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

MaxPool1DLayerDenseBatch.java

/* file: MaxPool1DLayerDenseBatch.java */
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/*
// Content:
// Java example of neural network forward and backward one-dimensional maximum pooling layers usage
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.maximum_pooling1d.*;
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.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class MaxPool1DLayerDenseBatch {
private static final String datasetFileName = "../data/batch/layer.csv";
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 input data */
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
long nDim = data.getDimensions().length;
/* Print the input of the forward one-dimensional pooling */
Service.printTensor("Forward one-dimensional maximum pooling layer input (first 10 rows):", data, 10, 0);
/* Create an algorithm to compute forward one-dimensional pooling layer results using maximum method */
MaximumPooling1dForwardBatch maximumPooling1DLayerForward = new MaximumPooling1dForwardBatch(context, Float.class, MaximumPooling1dMethod.defaultDense, nDim);
/* Set input objects for the forward one-dimensional pooling */
maximumPooling1DLayerForward.input.set(ForwardInputId.data, data);
/* Compute forward one-dimensional pooling results */
MaximumPooling1dForwardResult forwardResult = maximumPooling1DLayerForward.compute();
/* Print the results of the forward one-dimensional maximum pooling layer */
Service.printTensor("Forward one-dimensional maximum pooling layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printTensor("Forward one-dimensional maximum pooling layer selected indices (first 5 rows):",
forwardResult.get(MaximumPooling1dLayerDataId.auxSelectedIndices), 5, 0);
/* Create an algorithm to compute backward one-dimensional pooling layer results using maximum method */
MaximumPooling1dBackwardBatch maximumPooling1DLayerBackward = new MaximumPooling1dBackwardBatch(context, Float.class, MaximumPooling1dMethod.defaultDense, nDim);
/* Set input objects for the backward one-dimensional maximum pooling layer */
maximumPooling1DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
maximumPooling1DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward one-dimensional pooling results */
MaximumPooling1dBackwardResult backwardResult = maximumPooling1DLayerBackward.compute();
/* Print the results of the backward one-dimensional maximum pooling layer */
Service.printTensor("Backward one-dimensional maximum pooling layer result (first 10 rows):", backwardResult.get(BackwardResultId.gradient), 10, 0);
context.dispose();
}
}

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