New Error

org.deeplearning4j.exception.DL4JInvalidInputException: Cannot do forward pass in Convolution layer (layer name = layer0, layer index = 0): input array channels does not match CNN layer configuration (data input channels = 1, [minibatch,inputDepth,height,width]=[64, 1, 5, 1]; expected input channels = 5) (layer name: layer0, layer index: 0, layer type: Convolution1DLayer)
	at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.preOutput(ConvolutionLayer.java:282)
	at org.deeplearning4j.nn.layers.convolution.Convolution1DLayer.preOutput(Convolution1DLayer.java:79)
	at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.activate(ConvolutionLayer.java:392)
	at org.deeplearning4j.nn.layers.convolution.Convolution1DLayer.activate(Convolution1DLayer.java:92)
	at org.deeplearning4j.nn.layers.AbstractLayer.activate(AbstractLayer.java:255)
	at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.ffToLayerActivationsInWs(MultiLayerNetwork.java:966)
	at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:2429)
	at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:2395)
	at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:178)
	at org.deeplearning4j.optimize.solvers.StochasticGradientDescent.optimize(StochasticGradientDescent.java:60)
	at org.deeplearning4j.optimize.Solver.optimize(Solver.java:54)
	at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:2030)
	at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:2051)
	at org.deeplearning4j.spark.impl.paramavg.ParameterAveragingTrainingWorker.processMinibatch(ParameterAveragingTrainingWorker.java:179)
	at org.deeplearning4j.spark.impl.paramavg.ParameterAveragingTrainingWorker.processMinibatch(ParameterAveragingTrainingWorker.java:41)
	at org.deeplearning4j.spark.api.worker.ExecuteWorkerFlatMapAdapter.call(ExecuteWorkerFlatMap.java:125)
	at org.deeplearning4j.spark.api.worker.ExecuteWorkerFlatMapAdapter.call(ExecuteWorkerFlatMap.java:42)
	at org.deeplearning4j.spark.api.worker.ExecuteWorkerPathFlatMapAdapter.call(ExecuteWorkerPathFlatMap.java:71)
	at org.deeplearning4j.spark.api.worker.ExecuteWorkerPathFlatMapAdapter.call(ExecuteWorkerPathFlatMap.java:38)
	at org.datavec.spark.transform.BaseFlatMapFunctionAdaptee.call(BaseFlatMapFunctionAdaptee.java:24)
	at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$4$1.apply(JavaRDDLike.scala:153)
	at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$4$1.apply(JavaRDDLike.scala:153)
	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
	at org.apache.spark.scheduler.Task.run(Task.scala:109)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)