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Fixing Tests

master
Brian Rosenberger 2023-05-08 09:34:44 +02:00
parent 581a14118c
commit 24466a8fd4
437 changed files with 6263 additions and 3645 deletions
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13
.gitignore vendored
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@ -36,6 +36,8 @@ pom.xml.versionsBackup
pom.xml.next
release.properties
*dependency-reduced-pom.xml
**/build/*
.gradle/*
# Specific for Nd4j
*.md5
@ -83,3 +85,14 @@ bruai4j-native-common/cmake*
/bruai4j-native/bruai4j-native-common/blasbuild/
/bruai4j-native/bruai4j-native-common/build/
/cavis-native/cavis-native-lib/blasbuild/
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/classes/org.deeplearning4j.gradientcheck.AttentionLayerTest.html
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/css/base-style.css
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/css/style.css
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/js/report.js
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/packages/org.deeplearning4j.gradientcheck.html
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/index.html
/cavis-dnn/cavis-dnn-core/build/resources/main/iris.dat
/cavis-dnn/cavis-dnn-core/build/resources/test/junit-platform.properties
/cavis-dnn/cavis-dnn-core/build/resources/test/logback-test.xml
/cavis-dnn/cavis-dnn-core/build/test-results/cudaTest/TEST-org.deeplearning4j.gradientcheck.AttentionLayerTest.xml
/cavis-dnn/cavis-dnn-core/build/tmp/jar/MANIFEST.MF

167
brutex-extended-tests/src/test/java/net/brutex/ai/nd4j/tests/ExploreParamsTest.java Normal file
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@ -0,0 +1,167 @@
/*
*
* ******************************************************************************
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*
*/
package net.brutex.ai.nd4j.tests;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.datasets.iterator.INDArrayDataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.jupiter.api.Test;
import org.nd4j.common.primitives.Pair;
import org.nd4j.evaluation.classification.Evaluation;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;
@Slf4j
public class ExploreParamsTest {
@Test
public void testParam() {
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.seed(12345)
.dataType(DataType.DOUBLE)
.layer(
DenseLayer.builder().nIn(4).nOut(30).name("1. Dense").activation(Activation.TANH))
.layer(DenseLayer.builder().nIn(30).nOut(10).name("2. Dense"))
// .layer(FrozenLayer.builder(DenseLayer.builder().nOut(6).build()).build())
.layer(
OutputLayer.builder()
.nOut(3)
.lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.SOFTMAX))
.build();
MultiLayerNetwork nn = new MultiLayerNetwork(conf);
nn.init();
log.info(nn.summary());
// INDArray input = Nd4j.rand(10,4);
INDArray labels = Nd4j.zeros(9, 3);
INDArray input =
Nd4j.create(
new double[][] {
{5.15, 3.5, 1.4, 0.21}, // setosa
{4.9, 3.2, 1.4, 0.2}, // setosa
{4.7, 3.2, 1.23, 0.2}, // setosa
{7, 3.25, 4.7, 1.41}, // versicolor
{6.4, 3.2, 4.54, 1.5}, // versicolor
{6.9, 3.1, 4.92, 1.5}, // versicolor
{7.7, 3, 6.1, 2.3}, // virginica
{6.3, 3.4, 5.6, 2.45}, // virginica
{6.4, 3.12, 5.5, 1.8} // virginica
});
labels.putScalar(0, 1);
labels.putScalar(3, 1);
labels.putScalar(6, 1);
labels.putScalar(10, 1);
labels.putScalar(13, 1);
labels.putScalar(16, 1);
labels.putScalar(20, 1);
labels.putScalar(23, 1);
labels.putScalar(26, 1);
IrisDataSetIterator iter = new IrisDataSetIterator();
//Iterable<Pair<INDArray, INDArray>> it = List.of(new Pair<INDArray, INDArray>(input, labels));
List l = new ArrayList<>();
for (int i=0; i< input.rows(); i++) {
l.add(new Pair(input.getRow(i), labels.getRow(i)));
}
Iterable<Pair<INDArray, INDArray>> it = l;
INDArrayDataSetIterator diter = new INDArrayDataSetIterator(it, 1);
for (int i = 0; i < 100; i++) {
// nn.fit(input, labels);
// nn.fit( input, labels);
nn.fit(diter);
// nn.feedForward(input);
if(i%20==0) log.info("Score: {}", nn.getScore());
}
Evaluation eval = nn.evaluate(iter, List.of("setosa", "vericolor", "virginica"));
log.info("\n{}", eval.stats());
}
@Test
public void testParam2() throws IOException {
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.seed(12345)
.layer(
DenseLayer.builder().nIn(784).nOut(20).name("1. Dense"))
.layer(DenseLayer.builder().nIn(20).nOut(10).name("2. Dense"))
.layer(
OutputLayer.builder()
.nOut(10)
.lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.SOFTMAX))
.build();
MultiLayerNetwork nn = new MultiLayerNetwork(conf);
nn.init();
log.info(nn.summary());
NeuralNetConfiguration conf2 =
NeuralNetConfiguration.builder()
.seed(12345)
.layer(
DenseLayer.builder().nIn(784).nOut(20).name("1. Dense").dropOut(0.7))
.layer(DenseLayer.builder().nIn(20).nOut(10).name("2. Dense"))
.layer(
OutputLayer.builder()
.nOut(10)
.lossFunction(LossFunctions.LossFunction.MSE)
.activation(Activation.SOFTMAX))
.build();
MultiLayerNetwork nn2 = new MultiLayerNetwork(conf2);
nn2.init();
log.info(nn2.summary());
MnistDataSetIterator iter = new MnistDataSetIterator(10, 500);
MnistDataSetIterator iter2 = new MnistDataSetIterator(10, 50);
for (int i = 0; i < 200; i++) {
nn.fit(iter);
nn2.fit(iter);
if(i%20==0) log.info("Score: {} vs. {}", nn.getScore(), nn2.getScore());
}
Evaluation eval = nn.evaluate(iter2);
Evaluation eval2 = nn2.evaluate(iter2);
log.info("\n{} \n{}", eval.stats(), eval2.stats());
}
}

43
brutex-extended-tests/src/test/java/net/brutex/gan/App.java
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@ -45,6 +45,7 @@ import org.datavec.image.transform.PipelineImageTransform;
import org.datavec.image.transform.ResizeImageTransform;
import org.datavec.image.transform.ShowImageTransform;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.distribution.Distribution;
@ -65,6 +66,7 @@ import org.deeplearning4j.optimize.listeners.ScoreToChartListener;
import org.junit.jupiter.api.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.impl.ActivationLReLU;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
@ -80,11 +82,11 @@ public class App {
private static final int X_DIM = 20 ;
private static final int Y_DIM = 20;
private static final int CHANNELS = 1;
private static final int batchSize = 10;
private static final int CHANNELS = 3;
private static final int batchSize = 50;
private static final int INPUT = 128;
private static final int OUTPUT_PER_PANEL = 4;
private static final int OUTPUT_PER_PANEL = 16;
private static final int ARRAY_SIZE_PER_SAMPLE = X_DIM*Y_DIM*CHANNELS;
private static final IUpdater UPDATER = Adam.builder().learningRate(LEARNING_RATE).beta1(0.5).build();
@ -146,7 +148,7 @@ public class App {
ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
DropoutLayer.builder(1 - 0.5).build(),
OutputLayer.builder().name("dis-output").lossFunction(LossFunction.XENT).nIn(X_DIM*Y_DIM).nOut(1).activation(Activation.SIGMOID).build()
OutputLayer.builder().name("dis-output").lossFunction(LossFunction.MCXENT).nIn(X_DIM*Y_DIM).nOut(1).activation(Activation.SIGMOID).build()
};
}
@ -196,6 +198,7 @@ public class App {
.activation( Activation.IDENTITY )
.layersFromArray( layers )
.inputType( InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
.dataType(DataType.FLOAT)
.build();
((NeuralNetConfiguration) conf).init();
return conf;
@ -212,8 +215,8 @@ public class App {
log.info("\u001B[32m Some \u001B[1m green \u001B[22m text \u001B[0m \u001B[7m Inverted\u001B[0m ");
Nd4j.getMemoryManager().setAutoGcWindow(500);
// MnistDataSetIterator trainData = new MnistDataSetIterator(128, true, 45);
// FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/flowers"), NativeImageLoader.getALLOWED_FORMATS());
//MnistDataSetIterator trainData = new MnistDataSetIterator(128, true, 45);
//FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/flowers"), NativeImageLoader.getALLOWED_FORMATS());
FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/humans"), NativeImageLoader.getALLOWED_FORMATS());
@ -223,7 +226,7 @@ public class App {
ImageTransform transform3 = new ResizeImageTransform(X_DIM, Y_DIM);
ImageTransform tr = new PipelineImageTransform.Builder()
.addImageTransform(transform) //convert to GREY SCALE
//.addImageTransform(transform) //convert to GREY SCALE
.addImageTransform(transform3)
//.addImageTransform(transform2)
.build();
@ -270,10 +273,10 @@ public class App {
break;
}
if(i%20 == 0) {
// frame2 = visualize(new INDArray[]{real}, batchSize,
// frame2 == null ? new JFrame() : frame2, true); //real has batchsize number of images
}
//if(i%20 == 0) {
frame2 = visualize(new INDArray[]{real}, batchSize,
frame2 == null ? new JFrame() : frame2, true); //real has batchsize number of images
//}
real.divi(255f);
// int batchSize = (int) real.shape()[0];
@ -290,7 +293,7 @@ public class App {
DataSet data = DataSet.merge(Arrays.asList(realSet, fakeSet));
dis.fit(data);
dis.fit(data);
//dis.fit(data);
// Update the discriminator in the GAN network
updateGan(gen, dis, gan);
@ -298,7 +301,7 @@ public class App {
//gan.fit(new DataSet(Nd4j.rand(batchSize, INPUT), Nd4j.zeros(batchSize, 1)));
gan.fit(new DataSet(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1)));
//Visualize and reporting
if (j % 10 == 1) {
System.out.println("Iteration " + j + " Visualizing...");
INDArray[] samples = batchSize > OUTPUT_PER_PANEL ? new INDArray[OUTPUT_PER_PANEL] : new INDArray[batchSize];
@ -320,11 +323,16 @@ public class App {
frame = visualize(samples, 1, frame == null ? new JFrame() : frame, false); //each samples only has 1 image, thus batchElements=1
}
}
if (trainData.resetSupported()) {
trainData.reset();
} else {
log.error("Trainingdata {} does not support reset.", trainData.toString());
}
// Copy the GANs generator to gen.
updateGen(gen, gan);
}
gen.save(new File("mnist-mlp-generator.dlj"));
}
@ -383,7 +391,12 @@ public class App {
}
private static JLabel getImage(INDArray tensor, int batchElement, boolean isOrig) {
final BufferedImage bi = new BufferedImage(X_DIM, Y_DIM, BufferedImage.TYPE_BYTE_GRAY);
final BufferedImage bi;
if(CHANNELS>1) {
bi = new BufferedImage(X_DIM, Y_DIM, BufferedImage.TYPE_INT_RGB); //need to change here based on channels
} else {
bi = new BufferedImage(X_DIM, Y_DIM, BufferedImage.TYPE_BYTE_GRAY); //need to change here based on channels
}
final int imageSize = X_DIM * Y_DIM;
final int offset = batchElement * imageSize;
int pxl = offset * CHANNELS; //where to start in the INDArray

8
brutex-extended-tests/src/test/java/net/brutex/gan/MnistSimpleGAN.java
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@ -24,12 +24,14 @@ package net.brutex.gan;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ActivationLayer;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.DropoutLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.junit.jupiter.api.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.impl.ActivationLReLU;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -98,7 +100,10 @@ public class MnistSimpleGAN {
return new MultiLayerNetwork(discConf);
}
@Test
public void runTest() throws Exception {
main(null);
}
public static void main(String[] args) throws Exception {
GAN gan = new GAN.Builder()
.generator(MnistSimpleGAN::getGenerator)
@ -108,6 +113,7 @@ public class MnistSimpleGAN {
.updater(UPDATER)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.gradientNormalizationThreshold(100)
.build();
Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);

2
brutex-extended-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN1DTestCases.java
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@ -88,7 +88,7 @@ public class CNN1DTestCases {
.convolutionMode(ConvolutionMode.Same))
.graphBuilder()
.addInputs("in")
.layer("0", Convolution1DLayer.builder().nOut(32).activation(Activation.TANH).kernelSize(3).stride(1).build(), "in")
.layer("0", Convolution1D.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")

6
cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java
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@ -2386,7 +2386,11 @@ public interface INDArray extends Serializable, AutoCloseable {
long[] stride();
/**
* Return the ordering (fortran or c 'f' and 'c' respectively) of this ndarray
* Return the ordering (fortran or c 'f' and 'c' respectively) of this ndarray <br/><br/>
* C Is Contiguous layout. Mathematically speaking, row major.<br/>
* F Is Fortran contiguous layout. Mathematically speaking, column major.<br/>
* {@see https://en.wikipedia.org/wiki/Row-_and_column-major_order}<br/>
*
* @return the ordering of this ndarray
*/
char ordering();

1
cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/dataset/DataSet.java
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@ -334,6 +334,7 @@ public class DataSet implements org.nd4j.linalg.dataset.api.DataSet {
public void save(File to) {
try (FileOutputStream fos = new FileOutputStream(to, false);
BufferedOutputStream bos = new BufferedOutputStream(fos)) {
to.mkdirs();
save(bos);
} catch (IOException e) {
throw new RuntimeException(e);

2
cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java
View File

@ -5121,7 +5121,7 @@ public class Nd4j {
Nd4j.backend = backend;
updateNd4jContext();
props = Nd4jContext.getInstance().getConf();
logger.info("Properties for Nd4jContext " + props);
log.debug("Properties for Nd4jContext {}", props);
PropertyParser pp = new PropertyParser(props);
String otherDtype = pp.toString(ND4JSystemProperties.DTYPE);

6
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/datasets/iterator/DataSetIteratorTest.java
View File

@ -166,10 +166,10 @@ public class DataSetIteratorTest extends BaseDL4JTest {
int seed = 123;
int listenerFreq = 1;
LFWDataSetIterator lfw = new LFWDataSetIterator(batchSize, numSamples,
final LFWDataSetIterator lfw = new LFWDataSetIterator(batchSize, numSamples,
new int[] {numRows, numColumns, numChannels}, outputNum, false, true, 1.0, new Random(seed));
NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed)
final var builder = NeuralNetConfiguration.builder().seed(seed)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.layer(0, ConvolutionLayer.builder(5, 5).nIn(numChannels).nOut(6)
@ -181,7 +181,7 @@ public class DataSetIteratorTest extends BaseDL4JTest {
.build())
.inputType(InputType.convolutionalFlat(numRows, numColumns, numChannels));
MultiLayerNetwork model = new MultiLayerNetwork(builder.build());
final MultiLayerNetwork model = new MultiLayerNetwork(builder.build());
model.init();
model.addTrainingListeners(new ScoreIterationListener(listenerFreq));

