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Using @SuperBuilder for LayerConfigurations 

Signed-off-by: brian <brian@brutex.de>
master
Brian Rosenberger 2023-04-25 16:44:47 +02:00
parent 8f524827e4
commit 3267b06bde
62 changed files with 122 additions and 108 deletions
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8
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/RnnGradientChecks.java
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@ -267,11 +267,11 @@ public class RnnGradientChecks extends BaseDL4JTest {
.activation(Activation.TANH)
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.list()
.layer(simple ? SimpleRnn.builder().nOut(layerSize).hasLayerNorm(hasLayerNorm).build() :
LSTM.builder().nOut(layerSize).build())
.layer(new LastTimeStep(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))
@ -335,7 +335,7 @@ public class RnnGradientChecks extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER)
.list()
.layer(LSTM.builder().nOut(layerSize).build())
.layer(new TimeDistributed(DenseLayer.builder().nOut(layerSize).activation(Activation.SOFTMAX).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))

10
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/dtypes/DTypeTests.java
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@ -482,7 +482,7 @@ public class DTypeTests extends BaseDL4JTest {
break;
case 1:
ol = LossLayer.builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build();
secondLast = new FrozenLayerWithBackprop(DenseLayer.builder().nOut(10).activation(Activation.SIGMOID).build());
secondLast = FrozenLayerWithBackprop.builder().underlying(DenseLayer.builder().nOut(10).activation(Activation.SIGMOID).build()).build();
break;
case 2:
ol =CenterLossOutputLayer.builder().nOut(10).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build();
@ -889,7 +889,7 @@ public class DTypeTests extends BaseDL4JTest {
break;
case 2:
ol = OutputLayer.builder().nOut(5).build();
secondLast = new LastTimeStep(SimpleRnn.builder().nOut(5).activation(Activation.TANH).build());
secondLast = LastTimeStep.builder().underlying(SimpleRnn.builder().nOut(5).activation(Activation.TANH).build()).build();
break;
default:
throw new RuntimeException();
@ -905,7 +905,7 @@ public class DTypeTests extends BaseDL4JTest {
.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(new TimeDistributed(DenseLayer.builder().nIn(10).nOut(5).activation(Activation.TANH).build()))
.layer(TimeDistributed.builder().underlying(DenseLayer.builder().nIn(10).nOut(5).activation(Activation.TANH).build()).build())
.layer(SimpleRnn.builder().nIn(5).nOut(5).build())
.layer(MaskZeroLayer.builder().underlying(SimpleRnn.builder().nIn(5).nOut(5).build()).maskingValue(0.0).build())
.layer(secondLast)
@ -1062,7 +1062,7 @@ public class DTypeTests extends BaseDL4JTest {
INDArray input;
if (test == 0) {
if (frozen) {
conf.layer("0", new FrozenLayer(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", EmbeddingLayer.builder().nIn(5).nOut(5).build(), "in");
}
@ -1071,7 +1071,7 @@ public class DTypeTests extends BaseDL4JTest {
conf.setInputTypes(InputType.feedForward(1));
} else if (test == 1) {
if (frozen) {
conf.layer("0", new FrozenLayer(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", EmbeddingSequenceLayer.builder().nIn(5).nOut(5).build(), "in");
}

2
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/graph/TestComputationGraphNetwork.java
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@ -1925,7 +1925,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
.setOutputs("output")
.addLayer("0", ConvolutionLayer.builder().nOut(5).convolutionMode(ConvolutionMode.Same).build(),"input" )
.addVertex("dummyAdd", new ElementWiseVertex(ElementWiseVertex.Op.Add), "0")
.addLayer("output", new CnnLossLayer(), "dummyAdd")
.addLayer("output", CnnLossLayer.builder(), "dummyAdd")
.build());
graph.init();
graph.outputSingle(Nd4j.randn(1, 2, 10, 10));

8
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/FrozenLayerTest.java
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@ -289,11 +289,11 @@ public class FrozenLayerTest extends BaseDL4JTest {
.build();
NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder().seed(12345).list().layer(0,
new org.deeplearning4j.nn.conf.layers.misc.FrozenLayer(DenseLayer.builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()))
.layer(1, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayer(
org.deeplearning4j.nn.conf.layers.misc.FrozenLayer.builder(DenseLayer.builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()).build())
.layer(1, org.deeplearning4j.nn.conf.layers.misc.FrozenLayer.builder(
DenseLayer.builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()))
.weightInit(WeightInit.XAVIER).build()).build())
.layer(2, org.deeplearning4j.nn.conf.layers.OutputLayer.builder().lossFunction(
LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10)
.nOut(10).build())

