52 lines
1.7 KiB
C++
52 lines
1.7 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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// Created by GS <sgazeos@gmail.com> 31.01.2018
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_l2_loss)
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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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CUSTOM_OP_IMPL(l2_loss, 1, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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REQUIRE_TRUE(output->isScalar(), 0, "Rank output should be scalar");
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// FIXME: output should be used directly here, to avoid sum
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input->reduceNumber(reduce::SquaredNorm, *output);
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(*output) /= 2.;
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return Status::OK();
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}
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DECLARE_SHAPE_FN(l2_loss) {
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return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(ArrayOptions::dataType(inputShape->at(0))));
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}
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DECLARE_TYPES(l2_loss) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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}
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}
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#endif
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