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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(
NeuralNetConfiguration.builder()
.list(
new OutputLayer.Builder(LossFunction.MSE)
OutputLayer.builder(LossFunction.MSE)
.nIn(3)
.nOut(1)
.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()
.list(
new OutputLayer.Builder()
OutputLayer.builder()
.nIn(numInputs)
.nOut(numOutputs)
.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()
.list(
new OutputLayer.Builder()
OutputLayer.builder()
.nIn(numInputs)
.nOut(numOutputs)
.activation(Activation.IDENTITY)
@ -278,7 +278,7 @@ public class ModelTupleStreamTest {
.graphBuilder()
.addInputs("inputLayer")
.addLayer("outputLayer",
new OutputLayer.Builder()
OutputLayer.builder()
.nIn(numInputs)
.nOut(numOutputs)
.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()
.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();
@ -221,7 +221,7 @@ public class ScoringModelTest {
.graphBuilder()
.addInputs("inputLayer")
.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")
.setOutputs("outputLayer")
.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))
.weightInit(WeightInit.XAVIER)
.list()
.layer(0, new 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(0, DenseLayer.builder().activation(Activation.TANH).nIn(4).nOut(10).build())
.layer(1, OutputLayer.builder(LossFunctions.LossFunction.SQUARED_LOSS).activation(Activation.SIGMOID).nIn(10).nOut(1).build())
.build();
model = new MultiLayerNetwork(conf);
@ -543,8 +543,8 @@ public class JsonModelServerTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.list()
.layer(new DenseLayer.Builder().nIn(784).nOut(10).build())
.layer(new LossLayer.Builder().activation(Activation.SOFTMAX).build())
.layer(DenseLayer.builder().nIn(784).nOut(10).build())
.layer(LossLayer.builder().lossFunction().activation(Activation.SOFTMAX).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -600,10 +600,10 @@ public class JsonModelServerTest extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder()
.addInputs("input1", "input2")
.addLayer("L1", new DenseLayer.Builder().nIn(3).nOut(4).build(), "input1")
.addLayer("L2", new DenseLayer.Builder().nIn(3).nOut(4).build(), "input2")
.addLayer("L1", DenseLayer.builder().nIn(3).nOut(4).build(), "input1")
.addLayer("L2", DenseLayer.builder().nIn(3).nOut(4).build(), "input2")
.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")
.build();
@ -656,11 +656,11 @@ public class JsonModelServerTest extends BaseDL4JTest {
.updater(new Sgd(0.01))
.graphBuilder()
.addInputs("input")
.addLayer("L1", new DenseLayer.Builder().nIn(8).nOut(4).build(), "input")
.addLayer("out1", new OutputLayer.Builder()
.addLayer("L1", DenseLayer.builder().nIn(8).nOut(4).build(), "input")
.addLayer("out1", OutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nIn(4).nOut(3).build(), "L1")
.addLayer("out2", new OutputLayer.Builder()
.addLayer("out2", OutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MSE)
.nIn(4).nOut(2).build(), "L1")
.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() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().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())
.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)
.activation(Activation.SOFTMAX).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))
.graphBuilder()
.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")
.setOutputs("out")
.build();
@ -272,15 +272,15 @@ public class GradientSharingTrainingTest extends BaseSparkTest {
.weightInit(WeightInit.XAVIER)
.seed(12345)
.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();
} else {
conf = NeuralNetConfiguration.builder()
.weightInit(WeightInit.XAVIER)
.seed(12345)
.list()
.layer(new 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(DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build())
.layer(OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
}
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -358,7 +358,7 @@ public class GradientSharingTrainingTest extends BaseSparkTest {
.updater(new AMSGrad(0.001))
.graphBuilder()
.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")
.setOutputs("out")
.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() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().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())
.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)
.activation(Activation.SOFTMAX).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()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -127,7 +127,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(10.0)) //Intentionally huge LR
.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())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -166,7 +166,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -212,7 +212,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -249,7 +249,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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())
.build();
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()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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")
.setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf);
@ -128,7 +128,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(2.0)) //Intentionally huge LR
.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")
.setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf);
@ -169,7 +169,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(1e-6)).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")
.setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf);
@ -217,7 +217,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.0)).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")
.setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf);
@ -256,7 +256,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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")
.setOutputs("0").build();
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(10, 0.1);
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();
testSerialization(mlc, si);
@ -79,23 +79,23 @@ public class TestKryo extends BaseSparkKryoTest {
.dist(new UniformDistribution(-1, 1))
.updater(new Adam(new MapSchedule(ScheduleType.ITERATION,m)))
.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();
testSerialization(cgc, si);
//Check main layers:
Layer[] layers = new Layer[] {new OutputLayer.Builder().nIn(10).nOut(10).build(),
new RnnOutputLayer.Builder().nIn(10).nOut(10).build(), new LossLayer.Builder().build(),
new CenterLossOutputLayer.Builder().nIn(10).nOut(10).build(),
new DenseLayer.Builder().nIn(10).nOut(10).build(),
new ConvolutionLayer.Builder().nIn(10).nOut(10).build(), new SubsamplingLayer.Builder().build(),
Layer[] layers = new Layer[] {OutputLayer.builder().nIn(10).nOut(10).build(),
RnnOutputLayer.builder().nIn(10).nOut(10).build(), LossLayer.builder().lossFunction().build(),
CenterLossOutputLayer.builder().nIn(10).nOut(10).build(),
DenseLayer.builder().nIn(10).nOut(10).build(),
ConvolutionLayer.builder().nIn(10).nOut(10).build(), SubsamplingLayer.builder().build(),
new Convolution1DLayer.Builder(2, 2).nIn(10).nOut(10).build(),
new ActivationLayer.Builder().activation(Activation.TANH).build(),
new GlobalPoolingLayer.Builder().build(), new GravesLSTM.Builder().nIn(10).nOut(10).build(),
new LSTM.Builder().nIn(10).nOut(10).build(), new DropoutLayer.Builder(0.5).build(),
new BatchNormalization.Builder().build(), new LocalResponseNormalization.Builder().build()};
ActivationLayer.builder().activation(Activation.TANH).build(),
GlobalPoolingLayer.builder().build(), GravesLSTM.builder().nIn(10).nOut(10).build(),
LSTM.builder().nIn(10).nOut(10).build(), DropoutLayer.builder(0.5).build(),
BatchNormalization.builder().build(), LocalResponseNormalization.builder().build()};
for (Layer l : layers) {
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)
.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())
.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)
.build())
.build();
@ -137,9 +137,9 @@ public class TestPreProcessedData extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(Updater.RMSPROP)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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")
.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)
.build(),
"0")
@ -191,9 +191,9 @@ public class TestPreProcessedData extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(Updater.RMSPROP)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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")
.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)
.build(),
"0")

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

@ -41,7 +41,7 @@ public class TestKryoWarning {
try {
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();
TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(1).build();
@ -58,7 +58,7 @@ public class TestKryoWarning {
try {
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();
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
View File

@ -53,9 +53,9 @@ public class TestCustomLayer extends BaseSparkTest {
//Custom layers are tested more extensively in dl4j core
NeuralNetConfiguration conf =
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,
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.nIn(10).nOut(10).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)
.graphBuilder().addInputs("in")
.addLayer("l0", new DenseLayer.Builder().nIn(4).nOut(10).build(), "in")
.addLayer("l1", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.addLayer("l0", DenseLayer.builder().nIn(4).nOut(10).build(), "in")
.addLayer("l1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(2).build(), "l0")
.setOutputs("l1").build();
@ -107,8 +107,8 @@ public class TestSparkComputationGraph extends BaseSparkTest {
ComputationGraphConfiguration config = NeuralNetConfiguration.builder()
.updater(new Sgd(0.1))
.graphBuilder().addInputs("in")
.addLayer("dense", new DenseLayer.Builder().nIn(4).nOut(2).build(), "in").addLayer("out",
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3)
.addLayer("dense", DenseLayer.builder().nIn(4).nOut(2).build(), "in").addLayer("out",
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3)
.build(),
"dense")
.setOutputs("out").build();
@ -141,9 +141,9 @@ public class TestSparkComputationGraph extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().l1(0.1).l2(0.1)
.seed(123).updater(new Nesterovs(0.1, 0.9)).graphBuilder()
.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")
.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)
.activation(Activation.SOFTMAX).build(),
"0")
@ -220,9 +220,9 @@ public class TestSparkComputationGraph extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(Updater.RMSPROP)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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")
.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)
.build(),
"0")
@ -421,8 +421,8 @@ public class TestSparkComputationGraph extends BaseSparkTest {
.graphBuilder()
.addInputs("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("output",new OutputLayer.Builder().nIn(featSize / 2).nOut(2).lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).hasBias(false).build(),"dense")
.addLayer("dense",DenseLayer.builder().dropOut(0.9).nIn(featSize).nOut(featSize / 2).build(),"avg")
.addLayer("output",OutputLayer.builder().nIn(featSize / 2).nOut(2).lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).hasBias(false).build(),"dense")
.setOutputs("output")
.build();

16
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/misc/TestFrozenLayers.java
View File

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

20
.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/multilayer/TestMiscFunctions.java
View File

@ -58,8 +58,8 @@ public class TestMiscFunctions extends BaseSparkTest {
public void testFeedForwardWithKey() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(3).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3)
.layer(0, DenseLayer.builder().nIn(4).nOut(3).build())
.layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3)
.activation(Activation.SOFTMAX).build())
.build();
@ -109,9 +109,9 @@ public class TestMiscFunctions extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER)
.list()
.layer( new LSTM.Builder().nIn(4).nOut(3).build())
.layer(new GlobalPoolingLayer(PoolingType.AVG))
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3)
.layer( LSTM.builder().nIn(4).nOut(3).build())
.layer(GlobalPoolingLayer.builder(PoolingType.AVG))
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3)
.activation(Activation.SOFTMAX).build())
.build();
@ -164,9 +164,9 @@ public class TestMiscFunctions extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER)
.graphBuilder().addInputs("in1", "in2")
.addLayer("0", new DenseLayer.Builder().nIn(4).nOut(3).build(), "in1")
.addLayer("1", new DenseLayer.Builder().nIn(4).nOut(3).build(), "in2").addLayer("2",
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(6).nOut(3)
.addLayer("0", DenseLayer.builder().nIn(4).nOut(3).build(), "in1")
.addLayer("1", DenseLayer.builder().nIn(4).nOut(3).build(), "in2").addLayer("2",
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(6).nOut(3)
.activation(Activation.SOFTMAX).build(),
"0", "1")
.setOutputs("2").build();
@ -221,7 +221,7 @@ public class TestMiscFunctions extends BaseSparkTest {
int nIn = 10;
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(
new GaussianReconstructionDistribution(Activation.IDENTITY))
.nIn(nIn).nOut(5).encoderLayerSizes(12).decoderLayerSizes(13).build())
@ -261,7 +261,7 @@ public class TestMiscFunctions extends BaseSparkTest {
NeuralNetConfiguration mlc = NeuralNetConfiguration.builder()
.list().layer(0,
new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder()
org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.builder()
.reconstructionDistribution(new LossFunctionWrapper(
Activation.IDENTITY, new LossMSE()))
.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))
.l2(1e-5)
.list()
.layer(0, new DenseLayer.Builder().nIn(28 * 28).nOut(500).build())
.layer(1, new DenseLayer.Builder().nIn(500).nOut(100).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.layer(0, DenseLayer.builder().nIn(28 * 28).nOut(500).build())
.layer(1, DenseLayer.builder().nIn(500).nOut(100).build())
.layer(2, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.activation(Activation.SOFTMAX).nIn(100).nOut(10).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()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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())
.build();
return conf;
@ -79,11 +79,11 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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())
.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())
.layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nOut(10)
.layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nOut(10)
.build())
.inputType(InputType.convolutional(10, 10, 3)).build();
return conf;
@ -95,8 +95,8 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.XAVIER).updater(updater).seed(seed).graphBuilder()
.addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in").addLayer("1",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10)
.addLayer("0", DenseLayer.builder().nIn(10).nOut(10).build(), "in").addLayer("1",
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10)
.nOut(10).build(),
"0")
.setOutputs("1").build();
@ -109,11 +109,11 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.XAVIER).updater(updater).seed(seed).graphBuilder()
.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")
.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")
.addLayer("2", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nOut(10)
.addLayer("2", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nOut(10)
.build(), "1")
.setOutputs("2").setInputTypes(InputType.convolutional(10, 10, 3))
.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();
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
.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())
.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)
.activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER)
.build())
@ -167,9 +167,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
.updater(new Adam(1e-6))
.weightInit(WeightInit.XAVIER)
.list()
.layer(new BatchNormalization.Builder().nIn(4).nOut(4).build())
.layer(new 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(BatchNormalization.builder().nIn(4).nOut(4).build())
.layer(DenseLayer.builder().nIn(4).nOut(32).activation(Activation.RELU).build())
.layer(org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(32).nOut(3)
.activation(Activation.SOFTMAX).build())
.build();
@ -277,9 +277,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
}
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.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())
.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)
.build())
.build();
@ -302,9 +302,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l1(0.1).l2(0.1)
.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())
.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)
.activation(Activation.SOFTMAX).build())
.build();
@ -391,9 +391,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.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())
.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)
.activation(Activation.SOFTMAX).build())
.build();
@ -455,9 +455,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.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())
.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)
.activation(Activation.SOFTMAX).build())
.build();
@ -525,9 +525,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.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())
.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)
.activation(Activation.SOFTMAX).build())
.build();
@ -614,9 +614,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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")
.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)
.activation(Activation.SOFTMAX).build(),
"0")
@ -687,9 +687,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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())
.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)
.build())
.build();
@ -771,9 +771,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.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())
.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)
.activation(Activation.SOFTMAX).build())
.build();
@ -822,9 +822,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().updater(new RmsProp())
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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")
.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)
.activation(Activation.SOFTMAX).build(),
"0")
@ -862,7 +862,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
Nd4j.getRandom().setSeed(12345);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(new RmsProp())
.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(
new GaussianReconstructionDistribution(Activation.IDENTITY))
.build())
@ -898,7 +898,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
Nd4j.getRandom().setSeed(12345);
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().seed(12345).updater(new RmsProp())
.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(
new GaussianReconstructionDistribution(Activation.IDENTITY))
.build(), "in")
@ -938,8 +938,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(layerSize).build())
.layer(1, new OutputLayer.Builder().nIn(layerSize).nOut(nOut)
.layer(0, DenseLayer.builder().nIn(nIn).nOut(layerSize).build())
.layer(1, OutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX).lossFunction(
LossFunctions.LossFunction.MCXENT)
.build())
@ -993,8 +993,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(layerSize).build())
.layer(1, new OutputLayer.Builder().nIn(layerSize).nOut(nOut)
.layer(0, DenseLayer.builder().nIn(nIn).nOut(layerSize).build())
.layer(1, OutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX).lossFunction(
LossFunctions.LossFunction.MCXENT)
.build())
@ -1047,13 +1047,13 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
}
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.list()
.layer(new OutputLayer.Builder().nIn(4).nOut(3).build())
.layer(OutputLayer.builder().nIn(4).nOut(3).build())
.build();
ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder()
.graphBuilder()
.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")
.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()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).build())
.layer(0, DenseLayer.builder().nIn(10).nOut(10).build())
.layer(1, OutputLayer.builder().nIn(10).nOut(10).build())
.build();
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)
.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())
.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)
.activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER)
.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)
.l2(conf.getL2()).graphBuilder()
.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(),
"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()) {
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");
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");
} 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");
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");
}

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()
.setInputTypes(conf.isUseLSTM() ? InputType.recurrent(nIn)
: 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(),
"input");
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) + "");
}
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) + "");
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() + "");
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() + "");
} 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) + "");
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) + "");
}

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())
.weightInit(WeightInit.XAVIER)
.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());
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());
}
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());
} 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());
}
@ -96,22 +96,22 @@ public class ActorCriticFactorySeparateStdDense implements ActorCriticFactorySep
.weightInit(WeightInit.XAVIER)
//.regularization(true)
//.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());
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());
}
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());
} 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());
}

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())
.updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam())
.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());
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());
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())
.list()
.layer(0,
new DenseLayer.Builder()
DenseLayer.builder()
.nIn(nIn)
.nOut(conf.getNumHiddenNodes())
.activation(Activation.RELU).build()
@ -69,12 +69,12 @@ public class DQNFactoryStdDense implements DQNFactory {
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());
}
confB.layer(conf.getNumLayers(),
new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.IDENTITY)
.nIn(conf.getNumHiddenNodes())
.nOut(numOutputs)

14
.old/rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/NStepRnn.java
View File

@ -141,16 +141,16 @@ public class NStepRnn {
.graphBuilder()
.addInputs("input")
.setInputTypes(InputType.recurrent(NUM_INPUTS))
.addLayer("lstm", new LSTM.Builder().nOut(lstmLayerSize).activation(Activation.TANH).build(), "input")
.addLayer("dl", new DenseLayer.Builder().nOut(dl1Size).activation(Activation.RELU).build(), "input", "lstm")
.addLayer("dl-1", new DenseLayer.Builder().nOut(dl2Size).activation(Activation.RELU).build(), "dl")
.addLayer("lstm", LSTM.builder().nOut(lstmLayerSize).activation(Activation.TANH).build(), "input")
.addLayer("dl", DenseLayer.builder().nOut(dl1Size).activation(Activation.RELU).build(), "input", "lstm")
.addLayer("dl-1", DenseLayer.builder().nOut(dl2Size).activation(Activation.RELU).build(), "dl")
.addVertex("dl-rnn", new PreprocessorVertex(new FeedForwardToRnnPreProcessor()), "dl-1");
}
private static ITrainableNeuralNet buildActorCriticNetwork() {
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("softmax", new RnnOutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX).nOut(NUM_ACTIONS).build(), "dl-rnn", "lstm")
.addLayer("value", RnnOutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nOut(1).build(), "dl-rnn", "lstm")
.addLayer("softmax", RnnOutputLayer.builder(new ActorCriticLoss()).activation(Activation.SOFTMAX).nOut(NUM_ACTIONS).build(), "dl-rnn", "lstm")
.setOutputs("value", "softmax")
.build();
@ -164,12 +164,12 @@ public class NStepRnn {
private static ITrainableNeuralNet buildSeparateActorCriticNetwork() {
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")
.build();
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")
.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 )
.addInputs("tracker-in", "radar-in")
.layer("dl_1", new 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_1", DenseLayer.builder().activation(Activation.RELU).nOut(40).build(), "tracker-in", "radar-in")
.layer("dl_out", DenseLayer.builder().activation(Activation.RELU).nOut(40).build(), "dl_1");
}
private static ITrainableNeuralNet buildQNetwork() {
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")
.setOutputs("output")
@ -220,9 +220,9 @@ public class RobotLakeExample {
private static ITrainableNeuralNet buildActorCriticNetwork() {
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")
.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")
.setOutputs("value", "softmax")
.build();

12
.old/rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/TMazeExample.java
View File

@ -181,18 +181,18 @@ public class TMazeExample {
.graphBuilder()
.setInputTypes(InputType.recurrent(NUM_INPUTS))
.addInputs("input")
.addLayer("goal", new LSTM.Builder()
.addLayer("goal", LSTM.builder()
.nOut(40)
.activation(Activation.TANH)
.build(), "input")
.addLayer("corridor", new 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", DenseLayer.builder().nOut(40).activation(Activation.RELU).build(), "input", "goal")
.addLayer("corridor-1", DenseLayer.builder().nOut(20).activation(Activation.RELU).build(), "corridor")
.addVertex("corridor-rnn", new PreprocessorVertex(new FeedForwardToRnnPreProcessor()), "corridor-1");
}
private static ITrainableNeuralNet buildQNetwork() {
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")
.setOutputs("output")
@ -207,9 +207,9 @@ public class TMazeExample {
private static ITrainableNeuralNet buildActorCriticNetwork() {
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")
.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")
.setOutputs("value", "softmax")
.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
public void testACPolicy() throws Exception {
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()
.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());