36
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/FrozenLayerWithBackpropTest.java
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@ -60,11 +60,11 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
.build();
NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder().seed(12345).list().layer(0,
new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(DenseLayer.builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()))
.layer(1, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(DenseLayer.builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()).build())
.layer(1, org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
DenseLayer.builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()))
.weightInit(WeightInit.XAVIER).build()).build())
.layer(2, OutputLayer.builder(
LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10)
.nOut(10).build())
@ -113,10 +113,10 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
ComputationGraphConfiguration conf2 = NeuralNetConfiguration.builder().seed(12345).graphBuilder()
.addInputs("in")
.addLayer("0", new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.addLayer("0", org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
DenseLayer.builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()), "in")
.addLayer("1", new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.addLayer("1", org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
DenseLayer.builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()), "0")
.addLayer("2", OutputLayer.builder(
@ -160,11 +160,11 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
.updater(new Sgd(2))
.list()
.layer(DenseLayer.builder().nIn(4).nOut(3).build())
.layer(new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.layer(org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
DenseLayer.builder().nIn(3).nOut(4).build()))
.layer(new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.layer(org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
DenseLayer.builder().nIn(4).nOut(2).build()))
.layer(new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.layer(org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.TANH).nIn(2).nOut(1).build()))
.build();
@ -213,15 +213,15 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
.addInputs("input")
.addLayer(initialLayer, DenseLayer.builder().nIn(4).nOut(4).build(),"input")
.addLayer(frozenBranchUnfrozenLayer0, DenseLayer.builder().nIn(4).nOut(3).build(),initialLayer)
.addLayer(frozenBranchFrozenLayer1, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.addLayer(frozenBranchFrozenLayer1, org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
DenseLayer.builder().nIn(3).nOut(4).build()),frozenBranchUnfrozenLayer0)
.addLayer(frozenBranchFrozenLayer2, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.addLayer(frozenBranchFrozenLayer2, org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
DenseLayer.builder().nIn(4).nOut(2).build()),frozenBranchFrozenLayer1)
.addLayer(unfrozenLayer0, DenseLayer.builder().nIn(4).nOut(4).build(),initialLayer)
.addLayer(unfrozenLayer1, DenseLayer.builder().nIn(4).nOut(2).build(),unfrozenLayer0)
.addLayer(unfrozenBranch2, DenseLayer.builder().nIn(2).nOut(1).build(),unfrozenLayer1)
.addVertex("merge", new MergeVertex(), frozenBranchFrozenLayer2, unfrozenBranch2)
.addLayer(frozenBranchOutput,new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.addLayer(frozenBranchOutput,org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.TANH).nIn(3).nOut(1).build()),"merge")
.setOutputs(frozenBranchOutput)
.build();
@ -269,9 +269,9 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
.updater(new Sgd(2))
.list()
.layer(0,DenseLayer.builder().nIn(4).nOut(3).build())
.layer(1,new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(DenseLayer.builder().nIn(3).nOut(4).build()))
.layer(2,new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(DenseLayer.builder().nIn(4).nOut(2).build()))
.layer(3,new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.TANH).nIn(2).nOut(1).build()))
.layer(1,org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(DenseLayer.builder().nIn(3).nOut(4).build()))
.layer(2,org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(DenseLayer.builder().nIn(4).nOut(2).build()))
.layer(3,org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.TANH).nIn(2).nOut(1).build()))
.build();
MultiLayerNetwork frozenNetwork = new MultiLayerNetwork(confFrozen);
frozenNetwork.init();
@ -327,16 +327,16 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
.addInputs("input")
.addLayer(initialLayer,DenseLayer.builder().nIn(4).nOut(4).build(),"input")
.addLayer(frozenBranchUnfrozenLayer0,DenseLayer.builder().nIn(4).nOut(3).build(), initialLayer)
.addLayer(frozenBranchFrozenLayer1,new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.addLayer(frozenBranchFrozenLayer1,org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
DenseLayer.builder().nIn(3).nOut(4).build()),frozenBranchUnfrozenLayer0)
.addLayer(frozenBranchFrozenLayer2,
new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
DenseLayer.builder().nIn(4).nOut(2).build()),frozenBranchFrozenLayer1)
.addLayer(unfrozenLayer0,DenseLayer.builder().nIn(4).nOut(4).build(),initialLayer)
.addLayer(unfrozenLayer1,DenseLayer.builder().nIn(4).nOut(2).build(),unfrozenLayer0)
.addLayer(unfrozenBranch2,DenseLayer.builder().nIn(2).nOut(1).build(),unfrozenLayer1)
.addVertex("merge",new MergeVertex(), frozenBranchFrozenLayer2, unfrozenBranch2)
.addLayer(frozenBranchOutput, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
.addLayer(frozenBranchOutput, org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop.builder(
OutputLayer.builder().lossFunction(LossFunctions.LossFunction.MSE).activation(Activation.TANH).nIn(3).nOut(1).build()),"merge")
.setOutputs(frozenBranchOutput)
.build();

