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Using @SuperBuilder for LayerConfigurations

master
Brian Rosenberger 2023-04-24 18:09:11 +02:00
parent 4482113f23
commit 396dbec24e
549 changed files with 18468 additions and 20124 deletions
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2
.old/deeplearning4j/deeplearning4j-dataimport-solrj/src/test/java/org/deeplearning4j/nn/dataimport/solr/client/solrj/io/stream/TupleStreamDataSetIteratorTest.java
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@ -207,7 +207,7 @@ public class TupleStreamDataSetIteratorTest extends SolrCloudTestCase {
final MultiLayerNetwork model = new MultiLayerNetwork( final MultiLayerNetwork model = new MultiLayerNetwork(
NeuralNetConfiguration.builder() NeuralNetConfiguration.builder()
.list( .list(
new OutputLayer.Builder(LossFunction.MSE) OutputLayer.builder(LossFunction.MSE)
.nIn(3) .nIn(3)
.nOut(1) .nOut(1)
.weightInit(WeightInit.ONES) .weightInit(WeightInit.ONES)

2
.old/deeplearning4j/deeplearning4j-modelexport-solr/src/test/java/org/deeplearning4j/nn/modelexport/solr/handler/ModelTupleStreamIntegrationTest.java
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@ -155,7 +155,7 @@ public class ModelTupleStreamIntegrationTest extends SolrCloudTestCase {
final NeuralNetConfiguration conf = NeuralNetConfiguration.builder() final NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.list( .list(
new OutputLayer.Builder() OutputLayer.builder()
.nIn(numInputs) .nIn(numInputs)
.nOut(numOutputs) .nOut(numOutputs)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)

4
.old/deeplearning4j/deeplearning4j-modelexport-solr/src/test/java/org/deeplearning4j/nn/modelexport/solr/handler/ModelTupleStreamTest.java
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@ -244,7 +244,7 @@ public class ModelTupleStreamTest {
final NeuralNetConfiguration conf = NeuralNetConfiguration.builder() final NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.list( .list(
new OutputLayer.Builder() OutputLayer.builder()
.nIn(numInputs) .nIn(numInputs)
.nOut(numOutputs) .nOut(numOutputs)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)
@ -278,7 +278,7 @@ public class ModelTupleStreamTest {
.graphBuilder() .graphBuilder()
.addInputs("inputLayer") .addInputs("inputLayer")
.addLayer("outputLayer", .addLayer("outputLayer",
new OutputLayer.Builder() OutputLayer.builder()
.nIn(numInputs) .nIn(numInputs)
.nOut(numOutputs) .nOut(numOutputs)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)

4
.old/deeplearning4j/deeplearning4j-modelexport-solr/src/test/java/org/deeplearning4j/nn/modelexport/solr/ltr/model/ScoringModelTest.java
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@ -194,7 +194,7 @@ public class ScoringModelTest {
final NeuralNetConfiguration conf = NeuralNetConfiguration.builder() final NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.list( .list(
new OutputLayer.Builder().nIn(numFeatures).nOut(1).lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).build() OutputLayer.builder().nIn(numFeatures).nOut(1).lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).build()
) )
.build(); .build();
@ -221,7 +221,7 @@ public class ScoringModelTest {
.graphBuilder() .graphBuilder()
.addInputs("inputLayer") .addInputs("inputLayer")
.addLayer("outputLayer", .addLayer("outputLayer",
new OutputLayer.Builder().nIn(numFeatures).nOut(1).lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).build(), OutputLayer.builder().nIn(numFeatures).nOut(1).lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).build(),
"inputLayer") "inputLayer")
.setOutputs("outputLayer") .setOutputs("outputLayer")
.build(); .build();

20
.old/deeplearning4j/deeplearning4j-remote/deeplearning4j-json-server/src/test/java/org/deeplearning4j/remote/JsonModelServerTest.java
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@ -75,8 +75,8 @@ public class JsonModelServerTest extends BaseDL4JTest {
.updater(new Adam(0.119f)) .updater(new Adam(0.119f))
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .list()
.layer(0, new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(10).build()) .layer(0, DenseLayer.builder().activation(Activation.TANH).nIn(4).nOut(10).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.SQUARED_LOSS).activation(Activation.SIGMOID).nIn(10).nOut(1).build()) .layer(1, OutputLayer.builder(LossFunctions.LossFunction.SQUARED_LOSS).activation(Activation.SIGMOID).nIn(10).nOut(1).build())
.build(); .build();
model = new MultiLayerNetwork(conf); model = new MultiLayerNetwork(conf);
@ -543,8 +543,8 @@ public class JsonModelServerTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.list() .list()
.layer(new DenseLayer.Builder().nIn(784).nOut(10).build()) .layer(DenseLayer.builder().nIn(784).nOut(10).build())
.layer(new LossLayer.Builder().activation(Activation.SOFTMAX).build()) .layer(LossLayer.builder().lossFunction().activation(Activation.SOFTMAX).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -600,10 +600,10 @@ public class JsonModelServerTest extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("input1", "input2") .addInputs("input1", "input2")
.addLayer("L1", new DenseLayer.Builder().nIn(3).nOut(4).build(), "input1") .addLayer("L1", DenseLayer.builder().nIn(3).nOut(4).build(), "input1")
.addLayer("L2", new DenseLayer.Builder().nIn(3).nOut(4).build(), "input2") .addLayer("L2", DenseLayer.builder().nIn(3).nOut(4).build(), "input2")
.addVertex("merge", new MergeVertex(), "L1", "L2") .addVertex("merge", new MergeVertex(), "L1", "L2")
.addLayer("out", new OutputLayer.Builder().nIn(4+4).nOut(3).build(), "merge") .addLayer("out", OutputLayer.builder().nIn(4+4).nOut(3).build(), "merge")
.setOutputs("out") .setOutputs("out")
.build(); .build();
@ -656,11 +656,11 @@ public class JsonModelServerTest extends BaseDL4JTest {
.updater(new Sgd(0.01)) .updater(new Sgd(0.01))
.graphBuilder() .graphBuilder()
.addInputs("input") .addInputs("input")
.addLayer("L1", new DenseLayer.Builder().nIn(8).nOut(4).build(), "input") .addLayer("L1", DenseLayer.builder().nIn(8).nOut(4).build(), "input")
.addLayer("out1", new OutputLayer.Builder() .addLayer("out1", OutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nIn(4).nOut(3).build(), "L1") .nIn(4).nOut(3).build(), "L1")
.addLayer("out2", new OutputLayer.Builder() .addLayer("out2", OutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MSE) .lossFunction(LossFunctions.LossFunction.MSE)
.nIn(4).nOut(2).build(), "L1") .nIn(4).nOut(2).build(), "L1")
.setOutputs("out1","out2") .setOutputs("out1","out2")

4
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-parameterserver/src/test/java/org/deeplearning4j/spark/parameterserver/BaseSparkTest.java
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@ -129,9 +129,9 @@ public abstract class BaseSparkTest extends BaseDL4JTest implements Serializable
protected NeuralNetConfiguration getBasicConf() { protected NeuralNetConfiguration getBasicConf() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
.updater(new Nesterovs(0.1, 0.9)).list() .updater(new Nesterovs(0.1, 0.9)).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(nIn).nOut(3) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(nIn).nOut(3)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(3).nOut(nOut) LossFunctions.LossFunction.MCXENT).nIn(3).nOut(nOut)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();

10
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-parameterserver/src/test/java/org/deeplearning4j/spark/parameterserver/train/GradientSharingTrainingTest.java
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@ -137,7 +137,7 @@ public class GradientSharingTrainingTest extends BaseSparkTest {
.updater(new AMSGrad(0.1)) .updater(new AMSGrad(0.1))
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("out", new OutputLayer.Builder().nIn(784).nOut(10).activation(Activation.SOFTMAX) .layer("out", OutputLayer.builder().nIn(784).nOut(10).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("out") .setOutputs("out")
.build(); .build();
@ -272,15 +272,15 @@ public class GradientSharingTrainingTest extends BaseSparkTest {
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.seed(12345) .seed(12345)
.list() .list()
.layer(new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build()) .layer(OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
} else { } else {
conf = NeuralNetConfiguration.builder() conf = NeuralNetConfiguration.builder()
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.seed(12345) .seed(12345)
.list() .list()
.layer(new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()) .layer(DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build())
.layer(new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build()) .layer(OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
} }
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -358,7 +358,7 @@ public class GradientSharingTrainingTest extends BaseSparkTest {
.updater(new AMSGrad(0.001)) .updater(new AMSGrad(0.001))
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("out", new OutputLayer.Builder().nIn(784).nOut(10).activation(Activation.SOFTMAX) .layer("out", OutputLayer.builder().nIn(784).nOut(10).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("out") .setOutputs("out")
.build(); .build();

4
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/BaseSparkTest.java
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@ -132,9 +132,9 @@ public abstract class BaseSparkTest extends BaseDL4JTest implements Serializable
protected NeuralNetConfiguration getBasicConf() { protected NeuralNetConfiguration getBasicConf() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
.updater(new Nesterovs(0.1, 0.9)).list() .updater(new Nesterovs(0.1, 0.9)).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(nIn).nOut(3) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(nIn).nOut(3)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(3).nOut(nOut) LossFunctions.LossFunction.MCXENT).nIn(3).nOut(nOut)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();

10
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/TestEarlyStoppingSpark.java
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@ -71,7 +71,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd()).weightInit(WeightInit.XAVIER).list() .updater(new Sgd()).weightInit(WeightInit.XAVIER).list()
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -127,7 +127,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(10.0)) //Intentionally huge LR .updater(new Sgd(10.0)) //Intentionally huge LR
.weightInit(WeightInit.XAVIER).list() .weightInit(WeightInit.XAVIER).list()
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.IDENTITY) .layer(0, OutputLayer.builder().nIn(4).nOut(3).activation(Activation.IDENTITY)
.lossFunction(LossFunctions.LossFunction.MSE).build()) .lossFunction(LossFunctions.LossFunction.MSE).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -166,7 +166,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(1e-6)).weightInit(WeightInit.XAVIER).list() .updater(new Sgd(1e-6)).weightInit(WeightInit.XAVIER).list()
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -212,7 +212,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.0)).weightInit(WeightInit.XAVIER).list() .updater(new Sgd(0.0)).weightInit(WeightInit.XAVIER).list()
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -249,7 +249,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd()).weightInit(WeightInit.XAVIER).list() .updater(new Sgd()).weightInit(WeightInit.XAVIER).list()
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);

10
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/TestEarlyStoppingSparkCompGraph.java
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@ -74,7 +74,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd()).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in") .updater(new Sgd()).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf); ComputationGraph net = new ComputationGraph(conf);
@ -128,7 +128,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(2.0)) //Intentionally huge LR .updater(new Sgd(2.0)) //Intentionally huge LR
.weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in") .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.IDENTITY) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3).activation(Activation.IDENTITY)
.lossFunction(LossFunctions.LossFunction.MSE).build(), "in") .lossFunction(LossFunctions.LossFunction.MSE).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf); ComputationGraph net = new ComputationGraph(conf);
@ -169,7 +169,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(1e-6)).weightInit(WeightInit.XAVIER).graphBuilder() .updater(new Sgd(1e-6)).weightInit(WeightInit.XAVIER).graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf); ComputationGraph net = new ComputationGraph(conf);
@ -217,7 +217,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.0)).weightInit(WeightInit.XAVIER).graphBuilder() .updater(new Sgd(0.0)).weightInit(WeightInit.XAVIER).graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf); ComputationGraph net = new ComputationGraph(conf);
@ -256,7 +256,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd()).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in") .updater(new Sgd()).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf); ComputationGraph net = new ComputationGraph(conf);

22
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/TestKryo.java
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@ -69,7 +69,7 @@ public class TestKryo extends BaseSparkKryoTest {
m.put(0, 0.5); m.put(0, 0.5);
m.put(10, 0.1); m.put(10, 0.1);
NeuralNetConfiguration mlc = NeuralNetConfiguration.builder() NeuralNetConfiguration mlc = NeuralNetConfiguration.builder()
.updater(new Nadam(new MapSchedule(ScheduleType.ITERATION,m))).list().layer(0, new OutputLayer.Builder().nIn(10).nOut(10).build()) .updater(new Nadam(new MapSchedule(ScheduleType.ITERATION,m))).list().layer(0, OutputLayer.builder().nIn(10).nOut(10).build())
.build(); .build();
testSerialization(mlc, si); testSerialization(mlc, si);
@ -79,23 +79,23 @@ public class TestKryo extends BaseSparkKryoTest {
.dist(new UniformDistribution(-1, 1)) .dist(new UniformDistribution(-1, 1))
.updater(new Adam(new MapSchedule(ScheduleType.ITERATION,m))) .updater(new Adam(new MapSchedule(ScheduleType.ITERATION,m)))
.graphBuilder() .graphBuilder()
.addInputs("in").addLayer("out", new OutputLayer.Builder().nIn(10).nOut(10).build(), "in") .addInputs("in").addLayer("out", OutputLayer.builder().nIn(10).nOut(10).build(), "in")
.setOutputs("out").build(); .setOutputs("out").build();
testSerialization(cgc, si); testSerialization(cgc, si);
//Check main layers: //Check main layers:
Layer[] layers = new Layer[] {new OutputLayer.Builder().nIn(10).nOut(10).build(), Layer[] layers = new Layer[] {OutputLayer.builder().nIn(10).nOut(10).build(),
new RnnOutputLayer.Builder().nIn(10).nOut(10).build(), new LossLayer.Builder().build(), RnnOutputLayer.builder().nIn(10).nOut(10).build(), LossLayer.builder().lossFunction().build(),
new CenterLossOutputLayer.Builder().nIn(10).nOut(10).build(), CenterLossOutputLayer.builder().nIn(10).nOut(10).build(),
new DenseLayer.Builder().nIn(10).nOut(10).build(), DenseLayer.builder().nIn(10).nOut(10).build(),
new ConvolutionLayer.Builder().nIn(10).nOut(10).build(), new SubsamplingLayer.Builder().build(), ConvolutionLayer.builder().nIn(10).nOut(10).build(), SubsamplingLayer.builder().build(),
new Convolution1DLayer.Builder(2, 2).nIn(10).nOut(10).build(), new Convolution1DLayer.Builder(2, 2).nIn(10).nOut(10).build(),
new ActivationLayer.Builder().activation(Activation.TANH).build(), ActivationLayer.builder().activation(Activation.TANH).build(),
new GlobalPoolingLayer.Builder().build(), new GravesLSTM.Builder().nIn(10).nOut(10).build(), GlobalPoolingLayer.builder().build(), GravesLSTM.builder().nIn(10).nOut(10).build(),
new LSTM.Builder().nIn(10).nOut(10).build(), new DropoutLayer.Builder(0.5).build(), LSTM.builder().nIn(10).nOut(10).build(), DropoutLayer.builder(0.5).build(),
new BatchNormalization.Builder().build(), new LocalResponseNormalization.Builder().build()}; BatchNormalization.builder().build(), LocalResponseNormalization.builder().build()};
for (Layer l : layers) { for (Layer l : layers) {
testSerialization(l, si); testSerialization(l, si);

12
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/datavec/TestPreProcessedData.java
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@ -86,9 +86,9 @@ public class TestPreProcessedData extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(Updater.RMSPROP) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(Updater.RMSPROP)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(4).nOut(3) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(4).nOut(3)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).activation(Activation.SOFTMAX) LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).activation(Activation.SOFTMAX)
.build()) .build())
.build(); .build();
@ -137,9 +137,9 @@ public class TestPreProcessedData extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(Updater.RMSPROP) ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(Updater.RMSPROP)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.graphBuilder().addInputs("in") .graphBuilder().addInputs("in")
.addLayer("0", new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(4).nOut(3) .addLayer("0", org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(4).nOut(3)
.activation(Activation.TANH).build(), "in") .activation(Activation.TANH).build(), "in")
.addLayer("1", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .addLayer("1", org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).activation(Activation.SOFTMAX) LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).activation(Activation.SOFTMAX)
.build(), .build(),
"0") "0")
@ -191,9 +191,9 @@ public class TestPreProcessedData extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(Updater.RMSPROP) ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(Updater.RMSPROP)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.graphBuilder().addInputs("in") .graphBuilder().addInputs("in")
.addLayer("0", new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(4).nOut(3) .addLayer("0", org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(4).nOut(3)
.activation(Activation.TANH).build(), "in") .activation(Activation.TANH).build(), "in")
.addLayer("1", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .addLayer("1", org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).activation(Activation.SOFTMAX) LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).activation(Activation.SOFTMAX)
.build(), .build(),
"0") "0")

4
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/TestKryoWarning.java
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@ -41,7 +41,7 @@ public class TestKryoWarning {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new OutputLayer.Builder().nIn(10).nOut(10).build()) .layer(0, OutputLayer.builder().nIn(10).nOut(10).build())
.build(); .build();
TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(1).build(); TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(1).build();
@ -58,7 +58,7 @@ public class TestKryoWarning {
try { try {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in") ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(10).nOut(10).build(), "in").setOutputs("0") .addLayer("0", OutputLayer.builder().nIn(10).nOut(10).build(), "in").setOutputs("0")
.build(); .build();
TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(1).build(); TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(1).build();

4
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/customlayer/TestCustomLayer.java
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@ -53,9 +53,9 @@ public class TestCustomLayer extends BaseSparkTest {
//Custom layers are tested more extensively in dl4j core //Custom layers are tested more extensively in dl4j core
NeuralNetConfiguration conf = NeuralNetConfiguration conf =
NeuralNetConfiguration.builder().updater(new Sgd(0.1)).list() NeuralNetConfiguration.builder().updater(new Sgd(0.1)).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()) .layer(0, DenseLayer.builder().nIn(10).nOut(10).build())
.layer(1, new CustomLayer(3.14159)).layer(2, .layer(1, new CustomLayer(3.14159)).layer(2,
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.nIn(10).nOut(10).build()) .nIn(10).nOut(10).build())
.build(); .build();

20
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/graph/TestSparkComputationGraph.java
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@ -79,8 +79,8 @@ public class TestSparkComputationGraph extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER) ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER)
.graphBuilder().addInputs("in") .graphBuilder().addInputs("in")
.addLayer("l0", new DenseLayer.Builder().nIn(4).nOut(10).build(), "in") .addLayer("l0", DenseLayer.builder().nIn(4).nOut(10).build(), "in")
.addLayer("l1", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .addLayer("l1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(2).build(), "l0") .activation(Activation.SOFTMAX).nIn(10).nOut(2).build(), "l0")
.setOutputs("l1").build(); .setOutputs("l1").build();
@ -107,8 +107,8 @@ public class TestSparkComputationGraph extends BaseSparkTest {
ComputationGraphConfiguration config = NeuralNetConfiguration.builder() ComputationGraphConfiguration config = NeuralNetConfiguration.builder()
.updater(new Sgd(0.1)) .updater(new Sgd(0.1))
.graphBuilder().addInputs("in") .graphBuilder().addInputs("in")
.addLayer("dense", new DenseLayer.Builder().nIn(4).nOut(2).build(), "in").addLayer("out", .addLayer("dense", DenseLayer.builder().nIn(4).nOut(2).build(), "in").addLayer("out",
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3)
.build(), .build(),
"dense") "dense")
.setOutputs("out").build(); .setOutputs("out").build();
@ -141,9 +141,9 @@ public class TestSparkComputationGraph extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().l1(0.1).l2(0.1) ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().l1(0.1).l2(0.1)
.seed(123).updater(new Nesterovs(0.1, 0.9)).graphBuilder() .seed(123).updater(new Nesterovs(0.1, 0.9)).graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(nIn).nOut(3) .addLayer("0", org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(nIn).nOut(3)
.activation(Activation.TANH).build(), "in") .activation(Activation.TANH).build(), "in")
.addLayer("1", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .addLayer("1", org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(3).nOut(nOut) LossFunctions.LossFunction.MCXENT).nIn(3).nOut(nOut)
.activation(Activation.SOFTMAX).build(), .activation(Activation.SOFTMAX).build(),
"0") "0")
@ -220,9 +220,9 @@ public class TestSparkComputationGraph extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(Updater.RMSPROP) ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(Updater.RMSPROP)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in") .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.addLayer("0", new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(4).nOut(4) .addLayer("0", org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(4).nOut(4)
.activation(Activation.TANH).build(), "in") .activation(Activation.TANH).build(), "in")
.addLayer("1", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .addLayer("1", org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(4).nOut(3).activation(Activation.SOFTMAX) LossFunctions.LossFunction.MCXENT).nIn(4).nOut(3).activation(Activation.SOFTMAX)
.build(), .build(),
"0") "0")
@ -421,8 +421,8 @@ public class TestSparkComputationGraph extends BaseSparkTest {
.graphBuilder() .graphBuilder()
.addInputs("input1", "input2") .addInputs("input1", "input2")
.addVertex("avg",new ElementWiseVertex(ElementWiseVertex.Op.Average),"input1","input2") .addVertex("avg",new ElementWiseVertex(ElementWiseVertex.Op.Average),"input1","input2")
.addLayer("dense",new DenseLayer.Builder().dropOut(0.9).nIn(featSize).nOut(featSize / 2).build(),"avg") .addLayer("dense",DenseLayer.builder().dropOut(0.9).nIn(featSize).nOut(featSize / 2).build(),"avg")
.addLayer("output",new OutputLayer.Builder().nIn(featSize / 2).nOut(2).lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).hasBias(false).build(),"dense") .addLayer("output",OutputLayer.builder().nIn(featSize / 2).nOut(2).lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).hasBias(false).build(),"dense")
.setOutputs("output") .setOutputs("output")
.build(); .build();

16
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/misc/TestFrozenLayers.java
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@ -62,10 +62,10 @@ public class TestFrozenLayers extends BaseSparkTest {
int nOut = 3; int nOut = 3;
MultiLayerNetwork origModel = new MultiLayerNetwork(overallConf.clone().list() MultiLayerNetwork origModel = new MultiLayerNetwork(overallConf.clone().list()
.layer(0, new DenseLayer.Builder().nIn(6).nOut(5).build()) .layer(0, DenseLayer.builder().nIn(6).nOut(5).build())
.layer(1, new DenseLayer.Builder().nIn(5).nOut(4).build()) .layer(1, DenseLayer.builder().nIn(5).nOut(4).build())
.layer(2, new DenseLayer.Builder().nIn(4).nOut(3).build()) .layer(2, DenseLayer.builder().nIn(4).nOut(3).build())
.layer(3, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(3, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3) LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3)
.build()) .build())
.build()); .build());
@ -138,10 +138,10 @@ public class TestFrozenLayers extends BaseSparkTest {
ComputationGraph origModel = new ComputationGraph(NeuralNetConfiguration.builder().updater(new Sgd(0.1)) ComputationGraph origModel = new ComputationGraph(NeuralNetConfiguration.builder().updater(new Sgd(0.1))
.activation(Activation.TANH).graphBuilder().addInputs("in") .activation(Activation.TANH).graphBuilder().addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(6).nOut(5).build(), "in") .addLayer("0", DenseLayer.builder().nIn(6).nOut(5).build(), "in")
.addLayer("1", new DenseLayer.Builder().nIn(5).nOut(4).build(), "0") .addLayer("1", DenseLayer.builder().nIn(5).nOut(4).build(), "0")
.addLayer("2", new DenseLayer.Builder().nIn(4).nOut(3).build(), "1") .addLayer("2", DenseLayer.builder().nIn(4).nOut(3).build(), "1")
.addLayer("3", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .addLayer("3", org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3) LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3)
.build(), .build(),
"2") "2")

20
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/multilayer/TestMiscFunctions.java
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@ -58,8 +58,8 @@ public class TestMiscFunctions extends BaseSparkTest {
public void testFeedForwardWithKey() { public void testFeedForwardWithKey() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(3).build()) .layer(0, DenseLayer.builder().nIn(4).nOut(3).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3) .layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();
@ -109,9 +109,9 @@ public class TestMiscFunctions extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER)
.list() .list()
.layer( new LSTM.Builder().nIn(4).nOut(3).build()) .layer( LSTM.builder().nIn(4).nOut(3).build())
.layer(new GlobalPoolingLayer(PoolingType.AVG)) .layer(GlobalPoolingLayer.builder(PoolingType.AVG))
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();
@ -164,9 +164,9 @@ public class TestMiscFunctions extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER) ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER)
.graphBuilder().addInputs("in1", "in2") .graphBuilder().addInputs("in1", "in2")
.addLayer("0", new DenseLayer.Builder().nIn(4).nOut(3).build(), "in1") .addLayer("0", DenseLayer.builder().nIn(4).nOut(3).build(), "in1")
.addLayer("1", new DenseLayer.Builder().nIn(4).nOut(3).build(), "in2").addLayer("2", .addLayer("1", DenseLayer.builder().nIn(4).nOut(3).build(), "in2").addLayer("2",
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(6).nOut(3) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(6).nOut(3)
.activation(Activation.SOFTMAX).build(), .activation(Activation.SOFTMAX).build(),
"0", "1") "0", "1")
.setOutputs("2").build(); .setOutputs("2").build();
@ -221,7 +221,7 @@ public class TestMiscFunctions extends BaseSparkTest {
int nIn = 10; int nIn = 10;
NeuralNetConfiguration mlc = NeuralNetConfiguration.builder().list() NeuralNetConfiguration mlc = NeuralNetConfiguration.builder().list()
.layer(0, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder() .layer(0, org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.builder()
.reconstructionDistribution( .reconstructionDistribution(
new GaussianReconstructionDistribution(Activation.IDENTITY)) new GaussianReconstructionDistribution(Activation.IDENTITY))
.nIn(nIn).nOut(5).encoderLayerSizes(12).decoderLayerSizes(13).build()) .nIn(nIn).nOut(5).encoderLayerSizes(12).decoderLayerSizes(13).build())
@ -261,7 +261,7 @@ public class TestMiscFunctions extends BaseSparkTest {
NeuralNetConfiguration mlc = NeuralNetConfiguration.builder() NeuralNetConfiguration mlc = NeuralNetConfiguration.builder()
.list().layer(0, .list().layer(0,
new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder() org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.builder()
.reconstructionDistribution(new LossFunctionWrapper( .reconstructionDistribution(new LossFunctionWrapper(
Activation.IDENTITY, new LossMSE())) Activation.IDENTITY, new LossMSE()))
.nIn(nIn).nOut(5).encoderLayerSizes(12).decoderLayerSizes(13) .nIn(nIn).nOut(5).encoderLayerSizes(12).decoderLayerSizes(13)

6
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/multilayer/TestSparkDl4jMultiLayer.java
View File

@ -111,9 +111,9 @@ public class TestSparkDl4jMultiLayer extends BaseSparkTest {
.updater(new Adam(1e-3)) .updater(new Adam(1e-3))
.l2(1e-5) .l2(1e-5)
.list() .list()
.layer(0, new DenseLayer.Builder().nIn(28 * 28).nOut(500).build()) .layer(0, DenseLayer.builder().nIn(28 * 28).nOut(500).build())
.layer(1, new DenseLayer.Builder().nIn(500).nOut(100).build()) .layer(1, DenseLayer.builder().nIn(500).nOut(100).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .layer(2, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.activation(Activation.SOFTMAX).nIn(100).nOut(10).build()) .activation(Activation.SOFTMAX).nIn(100).nOut(10).build())
.build(); .build();

18
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/paramavg/TestCompareParameterAveragingSparkVsSingleMachine.java
View File

@ -68,7 +68,7 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.XAVIER).updater(updater).seed(seed).list() .weightInit(WeightInit.XAVIER).updater(updater).seed(seed).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new OutputLayer.Builder() .layer(0, DenseLayer.builder().nIn(10).nOut(10).build()).layer(1, OutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(10).build()) .lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(10).build())
.build(); .build();
return conf; return conf;
@ -79,11 +79,11 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.XAVIER).updater(updater).seed(seed).list() .weightInit(WeightInit.XAVIER).updater(updater).seed(seed).list()
.layer(0, new ConvolutionLayer.Builder().nOut(3).kernelSize(2, 2).stride(1, 1).padding(0, 0) .layer(0, ConvolutionLayer.builder().nOut(3).kernelSize(2, 2).stride(1, 1).padding(0, 0)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new ConvolutionLayer.Builder().nOut(3).kernelSize(2, 2).stride(1, 1).padding(0, 0) .layer(1, ConvolutionLayer.builder().nOut(3).kernelSize(2, 2).stride(1, 1).padding(0, 0)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nOut(10) .layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nOut(10)
.build()) .build())
.inputType(InputType.convolutional(10, 10, 3)).build(); .inputType(InputType.convolutional(10, 10, 3)).build();
return conf; return conf;
@ -95,8 +95,8 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.XAVIER).updater(updater).seed(seed).graphBuilder() .weightInit(WeightInit.XAVIER).updater(updater).seed(seed).graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in").addLayer("1", .addLayer("0", DenseLayer.builder().nIn(10).nOut(10).build(), "in").addLayer("1",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10)
.nOut(10).build(), .nOut(10).build(),
"0") "0")
.setOutputs("1").build(); .setOutputs("1").build();
@ -109,11 +109,11 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.XAVIER).updater(updater).seed(seed).graphBuilder() .weightInit(WeightInit.XAVIER).updater(updater).seed(seed).graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new ConvolutionLayer.Builder().nOut(3).kernelSize(2, 2).stride(1, 1) .addLayer("0", ConvolutionLayer.builder().nOut(3).kernelSize(2, 2).stride(1, 1)
.padding(0, 0).activation(Activation.TANH).build(), "in") .padding(0, 0).activation(Activation.TANH).build(), "in")
.addLayer("1", new ConvolutionLayer.Builder().nOut(3).kernelSize(2, 2).stride(1, 1) .addLayer("1", ConvolutionLayer.builder().nOut(3).kernelSize(2, 2).stride(1, 1)
.padding(0, 0).activation(Activation.TANH).build(), "0") .padding(0, 0).activation(Activation.TANH).build(), "0")
.addLayer("2", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nOut(10) .addLayer("2", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nOut(10)
.build(), "1") .build(), "1")
.setOutputs("2").setInputTypes(InputType.convolutional(10, 10, 3)) .setOutputs("2").setInputTypes(InputType.convolutional(10, 10, 3))
.build(); .build();

62
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/paramavg/TestSparkMultiLayerParameterAveraging.java
View File

@ -129,9 +129,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
DataSet d = new IrisDataSetIterator(150, 150).next(); DataSet d = new IrisDataSetIterator(150, 150).next();
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(100).weightInit(WeightInit.XAVIER) .layer(0, DenseLayer.builder().nIn(4).nOut(100).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(100).nOut(3) LossFunctions.LossFunction.MCXENT).nIn(100).nOut(3)
.activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER) .activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER)
.build()) .build())
@ -167,9 +167,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
.updater(new Adam(1e-6)) .updater(new Adam(1e-6))
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .list()
.layer(new BatchNormalization.Builder().nIn(4).nOut(4).build()) .layer(BatchNormalization.builder().nIn(4).nOut(4).build())
.layer(new DenseLayer.Builder().nIn(4).nOut(32).activation(Activation.RELU).build()) .layer(DenseLayer.builder().nIn(4).nOut(32).activation(Activation.RELU).build())
.layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(32).nOut(3) .layer(org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(32).nOut(3)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();
@ -277,9 +277,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
} }
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp()) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(nIn).nOut(3) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(nIn).nOut(3)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder(
LossFunctions.LossFunction.MSE).nIn(3).nOut(nOut).activation(Activation.SOFTMAX) LossFunctions.LossFunction.MSE).nIn(3).nOut(nOut).activation(Activation.SOFTMAX)
.build()) .build())
.build(); .build();
@ -302,9 +302,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l1(0.1).l2(0.1) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l1(0.1).l2(0.1)
.seed(123).updater(new Nesterovs(0.1, 0.9)).list() .seed(123).updater(new Nesterovs(0.1, 0.9)).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(nIn).nOut(3) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(nIn).nOut(3)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(3).nOut(nOut) LossFunctions.LossFunction.MCXENT).nIn(3).nOut(nOut)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();
@ -391,9 +391,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp()) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(28 * 28).nOut(50) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(28 * 28).nOut(50)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10) LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();
@ -455,9 +455,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp()) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(28 * 28).nOut(50) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(28 * 28).nOut(50)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10) LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();
@ -525,9 +525,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp()) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(28 * 28).nOut(50) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(28 * 28).nOut(50)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10) LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();
@ -614,9 +614,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp()) ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.graphBuilder().addInputs("in") .graphBuilder().addInputs("in")
.addLayer("0", new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(28 * 28).nOut(50) .addLayer("0", org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(28 * 28).nOut(50)
.activation(Activation.TANH).build(), "in") .activation(Activation.TANH).build(), "in")
.addLayer("1", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .addLayer("1", org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10) LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10)
.activation(Activation.SOFTMAX).build(), .activation(Activation.SOFTMAX).build(),
"0") "0")
@ -687,9 +687,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(new RmsProp()) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.XAVIER).list() .weightInit(WeightInit.XAVIER).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(4).nOut(4) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(4).nOut(4)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(4).nOut(3).activation(Activation.SOFTMAX) LossFunctions.LossFunction.MCXENT).nIn(4).nOut(3).activation(Activation.SOFTMAX)
.build()) .build())
.build(); .build();
@ -771,9 +771,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp()) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(28 * 28).nOut(50) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(28 * 28).nOut(50)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10) LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();
@ -822,9 +822,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp()) ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.graphBuilder().addInputs("in") .graphBuilder().addInputs("in")
.addLayer("0", new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(28 * 28).nOut(50) .addLayer("0", org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(28 * 28).nOut(50)
.activation(Activation.TANH).build(), "in") .activation(Activation.TANH).build(), "in")
.addLayer("1", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .addLayer("1", org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10) LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10)
.activation(Activation.SOFTMAX).build(), .activation(Activation.SOFTMAX).build(),
"0") "0")
@ -862,7 +862,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
Nd4j.getRandom().setSeed(12345); Nd4j.getRandom().setSeed(12345);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(new RmsProp()) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(new RmsProp())
.weightInit(WeightInit.XAVIER).list() .weightInit(WeightInit.XAVIER).list()
.layer(0, new VariationalAutoencoder.Builder().nIn(8).nOut(10).encoderLayerSizes(12) .layer(0, VariationalAutoencoder.builder().nIn(8).nOut(10).encoderLayerSizes(12)
.decoderLayerSizes(13).reconstructionDistribution( .decoderLayerSizes(13).reconstructionDistribution(
new GaussianReconstructionDistribution(Activation.IDENTITY)) new GaussianReconstructionDistribution(Activation.IDENTITY))
.build()) .build())
@ -898,7 +898,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
Nd4j.getRandom().setSeed(12345); Nd4j.getRandom().setSeed(12345);
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(new RmsProp()) ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(new RmsProp())
.weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in") .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.addLayer("0", new VariationalAutoencoder.Builder().nIn(8).nOut(10).encoderLayerSizes(12) .addLayer("0", VariationalAutoencoder.builder().nIn(8).nOut(10).encoderLayerSizes(12)
.decoderLayerSizes(13).reconstructionDistribution( .decoderLayerSizes(13).reconstructionDistribution(
new GaussianReconstructionDistribution(Activation.IDENTITY)) new GaussianReconstructionDistribution(Activation.IDENTITY))
.build(), "in") .build(), "in")
@ -938,8 +938,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration conf =
NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list() NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(layerSize).build()) .layer(0, DenseLayer.builder().nIn(nIn).nOut(layerSize).build())
.layer(1, new OutputLayer.Builder().nIn(layerSize).nOut(nOut) .layer(1, OutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX).lossFunction( .activation(Activation.SOFTMAX).lossFunction(
LossFunctions.LossFunction.MCXENT) LossFunctions.LossFunction.MCXENT)
.build()) .build())
@ -993,8 +993,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration conf =
NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list() NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(layerSize).build()) .layer(0, DenseLayer.builder().nIn(nIn).nOut(layerSize).build())
.layer(1, new OutputLayer.Builder().nIn(layerSize).nOut(nOut) .layer(1, OutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX).lossFunction( .activation(Activation.SOFTMAX).lossFunction(
LossFunctions.LossFunction.MCXENT) LossFunctions.LossFunction.MCXENT)
.build()) .build())
@ -1047,13 +1047,13 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
} }
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.list() .list()
.layer(new OutputLayer.Builder().nIn(4).nOut(3).build()) .layer(OutputLayer.builder().nIn(4).nOut(3).build())
.build(); .build();
ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("out", new OutputLayer.Builder().nIn(4).nOut(3).build(), "in") .addLayer("out", OutputLayer.builder().nIn(4).nOut(3).build(), "in")
.setOutputs("out") .setOutputs("out")
.build(); .build();

4
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/stats/TestTrainingStatsCollection.java
View File

@ -69,8 +69,8 @@ public class TestTrainingStatsCollection extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()) .layer(0, DenseLayer.builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).build()) .layer(1, OutputLayer.builder().nIn(10).nOut(10).build())
.build(); .build();
int miniBatchSizePerWorker = 10; int miniBatchSizePerWorker = 10;

4
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/ui/TestListeners.java
View File

@ -62,9 +62,9 @@ public class TestListeners extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(100).weightInit(WeightInit.XAVIER) .layer(0, DenseLayer.builder().nIn(4).nOut(100).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(100).nOut(3) LossFunctions.LossFunction.MCXENT).nIn(100).nOut(3)
.activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER) .activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER)
.build()) .build())

16
.old/rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/ac/ActorCriticFactoryCompGraphStdConv.java
View File

@ -65,27 +65,27 @@ public class ActorCriticFactoryCompGraphStdConv implements ActorCriticFactoryCom
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.l2(conf.getL2()).graphBuilder() .l2(conf.getL2()).graphBuilder()
.addInputs("input").addLayer("0", .addInputs("input").addLayer("0",
new ConvolutionLayer.Builder(8, 8).nIn(shapeInputs[0]).nOut(16) ConvolutionLayer.builder(8, 8).nIn(shapeInputs[0]).nOut(16)
.stride(4, 4).activation(Activation.RELU).build(), .stride(4, 4).activation(Activation.RELU).build(),
"input"); "input");
confB.addLayer("1", new ConvolutionLayer.Builder(4, 4).nIn(16).nOut(32).stride(2, 2).activation(Activation.RELU).build(), "0"); confB.addLayer("1", ConvolutionLayer.builder(4, 4).nIn(16).nOut(32).stride(2, 2).activation(Activation.RELU).build(), "0");
confB.addLayer("2", new DenseLayer.Builder().nIn(w * h * 32).nOut(256).activation(Activation.RELU).build(), "1"); confB.addLayer("2", DenseLayer.builder().nIn(w * h * 32).nOut(256).activation(Activation.RELU).build(), "1");
if (conf.isUseLSTM()) { if (conf.isUseLSTM()) {
confB.addLayer("3", new LSTM.Builder().nIn(256).nOut(256).activation(Activation.TANH).build(), "2"); confB.addLayer("3", LSTM.builder().nIn(256).nOut(256).activation(Activation.TANH).build(), "2");
confB.addLayer("value", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) confB.addLayer("value", RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nIn(256).nOut(1).build(), "3"); .nIn(256).nOut(1).build(), "3");
confB.addLayer("softmax", new RnnOutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX) confB.addLayer("softmax", RnnOutputLayer.builder(new ActorCriticLoss()).activation(Activation.SOFTMAX)
.nIn(256).nOut(numOutputs).build(), "3"); .nIn(256).nOut(numOutputs).build(), "3");
} else { } else {
confB.addLayer("value", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) confB.addLayer("value", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nIn(256).nOut(1).build(), "2"); .nIn(256).nOut(1).build(), "2");
confB.addLayer("softmax", new OutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX) confB.addLayer("softmax", OutputLayer.builder(new ActorCriticLoss()).activation(Activation.SOFTMAX)
.nIn(256).nOut(numOutputs).build(), "2"); .nIn(256).nOut(numOutputs).build(), "2");
} }

14
.old/rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/ac/ActorCriticFactoryCompGraphStdDense.java
View File

@ -56,31 +56,31 @@ public class ActorCriticFactoryCompGraphStdDense implements ActorCriticFactoryCo
.l2(conf.getL2()).graphBuilder() .l2(conf.getL2()).graphBuilder()
.setInputTypes(conf.isUseLSTM() ? InputType.recurrent(nIn) .setInputTypes(conf.isUseLSTM() ? InputType.recurrent(nIn)
: InputType.feedForward(nIn)).addInputs("input") : InputType.feedForward(nIn)).addInputs("input")
.addLayer("0", new DenseLayer.Builder().nIn(nIn) .addLayer("0", DenseLayer.builder().nIn(nIn)
.nOut(conf.getNumHiddenNodes()).activation(Activation.RELU).build(), .nOut(conf.getNumHiddenNodes()).activation(Activation.RELU).build(),
"input"); "input");
for (int i = 1; i < conf.getNumLayers(); i++) { for (int i = 1; i < conf.getNumLayers(); i++) {
confB.addLayer(i + "", new DenseLayer.Builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes()) confB.addLayer(i + "", DenseLayer.builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes())
.activation(Activation.RELU).build(), (i - 1) + ""); .activation(Activation.RELU).build(), (i - 1) + "");
} }
if (conf.isUseLSTM()) { if (conf.isUseLSTM()) {
confB.addLayer(getConf().getNumLayers() + "", new LSTM.Builder().activation(Activation.TANH) confB.addLayer(getConf().getNumLayers() + "", LSTM.builder().activation(Activation.TANH)
.nOut(conf.getNumHiddenNodes()).build(), (getConf().getNumLayers() - 1) + ""); .nOut(conf.getNumHiddenNodes()).build(), (getConf().getNumLayers() - 1) + "");
confB.addLayer("value", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) confB.addLayer("value", RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nOut(1).build(), getConf().getNumLayers() + ""); .nOut(1).build(), getConf().getNumLayers() + "");
confB.addLayer("softmax", new RnnOutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX) confB.addLayer("softmax", RnnOutputLayer.builder(new ActorCriticLoss()).activation(Activation.SOFTMAX)
.nOut(numOutputs).build(), getConf().getNumLayers() + ""); .nOut(numOutputs).build(), getConf().getNumLayers() + "");
} else { } else {
confB.addLayer("value", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) confB.addLayer("value", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nOut(1).build(), (getConf().getNumLayers() - 1) + ""); .nOut(1).build(), (getConf().getNumLayers() - 1) + "");
confB.addLayer("softmax", new OutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX) confB.addLayer("softmax", OutputLayer.builder(new ActorCriticLoss()).activation(Activation.SOFTMAX)
.nOut(numOutputs).build(), (getConf().getNumLayers() - 1) + ""); .nOut(numOutputs).build(), (getConf().getNumLayers() - 1) + "");
} }

20
.old/rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/ac/ActorCriticFactorySeparateStdDense.java
View File

@ -61,22 +61,22 @@ public class ActorCriticFactorySeparateStdDense implements ActorCriticFactorySep
.updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam()) .updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.l2(conf.getL2()) .l2(conf.getL2())
.list().layer(0, new DenseLayer.Builder().nIn(nIn).nOut(conf.getNumHiddenNodes()) .list().layer(0, DenseLayer.builder().nIn(nIn).nOut(conf.getNumHiddenNodes())
.activation(Activation.RELU).build()); .activation(Activation.RELU).build());
for (int i = 1; i < conf.getNumLayers(); i++) { for (int i = 1; i < conf.getNumLayers(); i++) {
confB.layer(i, new DenseLayer.Builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes()) confB.layer(i, DenseLayer.builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes())
.activation(Activation.RELU).build()); .activation(Activation.RELU).build());
} }
if (conf.isUseLSTM()) { if (conf.isUseLSTM()) {
confB.layer(conf.getNumLayers(), new LSTM.Builder().nOut(conf.getNumHiddenNodes()).activation(Activation.TANH).build()); confB.layer(conf.getNumLayers(), LSTM.builder().nOut(conf.getNumHiddenNodes()).activation(Activation.TANH).build());
confB.layer(conf.getNumLayers() + 1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) confB.layer(conf.getNumLayers() + 1, RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nIn(conf.getNumHiddenNodes()).nOut(1).build()); .nIn(conf.getNumHiddenNodes()).nOut(1).build());
} else { } else {
confB.layer(conf.getNumLayers(), new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) confB.layer(conf.getNumLayers(), OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nIn(conf.getNumHiddenNodes()).nOut(1).build()); .nIn(conf.getNumHiddenNodes()).nOut(1).build());
} }
@ -96,22 +96,22 @@ public class ActorCriticFactorySeparateStdDense implements ActorCriticFactorySep
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
//.regularization(true) //.regularization(true)
//.l2(conf.getL2()) //.l2(conf.getL2())
.list().layer(0, new DenseLayer.Builder().nIn(nIn).nOut(conf.getNumHiddenNodes()) .list().layer(0, DenseLayer.builder().nIn(nIn).nOut(conf.getNumHiddenNodes())
.activation(Activation.RELU).build()); .activation(Activation.RELU).build());
for (int i = 1; i < conf.getNumLayers(); i++) { for (int i = 1; i < conf.getNumLayers(); i++) {
confB2.layer(i, new DenseLayer.Builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes()) confB2.layer(i, DenseLayer.builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes())
.activation(Activation.RELU).build()); .activation(Activation.RELU).build());
} }
if (conf.isUseLSTM()) { if (conf.isUseLSTM()) {
confB2.layer(conf.getNumLayers(), new LSTM.Builder().nOut(conf.getNumHiddenNodes()).activation(Activation.TANH).build()); confB2.layer(conf.getNumLayers(), LSTM.builder().nOut(conf.getNumHiddenNodes()).activation(Activation.TANH).build());
confB2.layer(conf.getNumLayers() + 1, new RnnOutputLayer.Builder(new ActorCriticLoss()) confB2.layer(conf.getNumLayers() + 1, RnnOutputLayer.builder(new ActorCriticLoss())
.activation(Activation.SOFTMAX).nIn(conf.getNumHiddenNodes()).nOut(numOutputs).build()); .activation(Activation.SOFTMAX).nIn(conf.getNumHiddenNodes()).nOut(numOutputs).build());
} else { } else {
confB2.layer(conf.getNumLayers(), new OutputLayer.Builder(new ActorCriticLoss()) confB2.layer(conf.getNumLayers(), OutputLayer.builder(new ActorCriticLoss())
.activation(Activation.SOFTMAX).nIn(conf.getNumHiddenNodes()).nOut(numOutputs).build()); .activation(Activation.SOFTMAX).nIn(conf.getNumHiddenNodes()).nOut(numOutputs).build());
} }

