kernelSize(2,2).stride(1,1).build())
            .layer(new ConvolutionLayer.Builder().nIn(3).nOut(3).kernelSize(2,2).stride(1,1).build())
            .layer(new DenseLayer.Builder().nOut(64).build())
            .layer(new DenseLayer.Builder().nIn(64).nOut(64).build())
            .layer(new OutputLayer.Builder().nIn(64).nOut(10).lossFunction(LossFunctions.LossFunction.MSE).build())
            .setInputType(InputType.convolutionalFlat(28,28,1))
            .build();


    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    return net;
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
public static ComputationGraph getOriginalGraph(int seed){
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(seed)
            .weightInit(WeightInit.XAVIER)
            .activation(Activation.TANH)
            .convolutionMode(ConvolutionMode.Same)
            .updater(new Sgd(0.3))
            .graphBuilder()
            .addInputs("in")
            .layer("0", new ConvolutionLayer.Builder().nOut(3).kernelSize(2,2).stride(1,1).build(), "in")
            .layer("1", new SubsamplingLayer.Builder().kernelSize(2,2).stride(1,1).build(), "0")
            .layer("2", new ConvolutionLayer.Builder().nIn(3).nOut(3).kernelSize(2,2).stride(1,1).build(), "1")
            .layer("3", new DenseLayer.Builder().nOut(64).build(), "2")
            .layer("4", new DenseLayer.Builder().nIn(64).nOut(64).build(), "3")
            .layer("5", new OutputLayer.Builder().nIn(64).nOut(10).lossFunction(LossFunctions.LossFunction.MSE).build(), "4")
            .setOutputs("5")
            .setInputTypes(InputType.convolutionalFlat(28,28,1))
            .build();


    ComputationGraph net = new ComputationGraph(conf);
    net.init();
    return net;
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
@Test
public void testJsonComputationGraph() {
    //ComputationGraph with a custom layer; check JSON and YAML config actually works...

    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder()
                    .addInputs("in").addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in")
                    .addLayer("1", new CustomLayer(3.14159), "0").addLayer("2",
                                    new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10)
                                                    .build(),
                                    "1")
                    .setOutputs("2").pretrain(false).backprop(true).build();

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

    System.out.println(json);

    ComputationGraphConfiguration confFromJson = ComputationGraphConfiguration.fromJson(json);
    assertEquals(conf, confFromJson);

    ComputationGraphConfiguration confFromYaml = ComputationGraphConfiguration.fromYaml(yaml);
    assertEquals(conf, confFromYaml);
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
@Test
public void checkInitializationFF() {
    //Actually create a network with a custom layer; check initialization and forward pass

    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
                    .layer(0, new DenseLayer.Builder().nIn(9).nOut(10).build()).layer(1, new CustomLayer(3.14159)) //hard-coded nIn/nOut of 10
                    .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(11).build())
                    .pretrain(false).backprop(true).build();

    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();

    assertEquals(9 * 10 + 10, net.getLayer(0).numParams());
    assertEquals(10 * 10 + 10, net.getLayer(1).numParams());
    assertEquals(10 * 11 + 11, net.getLayer(2).numParams());

    //Check for exceptions...
    net.output(Nd4j.rand(1, 9));
    net.fit(new DataSet(Nd4j.rand(1, 9), Nd4j.rand(1, 11)));
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
@Test
public void testMultiCNNLayer() throws Exception {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).seed(123).list()
                    .layer(0, new ConvolutionLayer.Builder().nIn(1).nOut(6).weightInit(WeightInit.XAVIER)
                                    .activation(Activation.RELU).build())
                    .layer(1, new LocalResponseNormalization.Builder().build()).layer(2,
                                    new DenseLayer.Builder()
                                                    .nOut(2).build())
                    .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                                    .weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(2).nOut(10)
                                    .build())
                    .backprop(true).pretrain(false).setInputType(InputType.convolutionalFlat(28, 28, 1)).build();

    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    DataSetIterator iter = new MnistDataSetIterator(2, 2);
    DataSet next = iter.next();

    network.fit(next);
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
private static MultiLayerNetwork getDenseMLNConfig(boolean backprop, boolean pretrain) {
    int numInputs = 4;
    int outputNum = 3;
    long seed = 6;

    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed)
                    .updater(new Sgd(1e-3)).l1(0.3).l2(1e-3).list()
                    .layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(numInputs).nOut(3)
                                    .activation(Activation.TANH).weightInit(WeightInit.XAVIER).build())
                    .layer(1, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(3).nOut(2)
                                    .activation(Activation.TANH).weightInit(WeightInit.XAVIER).build())
                    .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                                    .weightInit(WeightInit.XAVIER).nIn(2).nOut(outputNum).build())
                    .backprop(backprop).pretrain(pretrain).build();

    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    return model;

