Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

EltwiseSumLayerDenseBatch.java

/* file: EltwiseSumLayerDenseBatch.java */
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/*
// Content:
// Java example of element-wise sum layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.eltwise_sum.*;
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 EltwiseSumLayerDenseBatch {
private static DaalContext context = new DaalContext();
private static final String datasetFileName = "../data/batch/layer.csv";
private static final long nInputs = 3;
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Create an algorithm to compute forward concat layer results using default method */
EltwiseSumForwardBatch forwardLayer = new EltwiseSumForwardBatch(context, Float.class, EltwiseSumMethod.defaultDense);
/* Read datasetFileName from a file and create a tensor to store forward input data */
for (int i = 0; i < nInputs; i++) {
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
/* Set input objects for the forward concat layer */
forwardLayer.input.set(ForwardInputLayerDataId.inputLayerData, data, i);
}
/* Compute forward concat layer results */
EltwiseSumForwardResult forwardResult = forwardLayer.compute();
/* Print the results of the forward concat layer */
Service.printTensor("Forward element-wise sum layer result (first 5 rows):",
forwardResult.get(ForwardResultId.value), 5, 0);
Service.printNumericTable("Forward element-wise sum layer number of inputs (number of coefficients)",
forwardResult.get(EltwiseSumLayerDataNumericTableId.auxNumberOfCoefficients), 1, 0);
/* Create an algorithm to compute backward concat layer results using default method */
EltwiseSumBackwardBatch backwardLayer = new EltwiseSumBackwardBatch(context, Float.class, EltwiseSumMethod.defaultDense);
/* Read input gradient from CSV, for brevity use the same file as for an input data */
Tensor inputGradient = Service.readTensorFromCSV(context, datasetFileName);
/* Set input objects for the backward concat layer */
backwardLayer.input.set(BackwardInputId.inputGradient, inputGradient);
backwardLayer.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward concat layer results */
EltwiseSumBackwardResult backwardResult = backwardLayer.compute();
/* Print the results of the backward concat layer */
for (int i = 0; i < nInputs; i++) {
Service.printTensor("Backward element-wise sum layer backward result (first 5 rows):",
backwardResult.get(BackwardResultLayerDataId.resultLayerData, i), 5, 0);
}
context.dispose();
}
}

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