8
.old/rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/dqn/DQNFactoryStdConv.java
View File

@ -60,15 +60,15 @@ public class DQNFactoryStdConv implements DQNFactory {
.l2(conf.getL2()) .l2(conf.getL2())
.updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam()) .updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam())
.weightInit(WeightInit.XAVIER).l2(conf.getL2()).list() .weightInit(WeightInit.XAVIER).l2(conf.getL2()).list()
.layer(0, new ConvolutionLayer.Builder(8, 8).nIn(shapeInputs[0]).nOut(16).stride(4, 4) .layer(0, ConvolutionLayer.builder(8, 8).nIn(shapeInputs[0]).nOut(16).stride(4, 4)
.activation(Activation.RELU).build()); .activation(Activation.RELU).build());
confB.layer(1, new ConvolutionLayer.Builder(4, 4).nOut(32).stride(2, 2).activation(Activation.RELU).build()); confB.layer(1, ConvolutionLayer.builder(4, 4).nOut(32).stride(2, 2).activation(Activation.RELU).build());
confB.layer(2, new DenseLayer.Builder().nOut(256).activation(Activation.RELU).build()); confB.layer(2, DenseLayer.builder().nOut(256).activation(Activation.RELU).build());
confB.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nOut(numOutputs) confB.layer(3, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nOut(numOutputs)
.build()); .build());
confB.inputType(InputType.convolutional(shapeInputs[1], shapeInputs[2], shapeInputs[0])); confB.inputType(InputType.convolutional(shapeInputs[1], shapeInputs[2], shapeInputs[0]));

6
.old/rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/dqn/DQNFactoryStdDense.java
View File

@ -61,7 +61,7 @@ public class DQNFactoryStdDense implements DQNFactory {
.l2(conf.getL2()) .l2(conf.getL2())
.list() .list()
.layer(0, .layer(0,
new DenseLayer.Builder() DenseLayer.builder()
.nIn(nIn) .nIn(nIn)
.nOut(conf.getNumHiddenNodes()) .nOut(conf.getNumHiddenNodes())
.activation(Activation.RELU).build() .activation(Activation.RELU).build()
@ -69,12 +69,12 @@ public class DQNFactoryStdDense implements DQNFactory {
for (int i = 1; i < conf.getNumLayers(); i++) { for (int i = 1; i < conf.getNumLayers(); i++) {
confB.layer(i, new DenseLayer.Builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes()) confB.layer(i, DenseLayer.builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes())
.activation(Activation.RELU).build()); .activation(Activation.RELU).build());
} }
confB.layer(conf.getNumLayers(), confB.layer(conf.getNumLayers(),
new OutputLayer.Builder(LossFunctions.LossFunction.MSE) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)
.nIn(conf.getNumHiddenNodes()) .nIn(conf.getNumHiddenNodes())
.nOut(numOutputs) .nOut(numOutputs)

14
.old/rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/NStepRnn.java
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@ -141,16 +141,16 @@ public class NStepRnn {
.graphBuilder() .graphBuilder()
.addInputs("input") .addInputs("input")
.setInputTypes(InputType.recurrent(NUM_INPUTS)) .setInputTypes(InputType.recurrent(NUM_INPUTS))
.addLayer("lstm", new LSTM.Builder().nOut(lstmLayerSize).activation(Activation.TANH).build(), "input") .addLayer("lstm", LSTM.builder().nOut(lstmLayerSize).activation(Activation.TANH).build(), "input")
.addLayer("dl", new DenseLayer.Builder().nOut(dl1Size).activation(Activation.RELU).build(), "input", "lstm") .addLayer("dl", DenseLayer.builder().nOut(dl1Size).activation(Activation.RELU).build(), "input", "lstm")
.addLayer("dl-1", new DenseLayer.Builder().nOut(dl2Size).activation(Activation.RELU).build(), "dl") .addLayer("dl-1", DenseLayer.builder().nOut(dl2Size).activation(Activation.RELU).build(), "dl")
.addVertex("dl-rnn", new PreprocessorVertex(new FeedForwardToRnnPreProcessor()), "dl-1"); .addVertex("dl-rnn", new PreprocessorVertex(new FeedForwardToRnnPreProcessor()), "dl-1");
} }
private static ITrainableNeuralNet buildActorCriticNetwork() { private static ITrainableNeuralNet buildActorCriticNetwork() {
ComputationGraphConfiguration valueConfiguration = buildBaseNetworkConfiguration(COMBINED_LSTM_LAYER_SIZE, COMBINED_DL1_LAYER_SIZE, COMBINED_DL2_LAYER_SIZE) ComputationGraphConfiguration valueConfiguration = buildBaseNetworkConfiguration(COMBINED_LSTM_LAYER_SIZE, COMBINED_DL1_LAYER_SIZE, COMBINED_DL2_LAYER_SIZE)
.addLayer("value", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nOut(1).build(), "dl-rnn", "lstm") .addLayer("value", RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nOut(1).build(), "dl-rnn", "lstm")
.addLayer("softmax", new RnnOutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX).nOut(NUM_ACTIONS).build(), "dl-rnn", "lstm") .addLayer("softmax", RnnOutputLayer.builder(new ActorCriticLoss()).activation(Activation.SOFTMAX).nOut(NUM_ACTIONS).build(), "dl-rnn", "lstm")
.setOutputs("value", "softmax") .setOutputs("value", "softmax")
.build(); .build();
@ -164,12 +164,12 @@ public class NStepRnn {
private static ITrainableNeuralNet buildSeparateActorCriticNetwork() { private static ITrainableNeuralNet buildSeparateActorCriticNetwork() {
ComputationGraphConfiguration valueConfiguration = buildBaseNetworkConfiguration(SEPARATE_LSTM_LAYER_SIZE, SEPARATE_DL1_LAYER_SIZE, SEPARATE_DL2_LAYER_SIZE) ComputationGraphConfiguration valueConfiguration = buildBaseNetworkConfiguration(SEPARATE_LSTM_LAYER_SIZE, SEPARATE_DL1_LAYER_SIZE, SEPARATE_DL2_LAYER_SIZE)
.addLayer("value", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nOut(1).build(), "dl-rnn", "lstm") .addLayer("value", RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nOut(1).build(), "dl-rnn", "lstm")
.setOutputs("value") .setOutputs("value")
.build(); .build();
ComputationGraphConfiguration policyConfiguration = buildBaseNetworkConfiguration(SEPARATE_LSTM_LAYER_SIZE, SEPARATE_DL1_LAYER_SIZE, SEPARATE_DL2_LAYER_SIZE) ComputationGraphConfiguration policyConfiguration = buildBaseNetworkConfiguration(SEPARATE_LSTM_LAYER_SIZE, SEPARATE_DL1_LAYER_SIZE, SEPARATE_DL2_LAYER_SIZE)
.addLayer("softmax", new RnnOutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX).nOut(NUM_ACTIONS).build(), "dl-rnn", "lstm") .addLayer("softmax", RnnOutputLayer.builder(new ActorCriticLoss()).activation(Activation.SOFTMAX).nOut(NUM_ACTIONS).build(), "dl-rnn", "lstm")
.setOutputs("softmax") .setOutputs("softmax")
.build(); .build();

10
.old/rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/RobotLakeExample.java
View File

@ -197,13 +197,13 @@ public class RobotLakeExample {
InputType.feedForward(4)) // radar ) InputType.feedForward(4)) // radar )
.addInputs("tracker-in", "radar-in") .addInputs("tracker-in", "radar-in")
.layer("dl_1", new DenseLayer.Builder().activation(Activation.RELU).nOut(40).build(), "tracker-in", "radar-in") .layer("dl_1", DenseLayer.builder().activation(Activation.RELU).nOut(40).build(), "tracker-in", "radar-in")
.layer("dl_out", new DenseLayer.Builder().activation(Activation.RELU).nOut(40).build(), "dl_1"); .layer("dl_out", DenseLayer.builder().activation(Activation.RELU).nOut(40).build(), "dl_1");
} }
private static ITrainableNeuralNet buildQNetwork() { private static ITrainableNeuralNet buildQNetwork() {
ComputationGraphConfiguration conf = buildBaseNetworkConfiguration() ComputationGraphConfiguration conf = buildBaseNetworkConfiguration()
.addLayer("output", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) .addLayer("output", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nOut(RobotLake.NUM_ACTIONS).build(), "dl_out") .nOut(RobotLake.NUM_ACTIONS).build(), "dl_out")
.setOutputs("output") .setOutputs("output")
@ -220,9 +220,9 @@ public class RobotLakeExample {
private static ITrainableNeuralNet buildActorCriticNetwork() { private static ITrainableNeuralNet buildActorCriticNetwork() {
ComputationGraphConfiguration conf = buildBaseNetworkConfiguration() ComputationGraphConfiguration conf = buildBaseNetworkConfiguration()
.addLayer("value", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) .addLayer("value", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nOut(1).build(), "dl_out") .nOut(1).build(), "dl_out")
.addLayer("softmax", new OutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX) .addLayer("softmax", OutputLayer.builder(new ActorCriticLoss()).activation(Activation.SOFTMAX)
.nOut(RobotLake.NUM_ACTIONS).build(), "dl_out") .nOut(RobotLake.NUM_ACTIONS).build(), "dl_out")
.setOutputs("value", "softmax") .setOutputs("value", "softmax")
.build(); .build();

12
.old/rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/TMazeExample.java
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@ -181,18 +181,18 @@ public class TMazeExample {
.graphBuilder() .graphBuilder()
.setInputTypes(InputType.recurrent(NUM_INPUTS)) .setInputTypes(InputType.recurrent(NUM_INPUTS))
.addInputs("input") .addInputs("input")
.addLayer("goal", new LSTM.Builder() .addLayer("goal", LSTM.builder()
.nOut(40) .nOut(40)
.activation(Activation.TANH) .activation(Activation.TANH)
.build(), "input") .build(), "input")
.addLayer("corridor", new DenseLayer.Builder().nOut(40).activation(Activation.RELU).build(), "input", "goal") .addLayer("corridor", DenseLayer.builder().nOut(40).activation(Activation.RELU).build(), "input", "goal")
.addLayer("corridor-1", new DenseLayer.Builder().nOut(20).activation(Activation.RELU).build(), "corridor") .addLayer("corridor-1", DenseLayer.builder().nOut(20).activation(Activation.RELU).build(), "corridor")
.addVertex("corridor-rnn", new PreprocessorVertex(new FeedForwardToRnnPreProcessor()), "corridor-1"); .addVertex("corridor-rnn", new PreprocessorVertex(new FeedForwardToRnnPreProcessor()), "corridor-1");
} }
private static ITrainableNeuralNet buildQNetwork() { private static ITrainableNeuralNet buildQNetwork() {
ComputationGraphConfiguration conf = buildBaseNetworkConfiguration() ComputationGraphConfiguration conf = buildBaseNetworkConfiguration()
.addLayer("output", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) .addLayer("output", RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nOut(NUM_ACTIONS).build(), "goal", "corridor-rnn") .nOut(NUM_ACTIONS).build(), "goal", "corridor-rnn")
.setOutputs("output") .setOutputs("output")
@ -207,9 +207,9 @@ public class TMazeExample {
private static ITrainableNeuralNet buildActorCriticNetwork() { private static ITrainableNeuralNet buildActorCriticNetwork() {
ComputationGraphConfiguration conf = buildBaseNetworkConfiguration() ComputationGraphConfiguration conf = buildBaseNetworkConfiguration()
.addLayer("value", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY) .addLayer("value", RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
.nOut(1).build(), "goal", "corridor-rnn") .nOut(1).build(), "goal", "corridor-rnn")
.addLayer("softmax", new RnnOutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX) .addLayer("softmax", RnnOutputLayer.builder(new ActorCriticLoss()).activation(Activation.SOFTMAX)
.nOut(NUM_ACTIONS).build(), "goal", "corridor-rnn") .nOut(NUM_ACTIONS).build(), "goal", "corridor-rnn")
.setOutputs("value", "softmax") .setOutputs("value", "softmax")
.build(); .build();

4
.old/rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/policy/PolicyTest.java
View File

@ -167,9 +167,9 @@ public class PolicyTest {
@Test @Test
public void testACPolicy() throws Exception { public void testACPolicy() throws Exception {
ComputationGraph cg = new ComputationGraph(NeuralNetConfiguration.builder().seed(444).graphBuilder().addInputs("input") ComputationGraph cg = new ComputationGraph(NeuralNetConfiguration.builder().seed(444).graphBuilder().addInputs("input")
.addLayer("output", new OutputLayer.Builder().nOut(1).lossFunction(LossFunctions.LossFunction.XENT).activation(Activation.SIGMOID).build(), "input").setOutputs("output").build()); .addLayer("output", OutputLayer.builder().nOut(1).lossFunction(LossFunctions.LossFunction.XENT).activation(Activation.SIGMOID).build(), "input").setOutputs("output").build());
MultiLayerNetwork mln = new MultiLayerNetwork(NeuralNetConfiguration.builder().seed(555).list() MultiLayerNetwork mln = new MultiLayerNetwork(NeuralNetConfiguration.builder().seed(555).list()
.layer(0, new OutputLayer.Builder().nOut(1).lossFunction(LossFunctions.LossFunction.XENT).activation(Activation.SIGMOID).build()).build()); .layer(0, OutputLayer.builder().nOut(1).lossFunction(LossFunctions.LossFunction.XENT).activation(Activation.SIGMOID).build()).build());
ACPolicy policy = new ACPolicy(new DummyAC(mln), true, Nd4j.getRandom()); ACPolicy policy = new ACPolicy(new DummyAC(mln), true, Nd4j.getRandom());

43
brutex-extended-tests/src/test/java/net/brutex/gan/App.java
View File

@ -95,13 +95,13 @@ public class App {
private static LayerConfiguration[] genLayers() { private static LayerConfiguration[] genLayers() {
return new LayerConfiguration[] { return new LayerConfiguration[] {
new DenseLayer.Builder().nIn(INPUT).nOut(X_DIM*Y_DIM*CHANNELS).weightInit(WeightInit.NORMAL).build(), DenseLayer.builder().nIn(INPUT).nOut(X_DIM*Y_DIM*CHANNELS).weightInit(WeightInit.NORMAL).build(),
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(), ActivationLayer.builder(Activation.LEAKYRELU).build(),
new DenseLayer.Builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(), DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(), ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
new DenseLayer.Builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM).build(), DenseLayer.builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM).build(),
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(), ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
new DenseLayer.Builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM*CHANNELS).activation(Activation.TANH) DenseLayer.builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM*CHANNELS).activation(Activation.TANH)
.build() .build()
}; };
} }
@ -131,19 +131,19 @@ public class App {
private static LayerConfiguration[] disLayers() { private static LayerConfiguration[] disLayers() {
return new LayerConfiguration[]{ return new LayerConfiguration[]{
new DenseLayer.Builder().nOut(X_DIM*Y_DIM*CHANNELS*2).build(), //input is set by setInputType on the network DenseLayer.builder().nOut(X_DIM*Y_DIM*CHANNELS*2).build(), //input is set by setInputType on the network
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(), ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
new DropoutLayer.Builder(1 - 0.5).build(), DropoutLayer.builder(1 - 0.5).build(),
new DenseLayer.Builder().nIn(X_DIM * Y_DIM*CHANNELS*2).nOut(X_DIM*Y_DIM*CHANNELS*4).build(), //HxBxC DenseLayer.builder().nIn(X_DIM * Y_DIM*CHANNELS*2).nOut(X_DIM*Y_DIM*CHANNELS*4).build(), //HxBxC
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(), ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
new DropoutLayer.Builder(1 - 0.5).build(), DropoutLayer.builder(1 - 0.5).build(),
new DenseLayer.Builder().nIn(X_DIM*Y_DIM*CHANNELS*4).nOut(X_DIM*Y_DIM*CHANNELS).build(), DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS*4).nOut(X_DIM*Y_DIM*CHANNELS).build(),
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(), ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
new DropoutLayer.Builder(1 - 0.5).build(), DropoutLayer.builder(1 - 0.5).build(),
new DenseLayer.Builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(), DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(), ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
new DropoutLayer.Builder(1 - 0.5).build(), DropoutLayer.builder(1 - 0.5).build(),
new OutputLayer.Builder(LossFunction.XENT).nIn(X_DIM*Y_DIM).nOut(1).activation(Activation.SIGMOID).build() OutputLayer.builder().lossFunction(LossFunction.XENT).nIn(X_DIM*Y_DIM).nOut(1).activation(Activation.SIGMOID).build()
}; };
} }
@ -171,7 +171,7 @@ public class App {
LayerConfiguration[] disLayers = Arrays.stream(disLayers()) LayerConfiguration[] disLayers = Arrays.stream(disLayers())
.map((layer) -> { .map((layer) -> {
if (layer instanceof DenseLayer || layer instanceof OutputLayer) { if (layer instanceof DenseLayer || layer instanceof OutputLayer) {
return new FrozenLayerWithBackprop(layer); return FrozenLayerWithBackprop.builder(layer);
} else { } else {
return layer; return layer;
} }
@ -242,6 +242,7 @@ public class App {
gan.addTrainingListeners(new ScoreToChartListener("gan")); gan.addTrainingListeners(new ScoreToChartListener("gan"));
//dis.setListeners(new ScoreToChartListener("dis")); //dis.setListeners(new ScoreToChartListener("dis"));
System.out.println(gan.toString());
gan.fit(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1)); gan.fit(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1));
//gan.fit(new DataSet(trainData.next().getFeatures(), Nd4j.zeros(batchSize, 1))); //gan.fit(new DataSet(trainData.next().getFeatures(), Nd4j.zeros(batchSize, 1)));

28
brutex-extended-tests/src/test/java/net/brutex/gan/MnistDCGANExample.java
View File

@ -101,21 +101,21 @@ public class MnistDCGANExample {
public static void main(String[] args) throws Exception { public static void main(String[] args) throws Exception {
Supplier<MultiLayerNetwork> genSupplier = () -> { Supplier<MultiLayerNetwork> genSupplier = () -> {
return new MultiLayerNetwork(NeuralNetConfiguration.builder() return new MultiLayerNetwork(NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().nIn(latentDim).nOut(width / 2 * height / 2 * 128) .layer(0, DenseLayer.builder().nIn(latentDim).nOut(width / 2 * height / 2 * 128)
.activation(Activation.LEAKYRELU).weightInit(WeightInit.NORMAL).build()) .activation(Activation.LEAKYRELU).weightInit(WeightInit.NORMAL).build())
.layer(1, new Convolution2D.Builder().nIn(128).nOut(128).kernelSize(5, 5) .layer(1, Convolution2D.builder().nIn(128).nOut(128).kernelSize(5, 5)
.convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU).build()) .convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU).build())
// Up-sampling to 28x28x256 // Up-sampling to 28x28x256
.layer(2, new Deconvolution2D.Builder().nIn(128).nOut(128).stride(2, 2) .layer(2, Deconvolution2D.builder().nIn(128).nOut(128).stride(2, 2)
.kernelSize(5, 5).convolutionMode(ConvolutionMode.Same) .kernelSize(5, 5).convolutionMode(ConvolutionMode.Same)
.activation(Activation.LEAKYRELU).build()) .activation(Activation.LEAKYRELU).build())
.layer(3, new Convolution2D.Builder().nIn(128).nOut(128).kernelSize(5, 5) .layer(3, Convolution2D.builder().nIn(128).nOut(128).kernelSize(5, 5)
.convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU).build()) .convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU).build())
.layer(4, new Convolution2D.Builder().nIn(128).nOut(128).kernelSize(5, 5) .layer(4, Convolution2D.builder().nIn(128).nOut(128).kernelSize(5, 5)
.convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU).build()) .convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU).build())
.layer(5, new Convolution2D.Builder().nIn(128).nOut(channels).kernelSize(7, 7) .layer(5, Convolution2D.builder().nIn(128).nOut(channels).kernelSize(7, 7)
.convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU).build()) .convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU).build())
.layer(6, new ActivationLayer.Builder().activation(Activation.TANH).build()) .layer(6, ActivationLayer.builder().activation(Activation.TANH).build())
.inputPreProcessor(1, .inputPreProcessor(1,
new FeedForwardToCnnPreProcessor(height / 2, width / 2, 128)) new FeedForwardToCnnPreProcessor(height / 2, width / 2, 128))
.inputPreProcessor(6, new CnnToFeedForwardPreProcessor(height, width, channels)) .inputPreProcessor(6, new CnnToFeedForwardPreProcessor(height, width, channels))
@ -129,17 +129,17 @@ public class MnistDCGANExample {
//.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue) //.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
//.gradientNormalizationThreshold(100.0) //.gradientNormalizationThreshold(100.0)
.layer(0, new Convolution2D.Builder().nIn(channels).nOut(64).kernelSize(3, 3) .layer(0, Convolution2D.builder().nIn(channels).nOut(64).kernelSize(3, 3)
.activation(Activation.LEAKYRELU).build()) .activation(Activation.LEAKYRELU).build())
.layer(1, new Convolution2D.Builder().nIn(64).nOut(64).kernelSize(3, 3).stride(2, 2) .layer(1, Convolution2D.builder().nIn(64).nOut(64).kernelSize(3, 3).stride(2, 2)
.activation(Activation.LEAKYRELU).build()) .activation(Activation.LEAKYRELU).build())
.layer(2, new Convolution2D.Builder().nIn(64).nOut(64).kernelSize(3, 3).stride(2, 2) .layer(2, Convolution2D.builder().nIn(64).nOut(64).kernelSize(3, 3).stride(2, 2)
.activation(Activation.LEAKYRELU).build()) .activation(Activation.LEAKYRELU).build())
.layer(3, new Convolution2D.Builder().nIn(64).nOut(64).kernelSize(3, 3).stride(2, 2) .layer(3, Convolution2D.builder().nIn(64).nOut(64).kernelSize(3, 3).stride(2, 2)
.activation(Activation.LEAKYRELU).build()) .activation(Activation.LEAKYRELU).build())
.layer(4, new DropoutLayer.Builder().dropOut(0.5).build()) .layer(4, DropoutLayer.builder().dropOut(0.5).build())
.layer(5, new DenseLayer.Builder().nIn(64 * 2 * 2).nOut(1).activation(Activation.SIGMOID).build()) .layer(5, DenseLayer.builder().nIn(64 * 2 * 2).nOut(1).activation(Activation.SIGMOID).build())
.layer(6, new LossLayer.Builder().lossFunction(LossFunctions.LossFunction.XENT).build()) .layer(6, LossLayer.builder().lossFunction(LossFunctions.LossFunction.XENT.getILossFunction()).build())
.inputPreProcessor(0, new FeedForwardToCnnPreProcessor(height, width, channels)) .inputPreProcessor(0, new FeedForwardToCnnPreProcessor(height, width, channels))
.inputPreProcessor(4, new CnnToFeedForwardPreProcessor(2, 2, 64)) .inputPreProcessor(4, new CnnToFeedForwardPreProcessor(2, 2, 64))
.inputType(InputType.convolutionalFlat(height, width, channels)) .inputType(InputType.convolutionalFlat(height, width, channels))

34
brutex-extended-tests/src/test/java/net/brutex/gan/MnistSimpleGAN.java
View File

@ -62,13 +62,13 @@ public class MnistSimpleGAN {
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.gradientNormalizationThreshold(100) .gradientNormalizationThreshold(100)
.layer(new DenseLayer.Builder().nIn(100).nOut(256).weightInit(WeightInit.NORMAL).build()) .layer(DenseLayer.builder().nIn(100).nOut(256).weightInit(WeightInit.NORMAL).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build()) .layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(new DenseLayer.Builder().nIn(256).nOut(512).build()) .layer(DenseLayer.builder().nIn(256).nOut(512).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build()) .layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(new DenseLayer.Builder().nIn(512).nOut(1024).build()) .layer(DenseLayer.builder().nIn(512).nOut(1024).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build()) .layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(new DenseLayer.Builder().nIn(1024).nOut(784).activation(Activation.TANH).build()) .layer(DenseLayer.builder().nIn(1024).nOut(784).activation(Activation.TANH).build())
.build(); .build();
return new MultiLayerNetwork(genConf); return new MultiLayerNetwork(genConf);
} }
@ -83,16 +83,16 @@ public class MnistSimpleGAN {
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.gradientNormalizationThreshold(100) .gradientNormalizationThreshold(100)
.layer(new DenseLayer.Builder().nIn(784).nOut(1024).updater(updater).build()) .layer(DenseLayer.builder().nIn(784).nOut(1024).updater(updater).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build()) .layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(new DropoutLayer.Builder(1 - 0.5).build()) .layer(DropoutLayer.builder(1 - 0.5).build())
.layer(new DenseLayer.Builder().nIn(1024).nOut(512).updater(updater).build()) .layer(DenseLayer.builder().nIn(1024).nOut(512).updater(updater).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build()) .layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(new DropoutLayer.Builder(1 - 0.5).build()) .layer(DropoutLayer.builder(1 - 0.5).build())
.layer(new DenseLayer.Builder().nIn(512).nOut(256).updater(updater).build()) .layer(DenseLayer.builder().nIn(512).nOut(256).updater(updater).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build()) .layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(new DropoutLayer.Builder(1 - 0.5).build()) .layer(DropoutLayer.builder(1 - 0.5).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.XENT).nIn(256).nOut(1) .layer(OutputLayer.builder(LossFunctions.LossFunction.XENT).nIn(256).nOut(1)
.activation(Activation.SIGMOID).updater(updater).build()) .activation(Activation.SIGMOID).updater(updater).build())
.build(); .build();

6
brutex-extended-tests/src/test/java/net/brutex/spark/BrianTest.java
View File

@ -288,12 +288,12 @@ public class BrianTest extends BaseSparkSessionTest {
.seed(123) .seed(123)
.updater(new Nesterovs(0.1, 0.9)) .updater(new Nesterovs(0.1, 0.9))
.layer(0, new DenseLayer.Builder().nIn(5).nOut(20).weightInit(WeightInit.XAVIER) .layer(0, DenseLayer.builder().nIn(5).nOut(20).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).l2(0.001).build()) .activation(Activation.RELU).l2(0.001).build())
.layer(1, new DenseLayer.Builder().nIn(20).nOut(20).weightInit(WeightInit.XAVIER) .layer(1, DenseLayer.builder().nIn(20).nOut(20).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
//.layer(2, new DenseLayerConfiguration.Builder().nIn(9).nOut(9).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()) //.layer(2, new DenseLayerConfiguration.Builder().nIn(9).nOut(9).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.XENT).nIn(20).nOut(4) .layer(2, OutputLayer.builder(LossFunctions.LossFunction.XENT).nIn(20).nOut(4)
.weightInit(WeightInit.XAVIER).activation(Activation.SIGMOID).build()) .weightInit(WeightInit.XAVIER).activation(Activation.SIGMOID).build())
.build(); .build();

6
brutex-extended-tests/src/test/java/net/brutex/spark/BrianTest2.java
View File

@ -298,10 +298,10 @@ public class BrianTest2 /*extends BaseDL4JTest*/ {
.seed(123) .seed(123)
.updater(new Nesterovs(0.1, 0.9)) .updater(new Nesterovs(0.1, 0.9))
.layer(0, new DenseLayer.Builder().nIn(5).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.RELU).l2(0.001).build()) .layer(0, DenseLayer.builder().nIn(5).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.RELU).l2(0.001).build())
.layer(1, new DenseLayer.Builder().nIn(20).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()) .layer(1, DenseLayer.builder().nIn(20).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
//.layer(2, new DenseLayerConfiguration.Builder().nIn(9).nOut(9).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()) //.layer(2, new DenseLayerConfiguration.Builder().nIn(9).nOut(9).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.XENT).nIn(20).nOut(4).weightInit(WeightInit.XAVIER).activation(Activation.SIGMOID).build()) .layer(2, OutputLayer.builder(LossFunctions.LossFunction.XENT).nIn(20).nOut(4).weightInit(WeightInit.XAVIER).activation(Activation.SIGMOID).build())
.build(); .build();
//Define SparkNet //Define SparkNet

14
brutex-extended-tests/src/test/java/net/brutex/spark/TestServer.java
View File

@ -87,15 +87,15 @@ public class TestServer {
.activation(Activation.RELU) .activation(Activation.RELU)
.l2(0) .l2(0)
//.layer(0, new ConvolutionLayer.Builder().nIn(1).kernelSize(1, 5).stride(1,1).padding(0,2).nOut(1).name("1st Filter").updater(new Adam.Builder().learningRate(0.2).build()).build()) //.layer(0, ConvolutionLayer.builder().nIn(1).kernelSize(1, 5).stride(1,1).padding(0,2).nOut(1).name("1st Filter").updater(new Adam.Builder().learningRate(0.2).build()).build())
//.layer(1, new ConvolutionLayer.Builder().nIn(1).kernelSize(1, 2).stride(1,2).padding(0,0).nOut(1).name("2nd Filter").updater(new Adam.Builder().learningRate(0.1).build()).build()) //.layer(1, ConvolutionLayer.builder().nIn(1).kernelSize(1, 2).stride(1,2).padding(0,0).nOut(1).name("2nd Filter").updater(new Adam.Builder().learningRate(0.1).build()).build())
// .layer(1, new DenseLayerConfiguration.Builder().nIn(10).nOut(64).activation(Activation.RELU).build()) // .layer(1, new DenseLayerConfiguration.Builder().nIn(10).nOut(64).activation(Activation.RELU).build())
.layer(0, new DenseLayer.Builder().nIn(10).nOut(100).activation(Activation.RELU).l2(0.003).build()) .layer(0, DenseLayer.builder().nIn(10).nOut(100).activation(Activation.RELU).l2(0.003).build())
.layer(1, new LSTM.Builder().nIn(100).nOut(100).activation(Activation.TANH).build()) .layer(1, LSTM.builder().nIn(100).nOut(100).activation(Activation.TANH).build())
.layer(2, new LSTM.Builder().nIn(100).nOut(100).activation(Activation.TANH).build()) .layer(2, LSTM.builder().nIn(100).nOut(100).activation(Activation.TANH).build())
.layer(3, new DenseLayer.Builder().nIn(100).nOut(16).activation(Activation.RELU).l2(0.001).build()) .layer(3, DenseLayer.builder().nIn(100).nOut(16).activation(Activation.RELU).l2(0.001).build())
.layer(4, new OutputLayer.Builder().nIn(16).nOut(numClasses) .layer(4, OutputLayer.builder().nIn(16).nOut(numClasses)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(new LossMCXENT()) .lossFunction(new LossMCXENT())
.build() .build()

14
brutex-extended-tests/src/test/java/net/brutex/spark/TestServer2.java
View File

@ -127,15 +127,15 @@ public class TestServer2 {
.activation(Activation.RELU) .activation(Activation.RELU)
.l2(0) .l2(0)
//.layer(0, new ConvolutionLayer.Builder().nIn(1).kernelSize(1, 5).stride(1,1).padding(0,2).nOut(1).name("1st Filter").updater(new Adam.Builder().learningRate(0.2).build()).build()) //.layer(0, ConvolutionLayer.builder().nIn(1).kernelSize(1, 5).stride(1,1).padding(0,2).nOut(1).name("1st Filter").updater(new Adam.Builder().learningRate(0.2).build()).build())
//.layer(1, new ConvolutionLayer.Builder().nIn(1).kernelSize(1, 2).stride(1,2).padding(0,0).nOut(1).name("2nd Filter").updater(new Adam.Builder().learningRate(0.1).build()).build()) //.layer(1, ConvolutionLayer.builder().nIn(1).kernelSize(1, 2).stride(1,2).padding(0,0).nOut(1).name("2nd Filter").updater(new Adam.Builder().learningRate(0.1).build()).build())
// .layer(1, new DenseLayerConfiguration.Builder().nIn(10).nOut(64).activation(Activation.RELU).build()) // .layer(1, new DenseLayerConfiguration.Builder().nIn(10).nOut(64).activation(Activation.RELU).build())
.layer(0, new DenseLayer.Builder().nIn(10).nOut(100).activation(Activation.RELU).l2(0.003).build()) .layer(0, DenseLayer.builder().nIn(10).nOut(100).activation(Activation.RELU).l2(0.003).build())
.layer(1, new LSTM.Builder().nIn(100).nOut(100).activation(Activation.TANH).build()) .layer(1, LSTM.builder().nIn(100).nOut(100).activation(Activation.TANH).build())
.layer(2, new LSTM.Builder().nIn(100).nOut(100).activation(Activation.TANH).build()) .layer(2, LSTM.builder().nIn(100).nOut(100).activation(Activation.TANH).build())
.layer(3, new DenseLayer.Builder().nIn(100).nOut(16).activation(Activation.RELU).l2(0.001).build()) .layer(3, DenseLayer.builder().nIn(100).nOut(16).activation(Activation.RELU).l2(0.001).build())
.layer(4, new OutputLayer.Builder().nIn(16).nOut(numClasses) .layer(4, OutputLayer.builder().nIn(16).nOut(numClasses)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(new LossMCXENT()) .lossFunction(new LossMCXENT())
.build() .build()

2
brutex-extended-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestRunner.java
View File

@ -832,7 +832,7 @@ public class IntegrationTestRunner {
if(m instanceof MultiLayerNetwork){ if(m instanceof MultiLayerNetwork){
paramPrefix = l.getIndex() + "_"; paramPrefix = l.getIndex() + "_";
} else { } else {
paramPrefix = l.getLayerConfiguration().getLayerName() + "_"; paramPrefix = l.getLayerConfiguration().getName() + "_";
} }
Map<String,INDArray> paramTable = l.getParamTable(); Map<String,INDArray> paramTable = l.getParamTable();
for(Map.Entry<String,INDArray> e : paramTable.entrySet()){ for(Map.Entry<String,INDArray> e : paramTable.entrySet()){

10
brutex-extended-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN1DTestCases.java
View File

@ -88,11 +88,11 @@ public class CNN1DTestCases {
.convolutionMode(ConvolutionMode.Same)) .convolutionMode(ConvolutionMode.Same))
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new Convolution1DLayer.Builder().nOut(32).activation(Activation.TANH).kernelSize(3).stride(1).build(), "in") .layer("0", Convolution1DLayer.builder().nOut(32).activation(Activation.TANH).kernelSize(3).stride(1).build(), "in")
.layer("1", new Subsampling1DLayer.Builder().kernelSize(2).stride(1).poolingType(SubsamplingLayer.PoolingType.MAX).build(), "0") .layer("1", Subsampling1DLayer.builder().kernelSize(2).stride(1).poolingType(SubsamplingLayer.PoolingType.MAX.toPoolingType()).build(), "0")
.layer("2", new Cropping1D(1), "1") .layer("2", Cropping1D.builder(1).build(), "1")
.layer("3", new ZeroPadding1DLayer(1), "2") .layer("3", ZeroPadding1DLayer.builder(1).build(), "2")
.layer("out", new RnnOutputLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).nOut(nOut).build(), "3") .layer("out", RnnOutputLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).nOut(nOut).build(), "3")
.setInputTypes(InputType.recurrent(nOut)) .setInputTypes(InputType.recurrent(nOut))
.setOutputs("out") .setOutputs("out")
.build(); .build();

32
brutex-extended-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN2DTestCases.java
View File

@ -105,30 +105,30 @@ public class CNN2DTestCases {
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.updater(new Nesterovs(0.01, 0.9)) .updater(new Nesterovs(0.01, 0.9))
.layer(0, new ConvolutionLayer.Builder(5, 5) .layer(0, ConvolutionLayer.builder(5, 5)
//nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied //nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
.nIn(nChannels) .nIn(nChannels)
.stride(1, 1) .stride(1, 1)
.nOut(20) .nOut(20)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)
.build()) .build())
.layer(1, new SubsamplingLayer.Builder(PoolingType.MAX) .layer(1, SubsamplingLayer.builder(PoolingType.MAX)
.kernelSize(2, 2) .kernelSize(2, 2)
.stride(2, 2) .stride(2, 2)
.build()) .build())
.layer(2, new ConvolutionLayer.Builder(5, 5) .layer(2, ConvolutionLayer.builder(5, 5)
//Note that nIn need not be specified in later layers //Note that nIn need not be specified in later layers
.stride(1, 1) .stride(1, 1)
.nOut(50) .nOut(50)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)
.build()) .build())
.layer(3, new SubsamplingLayer.Builder(PoolingType.MAX) .layer(3, SubsamplingLayer.builder(PoolingType.MAX)
.kernelSize(2, 2) .kernelSize(2, 2)
.stride(2, 2) .stride(2, 2)
.build()) .build())
.layer(4, new DenseLayer.Builder().activation(Activation.RELU) .layer(4, DenseLayer.builder().activation(Activation.RELU)
.nOut(500).build()) .nOut(500).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .layer(5, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum) .nOut(outputNum)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build()) .build())
@ -221,7 +221,7 @@ public class CNN2DTestCases {
.seed(12345) .seed(12345)
.build()) .build())
.removeVertexKeepConnections("predictions") .removeVertexKeepConnections("predictions")
.addLayer("predictions", new OutputLayer.Builder() .addLayer("predictions", OutputLayer.builder()
.nIn(4096) .nIn(4096)
.nOut(200) //Tiny imagenet .nOut(200) //Tiny imagenet
.build(), "fc2") .build(), "fc2")
@ -321,7 +321,7 @@ public class CNN2DTestCases {
.removeVertexKeepConnections("conv2d_9") .removeVertexKeepConnections("conv2d_9")
.removeVertexAndConnections("outputs") .removeVertexAndConnections("outputs")
.addLayer("convolution2d_9", .addLayer("convolution2d_9",
new ConvolutionLayer.Builder(1,1) ConvolutionLayer.builder(1,1)
.nIn(1024) .nIn(1024)
.nOut(nBoxes * (5 + nClasses)) .nOut(nBoxes * (5 + nClasses))
.stride(1,1) .stride(1,1)
@ -331,10 +331,10 @@ public class CNN2DTestCases {
.build(), .build(),
"leaky_re_lu_8") "leaky_re_lu_8")
.addLayer("outputs", .addLayer("outputs",
new Yolo2OutputLayer.Builder() Yolo2OutputLayer.builder()
.lambdaNoObj(lambdaNoObj) .lambdaNoObj(lambdaNoObj)
.lambdaCoord(lambdaCoord) .lambdaCoord(lambdaCoord)
.boundingBoxPriors(priors) .boundingBoxes(priors)
.build(), .build(),
"convolution2d_9") "convolution2d_9")
.setOutputs("outputs") .setOutputs("outputs")
@ -417,32 +417,32 @@ public class CNN2DTestCases {
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.updater(new Nesterovs(0.01, 0.9)) .updater(new Nesterovs(0.01, 0.9))
.layer(0, new ConvolutionLayer.Builder(5, 5) .layer(0, ConvolutionLayer.builder(5, 5)
//nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied //nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
.nIn(1) .nIn(1)
.stride(1, 1) .stride(1, 1)
.nOut(20) .nOut(20)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)
.build()) .build())
.layer(1, new SubsamplingLayer.Builder(PoolingType.MAX) .layer(1, SubsamplingLayer.builder(PoolingType.MAX)
.kernelSize(2, 2) .kernelSize(2, 2)
.stride(2, 2) .stride(2, 2)
.build()) .build())
.layer(2, new ConvolutionLayer.Builder(5, 5) .layer(2, ConvolutionLayer.builder(5, 5)
//Note that nIn need not be specified in later layers //Note that nIn need not be specified in later layers
.stride(1, 1) .stride(1, 1)
.nOut(50) .nOut(50)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)
.dropOut(0.5) //**** Dropout on conv layer .dropOut(0.5) //**** Dropout on conv layer
.build()) .build())
.layer(3, new SubsamplingLayer.Builder(PoolingType.MAX) .layer(3, SubsamplingLayer.builder(PoolingType.MAX)
.kernelSize(2, 2) .kernelSize(2, 2)
.stride(2, 2) .stride(2, 2)
.build()) .build())
.layer(4, new DenseLayer.Builder().activation(Activation.RELU) .layer(4, DenseLayer.builder().activation(Activation.RELU)
.dropOut(0.5) //**** Dropout on dense layer .dropOut(0.5) //**** Dropout on dense layer
.nOut(500).build()) .nOut(500).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .layer(5, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(10) .nOut(10)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build()) .build())

6
brutex-extended-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN3DTestCases.java
View File

@ -82,18 +82,18 @@ public class CNN3DTestCases {
.updater(new Nesterovs(0.01, 0.9)) .updater(new Nesterovs(0.01, 0.9))
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.layer(new Convolution3D.Builder(3,3,3) .layer(Convolution3D.builder(3,3,3)
.dataFormat(Convolution3D.DataFormat.NCDHW) .dataFormat(Convolution3D.DataFormat.NCDHW)
.nIn(nChannels) .nIn(nChannels)
.stride(2, 2, 2) .stride(2, 2, 2)
.nOut(8) .nOut(8)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)
.build()) .build())
.layer(new Subsampling3DLayer.Builder(PoolingType.MAX) .layer(Subsampling3DLayer.builder(PoolingType.MAX)
.kernelSize(2, 2, 2) .kernelSize(2, 2, 2)
.stride(2, 2, 2) .stride(2, 2, 2)
.build()) .build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .layer(OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum) .nOut(outputNum)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build()) .build())

8
brutex-extended-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/MLPTestCases.java
View File

@ -104,8 +104,8 @@ public class MLPTestCases {
.build())) .build()))
.l1(1e-3).l2(1e-3) .l1(1e-3).l2(1e-3)
.layer(new DenseLayer.Builder().activation(Activation.TANH).nOut(64).build()) .layer(DenseLayer.builder().activation(Activation.TANH).nOut(64).build())
.layer(new OutputLayer.Builder().nOut(10) .layer(OutputLayer.builder().nOut(10)
.lossFunction(LossFunctions.LossFunction.MCXENT) .lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build()) .build())
@ -202,11 +202,11 @@ public class MLPTestCases {
.seed(seed) .seed(seed)
.updater(new Nesterovs(learningRate, 0.9)) .updater(new Nesterovs(learningRate, 0.9))
.layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .layer(0, DenseLayer.builder().nIn(numInputs).nOut(numHiddenNodes)
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.activation(Activation.RELU) .activation(Activation.RELU)
.build()) .build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .layer(1, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.nIn(numHiddenNodes).nOut(numOutputs).build()) .nIn(numHiddenNodes).nOut(numOutputs).build())

18
brutex-extended-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/RNNTestCases.java
View File

@ -119,11 +119,11 @@ public class RNNTestCases {
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.updater(new Adam(1e-3)) .updater(new Adam(1e-3))
.layer(0, new LSTM.Builder().nIn(iter.inputColumns()).nOut(lstmLayerSize) .layer(0, LSTM.builder().nIn(iter.inputColumns()).nOut(lstmLayerSize)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new LSTM.Builder().nIn(lstmLayerSize).nOut(lstmLayerSize) .layer(1, LSTM.builder().nIn(lstmLayerSize).nOut(lstmLayerSize)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(2, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX) //MCXENT + softmax for classification .layer(2, RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX) //MCXENT + softmax for classification
.nIn(lstmLayerSize).nOut(nOut).build()) .nIn(lstmLayerSize).nOut(nOut).build())
.backpropType(BackpropType.TruncatedBPTT).tbpttFwdLength(tbpttLength).tbpttBackLength(tbpttLength) .backpropType(BackpropType.TruncatedBPTT).tbpttFwdLength(tbpttLength).tbpttBackLength(tbpttLength)
@ -201,9 +201,9 @@ public class RNNTestCases {
.updater(new Adam(5e-2)) .updater(new Adam(5e-2))
.l1(1e-3).l2(1e-3) .l1(1e-3).l2(1e-3)
.layer(0, new LSTM.Builder().activation(Activation.TANH).nOut(10).build()) .layer(0, LSTM.builder().activation(Activation.TANH).nOut(10).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build()) .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nOut(6) .layer(OutputLayer.builder().nOut(6)
.lossFunction(LossFunctions.LossFunction.MCXENT) .lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build()) .build())
@ -322,9 +322,9 @@ public class RNNTestCases {
.updater(new Adam(5e-2)) .updater(new Adam(5e-2))
.l1(1e-3).l2(1e-3) .l1(1e-3).l2(1e-3)
.layer(0, new Bidirectional(new LSTM.Builder().activation(Activation.TANH).nOut(10).build())) .layer(0, Bidirectional.builder(LSTM.builder().activation(Activation.TANH).nOut(10).build()).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build()) .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nOut(6) .layer(OutputLayer.builder().nOut(6)
.lossFunction(LossFunctions.LossFunction.MCXENT) .lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build()) .build())