9
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStopping.java
View File

@ -45,6 +45,7 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution;
import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder;
import org.deeplearning4j.nn.conf.serde.CavisMapper;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.api.BaseTrainingListener;
@ -924,8 +925,8 @@ public class TestEarlyStopping extends BaseDL4JTest {
};
for(EpochTerminationCondition e : etc ){
String s = NeuralNetConfiguration.mapper().writeValueAsString(e);
EpochTerminationCondition c = NeuralNetConfiguration.mapper().readValue(s, EpochTerminationCondition.class);
String s = CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(e);
EpochTerminationCondition c = CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(s, EpochTerminationCondition.class);
assertEquals(e, c);
}
@ -936,8 +937,8 @@ public class TestEarlyStopping extends BaseDL4JTest {
};
for(IterationTerminationCondition i : itc ){
String s = NeuralNetConfiguration.mapper().writeValueAsString(i);
IterationTerminationCondition c = NeuralNetConfiguration.mapper().readValue(s, IterationTerminationCondition.class);
String s = CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(i);
IterationTerminationCondition c = CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(s, IterationTerminationCondition.class);
assertEquals(i, c);
}
}

2
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/exceptions/TestInvalidConfigurations.java
View File

@ -309,7 +309,7 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict)
.list()
.layer(0, ConvolutionLayer.builder().kernelSize(2, 3).stride(2, 2).padding(0, 0).nOut(5)
.build())
.layer(1, OutputLayer.builder().nOut(10).build())

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

@ -77,7 +77,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
NeuralNetConfiguration.builder().updater(new NoOp())
.dataType(DataType.DOUBLE)
.seed(12345L)
.dist(new NormalDistribution(0, 1)).list()
.weightInit(new NormalDistribution(0, 1))
.layer(0, DenseLayer.builder().nIn(4).nOut(3)
.activation(Activation.IDENTITY).build())
.layer(1,BatchNormalization.builder().useLogStd(useLogStd).nOut(3).build())
@ -122,7 +122,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.updater(new NoOp()).seed(12345L)
.dist(new NormalDistribution(0, 2)).list()
.layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2)
.layer(0, Convolution2D.builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2)
.activation(Activation.IDENTITY).build())
.layer(1,BatchNormalization.builder().useLogStd(useLogStd).build())
.layer(2, ActivationLayer.builder().activation(Activation.TANH).build())

25
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DGradientCheckTest.java
View File

@ -91,9 +91,8 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp())
.dist(new NormalDistribution(0, 1))
.convolutionMode(ConvolutionMode.Same)
.list()
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -202,7 +201,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1))
.convolutionMode(ConvolutionMode.Same)
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -211,7 +210,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.build())
.layer(Cropping1D.builder(cropping).build())
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -317,7 +316,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.dist(new NormalDistribution(0, 1))
.convolutionMode(ConvolutionMode.Same)
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -326,7 +325,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.build())
.layer(ZeroPadding1DLayer.builder(zeroPadding).build())
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -435,10 +434,9 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp())
.dist(new NormalDistribution(0, 1))
.convolutionMode(ConvolutionMode.Same)
.list()
.layer(
0,
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -447,7 +445,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.build())
.layer(
1,
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -461,6 +459,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.stride(stride)
.padding(padding)
.pnorm(pnorm)
.name("SubsamplingLayer")
.build())
.layer(
3,
@ -548,7 +547,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.seed(12345)
.list()
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.kernelSize(2)
.rnnDataFormat(RNNFormat.NCW)
.stride(stride)
@ -562,7 +561,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.pnorm(pnorm)
.build())
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.kernelSize(2)
.rnnDataFormat(RNNFormat.NCW)
.stride(stride)
@ -655,7 +654,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.seed(12345)
.list()
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.kernelSize(k)
.dilation(d)
.hasBias(hasBias)
@ -664,7 +663,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.nOut(convNOut1)
.build())
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.kernelSize(k)
.dilation(d)
.convolutionMode(ConvolutionMode.Causal)

811
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DNewGradientCheckTest.java Normal file
View File

@ -0,0 +1,811 @@
/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.deeplearning4j.gradientcheck;
import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertTrue;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.TestUtils;
import org.deeplearning4j.datasets.iterator.INDArrayDataSetIterator;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.Convolution1DUtils;
import org.junit.jupiter.api.Test;
import org.nd4j.common.primitives.Pair;
import org.nd4j.evaluation.classification.Evaluation;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
@Slf4j
public class CNN1DNewGradientCheckTest extends BaseDL4JTest {
private static final boolean PRINT_RESULTS = true;
private static final boolean RETURN_ON_FIRST_FAILURE = false;
private static final double DEFAULT_EPS = 1e-6;
private static final double DEFAULT_MAX_REL_ERROR = 1e-3;
private static final double DEFAULT_MIN_ABS_ERROR = 1e-8;
static {
Nd4j.setDataType(DataType.DOUBLE);
}
@Test
public void testCnn1D() {
int minibatchSize = 4;
int[] dataChannels = {4, 10}; //the input
int[] kernels = {2,4,5,8};
int stride = 2;
int padding = 3;
int seriesLength = 300;
for (int kernel : kernels) {
for (int dChannels : dataChannels) {
int numLabels = ((seriesLength + (2 * padding) - kernel) / stride) + 1;
final NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1))
.convolutionMode(ConvolutionMode.Same)
.layer(
Convolution1DNew.builder()
.activation(Activation.RELU)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nIn(dChannels) // channels
.nOut(3)
.rnnDataFormat(RNNFormat.NCW)
.build())
.layer(
RnnOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(4)
.build())
.inputType(InputType.recurrent(dChannels, seriesLength))
.build();
INDArray input = Nd4j.rand(minibatchSize, dChannels, seriesLength);
INDArray labels = Nd4j.zeros(minibatchSize, 4, numLabels);
for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < numLabels; j++) {
labels.putScalar(new int[] {i, i % 4, j}, 1.0);
}
}
final MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg =
"Minibatch="
+ minibatchSize
+ ", activationFn="
+ Activation.RELU
+ ", kernel = "
+ kernel;
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
/**
List<Pair<INDArray, INDArray>> iter = new java.util.ArrayList<>(Collections.emptyList());
iter.add(new Pair<>(input, labels));
for(int x=0;x<100; x++) net.fit(input, labels);
Evaluation eval = net.evaluate(new INDArrayDataSetIterator(iter,2), Arrays.asList(new String[]{"One", "Two", "Three", "Four"}));
// net.fit(input, labels);
eval.eval(labels, net.output(input));
**/
boolean gradOK =
GradientCheckUtil.checkGradients(
net,
DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE,
input,
labels);
assertTrue(gradOK, msg);
TestUtils.testModelSerialization(net);
}
}
}
@Test
public void testCnn1DWithLocallyConnected1D() {
Nd4j.getRandom().setSeed(1337);
int[] minibatchSizes = {2, 3};
int length = 25;
int convNIn = 18;
int convNOut1 = 3;
int convNOut2 = 4;
int finalNOut = 4;
int[] kernels = {1,2,4};
int stride = 1;
int padding = 0;
Activation[] activations = {Activation.SIGMOID};
for (Activation afn : activations) {
for (int minibatchSize : minibatchSizes) {
for (int kernel : kernels) {
INDArray input = Nd4j.rand(minibatchSize, convNIn, length);
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length);
for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < length; j++) {
labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
}
}
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1))
.convolutionMode(ConvolutionMode.Same)
.layer(
Convolution1DNew.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nIn(convNIn)
.nOut(convNOut1)
.rnnDataFormat(RNNFormat.NCW)
.build())
.layer(
LocallyConnected1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nIn(convNOut1)
.nOut(convNOut2)
.hasBias(false)
.build())
.layer(
RnnOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length))
.build();
String json = conf.toJson();
NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg =
"Minibatch=" + minibatchSize + ", activationFn=" + afn + ", kernel = " + kernel;
if (PRINT_RESULTS) {
System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("ILayer " + j + " # params: " +
// net.getLayer(j).numParams());
}
boolean gradOK =
GradientCheckUtil.checkGradients(
net,
DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE,
input,
labels);
assertTrue(gradOK, msg);
TestUtils.testModelSerialization(net);
}
}
}
}
@Test
public void testCnn1DWithCropping1D() {
Nd4j.getRandom().setSeed(1337);
int[] minibatchSizes = {1, 3};
int length = 7;
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int finalNOut = 4;
int[] kernels = {1, 2, 4};
int stride = 1;
int padding = 0;
int cropping = 1;
int croppedLength = length - 2 * cropping;
Activation[] activations = {Activation.SIGMOID};
SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[] {
SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG,
SubsamplingLayer.PoolingType.PNORM
};
for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
for (int kernel : kernels) {
INDArray input = Nd4j.rand(minibatchSize, convNIn, length);
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, croppedLength);
for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < croppedLength; j++) {
labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
}
}
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1))
.convolutionMode(ConvolutionMode.Same)
.layer(
Convolution1DNew.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut1)
.build())
.layer(Cropping1D.builder(cropping).build())
.layer(
Convolution1DNew.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut2)
.build())
.layer(
RnnOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
.build();
String json = conf.toJson();
NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg =
"PoolingType="
+ poolingType
+ ", minibatch="
+ minibatchSize
+ ", activationFn="
+ afn
+ ", kernel = "
+ kernel;
if (PRINT_RESULTS) {
System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("ILayer " + j + " # params: " +
// net.getLayer(j).numParams());
}
boolean gradOK =
GradientCheckUtil.checkGradients(
net,
DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE,
input,
labels);
assertTrue(gradOK, msg);
TestUtils.testModelSerialization(net);
}
}
}
}
}
@Test
public void testCnn1DWithZeroPadding1D() {
Nd4j.getRandom().setSeed(1337);
int[] minibatchSizes = {1, 3};
int length = 7;
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int finalNOut = 4;
int[] kernels = {1, 2, 4};
int stride = 1;
int pnorm = 2;
int padding = 0;
int zeroPadding = 2;
int paddedLength = length + 2 * zeroPadding;
Activation[] activations = {Activation.SIGMOID};
SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[] {
SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG,
SubsamplingLayer.PoolingType.PNORM
};
for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
for (int kernel : kernels) {
INDArray input = Nd4j.rand(minibatchSize, convNIn, length);
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, paddedLength);
for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < paddedLength; j++) {
labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
}
}
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1))
.convolutionMode(ConvolutionMode.Same)
.layer(
Convolution1DNew.builder()
.activation(afn)
.kernelSize(2, kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut1)
.build())
.layer(ZeroPadding1DLayer.builder(zeroPadding).build())
.layer(
Convolution1DNew.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut2)
.build())
.layer(ZeroPadding1DLayer.builder(0).build())
.layer(
Subsampling1DLayer.builder(poolingType)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.pnorm(pnorm)
.build())
.layer(
RnnOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
.build();
String json = conf.toJson();
NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg =
"PoolingType="
+ poolingType
+ ", minibatch="
+ minibatchSize
+ ", activationFn="
+ afn
+ ", kernel = "
+ kernel;
if (PRINT_RESULTS) {
System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("ILayer " + j + " # params: " +
// net.getLayer(j).numParams());
}
boolean gradOK =
GradientCheckUtil.checkGradients(
net,
DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE,
input,
labels);
assertTrue(gradOK, msg);
TestUtils.testModelSerialization(net);
}
}
}
}
}
@Test
public void testCnn1DWithSubsampling1D() {
Nd4j.getRandom().setSeed(12345);
int[] minibatchSizes = {1, 3};
int length = 7;
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int finalNOut = 4;
int[] kernels = {1, 2, 4};
int stride = 1;
int padding = 0;
int pnorm = 2;
Activation[] activations = {Activation.SIGMOID, Activation.TANH};
SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[] {
SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG,
SubsamplingLayer.PoolingType.PNORM
};
for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
for (int kernel : kernels) {
INDArray input = Nd4j.rand(minibatchSize, convNIn, length);
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length);
for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < length; j++) {
labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
}
}
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1))
.convolutionMode(ConvolutionMode.Same)
.layer(
0,
Convolution1DNew.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut1)
.build())
.layer(
1,
Convolution1DNew.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.nOut(convNOut2)
.build())
.layer(
2,
Subsampling1DLayer.builder(poolingType)
.kernelSize(kernel)
.stride(stride)
.padding(padding)
.pnorm(pnorm)
.name("SubsamplingLayer")
.build())
.layer(
3,
RnnOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
.build();
String json = conf.toJson();
NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg =
"PoolingType="
+ poolingType
+ ", minibatch="
+ minibatchSize
+ ", activationFn="
+ afn
+ ", kernel = "
+ kernel;
if (PRINT_RESULTS) {
System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("ILayer " + j + " # params: " +
// net.getLayer(j).numParams());
}
boolean gradOK =
GradientCheckUtil.checkGradients(
net,
DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE,
input,
labels);
assertTrue(gradOK, msg);
TestUtils.testModelSerialization(net);
}
}
}
}
}
@Test
public void testCnn1dWithMasking() {
int length = 12;
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int finalNOut = 3;
int pnorm = 2;
SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[] {
SubsamplingLayer.PoolingType.MAX, SubsamplingLayer.PoolingType.AVG
};
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (ConvolutionMode cm :
new ConvolutionMode[] {ConvolutionMode.Same, ConvolutionMode.Truncate}) {
for (int stride : new int[] {1, 2}) {
String s = cm + ", stride=" + stride + ", pooling=" + poolingType;
log.info("Starting test: " + s);
Nd4j.getRandom().setSeed(12345);
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.activation(Activation.TANH)
.dist(new NormalDistribution(0, 1))
.convolutionMode(cm)
.seed(12345)
.layer(
Convolution1DNew.builder()
.kernelSize(2)
.rnnDataFormat(RNNFormat.NCW)
.stride(stride)
.nIn(convNIn)
.nOut(convNOut1)
.build())
.layer(
Subsampling1DLayer.builder(poolingType)
.kernelSize(2)
.stride(stride)
.pnorm(pnorm)
.build())
.layer(
Convolution1DNew.builder()
.kernelSize(2)
.rnnDataFormat(RNNFormat.NCW)
.stride(stride)
.nIn(convNOut1)
.nOut(convNOut2)
.build())
.layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
.layer(
OutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
INDArray f = Nd4j.rand(2, convNIn, length);
INDArray fm = Nd4j.create(2, length);
fm.get(NDArrayIndex.point(0), NDArrayIndex.all()).assign(1);
fm.get(NDArrayIndex.point(1), NDArrayIndex.interval(0, 6)).assign(1);
INDArray label = TestUtils.randomOneHot(2, finalNOut);
boolean gradOK =
GradientCheckUtil.checkGradients(
new GradientCheckUtil.MLNConfig().net(net).input(f).labels(label).inputMask(fm));
assertTrue(gradOK, s);
TestUtils.testModelSerialization(net);
// TODO also check that masked step values don't impact forward pass, score or gradients
DataSet ds = new DataSet(f, label, fm, null);
double scoreBefore = net.score(ds);
net.setInput(f);
net.setLabels(label);
net.setLayerMaskArrays(fm, null);
net.computeGradientAndScore();
INDArray gradBefore = net.getFlattenedGradients().dup();
f.putScalar(1, 0, 10, 10.0);
f.putScalar(1, 1, 11, 20.0);
double scoreAfter = net.score(ds);
net.setInput(f);
net.setLabels(label);
net.setLayerMaskArrays(fm, null);
net.computeGradientAndScore();
INDArray gradAfter = net.getFlattenedGradients().dup();
assertEquals(scoreBefore, scoreAfter, 1e-6);
assertEquals(gradBefore, gradAfter);
}
}
}
}
@Test
public void testCnn1Causal() throws Exception {
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int finalNOut = 3;
int[] lengths = {11, 12, 13, 9, 10, 11};
int[] kernels = {2, 3, 2, 4, 2, 3};
int[] dilations = {1, 1, 2, 1, 2, 1};
int[] strides = {1, 2, 1, 2, 1, 1};
boolean[] masks = {false, true, false, true, false, true};
boolean[] hasB = {true, false, true, false, true, true};
for (int i = 0; i < lengths.length; i++) {
int length = lengths[i];
int k = kernels[i];
int d = dilations[i];
int st = strides[i];
boolean mask = masks[i];
boolean hasBias = hasB[i];
// TODO has bias
String s = "k=" + k + ", s=" + st + " d=" + d + ", seqLen=" + length;
log.info("Starting test: " + s);
Nd4j.getRandom().setSeed(12345);
NeuralNetConfiguration conf =
NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.activation(Activation.TANH)
.weightInit(new NormalDistribution(0, 1))
.seed(12345)
.layer(
Convolution1DNew.builder()
.kernelSize(k)
.dilation(d)
.hasBias(hasBias)
.convolutionMode(ConvolutionMode.Causal)
.stride(st)
.nOut(convNOut1)
.build())
.layer(
Convolution1DNew.builder()
.kernelSize(k)
.dilation(d)
.convolutionMode(ConvolutionMode.Causal)
.stride(st)
.nOut(convNOut2)
.build())
.layer(
RnnOutputLayer.builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(finalNOut)
.build())
.inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
INDArray f = Nd4j.rand(DataType.DOUBLE, 2, convNIn, length);
INDArray fm = null;
if (mask) {
fm = Nd4j.create(2, length);
fm.get(NDArrayIndex.point(0), NDArrayIndex.all()).assign(1);
fm.get(NDArrayIndex.point(1), NDArrayIndex.interval(0, length - 2)).assign(1);
}
long outSize1 = Convolution1DUtils.getOutputSize(length, k, st, 0, ConvolutionMode.Causal, d);
long outSize2 =
Convolution1DUtils.getOutputSize(outSize1, k, st, 0, ConvolutionMode.Causal, d);
INDArray label = TestUtils.randomOneHotTimeSeries(2, finalNOut, (int) outSize2);
String msg =
"Minibatch="
+ 1
+ ", activationFn="
+ Activation.RELU
+ ", kernel = "
+ k;
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
boolean gradOK =
GradientCheckUtil.checkGradients(
new GradientCheckUtil.MLNConfig().net(net).input(f).labels(label).inputMask(fm));
assertTrue(gradOK, s);
TestUtils.testModelSerialization(net);
}
}
}

