C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

split_layer_dense_batch.cpp

/* file: split_layer_dense_batch.cpp */
/*******************************************************************************
* 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:
! C++ example of forward and backward split layer usage
!
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::algorithms::neural_networks::layers;
using namespace daal::data_management;
using namespace daal::services;
/* Input data set parameters */
string datasetName = "../data/batch/layer.csv";
const size_t nOutputs = 3;
const size_t nInputs = 3;
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetName);
/* Read datasetFileName from a file and create a tensor to store input data */
TensorPtr tensorData = readTensorFromCSV(datasetName);
/* Create an algorithm to compute forward split layer results using default method */
split::forward::Batch<> splitLayerForward;
/* Set parameters for the forward split layer */
splitLayerForward.parameter.nOutputs = nOutputs;
splitLayerForward.parameter.nInputs = nInputs;
/* Set input objects for the forward split layer */
splitLayerForward.input.set(forward::data, tensorData);
printTensor(tensorData, "Split layer input (first 5 rows):", 5);
/* Compute forward split layer results */
splitLayerForward.compute();
/* Print the results of the forward split layer */
split::forward::ResultPtr forwardResult = splitLayerForward.getResult();
for(size_t i = 0; i < nOutputs; i++)
{
printTensor(forwardResult->get(split::forward::valueCollection, i), "Forward split layer result (first 5 rows):", 5);
}
/* Create an algorithm to compute backward split layer results using default method */
split::backward::Batch<> splitLayerBackward;
/* Set parameters for the backward split layer */
splitLayerBackward.parameter.nOutputs = nOutputs;
splitLayerBackward.parameter.nInputs = nInputs;
/* Set input objects for the backward split layer */
splitLayerBackward.input.set(split::backward::inputGradientCollection, forwardResult->get(split::forward::valueCollection));
/* Compute backward split layer results */
splitLayerBackward.compute();
/* Print the results of the backward split layer */
backward::ResultPtr backwardResult = splitLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient), "Backward split layer result (first 5 rows):", 5);
return 0;
}

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