package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.average_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.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class AvePool2DLayerDenseBatch {
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 {
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
long nDim = data.getDimensions().length;
Service.printTensor("Forward two-dimensional pooling input (first 10 rows):", data, 10, 0);
AveragePooling2dForwardBatch averagePooling2DLayerForward = new AveragePooling2dForwardBatch(context, Float.class, AveragePooling2dMethod.defaultDense, nDim);
averagePooling2DLayerForward.input.set(ForwardInputId.data, data);
AveragePooling2dForwardResult forwardResult = averagePooling2DLayerForward.compute();
Service.printTensor("Forward two-dimensional pooling result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printNumericTable("Forward two-dimensional average pooling layer input dimensions:",
forwardResult.get(AveragePooling2dLayerDataId.auxInputDimensions));
AveragePooling2dBackwardBatch averagePooling2DLayerBackward = new AveragePooling2dBackwardBatch(context, Float.class, AveragePooling2dMethod.defaultDense, nDim);
averagePooling2DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
averagePooling2DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
AveragePooling2dBackwardResult backwardResult = averagePooling2DLayerBackward.compute();
Service.printTensor("Backward two-dimensional pooling result (first 10 rows):", backwardResult.get(BackwardResultId.gradient), 10, 0);
context.dispose();
}
}