DKPro TC Deep Learning

Deep learning experiments with DKPro TC

At the moment, three deep learning experiments are supported by DKPro TC:

  • Deeplearning 4j (
  • DyNet (
  • Keras (

DyNet and Keras are used via Python, using these two frameworks in DKPro TC requires that:

  • Python is locally installed
  • the deep learning framework with all dependencies are locally installed

Deeplearning 4j is written in Java and requires no additional installation effort.

Python-based Deep Learning Experiments in DKPro TC

Python-based frameworks are not as straight-forward to integrate as Java-based frameworks. We discuss subsequently how using Python-based frameworks in DKPro TC and how the interfacing between Java/Python works. The code snipped below shows a setup to configure a Python-based DKPro TC deep learning experiment. The biggest difference to a shallow learning experiment is the wiring of the ParameterSpace, which uses a few more additional dimensions.

CollectionReaderDescription trainReader = createReaderTrain(...)

CollectionReaderDescription testReader = createReaderTest(...)		
Map<String, Object> dimReaders = new HashMap<String, Object>();
dimReaders.put(DIM_READER_TRAIN, trainReader);
dimReaders.put(DIM_READER_TEST, testReader);
ParameterSpace pSpace = new ParameterSpace(
//same as for shallow 
Dimension.createBundle("readers", dimReaders), 
Dimension.create(DIM_FEATURE_MODE, Constants.FM_SEQUENCE),
Dimension.create(DIM_LEARNING_MODE, Constants.LM_SINGLE_LABEL), 
Additional deep learning framework specific dimensions
// absolute path to the python installation for which the deep learning framework is installed
Dimension.create(DIM_PYTHON_INSTALLATION, python3),
// seed value that is passed to the deep learning 
Dimension.create(DIM_SEED_VALUE, 12345),
// the working memory that shall be used, 
// i.e. requries that the Python-based framework supports memory limits
Dimension.create(DIM_RAM_WORKING_MEMORY, 5000), 
// file path to the word embeddings file that shall be used in the experiment        
Dimension.create(DIM_PRETRAINED_EMBEDDINGS, embedding),
// automatically translates all words in the input text into integer values and stores a mapping
Dimension.create(DIM_VECTORIZE_TO_INTEGER, true), 
// file path to a code snipped,
// which defines the actual Python-framework-specific deep learning code
Dimension.create(DIM_USER_CODE, dyNetUserCode)

/* Experiment instantiation, note that deep learning experiments use `DeepLearningExperimentTrainTest` 
   while shallow learning experiments use `ExperimentTrainTest` */
DeepLearningExperimentTrainTest experiment = new DeepLearningExperimentTrainTest("Experiment", DynetAdapter.class);

When the experiment is executed, the vectorization into integer is automatically performed on the training and testing data, the word embeddings are pruned to contain only occuring vocabulary, and are all passed to the code-snipped provided as file path in the dimension DIM_USER_CODE.

The receiving Python code has then eventually to take care of loading the provided data files into the data format the framework expects.

Results of an experiment

The results are written to the folder provided as DKPRO_HOME directory. The subfolder contain all output written by an experiment, and not just the final results. The folder with the results is the Evaluation-* folder. The other folders are probably not of importance for using DKPRo TC, but we explain their content yet briefly. For a train-test experiment, the following folders are created:

  • InitTaskDeep-Train-ExperimentName-*
  • InitTaskDeep-Test-ExperimentName-*
  • EmbeddingTask-ExperimentName-*
  • VectorizationTask-Train-ExperimentName-*
  • VectorizationTask-Test-ExperimentName-*
  • DKProTcShallowTestTask-ExperimentName-*
  • <MachineLearningAdapter>-ExperimentName-*
  • Evaluation-ExperimentName-*

The InitTaskDeep folders contain the provided training and testing data converted into an internal data format. EmbeddingTask takes care of pruning the provied embedding (if one was provided) or initializes missing words with a random vector. This step does nothing if no embedding is provided. VectorizationTask transforms the training and testing data into a flat file format, which is provied in <MachineLearningAdapter> to the deep learning code. The results per instance and some more low-level information can be found in the <MachineLearningAdapter> folder.