2
brutex-extended-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/UnsupervisedTestCases.java
View File

@ -79,7 +79,7 @@ public class UnsupervisedTestCases {
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.l2(1e-4) .l2(1e-4)
.layer(0, new VariationalAutoencoder.Builder() .layer(0, VariationalAutoencoder.builder()
.activation(Activation.TANH) .activation(Activation.TANH)
.encoderLayerSizes(256, 256) //2 encoder layers, each of size 256 .encoderLayerSizes(256, 256) //2 encoder layers, each of size 256
.decoderLayerSizes(256, 256) //2 decoder layers, each of size 256 .decoderLayerSizes(256, 256) //2 decoder layers, each of size 256

2
cavis-common-platform/build.gradle
View File

@ -71,7 +71,7 @@ dependencies {
// api "com.fasterxml.jackson.module:jackson-module-scala_${scalaVersion}" // api "com.fasterxml.jackson.module:jackson-module-scala_${scalaVersion}"
api "org.projectlombok:lombok:1.18.24" api "org.projectlombok:lombok:1.18.26"
/*Logging*/ /*Logging*/
api 'org.slf4j:slf4j-api:2.0.3' api 'org.slf4j:slf4j-api:2.0.3'

2
cavis-datavec/cavis-datavec-api/src/main/java/org/datavec/api/transform/analysis/columns/NumericalColumnAnalysis.java
View File

@ -90,7 +90,7 @@ public abstract class NumericalColumnAnalysis implements ColumnAnalysis {
public abstract double getMaxDouble(); public abstract double getMaxDouble();
@SuppressWarnings("unchecked") @SuppressWarnings("unchecked")
public abstract static class Builder<T extends Builder<T>> { public static abstract class Builder<T extends Builder<T>> {
protected double mean; protected double mean;
protected double sampleStdev; protected double sampleStdev;
protected double sampleVariance; protected double sampleVariance;

2
cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/autodiff/samediff/internal/InferenceSession.java
View File

@ -970,7 +970,7 @@ public class InferenceSession extends AbstractSession<INDArray, Pair<SameDiffOp,
} }
@Data @Data
public abstract static class Dep { public static abstract class Dep {
protected String frame; protected String frame;
protected FrameIter parentFrame; protected FrameIter parentFrame;
} }

44
cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/activations/Activation.java
View File

@ -22,9 +22,11 @@ package org.nd4j.linalg.activations;
import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.activations.impl.*; import org.nd4j.linalg.activations.impl.*;
import org.nd4j.linalg.api.ndarray.INDArray;
public enum Activation { public enum Activation implements IActivation {
CUBE, ELU, HARDSIGMOID, HARDTANH, IDENTITY, LEAKYRELU, RATIONALTANH, RELU, RELU6, CUBE, ELU, HARDSIGMOID, HARDTANH, IDENTITY, LEAKYRELU, RATIONALTANH, RELU, RELU6,
RRELU, SIGMOID, SOFTMAX, SOFTPLUS, SOFTSIGN, TANH, RECTIFIEDTANH, SELU, SWISH, RRELU, SIGMOID, SOFTMAX, SOFTPLUS, SOFTSIGN, TANH, RECTIFIEDTANH, SELU, SWISH,
THRESHOLDEDRELU, GELU, MISH; THRESHOLDEDRELU, GELU, MISH;
@ -149,4 +151,44 @@ public enum Activation {
throw new UnsupportedOperationException("Activation function not yet supported: " + this); throw new UnsupportedOperationException("Activation function not yet supported: " + this);
} }
} }
/**
* Carry out activation function on the input array (usually known as 'preOut' or 'z')
* Implementations must overwrite "in", transform in place and return "in"
* Can support separate behaviour during test
*
* @param in input array.
* @param training true when training.
* @return transformed activation
*/
@Override
public INDArray getActivation(INDArray in, boolean training) {
return getActivationFunction().getActivation(in, training);
}
/**
* Backpropagate the errors through the activation function, given input z and epsilon dL/da.<br>
* Returns 2 INDArrays:<br>
* (a) The gradient dL/dz, calculated from dL/da, and<br>
* (b) The parameter gradients dL/dW, where w is the weights in the activation function. For activation functions
* with no gradients, this will be null.
*
* @param in Input, before applying the activation function (z, or 'preOut')
* @param epsilon Gradient to be backpropagated: dL/da, where L is the loss function
* @return dL/dz and dL/dW, for weights w (null if activation function has no weights)
*/
@Override
public Pair<INDArray, INDArray> backprop(INDArray in, INDArray epsilon) {
return getActivationFunction().backprop(in, epsilon);
}
/**
*
* @param inputSize
* @return
*/
@Override
public int numParams(int inputSize) {
return getActivationFunction().numParams(inputSize);
}
} }

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/RandomTests.java
View File

@ -42,9 +42,9 @@ public class RandomTests extends BaseDL4JTest {
final NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp()) final NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(28 * 28).nOut(10) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(28 * 28).nOut(10)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder(
LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10) LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();

6
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/TestUtils.java
View File

@ -73,7 +73,7 @@ public class TestUtils {
throw new RuntimeException(e); throw new RuntimeException(e);
} }
//Also check the NeuralNetConfiguration is serializable (required by Spark etc) //Also check the NeuralNetConfiguration is serializable (required by Spark etc.)
NeuralNetConfiguration conf = net.getNetConfiguration(); NeuralNetConfiguration conf = net.getNetConfiguration();
serializeDeserializeJava(conf); serializeDeserializeJava(conf);
@ -317,14 +317,14 @@ public class TestUtils {
for(Layer l : layers){ for(Layer l : layers){
//Don't use instanceof here - there are sub conv subclasses //Don't use instanceof here - there are sub conv subclasses
if(l.getClass() == ConvolutionLayer.class || l instanceof SubsamplingLayer || l instanceof BatchNormalization || l instanceof LSTM){ if(l.getClass() == ConvolutionLayer.class || l instanceof SubsamplingLayer || l instanceof BatchNormalization || l instanceof LSTM){
Preconditions.checkNotNull(l.getHelper(), l.getLayerConfiguration().getLayerName()); Preconditions.checkNotNull(l.getHelper(), l.getLayerConfiguration().getName());
} }
} }
} }
public static void assertHelpersAbsent(Layer[] layers) throws Exception { public static void assertHelpersAbsent(Layer[] layers) throws Exception {
for(Layer l : layers){ for(Layer l : layers){
Preconditions.checkState(l.getHelper() == null, l.getLayerConfiguration().getLayerName()); Preconditions.checkState(l.getHelper() == null, l.getLayerConfiguration().getName());
} }
} }
} }

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetiteratorTest.java
View File

@ -473,9 +473,7 @@ public class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
public Pair<double[][],File> makeRandomCSV(String tempFile, int nLines, int nFeatures) throws IOException { public Pair<double[][],File> makeRandomCSV(String tempFile, int nLines, int nFeatures) throws IOException {
File temp = temporaryFolder; File temp = new File(temporaryFolder, "makeRandomCSV.csv");
temp.mkdirs();
temp.deleteOnExit();
Random rand = new Random(12345); Random rand = new Random(12345);
double[][] dArr = new double[nLines][nFeatures + 1]; double[][] dArr = new double[nLines][nFeatures + 1];

2
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderMultiDataSetIteratorTest.java
View File

@ -774,7 +774,7 @@ public class RecordReaderMultiDataSetIteratorTest extends BaseDL4JTest {
@Test @Test
public void testExcludeStringColCSV() throws Exception { public void testExcludeStringColCSV() throws Exception {
File csvFile = temporaryFolder; File csvFile = new File(temporaryFolder, "test.csv");
StringBuilder sb = new StringBuilder(); StringBuilder sb = new StringBuilder();
for(int i=1; i<=10; i++ ){ for(int i=1; i<=10; i++ ){

17
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/datasets/iterator/DataSetIteratorTest.java
View File

@ -41,7 +41,9 @@ import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.CollectScoresIterationListener; import org.deeplearning4j.optimize.listeners.CollectScoresIterationListener;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener; import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.junit.jupiter.api.Disabled;
import org.junit.jupiter.api.Test; import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.Timeout;
import org.nd4j.linalg.activations.Activation; import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.dataset.DataSet;
@ -170,11 +172,11 @@ public class DataSetIteratorTest extends BaseDL4JTest {
NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed) NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.layer(0, new ConvolutionLayer.Builder(5, 5).nIn(numChannels).nOut(6) .layer(0, ConvolutionLayer.builder(5, 5).nIn(numChannels).nOut(6)
.weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()) .weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2}) .layer(1, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2})
.stride(1, 1).build()) .stride(1, 1).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .layer(2, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX) .nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.build()) .build())
.inputType(InputType.convolutionalFlat(numRows, numColumns, numChannels)); .inputType(InputType.convolutionalFlat(numRows, numColumns, numChannels));
@ -207,7 +209,8 @@ public class DataSetIteratorTest extends BaseDL4JTest {
} }
@Test //@Ignore //Ignored for now - CIFAR iterator needs work - https://github.com/eclipse/deeplearning4j/issues/4673 @Test @Timeout(1200) @Disabled("Runs quite some time.")
//Ignored for now - CIFAR iterator needs work - https://github.com/eclipse/deeplearning4j/issues/4673
public void testCifarModel() throws Exception { public void testCifarModel() throws Exception {
// Streaming // Streaming
runCifar(false); runCifar(false);
@ -230,11 +233,11 @@ public class DataSetIteratorTest extends BaseDL4JTest {
NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed) NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.layer(0, new ConvolutionLayer.Builder(5, 5).nIn(channels).nOut(6).weightInit(WeightInit.XAVIER) .layer(0, ConvolutionLayer.builder(5, 5).nIn(channels).nOut(6).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2}) .layer(1, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2})
.build()) .build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .layer(2, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX) .nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.build()) .build())

7
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/datasets/iterator/TestFileIterators.java
View File

@ -76,10 +76,12 @@ public class TestFileIterators extends BaseDL4JTest {
assertEquals(exp, act); assertEquals(exp, act);
//Test multiple directories //Test multiple directories
File f2a = new File(folder2, "f2a"); File f2a = new File(folder2, "f2a");
f2a.mkdirs();
File f2b = new File(folder2, "f2b"); File f2b = new File(folder2, "f2b");
f2b.mkdirs();
File f2c = new File(folder2, "f2c"); File f2c = new File(folder2, "f2c");
f2c.mkdirs();
d1.save(new File(f2a, "d1.bin")); d1.save(new File(f2a, "d1.bin"));
d2.save(new File(f2a, "d2.bin")); d2.save(new File(f2a, "d2.bin"));
d3.save(new File(f2b, "d3.bin")); d3.save(new File(f2b, "d3.bin"));
@ -188,8 +190,11 @@ public class TestFileIterators extends BaseDL4JTest {
//Test multiple directories //Test multiple directories
File f2a = new File(folder2, "2-f2a"); File f2a = new File(folder2, "2-f2a");
f2a.mkdirs();
File f2b = new File(folder2, "2-f2b"); File f2b = new File(folder2, "2-f2b");
f2b.mkdirs();
File f2c = new File(folder2, "2-f2C"); File f2c = new File(folder2, "2-f2C");
f2c.mkdirs();
d1.save(new File(f2a, "d1.bin")); d1.save(new File(f2a, "d1.bin"));
d2.save(new File(f2a, "d2.bin")); d2.save(new File(f2a, "d2.bin"));
d3.save(new File(f2b, "d3.bin")); d3.save(new File(f2b, "d3.bin"));

44
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStopping.java
View File

@ -135,8 +135,8 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.seed(12345) .seed(12345)
.updater(new Sgd(0.5)).weightInit(WeightInit.XAVIER) .updater(new Sgd(0.5)).weightInit(WeightInit.XAVIER)
.layer(new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()) .layer(DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build())
.layer(new OutputLayer.Builder().nIn(4).nOut(3) .layer(OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
@ -221,7 +221,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.01)).weightInit(WeightInit.XAVIER) .updater(new Sgd(0.01)).weightInit(WeightInit.XAVIER)
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
@ -250,7 +250,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER) .updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER)
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
@ -300,7 +300,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(5.0)) //Intentionally huge LR .updater(new Sgd(5.0)) //Intentionally huge LR
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX) .layer(0, OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -338,7 +338,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(1e-6)).weightInit(WeightInit.XAVIER) .updater(new Sgd(1e-6)).weightInit(WeightInit.XAVIER)
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
@ -381,7 +381,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.0)).weightInit(WeightInit.XAVIER) .updater(new Sgd(0.0)).weightInit(WeightInit.XAVIER)
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
@ -421,11 +421,11 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Nesterovs(0.0,0.9)) .updater(new Nesterovs(0.0,0.9))
.layer(0, new DenseLayer.Builder().nIn(1).nOut(20) .layer(0, DenseLayer.builder().nIn(1).nOut(20)
.weightInit(WeightInit.XAVIER).activation( .weightInit(WeightInit.XAVIER).activation(
Activation.TANH) Activation.TANH)
.build()) .build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).weightInit(WeightInit.XAVIER) .layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).weightInit(WeightInit.XAVIER)
.activation(Activation.IDENTITY).weightInit(WeightInit.XAVIER).nIn(20).nOut(1) .activation(Activation.IDENTITY).weightInit(WeightInit.XAVIER).nIn(20).nOut(1)
.build()) .build())
.build(); .build();
@ -468,7 +468,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER) .updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER)
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
@ -506,7 +506,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER) .updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER)
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
@ -570,8 +570,8 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(784).nOut(32).build()) .layer(DenseLayer.builder().nIn(784).nOut(32).build())
.layer(new OutputLayer.Builder().nIn(32).nOut(784).activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.MSE).build()) .layer(OutputLayer.builder().nIn(32).nOut(784).activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.MSE).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -613,7 +613,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new AutoEncoder.Builder().nIn(784).nOut(32).build()) .layer(AutoEncoder.builder().nIn(784).nOut(32).build())
.build(); .build();
@ -656,7 +656,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new VariationalAutoencoder.Builder() .layer(VariationalAutoencoder.builder()
.nIn(784).nOut(32) .nIn(784).nOut(32)
.encoderLayerSizes(64) .encoderLayerSizes(64)
.decoderLayerSizes(64) .decoderLayerSizes(64)
@ -701,7 +701,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new VariationalAutoencoder.Builder() .layer(VariationalAutoencoder.builder()
.nIn(784).nOut(32) .nIn(784).nOut(32)
.encoderLayerSizes(64) .encoderLayerSizes(64)
.decoderLayerSizes(64) .decoderLayerSizes(64)
@ -748,8 +748,8 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(784).nOut(32).build()) .layer(DenseLayer.builder().nIn(784).nOut(32).build())
.layer(new OutputLayer.Builder().nIn(32).nOut(10).activation(Activation.SOFTMAX).build()) .layer(OutputLayer.builder().nIn(32).nOut(10).activation(Activation.SOFTMAX).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -785,7 +785,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
public void testEarlyStoppingListeners() { public void testEarlyStoppingListeners() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER) .updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER)
.layer(0, new OutputLayer.Builder().nIn(4).nOut(3) .layer(0, OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
@ -868,14 +868,14 @@ public class TestEarlyStopping extends BaseDL4JTest {
.ClipElementWiseAbsoluteValue) .ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(1.0) .gradientNormalizationThreshold(1.0)
.layer(0, new LSTM.Builder() .layer(0, LSTM.builder()
.nIn(10) .nIn(10)
.nOut(10) .nOut(10)
.activation(Activation.TANH) .activation(Activation.TANH)
.gateActivationFunction(Activation.SIGMOID) .gateActivationFunction(Activation.SIGMOID.getActivationFunction())
.dropOut(0.5) .dropOut(0.5)
.build()) .build())
.layer(1, new RnnOutputLayer.Builder() .layer(1, RnnOutputLayer.builder()
.nIn(10) .nIn(10)
.nOut(outputs) .nOut(outputs)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)

26
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStoppingCompGraph.java
View File

@ -79,7 +79,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in") .updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
@ -124,7 +124,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(5.0)) //Intentionally huge LR .updater(new Sgd(5.0)) //Intentionally huge LR
.weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in") .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf); ComputationGraph net = new ComputationGraph(conf);
@ -160,7 +160,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(1e-6)).weightInit(WeightInit.XAVIER).graphBuilder() .updater(new Sgd(1e-6)).weightInit(WeightInit.XAVIER).graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
@ -202,7 +202,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.0)).weightInit(WeightInit.XAVIER).graphBuilder() .updater(new Sgd(0.0)).weightInit(WeightInit.XAVIER).graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
@ -236,7 +236,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in") .updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3) .addLayer("0", OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();
@ -300,8 +300,8 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new DenseLayer.Builder().nIn(784).nOut(32).build(), "in") .layer("0", DenseLayer.builder().nIn(784).nOut(32).build(), "in")
.layer("1", new OutputLayer.Builder().nIn(32).nOut(784).activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.MSE).build(), "0") .layer("1", OutputLayer.builder().nIn(32).nOut(784).activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.MSE).build(), "0")
.setOutputs("1") .setOutputs("1")
.build(); .build();
@ -346,7 +346,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new AutoEncoder.Builder().nIn(784).nOut(32).build(), "in") .layer("0", AutoEncoder.builder().nIn(784).nOut(32).build(), "in")
.setOutputs("0") .setOutputs("0")
.build(); .build();
@ -391,7 +391,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new VariationalAutoencoder.Builder() .layer("0", VariationalAutoencoder.builder()
.nIn(784).nOut(32) .nIn(784).nOut(32)
.encoderLayerSizes(64) .encoderLayerSizes(64)
.decoderLayerSizes(64) .decoderLayerSizes(64)
@ -439,7 +439,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.updater(new Adam(1e-5)) .updater(new Adam(1e-5))
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new VariationalAutoencoder.Builder() .layer("0", VariationalAutoencoder.builder()
.nIn(784).nOut(32) .nIn(784).nOut(32)
.encoderLayerSizes(64) .encoderLayerSizes(64)
.decoderLayerSizes(64) .decoderLayerSizes(64)
@ -489,8 +489,8 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new DenseLayer.Builder().nIn(784).nOut(32).build(), "in") .layer("0", DenseLayer.builder().nIn(784).nOut(32).build(), "in")
.layer("1", new OutputLayer.Builder().nIn(32).nOut(10).activation(Activation.SOFTMAX).build(), "0") .layer("1", OutputLayer.builder().nIn(32).nOut(10).activation(Activation.SOFTMAX).build(), "0")
.setOutputs("1") .setOutputs("1")
.build(); .build();
@ -530,7 +530,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER) .updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER)
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new OutputLayer.Builder().nIn(4).nOut(3) .layer("0", OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0") .setOutputs("0")

48
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/eval/EvalTest.java
View File

@ -73,9 +73,9 @@ public class EvalTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).seed(42) .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).seed(42)
.updater(new Sgd(1e-6)).list() .updater(new Sgd(1e-6)).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(2).activation(Activation.TANH) .layer(0, DenseLayer.builder().nIn(4).nOut(2).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()) .weightInit(WeightInit.XAVIER).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).weightInit(WeightInit.XAVIER) LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).weightInit(WeightInit.XAVIER)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
@ -180,7 +180,7 @@ public class EvalTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new Sgd(0.1)) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new Sgd(0.1))
.list() .list()
.layer(0, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(0, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(4).nOut(3).build()) .activation(Activation.SOFTMAX).nIn(4).nOut(3).build())
.build(); .build();
@ -300,8 +300,8 @@ public class EvalTest extends BaseDL4JTest {
.trainingWorkspaceMode(ws) .trainingWorkspaceMode(ws)
.inferenceWorkspaceMode(ws) .inferenceWorkspaceMode(ws)
.list() .list()
.layer(new LSTM.Builder().nIn(nIn).nOut(layerSize).build()) .layer(LSTM.builder().nIn(nIn).nOut(layerSize).build())
.layer(new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut) .layer(RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build()) .build())
.build(); .build();
@ -311,8 +311,8 @@ public class EvalTest extends BaseDL4JTest {
.trainingWorkspaceMode(ws) .trainingWorkspaceMode(ws)
.inferenceWorkspaceMode(ws) .inferenceWorkspaceMode(ws)
.list() .list()
.layer(new LSTM.Builder().nIn(nIn).nOut(layerSize).build()) .layer(LSTM.builder().nIn(nIn).nOut(layerSize).build())
.layer(new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut) .layer(RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.tbpttFwdLength(10).tbpttBackLength(10) .tbpttFwdLength(10).tbpttBackLength(10)
.backpropType(BackpropType.TruncatedBPTT) .backpropType(BackpropType.TruncatedBPTT)
@ -377,8 +377,8 @@ public class EvalTest extends BaseDL4JTest {
.inferenceWorkspaceMode(ws) .inferenceWorkspaceMode(ws)
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new LSTM.Builder().nIn(nIn).nOut(layerSize).build(), "in") .addLayer("0", LSTM.builder().nIn(nIn).nOut(layerSize).build(), "in")
.addLayer("1", new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut) .addLayer("1", RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build(), "0") .build(), "0")
.setOutputs("1") .setOutputs("1")
@ -390,8 +390,8 @@ public class EvalTest extends BaseDL4JTest {
.inferenceWorkspaceMode(ws) .inferenceWorkspaceMode(ws)
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new LSTM.Builder().nIn(nIn).nOut(layerSize).build(), "in") .addLayer("0", LSTM.builder().nIn(nIn).nOut(layerSize).build(), "in")
.addLayer("1", new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut) .addLayer("1", RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build(), "0") .build(), "0")
.setOutputs("1") .setOutputs("1")
@ -457,8 +457,8 @@ public class EvalTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
.list() .list()
.layer(0, new LSTM.Builder().activation(Activation.TANH).nIn(3).nOut(3).build()) .layer(0, LSTM.builder().activation(Activation.TANH).nIn(3).nOut(3).build())
.layer(1, new RnnOutputLayer.Builder().activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.XENT) .layer(1, RnnOutputLayer.builder().activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.XENT)
.nIn(3).nOut(1).build()) .nIn(3).nOut(1).build())
.backpropType(BackpropType.TruncatedBPTT).tbpttFwdLength(10).tbpttBackLength(10) .backpropType(BackpropType.TruncatedBPTT).tbpttFwdLength(10).tbpttBackLength(10)
.build(); .build();
@ -477,9 +477,9 @@ public class EvalTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).seed(42) .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).seed(42)
.updater(new Sgd(1e-6)).list() .updater(new Sgd(1e-6)).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(2).activation(Activation.TANH) .layer(0, DenseLayer.builder().nIn(4).nOut(2).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()) .weightInit(WeightInit.XAVIER).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder( .layer(1, org.deeplearning4j.nn.conf.layers.OutputLayer.builder(
LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).weightInit(WeightInit.XAVIER) LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).weightInit(WeightInit.XAVIER)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.build(); .build();
@ -507,8 +507,8 @@ public class EvalTest extends BaseDL4JTest {
.seed(12345) .seed(12345)
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("out1", new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).build(), "in") .addLayer("out1", OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).build(), "in")
.addLayer("out2", new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).build(), "in") .addLayer("out2", OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).build(), "in")
.setOutputs("out1", "out2") .setOutputs("out1", "out2")
.build(); .build();
@ -541,11 +541,11 @@ public class EvalTest extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new EmbeddingSequenceLayer.Builder().nIn(10).nOut(10).build(), "in") .layer("0", EmbeddingSequenceLayer.builder().nIn(10).nOut(10).build(), "in")
.layer("1", new LSTM.Builder().nIn(10).nOut(10).build(), "0") .layer("1", LSTM.builder().nIn(10).nOut(10).build(), "0")
.layer("2", new LSTM.Builder().nIn(10).nOut(10).build(), "0") .layer("2", LSTM.builder().nIn(10).nOut(10).build(), "0")
.layer("out1", new RnnOutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build(), "1") .layer("out1", RnnOutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build(), "1")
.layer("out2", new RnnOutputLayer.Builder().nIn(10).nOut(20).activation(Activation.SOFTMAX).build(), "2") .layer("out2", RnnOutputLayer.builder().nIn(10).nOut(20).activation(Activation.SOFTMAX).build(), "2")
.setOutputs("out1", "out2") .setOutputs("out1", "out2")
.build(); .build();
@ -569,8 +569,8 @@ public class EvalTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.list() .list()
.layer(new DenseLayer.Builder().nIn(4).nOut(10).build()) .layer(DenseLayer.builder().nIn(4).nOut(10).build())
.layer(new OutputLayer.Builder().nIn(10).nOut(3).lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.RELU).build()) .layer(OutputLayer.builder().nIn(10).nOut(3).lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.RELU).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);

8
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/eval/EvaluationToolsTests.java
View File

@ -48,8 +48,8 @@ public class EvaluationToolsTests extends BaseDL4JTest {
DataSetIterator iter = new IrisDataSetIterator(150, 150); DataSetIterator iter = new IrisDataSetIterator(150, 150);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1, .layer(0, DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1,
new OutputLayer.Builder().nIn(4).nOut(2).activation(Activation.SOFTMAX) OutputLayer.builder().nIn(4).nOut(2).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -90,8 +90,8 @@ public class EvaluationToolsTests extends BaseDL4JTest {
DataSetIterator iter = new IrisDataSetIterator(150, 150); DataSetIterator iter = new IrisDataSetIterator(150, 150);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1, .layer(0, DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1,
new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX) OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/eval/ROCTest.java
View File

@ -84,8 +84,8 @@ public class ROCTest extends BaseDL4JTest {
Nd4j.getRandom().setSeed(12345); Nd4j.getRandom().setSeed(12345);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).seed(12345)
.list() .list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1, .layer(0, DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1,
new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX) OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/eval/RegressionEvalTest.java
View File

@ -49,7 +49,7 @@ public class RegressionEvalTest extends BaseDL4JTest {
//Basic sanity check //Basic sanity check
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.ZERO).list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.ZERO).list()
.layer(0, new OutputLayer.Builder().activation(Activation.TANH) .layer(0, OutputLayer.builder().activation(Activation.TANH)
.lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(5).build()) .lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(5).build())
.build(); .build();
@ -71,7 +71,7 @@ public class RegressionEvalTest extends BaseDL4JTest {
ComputationGraphConfiguration graphConf = ComputationGraphConfiguration graphConf =
NeuralNetConfiguration.builder().weightInit(WeightInit.ZERO).graphBuilder() NeuralNetConfiguration.builder().weightInit(WeightInit.ZERO).graphBuilder()
.addInputs("in").addLayer("0", new OutputLayer.Builder() .addInputs("in").addLayer("0", OutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MSE) .lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.TANH).nIn(10).nOut(5).build(), "in") .activation(Activation.TANH).nIn(10).nOut(5).build(), "in")
.setOutputs("0").build(); .setOutputs("0").build();

72
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/exceptions/TestInvalidConfigurations.java
View File

@ -41,8 +41,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public static MultiLayerNetwork getDensePlusOutput(int nIn, int nOut) { public static MultiLayerNetwork getDensePlusOutput(int nIn, int nOut) {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(10).build()) .layer(0, DenseLayer.builder().nIn(nIn).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(nOut).build()).build(); .layer(1, OutputLayer.builder().nIn(10).nOut(nOut).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -52,8 +52,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public static MultiLayerNetwork getLSTMPlusRnnOutput(int nIn, int nOut) { public static MultiLayerNetwork getLSTMPlusRnnOutput(int nIn, int nOut) {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(10).build()) .layer(0, GravesLSTM.builder().nIn(nIn).nOut(10).build())
.layer(1, new RnnOutputLayer.Builder().nIn(10).nOut(nOut).build()).build(); .layer(1, RnnOutputLayer.builder().nIn(10).nOut(nOut).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -63,8 +63,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public static MultiLayerNetwork getCnnPlusOutputLayer(int depthIn, int inH, int inW, int nOut) { public static MultiLayerNetwork getCnnPlusOutputLayer(int depthIn, int inH, int inW, int nOut) {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().nIn(depthIn).nOut(5).build()) .layer(0, ConvolutionLayer.builder().nIn(depthIn).nOut(5).build())
.layer(1, new OutputLayer.Builder().nOut(nOut).build()) .layer(1, OutputLayer.builder().nOut(nOut).build())
.inputType(InputType.convolutional(inH, inW, depthIn)).build(); .inputType(InputType.convolutional(inH, inW, depthIn)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -90,8 +90,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public void testDenseNout0() { public void testDenseNout0() {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(0).build()) .layer(0, DenseLayer.builder().nIn(10).nOut(0).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).build()).build(); .layer(1, OutputLayer.builder().nIn(10).nOut(10).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -147,8 +147,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public void testLSTMNOut0() { public void testLSTMNOut0() {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new GravesLSTM.Builder().nIn(10).nOut(0).build()) .layer(0, GravesLSTM.builder().nIn(10).nOut(0).build())
.layer(1, new RnnOutputLayer.Builder().nIn(10).nOut(10).build()).build(); .layer(1, RnnOutputLayer.builder().nIn(10).nOut(10).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -178,8 +178,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public void testConvolutionalNOut0() { public void testConvolutionalNOut0() {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().nIn(5).nOut(0).build()) .layer(0, ConvolutionLayer.builder().nIn(5).nOut(0).build())
.layer(1, new OutputLayer.Builder().nOut(10).build()) .layer(1, OutputLayer.builder().nOut(10).build())
.inputType(InputType.convolutional(10, 10, 5)).build(); .inputType(InputType.convolutional(10, 10, 5)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -208,9 +208,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict)
.list() .list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(3, 2).stride(2, 2).padding(0, 0).nOut(5) .layer(0, ConvolutionLayer.builder().kernelSize(3, 2).stride(2, 2).padding(0, 0).nOut(5)
.build()) .build())
.layer(1, new OutputLayer.Builder().nOut(10).build()) .layer(1, OutputLayer.builder().nOut(10).build())
.inputType(InputType.convolutional(hIn, wIn, depthIn)).build(); .inputType(InputType.convolutional(hIn, wIn, depthIn)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -234,9 +234,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
int wIn = 10; int wIn = 10;
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(7, 7).stride(1, 1).padding(0, 0).nOut(5) .layer(0, ConvolutionLayer.builder().kernelSize(7, 7).stride(1, 1).padding(0, 0).nOut(5)
.build()) .build())
.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build()) .layer(1, OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(hIn, wIn, depthIn)).build(); .inputType(InputType.convolutional(hIn, wIn, depthIn)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -266,9 +266,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration conf =
NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict).list() NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict).list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(3, 3).stride(2, 2) .layer(0, ConvolutionLayer.builder().kernelSize(3, 3).stride(2, 2)
.padding(0, 0).nIn(depthIn).nOut(5).build()) .padding(0, 0).nIn(depthIn).nOut(5).build())
.layer(1, new OutputLayer.Builder().nIn(5 * 4 * 4).nOut(10).activation(Activation.SOFTMAX).build()) .layer(1, OutputLayer.builder().nIn(5 * 4 * 4).nOut(10).activation(Activation.SOFTMAX).build())
.inputPreProcessor(1, new CnnToFeedForwardPreProcessor()).build(); .inputPreProcessor(1, new CnnToFeedForwardPreProcessor()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -299,9 +299,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 3).stride(2, 2).padding(0, 0).nOut(5) .layer(0, ConvolutionLayer.builder().kernelSize(2, 3).stride(2, 2).padding(0, 0).nOut(5)
.build()) .build())
.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build()) .layer(1, OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(hIn, wIn, depthIn)).build(); .inputType(InputType.convolutional(hIn, wIn, depthIn)).build();
} catch (Exception e) { } catch (Exception e) {
fail("Did not expect exception with default (truncate)"); fail("Did not expect exception with default (truncate)");
@ -310,9 +310,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict)
.list() .list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 3).stride(2, 2).padding(0, 0).nOut(5) .layer(0, ConvolutionLayer.builder().kernelSize(2, 3).stride(2, 2).padding(0, 0).nOut(5)
.build()) .build())
.layer(1, new OutputLayer.Builder().nOut(10).build()) .layer(1, OutputLayer.builder().nOut(10).build())
.inputType(InputType.convolutional(hIn, wIn, depthIn)).build(); .inputType(InputType.convolutional(hIn, wIn, depthIn)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -339,9 +339,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict)
.list() .list()
.layer(0, new SubsamplingLayer.Builder().kernelSize(2, 3).stride(2, 2).padding(0, 0) .layer(0, SubsamplingLayer.builder().kernelSize(2, 3).stride(2, 2).padding(0, 0)
.build()) .build())
.layer(1, new OutputLayer.Builder().nOut(10).build()) .layer(1, OutputLayer.builder().nOut(10).build())
.inputType(InputType.convolutional(hIn, wIn, depthIn)).build(); .inputType(InputType.convolutional(hIn, wIn, depthIn)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -358,84 +358,84 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
@Test @Test
public void testCnnInvalidKernel() { public void testCnnInvalidKernel() {
assertThrows(IllegalStateException.class, () -> { assertThrows(IllegalStateException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(3, 0).build(); ConvolutionLayer.builder().kernelSize(3, 0).build();
}); });
} }
@Test @Test
public void testCnnInvalidKernel2() { public void testCnnInvalidKernel2() {
assertThrows(IllegalStateException.class, () -> { assertThrows(IllegalStateException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(2, 2, 2).build(); ConvolutionLayer.builder().kernelSize(2, 2, 2).build();
}); });
} }
@Test @Test
public void testCnnInvalidStride() { public void testCnnInvalidStride() {
assertThrows(IllegalStateException.class, () -> { assertThrows(IllegalStateException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(3, 3).stride(0, 1).build(); ConvolutionLayer.builder().kernelSize(3, 3).stride(0, 1).build();
}); });
} }
@Test @Test
public void testCnnInvalidStride2() { public void testCnnInvalidStride2() {
assertThrows(IllegalArgumentException.class, () -> { assertThrows(IllegalArgumentException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1).build(); ConvolutionLayer.builder().kernelSize(3, 3).stride(1).build();
}); });
} }
@Test @Test
public void testCnnInvalidPadding() { public void testCnnInvalidPadding() {
assertThrows(IllegalArgumentException.class, () -> { assertThrows(IllegalArgumentException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(-1, 0).build(); ConvolutionLayer.builder().kernelSize(3, 3).stride(1, 1).padding(-1, 0).build();
}); });
} }
@Test @Test
public void testCnnInvalidPadding2() { public void testCnnInvalidPadding2() {
assertThrows(IllegalArgumentException.class, () -> { assertThrows(IllegalArgumentException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(0, 0, 0).build(); ConvolutionLayer.builder().kernelSize(3, 3).stride(1, 1).padding(0, 0, 0).build();
}); });
} }
@Test @Test
public void testSubsamplingInvalidKernel() { public void testSubsamplingInvalidKernel() {
assertThrows(IllegalStateException.class, () -> { assertThrows(IllegalStateException.class, () -> {
new SubsamplingLayer.Builder().kernelSize(3, 0).build(); SubsamplingLayer.builder().kernelSize(3, 0).build();
}); });
} }
@Test @Test
public void testSubsamplingInvalidKernel2() { public void testSubsamplingInvalidKernel2() {
assertThrows(IllegalArgumentException.class, () -> { assertThrows(IllegalArgumentException.class, () -> {
new SubsamplingLayer.Builder().kernelSize(2).build(); SubsamplingLayer.builder().kernelSize(2).build();
}); });
} }
@Test @Test
public void testSubsamplingInvalidStride() { public void testSubsamplingInvalidStride() {
assertThrows(IllegalStateException.class, () -> { assertThrows(IllegalStateException.class, () -> {
new SubsamplingLayer.Builder().kernelSize(3, 3).stride(0, 1).build(); SubsamplingLayer.builder().kernelSize(3, 3).stride(0, 1).build();
}); });
} }
@Test @Test
public void testSubsamplingInvalidStride2() { public void testSubsamplingInvalidStride2() {
assertThrows(RuntimeException.class, () -> { assertThrows(RuntimeException.class, () -> {
new SubsamplingLayer.Builder().kernelSize(3, 3).stride(1, 1, 1).build(); SubsamplingLayer.builder().kernelSize(3, 3).stride(1, 1, 1).build();
}); });
} }
@Test @Test
public void testSubsamplingInvalidPadding() { public void testSubsamplingInvalidPadding() {
assertThrows(IllegalArgumentException.class, () -> { assertThrows(IllegalArgumentException.class, () -> {
new SubsamplingLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(-1, 0).build(); SubsamplingLayer.builder().kernelSize(3, 3).stride(1, 1).padding(-1, 0).build();
}); });
} }
@Test @Test
public void testSubsamplingInvalidPadding2() { public void testSubsamplingInvalidPadding2() {
assertThrows(RuntimeException.class, () -> { assertThrows(RuntimeException.class, () -> {
new SubsamplingLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(0).build(); SubsamplingLayer.builder().kernelSize(3, 3).stride(1, 1).padding(0).build();
}); });
} }

44
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/exceptions/TestInvalidInput.java
View File

@ -43,8 +43,8 @@ public class TestInvalidInput extends BaseDL4JTest {
@Test @Test
public void testInputNinMismatchDense() { public void testInputNinMismatchDense() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()) .layer(0, DenseLayer.builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build(); .layer(1, OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -64,8 +64,8 @@ public class TestInvalidInput extends BaseDL4JTest {
@Test @Test
public void testLabelsNOutMismatchOutputLayer() { public void testLabelsNOutMismatchOutputLayer() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()) .layer(0, DenseLayer.builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build(); .layer(1, OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -85,8 +85,8 @@ public class TestInvalidInput extends BaseDL4JTest {
@Test @Test
public void testLabelsNOutMismatchRnnOutputLayer() { public void testLabelsNOutMismatchRnnOutputLayer() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new LSTM.Builder().nIn(5).nOut(5).build()) .layer(0, LSTM.builder().nIn(5).nOut(5).build())
.layer(1, new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build(); .layer(1, RnnOutputLayer.builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -112,8 +112,8 @@ public class TestInvalidInput extends BaseDL4JTest {
int d = 3; int d = 3;
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().nIn(d).nOut(5).build()) .layer(0, ConvolutionLayer.builder().nIn(d).nOut(5).build())
.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build()) .layer(1, OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(h, w, d)).build(); .inputType(InputType.convolutional(h, w, d)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -139,8 +139,8 @@ public class TestInvalidInput extends BaseDL4JTest {
int d = 3; int d = 3;
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().nIn(d).nOut(5).build()) .layer(0, ConvolutionLayer.builder().nIn(d).nOut(5).build())
.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build()) .layer(1, OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(h, w, d)).build(); .inputType(InputType.convolutional(h, w, d)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -165,8 +165,8 @@ public class TestInvalidInput extends BaseDL4JTest {
int d = 3; int d = 3;
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new SubsamplingLayer.Builder().kernelSize(2, 2).build()) .layer(0, SubsamplingLayer.builder().kernelSize(2, 2).build())
.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build()) .layer(1, OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(h, w, d)).build(); .inputType(InputType.convolutional(h, w, d)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -188,8 +188,8 @@ public class TestInvalidInput extends BaseDL4JTest {
public void testInputNinMismatchLSTM() { public void testInputNinMismatchLSTM() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new GravesLSTM.Builder().nIn(5).nOut(5).build()) .layer(0, GravesLSTM.builder().nIn(5).nOut(5).build())
.layer(1, new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build(); .layer(1, RnnOutputLayer.builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -209,8 +209,8 @@ public class TestInvalidInput extends BaseDL4JTest {
public void testInputNinMismatchBidirectionalLSTM() { public void testInputNinMismatchBidirectionalLSTM() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new GravesBidirectionalLSTM.Builder().nIn(5).nOut(5).build()) .layer(0, GravesBidirectionalLSTM.builder().nIn(5).nOut(5).build())
.layer(1, new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build(); .layer(1, RnnOutputLayer.builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -231,8 +231,8 @@ public class TestInvalidInput extends BaseDL4JTest {
public void testInputNinMismatchEmbeddingLayer() { public void testInputNinMismatchEmbeddingLayer() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new EmbeddingLayer.Builder().nIn(10).nOut(10).build()) .layer(0, EmbeddingLayer.builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build(); .layer(1, OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -259,13 +259,13 @@ public class TestInvalidInput extends BaseDL4JTest {
LayerConfiguration l; LayerConfiguration l;
switch (layerType){ switch (layerType){
case "simple": case "simple":
l = new SimpleRnn.Builder().nIn(5).nOut(5).build(); l = SimpleRnn.builder().nIn(5).nOut(5).build();
break; break;
case "lstm": case "lstm":
l = new LSTM.Builder().nIn(5).nOut(5).build(); l = LSTM.builder().nIn(5).nOut(5).build();
break; break;
case "graves": case "graves":
l = new GravesLSTM.Builder().nIn(5).nOut(5).build(); l = GravesLSTM.builder().nIn(5).nOut(5).build();
break; break;
default: default:
throw new RuntimeException(); throw new RuntimeException();
@ -273,7 +273,7 @@ public class TestInvalidInput extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(l) .layer(l)
.layer(new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build(); .layer(RnnOutputLayer.builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();

63
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/AttentionLayerTest.java
View File

@ -88,14 +88,13 @@ public class AttentionLayerTest extends BaseDL4JTest {
.activation(Activation.TANH) .activation(Activation.TANH)
.updater(new NoOp()) .updater(new NoOp())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .layer(LSTM.builder().nOut(layerSize).build())
.layer(new LSTM.Builder().nOut(layerSize).build())
.layer( projectInput ? .layer( projectInput ?
new SelfAttentionLayer.Builder().nOut(4).nHeads(2).projectInput(true).build() SelfAttentionLayer.builder().nOut(4).nHeads(2).projectInput(true).build()
: new SelfAttentionLayer.Builder().nHeads(1).projectInput(false).build() : SelfAttentionLayer.builder().nHeads(1).projectInput(false).build()
) )
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build()) .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX) .layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn)) .inputType(InputType.recurrent(nIn))
.build(); .build();
@ -150,13 +149,13 @@ public class AttentionLayerTest extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .list()
.layer(new LSTM.Builder().nOut(layerSize).build()) .layer(LSTM.builder().nOut(layerSize).build())
.layer( projectInput ? .layer( projectInput ?
new LearnedSelfAttentionLayer.Builder().nOut(4).nHeads(2).nQueries(numQueries).projectInput(true).build() LearnedSelfAttentionLayer.builder().nOut(4).nHeads(2).nQueries(numQueries).projectInput(true).build()
: new LearnedSelfAttentionLayer.Builder().nHeads(1).nQueries(numQueries).projectInput(false).build() : LearnedSelfAttentionLayer.builder().nHeads(1).nQueries(numQueries).projectInput(false).build()
) )
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build()) .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX) .layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn)) .inputType(InputType.recurrent(nIn))
.build(); .build();
@ -190,13 +189,13 @@ public class AttentionLayerTest extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .list()
.layer(new LSTM.Builder().nOut(layerSize).build()) .layer(LSTM.builder().nOut(layerSize).build())
.layer( projectInput ? .layer( projectInput ?
new LearnedSelfAttentionLayer.Builder().nOut(4).nHeads(2).nQueries(numQueries).projectInput(true).build() LearnedSelfAttentionLayer.builder().nOut(4).nHeads(2).nQueries(numQueries).projectInput(true).build()
: new LearnedSelfAttentionLayer.Builder().nHeads(1).nQueries(numQueries).projectInput(false).build() : LearnedSelfAttentionLayer.builder().nHeads(1).nQueries(numQueries).projectInput(false).build()
) )
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build()) .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX) .layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn)) .inputType(InputType.recurrent(nIn))
.build(); .build();
@ -245,10 +244,10 @@ public class AttentionLayerTest extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .list()
.layer(new LSTM.Builder().nOut(layerSize).build()) .layer(LSTM.builder().nOut(layerSize).build())
.layer(new RecurrentAttentionLayer.Builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build()) .layer(RecurrentAttentionLayer.builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build()) .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX) .layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn)) .inputType(InputType.recurrent(nIn))
.build(); .build();
@ -308,10 +307,10 @@ public class AttentionLayerTest extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .list()
.layer(new LSTM.Builder().nOut(layerSize).build()) .layer(LSTM.builder().nOut(layerSize).build())
.layer(new RecurrentAttentionLayer.Builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build()) .layer(RecurrentAttentionLayer.builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build()) .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX) .layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn)) .inputType(InputType.recurrent(nIn))
.build(); .build();
@ -367,15 +366,15 @@ public class AttentionLayerTest extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.graphBuilder() .graphBuilder()
.addInputs("input") .addInputs("input")
.addLayer("rnnKeys", new SimpleRnn.Builder().nOut(layerSize).build(), "input") .addLayer("rnnKeys", SimpleRnn.builder().nOut(layerSize).build(), "input")
.addLayer("rnnQueries", new SimpleRnn.Builder().nOut(layerSize).build(), "input") .addLayer("rnnQueries", SimpleRnn.builder().nOut(layerSize).build(), "input")
.addLayer("rnnValues", new SimpleRnn.Builder().nOut(layerSize).build(), "input") .addLayer("rnnValues", SimpleRnn.builder().nOut(layerSize).build(), "input")
.addVertex("attention", .addVertex("attention",
projectInput ? projectInput ?
new AttentionVertex.Builder().nOut(4).nHeads(2).projectInput(true).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build() new AttentionVertex.Builder().nOut(4).nHeads(2).projectInput(true).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build()
: new AttentionVertex.Builder().nOut(3).nHeads(1).projectInput(false).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build(), "rnnQueries", "rnnKeys", "rnnValues") : new AttentionVertex.Builder().nOut(3).nHeads(1).projectInput(false).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build(), "rnnQueries", "rnnKeys", "rnnValues")
.addLayer("pooling", new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build(), "attention") .addLayer("pooling", GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build(), "attention")
.addLayer("output", new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling") .addLayer("output", OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling")
.setOutputs("output") .setOutputs("output")
.setInputTypes(InputType.recurrent(nIn)) .setInputTypes(InputType.recurrent(nIn))
.build(); .build();
@ -431,13 +430,13 @@ public class AttentionLayerTest extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.graphBuilder() .graphBuilder()
.addInputs("input") .addInputs("input")
.addLayer("rnn", new SimpleRnn.Builder().activation(Activation.TANH).nOut(layerSize).build(), "input") .addLayer("rnn", SimpleRnn.builder().activation(Activation.TANH).nOut(layerSize).build(), "input")
.addVertex("attention", .addVertex("attention",
projectInput ? projectInput ?
new AttentionVertex.Builder().nOut(4).nHeads(2).projectInput(true).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build() new AttentionVertex.Builder().nOut(4).nHeads(2).projectInput(true).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build()
: new AttentionVertex.Builder().nOut(4).nHeads(1).projectInput(false).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build(), "rnn", "rnn", "rnn") : new AttentionVertex.Builder().nOut(4).nHeads(1).projectInput(false).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build(), "rnn", "rnn", "rnn")
.addLayer("pooling", new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build(), "attention") .addLayer("pooling", GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build(), "attention")
.addLayer("output", new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling") .addLayer("output", OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling")
.setOutputs("output") .setOutputs("output")
.setInputTypes(InputType.recurrent(nIn)) .setInputTypes(InputType.recurrent(nIn))
.build(); .build();

