package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.softmax.*;
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 SoftmaxLayerDenseBatch {
private static final String datasetFileName = "../data/batch/layer.csv";
private static final long dimension = 1;
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);
SoftmaxForwardBatch softmaxLayerForward = new SoftmaxForwardBatch(context, Float.class, SoftmaxMethod.defaultDense);
softmaxLayerForward.parameter.setDimension(dimension);
softmaxLayerForward.input.set(ForwardInputId.data, data);
SoftmaxForwardResult forwardResult = softmaxLayerForward.compute();
Service.printTensor("Forward softmax layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
int nSize = (int)forwardResult.get(ForwardResultId.value).getSize();
long[] dims = forwardResult.get(ForwardResultId.value).getDimensions();
double[] backData = new double[nSize];
Tensor tensorDataBack = new HomogenTensor(context, dims, backData, 0.01);
SoftmaxBackwardBatch softmaxLayerBackward = new SoftmaxBackwardBatch(context, Float.class, SoftmaxMethod.defaultDense);
softmaxLayerBackward.input.set(BackwardInputId.inputGradient, tensorDataBack);
softmaxLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
SoftmaxBackwardResult backwardResult = softmaxLayerBackward.compute();
Service.printTensor("Backward softmax layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
context.dispose();
}
}