2
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/RnnDataFormatTests.java
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@ -243,7 +243,7 @@ public class RnnDataFormatTests extends BaseDL4JTest {
layer = MaskZeroLayer.builder().maskingValue(0.).underlying(layer).build();
}
if(lastTimeStep){
layer = new LastTimeStep(layer);
layer = LastTimeStep.builder(layer);
}
NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = (NeuralNetConfiguration.NeuralNetConfigurationBuilder) NeuralNetConfiguration.builder()
.seed(12345)

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestLastTimeStepLayer.java
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@ -63,7 +63,7 @@ public class TestLastTimeStepLayer extends BaseDL4JTest {
public void testLastTimeStepVertex() {
ComputationGraphConfiguration conf = NeuralNetConfiguration.builder().graphBuilder().addInputs("in")
.addLayer("lastTS", new LastTimeStep(SimpleRnn.builder()
.addLayer("lastTS", LastTimeStep.builder(SimpleRnn.builder()
.nIn(5).nOut(6).dataFormat(rnnDataFormat).build()), "in")
.setOutputs("lastTS")
.build();
@ -134,7 +134,7 @@ public class TestLastTimeStepLayer extends BaseDL4JTest {
.graphBuilder()
.addInputs("in")
.setInputTypes(InputType.recurrent(1, rnnDataFormat))
.addLayer("RNN", new LastTimeStep(LSTM.builder()
.addLayer("RNN", LastTimeStep.builder(LSTM.builder()
.nOut(10).dataFormat(rnnDataFormat)
.build()), "in")
.addLayer("dense", DenseLayer.builder()

3
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestTimeDistributed.java
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@ -79,9 +79,8 @@ public class TestTimeDistributed extends BaseDL4JTest {
.inferenceWorkspaceMode(wsm)
.seed(12345)
.updater(new Adam(0.1))
.list()
.layer(LSTM.builder().nIn(3).nOut(3).dataFormat(rnnDataFormat).build())
.layer(new TimeDistributed(DenseLayer.builder().nIn(3).nOut(3).activation(Activation.TANH).build(), rnnDataFormat))
.layer(TimeDistributed.builder().underlying(DenseLayer.builder().nIn(3).nOut(3).activation(Activation.TANH).build()).rnnDataFormat(rnnDataFormat))
.layer(RnnOutputLayer.builder().nIn(3).nOut(3).activation(Activation.SOFTMAX).dataFormat(rnnDataFormat)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.inputType(InputType.recurrent(3, rnnDataFormat))

2
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/multilayer/TestMasking.java
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@ -314,7 +314,7 @@ public class TestMasking extends BaseDL4JTest {
)
.addInputs("m1", "m2")
.addVertex("stack", new StackVertex(), "m1", "m2")
.addLayer("lastUnStacked", new LastTimeStep(LSTM.builder().nIn(3).nOut(1).activation(Activation.TANH).build()), "stack")
.addLayer("lastUnStacked", LastTimeStep.builder(LSTM.builder().nIn(3).nOut(1).activation(Activation.TANH).build()), "stack")
.addVertex("unstacked1", new UnstackVertex(0, 2), "lastUnStacked")
.addVertex("unstacked2", new UnstackVertex(1, 2), "lastUnStacked")
.addVertex("restacked", new StackVertex(), "unstacked1", "unstacked2")