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java
View File

@ -112,9 +112,8 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1))
.list()
.layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
.stride(stride).nIn(convNIn).nOut(convNOut1).hasBias(false)
.convolutionMode(mode).dataFormat(df)
@ -400,7 +399,6 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1))
.seed(12345)
.list()
.layer(0, Convolution3D.builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNIn).nOut(convNOut).hasBias(false)
.convolutionMode(mode).dataFormat(df)

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

@ -108,8 +108,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.seed(12345L)
.list()
.layer(0, ConvolutionLayer.builder(1, 1).nOut(6).activation(afn).build())
.layer(0, Convolution2D.builder().kernelSize(1).stride(1).nOut(6).activation(afn).build())
.layer(1, OutputLayer.builder(lf).activation(outputActivation).nOut(3).build())
.inputType(InputType.convolutionalFlat(1, 4, 1));

3
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/LossFunctionGradientCheck.java
View File

@ -32,6 +32,7 @@ import org.deeplearning4j.nn.conf.distribution.UniformDistribution;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.LossLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.serde.CavisMapper;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.jupiter.api.Test;
import org.nd4j.linalg.activations.Activation;
@ -336,7 +337,7 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
// to ensure that we carry the parameters through
// the serializer.
try{
ObjectMapper m = NeuralNetConfiguration.mapper();
ObjectMapper m = CavisMapper.getMapper(CavisMapper.Type.JSON);
String s = m.writeValueAsString(lossFunctions[i]);
ILossFunction lf2 = m.readValue(s, lossFunctions[i].getClass());
lossFunctions[i] = lf2;

2
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/dtypes/DTypeTests.java
View File

@ -180,7 +180,7 @@ public class DTypeTests extends BaseDL4JTest {
Pooling2D.class, //Alias for SubsamplingLayer
Convolution2D.class, //Alias for ConvolutionLayer
Pooling1D.class, //Alias for Subsampling1D
Convolution1D.class, //Alias for Convolution1DLayer
Convolution1D.class, //Alias for Convolution1D
TensorFlowCnnToFeedForwardPreProcessor.class //Deprecated
));

4
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/ConvDataFormatTests.java
View File

@ -37,7 +37,7 @@ import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.util.ConvolutionUtils;
import org.deeplearning4j.util.Convolution2DUtils;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.Timeout;
import org.nd4j.linalg.activations.Activation;
@ -1026,7 +1026,7 @@ public class ConvDataFormatTests extends BaseDL4JTest {
} catch (DL4JInvalidInputException e) {
// e.printStackTrace();
String msg = e.getMessage();
assertTrue(msg.contains(ConvolutionUtils.NCHW_NHWC_ERROR_MSG) || msg.contains("input array channels does not match CNN layer configuration"), msg);
assertTrue(msg.contains(Convolution2DUtils.NCHW_NHWC_ERROR_MSG) || msg.contains("input array channels does not match CNN layer configuration"), msg);
}
}
}

6
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/ConvolutionLayerTest.java
View File

@ -36,7 +36,7 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.conf.layers.Convolution1D;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
@ -921,7 +921,7 @@ public class ConvolutionLayerTest extends BaseDL4JTest {
NeuralNetConfiguration.builder()
.convolutionMode(ConvolutionMode.Same)
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.nOut(3)
.kernelSize(2)
.activation(Activation.TANH)
@ -975,7 +975,7 @@ public class ConvolutionLayerTest extends BaseDL4JTest {
@Test
public void testConv1dCausalAllowed() {
Convolution1DLayer.builder().convolutionMode(ConvolutionMode.Causal).kernelSize(2).build();
Convolution1D.builder().convolutionMode(ConvolutionMode.Causal).kernelSize(2).build();
Subsampling1DLayer.builder().convolutionMode(ConvolutionMode.Causal).kernelSize(2).build();
}

14
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/TestConvolutionModes.java
View File

@ -33,7 +33,7 @@ import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.util.ConvolutionUtils;
import org.deeplearning4j.util.Convolution2DUtils;
import org.junit.jupiter.api.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -346,7 +346,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
assertEquals(2, it.getHeight());
assertEquals(2, it.getWidth());
assertEquals(dOut, it.getChannels());
int[] outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Strict);
int[] outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Strict);
assertEquals(2, outSize[0]);
assertEquals(2, outSize[1]);
@ -357,7 +357,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
assertEquals(2, it.getHeight());
assertEquals(2, it.getWidth());
assertEquals(dOut, it.getChannels());
outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Truncate);
outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Truncate);
assertEquals(2, outSize[0]);
assertEquals(2, outSize[1]);
@ -367,7 +367,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
assertEquals(3, it.getHeight());
assertEquals(3, it.getWidth());
assertEquals(dOut, it.getChannels());
outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, null, ConvolutionMode.Same);
outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, null, ConvolutionMode.Same);
assertEquals(3, outSize[0]);
assertEquals(3, outSize[1]);
@ -397,7 +397,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
System.out.println(e.getMessage());
}
try {
outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Strict);
outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Strict);
fail("Exception expected");
} catch (DL4JException e) {
System.out.println(e.getMessage());
@ -409,7 +409,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
assertEquals(1, it.getHeight());
assertEquals(1, it.getWidth());
assertEquals(dOut, it.getChannels());
outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Truncate);
outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Truncate);
assertEquals(1, outSize[0]);
assertEquals(1, outSize[1]);
@ -419,7 +419,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
assertEquals(2, it.getHeight());
assertEquals(2, it.getWidth());
assertEquals(dOut, it.getChannels());
outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, null, ConvolutionMode.Same);
outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, null, ConvolutionMode.Same);
assertEquals(2, outSize[0]);
assertEquals(2, outSize[1]);
}

2
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/normalization/BatchNormalizationTest.java
View File

@ -732,7 +732,7 @@ public class BatchNormalizationTest extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER)
.convolutionMode(ConvolutionMode.Same)
.layer(rnn ? LSTM.builder().nOut(3).build() :
Convolution1DLayer.builder().kernelSize(3).stride(1).nOut(3).build())
Convolution1D.builder().kernelSize(3).stride(1).nOut(3).build())
.layer(BatchNormalization.builder().build())
.layer(RnnOutputLayer.builder().nOut(3).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build())
.inputType(InputType.recurrent(3))

2
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/weights/WeightInitIdentityTest.java
View File

@ -52,7 +52,7 @@ public class WeightInitIdentityTest extends BaseDL4JTest {
.graphBuilder()
.addInputs(inputName)
.setOutputs(output)
.layer(conv, Convolution1DLayer.builder(7)
.layer(conv, Convolution1D.builder(7)
.convolutionMode(ConvolutionMode.Same)
.nOut(input.size(1))
.weightInit(new WeightInitIdentity())

5
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/TestDistributionDeserializer.java
View File

@ -23,6 +23,7 @@ package org.deeplearning4j.regressiontest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.distribution.*;
import org.deeplearning4j.nn.conf.serde.CavisMapper;
import org.junit.jupiter.api.Test;
import com.fasterxml.jackson.databind.ObjectMapper;
@ -38,7 +39,7 @@ public class TestDistributionDeserializer extends BaseDL4JTest {
new Distribution[] {new NormalDistribution(3, 0.5), new UniformDistribution(-2, 1),
new GaussianDistribution(2, 1.0), new BinomialDistribution(10, 0.3)};
ObjectMapper om = NeuralNetConfiguration.mapper();
ObjectMapper om = CavisMapper.getMapper(CavisMapper.Type.JSON);
for (Distribution d : distributions) {
String json = om.writeValueAsString(d);
@ -50,7 +51,7 @@ public class TestDistributionDeserializer extends BaseDL4JTest {
@Test
public void testDistributionDeserializerLegacyFormat() throws Exception {
ObjectMapper om = NeuralNetConfiguration.mapper();
ObjectMapper om = CavisMapper.getMapper(CavisMapper.Type.JSON);
String normalJson = "{\n" + " \"normal\" : {\n" + " \"mean\" : 0.1,\n"
+ " \"std\" : 1.2\n" + " }\n" + " }";

10
cavis-dnn/cavis-dnn-cudnn/src/main/java/org/deeplearning4j/cuda/convolution/CudnnConvolutionHelper.java
View File

@ -38,7 +38,7 @@ import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.cuda.BaseCudnnHelper;
import org.deeplearning4j.nn.layers.convolution.ConvolutionHelper;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer;
import org.deeplearning4j.util.ConvolutionUtils;
import org.deeplearning4j.util.Convolution2DUtils;
import org.nd4j.jita.allocator.Allocator;
import org.nd4j.jita.allocator.impl.AtomicAllocator;
import org.nd4j.jita.conf.CudaEnvironment;
@ -681,9 +681,9 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
padding = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
int[] padBottomRight = ConvolutionUtils.getSameModeBottomRightPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
outSize = Convolution2DUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
padding = Convolution2DUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
int[] padBottomRight = Convolution2DUtils.getSameModeBottomRightPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
if(!Arrays.equals(padding, padBottomRight)){
/*
CuDNN - even as of 7.1 (CUDA 9.1) still doesn't have support for proper SAME mode padding (i.e., asymmetric
@ -731,7 +731,7 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
// CuDNN handle
}
} else {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, padding, convolutionMode, dilation, format); //Also performs validation
outSize = Convolution2DUtils.getOutputSize(input, kernel, strides, padding, convolutionMode, dilation, format); //Also performs validation
}
return new CudnnForwardArgs(manualPadBottom, manualPadRight, input, origInput, padding, outSize);

6
cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/KerasModel.java
View File

@ -42,7 +42,7 @@ import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasOptimizerUtils;
import org.deeplearning4j.util.ConvolutionUtils;
import org.deeplearning4j.util.Convolution2DUtils;
import org.nd4j.common.primitives.Counter;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.learning.config.IUpdater;
@ -442,8 +442,8 @@ public class KerasModel {
KerasInput kerasInput = (KerasInput) layer;
LayerConfiguration layer1 = layersOrdered.get(kerasLayerIdx + 1).layer;
//no dim order, try to pull it from the next layer if there is one
if(ConvolutionUtils.layerHasConvolutionLayout(layer1)) {
CNN2DFormat formatForLayer = ConvolutionUtils.getFormatForLayer(layer1);
if(Convolution2DUtils.layerHasConvolutionLayout(layer1)) {
CNN2DFormat formatForLayer = Convolution2DUtils.getFormatForLayer(layer1);
if(formatForLayer == CNN2DFormat.NCHW) {
dimOrder = KerasLayer.DimOrder.THEANO;
} else if(formatForLayer == CNN2DFormat.NHWC) {

