SpatMaxPool2DLayerDenseBatch.java

/* file: SpatMaxPool2DLayerDenseBatch.java */
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/*
 //  Content:
 //  Java example of neural network forward and backward two-dimensional maximum pooling layers usage
 */

package com.intel.daal.examples.neural_networks;

import com.intel.daal.algorithms.neural_networks.layers.spatial_maximum_pooling2d.*;
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;

import com.intel.daal.data_management.data.NumericTable;
class SpatMaxPool2DLayerDenseBatch {
    private static final String datasetFileName = "../data/batch/layer.csv";
    private static DaalContext context = new DaalContext();
    private static long pyramidHeight = 2;

    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 */
        /* Create a collection of dimension sizes of input data */
        long[] dimensionSizes = new long[4];
        dimensionSizes[0] = 2;
        dimensionSizes[1] = 3;
        dimensionSizes[2] = 2;
        dimensionSizes[3] = 4;

        /* Create input data tensor */
        float[] data = {1,  2,  3,  4,
                        5,  6,  7,  8,
                                    9, 10, 11, 12,
                                    13, 14, 15, 16,
                                                17, 18, 19, 20,
                                                21, 22, 23, 24,
                                    -1, -2, -3, -4,
                                    -5, -6, -7, -8,
                                                -9, -10, -11, -12,
                                                -13, -14, -15, -16,
                                                -17, -18, -19, -20,
                                                -21, -22, -23, -24};
        Tensor dataTensor = new HomogenTensor(context, dimensionSizes, data);

        long nDim = dataTensor.getDimensions().length;

        /* Print the input of the forward two-dimensional pooling */
        Service.printTensor("Forward two-dimensional spatial pyramid maximum pooling layer input (first 10 rows):", dataTensor, 10, 0);

        /* Create an algorithm to compute forward two-dimensional pooling results using default method */
        SpatialMaximumPooling2dForwardBatch spatialMaxPooling2DLayerForward = new SpatialMaximumPooling2dForwardBatch(context, Float.class,
                                                                                                                      SpatialMaximumPooling2dMethod.defaultDense,
                                                                                                                      pyramidHeight, nDim);

        /* Set input objects for the forward two-dimensional pooling */
        spatialMaxPooling2DLayerForward.input.set(ForwardInputId.data, dataTensor);

        /* Compute forward two-dimensional pooling results */
        SpatialMaximumPooling2dForwardResult forwardResult = spatialMaxPooling2DLayerForward.compute();

        /* Print the results of the forward two-dimensional pooling */
        Service.printTensor("Forward two-dimensional spatial pyramid maximum pooling layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
        Service.printTensor("Forward two-dimensional spatial pyramid maximum pooling layer selected indices (first 10 rows):",
                            forwardResult.get(SpatialMaximumPooling2dLayerDataId.auxSelectedIndices), 10, 0);

        /* Create an algorithm to compute backward two-dimensional pooling results using default method */
        SpatialMaximumPooling2dBackwardBatch spatialMaxPooling2DLayerBackward = new SpatialMaximumPooling2dBackwardBatch(context, Float.class,
                                                                                                                         SpatialMaximumPooling2dMethod.defaultDense,
                                                                                                                         pyramidHeight, nDim);

        /* Set input objects for the backward two-dimensional pooling */
        spatialMaxPooling2DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
        spatialMaxPooling2DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
                forwardResult.get(ForwardResultLayerDataId.resultForBackward));

        /* Compute backward two-dimensional pooling results */
        SpatialMaximumPooling2dBackwardResult backwardResult = spatialMaxPooling2DLayerBackward.compute();

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

        context.dispose();
    }
}
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