# How to convert CSV to the input of 1D convolutional layer
**URL:** https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292
**Category:** DL4J
**Created:** [March 19, 2020, 12:43pm UTC](https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292 "2020-03-19T12:43:11Z")
**Posts on this page:** 8
**Page:** 1
### Author:  [@amirshamaei](https://community.konduit.ai/u/amirshamaei)
#### Post date: [March 19, 2020, 12:43pm UTC](https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292/1 "2020-03-19T12:43:11Z")
My problem is “fitting a 1D signal”. the signals are stored in a CSV file with the size of 1000 \* 2069 (1:2048 → features, 2049:2069 → targets/labels).
I loaded a CSV file using this piece of code:
> ```
> RecordReader recordReader = new CSVRecordReader(0,',');
> recordReader.initialize(new FileSplit(new File("D:\\deep fitting\\Labels.csv")));
> DataSetIterator iterator = new RecordReaderDataSetIterator.Builder(recordReader,1000)
> .regression(2048, 2068)
> .build();
> DataSet allData = iterator.next();
>
> ```
However, the input of my network is 1D Conv layer:
> ```
> .layer(new Convolution1DLayer.Builder().kernelSize(5).convolutionMode(ConvolutionMode.Same)
> .nIn(1).nOut(16).build())
>
> ```
when i run my code, i encountered this Error:
> `Invalid input: expect CNN activations with rank 4 (received input with shape [800, 2048])`
which is logical, so I convert the shape of features matrix.
> `train.setFeatures(train.getFeatures().reshape(800,2048,1,1));`
(I do not know it is okay or not) however this time i got this error:
> `Exception in thread "main" java.lang.IllegalStateException: Invalid input, does not match configuration: expected [minibatch, numChannels=1, inputHeight=1, inputWidth=2048] but got input array ofshape [800, 2048, 1, 1]`
---
### Author:  [@StoicProgrammer](https://community.konduit.ai/u/StoicProgrammer)
#### Post date: [March 19, 2020, 1:48pm UTC](https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292/2 "2020-03-19T13:48:49Z")
By reading the message:
> expected [minibatch, numChannels=1, inputHeight=1, inputWidth=2048] but got input array ofshape [800, 2048, 1, 1]
I think it is expecting shape `[800, 1, 1, 2048]` but got `[800, 2048, 1, 1]`.
Maybe you have to change your reshape from:
> train.setFeatures(train.getFeatures().reshape(800,2048,1,1));
To this:
> train.setFeatures(train.getFeatures().reshape(800,1,1,2048));
---
### Author:  [@treo](https://community.konduit.ai/u/treo)
#### Post date: [March 19, 2020, 2:07pm UTC](https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292/3 "2020-03-19T14:07:24Z")
Can you please share how exactly you’ve configured your network and the full stacktrace?
---
### Author:  [@amirshamaei](https://community.konduit.ai/u/amirshamaei)
#### Post date: [March 20, 2020, 5:13pm UTC](https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292/4 "2020-03-20T17:13:32Z")
Thanks @StoicProgrammer I did it; however, It did not work
---
### Author:  [@amirshamaei](https://community.konduit.ai/u/amirshamaei)
#### Post date: [March 20, 2020, 5:15pm UTC](https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292/5 "2020-03-20T17:15:52Z")
yes, sure. you can find my network
> ```java
> private static long seed = 123L;
>
> public static void main(String[] args) throws IOException, InterruptedException {
> MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
> .seed(seed)
> .updater(new AdaDelta())
> .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
> .weightInit(WeightInit.XAVIER)
> .list()
> .layer(new Convolution1DLayer.Builder().kernelSize(5).convolutionMode(ConvolutionMode.Same)
> .nIn(1).nOut(16).build())
> .layer(new Subsampling1DLayer.Builder().kernelSize(2).stride(2).poolingType(SubsamplingLayer.PoolingType.MAX).build())
> .layer(new Convolution1DLayer.Builder().kernelSize(5).convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU)
> .nOut(32).build())
> .layer(new Subsampling1DLayer.Builder().kernelSize(2).stride(2).build())
> .layer(new Convolution1DLayer.Builder().kernelSize(5).convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU)
> .nOut(64).build())
> .layer(new Subsampling1DLayer.Builder().kernelSize(2).stride(2).build())
> .layer(new Convolution1DLayer.Builder().kernelSize(3).convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU)
> .nOut(128).build())
> .layer(new Subsampling1DLayer.Builder().kernelSize(2).stride(2).build())
