package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.batch_normalization.*;
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;
class BatchNormLayerDenseBatch {
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 {
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
Service.printTensor("Forward batch normalization layer input (first 5 rows):", data, 5, 0);
BatchNormalizationForwardBatch forwardLayer = new BatchNormalizationForwardBatch(context, Float.class, BatchNormalizationMethod.defaultDense);
long[] dataDims = data.getDimensions();
int dimensionSize = (int)dataDims[(int)forwardLayer.parameter.getDimension()];
long[] dimensionSizes = new long[1];
dimensionSizes[0] = dimensionSize;
double[] weightsData = new double[dimensionSize];
Tensor weights = new HomogenTensor(context, dimensionSizes, weightsData, 1.0);
double[] biasesData = new double[dimensionSize];
Tensor biases = new HomogenTensor(context, dimensionSizes, biasesData, 2.0);
double[] populationMeanData = new double[dimensionSize];
Tensor populationMean = new HomogenTensor(context, dimensionSizes, populationMeanData, 0.0);
double[] populationVarianceData = new double[dimensionSize];
Tensor populationVariance = new HomogenTensor(context, dimensionSizes, populationVarianceData, 0.0);
forwardLayer.input.set(ForwardInputId.data, data);
forwardLayer.input.set(ForwardInputId.weights, weights);
forwardLayer.input.set(ForwardInputId.biases, biases);
forwardLayer.input.set(BatchNormalizationForwardInputLayerDataId.populationMean, populationMean);
forwardLayer.input.set(BatchNormalizationForwardInputLayerDataId.populationVariance, populationVariance);
BatchNormalizationForwardResult forwardResult = forwardLayer.compute();
Service.printTensor("Forward batch normalization layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printTensor("Mini-batch mean (first 5 values):", forwardResult.get(BatchNormalizationLayerDataId.auxMean), 5, 0);
Service.printTensor("Mini-batch standard deviation (first 5 values):", forwardResult.get(BatchNormalizationLayerDataId.auxStandardDeviation), 5, 0);
Service.printTensor("Population mean (first 5 values):", forwardResult.get(BatchNormalizationLayerDataId.auxPopulationMean), 5, 0);
Service.printTensor("Population variance (first 5 values):", forwardResult.get(BatchNormalizationLayerDataId.auxPopulationVariance), 5, 0);
double[] inputGradientData = new double[(int)data.getSize()];
Tensor inputGradient = new HomogenTensor(context, dataDims, inputGradientData, 10.0);
BatchNormalizationBackwardBatch backwardLayer = new BatchNormalizationBackwardBatch(context, Float.class, BatchNormalizationMethod.defaultDense);
backwardLayer.input.set(BackwardInputId.inputGradient, inputGradient);
backwardLayer.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
BatchNormalizationBackwardResult backwardResult = backwardLayer.compute();
Service.printTensor("Backward batch normalization layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
Service.printTensor("Weight derivatives (first 5 values):", backwardResult.get(BackwardResultId.weightDerivatives), 5, 0);
Service.printTensor("Bias derivatives (first 5 values):", backwardResult.get(BackwardResultId.biasDerivatives), 5, 0);
context.dispose();
}
}