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() {
return new LayerConfiguration[] {
new DenseLayer.Builder().nIn(INPUT).nOut(X_DIM*Y_DIM*CHANNELS).weightInit(WeightInit.NORMAL).build(),
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
new DenseLayer.Builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
new DenseLayer.Builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM).build(),
new 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(INPUT).nOut(X_DIM*Y_DIM*CHANNELS).weightInit(WeightInit.NORMAL).build(),
ActivationLayer.builder(Activation.LEAKYRELU).build(),
DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
DenseLayer.builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM).build(),
ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
DenseLayer.builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM*CHANNELS).activation(Activation.TANH)
.build()
};
}
@ -131,19 +131,19 @@ public class App {
private static LayerConfiguration[] disLayers() {
return new LayerConfiguration[]{
new 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(),
new 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
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
new DropoutLayer.Builder(1 - 0.5).build(),
new DenseLayer.Builder().nIn(X_DIM*Y_DIM*CHANNELS*4).nOut(X_DIM*Y_DIM*CHANNELS).build(),
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
new DropoutLayer.Builder(1 - 0.5).build(),
new DenseLayer.Builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
new DropoutLayer.Builder(1 - 0.5).build(),
new OutputLayer.Builder(LossFunction.XENT).nIn(X_DIM*Y_DIM).nOut(1).activation(Activation.SIGMOID).build()
DenseLayer.builder().nOut(X_DIM*Y_DIM*CHANNELS*2).build(), //input is set by setInputType on the network
ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
DropoutLayer.builder(1 - 0.5).build(),
DenseLayer.builder().nIn(X_DIM * Y_DIM*CHANNELS*2).nOut(X_DIM*Y_DIM*CHANNELS*4).build(), //HxBxC
ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
DropoutLayer.builder(1 - 0.5).build(),
DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS*4).nOut(X_DIM*Y_DIM*CHANNELS).build(),
ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
DropoutLayer.builder(1 - 0.5).build(),
DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
DropoutLayer.builder(1 - 0.5).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())
.map((layer) -> {
if (layer instanceof DenseLayer || layer instanceof OutputLayer) {
return new FrozenLayerWithBackprop(layer);
return FrozenLayerWithBackprop.builder(layer);
} else {
return layer;
}
@ -242,6 +242,7 @@ public class App {
gan.addTrainingListeners(new ScoreToChartListener("gan"));
//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(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 {
Supplier<MultiLayerNetwork> genSupplier = () -> {
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())
.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())
// 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)
.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())
.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())
.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())
.layer(6, new ActivationLayer.Builder().activation(Activation.TANH).build())
.layer(6, ActivationLayer.builder().activation(Activation.TANH).build())
.inputPreProcessor(1,
new FeedForwardToCnnPreProcessor(height / 2, width / 2, 128))
.inputPreProcessor(6, new CnnToFeedForwardPreProcessor(height, width, channels))
@ -129,17 +129,17 @@ public class MnistDCGANExample {
//.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
//.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())
.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())
.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())
.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())
.layer(4, new DropoutLayer.Builder().dropOut(0.5).build())
.layer(5, new DenseLayer.Builder().nIn(64 * 2 * 2).nOut(1).activation(Activation.SIGMOID).build())
.layer(6, new LossLayer.Builder().lossFunction(LossFunctions.LossFunction.XENT).build())
.layer(4, DropoutLayer.builder().dropOut(0.5).build())
.layer(5, DenseLayer.builder().nIn(64 * 2 * 2).nOut(1).activation(Activation.SIGMOID).build())
.layer(6, LossLayer.builder().lossFunction(LossFunctions.LossFunction.XENT.getILossFunction()).build())
.inputPreProcessor(0, new FeedForwardToCnnPreProcessor(height, width, channels))
.inputPreProcessor(4, new CnnToFeedForwardPreProcessor(2, 2, 64))
.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)
.gradientNormalizationThreshold(100)
.layer(new DenseLayer.Builder().nIn(100).nOut(256).weightInit(WeightInit.NORMAL).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
.layer(new DenseLayer.Builder().nIn(256).nOut(512).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
.layer(new DenseLayer.Builder().nIn(512).nOut(1024).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
.layer(new DenseLayer.Builder().nIn(1024).nOut(784).activation(Activation.TANH).build())
.layer(DenseLayer.builder().nIn(100).nOut(256).weightInit(WeightInit.NORMAL).build())
.layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(DenseLayer.builder().nIn(256).nOut(512).build())
.layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(DenseLayer.builder().nIn(512).nOut(1024).build())
.layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(DenseLayer.builder().nIn(1024).nOut(784).activation(Activation.TANH).build())
.build();
return new MultiLayerNetwork(genConf);
}
@ -83,16 +83,16 @@ public class MnistSimpleGAN {
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.gradientNormalizationThreshold(100)
.layer(new DenseLayer.Builder().nIn(784).nOut(1024).updater(updater).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
.layer(new DropoutLayer.Builder(1 - 0.5).build())
.layer(new DenseLayer.Builder().nIn(1024).nOut(512).updater(updater).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
.layer(new DropoutLayer.Builder(1 - 0.5).build())
.layer(new DenseLayer.Builder().nIn(512).nOut(256).updater(updater).build())
.layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
.layer(new DropoutLayer.Builder(1 - 0.5).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.XENT).nIn(256).nOut(1)
.layer(DenseLayer.builder().nIn(784).nOut(1024).updater(updater).build())
.layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(DropoutLayer.builder(1 - 0.5).build())
.layer(DenseLayer.builder().nIn(1024).nOut(512).updater(updater).build())
.layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(DropoutLayer.builder(1 - 0.5).build())
.layer(DenseLayer.builder().nIn(512).nOut(256).updater(updater).build())
.layer(ActivationLayer.builder(new ActivationLReLU(0.2)).build())
.layer(DropoutLayer.builder(1 - 0.5).build())
.layer(OutputLayer.builder(LossFunctions.LossFunction.XENT).nIn(256).nOut(1)
.activation(Activation.SIGMOID).updater(updater).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)
.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())
.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())
//.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())
.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)
.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(1, new DenseLayer.Builder().nIn(20).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
.layer(0, DenseLayer.builder().nIn(5).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.RELU).l2(0.001).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 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();
//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)
.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(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(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, 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(0, new 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(2, new 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(0, DenseLayer.builder().nIn(10).nOut(100).activation(Activation.RELU).l2(0.003).build())
.layer(1, LSTM.builder().nIn(100).nOut(100).activation(Activation.TANH).build())
.layer(2, LSTM.builder().nIn(100).nOut(100).activation(Activation.TANH).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)
.lossFunction(new LossMCXENT())
.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)
.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(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(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, 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(0, new 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(2, new 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(0, DenseLayer.builder().nIn(10).nOut(100).activation(Activation.RELU).l2(0.003).build())
.layer(1, LSTM.builder().nIn(100).nOut(100).activation(Activation.TANH).build())
.layer(2, LSTM.builder().nIn(100).nOut(100).activation(Activation.TANH).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)
.lossFunction(new LossMCXENT())
.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){
paramPrefix = l.getIndex() + "_";
} else {
paramPrefix = l.getLayerConfiguration().getLayerName() + "_";
paramPrefix = l.getLayerConfiguration().getName() + "_";
}
Map<String,INDArray> paramTable = l.getParamTable();
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))
.graphBuilder()
.addInputs("in")
.layer("0", new 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("2", new Cropping1D(1), "1")
.layer("3", new ZeroPadding1DLayer(1), "2")
.layer("out", new RnnOutputLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).nOut(nOut).build(), "3")
.layer("0", Convolution1DLayer.builder().nOut(32).activation(Activation.TANH).kernelSize(3).stride(1).build(), "in")
.layer("1", Subsampling1DLayer.builder().kernelSize(2).stride(1).poolingType(SubsamplingLayer.PoolingType.MAX.toPoolingType()).build(), "0")
.layer("2", Cropping1D.builder(1).build(), "1")
.layer("3", ZeroPadding1DLayer.builder(1).build(), "2")
.layer("out", RnnOutputLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).nOut(nOut).build(), "3")
.setInputTypes(InputType.recurrent(nOut))
.setOutputs("out")
.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)
.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(nChannels)
.stride(1, 1)
.nOut(20)
.activation(Activation.IDENTITY)
.build())
.layer(1, new SubsamplingLayer.Builder(PoolingType.MAX)
.layer(1, SubsamplingLayer.builder(PoolingType.MAX)
.kernelSize(2, 2)
.stride(2, 2)
.build())
.layer(2, new ConvolutionLayer.Builder(5, 5)
.layer(2, ConvolutionLayer.builder(5, 5)
//Note that nIn need not be specified in later layers
.stride(1, 1)
.nOut(50)
.activation(Activation.IDENTITY)
.build())
.layer(3, new SubsamplingLayer.Builder(PoolingType.MAX)
.layer(3, SubsamplingLayer.builder(PoolingType.MAX)
.kernelSize(2, 2)
.stride(2, 2)
.build())
.layer(4, new DenseLayer.Builder().activation(Activation.RELU)
.layer(4, DenseLayer.builder().activation(Activation.RELU)
.nOut(500).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.layer(5, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum)
.activation(Activation.SOFTMAX)
.build())
@ -221,7 +221,7 @@ public class CNN2DTestCases {
.seed(12345)
.build())
.removeVertexKeepConnections("predictions")
.addLayer("predictions", new OutputLayer.Builder()
.addLayer("predictions", OutputLayer.builder()
.nIn(4096)
.nOut(200) //Tiny imagenet
.build(), "fc2")
@ -321,7 +321,7 @@ public class CNN2DTestCases {
.removeVertexKeepConnections("conv2d_9")
.removeVertexAndConnections("outputs")
.addLayer("convolution2d_9",
new ConvolutionLayer.Builder(1,1)
ConvolutionLayer.builder(1,1)
.nIn(1024)
.nOut(nBoxes * (5 + nClasses))
.stride(1,1)
@ -331,10 +331,10 @@ public class CNN2DTestCases {
.build(),
"leaky_re_lu_8")
.addLayer("outputs",
new Yolo2OutputLayer.Builder()
Yolo2OutputLayer.builder()
.lambdaNoObj(lambdaNoObj)
.lambdaCoord(lambdaCoord)
.boundingBoxPriors(priors)
.boundingBoxes(priors)
.build(),
"convolution2d_9")
.setOutputs("outputs")
@ -417,32 +417,32 @@ public class CNN2DTestCases {
.weightInit(WeightInit.XAVIER)
.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(1)
.stride(1, 1)
.nOut(20)
.activation(Activation.IDENTITY)
.build())
.layer(1, new SubsamplingLayer.Builder(PoolingType.MAX)
.layer(1, SubsamplingLayer.builder(PoolingType.MAX)
.kernelSize(2, 2)
.stride(2, 2)
.build())
.layer(2, new ConvolutionLayer.Builder(5, 5)
.layer(2, ConvolutionLayer.builder(5, 5)
//Note that nIn need not be specified in later layers
.stride(1, 1)
.nOut(50)
.activation(Activation.IDENTITY)
.dropOut(0.5) //**** Dropout on conv layer
.build())
.layer(3, new SubsamplingLayer.Builder(PoolingType.MAX)
.layer(3, SubsamplingLayer.builder(PoolingType.MAX)
.kernelSize(2, 2)
.stride(2, 2)
.build())
.layer(4, new DenseLayer.Builder().activation(Activation.RELU)
.layer(4, DenseLayer.builder().activation(Activation.RELU)
.dropOut(0.5) //**** Dropout on dense layer
.nOut(500).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.layer(5, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(10)
.activation(Activation.SOFTMAX)
.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))
.convolutionMode(ConvolutionMode.Same)
.layer(new Convolution3D.Builder(3,3,3)
.layer(Convolution3D.builder(3,3,3)
.dataFormat(Convolution3D.DataFormat.NCDHW)
.nIn(nChannels)
.stride(2, 2, 2)
.nOut(8)
.activation(Activation.IDENTITY)
.build())
.layer(new Subsampling3DLayer.Builder(PoolingType.MAX)
.layer(Subsampling3DLayer.builder(PoolingType.MAX)
.kernelSize(2, 2, 2)
.stride(2, 2, 2)
.build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.layer(OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum)
.activation(Activation.SOFTMAX)
.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()))
.l1(1e-3).l2(1e-3)
.layer(new DenseLayer.Builder().activation(Activation.TANH).nOut(64).build())
.layer(new OutputLayer.Builder().nOut(10)
.layer(DenseLayer.builder().activation(Activation.TANH).nOut(64).build())
.layer(OutputLayer.builder().nOut(10)
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.build())
@ -202,11 +202,11 @@ public class MLPTestCases {
.seed(seed)
.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)
.activation(Activation.RELU)
.build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.layer(1, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.weightInit(WeightInit.XAVIER)
.activation(Activation.SOFTMAX)
.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)
.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())
.layer(1, new LSTM.Builder().nIn(lstmLayerSize).nOut(lstmLayerSize)
.layer(1, LSTM.builder().nIn(lstmLayerSize).nOut(lstmLayerSize)
.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())
.backpropType(BackpropType.TruncatedBPTT).tbpttFwdLength(tbpttLength).tbpttBackLength(tbpttLength)
@ -201,9 +201,9 @@ public class RNNTestCases {
.updater(new Adam(5e-2))
.l1(1e-3).l2(1e-3)
.layer(0, new LSTM.Builder().activation(Activation.TANH).nOut(10).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nOut(6)
.layer(0, LSTM.builder().activation(Activation.TANH).nOut(10).build())
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(OutputLayer.builder().nOut(6)
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.build())
@ -322,9 +322,9 @@ public class RNNTestCases {
.updater(new Adam(5e-2))
.l1(1e-3).l2(1e-3)
.layer(0, new Bidirectional(new LSTM.Builder().activation(Activation.TANH).nOut(10).build()))
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nOut(6)
.layer(0, Bidirectional.builder(LSTM.builder().activation(Activation.TANH).nOut(10).build()).build())
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(OutputLayer.builder().nOut(6)
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.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)
.l2(1e-4)
.layer(0, new VariationalAutoencoder.Builder()
.layer(0, VariationalAutoencoder.builder()
.activation(Activation.TANH)
.encoderLayerSizes(256, 256) //2 encoder 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 "org.projectlombok:lombok:1.18.24"
api "org.projectlombok:lombok:1.18.26"
/*Logging*/
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();
@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 sampleStdev;
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
public abstract static class Dep {
public static abstract class Dep {
protected String frame;
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.SameDiff;
import org.nd4j.common.primitives.Pair;
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,
RRELU, SIGMOID, SOFTMAX, SOFTPLUS, SOFTSIGN, TANH, RECTIFIEDTANH, SELU, SWISH,
THRESHOLDEDRELU, GELU, MISH;
@ -149,4 +151,44 @@ public enum Activation {
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())
.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())
.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)
.activation(Activation.SOFTMAX).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);
}
//Also check the NeuralNetConfiguration is serializable (required by Spark etc)
//Also check the NeuralNetConfiguration is serializable (required by Spark etc.)
NeuralNetConfiguration conf = net.getNetConfiguration();
serializeDeserializeJava(conf);
@ -317,14 +317,14 @@ public class TestUtils {
for(Layer l : layers){
//Don't use instanceof here - there are sub conv subclasses
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 {
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 {
File temp = temporaryFolder;
temp.mkdirs();
temp.deleteOnExit();
File temp = new File(temporaryFolder, "makeRandomCSV.csv");
Random rand = new Random(12345);
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
public void testExcludeStringColCSV() throws Exception {
File csvFile = temporaryFolder;
File csvFile = new File(temporaryFolder, "test.csv");
StringBuilder sb = new StringBuilder();
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.ScoreIterationListener;
import org.junit.jupiter.api.Disabled;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.Timeout;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
@ -170,11 +172,11 @@ public class DataSetIteratorTest extends BaseDL4JTest {
NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.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())
.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())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.layer(2, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.build())
.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 {
// Streaming
runCifar(false);
@ -230,11 +233,11 @@ public class DataSetIteratorTest extends BaseDL4JTest {
NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.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())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2})
.layer(1, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2})
.build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.layer(2, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.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);
//Test multiple directories
File f2a = new File(folder2, "f2a");
f2a.mkdirs();
File f2b = new File(folder2, "f2b");
f2b.mkdirs();
File f2c = new File(folder2, "f2c");
f2c.mkdirs();
d1.save(new File(f2a, "d1.bin"));
d2.save(new File(f2a, "d2.bin"));
d3.save(new File(f2b, "d3.bin"));
@ -188,8 +190,11 @@ public class TestFileIterators extends BaseDL4JTest {
//Test multiple directories
File f2a = new File(folder2, "2-f2a");
f2a.mkdirs();
File f2b = new File(folder2, "2-f2b");
f2b.mkdirs();
File f2c = new File(folder2, "2-f2C");
f2c.mkdirs();
d1.save(new File(f2a, "d1.bin"));
d2.save(new File(f2a, "d2.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()
.seed(12345)
.updater(new Sgd(0.5)).weightInit(WeightInit.XAVIER)
.layer(new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build())
.layer(new OutputLayer.Builder().nIn(4).nOut(3)
.layer(DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build())
.layer(OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
@ -221,7 +221,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
@ -250,7 +250,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
@ -300,7 +300,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(5.0)) //Intentionally huge LR
.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())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -338,7 +338,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
@ -381,7 +381,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
@ -421,11 +421,11 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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(
Activation.TANH)
.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)
.build())
.build();
@ -468,7 +468,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
@ -506,7 +506,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
@ -570,8 +570,8 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(784).nOut(32).build())
.layer(new OutputLayer.Builder().nIn(32).nOut(784).activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.MSE).build())
.layer(DenseLayer.builder().nIn(784).nOut(32).build())
.layer(OutputLayer.builder().nIn(32).nOut(784).activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.MSE).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -613,7 +613,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new AutoEncoder.Builder().nIn(784).nOut(32).build())
.layer(AutoEncoder.builder().nIn(784).nOut(32).build())
.build();
@ -656,7 +656,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new VariationalAutoencoder.Builder()
.layer(VariationalAutoencoder.builder()
.nIn(784).nOut(32)
.encoderLayerSizes(64)
.decoderLayerSizes(64)
@ -701,7 +701,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new VariationalAutoencoder.Builder()
.layer(VariationalAutoencoder.builder()
.nIn(784).nOut(32)
.encoderLayerSizes(64)
.decoderLayerSizes(64)
@ -748,8 +748,8 @@ public class TestEarlyStopping extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(784).nOut(32).build())
.layer(new OutputLayer.Builder().nIn(32).nOut(10).activation(Activation.SOFTMAX).build())
.layer(DenseLayer.builder().nIn(784).nOut(32).build())
.layer(OutputLayer.builder().nIn(32).nOut(10).activation(Activation.SOFTMAX).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -785,7 +785,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
public void testEarlyStoppingListeners() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
@ -868,14 +868,14 @@ public class TestEarlyStopping extends BaseDL4JTest {
.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(1.0)
.layer(0, new LSTM.Builder()
.layer(0, LSTM.builder()
.nIn(10)
.nOut(10)
.activation(Activation.TANH)
.gateActivationFunction(Activation.SIGMOID)
.gateActivationFunction(Activation.SIGMOID.getActivationFunction())
.dropOut(0.5)
.build())
.layer(1, new RnnOutputLayer.Builder()
.layer(1, RnnOutputLayer.builder()
.nIn(10)
.nOut(outputs)
.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()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build();
@ -124,7 +124,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(5.0)) //Intentionally huge LR
.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")
.setOutputs("0").build();
ComputationGraph net = new ComputationGraph(conf);
@ -160,7 +160,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(1e-6)).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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build();
@ -202,7 +202,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(0.0)).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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build();
@ -236,7 +236,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0").build();
@ -300,8 +300,8 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder()
.addInputs("in")
.layer("0", new 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("0", DenseLayer.builder().nIn(784).nOut(32).build(), "in")
.layer("1", OutputLayer.builder().nIn(32).nOut(784).activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.MSE).build(), "0")
.setOutputs("1")
.build();
@ -346,7 +346,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder()
.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")
.build();
@ -391,7 +391,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder()
.addInputs("in")
.layer("0", new VariationalAutoencoder.Builder()
.layer("0", VariationalAutoencoder.builder()
.nIn(784).nOut(32)
.encoderLayerSizes(64)
.decoderLayerSizes(64)
@ -439,7 +439,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.updater(new Adam(1e-5))
.graphBuilder()
.addInputs("in")
.layer("0", new VariationalAutoencoder.Builder()
.layer("0", VariationalAutoencoder.builder()
.nIn(784).nOut(32)
.encoderLayerSizes(64)
.decoderLayerSizes(64)
@ -489,8 +489,8 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder()
.addInputs("in")
.layer("0", new DenseLayer.Builder().nIn(784).nOut(32).build(), "in")
.layer("1", new OutputLayer.Builder().nIn(32).nOut(10).activation(Activation.SOFTMAX).build(), "0")
.layer("0", DenseLayer.builder().nIn(784).nOut(32).build(), "in")
.layer("1", OutputLayer.builder().nIn(32).nOut(10).activation(Activation.SOFTMAX).build(), "0")
.setOutputs("1")
.build();
@ -530,7 +530,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
.updater(new Sgd(0.001)).weightInit(WeightInit.XAVIER)
.graphBuilder()
.addInputs("in")
.layer("0", new OutputLayer.Builder().nIn(4).nOut(3)
.layer("0", OutputLayer.builder().nIn(4).nOut(3)
.activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.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)
.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())
.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)
.activation(Activation.SOFTMAX).build())
@ -180,7 +180,7 @@ public class EvalTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new Sgd(0.1))
.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())
.build();
@ -300,8 +300,8 @@ public class EvalTest extends BaseDL4JTest {
.trainingWorkspaceMode(ws)
.inferenceWorkspaceMode(ws)
.list()
.layer(new LSTM.Builder().nIn(nIn).nOut(layerSize).build())
.layer(new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut)
.layer(LSTM.builder().nIn(nIn).nOut(layerSize).build())
.layer(RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX)
.build())
.build();
@ -311,8 +311,8 @@ public class EvalTest extends BaseDL4JTest {
.trainingWorkspaceMode(ws)
.inferenceWorkspaceMode(ws)
.list()
.layer(new LSTM.Builder().nIn(nIn).nOut(layerSize).build())
