1. Call the load() method to import the model from the saved location:

File savedLocation = new File("model.zip");
 boolean saveUpdater = true;
 MultiLayerNetwork restored = MultiLayerNetwork.load(savedLocation, saveUpdater);

2. Add the required pom dependency to use the deeplearning4j-zoo module:

<dependency>
 <groupId>org.deeplearning4j</groupId>
 <artifactId>deeplearning4j-zoo</artifactId>
 <version>1.0.0-beta3</version>
 </dependency>

3. Add the fine-tuning configuration for MultiLayerNetwork using the TransferLearning API:

MultiLayerNetwork newModel = new TransferLearning.Builder(oldModel)
 .fineTuneConfiguration(fineTuneConf)
 .build();

4. Add the fine-tuning configuration for ComputationGraph using the TransferLearning API:

ComputationGraph newModel = new TransferLearning.GraphBuilder(oldModel).
 .fineTuneConfiguration(fineTuneConf)
 .build();

5. Configure the training session using TransferLearningHelper. TransferLearningHelper can be created in two ways:
   • Pass in the model object that was created using the transfer learning builder (step 2) with the frozen layers mentioned:

 TransferLearningHelper tHelper = new TransferLearningHelper(newModel);

1. • Create it directly from the imported model by specifying the frozen layers explicitly:

TransferLearningHelper tHelper = new TransferLearningHelper(oldModel, "layer1")

6. Featurize the train/test data using the featurize() method:

  while(iterator.hasNext()) {
         DataSet currentFeaturized = transferLearningHelper.featurize(iterator.next());
         saveToDisk(currentFeaturized); //save the featurized date to disk 
        }

7. Create train/test iterators by using ExistingMiniBatchDataSetIterator:

DataSetIterator existingTrainingData = new ExistingMiniBatchDataSetIterator(new File("trainFolder"),"churn-"+featureExtractorLayer+"-train-%d.bin");
 DataSetIterator existingTestData = new ExistingMiniBatchDataSetIterator(new File("testFolder"),"churn-"+featureExtractorLayer+"-test-%d.bin");

8. Start the training instance on top of the featurized data by calling fitFeaturized():

 transferLearningHelper.fitFeaturized(existingTrainingData);

9. Evaluate the model by calling evaluate() for unfrozen layers:

 transferLearningHelper.unfrozenMLN().evaluate(existingTestData);