Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

NeuralNetPredicDenseBatch.java

/* file: NeuralNetPredicDenseBatch.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.Parameter;
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.ForwardInput;
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 NeuralNetPredicDenseBatch {
/* Input data set parameters */
private static final String testDatasetFile = "../data/batch/neural_network_test.csv";
private static final String testGroundTruthFile = "../data/batch/neural_network_test_ground_truth.csv";
/* Weights and biases obtained on the training stage */
private static final String fc1WeightsFile = "../data/batch/fc1_weights.csv";
private static final String fc1BiasesFile = "../data/batch/fc1_biases.csv";
private static final String fc2WeightsFile = "../data/batch/fc2_weights.csv";
private static final String fc2BiasesFile = "../data/batch/fc2_biases.csv";
private static Tensor predictionData;
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 {
createModel();
testModel();
printResults();
context.dispose();
}
private static void createModel() throws java.io.FileNotFoundException, java.io.IOException {
/* Read testing data set from a .csv file and create a tensor to store input data */
predictionData = Service.readTensorFromCSV(context, testDatasetFile);
/* Retrieve training and prediction models of the neural network */
PredictionTopology topology = NeuralNetPredicConfigurator.configureNet(context);
predictionModel = new PredictionModel(context, topology);
/* Read 1st fully-connected layer weights and biases from CSV file */
/* 1st fully-connected layer weights are a 2D tensor of size 5 x 20 */
Tensor fc1Weights = Service.readTensorFromCSV(context, fc1WeightsFile);
/* 1st fully-connected layer biases are a 1D tensor of size 5 */
Tensor fc1Biases = Service.readTensorFromCSV(context, fc1BiasesFile);
/* Set weights and biases of the 1st fully-connected layer */
ForwardInput fc1Input = predictionModel.getLayer(0).getLayerInput();
fc1Input.set(ForwardInputId.weights, fc1Weights);
fc1Input.set(ForwardInputId.biases, fc1Biases);
/* Set flag that specifies that weights and biases of the 1st fully-connected layer are initialized */
predictionModel.getLayer(0).getLayerParameter().setWeightsAndBiasesInitializationFlag(true);
/* Read 2nd fully-connected layer weights and biases from CSV file */
/* 2nd fully-connected layer weights are a 2D tensor of size 2 x 5 */
Tensor fc2Weights = Service.readTensorFromCSV(context, fc2WeightsFile);
/* 2nd fully-connected layer biases are a 1D tensor of size 2 */
Tensor fc2Biases = Service.readTensorFromCSV(context, fc2BiasesFile);
/* Set weights and biases of the 2nd fully-connected layer */
ForwardInput fc2Input = predictionModel.getLayer(1).getLayerInput();
fc2Input.set(ForwardInputId.weights, fc2Weights);
fc2Input.set(ForwardInputId.biases, fc2Biases);
/* Set flag that specifies that weights and biases of the 2nd fully-connected layer are initialized */
predictionModel.getLayer(1).getLayerParameter().setWeightsAndBiasesInitializationFlag(true);
}
private static void testModel() {
/* 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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