Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

ConcatLayerDenseBatch.java

/* file: ConcatLayerDenseBatch.java */
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/*
// Content:
// Java example of concat layer in the batch processing mode
*/
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 {
/* Read datasetFileName from a file and create a tensor to store forward input data */
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
KeyValueDataCollection dataCollection = new KeyValueDataCollection(context);
for (int i = 0; i < 3; i++) {
dataCollection.set(i, data);
}
/* Create an algorithm to compute forward concat layer results using default method */
ConcatForwardBatch forwardLayer = new ConcatForwardBatch(context, Float.class, ConcatMethod.defaultDense);
forwardLayer.parameter.setConcatDimension(concatDimension);
/* Set input objects for the forward concat layer */
forwardLayer.input.set(ForwardInputLayerDataId.inputLayerData, dataCollection);
/* Compute forward concat layer results */
ConcatForwardResult forwardResult = forwardLayer.compute();
/* Print the results of the forward concat layer */
Service.printTensor("Forward concatenation layer result value (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
/* Create an algorithm to compute backward concat layer results using default method */
ConcatBackwardBatch backwardLayer = new ConcatBackwardBatch(context, Float.class, ConcatMethod.defaultDense);
backwardLayer.parameter.setConcatDimension(concatDimension);
/* Set input objects for the backward concat layer */
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);
/* Compute backward concat layer results */
ConcatBackwardResult backwardResult = backwardLayer.compute();
/* Print the results of the backward concat layer */
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();
}
}

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