package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.concat.*;
import com.intel.daal.algorithms.neural_networks.layers.ForwardResultId;
import com.intel.daal.algorithms.neural_networks.layers.ForwardResultLayerDataId;
import com.intel.daal.algorithms.neural_networks.layers.ForwardInputLayerDataId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardResultLayerDataId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardInputId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardInputLayerDataId;
import com.intel.daal.data_management.data.Tensor;
import com.intel.daal.data_management.data.HomogenTensor;
import com.intel.daal.data_management.data.KeyValueDataCollection;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class ConcatLayerDenseBatch {
private static final String datasetFileName = "../data/batch/layer.csv";
private static DaalContext context = new DaalContext();
private static final long concatDimension = 1;
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
KeyValueDataCollection dataCollection = new KeyValueDataCollection(context);
for (int i = 0; i < 3; i++) {
dataCollection.set(i, data);
}
ConcatForwardBatch forwardLayer = new ConcatForwardBatch(context, Float.class, ConcatMethod.defaultDense);
forwardLayer.parameter.setConcatDimension(concatDimension);
forwardLayer.input.set(ForwardInputLayerDataId.inputLayerData, dataCollection);
ConcatForwardResult forwardResult = forwardLayer.compute();
Service.printTensor("Forward concatenation layer result value (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
ConcatBackwardBatch backwardLayer = new ConcatBackwardBatch(context, Float.class, ConcatMethod.defaultDense);
backwardLayer.parameter.setConcatDimension(concatDimension);
backwardLayer.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
backwardLayer.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
Service.printNumericTable("auxInputDimensions ", forwardResult.get(ConcatLayerDataId.auxInputDimensions), 5, 0);
ConcatBackwardResult backwardResult = backwardLayer.compute();
for (int i = 0; i < dataCollection.size(); i++) {
Service.printTensor("Backward concatenation layer backward result (first 5 rows):",
backwardResult.get(BackwardResultLayerDataId.resultLayerData, i), 5, 0);
}
context.dispose();
}
}