70
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/BNGradientCheckTest.java
View File

@ -78,11 +78,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.seed(12345L) .seed(12345L)
.dist(new NormalDistribution(0, 1)).list() .dist(new NormalDistribution(0, 1)).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(3) .layer(0, DenseLayer.builder().nIn(4).nOut(3)
.activation(Activation.IDENTITY).build()) .activation(Activation.IDENTITY).build())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).nOut(3).build()) .layer(1,BatchNormalization.builder().useLogStd(useLogStd).nOut(3).build())
.layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build()) .layer(2, ActivationLayer.builder().activation(Activation.TANH).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(3, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(3).nOut(3).build()); .activation(Activation.SOFTMAX).nIn(3).nOut(3).build());
MultiLayerNetwork mln = new MultiLayerNetwork(builder.build()); MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
@ -122,11 +122,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()).seed(12345L) .updater(new NoOp()).seed(12345L)
.dist(new NormalDistribution(0, 2)).list() .dist(new NormalDistribution(0, 2)).list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2) .layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2)
.activation(Activation.IDENTITY).build()) .activation(Activation.IDENTITY).build())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).build()) .layer(1,BatchNormalization.builder().useLogStd(useLogStd).build())
.layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build()) .layer(2, ActivationLayer.builder().activation(Activation.TANH).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(3, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build()) .activation(Activation.SOFTMAX).nOut(nOut).build())
.inputType(InputType.convolutional(hw, hw, depth)); .inputType(InputType.convolutional(hw, hw, depth));
@ -193,14 +193,14 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
.updater(new NoOp()) .updater(new NoOp())
.dist(new UniformDistribution(-2, 2)).seed(12345L).list() .dist(new UniformDistribution(-2, 2)).seed(12345L).list()
.layer(0, new ConvolutionLayer.Builder(2, 2).stride(1, 1).nOut(3) .layer(0, ConvolutionLayer.builder(2, 2).stride(1, 1).nOut(3)
.activation(afn).build()) .activation(afn).build())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).build()) .layer(1,BatchNormalization.builder().useLogStd(useLogStd).build())
.layer(2, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) .layer(2, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2).stride(1, 1).build()) .kernelSize(2, 2).stride(1, 1).build())
.layer(3, new BatchNormalization()) .layer(3, BatchNormalization.builder().build())
.layer(4, new ActivationLayer.Builder().activation(afn).build()) .layer(4, ActivationLayer.builder().activation(afn).build())
.layer(5, new OutputLayer.Builder(lf).activation(outputActivation).nOut(nOut) .layer(5, OutputLayer.builder(lf).activation(outputActivation).nOut(nOut)
.build()) .build())
.inputType(InputType.convolutional(hw, hw, depth)); .inputType(InputType.convolutional(hw, hw, depth));
@ -300,12 +300,12 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT) .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.updater(new NoOp()) .updater(new NoOp())
.dist(new UniformDistribution(-2, 2)).seed(12345L).list() .dist(new UniformDistribution(-2, 2)).seed(12345L).list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(4) .layer(0, DenseLayer.builder().nIn(nIn).nOut(4)
.activation(afn).build()) .activation(afn).build())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).build()) .layer(1,BatchNormalization.builder().useLogStd(useLogStd).build())
.layer(2, new DenseLayer.Builder().nIn(4).nOut(4).build()) .layer(2, DenseLayer.builder().nIn(4).nOut(4).build())
.layer(3, new BatchNormalization.Builder().useLogStd(useLogStd).build()) .layer(3,BatchNormalization.builder().useLogStd(useLogStd).build())
.layer(4, new OutputLayer.Builder(lf) .layer(4, OutputLayer.builder(lf)
.activation(outputActivation).nOut(nOut) .activation(outputActivation).nOut(nOut)
.build()); .build());
@ -373,11 +373,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.seed(12345L) .seed(12345L)
.dist(new NormalDistribution(0, 1)).list() .dist(new NormalDistribution(0, 1)).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(3).activation(Activation.IDENTITY).build()) .layer(0, DenseLayer.builder().nIn(4).nOut(3).activation(Activation.IDENTITY).build())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).lockGammaBeta(true).gamma(2.0).beta(0.5).nOut(3) .layer(1,BatchNormalization.builder().useLogStd(useLogStd).lockGammaBeta(true).gamma(2.0).beta(0.5).nOut(3)
.build()) .build())
.layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build()) .layer(2, ActivationLayer.builder().activation(Activation.TANH).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(3, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(3).nOut(3).build()); .activation(Activation.SOFTMAX).nIn(3).nOut(3).build());
MultiLayerNetwork mln = new MultiLayerNetwork(builder.build()); MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
@ -417,11 +417,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.seed(12345L) .seed(12345L)
.dist(new NormalDistribution(0, 2)).list() .dist(new NormalDistribution(0, 2)).list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2) .layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2)
.activation(Activation.IDENTITY).build()) .activation(Activation.IDENTITY).build())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).lockGammaBeta(true).gamma(2.0).beta(0.5).build()) .layer(1,BatchNormalization.builder().useLogStd(useLogStd).lockGammaBeta(true).gamma(2.0).beta(0.5).build())
.layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build()) .layer(2, ActivationLayer.builder().activation(Activation.TANH).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(3, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build()) .activation(Activation.SOFTMAX).nOut(nOut).build())
.inputType(InputType.convolutional(hw, hw, depth)); .inputType(InputType.convolutional(hw, hw, depth));
@ -460,8 +460,8 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in") .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.setInputTypes(InputType.convolutional(height, width, channels)) .setInputTypes(InputType.convolutional(height, width, channels))
.addLayer("bn", new BatchNormalization.Builder().useLogStd(useLogStd).build(), "in") .addLayer("bn",BatchNormalization.builder().useLogStd(useLogStd).build(), "in")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT) .addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(numClasses).build(), "bn") .activation(Activation.SOFTMAX).nOut(numClasses).build(), "bn")
.setOutputs("out").build(); .setOutputs("out").build();
@ -531,14 +531,14 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.dist(new UniformDistribution(-2, 2)).seed(12345L).graphBuilder() .dist(new UniformDistribution(-2, 2)).seed(12345L).graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new ConvolutionLayer.Builder(2, 2).stride(1, 1).nOut(3) .addLayer("0", ConvolutionLayer.builder(2, 2).stride(1, 1).nOut(3)
.activation(afn).build(), "in") .activation(afn).build(), "in")
.addLayer("1", new BatchNormalization.Builder().useLogStd(useLogStd).build(), "0") .addLayer("1",BatchNormalization.builder().useLogStd(useLogStd).build(), "0")
.addLayer("2", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) .addLayer("2", SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2).stride(1, 1).build(), "1") .kernelSize(2, 2).stride(1, 1).build(), "1")
.addLayer("3", new BatchNormalization.Builder().useLogStd(useLogStd).build(), "2") .addLayer("3",BatchNormalization.builder().useLogStd(useLogStd).build(), "2")
.addLayer("4", new ActivationLayer.Builder().activation(afn).build(), "3") .addLayer("4", ActivationLayer.builder().activation(afn).build(), "3")
.addLayer("5", new OutputLayer.Builder(lf).activation(outputActivation) .addLayer("5", OutputLayer.builder(lf).activation(outputActivation)
.nOut(nOut).build(), "4") .nOut(nOut).build(), "4")
.setOutputs("5").setInputTypes(InputType.convolutional(hw, hw, depth)) .setOutputs("5").setInputTypes(InputType.convolutional(hw, hw, depth))
.build(); .build();

424
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DGradientCheckTest.java
View File

@ -20,6 +20,9 @@
package org.deeplearning4j.gradientcheck; package org.deeplearning4j.gradientcheck;
import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertTrue;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.TestUtils; import org.deeplearning4j.TestUtils;
@ -42,9 +45,6 @@ import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.learning.config.NoOp; import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions; import org.nd4j.linalg.lossfunctions.LossFunctions;
import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertTrue;
@Slf4j @Slf4j
public class CNN1DGradientCheckTest extends BaseDL4JTest { public class CNN1DGradientCheckTest extends BaseDL4JTest {
private static final boolean PRINT_RESULTS = true; private static final boolean PRINT_RESULTS = true;
@ -81,24 +81,45 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length); INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length);
for (int i = 0; i < minibatchSize; i++) { for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < length; j++) { for (int j = 0; j < length; j++) {
labels.putScalar(new int[]{i, i % finalNOut, j}, 1.0); labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
} }
} }
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.dist(new NormalDistribution(0, 1)).convolutionMode(ConvolutionMode.Same).list() .dist(new NormalDistribution(0, 1))
.layer(new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel) .convolutionMode(ConvolutionMode.Same)
.stride(stride).padding(padding).nIn(convNIn).nOut(convNOut1) .list()
.layer(
Convolution1DLayer.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nIn(convNIn)
.nOut(convNOut1)
.rnnDataFormat(RNNFormat.NCW) .rnnDataFormat(RNNFormat.NCW)
.build()) .build())
.layer(new LocallyConnected1D.Builder().activation(afn).kernelSize(kernel) .layer(
.stride(stride).padding(padding).nIn(convNOut1).nOut(convNOut2).hasBias(false) LocallyConnected1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nIn(convNOut1)
.nOut(convNOut2)
.hasBias(false)
.build()) .build())
.layer(new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) RnnOutputLayer.builder()
.inputType(InputType.recurrent(convNIn, length)).build(); .lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length))
.build();
String json = conf.toJson(); String json = conf.toJson();
NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json); NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
@ -107,28 +128,35 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
String msg = "Minibatch=" + minibatchSize + ", activationFn=" String msg =
+ afn + ", kernel = " + kernel; "Minibatch=" + minibatchSize + ", activationFn=" + afn + ", kernel = " + kernel;
if (PRINT_RESULTS) { if (PRINT_RESULTS) {
System.out.println(msg); System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++) // for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams()); // System.out.println("ILayer " + j + " # params: " +
// net.getLayer(j).numParams());
} }
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, boolean gradOK =
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels); GradientCheckUtil.checkGradients(
net,
DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE,
input,
labels);
assertTrue(gradOK, msg); assertTrue(gradOK, msg);
TestUtils.testModelSerialization(net); TestUtils.testModelSerialization(net);
} }
} }
} }
} }
@Test @Test
public void testCnn1DWithCropping1D() { public void testCnn1DWithCropping1D() {
Nd4j.getRandom().setSeed(1337); Nd4j.getRandom().setSeed(1337);
@ -140,7 +168,6 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
int convNOut2 = 4; int convNOut2 = 4;
int finalNOut = 4; int finalNOut = 4;
int[] kernels = {1, 2, 4}; int[] kernels = {1, 2, 4};
int stride = 1; int stride = 1;
@ -150,8 +177,11 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
Activation[] activations = {Activation.SIGMOID}; Activation[] activations = {Activation.SIGMOID};
SubsamplingLayer.PoolingType[] poolingTypes = SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[]{SubsamplingLayer.PoolingType.MAX, new SubsamplingLayer.PoolingType[] {
SubsamplingLayer.PoolingType.AVG, SubsamplingLayer.PoolingType.PNORM}; SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG,
SubsamplingLayer.PoolingType.PNORM
};
for (Activation afn : activations) { for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) { for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
@ -161,24 +191,41 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, croppedLength); INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, croppedLength);
for (int i = 0; i < minibatchSize; i++) { for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < croppedLength; j++) { for (int j = 0; j < croppedLength; j++) {
labels.putScalar(new int[]{i, i % finalNOut, j}, 1.0); labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
} }
} }
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.dist(new NormalDistribution(0, 1)).convolutionMode(ConvolutionMode.Same).list() .dist(new NormalDistribution(0, 1))
.layer(new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel) .convolutionMode(ConvolutionMode.Same)
.stride(stride).padding(padding).nOut(convNOut1) .layer(
Convolution1DLayer.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut1)
.build()) .build())
.layer(new Cropping1D.Builder(cropping).build()) .layer(Cropping1D.builder(cropping).build())
.layer(new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel) .layer(
.stride(stride).padding(padding).nOut(convNOut2) Convolution1DLayer.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut2)
.build()) .build())
.layer(new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) RnnOutputLayer.builder()
.inputType(InputType.recurrent(convNIn, length,RNNFormat.NCW)).build(); .lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
.build();
String json = conf.toJson(); String json = conf.toJson();
NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json); NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
@ -187,17 +234,33 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
String msg = "PoolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn=" String msg =
+ afn + ", kernel = " + kernel; "PoolingType="
+ poolingType
+ ", minibatch="
+ minibatchSize
+ ", activationFn="
+ afn
+ ", kernel = "
+ kernel;
if (PRINT_RESULTS) { if (PRINT_RESULTS) {
System.out.println(msg); System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++) // for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams()); // System.out.println("ILayer " + j + " # params: " +
// net.getLayer(j).numParams());
} }
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, boolean gradOK =
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels); GradientCheckUtil.checkGradients(
net,
DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE,
input,
labels);
assertTrue(gradOK, msg); assertTrue(gradOK, msg);
@ -208,7 +271,6 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
} }
} }
@Test @Test
public void testCnn1DWithZeroPadding1D() { public void testCnn1DWithZeroPadding1D() {
Nd4j.getRandom().setSeed(1337); Nd4j.getRandom().setSeed(1337);
@ -220,7 +282,6 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
int convNOut2 = 4; int convNOut2 = 4;
int finalNOut = 4; int finalNOut = 4;
int[] kernels = {1, 2, 4}; int[] kernels = {1, 2, 4};
int stride = 1; int stride = 1;
int pnorm = 2; int pnorm = 2;
@ -231,8 +292,11 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
Activation[] activations = {Activation.SIGMOID}; Activation[] activations = {Activation.SIGMOID};
SubsamplingLayer.PoolingType[] poolingTypes = SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[]{SubsamplingLayer.PoolingType.MAX, new SubsamplingLayer.PoolingType[] {
SubsamplingLayer.PoolingType.AVG, SubsamplingLayer.PoolingType.PNORM}; SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG,
SubsamplingLayer.PoolingType.PNORM
};
for (Activation afn : activations) { for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) { for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
@ -242,27 +306,49 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, paddedLength); INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, paddedLength);
for (int i = 0; i < minibatchSize; i++) { for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < paddedLength; j++) { for (int j = 0; j < paddedLength; j++) {
labels.putScalar(new int[]{i, i % finalNOut, j}, 1.0); labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
} }
} }
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.dist(new NormalDistribution(0, 1)).convolutionMode(ConvolutionMode.Same).list() .dist(new NormalDistribution(0, 1))
.layer(new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel) .convolutionMode(ConvolutionMode.Same)
.stride(stride).padding(padding).nOut(convNOut1) .layer(
Convolution1DLayer.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut1)
.build()) .build())
.layer(new ZeroPadding1DLayer.Builder(zeroPadding).build()) .layer(ZeroPadding1DLayer.builder(zeroPadding).build())
.layer(new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel) .layer(
.stride(stride).padding(padding).nOut(convNOut2) Convolution1DLayer.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut2)
.build()) .build())
.layer(new ZeroPadding1DLayer.Builder(0).build()) .layer(ZeroPadding1DLayer.builder(0).build())
.layer(new Subsampling1DLayer.Builder(poolingType).kernelSize(kernel) .layer(
.stride(stride).padding(padding).pnorm(pnorm).build()) Subsampling1DLayer.builder(poolingType)
.layer(new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .kernelSize(kernel)
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) .stride(stride)
.inputType(InputType.recurrent(convNIn, length,RNNFormat.NCW)).build(); .padding(padding)
.pnorm(pnorm)
.build())
.layer(
RnnOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
.build();
String json = conf.toJson(); String json = conf.toJson();
NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json); NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
@ -271,17 +357,33 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
String msg = "PoolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn=" String msg =
+ afn + ", kernel = " + kernel; "PoolingType="
+ poolingType
+ ", minibatch="
+ minibatchSize
+ ", activationFn="
+ afn
+ ", kernel = "
+ kernel;
if (PRINT_RESULTS) { if (PRINT_RESULTS) {
System.out.println(msg); System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++) // for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams()); // System.out.println("ILayer " + j + " # params: " +
// net.getLayer(j).numParams());
} }
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, boolean gradOK =
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels); GradientCheckUtil.checkGradients(
net,
DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE,
input,
labels);
assertTrue(gradOK, msg); assertTrue(gradOK, msg);
TestUtils.testModelSerialization(net); TestUtils.testModelSerialization(net);
@ -291,7 +393,6 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
} }
} }
@Test @Test
public void testCnn1DWithSubsampling1D() { public void testCnn1DWithSubsampling1D() {
Nd4j.getRandom().setSeed(12345); Nd4j.getRandom().setSeed(12345);
@ -310,8 +411,11 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
Activation[] activations = {Activation.SIGMOID, Activation.TANH}; Activation[] activations = {Activation.SIGMOID, Activation.TANH};
SubsamplingLayer.PoolingType[] poolingTypes = SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[]{SubsamplingLayer.PoolingType.MAX, new SubsamplingLayer.PoolingType[] {
SubsamplingLayer.PoolingType.AVG, SubsamplingLayer.PoolingType.PNORM}; SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG,
SubsamplingLayer.PoolingType.PNORM
};
for (Activation afn : activations) { for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) { for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
@ -321,25 +425,52 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length); INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length);
for (int i = 0; i < minibatchSize; i++) { for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < length; j++) { for (int j = 0; j < length; j++) {
labels.putScalar(new int[]{i, i % finalNOut, j}, 1.0); labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
} }
} }
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.dist(new NormalDistribution(0, 1)).convolutionMode(ConvolutionMode.Same).list() .dist(new NormalDistribution(0, 1))
.layer(0, new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel) .convolutionMode(ConvolutionMode.Same)
.stride(stride).padding(padding).nOut(convNOut1) .list()
.layer(
0,
Convolution1DLayer.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut1)
.build()) .build())
.layer(1, new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel) .layer(
.stride(stride).padding(padding).nOut(convNOut2) 1,
Convolution1DLayer.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut2)
.build()) .build())
.layer(2, new Subsampling1DLayer.Builder(poolingType).kernelSize(kernel) .layer(
.stride(stride).padding(padding).pnorm(pnorm).build()) 2,
.layer(3, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT) Subsampling1DLayer.builder(poolingType)
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) .kernelSize(kernel)
.inputType(InputType.recurrent(convNIn, length,RNNFormat.NCW)).build(); .stride(stride)
.padding(padding)
.pnorm(pnorm)
.build())
.layer(
3,
RnnOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
.build();
String json = conf.toJson(); String json = conf.toJson();
NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json); NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
@ -348,17 +479,33 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
String msg = "PoolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn=" String msg =
+ afn + ", kernel = " + kernel; "PoolingType="
+ poolingType
+ ", minibatch="
+ minibatchSize
+ ", activationFn="
+ afn
+ ", kernel = "
+ kernel;
if (PRINT_RESULTS) { if (PRINT_RESULTS) {
System.out.println(msg); System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++) // for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams()); // System.out.println("ILayer " + j + " # params: " +
// net.getLayer(j).numParams());
} }
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, boolean gradOK =
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels); GradientCheckUtil.checkGradients(
net,
DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE,
input,
labels);
assertTrue(gradOK, msg); assertTrue(gradOK, msg);
TestUtils.testModelSerialization(net); TestUtils.testModelSerialization(net);
@ -369,7 +516,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
} }
@Test @Test
public void testCnn1dWithMasking(){ public void testCnn1dWithMasking() {
int length = 12; int length = 12;
int convNIn = 2; int convNIn = 2;
int convNOut1 = 3; int convNOut1 = 3;
@ -379,36 +526,58 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
int pnorm = 2; int pnorm = 2;
SubsamplingLayer.PoolingType[] poolingTypes = SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[] {SubsamplingLayer.PoolingType.MAX, SubsamplingLayer.PoolingType.AVG}; new SubsamplingLayer.PoolingType[] {
SubsamplingLayer.PoolingType.MAX, SubsamplingLayer.PoolingType.AVG
};
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) { for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for(ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Same, ConvolutionMode.Truncate}) { for (ConvolutionMode cm :
for( int stride : new int[]{1, 2}){ new ConvolutionMode[] {ConvolutionMode.Same, ConvolutionMode.Truncate}) {
for (int stride : new int[] {1, 2}) {
String s = cm + ", stride=" + stride + ", pooling=" + poolingType; String s = cm + ", stride=" + stride + ", pooling=" + poolingType;
log.info("Starting test: " + s); log.info("Starting test: " + s);
Nd4j.getRandom().setSeed(12345); Nd4j.getRandom().setSeed(12345);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.activation(Activation.TANH) .activation(Activation.TANH)
.dist(new NormalDistribution(0, 1)).convolutionMode(cm) .dist(new NormalDistribution(0, 1))
.convolutionMode(cm)
.seed(12345) .seed(12345)
.list() .list()
.layer(new Convolution1DLayer.Builder().kernelSize(2) .layer(
Convolution1DLayer.builder()
.kernelSize(2)
.rnnDataFormat(RNNFormat.NCW) .rnnDataFormat(RNNFormat.NCW)
.stride(stride).nIn(convNIn).nOut(convNOut1) .stride(stride)
.nIn(convNIn)
.nOut(convNOut1)
.build()) .build())
.layer(new Subsampling1DLayer.Builder(poolingType).kernelSize(2) .layer(
.stride(stride).pnorm(pnorm).build()) Subsampling1DLayer.builder(poolingType)
.layer(new Convolution1DLayer.Builder().kernelSize(2) .kernelSize(2)
.stride(stride)
.pnorm(pnorm)
.build())
.layer(
Convolution1DLayer.builder()
.kernelSize(2)
.rnnDataFormat(RNNFormat.NCW) .rnnDataFormat(RNNFormat.NCW)
.stride(stride).nIn(convNOut1).nOut(convNOut2) .stride(stride)
.nIn(convNOut1)
.nOut(convNOut2)
.build()) .build())
.layer(new GlobalPoolingLayer(PoolingType.AVG)) .layer( GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) OutputLayer.builder()
.inputType(InputType.recurrent(convNIn, length)).build(); .lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -416,19 +585,20 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
INDArray f = Nd4j.rand(2, convNIn, length); INDArray f = Nd4j.rand(2, convNIn, length);
INDArray fm = Nd4j.create(2, length); INDArray fm = Nd4j.create(2, length);
fm.get(NDArrayIndex.point(0), NDArrayIndex.all()).assign(1); fm.get(NDArrayIndex.point(0), NDArrayIndex.all()).assign(1);
fm.get(NDArrayIndex.point(1), NDArrayIndex.interval(0,6)).assign(1); fm.get(NDArrayIndex.point(1), NDArrayIndex.interval(0, 6)).assign(1);
INDArray label = TestUtils.randomOneHot(2, finalNOut); INDArray label = TestUtils.randomOneHot(2, finalNOut);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(f) boolean gradOK =
.labels(label).inputMask(fm)); GradientCheckUtil.checkGradients(
new GradientCheckUtil.MLNConfig().net(net).input(f).labels(label).inputMask(fm));
assertTrue(gradOK, s); assertTrue(gradOK, s);
TestUtils.testModelSerialization(net); TestUtils.testModelSerialization(net);
//TODO also check that masked step values don't impact forward pass, score or gradients // TODO also check that masked step values don't impact forward pass, score or gradients
DataSet ds = new DataSet(f,label,fm,null); DataSet ds = new DataSet(f, label, fm, null);
double scoreBefore = net.score(ds); double scoreBefore = net.score(ds);
net.setInput(f); net.setInput(f);
net.setLabels(label); net.setLabels(label);
@ -471,32 +641,44 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
int st = strides[i]; int st = strides[i];
boolean mask = masks[i]; boolean mask = masks[i];
boolean hasBias = hasB[i]; boolean hasBias = hasB[i];
//TODO has bias // TODO has bias
String s = "k=" + k + ", s=" + st + " d=" + d + ", seqLen=" + length; String s = "k=" + k + ", s=" + st + " d=" + d + ", seqLen=" + length;
log.info("Starting test: " + s); log.info("Starting test: " + s);
Nd4j.getRandom().setSeed(12345); Nd4j.getRandom().setSeed(12345);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.activation(Activation.TANH) .activation(Activation.TANH)
.weightInit(new NormalDistribution(0, 1)) .weightInit(new NormalDistribution(0, 1))
.seed(12345) .seed(12345)
.list() .list()
.layer(new Convolution1DLayer.Builder().kernelSize(k) .layer(
Convolution1DLayer.builder()
.kernelSize(k)
.dilation(d) .dilation(d)
.hasBias(hasBias) .hasBias(hasBias)
.convolutionMode(ConvolutionMode.Causal) .convolutionMode(ConvolutionMode.Causal)
.stride(st).nOut(convNOut1) .stride(st)
.nOut(convNOut1)
.build()) .build())
.layer(new Convolution1DLayer.Builder().kernelSize(k) .layer(
Convolution1DLayer.builder()
.kernelSize(k)
.dilation(d) .dilation(d)
.convolutionMode(ConvolutionMode.Causal) .convolutionMode(ConvolutionMode.Causal)
.stride(st).nOut(convNOut2) .stride(st)
.nOut(convNOut2)
.build()) .build())
.layer(new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) RnnOutputLayer.builder()
.inputType(InputType.recurrent(convNIn, length,RNNFormat.NCW)).build(); .lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -510,12 +692,14 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
} }
long outSize1 = Convolution1DUtils.getOutputSize(length, k, st, 0, ConvolutionMode.Causal, d); long outSize1 = Convolution1DUtils.getOutputSize(length, k, st, 0, ConvolutionMode.Causal, d);
long outSize2 = Convolution1DUtils.getOutputSize(outSize1, k, st, 0, ConvolutionMode.Causal, d); long outSize2 =
Convolution1DUtils.getOutputSize(outSize1, k, st, 0, ConvolutionMode.Causal, d);
INDArray label = TestUtils.randomOneHotTimeSeries(2, finalNOut, (int)outSize2); INDArray label = TestUtils.randomOneHotTimeSeries(2, finalNOut, (int) outSize2);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(f) boolean gradOK =
.labels(label).inputMask(fm)); GradientCheckUtil.checkGradients(
new GradientCheckUtil.MLNConfig().net(net).input(f).labels(label).inputMask(fm));
assertTrue(gradOK, s); assertTrue(gradOK, s);
TestUtils.testModelSerialization(net); TestUtils.testModelSerialization(net);

52
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java
View File

@ -115,16 +115,16 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL) .updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.list() .list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel) .layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
.stride(stride).nIn(convNIn).nOut(convNOut1).hasBias(false) .stride(stride).nIn(convNIn).nOut(convNOut1).hasBias(false)
.convolutionMode(mode).dataFormat(df) .convolutionMode(mode).dataFormat(df)
.build()) .build())
.layer(1, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1) .layer(1, Convolution3D.builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNOut1).nOut(convNOut2).hasBias(false) .nIn(convNOut1).nOut(convNOut2).hasBias(false)
.convolutionMode(mode).dataFormat(df) .convolutionMode(mode).dataFormat(df)
.build()) .build())
.layer(2, new DenseLayer.Builder().nOut(denseNOut).build()) .layer(2, DenseLayer.builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) .activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(2, .inputPreProcessor(2,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth, new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
@ -218,17 +218,17 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL) .updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.list() .list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel) .layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
.nIn(convNIn).nOut(convNOut1).hasBias(false) .nIn(convNIn).nOut(convNOut1).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW) .convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build()) .build())
.layer(1, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1) .layer(1, Convolution3D.builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNOut1).nOut(convNOut2).hasBias(false) .nIn(convNOut1).nOut(convNOut2).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW) .convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build()) .build())
.layer(2, new ZeroPadding3DLayer.Builder(zeroPadding).build()) .layer(2, ZeroPadding3DLayer.builder(zeroPadding).build())
.layer(3, new DenseLayer.Builder().nOut(denseNOut).build()) .layer(3, DenseLayer.builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) .activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(3, .inputPreProcessor(3,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth, new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
@ -314,14 +314,14 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.list() .list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1) .layer(0, Convolution3D.builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNIn).nOut(convNOut).hasBias(false) .nIn(convNIn).nOut(convNOut).hasBias(false)
.convolutionMode(mode).dataFormat(df) .convolutionMode(mode).dataFormat(df)
.build()) .build())
.layer(1, new Subsampling3DLayer.Builder(kernel) .layer(1,Subsampling3DLayer.builder(kernel)
.poolingType(pool).convolutionMode(mode).dataFormat(df).build()) .poolingType(pool.toPoolingType()).convolutionMode(mode).dataFormat(df).build())
.layer(2, new DenseLayer.Builder().nOut(denseNOut).build()) .layer(2, DenseLayer.builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) .activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(2, .inputPreProcessor(2,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,convNOut, df)) new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,convNOut, df))
@ -401,13 +401,13 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.seed(12345) .seed(12345)
.list() .list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1) .layer(0, Convolution3D.builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNIn).nOut(convNOut).hasBias(false) .nIn(convNIn).nOut(convNOut).hasBias(false)
.convolutionMode(mode).dataFormat(df) .convolutionMode(mode).dataFormat(df)
.build()) .build())
.layer(1, new Upsampling3D.Builder(upsamplingSize[0]).dataFormat(df).build()) .layer(1, Upsampling3D.builder(upsamplingSize[0]).dataFormat(df).build())
.layer(2, new DenseLayer.Builder().nOut(denseNOut).build()) .layer(2, DenseLayer.builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) .activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(2, .inputPreProcessor(2,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth, new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
@ -496,17 +496,17 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL) .updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.list() .list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel) .layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
.nIn(convNIn).nOut(convNOut1).hasBias(false) .nIn(convNIn).nOut(convNOut1).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW) .convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build()) .build())
.layer(1, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1) .layer(1, Convolution3D.builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNOut1).nOut(convNOut2).hasBias(false) .nIn(convNOut1).nOut(convNOut2).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW) .convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build()) .build())
.layer(2, new Cropping3D.Builder(cropping).build()) .layer(2, Cropping3D.builder(cropping).build())
.layer(3, new DenseLayer.Builder().nOut(denseNOut).build()) .layer(3, DenseLayer.builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) .activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(3, .inputPreProcessor(3,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth, new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
@ -595,15 +595,15 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.weightInit(new NormalDistribution(0, 0.1)) .weightInit(new NormalDistribution(0, 0.1))
.list() .list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel) .layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
.stride(stride).nIn(convNIn).nOut(dOut).hasBias(false) .stride(stride).nIn(convNIn).nOut(dOut).hasBias(false)
.convolutionMode(mode).dataFormat(df) .convolutionMode(mode).dataFormat(df)
.build()) .build())
.layer(1, new Deconvolution3D.Builder().activation(afn).kernelSize(kernel) .layer(1, Deconvolution3D.builder().activation(afn).kernelSize(kernel)
.stride(stride).nOut(dOut).hasBias(false) .stride(stride).nOut(dOut).hasBias(false)
.convolutionMode(mode).dataFormat(df) .convolutionMode(mode).dataFormat(df)
.build()) .build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build()) .activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputType(InputType.convolutional3D(df, depth, height, width, convNIn)).build(); .inputType(InputType.convolutional3D(df, depth, height, width, convNIn)).build();

1381
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java
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10
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CapsnetGradientCheckTest.java
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@ -82,14 +82,14 @@ public class CapsnetGradientCheckTest extends BaseDL4JTest {
.seed(123) .seed(123)
.updater(new NoOp()) .updater(new NoOp())
.dist(new UniformDistribution(-6, 6)) .dist(new UniformDistribution(-6, 6))
.layer(new PrimaryCapsules.Builder(primaryCapsDim, primarpCapsChannel) .layer(PrimaryCapsules.builder(primaryCapsDim, primarpCapsChannel)
.kernelSize(3, 3) .kernelSize(3, 3)
.stride(2, 2) .stride(2, 2)
.build()) .build())
.layer(new CapsuleLayer.Builder(capsule, capsuleDim, routing).build()) .layer(CapsuleLayer.builder(capsule, capsuleDim, routing).build())
.layer(new CapsuleStrengthLayer.Builder().build()) .layer(CapsuleStrengthLayer.builder().build())
.layer(new ActivationLayer.Builder(new ActivationSoftmax()).build()) .layer(ActivationLayer.builder(new ActivationSoftmax()).build())
.layer(new LossLayer.Builder(new LossNegativeLogLikelihood()).build()) .layer(LossLayer.builder().lossFunction(new LossNegativeLogLikelihood()).build())
.inputType(InputType.convolutional(height, width, inputDepth)) .inputType(InputType.convolutional(height, width, inputDepth))
.build(); .build();

25
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/DropoutGradientCheck.java
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@ -97,24 +97,23 @@ public class DropoutGradientCheck extends BaseDL4JTest {
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.dropOut(dropout) .dropOut(dropout)
.activation(Activation.TANH) .activation(Activation.TANH)
.updater(new NoOp()) .updater(new NoOp());
.list();
if(cnn){ if(cnn){
builder.layer(new ConvolutionLayer.Builder().kernelSize(3,3).stride(2,2).nOut(2).build()); builder.layer(ConvolutionLayer.builder().kernelSize(3,3).stride(2,2).nOut(2).build());
builder.layer(new ConvolutionLayer.Builder().kernelSize(3,3).stride(2,2).nOut(2).build()); builder.layer(ConvolutionLayer.builder().kernelSize(3,3).stride(2,2).nOut(2).build());
builder.inputType(InputType.convolutional(6,6,2)); builder.inputType(InputType.convolutional(6,6,2));
} else { } else {
builder.layer(new DenseLayer.Builder().nOut(3).build()); builder.layer(DenseLayer.builder().nOut(3).build());
builder.layer(new DenseLayer.Builder().nOut(3).build()); builder.layer(DenseLayer.builder().nOut(3).build());
builder.inputType(InputType.feedForward(6)); builder.inputType(InputType.feedForward(6));
} }
builder.layer(new OutputLayer.Builder().nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunction.MCXENT).build()); builder.layer(OutputLayer.builder().nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunction.MCXENT).build());
NeuralNetConfiguration conf = builder.build(); NeuralNetConfiguration conf = builder.build();
//Remove spatial dropout from output layer - can't be used for 2d input //Remove spatial dropout from output layer - can't be used for 2d input
if(i == 4){ if(i == 4){
conf.getFlattenedLayerConfigurations().get(2).setIDropout(null); conf.getFlattenedLayerConfigurations().get(2).setDropOut(null);
} }
MultiLayerNetwork mln = new MultiLayerNetwork(conf); MultiLayerNetwork mln = new MultiLayerNetwork(conf);
@ -157,11 +156,11 @@ public class DropoutGradientCheck extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(5).nOut(5).build(), "in") .addLayer("0", DenseLayer.builder().nIn(5).nOut(5).build(), "in")
.addLayer("1", new DenseLayer.Builder().nIn(5).nOut(5).build(), "0") .addLayer("1", DenseLayer.builder().nIn(5).nOut(5).build(), "0")
.addLayer("2", new DenseLayer.Builder().nIn(5).nOut(5).build(), "0") .addLayer("2", DenseLayer.builder().nIn(5).nOut(5).build(), "0")
.addLayer("3", new DenseLayer.Builder().nIn(5).nOut(5).build(), "0") .addLayer("3", DenseLayer.builder().nIn(5).nOut(5).build(), "0")
.addLayer("out", new OutputLayer.Builder().nIn(15).nOut(5).activation(Activation.SOFTMAX) .addLayer("out", OutputLayer.builder().nIn(15).nOut(5).activation(Activation.SOFTMAX)
.lossFunction(LossFunction.MCXENT).build(), "1", "2", "3") .lossFunction(LossFunction.MCXENT).build(), "1", "2", "3")
.setOutputs("out") .setOutputs("out")
.build(); .build();

26
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/GlobalPoolingGradientCheckTests.java
View File

@ -75,10 +75,10 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).seed(12345L).list() .dist(new NormalDistribution(0, 1.0)).seed(12345L).list()
.layer(0, new SimpleRnn.Builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH) .layer(0, SimpleRnn.builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH)
.build()) .build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(pt).build()) .layer(1, GlobalPoolingLayer.builder().poolingType(pt).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).build()) .activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).build())
.build(); .build();
@ -130,12 +130,12 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).seed(12345L).list() .dist(new NormalDistribution(0, 1.0)).seed(12345L).list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1) .layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1)
.dataFormat(nchw ? CNN2DFormat.NCHW : CNN2DFormat.NHWC) .convFormat(nchw ? CNN2DFormat.NCHW : CNN2DFormat.NHWC)
.nOut(layerDepth) .nOut(layerDepth)
.build()) .build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(pt).build()) .layer(1, GlobalPoolingLayer.builder().poolingType(pt).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build()) .activation(Activation.SOFTMAX).nOut(nOut).build())
.inputType(InputType.convolutional(inputH, inputW, inputDepth, nchw ? CNN2DFormat.NCHW : CNN2DFormat.NHWC)).build(); .inputType(InputType.convolutional(inputH, inputW, inputDepth, nchw ? CNN2DFormat.NCHW : CNN2DFormat.NHWC)).build();
@ -188,10 +188,10 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).seed(12345L).list() .dist(new NormalDistribution(0, 1.0)).seed(12345L).list()
.layer(0, new LSTM.Builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH) .layer(0, LSTM.builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH)
.build()) .build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(pt).build()) .layer(1, GlobalPoolingLayer.builder().poolingType(pt).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).build()) .activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).build())
.build(); .build();
@ -263,10 +263,10 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).convolutionMode(ConvolutionMode.Same) .dist(new NormalDistribution(0, 1.0)).convolutionMode(ConvolutionMode.Same)
.seed(12345L).list() .seed(12345L).list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(kernel).stride(stride) .layer(0, ConvolutionLayer.builder().kernelSize(kernel).stride(stride)
.nOut(layerDepth).build()) .nOut(layerDepth).build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(pt).build()) .layer(1, GlobalPoolingLayer.builder().poolingType(pt).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build()) .activation(Activation.SOFTMAX).nOut(nOut).build())
.inputType(InputType.convolutional(inputH, inputW, inputDepth)).build(); .inputType(InputType.convolutional(inputH, inputW, inputDepth)).build();

46
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTests.java
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@ -78,11 +78,11 @@ public class GradientCheckTests extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new NoOp()) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new NoOp())
.list() .list()
.layer(0, .layer(0,
new DenseLayer.Builder().nIn(4).nOut(3) DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH) .activation(Activation.TANH)
.build()) .build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(3).nOut(3).build()) .activation(Activation.SOFTMAX).nIn(3).nOut(3).build())
.build(); .build();
@ -168,11 +168,11 @@ public class GradientCheckTests extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).updater(new NoOp()) .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).updater(new NoOp())
.seed(12345L) .seed(12345L)
.list().layer(0, .list().layer(0,
new DenseLayer.Builder().nIn(4).nOut(3) DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(afn) .activation(afn)
.build()) .build())
.layer(1, new OutputLayer.Builder(lf).activation(outputActivation).nIn(3).nOut(3) .layer(1, OutputLayer.builder(lf).activation(outputActivation).nIn(3).nOut(3)
.dist(new NormalDistribution(0, 1)).build()) .dist(new NormalDistribution(0, 1)).build())
.build(); .build();
@ -259,12 +259,12 @@ public class GradientCheckTests extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT) .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.seed(12345L) .seed(12345L)
.list().layer(0, .list().layer(0,
new DenseLayer.Builder().nIn(4).nOut(3) DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0, .dist(new NormalDistribution(0,
1)) 1))
.updater(new NoOp()) .updater(new NoOp())
.activation(afn).build()) .activation(afn).build())
.layer(1, new OutputLayer.Builder(lf).nIn(3).nOut(3) .layer(1, OutputLayer.builder(lf).nIn(3).nOut(3)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()) .updater(new NoOp())
.activation(outputActivation).build()) .activation(outputActivation).build())
@ -327,10 +327,10 @@ public class GradientCheckTests extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l2(0.2).l1(0.1) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l2(0.2).l1(0.1)
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L)
.list().layer(new EmbeddingLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER) .list().layer(EmbeddingLayer.builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)
.updater(new NoOp()).build()) .updater(new NoOp()).build())
.layer(new PReLULayer.Builder().inputShape(3).sharedAxes(1).updater(new NoOp()).build()) .layer(PReLULayer.builder().inputShape(3).sharedAxes(1).updater(new NoOp()).build())
.layer(new OutputLayer.Builder(LossFunction.MCXENT).nIn(3).nOut(3) .layer(OutputLayer.builder().lossFunction(LossFunction.MCXENT).nIn(3).nOut(3)
.weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1)) .weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.SOFTMAX).build()) .updater(new NoOp()).activation(Activation.SOFTMAX).build())
.build(); .build();
@ -365,12 +365,12 @@ public class GradientCheckTests extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l2(0.2).l1(0.1) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l2(0.2).l1(0.1)
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L)
.list().layer(0, .layer(0,
new EmbeddingLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER) EmbeddingLayer.builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)
.updater(new NoOp()).activation( .updater(new NoOp()).activation(
Activation.TANH) Activation.TANH)
.build()) .build())
.layer(1, new OutputLayer.Builder(LossFunction.MCXENT).nIn(3).nOut(3) .layer(1, OutputLayer.builder().lossFunction(LossFunction.MCXENT).nIn(3).nOut(3)
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.updater(new NoOp()).activation(Activation.SOFTMAX).build()) .updater(new NoOp()).activation(Activation.SOFTMAX).build())
.build(); .build();
@ -437,9 +437,9 @@ public class GradientCheckTests extends BaseDL4JTest {
.seed(12345L) .seed(12345L)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.list().layer(0, .list().layer(0,
new AutoEncoder.Builder().nIn(4).nOut(3) AutoEncoder.builder().nIn(4).nOut(3)
.activation(afn).build()) .activation(afn).build())
.layer(1, new OutputLayer.Builder(lf).nIn(3).nOut(3) .layer(1, OutputLayer.builder(lf).nIn(3).nOut(3)
.activation(outputActivation).build()) .activation(outputActivation).build())
.build(); .build();
@ -497,13 +497,13 @@ public class GradientCheckTests extends BaseDL4JTest {
.weightInit(new UniformDistribution(0, 1)) .weightInit(new UniformDistribution(0, 1))
.graphBuilder() .graphBuilder()
.addInputs("features") .addInputs("features")
.addLayer("dense", new DenseLayer.Builder().nIn(4).nOut(4) .addLayer("dense", DenseLayer.builder().nIn(4).nOut(4)
.activation(Activation.TANH) .activation(Activation.TANH)
.build(), "features") .build(), "features")
.addLayer("elementWiseMul", new ElementWiseMultiplicationLayer.Builder().nIn(4).nOut(4) .addLayer("elementWiseMul", ElementWiseMultiplicationLayer.builder().nIn(4).nOut(4)
.activation(a) .activation(a)
.build(), "dense") .build(), "dense")
.addLayer("loss", new LossLayer.Builder(LossFunctions.LossFunction.COSINE_PROXIMITY) .addLayer("loss", LossLayer.builder().lossFunction(LossFunctions.LossFunction.COSINE_PROXIMITY.getILossFunction())
.activation(Activation.IDENTITY).build(), "elementWiseMul") .activation(Activation.IDENTITY).build(), "elementWiseMul")
.setOutputs("loss") .setOutputs("loss")
.build(); .build();
@ -566,12 +566,12 @@ public class GradientCheckTests extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.weightInit(new NormalDistribution(0, 1)) .weightInit(new NormalDistribution(0, 1))
.list() .list()
.layer(new EmbeddingSequenceLayer.Builder() .layer(EmbeddingSequenceLayer.builder()
.nIn(8) .nIn(8)
.nOut(4) .nOut(4)
.outputDataFormat(seqOutputFormat) .outputDataFormat(seqOutputFormat)
.build()) .build())
.layer(new RnnOutputLayer.Builder().nIn(4).nOut(3).activation(Activation.TANH) .layer(RnnOutputLayer.builder().nIn(4).nOut(3).activation(Activation.TANH)
.dataFormat(seqOutputFormat) .dataFormat(seqOutputFormat)
.lossFunction(LossFunction.MSE).build()) .lossFunction(LossFunction.MSE).build())
.build(); .build();
@ -679,12 +679,12 @@ public class GradientCheckTests extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT) .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.seed(12345L) .seed(12345L)
.list().layer(0, .list().layer(0,
new DenseLayer.Builder().nIn(4).nOut(3) DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0, .dist(new NormalDistribution(0,
1)) 1))
.updater(new NoOp()) .updater(new NoOp())
.activation(afn).build()) .activation(afn).build())
.layer(1, new OutputLayer.Builder(lf).nIn(3).nOut(3) .layer(1, OutputLayer.builder(lf).nIn(3).nOut(3)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()) .updater(new NoOp())
.activation(outputActivation).build()) .activation(outputActivation).build())
@ -740,12 +740,12 @@ public class GradientCheckTests extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).updater(new NoOp()) .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).updater(new NoOp())
.seed(12345L) .seed(12345L)
.list().layer(0, .list().layer(0,
new DenseLayer.Builder().nIn(4).nOut(3) DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.hasLayerNorm(layerNorm) .hasLayerNorm(layerNorm)
.activation(afn) .activation(afn)
.build()) .build())
.layer(1, new OutputLayer.Builder(lf).activation(outputActivation).nIn(3).nOut(3) .layer(1, OutputLayer.builder(lf).activation(outputActivation).nIn(3).nOut(3)
.dist(new NormalDistribution(0, 1)).build()) .dist(new NormalDistribution(0, 1)).build())
.build(); .build();

