cavis/libnd4j/include/ops/declarable/helpers/cuda/legacy/relu.cu

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/*******************************************************************************
* 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/legacy_helpers.h>
#include <ops/ops.h>
#include <NDArrayFactory.h>
#include <op_boilerplate.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
linkage void reluDerivative__(NDArray* theFirst, NDArray* theSecond) {
auto functor = LAMBDA_TT(x, y){
return x > (T) 0.f ? y : T(0.f);
};
theFirst->applyPairwiseLambda(*theSecond, functor, *theFirst);
}
void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative__, (theFirst, theSecond), FLOAT_TYPES);
}
template <typename T>
linkage void reluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
auto functor = LAMBDA_TT(x, y){
return x > (T)0.f ? y : T(0.f);
};
input->applyPairwiseLambda(*epsilon, functor, *output);
}
void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
}
template <typename T>
linkage void relu6Derivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
auto functor = LAMBDA_TT(x, y){
return x > (T)0.f && x < (T)6.f? y : T(0.f);
};
input->applyPairwiseLambda(*epsilon, functor, *output);
}
void relu6Derivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), relu6Derivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
}
template <typename T>
linkage void leakyReluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output, const float alpha) {
const T alphaT = static_cast<T>(alpha);
auto functor = LAMBDA_TT(x, y, alphaT) {
return x < 0 ? alphaT * y : y;
};
input->applyPairwiseLambda(*epsilon, functor, *output);
}
void leakyReluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), leakyReluDerivative_, (theFirst, theSecond, theOutput, alpha), FLOAT_TYPES);
}
template <typename T>
linkage void eluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output, const float alpha) {
const T alphaT = static_cast<T>(alpha);
auto functor = LAMBDA_TT(x, y, alphaT){
return y * nd4j::math::nd4j_eluderivative<T,T>(x, alphaT);
};
input->applyPairwiseLambda(*epsilon, functor, *output);
}
void eluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), eluDerivative_, (theFirst, theSecond, theOutput, alpha), FLOAT_TYPES);
}
template <typename T>
linkage void seluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
auto functor = LAMBDA_TT(x, y){
return y * simdOps::SELUDerivative<T>::op(x, nullptr);
};
input->applyPairwiseLambda(*epsilon, functor, *output);
}
void seluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), seluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
}
}
}
}