package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.split.*;
import com.intel.daal.algorithms.neural_networks.layers.ForwardResultId;
import com.intel.daal.algorithms.neural_networks.layers.ForwardInputId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardResultId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardInputId;
import com.intel.daal.data_management.data.Tensor;
import com.intel.daal.data_management.data.HomogenTensor;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class SplitLayerDenseBatch {
private static final String datasetFileName = "../data/batch/layer.csv";
private static DaalContext context = new DaalContext();
private static final long nOutputs = 3;
private static final long nInputs = 3;
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
SplitForwardBatch forwardLayer = new SplitForwardBatch(context, Float.class, SplitMethod.defaultDense);
forwardLayer.parameter.setNOutputs(nOutputs);
forwardLayer.parameter.setNInputs(nInputs);
forwardLayer.input.set(ForwardInputId.data, data);
Service.printTensor("Split layer input (first 5 rows):", data, 5, 0);
SplitForwardResult forwardResult = forwardLayer.compute();
for (int i = 0; i < (int)nOutputs; i++) {
Service.printTensor("Forward split layer result (first 5 rows):", forwardResult.get(SplitForwardResultLayerDataId.valueCollection, i), 5, 0);
}
SplitBackwardBatch backwardLayer = new SplitBackwardBatch(context, Float.class, SplitMethod.defaultDense);
backwardLayer.parameter.setNOutputs(nOutputs);
backwardLayer.parameter.setNInputs(nInputs);
backwardLayer.input.set(SplitBackwardInputLayerDataId.inputGradientCollection, forwardResult.get(SplitForwardResultLayerDataId.valueCollection));
SplitBackwardResult backwardResult = backwardLayer.compute();
Service.printTensor("Backward split layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
context.dispose();
}
}