# A fatal error has been detected by the Java Runtime Environment **URL:** \ **Category:** DL4J\ **Created:** [October 21, 2024, 9:47am UTC](https://community.konduit.ai/t/a-fatal-error-has-been-detected-by-the-java-runtime-environment/3259 "2024-10-21T09:47:42Z")\ **Posts on this page:** 6\ **Page:** 1
**Author:** ![MRV](https://avatars.discourse-cdn.com/v4/letter/m/848f3c/32.png) [@MRV](https://community.konduit.ai/u/MRV)\ **Post date:** [October 21, 2024, 9:47am UTC](https://community.konduit.ai/t/a-fatal-error-has-been-detected-by-the-java-runtime-environment/3259/1 "2024-10-21T09:47:42Z")
I am trying to load a .h5 model (tf.keras) in scala using deeplearning4j. I’m using SBT with following dependencies : ```auto libraryDependencies ++= Seq( "org.deeplearning4j" % "deeplearning4j-core" % "1.0.0-M2.1", "org.nd4j" % "nd4j-native" % "1.0.0-M2.1", ) ``` When I try to load models containing LSTM or RNN layers, the following error is raised: ```auto # # A fatal error has been detected by the Java Runtime Environment: # # SIGSEGV (0xb) at pc=0x000072104b173475, pid=76220, tid=76222 # # JRE version: OpenJDK Runtime Environment (11.0.24+8) (build 11.0.24+8-post-Ubuntu-1ubuntu322.04) # Java VM: OpenJDK 64-Bit Server VM (11.0.24+8-post-Ubuntu-1ubuntu322.04, mixed mode, sharing, tiered, compressed oops, g1 gc, linux-amd64) # Problematic frame: # C [libnd4jcpu.so+0x1573475] sd::NDArray::getOffset(long long) const+0x25 # # Core dump will be written. Default location: Core dumps may be processed with "/usr/share/apport/apport -p%p -s%s -c%c -d%d -P%P -u%u -g%g -- %E" (or dumping to /home/userr/IdeaProjects/Tor_scala/core.76220) # # An error report file with more information is saved as: # /home/userr/IdeaProjects/Tor_scala/hs_err_pid76220.log Process finished with exit code 130 (interrupted by signal 2:SIGINT) ``` However, models containing layers other than LSTM/RNN doesnt raise any errors. I tried loading models containing Dense layers and CNN. They worked perfectly. This issue was seen only when I try with LSTM/RNN layers. I tried with other versions - M2, M1.1 and M1 The same issue was seen with M2 version too. But when I used M1.1 and M1, I got the following error for all models regardless of the type of layers used in the model: ```auto Debug: Deallocating org.bytedeco.hdf5.Group[address=0x71ce3568b530,position=0,limit=1,capacity=1,deallocator=org.bytedeco.javacpp.Pointer$NativeDeallocator[ownerAddress=0x71ce3568b530,deallocatorAddress=0x71ce026a7dc0]] [error] java.lang.NullPointerException: Cannot invoke "org.deeplearning4j.nn.modelimport.keras.KerasLayer.getNumParams()" because the return value of "java.util.Map.get(Object)" is null [error] at org.deeplearning4j.nn.modelimport.keras.utils.KerasModelUtils.importWeights(KerasModelUtils.java:235) [error] at org.deeplearning4j.nn.modelimport.keras.KerasModel.(KerasModel.java:190) [error] at org.deeplearning4j.nn.modelimport.keras.KerasModel.