java.lang.NullPointerException

JIRA | Eric Eckstrand | 1 year ago
  1. 0

    doing this (python): iris = h2o.import_file(h2o.locate("smalldata/iris/iris.csv")) s = iris.runif(seed=12345) train1 = iris[s >= 0.5] train2 = iris[s < 0.5] m1 = h2o.deeplearning(x=train1[0:4], y=train1[4], epochs=100) # update m1 with new training data m2 = h2o.deeplearning(x=train2[0:4], y=train2[4], epochs=200, checkpoint=m1.id) h2o logs: INFO: Resuming from checkpoint. 09-08 17:57:55.957 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_epochs': 100.0 -> 200.0 09-08 17:57:55.957 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_seed': -7534157001258414428 -> -606642242870538099 09-08 17:57:55.957 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_max_w2': 1000.0 -> Infinity 09-08 17:57:55.957 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_loss': CrossEntropy -> Automatic 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_replicate_training_data': false -> true 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: _replicate_training_data: Disabling replicate_training_data on 1 node. 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: _adaptive_rate: Using automatic learning rate. Ignoring the following input parameters: rate, rate_decay, rate_annealing, momentum_start, momentum_ramp, momentum_stable. 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: _max_w2: Automatically setting max_w2 to 1000 to keep (unbounded) Rectifier activation in check. 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: Adding 100.000 epochs from the checkpointed model. 09-08 17:57:55.961 172.16.2.57:54321 63536 FJ-0-1 INFO: Model category: Classification 09-08 17:57:55.961 172.16.2.57:54321 63536 FJ-0-1 INFO: Number of model parameters (weights/biases): 41,803 java.lang.NullPointerException at hex.deeplearning.DeepLearning$DeepLearningDriver.trainModel(DeepLearning.java:300) at hex.deeplearning.DeepLearning$DeepLearningDriver.buildModel(DeepLearning.java:248) at hex.deeplearning.DeepLearning$DeepLearningDriver.compute2(DeepLearning.java:171) at water.H2O$H2OCountedCompleter.compute(H2O.java:1005) at jsr166y.CountedCompleter.exec(CountedCompleter.java:429) at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263) at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974) at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477) at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104) barrier onExCompletion for hex.deeplearning.DeepLearning$DeepLearningDriver@4708993a java.lang.NullPointerException at hex.deeplearning.DeepLearning$DeepLearningDriver.trainModel(DeepLearning.java:300) at hex.deeplearning.DeepLearning$DeepLearningDriver.buildModel(DeepLearning.java:248) at hex.deeplearning.DeepLearning$DeepLearningDriver.compute2(DeepLearning.java:171) at water.H2O$H2OCountedCompleter.compute(H2O.java:1005) at jsr166y.CountedCompleter.exec(CountedCompleter.java:429) at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263) at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974) at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477) at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104) *the above fails, even if the same training frame is used model-building phases **this workflow works from the R interface - looking at the json request sent by the R/Python interfaces, the only notable difference is: in R: "_ignored_columns":[""] in Python: "_ignored_columns":null in R: INFO: Dropping ignored columns: []

    JIRA | 1 year ago | Eric Eckstrand
    java.lang.NullPointerException
  2. 0

    doing this (python): iris = h2o.import_file(h2o.locate("smalldata/iris/iris.csv")) s = iris.runif(seed=12345) train1 = iris[s >= 0.5] train2 = iris[s < 0.5] m1 = h2o.deeplearning(x=train1[0:4], y=train1[4], epochs=100) # update m1 with new training data m2 = h2o.deeplearning(x=train2[0:4], y=train2[4], epochs=200, checkpoint=m1.id) h2o logs: INFO: Resuming from checkpoint. 09-08 17:57:55.957 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_epochs': 100.0 -> 200.0 09-08 17:57:55.957 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_seed': -7534157001258414428 -> -606642242870538099 09-08 17:57:55.957 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_max_w2': 1000.0 -> Infinity 09-08 17:57:55.957 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_loss': CrossEntropy -> Automatic 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: Applying user-requested modification of '_replicate_training_data': false -> true 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: _replicate_training_data: Disabling replicate_training_data on 1 node. 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: _adaptive_rate: Using automatic learning rate. Ignoring the following input parameters: rate, rate_decay, rate_annealing, momentum_start, momentum_ramp, momentum_stable. 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: _max_w2: Automatically setting max_w2 to 1000 to keep (unbounded) Rectifier activation in check. 09-08 17:57:55.958 172.16.2.57:54321 63536 FJ-0-1 INFO: Adding 100.000 epochs from the checkpointed model. 09-08 17:57:55.961 172.16.2.57:54321 63536 FJ-0-1 INFO: Model category: Classification 09-08 17:57:55.961 172.16.2.57:54321 63536 FJ-0-1 INFO: Number of model parameters (weights/biases): 41,803 java.lang.NullPointerException at hex.deeplearning.DeepLearning$DeepLearningDriver.trainModel(DeepLearning.java:300) at hex.deeplearning.DeepLearning$DeepLearningDriver.buildModel(DeepLearning.java:248) at hex.deeplearning.DeepLearning$DeepLearningDriver.compute2(DeepLearning.java:171) at water.H2O$H2OCountedCompleter.compute(H2O.java:1005) at jsr166y.CountedCompleter.exec(CountedCompleter.java:429) at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263) at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974) at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477) at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104) barrier onExCompletion for hex.deeplearning.DeepLearning$DeepLearningDriver@4708993a java.lang.NullPointerException at hex.deeplearning.DeepLearning$DeepLearningDriver.trainModel(DeepLearning.java:300) at hex.deeplearning.DeepLearning$DeepLearningDriver.buildModel(DeepLearning.java:248) at hex.deeplearning.DeepLearning$DeepLearningDriver.compute2(DeepLearning.java:171) at water.H2O$H2OCountedCompleter.compute(H2O.java:1005) at jsr166y.CountedCompleter.exec(CountedCompleter.java:429) at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263) at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974) at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477) at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104) *the above fails, even if the same training frame is used model-building phases **this workflow works from the R interface - looking at the json request sent by the R/Python interfaces, the only notable difference is: in R: "_ignored_columns":[""] in Python: "_ignored_columns":null in R: INFO: Dropping ignored columns: []

    JIRA | 1 year ago | Eric Eckstrand
    java.lang.NullPointerException
  3. 0

    Android: Saving Map State in Google map

    Stack Overflow | 11 months ago | Junie Negentien
    java.lang.RuntimeException: Unable to resume activity {com.ourThesis.junieNegentien2015/com.ourThesis.junieNegentien2015.MainActivity}: java.lang.NullPointerException
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    Root Cause Analysis

    1. java.lang.NullPointerException

      No message provided

      at hex.deeplearning.DeepLearning$DeepLearningDriver.trainModel()
    2. hex.deeplearning
      DeepLearning$DeepLearningDriver.compute2
      1. hex.deeplearning.DeepLearning$DeepLearningDriver.trainModel(DeepLearning.java:300)
      2. hex.deeplearning.DeepLearning$DeepLearningDriver.buildModel(DeepLearning.java:248)
      3. hex.deeplearning.DeepLearning$DeepLearningDriver.compute2(DeepLearning.java:171)
      3 frames
    3. water
      H2O$H2OCountedCompleter.compute
      1. water.H2O$H2OCountedCompleter.compute(H2O.java:1005)
      1 frame
    4. jsr166y
      ForkJoinWorkerThread.run
      1. jsr166y.CountedCompleter.exec(CountedCompleter.java:429)
      2. jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
      3. jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
      4. jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
      5. jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)
      5 frames