190
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTestsComputationGraph.java
View File

@ -76,10 +76,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1)).updater(new NoOp()) .dist(new NormalDistribution(0, 1)).updater(new NoOp())
.graphBuilder().addInputs("input") .graphBuilder().addInputs("input")
.addLayer("firstLayer", .addLayer("firstLayer",
new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(), DenseLayer.builder().nIn(4).nOut(5).activation(Activation.TANH).build(),
"input") "input")
.addLayer("outputLayer", .addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5).nOut(3).build(), .activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
"firstLayer") "firstLayer")
.setOutputs("outputLayer").build(); .setOutputs("outputLayer").build();
@ -121,13 +121,13 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)).updater(new NoOp()) .dist(new NormalDistribution(0, 1)).updater(new NoOp())
.graphBuilder().addInputs("input") .graphBuilder().addInputs("input")
.addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(), .addLayer("l1", DenseLayer.builder().nIn(4).nOut(5).activation(Activation.TANH).build(),
"input") "input")
.addLayer("l2", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(), .addLayer("l2", DenseLayer.builder().nIn(4).nOut(5).activation(Activation.TANH).build(),
"input") "input")
.addVertex("merge", new MergeVertex(), "l1", "l2") .addVertex("merge", new MergeVertex(), "l1", "l2")
.addLayer("outputLayer", .addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5 + 5).nOut(3).build(), .activation(Activation.SOFTMAX).nIn(5 + 5).nOut(3).build(),
"merge") "merge")
.setOutputs("outputLayer").build(); .setOutputs("outputLayer").build();
@ -178,13 +178,13 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()).graphBuilder().addInputs("input") .updater(new NoOp()).graphBuilder().addInputs("input")
.addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(), .addLayer("l1", DenseLayer.builder().nIn(4).nOut(5).activation(Activation.TANH).build(),
"input") "input")
.addLayer("l2", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.SIGMOID) .addLayer("l2", DenseLayer.builder().nIn(4).nOut(5).activation(Activation.SIGMOID)
.build(), "input") .build(), "input")
.addVertex("elementwise", new ElementWiseVertex(op), "l1", "l2") .addVertex("elementwise", new ElementWiseVertex(op), "l1", "l2")
.addLayer("outputLayer", .addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5).nOut(3).build(), .activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
"elementwise") "elementwise")
.setOutputs("outputLayer").build(); .setOutputs("outputLayer").build();
@ -236,15 +236,15 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()).graphBuilder().addInputs("input") .updater(new NoOp()).graphBuilder().addInputs("input")
.addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(), .addLayer("l1", DenseLayer.builder().nIn(4).nOut(5).activation(Activation.TANH).build(),
"input") "input")
.addLayer("l2", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.SIGMOID) .addLayer("l2", DenseLayer.builder().nIn(4).nOut(5).activation(Activation.SIGMOID)
.build(), "input") .build(), "input")
.addLayer("l3", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.RELU).build(), .addLayer("l3", DenseLayer.builder().nIn(4).nOut(5).activation(Activation.RELU).build(),
"input") "input")
.addVertex("elementwise", new ElementWiseVertex(op), "l1", "l2", "l3") .addVertex("elementwise", new ElementWiseVertex(op), "l1", "l2", "l3")
.addLayer("outputLayer", .addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5).nOut(3).build(), .activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
"elementwise") "elementwise")
.setOutputs("outputLayer").build(); .setOutputs("outputLayer").build();
@ -299,10 +299,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.setOutputs("out") .setOutputs("out")
.layer("l1", new DenseLayer.Builder().nIn(3).nOut(firstSmaller ? 1 : 3).build(), "in") //[mb,3] .layer("l1", DenseLayer.builder().nIn(3).nOut(firstSmaller ? 1 : 3).build(), "in") //[mb,3]
.layer("l2", new DenseLayer.Builder().nIn(3).nOut(firstSmaller ? 3 : 1).build(), "in") //[mb,1] .layer("l2", DenseLayer.builder().nIn(3).nOut(firstSmaller ? 3 : 1).build(), "in") //[mb,1]
.addVertex("ew", new ElementWiseVertex(op), "l1", "l2") .addVertex("ew", new ElementWiseVertex(op), "l1", "l2")
.layer("out", new OutputLayer.Builder().nIn(3).nOut(2).lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).build(), "ew") .layer("out", OutputLayer.builder().nIn(3).nOut(2).lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).build(), "ew")
.build(); .build();
ComputationGraph graph = new ComputationGraph(conf); ComputationGraph graph = new ComputationGraph(conf);
@ -344,15 +344,15 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 0.1)) .dist(new NormalDistribution(0, 0.1))
.updater(new NoOp()).graphBuilder().addInputs("input") .updater(new NoOp()).graphBuilder().addInputs("input")
.addLayer("l1", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0) .addLayer("l1", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).padding(0, 0)
.dataFormat(format) .convFormat(format)
.nIn(2).nOut(2).activation(Activation.TANH).build(), "input") .nIn(2).nOut(2).activation(Activation.TANH).build(), "input")
.addLayer("l2", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1) .addLayer("l2", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1)
.padding(0, 0).dataFormat(format) .padding(0, 0).convFormat(format)
.nIn(2).nOut(2).activation(Activation.TANH).build(), "input") .nIn(2).nOut(2).activation(Activation.TANH).build(), "input")
.addVertex("merge", new MergeVertex(), "l1", "l2") .addVertex("merge", new MergeVertex(), "l1", "l2")
.addLayer("outputLayer", .addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5 * 5 * (2 + 2)).nOut(3) .activation(Activation.SOFTMAX).nIn(5 * 5 * (2 + 2)).nOut(3)
.build(), .build(),
"merge") "merge")
@ -401,23 +401,23 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.updater(new NoOp()).graphBuilder().addInputs("input") .updater(new NoOp()).graphBuilder().addInputs("input")
.setOutputs("out") .setOutputs("out")
.addLayer("rnn1", .addLayer("rnn1",
new SimpleRnn.Builder().nOut(3) SimpleRnn.builder().nOut(3)
.activation(Activation.TANH).build(), .activation(Activation.TANH).build(),
"input") "input")
.addLayer("rnn2", .addLayer("rnn2",
new SimpleRnn.Builder().nOut(3) SimpleRnn.builder().nOut(3)
.activation(Activation.TANH).build(), .activation(Activation.TANH).build(),
"rnn1") "rnn1")
.addLayer("dense1", .addLayer("dense1",
new DenseLayer.Builder().nOut(3) DenseLayer.builder().nOut(3)
.activation(Activation.SIGMOID).build(), .activation(Activation.SIGMOID).build(),
"rnn1") "rnn1")
.addLayer("rnn3", .addLayer("rnn3",
new SimpleRnn.Builder().nOut(3) SimpleRnn.builder().nOut(3)
.activation(Activation.TANH).build(), .activation(Activation.TANH).build(),
"dense1") "dense1")
.addVertex("merge", new MergeVertex(), "rnn2", "rnn3") .addVertex("merge", new MergeVertex(), "rnn2", "rnn3")
.addLayer("out", new RnnOutputLayer.Builder().nOut(outSize) .addLayer("out", RnnOutputLayer.builder().nOut(outSize)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), .lossFunction(LossFunctions.LossFunction.MCXENT).build(),
@ -457,10 +457,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.weightInit(new NormalDistribution(0, 1)) .weightInit(new NormalDistribution(0, 1))
.updater(new NoOp()).graphBuilder().addInputs("input").setOutputs("out") .updater(new NoOp()).graphBuilder().addInputs("input").setOutputs("out")
.addLayer("lstm1", new LSTM.Builder().nOut(6).activation(Activation.TANH).build(), .addLayer("lstm1", LSTM.builder().nOut(6).activation(Activation.TANH).build(),
"input") "input")
.addVertex("subset", new SubsetVertex(0, 2), "lstm1") .addVertex("subset", new SubsetVertex(0, 2), "lstm1")
.addLayer("out", new RnnOutputLayer.Builder().nOut(2).activation(Activation.SOFTMAX) .addLayer("out", RnnOutputLayer.builder().nOut(2).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "subset") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "subset")
.setInputTypes(InputType.recurrent(inLength,timeSeriesLength,RNNFormat.NCW)) .setInputTypes(InputType.recurrent(inLength,timeSeriesLength,RNNFormat.NCW))
.build(); .build();
@ -494,10 +494,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()).graphBuilder().addInputs("input").setOutputs("out") .updater(new NoOp()).graphBuilder().addInputs("input").setOutputs("out")
.addLayer("lstm1", new LSTM.Builder().nIn(3).nOut(4).activation(Activation.TANH).build(), .addLayer("lstm1", LSTM.builder().nIn(3).nOut(4).activation(Activation.TANH).build(),
"input") "input")
.addVertex("lastTS", new LastTimeStepVertex("input"), "lstm1") .addVertex("lastTS", new LastTimeStepVertex("input"), "lstm1")
.addLayer("out", new OutputLayer.Builder().nIn(4).nOut(2).activation(Activation.SOFTMAX) .addLayer("out", OutputLayer.builder().nIn(4).nOut(2).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "lastTS") .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "lastTS")
.build(); .build();
@ -548,16 +548,16 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.updater(new NoOp()).graphBuilder() .updater(new NoOp()).graphBuilder()
.addInputs("input1", "input2").setOutputs("out") .addInputs("input1", "input2").setOutputs("out")
.addLayer("lstm1", .addLayer("lstm1",
new LSTM.Builder().nIn(3).nOut(3) LSTM.builder().nIn(3).nOut(3)
.activation(Activation.TANH).build(), .activation(Activation.TANH).build(),
"input1") "input1")
.addLayer("lstm2", .addLayer("lstm2",
new LSTM.Builder().nIn(2).nOut(4) LSTM.builder().nIn(2).nOut(4)
.activation(Activation.SOFTSIGN).build(), .activation(Activation.SOFTSIGN).build(),
"input2") "input2")
.addVertex("lastTS", new LastTimeStepVertex("input2"), "lstm2") .addVertex("lastTS", new LastTimeStepVertex("input2"), "lstm2")
.addVertex("duplicate", new DuplicateToTimeSeriesVertex("input2"), "lastTS") .addVertex("duplicate", new DuplicateToTimeSeriesVertex("input2"), "lastTS")
.addLayer("out", new RnnOutputLayer.Builder().nIn(3+4).nOut(2) .addLayer("out", RnnOutputLayer.builder().nIn(3+4).nOut(2)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), .lossFunction(LossFunctions.LossFunction.MCXENT).build(),
"lstm1", "duplicate") "lstm1", "duplicate")
@ -598,16 +598,16 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.updater(new NoOp()).graphBuilder() .updater(new NoOp()).graphBuilder()
.addInputs("input").setOutputs("out") .addInputs("input").setOutputs("out")
.addLayer("lstm_a", .addLayer("lstm_a",
new LSTM.Builder().nIn(2).nOut(3) LSTM.builder().nIn(2).nOut(3)
.activation(Activation.TANH).build(), .activation(Activation.TANH).build(),
"input") "input")
.addVertex("input_rev", new ReverseTimeSeriesVertex("input"), "input") .addVertex("input_rev", new ReverseTimeSeriesVertex("input"), "input")
.addLayer("lstm_b", .addLayer("lstm_b",
new LSTM.Builder().nIn(2).nOut(3) LSTM.builder().nIn(2).nOut(3)
.activation(Activation.TANH).build(), .activation(Activation.TANH).build(),
"input_rev") "input_rev")
.addVertex("lstm_b_rev", new ReverseTimeSeriesVertex("input"), "lstm_b") .addVertex("lstm_b_rev", new ReverseTimeSeriesVertex("input"), "lstm_b")
.addLayer("out", new RnnOutputLayer.Builder().nIn(3 + 3).nOut(2) .addLayer("out", RnnOutputLayer.builder().nIn(3 + 3).nOut(2)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), .lossFunction(LossFunctions.LossFunction.MCXENT).build(),
"lstm_a", "lstm_b_rev") "lstm_a", "lstm_b_rev")
@ -655,11 +655,11 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("i0", "i1", "i2") .updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("i0", "i1", "i2")
.addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i0") .addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "i0")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i1") .addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "i1")
.addLayer("d2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i2") .addLayer("d2", DenseLayer.builder().nIn(2).nOut(2).build(), "i2")
.addLayer("d3", new DenseLayer.Builder().nIn(6).nOut(2).build(), "d0", "d1", "d2") .addLayer("d3", DenseLayer.builder().nIn(6).nOut(2).build(), "d0", "d1", "d2")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(2) .addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(2)
.nOut(2).build(), "d3") .nOut(2).build(), "d3")
.setOutputs("out").build(); .setOutputs("out").build();
@ -698,11 +698,11 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("i0") .updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("i0")
.addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i0") .addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "i0")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "d0") .addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "d0")
.addLayer("d2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "d0") .addLayer("d2", DenseLayer.builder().nIn(2).nOut(2).build(), "d0")
.addLayer("d3", new DenseLayer.Builder().nIn(2).nOut(2).build(), "d0") .addLayer("d3", DenseLayer.builder().nIn(2).nOut(2).build(), "d0")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(6) .addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(6)
.nOut(2).build(), "d1", "d2", "d3") .nOut(2).build(), "d1", "d2", "d3")
.setOutputs("out").build(); .setOutputs("out").build();
@ -738,14 +738,14 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("i0", "i1", "i2") .updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("i0", "i1", "i2")
.addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i0") .addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "i0")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i1") .addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "i1")
.addLayer("d2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i2") .addLayer("d2", DenseLayer.builder().nIn(2).nOut(2).build(), "i2")
.addVertex("m", new MergeVertex(), "d0", "d1", "d2") .addVertex("m", new MergeVertex(), "d0", "d1", "d2")
.addLayer("D0", new DenseLayer.Builder().nIn(6).nOut(2).build(), "m") .addLayer("D0", DenseLayer.builder().nIn(6).nOut(2).build(), "m")
.addLayer("D1", new DenseLayer.Builder().nIn(6).nOut(2).build(), "m") .addLayer("D1", DenseLayer.builder().nIn(6).nOut(2).build(), "m")
.addLayer("D2", new DenseLayer.Builder().nIn(6).nOut(2).build(), "m") .addLayer("D2", DenseLayer.builder().nIn(6).nOut(2).build(), "m")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(6) .addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(6)
.nOut(2).build(), "D0", "D1", "D2") .nOut(2).build(), "D0", "D1", "D2")
.setOutputs("out").build(); .setOutputs("out").build();
@ -787,18 +787,18 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("input") .updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("input")
.addLayer("l0", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0) .addLayer("l0", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).padding(0, 0)
.nIn(2).nOut(2).activation(Activation.TANH).build(), "input") .nIn(2).nOut(2).activation(Activation.TANH).build(), "input")
.addLayer("l1", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0) .addLayer("l1", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).padding(0, 0)
.nIn(2).nOut(2).activation(Activation.TANH).build(), "l0") .nIn(2).nOut(2).activation(Activation.TANH).build(), "l0")
.addLayer("l2", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0) .addLayer("l2", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).padding(0, 0)
.nIn(2).nOut(2).activation(Activation.TANH).build(), "l0") .nIn(2).nOut(2).activation(Activation.TANH).build(), "l0")
.addVertex("m", new MergeVertex(), "l1", "l2") .addVertex("m", new MergeVertex(), "l1", "l2")
.addLayer("l3", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0) .addLayer("l3", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).padding(0, 0)
.nIn(4).nOut(2).activation(Activation.TANH).build(), "m") .nIn(4).nOut(2).activation(Activation.TANH).build(), "m")
.addLayer("l4", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0) .addLayer("l4", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).padding(0, 0)
.nIn(4).nOut(2).activation(Activation.TANH).build(), "m") .nIn(4).nOut(2).activation(Activation.TANH).build(), "m")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE) .addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.IDENTITY).nOut(2) .activation(Activation.IDENTITY).nOut(2)
.build(), "l3", "l4") .build(), "l3", "l4")
.setOutputs("out").setInputTypes(InputType.convolutional(inH, inW, 2)) .setOutputs("out").setInputTypes(InputType.convolutional(inH, inW, 2))
@ -839,7 +839,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.updater(new NoOp()).graphBuilder() .updater(new NoOp()).graphBuilder()
.addInputs("input1", "input2", "input3") .addInputs("input1", "input2", "input3")
.addVertex("stack1", new StackVertex(), "input1", "input2", "input3") .addVertex("stack1", new StackVertex(), "input1", "input2", "input3")
.addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5) .addLayer("l1", DenseLayer.builder().nIn(4).nOut(5)
.activation(Activation.TANH).build(), "stack1") .activation(Activation.TANH).build(), "stack1")
.addVertex("unstack0", new UnstackVertex(0, 3), "l1") .addVertex("unstack0", new UnstackVertex(0, 3), "l1")
.addVertex("unstack1", new UnstackVertex(1, 3), "l1") .addVertex("unstack1", new UnstackVertex(1, 3), "l1")
@ -847,8 +847,8 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.addVertex("l2-1", new L2Vertex(), "unstack1", "unstack0") // x - x- .addVertex("l2-1", new L2Vertex(), "unstack1", "unstack0") // x - x-
.addVertex("l2-2", new L2Vertex(), "unstack1", "unstack2") // x - x+ .addVertex("l2-2", new L2Vertex(), "unstack1", "unstack2") // x - x+
.addLayer("lossLayer", .addLayer("lossLayer",
new LossLayer.Builder() LossLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT) .lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction())
.activation(Activation.SOFTMAX).build(), .activation(Activation.SOFTMAX).build(),
"l2-1", "l2-2") "l2-1", "l2-2")
.setOutputs("lossLayer").build(); .setOutputs("lossLayer").build();
@ -911,9 +911,9 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new GaussianDistribution(0, 1)) .dist(new GaussianDistribution(0, 1))
.updater(new NoOp()).graphBuilder().addInputs("input1") .updater(new NoOp()).graphBuilder().addInputs("input1")
.addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH) .addLayer("l1", DenseLayer.builder().nIn(4).nOut(5).activation(Activation.TANH)
.build(), "input1") .build(), "input1")
.addLayer("cl", new CenterLossOutputLayer.Builder() .addLayer("cl",CenterLossOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT).nIn(5).nOut(numLabels) .lossFunction(LossFunctions.LossFunction.MCXENT).nIn(5).nOut(numLabels)
.alpha(1.0).lambda(lambda).gradientCheck(true) .alpha(1.0).lambda(lambda).gradientCheck(true)
.activation(Activation.SOFTMAX).build(), "l1") .activation(Activation.SOFTMAX).build(), "l1")
@ -975,9 +975,9 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).seed(12345L).list() .dist(new NormalDistribution(0, 1.0)).seed(12345L).list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(3).build()) .layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(3).build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build()) .layer(1, GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(2, new CenterLossOutputLayer.Builder() .layer(2,CenterLossOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT).nOut(numLabels) .lossFunction(LossFunctions.LossFunction.MCXENT).nOut(numLabels)
.alpha(1.0).lambda(lambda).gradientCheck(true) .alpha(1.0).lambda(lambda).gradientCheck(true)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
@ -1030,10 +1030,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder() .activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in1") .addInputs("in1", "in2").addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "in1")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in2") .addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "in2")
.addVertex("l2", new L2Vertex(), "d0", "d1") .addVertex("l2", new L2Vertex(), "d0", "d1")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nIn(1) .addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2).nIn(1)
.nOut(1).activation(Activation.IDENTITY).build(), "l2") .nOut(1).activation(Activation.IDENTITY).build(), "l2")
.setOutputs("out").build(); .setOutputs("out").build();
@ -1083,14 +1083,14 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder() .activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2") .addInputs("in1", "in2")
.addLayer("d0", new DenseLayer.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in1") .addLayer("d0", DenseLayer.builder().nIn(layerSizes).nOut(layerSizes).build(), "in1")
.addLayer("d1", new DenseLayer.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in2") .addLayer("d1", DenseLayer.builder().nIn(layerSizes).nOut(layerSizes).build(), "in2")
.addVertex("stack", new StackVertex(), "d0", "d1") .addVertex("stack", new StackVertex(), "d0", "d1")
.addLayer("d2", new DenseLayer.Builder().nIn(layerSizes).nOut(layerSizes).build(), "stack") .addLayer("d2", DenseLayer.builder().nIn(layerSizes).nOut(layerSizes).build(), "stack")
.addVertex("u1", new UnstackVertex(0, 2), "d2").addVertex("u2", new UnstackVertex(1, 2), "d2") .addVertex("u1", new UnstackVertex(0, 2), "d2").addVertex("u2", new UnstackVertex(1, 2), "d2")
.addLayer("out1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2) .addLayer("out1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2)
.nIn(layerSizes).nOut(layerSizes).activation(Activation.IDENTITY).build(), "u1") .nIn(layerSizes).nOut(layerSizes).activation(Activation.IDENTITY).build(), "u1")
.addLayer("out2", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2) .addLayer("out2", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2)
.nIn(layerSizes).nOut(2).activation(Activation.IDENTITY).build(), "u2") .nIn(layerSizes).nOut(2).activation(Activation.IDENTITY).build(), "u2")
.setOutputs("out1", "out2").build(); .setOutputs("out1", "out2").build();
@ -1137,17 +1137,17 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder() .activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in1") .addInputs("in1", "in2").addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "in1")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in2") .addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "in2")
.addVertex("stack", new StackVertex(), "d0", "d1") .addVertex("stack", new StackVertex(), "d0", "d1")
.addVertex("u0", new UnstackVertex(0, 2), "stack") .addVertex("u0", new UnstackVertex(0, 2), "stack")
.addVertex("u1", new UnstackVertex(1, 2), "stack") .addVertex("u1", new UnstackVertex(1, 2), "stack")
.addLayer("out1", .addLayer("out1",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2)
.nOut(2).activation(Activation.IDENTITY).build(), .nOut(2).activation(Activation.IDENTITY).build(),
"u0") "u0")
.addLayer("out2", .addLayer("out2",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2)
.nOut(2).activation(Activation.IDENTITY).build(), .nOut(2).activation(Activation.IDENTITY).build(),
"u1") "u1")
.setOutputs("out1", "out2").build(); .setOutputs("out1", "out2").build();
@ -1198,16 +1198,16 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder() .activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2") .addInputs("in1", "in2")
.addLayer("d0", new SimpleRnn.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in1") .addLayer("d0", SimpleRnn.builder().nIn(layerSizes).nOut(layerSizes).build(), "in1")
.addLayer("d1", new SimpleRnn.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in2") .addLayer("d1", SimpleRnn.builder().nIn(layerSizes).nOut(layerSizes).build(), "in2")
.addVertex("stack", new StackVertex(), "d0", "d1") .addVertex("stack", new StackVertex(), "d0", "d1")
.addLayer("d2", new SimpleRnn.Builder().nIn(layerSizes).nOut(layerSizes).build(), "stack") .addLayer("d2", SimpleRnn.builder().nIn(layerSizes).nOut(layerSizes).build(), "stack")
.addVertex("u1", new UnstackVertex(0, 2), "d2").addVertex("u2", new UnstackVertex(1, 2), "d2") .addVertex("u1", new UnstackVertex(0, 2), "d2").addVertex("u2", new UnstackVertex(1, 2), "d2")
.addLayer("p1", new GlobalPoolingLayer.Builder(PoolingType.AVG).build(), "u1") .addLayer("p1", GlobalPoolingLayer.builder(PoolingType.AVG).build(), "u1")
.addLayer("p2", new GlobalPoolingLayer.Builder(PoolingType.AVG).build(), "u2") .addLayer("p2", GlobalPoolingLayer.builder(PoolingType.AVG).build(), "u2")
.addLayer("out1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2) .addLayer("out1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2)
.nIn(layerSizes).nOut(layerSizes).activation(Activation.IDENTITY).build(), "p1") .nIn(layerSizes).nOut(layerSizes).activation(Activation.IDENTITY).build(), "p1")
.addLayer("out2", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2) .addLayer("out2", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2)
.nIn(layerSizes).nOut(2).activation(Activation.IDENTITY).build(), "p2") .nIn(layerSizes).nOut(2).activation(Activation.IDENTITY).build(), "p2")
.setOutputs("out1", "out2").build(); .setOutputs("out1", "out2").build();
@ -1260,14 +1260,14 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder() .activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in1") .addInputs("in1", "in2").addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "in1")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in2") .addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "in2")
.addLayer("out1", .addLayer("out1",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2)
.nOut(2).activation(Activation.IDENTITY).build(), .nOut(2).activation(Activation.IDENTITY).build(),
"d0") "d0")
.addLayer("out2", .addLayer("out2",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2)
.nOut(2).activation(Activation.IDENTITY).build(), .nOut(2).activation(Activation.IDENTITY).build(),
"d1") "d1")
.setOutputs("out1", "out2").build(); .setOutputs("out1", "out2").build();
@ -1320,10 +1320,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.activation(Activation.TANH).updater(new NoOp()).graphBuilder() .activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1").addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(3).build(), "in1") .addInputs("in1").addLayer("d1", DenseLayer.builder().nIn(2).nOut(3).build(), "in1")
.addVertex("norm", new L2NormalizeVertex(definition,L2NormalizeVertex.DEFAULT_EPS), "d1") .addVertex("norm", new L2NormalizeVertex(definition,L2NormalizeVertex.DEFAULT_EPS), "d1")
.addLayer("out1", .addLayer("out1",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nIn(3) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2).nIn(3)
.nOut(2).activation(Activation.IDENTITY).build(), .nOut(2).activation(Activation.IDENTITY).build(),
"norm") "norm")
.setOutputs("out1").build(); .setOutputs("out1").build();
@ -1370,11 +1370,11 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder() .activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1") .addInputs("in1")
.addLayer("d1", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(2).build(), .addLayer("d1", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(2).build(),
"in1") "in1")
.addVertex("norm", new L2NormalizeVertex(), "d1") .addVertex("norm", new L2NormalizeVertex(), "d1")
.addLayer("out1", .addLayer("out1",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nOut(2) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2).nOut(2)
.activation(Activation.IDENTITY).build(), .activation(Activation.IDENTITY).build(),
"norm") "norm")
.setOutputs("out1").setInputTypes(InputType.convolutional(h, w, dIn)).build(); .setOutputs("out1").setInputTypes(InputType.convolutional(h, w, dIn)).build();
@ -1420,9 +1420,9 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L)
.updater(new NoOp()).graphBuilder().addInputs("in") .updater(new NoOp()).graphBuilder().addInputs("in")
.addLayer("0", new EmbeddingLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER) .addLayer("0", EmbeddingLayer.builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)
.activation(Activation.TANH).build(), "in") .activation(Activation.TANH).build(), "in")
.addLayer("1", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3) .addLayer("1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3)
.activation(Activation.SOFTMAX).build(), "0") .activation(Activation.SOFTMAX).build(), "0")
.setOutputs("1").build(); .setOutputs("1").build();

36
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTestsMasking.java
View File

@ -119,10 +119,10 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345L) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345L)
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.list()
.layer(0, new SimpleRnn.Builder().nIn(nIn).nOut(layerSize) .layer(0, SimpleRnn.builder().nIn(nIn).nOut(layerSize)
.weightInit(new NormalDistribution(0, 1)).build()) .weightInit(new NormalDistribution(0, 1)).build())
.layer(1, new RnnOutputLayer.Builder(s.lf).activation(s.act).nIn(layerSize).nOut(s.nOut) .layer(1, RnnOutputLayer.builder().lossFunction(s.lf).activation(s.act).nIn(layerSize).nOut(s.nOut)
.weightInit(new NormalDistribution(0, 1)).build()) .weightInit(new NormalDistribution(0, 1)).build())
.build(); .build();
MultiLayerNetwork mln = new MultiLayerNetwork(conf); MultiLayerNetwork mln = new MultiLayerNetwork(conf);
@ -161,10 +161,10 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1.0)).seed(12345L).list() .dist(new NormalDistribution(0, 1.0)).seed(12345L).list()
.layer(0, new SimpleRnn.Builder().nIn(nIn).nOut(2).activation(Activation.TANH).build()) .layer(0, SimpleRnn.builder().nIn(nIn).nOut(2).activation(Activation.TANH).build())
.layer(1, new GravesBidirectionalLSTM.Builder().nIn(2).nOut(layerSize) .layer(1, GravesBidirectionalLSTM.builder().nIn(2).nOut(layerSize)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(2, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(2, RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).build()) .activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).build())
.build(); .build();
@ -241,9 +241,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1)).seed(12345) .dist(new NormalDistribution(0, 1)).seed(12345)
.list() .list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH) .layer(0, DenseLayer.builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH)
.build()) .build())
.layer(1, new OutputLayer.Builder().nIn(layerSize).nOut(nOut).lossFunction(lf) .layer(1, OutputLayer.builder().nIn(layerSize).nOut(nOut).lossFunction(lf)
.activation(a).build()) .activation(a).build())
.validateOutputLayerConfig(false) .validateOutputLayerConfig(false)
.build(); .build();
@ -335,9 +335,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1)).seed(12345) .dist(new NormalDistribution(0, 1)).seed(12345)
.list() .list()
.layer(0, new SimpleRnn.Builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH) .layer(0, SimpleRnn.builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH)
.build()) .build())
.layer(1, new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut).lossFunction(lf) .layer(1, RnnOutputLayer.builder().nIn(layerSize).nOut(nOut).lossFunction(lf)
.activation(a).build()) .activation(a).build())
.validateOutputLayerConfig(false) .validateOutputLayerConfig(false)
.inputType(InputType.recurrent(nIn,tsLength, RNNFormat.NCW)) .inputType(InputType.recurrent(nIn,tsLength, RNNFormat.NCW))
@ -368,9 +368,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 2)).seed(12345) .dist(new NormalDistribution(0, 2)).seed(12345)
.graphBuilder().addInputs("in") .graphBuilder().addInputs("in")
.addLayer("0", new SimpleRnn.Builder().nOut(layerSize) .addLayer("0", SimpleRnn.builder().nOut(layerSize)
.activation(Activation.TANH).build(), "in") .activation(Activation.TANH).build(), "in")
.addLayer("1", new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut).lossFunction(lf) .addLayer("1", RnnOutputLayer.builder().nIn(layerSize).nOut(nOut).lossFunction(lf)
.activation(a).build(), "0") .activation(a).build(), "0")
.setOutputs("1").validateOutputLayerConfig(false) .setOutputs("1").validateOutputLayerConfig(false)
.setInputTypes(InputType.recurrent(nIn,tsLength,RNNFormat.NCW)) .setInputTypes(InputType.recurrent(nIn,tsLength,RNNFormat.NCW))
@ -401,9 +401,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.weightInit(new NormalDistribution(0,2)) .weightInit(new NormalDistribution(0,2))
.updater(new NoOp()) .updater(new NoOp())
.list() .list()
.layer(new LSTM.Builder().nIn(3).nOut(3).build()) .layer(LSTM.builder().nIn(3).nOut(3).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build()) .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nIn(3).nOut(3).activation(Activation.SOFTMAX).build()) .layer(OutputLayer.builder().nIn(3).nOut(3).activation(Activation.SOFTMAX).build())
.inputType(InputType.recurrent(3)) .inputType(InputType.recurrent(3))
.build(); .build();
@ -457,9 +457,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new LSTM.Builder().nIn(3).nOut(3).build(), "in") .layer("0", LSTM.builder().nIn(3).nOut(3).build(), "in")
.layer("1", new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build(), "0") .layer("1", GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build(), "0")
.layer("out", new OutputLayer.Builder().nIn(3).nOut(3).activation(Activation.SOFTMAX).build(), "1") .layer("out", OutputLayer.builder().nIn(3).nOut(3).activation(Activation.SOFTMAX).build(), "1")
.setOutputs("out") .setOutputs("out")
.setInputTypes(InputType.recurrent(3)) .setInputTypes(InputType.recurrent(3))
.build(); .build();

6
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/LRNGradientCheckTests.java
View File

@ -72,10 +72,10 @@ public class LRNGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.seed(12345L) .seed(12345L)
.dist(new NormalDistribution(0, 2)).list() .dist(new NormalDistribution(0, 2)).list()
.layer(0, new ConvolutionLayer.Builder().nOut(6).kernelSize(2, 2).stride(1, 1) .layer(0, ConvolutionLayer.builder().nOut(6).kernelSize(2, 2).stride(1, 1)
.activation(Activation.TANH).build()) .activation(Activation.TANH).build())
.layer(1, new LocalResponseNormalization.Builder().build()) .layer(1, LocalResponseNormalization.builder().build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build()) .activation(Activation.SOFTMAX).nOut(nOut).build())
.inputType(InputType.convolutional(hw, hw, depth)); .inputType(InputType.convolutional(hw, hw, depth));

40
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/LSTMGradientCheckTests.java
View File

@ -73,17 +73,17 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
LayerConfiguration l0; LayerConfiguration l0;
LayerConfiguration l1; LayerConfiguration l1;
if (graves) { if (graves) {
l0 = new GravesLSTM.Builder().nIn(nIn).nOut(layerSize).activation(Activation.SIGMOID) l0 = GravesLSTM.builder().nIn(nIn).nOut(layerSize).activation(Activation.SIGMOID)
.dist(new NormalDistribution(0, 1.0)) .dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build(); .updater(new NoOp()).build();
l1 = new GravesLSTM.Builder().nIn(layerSize).nOut(layerSize).activation(Activation.SIGMOID) l1 = GravesLSTM.builder().nIn(layerSize).nOut(layerSize).activation(Activation.SIGMOID)
.dist(new NormalDistribution(0, 1.0)) .dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build(); .updater(new NoOp()).build();
} else { } else {
l0 = new LSTM.Builder().nIn(nIn).nOut(layerSize).activation(Activation.SIGMOID) l0 = LSTM.builder().nIn(nIn).nOut(layerSize).activation(Activation.SIGMOID)
.dist(new NormalDistribution(0, 1.0)) .dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build(); .updater(new NoOp()).build();
l1 = new LSTM.Builder().nIn(layerSize).nOut(layerSize).activation(Activation.SIGMOID) l1 = LSTM.builder().nIn(layerSize).nOut(layerSize).activation(Activation.SIGMOID)
.dist(new NormalDistribution(0, 1.0)) .dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build(); .updater(new NoOp()).build();
} }
@ -94,7 +94,7 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
.list() .list()
.layer(0, l0).layer(1, .layer(0, l0).layer(1,
l1) l1)
.layer(2, new RnnOutputLayer.Builder(LossFunction.MCXENT) .layer(2, RnnOutputLayer.builder().lossFunction(LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut) .activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut)
.dist(new NormalDistribution(0, 1.0)).updater(new NoOp()) .dist(new NormalDistribution(0, 1.0)).updater(new NoOp())
@ -196,14 +196,14 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
LayerConfiguration layer; LayerConfiguration layer;
if (graves) { if (graves) {
layer = new GravesLSTM.Builder().nIn(nIn).nOut(layerSize).activation(afn).build(); layer = GravesLSTM.builder().nIn(nIn).nOut(layerSize).activation(afn).build();
} else { } else {
layer = new LSTM.Builder().nIn(nIn).nOut(layerSize).activation(afn).build(); layer = LSTM.builder().nIn(nIn).nOut(layerSize).activation(afn).build();
} }
NeuralNetConfiguration.NeuralNetConfigurationBuilder conf2 = (NeuralNetConfigurationBuilder) conf NeuralNetConfiguration.NeuralNetConfigurationBuilder conf2 = (NeuralNetConfigurationBuilder) conf
.layer(0, layer) .layer(0, layer)
.layer(1, new RnnOutputLayer.Builder(lf).activation(outputActivation) .layer(1, RnnOutputLayer.builder(lf).activation(outputActivation)
.nIn(layerSize).nOut(nOut).build()); .nIn(layerSize).nOut(nOut).build());
MultiLayerNetwork mln = new MultiLayerNetwork(conf2.build()); MultiLayerNetwork mln = new MultiLayerNetwork(conf2.build());
@ -251,16 +251,16 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
LayerConfiguration layer; LayerConfiguration layer;
if (graves) { if (graves) {
layer = new GravesLSTM.Builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH).build(); layer = GravesLSTM.builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH).build();
} else { } else {
layer = new LSTM.Builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH).build(); layer = LSTM.builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH).build();
} }
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345L) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345L)
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.updater(new NoOp()).list().layer(0, layer) .updater(new NoOp()).list().layer(0, layer)
.layer(1, new RnnOutputLayer.Builder(LossFunction.MCXENT).activation(Activation.SOFTMAX) .layer(1, RnnOutputLayer.builder().lossFunction(LossFunction.MCXENT).activation(Activation.SOFTMAX)
.nIn(layerSize).nOut(nOut).build()) .nIn(layerSize).nOut(nOut).build())
.build(); .build();
MultiLayerNetwork mln = new MultiLayerNetwork(conf); MultiLayerNetwork mln = new MultiLayerNetwork(conf);
@ -324,11 +324,11 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.layer(0, .layer(0,
new GravesBidirectionalLSTM.Builder().nIn(nIn).nOut(layerSize) GravesBidirectionalLSTM.builder().nIn(nIn).nOut(layerSize)
.weightInit(new NormalDistribution(0, 1)) .weightInit(new NormalDistribution(0, 1))
.activation(afn) .activation(afn)
.build()) .build())
.layer(1, new RnnOutputLayer.Builder(lf).activation(outputActivation).nIn(layerSize) .layer(1, RnnOutputLayer.builder(lf).activation(outputActivation).nIn(layerSize)
.nOut(nOut) .nOut(nOut)
.dist(new NormalDistribution(0, 1)).updater(new NoOp()).build()) .dist(new NormalDistribution(0, 1)).updater(new NoOp()).build())
.build(); .build();
@ -383,12 +383,12 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345L) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345L)
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.list() .list()
.layer(0, new GravesBidirectionalLSTM.Builder().nIn(nIn).nOut(layerSize) .layer(0, GravesBidirectionalLSTM.builder().nIn(nIn).nOut(layerSize)
.dist(new NormalDistribution(0, 1)).updater( .dist(new NormalDistribution(0, 1)).updater(
Updater.NONE) Updater.NONE)
.build()) .build())
.layer(1, new RnnOutputLayer.Builder(LossFunction.MCXENT).activation(Activation.SOFTMAX) .layer(1, RnnOutputLayer.builder().lossFunction(LossFunction.MCXENT).activation(Activation.SOFTMAX)
.nIn(layerSize).nOut(nOut) .nIn(layerSize).nOut(nOut)
.dist(new NormalDistribution(0, 1)).updater(new NoOp()).build()) .dist(new NormalDistribution(0, 1)).updater(new NoOp()).build())
.build(); .build();
@ -432,13 +432,13 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new NoOp()).seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new NoOp()).seed(12345)
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.dist(new UniformDistribution(-2, 2)).list() .dist(new UniformDistribution(-2, 2)).list()
.layer(0, new ConvolutionLayer.Builder(3, 3).nIn(2).nOut(3).stride(1, 1) .layer(0, ConvolutionLayer.builder(3, 3).nIn(2).nOut(3).stride(1, 1)
.activation(Activation.TANH).build()) //Out: (10-5)/1+1 = 6 -> 6x6x5 .activation(Activation.TANH).build()) //Out: (10-5)/1+1 = 6 -> 6x6x5
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2) .layer(1, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
.stride(1, 1).build()) //Out: (6-2)/1+1 = 5 -> 5x5x5 .stride(1, 1).build()) //Out: (6-2)/1+1 = 5 -> 5x5x5
.layer(2, new DenseLayer.Builder().nIn(27).nOut(4).activation(Activation.TANH).build()) .layer(2, DenseLayer.builder().nIn(27).nOut(4).activation(Activation.TANH).build())
.layer(3, new GravesLSTM.Builder().nIn(4).nOut(3).activation(Activation.TANH).build()) .layer(3, GravesLSTM.builder().nIn(4).nOut(3).activation(Activation.TANH).build())
.layer(4, new RnnOutputLayer.Builder().lossFunction(LossFunction.MCXENT).nIn(3).nOut(nClasses) .layer(4, RnnOutputLayer.builder().lossFunction(LossFunction.MCXENT).nIn(3).nOut(nClasses)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(6, 6, 2)).build(); .inputType(InputType.convolutional(6, 6, 2)).build();

14
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/LossFunctionGradientCheck.java
View File

@ -187,8 +187,8 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345)
.updater(new NoOp()) .updater(new NoOp())
.dist(new UniformDistribution(-2, 2)).list() .dist(new UniformDistribution(-2, 2)).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()) .layer(0, DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build())
.layer(1, new OutputLayer.Builder().lossFunction(lossFunctions[i]) .layer(1, OutputLayer.builder().lossFunction(lossFunctions[i])
.activation(outputActivationFn[i]).nIn(4).nOut(nOut[i]).build()) .activation(outputActivationFn[i]).nIn(4).nOut(nOut[i]).build())
.validateOutputLayerConfig(false) .validateOutputLayerConfig(false)
.build(); .build();
@ -351,9 +351,9 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345)
.updater(new NoOp()) .updater(new NoOp())
.dist(new UniformDistribution(-2, 2)).list() .dist(new UniformDistribution(-2, 2)).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(nOut[i]).activation(Activation.TANH) .layer(0, DenseLayer.builder().nIn(4).nOut(nOut[i]).activation(Activation.TANH)
.build()) .build())
.layer(1, new LossLayer.Builder().lossFunction(lossFunctions[i]) .layer(1, LossLayer.builder().lossFunction(lossFunctions[i])
.activation(outputActivationFn[i]).build()) .activation(outputActivationFn[i]).build())
.validateOutputLayerConfig(false) .validateOutputLayerConfig(false)
.build(); .build();
@ -361,7 +361,7 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
assertSame(((LossLayer) net.getLayer(1).getLayerConfiguration()).getLossFn().getClass(), lossFunctions[i] assertSame(((LossLayer) net.getLayer(1).getLayerConfiguration()).getLossFunction().getClass(), lossFunctions[i]
.getClass()); .getClass());
INDArray[] inOut = getFeaturesAndLabels(lossFunctions[i], minibatchSizes[j], 4, nOut[i], 12345); INDArray[] inOut = getFeaturesAndLabels(lossFunctions[i], minibatchSizes[j], 4, nOut[i], 12345);
@ -655,9 +655,9 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
// .dist(new UniformDistribution(-3, 3)) // .dist(new UniformDistribution(-3, 3))
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.list() .list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH) .layer(0, DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH)
.build()) .build())
.layer(1, new OutputLayer.Builder().lossFunction(lossFunctions[i]) .layer(1, OutputLayer.builder().lossFunction(lossFunctions[i])
.activation(outputActivationFn[i]).nIn(4).nOut(3).build()) .activation(outputActivationFn[i]).nIn(4).nOut(3).build())
.validateOutputLayerConfig(false) .validateOutputLayerConfig(false)
.build(); .build();

