70 lines
2.3 KiB
C++
70 lines
2.3 KiB
C++
/*******************************************************************************
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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_elu)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/legacy_helpers.h>
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namespace nd4j {
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namespace ops {
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CONFIGURABLE_OP_IMPL(elu, 1, 1, true, -2, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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const auto alpha = block.numT() > 0 ? T_ARG(0) : 1.f;
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input->applyScalar(nd4j::scalar::ELU, alpha, *output);
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return Status::OK();
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}
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DECLARE_TYPES(elu) {
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getOpDescriptor()
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->setAllowedInputTypes(0, DataType::ANY)
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->setAllowedOutputTypes(0, {ALL_FLOATS});
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}
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CONFIGURABLE_OP_IMPL(elu_bp, 2, 1, true, -2, 0) {
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auto input = INPUT_VARIABLE(0);
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auto epsilon = INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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const auto alpha = block.numT() > 0 ? T_ARG(0) : 1.f;
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// input->applyPairwiseTransform(pairwise::ELUDerivativeE, epsilon, output);
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helpers::eluDerivative(block.launchContext(), input, epsilon, output, alpha);
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return Status::OK();
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}
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DECLARE_TYPES(elu_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(0, DataType::ANY)
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->setAllowedInputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
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->setAllowedOutputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF});
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}
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}
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}
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#endif |