The PR is shown below. Basically, i was trying to feed my input to LSTM cell, normalize and trying to feed the Convolution layer. And My stacktrace is shown below. I don't know where did i get it wrong.

gistfile1.txt

MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
            .updater(Updater.NESTEROVS).adamMeanDecay(0.9).adamVarDecay(0.999)
            .regularization(true).l2(1e-5).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
            .weightInit(WeightInit.XAVIER)
            .convolutionMode(ConvolutionMode.Truncate)
            .gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue).gradientNormalizationThreshold(1.0)
            .learningRate(2e-2)
            .list()
                .layer(0, new GravesBidirectionalLSTM.Builder()
                        .name("LSTMCell_1")
                        .nIn(vectorSize)
                        .activation(Activation.SIGMOID)
                        .nOut(100)
                        .build())
                .layer(1, new DenseLayer.Builder()
                        .name("Normalization_layer")
                        .gradientNormalization(GradientNormalization.ClipL2PerParamType)
                        .nOut(100)
                        .build())
                .layer(2, new ConvolutionLayer.Builder()
                        .kernelSize(5,1)
                        .stride(1,1)
                        .activation(Activation.SIGMOID)
                        .build())
                .layer(3, new GlobalPoolingLayer.Builder()
                        .activation(Activation.RELU)
                        .build())
                .layer(4, new OutputLayer.Builder()
                        .activation(Activation.SOFTMAX)
                        .nOut(2)
                        .build())
            .inputPreProcessor(2, new FeedForwardToCnnPreProcessor(100,100,1))
            .pretrain(false).backprop(true)
            .build()
            
// MY STACKTRACE
Exception in thread "main" java.lang.IllegalStateException: Invalid input: expected FeedForward input of size 10000 = (d=1 * w=100 * h=100), got InputTypeFeedForward(100)
	at org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor.getOutputType
  ...