22
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/NoBiasGradientCheckTests.java
View File

@ -73,19 +73,19 @@ public class NoBiasGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.seed(12345L) .seed(12345L)
.list() .list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(layerSize) .layer(0, DenseLayer.builder().nIn(nIn).nOut(layerSize)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH) .activation(Activation.TANH)
.hasBias(true) //ILayer 0: Always have a bias .hasBias(true) //ILayer 0: Always have a bias
.build()) .build())
.layer(1, new DenseLayer.Builder().nIn(layerSize).nOut(layerSize) .layer(1, DenseLayer.builder().nIn(layerSize).nOut(layerSize)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH) .activation(Activation.TANH)
.hasBias(denseHasBias) .hasBias(denseHasBias)
.build()) .build())
.layer(2, new OutputLayer.Builder(LossFunction.MCXENT) .layer(2, OutputLayer.builder().lossFunction(LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut) .activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
@ -144,12 +144,12 @@ public class NoBiasGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.seed(12345L) .seed(12345L)
.list() .list()
.layer(0, new LSTM.Builder().nIn(nIn).nOut(layerSize) .layer(0, LSTM.builder().nIn(nIn).nOut(layerSize)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH) .activation(Activation.TANH)
.build()) .build())
.layer(1, new RnnOutputLayer.Builder(LossFunction.MCXENT) .layer(1, RnnOutputLayer.builder().lossFunction(LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut) .activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
@ -205,13 +205,13 @@ public class NoBiasGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.seed(12345L) .seed(12345L)
.list() .list()
.layer(0, new EmbeddingLayer.Builder().nIn(nIn).nOut(layerSize) .layer(0, EmbeddingLayer.builder().nIn(nIn).nOut(layerSize)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(Activation.TANH) .activation(Activation.TANH)
.hasBias(embeddingHasBias) .hasBias(embeddingHasBias)
.build()) .build())
.layer(1, new OutputLayer.Builder(LossFunction.MCXENT) .layer(1, OutputLayer.builder().lossFunction(LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut) .activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
@ -271,17 +271,17 @@ public class NoBiasGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.list() .list()
.layer(new ConvolutionLayer.Builder(kernel, .layer(ConvolutionLayer.builder(kernel,
stride, padding).nIn(inputDepth) stride, padding).nIn(inputDepth)
.hasBias(false) .hasBias(false)
.nOut(3).build())//output: (5-2+0)/1+1 = 4 .nOut(3).build())//output: (5-2+0)/1+1 = 4
.layer(new SubsamplingLayer.Builder(PoolingType.MAX) .layer(SubsamplingLayer.builder(PoolingType.MAX)
.kernelSize(kernel).stride(stride).padding(padding) .kernelSize(kernel).stride(stride).padding(padding)
.pnorm(pNorm).build()) //output: (4-2+0)/1+1 =3 -> 3x3x3 .pnorm(pNorm).build()) //output: (4-2+0)/1+1 =3 -> 3x3x3
.layer(new ConvolutionLayer.Builder(kernel, stride, padding) .layer(ConvolutionLayer.builder(kernel, stride, padding)
.hasBias(cnnHasBias) .hasBias(cnnHasBias)
.nOut(2).build()) //Output: (3-2+0)/1+1 = 2 .nOut(2).build()) //Output: (3-2+0)/1+1 = 2
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.nOut(4).build()) .nOut(4).build())
.inputType(InputType.convolutionalFlat(height, width, inputDepth)) .inputType(InputType.convolutionalFlat(height, width, inputDepth))

12
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/OutputLayerGradientChecks.java
View File

@ -121,10 +121,10 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.list() .list()
.layer(new LSTM.Builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH) .layer(LSTM.builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH)
.dist(new NormalDistribution(0, 1.0)) .dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build()) .updater(new NoOp()).build())
.layer(new RnnLossLayer.Builder(lf) .layer(RnnLossLayer.builder().lossFunction(lf)
.activation(oa) .activation(oa)
.build()) .build())
.validateOutputLayerConfig(false).build(); .validateOutputLayerConfig(false).build();
@ -228,10 +228,10 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.list() .list()
.layer(new ConvolutionLayer.Builder().nIn(dIn).nOut(dOut).activation(Activation.TANH) .layer(ConvolutionLayer.builder().nIn(dIn).nOut(dOut).activation(Activation.TANH)
.dist(new NormalDistribution(0, 1.0)) .dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build()) .updater(new NoOp()).build())
.layer(new CnnLossLayer.Builder(lf) .layer(CnnLossLayer.builder().lossFunction(lf)
.activation(oa) .activation(oa)
.build()) .build())
.validateOutputLayerConfig(false).build(); .validateOutputLayerConfig(false).build();
@ -375,11 +375,11 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.list() .list()
.layer(new Convolution3D.Builder().nIn(chIn).nOut(chOut).activation(Activation.TANH) .layer(Convolution3D.builder().nIn(chIn).nOut(chOut).activation(Activation.TANH)
.dist(new NormalDistribution(0, 1.0)) .dist(new NormalDistribution(0, 1.0))
.dataFormat(dataFormat) .dataFormat(dataFormat)
.updater(new NoOp()).build()) .updater(new NoOp()).build())
.layer(new Cnn3DLossLayer.Builder(dataFormat) .layer(Cnn3DLossLayer.builder().dataFormat(dataFormat)
.lossFunction(lf) .lossFunction(lf)
.activation(oa) .activation(oa)
.build()) .build())

34
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/RnnGradientChecks.java
View File

@ -112,12 +112,12 @@ public class RnnGradientChecks extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .list()
.layer(new LSTM.Builder().nIn(nIn).nOut(3).build()) .layer(LSTM.builder().nIn(nIn).nOut(3).build())
.layer(new Bidirectional(m, .layer(Bidirectional.builder(m,
(simple ? (simple ?
new SimpleRnn.Builder().nIn(3).nOut(3).hasLayerNorm(hasLayerNorm).build() : SimpleRnn.builder().nIn(3).nOut(3).hasLayerNorm(hasLayerNorm).build() :
new LSTM.Builder().nIn(3).nOut(3).build()))) LSTM.builder().nIn(3).nOut(3).build())).build())
.layer(new RnnOutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX).build()) .layer(RnnOutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX).build())
.build(); .build();
@ -194,9 +194,9 @@ public class RnnGradientChecks extends BaseDL4JTest {
.l1(l1s[l]) .l1(l1s[l])
.l2(l2s[l]) .l2(l2s[l])
.list() .list()
.layer(new SimpleRnn.Builder().nIn(nIn).nOut(layerSize).hasLayerNorm(hasLayerNorm).build()) .layer(SimpleRnn.builder().nIn(nIn).nOut(layerSize).hasLayerNorm(hasLayerNorm).build())
.layer(new SimpleRnn.Builder().nIn(layerSize).nOut(layerSize).hasLayerNorm(hasLayerNorm).build()) .layer(SimpleRnn.builder().nIn(layerSize).nOut(layerSize).hasLayerNorm(hasLayerNorm).build())
.layer(new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut) .layer(RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT) .activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT)
.build()) .build())
.build(); .build();
@ -267,12 +267,12 @@ public class RnnGradientChecks extends BaseDL4JTest {
.activation(Activation.TANH) .activation(Activation.TANH)
.updater(new NoOp()) .updater(new NoOp())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list()
.layer(simple ? new SimpleRnn.Builder().nOut(layerSize).hasLayerNorm(hasLayerNorm).build() : .layer(simple ? SimpleRnn.builder().nOut(layerSize).hasLayerNorm(hasLayerNorm).build() :
new LSTM.Builder().nOut(layerSize).build()) LSTM.builder().nOut(layerSize).build())
.layer(new LastTimeStep(simple ? new SimpleRnn.Builder().nOut(layerSize).hasLayerNorm(hasLayerNorm).build() : .layer(LastTimeStep.builder().underlying(simple ? SimpleRnn.builder().nOut(layerSize).hasLayerNorm(hasLayerNorm).build() :
new LSTM.Builder().nOut(layerSize).build())) LSTM.builder().nOut(layerSize).build()).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX) .layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn)) .inputType(InputType.recurrent(nIn))
.build(); .build();
@ -334,9 +334,9 @@ public class RnnGradientChecks extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .list()
.layer(new LSTM.Builder().nOut(layerSize).build()) .layer(LSTM.builder().nOut(layerSize).build())
.layer(new TimeDistributed(new DenseLayer.Builder().nOut(layerSize).activation(Activation.SOFTMAX).build())) .layer(TimeDistributed.builder().underlying(DenseLayer.builder().nOut(layerSize).activation(Activation.SOFTMAX).build()).build())
.layer(new RnnOutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX) .layer(RnnOutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn)) .inputType(InputType.recurrent(nIn))
.build(); .build();

40
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/UtilLayerGradientChecks.java
View File

@ -132,27 +132,27 @@ public class UtilLayerGradientChecks extends BaseDL4JTest {
InputType it; InputType it;
switch (inputRank){ switch (inputRank){
case 2: case 2:
l1 = new DenseLayer.Builder().nOut(3).build(); l1 = DenseLayer.builder().nOut(3).build();
l2 = new DenseLayer.Builder().nOut(3).build(); l2 = DenseLayer.builder().nOut(3).build();
l3 = new OutputLayer.Builder().nOut(3).lossFunction(LossFunctions.LossFunction.MSE) l3 = OutputLayer.builder().nOut(3).lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.TANH).build(); .activation(Activation.TANH).build();
it = InputType.feedForward(3); it = InputType.feedForward(3);
break; break;
case 3: case 3:
l1 = new SimpleRnn.Builder().nIn(3).nOut(3).activation(Activation.TANH).build(); l1 = SimpleRnn.builder().nIn(3).nOut(3).activation(Activation.TANH).build();
l2 = new SimpleRnn.Builder().nIn(3).nOut(3).activation(Activation.TANH).build(); l2 = SimpleRnn.builder().nIn(3).nOut(3).activation(Activation.TANH).build();
l3 = new RnnOutputLayer.Builder().nIn(3).nOut(3).lossFunction(LossFunctions.LossFunction.SQUARED_LOSS) l3 = RnnOutputLayer.builder().nIn(3).nOut(3).lossFunction(LossFunctions.LossFunction.SQUARED_LOSS)
.activation(Activation.IDENTITY).build(); .activation(Activation.IDENTITY).build();
it = InputType.recurrent(3); it = InputType.recurrent(3);
break; break;
case 4: case 4:
l1 = new ConvolutionLayer.Builder().nOut(5).convolutionMode(ConvolutionMode.Truncate) l1 = ConvolutionLayer.builder().nOut(5).convolutionMode(ConvolutionMode.Truncate)
.stride(1,1).kernelSize(2,2).padding(0,0) .stride(1,1).kernelSize(2,2).padding(0,0)
.build(); .build();
l2 = new ConvolutionLayer.Builder().nOut(5).convolutionMode(ConvolutionMode.Truncate) l2 = ConvolutionLayer.builder().nOut(5).convolutionMode(ConvolutionMode.Truncate)
.stride(1,1).kernelSize(2,2).padding(0,0) .stride(1,1).kernelSize(2,2).padding(0,0)
.build(); .build();
l3 = new OutputLayer.Builder().nOut(5).lossFunction(LossFunctions.LossFunction.SQUARED_LOSS) l3 = OutputLayer.builder().nOut(5).lossFunction(LossFunctions.LossFunction.SQUARED_LOSS)
.activation(Activation.IDENTITY) .activation(Activation.IDENTITY)
.build(); .build();
it = InputType.convolutional(5,5,1); it = InputType.convolutional(5,5,1);
@ -162,20 +162,20 @@ public class UtilLayerGradientChecks extends BaseDL4JTest {
} }
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.updater(new NoOp()) .updater(new NoOp())
.activation(Activation.TANH) .activation(Activation.TANH)
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0,2)) .dist(new NormalDistribution(0, 2))
.list() .list()
.layer(l1) .layer(l1)
.layer(new MaskLayer()) .layer(MaskLayer.builder().build())
.layer(l2) .layer(l2)
.layer(l3) .layer(l3)
.inputType(it) .inputType(it)
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -201,14 +201,14 @@ public class UtilLayerGradientChecks extends BaseDL4JTest {
.seed(12345) .seed(12345)
.updater(Updater.NONE.getIUpdaterWithDefaultConfig()) .updater(Updater.NONE.getIUpdaterWithDefaultConfig())
.list() .list()
.layer(new DenseLayer.Builder().nIn(10).nOut(10) .layer(DenseLayer.builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()) .activation(Activation.TANH).weightInit(WeightInit.XAVIER).build())
.layer(new FrozenLayerWithBackprop(new DenseLayer.Builder().nIn(10).nOut(10) .layer(FrozenLayerWithBackprop.builder().underlying(DenseLayer.builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build())) .activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()).build())
.layer(new FrozenLayerWithBackprop( .layer(FrozenLayerWithBackprop.builder().underlying(
new DenseLayer.Builder().nIn(10).nOut(10).activation(Activation.TANH) DenseLayer.builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build())) .weightInit(WeightInit.XAVIER).build()).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(10).build()) .activation(Activation.SOFTMAX).nIn(10).nOut(10).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf2); MultiLayerNetwork net = new MultiLayerNetwork(conf2);

10
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/VaeGradientCheckTests.java
View File

@ -99,14 +99,14 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.l2Bias(biasL2[i]).l1Bias(biasL1[i]) .l2Bias(biasL2[i]).l1Bias(biasL1[i])
.updater(new NoOp()).seed(12345L).list() .updater(new NoOp()).seed(12345L).list()
.layer(0, new VariationalAutoencoder.Builder().nIn(4) .layer(0, VariationalAutoencoder.builder().nIn(4)
.nOut(3).encoderLayerSizes(encoderSizes) .nOut(3).encoderLayerSizes(encoderSizes)
.decoderLayerSizes(decoderSizes) .decoderLayerSizes(decoderSizes)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
.activation(afn) .activation(afn)
.build()) .build())
.layer(1, new OutputLayer.Builder(lf) .layer(1, OutputLayer.builder(lf)
.activation(outputActivation).nIn(3).nOut(3) .activation(outputActivation).nIn(3).nOut(3)
.dist(new NormalDistribution(0, 1)) .dist(new NormalDistribution(0, 1))
@ -173,7 +173,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.l1(l1).l2Bias(biasL2[i]).l1Bias(biasL1[i]).updater(new NoOp()) .l1(l1).l2Bias(biasL2[i]).l1Bias(biasL1[i]).updater(new NoOp())
.seed(12345L).weightInit(WeightInit.XAVIER).list() .seed(12345L).weightInit(WeightInit.XAVIER).list()
.layer(0, new VariationalAutoencoder.Builder().nIn(4).nOut(3) .layer(0, VariationalAutoencoder.builder().nIn(4).nOut(3)
.encoderLayerSizes(encoderSizes).decoderLayerSizes(decoderSizes) .encoderLayerSizes(encoderSizes).decoderLayerSizes(decoderSizes)
.pzxActivationFunction(pzxAfn) .pzxActivationFunction(pzxAfn)
.reconstructionDistribution( .reconstructionDistribution(
@ -263,7 +263,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.seed(12345L).dist(new NormalDistribution(0, 1)) .seed(12345L).dist(new NormalDistribution(0, 1))
.list().layer(0, .list().layer(0,
new VariationalAutoencoder.Builder().nIn(inOutSize).nOut(3) VariationalAutoencoder.builder().nIn(inOutSize).nOut(3)
.encoderLayerSizes(4).decoderLayerSizes(3) .encoderLayerSizes(4).decoderLayerSizes(3)
.pzxActivationFunction(Activation.TANH) .pzxActivationFunction(Activation.TANH)
.reconstructionDistribution( .reconstructionDistribution(
@ -306,7 +306,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.updater(new NoOp()) .updater(new NoOp())
.seed(12345L).weightInit(WeightInit.XAVIER).list() .seed(12345L).weightInit(WeightInit.XAVIER).list()
.layer(0, new VariationalAutoencoder.Builder().nIn(4).nOut(3).encoderLayerSizes(2, 3) .layer(0, VariationalAutoencoder.builder().nIn(4).nOut(3).encoderLayerSizes(2, 3)
.decoderLayerSizes(4, 3).pzxActivationFunction(Activation.TANH) .decoderLayerSizes(4, 3).pzxActivationFunction(Activation.TANH)
.reconstructionDistribution( .reconstructionDistribution(
new GaussianReconstructionDistribution(Activation.TANH)) new GaussianReconstructionDistribution(Activation.TANH))

19
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/YoloGradientCheckTests.java
View File

@ -115,12 +115,11 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
.activation(a) .activation(a)
.l1(l1[i]).l2(l2[i]) .l1(l1[i]).l2(l2[i])
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.list() .layer(ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1)
.layer(new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1) .convFormat(format)
.dataFormat(format)
.nIn(depthIn).nOut(yoloDepth).build())//output: (5-2+0)/1+1 = 4 .nIn(depthIn).nOut(yoloDepth).build())//output: (5-2+0)/1+1 = 4
.layer(new Yolo2OutputLayer.Builder() .layer(Yolo2OutputLayer.builder()
.boundingBoxPriors(bbPrior) .boundingBoxes(bbPrior)
.build()) .build())
.inputType(InputType.convolutional(h, w, depthIn, format)) .inputType(InputType.convolutional(h, w, depthIn, format))
.build(); .build();
@ -234,11 +233,11 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
.dist(new GaussianDistribution(0,0.1)) .dist(new GaussianDistribution(0,0.1))
.seed(12345) .seed(12345)
.list() .list()
.layer(new ConvolutionLayer.Builder().kernelSize(3,3).stride(1,1).nOut(4).build()) .layer(ConvolutionLayer.builder().kernelSize(3,3).stride(1,1).nOut(4).build())
.layer(new SubsamplingLayer.Builder().kernelSize(2,2).stride(2,2).build()) .layer(SubsamplingLayer.builder().kernelSize(2,2).stride(2,2).build())
.layer(new ConvolutionLayer.Builder().activation(Activation.IDENTITY).kernelSize(3,3).stride(1,1).nOut(depthOut).build()) .layer(ConvolutionLayer.builder().activation(Activation.IDENTITY).kernelSize(3,3).stride(1,1).nOut(depthOut).build())
.layer(new Yolo2OutputLayer.Builder() .layer(Yolo2OutputLayer.builder()
.boundingBoxPriors(bbPriors) .boundingBoxes(bbPriors)
.build()) .build())
.inputType(InputType.convolutional(h,w,c)) .inputType(InputType.convolutional(h,w,c))
.build(); .build();

86
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/ComputationGraphConfigurationTest.java
View File

@ -62,9 +62,9 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1)).updater(new NoOp()) .dist(new NormalDistribution(0, 1)).updater(new NoOp())
.graphBuilder().addInputs("input") .graphBuilder().addInputs("input")
.appendLayer("firstLayer", .appendLayer("firstLayer",
new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build()) DenseLayer.builder().nIn(4).nOut(5).activation(Activation.TANH).build())
.addLayer("outputLayer", .addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5).nOut(3).build(), .activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
"firstLayer") "firstLayer")
.setOutputs("outputLayer").build(); .setOutputs("outputLayer").build();
@ -83,20 +83,20 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.graphBuilder().addInputs("input") .graphBuilder().addInputs("input")
.addLayer("cnn1", .addLayer("cnn1",
new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5) ConvolutionLayer.builder(2, 2).stride(2, 2).nIn(1).nOut(5)
.build(), .build(),
"input") "input")
.addLayer("cnn2", .addLayer("cnn2",
new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5) ConvolutionLayer.builder(2, 2).stride(2, 2).nIn(1).nOut(5)
.build(), .build(),
"input") "input")
.addLayer("max1", .addLayer("max1",
new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2).build(), .kernelSize(2, 2).build(),
"cnn1", "cnn2") "cnn1", "cnn2")
.addLayer("dnn1", new DenseLayer.Builder().nOut(7).build(), "max1") .addLayer("dnn1", DenseLayer.builder().nOut(7).build(), "max1")
.addLayer("max2", new SubsamplingLayer.Builder().build(), "max1") .addLayer("max2", SubsamplingLayer.builder().build(), "max1")
.addLayer("output", new OutputLayer.Builder().nIn(7).nOut(10).activation(Activation.SOFTMAX).build(), "dnn1", .addLayer("output", OutputLayer.builder().nIn(7).nOut(10).activation(Activation.SOFTMAX).build(), "dnn1",
"max2") "max2")
.setOutputs("output") .setOutputs("output")
.inputPreProcessor("cnn1", new FeedForwardToCnnPreProcessor(32, 32, 3)) .inputPreProcessor("cnn1", new FeedForwardToCnnPreProcessor(32, 32, 3))
@ -119,20 +119,20 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.graphBuilder().addInputs("input1", "input2") .graphBuilder().addInputs("input1", "input2")
.addLayer("cnn1", .addLayer("cnn1",
new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5) ConvolutionLayer.builder(2, 2).stride(2, 2).nIn(1).nOut(5)
.build(), .build(),
"input1") "input1")
.addLayer("cnn2", .addLayer("cnn2",
new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5) ConvolutionLayer.builder(2, 2).stride(2, 2).nIn(1).nOut(5)
.build(), .build(),
"input2") "input2")
.addVertex("merge1", new MergeVertex(), "cnn1", "cnn2") .addVertex("merge1", new MergeVertex(), "cnn1", "cnn2")
.addVertex("subset1", new SubsetVertex(0, 1), "merge1") .addVertex("subset1", new SubsetVertex(0, 1), "merge1")
.addLayer("dense1", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1") .addLayer("dense1", DenseLayer.builder().nIn(20).nOut(5).build(), "subset1")
.addLayer("dense2", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1") .addLayer("dense2", DenseLayer.builder().nIn(20).nOut(5).build(), "subset1")
.addVertex("add", new ElementWiseVertex(ElementWiseVertex.Op.Add), "dense1", .addVertex("add", new ElementWiseVertex(ElementWiseVertex.Op.Add), "dense1",
"dense2") "dense2")
.addLayer("out", new OutputLayer.Builder().nIn(1).nOut(1).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build(), "add") .addLayer("out", OutputLayer.builder().nIn(1).nOut(1).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build(), "add")
.setOutputs("out").build(); .setOutputs("out").build();
String json = conf.toJson(); String json = conf.toJson();
@ -150,8 +150,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test no inputs for a layer: //Test no inputs for a layer:
try { try {
NeuralNetConfiguration.builder().graphBuilder().addInputs("input1") NeuralNetConfiguration.builder().graphBuilder().addInputs("input1")
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1") .addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build()).setOutputs("out") .addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build()).setOutputs("out")
.build(); .build();
fail("No exception thrown for invalid configuration"); fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) { } catch (IllegalStateException e) {
@ -162,8 +162,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
// Use appendLayer on first layer // Use appendLayer on first layer
try { try {
NeuralNetConfiguration.builder().graphBuilder() NeuralNetConfiguration.builder().graphBuilder()
.appendLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build()) .appendLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build())
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build()).setOutputs("out") .addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build()).setOutputs("out")
.build(); .build();
fail("No exception thrown for invalid configuration"); fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) { } catch (IllegalStateException e) {
@ -174,8 +174,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test no network inputs //Test no network inputs
try { try {
NeuralNetConfiguration.builder().graphBuilder() NeuralNetConfiguration.builder().graphBuilder()
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1") .addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build(), "dense1") .addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build(), "dense1")
.setOutputs("out").build(); .setOutputs("out").build();
fail("No exception thrown for invalid configuration"); fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) { } catch (IllegalStateException e) {
@ -186,8 +186,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test no network outputs //Test no network outputs
try { try {
NeuralNetConfiguration.builder().graphBuilder().addInputs("input1") NeuralNetConfiguration.builder().graphBuilder().addInputs("input1")
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1") .addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build(), "dense1").build(); .addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build(), "dense1").build();
fail("No exception thrown for invalid configuration"); fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) { } catch (IllegalStateException e) {
//OK - exception is good //OK - exception is good
@ -197,8 +197,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test: invalid input //Test: invalid input
try { try {
NeuralNetConfiguration.builder().graphBuilder().addInputs("input1") NeuralNetConfiguration.builder().graphBuilder().addInputs("input1")
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1") .addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build(), "thisDoesntExist") .addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build(), "thisDoesntExist")
.setOutputs("out").build(); .setOutputs("out").build();
fail("No exception thrown for invalid configuration"); fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) { } catch (IllegalStateException e) {
@ -209,10 +209,10 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test: graph with cycles //Test: graph with cycles
try { try {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("input1") ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("input1")
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1", "dense3") .addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1", "dense3")
.addLayer("dense2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "dense1") .addLayer("dense2", DenseLayer.builder().nIn(2).nOut(2).build(), "dense1")
.addLayer("dense3", new DenseLayer.Builder().nIn(2).nOut(2).build(), "dense2") .addLayer("dense3", DenseLayer.builder().nIn(2).nOut(2).build(), "dense2")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).lossFunction(LossFunctions.LossFunction.MSE).build(), "dense1") .addLayer("out", OutputLayer.builder().nIn(2).nOut(2).lossFunction(LossFunctions.LossFunction.MSE).build(), "dense1")
.setOutputs("out").build(); .setOutputs("out").build();
//Cycle detection happens in ComputationGraph.init() //Cycle detection happens in ComputationGraph.init()
ComputationGraph graph = new ComputationGraph(conf); ComputationGraph graph = new ComputationGraph(conf);
@ -229,20 +229,20 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration.builder().graphBuilder().addInputs("input1", "input2") NeuralNetConfiguration.builder().graphBuilder().addInputs("input1", "input2")
.setInputTypes(new InputType.InputTypeRecurrent(10, 12)) .setInputTypes(new InputType.InputTypeRecurrent(10, 12))
.addLayer("cnn1", .addLayer("cnn1",
new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5) ConvolutionLayer.builder(2, 2).stride(2, 2).nIn(1).nOut(5)
.build(), .build(),
"input1") "input1")
.addLayer("cnn2", .addLayer("cnn2",
new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5) ConvolutionLayer.builder(2, 2).stride(2, 2).nIn(1).nOut(5)
.build(), .build(),
"input2") "input2")
.addVertex("merge1", new MergeVertex(), "cnn1", "cnn2") .addVertex("merge1", new MergeVertex(), "cnn1", "cnn2")
.addVertex("subset1", new SubsetVertex(0, 1), "merge1") .addVertex("subset1", new SubsetVertex(0, 1), "merge1")
.addLayer("dense1", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1") .addLayer("dense1", DenseLayer.builder().nIn(20).nOut(5).build(), "subset1")
.addLayer("dense2", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1") .addLayer("dense2", DenseLayer.builder().nIn(20).nOut(5).build(), "subset1")
.addVertex("add", new ElementWiseVertex(ElementWiseVertex.Op.Add), "dense1", .addVertex("add", new ElementWiseVertex(ElementWiseVertex.Op.Add), "dense1",
"dense2") "dense2")
.addLayer("out", new OutputLayer.Builder().nIn(1).nOut(1).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build(), "add") .addLayer("out", OutputLayer.builder().nIn(1).nOut(1).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build(), "add")
.setOutputs("out").build(); .setOutputs("out").build();
fail("No exception thrown for invalid configuration"); fail("No exception thrown for invalid configuration");
} catch (IllegalArgumentException e) { } catch (IllegalArgumentException e) {
@ -283,9 +283,9 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
@Test @Test
public void testOutputOrderDoesntChangeWhenCloning() { public void testOutputOrderDoesntChangeWhenCloning() {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in") ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in")
.addLayer("out1", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in") .addLayer("out1", OutputLayer.builder().nIn(1).nOut(1).build(), "in")
.addLayer("out2", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in") .addLayer("out2", OutputLayer.builder().nIn(1).nOut(1).build(), "in")
.addLayer("out3", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in") .addLayer("out3", OutputLayer.builder().nIn(1).nOut(1).build(), "in")
.validateOutputLayerConfig(false) .validateOutputLayerConfig(false)
.setOutputs("out1", "out2", "out3").build(); .setOutputs("out1", "out2", "out3").build();
@ -301,14 +301,14 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
public void testAllowDisconnectedLayers() { public void testAllowDisconnectedLayers() {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in") ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in")
.addLayer("bidirectional", .addLayer("bidirectional",
new Bidirectional(new LSTM.Builder().activation(Activation.TANH).nOut(10).build()), Bidirectional.builder(LSTM.builder().activation(Activation.TANH).nOut(10).build()).build(),
"in") "in")
.addLayer("out", new RnnOutputLayer.Builder().nOut(6) .addLayer("out", RnnOutputLayer.builder().nOut(6)
.lossFunction(LossFunctions.LossFunction.MCXENT) .lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build(), "bidirectional") .build(), "bidirectional")
.addLayer("disconnected_layer", .addLayer("disconnected_layer",
new Bidirectional(new LSTM.Builder().activation(Activation.TANH).nOut(10).build()), Bidirectional.builder(LSTM.builder().activation(Activation.TANH).nOut(10).build()).build(),
"in") "in")
.setOutputs("out") .setOutputs("out")
.setInputTypes(new InputType.InputTypeRecurrent(10, 12)) .setInputTypes(new InputType.InputTypeRecurrent(10, 12))
@ -323,9 +323,9 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
public void testBidirectionalGraphSummary() { public void testBidirectionalGraphSummary() {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in") ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in")
.addLayer("bidirectional", .addLayer("bidirectional",
new Bidirectional(new LSTM.Builder().activation(Activation.TANH).nOut(10).build()), Bidirectional.builder(LSTM.builder().activation(Activation.TANH).nOut(10).build()).build(),
"in") "in")
.addLayer("out", new RnnOutputLayer.Builder().nOut(6) .addLayer("out", RnnOutputLayer.builder().nOut(6)
.lossFunction(LossFunctions.LossFunction.MCXENT) .lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX) .activation(Activation.SOFTMAX)
.build(), "bidirectional") .build(), "bidirectional")
@ -411,10 +411,10 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration.builder() NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in") .layer("0", DenseLayer.builder().nIn(10).nOut(10).build(), "in")
.layer("1", .layer("1",
!lossLayer ? new OutputLayer.Builder().nIn(10).nOut(nOut[i]).activation(activations[i]).lossFunction(lf[i]).build() !lossLayer ? OutputLayer.builder().nIn(10).nOut(nOut[i]).activation(activations[i]).lossFunction(lf[i]).build()
: new LossLayer.Builder().activation(activations[i]).lossFunction(lf[i]).build(), "0") : LossLayer.builder().activation(activations[i]).lossFunction(lf[i].getILossFunction()).build(), "0")
.setOutputs("1") .setOutputs("1")
.validateOutputLayerConfig(validate) .validateOutputLayerConfig(validate)
.build(); .build();

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/JsonTest.java
View File

@ -99,8 +99,8 @@ public class JsonTest extends BaseDL4JTest {
for (int i = 0; i < lossFunctions.length; i++) { for (int i = 0; i < lossFunctions.length; i++) {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(Updater.ADAM.getIUpdaterWithDefaultConfig()) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(Updater.ADAM.getIUpdaterWithDefaultConfig())
.layer(0, new DenseLayer.Builder().nIn(4).nOut(nOut[i]).activation(Activation.TANH).build()) .layer(0, DenseLayer.builder().nIn(4).nOut(nOut[i]).activation(Activation.TANH).build())
.layer(1, new LossLayer.Builder().lossFunction(lossFunctions[i]) .layer(1, LossLayer.builder().lossFunction(lossFunctions[i])
.activation(outputActivationFn[i]).build()) .activation(outputActivationFn[i]).build())
.validateOutputLayerConfig(false).build(); .validateOutputLayerConfig(false).build();

78
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/MultiLayerNeuralNetConfigurationTest.java
View File

@ -69,9 +69,9 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
private static NeuralNetConfiguration getConf() { private static NeuralNetConfiguration getConf() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345L) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345L)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2) .layer(0, DenseLayer.builder().nIn(2).nOut(2)
.dist(new NormalDistribution(0, 1)).build()) .dist(new NormalDistribution(0, 1)).build())
.layer(1, new OutputLayer.Builder().nIn(2).nOut(1) .layer(1, OutputLayer.builder().nIn(2).nOut(1)
.activation(Activation.TANH) .activation(Activation.TANH)
.dist(new NormalDistribution(0, 1)).lossFunction(LossFunctions.LossFunction.MSE) .dist(new NormalDistribution(0, 1)).lossFunction(LossFunctions.LossFunction.MSE)
.build()) .build())
@ -82,7 +82,7 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
@Test @Test
public void testJson() throws Exception { public void testJson() throws Exception {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().dist(new NormalDistribution(1, 1e-1)).build()) .layer(0, DenseLayer.builder().dist(new NormalDistribution(1, 1e-1)).build())
.inputPreProcessor(0, new CnnToFeedForwardPreProcessor()).build(); .inputPreProcessor(0, new CnnToFeedForwardPreProcessor()).build();
String json = conf.toJson(); String json = conf.toJson();
@ -123,17 +123,17 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
.l1(1e-1).l2(2e-4).weightNoise(new DropConnect(0.5)).miniBatch(true) .l1(1e-1).l2(2e-4).weightNoise(new DropConnect(0.5)).miniBatch(true)
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT) .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.layer(0, .layer(0,
new ConvolutionLayer.Builder(5, 5).nOut(5).dropOut(0.5).weightInit(WeightInit.XAVIER) ConvolutionLayer.builder(5, 5).nOut(5).dropOut(0.5).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2}) .layer(1, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.build()) .build())
.layer(2, .layer(2,
new ConvolutionLayer.Builder(3, 3).nOut(10).dropOut(0.5).weightInit(WeightInit.XAVIER) ConvolutionLayer.builder(3, 3).nOut(10).dropOut(0.5).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2}) .layer(3, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.build()) .build())
.layer(4, new DenseLayer.Builder().nOut(100).activation(Activation.RELU).build()) .layer(4, DenseLayer.builder().nOut(100).activation(Activation.RELU).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .layer(5, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX) .nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.build()) .build())
@ -157,15 +157,15 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed) NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed)
.l1(1e-1).l2(2e-4).dropOut(0.5).miniBatch(true) .l1(1e-1).l2(2e-4).dropOut(0.5).miniBatch(true)
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT) .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.layer(new ConvolutionLayer.Builder(5, 5).nOut(5).dropOut(0.5).weightInit(WeightInit.XAVIER) .layer(ConvolutionLayer.builder(5, 5).nOut(5).dropOut(0.5).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(new Upsampling2D.Builder().size(2).build()) .layer(Upsampling2D.builder().size(2).build())
.layer(2, .layer(2,
new ConvolutionLayer.Builder(3, 3).nOut(10).dropOut(0.5).weightInit(WeightInit.XAVIER) ConvolutionLayer.builder(3, 3).nOut(10).dropOut(0.5).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(new Upsampling2D.Builder().size(2).build()) .layer(Upsampling2D.builder().size(2).build())
.layer(4, new DenseLayer.Builder().nOut(100).activation(Activation.RELU).build()) .layer(4, DenseLayer.builder().nOut(100).activation(Activation.RELU).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .layer(5, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX) .nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.build()) .build())
@ -181,9 +181,9 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
public void testGlobalPoolingJson() { public void testGlobalPoolingJson() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new NoOp()) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).seed(12345L) .dist(new NormalDistribution(0, 1.0)).seed(12345L)
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(5).build()) .layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(5).build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(PoolingType.PNORM).pnorm(3).build()) .layer(1, GlobalPoolingLayer.builder().poolingType(PoolingType.PNORM).pnorm(3).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(3).build()) .activation(Activation.SOFTMAX).nOut(3).build())
.inputType(InputType.convolutional(32, 32, 1)).build(); .inputType(InputType.convolutional(32, 32, 1)).build();
@ -196,7 +196,7 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
@Test @Test
public void testYaml() throws Exception { public void testYaml() throws Exception {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().dist(new NormalDistribution(1, 1e-1)).build()) .layer(0, DenseLayer.builder().dist(new NormalDistribution(1, 1e-1)).build())
.inputPreProcessor(0, new CnnToFeedForwardPreProcessor()).build(); .inputPreProcessor(0, new CnnToFeedForwardPreProcessor()).build();
String json = conf.toYaml(); String json = conf.toYaml();
NeuralNetConfiguration from = NeuralNetConfiguration.fromYaml(json); NeuralNetConfiguration from = NeuralNetConfiguration.fromYaml(json);
@ -226,8 +226,8 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
@Test @Test
public void testClone() { public void testClone() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().build()) .layer(0, DenseLayer.builder().build())
.layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).build()) .layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).build())
.inputPreProcessor(1, new CnnToFeedForwardPreProcessor()).build(); .inputPreProcessor(1, new CnnToFeedForwardPreProcessor()).build();
NeuralNetConfiguration conf2 = conf.clone(); NeuralNetConfiguration conf2 = conf.clone();
@ -301,8 +301,8 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.layer(1, new DenseLayer.Builder().nIn(3).nOut(4).build()) .layer(1, DenseLayer.builder().nIn(3).nOut(4).build())
.layer(2, new OutputLayer.Builder().nIn(4).nOut(5).build()) .layer(2, OutputLayer.builder().nIn(4).nOut(5).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -317,8 +317,8 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
try { try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build()) .layer(0, DenseLayer.builder().nIn(3).nOut(4).build())
.layer(2, new OutputLayer.Builder().nIn(4).nOut(5).build()) .layer(2, OutputLayer.builder().nIn(4).nOut(5).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -336,8 +336,8 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
public void testListOverloads() { public void testListOverloads() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build()) .layer(0, DenseLayer.builder().nIn(3).nOut(4).build())
.layer(1, new OutputLayer.Builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build()) .layer(1, OutputLayer.builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -350,16 +350,16 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
assertEquals(5, ol.getNOut()); assertEquals(5, ol.getNOut());
NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder().seed(12345)
.layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build()) .layer(0, DenseLayer.builder().nIn(3).nOut(4).build())
.layer(1, new OutputLayer.Builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build()) .layer(1, OutputLayer.builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build())
.build(); .build();
MultiLayerNetwork net2 = new MultiLayerNetwork(conf2); MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
net2.init(); net2.init();
NeuralNetConfiguration conf3 = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf3 = NeuralNetConfiguration.builder().seed(12345)
.layer(new DenseLayer.Builder().nIn(3).nOut(4).build()) .layer(DenseLayer.builder().nIn(3).nOut(4).build())
.layer( .layer(
new OutputLayer.Builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build()) OutputLayer.builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build())
.build(); .build();
MultiLayerNetwork net3 = new MultiLayerNetwork(conf3); MultiLayerNetwork net3 = new MultiLayerNetwork(conf3);
net3.init(); net3.init();
@ -375,14 +375,16 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.updater(new Adam(1e-2)) .updater(new Adam(1e-2))
.biasUpdater(new Adam(0.5)) .biasUpdater(new Adam(0.5))
.layer(0, new ConvolutionLayer.Builder(5, 5).nOut(5).weightInit(WeightInit.XAVIER) .layer(0, ConvolutionLayer.builder(5, 5).nOut(5).weightInit(WeightInit.XAVIER)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(1, new DenseLayer.Builder().nOut(100).activation(Activation.RELU).build()) .layer(1, DenseLayer.builder().nOut(100).activation(Activation.RELU).build())
.layer(2, new DenseLayer.Builder().nOut(100).activation(Activation.RELU).build()) .layer(2, DenseLayer.builder().nOut(100).activation(Activation.RELU).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10) .layer(3, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10)
.weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build()) .weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(28, 28, 1)).build(); .inputType(InputType.convolutional(28, 28, 1)).build();
conf.init();
BaseLayerConfiguration l0 = (BaseLayerConfiguration) conf.getConf(0).getLayer(); BaseLayerConfiguration l0 = (BaseLayerConfiguration) conf.getConf(0).getLayer();
BaseLayerConfiguration l1 = (BaseLayerConfiguration) conf.getConf(1).getLayer(); BaseLayerConfiguration l1 = (BaseLayerConfiguration) conf.getConf(1).getLayer();
BaseLayerConfiguration l2 = (BaseLayerConfiguration) conf.getConf(2).getLayer(); BaseLayerConfiguration l2 = (BaseLayerConfiguration) conf.getConf(2).getLayer();
@ -432,10 +434,10 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
try { try {
NeuralNetConfiguration.builder() NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(10).nOut(10).build()) .layer(DenseLayer.builder().nIn(10).nOut(10).build())
.layer(!lossLayer ? new OutputLayer.Builder().nIn(10).nOut(nOut[i]) .layer(!lossLayer ? OutputLayer.builder().nIn(10).nOut(nOut[i])
.activation(activations[i]).lossFunction(lf[i]).build() .activation(activations[i]).lossFunction(lf[i]).build()
: new LossLayer.Builder().activation(activations[i]).lossFunction(lf[i]) : LossLayer.builder().activation(activations[i]).lossFunction(lf[i].getILossFunction())
.build()) .build())
.validateOutputLayerConfig(validate) .validateOutputLayerConfig(validate)
.build(); .build();

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/MultiNeuralNetConfLayerBuilderTest.java
View File

@ -67,9 +67,9 @@ public class MultiNeuralNetConfLayerBuilderTest extends BaseDL4JTest {
NeuralNetConfiguration multiConf1 = NeuralNetConfiguration multiConf1 =
NeuralNetConfiguration.builder() NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().nIn(newNumIn).nOut(newNumOut).activation(act) .layer(0, DenseLayer.builder().nIn(newNumIn).nOut(newNumOut).activation(act)
.build()) .build())
.layer(1, new DenseLayer.Builder().nIn(newNumIn + 1).nOut(newNumOut + 1) .layer(1, DenseLayer.builder().nIn(newNumIn + 1).nOut(newNumOut + 1)
.activation(act).build()) .activation(act).build())
.build(); .build();
NeuralNetConfiguration firstLayer = multiConf1.getConf(0).getNetConfiguration(); NeuralNetConfiguration firstLayer = multiConf1.getConf(0).getNetConfiguration();

20
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/NeuralNetConfigurationTest.java
View File

@ -113,7 +113,7 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
@Test @Test
public void testRNG() { public void testRNG() {
DenseLayer layer = new DenseLayer.Builder().nIn(trainingSet.numInputs()).nOut(trainingSet.numOutcomes()) DenseLayer layer = DenseLayer.builder().nIn(trainingSet.numInputs()).nOut(trainingSet.numOutcomes())
.weightInit(WeightInit.UNIFORM).activation(Activation.TANH).build(); .weightInit(WeightInit.UNIFORM).activation(Activation.TANH).build();
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
@ -125,7 +125,7 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
INDArray modelWeights = model.getParam(DefaultParamInitializer.WEIGHT_KEY); INDArray modelWeights = model.getParam(DefaultParamInitializer.WEIGHT_KEY);
DenseLayer layer2 = new DenseLayer.Builder().nIn(trainingSet.numInputs()).nOut(trainingSet.numOutcomes()) DenseLayer layer2 = DenseLayer.builder().nIn(trainingSet.numInputs()).nOut(trainingSet.numOutcomes())
.weightInit(WeightInit.UNIFORM).activation(Activation.TANH).build(); .weightInit(WeightInit.UNIFORM).activation(Activation.TANH).build();
NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder().seed(123) NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder().seed(123)
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).layer(layer2).build(); .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).layer(layer2).build();
@ -197,7 +197,7 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
private static NeuralNetConfiguration getConfig(int nIn, int nOut, IWeightInit weightInit, boolean pretrain) { private static NeuralNetConfiguration getConfig(int nIn, int nOut, IWeightInit weightInit, boolean pretrain) {
DenseLayer layer = new DenseLayer.Builder().nIn(nIn).nOut(nOut).weightInit(weightInit) DenseLayer layer = DenseLayer.builder().nIn(nIn).nOut(nOut).weightInit(weightInit)
.activation(Activation.TANH).build(); .activation(Activation.TANH).build();
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
@ -226,10 +226,10 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
INDArray gradientW = Nd4j.ones(nIns[0], nOuts[0]); INDArray gradientW = Nd4j.ones(nIns[0], nOuts[0]);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3)) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3))
.layer(0, new DenseLayer.Builder().nIn(nIns[0]).nOut(nOuts[0]) .layer(0, DenseLayer.builder().nIn(nIns[0]).nOut(nOuts[0])
.updater(new Sgd(lr)).biasUpdater(new Sgd(biasLr)).build()) .updater(new Sgd(lr)).biasUpdater(new Sgd(biasLr)).build())
.layer(1, new BatchNormalization.Builder().nIn(nIns[1]).nOut(nOuts[1]).updater(new Sgd(0.7)).build()) .layer(1,BatchNormalization.builder().nIn(nIns[1]).nOut(nOuts[1]).updater(new Sgd(0.7)).build())
.layer(2, new OutputLayer.Builder().nIn(nIns[2]).nOut(nOuts[2]).lossFunction(LossFunctions.LossFunction.MSE).build()) .layer(2, OutputLayer.builder().nIn(nIns[2]).nOut(nOuts[2]).lossFunction(LossFunctions.LossFunction.MSE).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -287,9 +287,9 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l1(l1) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l1(l1)
.l2(l2) .l2(l2)
.layer(0, new DenseLayer.Builder().nIn(nIns[0]).nOut(nOuts[0]).build()) .layer(0, DenseLayer.builder().nIn(nIns[0]).nOut(nOuts[0]).build())
.layer(1, new BatchNormalization.Builder().nIn(nIns[1]).nOut(nOuts[1]).l2(0.5).build()) .layer(1,BatchNormalization.builder().nIn(nIns[1]).nOut(nOuts[1]).l2(0.5).build())
.layer(2, new OutputLayer.Builder().nIn(nIns[2]).nOut(nOuts[2]).lossFunction(LossFunctions.LossFunction.MSE).build()) .layer(2, OutputLayer.builder().nIn(nIns[2]).nOut(nOuts[2]).lossFunction(LossFunctions.LossFunction.MSE).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -318,7 +318,7 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
public void testLayerPretrainConfig() { public void testLayerPretrainConfig() {
boolean pretrain = true; boolean pretrain = true;
VariationalAutoencoder layer = new VariationalAutoencoder.Builder() VariationalAutoencoder layer = VariationalAutoencoder.builder()
.nIn(10).nOut(5).updater(new Sgd(1e-1)) .nIn(10).nOut(5).updater(new Sgd(1e-1))
.lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build(); .lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build();

