package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.initializers.*;
import com.intel.daal.algorithms.neural_networks.initializers.gaussian.*;
import com.intel.daal.algorithms.neural_networks.initializers.uniform.*;
import com.intel.daal.algorithms.neural_networks.initializers.truncated_gaussian.*;
import com.intel.daal.algorithms.neural_networks.initializers.xavier.*;
import com.intel.daal.algorithms.neural_networks.layers.fullyconnected.*;
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 InitializersDenseBatch {
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
long[] dimensionSizes = new long[4];
dimensionSizes[0] = 2;
dimensionSizes[1] = 1;
dimensionSizes[2] = 3;
dimensionSizes[3] = 4;
float[] data = new float[24];
Tensor dataTensor = new HomogenTensor(context, dimensionSizes, data);
TruncatedGaussianBatch truncatedGaussInitializer = new TruncatedGaussianBatch(context, Float.class, TruncatedGaussianMethod.defaultDense, 0.0, 1.0);
truncatedGaussInitializer.input.set(InputId.data, dataTensor);
truncatedGaussInitializer.compute();
Service.printTensor("Data with truncated gaussian distribution:", dataTensor, 5, 0);
GaussianBatch gaussInitializer = new GaussianBatch(context, Float.class, GaussianMethod.defaultDense, 1.0, 0.5);
gaussInitializer.input.set(InputId.data, dataTensor);
gaussInitializer.compute();
Service.printTensor("Data with gaussian distribution:", dataTensor, 5, 0);
UniformBatch uniformInitializer = new UniformBatch(context, Float.class, UniformMethod.defaultDense, -5.0, 5.0);
uniformInitializer.input.set(InputId.data, dataTensor);
uniformInitializer.compute();
Service.printTensor("Data with uniform distribution:", dataTensor, 5, 0);
FullyConnectedForwardBatch fullyconnectedLayerForward = new FullyConnectedForwardBatch(context, Float.class, FullyConnectedMethod.defaultDense, 5);
fullyconnectedLayerForward.input.set(ForwardInputId.data, dataTensor);
fullyconnectedLayerForward.parameter.setWeightsInitializer(new XavierBatch(context, Float.class, XavierMethod.defaultDense));
FullyConnectedForwardResult forwardResult = fullyconnectedLayerForward.compute();
Service.printTensor("Weights filled by xavier initializer:", forwardResult.get(FullyConnectedLayerDataId.auxWeights), 5, 0);
context.dispose();
}
}