.layer(new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut)
.layer(LSTM.builder().nIn(nIn).nOut(layerSize).build())
.layer(RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX).build())
.tbpttFwdLength(10).tbpttBackLength(10)
.backpropType(BackpropType.TruncatedBPTT)
@ -377,8 +377,8 @@ public class EvalTest extends BaseDL4JTest {
.inferenceWorkspaceMode(ws)
.graphBuilder()
.addInputs("in")
.addLayer("0", new LSTM.Builder().nIn(nIn).nOut(layerSize).build(), "in")
.addLayer("1", new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut)
.addLayer("0", LSTM.builder().nIn(nIn).nOut(layerSize).build(), "in")
.addLayer("1", RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX)
.build(), "0")
.setOutputs("1")
@ -390,8 +390,8 @@ public class EvalTest extends BaseDL4JTest {
.inferenceWorkspaceMode(ws)
.graphBuilder()
.addInputs("in")
.addLayer("0", new LSTM.Builder().nIn(nIn).nOut(layerSize).build(), "in")
.addLayer("1", new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut)
.addLayer("0", LSTM.builder().nIn(nIn).nOut(layerSize).build(), "in")
.addLayer("1", RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX)
.build(), "0")
.setOutputs("1")
@ -457,8 +457,8 @@ public class EvalTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
.list()
.layer(0, new LSTM.Builder().activation(Activation.TANH).nIn(3).nOut(3).build())
.layer(1, new RnnOutputLayer.Builder().activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.XENT)
.layer(0, LSTM.builder().activation(Activation.TANH).nIn(3).nOut(3).build())
.layer(1, RnnOutputLayer.builder().activation(Activation.SIGMOID).lossFunction(LossFunctions.LossFunction.XENT)
.nIn(3).nOut(1).build())
.backpropType(BackpropType.TruncatedBPTT).tbpttFwdLength(10).tbpttBackLength(10)
.build();
@ -477,9 +477,9 @@ public class EvalTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).seed(42)
.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())
.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)
.activation(Activation.SOFTMAX).build())
.build();
@ -507,8 +507,8 @@ public class EvalTest extends BaseDL4JTest {
.seed(12345)
.graphBuilder()
.addInputs("in")
.addLayer("out1", new 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("out1", 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")
.build();
@ -541,11 +541,11 @@ public class EvalTest extends BaseDL4JTest {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder()
.graphBuilder()
.addInputs("in")
.layer("0", new EmbeddingSequenceLayer.Builder().nIn(10).nOut(10).build(), "in")
.layer("1", new LSTM.Builder().nIn(10).nOut(10).build(), "0")
.layer("2", new LSTM.Builder().nIn(10).nOut(10).build(), "0")
.layer("out1", new 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("0", EmbeddingSequenceLayer.builder().nIn(10).nOut(10).build(), "in")
.layer("1", LSTM.builder().nIn(10).nOut(10).build(), "0")
.layer("2", LSTM.builder().nIn(10).nOut(10).build(), "0")
.layer("out1", RnnOutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build(), "1")
.layer("out2", RnnOutputLayer.builder().nIn(10).nOut(20).activation(Activation.SOFTMAX).build(), "2")
.setOutputs("out1", "out2")
.build();
@ -569,8 +569,8 @@ public class EvalTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.list()
.layer(new DenseLayer.Builder().nIn(4).nOut(10).build())
.layer(new OutputLayer.Builder().nIn(10).nOut(3).lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.RELU).build())
.layer(DenseLayer.builder().nIn(4).nOut(10).build())
.layer(OutputLayer.builder().nIn(10).nOut(3).lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.RELU).build())
.build();
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);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1,
new OutputLayer.Builder().nIn(4).nOut(2).activation(Activation.SOFTMAX)
.layer(0, DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1,
OutputLayer.builder().nIn(4).nOut(2).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -90,8 +90,8 @@ public class EvaluationToolsTests extends BaseDL4JTest {
DataSetIterator iter = new IrisDataSetIterator(150, 150);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1,
new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
.layer(0, DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1,
OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
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);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().weightInit(WeightInit.XAVIER).seed(12345)
.list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1,
new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
.layer(0, DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1,
OutputLayer.builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
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
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())
.build();
@ -71,7 +71,7 @@ public class RegressionEvalTest extends BaseDL4JTest {
ComputationGraphConfiguration graphConf =
NeuralNetConfiguration.builder().weightInit(WeightInit.ZERO).graphBuilder()
.addInputs("in").addLayer("0", new OutputLayer.Builder()
.addInputs("in").addLayer("0", OutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.TANH).nIn(10).nOut(5).build(), "in")
.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) {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(nOut).build()).build();
.layer(0, DenseLayer.builder().nIn(nIn).nOut(10).build())
.layer(1, OutputLayer.builder().nIn(10).nOut(nOut).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -52,8 +52,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public static MultiLayerNetwork getLSTMPlusRnnOutput(int nIn, int nOut) {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(10).build())
.layer(1, new RnnOutputLayer.Builder().nIn(10).nOut(nOut).build()).build();
.layer(0, GravesLSTM.builder().nIn(nIn).nOut(10).build())
.layer(1, RnnOutputLayer.builder().nIn(10).nOut(nOut).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -63,8 +63,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public static MultiLayerNetwork getCnnPlusOutputLayer(int depthIn, int inH, int inW, int nOut) {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().nIn(depthIn).nOut(5).build())
.layer(1, new OutputLayer.Builder().nOut(nOut).build())
.layer(0, ConvolutionLayer.builder().nIn(depthIn).nOut(5).build())
.layer(1, OutputLayer.builder().nOut(nOut).build())
.inputType(InputType.convolutional(inH, inW, depthIn)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -90,8 +90,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public void testDenseNout0() {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(0).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).build()).build();
.layer(0, DenseLayer.builder().nIn(10).nOut(0).build())
.layer(1, OutputLayer.builder().nIn(10).nOut(10).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -147,8 +147,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public void testLSTMNOut0() {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new GravesLSTM.Builder().nIn(10).nOut(0).build())
.layer(1, new RnnOutputLayer.Builder().nIn(10).nOut(10).build()).build();
.layer(0, GravesLSTM.builder().nIn(10).nOut(0).build())
.layer(1, RnnOutputLayer.builder().nIn(10).nOut(10).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -178,8 +178,8 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
public void testConvolutionalNOut0() {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().nIn(5).nOut(0).build())
.layer(1, new OutputLayer.Builder().nOut(10).build())
.layer(0, ConvolutionLayer.builder().nIn(5).nOut(0).build())
.layer(1, OutputLayer.builder().nOut(10).build())
.inputType(InputType.convolutional(10, 10, 5)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -208,9 +208,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict)
.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())
.layer(1, new OutputLayer.Builder().nOut(10).build())
.layer(1, OutputLayer.builder().nOut(10).build())
.inputType(InputType.convolutional(hIn, wIn, depthIn)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -234,9 +234,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
int wIn = 10;
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())
.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();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -266,9 +266,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
NeuralNetConfiguration conf =
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())
.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();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -299,9 +299,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try {
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())
.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();
} catch (Exception e) {
fail("Did not expect exception with default (truncate)");
@ -310,9 +310,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict)
.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())
.layer(1, new OutputLayer.Builder().nOut(10).build())
.layer(1, OutputLayer.builder().nOut(10).build())
.inputType(InputType.convolutional(hIn, wIn, depthIn)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -339,9 +339,9 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict)
.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())
.layer(1, new OutputLayer.Builder().nOut(10).build())
.layer(1, OutputLayer.builder().nOut(10).build())
.inputType(InputType.convolutional(hIn, wIn, depthIn)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -358,84 +358,84 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
@Test
public void testCnnInvalidKernel() {
assertThrows(IllegalStateException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(3, 0).build();
ConvolutionLayer.builder().kernelSize(3, 0).build();
});
}
@Test
public void testCnnInvalidKernel2() {
assertThrows(IllegalStateException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(2, 2, 2).build();
ConvolutionLayer.builder().kernelSize(2, 2, 2).build();
});
}
@Test
public void testCnnInvalidStride() {
assertThrows(IllegalStateException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(3, 3).stride(0, 1).build();
ConvolutionLayer.builder().kernelSize(3, 3).stride(0, 1).build();
});
}
@Test
public void testCnnInvalidStride2() {
assertThrows(IllegalArgumentException.class, () -> {
new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1).build();
ConvolutionLayer.builder().kernelSize(3, 3).stride(1).build();
});
}
@Test
public void testCnnInvalidPadding() {
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
public void testCnnInvalidPadding2() {
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
public void testSubsamplingInvalidKernel() {
assertThrows(IllegalStateException.class, () -> {
new SubsamplingLayer.Builder().kernelSize(3, 0).build();
SubsamplingLayer.builder().kernelSize(3, 0).build();
});
}
@Test
public void testSubsamplingInvalidKernel2() {
assertThrows(IllegalArgumentException.class, () -> {
new SubsamplingLayer.Builder().kernelSize(2).build();
SubsamplingLayer.builder().kernelSize(2).build();
});
}
@Test
public void testSubsamplingInvalidStride() {
assertThrows(IllegalStateException.class, () -> {
new SubsamplingLayer.Builder().kernelSize(3, 3).stride(0, 1).build();
SubsamplingLayer.builder().kernelSize(3, 3).stride(0, 1).build();
});
}
@Test
public void testSubsamplingInvalidStride2() {
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
public void testSubsamplingInvalidPadding() {
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
public void testSubsamplingInvalidPadding2() {
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
public void testInputNinMismatchDense() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build();
.layer(0, DenseLayer.builder().nIn(10).nOut(10).build())
.layer(1, OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -64,8 +64,8 @@ public class TestInvalidInput extends BaseDL4JTest {
@Test
public void testLabelsNOutMismatchOutputLayer() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build();
.layer(0, DenseLayer.builder().nIn(10).nOut(10).build())
.layer(1, OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -85,8 +85,8 @@ public class TestInvalidInput extends BaseDL4JTest {
@Test
public void testLabelsNOutMismatchRnnOutputLayer() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new LSTM.Builder().nIn(5).nOut(5).build())
.layer(1, new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
.layer(0, LSTM.builder().nIn(5).nOut(5).build())
.layer(1, RnnOutputLayer.builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -112,8 +112,8 @@ public class TestInvalidInput extends BaseDL4JTest {
int d = 3;
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().nIn(d).nOut(5).build())
.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build())
.layer(0, ConvolutionLayer.builder().nIn(d).nOut(5).build())
.layer(1, OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(h, w, d)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -139,8 +139,8 @@ public class TestInvalidInput extends BaseDL4JTest {
int d = 3;
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new ConvolutionLayer.Builder().nIn(d).nOut(5).build())
.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build())
.layer(0, ConvolutionLayer.builder().nIn(d).nOut(5).build())
.layer(1, OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(h, w, d)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -165,8 +165,8 @@ public class TestInvalidInput extends BaseDL4JTest {
int d = 3;
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new SubsamplingLayer.Builder().kernelSize(2, 2).build())
.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build())
.layer(0, SubsamplingLayer.builder().kernelSize(2, 2).build())
.layer(1, OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(h, w, d)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -188,8 +188,8 @@ public class TestInvalidInput extends BaseDL4JTest {
public void testInputNinMismatchLSTM() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new GravesLSTM.Builder().nIn(5).nOut(5).build())
.layer(1, new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
.layer(0, GravesLSTM.builder().nIn(5).nOut(5).build())
.layer(1, RnnOutputLayer.builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -209,8 +209,8 @@ public class TestInvalidInput extends BaseDL4JTest {
public void testInputNinMismatchBidirectionalLSTM() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new GravesBidirectionalLSTM.Builder().nIn(5).nOut(5).build())
.layer(1, new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
.layer(0, GravesBidirectionalLSTM.builder().nIn(5).nOut(5).build())
.layer(1, RnnOutputLayer.builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -231,8 +231,8 @@ public class TestInvalidInput extends BaseDL4JTest {
public void testInputNinMismatchEmbeddingLayer() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.layer(0, new EmbeddingLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build();
.layer(0, EmbeddingLayer.builder().nIn(10).nOut(10).build())
.layer(1, OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -259,13 +259,13 @@ public class TestInvalidInput extends BaseDL4JTest {
LayerConfiguration l;
switch (layerType){
case "simple":
l = new SimpleRnn.Builder().nIn(5).nOut(5).build();
l = SimpleRnn.builder().nIn(5).nOut(5).build();
break;
case "lstm":
l = new LSTM.Builder().nIn(5).nOut(5).build();
l = LSTM.builder().nIn(5).nOut(5).build();
break;
case "graves":
l = new GravesLSTM.Builder().nIn(5).nOut(5).build();
l = GravesLSTM.builder().nIn(5).nOut(5).build();
break;
default:
throw new RuntimeException();
@ -273,7 +273,7 @@ public class TestInvalidInput extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().list()
.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);
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)
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.list()
.layer(new LSTM.Builder().nOut(layerSize).build())
.layer(LSTM.builder().nOut(layerSize).build())
.layer( projectInput ?
new SelfAttentionLayer.Builder().nOut(4).nHeads(2).projectInput(true).build()
: new SelfAttentionLayer.Builder().nHeads(1).projectInput(false).build()
SelfAttentionLayer.builder().nOut(4).nHeads(2).projectInput(true).build()
: SelfAttentionLayer.builder().nHeads(1).projectInput(false).build()
)
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX)
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build())
.layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn))
.build();
@ -150,13 +149,13 @@ public class AttentionLayerTest extends BaseDL4JTest {
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.list()
.layer(new LSTM.Builder().nOut(layerSize).build())
.layer(LSTM.builder().nOut(layerSize).build())
.layer( projectInput ?
new LearnedSelfAttentionLayer.Builder().nOut(4).nHeads(2).nQueries(numQueries).projectInput(true).build()
: new LearnedSelfAttentionLayer.Builder().nHeads(1).nQueries(numQueries).projectInput(false).build()
LearnedSelfAttentionLayer.builder().nOut(4).nHeads(2).nQueries(numQueries).projectInput(true).build()
: LearnedSelfAttentionLayer.builder().nHeads(1).nQueries(numQueries).projectInput(false).build()
)
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX)
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build())
.layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn))
.build();
@ -190,13 +189,13 @@ public class AttentionLayerTest extends BaseDL4JTest {
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.list()
.layer(new LSTM.Builder().nOut(layerSize).build())
.layer(LSTM.builder().nOut(layerSize).build())
.layer( projectInput ?
new LearnedSelfAttentionLayer.Builder().nOut(4).nHeads(2).nQueries(numQueries).projectInput(true).build()
: new LearnedSelfAttentionLayer.Builder().nHeads(1).nQueries(numQueries).projectInput(false).build()
LearnedSelfAttentionLayer.builder().nOut(4).nHeads(2).nQueries(numQueries).projectInput(true).build()
: LearnedSelfAttentionLayer.builder().nHeads(1).nQueries(numQueries).projectInput(false).build()
)
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX)
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build())
.layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn))
.build();
@ -245,10 +244,10 @@ public class AttentionLayerTest extends BaseDL4JTest {
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.list()
.layer(new LSTM.Builder().nOut(layerSize).build())
.layer(new RecurrentAttentionLayer.Builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX)
.layer(LSTM.builder().nOut(layerSize).build())
.layer(RecurrentAttentionLayer.builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build())
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn))
.build();
@ -308,10 +307,10 @@ public class AttentionLayerTest extends BaseDL4JTest {
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.list()
.layer(new LSTM.Builder().nOut(layerSize).build())
.layer(new RecurrentAttentionLayer.Builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX)
.layer(LSTM.builder().nOut(layerSize).build())
.layer(RecurrentAttentionLayer.builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build())
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn))
.build();
@ -367,15 +366,15 @@ public class AttentionLayerTest extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER)
.graphBuilder()
.addInputs("input")
.addLayer("rnnKeys", new SimpleRnn.Builder().nOut(layerSize).build(), "input")
.addLayer("rnnQueries", new SimpleRnn.Builder().nOut(layerSize).build(), "input")
.addLayer("rnnValues", new SimpleRnn.Builder().nOut(layerSize).build(), "input")
.addLayer("rnnKeys", SimpleRnn.builder().nOut(layerSize).build(), "input")
.addLayer("rnnQueries", SimpleRnn.builder().nOut(layerSize).build(), "input")
.addLayer("rnnValues", SimpleRnn.builder().nOut(layerSize).build(), "input")
.addVertex("attention",
projectInput ?
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")
.addLayer("pooling", new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build(), "attention")
.addLayer("output", new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling")
.addLayer("pooling", GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build(), "attention")
.addLayer("output", OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling")
.setOutputs("output")
.setInputTypes(InputType.recurrent(nIn))
.build();
@ -431,13 +430,13 @@ public class AttentionLayerTest extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER)
.graphBuilder()
.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",
projectInput ?
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")
.addLayer("pooling", new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build(), "attention")
.addLayer("output", new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling")
.addLayer("pooling", GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build(), "attention")
.addLayer("output", OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling")
.setOutputs("output")
.setInputTypes(InputType.recurrent(nIn))
.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)
.seed(12345L)
.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())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).nOut(3).build())
.layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1,BatchNormalization.builder().useLogStd(useLogStd).nOut(3).build())
.layer(2, ActivationLayer.builder().activation(Activation.TANH).build())
.layer(3, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(3).nOut(3).build());
MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
@ -122,11 +122,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.updater(new NoOp()).seed(12345L)
.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())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).build())
.layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1,BatchNormalization.builder().useLogStd(useLogStd).build())
.layer(2, ActivationLayer.builder().activation(Activation.TANH).build())
.layer(3, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.inputType(InputType.convolutional(hw, hw, depth));
@ -193,14 +193,14 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
.updater(new NoOp())
.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())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).build())
.layer(2, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.layer(1,BatchNormalization.builder().useLogStd(useLogStd).build())
.layer(2, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2).stride(1, 1).build())
.layer(3, new BatchNormalization())
.layer(4, new ActivationLayer.Builder().activation(afn).build())
.layer(5, new OutputLayer.Builder(lf).activation(outputActivation).nOut(nOut)
.layer(3, BatchNormalization.builder().build())
.layer(4, ActivationLayer.builder().activation(afn).build())
.layer(5, OutputLayer.builder(lf).activation(outputActivation).nOut(nOut)
.build())
.inputType(InputType.convolutional(hw, hw, depth));
@ -300,12 +300,12 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.updater(new NoOp())
.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())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).build())
.layer(2, new DenseLayer.Builder().nIn(4).nOut(4).build())
.layer(3, new BatchNormalization.Builder().useLogStd(useLogStd).build())
.layer(4, new OutputLayer.Builder(lf)
.layer(1,BatchNormalization.builder().useLogStd(useLogStd).build())
.layer(2, DenseLayer.builder().nIn(4).nOut(4).build())
.layer(3,BatchNormalization.builder().useLogStd(useLogStd).build())
.layer(4, OutputLayer.builder(lf)
.activation(outputActivation).nOut(nOut)
.build());
@ -373,11 +373,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.seed(12345L)
.dist(new NormalDistribution(0, 1)).list()
.layer(0, new 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(0, DenseLayer.builder().nIn(4).nOut(3).activation(Activation.IDENTITY).build())
.layer(1,BatchNormalization.builder().useLogStd(useLogStd).lockGammaBeta(true).gamma(2.0).beta(0.5).nOut(3)
.build())
.layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(2, ActivationLayer.builder().activation(Activation.TANH).build())
.layer(3, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(3).nOut(3).build());
MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
@ -417,11 +417,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.seed(12345L)
.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())
.layer(1, new BatchNormalization.Builder().useLogStd(useLogStd).lockGammaBeta(true).gamma(2.0).beta(0.5).build())
.layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1,BatchNormalization.builder().useLogStd(useLogStd).lockGammaBeta(true).gamma(2.0).beta(0.5).build())
.layer(2, ActivationLayer.builder().activation(Activation.TANH).build())
.layer(3, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.inputType(InputType.convolutional(hw, hw, depth));
@ -460,8 +460,8 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
.setInputTypes(InputType.convolutional(height, width, channels))
.addLayer("bn", new BatchNormalization.Builder().useLogStd(useLogStd).build(), "in")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.addLayer("bn",BatchNormalization.builder().useLogStd(useLogStd).build(), "in")
.addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(numClasses).build(), "bn")
.setOutputs("out").build();
@ -531,14 +531,14 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp())
.dist(new UniformDistribution(-2, 2)).seed(12345L).graphBuilder()
.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")
.addLayer("1", new BatchNormalization.Builder().useLogStd(useLogStd).build(), "0")
.addLayer("2", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.addLayer("1",BatchNormalization.builder().useLogStd(useLogStd).build(), "0")
.addLayer("2", SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2).stride(1, 1).build(), "1")
.addLayer("3", new BatchNormalization.Builder().useLogStd(useLogStd).build(), "2")
.addLayer("4", new ActivationLayer.Builder().activation(afn).build(), "3")
.addLayer("5", new OutputLayer.Builder(lf).activation(outputActivation)
.addLayer("3",BatchNormalization.builder().useLogStd(useLogStd).build(), "2")
.addLayer("4", ActivationLayer.builder().activation(afn).build(), "3")
.addLayer("5", OutputLayer.builder(lf).activation(outputActivation)
.nOut(nOut).build(), "4")
.setOutputs("5").setInputTypes(InputType.convolutional(hw, hw, depth))
.build();

