02-25 21:14:08.391 16707 16707 E AndroidRuntime: Caused by: org.deeplearning4j.exception.DL4JInvalidConfigException: ConvolutionLayer (index=2, name=(name not set)) nIn=0, nOut=50; nIn and nOut must be > 0

gistfile1.txt

        /* Neural Dependencies. */
        private static final int IMAGE_WIDTH  = 64;
        private static final int IMAGE_HEIGHT = 80;
        private static final int SEED = 123;
        private static final int ITERATIONS = 1;
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(SEED)
                .iterations(ITERATIONS) // Training iterations as above
                .regularization(true).l2(0.0005)
                /*  
                    Uncomment the following for learning decay and bias
                 */
                .learningRate(.01)//.biasLearningRate(0.02)
                //.learningRateDecayPolicy(LearningRatePolicy.Inverse).lrPolicyDecayRate(0.001).lrPolicyPower(0.75)
                .weightInit(WeightInit.XAVIER)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(Updater.NESTEROVS).momentum(0.9)
                .list()
                .layer(0, new ConvolutionLayer.Builder(5, 5)
                        //nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
                        .nIn(IMAGE_WIDTH * IMAGE_HEIGHT)
                        .stride(1, 1)
                        .nOut(20)
                        .activation(Activation.IDENTITY)
                        .build())
                .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
                        .kernelSize(2,2)
                        .stride(2,2)
                        .build())
                .layer(2, new ConvolutionLayer.Builder(5, 5)
                        //Note that nIn need not be specified in later layers
                        .stride(1, 1)
                        .nOut(50)
                        .activation(Activation.IDENTITY)
                        .build())
                .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
                        .kernelSize(2,2)
                        .stride(2,2)
                        .build())
                .layer(4, new DenseLayer.Builder().activation(Activation.RELU)
                        .nOut(500).build())
                .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nOut(CharacterizationUtils.CARD_NUM_SIGNALS)
                        .activation(Activation.SOFTMAX)
                        .build())
                .backprop(true).pretrain(false).build();

                MultiLayerNetwork myNetwork = new MultiLayerNetwork(conf);