Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

LossLogisticEntrLayerDenseBatch.java

/* file: LossLogisticEntrLayerDenseBatch.java */
/*******************************************************************************
* Copyright 2014-2018 Intel Corporation
* All Rights Reserved.
*
* If this software was obtained under the Intel Simplified Software License,
* the following terms apply:
*
* The source code, information and material ("Material") contained herein is
* owned by Intel Corporation or its suppliers or licensors, and title to such
* Material remains with Intel Corporation or its suppliers or licensors. The
* Material contains proprietary information of Intel or its suppliers and
* licensors. The Material is protected by worldwide copyright laws and treaty
* provisions. No part of the Material may be used, copied, reproduced,
* modified, published, uploaded, posted, transmitted, distributed or disclosed
* in any way without Intel's prior express written permission. No license under
* any patent, copyright or other intellectual property rights in the Material
* is granted to or conferred upon you, either expressly, by implication,
* inducement, estoppel or otherwise. Any license under such intellectual
* property rights must be express and approved by Intel in writing.
*
* Unless otherwise agreed by Intel in writing, you may not remove or alter this
* notice or any other notice embedded in Materials by Intel or Intel's
* suppliers or licensors in any way.
*
*
* If this software was obtained under the Apache License, Version 2.0 (the
* "License"), the following terms apply:
*
* You may not use this file except in compliance with the License. You may
* obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
/*
// Content:
// Java example of logistic cross-entropy layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.logistic_cross.*;
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.loss.LossForwardInputId;
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 LossLogisticEntrLayerDenseBatch {
private static final String datasetFileName = "../data/batch/logistic_cross_entropy_layer.csv";
private static final String datasetGroundTruthFileName = "../data/batch/logistic_cross_entropy_layer_ground_truth.csv";
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Read datasetFileName from a file and create a tensor to store forward input data */
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
Tensor groundTruth = Service.readTensorFromCSV(context, datasetGroundTruthFileName);
/* Create an algorithm to compute forward logistic cross-entropy layer results using default method */
LogisticCrossForwardBatch forwardLayer = new LogisticCrossForwardBatch(context, Float.class, LogisticCrossMethod.defaultDense);
/* Set input objects for the forward logistic cross-entropy layer */
forwardLayer.input.set(LossForwardInputId.data, data);
forwardLayer.input.set(LossForwardInputId.groundTruth, groundTruth);
/* Compute forward logistic cross-entropy layer results */
LogisticCrossForwardResult forwardResult = forwardLayer.compute();
/* Print the results of the forward logistic cross-entropy layer */
Service.printTensor("Forward logistic cross-entropy layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printTensor("Logistic cross-Entropy layer probabilities estimations (first 5 rows):", forwardResult.get(LogisticCrossLayerDataId.auxGroundTruth), 5, 0);
/* Create an algorithm to compute backward logistic cross-entropy layer results using default method */
LogisticCrossBackwardBatch backwardLayer = new LogisticCrossBackwardBatch(context, Float.class, LogisticCrossMethod.defaultDense);
/* Set input objects for the backward logistic cross-entropy layer */
backwardLayer.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward logistic cross-entropy layer results */
LogisticCrossBackwardResult backwardResult = backwardLayer.compute();
/* Print the results of the backward logistic cross-entropy layer */
Service.printTensor("Backward logistic cross-entropy layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
context.dispose();
}
}

For more complete information about compiler optimizations, see our Optimization Notice.