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