text: /* file: conv2d_layer_dense_batch.cpp */
  /*******************************************************************************
  * Copyright 2014-2019 Intel Corporation
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * 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 two-dimensional convolution layer usage
  !
  !******************************************************************************/
  
  /**
   * <a name="DAAL-EXAMPLE-CPP-CONVOLUTION2D_LAYER_BATCH"></a>
   * \example conv2d_layer_dense_batch.cpp
   */
  
  #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 name */
  string datasetFileName = "../data/batch/layer.csv";
  
  int main(int argc, char *argv[])
  {
      checkArguments(argc, argv, 1, &datasetFileName);
  
      /* Create collection of dimension sizes of the input data tensor */
      Collection<size_t> inDims;
      inDims.push_back(2);
      inDims.push_back(1);
      inDims.push_back(16);
      inDims.push_back(16);
      TensorPtr tensorData = TensorPtr(new HomogenTensor<>(inDims, Tensor::doAllocate, 1.0f));
  
      /* Create an algorithm to compute forward two-dimensional convolution layer results using default method */
      convolution2d::forward::Batch<> convolution2dLayerForward;
      convolution2dLayerForward.input.set(forward::data, tensorData);
  
      /* Compute forward two-dimensional convolution layer results */
      convolution2dLayerForward.compute();
  
      /* Get the computed forward two-dimensional convolution layer results */
      convolution2d::forward::ResultPtr forwardResult = convolution2dLayerForward.getResult();
      printTensor(forwardResult->get(forward::value), "Two-dimensional convolution layer result (first 5 rows):", 5, 15);
      printTensor(forwardResult->get(convolution2d::auxWeights), "Two-dimensional convolution layer weights (first 5 rows):", 5, 15);
  
      const Collection<size_t> &gDims = forwardResult->get(forward::value)->getDimensions();
      /* Create input gradient tensor for backward two-dimensional convolution layer */
      TensorPtr tensorDataBack = TensorPtr(new HomogenTensor<>(gDims, Tensor::doAllocate, 0.01f));
  
      /* Create an algorithm to compute backward two-dimensional convolution layer results using default method */
      convolution2d::backward::Batch<> convolution2dLayerBackward;
      convolution2dLayerBackward.input.set(backward::inputGradient, tensorDataBack);
      convolution2dLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
  
      /* Compute backward two-dimensional convolution layer results */
      convolution2dLayerBackward.compute();
  
      /* Get the computed backward two-dimensional convolution layer results */
      backward::ResultPtr backwardResult = convolution2dLayerBackward.getResult();
      printTensor(backwardResult->get(backward::gradient),
                  "Two-dimensional convolution layer backpropagation gradient result (first 5 rows):", 5, 15);
      printTensor(backwardResult->get(backward::weightDerivatives),
                  "Two-dimensional convolution layer backpropagation weightDerivative result (first 5 rows):", 5, 15);
      printTensor(backwardResult->get(backward::biasDerivatives),
                  "Two-dimensional convolution layer backpropagation biasDerivative result (first 5 rows):", 5, 15);
  
      return 0;
  }
  