10
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningCompGraphTest.java
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@ -336,12 +336,12 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
new ComputationGraph(overallConf.graphBuilder().addInputs("layer0In")
.setInputTypes(InputType.convolutionalFlat(28,28, 3))
.addLayer("layer0",
new FrozenLayer(ConvolutionLayer.builder(5, 5).nIn(3)
FrozenLayer.builder(ConvolutionLayer.builder(5, 5).nIn(3)
.stride(1, 1).nOut(20)
.activation(Activation.IDENTITY).build()),
"layer0In")
.addLayer("layer1",
new FrozenLayer(SubsamplingLayer.builder(
FrozenLayer.builder(SubsamplingLayer.builder(
SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2).stride(2, 2)
.build()),
@ -430,11 +430,11 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER)
.graphBuilder().addInputs("in")
.addLayer("blstm1",
new FrozenLayer(GravesBidirectionalLSTM.builder().nIn(10).nOut(10)
FrozenLayer.builder(GravesBidirectionalLSTM.builder().nIn(10).nOut(10)
.activation(Activation.TANH).build()),
"in")
.addLayer("pool", new FrozenLayer(GlobalPoolingLayer.builder().build()), "blstm1")
.addLayer("dense", new FrozenLayer(DenseLayer.builder().nIn(10).nOut(10).build()), "pool")
.addLayer("pool", FrozenLayer.builder(GlobalPoolingLayer.builder().build()), "blstm1")
.addLayer("dense", FrozenLayer.builder(DenseLayer.builder().nIn(10).nOut(10).build()), "pool")
.addLayer("out", OutputLayer.builder().nIn(10).nOut(5).activation(Activation.SOFTMAX)
.updater(new Adam(0.1))
.lossFunction(LossFunctions.LossFunction.MCXENT).build(), "dense")

2
cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasLSTM.java
View File

@ -203,7 +203,7 @@ public class KerasLSTM extends KerasLayer {
this.layer = builder.build();
if (!returnSequences) {
this.layer = new LastTimeStep(this.layer);
this.layer = LastTimeStep.builder(this.layer);
}
if (maskingConfig.getFirst()) {
this.layer = new MaskZeroLayer(this.layer, maskingConfig.getSecond());

2
cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasSimpleRnn.java
View File

@ -174,7 +174,7 @@ public class KerasSimpleRnn extends KerasLayer {
this.layer = builder.build();
if (!returnSequences) {
this.layer = new LastTimeStep(this.layer);
this.layer = LastTimeStep.builder(this.layer);
}
if (maskingConfig.getFirst()) {
this.layer = new MaskZeroLayer(this.layer, maskingConfig.getSecond());

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.java
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@ -819,6 +819,9 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
public GraphBuilder addLayer(String layerName, LayerConfiguration layer, String... layerInputs) {
return addLayer(layerName, layer, null, layerInputs);
}
public GraphBuilder addLayer(String layerName, LayerConfiguration.LayerConfigurationBuilder<?,?> layer, String... layerInputs) {
return addLayer(layerName, layer.build(), null, layerInputs);
}
/**
* Add a layer, with no {@link InputPreProcessor}, with the specified name

15
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/NeuralNetBaseBuilderConfiguration.java
View File

@ -661,7 +661,17 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B layer(Integer index, @NonNull LayerConfiguration layer) {
innerConfigurations$value.add(index, layer);
innerConfigurations$set = true;
return (B) this;
return self();
}
/**
* Set layer at index
*
* @param index where to insert
* @param layer the layer
* @return builder
*/
public B layer(Integer index, @NonNull LayerConfiguration.LayerConfigurationBuilder<?,?> layer) {
return this.layer(index, layer.build());
}
/**
@ -675,6 +685,9 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
innerConfigurations$set = true;
return (B) this;
}
public B layer(@NonNull LayerConfiguration.LayerConfigurationBuilder<?, ?> layer) {
return this.layer(layer.build());
}
// TODO this is a dirty workaround
public boolean isOverrideNinUponBuild() {

10
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/BaseLayerConfiguration.java
View File

@ -212,6 +212,16 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration
C extends BaseLayerConfiguration, B extends BaseLayerConfigurationBuilder<C, B>>
extends LayerConfigurationBuilder<C, B> {
public B updater(Updater upd) {
this.updater = upd.getIUpdaterWithDefaultConfig();
return self();
}
public B updater(IUpdater upd) {
this.updater = upd;
return self();
}
/**
* Set weight initialization scheme to random sampling via the specified distribution.

