# CNN Multi feature Exception

**URL:** <https://community.konduit.ai/t/cnn-multi-feature-exception/1237>\
**Category:** DL4J\
**Created:** [March 10, 2021, 10:42am UTC](https://community.konduit.ai/t/cnn-multi-feature-exception/1237 "2021-03-10T10:42:30Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![anonhm09](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/anonhm09/32/478_2.png) [@anonhm09](https://community.konduit.ai/u/anonhm09)\
**Post date:** [March 10, 2021, 10:42am UTC](https://community.konduit.ai/t/cnn-multi-feature-exception/1237/1 "2021-03-10T10:42:30Z")

</div>

Hi,  
I am getting below exception :  
_Invalid input, does not match configuration: expected [minibatch, numChannels=10, inputHeight=8, inputWidth=8] but got input array of shape [43, 10, 8, 3]_

- at org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor.preProcess(CnnToFeedForwardPreProcessor.java:112)\*

---

<div class="post-metadata">

**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\
**Post date:** [March 10, 2021, 10:58am UTC](https://community.konduit.ai/t/cnn-multi-feature-exception/1237/2 "2021-03-10T10:58:32Z")

</div>

@anonhm09 post your configuration please. Ideally it’s something we can run standalone ourselves. That would include a sample input as well.

---

<div class="post-metadata">

**Author:** ![anonhm09](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/anonhm09/32/478_2.png) [@anonhm09](https://community.konduit.ai/u/anonhm09)\
**Post date:** [March 10, 2021, 11:43am UTC](https://community.konduit.ai/t/cnn-multi-feature-exception/1237/3 "2021-03-10T11:43:57Z")

</div>

@agibsonccc

> **[GitHub - anonhm09/dl4jdataset: Info](https://github.com/anonhm09/dl4jdataset/)**
>
> Info. Contribute to anonhm09/dl4jdataset development by creating an account on GitHub.

dataset can be download from here.

DataSet dataSet = new DataSet() ;  
dataSet.load(new File());

Features Rank: 4, DataType: DOUBLE, Offset: 0, Order: c, Shape: [43,1,7,2], Stride: [14,14,2,1]  
Labels Rank: 3, DataType: DOUBLE, Offset: 0, Order: c, Shape: [43,1,1], Stride: [1,1,1]

Here Model conf:

```
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
			.trainingWorkspaceMode(WorkspaceMode.ENABLED).inferenceWorkspaceMode(WorkspaceMode.ENABLED)
			.seed(RANDOM_SEED)
			.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
		    .updater(new RmsProp.Builder().learningRate(.001).rmsDecay(.001).build())
			.layer(0, new ConvolutionLayer.Builder(2,2).nIn(1).nOut(10).padding(1,1).stride(1,1).activation(Activation.TANH).build())
		    .layer(1, new SubsamplingLayer.Builder(PoolingType.MAX).kernelSize(1,1) .stride(1,1).build())
		 	.layer(2, new DenseLayer.Builder().nOut(40).activation(Activation.TANH).build())
		 	.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MEAN_ABSOLUTE_ERROR).nOut(1).activation(Activation.TANH)
			.setInputType(InputType.convolutional(7, 7,1))
			.build();
			
	MultiLayerNetwork model = new MultiLayerNetwork(conf);
	model.setInputMiniBatchSize(10);
	model.init();

```

training sample input dataSet(github file) :  
===========INPUT===================  
[[[[12.2289, 1.0000],  
[13.4411, 2.0000],  
[10.6273, 3.0000],  
[10.4183, 4.0000],  
[10.9446, 5.0000],  
[10.3468, 6.0000],  
[7.5371, 7.0000]]]]  
=================OUTPUT==================  
[[[7.3623]]]

total 43 is same as above.

---

<div class="post-metadata">

**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\
**Post date:** [March 11, 2021, 12:21pm UTC](https://community.konduit.ai/t/cnn-multi-feature-exception/1237/4 "2021-03-11T12:21:12Z")

</div>

> [@anonhm09](#):
>
> `.setInputType(InputType.convolutional(7, 7,1))`

@anonhm09 sorry just had a chance to look at this.  
For the future: please just give me an example I can run. Your code was incomplete (it didn’t even compile)  
I was able to piece together what you wanted to do. The input data set is in the format NCHW:  
Number of examples, Number of Channels, Height, Width

Your input dataset was a height of 7 and a width of 2.

This will work:

```auto
 ConvolutionLayer build = new ConvolutionLayer.Builder(2, 2).nOut(10).padding(1, 1).stride(1, 1)
                .activation(Activation.TANH).build();
        long RANDOM_SEED = 12345;
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .updater(new RmsProp.Builder().learningRate(.001).rmsDecay(.001).build())
                .list(build,
                        new SubsamplingLayer.Builder(PoolingType.MAX).kernelSize(1,1) .stride(1,1).build(),
                        new DenseLayer.Builder().nOut(40).activation(Activation.TANH).build(),
                        new OutputLayer.Builder(LossFunctions.LossFunction.MEAN_ABSOLUTE_ERROR).nOut(1).activation(Activation.TANH).build())

                .setInputType(InputType.convolutional(7, 2,1))
                .build();

        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();

        DataSet dataSet = new DataSet();
        dataSet.load(new File("abc.bin"));
        INDArray features = dataSet.getFeatures();
        model.output(features);

```

Also, not sure what version you were using. Please try to stick to the examples if you don’t know the API very well. That has best practices. You can find those here:

> **[GitHub - deeplearning4j/deeplearning4j-examples: Deeplearning4j Examples...](https://github.com/deeplearning4j/deeplearning4j-examples)**
>
> Deeplearning4j Examples (DL4J, DL4J Spark, DataVec) - GitHub - deeplearning4j/deeplearning4j-examples: Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)