1074
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DGradientCheckTest.java
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52
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java
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@ -115,16 +115,16 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1))
.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)
.convolutionMode(mode).dataFormat(df)
.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)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(2, new DenseLayer.Builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(2, DenseLayer.builder().nOut(denseNOut).build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(2,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
@ -218,17 +218,17 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1))
.list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel)
.layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
.nIn(convNIn).nOut(convNOut1).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.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)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build())
.layer(2, new ZeroPadding3DLayer.Builder(zeroPadding).build())
.layer(3, new DenseLayer.Builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(2, ZeroPadding3DLayer.builder(zeroPadding).build())
.layer(3, DenseLayer.builder().nOut(denseNOut).build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(3,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
@ -314,14 +314,14 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER)
.dist(new NormalDistribution(0, 1))
.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)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(1, new Subsampling3DLayer.Builder(kernel)
.poolingType(pool).convolutionMode(mode).dataFormat(df).build())
.layer(2, new DenseLayer.Builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1,Subsampling3DLayer.builder(kernel)
.poolingType(pool.toPoolingType()).convolutionMode(mode).dataFormat(df).build())
.layer(2, DenseLayer.builder().nOut(denseNOut).build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(2,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,convNOut, df))
@ -401,13 +401,13 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1))
.seed(12345)
.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)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(1, new Upsampling3D.Builder(upsamplingSize[0]).dataFormat(df).build())
.layer(2, new DenseLayer.Builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1, Upsampling3D.builder(upsamplingSize[0]).dataFormat(df).build())
.layer(2, DenseLayer.builder().nOut(denseNOut).build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(2,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
@ -496,17 +496,17 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1))
.list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel)
.layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
.nIn(convNIn).nOut(convNOut1).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.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)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build())
.layer(2, new Cropping3D.Builder(cropping).build())
.layer(3, new DenseLayer.Builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(2, Cropping3D.builder(cropping).build())
.layer(3, DenseLayer.builder().nOut(denseNOut).build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(3,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
@ -595,15 +595,15 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp())
.weightInit(new NormalDistribution(0, 0.1))
.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)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(1, new Deconvolution3D.Builder().activation(afn).kernelSize(kernel)
.layer(1, Deconvolution3D.builder().activation(afn).kernelSize(kernel)
.stride(stride).nOut(dOut).hasBias(false)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputType(InputType.convolutional3D(df, depth, height, width, convNIn)).build();