14
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/CapsuleLayer.java
View File

@ -38,7 +38,7 @@ import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
@Data
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild", builderMethodName = "innerBuilder")
public class CapsuleLayer extends SameDiffLayer {
@ -50,33 +50,33 @@ public class CapsuleLayer extends SameDiffLayer {
* @param hasBias
* @return
*/
@Builder.Default private boolean hasBias = false;
@Builder.Default @Getter @Setter private boolean hasBias = false;
/**
* Usually inferred automatically.
* @param inputCapsules
* @return
*/
@Builder.Default private long inputCapsules = 0;
@Builder.Default @Getter @Setter private long inputCapsules = 0;
/**
* Usually inferred automatically.
* @param inputCapsuleDimensions
* @return
*/
@Builder.Default private long inputCapsuleDimensions = 0;
@Builder.Default @Getter @Setter private long inputCapsuleDimensions = 0;
/**
* Set the number of capsules to use.
* @param capsules
* @return
*/
private int capsules;
private int capsuleDimensions;
@Getter @Setter private int capsules;
@Getter @Setter private int capsuleDimensions;
/**
* Set the number of dynamic routing iterations to use.
* The default is 3 (recommendedded in Dynamic Routing Between Capsules)
* @param routings
* @return
*/
@Builder.Default private int routings = 3;
@Builder.Default @Getter @Setter private int routings = 3;
@Override
public void setNIn(InputType inputType, boolean override) {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/CapsuleStrengthLayer.java
View File

@ -32,7 +32,6 @@ import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder
public class CapsuleStrengthLayer extends SameDiffLambdaLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/CnnLossLayer.java
View File

@ -39,7 +39,6 @@ import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.ILossFunction;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder

11
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ConvolutionLayer.java
View File

@ -46,7 +46,7 @@ import org.nd4j.linalg.api.ndarray.INDArray;
* to be used in the net or in other words the channels The builder specifies the filter/kernel
* size, the stride and padding The pooling layer takes the kernel size
*/
@Data
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild", builderMethodName = "innerBuilder")
@ -72,7 +72,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
*
* @param format Format for activations (in and out)
*/
@Builder.Default
@Builder.Default @Getter @Setter
private CNN2DFormat convFormat =
CNN2DFormat.NCHW; // default value for legacy serialization reasons
@ -86,24 +86,29 @@ public class ConvolutionLayer extends FeedForwardLayer {
* http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html#dilated-convolutions</a>
* <br>
*/
@Getter @Setter
private @Builder.Default int[] dilation = new int[] {1, 1};
/** Default is 2. Down-sample by a factor of 2 */
@Getter @Setter
private @Builder.Default int[] stride = new int[] {1, 1};
@Getter @Setter
private @Builder.Default int[] padding = new int[] {0, 0};
/**
* When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation
* be allowed? If set to false, an exception in CuDNN will be propagated back to the user. If
* false, the built-in (non-CuDNN) implementation for ConvolutionLayer will be used
*/
@Getter
@Builder.Default private boolean cudnnAllowFallback = true;
/** Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory. */
@Getter
@Builder.Default private AlgoMode cudnnAlgoMode = AlgoMode.PREFER_FASTEST;
private FwdAlgo cudnnFwdAlgo;
private BwdFilterAlgo cudnnBwdFilterAlgo;
private BwdDataAlgo cudnnBwdDataAlgo;
@Getter @Setter
@Builder.Default private int convolutionDim = 2; // 2D convolution by default
/** Causal convolution - allowed for 1D only */
@Builder.Default private boolean allowCausal = false;

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingLayer.java
View File

@ -45,7 +45,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild", builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingSequenceLayer.java
View File

@ -44,7 +44,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild", builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/GravesBidirectionalLSTM.java
View File

