67 lines
2.3 KiB
C++
67 lines
2.3 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 raver119@gmail.com
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_apply_sgd)
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#include <ops/declarable/CustomOperations.h>
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namespace nd4j {
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namespace ops {
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CONFIGURABLE_OP_IMPL(apply_sgd, 2, 1, true, -2, 0) {
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auto parameters = INPUT_VARIABLE(0);
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auto gradients = INPUT_VARIABLE(1);
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double lr = 0.0;
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REQUIRE_TRUE(parameters->isSameShape(gradients), 0, "ApplySGD: parameters and gradients should have the same shape, but got parameters = %s and gradients = %s !", ShapeUtils::shapeAsString(parameters).c_str(), ShapeUtils::shapeAsString(gradients).c_str());
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if (block.width() == 3) {
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auto tarr = INPUT_VARIABLE(2);
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lr = tarr->e<double>(0);
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} else if (block.getTArguments()->size() == 1) {
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lr = T_ARG(0);
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} else {
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REQUIRE_TRUE(false, 0, "ApplyGradients op should have LR announced either es T argument or additional NDArray!");
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}
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auto Z = OUTPUT_VARIABLE(0);
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// FIXME: lambda
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/*
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auto lambda = LAMBDA_TT(_x, _y, lr) {
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return _x - (_y * lr);
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};
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parameters->applyPairwiseLambda(gradients, lambda, Z);
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*/
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return Status::OK();
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}
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DECLARE_SYN(ApplyGradientDescent, apply_sgd);
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}
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DECLARE_TYPES(apply_sgd) {
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getOpDescriptor()
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->setAllowedInputTypes({ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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}
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#endif
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