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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)
.updater(new NoOp())
.dist(new UniformDistribution(-6, 6))
.layer(new PrimaryCapsules.Builder(primaryCapsDim, primarpCapsChannel)
.layer(PrimaryCapsules.builder(primaryCapsDim, primarpCapsChannel)
.kernelSize(3, 3)
.stride(2, 2)
.build())
.layer(new CapsuleLayer.Builder(capsule, capsuleDim, routing).build())
.layer(new CapsuleStrengthLayer.Builder().build())
.layer(new ActivationLayer.Builder(new ActivationSoftmax()).build())
.layer(new LossLayer.Builder(new LossNegativeLogLikelihood()).build())
.layer(CapsuleLayer.builder(capsule, capsuleDim, routing).build())
.layer(CapsuleStrengthLayer.builder().build())
.layer(ActivationLayer.builder(new ActivationSoftmax()).build())
.layer(LossLayer.builder().lossFunction(new LossNegativeLogLikelihood()).build())
.inputType(InputType.convolutional(height, width, inputDepth))
.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)
.dropOut(dropout)
.activation(Activation.TANH)
.updater(new NoOp())
.list();
.updater(new NoOp());
if(cnn){
builder.layer(new 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.layer(ConvolutionLayer.builder().kernelSize(3,3).stride(2,2).nOut(2).build());
builder.inputType(InputType.convolutional(6,6,2));
} else {
builder.layer(new DenseLayer.Builder().nOut(3).build());
builder.layer(new DenseLayer.Builder().nOut(3).build());
builder.layer(DenseLayer.builder().nOut(3).build());
builder.layer(DenseLayer.builder().nOut(3).build());
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();
//Remove spatial dropout from output layer - can't be used for 2d input
if(i == 4){
conf.getFlattenedLayerConfigurations().get(2).setIDropout(null);
conf.getFlattenedLayerConfigurations().get(2).setDropOut(null);
}
MultiLayerNetwork mln = new MultiLayerNetwork(conf);
@ -157,11 +156,11 @@ public class DropoutGradientCheck extends BaseDL4JTest {
.updater(new NoOp())
.graphBuilder()
.addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(5).nOut(5).build(), "in")
.addLayer("1", new DenseLayer.Builder().nIn(5).nOut(5).build(), "0")
.addLayer("2", new DenseLayer.Builder().nIn(5).nOut(5).build(), "0")
.addLayer("3", new DenseLayer.Builder().nIn(5).nOut(5).build(), "0")
.addLayer("out", new OutputLayer.Builder().nIn(15).nOut(5).activation(Activation.SOFTMAX)
.addLayer("0", DenseLayer.builder().nIn(5).nOut(5).build(), "in")
.addLayer("1", DenseLayer.builder().nIn(5).nOut(5).build(), "0")
.addLayer("2", DenseLayer.builder().nIn(5).nOut(5).build(), "0")
.addLayer("3", DenseLayer.builder().nIn(5).nOut(5).build(), "0")
.addLayer("out", OutputLayer.builder().nIn(15).nOut(5).activation(Activation.SOFTMAX)
.lossFunction(LossFunction.MCXENT).build(), "1", "2", "3")
.setOutputs("out")
.build();

