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80 lines
2.2 KiB
80 lines
2.2 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 "AddtoLayer.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(addto, AddtoLayer);
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bool AddtoLayer::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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/* initialize biases_ */
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if (biasParameter_.get() != NULL) {
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biases_ = std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
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}
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return true;
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}
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void AddtoLayer::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 = getInputValue(0)->getHeight();
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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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for (size_t i = 0; i != inputLayers_.size(); ++i) {
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MatrixPtr input = getInputValue(i);
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i == 0 ? outV->assign(*input) : outV->add(*input);
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}
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/* add the bias-vector */
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if (biases_.get() != NULL) {
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outV->addBias(*(biases_->getW()), 1);
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}
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/* activation */ { forwardActivation(); }
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}
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void AddtoLayer::backward(const UpdateCallback& callback) {
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/* Do derivation */ { backwardActivation(); }
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if (biases_ && biases_->getWGrad()) {
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biases_->getWGrad()->collectBias(*getOutputGrad(), 1);
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/* Increasing the number of gradient */
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biases_->getParameterPtr()->incUpdate(callback);
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}
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for (size_t i = 0; i != inputLayers_.size(); ++i) {
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/* Calculate the input layers error */
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MatrixPtr preGrad = getInputGrad(i);
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if (NULL != preGrad) {
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preGrad->add(*getOutputGrad());
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}
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}
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}
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} // namespace paddle
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