# RuntimeException in Bidirectional trainning

**URL:** <https://community.konduit.ai/t/runtimeexception-in-bidirectional-trainning/1005>\
**Category:** DL4J\
**Created:** [November 22, 2020, 12:33am UTC](https://community.konduit.ai/t/runtimeexception-in-bidirectional-trainning/1005 "2020-11-22T00:33:53Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![SidneyLann](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/sidneylann/32/367_2.png) [@SidneyLann](https://community.konduit.ai/u/SidneyLann)\
**Post date:** [November 22, 2020, 12:33am UTC](https://community.konduit.ai/t/runtimeexception-in-bidirectional-trainning/1005/1 "2020-11-22T00:33:53Z")

</div>

Bidirectional bidirectional = new Bidirectional.Builder().mode(Bidirectional.Mode.ADD).rnnLayer(new LSTM.Builder().nOut(250).l2(0.003495283883324279).dropOut(0.6816483486423628).activation(Activation.TANH)  
.updater(new Adam(new StepSchedule(ScheduleType.EPOCH, learningRate, decayRate, step))).build()).build();

Can Bidirectional be used like this way? RuntimeException thrown when trainning:

[main] WARN org.deeplearning4j.nn.layers.recurrent.LSTMHelpers - MKL/CuDNN execution failed - falling back on built-in implementation  
java.lang.RuntimeException: cuDNN status = 8: CUDNN\_STATUS\_EXECUTION\_FAILED  
at org.deeplearning4j.cuda.BaseCudnnHelper.checkCudnn(BaseCudnnHelper.java:48)  
at org.deeplearning4j.cuda.recurrent.CudnnLSTMHelper.activate(CudnnLSTMHelper.java:469)  
at org.deeplearning4j.nn.layers.recurrent.LSTMHelpers.activateHelper(LSTMHelpers.java:205)  
at org.deeplearning4j.nn.layers.recurrent.LSTM.activateHelper(LSTM.java:177)  
at org.deeplearning4j.nn.layers.recurrent.LSTM.activate(LSTM.java:147)  
at org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer.activate(BidirectionalLayer.java:201)  
at org.deeplearning4j.nn.graph.vertex.impl.LayerVertex.doForward(LayerVertex.java:111)  
at org.deeplearning4j.nn.graph.ComputationGraph.ffToLayerActivationsInWS(ComputationGraph.java:2136)  
at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1373)  
at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1342)  
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.graph.ComputationGraph.fitHelper(ComputationGraph.java:1166)  
at org.deeplearning4j.nn.graph.ComputationGraph.fit(ComputationGraph.java:1116)  
at org.deeplearning4j.nn.graph.ComputationGraph.fit(ComputationGraph.java:1083)

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<div class="post-metadata">

**Author:** ![SidneyLann](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/sidneylann/32/367_2.png) [@SidneyLann](https://community.konduit.ai/u/SidneyLann)\
**Post date:** [November 22, 2020, 12:37am UTC](https://community.konduit.ai/t/runtimeexception-in-bidirectional-trainning/1005/2 "2020-11-22T00:37:32Z")

</div>

Can train on CPU successfully but GPU can’t do it.

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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:** [November 22, 2020, 1:25am UTC](https://community.konduit.ai/t/runtimeexception-in-bidirectional-trainning/1005/3 "2020-11-22T01:25:53Z")

</div>

@SidneyLann you likely could without cudnn. Could you give me something end to end (eg: not just the snippet) I could run to verify this behavior? Thanks!

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<div class="post-metadata">

**Author:** ![SidneyLann](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/sidneylann/32/367_2.png) [@SidneyLann](https://community.konduit.ai/u/SidneyLann)\
**Post date:** [November 22, 2020, 3:09am UTC](https://community.konduit.ai/t/runtimeexception-in-bidirectional-trainning/1005/4 "2020-11-22T03:09:51Z")

</div>

```
Bidirectional bidirectional = new Bidirectional.Builder().mode(Bidirectional.Mode.ADD).rnnLayer(new LSTM.Builder().nOut(250).l2(0.003495283883324279).dropOut(0.6816483486423628).activation(Activation.TANH)
        .updater(new Adam(new StepSchedule(ScheduleType.EPOCH, learningRate, decayRate, step))).build()).build();
    
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).weightInit(WeightInit.XAVIER).graphBuilder()
        .setInputTypes(InputType.recurrent(NerUtil.MAX_SENTENCE_LENGTH * 6)).addInputs("input").addLayer("lstm", bidirectional, "input")
        .addLayer("output",
            new RnnOutputLayer.Builder(new LossMCXENT(Nd4j.create(NerUtil.labelWeight1d()))).nOut(NerUtil.getLabelNum()).l2(0.006332749296897214).dropOut(0.45063086836326066)
                .activation(Activation.SOFTMAX).updater(new Adam(new StepSchedule(ScheduleType.EPOCH, learningRate, decayRate, step))).build(),
            "lstm")
        .setOutputs("output").backpropType(BackpropType.Standard).build();

    net = new ComputationGraph(conf);
    net.init();

```

I use BERT output as my input, just one hidden layer in my model to do NER job.