Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

InitializersDenseBatch.java

/* file: InitializersDenseBatch.java */
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/*
// Content:
// Java example of initializers
*/
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 {
/* Create a collection of dimension sizes of input data */
long[] dimensionSizes = new long[4];
dimensionSizes[0] = 2;
dimensionSizes[1] = 1;
dimensionSizes[2] = 3;
dimensionSizes[3] = 4;
/* Create input daat tensor */
float[] data = new float[24];
Tensor dataTensor = new HomogenTensor(context, dimensionSizes, data);
/* Fill tensor data using truncated gaussian initializer */
/* Create an algorithm to initialize data using default method */
TruncatedGaussianBatch truncatedGaussInitializer = new TruncatedGaussianBatch(context, Float.class, TruncatedGaussianMethod.defaultDense, 0.0, 1.0);
/* Set input object and parameters for the truncated gaussian initializer */
truncatedGaussInitializer.input.set(InputId.data, dataTensor);
/* Compute truncated gaussian initializer */
truncatedGaussInitializer.compute();
/* Print the results of the truncated gaussian initializer */
Service.printTensor("Data with truncated gaussian distribution (first 5 rows):", dataTensor, 5, 0);
/* Fill tensor data using gaussian initializer */
/* Create an algorithm to initialize data using default method */
GaussianBatch gaussInitializer = new GaussianBatch(context, Float.class, GaussianMethod.defaultDense, 1.0, 0.5);
/* Set input object for the gaussian initializer */
gaussInitializer.input.set(InputId.data, dataTensor);
/* Compute gaussian initializer */
gaussInitializer.compute();
/* Print the results of the gaussian initializer */
Service.printTensor("Data with gaussian distribution (first 5 rows):", dataTensor, 5, 0);
/* Fill tensor data using uniform initializer */
/* Create an algorithm to initialize data using default method */
UniformBatch uniformInitializer = new UniformBatch(context, Float.class, UniformMethod.defaultDense, -5.0, 5.0);
/* Set input object and parameters for the uniform initializer */
uniformInitializer.input.set(InputId.data, dataTensor);
/* Compute uniform initializer */
uniformInitializer.compute();
/* Print the results of the uniform initializer */
Service.printTensor("Data with uniform distribution (first 5 rows):", dataTensor, 5, 0);
/* Fill layer weights using xavier initializer */
/* Create an algorithm to compute forward fully-connected layer results using default method */
FullyConnectedForwardBatch fullyconnectedLayerForward = new FullyConnectedForwardBatch(context, Float.class, FullyConnectedMethod.defaultDense, 5);
/* Set input objects and parameter for the forward fully-connected layer */
fullyconnectedLayerForward.input.set(ForwardInputId.data, dataTensor);
fullyconnectedLayerForward.parameter.setWeightsInitializer(new XavierBatch(context, Float.class, XavierMethod.defaultDense));
/* Compute forward fully-connected layer results */
FullyConnectedForwardResult forwardResult = fullyconnectedLayerForward.compute();
/* Print the results of the xavier initializer */
Service.printTensor("Weights filled by xavier initializer (first 5 rows):", forwardResult.get(FullyConnectedLayerDataId.auxWeights), 5, 0);
context.dispose();
}
}

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