26
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/GlobalPoolingGradientCheckTests.java
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@ -75,10 +75,10 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.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())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(pt).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1, GlobalPoolingLayer.builder().poolingType(pt).build())
.layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).build())
.build();
@ -130,12 +130,12 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).seed(12345L).list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1)
.dataFormat(nchw ? CNN2DFormat.NCHW : CNN2DFormat.NHWC)
.layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1)
.convFormat(nchw ? CNN2DFormat.NCHW : CNN2DFormat.NHWC)
.nOut(layerDepth)
.build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(pt).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1, GlobalPoolingLayer.builder().poolingType(pt).build())
.layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).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)
.updater(new NoOp())
.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())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(pt).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1, GlobalPoolingLayer.builder().poolingType(pt).build())
.layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).build())
.build();
@ -263,10 +263,10 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).convolutionMode(ConvolutionMode.Same)
.seed(12345L).list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(kernel).stride(stride)
.layer(0, ConvolutionLayer.builder().kernelSize(kernel).stride(stride)
.nOut(layerDepth).build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(pt).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1, GlobalPoolingLayer.builder().poolingType(pt).build())
.layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).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())
.list()
.layer(0,
new DenseLayer.Builder().nIn(4).nOut(3)
DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH)
.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())
.build();
@ -168,11 +168,11 @@ public class GradientCheckTests extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).updater(new NoOp())
.seed(12345L)
.list().layer(0,
new DenseLayer.Builder().nIn(4).nOut(3)
DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0, 1))
.activation(afn)
.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())
.build();
@ -259,12 +259,12 @@ public class GradientCheckTests extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.seed(12345L)
.list().layer(0,
new DenseLayer.Builder().nIn(4).nOut(3)
DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0,
1))
.updater(new NoOp())
.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))
.updater(new NoOp())
.activation(outputActivation).build())
@ -327,10 +327,10 @@ public class GradientCheckTests extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l2(0.2).l1(0.1)
.dataType(DataType.DOUBLE)
.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())
.layer(new PReLULayer.Builder().inputShape(3).sharedAxes(1).updater(new NoOp()).build())
.layer(new OutputLayer.Builder(LossFunction.MCXENT).nIn(3).nOut(3)
.layer(PReLULayer.builder().inputShape(3).sharedAxes(1).updater(new NoOp()).build())
.layer(OutputLayer.builder().lossFunction(LossFunction.MCXENT).nIn(3).nOut(3)
.weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.SOFTMAX).build())
.build();
@ -365,12 +365,12 @@ public class GradientCheckTests extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l2(0.2).l1(0.1)
.dataType(DataType.DOUBLE)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L)
.list().layer(0,
new EmbeddingLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)
.layer(0,
EmbeddingLayer.builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)
.updater(new NoOp()).activation(
Activation.TANH)
.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)
.updater(new NoOp()).activation(Activation.SOFTMAX).build())
.build();
@ -437,9 +437,9 @@ public class GradientCheckTests extends BaseDL4JTest {
.seed(12345L)
.dist(new NormalDistribution(0, 1))
.list().layer(0,
new AutoEncoder.Builder().nIn(4).nOut(3)
AutoEncoder.builder().nIn(4).nOut(3)
.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())
.build();
@ -497,13 +497,13 @@ public class GradientCheckTests extends BaseDL4JTest {
.weightInit(new UniformDistribution(0, 1))
.graphBuilder()
.addInputs("features")
.addLayer("dense", new DenseLayer.Builder().nIn(4).nOut(4)
.addLayer("dense", DenseLayer.builder().nIn(4).nOut(4)
.activation(Activation.TANH)
.build(), "features")
.addLayer("elementWiseMul", new ElementWiseMultiplicationLayer.Builder().nIn(4).nOut(4)
.addLayer("elementWiseMul", ElementWiseMultiplicationLayer.builder().nIn(4).nOut(4)
.activation(a)
.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")
.setOutputs("loss")
.build();
@ -566,12 +566,12 @@ public class GradientCheckTests extends BaseDL4JTest {
.updater(new NoOp())
.weightInit(new NormalDistribution(0, 1))
.list()
.layer(new EmbeddingSequenceLayer.Builder()
.layer(EmbeddingSequenceLayer.builder()
.nIn(8)
.nOut(4)
.outputDataFormat(seqOutputFormat)
.build())
.layer(new RnnOutputLayer.Builder().nIn(4).nOut(3).activation(Activation.TANH)
.layer(RnnOutputLayer.builder().nIn(4).nOut(3).activation(Activation.TANH)
.dataFormat(seqOutputFormat)
.lossFunction(LossFunction.MSE).build())
.build();
@ -679,12 +679,12 @@ public class GradientCheckTests extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.seed(12345L)
.list().layer(0,
new DenseLayer.Builder().nIn(4).nOut(3)
DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0,
1))
.updater(new NoOp())
.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))
.updater(new NoOp())
.activation(outputActivation).build())
@ -740,12 +740,12 @@ public class GradientCheckTests extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).updater(new NoOp())
.seed(12345L)
.list().layer(0,
new DenseLayer.Builder().nIn(4).nOut(3)
DenseLayer.builder().nIn(4).nOut(3)
.dist(new NormalDistribution(0, 1))
.hasLayerNorm(layerNorm)
.activation(afn)
.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())
.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())
.graphBuilder().addInputs("input")
.addLayer("firstLayer",
new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(),
DenseLayer.builder().nIn(4).nOut(5).activation(Activation.TANH).build(),
"input")
.addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
"firstLayer")
.setOutputs("outputLayer").build();
@ -121,13 +121,13 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1)).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")
.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")
.addVertex("merge", new MergeVertex(), "l1", "l2")
.addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5 + 5).nOut(3).build(),
"merge")
.setOutputs("outputLayer").build();
@ -178,13 +178,13 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.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")
.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")
.addVertex("elementwise", new ElementWiseVertex(op), "l1", "l2")
.addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
"elementwise")
.setOutputs("outputLayer").build();
@ -236,15 +236,15 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.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")
.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")
.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")
.addVertex("elementwise", new ElementWiseVertex(op), "l1", "l2", "l3")
.addLayer("outputLayer",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
"elementwise")
.setOutputs("outputLayer").build();
@ -299,10 +299,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.graphBuilder()
.addInputs("in")
.setOutputs("out")
.layer("l1", new 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("l1", DenseLayer.builder().nIn(3).nOut(firstSmaller ? 1 : 3).build(), "in") //[mb,3]
.layer("l2", DenseLayer.builder().nIn(3).nOut(firstSmaller ? 3 : 1).build(), "in") //[mb,1]
.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();
ComputationGraph graph = new ComputationGraph(conf);
@ -344,15 +344,15 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 0.1))
.updater(new NoOp()).graphBuilder().addInputs("input")
.addLayer("l1", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0)
.dataFormat(format)
.addLayer("l1", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).padding(0, 0)
.convFormat(format)
.nIn(2).nOut(2).activation(Activation.TANH).build(), "input")
.addLayer("l2", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1)
.padding(0, 0).dataFormat(format)
.addLayer("l2", ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1)
.padding(0, 0).convFormat(format)
.nIn(2).nOut(2).activation(Activation.TANH).build(), "input")
.addVertex("merge", new MergeVertex(), "l1", "l2")
.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)
.build(),
"merge")
@ -401,23 +401,23 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.updater(new NoOp()).graphBuilder().addInputs("input")
.setOutputs("out")
.addLayer("rnn1",
new SimpleRnn.Builder().nOut(3)
SimpleRnn.builder().nOut(3)
.activation(Activation.TANH).build(),
"input")
.addLayer("rnn2",
new SimpleRnn.Builder().nOut(3)
SimpleRnn.builder().nOut(3)
.activation(Activation.TANH).build(),
"rnn1")
.addLayer("dense1",
new DenseLayer.Builder().nOut(3)
DenseLayer.builder().nOut(3)
.activation(Activation.SIGMOID).build(),
"rnn1")
.addLayer("rnn3",
new SimpleRnn.Builder().nOut(3)
SimpleRnn.builder().nOut(3)
.activation(Activation.TANH).build(),
"dense1")
.addVertex("merge", new MergeVertex(), "rnn2", "rnn3")
.addLayer("out", new RnnOutputLayer.Builder().nOut(outSize)
.addLayer("out", RnnOutputLayer.builder().nOut(outSize)
.activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(),
@ -457,10 +457,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.weightInit(new NormalDistribution(0, 1))
.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")
.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")
.setInputTypes(InputType.recurrent(inLength,timeSeriesLength,RNNFormat.NCW))
.build();
@ -494,10 +494,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.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")
.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")
.build();
@ -548,16 +548,16 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.updater(new NoOp()).graphBuilder()
.addInputs("input1", "input2").setOutputs("out")
.addLayer("lstm1",
new LSTM.Builder().nIn(3).nOut(3)
LSTM.builder().nIn(3).nOut(3)
.activation(Activation.TANH).build(),
"input1")
.addLayer("lstm2",
new LSTM.Builder().nIn(2).nOut(4)
LSTM.builder().nIn(2).nOut(4)
.activation(Activation.SOFTSIGN).build(),
"input2")
.addVertex("lastTS", new LastTimeStepVertex("input2"), "lstm2")
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(),
"lstm1", "duplicate")
@ -598,16 +598,16 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.updater(new NoOp()).graphBuilder()
.addInputs("input").setOutputs("out")
.addLayer("lstm_a",
new LSTM.Builder().nIn(2).nOut(3)
LSTM.builder().nIn(2).nOut(3)
.activation(Activation.TANH).build(),
"input")
.addVertex("input_rev", new ReverseTimeSeriesVertex("input"), "input")
.addLayer("lstm_b",
new LSTM.Builder().nIn(2).nOut(3)
LSTM.builder().nIn(2).nOut(3)
.activation(Activation.TANH).build(),
"input_rev")
.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)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(),
"lstm_a", "lstm_b_rev")
@ -655,11 +655,11 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("i0", "i1", "i2")
.addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i0")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i1")
.addLayer("d2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i2")
.addLayer("d3", new DenseLayer.Builder().nIn(6).nOut(2).build(), "d0", "d1", "d2")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(2)
.addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "i0")
.addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "i1")
.addLayer("d2", DenseLayer.builder().nIn(2).nOut(2).build(), "i2")
.addLayer("d3", DenseLayer.builder().nIn(6).nOut(2).build(), "d0", "d1", "d2")
.addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(2)
.nOut(2).build(), "d3")
.setOutputs("out").build();
@ -698,11 +698,11 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("i0")
.addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i0")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "d0")
.addLayer("d2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "d0")
.addLayer("d3", new DenseLayer.Builder().nIn(2).nOut(2).build(), "d0")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(6)
.addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "i0")
.addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "d0")
.addLayer("d2", DenseLayer.builder().nIn(2).nOut(2).build(), "d0")
.addLayer("d3", DenseLayer.builder().nIn(2).nOut(2).build(), "d0")
.addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(6)
.nOut(2).build(), "d1", "d2", "d3")
.setOutputs("out").build();
@ -738,14 +738,14 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.updater(new NoOp()).activation(Activation.TANH).graphBuilder().addInputs("i0", "i1", "i2")
.addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i0")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i1")
.addLayer("d2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i2")
.addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "i0")
.addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "i1")
.addLayer("d2", DenseLayer.builder().nIn(2).nOut(2).build(), "i2")
.addVertex("m", new MergeVertex(), "d0", "d1", "d2")
.addLayer("D0", new DenseLayer.Builder().nIn(6).nOut(2).build(), "m")
.addLayer("D1", new DenseLayer.Builder().nIn(6).nOut(2).build(), "m")
.addLayer("D2", new DenseLayer.Builder().nIn(6).nOut(2).build(), "m")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(6)
.addLayer("D0", DenseLayer.builder().nIn(6).nOut(2).build(), "m")
.addLayer("D1", DenseLayer.builder().nIn(6).nOut(2).build(), "m")
.addLayer("D2", DenseLayer.builder().nIn(6).nOut(2).build(), "m")
.addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(6)
.nOut(2).build(), "D0", "D1", "D2")
.setOutputs("out").build();
@ -787,18 +787,18 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.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")
.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")
.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")
.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")
.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")
.addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE)
.addLayer("out", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.IDENTITY).nOut(2)
.build(), "l3", "l4")
.setOutputs("out").setInputTypes(InputType.convolutional(inH, inW, 2))
@ -839,7 +839,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.updater(new NoOp()).graphBuilder()
.addInputs("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")
.addVertex("unstack0", new UnstackVertex(0, 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("l2-2", new L2Vertex(), "unstack1", "unstack2") // x - x+
.addLayer("lossLayer",
new LossLayer.Builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
LossLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction())
.activation(Activation.SOFTMAX).build(),
"l2-1", "l2-2")
.setOutputs("lossLayer").build();
@ -911,9 +911,9 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new GaussianDistribution(0, 1))
.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")
.addLayer("cl", new CenterLossOutputLayer.Builder()
.addLayer("cl",CenterLossOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT).nIn(5).nOut(numLabels)
.alpha(1.0).lambda(lambda).gradientCheck(true)
.activation(Activation.SOFTMAX).build(), "l1")
@ -975,9 +975,9 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).seed(12345L).list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(3).build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build())
.layer(2, new CenterLossOutputLayer.Builder()
.layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(3).build())
.layer(1, GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(2,CenterLossOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT).nOut(numLabels)
.alpha(1.0).lambda(lambda).gradientCheck(true)
.activation(Activation.SOFTMAX).build())
@ -1030,10 +1030,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in1")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in2")
.addInputs("in1", "in2").addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "in1")
.addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "in2")
.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")
.setOutputs("out").build();
@ -1083,14 +1083,14 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2")
.addLayer("d0", new DenseLayer.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in1")
.addLayer("d1", new DenseLayer.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in2")
.addLayer("d0", DenseLayer.builder().nIn(layerSizes).nOut(layerSizes).build(), "in1")
.addLayer("d1", DenseLayer.builder().nIn(layerSizes).nOut(layerSizes).build(), "in2")
.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")
.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")
.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")
.setOutputs("out1", "out2").build();
@ -1137,17 +1137,17 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in1")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in2")
.addInputs("in1", "in2").addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "in1")
.addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "in2")
.addVertex("stack", new StackVertex(), "d0", "d1")
.addVertex("u0", new UnstackVertex(0, 2), "stack")
.addVertex("u1", new UnstackVertex(1, 2), "stack")
.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(),
"u0")
.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(),
"u1")
.setOutputs("out1", "out2").build();
@ -1198,16 +1198,16 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2")
.addLayer("d0", new SimpleRnn.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in1")
.addLayer("d1", new SimpleRnn.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in2")
.addLayer("d0", SimpleRnn.builder().nIn(layerSizes).nOut(layerSizes).build(), "in1")
.addLayer("d1", SimpleRnn.builder().nIn(layerSizes).nOut(layerSizes).build(), "in2")
.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")
.addLayer("p1", new GlobalPoolingLayer.Builder(PoolingType.AVG).build(), "u1")
.addLayer("p2", new GlobalPoolingLayer.Builder(PoolingType.AVG).build(), "u2")
.addLayer("out1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2)
.addLayer("p1", GlobalPoolingLayer.builder(PoolingType.AVG).build(), "u1")
.addLayer("p2", GlobalPoolingLayer.builder(PoolingType.AVG).build(), "u2")
.addLayer("out1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2)
.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")
.setOutputs("out1", "out2").build();
@ -1260,14 +1260,14 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in1")
.addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in2")
.addInputs("in1", "in2").addLayer("d0", DenseLayer.builder().nIn(2).nOut(2).build(), "in1")
.addLayer("d1", DenseLayer.builder().nIn(2).nOut(2).build(), "in2")
.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(),
"d0")
.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(),
"d1")
.setOutputs("out1", "out2").build();
@ -1320,10 +1320,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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")
.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(),
"norm")
.setOutputs("out1").build();
@ -1370,11 +1370,11 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.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")
.addVertex("norm", new L2NormalizeVertex(), "d1")
.addLayer("out1",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nOut(2)
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.L2).nOut(2)
.activation(Activation.IDENTITY).build(),
"norm")
.setOutputs("out1").setInputTypes(InputType.convolutional(h, w, dIn)).build();
@ -1420,9 +1420,9 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L)
.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")
.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")
.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)
.dataType(DataType.DOUBLE)
.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())
.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())
.build();
MultiLayerNetwork mln = new MultiLayerNetwork(conf);
@ -161,10 +161,10 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.updater(new NoOp())
.dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1.0)).seed(12345L).list()
.layer(0, new SimpleRnn.Builder().nIn(nIn).nOut(2).activation(Activation.TANH).build())
.layer(1, new GravesBidirectionalLSTM.Builder().nIn(2).nOut(layerSize)
.layer(0, SimpleRnn.builder().nIn(nIn).nOut(2).activation(Activation.TANH).build())
.layer(1, GravesBidirectionalLSTM.builder().nIn(2).nOut(layerSize)
.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())
.build();
@ -241,9 +241,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1)).seed(12345)
.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())
.layer(1, new OutputLayer.Builder().nIn(layerSize).nOut(nOut).lossFunction(lf)
.layer(1, OutputLayer.builder().nIn(layerSize).nOut(nOut).lossFunction(lf)
.activation(a).build())
.validateOutputLayerConfig(false)
.build();
@ -335,9 +335,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1)).seed(12345)
.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())
.layer(1, new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut).lossFunction(lf)
.layer(1, RnnOutputLayer.builder().nIn(layerSize).nOut(nOut).lossFunction(lf)
.activation(a).build())
.validateOutputLayerConfig(false)
.inputType(InputType.recurrent(nIn,tsLength, RNNFormat.NCW))
@ -368,9 +368,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 2)).seed(12345)
.graphBuilder().addInputs("in")
.addLayer("0", new SimpleRnn.Builder().nOut(layerSize)
.addLayer("0", SimpleRnn.builder().nOut(layerSize)
.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")
.setOutputs("1").validateOutputLayerConfig(false)
.setInputTypes(InputType.recurrent(nIn,tsLength,RNNFormat.NCW))
@ -401,9 +401,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.weightInit(new NormalDistribution(0,2))
.updater(new NoOp())
.list()
.layer(new LSTM.Builder().nIn(3).nOut(3).build())
.layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build())
.layer(new OutputLayer.Builder().nIn(3).nOut(3).activation(Activation.SOFTMAX).build())
.layer(LSTM.builder().nIn(3).nOut(3).build())
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(OutputLayer.builder().nIn(3).nOut(3).activation(Activation.SOFTMAX).build())
.inputType(InputType.recurrent(3))
.build();
@ -457,9 +457,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
.updater(new NoOp())
.graphBuilder()
.addInputs("in")
.layer("0", new LSTM.Builder().nIn(3).nOut(3).build(), "in")
.layer("1", new GlobalPoolingLayer.Builder().poolingType(PoolingType.AVG).build(), "0")
.layer("out", new OutputLayer.Builder().nIn(3).nOut(3).activation(Activation.SOFTMAX).build(), "1")
.layer("0", LSTM.builder().nIn(3).nOut(3).build(), "in")
.layer("1", GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build(), "0")
.layer("out", OutputLayer.builder().nIn(3).nOut(3).activation(Activation.SOFTMAX).build(), "1")
.setOutputs("out")
.setInputTypes(InputType.recurrent(3))
.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)
.seed(12345L)
.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())
.layer(1, new LocalResponseNormalization.Builder().build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(1, LocalResponseNormalization.builder().build())
.layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.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 l1;
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))
.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))
.updater(new NoOp()).build();
} 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))
.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))
.updater(new NoOp()).build();
}
@ -94,7 +94,7 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
.list()
.layer(0, l0).layer(1,
l1)
.layer(2, new RnnOutputLayer.Builder(LossFunction.MCXENT)
.layer(2, RnnOutputLayer.builder().lossFunction(LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut)
.dist(new NormalDistribution(0, 1.0)).updater(new NoOp())
@ -196,14 +196,14 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
LayerConfiguration layer;
if (graves) {
layer = new GravesLSTM.Builder().nIn(nIn).nOut(layerSize).activation(afn).build();
layer = GravesLSTM.builder().nIn(nIn).nOut(layerSize).activation(afn).build();
} 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
.layer(0, layer)
.layer(1, new RnnOutputLayer.Builder(lf).activation(outputActivation)
.layer(1, RnnOutputLayer.builder(lf).activation(outputActivation)
.nIn(layerSize).nOut(nOut).build());
MultiLayerNetwork mln = new MultiLayerNetwork(conf2.build());
@ -251,16 +251,16 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
LayerConfiguration layer;
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 {
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)
.dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1))
.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())
.build();
MultiLayerNetwork mln = new MultiLayerNetwork(conf);
@ -324,11 +324,11 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.layer(0,
new GravesBidirectionalLSTM.Builder().nIn(nIn).nOut(layerSize)
GravesBidirectionalLSTM.builder().nIn(nIn).nOut(layerSize)
.weightInit(new NormalDistribution(0, 1))
.activation(afn)
.build())
.layer(1, new RnnOutputLayer.Builder(lf).activation(outputActivation).nIn(layerSize)
.layer(1, RnnOutputLayer.builder(lf).activation(outputActivation).nIn(layerSize)
.nOut(nOut)
.dist(new NormalDistribution(0, 1)).updater(new NoOp()).build())
.build();
@ -383,12 +383,12 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345L)
.dataType(DataType.DOUBLE)
.list()
.layer(0, new GravesBidirectionalLSTM.Builder().nIn(nIn).nOut(layerSize)
.layer(0, GravesBidirectionalLSTM.builder().nIn(nIn).nOut(layerSize)
.dist(new NormalDistribution(0, 1)).updater(
Updater.NONE)
.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)
.dist(new NormalDistribution(0, 1)).updater(new NoOp()).build())
.build();
@ -432,13 +432,13 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new NoOp()).seed(12345)
.dataType(DataType.DOUBLE)
.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
.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
.layer(2, new DenseLayer.Builder().nIn(27).nOut(4).activation(Activation.TANH).build())
.layer(3, new GravesLSTM.Builder().nIn(4).nOut(3).activation(Activation.TANH).build())
.layer(4, new RnnOutputLayer.Builder().lossFunction(LossFunction.MCXENT).nIn(3).nOut(nClasses)
.layer(2, DenseLayer.builder().nIn(27).nOut(4).activation(Activation.TANH).build())
.layer(3, GravesLSTM.builder().nIn(4).nOut(3).activation(Activation.TANH).build())
.layer(4, RnnOutputLayer.builder().lossFunction(LossFunction.MCXENT).nIn(3).nOut(nClasses)
.activation(Activation.SOFTMAX).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)
.updater(new NoOp())
.dist(new UniformDistribution(-2, 2)).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build())
.layer(1, new OutputLayer.Builder().lossFunction(lossFunctions[i])
.layer(0, DenseLayer.builder().nIn(4).nOut(4).activation(Activation.TANH).build())
.layer(1, OutputLayer.builder().lossFunction(lossFunctions[i])
.activation(outputActivationFn[i]).nIn(4).nOut(nOut[i]).build())
.validateOutputLayerConfig(false)
.build();
@ -351,9 +351,9 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345)
.updater(new NoOp())
.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())
.layer(1, new LossLayer.Builder().lossFunction(lossFunctions[i])
.layer(1, LossLayer.builder().lossFunction(lossFunctions[i])
.activation(outputActivationFn[i]).build())
.validateOutputLayerConfig(false)
.build();
@ -361,7 +361,7 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
assertSame(((LossLayer) net.getLayer(1).getLayerConfiguration()).getLossFn().getClass(), lossFunctions[i]
assertSame(((LossLayer) net.getLayer(1).getLayerConfiguration()).getLossFunction().getClass(), lossFunctions[i]
.getClass());
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 NormalDistribution(0, 1))
.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())
.layer(1, new OutputLayer.Builder().lossFunction(lossFunctions[i])
.layer(1, OutputLayer.builder().lossFunction(lossFunctions[i])
.activation(outputActivationFn[i]).nIn(4).nOut(3).build())
.validateOutputLayerConfig(false)
.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())
.seed(12345L)
.list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(layerSize)
.layer(0, DenseLayer.builder().nIn(nIn).nOut(layerSize)
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH)
.hasBias(true) //ILayer 0: Always have a bias
.build())
.layer(1, new DenseLayer.Builder().nIn(layerSize).nOut(layerSize)
.layer(1, DenseLayer.builder().nIn(layerSize).nOut(layerSize)
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH)
.hasBias(denseHasBias)
.build())
.layer(2, new OutputLayer.Builder(LossFunction.MCXENT)
.layer(2, OutputLayer.builder().lossFunction(LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut)
.dist(new NormalDistribution(0, 1))
@ -144,12 +144,12 @@ public class NoBiasGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp())
.seed(12345L)
.list()
.layer(0, new LSTM.Builder().nIn(nIn).nOut(layerSize)
.layer(0, LSTM.builder().nIn(nIn).nOut(layerSize)
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH)
.build())
.layer(1, new RnnOutputLayer.Builder(LossFunction.MCXENT)
.layer(1, RnnOutputLayer.builder().lossFunction(LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut)
.dist(new NormalDistribution(0, 1))
@ -205,13 +205,13 @@ public class NoBiasGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp())
.seed(12345L)
.list()
.layer(0, new EmbeddingLayer.Builder().nIn(nIn).nOut(layerSize)
.layer(0, EmbeddingLayer.builder().nIn(nIn).nOut(layerSize)
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH)
.hasBias(embeddingHasBias)
.build())
.layer(1, new OutputLayer.Builder(LossFunction.MCXENT)
.layer(1, OutputLayer.builder().lossFunction(LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut)
.dist(new NormalDistribution(0, 1))
@ -271,17 +271,17 @@ public class NoBiasGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 1))
.list()
.layer(new ConvolutionLayer.Builder(kernel,
.layer(ConvolutionLayer.builder(kernel,
stride, padding).nIn(inputDepth)
.hasBias(false)
.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)
.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)
.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)
.nOut(4).build())
.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)
.updater(new NoOp())
.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))
.updater(new NoOp()).build())
.layer(new RnnLossLayer.Builder(lf)
.layer(RnnLossLayer.builder().lossFunction(lf)
.activation(oa)
.build())
.validateOutputLayerConfig(false).build();
@ -228,10 +228,10 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
.updater(new NoOp())
.convolutionMode(ConvolutionMode.Same)
.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))
.updater(new NoOp()).build())
.layer(new CnnLossLayer.Builder(lf)
.layer(CnnLossLayer.builder().lossFunction(lf)
.activation(oa)
.build())
.validateOutputLayerConfig(false).build();
@ -375,11 +375,11 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
.updater(new NoOp())
.convolutionMode(ConvolutionMode.Same)
.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))
.dataFormat(dataFormat)
.updater(new NoOp()).build())
.layer(new Cnn3DLossLayer.Builder(dataFormat)
.layer(Cnn3DLossLayer.builder().dataFormat(dataFormat)
.lossFunction(lf)
.activation(oa)
.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())
.weightInit(WeightInit.XAVIER)
.list()
.layer(new LSTM.Builder().nIn(nIn).nOut(3).build())
.layer(new Bidirectional(m,
.layer(LSTM.builder().nIn(nIn).nOut(3).build())
.layer(Bidirectional.builder(m,
(simple ?
new SimpleRnn.Builder().nIn(3).nOut(3).hasLayerNorm(hasLayerNorm).build() :
new LSTM.Builder().nIn(3).nOut(3).build())))
.layer(new RnnOutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX).build())
SimpleRnn.builder().nIn(3).nOut(3).hasLayerNorm(hasLayerNorm).build() :
LSTM.builder().nIn(3).nOut(3).build())).build())
.layer(RnnOutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX).build())
.build();
@ -194,9 +194,9 @@ public class RnnGradientChecks extends BaseDL4JTest {
.l1(l1s[l])
.l2(l2s[l])
.list()
.layer(new SimpleRnn.Builder().nIn(nIn).nOut(layerSize).hasLayerNorm(hasLayerNorm).build())
.layer(new SimpleRnn.Builder().nIn(layerSize).nOut(layerSize).hasLayerNorm(hasLayerNorm).build())
.layer(new RnnOutputLayer.Builder().nIn(layerSize).nOut(nOut)
.layer(SimpleRnn.builder().nIn(nIn).nOut(layerSize).hasLayerNorm(hasLayerNorm).build())
.layer(SimpleRnn.builder().nIn(layerSize).nOut(layerSize).hasLayerNorm(hasLayerNorm).build())
.layer(RnnOutputLayer.builder().nIn(layerSize).nOut(nOut)
.activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT)
.build())
.build();
@ -267,12 +267,12 @@ public class RnnGradientChecks extends BaseDL4JTest {
.activation(Activation.TANH)
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.list()
.layer(simple ? new SimpleRnn.Builder().nOut(layerSize).hasLayerNorm(hasLayerNorm).build() :
new LSTM.Builder().nOut(layerSize).build())
.layer(new LastTimeStep(simple ? new SimpleRnn.Builder().nOut(layerSize).hasLayerNorm(hasLayerNorm).build() :
new LSTM.Builder().nOut(layerSize).build()))
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX)
.layer(simple ? SimpleRnn.builder().nOut(layerSize).hasLayerNorm(hasLayerNorm).build() :
LSTM.builder().nOut(layerSize).build())
.layer(LastTimeStep.builder().underlying(simple ? SimpleRnn.builder().nOut(layerSize).hasLayerNorm(hasLayerNorm).build() :
LSTM.builder().nOut(layerSize).build()).build())
.layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn))
.build();
@ -334,9 +334,9 @@ public class RnnGradientChecks extends BaseDL4JTest {
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.list()
.layer(new LSTM.Builder().nOut(layerSize).build())
.layer(new TimeDistributed(new DenseLayer.Builder().nOut(layerSize).activation(Activation.SOFTMAX).build()))
.layer(new RnnOutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX)
.layer(LSTM.builder().nOut(layerSize).build())
.layer(TimeDistributed.builder().underlying(DenseLayer.builder().nOut(layerSize).activation(Activation.SOFTMAX).build()).build())
.layer(RnnOutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn))
.build();