@ -39,7 +39,6 @@ import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.*;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@Deprecated

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LSTM.java
View File

@ -41,7 +41,6 @@ import java.util.Collections;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild")

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LayerConfiguration.java
View File

@ -48,7 +48,7 @@ import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.linalg.learning.regularization.Regularization;
/** A neural network layer. */
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
//@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
@EqualsAndHashCode
// @JsonIdentityInfo(generator= ObjectIdGenerators.IntSequenceGenerator.class, property="@id")
@Slf4j

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocalResponseNormalization.java
View File

@ -40,7 +40,6 @@ import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.learning.regularization.Regularization;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocallyConnected2D.java
View File

@ -47,7 +47,6 @@ import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@JsonIgnoreProperties({"paramShapes"})
@SuperBuilder(buildMethodName = "initBuild")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LossLayer.java
View File

@ -40,7 +40,6 @@ import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.ILossFunction;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/OutputLayer.java
View File

@ -37,7 +37,6 @@ import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossFunctions;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild", builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/PReLULayer.java
View File

@ -38,7 +38,6 @@ import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild", builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Pooling1D.java
View File

@ -32,7 +32,6 @@ import lombok.ToString;
* @author Max Pumperla
*/
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class Pooling1D extends Subsampling1DLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Pooling2D.java
View File

@ -32,7 +32,6 @@ import lombok.ToString;
* @author Max Pumperla
*/
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class Pooling2D extends SubsamplingLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/PrimaryCapsules.java
View File

@ -40,7 +40,6 @@ import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
import org.nd4j.linalg.factory.Nd4j;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild", builderMethodName = "innerBuilder")
public class PrimaryCapsules extends SameDiffLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/RecurrentAttentionLayer.java
View File

@ -42,7 +42,6 @@ import org.nd4j.linalg.factory.Nd4j;
import java.util.Map;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild")
public class RecurrentAttentionLayer extends SameDiffLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/RnnLossLayer.java
View File

@ -40,7 +40,6 @@ import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossFunctions;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/RnnOutputLayer.java
View File

@ -40,7 +40,6 @@ import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.LossFunctions;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild", builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SelfAttentionLayer.java
View File

@ -38,7 +38,6 @@ import org.nd4j.linalg.factory.Nd4j;
@Data
@EqualsAndHashCode(callSuper = true)
@NoArgsConstructor()
@SuperBuilder(buildMethodName = "initBuild")
public class SelfAttentionLayer extends SameDiffLayer {
private static final String WEIGHT_KEY_QUERY_PROJECTION = "Wq";

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SpaceToBatchLayer.java
View File

@ -39,7 +39,6 @@ import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SpaceToDepthLayer.java
View File

@ -38,7 +38,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Subsampling1DLayer.java
View File

@ -48,7 +48,6 @@ import org.nd4j.linalg.api.ndarray.INDArray;
* wide.
*/
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Subsampling3DLayer.java
View File

@ -43,7 +43,6 @@ import org.nd4j.linalg.exception.ND4JArraySizeException;
import org.nd4j.linalg.learning.regularization.Regularization;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder", buildMethodName = "initBuild")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SubsamplingLayer.java
View File

@ -43,7 +43,6 @@ import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(buildMethodName = "initBuild", builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Upsampling1D.java
View File

@ -41,7 +41,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Upsampling2D.java
View File

@ -39,7 +39,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Upsampling3D.java
View File

@ -37,7 +37,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ZeroPadding1DLayer.java
View File

@ -38,7 +38,6 @@ import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")
public class ZeroPadding1DLayer extends NoParamLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ZeroPadding3DLayer.java
View File

@ -39,7 +39,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")
public class ZeroPadding3DLayer extends NoParamLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ZeroPaddingLayer.java
View File

@ -40,7 +40,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder", buildMethodName = "initBuild")
public class ZeroPaddingLayer extends NoParamLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/convolutional/Cropping1D.java
View File

@ -40,7 +40,6 @@ import org.nd4j.linalg.api.ndarray.INDArray;
/** Amount of cropping to apply to both the top and the bottom of the input activations */
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")
public class Cropping1D extends NoParamLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/convolutional/Cropping2D.java
View File

