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Fixing tests 

Signed-off-by: brian <brian@brutex.de>
enhance-build-infrastructure
Brian Rosenberger 2023-05-17 09:12:47 +02:00
parent 0bed17c97f
commit 997143b9dd
407 changed files with 1730 additions and 2012 deletions
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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")

2
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())

22
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DGradientCheckTest.java
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@ -93,7 +93,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.convolutionMode(ConvolutionMode.Same)
.list()
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -202,7 +202,7 @@ 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 +211,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.build())
.layer(Cropping1D.builder(cropping).build())
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -317,7 +317,7 @@ 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 +326,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.build())
.layer(ZeroPadding1DLayer.builder(zeroPadding).build())
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -438,7 +438,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.list()
.layer(
0,
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -447,7 +447,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.build())
.layer(
1,
Convolution1DLayer.builder()
Convolution1D.builder()
.activation(afn)
.kernelSize(kernel)
.stride(stride)
@ -548,7 +548,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.seed(12345)
.list()
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.kernelSize(2)
.rnnDataFormat(RNNFormat.NCW)
.stride(stride)
@ -562,7 +562,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.pnorm(pnorm)
.build())
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.kernelSize(2)
.rnnDataFormat(RNNFormat.NCW)
.stride(stride)
@ -655,7 +655,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.seed(12345)
.list()
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.kernelSize(k)
.dilation(d)
.hasBias(hasBias)
@ -664,7 +664,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
.nOut(convNOut1)
.build())
.layer(
Convolution1DLayer.builder()
Convolution1D.builder()
.kernelSize(k)
.dilation(d)
.convolutionMode(ConvolutionMode.Causal)

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())

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
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@ -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;
}
/**

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;
}

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
View File

@ -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
View File

@ -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
View File

@ -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
View File

@ -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
View File

@ -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
View File

@ -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
View File

@ -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
View File

@ -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 {

13
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.java
View File

@ -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;
@ -43,16 +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)

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 {

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

@ -23,12 +23,9 @@ 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.jackson.Jacksonized;
import lombok.extern.slf4j.Slf4j;
import net.brutex.ai.dnn.api.INeuralNetworkConfiguration;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
@ -37,11 +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.CavisMapper;
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;
@ -50,7 +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;
@ -60,9 +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

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

@ -326,7 +326,7 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
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) {
if (layer instanceof Convolution1D) {
if (l instanceof DenseLayer && getInputType() instanceof InputType.InputTypeRecurrent) {
FeedForwardLayer feedForwardLayer = (FeedForwardLayer) l;
if (getInputType() instanceof InputType.InputTypeRecurrent) {

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
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@ -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)

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/Distribution.java
View File

@ -20,12 +20,9 @@
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, property = "@class")
public abstract class Distribution implements Serializable, Cloneable {

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

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

@ -20,11 +20,10 @@
package org.deeplearning4j.nn.conf.distribution;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
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

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/OrthogonalDistribution.java
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@ -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"})

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/GaussianDropout.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 org.deeplearning4j.nn.workspace.ArrayType;
@ -30,8 +32,6 @@ import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
import org.nd4j.linalg.api.ops.random.impl.GaussianDistribution;
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({"noise"})

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/GaussianNoise.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.dropout;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -27,7 +28,6 @@ import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.AddOp;
import org.nd4j.linalg.api.ops.random.impl.GaussianDistribution;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.schedule.ISchedule;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
public class GaussianNoise implements IDropout {

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/IDropout.java
View File

@ -20,11 +20,10 @@
package org.deeplearning4j.nn.conf.dropout;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface IDropout extends Serializable, Cloneable {

4
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/SpatialDropout.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.val;
@ -31,8 +33,6 @@ import org.nd4j.linalg.api.ops.random.impl.DropOutInverted;
import org.nd4j.linalg.factory.Broadcast;
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"})

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/AttentionVertex.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.graph;
import com.google.common.base.Preconditions;
import java.util.Map;
import lombok.*;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -30,12 +31,10 @@ import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.WeightInitUtil;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.primitives.Pair;
import java.util.Map;
@NoArgsConstructor
@Data

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ElementWiseVertex.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.graph;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.val;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -30,7 +31,6 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
public class ElementWiseVertex extends GraphVertex {

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/FrozenVertex.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.graph;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -28,7 +29,6 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
@EqualsAndHashCode(callSuper = false)

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/GraphVertex.java
View File

@ -20,15 +20,14 @@
package org.deeplearning4j.nn.conf.graph;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public abstract class GraphVertex implements Cloneable, Serializable {

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/L2NormalizeVertex.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.graph;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.val;
@ -30,7 +31,6 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
@EqualsAndHashCode(callSuper = false)

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/LayerVertex.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.graph;
import java.util.Arrays;
import lombok.Data;
import lombok.Getter;
import lombok.NoArgsConstructor;
@ -34,8 +35,6 @@ import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Arrays;
@NoArgsConstructor
@Data
public class LayerVertex extends GraphVertex {

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/MergeVertex.java
View File

@ -22,7 +22,6 @@ package org.deeplearning4j.nn.conf.graph;
import lombok.Data;
import lombok.Setter;
import lombok.val;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.RNNFormat;

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ReshapeVertex.java
View File

@ -20,6 +20,8 @@
package org.deeplearning4j.nn.conf.graph;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.Arrays;
import lombok.Data;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
@ -29,9 +31,6 @@ import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.Arrays;
@Data
public class ReshapeVertex extends GraphVertex {

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ScaleVertex.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.graph;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
@ -28,7 +29,6 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
public class ScaleVertex extends GraphVertex {

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ShiftVertex.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.graph;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
@ -31,7 +32,6 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
@NoArgsConstructor

5
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/SubsetVertex.java
View File

@ -20,6 +20,8 @@
package org.deeplearning4j.nn.conf.graph;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.Arrays;
import lombok.Data;
import lombok.val;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -29,9 +31,6 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.Arrays;
@Data
public class SubsetVertex extends GraphVertex {

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/UnstackVertex.java
View File

@ -21,6 +21,7 @@
package org.deeplearning4j.nn.conf.graph;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Getter;
import lombok.val;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -30,7 +31,6 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
@Getter
public class UnstackVertex extends GraphVertex {

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/rnn/DuplicateToTimeSeriesVertex.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.graph.rnn;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.deeplearning4j.nn.conf.graph.GraphVertex;
@ -30,7 +31,6 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
@EqualsAndHashCode(callSuper = false)

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/graph/rnn/LastTimeStepVertex.java
View File

@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.graph.rnn;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import org.deeplearning4j.nn.conf.graph.GraphVertex;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -29,7 +30,6 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonProperty;
@Data
public class LastTimeStepVertex extends GraphVertex {

16
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/inputs/InputType.java
View File

@ -20,25 +20,23 @@
package org.deeplearning4j.nn.conf.inputs;
import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import java.util.Arrays;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.DataFormat;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.layers.Convolution3D;
import org.nd4j.common.base.Preconditions;
import org.nd4j.common.util.OneTimeLogger;
import org.nd4j.linalg.api.ndarray.INDArray;
import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import java.util.Arrays;
@JsonInclude(JsonInclude.Include.NON_NULL)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")

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