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

StochPool2DLayerDenseBatch.java

/* file: StochPool2DLayerDenseBatch.java */
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/*
// 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.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 StochPool2DLayerDenseBatch {
/* Input non-negative data set */
private static final String datasetFileName = "../data/batch/layer_non_negative.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 forward input data */
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
/* Get number of dimensions in input tensor */
long nDim = data.getDimensions().length;
/* Print the input of the forward two-dimensional pooling */
Service.printTensor("Forward two-dimensional stochastic pooling layer input (first 10 rows):", data, 10, 0);
/* Create an algorithm to compute forward two-dimensional pooling results using default method */
StochasticPooling2dForwardBatch stochasticPooling2DLayerForward = new StochasticPooling2dForwardBatch(context, Float.class, StochasticPooling2dMethod.defaultDense, nDim);
/* Set input objects for the forward two-dimensional pooling */
stochasticPooling2DLayerForward.input.set(ForwardInputId.data, data);
/* Compute forward two-dimensional pooling results */
StochasticPooling2dForwardResult forwardResult = stochasticPooling2DLayerForward.compute();
/* Print the results of the forward two-dimensional pooling */
Service.printTensor("Forward two-dimensional stochastic pooling layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printTensor("Forward two-dimensional stochastic pooling layer selected indices (first 10 rows):",
forwardResult.get(StochasticPooling2dLayerDataId.auxSelectedIndices), 10, 0);
/* Create an algorithm to compute backward two-dimensional pooling results using default method */
StochasticPooling2dBackwardBatch stochasticPooling2DLayerBackward = new StochasticPooling2dBackwardBatch(context, Float.class, StochasticPooling2dMethod.defaultDense, nDim);
/* Set input objects for the backward two-dimensional pooling */
stochasticPooling2DLayerBackward.input.set(BackwardInputId.inputGradient, forwardResult.get(ForwardResultId.value));
stochasticPooling2DLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward two-dimensional pooling results */
StochasticPooling2dBackwardResult backwardResult = stochasticPooling2DLayerBackward.compute();
/* Print the results of the backward two-dimensional pooling */
Service.printTensor("Backward two-dimensional stochastic pooling layer result (first 10 rows):", backwardResult.get(BackwardResultId.gradient), 10, 0);
context.dispose();
}
}

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