Package com.github.neuralnetworks.training

Examples of com.github.neuralnetworks.training.TrainingInputData

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    OneStepTrainer<?> t = (OneStepTrainer<?>) event.getSource();
    NeuralNetwork n = t.getNeuralNetwork();

    if (n.getLayerCalculator() != null) {
        Set<Layer> calculatedLayers = new UniqueList<>();
        TrainingInputData input = null;
        OutputError outputError = t.getOutputError();
        outputError.reset();
        inputProvider.reset();

        while ((input = inputProvider.getNextInput()) != null) {
      calculatedLayers.clear();
      calculatedLayers.add(n.getInputLayer());
      ValuesProvider vp = mbe.getResults();
      if (vp == null) {
          vp = new ValuesProvider();
      }

      vp.addValues(n.getInputLayer(), input.getInput());
      n.getLayerCalculator().calculate(n, n.getOutputLayer(), calculatedLayers, vp);

      outputError.addItem(vp.getValues(n.getOutputLayer()), input.getTarget());
        }

        float e = outputError.getTotalNetworkError();
        if (e <= acceptanceError) {
      System.out.println("Stopping at error " + e + " (" + (e * 100) + "%) for " + mbe.getBatchCount() + " minibatches");
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  }
    }

    @Override
    public TrainingInputData getNextUnmodifiedInput() {
  TrainingInputData result = null;

  if (elementsOrder.size() == 0 && currentEpoch < epochs) {
      resetOrder();
      currentEpoch++;
  }
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      vp = TensorFactory.tensorProvider(n, 1, Environment.getInstance().getUseDataSharedMemory());
        }
        if (vp.get(outputError) == null) {
      vp.add(outputError, vp.get(n.getOutputLayer()).getDimensions());
        }
        TrainingInputData input = new TrainingInputDataImpl(vp.get(n.getInputLayer()), vp.get(outputError));

        Set<Layer> calculatedLayers = new UniqueList<>();
        for (int i = 0; i < inputProvider.getInputSize(); i++) {
      inputProvider.populateNext(input);
      calculatedLayers.clear();
      calculatedLayers.add(n.getInputLayer());

      n.getLayerCalculator().calculate(n, n.getOutputLayer(), calculatedLayers, vp);

      outputError.addItem(vp.get(n.getOutputLayer()), input.getTarget());
        }

        float e = outputError.getTotalNetworkError();
        if (e <= acceptanceError) {
      System.out.println("Stopping at error " + e + " (" + (e * 100) + "%) for " + mbe.getBatchCount() + " minibatches");
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Related Classes of com.github.neuralnetworks.training.TrainingInputData

  • com.github.neuralnetworks.samples.mnist.MnistInputProvider
  • com.github.neuralnetworks.training.events.EarlyStoppingListener
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