Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

Conv2DLayerDenseBatch.java

/* file: Conv2DLayerDenseBatch.java */
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/*
// Content:
// Java example of 2D convolution layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.convolution2d.*;
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 Conv2DLayerDenseBatch {
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Create a collection of dimension sizes of input data */
long[] dimensionSizes = new long[4];
dimensionSizes[0] = 2;
dimensionSizes[1] = 1;
dimensionSizes[2] = 16;
dimensionSizes[3] = 16;
/* Create input daat tensor */
double[] data = new double[512];
Tensor dataTensor = new HomogenTensor(context, dimensionSizes, data, 1.0f);
/* Create an algorithm to compute forward 2D convolution layer results using default method */
Convolution2dForwardBatch convolution2DLayerForward = new Convolution2dForwardBatch(context, Float.class, Convolution2dMethod.defaultDense);
/* Set input objects for the forward 2D convolution layer */
convolution2DLayerForward.input.set(ForwardInputId.data, dataTensor);
/* Compute forward 2D convolution layer results */
Convolution2dForwardResult forwardResult = convolution2DLayerForward.compute();
/* Print the results of the forward 2D convolution layer */
Service.printTensor("Two-dimensional convolution layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 15);
Service.printTensor("Two-dimensional convolution layer weights (first 5 rows):", forwardResult.get(Convolution2dLayerDataId.auxWeights), 5, 15);
/* Get the size of forward 2D convolution 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[] backData = new double[nSize];
Tensor tensorDataBack = new HomogenTensor(context, dims, backData, 0.01f);
/* Create an algorithm to compute backward 2D convolution layer results using default method */
Convolution2dBackwardBatch convolution2DLayerBackward = new Convolution2dBackwardBatch(context, Float.class, Convolution2dMethod.defaultDense);
/* Set input objects for the backward 2D convolution layer */
convolution2DLayerBackward.input.set(BackwardInputId.inputGradient, tensorDataBack);
convolution2DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward 2D convolution layer results */
Convolution2dBackwardResult backwardResult = convolution2DLayerBackward.compute();
/* Print the results of the backward 2D convolution layer */
Service.printTensor("Two-dimensional convolution layer backpropagation gradient result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 15);
Service.printTensor("Two-dimensional convolution layer backpropagation weightDerivative result (first 5 rows):", backwardResult.get(BackwardResultId.weightDerivatives), 5, 15);
Service.printTensor("Two-dimensional convolution layer backpropagation biasDerivative result (first 5 rows):", backwardResult.get(BackwardResultId.biasDerivatives), 5, 15);
context.dispose();
}
}

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