18
cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution1D.java
View File

@ -23,7 +23,7 @@ package org.deeplearning4j.nn.modelimport.keras.layers.convolutional;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.conf.layers.Convolution1D;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
@ -84,29 +84,29 @@ public class KerasAtrousConvolution1D extends KerasConvolution {
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
ConvolutionLayer.ConvolutionLayerBuilder builder = Convolution1DLayer.builder().name(this.name)
var builder = Convolution1D.builder().name(this.name)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(init)
.dilation(getDilationRate(layerConfig, 1, conf, true)[0])
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 1, conf, kerasMajorVersion)[0])
.kernelSize(getKernelSizeFromConfig(layerConfig, 1, conf, kerasMajorVersion))
.hasBias(hasBias)
.rnnDataFormat(dimOrder == DimOrder.TENSORFLOW ? RNNFormat.NWC : RNNFormat.NCW)
.stride(getStrideFromConfig(layerConfig, 1, conf)[0]);
.stride(getStrideFromConfig(layerConfig, 1, conf));
int[] padding = getPaddingFromBorderModeConfig(layerConfig, 1, conf, kerasMajorVersion);
if (hasBias)
builder.biasInit(0.0);
if (padding != null)
builder.padding(padding[0]);
builder.padding(padding);
if (biasConstraint != null)
builder.constrainBias(biasConstraint);
if (weightConstraint != null)
builder.constrainWeights(weightConstraint);
this.layer = builder.build();
Convolution1DLayer convolution1DLayer = (Convolution1DLayer) layer;
convolution1DLayer.setDefaultValueOverriden(true);
Convolution1D convolution1D = (Convolution1D) layer;
convolution1D.setDefaultValueOverriden(true);
}
/**
@ -114,8 +114,8 @@ public class KerasAtrousConvolution1D extends KerasConvolution {
*
* @return ConvolutionLayer
*/
public Convolution1DLayer getAtrousConvolution1D() {
return (Convolution1DLayer) this.layer;
public Convolution1D getAtrousConvolution1D() {
return (Convolution1D) this.layer;
}
/**

3
cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution2D.java
View File

@ -24,6 +24,7 @@ import lombok.val;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution2D;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
@ -85,7 +86,7 @@ public class KerasAtrousConvolution2D extends KerasConvolution {
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
val builder = ConvolutionLayer.builder().name(this.name)
val builder = Convolution2D.builder().name(this.name)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(init)

14
cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution1D.java
View File

@ -28,7 +28,7 @@ import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.conf.layers.Convolution1D;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
@ -93,7 +93,7 @@ public class KerasConvolution1D extends KerasConvolution {
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
Convolution1DLayer.Convolution1DLayerBuilder builder = Convolution1DLayer.builder().name(this.name)
var builder = Convolution1D.builder().name(this.name)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(init)
@ -125,9 +125,9 @@ public class KerasConvolution1D extends KerasConvolution {
this.layer = builder.build();
//set this in order to infer the dimensional format
Convolution1DLayer convolution1DLayer = (Convolution1DLayer) this.layer;
convolution1DLayer.setDataFormat(dimOrder == DimOrder.TENSORFLOW ? CNN2DFormat.NHWC : CNN2DFormat.NCHW);
convolution1DLayer.setDefaultValueOverriden(true);
Convolution1D convolution1D = (Convolution1D) this.layer;
convolution1D.setDataFormat(dimOrder == DimOrder.TENSORFLOW ? CNN2DFormat.NHWC : CNN2DFormat.NCHW);
convolution1D.setDefaultValueOverriden(true);
}
/**
@ -135,8 +135,8 @@ public class KerasConvolution1D extends KerasConvolution {
*
* @return ConvolutionLayer
*/
public Convolution1DLayer getConvolution1DLayer() {
return (Convolution1DLayer) this.layer;
public Convolution1D getConvolution1DLayer() {
return (Convolution1D) this.layer;
}

3
cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution2D.java
View File

@ -28,6 +28,7 @@ import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution2D;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
@ -95,7 +96,7 @@ public class KerasConvolution2D extends KerasConvolution {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion);
final var builder = ConvolutionLayer.builder().name(this.name)
final var builder = Convolution2D.builder().name(this.name)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(init)

4
cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/JsonTest.java
View File

@ -41,8 +41,8 @@ public class JsonTest extends BaseDL4JTest {
};
for(InputPreProcessor p : pp ){
String s = NeuralNetConfiguration.mapper().writeValueAsString(p);
InputPreProcessor p2 = NeuralNetConfiguration.mapper().readValue(s, InputPreProcessor.class);
String s = CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(p);
InputPreProcessor p2 = CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(s, InputPreProcessor.class);
assertEquals(p, p2);
}

9
cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java
View File

@ -29,11 +29,8 @@ import org.deeplearning4j.gradientcheck.GradientCheckUtil;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.layers.IOutputLayer;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
import org.deeplearning4j.nn.conf.layers.LossLayer;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.Convolution1D;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.Hdf5Archive;
@ -656,7 +653,7 @@ public class KerasModelEndToEndTest extends BaseDL4JTest {
MultiLayerNetwork net = importEndModelTest(modelPath, inputsOutputPath, true, true,
true, true, false, null, null);
Layer l = net.getLayer(0);
Convolution1DLayer c1d = (Convolution1DLayer) l.getTrainingConfig();
Convolution1D c1d = (Convolution1D) l.getTrainingConfig();
assertEquals(ConvolutionMode.Causal, c1d.getConvolutionMode());
}
}

4
cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasAtrousConvolution1DTest.java
View File

@ -22,7 +22,7 @@ package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.dropout.Dropout;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.conf.layers.Convolution1D;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasTestUtils;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
@ -97,7 +97,7 @@ public class KerasAtrousConvolution1DTest extends BaseDL4JTest {
config.put(conf.getLAYER_FIELD_BORDER_MODE(), BORDER_MODE_VALID);
layerConfig.put(conf.getLAYER_FIELD_CONFIG(), config);
Convolution1DLayer layer = new KerasAtrousConvolution1D(layerConfig).getAtrousConvolution1D();
Convolution1D layer = new KerasAtrousConvolution1D(layerConfig).getAtrousConvolution1D();
assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
assertEquals(LAYER_NAME, layer.getName());
assertEquals(INIT_DL4J, layer.getWeightInit());

4
cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasConvolution1DTest.java
View File

@ -22,7 +22,7 @@ package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.dropout.Dropout;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.conf.layers.Convolution1D;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasTestUtils;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
@ -119,7 +119,7 @@ public class KerasConvolution1DTest extends BaseDL4JTest {
config.put(conf.getLAYER_FIELD_BORDER_MODE(), BORDER_MODE_VALID);
layerConfig.put(conf.getLAYER_FIELD_CONFIG(), config);
Convolution1DLayer layer = new KerasConvolution1D(layerConfig).getConvolution1DLayer();
Convolution1D layer = new KerasConvolution1D(layerConfig).getConvolution1DLayer();
assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
assertEquals(LAYER_NAME, layer.getName());
assertEquals(INIT_DL4J, layer.getWeightInit());

2
cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/api/INeuralNetworkConfiguration.java
View File

@ -22,8 +22,6 @@
package net.brutex.ai.dnn.api;
import java.io.Serializable;
import java.util.List;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
public interface INeuralNetworkConfiguration extends Serializable, Cloneable {

1
cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/api/NN.java
View File

@ -23,7 +23,6 @@ package net.brutex.ai.dnn.api;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration.NeuralNetConfigurationBuilder;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
/**
* A fluent API to configure and create artificial neural networks

2
cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/networks/ArtificialNeuralNetwork.java
View File

@ -23,7 +23,6 @@ package net.brutex.ai.dnn.networks;
import java.io.Serializable;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import lombok.Getter;
import lombok.NonNull;
@ -33,7 +32,6 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.nd4j.linalg.api.ndarray.INDArray;
/**
* Artificial Neural Network An artificial neural network (1) takes some input data, and (2)
* transforms this input data by calculating a weighted sum over the inputs and (3) applies a

9
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/EarlyStoppingConfiguration.java
View File

@ -20,6 +20,10 @@
package org.deeplearning4j.earlystopping;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import lombok.Data;
import lombok.NoArgsConstructor;
import net.brutex.ai.dnn.api.IModel;
@ -30,11 +34,6 @@ import org.deeplearning4j.earlystopping.termination.IterationTerminationConditio
import org.deeplearning4j.exception.DL4JInvalidConfigException;
import org.nd4j.common.function.Supplier;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
@Data
@NoArgsConstructor
public class EarlyStoppingConfiguration<T extends IModel> implements Serializable {

11
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/EarlyStoppingModelSaver.java
View File

@ -20,16 +20,15 @@
package org.deeplearning4j.earlystopping;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonSubTypes;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.IOException;
import java.io.Serializable;
import net.brutex.ai.dnn.api.IModel;
import org.deeplearning4j.earlystopping.saver.InMemoryModelSaver;
import org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver;
import org.deeplearning4j.earlystopping.saver.LocalFileModelSaver;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonSubTypes;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.IOException;
import java.io.Serializable;
@JsonInclude(JsonInclude.Include.NON_NULL)
@JsonSubTypes(value = {@JsonSubTypes.Type(value = InMemoryModelSaver.class, name = "InMemoryModelSaver"),

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/EarlyStoppingResult.java
View File

@ -20,11 +20,10 @@
package org.deeplearning4j.earlystopping;
import lombok.Data;
import net.brutex.ai.dnn.api.IModel;
import java.io.Serializable;
import java.util.Map;
import lombok.Data;
import net.brutex.ai.dnn.api.IModel;
@Data
public class EarlyStoppingResult<T extends IModel> implements Serializable {

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/saver/InMemoryModelSaver.java
View File

@ -20,10 +20,9 @@
package org.deeplearning4j.earlystopping.saver;
import org.deeplearning4j.earlystopping.EarlyStoppingModelSaver;
import net.brutex.ai.dnn.api.IModel;
import java.io.IOException;
import net.brutex.ai.dnn.api.IModel;
import org.deeplearning4j.earlystopping.EarlyStoppingModelSaver;
public class InMemoryModelSaver<T extends IModel> implements EarlyStoppingModelSaver<T> {

7
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/saver/LocalFileGraphSaver.java
View File

@ -20,15 +20,14 @@
package org.deeplearning4j.earlystopping.saver;
import java.io.File;
import java.io.IOException;
import java.nio.charset.Charset;
import org.apache.commons.io.FilenameUtils;
import org.deeplearning4j.earlystopping.EarlyStoppingModelSaver;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.util.ModelSerializer;
import java.io.File;
import java.io.IOException;
import java.nio.charset.Charset;
public class LocalFileGraphSaver implements EarlyStoppingModelSaver<ComputationGraph> {
private static final String BEST_GRAPH_BIN = "bestGraph.bin";

7
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/saver/LocalFileModelSaver.java
View File

@ -20,15 +20,14 @@
package org.deeplearning4j.earlystopping.saver;
import java.io.File;
import java.io.IOException;
import java.nio.charset.Charset;
import org.apache.commons.io.FilenameUtils;
import org.deeplearning4j.earlystopping.EarlyStoppingModelSaver;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.ModelSerializer;
import java.io.File;
import java.io.IOException;
import java.nio.charset.Charset;
public class LocalFileModelSaver implements EarlyStoppingModelSaver<MultiLayerNetwork> {
private static final String BEST_MODEL_BIN = "bestModel.bin";

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/AutoencoderScoreCalculator.java
View File

@ -26,11 +26,11 @@ import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.evaluation.regression.RegressionEvaluation;
import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
public class AutoencoderScoreCalculator extends BaseScoreCalculator<IModel> {

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.java
View File

@ -20,8 +20,9 @@
package org.deeplearning4j.earlystopping.scorecalc;
import org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator;
import com.fasterxml.jackson.annotation.JsonProperty;
import net.brutex.ai.dnn.api.IModel;
import org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -29,7 +30,6 @@ import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.MultiDataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
import com.fasterxml.jackson.annotation.JsonProperty;
public class DataSetLossCalculator extends BaseScoreCalculator<IModel> {

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.java
View File

@ -20,6 +20,8 @@
package org.deeplearning4j.earlystopping.scorecalc;
import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.NoArgsConstructor;
import lombok.val;
import org.deeplearning4j.nn.graph.ComputationGraph;
@ -27,8 +29,6 @@ import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.MultiDataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.annotation.JsonProperty;
@NoArgsConstructor
@Deprecated

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/ScoreCalculator.java
View File

@ -20,12 +20,11 @@
package org.deeplearning4j.earlystopping.scorecalc;
import net.brutex.ai.dnn.api.IModel;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonSubTypes;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import net.brutex.ai.dnn.api.IModel;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
@JsonInclude(JsonInclude.Include.NON_NULL)

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/VAEReconErrorScoreCalculator.java
View File

@ -26,11 +26,11 @@ import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.layers.variational.VariationalAutoencoder;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.evaluation.regression.RegressionEvaluation;
import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
public class VAEReconErrorScoreCalculator extends BaseScoreCalculator<IModel> {

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/base/BaseIEvaluationScoreCalculator.java
View File

@ -20,9 +20,9 @@
package org.deeplearning4j.earlystopping.scorecalc.base;
import net.brutex.ai.dnn.api.IModel;
import org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator;
import org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator;
import net.brutex.ai.dnn.api.IModel;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.evaluation.IEvaluation;

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/base/BaseScoreCalculator.java
View File

@ -21,8 +21,8 @@
package org.deeplearning4j.earlystopping.scorecalc.base;
import lombok.NonNull;
import org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator;
import net.brutex.ai.dnn.api.IModel;
import org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.MultiDataSet;

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/BestScoreEpochTerminationCondition.java
View File

@ -20,8 +20,8 @@
package org.deeplearning4j.earlystopping.termination;
import lombok.Data;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
@Data
public class BestScoreEpochTerminationCondition implements EpochTerminationCondition {

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/EpochTerminationCondition.java
View File

@ -22,9 +22,7 @@ package org.deeplearning4j.earlystopping.termination;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonSubTypes;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/IterationTerminationCondition.java
View File

@ -22,7 +22,6 @@ package org.deeplearning4j.earlystopping.termination;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/MaxEpochsTerminationCondition.java
View File

@ -20,10 +20,10 @@
package org.deeplearning4j.earlystopping.termination;
import lombok.Data;
import lombok.NoArgsConstructor;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.NoArgsConstructor;
@NoArgsConstructor
@Data

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/MaxScoreIterationTerminationCondition.java
View File

@ -20,8 +20,8 @@
package org.deeplearning4j.earlystopping.termination;
import lombok.Data;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
@Data
public class MaxScoreIterationTerminationCondition implements IterationTerminationCondition {

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/MaxTimeIterationTerminationCondition.java
View File

@ -20,10 +20,9 @@
package org.deeplearning4j.earlystopping.termination;
import lombok.Data;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.concurrent.TimeUnit;
import lombok.Data;
/**Terminate training based on max time.
*/

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/ScoreImprovementEpochTerminationCondition.java
View File

@ -20,9 +20,9 @@
package org.deeplearning4j.earlystopping.termination;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.extern.slf4j.Slf4j;
import com.fasterxml.jackson.annotation.JsonProperty;
@Slf4j
@Data

13
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/trainer/BaseEarlyStoppingTrainer.java
View File

