SpatStochPool2DLayerDenseBatch.java

/* file: SpatStochPool2DLayerDenseBatch.java */
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/*
 //  Content:
 //   Java example of neural network forward and backward two-dimensional stochastic pooling layers usage
 */

package com.intel.daal.examples.neural_networks;

import com.intel.daal.algorithms.neural_networks.layers.spatial_stochastic_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 SpatStochPool2DLayerDenseBatch {
    /* Input non-negative data set */
    private static final String datasetFileName = "../data/batch/layer_non_negative.csv";
    private static DaalContext context = new DaalContext();
    private static long pyramidHeight = 2;
    static 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,
                            10, 20, 30, 40, 50, 60, 70, 80,
                            90, 100, 110, 120, 130, 140, 150, 160,
                            170, 180, 190, 200, 210, 220, 230, 240 };

    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 */
        Tensor dataTensor = new HomogenTensor(context, dimensionSizes, data);

        /* Get number of dimensions in input tensor */
        long nDim = dataTensor.getDimensions().length;

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

        /* Create an algorithm to compute forward two-dimensional pooling results using default method */
        SpatialStochasticPooling2dForwardBatch spatialStochPooling2DLayerForward = new SpatialStochasticPooling2dForwardBatch(context, Float.class,
                                                                                                                              SpatialStochasticPooling2dMethod.defaultDense,
                                                                                                                              pyramidHeight, nDim);

        /* Set input objects for the forward two-dimensional pooling */
        spatialStochPooling2DLayerForward.input.set(ForwardInputId.data, dataTensor);

        /* Compute forward two-dimensional pooling results */
        SpatialStochasticPooling2dForwardResult forwardResult = spatialStochPooling2DLayerForward.compute();

        /* Print the results of the forward two-dimensional pooling */
        Service.printTensor("Forward two-dimensional spatial pyramid stochastic pooling layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
        Service.printTensor("Forward two-dimensional spatial pyramid stochastic pooling layer selected indices (first 10 rows):",
                            forwardResult.get(SpatialStochasticPooling2dLayerDataId.auxSelectedIndices), 5, 0);

        /* Create an algorithm to compute backward two-dimensional pooling results using default method */
        SpatialStochasticPooling2dBackwardBatch spatialStochPooling2DLayerBackward = new SpatialStochasticPooling2dBackwardBatch(context, Float.class,
                                                                                                                                 SpatialStochasticPooling2dMethod.defaultDense,
                                                                                                                                 pyramidHeight, nDim);

        /* Set input objects for the backward two-dimensional pooling */
        spatialStochPooling2DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
        spatialStochPooling2DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
                forwardResult.get(ForwardResultLayerDataId.resultForBackward));

        /* Compute backward two-dimensional pooling results */
        SpatialStochasticPooling2dBackwardResult backwardResult = spatialStochPooling2DLayerBackward.compute();

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

        context.dispose();
    }
}
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