Error msg in LSTM RNN

DL4J
1

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)
…

2

@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:

3

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)