cavis/libnd4j/include/ops/declarable/helpers/cuda/legacy/tanh.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 <NDArrayFactory.h>
#include <op_boilerplate.h>
namespace nd4j {
namespace ops {
namespace helpers {
////////////////////////////////////////////////////////////////////////
template <typename T>
linkage void tanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
auto functor = LAMBDA_TT(x, y){
T th = nd4j::math::nd4j_tanh<T,T>(x);
return y * ((T)1.0f - (th * th));
};
input->applyPairwiseLambda(epsilon, functor, output);
}
void tanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), tanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
}
// return static_cast<X>(d2) * simdOps::HardTanhDerivative<X>::op(d1, nullptr);
template <typename T>
linkage void hardTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
auto functor = LAMBDA_TT(x, y){
T th = nd4j::math::nd4j_tanh<T,T>(x);
return y * simdOps::HardTanhDerivative<T>::op(x, nullptr);
};
input->applyPairwiseLambda(epsilon, functor, output);
}
void hardTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
}
template <typename T>
linkage void rationalTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
auto functor = LAMBDA_TT(x, y){
return y * simdOps::RationalTanhDerivative<T>::op(x, nullptr);
};
input->applyPairwiseLambda(epsilon, functor, output);
}
void rationalTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), rationalTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
}
template <typename T>
linkage void rectifiedTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
auto functor = LAMBDA_TT(x, y){
return x > (T) 0.0f ? y * (nd4j::math::nd4j_tanhderivative<T,T>(x)) : (T) 0.0f;
};
input->applyPairwiseLambda(epsilon, functor, output);
}
void rectifiedTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
BUILD_SINGLE_SELECTOR(theFirst->dataType(), rectifiedTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
}
}
}
}