73 lines
3.0 KiB
C++
73 lines
3.0 KiB
C++
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/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author GS (sgazeos@gmail.com), created on 10/1/2018
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//
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#include<ops/declarable/helpers/cross.h>
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#include <helpers/ShapeUtils.h>
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#include <ops/declarable/CustomOperations.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void weightedCrossEntropyWithLogitsFunctor_(NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) {
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T posWeight = weights->e<T>(0);
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auto mainRoutineT1 = LAMBDA_TT(_x, _z, posWeight) {
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T targetWeight = (1. + (posWeight - (T)1.f) * _z);
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return (1. - _z) * _x +
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targetWeight * (nd4j::math::nd4j_log<T,T>((T)1.f + nd4j::math::nd4j_exp<T,T>(-nd4j::math::nd4j_abs(_x))) +
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nd4j::math::nd4j_max(-_x, T(0.f))
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);
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};
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auto mainRoutineT2 = LAMBDA_TTT(_x, _z, _w) {
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return (((T)1.0 - _z) * _x) +
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_w * (nd4j::math::nd4j_log<T,T>(T(1.) + nd4j::math::nd4j_exp<T,T>(-nd4j::math::nd4j_abs(_x))) +
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nd4j::math::nd4j_max(-_x, T(0.f)));
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};
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if (weights->isScalar()) {
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const_cast<NDArray*>(input)->applyPairwiseLambda<T>(const_cast<NDArray*>(targets), mainRoutineT1, output);
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}
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else
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{
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std::unique_ptr<NDArray> targetVector(new NDArray(*weights));
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targetVector->applyScalar(scalar::Add, -1.f);
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std::unique_ptr<NDArray> targetTensor(new NDArray(*targets));
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*targetTensor = (*targetVector * *targetTensor) + T(1.f);
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const_cast<NDArray*>(input)->applyTriplewiseLambda<T>(const_cast<NDArray*>(targets), targetTensor.get(), mainRoutineT2, output);
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}
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}
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void weightedCrossEntropyWithLogitsFunctor(nd4j::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) {
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BUILD_SINGLE_SELECTOR(targets->dataType(), weightedCrossEntropyWithLogitsFunctor_, (targets, input, weights, output), FLOAT_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void weightedCrossEntropyWithLogitsFunctor_, (NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output), FLOAT_TYPES);
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}
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}
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}
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