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70 lines
2.4 KiB
70 lines
2.4 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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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,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "ParameterReluLayer.h"
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#include "paddle/utils/Logging.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(prelu, ParameterReluLayer);
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bool ParameterReluLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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/* Initialize the basic parent class */
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(inputLayers_.size(), 1UL);
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CHECK_EQ(inputLayers_.size(), parameters_.size());
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partialSum_ = config_.partial_sum();
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CHECK_GT(partialSum_, 0UL) << "partial_sum must be larger than zero.";
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CHECK(!(inputLayers_[0]->getSize() % partialSum_))
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<< "Incorrect value for partialSum: " << partialSum_
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<< " must divide input size: " << inputLayers_[0]->getSize();
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CHECK_EQ(getSize() / partialSum_, parameters_[0]->getSize());
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weight_ = std::unique_ptr<Weight>(new Weight(
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1UL, inputLayers_[0]->getSize() / partialSum_, parameters_[0]));
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return true;
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}
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void ParameterReluLayer::forward(PassType passType) {
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Layer::forward(passType);
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/* malloc memory for the output_ if necessary */
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int batchSize = getInput(0).getBatchSize();
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int size = getSize();
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reserveOutput(batchSize, size);
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MatrixPtr outV = getOutputValue();
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{
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REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
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outV->paramReluForward(*(getInput(0).value), *(weight_->getW()));
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}
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}
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void ParameterReluLayer::backward(const UpdateCallback& callback) {
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if (weight_->getWGrad()) {
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weight_->getWGrad()->paramReluBackwardW(*getOutputGrad(),
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*(getInputValue(0)));
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}
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MatrixPtr preGrad = getInputGrad(0);
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preGrad->paramReluBackwardDiff(
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*getOutputGrad(), *(getInputValue(0)), *(weight_->getW()));
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{
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REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
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weight_->getParameterPtr()->incUpdate(callback);
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}
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}
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} // namespace paddle
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