36
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/constraints/TestConstraints.java
View File

@ -48,6 +48,7 @@ import org.nd4j.linalg.learning.config.RmsProp;
import org.nd4j.linalg.learning.config.Sgd; import org.nd4j.linalg.learning.config.Sgd;
import org.nd4j.linalg.lossfunctions.LossFunctions; import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.util.List;
import java.util.Map; import java.util.Map;
import static org.junit.jupiter.api.Assertions.assertEquals; import static org.junit.jupiter.api.Assertions.assertEquals;
@ -71,9 +72,9 @@ public class TestConstraints extends BaseDL4JTest {
.updater(new Sgd(0.0)) .updater(new Sgd(0.0))
.dist(new NormalDistribution(0, 5)) .dist(new NormalDistribution(0, 5))
.layer(new LSTM.Builder().nIn(12).nOut(10) .layer(LSTM.builder().nIn(12).nOut(10)
.constrainRecurrent(lc).build()) .constrainRecurrent(lc).build())
.layer(new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build()) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -124,9 +125,9 @@ public class TestConstraints extends BaseDL4JTest {
.dist(new NormalDistribution(0, 5)) .dist(new NormalDistribution(0, 5))
.biasInit(10.0) .biasInit(10.0)
.layer(new DenseLayer.Builder().nIn(12).nOut(10) .layer(DenseLayer.builder().nIn(12).nOut(10)
.constrainBias(lc).build()) .constrainBias(lc).build())
.layer(new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build()) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -176,9 +177,9 @@ public class TestConstraints extends BaseDL4JTest {
.updater(new Sgd(0.0)) .updater(new Sgd(0.0))
.dist(new NormalDistribution(0, 5)) .dist(new NormalDistribution(0, 5))
.layer(new DenseLayer.Builder().nIn(12).nOut(10) .layer(DenseLayer.builder().nIn(12).nOut(10)
.constrainWeights(lc).build()) .constrainWeights(lc).build())
.layer(new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build()) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -229,9 +230,10 @@ public class TestConstraints extends BaseDL4JTest {
.dist(new NormalDistribution(0, 5)) .dist(new NormalDistribution(0, 5))
.biasInit(0.2) .biasInit(0.2)
.layer(new DenseLayer.Builder().nIn(12).nOut(10) .layer(DenseLayer.builder().nIn(12).nOut(10)
.constrainAllParameters(lc).build()) .allParamConstraints(List.of(lc))
.layer(new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build()) .build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -290,9 +292,9 @@ public class TestConstraints extends BaseDL4JTest {
.dist(new NormalDistribution(0, 5)) .dist(new NormalDistribution(0, 5))
.biasInit(0.2) .biasInit(0.2)
.layer(new DenseLayer.Builder().nIn(12).nOut(10) .layer(DenseLayer.builder().nIn(12).nOut(10)
.constrainWeights(lc).constrainBias(lc).build()) .constrainWeights(lc).constrainBias(lc).build())
.layer(new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build()) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -351,8 +353,8 @@ public class TestConstraints extends BaseDL4JTest {
.dist(new NormalDistribution(0,5)) .dist(new NormalDistribution(0,5))
.biasInit(1) .biasInit(1)
.layer(new DenseLayer.Builder().nIn(12).nOut(10).build()) .layer(DenseLayer.builder().nIn(12).nOut(10).build())
.layer(new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build()) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -406,7 +408,7 @@ public class TestConstraints extends BaseDL4JTest {
.graphBuilder() .graphBuilder()
.addInputs("input_lstm", "input_cpc") .addInputs("input_lstm", "input_cpc")
.addLayer("first_lstm_layer", .addLayer("first_lstm_layer",
new LSTM.Builder() LSTM.builder()
.nIn(nIn) .nIn(nIn)
.nOut(lstmLayerSize) .nOut(lstmLayerSize)
.activation(Activation.RELU) .activation(Activation.RELU)
@ -417,7 +419,7 @@ public class TestConstraints extends BaseDL4JTest {
.addVertex("merge", new MergeVertex(), .addVertex("merge", new MergeVertex(),
"lastTimeStep", "input_cpc") "lastTimeStep", "input_cpc")
.addLayer("dense", .addLayer("dense",
new DenseLayer.Builder() DenseLayer.builder()
.constrainWeights(new NonNegativeConstraint()) .constrainWeights(new NonNegativeConstraint())
.nIn(lstmLayerSize + 1) .nIn(lstmLayerSize + 1)
.nOut(lstmLayerSize/2) .nOut(lstmLayerSize/2)
@ -425,7 +427,7 @@ public class TestConstraints extends BaseDL4JTest {
.build(), .build(),
"merge") "merge")
.addLayer("second_dense", .addLayer("second_dense",
new DenseLayer.Builder() DenseLayer.builder()
.constrainWeights(new NonNegativeConstraint()) .constrainWeights(new NonNegativeConstraint())
.nIn(lstmLayerSize/2) .nIn(lstmLayerSize/2)
.nOut(lstmLayerSize/8) .nOut(lstmLayerSize/8)
@ -433,7 +435,7 @@ public class TestConstraints extends BaseDL4JTest {
.build(), .build(),
"dense") "dense")
.addLayer("output_layer", .addLayer("output_layer",
new OutputLayer.Builder(LossFunctions.LossFunction.MSE) OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE)
.constrainWeights(new NonNegativeConstraint()) .constrainWeights(new NonNegativeConstraint())
.nIn(lstmLayerSize/8) .nIn(lstmLayerSize/8)
.nOut(1) .nOut(1)

42
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/dropout/TestDropout.java
View File

@ -62,29 +62,29 @@ public class TestDropout extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.dropOut(0.6) .dropOut(0.6)
.layer(new DenseLayer.Builder().nIn(10).nOut(10).build()) .layer(DenseLayer.builder().nIn(10).nOut(10).build())
.layer(new DenseLayer.Builder().nIn(10).nOut(10).dropOut(0.7).build()) .layer(DenseLayer.builder().nIn(10).nOut(10).dropOut(0.7).build())
.layer(new DenseLayer.Builder().nIn(10).nOut(10).dropOut(new AlphaDropout(0.5)).build()) .layer(DenseLayer.builder().nIn(10).nOut(10).dropOut(new AlphaDropout(0.5)).build())
.build(); .build();
assertEquals(new Dropout(0.6), conf.getFlattenedLayerConfigurations().get(0).getIDropout()); assertEquals(new Dropout(0.6), conf.getFlattenedLayerConfigurations().get(0).getDropOut());
assertEquals(new Dropout(0.7), conf.getFlattenedLayerConfigurations().get(1).getIDropout()); assertEquals(new Dropout(0.7), conf.getFlattenedLayerConfigurations().get(1).getDropOut());
assertEquals(new AlphaDropout(0.5), conf.getFlattenedLayerConfigurations().get(2).getIDropout()); assertEquals(new AlphaDropout(0.5), conf.getFlattenedLayerConfigurations().get(2).getDropOut());
ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder()
.dropOut( new Dropout(0.6)) .dropOut( new Dropout(0.6))
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in") .addLayer("0", DenseLayer.builder().nIn(10).nOut(10).build(), "in")
.addLayer("1", new DenseLayer.Builder().nIn(10).nOut(10).dropOut(0.7).build(), "0") .addLayer("1", DenseLayer.builder().nIn(10).nOut(10).dropOut(0.7).build(), "0")
.addLayer("2", new DenseLayer.Builder().nIn(10).nOut(10).dropOut(new AlphaDropout(0.5)).build(), "1") .addLayer("2", DenseLayer.builder().nIn(10).nOut(10).dropOut(new AlphaDropout(0.5)).build(), "1")
.setOutputs("2") .setOutputs("2")
.build(); .build();
assertEquals(new Dropout(0.6), ((LayerVertex)conf2.getVertices().get("0")).getLayerConfiguration().getIDropout()); assertEquals(new Dropout(0.6), ((LayerVertex)conf2.getVertices().get("0")).getLayerConfiguration().getDropOut());
assertEquals(new Dropout(0.7), ((LayerVertex)conf2.getVertices().get("1")).getLayerConfiguration().getIDropout()); assertEquals(new Dropout(0.7), ((LayerVertex)conf2.getVertices().get("1")).getLayerConfiguration().getDropOut());
assertEquals(new AlphaDropout(0.5), ((LayerVertex)conf2.getVertices().get("2")).getLayerConfiguration().getIDropout()); assertEquals(new AlphaDropout(0.5), ((LayerVertex)conf2.getVertices().get("2")).getLayerConfiguration().getDropOut());
} }
@Test @Test
@ -95,8 +95,8 @@ public class TestDropout extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(4).nOut(3).dropOut(d1).build()) .layer(DenseLayer.builder().nIn(4).nOut(3).dropOut(d1).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MSE).dropOut(d2).nIn(3).nOut(3).build()) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).dropOut(d2).nIn(3).nOut(3).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -131,8 +131,8 @@ public class TestDropout extends BaseDL4JTest {
ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(4).nOut(3).dropOut(d1).build(), "in") .addLayer("0", DenseLayer.builder().nIn(4).nOut(3).dropOut(d1).build(), "in")
.addLayer("1", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).dropOut(d2).nIn(3).nOut(3).build(), "0") .addLayer("1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).dropOut(d2).nIn(3).nOut(3).build(), "0")
.setOutputs("1") .setOutputs("1")
.build(); .build();
@ -188,8 +188,8 @@ public class TestDropout extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.dropOut(id) .dropOut(id)
.layer(new DenseLayer.Builder().nIn(4).nOut(3).build()) .layer(DenseLayer.builder().nIn(4).nOut(3).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(3).nOut(3).build()) .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(3).nOut(3).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -200,8 +200,8 @@ public class TestDropout extends BaseDL4JTest {
.dropOut(id) .dropOut(id)
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(4).nOut(3).build(), "in") .addLayer("0", DenseLayer.builder().nIn(4).nOut(3).build(), "in")
.addLayer("1", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(3).nOut(3).build(), "0") .addLayer("1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(3).nOut(3).build(), "0")
.setOutputs("1") .setOutputs("1")
.build(); .build();
@ -602,7 +602,7 @@ public class TestDropout extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DropoutLayer.Builder(new SpatialDropout(0.5)).build()) .layer(DropoutLayer.builder(new SpatialDropout(0.5)).build())
.build(); .build();
String asJson = conf.toJson(); String asJson = conf.toJson();

56
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/graph/ElementWiseVertexTest.java
View File

@ -73,7 +73,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder() ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder()
.addInputs("input1", "input2", "input3") .addInputs("input1", "input2", "input3")
.addLayer("denselayer", .addLayer("denselayer",
new DenseLayer.Builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY) DenseLayer.builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY)
.build(), .build(),
"input1") "input1")
/* denselayer is not actually used, but it seems that you _need_ to have trainable parameters, otherwise, you get /* denselayer is not actually used, but it seems that you _need_ to have trainable parameters, otherwise, you get
@ -87,7 +87,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
*/ */
.addVertex("elementwiseAdd", new ElementWiseVertex(ElementWiseVertex.Op.Add), "input1", .addVertex("elementwiseAdd", new ElementWiseVertex(ElementWiseVertex.Op.Add), "input1",
"input2", "input3") "input2", "input3")
.addLayer("Add", new ActivationLayer.Builder().activation(Activation.IDENTITY).build(), .addLayer("Add", ActivationLayer.builder().activation(Activation.IDENTITY).build(),
"elementwiseAdd") "elementwiseAdd")
.setOutputs("Add", "denselayer").build(); .setOutputs("Add", "denselayer").build();
@ -114,7 +114,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder() ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder()
.addInputs("input1", "input2", "input3") .addInputs("input1", "input2", "input3")
.addLayer("denselayer", .addLayer("denselayer",
new DenseLayer.Builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY) DenseLayer.builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY)
.build(), .build(),
"input1") "input1")
/* denselayer is not actually used, but it seems that you _need_ to have trainable parameters, otherwise, you get /* denselayer is not actually used, but it seems that you _need_ to have trainable parameters, otherwise, you get
@ -128,7 +128,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
*/ */
.addVertex("elementwiseProduct", new ElementWiseVertex(ElementWiseVertex.Op.Product), "input1", .addVertex("elementwiseProduct", new ElementWiseVertex(ElementWiseVertex.Op.Product), "input1",
"input2", "input3") "input2", "input3")
.addLayer("Product", new ActivationLayer.Builder().activation(Activation.IDENTITY).build(), .addLayer("Product", ActivationLayer.builder().activation(Activation.IDENTITY).build(),
"elementwiseProduct") "elementwiseProduct")
.setOutputs("Product", "denselayer").build(); .setOutputs("Product", "denselayer").build();
@ -155,7 +155,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder() ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder()
.addInputs("input1", "input2") .addInputs("input1", "input2")
.addLayer("denselayer", .addLayer("denselayer",
new DenseLayer.Builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY) DenseLayer.builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY)
.build(), .build(),
"input1") "input1")
/* denselayer is not actually used, but it seems that you _need_ to have trainable parameters, otherwise, you get /* denselayer is not actually used, but it seems that you _need_ to have trainable parameters, otherwise, you get
@ -169,7 +169,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
*/ */
.addVertex("elementwiseSubtract", new ElementWiseVertex(ElementWiseVertex.Op.Subtract), .addVertex("elementwiseSubtract", new ElementWiseVertex(ElementWiseVertex.Op.Subtract),
"input1", "input2") "input1", "input2")
.addLayer("Subtract", new ActivationLayer.Builder().activation(Activation.IDENTITY).build(), .addLayer("Subtract", ActivationLayer.builder().activation(Activation.IDENTITY).build(),
"elementwiseSubtract") "elementwiseSubtract")
.setOutputs("Subtract", "denselayer").build(); .setOutputs("Subtract", "denselayer").build();
@ -200,22 +200,22 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder()
.addInputs("input1", "input2", "input3") .addInputs("input1", "input2", "input3")
.addLayer("dense1", .addLayer("dense1",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz) DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(), .activation(Activation.TANH).build(),
"input1") "input1")
.addLayer("dense2", .addLayer("dense2",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz) DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(), .activation(Activation.TANH).build(),
"input2") "input2")
.addLayer("dense3", .addLayer("dense3",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz) DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(), .activation(Activation.TANH).build(),
"input3") "input3")
.addVertex("elementwiseAdd", new ElementWiseVertex(ElementWiseVertex.Op.Add), "dense1", .addVertex("elementwiseAdd", new ElementWiseVertex(ElementWiseVertex.Op.Add), "dense1",
"dense2", "dense3") "dense2", "dense3")
.addLayer("output", .addLayer("output",
new OutputLayer.Builder().nIn(midsz).nOut(outputsz) OutputLayer.builder().nIn(midsz).nOut(outputsz)
.activation(new ActivationSigmoid()) .activation(Activation.SIGMOID)
.lossFunction(LossFunction.MSE).build(), .lossFunction(LossFunction.MSE).build(),
"elementwiseAdd") "elementwiseAdd")
.setOutputs("output").build(); .setOutputs("output").build();
@ -376,22 +376,22 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder()
.addInputs("input1", "input2", "input3") .addInputs("input1", "input2", "input3")
.addLayer("dense1", .addLayer("dense1",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz) DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(), .activation(Activation.TANH).build(),
"input1") "input1")
.addLayer("dense2", .addLayer("dense2",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz) DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(), .activation(Activation.TANH).build(),
"input2") "input2")
.addLayer("dense3", .addLayer("dense3",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz) DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(), .activation(Activation.TANH).build(),
"input3") "input3")
.addVertex("elementwiseProduct", new ElementWiseVertex(ElementWiseVertex.Op.Product), "dense1", .addVertex("elementwiseProduct", new ElementWiseVertex(ElementWiseVertex.Op.Product), "dense1",
"dense2", "dense3") "dense2", "dense3")
.addLayer("output", .addLayer("output",
new OutputLayer.Builder().nIn(midsz).nOut(outputsz) OutputLayer.builder().nIn(midsz).nOut(outputsz)
.activation(new ActivationSigmoid()) .activation(Activation.SIGMOID)
.lossFunction(LossFunction.MSE).build(), .lossFunction(LossFunction.MSE).build(),
"elementwiseProduct") "elementwiseProduct")
.setOutputs("output").build(); .setOutputs("output").build();
@ -551,18 +551,18 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder()
.addInputs("input1", "input2") .addInputs("input1", "input2")
.addLayer("dense1", .addLayer("dense1",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz) DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(), .activation(Activation.TANH).build(),
"input1") "input1")
.addLayer("dense2", .addLayer("dense2",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz) DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(), .activation(Activation.TANH).build(),
"input2") "input2")
.addVertex("elementwiseSubtract", new ElementWiseVertex(ElementWiseVertex.Op.Subtract), .addVertex("elementwiseSubtract", new ElementWiseVertex(ElementWiseVertex.Op.Subtract),
"dense1", "dense2") "dense1", "dense2")
.addLayer("output", .addLayer("output",
new OutputLayer.Builder().nIn(midsz).nOut(outputsz) OutputLayer.builder().nIn(midsz).nOut(outputsz)
.activation(new ActivationSigmoid()) .activation(Activation.SIGMOID)
.lossFunction(LossFunction.MSE).build(), .lossFunction(LossFunction.MSE).build(),
"elementwiseSubtract") "elementwiseSubtract")
.setOutputs("output").build(); .setOutputs("output").build();

10
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/graph/ShiftVertexTest.java
View File

@ -86,7 +86,7 @@ public class ShiftVertexTest extends BaseDL4JTest {
double sf = 4.1; double sf = 4.1;
ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder().addInputs("input") ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder().addInputs("input")
.addLayer("denselayer", .addLayer("denselayer",
new DenseLayer.Builder().nIn(input.columns()).nOut(1) DenseLayer.builder().nIn(input.columns()).nOut(1)
.activation(Activation.IDENTITY).build(), .activation(Activation.IDENTITY).build(),
"input") "input")
/* denselayer is not actually used, but it seems that you _need_ to have trainable parameters, otherwise, you get /* denselayer is not actually used, but it seems that you _need_ to have trainable parameters, otherwise, you get
@ -99,10 +99,10 @@ public class ShiftVertexTest extends BaseDL4JTest {
* at org.deeplearning4j.nn.graph.ComputationGraph.init(ComputationGraph.java:341) * at org.deeplearning4j.nn.graph.ComputationGraph.init(ComputationGraph.java:341)
*/ */
.addLayer("identityinputactivation", .addLayer("identityinputactivation",
new ActivationLayer.Builder().activation(Activation.IDENTITY).build(), "input") ActivationLayer.builder().activation(Activation.IDENTITY).build(), "input")
.addVertex("shiftvertex", new ShiftVertex(sf), "identityinputactivation") .addVertex("shiftvertex", new ShiftVertex(sf), "identityinputactivation")
.addLayer("identityshiftvertex", .addLayer("identityshiftvertex",
new ActivationLayer.Builder().activation(Activation.IDENTITY).build(), ActivationLayer.builder().activation(Activation.IDENTITY).build(),
"shiftvertex") "shiftvertex")
.setOutputs("identityshiftvertex", "denselayer").build(); .setOutputs("identityshiftvertex", "denselayer").build();
@ -144,12 +144,12 @@ public class ShiftVertexTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder()
.addInputs("input") .addInputs("input")
.addLayer("denselayer", .addLayer("denselayer",
new DenseLayer.Builder().nIn(input.columns()).nOut(input.columns()) DenseLayer.builder().nIn(input.columns()).nOut(input.columns())
.activation(a1).build(), .activation(a1).build(),
"input") "input")
.addVertex("shiftvertex", new ShiftVertex(sf), "denselayer") .addVertex("shiftvertex", new ShiftVertex(sf), "denselayer")
.addLayer("output", .addLayer("output",
new OutputLayer.Builder().nIn(input.columns()).nOut(target.columns()) OutputLayer.builder().nIn(input.columns()).nOut(target.columns())
.activation(a2).lossFunction(LossFunction.MSE).build(), .activation(a2).lossFunction(LossFunction.MSE).build(),
"shiftvertex") "shiftvertex")
.setOutputs("output").build(); .setOutputs("output").build();

30
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/layers/LayerBuilderTest.java
View File

@ -67,7 +67,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testLayer() throws Exception { public void testLayer() throws Exception {
DenseLayer layer = new DenseLayer.Builder().activation(act).weightInit(weight).dropOut(dropOut) DenseLayer layer = DenseLayer.builder().activation(act).weightInit(weight).dropOut(dropOut)
.updater(updater).gradientNormalization(gradNorm) .updater(updater).gradientNormalization(gradNorm)
.gradientNormalizationThreshold(gradNormThreshold).build(); .gradientNormalizationThreshold(gradNormThreshold).build();
@ -75,7 +75,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
assertEquals(act, layer.getActivationFn()); assertEquals(act, layer.getActivationFn());
assertEquals(weight.getWeightInitFunction(), layer.getWeightInit()); assertEquals(weight.getWeightInitFunction(), layer.getWeightInit());
assertEquals(new Dropout(dropOut), layer.getIDropout()); assertEquals(new Dropout(dropOut), layer.getDropOut());
assertEquals(updater, layer.getIUpdater()); assertEquals(updater, layer.getIUpdater());
assertEquals(gradNorm, layer.getGradientNormalization()); assertEquals(gradNorm, layer.getGradientNormalization());
assertEquals(gradNormThreshold, layer.getGradientNormalizationThreshold(), 0.0); assertEquals(gradNormThreshold, layer.getGradientNormalizationThreshold(), 0.0);
@ -83,7 +83,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testFeedForwardLayer() throws Exception { public void testFeedForwardLayer() throws Exception {
DenseLayer ff = new DenseLayer.Builder().nIn(numIn).nOut(numOut).build(); DenseLayer ff = DenseLayer.builder().nIn(numIn).nOut(numOut).build();
checkSerialization(ff); checkSerialization(ff);
@ -93,7 +93,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testConvolutionLayer() throws Exception { public void testConvolutionLayer() throws Exception {
ConvolutionLayer conv = new ConvolutionLayer.Builder(kernelSize, stride, padding).build(); ConvolutionLayer conv = ConvolutionLayer.builder(kernelSize, stride, padding).build();
checkSerialization(conv); checkSerialization(conv);
@ -106,7 +106,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testSubsamplingLayer() throws Exception { public void testSubsamplingLayer() throws Exception {
SubsamplingLayer sample = SubsamplingLayer sample =
new SubsamplingLayer.Builder(poolType, stride).kernelSize(kernelSize).padding(padding).build(); SubsamplingLayer.builder(poolType, stride).kernelSize(kernelSize).padding(padding).build();
checkSerialization(sample); checkSerialization(sample);
@ -118,21 +118,21 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testOutputLayer() throws Exception { public void testOutputLayer() throws Exception {
OutputLayer out = new OutputLayer.Builder(loss).build(); OutputLayer out = OutputLayer.builder(loss).build();
checkSerialization(out); checkSerialization(out);
} }
@Test @Test
public void testRnnOutputLayer() throws Exception { public void testRnnOutputLayer() throws Exception {
RnnOutputLayer out = new RnnOutputLayer.Builder(loss).build(); RnnOutputLayer out = RnnOutputLayer.builder(loss).build();
checkSerialization(out); checkSerialization(out);
} }
@Test @Test
public void testAutoEncoder() throws Exception { public void testAutoEncoder() throws Exception {
AutoEncoder enc = new AutoEncoder.Builder().corruptionLevel(corruptionLevel).sparsity(sparsity).build(); AutoEncoder enc = AutoEncoder.builder().corruptionLevel(corruptionLevel).sparsity(sparsity).build();
checkSerialization(enc); checkSerialization(enc);
@ -142,7 +142,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testGravesLSTM() throws Exception { public void testGravesLSTM() throws Exception {
GravesLSTM glstm = new GravesLSTM.Builder().forgetGateBiasInit(1.5).activation(Activation.TANH).nIn(numIn) GravesLSTM glstm = GravesLSTM.builder().forgetGateBiasInit(1.5).activation(Activation.TANH).nIn(numIn)
.nOut(numOut).build(); .nOut(numOut).build();
checkSerialization(glstm); checkSerialization(glstm);
@ -155,7 +155,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testGravesBidirectionalLSTM() throws Exception { public void testGravesBidirectionalLSTM() throws Exception {
final GravesBidirectionalLSTM glstm = new GravesBidirectionalLSTM.Builder().forgetGateBiasInit(1.5) final GravesBidirectionalLSTM glstm = GravesBidirectionalLSTM.builder().forgetGateBiasInit(1.5)
.activation(Activation.TANH).nIn(numIn).nOut(numOut).build(); .activation(Activation.TANH).nIn(numIn).nOut(numOut).build();
checkSerialization(glstm); checkSerialization(glstm);
@ -168,7 +168,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testEmbeddingLayer() throws Exception { public void testEmbeddingLayer() throws Exception {
EmbeddingLayer el = new EmbeddingLayer.Builder().nIn(10).nOut(5).build(); EmbeddingLayer el = EmbeddingLayer.builder().nIn(10).nOut(5).build();
checkSerialization(el); checkSerialization(el);
assertEquals(10, el.getNIn()); assertEquals(10, el.getNIn());
@ -177,7 +177,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testBatchNormLayer() throws Exception { public void testBatchNormLayer() throws Exception {
BatchNormalization bN = new BatchNormalization.Builder().nIn(numIn).nOut(numOut).gamma(2).beta(1).decay(0.5) BatchNormalization bN =BatchNormalization.builder().nIn(numIn).nOut(numOut).gamma(2).beta(1).decay(0.5)
.lockGammaBeta(true).build(); .lockGammaBeta(true).build();
checkSerialization(bN); checkSerialization(bN);
@ -192,11 +192,11 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test @Test
public void testActivationLayer() throws Exception { public void testActivationLayer() throws Exception {
ActivationLayer activationLayer = new ActivationLayer.Builder().activation(act).build(); ActivationLayer activationLayer = ActivationLayer.builder().activation(act).build();
checkSerialization(activationLayer); checkSerialization(activationLayer);
assertEquals(act, activationLayer.activationFn); assertEquals(act, activationLayer.getActivation());
} }
private void checkSerialization(LayerConfiguration layer) throws Exception { private void checkSerialization(LayerConfiguration layer) throws Exception {
@ -225,7 +225,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
assertEquals(confExpected.getFlattenedLayerConfigurations().get(0), confActual.getFlattenedLayerConfigurations().get(0), "unequal YAML serialization"); assertEquals(confExpected.getFlattenedLayerConfigurations().get(0), confActual.getFlattenedLayerConfigurations().get(0), "unequal YAML serialization");
// check the layer's use of callSuper on equals method // check the layer's use of callSuper on equals method
confActual.getFlattenedLayerConfigurations().get(0).setIDropout(new Dropout(new java.util.Random().nextDouble())); confActual.getFlattenedLayerConfigurations().get(0).setDropOut(new Dropout(new java.util.Random().nextDouble()));
assertNotEquals( confExpected, confActual, "broken equals method (missing callSuper?)"); assertNotEquals( confExpected, confActual, "broken equals method (missing callSuper?)");
} }

66
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/layers/LayerConfigTest.java
View File

@ -53,13 +53,13 @@ public class LayerConfigTest extends BaseDL4JTest {
String name2 = "bill"; String name2 = "bill";
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).name(name1).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).name(name1).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).name(name2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).name(name2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
assertEquals(name1, conf.getConf(0).getLayer().getLayerName()); assertEquals(name1, conf.getConf(0).getLayer().getName());
assertEquals(name2, conf.getConf(1).getLayer().getLayerName()); assertEquals(name2, conf.getConf(1).getLayer().getName());
} }
@ -67,8 +67,8 @@ public class LayerConfigTest extends BaseDL4JTest {
public void testActivationLayerwiseOverride() { public void testActivationLayerwiseOverride() {
//Without layerwise override: //Without layerwise override:
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().activation(Activation.RELU) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().activation(Activation.RELU)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -77,8 +77,8 @@ public class LayerConfigTest extends BaseDL4JTest {
//With //With
conf = NeuralNetConfiguration.builder().activation(Activation.RELU) conf = NeuralNetConfiguration.builder().activation(Activation.RELU)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).activation(Activation.TANH).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).activation(Activation.TANH).build()).build();
net = new MultiLayerNetwork(conf); net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -94,8 +94,8 @@ public class LayerConfigTest extends BaseDL4JTest {
final Distribution defaultDistribution = new NormalDistribution(0, 1.0); final Distribution defaultDistribution = new NormalDistribution(0, 1.0);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.dist(defaultDistribution).biasInit(1) .dist(defaultDistribution).biasInit(1)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -109,8 +109,8 @@ public class LayerConfigTest extends BaseDL4JTest {
final Distribution overriddenDistribution = new UniformDistribution(0, 1); final Distribution overriddenDistribution = new UniformDistribution(0, 1);
conf = NeuralNetConfiguration.builder() conf = NeuralNetConfiguration.builder()
.dist(defaultDistribution).biasInit(1) .dist(defaultDistribution).biasInit(1)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()).layer(1, .layer(0, DenseLayer.builder().nIn(2).nOut(2).build()).layer(1,
new DenseLayer.Builder().nIn(2).nOut(2) DenseLayer.builder().nIn(2).nOut(2)
.dist(overriddenDistribution).biasInit(0).build()) .dist(overriddenDistribution).biasInit(0).build())
.build(); .build();
@ -181,23 +181,23 @@ public class LayerConfigTest extends BaseDL4JTest {
@Test @Test
public void testDropoutLayerwiseOverride() { public void testDropoutLayerwiseOverride() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().dropOut(1.0) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().dropOut(1.0)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
assertEquals(new Dropout(1.0), conf.getConf(0).getLayer().getIDropout()); assertEquals(new Dropout(1.0), conf.getConf(0).getLayer().getDropOut());
assertEquals(new Dropout(1.0), conf.getConf(1).getLayer().getIDropout()); assertEquals(new Dropout(1.0), conf.getConf(1).getLayer().getDropOut());
conf = NeuralNetConfiguration.builder().dropOut(1.0) conf = NeuralNetConfiguration.builder().dropOut(1.0)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).dropOut(2.0).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).dropOut(2.0).build()).build();
net = new MultiLayerNetwork(conf); net = new MultiLayerNetwork(conf);
net.init(); net.init();
assertEquals(new Dropout(1.0), conf.getConf(0).getLayer().getIDropout()); assertEquals(new Dropout(1.0), conf.getConf(0).getLayer().getDropOut());
assertEquals(new Dropout(2.0), conf.getConf(1).getLayer().getIDropout()); assertEquals(new Dropout(2.0), conf.getConf(1).getLayer().getDropOut());
} }
@Test @Test
@ -208,8 +208,8 @@ public class LayerConfigTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.updater(new Nesterovs(1.0, new MapSchedule(ScheduleType.ITERATION, testMomentumAfter))) .updater(new Nesterovs(1.0, new MapSchedule(ScheduleType.ITERATION, testMomentumAfter)))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -221,7 +221,7 @@ public class LayerConfigTest extends BaseDL4JTest {
conf = NeuralNetConfiguration.builder().updater(new Nesterovs(1.0, new MapSchedule(ScheduleType.ITERATION, testMomentumAfter) )) conf = NeuralNetConfiguration.builder().updater(new Nesterovs(1.0, new MapSchedule(ScheduleType.ITERATION, testMomentumAfter) ))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()).layer(1, new DenseLayer.Builder() .layer(0, DenseLayer.builder().nIn(2).nOut(2).build()).layer(1, DenseLayer.builder()
.nIn(2).nOut(2).updater(new Nesterovs(1.0, new MapSchedule(ScheduleType.ITERATION, testMomentumAfter2))).build()) .nIn(2).nOut(2).updater(new Nesterovs(1.0, new MapSchedule(ScheduleType.ITERATION, testMomentumAfter2))).build())
.build(); .build();
@ -234,8 +234,8 @@ public class LayerConfigTest extends BaseDL4JTest {
@Test @Test
public void testUpdaterRhoRmsDecayLayerwiseOverride() { public void testUpdaterRhoRmsDecayLayerwiseOverride() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new AdaDelta(0.5, 0.9)) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new AdaDelta(0.5, 0.9))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).updater(new AdaDelta(0.01,0.9)).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).updater(new AdaDelta(0.01,0.9)).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -245,8 +245,8 @@ public class LayerConfigTest extends BaseDL4JTest {
assertEquals(0.01, ((AdaDelta)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getRho(), 0.0); assertEquals(0.01, ((AdaDelta)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getRho(), 0.0);
conf = NeuralNetConfiguration.builder().updater(new RmsProp(1.0, 2.0, RmsProp.DEFAULT_RMSPROP_EPSILON)) conf = NeuralNetConfiguration.builder().updater(new RmsProp(1.0, 2.0, RmsProp.DEFAULT_RMSPROP_EPSILON))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).updater(new RmsProp(1.0, 1.0, RmsProp.DEFAULT_RMSPROP_EPSILON)).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).updater(new RmsProp(1.0, 1.0, RmsProp.DEFAULT_RMSPROP_EPSILON)).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).updater(new AdaDelta(0.5,AdaDelta.DEFAULT_ADADELTA_EPSILON)).build()) .layer(1, DenseLayer.builder().nIn(2).nOut(2).updater(new AdaDelta(0.5,AdaDelta.DEFAULT_ADADELTA_EPSILON)).build())
.build(); .build();
net = new MultiLayerNetwork(conf); net = new MultiLayerNetwork(conf);
@ -264,8 +264,8 @@ public class LayerConfigTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.updater(new Adam(1.0, 0.5, 0.5, 1e-8)) .updater(new Adam(1.0, 0.5, 0.5, 1e-8))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).updater(new Adam(1.0, 0.6, 0.7, 1e-8)).build()) .layer(1, DenseLayer.builder().nIn(2).nOut(2).updater(new Adam(1.0, 0.6, 0.7, 1e-8)).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -283,8 +283,8 @@ public class LayerConfigTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue) .gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(10) .gradientNormalizationThreshold(10)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
BaseLayerConfiguration bconf = (BaseLayerConfiguration) conf.getConf(0).getLayer(); BaseLayerConfiguration bconf = (BaseLayerConfiguration) conf.getConf(0).getLayer();
@ -297,8 +297,8 @@ public class LayerConfigTest extends BaseDL4JTest {
conf = NeuralNetConfiguration.builder() conf = NeuralNetConfiguration.builder()
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue) .gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(10) .gradientNormalizationThreshold(10)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2) .layer(1, DenseLayer.builder().nIn(2).nOut(2)
.gradientNormalization(GradientNormalization.None) .gradientNormalization(GradientNormalization.None)
.gradientNormalizationThreshold(2.5).build()) .gradientNormalizationThreshold(2.5).build())
.build(); .build();

40
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/layers/LayerConfigValidationTest.java
View File

@ -56,8 +56,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
public void testDropConnect() { public void testDropConnect() {
// Warning thrown only since some layers may not have l1 or l2 // Warning thrown only since some layers may not have l1 or l2
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.1)).weightNoise(new DropConnect(0.5)) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.1)).weightNoise(new DropConnect(0.5))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
} }
@ -67,8 +67,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
public void testL1L2NotSet() { public void testL1L2NotSet() {
// Warning thrown only since some layers may not have l1 or l2 // Warning thrown only since some layers may not have l1 or l2
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3)) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
} }
@ -78,8 +78,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
public void testRegNotSetL1Global() { public void testRegNotSetL1Global() {
assertThrows(IllegalStateException.class, () -> { assertThrows(IllegalStateException.class, () -> {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3)).l1(0.5) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3)).l1(0.5)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
}); });
@ -90,8 +90,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
public void testRegNotSetL2Local() { public void testRegNotSetL2Local() {
assertThrows(IllegalStateException.class, () -> { assertThrows(IllegalStateException.class, () -> {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3)) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).l2(0.5).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).l2(0.5).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
}); });
@ -102,8 +102,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
// Warning thrown only since global dist can be set with a different weight init locally // Warning thrown only since global dist can be set with a different weight init locally
NeuralNetConfiguration conf = NeuralNetConfiguration conf =
NeuralNetConfiguration.builder().updater(new Sgd(0.3)).dist(new GaussianDistribution(1e-3, 2)) NeuralNetConfiguration.builder().updater(new Sgd(0.3)).dist(new GaussianDistribution(1e-3, 2))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
} }
@ -116,8 +116,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration conf =
NeuralNetConfiguration.builder().updater(new Nesterovs(1.0, new MapSchedule(ScheduleType.ITERATION, testMomentumAfter))) NeuralNetConfiguration.builder().updater(new Nesterovs(1.0, new MapSchedule(ScheduleType.ITERATION, testMomentumAfter)))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
} }
@ -130,12 +130,12 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
/* Graph Builder */ /* Graph Builder */
.updater(Updater.RMSPROP.getIUpdaterWithDefaultConfig()).graphBuilder().addInputs("in") .updater(Updater.RMSPROP.getIUpdaterWithDefaultConfig()).graphBuilder().addInputs("in")
.addLayer("L" + 1, .addLayer("L" + 1,
new GravesLSTM.Builder().nIn(20).updater(Updater.RMSPROP).nOut(10) GravesLSTM.builder().nIn(20).updater(Updater.RMSPROP).nOut(10)
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.dropOut(0.4).l1(0.3).activation(Activation.SIGMOID).build(), .dropOut(0.4).l1(0.3).activation(Activation.SIGMOID).build(),
"in") "in")
.addLayer("output", .addLayer("output",
new RnnOutputLayer.Builder().nIn(20).nOut(10).activation(Activation.SOFTMAX) RnnOutputLayer.builder().nIn(20).nOut(10).activation(Activation.SOFTMAX)
.weightInit(WeightInit.RELU_UNIFORM).build(), .weightInit(WeightInit.RELU_UNIFORM).build(),
"L" + 1) "L" + 1)
.setOutputs("output"); .setOutputs("output");
@ -157,8 +157,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
// Nesterovs Updater // Nesterovs Updater
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Nesterovs(0.9)) NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Nesterovs(0.9))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).l2(0.5).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).l2(0.5).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).updater(new Nesterovs(0.3, 0.4)).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).updater(new Nesterovs(0.3, 0.4)).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -173,8 +173,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
// Adam Updater // Adam Updater
conf = NeuralNetConfiguration.builder().updater(new Adam(0.3)) conf = NeuralNetConfiguration.builder().updater(new Adam(0.3))
.weightInit(expectedDist) .weightInit(expectedDist)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).l2(0.5).l1(0.3).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).l2(0.5).l1(0.3).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
net = new MultiLayerNetwork(conf); net = new MultiLayerNetwork(conf);
net.init(); net.init();
@ -191,8 +191,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
//RMSProp Updater //RMSProp Updater
conf = NeuralNetConfiguration.builder().updater(new RmsProp(0.3)) conf = NeuralNetConfiguration.builder().updater(new RmsProp(0.3))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()) .layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).updater(new RmsProp(0.3, 0.4, RmsProp.DEFAULT_RMSPROP_EPSILON)).build()).build(); .layer(1, DenseLayer.builder().nIn(2).nOut(2).updater(new RmsProp(0.3, 0.4, RmsProp.DEFAULT_RMSPROP_EPSILON)).build()).build();
net = new MultiLayerNetwork(conf); net = new MultiLayerNetwork(conf);
net.init(); net.init();

10
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/preprocessor/CNNProcessorTest.java
View File

@ -249,7 +249,7 @@ public class CNNProcessorTest extends BaseDL4JTest {
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
// Building the DL4J network // Building the DL4J network
.layer(0, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray) .layer(0, ConvolutionLayer.builder(kernelArray, strideArray, zeroPaddingArray)
.name("cnn1") .name("cnn1")
.convolutionMode(ConvolutionMode.Strict) .convolutionMode(ConvolutionMode.Strict)
.nIn(2) // 2 input channels .nIn(2) // 2 input channels
@ -258,7 +258,7 @@ public class CNNProcessorTest extends BaseDL4JTest {
.activation(Activation.RELU) .activation(Activation.RELU)
.biasInit(1e-2).build()) .biasInit(1e-2).build())
.layer(1, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray) .layer(1, ConvolutionLayer.builder(kernelArray, strideArray, zeroPaddingArray)
.name("cnn2") .name("cnn2")
.convolutionMode(ConvolutionMode.Strict) .convolutionMode(ConvolutionMode.Strict)
.nOut(processWidth) .nOut(processWidth)
@ -267,21 +267,21 @@ public class CNNProcessorTest extends BaseDL4JTest {
.biasInit(1e-2) .biasInit(1e-2)
.build()) .build())
.layer(2, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray) .layer(2, ConvolutionLayer.builder(kernelArray, strideArray, zeroPaddingArray)
.name("cnn3") .name("cnn3")
.convolutionMode(ConvolutionMode.Strict) .convolutionMode(ConvolutionMode.Strict)
.nOut(processWidth) .nOut(processWidth)
.weightInit(WeightInit.XAVIER_UNIFORM) .weightInit(WeightInit.XAVIER_UNIFORM)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(3, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray) .layer(3, ConvolutionLayer.builder(kernelArray, strideArray, zeroPaddingArray)
.name("cnn4") .name("cnn4")
.convolutionMode(ConvolutionMode.Strict) .convolutionMode(ConvolutionMode.Strict)
.nOut(processWidth) .nOut(processWidth)
.weightInit(WeightInit.XAVIER_UNIFORM) .weightInit(WeightInit.XAVIER_UNIFORM)
.activation(Activation.RELU).build()) .activation(Activation.RELU).build())
.layer(4, new OutputLayer.Builder(LossFunctions.LossFunction.MSE) .layer(4, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE)
.name("output") .name("output")
.nOut(1) .nOut(1)
.activation(Activation.TANH) .activation(Activation.TANH)

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/preprocessor/CustomPreprocessorTest.java
View File

@ -39,8 +39,8 @@ public class CustomPreprocessorTest extends BaseDL4JTest {
//Second: let's create a MultiLayerCofiguration with one, and check JSON and YAML config actually works... //Second: let's create a MultiLayerCofiguration with one, and check JSON and YAML config actually works...
NeuralNetConfiguration conf = NeuralNetConfiguration conf =
NeuralNetConfiguration.builder() NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()) .layer(0, DenseLayer.builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10) .layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(10)
.activation(Activation.SOFTMAX).nOut(10).build()) .activation(Activation.SOFTMAX).nOut(10).build())
.inputPreProcessor(0, new MyCustomPreprocessor()) .inputPreProcessor(0, new MyCustomPreprocessor())
.build(); .build();

40
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/preprocessor/TestPreProcessors.java
View File

