Fixing tests

Signed-off-by: brian <brian@brutex.de>

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

@@ -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")

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

@@ -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())

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

@@ -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)

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

@@ -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
     ));
 

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

@@ -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);
                 }
             }
         }

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

@@ -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();
   }
 

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

@@ -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]);
     }

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

@@ -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))

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

@@ -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())

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

@@ -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
+            outSize = Convolution2DUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
-            padding = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
+            padding = Convolution2DUtils.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);
+            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);

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

@@ -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)) {
+                    if(Convolution2DUtils.layerHasConvolutionLayout(layer1)) {
-                        CNN2DFormat formatForLayer = ConvolutionUtils.getFormatForLayer(layer1);
+                        CNN2DFormat formatForLayer = Convolution2DUtils.getFormatForLayer(layer1);
                         if(formatForLayer == CNN2DFormat.NCHW) {
                             dimOrder = KerasLayer.DimOrder.THEANO;
                         }  else if(formatForLayer == CNN2DFormat.NHWC) {

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

@@ -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;
+        Convolution1D convolution1D = (Convolution1D) layer;
-        convolution1DLayer.setDefaultValueOverriden(true);
+        convolution1D.setDefaultValueOverriden(true);
     }
 
     /**
@@ -114,8 +114,8 @@ public class KerasAtrousConvolution1D extends KerasConvolution {
      *
      * @return ConvolutionLayer
      */
-    public Convolution1DLayer getAtrousConvolution1D() {
+    public Convolution1D getAtrousConvolution1D() {
-        return (Convolution1DLayer) this.layer;
+        return (Convolution1D) this.layer;
     }
 
     /**

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

@@ -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;
+        Convolution1D convolution1D = (Convolution1D) this.layer;
-        convolution1DLayer.setDataFormat(dimOrder == DimOrder.TENSORFLOW ? CNN2DFormat.NHWC : CNN2DFormat.NCHW);
+        convolution1D.setDataFormat(dimOrder == DimOrder.TENSORFLOW ? CNN2DFormat.NHWC : CNN2DFormat.NCHW);
-        convolution1DLayer.setDefaultValueOverriden(true);
+        convolution1D.setDefaultValueOverriden(true);
     }
 
     /**
@@ -135,8 +135,8 @@ public class KerasConvolution1D extends KerasConvolution {
      *
      * @return  ConvolutionLayer
      */
-    public Convolution1DLayer getConvolution1DLayer() {
+    public Convolution1D getConvolution1DLayer() {
-        return (Convolution1DLayer) this.layer;
+        return (Convolution1D) this.layer;
     }
 
 

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

@@ -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.*;
-import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
+import org.deeplearning4j.nn.conf.layers.Convolution1D;
-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.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());
         }
     }

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

@@ -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());

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

@@ -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());

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

@@ -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 {
 

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

@@ -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

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

@@ -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

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

@@ -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 {

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

@@ -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"),

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

@@ -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 {

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

@@ -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> {
 

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

@@ -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";

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

@@ -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";

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

@@ -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> {
 

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

@@ -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> {
 

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

@@ -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

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

@@ -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)

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

@@ -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> {
 

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

@@ -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;

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

@@ -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;

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

@@ -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 {

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

@@ -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")

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

@@ -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")

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

@@ -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

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

@@ -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 {

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

@@ -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.
  */

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

@@ -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

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

@@ -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);

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

@@ -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;

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

@@ -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)

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

@@ -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> {

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

@@ -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

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

@@ -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

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

@@ -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)

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

@@ -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

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

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

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

@@ -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 {

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

@@ -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

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/meta/Prediction.java

@@ -20,7 +20,6 @@
 
 package org.deeplearning4j.eval.meta;
 
-import lombok.AllArgsConstructor;
 import lombok.Data;
 
 @Data

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/adapters/YoloModelAdapter.java

@@ -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

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/AbstractParamInitializer.java

@@ -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;
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Classifier.java

@@ -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 {
 
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/ITraininableLayerConfiguration.java

@@ -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 {
 
     /**

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Layer.java

@@ -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 org.nd4j.linalg.api.ndarray.INDArray;
-import java.io.Serializable;
 
 /**
  * A layer is the highest-level building block in deep learning. A layer is a container that usually

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/ParamInitializer.java

@@ -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
  *

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Updater.java

@@ -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

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/IOutputLayer.java

@@ -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 {
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/LayerConstraint.java

@@ -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 {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/RecurrentLayer.java

@@ -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 org.nd4j.common.primitives.Pair;
-import java.util.Map;
+import org.nd4j.linalg.api.ndarray.INDArray;
 
 public interface RecurrentLayer extends Layer {
 

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

@@ -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)
@@ -161,7 +160,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
      */
     public static ComputationGraphConfiguration fromJson(String json) {
         //As per NeuralNetConfiguration.fromJson()
-        ObjectMapper mapper =CavisMapper.getMapper(CavisMapper.Type.JSON);
+        ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.JSON);
         ComputationGraphConfiguration conf;
         try {
             conf = mapper.readValue(json, ComputationGraphConfiguration.class);

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

@@ -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)

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

@@ -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 org.nd4j.common.primitives.Pair;
-
+import org.nd4j.linalg.api.ndarray.INDArray;
-import java.io.Serializable;
 
 @JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
 public interface InputPreProcessor extends Serializable, Cloneable {

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

@@ -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 java.util.*;
-import com.fasterxml.jackson.databind.ObjectMapper;
-import com.fasterxml.jackson.databind.node.ArrayNode;
 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

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

@@ -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) {

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

@@ -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

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

@@ -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 {

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

@@ -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)

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

@@ -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 {

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

@@ -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)

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

@@ -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 {
 

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

@@ -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.

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

@@ -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

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

@@ -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>

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

@@ -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

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

@@ -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)

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

@@ -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

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

@@ -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 {

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

@@ -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"})

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

@@ -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"})

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/GaussianDropout.java

@@ -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"})

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

@@ -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 {

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

@@ -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 {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/SpatialDropout.java

@@ -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"})

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

@@ -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

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

@@ -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 {

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

@@ -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)

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

@@ -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 {

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

@@ -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)

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

@@ -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 {

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

@@ -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;

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

@@ -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 {

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

@@ -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 {

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

@@ -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

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

@@ -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 {

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

@@ -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 {

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

@@ -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)

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

@@ -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 {

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

@@ -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")