package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.average_pooling1d.*;
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 AvePool1DLayerDenseBatch {
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 one-dimensional average pooling layer input (first 10 rows):", data, 10, 0);
AveragePooling1dForwardBatch averagePooling1DLayerForward = new AveragePooling1dForwardBatch(context, Float.class, AveragePooling1dMethod.defaultDense, nDim);
averagePooling1DLayerForward.input.set(ForwardInputId.data, data);
AveragePooling1dForwardResult forwardResult = averagePooling1DLayerForward.compute();
Service.printTensor("Forward one-dimensional average pooling layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printNumericTable("Forward one-dimensional average pooling layer input dimensions:",
forwardResult.get(AveragePooling1dLayerDataId.auxInputDimensions));
AveragePooling1dBackwardBatch averagePooling1DLayerBackward = new AveragePooling1dBackwardBatch(context, Float.class, AveragePooling1dMethod.defaultDense, nDim);
averagePooling1DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
averagePooling1DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
AveragePooling1dBackwardResult backwardResult = averagePooling1DLayerBackward.compute();
Service.printTensor("Backward one-dimensional average pooling layer result (first 10 rows):", backwardResult.get(BackwardResultId.gradient), 10, 0);
context.dispose();
}
}