43 lines
1.5 KiB
C++
43 lines
1.5 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 sgazeos@gmail.com
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//
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#include <ops/declarable/helpers/axis.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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template <typename T>
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static void applyGradientDescent_(NDArray* input, NDArray* step, double weight, NDArray* output) {
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auto lambda = LAMBDA_TT(_x, _y, weight) {
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return _x - (_y * weight);
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};
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input->applyPairwiseLambda<T>(step, lambda, output);
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}
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void applyGradientDescent(nd4j::LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), applyGradientDescent_, (input, step, weight, output), FLOAT_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void applyGradientDescent_, (NDArray* input, NDArray* step, double weight, NDArray* output), FLOAT_TYPES);
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}
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}
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}
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