58
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;
switch (inputRank){
case 2:
l1 = new DenseLayer.Builder().nOut(3).build();
l2 = new DenseLayer.Builder().nOut(3).build();
l3 = new OutputLayer.Builder().nOut(3).lossFunction(LossFunctions.LossFunction.MSE)
l1 = DenseLayer.builder().nOut(3).build();
l2 = DenseLayer.builder().nOut(3).build();
l3 = OutputLayer.builder().nOut(3).lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.TANH).build();
it = InputType.feedForward(3);
break;
case 3:
l1 = new SimpleRnn.Builder().nIn(3).nOut(3).activation(Activation.TANH).build();
l2 = new SimpleRnn.Builder().nIn(3).nOut(3).activation(Activation.TANH).build();
l3 = new RnnOutputLayer.Builder().nIn(3).nOut(3).lossFunction(LossFunctions.LossFunction.SQUARED_LOSS)
l1 = SimpleRnn.builder().nIn(3).nOut(3).activation(Activation.TANH).build();
l2 = SimpleRnn.builder().nIn(3).nOut(3).activation(Activation.TANH).build();
l3 = RnnOutputLayer.builder().nIn(3).nOut(3).lossFunction(LossFunctions.LossFunction.SQUARED_LOSS)
.activation(Activation.IDENTITY).build();
it = InputType.recurrent(3);
break;
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)
.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)
.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)
.build();
it = InputType.convolutional(5,5,1);
@ -162,19 +162,19 @@ public class UtilLayerGradientChecks extends BaseDL4JTest {
}
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.updater(new NoOp())
.activation(Activation.TANH)
.dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0,2))
.list()
.layer(l1)
.layer(new MaskLayer())
.layer(l2)
.layer(l3)
.inputType(it)
.build();
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.updater(new NoOp())
.activation(Activation.TANH)
.dataType(DataType.DOUBLE)
.dist(new NormalDistribution(0, 2))
.list()
.layer(l1)
.layer(MaskLayer.builder().build())
.layer(l2)
.layer(l3)
.inputType(it)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -201,14 +201,14 @@ public class UtilLayerGradientChecks extends BaseDL4JTest {
.seed(12345)
.updater(Updater.NONE.getIUpdaterWithDefaultConfig())
.list()
.layer(new DenseLayer.Builder().nIn(10).nOut(10)
.layer(DenseLayer.builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build())
.layer(new FrozenLayerWithBackprop(new DenseLayer.Builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()))
.layer(new FrozenLayerWithBackprop(
new DenseLayer.Builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()))
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(FrozenLayerWithBackprop.builder().underlying(DenseLayer.builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()).build())
.layer(FrozenLayerWithBackprop.builder().underlying(
DenseLayer.builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()).build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(10).build())
.build();
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())
.l2Bias(biasL2[i]).l1Bias(biasL1[i])
.updater(new NoOp()).seed(12345L).list()
.layer(0, new VariationalAutoencoder.Builder().nIn(4)
.layer(0, VariationalAutoencoder.builder().nIn(4)
.nOut(3).encoderLayerSizes(encoderSizes)
.decoderLayerSizes(decoderSizes)
.dist(new NormalDistribution(0, 1))
.activation(afn)
.build())
.layer(1, new OutputLayer.Builder(lf)
.layer(1, OutputLayer.builder(lf)
.activation(outputActivation).nIn(3).nOut(3)
.dist(new NormalDistribution(0, 1))
@ -173,7 +173,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.l1(l1).l2Bias(biasL2[i]).l1Bias(biasL1[i]).updater(new NoOp())
.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)
.pzxActivationFunction(pzxAfn)
.reconstructionDistribution(
@ -263,7 +263,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
.updater(new NoOp())
.seed(12345L).dist(new NormalDistribution(0, 1))
.list().layer(0,
new VariationalAutoencoder.Builder().nIn(inOutSize).nOut(3)
VariationalAutoencoder.builder().nIn(inOutSize).nOut(3)
.encoderLayerSizes(4).decoderLayerSizes(3)
.pzxActivationFunction(Activation.TANH)
.reconstructionDistribution(
@ -306,7 +306,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.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)
.reconstructionDistribution(
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)
.l1(l1[i]).l2(l2[i])
.convolutionMode(ConvolutionMode.Same)
.list()
.layer(new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1)
.dataFormat(format)
.layer(ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1)
.convFormat(format)
.nIn(depthIn).nOut(yoloDepth).build())//output: (5-2+0)/1+1 = 4
.layer(new Yolo2OutputLayer.Builder()
.boundingBoxPriors(bbPrior)
.layer(Yolo2OutputLayer.builder()
.boundingBoxes(bbPrior)
.build())
.inputType(InputType.convolutional(h, w, depthIn, format))
.build();
@ -234,11 +233,11 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
.dist(new GaussianDistribution(0,0.1))
.seed(12345)
.list()
.layer(new ConvolutionLayer.Builder().kernelSize(3,3).stride(1,1).nOut(4).build())
.layer(new 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(new Yolo2OutputLayer.Builder()
.boundingBoxPriors(bbPriors)
.layer(ConvolutionLayer.builder().kernelSize(3,3).stride(1,1).nOut(4).build())
.layer(SubsamplingLayer.builder().kernelSize(2,2).stride(2,2).build())
.layer(ConvolutionLayer.builder().activation(Activation.IDENTITY).kernelSize(3,3).stride(1,1).nOut(depthOut).build())
.layer(Yolo2OutputLayer.builder()
.boundingBoxes(bbPriors)
.build())
.inputType(InputType.convolutional(h,w,c))
.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())
.graphBuilder().addInputs("input")
.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",
new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
"firstLayer")
.setOutputs("outputLayer").build();
@ -83,20 +83,20 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.graphBuilder().addInputs("input")
.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(),
"input")
.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(),
"input")
.addLayer("max1",
new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2).build(),
"cnn1", "cnn2")
.addLayer("dnn1", new DenseLayer.Builder().nOut(7).build(), "max1")
.addLayer("max2", new SubsamplingLayer.Builder().build(), "max1")
.addLayer("output", new OutputLayer.Builder().nIn(7).nOut(10).activation(Activation.SOFTMAX).build(), "dnn1",
.addLayer("dnn1", DenseLayer.builder().nOut(7).build(), "max1")
.addLayer("max2", SubsamplingLayer.builder().build(), "max1")
.addLayer("output", OutputLayer.builder().nIn(7).nOut(10).activation(Activation.SOFTMAX).build(), "dnn1",
"max2")
.setOutputs("output")
.inputPreProcessor("cnn1", new FeedForwardToCnnPreProcessor(32, 32, 3))
@ -119,20 +119,20 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.graphBuilder().addInputs("input1", "input2")
.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(),
"input1")
.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(),
"input2")
.addVertex("merge1", new MergeVertex(), "cnn1", "cnn2")
.addVertex("subset1", new SubsetVertex(0, 1), "merge1")
.addLayer("dense1", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1")
.addLayer("dense2", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1")
.addLayer("dense1", 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",
"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();
String json = conf.toJson();
@ -150,8 +150,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test no inputs for a layer:
try {
NeuralNetConfiguration.builder().graphBuilder().addInputs("input1")
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build()).setOutputs("out")
.addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build()).setOutputs("out")
.build();
fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) {
@ -162,8 +162,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
// Use appendLayer on first layer
try {
NeuralNetConfiguration.builder().graphBuilder()
.appendLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build())
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build()).setOutputs("out")
.appendLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build())
.addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build()).setOutputs("out")
.build();
fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) {
@ -174,8 +174,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test no network inputs
try {
NeuralNetConfiguration.builder().graphBuilder()
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build(), "dense1")
.addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build(), "dense1")
.setOutputs("out").build();
fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) {
@ -186,8 +186,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test no network outputs
try {
NeuralNetConfiguration.builder().graphBuilder().addInputs("input1")
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build(), "dense1").build();
.addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build(), "dense1").build();
fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) {
//OK - exception is good
@ -197,8 +197,8 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test: invalid input
try {
NeuralNetConfiguration.builder().graphBuilder().addInputs("input1")
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build(), "thisDoesntExist")
.addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1")
.addLayer("out", OutputLayer.builder().nIn(2).nOut(2).build(), "thisDoesntExist")
.setOutputs("out").build();
fail("No exception thrown for invalid configuration");
} catch (IllegalStateException e) {
@ -209,10 +209,10 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
//Test: graph with cycles
try {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("input1")
.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1", "dense3")
.addLayer("dense2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "dense1")
.addLayer("dense3", new DenseLayer.Builder().nIn(2).nOut(2).build(), "dense2")
.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).lossFunction(LossFunctions.LossFunction.MSE).build(), "dense1")
.addLayer("dense1", DenseLayer.builder().nIn(2).nOut(2).build(), "input1", "dense3")
.addLayer("dense2", DenseLayer.builder().nIn(2).nOut(2).build(), "dense1")
.addLayer("dense3", DenseLayer.builder().nIn(2).nOut(2).build(), "dense2")
.addLayer("out", OutputLayer.builder().nIn(2).nOut(2).lossFunction(LossFunctions.LossFunction.MSE).build(), "dense1")
.setOutputs("out").build();
//Cycle detection happens in ComputationGraph.init()
ComputationGraph graph = new ComputationGraph(conf);
@ -229,20 +229,20 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration.builder().graphBuilder().addInputs("input1", "input2")
.setInputTypes(new InputType.InputTypeRecurrent(10, 12))
.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(),
"input1")
.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(),
"input2")
.addVertex("merge1", new MergeVertex(), "cnn1", "cnn2")
.addVertex("subset1", new SubsetVertex(0, 1), "merge1")
.addLayer("dense1", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1")
.addLayer("dense2", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1")
.addLayer("dense1", 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",
"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();
fail("No exception thrown for invalid configuration");
} catch (IllegalArgumentException e) {
@ -283,9 +283,9 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
@Test
public void testOutputOrderDoesntChangeWhenCloning() {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in")
.addLayer("out1", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in")
.addLayer("out2", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in")
.addLayer("out3", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in")
.addLayer("out1", OutputLayer.builder().nIn(1).nOut(1).build(), "in")
.addLayer("out2", OutputLayer.builder().nIn(1).nOut(1).build(), "in")
.addLayer("out3", OutputLayer.builder().nIn(1).nOut(1).build(), "in")
.validateOutputLayerConfig(false)
.setOutputs("out1", "out2", "out3").build();
@ -301,14 +301,14 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
public void testAllowDisconnectedLayers() {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in")
.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")
.addLayer("out", new RnnOutputLayer.Builder().nOut(6)
.addLayer("out", RnnOutputLayer.builder().nOut(6)
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.build(), "bidirectional")
.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")
.setOutputs("out")
.setInputTypes(new InputType.InputTypeRecurrent(10, 12))
@ -323,9 +323,9 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
public void testBidirectionalGraphSummary() {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in")
.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")
.addLayer("out", new RnnOutputLayer.Builder().nOut(6)
.addLayer("out", RnnOutputLayer.builder().nOut(6)
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.build(), "bidirectional")
@ -411,10 +411,10 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration.builder()
.graphBuilder()
.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",
!lossLayer ? new 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 ? OutputLayer.builder().nIn(10).nOut(nOut[i]).activation(activations[i]).lossFunction(lf[i]).build()
: LossLayer.builder().activation(activations[i]).lossFunction(lf[i].getILossFunction()).build(), "0")
.setOutputs("1")
.validateOutputLayerConfig(validate)
.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++) {
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(1, new LossLayer.Builder().lossFunction(lossFunctions[i])
.layer(0, DenseLayer.builder().nIn(4).nOut(nOut[i]).activation(Activation.TANH).build())
.layer(1, LossLayer.builder().lossFunction(lossFunctions[i])
.activation(outputActivationFn[i]).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() {
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())
.layer(1, new OutputLayer.Builder().nIn(2).nOut(1)
.layer(1, OutputLayer.builder().nIn(2).nOut(1)
.activation(Activation.TANH)
.dist(new NormalDistribution(0, 1)).lossFunction(LossFunctions.LossFunction.MSE)
.build())
@ -82,7 +82,7 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
@Test
public void testJson() throws Exception {
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();
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)
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.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())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.layer(1, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.build())
.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())
.layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.layer(3, SubsamplingLayer.builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.build())
.layer(4, new DenseLayer.Builder().nOut(100).activation(Activation.RELU).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.layer(4, DenseLayer.builder().nOut(100).activation(Activation.RELU).build())
.layer(5, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.build())
@ -157,15 +157,15 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed)
.l1(1e-1).l2(2e-4).dropOut(0.5).miniBatch(true)
.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())
.layer(new Upsampling2D.Builder().size(2).build())
.layer(Upsampling2D.builder().size(2).build())
.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())
.layer(new Upsampling2D.Builder().size(2).build())
.layer(4, new DenseLayer.Builder().nOut(100).activation(Activation.RELU).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.layer(Upsampling2D.builder().size(2).build())
.layer(4, DenseLayer.builder().nOut(100).activation(Activation.RELU).build())
.layer(5, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
.build())
@ -181,9 +181,9 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
public void testGlobalPoolingJson() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).seed(12345L)
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(5).build())
.layer(1, new GlobalPoolingLayer.Builder().poolingType(PoolingType.PNORM).pnorm(3).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(5).build())
.layer(1, GlobalPoolingLayer.builder().poolingType(PoolingType.PNORM).pnorm(3).build())
.layer(2, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(3).build())
.inputType(InputType.convolutional(32, 32, 1)).build();
@ -196,7 +196,7 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
@Test
public void testYaml() throws Exception {
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();
String json = conf.toYaml();
NeuralNetConfiguration from = NeuralNetConfiguration.fromYaml(json);
@ -226,8 +226,8 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
@Test
public void testClone() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().build())
.layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).build())
.layer(0, DenseLayer.builder().build())
.layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).build())
.inputPreProcessor(1, new CnnToFeedForwardPreProcessor()).build();
NeuralNetConfiguration conf2 = conf.clone();
@ -301,8 +301,8 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.layer(1, new DenseLayer.Builder().nIn(3).nOut(4).build())
.layer(2, new OutputLayer.Builder().nIn(4).nOut(5).build())
.layer(1, DenseLayer.builder().nIn(3).nOut(4).build())
.layer(2, OutputLayer.builder().nIn(4).nOut(5).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -317,8 +317,8 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build())
.layer(2, new OutputLayer.Builder().nIn(4).nOut(5).build())
.layer(0, DenseLayer.builder().nIn(3).nOut(4).build())
.layer(2, OutputLayer.builder().nIn(4).nOut(5).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -336,8 +336,8 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
public void testListOverloads() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build())
.layer(1, new OutputLayer.Builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build())
.layer(0, DenseLayer.builder().nIn(3).nOut(4).build())
.layer(1, OutputLayer.builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -350,16 +350,16 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
assertEquals(5, ol.getNOut());
NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder().seed(12345)
.layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build())
.layer(1, new OutputLayer.Builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build())
.layer(0, DenseLayer.builder().nIn(3).nOut(4).build())
.layer(1, OutputLayer.builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build())
.build();
MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
net2.init();
NeuralNetConfiguration conf3 = NeuralNetConfiguration.builder().seed(12345)
.layer(new DenseLayer.Builder().nIn(3).nOut(4).build())
.layer(DenseLayer.builder().nIn(3).nOut(4).build())
.layer(
new OutputLayer.Builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build())
OutputLayer.builder().nIn(4).nOut(5).activation(Activation.SOFTMAX).build())
.build();
MultiLayerNetwork net3 = new MultiLayerNetwork(conf3);
net3.init();
@ -375,14 +375,16 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(12345)
.updater(new Adam(1e-2))
.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())
.layer(1, new DenseLayer.Builder().nOut(100).activation(Activation.RELU).build())
.layer(2, new DenseLayer.Builder().nOut(100).activation(Activation.RELU).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10)
.layer(1, DenseLayer.builder().nOut(100).activation(Activation.RELU).build())
.layer(2, DenseLayer.builder().nOut(100).activation(Activation.RELU).build())
.layer(3, OutputLayer.builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10)
.weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(28, 28, 1)).build();
conf.init();
BaseLayerConfiguration l0 = (BaseLayerConfiguration) conf.getConf(0).getLayer();
BaseLayerConfiguration l1 = (BaseLayerConfiguration) conf.getConf(1).getLayer();
BaseLayerConfiguration l2 = (BaseLayerConfiguration) conf.getConf(2).getLayer();
@ -432,10 +434,10 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
try {
NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(!lossLayer ? new OutputLayer.Builder().nIn(10).nOut(nOut[i])
.layer(DenseLayer.builder().nIn(10).nOut(10).build())
.layer(!lossLayer ? OutputLayer.builder().nIn(10).nOut(nOut[i])
.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())
.validateOutputLayerConfig(validate)
.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.builder()
.layer(0, new DenseLayer.Builder().nIn(newNumIn).nOut(newNumOut).activation(act)
.layer(0, DenseLayer.builder().nIn(newNumIn).nOut(newNumOut).activation(act)
.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())
.build();
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
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();
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
@ -125,7 +125,7 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
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();
NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder().seed(123)
.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) {
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();
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
@ -226,10 +226,10 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
INDArray gradientW = Nd4j.ones(nIns[0], nOuts[0]);
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())
.layer(1, new 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(1,BatchNormalization.builder().nIn(nIns[1]).nOut(nOuts[1]).updater(new Sgd(0.7)).build())
.layer(2, OutputLayer.builder().nIn(nIns[2]).nOut(nOuts[2]).lossFunction(LossFunctions.LossFunction.MSE).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -287,9 +287,9 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().l1(l1)
.l2(l2)
.layer(0, new 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(2, new OutputLayer.Builder().nIn(nIns[2]).nOut(nOuts[2]).lossFunction(LossFunctions.LossFunction.MSE).build())
.layer(0, DenseLayer.builder().nIn(nIns[0]).nOut(nOuts[0]).build())
.layer(1,BatchNormalization.builder().nIn(nIns[1]).nOut(nOuts[1]).l2(0.5).build())
.layer(2, OutputLayer.builder().nIn(nIns[2]).nOut(nOuts[2]).lossFunction(LossFunctions.LossFunction.MSE).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -318,7 +318,7 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
public void testLayerPretrainConfig() {
boolean pretrain = true;
VariationalAutoencoder layer = new VariationalAutoencoder.Builder()
VariationalAutoencoder layer = VariationalAutoencoder.builder()
.nIn(10).nOut(5).updater(new Sgd(1e-1))
.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.lossfunctions.LossFunctions;
import java.util.List;
import java.util.Map;
import static org.junit.jupiter.api.Assertions.assertEquals;
@ -71,9 +72,9 @@ public class TestConstraints extends BaseDL4JTest {
.updater(new Sgd(0.0))
.dist(new NormalDistribution(0, 5))
.layer(new LSTM.Builder().nIn(12).nOut(10)
.layer(LSTM.builder().nIn(12).nOut(10)
.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();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -124,9 +125,9 @@ public class TestConstraints extends BaseDL4JTest {
.dist(new NormalDistribution(0, 5))
.biasInit(10.0)
.layer(new DenseLayer.Builder().nIn(12).nOut(10)
.layer(DenseLayer.builder().nIn(12).nOut(10)
.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();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -176,9 +177,9 @@ public class TestConstraints extends BaseDL4JTest {
.updater(new Sgd(0.0))
.dist(new NormalDistribution(0, 5))
.layer(new DenseLayer.Builder().nIn(12).nOut(10)
.layer(DenseLayer.builder().nIn(12).nOut(10)
.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();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -229,9 +230,10 @@ public class TestConstraints extends BaseDL4JTest {
.dist(new NormalDistribution(0, 5))
.biasInit(0.2)
.layer(new DenseLayer.Builder().nIn(12).nOut(10)
.constrainAllParameters(lc).build())
.layer(new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.layer(DenseLayer.builder().nIn(12).nOut(10)
.allParamConstraints(List.of(lc))
.build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -290,9 +292,9 @@ public class TestConstraints extends BaseDL4JTest {
.dist(new NormalDistribution(0, 5))
.biasInit(0.2)
.layer(new DenseLayer.Builder().nIn(12).nOut(10)
.layer(DenseLayer.builder().nIn(12).nOut(10)
.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();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -351,8 +353,8 @@ public class TestConstraints extends BaseDL4JTest {
.dist(new NormalDistribution(0,5))
.biasInit(1)
.layer(new DenseLayer.Builder().nIn(12).nOut(10).build())
.layer(new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.layer(DenseLayer.builder().nIn(12).nOut(10).build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(8).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -406,7 +408,7 @@ public class TestConstraints extends BaseDL4JTest {
.graphBuilder()
.addInputs("input_lstm", "input_cpc")
.addLayer("first_lstm_layer",
new LSTM.Builder()
LSTM.builder()
.nIn(nIn)
.nOut(lstmLayerSize)
.activation(Activation.RELU)
@ -417,7 +419,7 @@ public class TestConstraints extends BaseDL4JTest {
.addVertex("merge", new MergeVertex(),
"lastTimeStep", "input_cpc")
.addLayer("dense",
new DenseLayer.Builder()
DenseLayer.builder()
.constrainWeights(new NonNegativeConstraint())
.nIn(lstmLayerSize + 1)
.nOut(lstmLayerSize/2)
@ -425,7 +427,7 @@ public class TestConstraints extends BaseDL4JTest {
.build(),
"merge")
.addLayer("second_dense",
new DenseLayer.Builder()
DenseLayer.builder()
.constrainWeights(new NonNegativeConstraint())
.nIn(lstmLayerSize/2)
.nOut(lstmLayerSize/8)
@ -433,7 +435,7 @@ public class TestConstraints extends BaseDL4JTest {
.build(),
"dense")
.addLayer("output_layer",
new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE)
.constrainWeights(new NonNegativeConstraint())
.nIn(lstmLayerSize/8)
.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()
.dropOut(0.6)
.layer(new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(new 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).build())
.layer(DenseLayer.builder().nIn(10).nOut(10).dropOut(0.7).build())
.layer(DenseLayer.builder().nIn(10).nOut(10).dropOut(new AlphaDropout(0.5)).build())
.build();
assertEquals(new Dropout(0.6), conf.getFlattenedLayerConfigurations().get(0).getIDropout());
assertEquals(new Dropout(0.7), conf.getFlattenedLayerConfigurations().get(1).getIDropout());
assertEquals(new AlphaDropout(0.5), conf.getFlattenedLayerConfigurations().get(2).getIDropout());
assertEquals(new Dropout(0.6), conf.getFlattenedLayerConfigurations().get(0).getDropOut());
assertEquals(new Dropout(0.7), conf.getFlattenedLayerConfigurations().get(1).getDropOut());
assertEquals(new AlphaDropout(0.5), conf.getFlattenedLayerConfigurations().get(2).getDropOut());
ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder()
.dropOut( new Dropout(0.6))
.graphBuilder()
.addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in")
.addLayer("1", new 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("0", DenseLayer.builder().nIn(10).nOut(10).build(), "in")
.addLayer("1", DenseLayer.builder().nIn(10).nOut(10).dropOut(0.7).build(), "0")
.addLayer("2", DenseLayer.builder().nIn(10).nOut(10).dropOut(new AlphaDropout(0.5)).build(), "1")
.setOutputs("2")
.build();
assertEquals(new Dropout(0.6), ((LayerVertex)conf2.getVertices().get("0")).getLayerConfiguration().getIDropout());
assertEquals(new Dropout(0.7), ((LayerVertex)conf2.getVertices().get("1")).getLayerConfiguration().getIDropout());
assertEquals(new AlphaDropout(0.5), ((LayerVertex)conf2.getVertices().get("2")).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().getDropOut());
assertEquals(new AlphaDropout(0.5), ((LayerVertex)conf2.getVertices().get("2")).getLayerConfiguration().getDropOut());
}
@Test
@ -95,8 +95,8 @@ public class TestDropout extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(4).nOut(3).dropOut(d1).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MSE).dropOut(d2).nIn(3).nOut(3).build())
.layer(DenseLayer.builder().nIn(4).nOut(3).dropOut(d1).build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).dropOut(d2).nIn(3).nOut(3).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -131,8 +131,8 @@ public class TestDropout extends BaseDL4JTest {
ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder()
.graphBuilder()
.addInputs("in")
.addLayer("0", new 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("0", DenseLayer.builder().nIn(4).nOut(3).dropOut(d1).build(), "in")
.addLayer("1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).dropOut(d2).nIn(3).nOut(3).build(), "0")
.setOutputs("1")
.build();
@ -188,8 +188,8 @@ public class TestDropout extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.dropOut(id)
.layer(new DenseLayer.Builder().nIn(4).nOut(3).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(3).nOut(3).build())
.layer(DenseLayer.builder().nIn(4).nOut(3).build())
.layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(3).nOut(3).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -200,8 +200,8 @@ public class TestDropout extends BaseDL4JTest {
.dropOut(id)
.graphBuilder()
.addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(4).nOut(3).build(), "in")
.addLayer("1", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(3).nOut(3).build(), "0")
.addLayer("0", DenseLayer.builder().nIn(4).nOut(3).build(), "in")
.addLayer("1", OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(3).nOut(3).build(), "0")
.setOutputs("1")
.build();
@ -602,7 +602,7 @@ public class TestDropout extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DropoutLayer.Builder(new SpatialDropout(0.5)).build())
.layer(DropoutLayer.builder(new SpatialDropout(0.5)).build())
.build();
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()
.addInputs("input1", "input2", "input3")
.addLayer("denselayer",
new DenseLayer.Builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY)
DenseLayer.builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY)
.build(),
"input1")
/* 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",
"input2", "input3")
.addLayer("Add", new ActivationLayer.Builder().activation(Activation.IDENTITY).build(),
.addLayer("Add", ActivationLayer.builder().activation(Activation.IDENTITY).build(),
"elementwiseAdd")
.setOutputs("Add", "denselayer").build();
@ -114,7 +114,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder()
.addInputs("input1", "input2", "input3")
.addLayer("denselayer",
new DenseLayer.Builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY)
DenseLayer.builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY)
.build(),
"input1")
/* 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",
"input2", "input3")
.addLayer("Product", new ActivationLayer.Builder().activation(Activation.IDENTITY).build(),
.addLayer("Product", ActivationLayer.builder().activation(Activation.IDENTITY).build(),
"elementwiseProduct")
.setOutputs("Product", "denselayer").build();
@ -155,7 +155,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder()
.addInputs("input1", "input2")
.addLayer("denselayer",
new DenseLayer.Builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY)
DenseLayer.builder().nIn(featuresz).nOut(1).activation(Activation.IDENTITY)
.build(),
"input1")
/* 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),
"input1", "input2")
.addLayer("Subtract", new ActivationLayer.Builder().activation(Activation.IDENTITY).build(),
.addLayer("Subtract", ActivationLayer.builder().activation(Activation.IDENTITY).build(),
"elementwiseSubtract")
.setOutputs("Subtract", "denselayer").build();
@ -200,22 +200,22 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder()
.addInputs("input1", "input2", "input3")
.addLayer("dense1",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(),
DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(Activation.TANH).build(),
"input1")
.addLayer("dense2",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(),
DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(Activation.TANH).build(),
"input2")
.addLayer("dense3",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(),
DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(Activation.TANH).build(),
"input3")
.addVertex("elementwiseAdd", new ElementWiseVertex(ElementWiseVertex.Op.Add), "dense1",
"dense2", "dense3")
.addLayer("output",
new OutputLayer.Builder().nIn(midsz).nOut(outputsz)
.activation(new ActivationSigmoid())
OutputLayer.builder().nIn(midsz).nOut(outputsz)
.activation(Activation.SIGMOID)
.lossFunction(LossFunction.MSE).build(),
"elementwiseAdd")
.setOutputs("output").build();
@ -376,22 +376,22 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder()
.addInputs("input1", "input2", "input3")
.addLayer("dense1",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(),
DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(Activation.TANH).build(),
"input1")
.addLayer("dense2",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(),
DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(Activation.TANH).build(),
"input2")
.addLayer("dense3",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(),
DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(Activation.TANH).build(),
"input3")
.addVertex("elementwiseProduct", new ElementWiseVertex(ElementWiseVertex.Op.Product), "dense1",
"dense2", "dense3")
.addLayer("output",
new OutputLayer.Builder().nIn(midsz).nOut(outputsz)
.activation(new ActivationSigmoid())
OutputLayer.builder().nIn(midsz).nOut(outputsz)
.activation(Activation.SIGMOID)
.lossFunction(LossFunction.MSE).build(),
"elementwiseProduct")
.setOutputs("output").build();
@ -551,18 +551,18 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder()
.addInputs("input1", "input2")
.addLayer("dense1",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(),
DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(Activation.TANH).build(),
"input1")
.addLayer("dense2",
new DenseLayer.Builder().nIn(featuresz).nOut(midsz)
.activation(new ActivationTanH()).build(),
DenseLayer.builder().nIn(featuresz).nOut(midsz)
.activation(Activation.TANH).build(),
"input2")
.addVertex("elementwiseSubtract", new ElementWiseVertex(ElementWiseVertex.Op.Subtract),
"dense1", "dense2")
.addLayer("output",
new OutputLayer.Builder().nIn(midsz).nOut(outputsz)
.activation(new ActivationSigmoid())
OutputLayer.builder().nIn(midsz).nOut(outputsz)
.activation(Activation.SIGMOID)
.lossFunction(LossFunction.MSE).build(),
"elementwiseSubtract")
.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;
ComputationGraphConfiguration cgc = NeuralNetConfiguration.builder().graphBuilder().addInputs("input")
.addLayer("denselayer",
new DenseLayer.Builder().nIn(input.columns()).nOut(1)
DenseLayer.builder().nIn(input.columns()).nOut(1)
.activation(Activation.IDENTITY).build(),
"input")
/* 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)
*/
.addLayer("identityinputactivation",
new ActivationLayer.Builder().activation(Activation.IDENTITY).build(), "input")
ActivationLayer.builder().activation(Activation.IDENTITY).build(), "input")
.addVertex("shiftvertex", new ShiftVertex(sf), "identityinputactivation")
.addLayer("identityshiftvertex",
new ActivationLayer.Builder().activation(Activation.IDENTITY).build(),
ActivationLayer.builder().activation(Activation.IDENTITY).build(),
"shiftvertex")
.setOutputs("identityshiftvertex", "denselayer").build();
@ -144,12 +144,12 @@ public class ShiftVertexTest extends BaseDL4JTest {
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder()
.addInputs("input")
.addLayer("denselayer",
new DenseLayer.Builder().nIn(input.columns()).nOut(input.columns())
DenseLayer.builder().nIn(input.columns()).nOut(input.columns())
.activation(a1).build(),
"input")
.addVertex("shiftvertex", new ShiftVertex(sf), "denselayer")
.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(),
"shiftvertex")
.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
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)
.gradientNormalizationThreshold(gradNormThreshold).build();
@ -75,7 +75,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
assertEquals(act, layer.getActivationFn());
assertEquals(weight.getWeightInitFunction(), layer.getWeightInit());
assertEquals(new Dropout(dropOut), layer.getIDropout());
assertEquals(new Dropout(dropOut), layer.getDropOut());
assertEquals(updater, layer.getIUpdater());
assertEquals(gradNorm, layer.getGradientNormalization());
assertEquals(gradNormThreshold, layer.getGradientNormalizationThreshold(), 0.0);
@ -83,7 +83,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test
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);
@ -93,7 +93,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test
public void testConvolutionLayer() throws Exception {
ConvolutionLayer conv = new ConvolutionLayer.Builder(kernelSize, stride, padding).build();
ConvolutionLayer conv = ConvolutionLayer.builder(kernelSize, stride, padding).build();
checkSerialization(conv);
@ -106,7 +106,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test
public void testSubsamplingLayer() throws Exception {
SubsamplingLayer sample =
new SubsamplingLayer.Builder(poolType, stride).kernelSize(kernelSize).padding(padding).build();
SubsamplingLayer.builder(poolType, stride).kernelSize(kernelSize).padding(padding).build();
checkSerialization(sample);
@ -118,21 +118,21 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test
public void testOutputLayer() throws Exception {
OutputLayer out = new OutputLayer.Builder(loss).build();
OutputLayer out = OutputLayer.builder(loss).build();
checkSerialization(out);
}
@Test
public void testRnnOutputLayer() throws Exception {
RnnOutputLayer out = new RnnOutputLayer.Builder(loss).build();
RnnOutputLayer out = RnnOutputLayer.builder(loss).build();
checkSerialization(out);
}
@Test
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);
@ -142,7 +142,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test
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();
checkSerialization(glstm);
@ -155,7 +155,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test
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();
checkSerialization(glstm);
@ -168,7 +168,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test
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);
assertEquals(10, el.getNIn());
@ -177,7 +177,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test
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();
checkSerialization(bN);
@ -192,11 +192,11 @@ public class LayerBuilderTest extends BaseDL4JTest {
@Test
public void testActivationLayer() throws Exception {
ActivationLayer activationLayer = new ActivationLayer.Builder().activation(act).build();
ActivationLayer activationLayer = ActivationLayer.builder().activation(act).build();
checkSerialization(activationLayer);
assertEquals(act, activationLayer.activationFn);
assertEquals(act, activationLayer.getActivation());
}
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");
// 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?)");
}

