LCNLayerDenseBatch.java

/* file: LCNLayerDenseBatch.java */
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/*
 //  Content:
 //     Java example of local contrast normalization layer in the batch processing mode
 */

package com.intel.daal.examples.neural_networks;

import com.intel.daal.algorithms.neural_networks.layers.lcn.*;
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 LCNLayerDenseBatch {
    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
        /* Create collection of dimension sizes of the input data tensor */
        long[] dimensionSizes = new long[4];
        dimensionSizes[0] = 2;
        dimensionSizes[1] = 1;
        dimensionSizes[2] = 3;
        dimensionSizes[3] = 4;

        /* Create input daat tensor */
        float[] data = new float[24];
        Tensor dataTensor = new HomogenTensor(context, dimensionSizes, data, 1.0f);

        /* Create an algorithm to compute forward local contrast normalization layer results using default method */
        LcnForwardBatch lcnLayerForward = new LcnForwardBatch(context, Float.class, LcnMethod.defaultDense);

        /* Set input objects for the forward local contrast normalization layer */
        lcnLayerForward.input.set(ForwardInputId.data, dataTensor);

        /* Compute forward local contrast normalization layer results */
        LcnForwardResult forwardResult = lcnLayerForward.compute();

        /* Print the results of the forward local contrast normalization layer */
        Service.printTensor("Forward local contrast normalization layer result:", forwardResult.get(ForwardResultId.value),5,12);
        Service.printTensor("Centered data tensor:", forwardResult.get(LcnLayerDataId.auxCenteredData),5,12);
        Service.printTensor("Sigma tensor:", forwardResult.get(LcnLayerDataId.auxSigma),5,12);
        Service.printTensor("C tensor:", forwardResult.get(LcnLayerDataId.auxC),5,1);
        Service.printTensor("Inverted max(sigma, C):", forwardResult.get(LcnLayerDataId.auxInvMax),5,12);

        /* Create input gradient tensor for backward local contrast normalization layer */
        float[] backData = new float[24];
        Tensor tensorDataBack = new HomogenTensor(context, dimensionSizes, backData, 0.01f);

        /* Create an algorithm to compute backward local contrast normalization layer results using default method */
        LcnBackwardBatch lcnLayerBackward = new LcnBackwardBatch(context, Float.class, LcnMethod.defaultDense);

        /* Set input objects for the backward local contrast normalization layer */
        lcnLayerBackward.input.set(BackwardInputId.inputGradient, tensorDataBack);
        lcnLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
                                             forwardResult.get(ForwardResultLayerDataId.resultForBackward));

        /* Compute backward local contrast normalization layer results */
        LcnBackwardResult backwardResult = lcnLayerBackward.compute();

        /* Get the computed backward local contrast normalization layer results */
        Service.printTensor("Local contrast normalization layer backpropagation gradient result:", backwardResult.get(BackwardResultId.gradient),5,12);

        context.dispose();
    }
}
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