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

AvePool2DLayerDenseBatch.java

/* file: AvePool2DLayerDenseBatch.java */
/*******************************************************************************
* Copyright 2014-2019 Intel Corporation.
*
* This software and the related documents are Intel copyrighted materials, and
* your use of them is governed by the express license under which they were
* provided to you (License). Unless the License provides otherwise, you may not
* use, modify, copy, publish, distribute, disclose or transmit this software or
* the related documents without Intel's prior written permission.
*
* This software and the related documents are provided as is, with no express
* or implied warranties, other than those that are expressly stated in the
* License.
*******************************************************************************/
/*
// 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.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 {
/* Read datasetFileName from a file and create a tensor to store input data */
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
long nDim = data.getDimensions().length;
/* Print the input of the forward two-dimensional pooling */
Service.printTensor("Forward two-dimensional average pooling layer input (first 10 rows):", data, 10, 0);
/* Create an algorithm to compute forward two-dimensional pooling layer results using average method */
AveragePooling2dForwardBatch averagePooling2DLayerForward = new AveragePooling2dForwardBatch(context, Float.class, AveragePooling2dMethod.defaultDense, nDim);
/* Set input objects for the forward two-dimensional pooling */
averagePooling2DLayerForward.input.set(ForwardInputId.data, data);
/* Compute forward two-dimensional pooling results */
AveragePooling2dForwardResult forwardResult = averagePooling2DLayerForward.compute();
/* Print the results of the forward two-dimensional average pooling layer */
Service.printTensor("Forward two-dimensional average pooling layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printNumericTable("Forward two-dimensional average pooling layer input dimensions:",
forwardResult.get(AveragePooling2dLayerDataId.auxInputDimensions));
/* Create an algorithm to compute backward two-dimensional pooling layer results using average method */
AveragePooling2dBackwardBatch averagePooling2DLayerBackward = new AveragePooling2dBackwardBatch(context, Float.class, AveragePooling2dMethod.defaultDense, nDim);
/* Set input objects for the backward two-dimensional average pooling layer */
averagePooling2DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
averagePooling2DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward two-dimensional pooling results */
AveragePooling2dBackwardResult backwardResult = averagePooling2DLayerBackward.compute();
/* Print the results of the backward two-dimensional average pooling layer */
Service.printTensor("Backward two-dimensional average pooling layer result (first 10 rows):", backwardResult.get(BackwardResultId.gradient), 10, 0);
context.dispose();
}
}

For more complete information about compiler optimizations, see our Optimization Notice.