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

NeuralNetDenseBatch.java

/* file: NeuralNetDenseBatch.java */
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/*
// Content:
// Java example of neural network in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.*;
import com.intel.daal.algorithms.neural_networks.prediction.*;
import com.intel.daal.algorithms.neural_networks.training.*;
import com.intel.daal.algorithms.neural_networks.layers.LayerDescriptor;
import com.intel.daal.algorithms.neural_networks.layers.NextLayers;
import com.intel.daal.algorithms.neural_networks.layers.ForwardLayer;
import com.intel.daal.algorithms.neural_networks.layers.BackwardLayer;
import com.intel.daal.algorithms.neural_networks.layers.ForwardInputId;
import com.intel.daal.algorithms.neural_networks.layers.ForwardResultId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardInputId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardResultId;
import com.intel.daal.data_management.data.Tensor;
import com.intel.daal.data_management.data.HomogenTensor;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class NeuralNetDenseBatch {
/* Input data set parameters */
private static final String trainDatasetFile = "../data/batch/neural_network_train.csv";
private static final String trainGroundTruthFile = "../data/batch/neural_network_train_ground_truth.csv";
private static final String testDatasetFile = "../data/batch/neural_network_test.csv";
private static final String testGroundTruthFile = "../data/batch/neural_network_test_ground_truth.csv";
private static final long batchSize = 10;
private static PredictionModel predictionModel;
private static PredictionResult predictionResult;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
trainModel();
testModel();
printResults();
context.dispose();
}
private static void trainModel() throws java.io.FileNotFoundException, java.io.IOException {
/* Read training data set from a .csv file and create a tensor to store input data */
Tensor trainingData = Service.readTensorFromCSV(context, trainDatasetFile);
Tensor trainingGroundTruth = Service.readTensorFromCSV(context, trainGroundTruthFile, true);
/* Set learning rate for the optimization solver used in the neural network */
double[] learningRateArray = new double[1];
learningRateArray[0] = 0.001;
com.intel.daal.algorithms.optimization_solver.sgd.Batch sgdAlgorithm =
new com.intel.daal.algorithms.optimization_solver.sgd.Batch(context, Float.class, com.intel.daal.algorithms.optimization_solver.sgd.Method.defaultDense);
sgdAlgorithm.parameter.setLearningRateSequence(new HomogenNumericTable(context, learningRateArray, 1, 1));
sgdAlgorithm.parameter.setBatchSize(batchSize);
sgdAlgorithm.parameter.setNIterations(trainingData.getDimensions()[0] / batchSize);
/* Create an algorithm to compute neural network results using default method */
TrainingBatch net = new TrainingBatch(context, sgdAlgorithm);
long[] sampleSize = trainingData.getDimensions();
sampleSize[0] = batchSize;
/* Configure the neural network */
TrainingTopology topology = NeuralNetConfigurator.configureNet(context);
net.initialize(sampleSize, topology);
/* Set input objects for the neural network */
net.input.set(TrainingInputId.data, trainingData);
net.input.set(TrainingInputId.groundTruth, trainingGroundTruth);
/* Run the neural network training */
TrainingResult result = net.compute();
/* Get training and prediction models of the neural network */
TrainingModel trainingModel = result.get(TrainingResultId.model);
predictionModel = trainingModel.getPredictionModel(Float.class);
}
private static void testModel() throws java.io.FileNotFoundException, java.io.IOException {
/* Read testing data set from a .csv file and create a tensor to store input data */
Tensor predictionData = Service.readTensorFromCSV(context, testDatasetFile);
/* Create an algorithm to compute the neural network predictions */
PredictionBatch net = new PredictionBatch(context);
long[] predictionDimensions = predictionData.getDimensions();
net.parameter.setBatchSize(predictionDimensions[0]);
/* Set input objects for the prediction neural network */
net.input.set(PredictionTensorInputId.data, predictionData);
net.input.set(PredictionModelInputId.model, predictionModel);
/* Run the neural network prediction */
predictionResult = net.compute();
}
private static void printResults() throws java.io.FileNotFoundException, java.io.IOException {
/* Read testing ground truth from a .csv file and create a tensor to store the data */
Tensor predictionGroundTruth = Service.readTensorFromCSV(context, testGroundTruthFile);
/* Print results of the neural network prediction */
Service.printTensors("Ground truth", "Neural network predictions: each class probability",
"Neural network classification results (first 20 observations):",
predictionGroundTruth, predictionResult.get(PredictionResultId.prediction), 20);
}
}

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