Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

SoftmaxLayerDenseBatch.java

/* file: SoftmaxLayerDenseBatch.java */
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/*
// Content:
// Java example of softmax layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.softmax.*;
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.ForwardInputId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardResultId;
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.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class SoftmaxLayerDenseBatch {
private static final String datasetFileName = "../data/batch/layer.csv";
/* Softmax layer parameter */
private static final long dimension = 1; /* Starting data dimension index to compute softmax */
private static DaalContext context = new DaalContext();
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);
/* Create an algorithm to compute forward softmax layer results using default method */
SoftmaxForwardBatch softmaxLayerForward = new SoftmaxForwardBatch(context, Float.class, SoftmaxMethod.defaultDense);
/* Set algorithm parameters */
softmaxLayerForward.parameter.setDimension(dimension);
/* Set input objects for the forward softmax layer */
softmaxLayerForward.input.set(ForwardInputId.data, data);
/* Compute forward softmax layer results */
SoftmaxForwardResult forwardResult = softmaxLayerForward.compute();
/* Print the results of the forward softmax layer */
Service.printTensor("Forward softmax layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
/* Get the size of forward softmax layer output */
int nSize = (int)forwardResult.get(ForwardResultId.value).getSize();
long[] dims = forwardResult.get(ForwardResultId.value).getDimensions();
/* Create a tensor with backward input data */
double[] backData = new double[nSize];
Tensor tensorDataBack = new HomogenTensor(context, dims, backData, 0.01);
/* Create an algorithm to compute backward softmax layer results using default method */
SoftmaxBackwardBatch softmaxLayerBackward = new SoftmaxBackwardBatch(context, Float.class, SoftmaxMethod.defaultDense);
/* Set input objects for the backward softmax layer */
softmaxLayerBackward.input.set(BackwardInputId.inputGradient, tensorDataBack);
softmaxLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward softmax layer results */
SoftmaxBackwardResult backwardResult = softmaxLayerBackward.compute();
/* Print the results of the backward softmax layer */
Service.printTensor("Backward softmax layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
context.dispose();
}
}

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