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deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/helpers/cpu/gradient.cpp
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2026-07-13 12:47:05 +08:00

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/* ******************************************************************************
*
*
* 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.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* 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 sgazeos@gmail.com
//
#include <ops/declarable/helpers/axis.h>
#include <system/op_boilerplate.h>
namespace sd {
namespace ops {
namespace helpers {
template <typename T>
static void applyGradientDescent_(NDArray* input, NDArray* step, double weight, NDArray* output) {
auto lambda = LAMBDA_TT(_x, _y, weight) { return _x - (_y * weight); });
input->applyPairwiseLambda<T>(step, lambda, output);
}
void applyGradientDescent(sd::LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output) {
BUILD_SINGLE_SELECTOR(input->dataType(), applyGradientDescent_, (input, step, weight, output), SD_FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE( void applyGradientDescent_,
(NDArray * input, NDArray* step, double weight, NDArray* output), SD_FLOAT_TYPES);
} // namespace helpers
} // namespace ops
} // namespace sd