@ -20,6 +20,12 @@
package org.deeplearning4j.earlystopping.trainer;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.util.Collection;
import java.util.Iterator;
import java.util.LinkedHashMap;
import java.util.Map;
import net.brutex.ai.dnn.api.IModel;
import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;
import org.deeplearning4j.earlystopping.EarlyStoppingResult;
@ -40,13 +46,6 @@ import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.util.Collection;
import java.util.Iterator;
import java.util.LinkedHashMap;
import java.util.Map;
public abstract class BaseEarlyStoppingTrainer<T extends IModel> implements IEarlyStoppingTrainer<T> {
private static final Logger log = LoggerFactory.getLogger(BaseEarlyStoppingTrainer.class);

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/trainer/EarlyStoppingGraphTrainer.java
View File

@ -20,7 +20,6 @@
package org.deeplearning4j.earlystopping.trainer;
import org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator;
import org.deeplearning4j.datasets.iterator.impl.SingletonDataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator;
import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;

15
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/BaseEvaluation.java
View File

@ -20,6 +20,13 @@
package org.deeplearning4j.eval;
import com.fasterxml.jackson.annotation.JsonAutoDetect;
import com.fasterxml.jackson.databind.DeserializationFeature;
import com.fasterxml.jackson.databind.MapperFeature;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.SerializationFeature;
import com.fasterxml.jackson.databind.module.SimpleModule;
import com.fasterxml.jackson.dataformat.yaml.YAMLFactory;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import org.nd4j.common.primitives.AtomicBoolean;
@ -28,14 +35,6 @@ import org.nd4j.common.primitives.serde.JsonDeserializerAtomicBoolean;
import org.nd4j.common.primitives.serde.JsonDeserializerAtomicDouble;
import org.nd4j.common.primitives.serde.JsonSerializerAtomicBoolean;
import org.nd4j.common.primitives.serde.JsonSerializerAtomicDouble;
import com.fasterxml.jackson.annotation.JsonAutoDetect;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.DeserializationFeature;
import com.fasterxml.jackson.databind.MapperFeature;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.SerializationFeature;
import com.fasterxml.jackson.databind.module.SimpleModule;
import com.fasterxml.jackson.dataformat.yaml.YAMLFactory;
@Deprecated
@EqualsAndHashCode(callSuper = false)

7
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/ConfusionMatrix.java
View File

@ -20,15 +20,8 @@
package org.deeplearning4j.eval;
import com.google.common.collect.HashMultiset;
import com.google.common.collect.Multiset;
import lombok.Getter;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
@Deprecated
public class ConfusionMatrix<T extends Comparable<? super T>> extends org.nd4j.evaluation.classification.ConfusionMatrix<T> {

9
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/Evaluation.java
View File

@ -20,14 +20,11 @@
package org.deeplearning4j.eval;
import lombok.EqualsAndHashCode;
import lombok.NonNull;
import org.nd4j.evaluation.EvaluationAveraging;
import org.nd4j.evaluation.IEvaluation;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.List;
import java.util.Map;
import lombok.EqualsAndHashCode;
import lombok.NonNull;
import org.nd4j.linalg.api.ndarray.INDArray;
@EqualsAndHashCode(callSuper = true)
@Deprecated

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/EvaluationCalibration.java
View File

@ -20,9 +20,9 @@
package org.deeplearning4j.eval;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import com.fasterxml.jackson.annotation.JsonProperty;
@Deprecated
@Getter

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/RegressionEvaluation.java
View File

@ -20,11 +20,10 @@
package org.deeplearning4j.eval;
import java.util.List;
import lombok.Data;
import lombok.EqualsAndHashCode;
import java.util.List;
@Deprecated
@Data
@EqualsAndHashCode(callSuper = true)

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/curves/Histogram.java
View File

@ -20,10 +20,10 @@
package org.deeplearning4j.eval.curves;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.nd4j.evaluation.curves.BaseHistogram;
import com.fasterxml.jackson.annotation.JsonProperty;
@Deprecated
@Data

6
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/curves/PrecisionRecallCurve.java
View File

@ -20,13 +20,9 @@
package org.deeplearning4j.eval.curves;
import com.google.common.base.Preconditions;
import lombok.AllArgsConstructor;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.Arrays;
@Deprecated
@Data

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/curves/ReliabilityDiagram.java
View File

@ -20,8 +20,8 @@
package org.deeplearning4j.eval.curves;
import lombok.NonNull;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.NonNull;
@Deprecated
public class ReliabilityDiagram extends org.nd4j.evaluation.curves.ReliabilityDiagram {

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/curves/RocCurve.java
View File

@ -20,10 +20,9 @@
package org.deeplearning4j.eval.curves;
import com.google.common.base.Preconditions;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import com.fasterxml.jackson.annotation.JsonProperty;
@Deprecated
@Data

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/meta/Prediction.java
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@ -20,7 +20,6 @@
package org.deeplearning4j.eval.meta;
import lombok.AllArgsConstructor;
import lombok.Data;
@Data

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/adapters/YoloModelAdapter.java
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@ -20,6 +20,7 @@
package org.deeplearning4j.nn.adapters;
import java.util.List;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.NoArgsConstructor;
@ -32,8 +33,6 @@ import org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import java.util.List;
@Builder
@AllArgsConstructor
@NoArgsConstructor

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/AbstractParamInitializer.java
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@ -21,7 +21,6 @@
package org.deeplearning4j.nn.api;
import lombok.Getter;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.LayerConfiguration;

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Classifier.java
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@ -20,14 +20,12 @@
package org.deeplearning4j.nn.api;
import java.util.List;
import net.brutex.ai.dnn.api.IModel;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import java.util.List;
public interface Classifier extends IModel {

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/ITraininableLayerConfiguration.java
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@ -20,13 +20,12 @@
package org.deeplearning4j.nn.api;
import java.util.List;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.linalg.learning.regularization.Regularization;
import java.util.List;
public interface ITraininableLayerConfiguration {
/**

6
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Layer.java
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@ -21,7 +21,7 @@
package org.deeplearning4j.nn.api;
import java.util.Map;
import java.io.Serializable;
import net.brutex.ai.dnn.api.IModel;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -29,10 +29,8 @@ import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.LayerHelper;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;
import java.io.Serializable;
import org.nd4j.linalg.api.ndarray.INDArray;
/**
* A layer is the highest-level building block in deep learning. A layer is a container that usually

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/ParamInitializer.java
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@ -20,13 +20,12 @@
package org.deeplearning4j.nn.api;
import java.util.List;
import java.util.Map;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.List;
import java.util.Map;
/**
* Param initializer for a layer
*

7
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Updater.java
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@ -20,11 +20,10 @@
package org.deeplearning4j.nn.api;
import org.deeplearning4j.nn.gradient.Gradient;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import java.io.Serializable;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.ndarray.INDArray;
/**
* Update the model

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/IOutputLayer.java
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@ -22,8 +22,8 @@ package org.deeplearning4j.nn.api.layers;
import org.deeplearning4j.nn.api.Classifier;
import org.deeplearning4j.nn.api.Layer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.ndarray.INDArray;
public interface IOutputLayer extends Layer, Classifier {

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/LayerConstraint.java
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@ -20,11 +20,10 @@
package org.deeplearning4j.nn.api.layers;
import org.deeplearning4j.nn.api.Layer;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import java.util.Set;
import org.deeplearning4j.nn.api.Layer;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface LayerConstraint extends Cloneable, Serializable {

7
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/RecurrentLayer.java
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@ -20,13 +20,12 @@
package org.deeplearning4j.nn.api.layers;
import java.util.Map;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.gradient.Gradient;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import java.util.Map;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.api.ndarray.INDArray;
public interface RecurrentLayer extends Layer {

22
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.java
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@ -20,6 +20,12 @@
package org.deeplearning4j.nn.conf;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.exc.InvalidTypeIdException;
import java.io.IOException;
import java.io.Serializable;
import java.util.*;
import lombok.*;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.graph.GraphVertex;
@ -34,6 +40,7 @@ import org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.conf.memory.NetworkMemoryReport;
import org.deeplearning4j.nn.conf.serde.CavisMapper;
import org.deeplearning4j.nn.conf.serde.JsonMappers;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInit;
@ -42,16 +49,9 @@ import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.buffer.DataType;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.exc.InvalidTypeIdException;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
import java.io.Serializable;
import java.util.*;
@Data
@EqualsAndHashCode(exclude = {"trainingWorkspaceMode", "inferenceWorkspaceMode", "cacheMode", "topologicalOrder", "topologicalOrderStr"})
@AllArgsConstructor(access = AccessLevel.PRIVATE)
@ -110,7 +110,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
* @return YAML representation of configuration
*/
public String toYaml() {
ObjectMapper mapper = NeuralNetConfiguration.mapperYaml();
ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.YAML);
synchronized (mapper) {
try {
return mapper.writeValueAsString(this);
@ -127,7 +127,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
* @return {@link ComputationGraphConfiguration}
*/
public static ComputationGraphConfiguration fromYaml(String json) {
ObjectMapper mapper = NeuralNetConfiguration.mapperYaml();
ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.YAML);
try {
return mapper.readValue(json, ComputationGraphConfiguration.class);
} catch (IOException e) {
@ -140,7 +140,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
*/
public String toJson() {
//As per NeuralNetConfiguration.toJson()
ObjectMapper mapper = NeuralNetConfiguration.mapper();
ObjectMapper mapper =CavisMapper.getMapper(CavisMapper.Type.JSON);
synchronized (mapper) {
//JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields occasionally
//when writeValueAsString is used by multiple threads. This results in invalid JSON. See issue #3243
@ -160,7 +160,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
*/
public static ComputationGraphConfiguration fromJson(String json) {
//As per NeuralNetConfiguration.fromJson()
ObjectMapper mapper = NeuralNetConfiguration.mapper();
ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.JSON);
ComputationGraphConfiguration conf;
try {
conf = mapper.readValue(json, ComputationGraphConfiguration.class);

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/DataFormat.java
View File

@ -19,10 +19,10 @@
*/
package org.deeplearning4j.nn.conf;
import org.deeplearning4j.nn.conf.serde.format.DataFormatDeserializer;
import org.deeplearning4j.nn.conf.serde.format.DataFormatSerializer;
import com.fasterxml.jackson.databind.annotation.JsonDeserialize;
import com.fasterxml.jackson.databind.annotation.JsonSerialize;
import org.deeplearning4j.nn.conf.serde.format.DataFormatDeserializer;
import org.deeplearning4j.nn.conf.serde.format.DataFormatSerializer;
@JsonSerialize(using = DataFormatSerializer.class)
@JsonDeserialize(using = DataFormatDeserializer.class)

9
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/InputPreProcessor.java
View File

@ -21,14 +21,13 @@
package org.deeplearning4j.nn.conf;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.api.ndarray.INDArray;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface InputPreProcessor extends Serializable, Cloneable {

182
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/NeuralNetBaseBuilderConfiguration.java
View File