@ -58,7 +58,7 @@ public class TestPreProcessors extends BaseDL4JTest {
RnnToFeedForwardPreProcessor proc = new RnnToFeedForwardPreProcessor(); RnnToFeedForwardPreProcessor proc = new RnnToFeedForwardPreProcessor();
NeuralNetConfiguration nnc = NeuralNetConfiguration.builder() NeuralNetConfiguration nnc = NeuralNetConfiguration.builder()
.layer(new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(layerSize) .layer(org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(layerSize)
.nOut(layerSize).build()) .nOut(layerSize).build())
.build(); .build();
@ -143,7 +143,7 @@ public class TestPreProcessors extends BaseDL4JTest {
FeedForwardToRnnPreProcessor proc = new FeedForwardToRnnPreProcessor(); FeedForwardToRnnPreProcessor proc = new FeedForwardToRnnPreProcessor();
NeuralNetConfiguration nnc = NeuralNetConfiguration.builder() NeuralNetConfiguration nnc = NeuralNetConfiguration.builder()
.layer(new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(layerSize) .layer(org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(layerSize)
.nOut(layerSize).build()) .nOut(layerSize).build())
.build(); .build();
@ -227,7 +227,7 @@ public class TestPreProcessors extends BaseDL4JTest {
NeuralNetConfiguration nnc = NeuralNetConfiguration nnc =
NeuralNetConfiguration.builder() NeuralNetConfiguration.builder()
.layer(new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder( .layer(org.deeplearning4j.nn.conf.layers.ConvolutionLayer.builder(
inputWidth, inputHeight).nIn(cnnNChannelsIn) inputWidth, inputHeight).nIn(cnnNChannelsIn)
.nOut(nChannels).build()) .nOut(nChannels).build())
.build(); .build();
@ -309,7 +309,7 @@ public class TestPreProcessors extends BaseDL4JTest {
NeuralNetConfiguration nnc = NeuralNetConfiguration nnc =
NeuralNetConfiguration.builder() NeuralNetConfiguration.builder()
.layer(new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder( .layer(org.deeplearning4j.nn.conf.layers.ConvolutionLayer.builder(
inputWidth, inputHeight).nIn(cnnNChannelsIn) inputWidth, inputHeight).nIn(cnnNChannelsIn)
.nOut(nChannels).build()) .nOut(nChannels).build())
.build(); .build();
@ -397,12 +397,12 @@ public class TestPreProcessors extends BaseDL4JTest {
//FF->RNN and RNN->FF //FF->RNN and RNN->FF
NeuralNetConfiguration conf1 = NeuralNetConfiguration conf1 =
NeuralNetConfiguration.builder() NeuralNetConfiguration.builder()
.layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(5) .layer(0, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(5)
.nOut(6).build()) .nOut(6).build())
.layer(1, new GravesLSTM.Builder().nIn(6).nOut(7).build()) .layer(1, GravesLSTM.builder().nIn(6).nOut(7).build())
.layer(2, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(7) .layer(2, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(7)
.nOut(8).build()) .nOut(8).build())
.layer(3, new RnnOutputLayer.Builder().nIn(8).nOut(9).activation(Activation.SOFTMAX).build()).build(); .layer(3, RnnOutputLayer.builder().nIn(8).nOut(9).activation(Activation.SOFTMAX).build()).build();
//Expect preprocessors: layer1: FF->RNN; 2: RNN->FF; 3: FF->RNN //Expect preprocessors: layer1: FF->RNN; 2: RNN->FF; 3: FF->RNN
assertEquals(3, conf1.getInputPreProcessors().size()); assertEquals(3, conf1.getInputPreProcessors().size());
assertTrue(conf1.getInputPreProcess(1) instanceof FeedForwardToRnnPreProcessor); assertTrue(conf1.getInputPreProcess(1) instanceof FeedForwardToRnnPreProcessor);
@ -412,10 +412,10 @@ public class TestPreProcessors extends BaseDL4JTest {
//FF-> CNN, CNN-> FF, FF->RNN //FF-> CNN, CNN-> FF, FF->RNN
NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder() NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder()
.layer(0, new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder().nOut(10) .layer(0, org.deeplearning4j.nn.conf.layers.ConvolutionLayer.builder().nOut(10)
.kernelSize(5, 5).stride(1, 1).build()) .kernelSize(5, 5).stride(1, 1).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nOut(6).build()) .layer(1, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nOut(6).build())
.layer(2, new RnnOutputLayer.Builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build()) .layer(2, RnnOutputLayer.builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutionalFlat(28, 28, 1)).build(); .inputType(InputType.convolutionalFlat(28, 28, 1)).build();
//Expect preprocessors: 0: FF->CNN; 1: CNN->FF; 2: FF->RNN //Expect preprocessors: 0: FF->CNN; 1: CNN->FF; 2: FF->RNN
assertEquals(3, conf2.getInputPreProcessors().size()); assertEquals(3, conf2.getInputPreProcessors().size());
@ -425,10 +425,10 @@ public class TestPreProcessors extends BaseDL4JTest {
//CNN-> FF, FF->RNN - InputType.convolutional instead of convolutionalFlat //CNN-> FF, FF->RNN - InputType.convolutional instead of convolutionalFlat
NeuralNetConfiguration conf2a = NeuralNetConfiguration.builder() NeuralNetConfiguration conf2a = NeuralNetConfiguration.builder()
.layer(0, new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder().nOut(10) .layer(0, org.deeplearning4j.nn.conf.layers.ConvolutionLayer.builder().nOut(10)
.kernelSize(5, 5).stride(1, 1).build()) .kernelSize(5, 5).stride(1, 1).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nOut(6).build()) .layer(1, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nOut(6).build())
.layer(2, new RnnOutputLayer.Builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build()) .layer(2, RnnOutputLayer.builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(28, 28, 1)).build(); .inputType(InputType.convolutional(28, 28, 1)).build();
//Expect preprocessors: 1: CNN->FF; 2: FF->RNN //Expect preprocessors: 1: CNN->FF; 2: FF->RNN
assertEquals(2, conf2a.getInputPreProcessors().size()); assertEquals(2, conf2a.getInputPreProcessors().size());
@ -438,10 +438,10 @@ public class TestPreProcessors extends BaseDL4JTest {
//FF->CNN and CNN->RNN: //FF->CNN and CNN->RNN:
NeuralNetConfiguration conf3 = NeuralNetConfiguration.builder().list() NeuralNetConfiguration conf3 = NeuralNetConfiguration.builder().list()
.layer(0, new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder().nOut(10) .layer(0, org.deeplearning4j.nn.conf.layers.ConvolutionLayer.builder().nOut(10)
.kernelSize(5, 5).stride(1, 1).build()) .kernelSize(5, 5).stride(1, 1).build())
.layer(1, new GravesLSTM.Builder().nOut(6).build()) .layer(1, GravesLSTM.builder().nOut(6).build())
.layer(2, new RnnOutputLayer.Builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build()) .layer(2, RnnOutputLayer.builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutionalFlat(28, 28, 1)).build(); .inputType(InputType.convolutionalFlat(28, 28, 1)).build();
//Expect preprocessors: 0: FF->CNN, 1: CNN->RNN; //Expect preprocessors: 0: FF->CNN, 1: CNN->RNN;
assertEquals(2, conf3.getInputPreProcessors().size()); assertEquals(2, conf3.getInputPreProcessors().size());
@ -454,16 +454,16 @@ public class TestPreProcessors extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration conf =
NeuralNetConfiguration.builder() NeuralNetConfiguration.builder()
.list().layer(0, .list().layer(0,
new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder( org.deeplearning4j.nn.conf.layers.ConvolutionLayer.builder(
4, 4) // 28*28*1 => 15*15*10 4, 4) // 28*28*1 => 15*15*10
.nIn(1).nOut(10).padding(2, 2) .nIn(1).nOut(10).padding(2, 2)
.stride(2, 2) .stride(2, 2)
.weightInit(WeightInit.RELU) .weightInit(WeightInit.RELU)
.activation(Activation.RELU) .activation(Activation.RELU)
.build()) .build())
.layer(1, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder() .layer(1, org.deeplearning4j.nn.conf.layers.DenseLayer.builder()
.activation(Activation.RELU).nOut(200).build()) .activation(Activation.RELU).nOut(200).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(200) .layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(200)
.nOut(5).weightInit(WeightInit.RELU) .nOut(5).weightInit(WeightInit.RELU)
.activation(Activation.SOFTMAX).build()) .activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutionalFlat(28, 28, 1)) .inputType(InputType.convolutionalFlat(28, 28, 1))

26
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/weightnoise/TestWeightNoise.java
View File

@ -67,9 +67,9 @@ public class TestWeightNoise extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.weightNoise(wn) .weightNoise(wn)
.layer(new DenseLayer.Builder().nIn(10).nOut(10).build()) .layer(DenseLayer.builder().nIn(10).nOut(10).build())
.layer(new DenseLayer.Builder().nIn(10).nOut(10).weightNoise(new DropConnect(0.25)).build()) .layer(DenseLayer.builder().nIn(10).nOut(10).weightNoise(new DropConnect(0.25)).build())
.layer(new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()) .layer(OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -86,9 +86,9 @@ public class TestWeightNoise extends BaseDL4JTest {
.weightNoise(wn) .weightNoise(wn)
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in") .layer("0", DenseLayer.builder().nIn(10).nOut(10).build(), "in")
.layer("1", new DenseLayer.Builder().nIn(10).nOut(10).weightNoise(new DropConnect(0.25)).build(), "0") .layer("1", DenseLayer.builder().nIn(10).nOut(10).weightNoise(new DropConnect(0.25)).build(), "0")
.layer("2", new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build(), "1") .layer("2", OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build(), "1")
.setOutputs("2") .setOutputs("2")
.build(); .build();
@ -145,9 +145,9 @@ public class TestWeightNoise extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(10).nOut(10).weightNoise(wn1).build()) .layer(DenseLayer.builder().nIn(10).nOut(10).weightNoise(wn1).build())
.layer(new DenseLayer.Builder().nIn(10).nOut(10).weightNoise(wn2).build()) .layer(DenseLayer.builder().nIn(10).nOut(10).weightNoise(wn2).build())
.layer(new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).weightNoise(wn3).build()) .layer(OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).weightNoise(wn3).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -170,9 +170,9 @@ public class TestWeightNoise extends BaseDL4JTest {
ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder() ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder()
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("0", new DenseLayer.Builder().nIn(10).nOut(10).weightNoise(wn1).build(), "in") .layer("0", DenseLayer.builder().nIn(10).nOut(10).weightNoise(wn1).build(), "in")
.layer("1", new DenseLayer.Builder().nIn(10).nOut(10).weightNoise(wn2).build(), "0") .layer("1", DenseLayer.builder().nIn(10).nOut(10).weightNoise(wn2).build(), "0")
.layer("2", new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).weightNoise(wn3).build(), "1") .layer("2", OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).weightNoise(wn3).build(), "1")
.setOutputs("2") .setOutputs("2")
.build(); .build();
@ -249,7 +249,7 @@ public class TestWeightNoise extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder() NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.weightInit(WeightInit.ONES) .weightInit(WeightInit.ONES)
.layer(new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()) .layer(OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init(); net.init();

268
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/dtypes/DTypeTests.java
View File

@ -305,9 +305,9 @@ public class DTypeTests extends BaseDL4JTest {
.updater(new Adam(0.01)) .updater(new Adam(0.01))
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.list() .list()
.layer(new DenseLayer.Builder().activation(Activation.TANH).nIn(10).nOut(10).build()) .layer(DenseLayer.builder().activation(Activation.TANH).nIn(10).nOut(10).build())
.layer(new DenseLayer.Builder().activation(Activation.TANH).nIn(10).nOut(10).build()) .layer(DenseLayer.builder().activation(Activation.TANH).nIn(10).nOut(10).build())
.layer(new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build()) .layer(OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build(); .build();
MultiLayerNetwork net = new MultiLayerNetwork(conf); MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -389,9 +389,9 @@ public class DTypeTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE) .dataType(DataType.DOUBLE)
.graphBuilder() .graphBuilder()
.addInputs("in") .addInputs("in")
.layer("l0", new DenseLayer.Builder().activation(Activation.TANH).nIn(10).nOut(10).build(), "in") .layer("l0", DenseLayer.builder().activation(Activation.TANH).nIn(10).nOut(10).build(), "in")
.layer("l1", new DenseLayer.Builder().activation(Activation.TANH).nIn(10).nOut(10).build(), "l0") .layer("l1", DenseLayer.builder().activation(Activation.TANH).nIn(10).nOut(10).build(), "l0")
.layer("out", new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "l1") .layer("out", OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "l1")
.setOutputs("out") .setOutputs("out")
.build(); .build();
@ -477,24 +477,24 @@ public class DTypeTests extends BaseDL4JTest {
LayerConfiguration secondLast; LayerConfiguration secondLast;
switch (outputLayer) { switch (outputLayer) {
case 0: case 0:
ol = new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new GlobalPoolingLayer(PoolingType.MAX); secondLast = GlobalPoolingLayer.builder(PoolingType.MAX).build();
break; break;
case 1: case 1:
ol = new LossLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = LossLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build();
secondLast = new FrozenLayerWithBackprop(new DenseLayer.Builder().nOut(10).activation(Activation.SIGMOID).build()); secondLast = FrozenLayerWithBackprop.builder().underlying(DenseLayer.builder().nOut(10).activation(Activation.SIGMOID).build()).build();
break; break;
case 2: case 2:
ol = new CenterLossOutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol =CenterLossOutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new VariationalAutoencoder.Builder().encoderLayerSizes(10).decoderLayerSizes(10).nOut(10).activation(Activation.SIGMOID).build(); secondLast = VariationalAutoencoder.builder().encoderLayerSizes(10).decoderLayerSizes(10).nOut(10).activation(Activation.SIGMOID).build();
break; break;
case 3: case 3:
ol = new CnnLossLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = CnnLossLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build();
secondLast = new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(3).activation(Activation.TANH).build(); secondLast = ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(3).activation(Activation.TANH).build();
break; break;
case 4: case 4:
ol = new Yolo2OutputLayer.Builder().boundingBoxPriors(Nd4j.create(new double[][]{{1.0, 1.0}, {2.0, 2.0}}).castTo(networkDtype)).build(); ol = Yolo2OutputLayer.builder().boundingBoxes(Nd4j.create(new double[][]{{1.0, 1.0}, {2.0, 2.0}}).castTo(networkDtype)).build();
secondLast = new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(14).activation(Activation.TANH).build(); secondLast = ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(14).activation(Activation.TANH).build();
break; break;
default: default:
throw new RuntimeException(); throw new RuntimeException();
@ -505,28 +505,28 @@ public class DTypeTests extends BaseDL4JTest {
.dataType(networkDtype) .dataType(networkDtype)
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.updater(new Adam(1e-2)) .updater(new Adam(1e-2))
.list()
.layer(new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(3).activation(Activation.TANH).build()) .layer(ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(3).activation(Activation.TANH).build())
.layer(new LocalResponseNormalization()) .layer(LocalResponseNormalization.builder())
.layer(new DropoutLayer(0.5)) .layer(DropoutLayer.builder(0.5).build())
.layer(new DropoutLayer(new AlphaDropout(0.5))) .layer(DropoutLayer.builder(new AlphaDropout(0.5)).build())
.layer(new DropoutLayer(new GaussianDropout(0.5))) .layer(DropoutLayer.builder(new GaussianDropout(0.5)).build())
.layer(new DropoutLayer(new GaussianNoise(0.1))) .layer(DropoutLayer.builder(new GaussianNoise(0.1)).build())
.layer(new DropoutLayer(new SpatialDropout(0.5))) .layer(DropoutLayer.builder(new SpatialDropout(0.5)).build())
.layer(new SubsamplingLayer.Builder().poolingType(SubsamplingLayer.PoolingType.AVG).kernelSize(3, 3).stride(2, 2).build()) .layer(SubsamplingLayer.builder().poolingType(SubsamplingLayer.PoolingType.AVG.toPoolingType()).kernelSize(3, 3).stride(2, 2).build())
.layer(new Pooling2D.Builder().poolingType(SubsamplingLayer.PoolingType.AVG).kernelSize(2, 2).stride(1, 1).build()) .layer(Pooling2D.builder().poolingType(SubsamplingLayer.PoolingType.AVG.toPoolingType()).kernelSize(2, 2).stride(1, 1).build())
.layer(new Deconvolution2D.Builder().kernelSize(2, 2).stride(2, 2).nOut(3).activation(Activation.TANH).build()) .layer(Deconvolution2D.builder().kernelSize(2, 2).stride(2, 2).nOut(3).activation(Activation.TANH).build())
// .layer(new LocallyConnected2D.Builder().nOut(3).kernelSize(2,2).stride(1,1).activation(Activation.SIGMOID).build()) //EXCEPTION // .layer(LocallyConnected2D.builder().nOut(3).kernelSize(2,2).stride(1,1).activation(Activation.SIGMOID).build()) //EXCEPTION
.layer(new ZeroPaddingLayer(1, 1)) .layer(ZeroPaddingLayer.builder(1, 1).build())
.layer(new Cropping2D(1, 1)) .layer(Cropping2D.builder(1, 1).build())
.layer(new IdentityLayer()) .layer(IdentityLayer.builder())
.layer(new Upsampling2D.Builder().size(2).build()) .layer(Upsampling2D.builder().size(2).build())
.layer(new SubsamplingLayer.Builder().kernelSize(2, 2).stride(2, 2).build()) .layer(SubsamplingLayer.builder().kernelSize(2, 2).stride(2, 2).build())
.layer(new DepthwiseConvolution2D.Builder().nOut(3).activation(Activation.RELU).build()) .layer(DepthwiseConvolution2D.builder().nOut(3).activation(Activation.RELU).build())
.layer(new SeparableConvolution2D.Builder().nOut(3).activation(Activation.HARDTANH).build()) .layer(SeparableConvolution2D.builder().nOut(3).activation(Activation.HARDTANH).build())
.layer(new MaskLayer()) .layer(MaskLayer.builder())
.layer(new BatchNormalization.Builder().build()) .layer(BatchNormalization.builder().build())
.layer(new ActivationLayer(Activation.LEAKYRELU)) .layer(ActivationLayer.builder().activation(Activation.LEAKYRELU).build())
.layer(secondLast) .layer(secondLast)
.layer(ol) .layer(ol)
.inputType(InputType.convolutionalFlat(8, 8, 1)) .inputType(InputType.convolutionalFlat(8, 8, 1))
@ -603,16 +603,16 @@ public class DTypeTests extends BaseDL4JTest {
LayerConfiguration secondLast; LayerConfiguration secondLast;
switch (outputLayer) { switch (outputLayer) {
case 0: case 0:
ol = new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new GlobalPoolingLayer(PoolingType.AVG); secondLast = GlobalPoolingLayer.builder(PoolingType.AVG).build();
break; break;
case 1: case 1:
ol = new Cnn3DLossLayer.Builder(Convolution3D.DataFormat.NCDHW).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = Cnn3DLossLayer.builder().dataFormat(Convolution3D.DataFormat.NCDHW).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build();
secondLast = new Convolution3D.Builder().nOut(3).activation(Activation.ELU).build(); secondLast = Convolution3D.builder().nOut(3).activation(Activation.ELU).build();
break; break;
case 2: case 2:
ol = new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new Convolution3D.Builder().nOut(3).activation(Activation.ELU).build(); secondLast = Convolution3D.builder().nOut(3).activation(Activation.ELU).build();
break; break;
default: default:
throw new RuntimeException(); throw new RuntimeException();
@ -623,15 +623,15 @@ public class DTypeTests extends BaseDL4JTest {
.dataType(networkDtype) .dataType(networkDtype)
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.updater(new Nesterovs(1e-2, 0.9)) .updater(new Nesterovs(1e-2, 0.9))
.list()
.layer(new Convolution3D.Builder().kernelSize(2, 2, 2).stride(1, 1, 1).nOut(3).activation(Activation.TANH).build()) .layer(Convolution3D.builder().kernelSize(2, 2, 2).stride(1, 1, 1).nOut(3).activation(Activation.TANH).build())
.layer(new Convolution3D.Builder().kernelSize(2, 2, 2).stride(1, 1, 1).nOut(3).activation(Activation.TANH).build()) .layer(Convolution3D.builder().kernelSize(2, 2, 2).stride(1, 1, 1).nOut(3).activation(Activation.TANH).build())
.layer(new Subsampling3DLayer.Builder().poolingType(PoolingType.AVG).kernelSize(2, 2, 2).stride(2, 2, 2).build()) .layer(Subsampling3DLayer.builder().poolingType(PoolingType.AVG).kernelSize(2, 2, 2).stride(2, 2, 2).build())
.layer(new Deconvolution3D.Builder().kernelSize(2,2,2).stride(1,1,1).nIn(3).nOut(3).activation(Activation.TANH).build()) .layer(Deconvolution3D.builder().kernelSize(2,2,2).stride(1,1,1).nIn(3).nOut(3).activation(Activation.TANH).build())
.layer(new Cropping3D.Builder(1, 1, 1, 1, 1, 1).build()) .layer(Cropping3D.builder(1, 1, 1, 1, 1, 1).build())
.layer(new ZeroPadding3DLayer.Builder(1, 1, 1, 1, 1, 1).build()) .layer(ZeroPadding3DLayer.builder(1, 1, 1, 1, 1, 1).build())
.layer(new ActivationLayer(Activation.LEAKYRELU)) .layer(ActivationLayer.builder(Activation.LEAKYRELU).build())
.layer(new Upsampling3D.Builder().size(2).build()) .layer(Upsampling3D.builder().size(2).build())
.layer(secondLast) .layer(secondLast)
.layer(ol) .layer(ol)
.inputType(InputType.convolutional3D(Convolution3D.DataFormat.NCDHW, 8, 8, 8, 1)) .inputType(InputType.convolutional3D(Convolution3D.DataFormat.NCDHW, 8, 8, 8, 1))
@ -714,16 +714,16 @@ public class DTypeTests extends BaseDL4JTest {
LayerConfiguration secondLast; LayerConfiguration secondLast;
switch (outputLayer) { switch (outputLayer) {
case 0: case 0:
ol = new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new GlobalPoolingLayer(PoolingType.MAX); secondLast = GlobalPoolingLayer.builder(PoolingType.MAX).build();
break; break;
case 1: case 1:
ol = new RnnOutputLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).nOut(5).build(); ol = RnnOutputLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).nOut(5).build();
secondLast = new Convolution1D.Builder().kernelSize(2).nOut(5).build(); secondLast = Convolution1D.builder().kernelSize(2).nOut(5).build();
break; break;
case 2: case 2:
ol = new RnnLossLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = RnnLossLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build();
secondLast = new Convolution1D.Builder().kernelSize(2).nOut(5).build(); secondLast = Convolution1D.builder().kernelSize(2).nOut(5).build();
break; break;
default: default:
throw new RuntimeException(); throw new RuntimeException();
@ -737,14 +737,14 @@ public class DTypeTests extends BaseDL4JTest {
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.updater(new Adam(1e-2)) .updater(new Adam(1e-2))
.list() .list()
.layer(new Convolution1D.Builder() .layer(Convolution1D.builder()
.kernelSize(2) .kernelSize(2)
.stride(1).nOut(3). .stride(1).nOut(3).
activation(Activation.TANH).build()) activation(Activation.TANH).build())
.layer(new Subsampling1DLayer.Builder().poolingType(PoolingType.MAX).kernelSize(5).stride(1).build()) .layer(Subsampling1DLayer.builder().poolingType(PoolingType.MAX).kernelSize(5).stride(1).build())
.layer(new Cropping1D.Builder(1).build()) .layer(Cropping1D.builder(1).build())
.layer(new ZeroPadding1DLayer(1)) .layer(ZeroPadding1DLayer.builder(1).build())
.layer(new Upsampling1D.Builder(2).build()) .layer(Upsampling1D.builder(2).build())
.layer(secondLast) .layer(secondLast)
.layer(ol) .layer(ol)
.inputType(InputType.recurrent(5, 10,RNNFormat.NCW)) .inputType(InputType.recurrent(5, 10,RNNFormat.NCW))
@ -817,9 +817,9 @@ public class DTypeTests extends BaseDL4JTest {
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.updater(new Adam(1e-2)) .updater(new Adam(1e-2))
.list() .list()
.layer(new SpaceToBatchLayer.Builder().blocks(1, 1).build()) .layer(SpaceToBatchLayer.builder().blockSize(1, 1).build())
.layer(new SpaceToDepthLayer.Builder().blocks(2).build()) .layer(SpaceToDepthLayer.builder().blockSize(2).build())
.layer(new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build()) .layer(OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.convolutional(28, 28, 5)) .inputType(InputType.convolutional(28, 28, 5))
.build(); .build();
@ -880,16 +880,16 @@ public class DTypeTests extends BaseDL4JTest {
LayerConfiguration secondLast; LayerConfiguration secondLast;
switch (outputLayer) { switch (outputLayer) {
case 0: case 0:
ol = new RnnOutputLayer.Builder().nOut(5).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = RnnOutputLayer.builder().nOut(5).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new SimpleRnn.Builder().nOut(5).activation(Activation.TANH).build(); secondLast = SimpleRnn.builder().nOut(5).activation(Activation.TANH).build();
break; break;
case 1: case 1:
ol = new RnnLossLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(); ol = RnnLossLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new SimpleRnn.Builder().nOut(5).activation(Activation.TANH).build(); secondLast = SimpleRnn.builder().nOut(5).activation(Activation.TANH).build();
break; break;
case 2: case 2:
ol = new OutputLayer.Builder().nOut(5).build(); ol = OutputLayer.builder().nOut(5).build();
secondLast = new LastTimeStep(new SimpleRnn.Builder().nOut(5).activation(Activation.TANH).build()); secondLast = LastTimeStep.builder().underlying(SimpleRnn.builder().nOut(5).activation(Activation.TANH).build()).build();
break; break;
default: default:
throw new RuntimeException(); throw new RuntimeException();
@ -899,15 +899,15 @@ public class DTypeTests extends BaseDL4JTest {
.dataType(networkDtype) .dataType(networkDtype)
.convolutionMode(ConvolutionMode.Same) .convolutionMode(ConvolutionMode.Same)
.updater(new Adam(1e-2)) .updater(new Adam(1e-2))
.list()
.layer(new LSTM.Builder().nIn(5).nOut(5).activation(Activation.TANH).build()) .layer(LSTM.builder().nIn(5).nOut(5).activation(Activation.TANH).build())
.layer(new GravesLSTM.Builder().nIn(5).nOut(5).activation(Activation.TANH).build()) .layer(GravesLSTM.builder().nIn(5).nOut(5).activation(Activation.TANH).build())
.layer(new DenseLayer.Builder().nOut(5).build()) .layer(DenseLayer.builder().nOut(5).build())
.layer(new GravesBidirectionalLSTM.Builder().nIn(5).nOut(5).activation(Activation.TANH).build()) .layer(GravesBidirectionalLSTM.builder().nIn(5).nOut(5).activation(Activation.TANH).build())
.layer(new Bidirectional(new LSTM.Builder().nIn(5).nOut(5).activation(Activation.TANH).build())) .layer(Bidirectional.builder(LSTM.builder().nIn(5).nOut(5).activation(Activation.TANH).build()).build())
.layer(new TimeDistributed(new DenseLayer.Builder().nIn(10).nOut(5).activation(Activation.TANH).build())) .layer(TimeDistributed.builder().underlying(DenseLayer.builder().nIn(10).nOut(5).activation(Activation.TANH).build()).build())
.layer(new SimpleRnn.Builder().nIn(5).nOut(5).build()) .layer(SimpleRnn.builder().nIn(5).nOut(5).build())
.layer(new MaskZeroLayer.Builder().underlying(new SimpleRnn.Builder().nIn(5).nOut(5).build()).maskValue(0.0).build()) .layer(MaskZeroLayer.builder().underlying(SimpleRnn.builder().nIn(5).nOut(5).build()).maskingValue(0.0).build())
.layer(secondLast) .layer(secondLast)
.layer(ol) .layer(ol)
.build(); .build();
@ -986,14 +986,14 @@ public class DTypeTests extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.dist(new UniformDistribution(-6, 6)) .dist(new UniformDistribution(-6, 6))
.layer(new PrimaryCapsules.Builder(primaryCapsDim, primarpCapsChannel) .layer(PrimaryCapsules.builder(primaryCapsDim, primarpCapsChannel)
.kernelSize(3, 3) .kernelSize(3, 3)
.stride(2, 2) .stride(2, 2)
.build()) .build())
.layer(new CapsuleLayer.Builder(capsule, capsuleDim, routing).build()) .layer(CapsuleLayer.builder(capsule, capsuleDim, routing).build())
.layer(new CapsuleStrengthLayer.Builder().build()) .layer(CapsuleStrengthLayer.builder().build())
.layer(new ActivationLayer.Builder(new ActivationSoftmax()).build()) .layer(ActivationLayer.builder(new ActivationSoftmax()).build())
.layer(new LossLayer.Builder(new LossNegativeLogLikelihood()).build()) .layer(LossLayer.builder().lossFunction(new LossNegativeLogLikelihood()).build())
.inputType(InputType.convolutional(height, width, inputDepth)) .inputType(InputType.convolutional(height, width, inputDepth))
.build(); .build();
@ -1062,33 +1062,33 @@ public class DTypeTests extends BaseDL4JTest {
INDArray input; INDArray input;
if (test == 0) { if (test == 0) {
if (frozen) { if (frozen) {
conf.layer("0", new FrozenLayer(new EmbeddingLayer.Builder().nIn(5).nOut(5).build()), "in"); conf.layer("0", FrozenLayer.builder(EmbeddingLayer.builder().nIn(5).nOut(5).build()).build(), "in");
} else { } else {
conf.layer("0", new EmbeddingLayer.Builder().nIn(5).nOut(5).build(), "in"); conf.layer("0", EmbeddingLayer.builder().nIn(5).nOut(5).build(), "in");
} }
input = Nd4j.zeros(networkDtype, 10, 1).muli(5).castTo(DataType.INT); input = Nd4j.zeros(networkDtype, 10, 1).muli(5).castTo(DataType.INT);
conf.setInputTypes(InputType.feedForward(1)); conf.setInputTypes(InputType.feedForward(1));
} else if (test == 1) { } else if (test == 1) {
if (frozen) { if (frozen) {
conf.layer("0", new FrozenLayer(new EmbeddingSequenceLayer.Builder().nIn(5).nOut(5).build()), "in"); conf.layer("0", FrozenLayer.builder(EmbeddingSequenceLayer.builder().nIn(5).nOut(5).build()).build(), "in");
} else { } else {
conf.layer("0", new EmbeddingSequenceLayer.Builder().nIn(5).nOut(5).build(), "in"); conf.layer("0", EmbeddingSequenceLayer.builder().nIn(5).nOut(5).build(), "in");
} }
conf.layer("gp", new GlobalPoolingLayer.Builder(PoolingType.PNORM).pnorm(2).poolingDimensions(2).build(), "0"); conf.layer("gp", GlobalPoolingLayer.builder(PoolingType.PNORM).pnorm(2).poolingDimensions(2).build(), "0");
input = Nd4j.zeros(networkDtype, 10, 1, 5).muli(5).castTo(DataType.INT); input = Nd4j.zeros(networkDtype, 10, 1, 5).muli(5).castTo(DataType.INT);
conf.setInputTypes(InputType.recurrent(1)); conf.setInputTypes(InputType.recurrent(1));
} else { } else {
conf.layer("0", new RepeatVector.Builder().repetitionFactor(5).nOut(5).build(), "in"); conf.layer("0", RepeatVector.builder().repetitionFactor(5).nOut(5).build(), "in");
conf.layer("gp", new GlobalPoolingLayer.Builder(PoolingType.SUM).build(), "0"); conf.layer("gp", GlobalPoolingLayer.builder(PoolingType.SUM).build(), "0");
input = Nd4j.zeros(networkDtype, 10, 5); input = Nd4j.zeros(networkDtype, 10, 5);
conf.setInputTypes(InputType.feedForward(5)); conf.setInputTypes(InputType.feedForward(5));
} }
conf.appendLayer("el", new ElementWiseMultiplicationLayer.Builder().nOut(5).build()) conf.appendLayer("el", ElementWiseMultiplicationLayer.builder().nOut(5).build())
.appendLayer("ae", new AutoEncoder.Builder().nOut(5).build()) .appendLayer("ae", AutoEncoder.builder().nOut(5).build())
.appendLayer("prelu", new PReLULayer.Builder().nOut(5).inputShape(5).build()) .appendLayer("prelu", PReLULayer.builder().nOut(5).inputShape(5).build())
.appendLayer("out", new OutputLayer.Builder().nOut(10).build()); .appendLayer("out", OutputLayer.builder().nOut(10).build());
ComputationGraph net = new ComputationGraph(conf.build()); ComputationGraph net = new ComputationGraph(conf.build());
net.init(); net.init();
@ -1153,34 +1153,34 @@ public class DTypeTests extends BaseDL4JTest {
switch (test) { switch (test) {
case 0: case 0:
b.addInputs("in") b.addInputs("in")
.addLayer("l", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(1).build(), "in") .addLayer("l", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(1).build(), "in")
.addVertex("preproc", new PreprocessorVertex(new CnnToRnnPreProcessor(28, 28, 1)), "l") .addVertex("preproc", new PreprocessorVertex(new CnnToRnnPreProcessor(28, 28, 1)), "l")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "preproc") .addLayer("out", OutputLayer.builder().nOut(10).build(), "preproc")
.setInputTypes(InputType.convolutional(28, 28, 1)) .setInputTypes(InputType.convolutional(28, 28, 1))
.setOutputs("out"); .setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 28, 28)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 28, 28)};
break; break;
case 1: case 1:
b.addInputs("in") b.addInputs("in")
.addLayer("l", new DenseLayer.Builder().nOut(16).build(), "in") .addLayer("l", DenseLayer.builder().nOut(16).build(), "in")
.addVertex("preproc", new PreprocessorVertex(new FeedForwardToCnn3DPreProcessor(2, 2, 2, 2, true)), "l") .addVertex("preproc", new PreprocessorVertex(new FeedForwardToCnn3DPreProcessor(2, 2, 2, 2, true)), "l")
.addVertex("preproc2", new PreprocessorVertex(new PermutePreprocessor(0, 2, 3, 4, 1)), "preproc") .addVertex("preproc2", new PreprocessorVertex(new PermutePreprocessor(0, 2, 3, 4, 1)), "preproc")
.addVertex("preproc3", new PreprocessorVertex(new ReshapePreprocessor(new long[]{2, 2, 2, 2}, new long[]{16}, false)), "preproc2") .addVertex("preproc3", new PreprocessorVertex(new ReshapePreprocessor(new long[]{2, 2, 2, 2}, new long[]{16}, false)), "preproc2")
.addLayer("out", new OutputLayer.Builder().nIn(16).nOut(10).build(), "preproc3") .addLayer("out", OutputLayer.builder().nIn(16).nOut(10).build(), "preproc3")
.setInputTypes(InputType.feedForward(5)) .setInputTypes(InputType.feedForward(5))
.setOutputs("out"); .setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5)};
break; break;
case 2: case 2:
b.addInputs("in") b.addInputs("in")
.addLayer("1", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(1).build(), "in") .addLayer("1", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(1).build(), "in")
.addVertex("1a", new PoolHelperVertex(), "1") .addVertex("1a", new PoolHelperVertex(), "1")
.addVertex("2", new ShiftVertex(1), "1a") .addVertex("2", new ShiftVertex(1), "1a")
.addVertex("3", new ScaleVertex(2), "2") .addVertex("3", new ScaleVertex(2), "2")
.addVertex("4", new ReshapeVertex(2, -1), "3") .addVertex("4", new ReshapeVertex(2, -1), "3")
.addVertex("5", new SubsetVertex(0, 99), "4") .addVertex("5", new SubsetVertex(0, 99), "4")
.addVertex("6", new L2NormalizeVertex(), "5") .addVertex("6", new L2NormalizeVertex(), "5")
.addLayer("out", new OCNNOutputLayer.Builder().hiddenLayerSize(10).nIn(100).build(), "6") .addLayer("out",OCNNOutputLayer.builder().hiddenLayerSize(10).nIn(100).build(), "6")
.setInputTypes(InputType.convolutional(28, 28, 1)) .setInputTypes(InputType.convolutional(28, 28, 1))
.setOutputs("out"); .setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 28, 28)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 28, 28)};
@ -1193,23 +1193,23 @@ public class DTypeTests extends BaseDL4JTest {
.addVertex("3", new StackVertex(), "2a", "2b") .addVertex("3", new StackVertex(), "2a", "2b")
.addVertex("4", new DuplicateToTimeSeriesVertex("in3"), "3") .addVertex("4", new DuplicateToTimeSeriesVertex("in3"), "3")
.addVertex("5", new ReverseTimeSeriesVertex(), "4") .addVertex("5", new ReverseTimeSeriesVertex(), "4")
.addLayer("6", new GlobalPoolingLayer(PoolingType.AVG), "5") .addLayer("6", GlobalPoolingLayer.builder(PoolingType.AVG).build(), "5")
.addVertex("7", new LastTimeStepVertex("in3"), "in3") .addVertex("7", new LastTimeStepVertex("in3"), "in3")
.addVertex("8", new MergeVertex(), "6", "7") .addVertex("8", new MergeVertex(), "6", "7")
.addVertex("9", new PreprocessorVertex(new ComposableInputPreProcessor()), "8") .addVertex("9", new PreprocessorVertex(new ComposableInputPreProcessor()), "8")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "9") .addLayer("out", OutputLayer.builder().nOut(10).build(), "9")
.setInputTypes(InputType.feedForward(8), InputType.feedForward(8), InputType.recurrent(8)) .setInputTypes(InputType.feedForward(8), InputType.feedForward(8), InputType.recurrent(8))
.setOutputs("out"); .setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 8), Nd4j.rand(networkDtype, 2, 8), Nd4j.rand(networkDtype, 2, 8, 5)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 8), Nd4j.rand(networkDtype, 2, 8), Nd4j.rand(networkDtype, 2, 8, 5)};
break; break;
case 4: case 4:
b.addInputs("in1", "in2") b.addInputs("in1", "in2")
.addLayer("1", new LSTM.Builder().nOut(8).build(), "in1") .addLayer("1", LSTM.builder().nOut(8).build(), "in1")
.addVertex("preproc1", new PreprocessorVertex(new RnnToCnnPreProcessor(2, 2, 2)), "1") .addVertex("preproc1", new PreprocessorVertex(new RnnToCnnPreProcessor(2, 2, 2)), "1")
.addVertex("preproc2", new PreprocessorVertex(new CnnToRnnPreProcessor(2, 2, 2)), "preproc1") .addVertex("preproc2", new PreprocessorVertex(new CnnToRnnPreProcessor(2, 2, 2)), "preproc1")
.addLayer("pool", new GlobalPoolingLayer(), "preproc2") .addLayer("pool", GlobalPoolingLayer.builder().build(), "preproc2")
.addLayer("pool2", new GlobalPoolingLayer(), "in2") .addLayer("pool2", GlobalPoolingLayer.builder().build(), "in2")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "pool", "pool2") .addLayer("out", OutputLayer.builder().nOut(10).build(), "pool", "pool2")
.setInputTypes(InputType.recurrent(8), InputType.convolutional(28, 28, 1)) .setInputTypes(InputType.recurrent(8), InputType.convolutional(28, 28, 1))
.setOutputs("out"); .setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 8, 5), Nd4j.rand(networkDtype, 2, 1, 28, 28)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 8, 5), Nd4j.rand(networkDtype, 2, 1, 28, 28)};
@ -1217,28 +1217,28 @@ public class DTypeTests extends BaseDL4JTest {
case 5: case 5:
b.addInputs("in1", "in2") b.addInputs("in1", "in2")
.addVertex("fv", new FrozenVertex(new ScaleVertex(2.0)), "in1") .addVertex("fv", new FrozenVertex(new ScaleVertex(2.0)), "in1")
.addLayer("1", new DenseLayer.Builder().nOut(5).build(), "fv") .addLayer("1", DenseLayer.builder().nOut(5).build(), "fv")
.addLayer("2", new DenseLayer.Builder().nOut(5).build(), "in2") .addLayer("2", DenseLayer.builder().nOut(5).build(), "in2")
.addVertex("v", new L2Vertex(), "1", "2") .addVertex("v", new L2Vertex(), "1", "2")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "v") .addLayer("out", OutputLayer.builder().nOut(10).build(), "v")
.setInputTypes(InputType.feedForward(5), InputType.feedForward(5)) .setInputTypes(InputType.feedForward(5), InputType.feedForward(5))
.setOutputs("out"); .setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5), Nd4j.rand(networkDtype, 2, 5)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5), Nd4j.rand(networkDtype, 2, 5)};
break; break;
case 6: case 6:
b.addInputs("in") b.addInputs("in")
.addLayer("1", new LSTM.Builder().nOut(5).build(), "in") .addLayer("1", LSTM.builder().nOut(5).build(), "in")
.addVertex("2", new PreprocessorVertex(new KerasFlattenRnnPreprocessor(5, 4)), "1") .addVertex("2", new PreprocessorVertex(new KerasFlattenRnnPreprocessor(5, 4)), "1")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "2") .addLayer("out", OutputLayer.builder().nOut(10).build(), "2")
.setOutputs("out") .setOutputs("out")
.setInputTypes(InputType.recurrent(5, 4)); .setInputTypes(InputType.recurrent(5, 4));
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5, 4)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5, 4)};
break; break;
case 7: case 7:
b.addInputs("in") b.addInputs("in")
.addLayer("1", new ConvolutionLayer.Builder().kernelSize(2, 2).nOut(5).convolutionMode(ConvolutionMode.Same).build(), "in") .addLayer("1", ConvolutionLayer.builder().kernelSize(2, 2).nOut(5).convolutionMode(ConvolutionMode.Same).build(), "in")
.addVertex("2", new PreprocessorVertex(new CnnToFeedForwardPreProcessor(28, 28, 5)), "1") .addVertex("2", new PreprocessorVertex(new CnnToFeedForwardPreProcessor(28, 28, 5)), "1")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "2") .addLayer("out", OutputLayer.builder().nOut(10).build(), "2")
.setOutputs("out") .setOutputs("out")
.setInputTypes(InputType.convolutional(28, 28, 1)); .setInputTypes(InputType.convolutional(28, 28, 1));
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 28, 28)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 28, 28)};
@ -1311,9 +1311,9 @@ public class DTypeTests extends BaseDL4JTest {
switch (test) { switch (test) {
case 0: case 0:
b.addInputs("in") b.addInputs("in")
.addLayer("1", new LSTM.Builder().nOut(5).build(), "in") .addLayer("1", LSTM.builder().nOut(5).build(), "in")
.addLayer("2", new LocallyConnected1D.Builder().kernelSize(2).nOut(4).build(), "1") .addLayer("2", LocallyConnected1D.builder().kernelSize(2).nOut(4).build(), "1")
.addLayer("out", new RnnOutputLayer.Builder().nOut(10).build(), "2") .addLayer("out", RnnOutputLayer.builder().nOut(10).build(), "2")
.setOutputs("out") .setOutputs("out")
.setInputTypes(InputType.recurrent(5, 2)); .setInputTypes(InputType.recurrent(5, 2));
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5, 2)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5, 2)};
@ -1321,9 +1321,9 @@ public class DTypeTests extends BaseDL4JTest {
break; break;
case 1: case 1:
b.addInputs("in") b.addInputs("in")
.addLayer("1", new ConvolutionLayer.Builder().kernelSize(2, 2).nOut(5).convolutionMode(ConvolutionMode.Same).build(), "in") .addLayer("1", ConvolutionLayer.builder().kernelSize(2, 2).nOut(5).convolutionMode(ConvolutionMode.Same).build(), "in")
.addLayer("2", new LocallyConnected2D.Builder().kernelSize(2, 2).nOut(5).build(), "1") .addLayer("2", LocallyConnected2D.builder().kernelSize(2, 2).nOut(5).build(), "1")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "2") .addLayer("out", OutputLayer.builder().nOut(10).build(), "2")
.setOutputs("out") .setOutputs("out")
.setInputTypes(InputType.convolutional(8, 8, 1)); .setInputTypes(InputType.convolutional(8, 8, 1));
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 8, 8)}; in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 8, 8)};
@ -1399,12 +1399,12 @@ public class DTypeTests extends BaseDL4JTest {
.updater(new NoOp()) .updater(new NoOp())
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.list() .list()
.layer(new LSTM.Builder().nOut(layerSize).build()) .layer(LSTM.builder().nOut(layerSize).build())
.layer(new SelfAttentionLayer.Builder().nOut(8).nHeads(2).projectInput(true).build()) .layer(SelfAttentionLayer.builder().nOut(8).nHeads(2).projectInput(true).build())
.layer(new LearnedSelfAttentionLayer.Builder().nOut(8).nHeads(2).nQueries(numQueries).projectInput(true).build()) .layer(LearnedSelfAttentionLayer.builder().nOut(8).nHeads(2).nQueries(numQueries).projectInput(true).build())
.layer(new RecurrentAttentionLayer.Builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build()) .layer(RecurrentAttentionLayer.builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build()) .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX) .layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build()) .lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn)) .inputType(InputType.recurrent(nIn))
.build(); .build();
@ -1487,12 +1487,12 @@ public class DTypeTests extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER) .weightInit(WeightInit.XAVIER)
.graphBuilder() .graphBuilder()
.addInputs("input") .addInputs("input")
.addLayer("lstmKeys", new LSTM.Builder().nOut(layerSize).build(), "input") .addLayer("lstmKeys", LSTM.builder().nOut(layerSize).build(), "input")
.addLayer("lstmQueries", new LSTM.Builder().nOut(layerSize).build(), "input") .addLayer("lstmQueries", LSTM.builder().nOut(layerSize).build(), "input")
.addLayer("lstmValues", new LSTM.Builder().nOut(layerSize).build(), "input") .addLayer("lstmValues", LSTM.builder().nOut(layerSize).build(), "input")
.addVertex("attention", new AttentionVertex.Builder().nOut(8).nHeads(2).projectInput(true).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build(), "lstmQueries", "lstmKeys", "lstmValues") .addVertex("attention", new AttentionVertex.Builder().nOut(8).nHeads(2).projectInput(true).nInQueries(layerSize).nInKeys(layerSize).nInValues(layerSize).build(), "lstmQueries", "lstmKeys", "lstmValues")
.addLayer("pooling", new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build(), "attention") .addLayer("pooling", GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build(), "attention")
.addLayer("output", new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling") .addLayer("output", OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling")
.setOutputs("output") .setOutputs("output")
.setInputTypes(InputType.recurrent(nIn)) .setInputTypes(InputType.recurrent(nIn))
.build(); .build();

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