}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
private ComputationGraph getGraph(int numLabels, double lambda) {
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345)
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(new NoOp())
                    .graphBuilder().addInputs("input1")
                    .addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.RELU).build(),
                                    "input1")
                    .addLayer("lossLayer", new CenterLossOutputLayer.Builder()
                                    .lossFunction(LossFunctions.LossFunction.MCXENT).nIn(5).nOut(numLabels)
                                    .lambda(lambda).activation(Activation.SOFTMAX).build(), "l1")
                    .setOutputs("lossLayer").pretrain(false).backprop(true).build();

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

    return graph;
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
/** Very simple back-prop config set up for Iris.
 * Learning Rate = 0.1
 * No regularization, no Adagrad, no momentum etc. One iteration.
 */
private static MultiLayerConfiguration getIrisMLPSimpleConfig(int[] hiddenLayerSizes,
                Activation activationFunction) {
    NeuralNetConfiguration.ListBuilder lb = new NeuralNetConfiguration.Builder().updater(new Sgd(0.1))
                .seed(12345L).list();

    for (int i = 0; i < hiddenLayerSizes.length; i++) {
        int nIn = (i == 0 ? 4 : hiddenLayerSizes[i - 1]);
        lb.layer(i, new DenseLayer.Builder().nIn(nIn).nOut(hiddenLayerSizes[i]).weightInit(WeightInit.XAVIER)
                        .activation(activationFunction).build());
    }

    lb.layer(hiddenLayerSizes.length,
                    new OutputLayer.Builder(LossFunction.MCXENT).nIn(hiddenLayerSizes[hiddenLayerSizes.length - 1])
                                    .nOut(3).weightInit(WeightInit.XAVIER)
                                    .activation(activationFunction.equals(Activation.IDENTITY) ? Activation.IDENTITY
                                                    : Activation.SOFTMAX)
                                    .build());
    lb.pretrain(false).backprop(true);

    return lb.build();
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
@Test
public void testJSONBasic() {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345)
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(new NoOp())
                    .graphBuilder().addInputs("input")
                    .addLayer("firstLayer",
                                    new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(),
                                    "input")
                    .addLayer("outputLayer",
                                    new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
                                                    .activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
                                    "firstLayer")
                    .setOutputs("outputLayer").pretrain(false).backprop(true).build();

    String json = conf.toJson();
    ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);

    assertEquals(json, conf2.toJson());
    assertEquals(conf, conf2);
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
private MultiLayerNetwork getNetwork() {
    int nIn = 5;
    int nOut = 6;

    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).l1(0.01).l2(0.01)
            .updater(new Sgd(0.1)).activation(Activation.TANH).weightInit(WeightInit.XAVIER).list()
            .layer(0, new DenseLayer.Builder().nIn(nIn).nOut(20).build())
            .layer(1, new DenseLayer.Builder().nIn(20).nOut(30).build()).layer(2, new OutputLayer.Builder()
                    .lossFunction(LossFunctions.LossFunction.MSE).nIn(30).nOut(nOut).build())
            .build();

    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();

    return net;
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
@Test
public void testWriteCGModel() throws Exception {
    ComputationGraphConfiguration config = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new Sgd(0.1))
                    .graphBuilder().addInputs("in")
                    .addLayer("dense", new DenseLayer.Builder().nIn(4).nOut(2).build(), "in").addLayer("out",
                                    new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3)
                                                    .build(),
                                    "dense")
                    .setOutputs("out").pretrain(false).backprop(true).build();

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

    File tempFile = File.createTempFile("tsfs", "fdfsdf");
    tempFile.deleteOnExit();

    ModelSerializer.writeModel(cg, tempFile, true);

    ComputationGraph network = ModelSerializer.restoreComputationGraph(tempFile);

    assertEquals(network.getConfiguration().toJson(), cg.getConfiguration().toJson());
    assertEquals(cg.params(), network.params());
    assertEquals(cg.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
}
 

import org.deeplearning4j.nn.conf.layers.DenseLayer; //导入依赖的package包/类
@Test
public void testWriteCGModelInputStream() throws Exception {
    ComputationGraphConfiguration config = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new Sgd(0.1))
                    .graphBuilder().addInputs("in")
                    .addLayer("dense", new DenseLayer.Builder().nIn(4).nOut(2).build(), "in").addLayer("out",
                                    new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3)
                                                    .build(),
                                    "dense")
                    .setOutputs("out").pretrain(false).backprop(true).build();

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

    File tempFile = File.createTempFile("tsfs", "fdfsdf");
    tempFile.deleteOnExit();

    ModelSerializer.writeModel(cg, tempFile, true);
    FileInputStream fis = new FileInputStream(tempFile);

    ComputationGraph network = ModelSerializer.restoreComputationGraph(fis);

    assertEquals(network.getConfiguration().toJson(), cg.getConfiguration().toJson());
    assertEquals(cg.params(), network.params());
    assertEquals(cg.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
}