@ -21,10 +21,9 @@
package org.deeplearning4j.nn.conf;
import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.node.ArrayNode;
import java.util.*;
import lombok.*;
import lombok.experimental.SuperBuilder;
import lombok.extern.slf4j.Slf4j;
@ -35,10 +34,8 @@ import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.dropout.Dropout;
import org.deeplearning4j.nn.conf.dropout.IDropout;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
import org.deeplearning4j.nn.conf.serde.JsonMappers;
import org.deeplearning4j.nn.conf.stepfunctions.StepFunction;
import org.deeplearning4j.nn.conf.weightnoise.IWeightNoise;
import org.deeplearning4j.nn.weights.IWeightInit;
@ -47,7 +44,6 @@ import org.deeplearning4j.nn.weights.WeightInitDistribution;
import org.deeplearning4j.nn.weights.WeightInitXavier;
import org.deeplearning4j.util.NetworkUtils;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.learning.config.IUpdater;
@ -57,9 +53,6 @@ import org.nd4j.linalg.learning.regularization.L2Regularization;
import org.nd4j.linalg.learning.regularization.Regularization;
import org.nd4j.linalg.learning.regularization.WeightDecay;
import java.io.IOException;
import java.util.*;
/**
* Deeplearning4j is a domain-specific language to configure deep neural networks, which are made of
* multiple layers. Everything starts with a NeuralNetConfiguration, which organizes those layers
@ -331,7 +324,6 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
*/
@Getter @Setter @lombok.Builder.Default private IUpdater biasUpdater = null;
/**
* Weight initialization scheme to use, for initial weight values Note: values set by this method
* will be applied to all applicable layers in the network, unless a different value is explicitly
@ -339,6 +331,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* and can be overridden on a per-layer basis.
*/
@Getter @Setter @lombok.Builder.Default private IWeightInit weightInit = new WeightInitXavier();
/**
* Sets the convolution mode for convolutional layers, which impacts padding and output sizes. See
* {@link ConvolutionMode} for details. Defaults to ConvolutionMode.TRUNCATE<br>
@ -416,113 +409,6 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
@Getter @Setter @lombok.Builder.Default private double biasInit = 0.0;
@Getter @Setter @lombok.Builder.Default private double gainInit = 1.0;
/**
* Handle {@link WeightInit} and {@link Distribution} from legacy configs in Json format. Copied
* from handling of {@link Activation} above.
*
* @return True if all is well and layer iteration shall continue. False else-wise.
*/
private static boolean handleLegacyWeightInitFromJson(
String json, LayerConfiguration l, ObjectMapper mapper, JsonNode confs, int layerCount) {
if ((l instanceof BaseLayerConfiguration)
&& ((BaseLayerConfiguration) l).getWeightInit() == null) {
try {
JsonNode jsonNode = mapper.readTree(json);
if (confs == null) {
confs = jsonNode.get("confs");
}
if (confs instanceof ArrayNode) {
ArrayNode layerConfs = (ArrayNode) confs;
JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
if (outputLayerNNCNode == null) {
return false; // Should never happen...
}
JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
return true;
}
JsonNode layerNode = layerWrapperNode.elements().next();
JsonNode weightInit =
layerNode.get("weightInit"); // Should only have 1 element: "dense", "output", etc
JsonNode distribution = layerNode.get("dist");
Distribution dist = null;
if (distribution != null) {
dist = mapper.treeToValue(distribution, Distribution.class);
}
if (weightInit != null) {
final IWeightInit wi =
WeightInit.valueOf(weightInit.asText()).getWeightInitFunction(dist);
((BaseLayerConfiguration) l).setWeightInit(wi);
}
}
} catch (IOException e) {
log.warn(
"ILayer with null WeightInit detected: " + l.getName() + ", could not parse JSON",
e);
}
}
return true;
}
/**
* Object mapper for serialization of configurations
*
* @return
*/
public static ObjectMapper mapperYaml() {
return JsonMappers.getMapperYaml();
}
/**
* Object mapper for serialization of configurations
*
* @return
*/
public static ObjectMapper mapper() {
return JsonMappers.getMapper();
}
public static NeuralNetBaseBuilderConfiguration fromYaml(String input) {
throw new RuntimeException("Needs fixing - not supported."); // TODO
}
/**
* @return JSON representation of NN configuration
*/
public String toYaml() {
ObjectMapper mapper = NeuralNetBaseBuilderConfiguration.mapperYaml();
synchronized (mapper) {
try {
return mapper.writeValueAsString(this);
} catch (com.fasterxml.jackson.core.JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}
/**
* @return JSON representation of NN configuration
*/
public String toJson() {
ObjectMapper mapper = NeuralNetBaseBuilderConfiguration.mapper();
synchronized (mapper) {
// JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields
// occasionally
// when writeValueAsString is used by multiple threads. This results in invalid JSON. See
// issue #3243
try {
return mapper.writeValueAsString(this);
} catch (com.fasterxml.jackson.core.JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}
@Override
public NeuralNetBaseBuilderConfiguration clone() {
NeuralNetBaseBuilderConfiguration clone;
@ -561,14 +447,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
List<Object> innerConfigurations$value = new ArrayList<>(); // initialize with an empty list
public B activation(Activation activation) {
this.activation = activation;
return self();
}
public B activation(IActivation activation) {
this.activation = activation;
return self();
}
/**
* Set constraints to be applied to all layers. Default: no constraints.<br>
* Constraints can be used to enforce certain conditions (non-negativity of parameters, max-norm
@ -583,7 +462,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B constrainWeights(LayerConstraint... constraints) {
constrainWeights$value = Arrays.asList(constraints);
constrainWeights$set = true;
return (B) this;
return self();
}
/**
@ -618,7 +497,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B constrainAllParameters(LayerConstraint... constraints) {
allParamConstraints$value = Arrays.asList(constraints);
allParamConstraints$set = true;
return (B) this;
return self();
}
/**
@ -635,7 +514,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B constrainBias(LayerConstraint... constraints) {
biasConstraints$value = Arrays.asList(constraints);
biasConstraints$set = true;
return (B) this;
return self();
}
/**
@ -645,10 +524,11 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @param processor what to use to preProcess the data.
* @return builder pattern
*/
public B inputPreProcessor(Integer layer, InputPreProcessor processor) {
public B inputPreProcessor(@NonNull Integer layer, @NonNull InputPreProcessor processor) {
if(inputPreProcessors$value==null) inputPreProcessors$value=new LinkedHashMap<>();
inputPreProcessors$value.put(layer, processor);
inputPreProcessors$set = true;
return (B) this;
return self();
}
/**
@ -658,7 +538,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @param layer the layer
* @return builder
*/
public B layer(Integer index, @NonNull LayerConfiguration layer) {
public B layer(@NonNull Integer index, @NonNull LayerConfiguration layer) {
innerConfigurations$value.add(index, layer);
innerConfigurations$set = true;
return self();
@ -680,10 +560,11 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @param layer the layer
* @return builder
*/
@JsonIgnore
public B layer(@NonNull LayerConfiguration layer) {
innerConfigurations$value.add(layer);
innerConfigurations$set = true;
return (B) this;
return self();
}
public B layer(@NonNull LayerConfiguration.LayerConfigurationBuilder<?, ?> layer) {
return this.layer(layer.build());
@ -699,7 +580,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B layersFromArray(@NonNull LayerConfiguration[] arrLayers) {
innerConfigurations$value.addAll(List.of(arrLayers));
innerConfigurations$set = true;
return (B) this;
return self();
}
/** Specify additional layer configurations */
@ -707,7 +588,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B layersFromList(@NonNull List<LayerConfiguration> listLayers) {
innerConfigurations$value.addAll(listLayers);
innerConfigurations$set = true;
return (B) this;
return self();
}
/**
@ -723,7 +604,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularization$value.add(new L1Regularization(l1));
}
regularization$set = true;
return (B) this;
return self();
}
/**
@ -751,7 +632,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularization$value.add(new L2Regularization(l2));
}
regularization$set = true;
return (B) this;
return self();
}
/**
@ -766,7 +647,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularizationBias$value.add(new L1Regularization(l1Bias));
}
regularizationBias$set = true;
return (B) this;
return self();
}
/**
@ -791,7 +672,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
"L2 bias regularization removed: incompatible with added WeightDecay regularization");
regularizationBias$value.add(new L2Regularization(l2Bias));
}
return (B) this;
return self();
}
/**
@ -833,7 +714,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularization$value.add(new WeightDecay(coefficient, applyLR));
}
regularization$set = true;
return (B) this;
return self();
}
/**
@ -870,7 +751,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularizationBias$value.add(new WeightDecay(coefficient, applyLR));
}
regularization$set = true;
return (B) this;
return self();
}
@ -881,7 +762,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
*/
@Deprecated
public B list() {
return (B) this;
return self();
}
/**
@ -893,23 +774,24 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
*
* @param distribution Distribution to use for weight initialization
*/
@JsonIgnore
@JsonIgnore @Deprecated
public B weightInit(Distribution distribution) {
this.weightInit$value = new WeightInitDistribution(distribution);
this.weightInit$set = true;
return (B) this;
return self();
}
@JsonIgnore
public B weightInit(WeightInit weightInit) {
this.weightInit$value = weightInit.getWeightInitFunction();
this.weightInit$set = true;
return (B) this;
return self();
}
@JsonProperty("weightInit") //this is needed for Jackson < 2.4, otherwise JsonIgnore on the other setters will ignore this also
public B weightInit(IWeightInit iWeightInit) {
this.weightInit$value = iWeightInit;
this.weightInit$set = true;
return (B) this;
return self();
}
/**
@ -918,12 +800,13 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @param distribution
* @return
*/
@JsonIgnore
public B dist(@NonNull Distribution distribution) {
return (B) weightInit(distribution);
return weightInit(distribution);
}
public B dropOut(@NonNull IDropout dropout) {
return (B) idropOut(dropout);
return idropOut(dropout);
}
/**
@ -933,7 +816,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @return builder
*/
public B dropOut(double dropout) {
return (B) idropOut(new Dropout(dropout));
return idropOut(new Dropout(dropout));
}
/**
@ -946,7 +829,8 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B confs(@NonNull List<NeuralNetConfiguration> confs) {
innerConfigurations$value.addAll(confs);
innerConfigurations$set = true;
return (B) this;
return self();
}
}
}

619
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/NeuralNetConfiguration.java
View File

