025265b2cb29c098631ce7a644c601905c1d24f2,deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java,CNNGradientCheckTest,testGradientCNNL1L2MLN,#,132
Before Change
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
.regularization(true)
.l2(l2).l1(l1)
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.seed(12345L)
.list()
.layer(0, new ConvolutionLayer.Builder(new int[]{1, 1})
After Change
//use l2vals[i] with l1vals[i]
double[] l2vals = {0.4, 0.0, 0.4, 0.4};
double[] l1vals = {0.0, 0.0, 0.5, 0.0};
double[] biasL2 = {0.0, 0.0, 0.0, 0.2};
double[] biasL1 = {0.0, 0.0, 0.6, 0.0};
for( String afn : activFns ){
for( boolean doLearningFirst : characteristic ){
for( int i=0; i < lossFunctions.length; i++ ) {
for (int k = 0; k < l2vals.length; k++) {
LossFunctions.LossFunction lf = lossFunctions[i];
String outputActivation = outputActivations[i];
double l2 = l2vals[k];
double l1 = l1vals[k];
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
.regularization(true)
.l2(l2).l1(l1)
.l2Bias(biasL2[k]).l1Bias(biasL1[k])
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.seed(12345L)
.list()
.layer(0, new ConvolutionLayer.Builder(new int[]{1, 1})