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

SpatAvePool2DLayerDenseBatch.java

/* file: SpatAvePool2DLayerDenseBatch.java */
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/*
// Content:
// Java example of neural network forward and backward two-dimensional average pooling layers usage
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.spatial_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.data_management.data.HomogenTensor;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class SpatAvePool2DLayerDenseBatch {
private static final String datasetFileName = "../data/batch/layer.csv";
private static DaalContext context = new DaalContext();
private static long pyramidHeight = 2;
static float dataArray[] = {
2, 4, 6, 8,
10, 12, 14, 16,
18, 20, 22, 24,
26, 28, 30, 32,
34, 36, 38, 40,
42, 44, 46, 48,
-2, -4, -6, -8,
-10, -12, -14, -16,
- 18, -20, -22, -24,
-26, -28, -30, -32,
- 34, -36, -38, -40,
-42, -44, -46, -48
};
static long[] dims = {2, 3, 2, 4};
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Read datasetFileName from a file and create a tensor to store input data */
HomogenTensor dataTensor = new HomogenTensor(context, dims, dataArray);
long nDim = dataTensor.getDimensions().length;
/* Print the input of the forward two-dimensional pooling */
Service.printTensor("Forward two-dimensional spatial pyramid average pooling layer input (first 10 rows):", dataTensor, 10, 0);
/* Create an algorithm to compute forward two-dimensional pooling layer results using average method */
SpatialAveragePooling2dForwardBatch spatialAvePooling2DLayerForward = new SpatialAveragePooling2dForwardBatch(context, Float.class,
SpatialAveragePooling2dMethod.defaultDense,
pyramidHeight, nDim);
/* Set input objects for the forward two-dimensional pooling */
spatialAvePooling2DLayerForward.input.set(ForwardInputId.data, dataTensor);
/* Compute forward two-dimensional pooling results */
SpatialAveragePooling2dForwardResult forwardResult = spatialAvePooling2DLayerForward.compute();
/* Print the results of the forward two-dimensional average pooling layer */
Service.printTensor("Forward two-dimensional spatial pyramid average pooling layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printNumericTable("Forward two-dimensional spatial pyramid average pooling layer input dimensions:",
forwardResult.get(SpatialAveragePooling2dLayerDataId.auxInputDimensions));
/* Create an algorithm to compute backward two-dimensional pooling layer results using average method */
SpatialAveragePooling2dBackwardBatch spatialAvePooling2DLayerBackward = new SpatialAveragePooling2dBackwardBatch(context, Float.class,
SpatialAveragePooling2dMethod.defaultDense,
pyramidHeight, nDim);
/* Set input objects for the backward two-dimensional average pooling layer */
spatialAvePooling2DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
spatialAvePooling2DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward two-dimensional pooling results */
SpatialAveragePooling2dBackwardResult backwardResult = spatialAvePooling2DLayerBackward.compute();
/* Print the results of the backward two-dimensional average pooling layer */
Service.printTensor("Backward two-dimensional spatial pyramid average pooling layer result (first 10 rows):", backwardResult.get(BackwardResultId.gradient), 10, 0);
context.dispose();
}
}

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