@ -22,39 +22,26 @@ package org.deeplearning4j.nn.conf;
import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.exc.InvalidTypeIdException;
import com.fasterxml.jackson.databind.node.ArrayNode;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.*;
import java.util.*;
import java.util.stream.Collectors;
import lombok.*;
import lombok.experimental.SuperBuilder;
import lombok.extern.jackson.Jacksonized;
import lombok.extern.slf4j.Slf4j;
import net.brutex.ai.dnn.api.IModel;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.conf.memory.NetworkMemoryReport;
import org.deeplearning4j.nn.conf.serde.JsonMappers;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.conf.serde.CavisMapper;
import org.deeplearning4j.util.OutputLayerUtil;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.linalg.learning.config.Sgd;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.lossfunctions.impl.LossBinaryXENT;
import org.nd4j.linalg.lossfunctions.impl.LossMCXENT;
import org.nd4j.linalg.lossfunctions.impl.LossMSE;
import org.nd4j.linalg.lossfunctions.impl.LossNegativeLogLikelihood;
import java.io.IOException;
import java.util.*;
import java.util.stream.Collectors;
/**
* Deeplearning4j is a domain-specific language to configure deep neural networks, which are made of
@ -62,71 +49,50 @@ import java.util.stream.Collectors;
* and their hyperparameters. Hyperparameters are variables that determine how a neural network
* learns. They include how many times to update the weights of the model, how to initialize those
* weights, which activation function to attach to the nodes, which optimization algorithm to use,
* and how fast the model should learn. This is what one configuration would look like:
* <br/><br/>
*
* NeuralNetConfiguration conf = NeuralNetConfiguration.builder()<br/>
* .weightInit(WeightInit.XAVIER) .activation(Activation.RELU)<br/>
* .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)<br/>
* .updater(new Sgd(0.05)) //... other hyperparameters <br/>
* .backprop(true)<br/>
* .build();<br/><br/>
*
* With Deeplearning4j, you add a layer
* by calling layer on the NeuralNetConfiguration.NeuralNetConfigurationBuilder(), specifying its place in the order of
* and how fast the model should learn. This is what one configuration would look like: <br>
* <br>
* NeuralNetConfiguration conf = NeuralNetConfiguration.builder()<br>
* .weightInit(WeightInit.XAVIER) .activation(Activation.RELU)<br>
* .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)<br>
* .updater(new Sgd(0.05)) //... other hyperparameters <br>
* .backprop(true)<br>
* .build();<br>
* <br>
* With Deeplearning4j, you add a layer by calling layer on the
* NeuralNetConfiguration.NeuralNetConfigurationBuilder(), specifying its place in the order of
* layers (the zero-indexed layer below is the input layer), the number of input and output nodes,
* nIn and nOut, as well as the type: DenseLayer.<br/><br/>
*
* .layer(0, DenseLayer.builder().nIn(784).nOut(250)<br/>
* .build())<br/><br/>
*
* Once you've configured your net, you train the
* model with model.fit.
* nIn and nOut, as well as the type: DenseLayer.<br>
* <br>
* .layer(0, DenseLayer.builder().nIn(784).nOut(250)<br>
* .build())<br>
* <br>
* Once you've configured your net, you train the model with model.fit.
*/
@Data
@Slf4j
@Jacksonized
@JsonIgnoreProperties(value={"net"}, ignoreUnknown = true)
@EqualsAndHashCode(exclude = {"net"}, callSuper = true)
//@JsonIdentityInfo(generator= ObjectIdGenerators.IntSequenceGenerator.class, property="@id")
@JsonIgnoreProperties(value = {"net"})
@EqualsAndHashCode(callSuper = true)
// @JsonIdentityInfo(generator= ObjectIdGenerators.IntSequenceGenerator.class, property="@id")
//The inner builder, that we can then extend ...
@SuperBuilder //TODO fix access
// The inner builder, that we can then extend ...
@Jacksonized
@SuperBuilder // TODO fix access
public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
private IModel net;
private static final int DEFAULT_TBPTT_LENGTH = 20;
private boolean initCalled = false;
@Getter
@Setter
@NonNull
@lombok.Builder.Default
@Deprecated
@Getter @Setter @NonNull @lombok.Builder.Default @Deprecated
protected WorkspaceMode trainingWorkspaceMode = WorkspaceMode.ENABLED;
@Getter
@Setter
@NonNull
@lombok.Builder.Default
@Deprecated
@Getter @Setter @NonNull @lombok.Builder.Default @Deprecated
protected WorkspaceMode inferenceWorkspaceMode = WorkspaceMode.ENABLED;
@Getter
@Setter
@lombok.Builder.Default
protected int iterationCount = 0;
//Counter for the number of epochs completed so far. Used for per-epoch schedules
@Getter
@Setter
@lombok.Builder.Default
protected int epochCount = 0;
@lombok.Builder.Default
protected double dampingFactor = 100;
@Getter @Setter @lombok.Builder.Default protected int iterationCount = 0;
// Counter for the number of epochs completed so far. Used for per-epoch schedules
@Getter @Setter @lombok.Builder.Default protected int epochCount = 0;
@lombok.Builder.Default protected double dampingFactor = 100;
@EqualsAndHashCode.Exclude private IModel net;
private boolean initCalled = false;
// gradient keys used for ensuring order when getting and setting the gradient
@lombok.Builder.Default private LinkedHashSet<String> netWideVariables = new LinkedHashSet<>();
@ -141,22 +107,19 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
*/
@Getter @Setter @Builder.Default private IUpdater updater = new Sgd();
/**
* Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage of cuDNN.
* See {@link ConvolutionLayer.AlgoMode} for details. Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
* <br>
* Note: values set by this method will be applied to all applicable layers in the network, unless a different
* value is explicitly set on a given layer. In other words: values set via this method are used as the default
* value, and can be overridden on a per-layer basis.
* Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage
* of cuDNN. See {@link ConvolutionLayer.AlgoMode} for details. Defaults to "PREFER_FASTEST", but
* "NO_WORKSPACE" uses less memory. <br>
* Note: values set by this method will be applied to all applicable layers in the network, unless
* a different value is explicitly set on a given layer. In other words: values set via this
* method are used as the default value, and can be overridden on a per-layer basis.
*
* @param cudnnAlgoMode cuDNN algo mode to use
*/
@Getter
@Setter
@lombok.Builder.Default
@Getter @Setter @lombok.Builder.Default
private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
/**
* Create a neural net configuration from json
*
@ -164,260 +127,23 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
* @return {@link NeuralNetConfiguration}
*/
public static NeuralNetConfiguration fromJson(String json) {
NeuralNetConfiguration conf;
ObjectMapper mapper = NeuralNetConfiguration.mapper();
ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.JSON);
try {
conf = mapper.readValue(json, NeuralNetConfiguration.class);
} catch (InvalidTypeIdException e) {
if (e.getMessage().contains("@class")) {
try {
//JSON may be legacy (1.0.0-alpha or earlier), attempt to load it using old format
return JsonMappers.getLegacyMapper().readValue(json, NeuralNetConfiguration.class);
} catch (InvalidTypeIdException e2) {
//Check for legacy custom layers: "Could not resolve type id 'CustomLayer' as a subtype of [simple type, class org.deeplearning4j.nn.conf.layers.ILayer]: known type ids = [Bidirectional, CenterLossOutputLayer, CnnLossLayer, ..."
//1.0.0-beta5: dropping support for custom layers defined in pre-1.0.0-beta format. Built-in layers from these formats still work
String msg = e2.getMessage();
if (msg != null && msg.contains("Could not resolve type id")) {
throw new RuntimeException(
"Error deserializing NeuralNetConfiguration - configuration may have a custom " +
"layer, vertex or preprocessor, in pre version 1.0.0-beta JSON format.\nModels in legacy format with custom"
+
" layers should be loaded in 1.0.0-beta to 1.0.0-beta4 and saved again, before loading in the current version of DL4J",
e);
}
throw new RuntimeException(e2);
} catch (IOException e2) {
throw new RuntimeException(e2);
}
}
throw new RuntimeException(e);
} catch (IOException e) {
//Check if this exception came from legacy deserializer...
String msg = e.getMessage();
if (msg != null && msg.contains("legacy")) {
throw new RuntimeException(
"Error deserializing NeuralNetConfiguration - configuration may have a custom " +
"layer, vertex or preprocessor, in pre version 1.0.0-alpha JSON format. These layers can be "
+
"deserialized by first registering them with NeuralNetConfiguration.registerLegacyCustomClassesForJSON(Class...)",
e);
}
return mapper.readValue(json, NeuralNetConfiguration.class);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
//To maintain backward compatibility after loss function refactoring (configs generated with v0.5.0 or earlier)
// Previously: enumeration used for loss functions. Now: use classes
// IN the past, could have only been an OutputLayer or RnnOutputLayer using these enums
int layerCount = 0;
JsonNode confs = null;
for (LayerConfiguration nnc : conf.getFlattenedLayerConfigurations()) {
LayerConfiguration l = nnc;
if (l instanceof BaseOutputLayer && ((BaseOutputLayer) l).getLossFunction() == null) {
//lossFn field null -> may be an old config format, with lossFunction field being for the enum
//if so, try walking the JSON graph to extract out the appropriate enum value
BaseOutputLayer ol = (BaseOutputLayer) l;
try {
JsonNode jsonNode = mapper.readTree(json);
if (confs == null) {
confs = jsonNode.get("confs");
}
if (confs instanceof ArrayNode) {
ArrayNode layerConfs = (ArrayNode) confs;
JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
if (outputLayerNNCNode == null) {
throw new RuntimeException(
"should never happen"); //return conf; //Should never happen...
}
JsonNode outputLayerNode = outputLayerNNCNode.get("layer");
JsonNode lossFunctionNode = null;
if (outputLayerNode.has("output")) {
lossFunctionNode = outputLayerNode.get("output").get("lossFunction");
} else if (outputLayerNode.has("rnnoutput")) {
lossFunctionNode = outputLayerNode.get("rnnoutput").get("lossFunction");
}
if (lossFunctionNode != null) {
String lossFunctionEnumStr = lossFunctionNode.asText();
LossFunctions.LossFunction lossFunction = null;
try {
lossFunction = LossFunctions.LossFunction.valueOf(lossFunctionEnumStr);
} catch (Exception e) {
log.warn(
"OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON",
e);
}
if (lossFunction != null) {
switch (lossFunction) {
case MSE:
ol.setLossFunction(new LossMSE());
break;
case XENT:
ol.setLossFunction(new LossBinaryXENT());
break;
case NEGATIVELOGLIKELIHOOD:
ol.setLossFunction(new LossNegativeLogLikelihood());
break;
case MCXENT:
ol.setLossFunction(new LossMCXENT());
break;
//Remaining: TODO
case SQUARED_LOSS:
case RECONSTRUCTION_CROSSENTROPY:
default:
log.warn(
"OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not set loss function for {}",
lossFunction);
break;
}
}
}
} else {
log.warn(
"OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON: layer 'confs' field is not an ArrayNode (is: {})",
(confs != null ? confs.getClass() : null));
}
} catch (IOException e) {
log.warn(
"OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON",
e);
break;
}
}
//Also, pre 0.7.2: activation functions were Strings ("activationFunction" field), not classes ("activationFn")
//Try to load the old format if necessary, and create the appropriate IActivation instance
if ((l instanceof BaseLayerConfiguration) && ((BaseLayerConfiguration) l).getActivationFn() == null) {
try {
JsonNode jsonNode = mapper.readTree(json);
if (confs == null) {
confs = jsonNode.get("confs");
}
if (confs instanceof ArrayNode) {
ArrayNode layerConfs = (ArrayNode) confs;
JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
if (outputLayerNNCNode == null) {
throw new RuntimeException(
"Should never happen"); //return conf; //Should never happen...
}
JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
continue;
}
JsonNode layerNode = layerWrapperNode.elements().next();
JsonNode activationFunction = layerNode.get(
"activationFunction"); //Should only have 1 element: "dense", "output", etc
if (activationFunction != null) {
Activation ia = Activation.fromString(activationFunction.asText());
((BaseLayerConfiguration) l).setActivation(ia.getActivationFunction());
}
}
} catch (IOException e) {
log.warn(
"ILayer with null ActivationFn field or pre-0.7.2 activation function detected: could not parse JSON",
e);
}
}
if (!handleLegacyWeightInitFromJson(json, l, mapper, confs, layerCount)) {
return conf;
}
layerCount++;
}
return conf;
}
/**
* Handle {@link WeightInit} and {@link Distribution} from legacy configs in Json format. Copied
* from handling of {@link Activation} above.
*
* @return True if all is well and layer iteration shall continue. False else-wise.
*/
private static boolean handleLegacyWeightInitFromJson(String json, LayerConfiguration l,
ObjectMapper mapper,
JsonNode confs, int layerCount) {
if ((l instanceof BaseLayerConfiguration) && ((BaseLayerConfiguration) l).getWeightInit() == null) {
try {
JsonNode jsonNode = mapper.readTree(json);
if (confs == null) {
confs = jsonNode.get("confs");
}
if (confs instanceof ArrayNode) {
ArrayNode layerConfs = (ArrayNode) confs;
JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
if (outputLayerNNCNode == null) {
return false; //Should never happen...
}
JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
return true;
}
JsonNode layerNode = layerWrapperNode.elements().next();
JsonNode weightInit = layerNode.get(
"weightInit"); //Should only have 1 element: "dense", "output", etc
JsonNode distribution = layerNode.get("dist");
Distribution dist = null;
if (distribution != null) {
dist = mapper.treeToValue(distribution, Distribution.class);
}
if (weightInit != null) {
final IWeightInit wi = WeightInit.valueOf(weightInit.asText())
.getWeightInitFunction(dist);
((BaseLayerConfiguration) l).setWeightInit(wi);
}
}
} catch (IOException e) {
log.warn(
"ILayer with null WeightInit detected: " + l.getName() + ", could not parse JSON",
e);
}
}
return true;
}
/**
* Object mapper for serialization of configurations
*
* @return
*/
public static ObjectMapper mapperYaml() {
return JsonMappers.getMapperYaml();
}
/**
* Object mapper for serialization of configurations
*
* @return
*/
public static ObjectMapper mapper() {
return JsonMappers.getMapper();
}
public static NeuralNetConfiguration fromYaml(String input) {
throw new RuntimeException("Needs fixing - not supported."); //TODO
throw new RuntimeException("Needs fixing - not supported."); // TODO
}
/**
* @return JSON representation of NN configuration
*/
public String toYaml() {
ObjectMapper mapper = NeuralNetConfiguration.mapperYaml();
ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.YAML);
synchronized (mapper) {
try {
return mapper.writeValueAsString(this);
@ -431,10 +157,12 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
* @return JSON representation of NN configuration
*/
public String toJson() {
ObjectMapper mapper = NeuralNetConfiguration.mapper();
ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.JSON);
synchronized (mapper) {
//JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields occasionally
//when writeValueAsString is used by multiple threads. This results in invalid JSON. See issue #3243
// JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields
// occasionally
// when writeValueAsString is used by multiple threads. This results in invalid JSON. See
// issue #3243
try {
return mapper.writeValueAsString(this);
} catch (com.fasterxml.jackson.core.JsonProcessingException e) {
@ -453,7 +181,9 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
public NeuralNetConfiguration clone() {
NeuralNetConfiguration clone;
clone = (NeuralNetConfiguration) super.clone();
if(getStepFunction() != null) { clone.setStepFunction(getStepFunction().clone()); }
if (getStepFunction() != null) {
clone.setStepFunction(getStepFunction().clone());
}
clone.netWideVariables = new LinkedHashSet<>(netWideVariables);
clone.setInnerConfigurations(new ArrayList<>(innerConfigurations));
@ -473,98 +203,109 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
clone.setDataType(this.getDataType());
return clone;
}
/**
*
*/
/** */
@Override
public void init() {
if(initCalled) return;
initCalled=true;
if (initCalled) return;
initCalled = true;
/**
* Run init() for each layer
*/
for( NeuralNetConfiguration nconf : getNetConfigurations() ) {
/** Run init() for each layer */
for (NeuralNetConfiguration nconf : getNetConfigurations()) {
nconf.init();
}
//getNetConfigurations().stream().forEach( conf -> {
// getNetConfigurations().stream().forEach( conf -> {
// conf.init(); //do not call on self
//}); //call init on all embedded net configurations
// }); //call init on all embedded net configurations
//TODO do not put inside self to avoid serialization issues
// TODO do not put inside self to avoid serialization issues
// innerConfigurations.add(0, this); //put this configuration at first place
getLayerConfigurations().stream()
.forEach(
lconf ->
lconf.setNetConfiguration(
this)); // set this as net config for all layers (defined in here, not stacked
/**
* Inherit network wide configuration setting to those layer configurations
* that do not have an individual setting (nor a default)
* Inherit network wide configuration setting to those layer configurations that do not have an
* individual setting (nor a default)
*/
for(LayerConfiguration lconf : this.getFlattenedLayerConfigurations()) {
for (LayerConfiguration lconf : this.getFlattenedLayerConfigurations()) {
lconf.runInheritance();
}
getLayerConfigurations().stream().forEach( lconf -> lconf.setNetConfiguration(this)); //set this as net config for all layers (defined in here, not stacked
//Validate BackpropType setting
// Validate BackpropType setting
if ((tbpttBackLength != DEFAULT_TBPTT_LENGTH || tbpttFwdLength != DEFAULT_TBPTT_LENGTH)
&& backpropType != BackpropType.TruncatedBPTT) {
log.warn("Truncated backpropagation through time lengths have been configured with values "
log.warn(
"Truncated backpropagation through time lengths have been configured with values "
+ tbpttFwdLength
+ " and " + tbpttBackLength + " but backprop type is set to " + backpropType
+ ". TBPTT configuration" +
" settings will only take effect if backprop type is set to BackpropType.TruncatedBPTT");
+ " and "
+ tbpttBackLength
+ " but backprop type is set to "
+ backpropType
+ ". TBPTT configuration"
+ " settings will only take effect if backprop type is set to BackpropType.TruncatedBPTT");
}
if (backpropType == BackpropType.TruncatedBPTT && isValidateTbpttConfig()) {
//Check for invalid combination - tbptt plus LastTimeStepLayer or
// Check for invalid combination - tbptt plus LastTimeStepLayer or
for (int i = 0; i < getFlattenedLayerConfigurations().size(); i++) {
LayerConfiguration l = getFlattenedLayerConfigurations().get(i);
if (l instanceof LastTimeStep || l instanceof GlobalPoolingLayer) {
throw new IllegalStateException(
"Invalid network configuration detected: Truncated backpropagation through time (TBPTT)"
+
" cannot be used with layer " + i + " of type " + l.getClass().getName()
+ ": TBPTT is incompatible with this layer type (which is designed " +
"to process entire sequences at once, and does support the type of sequence segments that TPBTT uses).\n"
+
"This check can be disabled using validateTbpttConfig(false) but this is not recommended.");
+ " cannot be used with layer "
+ i
+ " of type "
+ l.getClass().getName()
+ ": TBPTT is incompatible with this layer type (which is designed "
+ "to process entire sequences at once, and does support the type of sequence segments that TPBTT uses).\n"
+ "This check can be disabled using validateTbpttConfig(false) but this is not recommended.");
}
}
}
if (getInputType() == null && inputPreProcessors.get(0) == null) {
//User hasn't set the InputType. Sometimes we can infer it...
// For example, Dense/RNN layers, where preprocessor isn't set -> user is *probably* going to feed in
// User hasn't set the InputType. Sometimes we can infer it...
// For example, Dense/RNN layers, where preprocessor isn't set -> user is *probably* going to
// feed in
// standard feedforward or RNN data
//This isn't the most elegant implementation, but should avoid breaking backward compatibility here
//Can't infer InputType for CNN layers, however (don't know image dimensions/depth)
// This isn't the most elegant implementation, but should avoid breaking backward
// compatibility here
// Can't infer InputType for CNN layers, however (don't know image dimensions/depth)
LayerConfiguration firstLayer = getFlattenedLayerConfigurations().get(0);
if (firstLayer instanceof BaseRecurrentLayer) {
BaseRecurrentLayer brl = (BaseRecurrentLayer) firstLayer;
val nIn = brl.getNIn();
if (nIn > 0) {
setInputType( InputType.recurrent(nIn, brl.getDataFormat()));
setInputType(InputType.recurrent(nIn, brl.getDataFormat()));
}
} else if (firstLayer instanceof DenseLayer || firstLayer instanceof EmbeddingLayer
} else if (firstLayer instanceof DenseLayer
|| firstLayer instanceof EmbeddingLayer
|| firstLayer instanceof OutputLayer) {
//Can't just use "instanceof FeedForwardLayer" here. ConvolutionLayer is also a FeedForwardLayer
// Can't just use "instanceof FeedForwardLayer" here. ConvolutionLayer is also a
// FeedForwardLayer
FeedForwardLayer ffl = (FeedForwardLayer) firstLayer;
val nIn = ffl.getNIn();
if (nIn > 0) {
setInputType( InputType.feedForward(nIn));
setInputType(InputType.feedForward(nIn));
}
}
}
//Add preprocessors and set nIns, if InputType has been set
// Add preprocessors and set nIns, if InputType has been set
// Builder.inputType field can be set in 1 of 4 ways:
// 1. User calls setInputType directly
// 2. Via ConvolutionLayerSetup -> internally calls setInputType(InputType.convolutional(...))
// 3. Via the above code: i.e., assume input is as expected by the RNN or dense layer -> sets the inputType field
if(inputPreProcessors == null) {
// 3. Via the above code: i.e., assume input is as expected by the RNN or dense layer -> sets
// the inputType field
if (inputPreProcessors == null) {
inputPreProcessors = new HashMap<>();
}
if (getInputType() != null) {
@ -572,7 +313,7 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
for (int i = 0; i < getFlattenedLayerConfigurations().size(); i++) {
LayerConfiguration l = getFlattenedLayerConfigurations().get(i);
if (inputPreProcessors.get(i) == null) {
//Don't override preprocessor setting, but set preprocessor if required...
// Don't override preprocessor setting, but set preprocessor if required...
@NonNull
InputPreProcessor inputPreProcessor = l.getPreProcessorForInputType(currentInputType);
if (inputPreProcessor != null) {
@ -586,41 +327,47 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
}
if (i > 0) {
LayerConfiguration layer = getFlattenedLayerConfigurations().get(i - 1);
//convolution 1d is an edge case where it has rnn input type but the filters
//should be the output
if (layer instanceof Convolution1DLayer) {
// convolution 1d is an edge case where it has rnn input type but the filters
// should be the output
if (layer instanceof Convolution1D || layer instanceof Convolution1DNew) {
if (l instanceof DenseLayer && getInputType() instanceof InputType.InputTypeRecurrent) {
FeedForwardLayer feedForwardLayer = (FeedForwardLayer) l;
if (getInputType() instanceof InputType.InputTypeRecurrent) {
InputType.InputTypeRecurrent recurrent = (InputType.InputTypeRecurrent) getInputType();
InputType.InputTypeRecurrent recurrent =
(InputType.InputTypeRecurrent) getInputType();
feedForwardLayer.setNIn(recurrent.getTimeSeriesLength());
}
} else {
l.setNIn(currentInputType,
isOverrideNinUponBuild()); //Don't override the nIn setting, if it's manually set by the user
l.setNIn(
currentInputType,
isOverrideNinUponBuild()); // Don't override the nIn setting, if it's manually set
// by the user
}
} else {
l.setNIn(currentInputType,
isOverrideNinUponBuild()); //Don't override the nIn setting, if it's manually set by the user
l.setNIn(
currentInputType,
isOverrideNinUponBuild()); // Don't override the nIn setting, if it's manually set
// by the user
}
} else {
l.setNIn(currentInputType,
isOverrideNinUponBuild()); //Don't override the nIn setting, if it's manually set by the user
l.setNIn(
currentInputType,
isOverrideNinUponBuild()); // Don't override the nIn setting, if it's manually set by
// the user
}
currentInputType = l.getOutputType(i, currentInputType);
}
}
Nd4j.getRandom().setSeed(getSeed());
//Validate output layer configuration
// Validate output layer configuration
if (isValidateOutputLayerConfig()) {
//Validate output layer configurations...
// Validate output layer configurations...
for (LayerConfiguration n : getFlattenedLayerConfigurations()) {
OutputLayerUtil.validateOutputLayer(n.getName(), n); //No-op for non output/loss layers
OutputLayerUtil.validateOutputLayer(n.getName(), n); // No-op for non output/loss layers
}
}
}
@ -646,26 +393,28 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
layerName = String.valueOf(i);
}
//Pass input type through preprocessor, if necessary
// Pass input type through preprocessor, if necessary
InputPreProcessor preproc = getInputPreProcess(i);
//TODO memory requirements for preprocessor
// TODO memory requirements for preprocessor
if (preproc != null) {
inputType = preproc.getOutputType(inputType);
}
LayerMemoryReport report = getFlattenedLayerConfigurations().get(i).getMemoryReport(inputType);
LayerMemoryReport report =
getFlattenedLayerConfigurations().get(i).getMemoryReport(inputType);
memoryReportMap.put(layerName, report);
inputType = getFlattenedLayerConfigurations().get(i).getOutputType(i, inputType);
}
return new NetworkMemoryReport(memoryReportMap, NeuralNetConfiguration.class,
"MultiLayerNetwork", inputType);
return new NetworkMemoryReport(
memoryReportMap, NeuralNetConfiguration.class, "MultiLayerNetwork", inputType);
}
/**
* For the given input shape/type for the network, return a list of activation sizes for each
* layer in the network.<br> i.e., list.get(i) is the output activation sizes for layer i
* layer in the network.<br>
* i.e., list.get(i) is the output activation sizes for layer i
*
* @param inputType Input type for the network
* @return A lits of activation types for the network, indexed by layer number
@ -699,38 +448,47 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
public void addNetWideVariable(String variable) {
if (!netWideVariables.contains(variable)) {
netWideVariables.add(variable);
log.trace("Adding neural network wide variable '{}' to the list of variables. New length is {}.", variable, netWideVariables.size());
log.trace(
"Adding neural network wide variable '{}' to the list of variables. New length is {}.",
variable,
netWideVariables.size());
}
log.trace("Skipped adding neural network wide variable '{}' to the list of variables. It was already present. Length remains {}.", variable, netWideVariables.size());
log.trace(
"Skipped adding neural network wide variable '{}' to the list of variables. It was already present. Length remains {}.",
variable,
netWideVariables.size());
}
public void clearNetWideVariable() {
netWideVariables.clear();
log.trace("Adding neural network wide variables have been cleared. New length is {}.", netWideVariables.size());
log.trace(
"Adding neural network wide variables have been cleared. New length is {}.",
netWideVariables.size());
}
/**
* From the list of layers and neural net configurations, only return the Layer Configurations that
* are defined in this neural network (it does not include embedded neural network configuration
* layers)
* From the list of layers and neural net configurations, only return the Layer Configurations
* that are defined in this neural network (it does not include embedded neural network
* configuration layers)
*
* @return list with layer configurations
*/
@JsonIgnore
public List<LayerConfiguration> getLayerConfigurations() {
return innerConfigurations.stream()
.filter(obj -> (obj instanceof LayerConfiguration))
.map( obj -> (LayerConfiguration)obj )
.collect( Collectors.toList());
.map(obj -> (LayerConfiguration) obj)
.collect(Collectors.toList());
}
/**
* From the list of layers and neural net configurations, only return the neural net configurations
* From the list of layers and neural net configurations, only return the neural net
* configurations
*
* @return list with neural net configurations
*/
//@Synchronized("innerConfigurationsLock")
// @Synchronized("innerConfigurationsLock")
@JsonIgnore
public List<NeuralNetConfiguration> getNetConfigurations() {
List<NeuralNetConfiguration> list;
@ -751,23 +509,29 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
* @return list of layer configurations
*/
public List<LayerConfiguration> getFlattenedLayerConfigurations(NeuralNetConfiguration conf) {
List<LayerConfiguration> ret = new ArrayList<>(); //create the final return list
//When properly initialized, _this_ configuration is set first in the list, however we
//can find cases where this is not true, thus the first configuration is another net or layer configuration
//and should not be skipped. In essence, skip first configuration if that is "this".
//TODO: skipping not needed anymore as we removed _this_ from innerConfigurations
List<LayerConfiguration> ret = new ArrayList<>(); // create the final return list
// When properly initialized, _this_ configuration is set first in the list, however we
// can find cases where this is not true, thus the first configuration is another net or layer
// configuration
// and should not be skipped. In essence, skip first configuration if that is "this".
// TODO: skipping not needed anymore as we removed _this_ from innerConfigurations
int iSkip = 0;
if(conf.getInnerConfigurations().size()>0 && conf.getInnerConfigurations().get(0).equals(this)) { iSkip=1;}
conf.getInnerConfigurations().stream().skip(iSkip)
.forEach(obj -> {
//if Layer Config, include in list and inherit parameters from this conf
//else if neural net configuration, call self recursively to resolve layer configurations
if (conf.getInnerConfigurations().size() > 0
&& conf.getInnerConfigurations().get(0).equals(this)) {
iSkip = 1;
}
conf.getInnerConfigurations().stream()
.skip(iSkip)
.forEach(
obj -> {
// if Layer Config, include in list and inherit parameters from this conf
// else if neural net configuration, call self recursively to resolve layer
// configurations
if (obj instanceof LayerConfiguration) {
((LayerConfiguration) obj).setNetConfiguration(conf);
ret.add((LayerConfiguration) obj);
} else if (obj instanceof NeuralNetConfiguration)
ret.addAll(getFlattenedLayerConfigurations(
(NeuralNetConfiguration) obj));
ret.addAll(getFlattenedLayerConfigurations((NeuralNetConfiguration) obj));
else {
log.error(
"The list of layers and neural network configurations does contain an object of {}. Element will be ignored.",
@ -778,8 +542,9 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
}
/**
* Sames as {@link #getFlattenedLayerConfigurations(NeuralNetConfiguration)}, but uses this configurations
* list of configurations
* Sames as {@link #getFlattenedLayerConfigurations(NeuralNetConfiguration)}, but uses this
* configurations list of configurations
*
* @return list of layer configurations
*/
@JsonIgnore
@ -789,6 +554,7 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
/**
* Add a new layer to the first position
*
* @param layer configuration
*/
public void setLayer(@NonNull LayerConfiguration layer) {
@ -801,26 +567,28 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
}
/**
* Deprecated, do not use. Workaround for old tests
* and getFlattenedLayerConfigurations().get(0);
* Deprecated, do not use. Workaround for old tests and getFlattenedLayerConfigurations().get(0);
*
* @return
*/
@Deprecated @JsonIgnore
@Deprecated
@JsonIgnore
public LayerConfiguration getFirstLayer() {
log.warn("This getFirstLayer method is an ugly workaround and will be removed.");
return getFlattenedLayerConfigurations().get(0);
}
/*
protected boolean canEqual(final Object other) {
return other instanceof NeuralNetConfiguration;
}
*/
public static abstract class NeuralNetConfigurationBuilder<C extends NeuralNetConfiguration,
B extends NeuralNetConfiguration.NeuralNetConfigurationBuilder<C, B>> extends
NeuralNetBaseBuilderConfigurationBuilder<C, B> {
public abstract static class NeuralNetConfigurationBuilder<
C extends NeuralNetConfiguration,
B extends NeuralNetConfiguration.NeuralNetConfigurationBuilder<C, B>>
extends NeuralNetBaseBuilderConfigurationBuilder<C, B> {
public ComputationGraphConfiguration.GraphBuilder graphBuilder() {
return new ComputationGraphConfiguration.GraphBuilder(this);
@ -829,10 +597,9 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
public NeuralNetConfigurationBuilder clone() {
try {
return (NeuralNetConfigurationBuilder) super.clone();
} catch(CloneNotSupportedException ex) {
} catch (CloneNotSupportedException ex) {
throw new RuntimeException(ex);
}
}
}
}