(KerasModel.java:99) [error] at org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder.buildModel(KerasModelBuilder.java:311) [error] at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasModelAndWeights(KerasModelImport.java:150) [error] at tesst$.main(tesst.scala:13) [error] at tesst.main(tesst.scala) [error] at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) [error] at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77) [error] at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) [error] at java.base/java.lang.reflect.Method.invoke(Method.java:569) [error] stack trace is suppressed; run last Compile / run for the full output [error] (Compile / run) java.lang.NullPointerException: Cannot invoke "org.deeplearning4j.nn.modelimport.keras.KerasLayer.getNumParams()" because the return value of "java.util.Map.get(Object)" is null [error] Total time: 3 s, completed 21-Oct-2024, 2:26:27 pm ``` My model was compiled and trained in Python and saved as .h5. Following is my scala code for loading the model: ```auto import org.deeplearning4j.nn.modelimport.keras.KerasModelImport import org.deeplearning4j.nn.graph.ComputationGraph object tesst { def main(args: Array[String]): Unit = { // Path to your .h5 file System.setProperty("org.bytedeco.javacpp.logger.debug", "true") System.setProperty("org.bytedeco.javacpp.logger", "sl4j") val modelPath="/home/userr/Models/lstm_actual.h5" val model: ComputationGraph = KerasModelImport.importKerasModelAndWeights(modelPath,false) println(model.summary()) } } ``` I’m new to DL4J. Can someone guide me and let me know why it happens? ---
**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:** [October 21, 2024, 11:28am UTC](https://community.konduit.ai/t/a-fatal-error-has-been-detected-by-the-java-runtime-environment/3259/2 "2024-10-21T11:28:22Z")
@MRV could you post the configuration and keras version? ---
**Author:** ![MRV](https://avatars.discourse-cdn.com/v4/letter/m/848f3c/32.png) [@MRV](https://community.konduit.ai/u/MRV)\ **Post date:** [October 22, 2024, 5:38am UTC](https://community.konduit.ai/t/a-fatal-error-has-been-detected-by-the-java-runtime-environment/3259/3 "2024-10-22T05:38:40Z")
@agibsonccc Model Type - Functional print(model. **class** ) \>\> \ TensorFlow version: 2.15.0 Keras version: 2.15.0 Keras backend: tensorflow ---
**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:** [October 24, 2024, 3:36am UTC](https://community.konduit.ai/t/a-fatal-error-has-been-detected-by-the-java-runtime-environment/3259/4 "2024-10-24T03:36:26Z")
@MRV that’s not what I needed. Can you dump the json of the file you’re importing or use model.summary()? ---
**Author:** ![MRV](https://avatars.discourse-cdn.com/v4/letter/m/848f3c/32.png) [@MRV](https://community.konduit.ai/u/MRV)\ **Post date:** [October 25, 2024, 4:42am UTC](https://community.konduit.ai/t/a-fatal-error-has-been-detected-by-the-java-runtime-environment/3259/5 "2024-10-25T04:42:52Z")
@agibsonccc The details of the model is given below: Model configuration: {‘name’: ‘model\_6’, ‘trainable’: True, ‘layers’: [{‘module’: ‘keras.layers’, ‘class\_name’: ‘InputLayer’, ‘config’: {‘batch\_input\_shape’: (None, 43), ‘dtype’: ‘int32’, ‘sparse’: False, ‘ragged’: False, ‘name’: ‘main\_input1’}, ‘registered\_name’: None, ‘name’: ‘main\_input1’, ‘inbound\_nodes’: }, {‘module’: ‘keras.layers’, ‘class\_name’: ‘Embedding’, ‘config’: {‘name’: ‘embedding\_10’, ‘trainable’: False, ‘dtype’: ‘float32’, ‘batch\_input\_shape’: (None, 43), ‘input\_dim’: 10000, ‘output\_dim’: 50, ‘embeddings\_initializer’: {‘module’: ‘keras.initializers’, ‘class\_name’: ‘RandomUniform’, ‘config’: {‘minval’: -0.05, ‘maxval’: 0.05, ‘seed’: None}, ‘registered\_name’: None}, ‘embeddings\_regularizer’: None, ‘activity\_regularizer’: None, ‘embeddings\_constraint’: None, ‘mask\_zero’: False, ‘input\_length’: 43}, ‘registered\_name’: None, ‘build\_config’: {‘input\_shape’: (None, 43)}, ‘name’: ‘embedding\_10’, ‘inbound\_nodes’: [[[‘main\_input1’, 0, 0, {}]]]}, {‘module’: ‘keras.layers’, ‘class\_name’: ‘InputLayer’, ‘config’: {‘batch\_input\_shape’: (None, 9), ‘dtype’: ‘float32’, ‘sparse’: False, ‘ragged’: False, ‘name’: ‘main\_input2’}, ‘registered\_name’: None, ‘name’: ‘main\_input2’, ‘inbound\_nodes’: }, {‘module’: ‘keras.layers’, ‘class\_name’: ‘LSTM’, ‘config’: {‘name’: ‘lstm\_2’, ‘trainable’: True, ‘dtype’: ‘float32’, ‘return\_sequences’: False, ‘return\_state’: False, ‘go\_backwards’: False, ‘stateful’: False, ‘unroll’: False, ‘time\_major’: False, ‘units’: 64, ‘activation’: ‘tanh’, ‘recurrent\_activation’: ‘sigmoid’, ‘use\_bias’: True, ‘kernel\_initializer’: {‘module’: ‘keras.initializers’, ‘class\_name’: ‘GlorotUniform’, ‘config’: {‘seed’: None}, ‘registered\_name’: None}, ‘recurrent\_initializer’: {‘module’: ‘keras.initializers’, ‘class\_name’: ‘Orthogonal’, ‘config’: {‘gain’: 1.0, ‘seed’: None}, ‘registered\_name’: None}, ‘bias\_initializer’: {‘module’: ‘keras.initializers’, ‘class\_name’: ‘Zeros’, ‘config’: {}, ‘registered\_name’: None}, ‘unit\_forget\_bias’: True, ‘kernel\_regularizer’: None, ‘recurrent\_regularizer’: None, ‘bias\_regularizer’: None, ‘activity\_regularizer’: None, ‘kernel\_constraint’: None, ‘recurrent\_constraint’: None, ‘bias\_constraint’: None, ‘dropout’: 0.0, ‘recurrent\_dropout’: 0.0, ‘implementation’: 2}, ‘registered\_name’: None, ‘build\_config’: {‘input\_shape’: (None, 43, 50)}, ‘name’: ‘lstm\_2’, ‘inbound\_nodes’: [[[‘embedding\_10’, 0, 0, {}]]]}, {‘module’: ‘keras.layers’, ‘class\_name’: ‘Dense’, ‘config’: {‘name’: ‘dense\_14’, ‘trainable’: True, ‘dtype’: ‘float32’, ‘units’: 64, ‘activation’: ‘relu’, ‘use\_bias’: True, ‘kernel\_initializer’: {‘module’: ‘keras.initializers’, ‘class\_name’: ‘GlorotUniform’, ‘config’: {‘seed’: None}, ‘registered\_name’: None}, ‘bias\_initializer’: {‘module’: ‘keras.initializers’, ‘class\_name’: ‘Zeros’, ‘config’: {}, ‘registered\_name’: None}, ‘kernel\_regularizer’: None, ‘bias\_regularizer’: None, ‘activity\_regularizer’: None, ‘kernel\_constraint’: None, ‘bias\_constraint’: None}, ‘registered\_name’: None, ‘build\_config’: {‘input\_shape’: (None, 9)}, ‘name’: ‘dense\_14’, ‘inbound\_nodes’: [[[‘main\_input2’, 0, 0, {}]]]}, {‘module’: ‘keras.layers’, ‘class\_name’: ‘Dropout’, ‘config’: {‘name’: ‘dropout\_16’, ‘trainable’: True, ‘dtype’: ‘float32’, ‘rate’: 0.5, ‘noise\_shape’: None, ‘seed’: None}, ‘registered\_name’: None, ‘build\_config’: {‘input\_shape’: (None, 64)}, ‘name’: ‘dropout\_16’, ‘inbound\_nodes’: [[[‘lstm\_2’, 0, 0, {}]]]}, {‘module’: ‘keras.layers’, ‘class\_name’: ‘Dropout’, ‘config’: {‘name’: ‘dropout\_17’, ‘trainable’: True, ‘dtype’: ‘float32’, ‘rate’: 0.1, ‘noise\_shape’: None, ‘seed’: None}, ‘registered\_name’: None, ‘build\_config’: {‘input\_shape’: (None, 64)}, ‘name’: ‘dropout\_17’, ‘inbound\_nodes’: [[[‘dense\_14’, 0, 0, {}]]]}, {‘module’: ‘keras.layers’, ‘class\_name’: ‘Concatenate’, ‘config’: {‘name’: ‘concatenate\_8’, ‘trainable’: True, ‘dtype’: ‘float32’, ‘axis’: -1}, ‘registered\_name’: None, ‘build\_config’: {‘input\_shape’: [(None, 64), (None, 64)]}, ‘name’: ‘concatenate\_8’, ‘inbound\_nodes’: [[[‘dropout\_16’, 0, 0, {}], [‘dropout\_17’, 0, 0, {}]]]}, {‘module’: ‘keras.layers’, ‘class\_name’: ‘Dense’, ‘config’: {‘name’: ‘dense\_15’, ‘trainable’: True, ‘dtype’: ‘float32’, ‘units’: 1, ‘activation’: ‘sigmoid’, ‘use\_bias’: True, ‘kernel\_initializer’: {‘module’: ‘keras.initializers’, ‘class\_name’: ‘GlorotUniform’, ‘config’: {‘seed’: None}, ‘registered\_name’: None}, ‘bias\_initializer’: {‘module’: ‘keras.initializers’, ‘class\_name’: ‘Zeros’, ‘config’: {}, ‘registered\_name’: None}, ‘kernel\_regularizer’: None, ‘bias\_regularizer’: None, ‘activity\_regularizer’: None, ‘kernel\_constraint’: None, ‘bias\_constraint’: None}, ‘registered\_name’: None, ‘build\_config’: {‘input\_shape’: (None, 128)}, ‘name’: ‘dense\_15’, ‘inbound\_nodes’: [[[‘concatenate\_8’, 0, 0, {}]]]}], ‘input\_layers’: [[‘main\_input1’, 0, 0], [‘main\_input2’, 0, 0]], ‘output\_layers’: [[‘dense\_15’, 0, 0]]} ```auto Model architecture: Model: "model_6" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== main_input1 (InputLayer) [(None, 43)] 0 [] embedding_10 (Embedding) (None, 43, 50) 500000 ['main_input1[0][0]'] main_input2 (InputLayer) [(None, 9)] 0 [] lstm_2 (LSTM) (None, 64) 29440 ['embedding_10[0][0]'] dense_14 (Dense) (None, 64) 640 ['main_input2[0][0]'] dropout_16 (Dropout) (None, 64) 0 ['lstm_2[0][0]'] dropout_17 (Dropout) (None, 64) 0 ['dense_14[0][0]'] concatenate_8 (Concatenate (None, 128) 0 ['dropout_16[0][0]', ) 'dropout_17[0][0]'] dense_15 (Dense) (None, 1) 129 ['concatenate_8[0][0]'] ================================================================================================== Total params: 530209 (2.02 MB) Trainable params: 30209 (118.00 KB) Non-trainable params: 500000 (1.91 MB) __________________________________________________________________________________________________ ``` Model type: Functional ---
**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:** [October 29, 2024, 12:40pm UTC](https://community.konduit.ai/t/a-fatal-error-has-been-detected-by-the-java-runtime-environment/3259/6 "2024-10-29T12:40:45Z")
@MRV apologies please ping me if I don’t get back to you. looking at the model again it might be an issue with functional models specifically. Could you try converting this to a sequential and see if you still have that issue? It’s still hard to tell why a nullpointer is being thrown there. It looks like a native crash but could also be a java one. There’s a mixed cause there…