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GUI Test

WekaDeeplearning4j comes with an automated JUnit test suite, however, some bugs may only occur in the GUI version of WEKA; because of this, it's important to test a range of cases in the GUI manually before confirming the package is ready for release.

These scenarios can be tested either:

  • Manually, as per the instructions, or;
  • Automatically, by running the parenthesised .sh script from weka-run-test-scripts/. This folder also contains run_all_tests.sh, which will run all of these tests (you should still check the output to ensure parameters are applied correctly). Check out the README for more info.

Scenarios

Dl4jMlpClassifier

The following are run with randomly generated data (using the default Generate window)

  • (Dl4jMlpClassifier_random_default.sh) Run with all default parameters

The following are run with the mnist-minimal dataset loaded, with the ImageInstanceIterator using the following settings:

  • directory of images pointing to the mnist-minimal/ image folder
  • desired width = 224
  • desired height = 224
  • desired number of channels = 3

You may like to set number of epochs to something smaller and set Test options to Percentage split to run these quicker.

  • (Dl4jMlpClassifier_mnist_default.sh) Run model with all default parameters
  • (Dl4jMlpClassifier_mnist_extraLayer.sh) Run model with an added DenseLayer with nOut = 32
  • (Dl4jMlpClassifier_mnist_AlexNet.sh) Run model with Dl4jAlexNet as the zoo model
  • (Dl4jMlpClassifier_mnist_ResNet50.sh) Run model with Dl4jResNet50 as the zoo model
  • (Dl4jMlpClassifier_mnist_EffNetB2.sh) Run model with KerasEfficientNet as the zoo model, with EFFICIENTNET_B2 as the variation
    • Ensure that the model actually uses the variation, you should see a log message something like ...Using cached model at /home/rhys/.deeplearning4j/models/keras_efficientnet/KerasEfficientNetB2.zip...

Dl4jMlpFilter

The following are run with the mnist-minimal dataset loaded. You'll need to click Undo after each test to revert the instances.

  • (Dl4jMlpFilter_mnist_default.sh) Run with default filter settings (uses Dl4jResNet50 as the model)
  • (Dl4jMlpFilter_mnist_extraLayer.sh) Run with Dl4jResNet50 as zoo model, set Use default feature layer to false, and add res4a_branch2b to the feature extraction layers
    • Ensure that the logging output contains something like ...Getting features from layers: [res4a_branch2b, flatten_1] and that there are attributes from both feature layers (i.e., named res4a_branch2b and flatten_1)
  • (Dl4jMlpFilter_mnist_DenseNet.sh) Run with KerasDenseNet as the zoo model and Use default feature extraction layer set to True
  • (Dl4jMlpFilter_mnist_ResNet101v2.sh) Run with KerasResNet as the zoo model, variation set to RESNET101V2, and Use default feature extraction layer set to True
    • Ensure that RESNET101V2 is actually used as the variation. You should see a logging message something like ...Using cached model at /home/rhys/.deeplearning4j/models/keras_resnet/KerasResNet101V2.zip
  • (Dl4jMlpFilter_mnist_EffNetB1ExtraLayer.sh) Run with KerasEfficientNet as the zoo model, variation set to EFFICIENTNET_B1, Use default feature layer to False, and add block4c_expand_conv to the feature extraction layers.
    • Again, ensure the variation is properly set, and the resulting attributes contain features from both layers.

The following are run using the mnist_784 convolutional dataset (src/test/resources/nominal/mnist_784_train_minimal.arff)

  • (Dl4jMlpFilter_mnist_LeNet.sh) Run with Dl4jLeNet as the zoo model, Use default feature layer to True, and a default ConvolutionInstanceIterator as the instance iterator