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

@ -21,7 +21,13 @@
package org.deeplearning4j.nn.conf;
/**
* N is the batch size<br/>
* C is the number of feature maps (that is,, number of channels)<br/>
* H is the image height (not used for 1D conv as this is an RNN format<br/>
* W is the image width<br/>
* **/
public enum RNNFormat implements DataFormat {
NCW,
NWC
/** n=batch size; c=channels/ features; w=width **/ NCW,
/** n=batch size; w=width; c=channels/ features **/ NWC
}

8
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/BaseConstraint.java
View File

@ -20,6 +20,9 @@
package org.deeplearning4j.nn.conf.constraint;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;
import lombok.*;
import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.nn.api.Layer;
@ -27,11 +30,6 @@ import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;
@AllArgsConstructor
@EqualsAndHashCode
@Data

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/MaxNormConstraint.java
View File

@ -20,6 +20,8 @@
package org.deeplearning4j.nn.conf.constraint;
import java.util.Collections;
import java.util.Set;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -27,9 +29,6 @@ import org.nd4j.linalg.factory.Broadcast;
import org.nd4j.linalg.indexing.BooleanIndexing;
import org.nd4j.linalg.indexing.conditions.Conditions;
import java.util.Collections;
import java.util.Set;
@Data
@EqualsAndHashCode(callSuper = true)
public class MaxNormConstraint extends BaseConstraint {

7
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/MinMaxNormConstraint.java
View File

@ -20,6 +20,8 @@
package org.deeplearning4j.nn.conf.constraint;
import java.util.Collections;
import java.util.Set;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -27,11 +29,6 @@ import org.nd4j.linalg.api.ops.CustomOp;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.factory.Broadcast;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.BooleanIndexing;
import org.nd4j.linalg.indexing.conditions.Conditions;
import java.util.Collections;
import java.util.Set;
@Data
@EqualsAndHashCode(callSuper = true)

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/UnitNormConstraint.java
View File

@ -20,14 +20,13 @@
package org.deeplearning4j.nn.conf.constraint;
import java.util.Collections;
import java.util.Set;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Broadcast;
import java.util.Collections;
import java.util.Set;
@Data
@EqualsAndHashCode(callSuper = true)
public class UnitNormConstraint extends BaseConstraint {

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/ConstantDistribution.java
View File

@ -20,10 +20,10 @@
package org.deeplearning4j.nn.conf.distribution;
import lombok.Data;
import lombok.EqualsAndHashCode;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
@Data
@EqualsAndHashCode(callSuper = false)

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/Distribution.java
View File

@ -20,13 +20,10 @@
package org.deeplearning4j.nn.conf.distribution;
import org.deeplearning4j.nn.conf.distribution.serde.LegacyDistributionHelper;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "type",
defaultImpl = LegacyDistributionHelper.class)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, property = "@class")
public abstract class Distribution implements Serializable, Cloneable {
private static final long serialVersionUID = 5401741214954998498L;

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/LogNormalDistribution.java
View File

@ -20,10 +20,10 @@
package org.deeplearning4j.nn.conf.distribution;
import lombok.Data;
import lombok.EqualsAndHashCode;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
/**
* A log-normal distribution, with two parameters: mean and standard deviation.

18
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/NormalDistribution.java
View File

@ -20,10 +20,10 @@
package org.deeplearning4j.nn.conf.distribution;
import lombok.Data;
import lombok.EqualsAndHashCode;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
/**
* A normal (Gaussian) distribution, with two parameters: mean and standard deviation
@ -48,21 +48,7 @@ public class NormalDistribution extends Distribution {
this.std = std;
}
public double getMean() {
return mean;
}
public void setMean(double mean) {
this.mean = mean;
}
public double getStd() {
return std;
}
public void setStd(double std) {
this.std = std;
}
@Override
public int hashCode() {

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/OrthogonalDistribution.java
View File

@ -20,10 +20,10 @@
package org.deeplearning4j.nn.conf.distribution;
import lombok.Data;
import lombok.EqualsAndHashCode;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
/**
* Orthogonal distribution, with gain parameter.<br>

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/TruncatedNormalDistribution.java
View File

@ -20,10 +20,10 @@
package org.deeplearning4j.nn.conf.distribution;
import lombok.Data;
import lombok.EqualsAndHashCode;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
@EqualsAndHashCode(callSuper = false)
@Data

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/UniformDistribution.java
View File

@ -20,12 +20,12 @@
package org.deeplearning4j.nn.conf.distribution;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
/**
* A uniform distribution, with two parameters: lower and upper - i.e., U(lower,upper)

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/serde/LegacyDistributionDeserializer.java
View File

@ -20,15 +20,13 @@
package org.deeplearning4j.nn.conf.distribution.serde;
import org.deeplearning4j.nn.conf.distribution.*;
import com.fasterxml.jackson.core.JsonParseException;
import com.fasterxml.jackson.core.JsonParser;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.DeserializationContext;
import com.fasterxml.jackson.databind.JsonDeserializer;
import com.fasterxml.jackson.databind.JsonNode;
import java.io.IOException;
import org.deeplearning4j.nn.conf.distribution.*;
public class LegacyDistributionDeserializer extends JsonDeserializer<Distribution> {
@Override

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/serde/LegacyDistributionHelper.java
View File

@ -20,8 +20,8 @@
package org.deeplearning4j.nn.conf.distribution.serde;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import com.fasterxml.jackson.databind.annotation.JsonDeserialize;
import org.deeplearning4j.nn.conf.distribution.Distribution;
@JsonDeserialize(using = LegacyDistributionDeserializer.class)
public class LegacyDistributionHelper extends Distribution {

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/AlphaDropout.java
View File

@ -20,6 +20,8 @@
package org.deeplearning4j.nn.conf.dropout;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NonNull;
@ -32,8 +34,6 @@ import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
import org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.schedule.ISchedule;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
@EqualsAndHashCode(exclude = {"lastPValue","alphaPrime","a","b", "mask"})

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/Dropout.java
View File

@ -20,6 +20,8 @@
package org.deeplearning4j.nn.conf.dropout;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.Getter;
@ -36,8 +38,6 @@ import org.nd4j.linalg.api.ops.random.impl.DropOutInverted;
import org.nd4j.linalg.exception.ND4JOpProfilerException;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.schedule.ISchedule;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
@JsonIgnoreProperties({"mask", "helper", "helperCountFail", "initializedHelper"})

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