@ -41,7 +41,6 @@ import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")
public class Cropping2D extends NoParamLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/convolutional/Cropping3D.java
View File

@ -40,7 +40,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")
public class Cropping3D extends NoParamLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/misc/ElementWiseMultiplicationLayer.java
View File

@ -42,7 +42,6 @@ import java.util.Map;
@Data
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@NoArgsConstructor
@SuperBuilder
public class ElementWiseMultiplicationLayer extends org.deeplearning4j.nn.conf.layers.FeedForwardLayer {

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/misc/FrozenLayerWithBackprop.java
View File

@ -57,6 +57,9 @@ public class FrozenLayerWithBackprop extends BaseWrapperLayerConfiguration {
public static FrozenLayerWithBackpropBuilder<?, ?> builder(LayerConfiguration innerConfiguration) {
return innerBuilder().underlying(innerConfiguration);
}
public static FrozenLayerWithBackpropBuilder<?, ?> builder(LayerConfigurationBuilder<?,?> innerConfiguration) {
return innerBuilder().underlying(innerConfiguration.build());
}
public NeuralNetConfiguration getInnerConf(NeuralNetConfiguration conf) {
NeuralNetConfiguration nnc = conf.clone();
nnc.getLayerConfigurations().add(0, underlying);

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/misc/RepeatVector.java
View File

@ -39,7 +39,6 @@ import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/recurrent/Bidirectional.java
View File

@ -48,7 +48,6 @@ import java.util.Map;
import static org.nd4j.linalg.indexing.NDArrayIndex.interval;
@NoArgsConstructor
@Data
@EqualsAndHashCode(callSuper = true, exclude = {"initializer"})
@JsonIgnoreProperties({"initializer"})

7
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/AbstractSameDiffLayer.java
View File

@ -61,6 +61,7 @@ public abstract class AbstractSameDiffLayer extends LayerConfiguration {
* @param regularization Regularization to apply for the network parameters/weights (excluding
* biases)
*/
@Getter
protected List<Regularization> regularization;
/**
* The regularization for the biases only - for example {@link WeightDecay} -- SETTER -- Set the
@ -68,6 +69,7 @@ public abstract class AbstractSameDiffLayer extends LayerConfiguration {
*
* @param regularizationBias Regularization to apply for the network biases only
*/
@Getter
protected List<Regularization> regularizationBias;
/**
* Gradient updater. For example, {@link org.nd4j.linalg.learning.config.Adam} or {@link
@ -83,10 +85,11 @@ public abstract class AbstractSameDiffLayer extends LayerConfiguration {
* @param biasUpdater Updater to use for bias parameters
*/
protected @Getter @Setter IUpdater biasUpdater;
@Getter @Setter
protected GradientNormalization gradientNormalization;
@Getter @Setter
protected double gradientNormalizationThreshold = Double.NaN;
@Getter @Setter
private SDLayerParams layerParams;
@Override

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/SameDiffLambdaLayer.java
View File

@ -28,7 +28,6 @@ import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Map;
@NoArgsConstructor
@SuperBuilder
public abstract class SameDiffLambdaLayer extends SameDiffLayer {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/variational/VariationalAutoencoder.java
View File

@ -45,7 +45,6 @@ import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossFunctions;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder
public class VariationalAutoencoder extends BasePretrainNetwork {

12
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/ocnn/OCNNOutputLayer.java
View File