> .layer(new Convolution1DLayer.Builder().kernelSize(3).convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU)
> .nOut(256).build())
> .layer(new Subsampling1DLayer.Builder().kernelSize(2).stride(2).build())
> .layer(new Convolution1DLayer.Builder().kernelSize(3).convolutionMode(ConvolutionMode.Same).activation(Activation.LEAKYRELU)
> .nOut(512).build())
> .layer(new Subsampling1DLayer.Builder().kernelSize(2).stride(2).build())
> .layer(new DropoutLayer(0.2))
> .layer(new DenseLayer.Builder().nOut(21).build())
> .layer(new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
> .name("output")
> .nOut(21)
> .activation(Activation.IDENTITY).build())
> .setInputType(InputType.convolutional(1,2048,1))
> .build();
>
> MultiLayerNetwork network = new MultiLayerNetwork(configuration);
> network.init();
> UIServer uiServer = UIServer.getInstance();
> StatsStorage statsStorage = new FileStatsStorage(new File(System.getProperty("java.io.tmpdir"), "ui-stats.dl4j"));
> uiServer.attach(statsStorage);
>
> RecordReader recordReader = new CSVRecordReader(0,',');
> recordReader.initialize(new FileSplit(new File("D:\\deep fitting\\Labels.csv")));
> DataSetIterator iterator = new RecordReaderDataSetIterator.Builder(recordReader,1000)
> .regression(2048, 2068)
> .build();
> DataSet allData = iterator.next();
> allData.shuffle();
> SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.8);
> DataSet train = testAndTrain.getTrain();
>
> train.setFeatures(train.getFeatures().reshape(800,2048,1,1));
>
> // train.setLabels(train.getLabels().reshape(800,1,21,1));
> DataSet test = testAndTrain.getTest();
> network.setListeners(new StatsListener( statsStorage), new ScoreIterationListener(1), new EvaluativeListener(iterator, 1, InvocationType.EPOCH_END));
> network.fit(train);
>
> ```
and the stacktrace:
> ```auto
> Exception in thread "main" java.lang.IllegalArgumentException: Invalid input: expect CNN activations with rank 4 (received input with shape [800, 2048])
> at org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor.preProcess(CnnToRnnPreProcessor.java:74)
> at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.ffToLayerActivationsInWs(MultiLayerNetwork.java:1122)
> at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:2750)
> at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:2708)
> at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:170)
> at org.deeplearning4j.optimize.solvers.StochasticGradientDescent.optimize(StochasticGradientDescent.java:63)
> at org.deeplearning4j.optimize.Solver.optimize(Solver.java:52)
> at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fitHelper(MultiLayerNetwork.java:2309)
> at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:2267)
> at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:2330)
> at org.deeplearning4j.examples.convolution.oneDConv.main(oneDConv.java:90)
>
> ```
---
### Author:  [@treo](https://community.konduit.ai/u/treo)
#### Post date: [March 23, 2020, 8:00am UTC](https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292/6 "2020-03-23T08:00:15Z")
What you can do, is add an input preprocessor that will do the proper reshaping for you.
You do this by adding this line to your model configuration:
```auto
.inputPreProcessor(0, new FeedForwardToCnnPreProcessor(2048, 1))
```
This turns your input into a 2048 wide and 1 high matrix with a single channel.
You will also have to change your `.setInputType` line to the following:
```auto
.setInputType(InputType.feedforward(2048))
```
That way, you don’t need to do any manual reshaping.
---
### Author:  [@amirshamaei](https://community.konduit.ai/u/amirshamaei)
#### Post date: [March 23, 2020, 2:49pm UTC](https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292/7 "2020-03-23T14:49:07Z")
Thank you very much @treo. It works. It should be mentioned that I changed the network layers from 1D to 2D; because after I changed setInputType to feedforward, I got an error in which stated Conv1D needs RNN input.
---
### Author:  [@sillyarms](https://community.konduit.ai/u/sillyarms)
#### Post date: [December 30, 2020, 10:33am UTC](https://community.konduit.ai/t/how-to-convert-csv-to-the-input-of-1d-convolutional-layer/292/8 "2020-12-30T10:33:52Z")
> [@amirshamaei](#):
>
> to
I am dealing with a similar issue, and can’t get it working. Is there any chance you could post your fixed code? There are not a lot of examples online how to get this to work.