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";
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).name(name1).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).name(name2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).name(name1).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).name(name2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
assertEquals(name1, conf.getConf(0).getLayer().getLayerName());
assertEquals(name2, conf.getConf(1).getLayer().getLayerName());
assertEquals(name1, conf.getConf(0).getLayer().getName());
assertEquals(name2, conf.getConf(1).getLayer().getName());
}
@ -67,8 +67,8 @@ public class LayerConfigTest extends BaseDL4JTest {
public void testActivationLayerwiseOverride() {
//Without layerwise override:
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().activation(Activation.RELU)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -77,8 +77,8 @@ public class LayerConfigTest extends BaseDL4JTest {
//With
conf = NeuralNetConfiguration.builder().activation(Activation.RELU)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).activation(Activation.TANH).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).activation(Activation.TANH).build()).build();
net = new MultiLayerNetwork(conf);
net.init();
@ -94,8 +94,8 @@ public class LayerConfigTest extends BaseDL4JTest {
final Distribution defaultDistribution = new NormalDistribution(0, 1.0);
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.dist(defaultDistribution).biasInit(1)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -109,8 +109,8 @@ public class LayerConfigTest extends BaseDL4JTest {
final Distribution overriddenDistribution = new UniformDistribution(0, 1);
conf = NeuralNetConfiguration.builder()
.dist(defaultDistribution).biasInit(1)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()).layer(1,
new DenseLayer.Builder().nIn(2).nOut(2)
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build()).layer(1,
DenseLayer.builder().nIn(2).nOut(2)
.dist(overriddenDistribution).biasInit(0).build())
.build();
@ -181,23 +181,23 @@ public class LayerConfigTest extends BaseDL4JTest {
@Test
public void testDropoutLayerwiseOverride() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().dropOut(1.0)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
assertEquals(new Dropout(1.0), conf.getConf(0).getLayer().getIDropout());
assertEquals(new Dropout(1.0), conf.getConf(1).getLayer().getIDropout());
assertEquals(new Dropout(1.0), conf.getConf(0).getLayer().getDropOut());
assertEquals(new Dropout(1.0), conf.getConf(1).getLayer().getDropOut());
conf = NeuralNetConfiguration.builder().dropOut(1.0)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).dropOut(2.0).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).dropOut(2.0).build()).build();
net = new MultiLayerNetwork(conf);
net.init();
assertEquals(new Dropout(1.0), conf.getConf(0).getLayer().getIDropout());
assertEquals(new Dropout(2.0), conf.getConf(1).getLayer().getIDropout());
assertEquals(new Dropout(1.0), conf.getConf(0).getLayer().getDropOut());
assertEquals(new Dropout(2.0), conf.getConf(1).getLayer().getDropOut());
}
@Test
@ -208,8 +208,8 @@ public class LayerConfigTest extends BaseDL4JTest {
NeuralNetConfiguration 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().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -221,7 +221,7 @@ public class LayerConfigTest extends BaseDL4JTest {
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())
.build();
@ -234,8 +234,8 @@ public class LayerConfigTest extends BaseDL4JTest {
@Test
public void testUpdaterRhoRmsDecayLayerwiseOverride() {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new AdaDelta(0.5, 0.9))
.layer(0, new 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(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).updater(new AdaDelta(0.01,0.9)).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -245,8 +245,8 @@ public class LayerConfigTest extends BaseDL4JTest {
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))
.layer(0, new 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(0, DenseLayer.builder().nIn(2).nOut(2).updater(new RmsProp(1.0, 1.0, RmsProp.DEFAULT_RMSPROP_EPSILON)).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).updater(new AdaDelta(0.5,AdaDelta.DEFAULT_ADADELTA_EPSILON)).build())
.build();
net = new MultiLayerNetwork(conf);
@ -264,8 +264,8 @@ public class LayerConfigTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.updater(new Adam(1.0, 0.5, 0.5, 1e-8))
.layer(0, new 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(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).updater(new Adam(1.0, 0.6, 0.7, 1e-8)).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -283,8 +283,8 @@ public class LayerConfigTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(10)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
BaseLayerConfiguration bconf = (BaseLayerConfiguration) conf.getConf(0).getLayer();
@ -297,8 +297,8 @@ public class LayerConfigTest extends BaseDL4JTest {
conf = NeuralNetConfiguration.builder()
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(10)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2)
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2)
.gradientNormalization(GradientNormalization.None)
.gradientNormalizationThreshold(2.5).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() {
// 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))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
}
@ -67,8 +67,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
public void testL1L2NotSet() {
// Warning thrown only since some layers may not have l1 or l2
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
}
@ -78,8 +78,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
public void testRegNotSetL1Global() {
assertThrows(IllegalStateException.class, () -> {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3)).l1(0.5)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
});
@ -90,8 +90,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
public void testRegNotSetL2Local() {
assertThrows(IllegalStateException.class, () -> {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Sgd(0.3))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).l2(0.5).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).l2(0.5).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
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
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder().updater(new Sgd(0.3)).dist(new GaussianDistribution(1e-3, 2))
.layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
.layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
}
@ -116,8 +116,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
NeuralNetConfiguration 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().nIn(2).nOut(2).build()).build();
.layer(0, DenseLayer.builder().nIn(2).nOut(2).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
}
@ -130,12 +130,12 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
/* Graph Builder */
.updater(Updater.RMSPROP.getIUpdaterWithDefaultConfig()).graphBuilder().addInputs("in")
.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)
.dropOut(0.4).l1(0.3).activation(Activation.SIGMOID).build(),
"in")
.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(),
"L" + 1)
.setOutputs("output");
@ -157,8 +157,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
// Nesterovs Updater
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().updater(new Nesterovs(0.9))
.layer(0, new 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(0, DenseLayer.builder().nIn(2).nOut(2).l2(0.5).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).updater(new Nesterovs(0.3, 0.4)).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -173,8 +173,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
// Adam Updater
conf = NeuralNetConfiguration.builder().updater(new Adam(0.3))
.weightInit(expectedDist)
.layer(0, new 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(0, DenseLayer.builder().nIn(2).nOut(2).l2(0.5).l1(0.3).build())
.layer(1, DenseLayer.builder().nIn(2).nOut(2).build()).build();
net = new MultiLayerNetwork(conf);
net.init();
@ -191,8 +191,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
//RMSProp Updater
conf = NeuralNetConfiguration.builder().updater(new RmsProp(0.3))
.layer(0, new 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(0, DenseLayer.builder().nIn(2).nOut(2).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.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)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
// Building the DL4J network
.layer(0, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray)
.layer(0, ConvolutionLayer.builder(kernelArray, strideArray, zeroPaddingArray)
.name("cnn1")
.convolutionMode(ConvolutionMode.Strict)
.nIn(2) // 2 input channels
@ -258,7 +258,7 @@ public class CNNProcessorTest extends BaseDL4JTest {
.activation(Activation.RELU)
.biasInit(1e-2).build())
.layer(1, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray)
.layer(1, ConvolutionLayer.builder(kernelArray, strideArray, zeroPaddingArray)
.name("cnn2")
.convolutionMode(ConvolutionMode.Strict)
.nOut(processWidth)
@ -267,21 +267,21 @@ public class CNNProcessorTest extends BaseDL4JTest {
.biasInit(1e-2)
.build())
.layer(2, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray)
.layer(2, ConvolutionLayer.builder(kernelArray, strideArray, zeroPaddingArray)
.name("cnn3")
.convolutionMode(ConvolutionMode.Strict)
.nOut(processWidth)
.weightInit(WeightInit.XAVIER_UNIFORM)
.activation(Activation.RELU).build())
.layer(3, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray)
.layer(3, ConvolutionLayer.builder(kernelArray, strideArray, zeroPaddingArray)
.name("cnn4")
.convolutionMode(ConvolutionMode.Strict)
.nOut(processWidth)
.weightInit(WeightInit.XAVIER_UNIFORM)
.activation(Activation.RELU).build())
.layer(4, new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
.layer(4, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE)
.name("output")
.nOut(1)
.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...
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10)
.layer(0, DenseLayer.builder().nIn(10).nOut(10).build())
.layer(1, OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(10)
.activation(Activation.SOFTMAX).nOut(10).build())
.inputPreProcessor(0, new MyCustomPreprocessor())
.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();
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())
.build();
@ -143,7 +143,7 @@ public class TestPreProcessors extends BaseDL4JTest {
FeedForwardToRnnPreProcessor proc = new FeedForwardToRnnPreProcessor();
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())
.build();
@ -227,7 +227,7 @@ public class TestPreProcessors extends BaseDL4JTest {
NeuralNetConfiguration nnc =
NeuralNetConfiguration.builder()
.layer(new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder(
.layer(org.deeplearning4j.nn.conf.layers.ConvolutionLayer.builder(
inputWidth, inputHeight).nIn(cnnNChannelsIn)
.nOut(nChannels).build())
.build();
@ -309,7 +309,7 @@ public class TestPreProcessors extends BaseDL4JTest {
NeuralNetConfiguration nnc =
NeuralNetConfiguration.builder()
.layer(new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder(
.layer(org.deeplearning4j.nn.conf.layers.ConvolutionLayer.builder(
inputWidth, inputHeight).nIn(cnnNChannelsIn)
.nOut(nChannels).build())
.build();
@ -397,12 +397,12 @@ public class TestPreProcessors extends BaseDL4JTest {
//FF->RNN and RNN->FF
NeuralNetConfiguration conf1 =
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())
.layer(1, new GravesLSTM.Builder().nIn(6).nOut(7).build())
.layer(2, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(7)
.layer(1, GravesLSTM.builder().nIn(6).nOut(7).build())
.layer(2, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nIn(7)
.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
assertEquals(3, conf1.getInputPreProcessors().size());
assertTrue(conf1.getInputPreProcess(1) instanceof FeedForwardToRnnPreProcessor);
@ -412,10 +412,10 @@ public class TestPreProcessors extends BaseDL4JTest {
//FF-> CNN, CNN-> FF, FF->RNN
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())
.layer(1, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nOut(6).build())
.layer(2, new RnnOutputLayer.Builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build())
.layer(1, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nOut(6).build())
.layer(2, RnnOutputLayer.builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutionalFlat(28, 28, 1)).build();
//Expect preprocessors: 0: FF->CNN; 1: CNN->FF; 2: FF->RNN
assertEquals(3, conf2.getInputPreProcessors().size());
@ -425,10 +425,10 @@ public class TestPreProcessors extends BaseDL4JTest {
//CNN-> FF, FF->RNN - InputType.convolutional instead of convolutionalFlat
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())
.layer(1, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nOut(6).build())
.layer(2, new RnnOutputLayer.Builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build())
.layer(1, org.deeplearning4j.nn.conf.layers.DenseLayer.builder().nOut(6).build())
.layer(2, RnnOutputLayer.builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutional(28, 28, 1)).build();
//Expect preprocessors: 1: CNN->FF; 2: FF->RNN
assertEquals(2, conf2a.getInputPreProcessors().size());
@ -438,10 +438,10 @@ public class TestPreProcessors extends BaseDL4JTest {
//FF->CNN and CNN->RNN:
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())
.layer(1, new GravesLSTM.Builder().nOut(6).build())
.layer(2, new RnnOutputLayer.Builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build())
.layer(1, GravesLSTM.builder().nOut(6).build())
.layer(2, RnnOutputLayer.builder().nIn(6).nOut(5).activation(Activation.SOFTMAX).build())
.inputType(InputType.convolutionalFlat(28, 28, 1)).build();
//Expect preprocessors: 0: FF->CNN, 1: CNN->RNN;
assertEquals(2, conf3.getInputPreProcessors().size());
@ -454,16 +454,16 @@ public class TestPreProcessors extends BaseDL4JTest {
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.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
.nIn(1).nOut(10).padding(2, 2)
.stride(2, 2)
.weightInit(WeightInit.RELU)
.activation(Activation.RELU)
.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())
.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)
.activation(Activation.SOFTMAX).build())
.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()
.weightNoise(wn)
.layer(new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(new 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(DenseLayer.builder().nIn(10).nOut(10).build())
.layer(DenseLayer.builder().nIn(10).nOut(10).weightNoise(new DropConnect(0.25)).build())
.layer(OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -86,9 +86,9 @@ public class TestWeightNoise extends BaseDL4JTest {
.weightNoise(wn)
.graphBuilder()
.addInputs("in")
.layer("0", new 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("2", new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build(), "1")
.layer("0", DenseLayer.builder().nIn(10).nOut(10).build(), "in")
.layer("1", DenseLayer.builder().nIn(10).nOut(10).weightNoise(new DropConnect(0.25)).build(), "0")
.layer("2", OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).build(), "1")
.setOutputs("2")
.build();
@ -145,9 +145,9 @@ public class TestWeightNoise extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.layer(new DenseLayer.Builder().nIn(10).nOut(10).weightNoise(wn1).build())
.layer(new DenseLayer.Builder().nIn(10).nOut(10).weightNoise(wn2).build())
.layer(new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).weightNoise(wn3).build())
.layer(DenseLayer.builder().nIn(10).nOut(10).weightNoise(wn1).build())
.layer(DenseLayer.builder().nIn(10).nOut(10).weightNoise(wn2).build())
.layer(OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).weightNoise(wn3).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -170,9 +170,9 @@ public class TestWeightNoise extends BaseDL4JTest {
ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder()
.graphBuilder()
.addInputs("in")
.layer("0", new DenseLayer.Builder().nIn(10).nOut(10).weightNoise(wn1).build(), "in")
.layer("1", new 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("0", DenseLayer.builder().nIn(10).nOut(10).weightNoise(wn1).build(), "in")
.layer("1", DenseLayer.builder().nIn(10).nOut(10).weightNoise(wn2).build(), "0")
.layer("2", OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).weightNoise(wn3).build(), "1")
.setOutputs("2")
.build();
@ -249,7 +249,7 @@ public class TestWeightNoise extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.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();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
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))
.dataType(DataType.DOUBLE)
.list()
.layer(new DenseLayer.Builder().activation(Activation.TANH).nIn(10).nOut(10).build())
.layer(new 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(DenseLayer.builder().activation(Activation.TANH).nIn(10).nOut(10).build())
.layer(DenseLayer.builder().activation(Activation.TANH).nIn(10).nOut(10).build())
.layer(OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -389,9 +389,9 @@ public class DTypeTests extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.graphBuilder()
.addInputs("in")
.layer("l0", new 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("out", new OutputLayer.Builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "l1")
.layer("l0", DenseLayer.builder().activation(Activation.TANH).nIn(10).nOut(10).build(), "in")
.layer("l1", DenseLayer.builder().activation(Activation.TANH).nIn(10).nOut(10).build(), "l0")
.layer("out", OutputLayer.builder().nIn(10).nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "l1")
.setOutputs("out")
.build();
@ -477,24 +477,24 @@ public class DTypeTests extends BaseDL4JTest {
LayerConfiguration secondLast;
switch (outputLayer) {
case 0:
ol = new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new GlobalPoolingLayer(PoolingType.MAX);
ol = OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = GlobalPoolingLayer.builder(PoolingType.MAX).build();
break;
case 1:
ol = new LossLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new FrozenLayerWithBackprop(new DenseLayer.Builder().nOut(10).activation(Activation.SIGMOID).build());
ol = LossLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build();
secondLast = FrozenLayerWithBackprop.builder().underlying(DenseLayer.builder().nOut(10).activation(Activation.SIGMOID).build()).build();
break;
case 2:
ol = new 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();
ol =CenterLossOutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = VariationalAutoencoder.builder().encoderLayerSizes(10).decoderLayerSizes(10).nOut(10).activation(Activation.SIGMOID).build();
break;
case 3:
ol = new CnnLossLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(3).activation(Activation.TANH).build();
ol = CnnLossLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build();
secondLast = ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(3).activation(Activation.TANH).build();
break;
case 4:
ol = new Yolo2OutputLayer.Builder().boundingBoxPriors(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();
ol = Yolo2OutputLayer.builder().boundingBoxes(Nd4j.create(new double[][]{{1.0, 1.0}, {2.0, 2.0}}).castTo(networkDtype)).build();
secondLast = ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(14).activation(Activation.TANH).build();
break;
default:
throw new RuntimeException();
@ -505,28 +505,28 @@ public class DTypeTests extends BaseDL4JTest {
.dataType(networkDtype)
.convolutionMode(ConvolutionMode.Same)
.updater(new Adam(1e-2))
.list()
.layer(new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nOut(3).activation(Activation.TANH).build())
.layer(new LocalResponseNormalization())
.layer(new DropoutLayer(0.5))
.layer(new DropoutLayer(new AlphaDropout(0.5)))
.layer(new DropoutLayer(new GaussianDropout(0.5)))
.layer(new DropoutLayer(new GaussianNoise(0.1)))
.layer(new DropoutLayer(new SpatialDropout(0.5)))
.layer(new SubsamplingLayer.Builder().poolingType(SubsamplingLayer.PoolingType.AVG).kernelSize(3, 3).stride(2, 2).build())
.layer(new Pooling2D.Builder().poolingType(SubsamplingLayer.PoolingType.AVG).kernelSize(2, 2).stride(1, 1).build())
.layer(new 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(new ZeroPaddingLayer(1, 1))
.layer(new Cropping2D(1, 1))
.layer(new IdentityLayer())
.layer(new Upsampling2D.Builder().size(2).build())
.layer(new SubsamplingLayer.Builder().kernelSize(2, 2).stride(2, 2).build())
.layer(new DepthwiseConvolution2D.Builder().nOut(3).activation(Activation.RELU).build())
.layer(new SeparableConvolution2D.Builder().nOut(3).activation(Activation.HARDTANH).build())
.layer(new MaskLayer())
.layer(new BatchNormalization.Builder().build())
.layer(new ActivationLayer(Activation.LEAKYRELU))
.layer(ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nOut(3).activation(Activation.TANH).build())
.layer(LocalResponseNormalization.builder())
.layer(DropoutLayer.builder(0.5).build())
.layer(DropoutLayer.builder(new AlphaDropout(0.5)).build())
.layer(DropoutLayer.builder(new GaussianDropout(0.5)).build())
.layer(DropoutLayer.builder(new GaussianNoise(0.1)).build())
.layer(DropoutLayer.builder(new SpatialDropout(0.5)).build())
.layer(SubsamplingLayer.builder().poolingType(SubsamplingLayer.PoolingType.AVG.toPoolingType()).kernelSize(3, 3).stride(2, 2).build())
.layer(Pooling2D.builder().poolingType(SubsamplingLayer.PoolingType.AVG.toPoolingType()).kernelSize(2, 2).stride(1, 1).build())
.layer(Deconvolution2D.builder().kernelSize(2, 2).stride(2, 2).nOut(3).activation(Activation.TANH).build())
// .layer(LocallyConnected2D.builder().nOut(3).kernelSize(2,2).stride(1,1).activation(Activation.SIGMOID).build()) //EXCEPTION
.layer(ZeroPaddingLayer.builder(1, 1).build())
.layer(Cropping2D.builder(1, 1).build())
.layer(IdentityLayer.builder())
.layer(Upsampling2D.builder().size(2).build())
.layer(SubsamplingLayer.builder().kernelSize(2, 2).stride(2, 2).build())
.layer(DepthwiseConvolution2D.builder().nOut(3).activation(Activation.RELU).build())
.layer(SeparableConvolution2D.builder().nOut(3).activation(Activation.HARDTANH).build())
.layer(MaskLayer.builder())
.layer(BatchNormalization.builder().build())
.layer(ActivationLayer.builder().activation(Activation.LEAKYRELU).build())
.layer(secondLast)
.layer(ol)
.inputType(InputType.convolutionalFlat(8, 8, 1))
@ -603,16 +603,16 @@ public class DTypeTests extends BaseDL4JTest {
LayerConfiguration secondLast;
switch (outputLayer) {
case 0:
ol = new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new GlobalPoolingLayer(PoolingType.AVG);
ol = OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = GlobalPoolingLayer.builder(PoolingType.AVG).build();
break;
case 1:
ol = new Cnn3DLossLayer.Builder(Convolution3D.DataFormat.NCDHW).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new Convolution3D.Builder().nOut(3).activation(Activation.ELU).build();
ol = Cnn3DLossLayer.builder().dataFormat(Convolution3D.DataFormat.NCDHW).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build();
secondLast = Convolution3D.builder().nOut(3).activation(Activation.ELU).build();
break;
case 2:
ol = new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new Convolution3D.Builder().nOut(3).activation(Activation.ELU).build();
ol = OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = Convolution3D.builder().nOut(3).activation(Activation.ELU).build();
break;
default:
throw new RuntimeException();
@ -623,15 +623,15 @@ public class DTypeTests extends BaseDL4JTest {
.dataType(networkDtype)
.convolutionMode(ConvolutionMode.Same)
.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(new 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(new 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(new ZeroPadding3DLayer.Builder(1, 1, 1, 1, 1, 1).build())
.layer(new ActivationLayer(Activation.LEAKYRELU))
.layer(new Upsampling3D.Builder().size(2).build())
.layer(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(Subsampling3DLayer.builder().poolingType(PoolingType.AVG).kernelSize(2, 2, 2).stride(2, 2, 2).build())
.layer(Deconvolution3D.builder().kernelSize(2,2,2).stride(1,1,1).nIn(3).nOut(3).activation(Activation.TANH).build())
.layer(Cropping3D.builder(1, 1, 1, 1, 1, 1).build())
.layer(ZeroPadding3DLayer.builder(1, 1, 1, 1, 1, 1).build())
.layer(ActivationLayer.builder(Activation.LEAKYRELU).build())
.layer(Upsampling3D.builder().size(2).build())
.layer(secondLast)
.layer(ol)
.inputType(InputType.convolutional3D(Convolution3D.DataFormat.NCDHW, 8, 8, 8, 1))
@ -714,16 +714,16 @@ public class DTypeTests extends BaseDL4JTest {
LayerConfiguration secondLast;
switch (outputLayer) {
case 0:
ol = new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new GlobalPoolingLayer(PoolingType.MAX);
ol = OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = GlobalPoolingLayer.builder(PoolingType.MAX).build();
break;
case 1:
ol = new RnnOutputLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).nOut(5).build();
secondLast = new Convolution1D.Builder().kernelSize(2).nOut(5).build();
ol = RnnOutputLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).nOut(5).build();
secondLast = Convolution1D.builder().kernelSize(2).nOut(5).build();
break;
case 2:
ol = new RnnLossLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new Convolution1D.Builder().kernelSize(2).nOut(5).build();
ol = RnnLossLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build();
secondLast = Convolution1D.builder().kernelSize(2).nOut(5).build();
break;
default:
throw new RuntimeException();
@ -737,14 +737,14 @@ public class DTypeTests extends BaseDL4JTest {
.convolutionMode(ConvolutionMode.Same)
.updater(new Adam(1e-2))
.list()
.layer(new Convolution1D.Builder()
.layer(Convolution1D.builder()
.kernelSize(2)
.stride(1).nOut(3).
activation(Activation.TANH).build())
.layer(new Subsampling1DLayer.Builder().poolingType(PoolingType.MAX).kernelSize(5).stride(1).build())
.layer(new Cropping1D.Builder(1).build())
.layer(new ZeroPadding1DLayer(1))
.layer(new Upsampling1D.Builder(2).build())
.layer(Subsampling1DLayer.builder().poolingType(PoolingType.MAX).kernelSize(5).stride(1).build())
.layer(Cropping1D.builder(1).build())
.layer(ZeroPadding1DLayer.builder(1).build())
.layer(Upsampling1D.builder(2).build())
.layer(secondLast)
.layer(ol)
.inputType(InputType.recurrent(5, 10,RNNFormat.NCW))
@ -817,9 +817,9 @@ public class DTypeTests extends BaseDL4JTest {
.convolutionMode(ConvolutionMode.Same)
.updater(new Adam(1e-2))
.list()
.layer(new SpaceToBatchLayer.Builder().blocks(1, 1).build())
.layer(new SpaceToDepthLayer.Builder().blocks(2).build())
.layer(new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
.layer(SpaceToBatchLayer.builder().blockSize(1, 1).build())
.layer(SpaceToDepthLayer.builder().blockSize(2).build())
.layer(OutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.convolutional(28, 28, 5))
.build();
@ -880,16 +880,16 @@ public class DTypeTests extends BaseDL4JTest {
LayerConfiguration secondLast;
switch (outputLayer) {
case 0:
ol = new RnnOutputLayer.Builder().nOut(5).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new SimpleRnn.Builder().nOut(5).activation(Activation.TANH).build();
ol = RnnOutputLayer.builder().nOut(5).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = SimpleRnn.builder().nOut(5).activation(Activation.TANH).build();
break;
case 1:
ol = new RnnLossLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = new SimpleRnn.Builder().nOut(5).activation(Activation.TANH).build();
ol = RnnLossLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
secondLast = SimpleRnn.builder().nOut(5).activation(Activation.TANH).build();
break;
case 2:
ol = new OutputLayer.Builder().nOut(5).build();
secondLast = new LastTimeStep(new SimpleRnn.Builder().nOut(5).activation(Activation.TANH).build());
ol = OutputLayer.builder().nOut(5).build();
secondLast = LastTimeStep.builder().underlying(SimpleRnn.builder().nOut(5).activation(Activation.TANH).build()).build();
break;
default:
throw new RuntimeException();
@ -899,15 +899,15 @@ public class DTypeTests extends BaseDL4JTest {
.dataType(networkDtype)
.convolutionMode(ConvolutionMode.Same)
.updater(new Adam(1e-2))
.list()
.layer(new LSTM.Builder().nIn(5).nOut(5).activation(Activation.TANH).build())
.layer(new GravesLSTM.Builder().nIn(5).nOut(5).activation(Activation.TANH).build())
.layer(new DenseLayer.Builder().nOut(5).build())
.layer(new 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(new TimeDistributed(new DenseLayer.Builder().nIn(10).nOut(5).activation(Activation.TANH).build()))
.layer(new 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(LSTM.builder().nIn(5).nOut(5).activation(Activation.TANH).build())
.layer(GravesLSTM.builder().nIn(5).nOut(5).activation(Activation.TANH).build())
.layer(DenseLayer.builder().nOut(5).build())
.layer(GravesBidirectionalLSTM.builder().nIn(5).nOut(5).activation(Activation.TANH).build())
.layer(Bidirectional.builder(LSTM.builder().nIn(5).nOut(5).activation(Activation.TANH).build()).build())
.layer(TimeDistributed.builder().underlying(DenseLayer.builder().nIn(10).nOut(5).activation(Activation.TANH).build()).build())
.layer(SimpleRnn.builder().nIn(5).nOut(5).build())
.layer(MaskZeroLayer.builder().underlying(SimpleRnn.builder().nIn(5).nOut(5).build()).maskingValue(0.0).build())
.layer(secondLast)
.layer(ol)
.build();
@ -986,14 +986,14 @@ public class DTypeTests extends BaseDL4JTest {
.updater(new NoOp())
.dist(new UniformDistribution(-6, 6))
.layer(new PrimaryCapsules.Builder(primaryCapsDim, primarpCapsChannel)
.layer(PrimaryCapsules.builder(primaryCapsDim, primarpCapsChannel)
.kernelSize(3, 3)
.stride(2, 2)
.build())
.layer(new CapsuleLayer.Builder(capsule, capsuleDim, routing).build())
.layer(new CapsuleStrengthLayer.Builder().build())
.layer(new ActivationLayer.Builder(new ActivationSoftmax()).build())
.layer(new LossLayer.Builder(new LossNegativeLogLikelihood()).build())
.layer(CapsuleLayer.builder(capsule, capsuleDim, routing).build())
.layer(CapsuleStrengthLayer.builder().build())
.layer(ActivationLayer.builder(new ActivationSoftmax()).build())
.layer(LossLayer.builder().lossFunction(new LossNegativeLogLikelihood()).build())
.inputType(InputType.convolutional(height, width, inputDepth))
.build();
@ -1062,33 +1062,33 @@ public class DTypeTests extends BaseDL4JTest {
INDArray input;
if (test == 0) {
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 {
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);
conf.setInputTypes(InputType.feedForward(1));
} else if (test == 1) {
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 {
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);
conf.setInputTypes(InputType.recurrent(1));
} else {
conf.layer("0", new RepeatVector.Builder().repetitionFactor(5).nOut(5).build(), "in");
conf.layer("gp", new GlobalPoolingLayer.Builder(PoolingType.SUM).build(), "0");
conf.layer("0", RepeatVector.builder().repetitionFactor(5).nOut(5).build(), "in");
conf.layer("gp", GlobalPoolingLayer.builder(PoolingType.SUM).build(), "0");
input = Nd4j.zeros(networkDtype, 10, 5);
conf.setInputTypes(InputType.feedForward(5));
}
conf.appendLayer("el", new ElementWiseMultiplicationLayer.Builder().nOut(5).build())
.appendLayer("ae", new AutoEncoder.Builder().nOut(5).build())
.appendLayer("prelu", new PReLULayer.Builder().nOut(5).inputShape(5).build())
.appendLayer("out", new OutputLayer.Builder().nOut(10).build());
conf.appendLayer("el", ElementWiseMultiplicationLayer.builder().nOut(5).build())
.appendLayer("ae", AutoEncoder.builder().nOut(5).build())
.appendLayer("prelu", PReLULayer.builder().nOut(5).inputShape(5).build())
.appendLayer("out", OutputLayer.builder().nOut(10).build());
ComputationGraph net = new ComputationGraph(conf.build());
net.init();
@ -1153,34 +1153,34 @@ public class DTypeTests extends BaseDL4JTest {
switch (test) {
case 0:
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")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "preproc")
.addLayer("out", OutputLayer.builder().nOut(10).build(), "preproc")
.setInputTypes(InputType.convolutional(28, 28, 1))
.setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 28, 28)};
break;
case 1:
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("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")
.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))
.setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5)};
break;
case 2:
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("2", new ShiftVertex(1), "1a")
.addVertex("3", new ScaleVertex(2), "2")
.addVertex("4", new ReshapeVertex(2, -1), "3")
.addVertex("5", new SubsetVertex(0, 99), "4")
.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))
.setOutputs("out");
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("4", new DuplicateToTimeSeriesVertex("in3"), "3")
.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("8", new MergeVertex(), "6", "7")
.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))
.setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 8), Nd4j.rand(networkDtype, 2, 8), Nd4j.rand(networkDtype, 2, 8, 5)};
break;
case 4:
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("preproc2", new PreprocessorVertex(new CnnToRnnPreProcessor(2, 2, 2)), "preproc1")
.addLayer("pool", new GlobalPoolingLayer(), "preproc2")
.addLayer("pool2", new GlobalPoolingLayer(), "in2")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "pool", "pool2")
.addLayer("pool", GlobalPoolingLayer.builder().build(), "preproc2")
.addLayer("pool2", GlobalPoolingLayer.builder().build(), "in2")
.addLayer("out", OutputLayer.builder().nOut(10).build(), "pool", "pool2")
.setInputTypes(InputType.recurrent(8), InputType.convolutional(28, 28, 1))
.setOutputs("out");
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:
b.addInputs("in1", "in2")
.addVertex("fv", new FrozenVertex(new ScaleVertex(2.0)), "in1")
.addLayer("1", new DenseLayer.Builder().nOut(5).build(), "fv")
.addLayer("2", new DenseLayer.Builder().nOut(5).build(), "in2")
.addLayer("1", DenseLayer.builder().nOut(5).build(), "fv")
.addLayer("2", DenseLayer.builder().nOut(5).build(), "in2")
.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))
.setOutputs("out");
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5), Nd4j.rand(networkDtype, 2, 5)};
break;
case 6:
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")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "2")
.addLayer("out", OutputLayer.builder().nOut(10).build(), "2")
.setOutputs("out")
.setInputTypes(InputType.recurrent(5, 4));
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5, 4)};
break;
case 7:
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")
.addLayer("out", new OutputLayer.Builder().nOut(10).build(), "2")
.addLayer("out", OutputLayer.builder().nOut(10).build(), "2")
.setOutputs("out")
.setInputTypes(InputType.convolutional(28, 28, 1));
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 28, 28)};
@ -1311,9 +1311,9 @@ public class DTypeTests extends BaseDL4JTest {
switch (test) {
case 0:
b.addInputs("in")
.addLayer("1", new LSTM.Builder().nOut(5).build(), "in")
.addLayer("2", new LocallyConnected1D.Builder().kernelSize(2).nOut(4).build(), "1")
.addLayer("out", new RnnOutputLayer.Builder().nOut(10).build(), "2")
.addLayer("1", LSTM.builder().nOut(5).build(), "in")
.addLayer("2", LocallyConnected1D.builder().kernelSize(2).nOut(4).build(), "1")
.addLayer("out", RnnOutputLayer.builder().nOut(10).build(), "2")
.setOutputs("out")
.setInputTypes(InputType.recurrent(5, 2));
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5, 2)};
@ -1321,9 +1321,9 @@ public class DTypeTests extends BaseDL4JTest {
break;
case 1:
b.addInputs("in")
.addLayer("1", new 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("out", new OutputLayer.Builder().nOut(10).build(), "2")
.addLayer("1", ConvolutionLayer.builder().kernelSize(2, 2).nOut(5).convolutionMode(ConvolutionMode.Same).build(), "in")
.addLayer("2", LocallyConnected2D.builder().kernelSize(2, 2).nOut(5).build(), "1")
.addLayer("out", OutputLayer.builder().nOut(10).build(), "2")
.setOutputs("out")
.setInputTypes(InputType.convolutional(8, 8, 1));
in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 8, 8)};
@ -1399,12 +1399,12 @@ public class DTypeTests extends BaseDL4JTest {
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.list()
.layer(new LSTM.Builder().nOut(layerSize).build())
.layer(new SelfAttentionLayer.Builder().nOut(8).nHeads(2).projectInput(true).build())
.layer(new 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(new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build())
.layer(new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX)
.layer(LSTM.builder().nOut(layerSize).build())
.layer(SelfAttentionLayer.builder().nOut(8).nHeads(2).projectInput(true).build())
.layer(LearnedSelfAttentionLayer.builder().nOut(8).nHeads(2).nQueries(numQueries).projectInput(true).build())
.layer(RecurrentAttentionLayer.builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build())
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build())
.layer(OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(nIn))
.build();
@ -1487,12 +1487,12 @@ public class DTypeTests extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER)
.graphBuilder()
.addInputs("input")
.addLayer("lstmKeys", new LSTM.Builder().nOut(layerSize).build(), "input")
.addLayer("lstmQueries", new LSTM.Builder().nOut(layerSize).build(), "input")
.addLayer("lstmValues", new LSTM.Builder().nOut(layerSize).build(), "input")
.addLayer("lstmKeys", LSTM.builder().nOut(layerSize).build(), "input")
.addLayer("lstmQueries", 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")
.addLayer("pooling", new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build(), "attention")
.addLayer("output", new OutputLayer.Builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling")
.addLayer("pooling", GlobalPoolingLayer.builder().poolingType(PoolingType.MAX).build(), "attention")
.addLayer("output", OutputLayer.builder().nOut(nOut).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "pooling")
.setOutputs("output")
.setInputTypes(InputType.recurrent(nIn))
.build();

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