@ -48,31 +48,31 @@ public class OCNNOutputLayer extends BaseOutputLayer {
* The hidden layer size for the one class neural network. Note this would be nOut on a dense
* layer. NOut in this neural net is always set to 1 though.
*/
@Builder.Default private int hiddenLayerSize; // embedded hidden layer size aka "K"
@Builder.Default @Getter private int hiddenLayerSize; // embedded hidden layer size aka "K"
/** For nu definition see the paper */
@Builder.Default private double nu = 0.04;
@Builder.Default @Getter private double nu = 0.04;
/**
* The number of examples to use for computing the quantile for the r value update. This value
* should generally be the same as the number of examples in the dataset
*/
@Builder.Default private int windowSize = 10000;
@Builder.Default @Getter private int windowSize = 10000;
/**
* The initial r value to use for ocnn for definition, see the paper, note this is only active
* when {@link #configureR} is specified as true
*/
@Builder.Default private double initialRValue = 0.1;
@Builder.Default @Getter private double initialRValue = 0.1;
/**
* Whether to use the specified {@link #initialRValue} or use the weight initialization with the
* neural network for the r value
*/
@Builder.Default private boolean configureR = true;
@Builder.Default @Getter private boolean configureR = true;
/**
* Psuedo code from keras: start_time = time.time() for epoch in range(100): # Train with each
* example sess.run(updates, feed_dict={X: train_X,r:rvalue}) rvalue = nnScore(train_X, w_1, w_2,
* g) with sess.as_default(): rvalue = rvalue.eval() rvalue = np.percentile(rvalue,q=100*nu)
* print("Epoch = %d, r = %f" % (epoch + 1,rvalue))
*/
@Builder.Default private int lastEpochSinceRUpdated = 0;
@Builder.Default @Getter @Setter private int lastEpochSinceRUpdated = 0;
@Override
public Layer instantiate(

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/layers/ocnn/OCNNOutputLayer.java
View File

@ -21,8 +21,10 @@
package org.deeplearning4j.nn.layers.ocnn;
import lombok.Builder;
import lombok.Getter;
import lombok.Setter;
import lombok.experimental.SuperBuilder;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
@ -46,10 +48,12 @@ import static org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer.R_KEY;
import static org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer.V_KEY;
import static org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer.W_KEY;
public class OCNNOutputLayer extends BaseOutputLayer<org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer> {
@Setter
@Getter
private IActivation activation = new ActivationReLU();
private static final IActivation relu = new ActivationReLU();

11
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/layers/util/IdentityLayer.java
View File

@ -21,16 +21,21 @@
package org.deeplearning4j.nn.layers.util;
import lombok.NoArgsConstructor;
import lombok.experimental.SuperBuilder;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
@NoArgsConstructor
@SuperBuilder(builderMethodName = "innerBuilder")
public class IdentityLayer extends SameDiffLambdaLayer {
public IdentityLayer(String name) {
this.name = name;
public static IdentityLayerBuilder<?,?> builder() {
return innerBuilder();
}
public static IdentityLayerBuilder<?,?> builder(String name) {
return innerBuilder()
.name(name);
}
@Override

8
cavis-native/cavis-native-lib/build.gradle
View File

@ -235,7 +235,10 @@ chipList.each { thisChip ->
/* Get VCVARS in case we want to build CUDA
* MinGW64 g++ on MSYS is used otherwise */
if (thisChip.equals('cuda') && osdetector.os.startsWith("win") && !VISUAL_STUDIO_INSTALL_DIR.isEmpty()) {
if (thisChip.equals('cuda') && osdetector.os.startsWith("win")
&& project.hasProperty("skip-native")
&& !project.getProperty("skip-native").equals("true")
&& !VISUAL_STUDIO_INSTALL_DIR.isEmpty()) {
def proc = ["cmd.exe", "/c", "${VISUAL_STUDIO_VCVARS_CMD} > nul && set"].execute()
it.environmentVariables = it.environmentVariables ?: [:]
def lines = proc.text.split("\\r?\\n")
@ -329,7 +332,8 @@ chipList.each { thisChip ->
thisTask.properties = getBuildPlatform( thisChip, thisTask )
if(thisChip.equals('cuda') && osdetector.os.startsWith("win") && !VISUAL_STUDIO_INSTALL_DIR.isEmpty()) {
if(thisChip.equals('cuda') && osdetector.os.startsWith("win") && project.hasProperty("skip-native")
&& !project.getProperty("skip-native").equals("true") && !VISUAL_STUDIO_INSTALL_DIR.isEmpty()) {
def proc = ["cmd.exe", "/c", "${VISUAL_STUDIO_VCVARS_CMD} > nul && where.exe cl.exe"].execute()
def outp = proc.text
def cl = outp.replace("\\", "\\\\").trim()

2
vsconfig.gradle
View File

@ -28,7 +28,9 @@
****************************************************************************/
if (!hasProperty("VISUAL_STUDIO_INSTALL_DIR") && osdetector.os.equals("windows")) {
if (project.hasProperty("skip-native") && !project.getProperty("skip-native").equals("true")) {
configureVisualStudio()
}
}
def configureVisualStudio() {