cavis/libnd4j/include/ops/declarable/helpers/cpu/confusion.cpp

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2019-06-06 14:21:15 +02:00
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author GS <sgazeos@gmail.com>
//
#include <ops/declarable/helpers/confusion.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
void _confusionFunctor(NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) {
std::unique_ptr<ResultSet> arrs(output->allTensorsAlongDimension({1}));
int lLen = labels->lengthOf();
PRAGMA_OMP_PARALLEL_FOR_IF(lLen > Environment::getInstance()->elementwiseThreshold())
for (int j = 0; j < lLen; ++j){
auto label = labels->e<Nd4jLong>(j);
auto pred = predictions->e<Nd4jLong>(j);
T value = (weights == nullptr ? (T)1.0f : weights->e<T>(j));
(*arrs->at(label)).p<T>(pred, value);
}
}
void confusionFunctor(nd4j::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) {
auto xType = output->dataType(); // weights can be null
BUILD_SINGLE_SELECTOR(xType, _confusionFunctor, (labels, predictions, weights, output), NUMERIC_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void _confusionFunctor, (NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output);, NUMERIC_TYPES);
}
}
}