# Error msg in LSTM RNN

**URL:** <https://community.konduit.ai/t/error-msg-in-lstm-rnn/3186>\
**Category:** DL4J\
**Created:** [July 10, 2024, 9:33am UTC](https://community.konduit.ai/t/error-msg-in-lstm-rnn/3186 "2024-07-10T09:33:06Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![hrivnac](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/hrivnac/32/1297_2.png) [@hrivnac](https://community.konduit.ai/u/hrivnac)\
**Post date:** [July 10, 2024, 9:33am UTC](https://community.konduit.ai/t/error-msg-in-lstm-rnn/3186/1 "2024-07-10T09:33:06Z")

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When I try the example Conv1DUCISequenceClassification without modification, I get following error. Any idea what’s wrong ?

Exception in thread “main” java.lang.IllegalStateException: Invalid mask array: per-example masking should be a column vector, per output masking arrays should be the same shape as the output/labels arrays. Mask shape: [450, 60], output shape: [26100, 6](layer name: layer1, layer index: 1, layer type: OutputLayer)  
at org.deeplearning4j.nn.layers.BaseOutputLayer.applyMask(BaseOutputLayer.java:339)  
…

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<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:** [July 10, 2024, 11:58am UTC](https://community.konduit.ai/t/error-msg-in-lstm-rnn/3186/2 "2024-07-10T11:58:39Z")

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@hrivnac is this with a fresh clone? It explains that the shapes are not correct. Make sure to read up on masking and how time series data works:

> **[Recurrent Layers | Deeplearning4j](https://deeplearning4j.konduit.ai/deeplearning4j/reference/recurrent-layers)**
>
> Recurrent Neural Network (RNN) implementations in DL4J.

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<div class="post-metadata">

**Author:** ![hrivnac](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/hrivnac/32/1297_2.png) [@hrivnac](https://community.konduit.ai/u/hrivnac)\
**Post date:** [July 10, 2024, 4:50pm UTC](https://community.konduit.ai/t/error-msg-in-lstm-rnn/3186/3 "2024-07-10T16:50:22Z")

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Yes, it’s the fresh clone.

The example seems to address our problem (classification of time-curves), so I wanted start with running the example as it is. But it fails. The complete error is this:

o.n.l.f.Nd4jBackend - Loaded [CpuBackend] backend  
o.n.n.NativeOpsHolder - Number of threads used for linear algebra: 4  
o.n.l.c.n.CpuNDArrayFactory - Binary level Generic x86 optimization level AVX/AVX2  
o.n.n.Nd4jBlas - Number of threads used for OpenMP BLAS: 4  
o.n.l.a.o.e.DefaultOpExecutioner - Backend used: [CPU]; OS: [Linux]  
o.n.l.a.o.e.DefaultOpExecutioner - Cores: [4]; Memory: [7.7GB];  
o.n.l.a.o.e.DefaultOpExecutioner - Blas vendor: [OPENBLAS]  
o.n.l.c.n.CpuBackend - Backend build information:  
GCC: “7.5.0”  
STD version: 201103L  
DEFAULT\_ENGINE: samediff::ENGINE\_CPU  
HAVE\_FLATBUFFERS  
HAVE\_OPENBLAS  
o.d.n.m.MultiLayerNetwork - Starting MultiLayerNetwork with WorkspaceModes set to [training: ENABLED; inference: ENABLED], cacheMode set to [NONE]  
o.d.e.q.m.c.Conv1DUCISequenceClassification - Starting training…  
Exception in thread “main” java.lang.IllegalStateException: Invalid mask array: per-example masking should be a column vector, per output masking arrays should be the same shape as the output/labels arrays. Mask shape: [10, 60], output shape: [580, 6](layer name: layer1, layer index: 1, layer type: OutputLayer)  
at org.deeplearning4j.nn.layers.BaseOutputLayer.applyMask(BaseOutputLayer.java:339)  
at org.deeplearning4j.nn.layers.BaseLayer.preOutputWithPreNorm(BaseLayer.java:336)  
at org.deeplearning4j.nn.layers.BaseLayer.preOutput(BaseLayer.java:296)  
at org.deeplearning4j.nn.layers.BaseOutputLayer.preOutput2d(BaseOutputLayer.java:325)  
at org.deeplearning4j.nn.layers.BaseOutputLayer.backpropGradient(BaseOutputLayer.java:144)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.calcBackpropGradients(MultiLayerNetwork.java:1998)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:2813)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:2756)  
at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:174)  
at org.deeplearning4j.optimize.solvers.StochasticGradientDescent.optimize(StochasticGradientDescent.java:61)  
at org.deeplearning4j.optimize.Solver.optimize(Solver.java:52)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fitHelper(MultiLayerNetwork.java:1767)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:1688)  
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:1675)  
at org.deeplearning4j.examples.quickstart.modeling.convolution.Conv1DUCISequenceClassification.main(Conv1DUCISequenceClassification.java:173)