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 {
long[] dimensionSizes = new long[4];
dimensionSizes[0] = 2;
dimensionSizes[1] = 1;
dimensionSizes[2] = 16;
dimensionSizes[3] = 16;
double[] data = new double[512];
Tensor dataTensor = new HomogenTensor(context, dimensionSizes, data, 1.0);
Convolution2dForwardBatch convolution2DLayerForward = new Convolution2dForwardBatch(context, Float.class, Convolution2dMethod.defaultDense);
convolution2DLayerForward.input.set(ForwardInputId.data, dataTensor);
Convolution2dForwardResult forwardResult = convolution2DLayerForward.compute();
Service.printTensor("Forward 2D convolution layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
int nSize = (int)forwardResult.get(ForwardResultId.value).getSize();
long[] dims = forwardResult.get(ForwardResultId.value).getDimensions();
double[] backData = new double[nSize];
Tensor tensorDataBack = new HomogenTensor(context, dims, backData, 0.01);
Convolution2dBackwardBatch convolution2DLayerBackward = new Convolution2dBackwardBatch(context, Float.class, Convolution2dMethod.defaultDense);
convolution2DLayerBackward.input.set(BackwardInputId.inputGradient, tensorDataBack);
convolution2DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
Convolution2dBackwardResult backwardResult = convolution2DLayerBackward.compute();
Service.printTensor("Backward 2D convolution layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
Service.printTensor("Weights derivative (first 5 rows):", backwardResult.get(BackwardResultId.weightDerivatives), 5, 0);
Service.printTensor("Biases derivative (first 5 rows):", backwardResult.get(BackwardResultId.biasDerivatives), 5, 0);
context.dispose();
}
}