AvePool1DLayerDenseBatch.java

/* file: AvePool1DLayerDenseBatch.java */
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/*
 //  Content:
 //  Java example of neural network forward and backward one-dimensional average pooling layers usage
 */

package com.intel.daal.examples.neural_networks;

import com.intel.daal.algorithms.neural_networks.layers.average_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 AvePool1DLayerDenseBatch {
    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 average pooling layer input (first 10 rows):", data, 10, 0);

        /* Create an algorithm to compute forward one-dimensional pooling layer results using average method */
        AveragePooling1dForwardBatch averagePooling1DLayerForward = new AveragePooling1dForwardBatch(context, Float.class, AveragePooling1dMethod.defaultDense, nDim);

        /* Set input objects for the forward one-dimensional pooling */
        averagePooling1DLayerForward.input.set(ForwardInputId.data, data);

        /* Compute forward one-dimensional pooling results */
        AveragePooling1dForwardResult forwardResult = averagePooling1DLayerForward.compute();

        /* Print the results of the forward one-dimensional average pooling layer */
        Service.printTensor("Forward one-dimensional average pooling layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
        Service.printNumericTable("Forward one-dimensional average pooling layer input dimensions:",
                                  forwardResult.get(AveragePooling1dLayerDataId.auxInputDimensions));

        /* Create an algorithm to compute backward one-dimensional pooling layer results using average method */
        AveragePooling1dBackwardBatch averagePooling1DLayerBackward = new AveragePooling1dBackwardBatch(context, Float.class, AveragePooling1dMethod.defaultDense, nDim);

        /* Set input objects for the backward one-dimensional average pooling layer */
        averagePooling1DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
        averagePooling1DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
                                                forwardResult.get(ForwardResultLayerDataId.resultForBackward));

        /* Compute backward one-dimensional pooling results */
        AveragePooling1dBackwardResult backwardResult = averagePooling1DLayerBackward.compute();

        /* Print the results of the backward one-dimensional average pooling layer */
        Service.printTensor("Backward one-dimensional average pooling layer result (first 10 rows):", backwardResult.get(BackwardResultId.gradient), 10, 0);

        context.dispose();
    }
}
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