Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

SpatStochPool2DLayerDenseBatch.java

/* file: SpatStochPool2DLayerDenseBatch.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 stochastic pooling layers usage
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.spatial_stochastic_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;
import com.intel.daal.data_management.data.NumericTable;
class SpatStochPool2DLayerDenseBatch {
/* Input non-negative data set */
private static final String datasetFileName = "../data/batch/layer_non_negative.csv";
private static DaalContext context = new DaalContext();
private static long pyramidHeight = 2;
static float data[] = { 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24,
10, 20, 30, 40, 50, 60, 70, 80,
90, 100, 110, 120, 130, 140, 150, 160,
170, 180, 190, 200, 210, 220, 230, 240 };
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Read datasetFileName from a file and create a tensor to store forward input data */
/* Create a collection of dimension sizes of input data */
long[] dimensionSizes = new long[4];
dimensionSizes[0] = 2;
dimensionSizes[1] = 3;
dimensionSizes[2] = 2;
dimensionSizes[3] = 4;
/* Create input data tensor */
Tensor dataTensor = new HomogenTensor(context, dimensionSizes, data);
/* Get number of dimensions in input tensor */
long nDim = dataTensor.getDimensions().length;
/* Print the input of the forward two-dimensional pooling */
Service.printTensor("Forward two-dimensional spatial pyramid stochastic pooling layer input (first 10 rows):", dataTensor, 10, 0);
/* Create an algorithm to compute forward two-dimensional pooling results using default method */
SpatialStochasticPooling2dForwardBatch spatialStochPooling2DLayerForward = new SpatialStochasticPooling2dForwardBatch(context, Float.class,
SpatialStochasticPooling2dMethod.defaultDense,
pyramidHeight, nDim);
/* Set input objects for the forward two-dimensional pooling */
spatialStochPooling2DLayerForward.input.set(ForwardInputId.data, dataTensor);
/* Compute forward two-dimensional pooling results */
SpatialStochasticPooling2dForwardResult forwardResult = spatialStochPooling2DLayerForward.compute();
/* Print the results of the forward two-dimensional pooling */
Service.printTensor("Forward two-dimensional spatial pyramid stochastic pooling layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printTensor("Forward two-dimensional spatial pyramid stochastic pooling layer selected indices (first 10 rows):",
forwardResult.get(SpatialStochasticPooling2dLayerDataId.auxSelectedIndices), 5, 0);
/* Create an algorithm to compute backward two-dimensional pooling results using default method */
SpatialStochasticPooling2dBackwardBatch spatialStochPooling2DLayerBackward = new SpatialStochasticPooling2dBackwardBatch(context, Float.class,
SpatialStochasticPooling2dMethod.defaultDense,
pyramidHeight, nDim);
/* Set input objects for the backward two-dimensional pooling */
spatialStochPooling2DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
spatialStochPooling2DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward two-dimensional pooling results */
SpatialStochasticPooling2dBackwardResult backwardResult = spatialStochPooling2DLayerBackward.compute();
/* Print the results of the backward two-dimensional pooling */
Service.printTensor("Backward two-dimensional spatial pyramid stochastic 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.