/* file: ReLULayerDenseBatch.java */
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* This software and the related documents  are provided as  is,  with no express
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 //  Content:
 //     Java example of ReLU layer in the batch processing mode

package com.intel.daal.examples.neural_networks;

import com.intel.daal.algorithms.neural_networks.layers.relu.*;
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 ReLULayerDenseBatch {
    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 forward input data */
        Tensor tensorData = Service.readTensorFromCSV(context, datasetFileName);

        /* Create an algorithm to compute forward relu layer results using default method */
        ReluForwardBatch reluLayerForward = new ReluForwardBatch(context, Float.class, ReluMethod.defaultDense);

        /* Set input objects for the forward relu layer */
        reluLayerForward.input.set(ForwardInputId.data, tensorData);

        /* Compute forward relu layer results */
        ReluForwardResult forwardResult = reluLayerForward.compute();

        /* Print the results of the forward relu layer */
        Service.printTensor("Forward relu layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);

        /* Get the size of forward relu layer output */
        int nSize = (int)forwardResult.get(ForwardResultId.value).getSize();
        long[] dims = forwardResult.get(ForwardResultId.value).getDimensions();

        /* Create a tensor with backward input data */
        double[] data = new double[nSize];
        Tensor tensorDataBack = new HomogenTensor(context, dims, data, 0.01);

        /* Create an algorithm to compute backward relu layer results using default method */
        ReluBackwardBatch reluLayerBackward = new ReluBackwardBatch(context, Float.class, ReluMethod.defaultDense);

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

        /* Compute backward relu layer results */
        ReluBackwardResult backwardResult = reluLayerBackward.compute();

        /* Print the results of the backward relu layer */
        Service.printTensor("Backward relu layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);

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